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National Institute of Technology Durgapur, INDIA

Students’ International Research Projects Technical Report 2011-2012

Volume 4

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From Director’s Desk The primary focus of National Institute of Technology Durgapur is to create a research and

academic environment that promotes innovation and excellence, associated with an

inclusive growth. The students are initiated to research very early and support the faculty

members in active research. They are financially supported by the Institute for their

research visits to conferences in India and abroad and for research internship abroad.

This has resulted in 102 students visiting premier universities and institutions all over the

world in the last three years and a few hundreds of them presenting papers in conferences

in India.

I am happy that, like the last three years, we are publishing the “Students’ International

Research Projects; Technical Report 2011-2012; Volume 4” this year too. This report

gives a brief overview of their research activities during internship and presentation in

international conferences abroad.

I hope the authors will continue to be associated with research and innovation in their

professional life.

Professor T. Kumar

Director

National Institute of Technology Durgapur

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AUTHORS

Amlan Kusum

Mukherjee Arindam Ghosh

Manali Adhikari

Nikita Agarwal

Nisha Chaurasia

Ratul Hazra

Shampa Biswas

Shankha Nag

Sharmili Adhikari

Sourayon Chanda

Abhisek

Mukhopadhyay Annesha Chaudhuri

Anwesh Mukherjee

Arka Halder

Dishari Chakraborty

Gaurav Khetan

AUTHORS

Ishita Rakshit

Naomi Joshi

Portia Banerjee

Prabisha Mallick

Randhir Kumar Saptarshi Mukherjee

Sonam Yangchen Soumi Bardhan

SoumyaSarkar

Sristi Agarwal

Sudipta Acharya

Contents Title Author Page

No.

RESEARCH INTERNSHIP:

FIRMWARE DEVELOPMENT TO USE THE CAEN V1495 MULTIPURPOSE BOARD FOR A SYNCHRONIZED LHC BUNCH PATTERN MEASUREMENT

Amlan Kusum Mukherjee

1

COOPERATIVE HYBRID ARQ SCHEME FOR UNDERWATER ACOUSTIC SENSOR NETWORKS

Arindam Ghosh

3

PRELIMINARY STUDY OF QUANTUM EFFICIENCY ENHANCEMENT IN GEM DETECTORS FOR ALICE UPGRADES

Manali Adhikari

5

IMPLEMENTING NEW COMMANDS FOR THE JAVA ALIEN SHELL

Nikita Agarwal 7

DATA CENTERS VIRTUALIZATION

Nisha Chaurasia 9

A LANGUAGE DRIVEN TOOL FOR FAULT INJECTION IN DISTRIBUTED SYSTEM

Ratul Hazra

11

DESIGNING OF RECTANGULAR MICROSTRIP PATCH ANTENNA OPERATING IN THE C-BAND FREQUENCY RANGE

Shampa Biswas

13

STUDY OF DIRECTIONALITY OF FUSED QUARTZ BAR FOR INSTALLATION IN THE MUON HALO DETECTOR

Shankha Nag

15

IMPACT OF LAYER STRAIN/LATTICE DEFORMATION ON THE DIELECTRIC PROPERTIES OF RARE EARTH OXIDES

Sharmili Adhikari

17

STOKES HYPOTHESIS

Sourayon Chanda

19

CONFERENCES:

OPTIMAL DESIGN OF LINEAR PHASE FIR BAND STOP FILTER USING PARTICLE SWARM OPTIMIZATION WITH IMPROVED INERTIA WEIGHT TECHNIQUE

Abhisek Mukhopadhyay

21

OPTIMIZATION OF IIR HIGH PASS FILTER USING CRAZINESS BASED PARTICLE SWARM OPTIMIZATION TECHNIQUE

Annesha Chaudhuri

23

GENERATION OF MULTIPLE SIDE LOBE LEVELS OF NON UNIFORMLY EXCITED LINEAR ARRAY ANTENNA USING ITERATIVE FAST FOURIER TRANSFORM

Anwesh Mukherjee

25

MOMENT BASED DELAY MODELLING FOR ON-CHIP RC GLOBAL VLSI INTERCONNECT FOR UNIT RAMP INPUT

Arka Halder

27

NOVEL PARTICLE SWARM OPTIMIZATION FOR HIGH PASS FIR FILTER DESIGN

Dishari Chakraborty

29

LOW POWER VLSI CIRCUIT IMPLEMENTATION USING MIXED Gaurav Khetan 31

STATIC CMOS AND DOMINO LOGIC WITH DELAY ELEMENTS

IIR SYSTEM IDENTIFICATION USING PARTICLE SWARM OPTIMIZATION WITH CONSTRICTION FACTOR AND INERTIA WEIGHT APPROACH

Ishita Rakshit

33

4-Π CROSSTALK NOISE MODEL FOR DEEP SUBMICRON VLSI GLOBAL RC INTERCONNECTS

Naomi Joshi

35

DELAY AND TRANSIENT RESPONSE MODELLING OF ON-CHIP RLCG INTERCONNECT USING TWO-PORT NETWORK FUNCTIONS

Portia Banerjee

37

OPTIMAL FIR BAND PASS FILTER DESIGN USING NOVEL PARTICLE SWARM OPTIMIZATION ALGORITHM

Prabisha Mallick

39

WIDE NULLS CONTROL OF LINEAR ANTENNA ARRAYS USING CRAZINESS BASED PARTICLE SWARM OPTIMIZATION

Randheer Kumar

41

LINEAR PHASE LOW PASS FIR FILTER DESIGN USING IMPROVED PARTICLE SWARM OPTIMIZATION

Saptarshi Mukherjee

43

DIGITAL STABLE IIR HIGH PASS FILTER OPTIMIZATION USING PSO-CFIWA

Sonam Yangchen

45

OPTIMIZATION OF LINEAR PHASE FIR BAND PASS FILTER USING PARTICLE SWARM OPTIMIZATION WITH CONSTRICTION FACTOR AND INERTIA WEIGHT APPROACH

Soumi Bardhan 47

DIGITAL STABLE IIR LOW PASS FILTER OPTIMIZATION USING PARTICLE SWARM OPTIMIZATION WITH IMPROVED INERTIA WEIGHT

Soumya Sarkar

49

ELMORE’S APPROXIMATIONS BASED EXPLICIT DELAY AND RISE TIME MODEL FOR DISTRIBUTED RLC ON-CHIP VLSI GLOBAL INTERCONNECT

Sristi Agarwal

51

AUTOMATED SOFTWARE DEVELOPMENT METHODOLOGY: AN AGENT ORIENTED APPROACH

Sudipta Acharya

53

National Institute of Technology Durgapur, INDIA Students’ International Research Projects Technical Report; Volume 4, 2011-12

FIRMWARE DEVELOPMENT TO USE THE CAEN V1495 MULTIPURPOSE BOARD FOR A SYNCHRONIZED LHC

BUNCH PATTERN MEASUREMENT

Amlan Kusum Mukherjee B. Tech. final year student, Department of Electronics & Communication Engineering


CMS Beam Rate Monitoring Group, CMS Experiment, CERN May 22 – July 13, 2012

Abstract The Beam Pickup Timing for Experiments of the BPTX is used to measure precisely the present of proton bunches in the Large Hadron Collider, popularly LHC. Presently the BPTX system uses various NIM modules for its entire back end electronics portion. The aim of the project is to translate the NIM modules to FPGA based modules so that the module can have all the advantages of the digital systems and remote handling. Introduction The Large Hadron Collider (LHC) accelerates protons to a relativistic speed and makes them collide to produce fundamental particles. It has two separate beam pipes, where the protons travel in opposite directions while being accelerated. The protons are grouped into bunches that are injected in the accelerator at intervals of 50ns (25ns after the so called Long Shutdown 1, two years of stop for upgrade starting on Feb 2013). When the protons reach relativistic speed, two bunches coming from opposite directions are collided, thus producing a variety of elementary particles that are detected in the CMS (Compact Muon Solenoid) detector. At the detector, bunches from both the beam pipes are filled into a single one for the collision to happen. After collision a burst of the smaller and more fundamental particles like Quarks, Muons, Pions, Kions, Nutrinos etc are produced which are detected in various layers of the detector and thus more information about them is obtained. The BPTX The Beam Pick-up Timing for Experiments, or shortly, BPTX is used to determine the presence of the proton-bunches at the two

sides of the interaction point in CMS. When a bunch passes the point at which sensors are attached, a current is generated in the BPTX and the analog signals are sent to the discriminator to produce digital signals. These digital signals are then logically manipulated to be used as trigger to the CMS. The BPTX modules are attached to the two separate beam pipes, having opposite directions of the bunch flow, at equal distances from the main Detector. If there is a coincident signal in the BPTXs, then there is a probability of collision at the main detector. So these coincidence signals are used to trigger the main CMS detector. Following is a table of Inputs sent to the trigger of the CMS. Presently CMS uses the trigger configured on NIM modules. The aim is to change the NIM modules to high speed FPGA boards for better, flexible and user friendly system. Aim of the Project The aim of the project is the design of a VME based, synchronous, back-end electronic logic circuits using FPGAs. The electronic circuits should acknowledge the electrical signals from the BPTX discriminator output and analyze them, to obtain the bunch pattern and timing from the LHC to be used as Trigger to the CMS detector. Design of the Circuit In the model we have two signals from the sensors of the BPTX coming to the discriminator that transforms them into digital pulses. These digital pulses are logically manipulated to produce various trigger signals to the detector. The desired input-output schematic is shown below.

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These signals are then copied into multiple signals, given adequate delay and width than used as trigger.

Results and Discussion The design is done in Altera Quartus II, to be implemented on CAEN v1495 general purpose module. The schematic of the system, simulations of signal flow and final results of the system are shown below.

Fig1: Schematic of the Design

Simulation results after Synchronization and Logical

manipulations

Simulation results with the gate signal and working of the

Delay width generator

Simulation results of all logic outputs having same pulse

widths and delays

Simulation results of Final Outputs: three types of output

pulses each having a particular delay and width. Conclusion When the development of the project gets implemented then the NIM modules can be replaced by the a FPGA board which *Can be controlled and updated from a remote server. *The Flexibility of the whole module will increase. *The delay due to multiple wires and connectors will decrease. *The output signals will be less distorted compared to what is obtained from the NIM module. *More number of Outputs can be remotely increased and configured for further use. Acknowledgement For this project, I want to thank my supervisors, Anne Dabrowski and Marina Giunta for their immense help and patience throughout the project. I also want to thank Vladimir Ryjov, Hans Henschel and Arkady Lokhovitskiy for their pioneering ideas which got me out of trouble in many situations. I would also like to thank Andrey Pozdnyakov and Stella Orfanelli for their guidance regarding the BPTX. A special thanks to Dr. Archana Sharma for providing the opportunity of working at CERN. Thanks to Prof. N.K. Roy and Dr. P. Kumbhakar for all the time and support without which it would have been impossible.

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COOPERATIVE HYBRID ARQ SCHEME FOR UNDERWATER ACOUSTIC SENSOR NETWORKS

Arindam Ghosh

B. Tech. final year student, Department of Electronics & Communication Engineering National Institute of Technology Durgapur, INDIA

School of Electronics Engineering, Kyungpook National University,

Daegu, South Korea, May 12 – July 22, 2012 Abstract The main objective was to develop and analyze a retransmission/error control scheme named cooperative hybrid automatic repeat request (C-HARQ) for underwater acoustic sensor networks. C-HARQ is an enhanced version of cooperative ARQ which exploits the efficiency of cooperative techniques along with the incremental error correction of HARQ using rate-compatible punctured convolution codes. Introduction Underwater acoustic sensor networks are gaining significant interest because of their wide variety of applications, such as environment monitoring, resource investigation, and detection of phenomena related to natural disaster. Naturally, all of these mission-critical applications require highly reliable data transmission techniques. However, underwater acoustic channels suffer from path loss, multi-path fading, Doppler spread, and aquatic noise, resulting in a high bit error rate (BER). To establish reliable communication under such poor channel conditions Stop and wait (S&W) ARQ scheme has been employed as the only method of retransmission because of the half duplexing mode of underwater acoustic modems. The cooperative ARQ (CARQ) scheme was proposed to substantially improve the data throughput. The proposed CHARQ scheme combines CARQ with type-II HARQ with rate-compatible punctured convolution (RCPC) codes to maximize reliability, throughput and energy efficiency.

Theory / Model A. Network Scenario We consider a single-hop network between the source (S-node) and the destination (D-node). As shown in Fig.1, the overlapped area between the two circles is called the cooperative region (C-Region). The neighbor nodes within this region are referred to as cooperative nodes (C-node).

Fig.1. Cooperative region for the target network

B. RCPC Codes & Puncturing For convolution coding, we use a systematic convolution encoder. From Fig.2, at first a k-bit data is encoded into a 2k-bit mother code (code rate = ½). Then the mother code is punctured using predefined puncturing patterns to get multiple packets Pac-0, Pac-1, Pac-2. Pac-0, which contains only data bits, can be called the original data packet. Redundant bits are enclosed in Pac-1 and Pac-2.

Fig. 2. Packet generation process via puncturing C. Scheme Description

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The core idea of C-HARQ scheme is to merge Type-II HARQ with Cooperative ARQ technique. Source (S-node) generates a data packet (Pac-0) and sends it to D-node. If it’s non erroneous, an ACK to S-node is sent otherwise D-node saves the erroneous Pac-0 and waits for C-flags (Cooperation flag) from C-nodes. Meanwhile, C-nodes overhear Pac-0 from source and immediately send C-flags to the D-node informing that they are available for cooperation. C-nodes get ready for cooperation by encoding Pac-0 into Pac-1…Pac-0. At D-node, if no C-flag is received, a NACK to S-node is sent, to ask for a retransmission of Pac-0. Otherwise if C-nodes are available, a NACK is sent to the closest C-node. The selected (current) C-node then sends Pac-1. D-node tries to recover the data by combining it with Pac-0. If recovery is successful, an ACK to S-node is sent while C-nodes are also able to recognize the successful recovery by overhearing ACK. Otherwise Pac-1 is saved and NACK is sent to the same C-node for next FEC packet i.e. Pac-2. If Pac-1 or Pac-2 is found in error, D-node approaches the next closest C-node and this process continues till an ACK is heard. Results and Discussions A. Performance Metrics We define two metrics, Time-throughput Efficiency (time-tpt) and Data-throughput Efficiency (data-tpt), as:

packet data adeliver correctly to timeTotalpacket data a of on timeTransmissi

=− tpttime

packet data adeliver correctly sent to bits Total(bits)length packet Data

=− tptdata

1. Channel Utilization: Fig.3.a shows that C-HARQ boosts the throughput efficiency of CARQ to a much higher value. The optimal data packet length for C-HARQ is found to be 1200 bits which is greater than that of CARQ (1000 bits); hence more data can be sent on a single transmission. 2. Energy Efficiency: Next we compare the Data-Throughput efficiency in Fig.3.b. It is clear that C-HARQ sends fewer bits for a successful transmission and hence is most

energy efficient. In Fig.3 nNC refers to the number of cooperative nodes.

Fig.3 (a) time-tpt (b) data-tpt vs. data packet length Conclusions Exploiting the reliability and efficiency of cooperative communication in sensor networks, an enhanced version of Cooperative ARQ scheme is proposed for underwater acoustic sensor networks. Computer simulations results show that this hybrid cooperation technique enhances the performance of Cooperative ARQ to a much higher level both in terms of channel utilization and energy efficiency. Acknowledgement I would like to sincerely thank my supervisor, Dr. Ho-Shin Cho, for mentoring my project. I would like to extend my gratitude to Mobile Communications Laboratory, School of Electronics Engineering, KNU, for their help and support throughout the internship period. My sincere regards to everyone from NIT Durgapur whose collective effort made this internship possible.

