syllabus for the academic year – 2019 - 2020 vii, viii...hardware for ultrasound scanning. explain...

SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU(A constituent College of Siddhartha Academy of Higher Education, Tumakuru)

Syllabus for the Academic Year – 2019 - 2020

Department: Medical Electronics

Semester: VII

Subject Name: Biomedical Digital Signal Processing

Subject Code: ML7T01 L-T-P-C: 4-0-0-4

Course Objectives :

Course Outcomes

Department

Sl.No Course Objectives

1

This course helps to understand the nature and difficulties to acquire bio-signal and its processing concepts for analysis.

2It also helps to bring out the concepts related Neurological signal processing and Sleep disorder.

3Explains the concept of data compression techniques.

4Emphasizes on Signal averaging, adaptive filers and its applications.

Course outcome

Descriptions

CO1On completion of the course the student can recallUnderstand the origin of EEG signals and their characteristics.

CO2 Understand the origin of ECG signals and their characteristics

CO3Understand the processing techniques required to analyze the bio medical signals

CO4 Understand data reduction techniques for ECG signal.


UNIT Description Hours

I

Introduction to Biomedical Signals: The nature of biomedicalsignals, the action potential, objectives of biomedical signal analysis,Difficulties in biomedical signal analysis, computer aided diagnosis. Neurological signal processing: The brain and its potentials, The electrophysiological origin of brain waves, The EEG signal and its characteristics, EEG analysis.

11

II

ECG Signal Processing: ECG data acquisition, ECG lead system, ECG parameters and their estimation, ECG QRS detection techniques:Template matching, differentiation based QRS detection techniques. Estimation of R-R Interval: Finite first difference method. The use of multi-scale analysis for parameter estimation of ECG waveforms, Arrhythmia analysis monitoring, long term continuous ECG recording.

11

III

Sleep EEG: Data acquisition and classification of sleep stages, The Markov model and Markov chains, Dynamics of sleep-wake transitions, Hypnogram model parameters, event history analysis for modeling sleep.

08

IV

ECG Data Reduction Techniques: direct data compression techniques, direct ECG data compression techniques: Turing point algorithm, AZTEC algorithm and FAN algorithm, other data compression techniques: data compression by DPCM, data compression method comparison.

10

V

Signal Averaging: Basics of signal averaging, signal averaging as adigital filter, a typical averager.

Adaptive Filters: Principle of an adaptive filter, the steepest descentalgorithm, adaptive noise canceller: (a)cancellation of 60 Hzinterference in electrocardiography, (b) Canceling donor-heartinterference in heart-transplant electrocardiography, (c)Cancellation ofECG signal from the electrical activity of the chest muscles,(d)canceling of maternal ECG in fetal ECG, (e)Cancellation of Highfrequency noise in Electro-surgery.

12

Question paper Pattern:

Department


Text Books:

Sl No

Text Book title Author Volume and Year of Edition

1Biomedical Digital Signal Processing

Willis J. Tompkins PHI.

2Biomedical Signal Processingprinciples and techniques D. C. Reddy Tata McGraw-Hill,

2005

3Biomedical Signal Analysis

Rangaraj M. Rangayyan,

IEEE Press, 2001.

Reference Book:

Sl No


1Biomedical Signal Processing

Akay M Academic: Press 1994

2Biomedical Signal Processing Cohen.A Vol. I Time &

FrequencyAnalysis, CRCPress, 1986.

Department




Semester: VII

Subject Name: Principles of Medical Imaging


Course Objectives :

Department


1

Build the physics background of interaction of radiationwith matter, enabling participants to understand projectionradiography, mammography, and fluoroscopy and train themto assess image distortions, image attenuation for X-rayradiography systems.

2

Expose students to the developments in X-ray ComputedTomography leading to modern day multi-slice, helical CTscanners and introduce the concept of computedtomography reconstruction

3

Divulge the image formation, image quality, and imaginghardware for ultrasound scanning. Explain the imagingprinciples and derive the fundamental equation of MRI.

4

Expose the participants to advanced MR techniquesincluding fast spin echoes, MR angiography, echo planarimaging, magnetization prepared sequences, diffusion andperfusion theory and sequences.


Course Outcomes

Department

Course outcome

Descriptions

CO1On the completion of the course the students shall be able To gain knowledge on X-rays and its generation.

CO2 To understand and distinguish different diagnostic method.

CO3 To explain concepts of CT, Projection functions of CT.

CO4Understand the principles of Radionuclide imaging and Magneticresonance imaging.



