About Sparse Theory and Applications

The sparse representation theory states that most natural signals can be

compactly expressed as a linear combination of a few elementary signals in some

representation matrix. Therefore, sparse representation has become an

invaluable tool as compared to direct time-domain and transform-domain signal

processing methods. Recently, the theory of sparse representation and l1-norm

minimization problem is widely applied in a variety of application areas such as

audio/image/video processing tasks (compression, denoising, deblurring,

inpainting, and superresolution), speech and object recognition, signal detection

and classification, face recognition, array processing, blind source separation,

time-delay estimation, sensor networks, cognitive radios, data acquisition,

solution of partial differential equations, and so on. Each of these applications

demands a design of an efficient and flexible representation matrix, which is

referred to as dictionary in the sparse theory. The sparse representation from

redundant dictionaries may provide better ways to reveal/capture the structures

and also offers better performance in signal modeling and classification problems.

The compressive sensing is a new DSP technique in data acquisition theory.

About CEN The Centre for Excellence in Computational Engineering and Networking (CEN)

is actively involved in research in the board areas of Machine Learning, Image

Processing, Biomedical Signal Processing, Graphics Processing Unit (GPU)

Computing, Software Defined Radio, and Natural Language Processing. The

centre has published more than 120 research papers in reputed peer reviewed

journals and international IEEE conferences. The Center offers two M.Tech

degree programs (Computational Engineering and Networking, and Remote

Sensing and Wireless Sensor Networks). Nine research students are pursuing

PhD degree in the field of Machine Translation for Indian Languages,

Biomedical Signal Processing and Kernel Methods. Many signal processing and

NLP tools have been developed at the Center. Ongoing and completed

research projects at the center are: Machine translation system (MHRD),

Factored SMT (CDAC-Pune), Source Code Plagiarism (DIT), Remote Sensing

Applications (ISRO), Video Summarization and Annotation (ADRIN), SVM-

based Target Classification (NPOL).

First National Workshop on

Sparse Image & Signal Processing (Focus: Sparse Theory, MATLAB & LATEX)

(SISP-2011) 23-26 December, 2011

Organized By

Centre for Excellence in Computational Engineering and Networking

Workshop Highlights

• Introduction to Linear Algebra

• Importance of Mathematics in Engineering

• Signal Transforms (Fourier, DCT, Wavelet)

• Optimization Algorithms

• Sparse Theory and Dictionary Learning

• Compressive Sensing and Imaging

• Single Pixel Camera and 1-bit Sampling

• What are the Open Research Problems?

• Medical Signal and Image Processing

• Hyperspectral Image Analysis

• Speech and Underwater Acoustic Signals

• Sparse Channel Estimation

• Distributed Compressive Video Sensing

• Sparse Image Processing (denoising, deblurring, inpainting, pattern recognition, and compression)

Signal/Image Features and Classifiers

• Feature Extraction Methods

• Supervised and Un-supervised Classifiers

• Importance of Gold Standard Database for Research Works

• Summary of Standard BioSignal , Speech, and Medical and Non-Medical Databases

• Performance Metrics for Validating Algorithms

MATLAB & LATEX Training • Implementation of Algorithms in MATLAB

• GUI Preparation

• Research Ethics

• Training for Preparing a Paper, Thesis , and PPT slides in Latex

Objectives of the Workshop:

The main focus of this workshop is to provide an opportunity for a multidisciplinary group

of academic and industry researchers, scientists, mathematicians, leading engineers,

professionals, scholar students to exchange and share their latest research, and new

ideas in the areas of sparse signal representations, optimization algorithms, and

applications of compressive sensing (CS). The SISP covers theory of sparse recovery and

optimizations, and also gives you a comprehensive understanding of MATALB Toolboxes

(Basic, Signal and Image Processing). The participants will be trained to prepare research

papers, thesis, and PPT slides in Latex.

Registration Form is available @ http://www.nlp.amrita.edu:8080/sisp/

Limited seat is available. Please confirm your participation through mail.

Resource Persons:

Dr. K.R. Ramakrishnan, IISc. Bangalore

Dr. S. R. M. Prasanna, IIT Guwahati

Dr. K. Rajamani, GE Research

Dr. Muralikrishna, NPOL, DRDO, Kochi

Dr. A. Unnikrishnan, NPOL, DRDO, Kochi

Dr. K. P. Soman

Dr. M. Sabarimalai Manikandan

Registration Fee:

• Student: 600

• Faculty: 1000

• Industry: Rs. 1500

Filled up registration form are to be sent

along with the DD for course material fee

drawn in favor of AMRITA CEN payable at

Coimbatore.

Course fee includes:

• Food and Accommodation

• Printed Copy of Text Book on Sparse Signal processing (500 pages)

• Databases (in-house) created for

our research works in the field of

Biometric, Speech, OCR, Transient

Signal Analysis

• Software Packages Created @ CEN

Address for Correspondence:

The coordinator 1

st National Workshop on Sparse Image and Signal Processing

Center for Excellence in Computational Engineering and Networking

Amrita School of Engineering, Amrita Vishwa Vidyapeetham

Amrita Nagar (P.O.), Ettimadai, Coimbatore -641112, Tamilnadu.

Mobile: 9843428800/9994329496/9486221489

E-mail: sispcen@gmail.com

Last Date for Registration: 15-12-2011