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PRELIMINARY STUDY OF QUANTUM EFFICIENCY ENHANCEMENT IN GEM DETECTORS FOR ALICE UPGRADES

Manali Adhikari

B. Tech. final year student, Department of Electrical Engineering National Institute of Technology Durgapur, INDIA

ALICE VHMPID TEAM, CERN, Geneva, Switzerland

May 21– July 15, 2012

Abstract The project was conceptualised to focus primarily on the first evaluation of Gas Electron Multipliers (GEMs) for their possible use in ALICE detector upgrade. Currently the ALICE team is considering plans to upgrade the present ALICE detectors to meet the physical challenges at high luminosity experiments. In these plans GEMs are being considered to replace MWPC in Very High Momentum Particle Identification Detector (VHMPID).The goal of the project was to make primary evaluation of GEMs and attempt an enhancement in the quantum efficiency for these applications. This report summarizes work done at CERN as part of the Summer Students 2012 program. Introduction Physics Motivation The purpose of the ALICE experiments is to identify and study the quark-gluon plasma (QGP) in heavy ion collisions at LHC energies .This may allow a lucid understanding of the creation of the Universe. In close context to the ALICE Upgrade program and the LOI, The proposed detector is a state-of-the-art Ring Imaging Cherenkov detector (RICH),

designed to meet the constraints of available space and structure inside ALICE,

without compromising the prospect for new physics.

Gas Electron Multipliers (GEMs) are being considered over MWPCs in this regard as they offer several advantages. Theory/Model A Very High Momentum Particle Detector (VHMPID) To significantly enhance ALICE's particle identification capabilities in the regime of particle-by-particle measurements, the upgrade proposition is to construct a new detector for ALICE, the Very High Momentum Particle Identification Detector (VHMPID).Unfortunately the beam test of GEM based prototype indicate that the number of detected Cherenkov photoelectrons as 0.7 compared to MWPC. In this project, the possibility to enhance the detector efficiency by adding ethylferrocene vapours (EF) is investigated. The possible contribution of EF could be:

1. Enhancement of the CsI Quantum efficiency(QE)due to the absorbed layer

2. Volume ionisation (see Fig.2)

Fig. 2: Diagram showing expected contribution from gas for volume ionisation.

Fig. 1:GEM detector

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Experimental

Fig. 3: Typical setup with a pulse D2 lamp (Ar flushed) and ORTEC preamplifier. Here EF flushed is heated in an attempt to enhance Quantum Efficiency (QE).

Fig. 4: Signal enhanced strongly when discharge lamp is flashed with Argon. In Fig, 5 is shown the pulse amplitude when the detector was heated. By reversing the earlier setup conditions, the contribution of the EF vapours is measured.

Fig. 5: Suppression of CSI signal and enhancement of EF signal.

Results and Discussion As followed from single-wire counter measurements, at room temperatures, the concentration of the EF vapours is very low .When the detector was heated, the EF layer was evaporated which decreased the CsI QE .From the TGEM measurements, it is observed that the signal from the gas (EF) is low .So approximately it has ~1/50th fraction on comparing with CsI, as observed on experimentation. Conclusion In the LOI, heating of the detector is proposed. A drop in the QE of CsI is expected. However, it is also believed that the contribution from the gas will be increased. To prove this hypothesis, a careful optimization of the drift volume and temperatures is required. The results obtained from the above project allows some important recommendation to be made, which holds great potential for further tests and studies in the framework of the ALICE Upgrade Program and the LHC community. Acknowledgement I would like to take this opportunity to express my sincere gratitude towards my supervisor, Vladimir Peskov, without whose exemplary patience and guidance this project would not have been possible. I am also indebted to J. Van Beelen, our Lab technician, and Paolo Martinengo, for tirelessly assisting in solving any problems, technical or otherwise. My sincere regards to Dr Archana Sharma, for her everlasting support and help. This list would remain incomplete without mentioning Dr. Bikash Sinha, Prof. N. K.Roy, Prof. A. K.Mitra, and Dr. Pathik Kumbhakar of NIT Durgapur for without their patronage and financial aid I perhaps would never have got the opportunity to work in a team comprising many eminent physicists and engineers, here at CERN. And finally I would like to thank the Almighty and my parents for bolstering my courage and giving me the strength in bringing this project to a satisfactory conclusion.

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IMPLEMENTING NEW COMMANDS FOR THE JAVA ALIEN SHELL

Nikita Agarwal B. Tech. final year student, Department of Computer Science & Engineering


Department of Computer Science & Engineering CERN European Organisation for Nuclear Research, May 22 – July 14, 2012

Abstract Since the ALICE experiment began data taking in early 2010, the amount of end user jobs on the AliEn Grid has increased significantly. Presently 1/3 of the 30K CPU cores available to ALICE are occupied by jobs submitted by about 400 distinct users. The overall stability of the AliEn middleware has been excellent throughout the 2 years of running, but the massive amount of end-user analysis and its specific requirements and load has revealed few components which can be improved. One of them is the interface between users and central AliEn services (catalogue, job submission system) which we are currently re-implementing in Java. The interface provides persistent connection with enhanced data and job submission authenticity. In this paper we will describe the architecture of the new interface, the ROOT binding which enables the use of a single interface in addition to the standard UNIX-like access shell, implementing the missing commands, arguments, or any new command in the jAliEn Environment. Introduction ALICE Computing Environment

AliEn (ALICE Environment) is a lightweight Open Source Grid Framework built around other Open Source components using the combination of a Web Service and a Distributed Agent Model. It was developed within the ALICE Offline Project at CERN and constitutes the production environment for simulation, reconstruction and analysis of physics data of the ALICE Experiment. The distributed computing infrastructure that makes up the ALICE Computing Grid

can be visualized online on the ALICE MonALISA Repository. Theory / Model Figure 1- Structure of the AliEn service AliEn has two types of services, some that run at each site and others that run centrally, as shown in Figure 1. The central services maintain the file catalog, jobs and transfer queues and offer authentication, authorization and accounting services. They also provide a uniform Grid access to the users. Each site runs a small set of services on a dedicated local server (called VoBox). These services are responsible for sending pilot jobs when jobs matching site resources are waiting in the queue, installing the required software packages and mediating jobs' interaction with the central services while they are running and

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monitoring the usage of site resources. Most of the services can be run in load-balancing mode, both centrally and on the sites, with several instances of the same service running on separate servers. As of the writing of this paper there are 86 sites and 110 VoBoxes on 5 continents providing access to more than 40,000 CPU cores, 16PB of disk storage in 60 storage elements and another 8PB of tape-backed storage elements for data archiving. The ALICE Computing Model requires that jobs are executed close to the data location thus spreading the data on all available storage elements is important in order to optimize the use of the available computing power.

All jobs and end-users interact with the central catalogue and job queues through a set of API servers that also handle the authentication and authorization while keeping persistent connections for performance reasons. Next section describes the current implementation and the improvements and changes in the model brought by the next version currently under development. Experimental All user interactions with AliEn are handled using client-server architecture. The API for all client applications is implemented in a library libgapiUI. So every client interface can communicate over a session-based connection with an API server. The API server exports to the client interface all functions, which are bridged from the AliEn PERL-UI via the Alien as PERL→C++ interface script. However the new interaction model will use native Java serialization for requests and replies, proxy-less authentication, support for signed JDLs, persistent SSL channels between components, with compression and native object reuse where possible, built-in monitoring exported via ApMon to MonALISA.

Results and Discussion There is list of commands in java shell which are available but some are not properly implemented. Some of the commands have missing arguments/ flags etc and others did not work properly. I studied how the objects are being passed from the client to the server and the modified object (by changing its fields) is sent back to the client and printing the contents there (just like jSh/ jBox/ jCentral communicate), worked upon the code for find, cat etc and implemented the missing flags. The arguments for the command “find” like –s, -d, -x “collection name”, -c “file name”, -l, -h. And for the command “cat”, arguments like -b, -t, -n, -e with their respective meanings were implemented by me in the java shell. The main objective is to speed up the execution of the command and find better and shorter ways for its execution. Conclusion To handle large amount of data and make the access of data faster and improved, the interface between users and central AliEn services (catalogue, job submission system) are being currently re-implemented in Java. The interface provides persistent connection with enhanced data and job submission authenticity. Thus this new improved technique will allow us to analyse the data in a better way and be platform independent while reducing communication overhead and the load on the central machines by the various caching mechanisms in the new framework. Acknowledgement I would like to thank my institute, NIT Durgapur who gave me a wonderful opportunity to work at CERN. I would like to acknowledge my supervisor Costin Grigoras who guided me throughout the work and helped me to successfully complete the project.

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DATA CENTERS VIRTUALIZATION

Nisha Chaurasia B. Tech. final year student, Department of Electronics & Communication Engineering


Department of Electrical & Electronics Engineering Nanyang Technological University, Singapore, June 04 – July 27, 2012

Abstract A data center is a centralised repository, either physical or virtual, for storage, management, and dissemination of data and information organised around a particular body of knowledge or pertaining to a particular business. With the rapid increase in the capacity and size of data centers, there is a continuous increase in the demand for energy consumption. These data centers not only consume a tremendous amount of energy but are riddled with IT inefficiencies. Data center are plagued with thousands of servers as major components which consume huge energy without performing useful work. This work studies a five-layer model using an emerging technology called virtualization to achieve energy efficient data centers. Introduction A Data Center is a pool of computing resources clustered together using communication networks to host applications and store data. Over the last decade, the rise of Internet and Web-based technologies has made it more strategic than ever, improving productivity, enhancing business processes, and accelerating change. They are the strategic focus of IT efforts to protect, optimize and grow the business. Major challenges in the data center network design includes:(a) scalability, (b) agility, (c) fault tolerance, (d) maximum end-to-end aggregate bandwidth, (e) automated naming and address allocation, and (f) backward compatibility. One of the most important goals of data-center management is to reduce cost through efficient use of resources. Virtualization techniques

provide the opportunity of carving individual physical servers into multiple virtual containers that can be run and managed separately. Theory / Model As data centers grow in size and proliferate, we have seen a wide range of applications evolve to take advantage of this environment. This new world presents challenges to both the owners of these data centers and the customers or users who run the applications. Data centers are the main culprits of consuming huge energy and emitting huge amount of CO2, which is very hazardous for global warming. Virtualization technology provides the solution but it has many overheads, like single point of failure, total cost of ownership, energy and efficiency calculations and return of investment. Virtualization technology is now becoming an important advancement in IT especially for business organizations and has become a top to bottom overhaul of the computing industry. Here, we study a model of five layer to be followed by IT managers to properly implement virtualization in their data centers to achieve efficiency and reduce carbon footprints and hence design the efficient and cost effective data centers. Experimental To properly implement virtualization, we define a five layers model. The components of the examined model provide a detailed treatment of state of art and emerging challenges faced by data centers managers to implement virtualization properly to achieve desired

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objectives. The components of the studied model are: 1) Inventory Process, 2) Type & Nature of Virtualization, 3) Hardware maximization, 4) Architecture, and 5) Managing Virtualization

Fig. 1 Process of Virtualization

Inventory process include information related to model and types of processor, memory size and speed, network type, applications installed, storage device, etc. After analyzing and categorizing servers and other resources, the 2nd step defines advantages, type, layers and most importantly vendor identification and selection. All the analysis should be done on time for a period of at least one month, this will generate high and low utilization ratios for each server. Then Hardware maximization can be achieved by purchasing a new quad processor which have better hardware utilization as well as less consumption of energy hence reducing the emission of CO2. The infrastructure helps to design, deploy and integrate a complete system which helps to achieve desirable objectives. Managing virtualization is another most important step that involves end users and the top management to decide whether to implement the virtualization or not and involves many factors like cost, return on investment, security, and service level agreements.

Results and Discussion Virtualization results in a number of compelling benefits including better utilization, faster provisioning and reduced impact of planned maintenance on application availability. Another key benefit of virtualization is the dynamical creation of multiple, separate IT infrastructures, secured and isolated from each other, yet running on a single physical infrastructure. It promises to radically transform computing for the better utilization of resources available in the data center reducing overall costs and increasing agility. Conclusion This work focuses on the importance of implementing the virtualization technology in data centers to maximize the efficiency and to reduce the cost by proposing a layered model consisting of five layers to properly implement the virtualization. Ongoing maintenance and management, reduction of hardware acquisition costs, and better architecture for availability, security and performance, etc. can be received by implementing virtualization. Acknowledgement I would like to extend my gratitude to Department of Electrical and Electronics Engineering at NTU, Singapore and Prof Zhong Wende for mentoring my project. I am extremely grateful to National Institute of Technology Durgapur for the financial support to partially cover my expenses to pursue the project successfully.

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A LANGUAGE DRIVEN TOOL FOR FAULT INJECTION IN DISTRIBUTED SYSTEM

Ratul Hazra


Network Performance and Analysis Laboratory

Université Pierre et Marie Curie Paris-VI, France, May 15-July 25, 2012

Abstract One of the topics of paramount importance in the development of Grid middleware is the impact of faults, since their probability of occurrence in a Grid infrastructure and in large-scale distributed systems is actually very high. The aim is to explore the versatility of this new tool for fault injection in distributed applications: FAIL-FCI. And also to incorporate the memory buffer overflow attack in the grid infrastructure through this new language driven tool. Introduction It is expected that Grid middleware is reliable and provides a comprehensive support for fault-tolerance mechanisms, such as failure-detection, check pointing and recovery replication, software rejuvenation, and component-based reconfiguration, among others. One of the techniques for evaluating the effectiveness of those fault-tolerance mechanisms and the reliability level of Grid middleware is to make use of some fault-injection tools and a robustness tester to conduct some experimental assessments of the dependability metrics of the target system. Some applications (for example peer to peer applications) involve a considerable number of users, e.g. to exchange files or to execute long calculations (SeTi@Home, Decrypthon, Xtremweb, Boinc, etc.). For those applications, the appearance and disappearance of participating machines are unpredictable, very frequent, and occur while the application is run eventually.

Theory/Model Details of the FAIL-FCI system architecture and the FAIL language are presented. First, FAIL (for Fault Injection Language) is a language that permits fault scenarios to be easily described. Second, FCI (for FAIL Cluster Implementation) is a distributed fault-injection platform whose input language for describing fault scenarios is FAIL. The FAIL language allows the defining of fault scenarios. A scenario describes, using a high-level abstract language, state machines which model fault occurrences. The FAIL language also describes the association between these state machines and a computer (or a group of computers) in the network. The FCI platform is composed of several building blocks: The FCI compiler: The fault scenarios written in FAIL are precompiled by the FCI compiler, which generates C++ source files and default configuration files. The FCI library: The files generated by the FCI compiler are bundled with the FCI library into several archives, and then distributed across the network to the target machines according to the user-defined configuration files. Both the FCI compiler generated files and the FCI library files are provided as source code archives, to enable support for heterogeneous clusters. The FCI daemon: The source files that have been distributed to the target machines are then extracted and compiled to generate specific executable files for every computer in the system. Those executable are referred to as the FCI daemons.

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Library Internal Structure The internal structure of the FAIL-FCI library can be divided in three main parts: the kernel, the external interface, and the network structure description. In the kernel The main class is Environment, that takes inputs from the Trigger class, and that controls the State Machine class. The Trigger class models events that are likely The State Machine class refers to the automata that describes the actions that are taken by the FAIL-FCI daemon when some events are triggered. The actual actions that are taken by the Environment are handled by three different classes (see the Figure below). The Timer Controller class permits to setup timeout events, the Network Controller permits to manage communication between FAIL-FCI daemons, and the Program Controller handles the program under test through a debugger. The Program Controller GDB permits to manage native programs through the gdb debugger, while the Program Controller JDB takes care of Java programs through the Java debugger. Further extensions for other languages and systems are straightforward with the new library structure carried out by the Environment.