I X-rays: Introduction to Electromagnetic Spectrum, Fundamentals of X-Rays, Generation and Detection of X-Rays, X-ray Diagnostic Methods.

11

II

X-Rays: Recent Developments, X-ray Imaging Characteristics,Biological effects of Ionizing radiation.

10

III

Ultrasound: Fundamentals of Acoustic Propagation, Generation andDetection of Ultrasound, Ultrasonic Diagnostics Methods, NewDevelopments, Image Characteristics, Biological effects ofUltrasounds.

10

IV

Radionuclide Imaging: Fundamentals of Radioactivity, Generationand Detection of nuclear emission, Diagnostic methods usingradiation detector probes, Radionuclide Imaging Systems, NewRadionuclide Imaging methods, Characteristics of RadionuclideImages, Internal radiation dosimeter and biological effects.

11

V

Magnetic Resonance ImagingFundamentals of nuclear magnetic resonance, Generation andDetection of NMR signal, Imaging Methods, In vivo NMR Spectroscopy,Characteristics of MRI, Biological Effects of Magnetic Fields. 10


Department


Text Books:

Sl No


1Principles of Medical Imaging

Shung K. Kirk, Tsui Benjamin, Smith.B.Michael

2Fundamentals of Medical Imaging

Suetens PaulCambridgeUniversity Press,2002

3

Reference Book:

Sl No


1 Handbook of Biomedical Instrumentation

Khandpur R.S. 2nd Ed., Tata-McGRaw Hill, 2003.

Department



Department: Semester: VII

Subject Name: IoT and SMART SENSORS


Course Objectives :

Course Outcomes

Department


1

Understand the purpose of measurement, themethods of measurements, errors associated withmeasurements.

2

Know the principle of transduction, classifications and the characteristics of different transducers and study its biomedical applications.


Department

Course outcome

Descriptions

CO1on the completion of this course the student will be able toExplain the basic design and requirement of IoT.

CO2Identify the importance of different types of protocols and modelsused with IoT.

CO3 Analyze the requirements of components of smart sensors.

CO4Determine the importance of communication protocol and standards that is used with smart sensors and improve the functionality of conventional systems using IoT.



I Introduction to IoT: Definition & Characteristics of IoT, PhysicalDesign of IoT, Logical Design of IoT, IoT Enabling Technologies, IoTLevels.

08

IIIoT System Management: Introduction, Machine-to-Machine (M2M),Difference between IoT and M2M, SDN and NFV for IoT, Need for IoTSystem Management, SNMP, Network Operator Requirements,NETCONF, YANG, IoT Systems Management with NETCONF-YANG.

08

IIIDomain Specific IoTs: Applications, Home Automation, Cities,Environment, Energy, Retail, Logistics, Agriculture, Industry, health &Lifestyle.

07

IV

Smart Sensors, Signal Conditioning and Control: Introduction,Smart Sensor Model, SLEEPMODETM Operational Amplifiers, Rail – to– Rail Operational Amplifiers, Switched Capacitor Amplifier, 4 – to 20mA Signal Transmitter, Analog to Digital Converter, MCU control,Modular MCU Design, DSP control.

08

V

Protocols and Standards for Smart Sensors: CAN protocol, CANModule, Neuron Chips, MCU Protocols, IEEE 1451 workingrelationship, IEEE 1451.1, IEEE 1451.2, IEEE P1451.3, IEEEP1451.4. 07


Department


Text Books:

Sl No


1Internet of Things – A hands-on approach, Arshdeep Bahga

and Vijay Madisetti

Universities Press (India) Private Ltd.,2015

2Understanding Smart Sensors

Randy Frank 2nd Edition, Artech House Publications, 2000.

Reference Book:

Sl No


1Rethinking the Internet of Things: AScalable Approach to ConnectingEverything

Francis daCosta and Byron Henderson

Apress Open, Intel Publication.2014

2Learning Internet of Things, Smart Peter Waher, PACKT Publishing,

2015

3Sensor Systems Gerard Meijer,

John – Wiley andSons

2008.

Department



Department: Semester: VII

Subject Name: Artificial Organs and Biomaterials

Subject Code: ML7PE21 L-T-P-C: 3-0-0-3

Course Objectives :

Course Outcomes

Department


1

Course Objectives:To create awareness to the student with modernartificial organs devices and methods used topartially support or completely replace pathologicalorgan

2Understand the design and working of artificialheart, kidney, and blood.