The

The FCI Platform

Conclusion Thus with this new tool Fault can be injected using a quantitative approach (as in most related studies) as well as the more original qualitative approach, where precise faults are inserted at precise logical states of the application under test. Although the set of possible fault injections is extremely large, the language that describes the faults scenario is high level and independent from the language used in the application. This enables decoupling between the application programmers and the test specifiers, so that their expertise is used in the proper domain. Acknowledgement I am extremely grateful to Professor Sebastien TIxeuil who provided me this opportunity and guided me throughout the project. I would also thank the French Embassy in India for the logistic and financial support that they provided or else this would not have been possible.

12


DESIGNING OF RECTANGULAR MICROSTRIP PATCH ANTENNA OPERATING IN THE C-BAND FREQUENCY RANGE

Shampa Biswas

B. Tech. final year student, Department of Electrical Engineering National Institute of Technology Durgapur, INDIA

Department of Electrical & Electronics Engineering

Universiti Tun Hussein Onn, Malaysia, May 10 – July 10, 2012

Abstract This project involves design, fabrication and testing of a rectangular microstrip antenna operating in C-band (4GHz-8GHz) frequency. The process includes choosing a suitable microwave substrate ,here RT5870 lossy is used and to choose an appropriate patch of microstrip line operating in the C-band by taking account the design procedure, selection of resonant frequency, choice of dielectric material, feeding technique, impedance matching ,simulation using CST microwave studio and finally to design and simulate the microstrip line. Introduction Microstrip antennas also called printed antennas are antenna that is constructed using printed fabrication techniques such that a portion of the metallization layer is responsible for radiation. In general, microstrip antenna has a conducting patch printed on a grounded microwave substrate. A microstrip antenna has some features of low profile, light weight, easy to fabricate and more. However, microstrip antenna inherently has a narrow bandwidth and bigger in size. People know that the bandwidth enhancement is usually demanded for practical applications. In addition, applications in present day in communication systems usually require smaller antenna size in order to meet the miniaturization requirements for mobile units. Thus, the size reduction and bandwidth enhancement are becoming major design considerations for practical applications of microstrip antennas. For this reason, studies to achieve compact and broadband operations of microstrip antennas have greatly increased.

Theory / Model In its most fundamental form, a rectangular patch antenna consists of a radiating patch on one side of a dielectric substrate which has a ground plane on the other side. The patch is generally made of conducting material such as copper. The radiating patch and the feed lines are usually photo etched on the dielectric substrate. The ground plane helps prevent excessive field leakage and thus reduce the radiation loss. The patch is selected to be very thin. The dielectric constant of the substrate (εr) is typically in the range 2.2 ≤ εr ≤ 12. The patch of the antenna is being excited by feed which is done by edge feed or a probe feed. When the patch is excited by feed a charge distribution is being established between the ground plane and the underneath of the patch. The underneath of the patch is charged to positive and the ground plane is charged to negative after the excitation by feed. The attractive forces are being setup between the planes i.e., patch underneath and the ground plane. The patch antennas radiate in the first case due to the fringing fields between the underneath of the patch and the ground plane. Experimental For this project, the transmission line feed with an inset cut in the middle of the patch structure is used as a feeder for the rectangular patch antenna and the operating frequency is chosen to be 4.5 GHz. By using the manual calculation as a guideline, CST Microwave Studio (CST MW) is used to simulate the rectangular patch antenna. After simulation process, the values of antenna parameters are used in the fabrication process. The proposed antenna

13


has been fabricated using Rogers 5870 substrate. AutoCAD software is used to export the dimensions of proposed antennas from CST microwave studio.

Fig 1: Designed antenna Fig 2: Fabricated antenna Results and Discussion The return loss and the resonating frequency of the patch antenna are compared for calculated, simulated and measured values. The simulation has been performed using CST microwave studio and the measured process has been carried out ZVB14 Rohde & Schwarz Vector Network Analyzer

Fig 3: Comparison of simulated and measured return loss.

Fig 4: Smith chart Fig 5: Directivity at far field region. region Input impedance of antenna is 50 Ω where discrete port is used in simulated process of the designed antennas. The directivity is high at the middle of the semi-sphere kind of

directivity radiation shape. Only half of the sphere is shown because the Zmin axis as been grounded using Et=0 condition at the boundary condition. Conclusion This work focuses on the designing of microstrip patch antenna operating in the C-band frequency range and the particular resonant frequency for this project is chosen to be 4.5GHz which is widely used for long-distance radio telecommunications such as wireless access point, satellite communications system and other wireless products. From simulated result, the resonant frequency is 4.49GHz but from measured result, the resonant frequency is about 4.48GHz. The return loss from simulated result is -48.745dB while from measured result, the return loss is-41.672dB. There is some discrepancy in the measured results compared to simulated results which is caused by the effect of soldering, ohmic losses, mismatch in the impedance and loss in cable of connectors. Acknowledgement I would pay my heartiest regards to Department of Electrical and Electronics Engineering at UTHM, Malaysia and Dr. Muhammad Yusof Ismail for guiding me all throughout my work and National Institute of Technology, Durgapur for assisting me with partial financial support without which it would never come to a successful completion.

14


STUDY OF DIRECTIONALITY OF FUSED QUARTZ BAR FOR INSTALLATION IN THE MUON HALO DETECTOR

Shankha Nag

B. Tech. final year student, Department of Metallurgical and Materials Engineering National Institute of Technology Durgapur, INDIA

Beam Radiation Monitoring Group, Department of Physics

CERN, Geneva, Switzerland, May 11 – July 10, 2011

Abstract The BRM group at CERN, Geneva has planned to use Fused Quartz material in the Directional Muon Halo Detector, to be installed during LS1 (the Long Shutdown 1 period starting in Feb 2013) for detecting Machined Induced Background in the CMS cavern. In this regard, the directionality of our Fused Quartz bar was studied by taking forward and backward acquisitions through the passage of cosmic muons in the bar that were directed towards and away from the Photomultiplier Tube (PMT) end respectively. Introduction Study of Directionality is important to judge the extent to which we can eliminate the background from our main Cherenkov readout. The Muon Halo Detector will receive particles due to Machine Induced Background (MIB) on one side and also particles from PP collision on the other side. Since we are interested to estimate the MIB we should be able to eliminate the signals produced due to the particles coming from PP collision. This requires our detector material to be directional.

Schematic of the interior of the CMS cavern with our detector (orange boxes). Beams B1 and B2 are the Machine Induced background which is to be estimated.

Basis of the Experiment The study of directionality was conducted employing cosmic muons. When a charged particle passes through matter at a speed greater than the speed of light in that medium, a directional electromagnetic shock wave is generated known as the Cherenkov Radiation. In our experiment we have compared the Cherenkov signals generated in the PMT due to the passage of cosmic muons through the bar, towards the PMT (Forward Acquisition) and in other case away from the PMT (Backward Acquisition) Experimental Setup

Forward Acquisition

Backward Acquisition

Results and Discussions Analysis of the Experimental Data centres on the following few parameters: (1) Signal Amplitude (2) Integrated Signal Amplitude

15


(3) Time interval for the primary peak to rise from 10mV threshold to the Absolute Amplitude (4) Full Width at Half Maxima Superimposed Gaussian Fits of Parameters for both Forward and Backward Acquisition Dual Fit of Absolute Amplitude:

Dual Fit of Integrated Signal Amplitude:

As evident from the Dual Fits of Absolute Amplitude and Integrated Signal Amplitude, there exist high rising peaks for the Forward Acquisition compared to the Backward Acquisition. Therefore a discriminator threshold can be defined so as to eliminate the background. To estimate this Threshold we need the cumulative plots.

Dual Fit of Full Width at Half Maxima:

In the LHC, the proton bunches are 25ns apart. So the width of the pulses needs to be less than 12.5ns for the signal to be distinguishable. Conclusion 1. Our Fused Quartz Bar is fairly directional since the Forward Acquisition shows very high mean Absolute Amplitude and Integrated Signal Amplitude values compared to the Backward Acquisition. 2. The discriminator cut voltage is 175mV which eliminates 94% of the background, substantiating good directionality. 3. FWHM for forward acquisition is much greater than that for the backward. In fact, FWHM for backward acquisition has a wider spread; thus the background can confuse with Cherenkov signal from the proton bunches 25ns apart. This is the reason we need to minimize the scintillation contribution to the background. Acknowledgment I am grateful to my institute for giving me this opportunity and providing the necessary funding. Also, Prof. N. K. Roy and Prof. Kumbhakar deserve recognition for guiding us with necessary assistance. Next I would like to thank Dr. Archana Sharma at CERN. Finally, a special regard for my supervisors Anne Dabrowski and Marina Giunta for their immense cooperation and support.

16


IMPACT OF LAYER STRAIN/LATTICE DEFORMATION ON THE DIELECTRIC PROPERTIES OF RARE EARTH OXIDES

Sharmili Adhikari


Institut für Materialen und Baulemente der Electronik (MBE)

(Electronics Materials & Devices) Leibniz Universität Hannover, Germany, May 10 – July 15, 2012

Abstract The ability to integrate crystalline metal oxide dielectric layers into silicon structures can pave the way for a miscellany of novel applications which enhances the functionality and flexibility, ranging from high-K replacements in future MOS devices to oxide/silicon/oxide heterostructures for nanoelectronic applications in quantum-effect devices. A very promising way to realize advanced future devices is using single-crystalline, closely lattice matched oxides, which will be deposited on the substrate of choice. Such an approach will annihilate the risk of re-crystallization, prevent the formation of interfacial layers and enable controllable interface properties. Thin epitaxial rare earth oxide layers on Si exhibit K values that are much larger than the known bulk values. One criteria is to investigate the thickness dependence of that enhancement effect for epitaxial Gd2O3 on Si(111). However, simple tetragonal distortion of the cubic lattice alone is not sufficient to explain the enhancement in K. Therefore, more severe strain induced structural phase deformations as one of the causative factors is proposed. Introduction One of the most critical challenges in semiconductor technology today is the replacement of the traditional silicon dioxide gate dielectric by new high- K materials. Binary crystalline rare earth oxides (REOs) have shown potential to replace silicon dioxide for end-of-roadmap CMOS technology. These crystalline oxides, if grown by Molecular Beam

Epitaxy (MBE), allow abrupt interfaces to silicon substrates, higher thermal stability and perfect lattice matching.

Fig 1: MBE system (DCA Instruments)

Theory / Model The fundamental origins of strain in heteroepitaxy are from two sources: i. The difference in lattice parameters. ii.Differential thermal expansion coefficients between epitaxial layer(s) and substrate. In oxides, the electrical and magnetic properties are very sensitive to strain. This is verified by investigating the change of the strain state in Gd2O3 and Nd2O3 on Si(111). This is followed by electrical characterisation of the samples obtained under experimental conditions. Experimental A Superlattice structure of Gd2O3/ Nd2O3/ Gd2O3 layers is grown on (111) oriented Si substrate.The layer thicknesses are around 3nm each to obtain a total thickness of 9nm, since Gadolinium is naturally relaxed at such thickness. A base pressure of 4E-10 torr and external oxygen flow is maintained.

17


Fig 2: RHEED pattern obtained after Ge passivation on Si(7x7) to form the (5x5) surface for layer structure evaluation The grown samples are evaluated electrically using Capacitance-Voltage and Current-Voltage measurements.

-2 -1 0 1 21E-7

1E-6

1E-5

1E-4

1E-3

0.01

I (Am

pere

)

Voltage (Volt)

CN055

Fig 3: The leakage current voltage (I-V) characteristics. For all investigated layers, the leakage current density measured at Vg=1 V is below 10-3 A/cm² Fig 4: Capacitance-voltage (C-V) measured at frequencies of 50 kHz and 100 kHz. To analyze the findings the following relation is employed-

…(1)

= area of the electrode = thickness of the sample

= oxide capacitance Results and Discussion The accumulation capacitance is used to extract the dielectric constant ( ); the derived value for the shown capacitor is calculated to be 18, which is greater than the bulk value of about 13.7. Indeed, an enhancement of the K value is clearly visible for thin layers. Conclusion In summary, molecular beam epitaxy is used to grow lanthanide oxide superlattices on Si(111) oriented surface. For thin layers, a strong enhancement effect in the K values is observed, as a result of tetragonal distortion of the cubic lattice in addition to severe strain induced structural phase deformations. Acknowledgement I consider it my prerogative to be able to thank my Professor, Prof. Dr. H. Jorg Osten and my supervisor, Dominik Schwendt, without whose extraordinary mentorship and patience this project would not have seen a successful completion. I would further like to extend my gratitude towards DAAD (German Academic Exchange Service) for awarding me the WISE- Working Internships in Science and Engineering –Scholarship which helped finance my stay and expenses in Germany.

I am indebted to my home institute, without the moral and financial support of which, the rare privilege of sharing office space with some of the scientific community’s renowned physicists and scholars would have been a distant possibility. And finally I would like to thank God and my parents for encouraging me throughout.

18


STOKES HYPOTHESIS

Sourayon Chanda B. Tech. final year student, Department of Mechanical Engineering


Institut für Thermodynamik der Luft-und Raumfahrt (ITLR) Unversität Stuttgart, Germany, May 10 – July 29, 2012

Abstract The present work is related to the age-old controversy regarding the Stokes hypothesis (1849) applied to formulate the Navier-Stokes equation. Even though this assumption, that the bulk viscosity is zero, has been found to be matching experimentally for some gases, many diatomic gases such as CO2, and even some mono-atomic gases are found to deviate from this hypothesis. This report is concerned with the introspection of the above controversy and analysis of the variation of bulk viscosity and related parameters with Mach number mainly for hypersonic flow, in case of CO2, where the effect of bulk viscosity is expected to be prominent. The ratio of bulk to shear viscosity used for CO2 in the analysis is in the range of 0.2 to 2.5, as confirmed by molecular dynamics simulations. Introduction Bulk viscosity, in very simple terms, may be defined as the coefficient which accounts for resistance to change in volume of the fluid element under consideration. Mathematically, it can be defined as:

23bμ λ μ= +

(1)

which arise as a consequence of representation of the stress tensor as a linear function of the rate of deformation tensor. According to the Stokes hypothesis, the bulk viscosity becomes zero for any fluid. This hypothesis reduces the number of physical properties which characterize the stress field from two to one. However, further developments in this field show that the existence of bulk viscosity, however insignificant it may be, cannot be denied.

The effect of bulk viscosity on heat transfer for hypersonic flows has been reported by Emanuel [1]. He numerically studied the flow over a flat plate and came to the conclusion that a high bulk viscosity significantly affects heat transfer for high Mach number flows. The mathematical formulation used in the present study in similar to the one used by Emanuel in his study. The present analysis of bulk viscosity has been done by considering flow over a flat plate as shown in figure 1.The parameters varied in the analysis to study the corresponding effects are bulk to shear viscosity ratio from 0.2 to 2.5, Mach number from 1 to 16 and wall to free-stream temperature ratio from 0.05 to 20. Even though the mathematical formulation used here is applicable for any ideal gas, the present study has been done with viscosity ratios similar to that expected to be found for CO2. The values of bulk viscosity used in this study have been obtained from molecular dynamics simulations for the same gas [2].

Fig. 1. Flow geometry (flow over a flat plate) Mathematical Formulation Laminar flow over a flat plate is considered for the analysis of the effect of bulk viscosity. The Levy-Lees scheme of transformation of the boundary layer

19


equations has been utilized. The two-dimensional, compressible, steady-state boundary layer equations have been implemented in this analysis. After the above stated transformation techniques have been used, the final combined form of the boundary layer equation is:

2 2 2 22 2G fG f G f Gηη η ξ Aξ′ ′+ = − + − (2) where the parameters are represented as:

( , )f f ξ η= (3)

( )0 0 0 0wh h h h G∞= + − w (4)

( )

( )( )

2

2

2 2

[ 1 2 2 (1

(1 ) ' ) '' 1 2

3 (1 )

w ww

w w

w w

ffA g Sf Sg

g f Sf ff g Sf

f f g g f Sf

⎡ ⎤′′′= − − −⎢ ⎥−⎣ ⎦

]

g +

′ ′ − − + − −

′ ′ − + − − ′

(5) ξ and η are the new coordinates required for non-similar boundary layer approximation. Results and Discussion The results obtained in the analysis are quite contradictory to that obtained by Emanuel [1]. The primary reason for this variation in results can be attributed to the fact that in Emanuel’s study, he considered the bulk to shear viscosity ratio to be of the order of 103, whereas in this analysis, it is taken in the range of 0.2-2.5, which matches with experimental as well as molecular simulation values. The other secondary reasons are the correction in the energy equation formulation and in the pressure ratio relation. The pressure variation in the boundary layer in case of the present geometry was found to be negligible, contrary to the results given by Emanuel [1]. The study of variation of A with η shows interesting results. One example is shown in Fig. 2. The variation is zero at the wall and free-stream; however the variation is notable in between. Even as the η increases, a point is reached where A again becomes zero and then, it changes its sign. The shape of the curve also changes with increase in Mach number. The effect of bulk viscosity is expected to be more prominent when

wg >1 because the variation in A is of greater magnitude in this case.

Fig. 2. The variation of A with η for 4M ∞ =

In the study, the effect of bulk viscosity on heat transfer is also found to be extremely small. The fraction contributed by bulk viscosity increases with increase in Mach number and for heat transfer from the wall to the free-stream, it increases with the increase in the difference between the wall and free-stream stagnation temperature. Conclusion The present study shows that for gases like CO2, the Stokes hypothesis can be fairly accurate for heat transfer analysis for low Mach numbers when the forces to cause a change in volume is not very pronounced. This fraction of heat transfer contributed by the bulk viscous effect attains a value of more than 10 per cent only when α goes as high as 2000, that too when the Mach number is around 16. Acknowledgement I am greatly indebted to Dr. Achintya Kumar Pramanick for his valuable recommendation and constant encouragement. I am very grateful to René Chatwell for his kind help, and to Prof. Dr. –ing. Habil Bernherd Weigand for efficiently guiding me throughout the internship. I would also like to thank NIT Durgapur for giving me this wonderful opportunity.

20


OPTIMAL DESIGN OF LINEAR PHASE FIR BAND STOP FILTER USING PARTICLE SWARM OPTIMIZATION WITH IMPROVED

INERTIA WEIGHT TECHNIQUE

Abhisek Mukhopadhyay B. Tech. third year student of Electronics & Communication Engineering Department


2012 Ninth International Joint Conference on Computer Science and Software Engineering (JCSSE), Bangkok, Thailand, May 29-June 02, 2012

Abstract Recently there has been a lot of research conducted on FIR filter design problem which involves multi-modal, multiparameter optimization techniques that can be utilized to determine the impulse response coefficient of a filter and try to meet the ideal frequency response characteristics. In this paper a recently proposed multi-objective swarm optimization algorithm called particle swarm optimization with improved inertia weight (PSOIIW) is applied for the design of optimal linear phase digital band stop finite impulse response (FIR) filter. PSOIIW adopts a new definition for the velocity vector and swarm updating and hence the solution quality is improved. The inertia weight has been modified for the PSO to enhance its search capability to obtain the global optimal solution. The key feature of the applied modified inertia weight mechanism is to monitor the weights of particles, which linearly decrease in general applications. In the design process, the filter length, pass band and stop band frequencies, feasible pass band and stop band ripple sizes are specified. The simulation results obtained prove the superiority of the algorithm compared to the other prevailing optimization algorithms like real code genetic algorithm (RGA), particle swarm optimization (PSO), and differential evolution (DE) for the solution of the multimodal, non-differentiable, highly nonlinear and constrained FIR filter design problems. Introduction Filtering is considered as one of the most challenging field of signal processing and

used to remove the frequencies in certain parts and to improve the magnitude, phase, or group delay in some other parts of the spectrum of a signal. A digital filter is simply a discrete time, discrete amplitude convolver that takes a digital input, gives a digital output, and consists of digital components. With the digital filter, one can achieve the target of a lower pass band ripple, faster transition, and higher stop band attenuation. Digital filters are mainly classified into two categories: •Finite impulse response (FIR) filters Advantages: They are always stable and can achieve linear phase frequency responses. •Infinite impulse response (IIR) filters Advantages: Easy to design as there are fixed optimal methods of design depending on the length of the impulse response. In this paper we take up the case of the design of a linear phase optimal bandstop FIR filter. Theory / Model Realisable digital FIR filter is characterized by,

H (z )= ∑n= 0

n= N

h(n) z−n

where N is the order of the filter which has (N+1) number of coefficients h(n) as the filter impulse responses. This paper presents the most widely used FIR filter with h(n) as even symmetric and the order is even. The length of h(n) is N+1 and the number of coefficients is also (N+1). Because the coefficients h(n) are symmetrical, the dimension of the problem is halved. The (N/2) coefficients are then flipped around the N/2+1th coefficient and

21


concatenated to find the required (N+1) number of coefficients. The optimization algorithm attains the least error between the desired frequency response and the actual frequency response by determining the optimal h(n) values after a certain maximum number of iterations. The optimal h(n) values, after concatenation, finally represent the filter with better frequency response.

Is taken as the fitness function for comparison of the results. The Improved PSO is given by:

where V i , k is the velocity of ith particle

vector at k th iteration. w is the weighting

function. C1 and C2 are the positive weighting factors rand1 and rand2 are the random numbers between 0 and 1. Xik is the current position of ith particle vector h(n) at k th

iteration; pbest I , k is the personal best of the i^th particle at the k th

iteration; gbest k

is the group best of the group at the k th

iteration. The first term of the formulation is the previous velocity of the particle vector. The second and third terms are used to change the velocity of the particle vector. Without the second and third terms, the particle vector will keep on ‘‘flying’’ in the same direction until it hits the boundary. Namely, it corresponds to a kind of inertia represented by the inertia constant, w and tries to explore new areas. This inertia weight w plays the important role of balancing the global and local exploration abilities. The searching point in the solution space and w may be modified by:

Results and Discussions The graphical results are given as under, the results clearly show an improvement over conventional methods of filter design,

as they show less stop band ripple and low pass band attenuation. Conclusion In this paper, a Particle Swarm Optimization with Improved Inertia Weight (PSOIIW) algorithm is applied for the solution of the constrained, multi-modal FIR filter design problem with optimal filter coefficients. Comparison of the results of PM,RGA, PSO, DE and the PSOIIW algorithm has been made. It is revealed that the PSOIIW has the ability to converge to the best quality near optimal solution and possesses the best convergence characteristics in much less execution times among the algorithms. The simulation results clearly indicate that the PSOIIW demonstrates better performance in terms of magnitude response, minimum stop band ripple and maximum stop band attenuation with almost same level of transition width. Thus, the PSOIIW may be used as a good optimizer for obtaining the optimal filter coefficients in any practical digital filter design problem of digital signal processing systems. Acknowledgement I am deeply thankful to NIT Durgapur for partially funding my visit, Thailand, and gratefully acknowledge the support and advice of Dr. Rajib Kar and Dr. Durbadal Mandal of ECE department.

22


OPTIMIZATION OF IIR HIGH PASS FILTER USING CRAZINESS BASED PARTICLE SWARM OPTIMIZATION TECHNIQUE

Annesha Chaudhuri

B. Tech. final student, Department of Electronics & Communication Engineering National Institute of Technology Durgapur, INDIA

Humanities, Science and Engineering Research (SHUSER), 2012 IEEE Symposium

Kuala Lumpur, Malaysia June 24-27, 2012 Abstract In this paper, a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) is used for the design of 8th order infinite impulse response (IIR) digital filter. From the simulation study it is established that the CRPSO outperforms RGA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and solution quality, i.e., the stop band attenuation, transition width, pass band and stop band ripples. Further, the pole-zero analysis justifies the stability of the designed optimized IIR filter. Introduction A filter is a frequency selective device which extracts the useful portion of input signal lying within its operating frequency range which could be contaminated with random noise due to unavoidable circumstances. In this paper, we try to optimize Digital IIR filters. To improve performance we try to minimize objective functions like mean square error between desired response and estimated filter output, this is traditionally performed by gradient based iterative search algorithms. However the classical gradient based optimization methods are not suitable for IIR filter optimization problem because of many reasons. So, evolutionary methods have been employed in the design of digital filters to design with better parameter control and to better approximate the ideal filter. This paper describes a novel technique for the IIR high pass digital filter design using craziness based particle swarm optimization (CRPSO). This tries to find the coefficients that closely match the ideal frequency response and gets a better

performance of the proposed designed method. Theory / Model The input output relation of IIR filter is governed by following difference equation.

Let and and Then the frequency response of the IIR filter becomes

The commonly used approach for IIR filter design is to represent the problem as an optimization problem with the mean square error (MSE) as the error fitness function expressed as in

Apart from the inherent advantages of PSO, the proposed algorithm CRPSO is also enriched with better searching capacity in a multidimensional search space. The basic velocity expression of conventional PSO is

The modification of searching point in the solution space may be expressed as

The modified velocity expression for CRPSO obtained from conventional PSO is

CRPSO is dictated as follows, say birds’ flocking for food, there could be some rare cases that after the position of the particle

23


is changed, a bird may not, due to inertia, fly towards a region at which it thinks is most promising for food. So, the direction of the bird’s velocity should be reversed in order for it to fly back to the promising region this is introduced for this purpose. This is described by using a ‘‘craziness’’ factor. Consequently, before updating its position the velocity of the particle is crazed by

Results and Discussion Analysis of Magnitude Response of IIR High pass filter: The MATLAB simulation has been performed extensively to realize the high pass IIR filter of the order of 8. fs =1 Hz , (δp) = 0.001, (δs) = 0.001, (ωp) = 0.35, (ωs) = 0.30.

The graphs of RGA and PSO are omitted RGA converges to the minimum error fitness value of 3.551 in more than 450 iteration cycles; PSO converges to 2.577 in more than 500 iteration; whereas, CRPSO converges to 0.9339 in less than 200 iteration. From the plot shown in Fig. 6, it is seen that the CRPSO algorithm is significantly for finding the optimum filter coefficients. Further, RGA and PSO yield suboptimal higher values of error but CRPSO yields near optimal error values.

Comparative effectiveness & convergence profiles of RGA, PSO and CRPSO:

Conclusion CRPSO not only provide the highest stop band attenuation but also the quality output in terms of ripple and transition width which are much improved than the others. Also the proposed CRPSO technique attains the highest convergence speed with the minimum error fitness value and hence the CRPSO is adequate enough for handling other related design problems. Acknowledgement My greatest gratitude is due to Dr. R. Kar and Dr. D. Mandal for helping me with the paper.

24


GENERATION OF MULTIPLE SIDE LOBE LEVELS OF NON UNIFORMLY EXCITED LINEAR ARRAY ANTENNA USING ITERATIVE

FAST FOURIER TRANSFORM

Anwesh Mukherjee B. Tech. final year student, Department of Electronics & Communications Engineering


IEEE Symposium on Computers & Informatics (ISCI 2012), Penang, Malaysia, March 18-20, 2012

Abstract This paper describes amplitude control method for the synthesis of broadside non-uniformly excited and uniformly spaced linear array antenna for generation of multiple side lobe levels using iterative Fast Fourier transform. Compared to other evolutionary methods this technique has higher computational speed. This is the major advantage of this technique i.e. it takes less computation time. This is because of the fact that the core calculations are based on direct and inverse fast Fourier transforms (FFT). An example has been presented with 30 isotropic antennas for producing two different side lobe levels of –50 dB and –30dB. Introduction Antenna arrays constitute one of the most versatile classes of radiators due to their capacity for beam shaping, beam steering and high gain [1]. Array antennas have been widely used in mobile, wireless, satellite and radar communications systems to improve signal quality, thereby increasing system coverage, capacity and link quality. Pattern synthesis is the process of choosing the antenna parameters, such as the specific position of the nulls, the desired side lobe level and beam width of antenna pattern, to obtain radiation pattern close to the desired one. One of the most important parameters in array designing is the side lobe level (SLL). Theory / Model We have considered a linear array of N isotropic antennas [1]. Antenna elements are equally spaced a distance d apart along the Z-axis as shown in Fig.1. The

free space [1] far-field broadside pattern F(u) in the principal vertical plane is given

by: Where n is the element number, λ is the wavelength, An is the excitation current amplitudes of the elements, i is the imaginary unit, k (=2π/ λ) is the wave number, d is the interelement spacing and u=cos θ, θ being the polar angle of farfield measured from broadside (0o to 180o). Normalized absolute power pattern in dB can be expressed as follows:

Array factor (AF) in the vertical plane is

given by:

Above mapping to Inverse Fast Fourier Transform is used for the following steps, Step1: Start the design process with uniform random excitation An between 0 and 1 for N elements. Step2: Using a K-point inverse FFT, with K>N compute AF from An.

25


Step3: Adapt AF to the prescribed multiple side lobe levels. Step4: Compute An for the adapted AF using a K-point direct FFT. Step5: Truncate An from K samples to N samples by making zero all samples outside the array and then calculate absolute value of An normalized to one. Step6: Repeat Steps 2-5 until the prescribed side lobe requirements for AF are satisfied, or the allowed number of iterations is reached. Experimental In this paper, a linear antenna array structure with equal spacing (0.5l) between any two consecutive elements has been considered. Multiple side lobe level pattern is generated using non-uniform excitation amplitudes of the elements. The design criterion considered here is generation of multiple side lobe levels. Program used here for the synthesis of linear array antenna is written in Matlab. We have used 4096-point IFFT padded with zeros if excitation current has less than 4096 points to get the desired result. The program is run for 1000 iterations. The computation time is measured with a PC with Intel corei3 processor of clock frequency 2.4 GHz and 4GB of RAM. Here we present an example of a linear array of 30 isotropic antennas for producing two different side lobe levels of -50dB and -30dB.Obtained results are shown in table I. Figure 2 and 3 show normalized absolute power pattern in dB and normalized amplitude distribution respectively for 30 elements linear array for generating multiple side level in the radiation pattern. Two side lobe levels are obtained in the radiation pattern with depth -50 dB and -30 dB. Figure 4 shows how the side level 2 changes with the number of iterations. Results Table I Design parameters

Desired side lobe level (dB)

Obtained side lobe level (dB)

Computation time

Side lobe level 1 (SLL1 in dB)

–50.00 –50.0963 3.9759 seconds

Side lobe level 2 (SLL2 in dB)

–30.00 –30.8212 3.9759 seconds

Conclusion In this paper, the usefulness of iterative Fourier technique algorithm is established to generate multiple side lobe levels in the radiation pattern of a non-uniformly excited and uniformly spaced linear antenna array by changing only the excitation amplitudes. IFFT thus serves as an alternative in array design to other algorithms based on nature laws such as GA, PSO etc. The principal advantage of this method is its simplicity that provides an easy, quick and effective resolution of medium or large problems. There is a very good agreement between the desired and obtained results. Future research will focus on achieving more control on suppressing side lobe level using the iterative fast Acknowledgement Help from Dr. Gautam Kumar Mahanti is Invaluable. Monetary help from our college made this experiment realisable.