3To know about working of heart valve. Design ofartificial heart valve

4

Study about biomaterial which is used for design ofartificial organ. Understand the characteristics ofpolymeric and metallic biomaterial.


UNIT Description HoursI ARTIFICIAL ORGANS: INTRODUCTION: Substitutive medicine,

outlook for organ replacement, design consideration, evaluationprocess.ARTIFICIAL HEART AND CIRCULATORY ASSIST DEVICES:Engineering design, Engg design of artificial heart and circulatoryassist devices.

07

II ARTIFICIAL KIDNEY: Functions of the kidneys, kidney disease, renalfailure, renal transplantation, artificial kidney, dialyzers, andmembranes for haemodialysis, haemodialysis machine, peritonealdialysis equipment-therapy format, fluid and solute removal.ARTIFICIAL BLOOD: Artificial oxygen carriers, fluorocarbons,hemoglo bin for oxygen carrying plasma expanders, hemoglobin basedartificial blood.

07

III

ARTIFICIAL LUNGS: Gas exchange systems, Cardiopulmonary bypass(heart-lung machine)-principle, block diagram and working, artificiallung versus natural lung.CARDIAC VALVE PROSTHESES: Mechanical valves, tissue valves,current types of prostheses, tissue versus mechanical, engineeringconcerns and hemodynamic assessment of prosthetic heart valves,implications for thrombus deposition, durability, current trends invalve design.

08

IV

CERAMIC BIOMATERIALS: Introduction, non absorbable/relativelybioinert bioceramics, biodegradable/restorable ceramics, bioreactiveceramics, deterioration of ceramics, bioceramic-manufacturingtechniquesPOLYMERIC BIOMATERIALS: Introduction, polymerization and basicstructure, polymers used as biomaterials, sterilization, surfacemodifications to for improving biocompatibility.

08

V

BIOMATERIALS: Introduction to biomaterials, uses of biomaterials,biomaterials in organs & body systems, materials for use in the body,performance of biomaterials. METALLIC BIOMATERIALS: Introduction, Stainless steel, Cobalt-Chromium alloy, Titanium alloys, Titanium-Nickel alloys, Dentalmetals, Corrosion of metallic implants, Manufacturing of implants.

09

Department

Course outcome

Descriptions

CO1

on the completion of this course the student will be able toUnderstand the need of artificial organs.

CO2Understand the function of various organs in your body.

CO3

Learn about the design of the various artificial organs using biomaterial.

CO4Understand the various biomaterials. Learn composite, biodegradable polymeric and tissue derived materials.



Text Books:

Sl No


1Biomedical Engineering Handbook- J.D.Bronzino Volume1(2nd Edition)

(CRC Press / IEEEPress, 2000).

2Biomedical Engineering Handbook J.D.Bronzino Volume 2 (2nd Edition)

(CRC Press / IEEEPress, 2000)

3Handbook of Biomedical Instrumentation

R.S.Khandpur (2nd Edition) by (Tata McGraw Hill, 2003)

Reference Books:

Sl No


1

2

3

Department




Semester: VII

Subject Name: Linear Algebra and Its Applications In Medicine


Course Objectives :

Course Outcomes

Department


1

Solve systems of linear equations using various methods including Gaussian and GaussJordan elimination and inverse matrices.

2Perform matrix algebra, invertibility, and the transpose and understand vector algebra in Rn .

3

Determine relationship between coefficient matrix invertibility and solutions to a system of linear equations and the inverse matrices.

4Find the dimension of spaces such as those associated with matrices and linear transformations.


UNIT Description HoursI Linear equations: Fields; system of linear equations, and its solution sets;

elementary row operations and echelon forms Matrix operations; invertiblematrices, LU-factorization. Vector spaces: Vector spaces; subspaces; bases and dimension; coordinates;summary of row-equivalence; computations concerning subspaces.

09

IILinear Transformations: Linear transformations; algebra of lineartransformations; isomorphism; representation of transformations by matrices;transpose of a linear transformation. 08

III Canonical Forms: Characteristic values; invariant subspaces; direct-sumdecompositions; invariant direct sums; primary decomposition theorem; cyclicbases; Jordan canonical form. 07

IV

Inner Product Spaces: Inner products; inner product spaces; orthogonal setsand projections.

08

V

Gram-Schmidt process; QR-factorization; least-squares problems; unitaryoperators Symmetric Matrices and Quadratic Forms: Digitalization; quadraticforms; constrained Optimization; singular value decomposition.

07


Department

Course outcome

Descriptions

CO1Understand LU factorization and elements of vector spaces.