26


MOMENT BASED DELAY MODELLING FOR ON-CHIP RC GLOBAL VLSI INTERCONNECT FOR UNIT RAMP INPUT

Arka Halder

B. Tech. third year student, Department of Electronics & Communication Engineering National Institute of Technology Durgapur, INDIA

2012 Ninth International Conference on Computer Science and Software Engineering

(JCSSE), School of Engineering, University of the Thai Chamber of Commerce, Bangkok, Thailand, May 30-June 1, 2012

Abstract . The Elmore does not have sufficient accuracy in measuring delay. This paper presents an accurate and efficient model to compute the delay metric of on-chip high speed VLSI interconnects for ramp inputs, based. The proposed model is based on the first three moments of the impulse response. Two pole RC model is developed based on the first, second and third moments’ effect onto the delay calculation for interconnect lines, which permits the pre-characterization of the interconnect delay. The proposed metric also provides an expression for impulse response. The SPICE simulation results justify the accuracy and efficacy of the proposed model. Introduction An approximation to the time at which the output voltage of the interconnect v(t), for a ramp input, reaches 50% of its final value. Since interconnect resistance is higher, its shielding effect is more important. Elmore delay, neglecting the resistance shielding, does not capture the correct sensitivity, which is very crucial and can lead to unacceptably large errors. While the Elmore delay is probably an upper bound for the 50% delay of large class of RC tree response. The tightness of the bound varies significantly from one node to create higher order (2-pole) moment matching models from which the delay can be approximated explicitly. This removes the disadvantages of Elmore delay, keeping the accuracy reliable.

Theory / Model

By using basic laws of electrical circuits in the 2 loops in the above interconnect, we find the transfer functions and the first three moments, given as: .

In terms of moments,

The 2 poles of the equation are:

Proceeding, the expression for dominant and coincident poles are given by:

for dominant pole and

27


for coincident pole. Experimental

Above show the results for dominant and coincident poles and the output voltage waveform. Results and Discussion The delay values for our proposed model are compared with HSPICE and Elmore delay equivalent simulation for different value of C, for both dominant and coincident poles. From the experimentally obtained values, we can analyze that the possible errors in the value of delay obtained in both cases with that of SPICE value is less than 10%. Advantages of the above model: 1. Accurate results. 2. Applicable to any type of interconnect. 3. Compatible with transient analysis and

wide band design. 4. Comparison with measurement data

shows its validity. Conclusion Proposed model efficiently estimates delay in interconnect lines and is applicable to any type of interconnect as this approach is not based on the analogy of the impulse response to a particular Probability Distribution Function (PDF). Experimental results verify the same. Acknowledgement As the paper is the result of a combined effort, i would like to show my gratitude to my co authors and also like to thank NIT Durgapur authorities for allowing a financial grant without which my successful participation in the conference could not have taken place.

28


NOVEL PARTICLE SWARM OPTIMIZATION FOR HIGH PASS

FIR FILTER DESIGN

Dishari Chakraborty B. Tech. final year student, Department of Electronics & Communication Engineering


2012 IEEE Symposium on Humanities, Science and Engineering (SHUSER 2012) Kuala Lumpur, Malaysia, June 24-27, 2012

Abstract This paper presents an optimal design of linear phase digital high pass finite impulse response (FIR) filter using Novel Particle Swarm Optimization (NPSO). A comparison of simulation results reveals the optimization efficacy of the algorithm over the prevailing optimization techniques for the solution of the multimodal, non-differentiable, highly non-linear, and constrained FIR filter design problems. Introduction Digital filter is an important part of digital signal processing (DSP) system. The objective function for the design of optimal digital filters involves accurate control of various parameters of frequency spectrum and is thus highly non-uniform, non-linear, non-differentiable and multimodal in nature. In this paper, the benefits of designing the FIR filter using a stochastic technique known as Particle Swarm Optimization (PSO) has been explored. Particle Swarm Optimization is an evolutionary optimization technique developed by Eberhart et al. The merits of PSO lie in its simplicity to implement as well as its convergence can be controlled via few parameters. Theory/Model The main advantage of the FIR filter structure is that it can achieve exactly linear-phase frequency responses. A digital FIR filter is characterized by:

Error fitness is the error between the frequency responses of the ideal filter and the designed approximate filter. In this paper, a novel error fitness function has

been adopted in order to achieve higher stop band attenuation and to have an accurate control on the transition width.

The error fitness function given represents the generalized fitness function to be minimized using evolutionary algorithms. Eberhart et al. developed PSO concept similar to the behaviour of a swarm of birds. Each particle tries to modify its position using the following information: • The distance between the current position and the pbest( best value of the particle). • The distance between the current position and the gbest (best value of the group). In NPSO weighing factor is given by:

Proper selection of Z provides a trade-off between global and local explorations, thus requiring less number of iteration cycles on average to find out the global optimal solution. The velocity is updated as

And position is given by:

The steps of NPSO as implemented for linear phase high FIR filter design arellows: Step 1: Initialization: Population (swarm size) of particle vectors =25; maximum iteration cycles=200; number of filter coefficients (h(n)), filter order, N=20; fixing values of C1, C2 as 2.05; minimum and maximum values of filter coefficients, hmin=-2,hmax=2;number of samples = 128; stop band and pass band ripple =0.01 and 0.1.Vi(max)=1 Vi(min)=0.01 & Z=100 Step 2: Generate initial particle vectors of

29


filter coefficients (N/2+1) randomly with limits; Computation of initial fitness values of the total population, np. Step 3: Computation of population based minimum error fitness value & computation of the personal best solution vectors (pbest), group best solution vector (gbest). Step 4: Record & update the best values. Step 5: Update the velocities, particle vectors, check against limit of filer coefficients, and compute updated values of error fitness function. Step 6: Repeat steps 3-5 until end conditions are reached. Results and Discussion This section presents the simulations performed in MATLAB 7.5 for the design of FIR high pass (HP) filter. The filter order (N) is taken as 20. Conclusion In this paper, a novel particle swarm optimization algorithm (NPSO) is applied to the solution of the constrained, multimodal FIR high pass filter design problem with optimal filter coefficients. Comparison of the results of PM, RGA, PSO, DE and the NPSO algorithm has been made .It revealed that the NPSO may be used as a good optimizer for obtaining the optimal filter coefficients in any practical digital filter design problem of digital signal processing systems.

30


LOW POWER VLSI CIRCUIT IMPLEMENTATION USING MIXED STATIC CMOS AND DOMINO LOGIC WITH DELAY ELEMENTS

Gaurav Khetan


International Conference on "Research & Development (IEEE SCORED 2011)"

Putrajaya, Malaysia, December 19-20, 2011 Abstract The advent of dynamic CMOS logic, more precisely domino logic, made them widely used for the implementation of low power VLSI circuits. However, the main drawback of this logic is the non-implementation of inverted logic. To implement the inverted logic, it is required to duplicate the logic circuit up to that part with inverted inputs. This obviously results the increase in area, delay as well as the power dissipation of the circuit. On the other hand, it is very simple to realize the circuit with both the inverted and non-inverted logic using static CMOS implementation. In this paper, this problem is addressed with the realization of the circuit which requires the implementation of inverted logic using mixed static and domino logic. To show the efficiency of the proposed model, a simple example like implementation of high fan-in NAND gate cascaded with AND gate is considered. With the comparison of all the three logics with a fixed fan-in of 7, 8 and 9 for both the gates, on an average 69.7% improvement is achieved in Power Delay Product (PDP), 11.4% improvement in area in terms of transistors using mixed logic implementation over static logic implementation and 68.64% improvement in PDP and 28.4% improvement in area over dynamic CMOS implementation when designed in 180nm technology. Introduction For the implementation of low-power and high-speed VLSI circuits, dynamic CMOS in particular domino logic is the logic of choice [1]. However, domino logic has many inherent limitations like charge leakage, charge sharing, clock skew etc. [2]. The main disadvantage in implementing domino logic is that it can

implement only non-inverting logic. The requirement of implementation of inverted logic forces the designer to duplicate the entire circuit before that inverter with opposite polarities of inputs which increases the number of gates in the circuit which in tern increases the power dissipation and delay of the entire circuit. Hence the efficiency of domino logic is challenged if the circuit requires the implementation of more intermediate inverters. The implementation of static CMOS is efficient as it can implement both the inverted and non-inverted logic. However static CMOS logic is slower than dynamic logic and suffers from large area and high short circuit power dissipation [6]. On the other hand dynamic CMOS logic has less transistor count and zero short circuit power dissipation. Now considering the advantages of both logic styles, in this paper, novel circuit architecture, using mixed static and dynamic CMOS logic has been proposed. Very few attempts have been made to implement a given circuit using mixed logic. Out of which an approach called two phase static-domino design [4-5] used two out of phase clocks, master and slave flip flops. In the first domino evolution phase, domino logic and static CMOS logic gets evaluated but the output of static CMOS logic is fed to domino logic only in the second phase of evaluation. The presence of two clocks in this design results in inevitable problems like clock skew and clock routing overheads. The requirement of mid-cycle latches between two domino phases often degrades the performance of the circuit. In another approach [3], instead of static inverter in a domino gate, complex static gate has been used, which follows strict sequence of domino gate-static gate-

31


domino gate, called DS domino gate. Whenever the intermediate inverter implementation was necessary, [3] used either a dual output domino gate or dual output static gate. This approach requires more number of parts to be implemented using static CMOS which degrades the circuit performance in terms of area, power dissipation and delay. In this paper, a given circuit is realized using mixed static CMOS logic and domino logic which need not be a sequence of Domino gate - Static gate - Domino gate [3] and the output of the Static gate can be fed to the next gate in every evolution phase not like in [4-5]. The parts of the circuit which requires the implementation of inverted logic is implemented using static CMOS logic where as others using domino logic. So this requires less transistor number compared to both the logics which in turn reduces the delay and power dissipation of the circuit. The rest of the paper is organized as follows: Section 2 discusses the design issues in implementation of mixed staticdomino logic, section 3 describes the proposed model of implementation using mixed logic and section 4 shows the schematics of the circuits using all the three logic styles designed in 180nm technology. Section 5 discusses the simulation results and finally section 6 concludes the paper. Theory / Model Using Mixed Logic Since the circuit demands for the implementation of inverted logic, the part of the circuit requiring the implementation of inverter can be replaced with proposed static gate as shown below. It is clear from the schematic that number of transistors required to implement using this logic is less compared to both static CMOS logic and domino logic. The part of the circuit in rectangular box is the static part following timing constraints. Proper delay is given to the inputs and clock, based on the propagation delay of the previous elements of the circuits to maintain the synchronization of the circuit and for efficient functioning of the logic. .

Results and Discussion The simulations are performed in 180nm technology node for all the three logics using same process parameters. The input to each circuit is pulsated in such a way that it covers all the possible inputs. The delay is calculated in ns, power dissipation in mW and area in terms of transistors. The following tables give the % of improvement in delay, area, power and Power Delay Product (PDP) for the mixed logic over static and domino logic. Conclusion Despite many difficulties in implementing domino logic, we implemented mixed logic effectively bounding to timing constraints. Using this mixed logic the problem of implementation of inverted logic can be eradicated. With this logic we achieved on an average 11.4% improvement in area, 69.7% improvement in PDP over static logic and 28.4% improvement in area, 68.64% improvement in PDP over domino logic for the standard examples. This model can be applied for implementation of any intermediate inverted logic and it need not be a strict domino gate – static gate domino gate and this can be extended for implementing not only an inverter but also other inverting gates Acknowledgement I am grateful to Dr. Rajib Kar of Electronics and Comm. Engineering Department of NIT Durgapur.

32


IIR SYSTEM IDENTIFICATION USING PARTICLE SWARM OPTIMIZATION WITH CONSTRICTION FACTOR AND

INERTIA WEIGHT APPROACH

Ishita Rakshit B. Tech. final Year student, Department of Electronics and Communication Engineering


2012 IEEE Symposium on Humanities, Science and Engineering Research, Kuala Lumpur, Malaysia, June, 24-27, 2012

Abstract In this paper a modified version of swarm intelligence technique called Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSO-CFIWA) is applied to IIR adaptive system design problem. The proposed technique PSO-CFIWA performs a structured randomized search of an unknown parameter within a multidimensional search space by manipulating a swarm of particles to converge to an optimal solution. The exploration and exploitation of entire search space can be handled efficiently with the proposed technique PSO-CFIWA along with the benefits of overcoming the premature convergence and stagnation problems. The simulation results justify the optimization efficacy of the proposed PSO-CFIWA over RGA and PSO. Introduction System identification is a challenging and complex optimization problem due to nonlinearity of the system and dynamic nature of the environment. To achieve a particular level of performance, an adaptive IIR filter requires lower order compared to adaptive FIR filter. In adaptive IIR filtering applications, non differentiable and multimodal nature of error surface is a major point of concern. Classical optimization methods are gradient based optimization techniques and are incapable to handle optimization problems. To overcome their shortfalls meta-heuristic search algorithms are used. An alternative modified technique PSO-CFIWA is employed for handling non linear system identification to overcome

conventional limitations of evolutionary algorithms like PSO. Theory/Model In the system identification configuration, the adaptive algorithm searches for the adaptive filter coefficients such that its input/output relationship matches closely to that of the unknown system.

Fig1: Basic block diagram of adaptive IIR filter for system identification The input output relation is governed by the difference equation:

Here, y(p)=filter’s output, x(p)= filter’s input The transfer function of the adaptive IIR filter is:

n=order of the filter The unknown plant of transfer function Hs(z) s is to be identified with the adaptive IIR filter Haf(z) in such a way that the output from both the systems matches closely.

J(w) is considered as the cost function where d(P)= desired response, Ns= the

33


number of samples, y(p)=the response of the adaptive IIR filter, error signal e(p). The cost function can be minimized by the coefficient vector w=[a0a1..anb0b1…bm]T Experimental For enhancement of the global search criteria, the basic equation of PSO has been modified to obtain the velocity expression of PSOCFIWA as: Vi

(k+1) = CFa*[w(k+1)*Vi(k)+ C1*rand1*(pbesti(k)

– Si(k))+C2*rand2*(gbest(k) – Si

(k))] Normally, C1=C2=1.5-2.5 In order to avoid the phenomenon of “swarm explosion”, the constriction factor is introduced :

where and inertia weight at kth cycle:

The searching point solution in space is: Results and Discussions Extensive MATLAB simulation studies have been performed for the performance comparison of three algorithms namely, RGA, PSO and PSO-CFIWA for nonlinear system identification optimization problem. All optimization programs are run in MATLAB 7.5 version on coreTM 2 duo processor, 3.00 GHz with 2GB RAM. The transfer function of the second order IIR plant to be modeled Hs(z) is

This modeling is done using a second order IIR filter having transfer function Haf(z):

Fig 2: convergence characteristics of 2nd order IIR system modeled using 2nd order IIR filter. PSO-CFIWA attains lowest MSE values for different runs, requires the least number of iteration cycles to converge to the minimum MSE level and also least deviation is observed for any coefficients under consideration. Thus it is the fastest among all. Conclusion Extensive simulation study with benchmarked IIR plants justify that PSO-CFIWA is a viable plant identification and optimization tool for modeling the nonlinear system. Successive experiments prove that PSO-CFIWA not only outperforms RGA and PSO in acquisition of lowest MSE values and fast convergence to the optimal solution but that it also has a greater accuracy in modeling an unknown system with the same or reduced order filter. Acknowledgement I am deeply indebted to Dr. Rajib Kar and Dr. Durbadal Mandal of the department of Electronics and Communication Engineering, NIT Durgapur for their help, stimulating suggestions, encouragement and knowledge which helped me during the study and analysis of the topic both in pre and post research period. I am also grateful to Dr. S.P.Ghoshal and Dr. M. C. Majumder for their invaluable guidance and support.

34


4-Π CROSSTALK NOISE MODEL FOR DEEP SUBMICRON VLSI GLOBAL RC INTERCONNECTS

Naomi Joshi


2012 IEEE Symposium on Humanities, Science and Engineering Research,

Kuala Lumpur, Malaysia, June 24-27, 2012 Abstract This paper presents an improved, highly accurate and efficient complete analytical 4-π crosstalk noise model which incorporates all physical properties such as victim and aggressor drivers, coupling locations in both lines (victim and aggressor) and distributed RC characteristics of on-chip VLSI interconnects. In this paper, various noise avoidance approaches are explained. Sensitivity expressions of parameters to peak noise and noise width are also used in this explanation. The analysis and evaluation of the parameters are done using the proposed model. In the explanation of crosstalk noise model various driver/interconnect parameters are included as done in any sensitivity based noise avoidance approach. Introduction Performance optimization has always been a critical step in the designing of integrated circuits. In the past, timing and power analysis have been critical criteria to be optimized in the design process. In today’s deep submicron designs, coupling capacitance between neighbours is a dominant component. According to the trends, the role of this coupling capacitance will be more dominant in the future as feature sizes shrink. Due to this coupling capacitance crosstalk noise is induced in the circuit, which degrades the performance and reliability of the circuit. One important effect of coupling capacitances is that they may induce unwanted voltage spikes in neighbouring nets. Crosstalk noise not only leads to excessive signal delays but also causes potential logic malfunctions. To deal with crosstalk noise estimation and avoidance

problems, various phenomenon, techniques and tools should be incorporated in early stages of the IC design cycle. Crosstalk noise modelling approaches are mainly classified into two categories based on their trade-off between accuracy and efficiency. One is analytical modelling and another is SPICE simulation. SPICE simulation is always computationally expensive and time consuming with the modern design containing millions of transistors and wires. This paper presents a 4-π model based on the approach explained. The Compete 4-π Crosstalk Noise Model We present a 4-π crosstalk noise model and derive analytical formulas for its time domain waveform, peak noise and peak width. The decoupled model to calculate transfer function is obtained as shown in Figure 1(a). Transfer function is computed from to , then we apply inV 2V ( )sV2 to the victim line as seen in Figure 3(b)

(a)

(b)

Figure.1. Decoupled model to calculate transfer function

From Figure 1(a),

( ) ( )11

1

2

22 ..

ZRZ

ZRZsVsV

athin ++

=

Then from the Figure 1(b) We have,

35


( ) ( )sV

sCZ

ZsV

c

2

4

45 .

1+

=

( ) ( )

32

35 1

1

.

vv

vout

sCR

sCsVsV

+=

Now, on putting the all values together,

( ) ( )sVZR

ZZR

Z

sCR

sC

sCZ

ZsV in

ath

Vv

v

c

out ...1

1

.1 11

1

2

2

32

3

4

4

++++=

Transfer function H(s) from to : inV outV

( )obsbsbsbs

asasasH

++++++++

=1

22

77

801

55

........

For the aggressor with saturated ramp input with normalized VDD=1 and transition time tr i.e.

( )⎪⎩

⎪⎨⎧

>

≤=

r

rrin

tt

tttt

tv1

Its Laplace transform is

( ) 2

1stesV

r

st

in

r−−=

Our aim is to find out simple analytical expression for important design metrics such as noise width and peak noise.

( ) ( ) ( )( )1

1

01

01

+−

=++

≈−

ststetsV

bsbasasV

vr

stx

inout

r

where the coefficients are ( cvhx CRRt 1+= )

))

( )( ) (

( ) ( thaaathaatha

vhvvvvcvhv

RRRCRRCRCCRCRCCCRRt

+++++++++++=

213121

132321

222

We get following time-domain waveform by inverse Laplace transform

( ) ( )

⎪⎪⎪

⎩

⎪⎪⎪

⎨

⎧

>⎟⎟⎠

⎞⎜⎜⎝

⎛−

≤⎟⎠⎞

⎜⎝⎛ −

=−−−

−

rr

tt

ttt

x

rr

tt

x

out

ttt

eet

ttt

et

tvvv

r

v1

The peak noise is ( )rout tv .

⎟⎟⎠

⎞⎜⎜⎝

⎛−=

−v

rt

t

r

xpeak e

tt

v 1

For a given threshold voltage level , the noise width for a pulse is defined to be the length of time interval that noise spike voltage is greater or equal to certain

threshold voltage .In this paper we consider the value of threshold voltage to be half of the peak noise voltage,

tv

v

tv

1

tv

2/maxvvt =

Noise width = 2 tt − Now if we simplify the analytical expression of noise width then we get, widtht

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

−

−−

−

vr

vr

tt

tt

v

e

e

1

1ln

2

+= rwidth ttt

Simulation Results We generated 1000 random circuits using the parameter ranges. The units are mV/fF for capacitances and mV/Ω for resistances. Figure 2(a) shows the effects of victim driver sizing on a short victim line, in this case peak noise voltage is reduced by 85mv/46.5% whereas noise width is reduced by 25ps/9.97% when victim driver size is doubled. Now as seen in Figure 2(b) victim driver sizing on a long victim line reduces noise width by 572ps/26% while peak noise is reduced by 0.64mv/1% when victim driver size is doubled.

Figure 2. Sensitivity of victim driver sizing

effects to victim line properties Acknowledgement I would like to extend my heartfelt gratitude to the Department of ECE of NIT Durgapur, especially to Dr. Rajib Kar and Dr. D. Mondal for their immense help whole throughout. I would also like to thank NIT Durgapur for the financial support.

36


DELAY AND TRANSIENT RESPONSE MODELLING OF ON-CHIP RLCG INTERCONNECT USING TWO-PORT NETWORK FUNCTIONS

Portia Banerjee

B. Tech. final year student, Department of Electronics and Communication Engineering National Institute of Technology Durgapur, INDIA

IEEE Symposium of Humanities, Science and Engineering (SHUSER 2012)

Kuala Lumpur, Malaysia, June 24-27, 2012 Abstract This paper presents a novel and accurate analytical approach for the efficient computation of the transient response and 50% delay of on-chip RLCG interconnect lines with a capacitive load. The proposed model is based on the two port representation of the transmission line. The simulation results are obtained by using the proposed model and found to be at good agreement with that of the SPICE simulation results. The results obtained justify the accuracy and the validity of the proposed transient response and the delay model for a wide range of load impedance values. The minimum error has been calculated to be 2.65% while the maximum error is found to be 8.33%. Introduction With the exponential reduction in the feature size, the delays due to interconnections have become a dominating factor in determining the circuit performance. This scaling has resulted in the interconnect delay to feature as a major bottleneck in today’s high speed circuits. Thus the distributed nature of interconnects must be taken into consideration for accurate on-chip performance modelling. Furthermore, with the IC operating frequency approaching multi-gigahertz, it is quite evident that the interconnect inductance also needs to be properly modelled. With the increase in frequency, the shunt lossy component has become a significant limiting factor for high speed on-chip interconnects modelling. In this paper, we begin with the two-port model of the transmission line and obtain the time-domain expression of the step response for a finite-length RLCG lines.

From the time domain response, 50% delay metric has been computed. Theory / Model Transmission Line Model

R = Series resistance per unit length L = Series inductance per unit length C = Shunt capacitance per unit length G = Shunt conductance per unit length By combining the Telegrapher’s equations for a transmission line we obtain the relation:

( ) ( ) ( ) ( ) ( )txRGVtxVRCLG

txVLC

xxV ,2

2

2

2+

∂∂

++∂

∂=

∂∂

Experimental Proposed Transient Response and Delay Model We start with the two port representation of transmission lines.

⎥⎦

⎤⎢⎣

⎡

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

+−

++=

⎥⎦

⎤⎢⎣

⎡⎥⎦

⎤⎢⎣

⎡=⎥

⎦

⎤⎢⎣

⎡

−−

−−

1

1

1

1

2

2

)(

)()(

21

IV

eesZee

sZeeee

IV

DCBA

IV

dd

o

ddo

dddd

γγγγ

γγγγ

where,

( )( )sCGsLRsZsCGsLRs o +

+=++= )(,)(γ

and d denotes the length of the transmission line. Knowing voltage at the output port is related to the current at the load capacitor, we find the transfer function. Expanding

)(sγ and in their McLaurin series and considering approximations on the basis of a lossy transmission line, adding a gain

)(sZo

37


compensation factor, the final transfer function is calculated. Two separate cases are considered assuming different values of )tanh( dγ in the expression of transfer function (H(s)) Case-I: When, )tanh( dγ =1

)()()( 21 tVtVtV outoutout += Where

⎟⎟⎠

⎞⎜⎜⎝

⎛−

−=

−−

tyz

xtout ee

xyzGxLtV )(1

( ) ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

−−

−−=−

−−t

yz

xtxtout ee

xyzzGxyRe

zGRtV 1)(2

Case-II: When, )tanh( dγ =0.5 dγ

)()()()( 321 tVtVtVtV outoutoutout ++= Where

222

2

1 2)(

zyzyeGyLtV

n

tyz

nout ++

=

−

αωω

( )22

2

2 )(αω

ω−

=n

nout z

GRtV

)2()(

222

22

3 zyzyzeGRy

tVn

tyz

nout

++−=

−

αωω

Results and Discussion Simulation Results We examined several cases with different values of load capacitance. The values of the transmission line parameters are R=50Ω, L=10nH, Rs=2KΩ, C=1pF and G=0.002S. The 50% delay is calculated for different values of load capacitance and those values have been compared with the SPICE results. Value of Load Capacitance (pF)

50% Delay by Proposed Model (ns)

50% Delay by SPICE (ns)

% of Error

1 0.0115 0.012 4.17 2 0.018 0.019 5.26 3 0.022 0.024 8.33 4 0.0275 0.029 5.17 5 0.031 0.033 6.06 6 0.0356 0.038 6.32 7 0.04 0.042 4.76 8 0.0456 0.0475 4.0 9 0.055 0.0565 2.65 10 0.057 0.0598 4.68

The maximum error obtained is around 8.33% and minimum error is 2.65%. The output response (output voltage vs time) derived by using the proposed method and the output response calculated by using SPICE have been plotted.

Output Response by Proposed Method

Output Response by using SPICE Conclusion In this paper we have proposed a novel and accurate method for obtaining the analytical expression for the step response of a lossy interconnect. We then obtained the step response of the system by doing some further simplification and we also calculated the 50% delay. The results show that the proposed method is able to obtain the step response and delay of the lossy interconnects with small error. Acknowledgement The research work has been recognized with a publication because of the hard work and guidance from Dr. Rajib Kar of NIT Durgapur. I am thankful to Institute administration for the support.

38


OPTIMAL FIR BAND PASS FILTER DESIGN USING NOVEL PARTICLE SWARM OPTIMIZATION ALGORITHM

Prabisha Mallick

B. Tech. final year student, Department of Electronics & Communication Engineering National Institute of Technology Durgapur, India

SHUSER 2012, IEEE Symposium on Humanities, Science and Engineering

Kuala Lumpur, Malaysia, 24-27 June 2012

Abstract FIR filter design involves multi-modal, multi-parameter optimization. Different optimization techniques can be utilized to determine the impulse response coefficient of a filter and try to meet the ideal frequency response characteristics. This paper presents an optimal design of linear phase digital band pass finite impulse response (FIR) filter using Novel Particle Swarm Optimization (NPSO) algorithm. NPSO is an improved particle swarm optimization (PSO) that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. The inertia weight has been modified for the PSO to enhance its search capability to obtain the global optimal solution. Evolutionary algorithms like real code genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), and the novel particle swarm optimization (NPSO) have been employed for the design of linear phase FIR band pass (BP) filter. A comparison ofsimulation results reveals the optimization efficacy of the algorithm over the prevailing optimization techniques.

Introduction Filtering is done for the passage of certain frequencies and simultaneously rejecting the unwanted frequency bands. Digital filters helps to achieve the target of lower pass band ripple, faster transition, and higher stop band attenuation. The objective function for the design of optimal digital filters involves accurate control of various parameters of frequency spectrum and is thus highly non-uniform, non-linear, non-differentiable and multimodal in nature. Classical optimization methods cannot optimize such objective functions and cannot converge to the global minimum solution. So, evolutionary

optimization methods have been implemented for the design of optimal digital filters with better control of parameters and the highest stop band attenuation. Theory FIR filter design involves multi-modal, multi-parameter optimization which cannot be obtained by classical gradient based solutions. Apart from the Optimization method the other traditional techniques have the following limitations:

I. Huge number of iterations is involved II. Low convergence speed

III. Large complexity To overcome these limitations and for better approximation of ideal filter, evolutionary optimization techniques are being implemented. A few of them are: Real coded Genetic Algorithm, Differential Evolution, Particle Swarm Optimisation, Novel Particle Swarm Optimisation. The drawbacks they face are:

GAs are inefficient in determining the local minimum in terms of convergence speed and solution quality.

PSOs may be influenced by stagnation problem and premature convergence Real Coded Genetic Algorithm (RGA): is mainly a probabilistic search technique based on the principles of natural selection & evolution. PSO: is a robust stochastic optimization technique based on the movement and intelligence of swarms with implicit parallelism, which can easily handle with non-differential objective functions, unlike traditional optimization methods. DE is an Evolutionary Algorithm. This Class also includes GA, Evolutionary Programming and Evolutionary Strategies NPSO: It’s an improved PSO that proposes a new definition for the velocity vector and

39


swarm updating and hence the solution is improved. NPSO is developed through simulation of bird flocking in multi-dimensional space. Bird flocking optimizes a certain objective function. Namely, each particle tries to modify its position using the following information:

• The distance between the current position and the pbest (its own best location).

• The distance between the current position and the gbest (the group’s recorded best location). The drawback of the conventional PSO used for the generation of optimal coefficients of filter design problem is that it results in sub-optimality problem. This paper describes an alternative technique for the FIR band pass digital filter design using a recently proposed Novel Particle Swarm Optimization (NPSO) algorithm. The inertia weight w plays the important rolocal exploration abilities. The search for the best location becomes more concentrated and localised to the optimum output with the increased number of iterations. Based on this concept the weighting factor, velocity and position’s expressions are modified to get a better result with lesser ripples in low transition width. Mathematically it optimises the fitness function which in this case is the error function where the absolute value of errors are summed up and hence minimised, to get the best solution.

le of balancing the global and

Results and discussion Analysis of Magnitude Response of Band pass FIR Filter and convergence profile of the evolutionary algorithms.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.2

0.4

0.6

0.8

1

1.2

1.4

Frequency

Mag

nitu

de (N

orm

aliz

ed)

PMRGAPSODENPSO

Fig. 1. Normalized plots for the FIR BP filter of order 20

0 50 100 150 200 250 300 350 400 450 5000

1

2

3

4

5

6

7

8

9

10

Iteration Cycles

Erro

r Fitn

ess

RGAPSODENPSO

Fig. 1. Normalized plots for the Fig.2 Convergence Profile for RGA, PSO, DE and NPSO in case of FIR Band pass Filter of Order 20.

Conclusion Comparison of the results of PM, RGA, PSO, DE and NPSO reveals that NPSO has the best quality optimal solution and possesses the best convergence profile in much less execution times. Thus NPSO can be used to obtain the optimal filter coefficients in any practical FIR filter design of DSP systems. Acknowledgement I would like to thank Prof. Durbadal Mandal, Prof. Rajib Kar of ECE Department and Prof. Sakti Prasad Ghoshal and Sangeeta Mandal of Electrical Department, NIT Durgapur, for their continuous technical support and help. I am thankful to my family members and friends for their moral support throughout this period.

FIR BP filter of order 20 Pass Band ripple (normalized) Stop Band Attenuation (dB)

Algorithm

Maximum Mean Variance Standard Deviation

Maximum Mean Variance Standard Deviation

PM 0.076 0.076 0 0 22.37 22.42 37.266 10−× 0.085245 RGA 0.167 0.1595 55.625 10−× 37.5 10−× 30.8 35.942 7.7522 2.784275 PSO 0.146 0.1445 52.25 10−× 31.5 10−× 32.03 35.964 10.0603 3.17179 DE 0.152 0.1495 66.25 10−× 32.5 10−× 32.58 36.88 7.66043 2.76775 NPSO 0.147 0.1405 54.225 10−× 36.5 10−× 33.96 36.98 2.613167 1.616529

40


WIDE NULLS CONTROL OF LINEAR ANTENNA ARRAYS USING CRAZINESS BASED PARTICLE SWARM OPTIMIZATION

Randheer Kumar


2011 IEEE Student Conference on Research and Development

(IEEE SCOReD 2011) in Putrajaya, Malaysia, December 19-20, 2011 Abstract In this paper, an evolutionary swarm intelligence technique; Craziness Particle Swarm Optimization (CRPSO) is propounded for nullifying the radiation pattern of a symmetrical linear antenna array in a particular direction. Multiple wide nulls are achieved by optimum perturbations of elements current amplitude weights to have symmetric nulls about the main beam. Different numerical examples are presented to illustrate the capability of CRPSO for pattern synthesis with a prescribed wide nulls locations and depths. Further, the peak sidelobe levels are also reduces when compared to a uniformly excited array having equal number of elements. Introduction At some applications antenna is required not to radiate to or receive from some particular directions. Thus the radiated (or received) power in (from) these directions should be negligible. Thus the radiation pattern of an antenna has to have a null in each such direction. A lot research works are going on for reducing the signal at nulls. In this paper, a symmetric linear antenna array structure with equal spacing between any two consecutive elements has been considered. The phase difference between any two elements is kept zero. This involves nonlinear dependence between array factor and antenna element parameters, which becomes a highly complex optimization problem. The classical optimization methods cannot bear the demand of such complex optimization problem.

Experiment An array of an even number of isotropic element 2M (where M is an integer) is positioned symmetrically along the z-axis, as shown in Fig. 1. The separation between the elements is d (d= λ/2), and M elements are placed on each side of the origin. Assuming that the amplitude excitation is symmetrical about origin, the array factor for the given non uniform amplitude broadside array is given by:

Where,ϕ = phase weight at element n In = Excitation Amplitude of nth element 2M = total number of elements in the array k = propagation constant d = spacing between elements θ = angle of radiation of electromagnetic plane wave All the antenna elements are assumed isotropic. Only Amplitude excitations of each element are used to change the antenna pattern. The Cost Function (CF) for imposing null is given below:-

‘ m’ is the maximum number of positions where nulls can be imposed. AF (null) is the Array factor value at the particular null. AF is the maximum of the array factor. In cost function, both the numerator and denominator are absolute values. Smaller value of the cost function means that array factor values at predefined positions are

41


less. Consequently, CRPSO controls the amplitude excitations in order to minimize the cost function. Conclusion In this paper, a PSO based technique is used to adjust the excitation weight of each element in the symmetric linear antenna array to obtain optimal null levels in a particular direction. The CRPSO algorithm can efficiently handle the design of non-uniformly excited symmetric linear antenna array by generating radiation patterns with maximum deeper nulls at a desired direction with respect to corresponding uniformly excited linear array with inter-element spacing of λ/ 2, for a given number of the array elements. The peak sidelobe levels for the optimal designs are also reduced with respect to the levels for the case of corresponding uniformly excited with d=λ/2 inter-element spacing linear array.

42


LINEAR PHASE LOW PASS FIR FILTER DESIGN USING IMPROVED PARTICLE SWARM OPTIMIZATION

Saptarshi Mukherjee


2011 IEEE Student Conference on Research and Development,

Cyberjaya, Selangor, Malaysia, December 19-20, 2011 Abstract In this paper, an optimal design of linear phase digital low pass finite impulse response (FIR) filter using Improved Particle Swarm Optimization (IPSO) is presented. In the design process, the filter length, pass band and stop band frequencies, feasible pass band and stop band ripple sizes are specified. An iterative method is introduced to find the optimal solution of FIR filter design problem. Genetic algorithm (GA), particle swarm optimization (PSO), improved particle swarm optimization (IPSO) has been used here. IPSO is an improved PSO that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. A comparison of simulation results reveals the optimization efficacy of the algorithm over the prevailing optimization techniques. Introduction Digital filter is essentially a system or network that improves the quality of a signal and/or extracts information from signals or separates two or more signals which are previously combined. Nowadays digital filters are replacing the traditional role of analog filters in many applications. There are mainly two types of filter algorithms. They are Finite Impulse Response filter (FIR) and Infinite Impulse Response filter (IIR). In case of a FIR filter, the response due to an impulse input will decay within a finite time. FIR filter is an attractive choice because of the ease in design and stability. Finite impulse response (FIR) digital filters are known to have many desirable features. Linear phase FIR filters are also required when time domain specifications are given.

Evolutionary methods have been employed in the design of digital filters to better approximate the ideal filter. Different heuristic optimization algorithms such as genetic algorithm, simulated annealing algorithms etc. have been widely used. Yet they are inefficient in determining the local minimum. This paper describes Improved Particle Swarm Optimization Approach (IPSO). IPSO algorithm tries to find the best coefficients that closely match the ideal frequency response. Based upon the IPSO approach, this paper presents a good and comprehensive set of results, and states arguments for the superiority of the algorithm. Simulation result demonstrates the effectiveness and better performance of the proposed designed method. Theory / Model An error function is defined as the difference between the actual and ideal frequency response of a filter. The algorithms try to minimize this error and increase the fitness for filter design. This greatly reduced the computational complexity of the algorithms. Standard Genetic Algorithm (also known as real coded GA) is mainly a probabilistic search technique, based on the principles of natural selection and evolution. Chromosomes are constructed over some particular alphabet. Each chromosome is evaluated by a function known as fitness function, which is usually the fitness function of the corresponding optimization problem. PSO is a flexible, robust population-based stochastic optimization technique which can easily handle with non-differential objective functions, unlike traditional optimization methods.PSO is developed through simulation of bird flocking in multi

43


dimensional space. The system starts with a population of random solutions. In each step velocity of each particle is accelerated towards its personal best and global best locations. Acceleration is weighed by random terms. The global search ability of traditional PSO is very much enhanced with IPSO. The cognitive and social parts are modified. The cognitive component is subdivided into good and bad experience components also. By using the bad experience component, the bird (particle) can bypass its previous worst position and always try to occupy a better position. Experimental In order to demonstrate the effectiveness of the proposed filter design method, several examples of FIR filter are constructed using RGA, PSO, IPSO algorithms. The MATLAB simulation has been performed extensively to realize the low pass FIR filter of the order of 20. The table above summarizes the maximum stop band attenuation achieved by using PM, RGA, PSO and IPSO algorithms. The figure shows the magnitude plot for the low pass FIR filter of the order of 20.From the figures, it is evident the proposed filter design approach IPSO produces higher stop band attenuation and smaller stop band ripple compared to that of PM, RGA and PSO.

Results and Discussion From the figures drawn for this filter, it is seen that the IPSO algorithm is significantly faster than the PSO algorithm for finding the optimum filter. Also, performance of IPSO technique is better as compared to RGA and PSO. Conclusion This paper presents a novel and accurate method for designing linear phase digital low pass FIR filters of order 20. The proposed algorithm outperforms RGA and classical PSO in the accuracy of the magnitude response the convergence speed. Acknowledgement I want to express my gratitude towards my college, for extending me the support and giving me the opportunity to represent NIT, Durgapur on such a global event. I am thankful to the professors, administrators of the college, my parents and my friends who guided me on my research and made this publication successful. Finally, I am also grateful to the organizers of the conference, whose amicable nature made me feel relaxed and at home.

44


DIGITAL STABLE IIR HIGH PASS FILTER OPTIMIZATION USING PSO-CFIWA

Sonam Yangchen


2012 IEEE Symposium on Humanities, Science and Engineering Research,

Kuala Lumpur, Malaysia, June 24 -27, 2012 Abstract In this paper, an optimal design of stable digital high pass infinite impulse response (IIR) filter using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSO-CFIWA) has been presented. The conventional gradient based optimization techniques are not efficient enough for handling digital IIR filter design due to the sub-optimality problem. The proposed optimization technique PSO-CFIWA is a heuristic search algorithm and capable enough to handle non differentiable optimization problem to find optimal solution in multidimensional search space. Performance of the proposed algorithm is compared with well accepted evolutionary algorithms such as particle swarm optimization (PSO) and real coded genetic algorithm (RGA). From the simulation study it is established that the PSOCFIWA outperforms RGA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and solution quality i.e. the stop band attenuation, transition width, pass band and stop band ripples. Further, the pole-zero analysis justifies the stability of the designed optimized IIR filter. Introduction The classical gradient based optimization methods are not suitable for IIR filter optimization because of the various reasons. So, evolutionary methods have been employed in the design of digital filters to design with better parameter control and to better approximate the ideal filter. Heuristic optimization methods that require no gradient and can achieve a near global optimal solution offer considerable advantages in solving these multi-modal objective function in digital filter design.

The paper describes an alternative technique for the IIR high pass digital filter design using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSO-CFIWA). This algorithm tries to find the best coefficients that closely match the ideal frequency response. Simulation result demonstrates the effectiveness and better performance of the proposed designed method and states arguments for the superiority of the algorithm. High Pass IIR Filter Design This section presents the design strategy of IIR filter based on PSO-CFIWA. The frequency response of IIR filter with the assumption a0 = 0 is expressed as:

Ω=2π( ) is the digital frequency, f is the analog frequency, and s f is the sampling frequency. The commonly used approach to IIR filter design is to represent the problem as an optimization problem with the mean square error (MSE) as the error fitness function, J (ω), expressed as

Ns is the number of samples used. A novel error fitness function is adopted in order to achieve higher stop band attenuation and to have more control on the transition width. Using the equation, it is found that the proposed filter design approach results in considerable improvement in stop band attenuation over other optimization techniques.

45


For the first term of equation ω € pass band including a portion of the transition band and for the second term of equation, ω € stop band including the rest portion of the transition band. The portions of the transition band chosen depend on pass band edge and stop band edge frequencies. The error fitness function given in equation represents the generalized fitness function to be minimized using evolutionary algorithms RGA, conventional PSO and the proposed PSO-CFIWA individually. Each algorithm tries to minimize this error fitness 1 J and thus optimizes the filter performance. Evolutionary Technique Employed Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSO-CFIWA): Evolutionary techniques RGA and PSO are used to make a comparative study of the results obtained with proposed algorithm PSO-CFIWA and those two algorithms are already discussed in [24-25]. PSO is a robust population-based stochastic search optimization technique with implicit parallelism, which can easily handle with non-differential objective functions, unlike traditional optimization methods. PSO is less susceptible to getting trapped on local optima unlike GA, simulated annealing, etc. Apart from inherent advantages of PSO, proposed algorithm PSO-CFIWA is also enriched with better searching capacity in a multidimensional search space. Design aim in the paper is to obtain the optimal combination of filter coefficients so as to acquire the maximum stop band attenuation with sharp transition width. Results and Discussion The MATLAB simulation has been performed extensively to realize the high pass IIR filter of the order of 8. The sampling frequency has been chosen as s f = 1Hz. Also, for all the simulations the sampling number is taken as 128. The parameters of the high pass filter to be designed are: pass band ripple (δp) = 0.001, Stop band ripple (δs) = 0.0001, pass band normalized edge frequency (ωp)

=0.35, Stop band normalized edge frequency (ωs) = 0.30. Each algorithm is run for 30 times for the best results, the best results are shown:

Conclusion This paper presents an alternative method for designing digital high pass IIR filters by using nonlinear stochastic global optimization technique called PSO-CFIWA. Small modifications in the conventional PSO in terms of constriction factor and iteration dependent value of inertia weight made appreciable improvement in the result obtained with simulation study. Results so obtained affirm that the proposed PSO-CFIWA optimization technique outperforms RGA and PSO not only in terms the accuracy of the magnitude response of the filter but also in the convergence speed and is adequate for use in other related design problems. Acknowledgement I would like to Dr. Rajib Kar, Dr. D. Mondal for their immense help throughout & NIT Durgapur for the financial support.

46

National Institute of Technology Durgapur, INDIA Students’ International Research Projects Technical Report; Volume 4, 2011-12 OPTIMIZATION OF LINEAR PHASE FIR BAND PASS FILTER USING PARTICLE SWARM OPTIMIZATION WITH CONSTRICTION FACTOR

AND INERTIA WEIGHT APPROACH

Soumi Bardhan B. Tech. final year student, Department of Electronics & Communication Engg

National Institute of Technology Durgapur, India

Industrial Electronics and Applications (ISIEA), 2011 IEEE Symposium Sept. 25-28, 2011

Abstract In this paper, swarm and evolutionary algorithms have been applied for the design of digital filters. Genetic algorithm (GA) and an improved Particle swarm optimization (PSO) called Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSOCFIWA) have been used here for the design of linear phase band pass finite impulse response (FIR) filters. The fitness function is based on the squared error between the actual and the ideal filter response. PSOCFIWA seems to be promising optimization tool for FIR filter design In this paper, we have introduced an iterative method to find the optimal solution of optimal FIR filter design. In this paper, for the given problem, the realization of the FIR band pass filters of different order has been performed. The simulation results have been compared with the well accepted evolutionary algorithm such as genetic algorithm (GA). The results justify that the proposed FIR filter design approach using PSOCFIWA outperforms to that of GA, not only in the accuracy of the designed filter but also in the convergence speed and solution quality. Introduction Filters are usually used to discriminate a frequency or a band of frequency from a given signal .Finite impulse response (FIR) digital filters are known to have many desirable features such as guaranteed stability, the possibility of exact linear phase characteristic at all frequencies and digital implementation as non-recursive structures. Particle Swarm Optimization (PSO) is an

evolutionary algorithm developed by Kennedy and Eberhart in 1995 .This paper describes the FIR band pass digital filter design using the PSO with constriction factor and inertia weight approach (PSO-CFIWA). PSO-CFIWA algorithm tries to find best coefficients that closely match the ideal frequency response. The results states arguments for the superiority of the algorithm. A digital FIR filter is characterized by,

( ) ( ) nN

n

znhzH −

=∑=

0 ( ) 00 ≠h ( ) 0≠nh Where N is the order of the filter which has N+1 coefficient (n) is the filter impulse response..N represents the order of the polynomial function. The length of h(n) is N+1 and the number of coefficients is also N+1. The individual represents h(n).The least square (LS) error is used to evaluate the individual. An ideal filter has a magnitude of 1 on the pass band and a magnitude of 0 in the stop band. So the error for this fitness function is the squared difference between the magnitudes of this filter and the filter designed using the evolutionary algorithms. The frequency response of the FIR digital filter can be calculated as,

( ) ( ) njwN

n

jw kk enheH −

=∑=

0 This is the FIR filter frequency response. We sample frequency in [0,π ] with N points, The expression of the LS function

is:( ) ( ) ( )

2

1∑

=

−=K

k

jd

ji

kk eHeHMinError ωωω

Real- Coded Genetic Algorithm

47

National Institute of Technology Durgapur, INDIA Students’ International Research Projects Technical Report; Volume 4, 2011-12 GA is mainly a probabilistic search technique, based on the principles of natural selection and evolution. Particle Swarm Optimization (PSO) PSO is a flexible, robust population-based stochastic search or optimization technique with implicit parallelism. Mathematically, velocities of the particles are modified as:

( )( ) **

***

22

111

ki

k

ki

ki

ki

ki

SgbestrandCSpbestrandCVwV

−+

−+=+

The searching point in the solution space can be modified as:

11 ++ += ki

ki

ki VSS

For Particle Swarm Optimization with Constriction Factor & Inertia Weight Approach (PSOCFIWA), the velocity is manipulated.

V ik1= CFa× wk1V i

kC 1rand 1pbest i− Sik

C 2rand 2gbest− Sik

ϕϕϕ 42

22 −−−

=CFa

In inertia weight approach (IWA), inertia weight ( 1+kw ) at (k+1)th cycle is

( )1max

minmaxmax

1 +×−

−=+ kk

wwwwk

Results and Discussions

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-70

-60

-50

-40

-30

-20

-10

0

Freguency

Mag

nitu

de (d

B)

GAPSO

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.2

0.4

0.6

0.8

1

Freguency

Mag

nitu

de (N

orm

aliz

ed)

GAPSO

Fig 1: Magnitude response of 30th order band -pass FIR filters

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-70

-60

-50

-40

-30

-20

-10

0

Freguency

Mag

nitu

de (d

B)

GAPSO

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.2

0.4

0.6

0.8

1

Freguency

Mag

nitu

de (N

orm

aliz

ed)

GAPSO

Fig 2: Magnitude response of 40th order band -pass FIR filters

0 100 200 300 400 500 6004

6

8

10

12

14

16

Iteration Cycle

Erro

r

Fig 3: Convergence profile for RGA in case of 40th order band -pass FIR filters.

0 100 200 300 400 500 6002

4

6

8

10

12

14

16

Iteration Cycle

Erro

r

Fig 4: Convergence profile for PSOCFIWA in case of 40th order band -pass FIR filters. Conclusion This paper presented a new method for designing linear phase digital band pass FIR filters by using nonlinear stochastic global optimization based on PSOCFIWA. Extensive simulation results justify that the proposed algorithm outperforms GA in the accuracy of magnitude response of the filter as well as in convergence speed. Acknowledgement It is my pleasure to express my gratitude to Dr. Rajib Kar, of department of ECE, NIT, Durgapur.

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DIGITAL STABLE IIR LOW PASS FILTER OPTIMIZATION USING PARTICLE SWARM OPTIMIZATION WITH IMPROVED INERTIA WEIGHT

Soumya Sarkar

B. Tech. third year student, Department of Electronics & Communication Engineering National Institute of Technology Durgapur, INDIA

2012 Ninth International Joint Conference on Computer Science & Software

Engineering, Bangkok, Thailand, May 30 - June 01, 2012 Abstract In this paper, a recently proposed global heuristic search algorithm, namely, particle swarm optimization with improved inertia weight(PSOIIW)approach is considered for the design of the 8th order infinite impulse response (IIR) low pass(LP) digital filter. Unlike conventional PSO, inertia weight component in the proposed technique is calculated separately for each particle and modification is based on the iteration dependent velocity and position components. With this approach better global and local exploration is achieved which resulted in faster convergence to near global optimal solution. Performance of the proposed PSOIIW based approach is compared with some well accepted evolutionary algorithms such as particle swarm optimization (PSO) and real coded genetic algorithm (RGA). From the simulation study it is established that the proposed optimization technique PSOIIW outperforms RGA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and solution quality, i.e., the stop band attenuation, transition width, pass band and stop band ripples. Further, the pole zero analysis justifies the stability of the designed optimal II R filter. Introduction In the signal processing, filtering holds a significant position which is involved with manipulation by modifying, reshaping or transforming the spectrum of signal. Fundamentally, filter operates on frequency Domain to permit certain band of frequencies to pass through and attenuates others. The Frequency, at which such phenomena

happens is a design dependence parameter called cut off frequency. This sort of frequency discrimination is of prime importance due to mixing information carrying signal with noise. digital filters perform mathematical operations on a sampled, discrete time signal to reduce or enhance the desired features of the applied signal. Analog filters are replaced by digital filers due to its wide range of applications and better performance. Digital filters are of two types: finite impulse response (FIR) and infinite impulse response (IIR) filter. Evolutionary methods have been employed in the design of digital filters to design with better parameter control and to better approximate the ideal filter. The approach detailed in this paper is one called Particle Swarm Optimization developed by Eberhart et al. In order to overcome the limitations of the conventional PSO, it has been modified and used for low pass filter design. Theory / Model This section proposes the design strategy of IIR filter based on PSOIIW. The input output relation of the 8th order Low pass IIR filter with 18 filter coefficients is governed by the difference equation approach. Our aim has been to represent the problem as an optimization problem with the mean square error function as the objective function. In this paper a novel error fitness function has been used to achieve higher stop-band attenuation and lesser transition width. The evolutionary technique used here is PSO which is a robust population-based stochastic search optimization technique with implicit parallelism, which can easily handle non differential objective functions. PSO is less susceptible to be trapped on

49

National Institute of Technology Durgapur, INDIA Students’ International Research Projects Technical Report; Volume 4, 2011-12 local optima like simulated annealing, GA etc. To enhance the advantages of PSO we use a modified version called Particle Swarm Optimization using Improved Inertia Weight. Experimental In conventional PSO the instantaneous position of the particle vector is dependent upon three factors, namely its current position, its personal best position and its group best position. In conventional PSO, the initial solutions are generally far from the global optimum; hence larger inertia weight may prove to be beneficial. Large inertia weight helps in global exploration while small inertia weight enables local exploration. In our approach to prevent being trapped in local optima the inertia weight is modified. It decreases if the direction of search is correct, otherwise constant. Using this inertia weight factor the subsequent expressions of PSO are modified and the algorithm is run in defined samples. Results and Discussion The algorithm was implemented in MATLAB and the best simulated results out of 30 independent runs have been reported in the paper. The maximum stop band attenuation obtained for the desired IIR filters using RGA, PSO and PSOIIW are 27.415 dB, 30.365 dB, 32.408 dB. Also it is seen that the PSOIIW approach takes 81 iterations to achieve an error value of 2.303, while 361 and 359 iterations are needed to achieve error values of 2.85 and 4.054 for PSO and RGA techniques. Also from the pole zero plot of the designed IIR filter the stability of the filter can be stated as all the poles and zeroes lie within the unit circle. Conclusion We observed that due adaptation of iteration dependent inertia weight factor results in better exploitation and exploration of the search space along with faster convergence to sub optimal solution. A comparative study between the proposed technique as well as

the conventional RGA and PSO affirmed that the proposed PSOIIW technique has the highest stop-band attenuation and quality output in terms of ripples and transition width. Also the proposed technique converges with the lowest number of iterations. Hence PSOIIW can be used as adequate method for handling other such optimization problems. Acknowledgement I am grateful to NIT Durgapur for providing me with this precious opportunity to present this paper in an international conference of repute. I am also grateful to Dr. Durbadal Mandal, Dr.Rajib Kar, Suman Saha of the Department of Electronics and Communication, NIT Durgapur and Dr. S. P. Ghoshal of Department of Electrical Engineering, NIT Durgapur for helping me in this project.

50


ELMORE’S APPROXIMATIONS BASED EXPLICIT DELAY AND RISE TIME MODEL FOR DISTRIBUTED RLC ON-CHIP

VLSI GLOBAL INTERCONNECT

Sristi Agarwal B. Tech. final year student, Department of Electronics & Communication Engineering


2012 IEEE Symposium on Humanities, Science and Engineering Research, Kuala Lumpur, Malaysia, June 24 -27, 2012

Abstract In this work, simple explicit delay and rise time expressions for uniformly distributed RLC on-chip interconnect line are derived based on Elmore’s approximations. Here, an n-cell RLC ladder network with capacitive load is used. Transfer function for the n-cell RLC ladder network is obtained by using the transmission line parameter matrix for each cell. In order to deduce the transfer function, the transmission line is modelled by a lumped parameter network. From this transfer function, explicit delay and rise time expressions are derived by using Elmore’s definitions. The calculated delay and rise times by the proposed closed form expressions are compared with the results obtained by SPICE simulation for n=2, 3, 4, 5, 6, 7 cell ladder networks with capacitive load.

Introduction

On-chip inductive effects are becoming predominant in deep submicron interconnects due to increasing clock speed, circuit complexity and interconnect lengths. Interconnects are an important performance limiting factor in today’s high-speed and high density VLSI designs. In CMOS circuits, if inductance is ignored, the on-chip interconnect is usually modelled as RC tree model But with faster rise times and lower resistance, long wide wires in the upper metal layers exhibit significant inductive effects. Furthermore, wire inductance can no longer be ignored, due to higher signal frequencies and longer wire lengths. Line inductance affects the circuit performance in two distinct ways.

Firstly, it can affect the rise/fall time (slew rate) and signal delay/integrity through the interconnect. Inductance causes overshoots and undershoots in the signal waveforms, which can adversely affect signal integrity. However, as the line inductance increases beyond a certain value, the actual delay and Elmore delay diverge and one needs to compute signal delay by accurately modelling of the line inductance. Modelling of Uniform Distributed RLC Line Let us consider one n cell ladder network as shown in Figure 1. The network is loaded by a capacitance C1 and excited by a sinusoidal voltage source. Uniformly distributed line, loaded with a capacitor, is modelled with n cascaded cell in the form of lumped parameter ladder network. In the complex frequency domain, impedances and admittances are defined

as, sLRZ += , sCY = , and , where Z and Y represent the impedance and admittance of the network respectively and s is the Laplace variable. Load

capacitance is chosen as

1' sCY =

CC α=1 , where α = 0, n, 2n, 3n

Delay and Rise Time Calculation

The network as shown in Figure 1 can be considered as cascaded, connecting n-cell in the form of ladder structure. The transmission parameter matrix of the network having n-cell without load is equivalent to the

nT where T is the

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transmission parameter matrix of one cell network shown in Figure 1 is given by ⎥

⎦

⎤⎢⎣

⎡=⎥

⎦

⎤⎢⎣

⎡

o

on

VYV

TIE

'1

From the above matrix, the transfer function of the network can be calculated as,

)( sH

])()[( )()(2)(

1

nnnn

n

babababasH

−−++−++=

+

ϕ

where , ZYa += 2 42 −= ab and

b)2ZY 1( αϕ =

+

characterizing time domain responses was proposed by Elmore. Elmore definitions of delay and rise times are valid for step response and has negligible or zero overshoot.

(3) ∫∞

=0

)( dttthDτ

])([20

22∫∞

−= DR dttht τπτ (4)

where Dτ and Rτ are the delay and rise time constant, respectively. is the impulse response and is defined as,

)(th

1)(0∫∞

th =dt

Now after doing some calculation and using n=2 we get

)23( ατ += RCD (5) ]644107[2 2222222 LCLCCRCRCRR −−++= αααπτ

(6) For n=3 we get :

)36( ατ += RCD (7)

] 12 6 9 2826[ 2 2222222 LCLCCRCRCRR −−++= αααπτ (8) Equations (5), (6) and (7), (8) give the closed form expressions of the delay and rise times for uniformly distributed RLC line for cell numbers n=2 and n=3 respectively. According to the equation (3), delay and rise times are functions of cell number, circuit parameters and load. Similarly we can find the expressions of delay and rise

time of the RLC interconnect for higher values of cell numbers n. Results and Discussion

The SPICE simulation was performed for n=2, 3, 4, 5, 6, 7 cell RLC ladder networks with capacitive load. Experimental parameters chosen were R=120 kΩ/m, L=270 nH/m, C=240 pF/m, d=10mm.It is observed that the accuracy of the delay time decreases as the load capacitance and the number of the cells increase. But the accuracy of the rise time increases as the load capacitance increases and the accuracy of the rise time decreases as the number of cells increases. And after examining the results, it is clear that the accuracy of the theoretical delay and the rise times depend on the cell number and the load capacitance Conclusion In this paper, simple explicit expressions of delay and rise times have been proposed for uniformly distributed RC lines with capacitive load. The derivation of the delay and rise time formulas has employed the Elmore’s definition.. It is observed that the accuracy of the delay time decreases as the load capacitance and the number of the cells increase. But the accuracy of the rise time increases as the load capacitance increases and the accuracy of the rise time decreases as the number of cells increases. The proposed expressions have been verified in the simple case for n=2, 3, 4, 5, 6, 7 cells and also the effect of number of cells and the load capacitance on the delay and rise times have been investigated. Acknowledgement I would like to extend my heartfelt gratitude to the Department of Electronics and Communication Engineering of NIT Durgapur, especially to Dr. Rajib Kar and Dr. D. Mondal for their immense help whole throughout. I would also like to thank NIT Durgapur for the financial support without which this would have been impossible.

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AUTOMATED SOFTWARE DEVELOPMENT METHODOLOGY: AN AGENT ORIENTED APPROACH

Sudipta Acharya

M. Tech final year student, Department of Information Technology National Institute of Technology Durgapur, INDIA

The 8th International Conference on Computing and Information Technology,

King Mongkut’s University of Technology, North Bangkok, May 9 – May 10, 2012 Abstract In this paper, we propose an automated software development methodology. The methodology is conceptualized with the notion of agents, which are autonomous goal-driven software entities. They coordinate and cooperate with each other, like humans in a society to achieve some goals by performing a set of tasks in the system. This automated system generates MAS (Multi Agent System) architecture and coordination of the agent society to satisfy the set of requirements by consulting with the domain ontology of the system. Introduction There are very few AOSE (Agent Oriented Software Engineering) methodologies for automated design of the system from user requirements. But, most of the work follows an informal approach due to which the system design may not totally satisfy the user requirements. Also the system design varies from developer to developer. In this paper, we develop an automated system which takes the user requirements as input and generates the MAS architecture and coordination with the help of domain knowledge. The basic requirements are analyzed in a goal oriented fashion and represented in the form of goal graph while the domain knowledge is represented with the help of ontology. The output of the developed system is MAS architecture which consists of a number of agents and their capabilities and MAS coordination represented through Task Petri Nets. Theory / Model Fig. 1 represents the architecture of our proposed automated system. The basic requirements are taken from the user as input and represented in the form of goal

graph. The domain knowledge is also an input and is represented in the form of ontology. The automated system returns the MAS Architecture and MAS Coordination as output. The MAS Coordination is represented in the form of Task Petri Nets. Thus, the automated system takes the requirements and the domain knowledge as input and generates the MAS Architecture and MAS Coordination as output. So, we can say, MAS Architecture= f (Requirements, Domain ontology) MAS Coordination=f (Requirements, Domain ontology, MAS architecture)

Figure 1. Architecture of the proposed automated system

Experiment with case study Let us start with the case study by applying our proposed methodology. We take “Library system” as our case study application. Fig. 3 shows snapshot of the ontology of a Library System.

Figure 2: Snapshot of Ontology of Library system

Now consider from user side requirements come as “Delete account of member with member id <i> and book with book id <j> from database.”It is the main goal.

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Results and Discussion Step 1: After Requirements Analysis, it is represented by Goal graph shown in Fig. 3. Step 2: The leaf node sub goals are given as input to the automated system. By semantic mapping system maps each basic keyword of leaf sub goals to the goal concepts of ontology, and finds out set of tasks required to be performed to achieve those sub-goals. This is shown in Fig. 4.

Figure 3: Goal Graph representation of basic requirements Step 3: The tasks that we get from step 2 are used to form task graph. Dependency between these tasks is known from ontology. The task graph is shown in Fig. 5 where task A implies “Check that requirement of book id <j> < threshold, if yes then continue, else stop. Task B implies “Check both book & member database whether member id <i> has not returned book, and any book <j> is not returned by any member.”, Task C implies “Delete member id <i> account.”, Task D implies “Remove book id <j> from library”, Task E implies “Delete entry of book id <j> from database”. Step 4: Using Task Graph of Fig. 5, we find out the number of agents and their capability set and the MAS architecture is <A1, A, B, C>, <A2, D, E>. Step 5: Using the Task Graph of Fig. 5 and MAS architecture developed in step 4, MAS Coordination is formed i.e. to satisfy user requirements, how a set of required agents (A1, A2) will perform a set of required tasks (A, B, C, D, E) collaboratively can be represented by Task Petri Nets (Fig. 6).

Figure 4:Semantic mapping

Figure 5: Task graph

Figure 6: MAS coordination

Conclusion In this paper, we have developed an automated system to generate MAS architecture and coordination from the user requirements and domain knowledge. It is a formal methodology which is developer independent i.e. it produces same MAS architecture and coordination for the same set of requirements and domain knowledge. Acknowledgement I would like to thank my guide Animesh Dutta and my senior Prajna Devi Upadhyay who are co-authors of this paper from Information Technology department of NIT Durgapur for their sincere contribution and guidance to complete this work.

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