CO2

Learn linear transformations and least square approximations to solve inconsistent systems, Orthonormal vectors using Gram-Schmidtt process and QR factorization.

CO3Understand concepts in Eigen spaces and its applications

CO4

Understand the concept of probability, distributions and its application in Biology and medical Science.


Text Books:

Sl No


1"Linear Algebra and its Applications", Gilbert Strang,

4thEdition, Thomson Learning Asia, 2007.

2"Linear Algebra and its Applications", David C. Lay,

3rd Edition, Pearson Education (Asia) Pvt. Ltd, 2005.

3"Introductory Linear Algebra with Applications” Bernard Kolman

and David R. Hill,Pearson Education (Asia) Pvt. Ltd, 7th edition, 2003.

Reference Book:

Sl No


1

2

Department




Semester: VII

Subject Name: Adaptive Signal Processing


Course Objectives:

Course Outcomes

Department


1

2

3

4


UNIT Description HoursI ADAPTIVE SYSTEMS: Definition and characteristics, Areas of

application, general properties, open and close loop adaptation,Application closed loop adaptation, examples of adaptive systems. Theadaptive linear combiner: General description, input signal and weightvectors, desired response and error, the performance function gradientand minimum mean square error. Example of a performance surface,alternative expression of the gradient, De correlation of error andinput components.

08

II

PROPERTIES OF QUADRATIC PERFORMANCE SURFACE: Normalform of input correlation Matrix, Eigen and eigen vectors of the inputcorrelation matrix. An example with two weights, geometricalsignificance of Eigen vectors and Eigen values. 08

III

SEARCHING THE PERFORMANCE SURFACE: Methods of searchingthe performance surface. Basic idea of gradient search methods, Asimple gradient search algorithm and its solution. 08

IV

SEARCHING THE PERFORMANCE SURFACE: Methods of searchingthe performance surface. Basic idea of gradient search methods, Asimple gradient search algorithm and its solution. Stability and rate ofconvergence, the learning curve, Gradient search by Newton’s methodin multi dimensional space, gradient search by the method of steepestdescent, comparison of learning curves.

09

V

GRADIENT ESTIMATION AND EFFECTS ON ADAPTATION:Gradient component estimation by derivatives measurements, theperformance penalty, derivative measurement and performancepenalties with multiple weights. 06

Department

Course outcome

Descriptions

CO1Describe optimal minimum mean square estimators and inparticular linear estimators.

CO2Hypothesize Wiener filters (FIR, non-causal, causal) and evaluatetheir performance.

CO3Apply combination of theory and software implementations tosolve adaptive signal problems.

CO4 Identify applications in which it would be possible to use the different adaptive filtering approaches.



Text Books:

Sl No


1Adaptive signal Processing

B. Widrow & S D Streans,

Pearson Education 1985.

23

Reference Book:Sl No


1 Adaptive filters C F N Cowan & P M Grant

Prentice Hall, 1985.

2

Department




Semester: VII

Subject Name: Brain Computer Interface


Course Objectives:

Department


Course Outcomes

UNIT Description HoursI Basic Neurosciences: Basic Neuroscience: Neurons, Action Potentials

or Spikes, Dendrites and Axons, Synapses, Spike Generation,Adapting the Connections: Synaptic Plasticity – (LTP, LTD, STDP,Short-Term Facilitation and Depression), Brain Organization,Anatomy, and Function.Recording and Stimulating the Brain: Recording Signals from theBrain: Invasive Techniques &Noninvasive Techniques. Stimulating theBrain - Invasive Techniques & nonTechniques. SimultaneousRecording and Stimulation: Multi-electrode Arrays, Neurochip.

08

II Signal Processing for BCI's: Spike Sorting, Frequency DomainAnalysis: Fourier analysis, Discrete Fourier Transform (DFT), FastFourier Transform (FFT), Spectral Features, Wavelet Analysis. TimeDomain Analysis: Hjorth Parameters , Fractal Dimension ,Autoregressive (AR) Modeling, Bayesian Filtering, Kalman Filtering,Particle Filtering), Spatial Filtering : (Bipolar, Laplacian, and CommonAverage Referencing ,Principal Component Analysis (PCA)

08

Department


1

This course aims for students to obtain the background to understand brain-computer interaction and human-computerinteraction;

2understand the literature in the field of brain sensing for human-computer interaction research;

3

Understand the various tools used in brain sensing,with a focus on functional near-infrared spectroscopy(fNIRS) research at Drexel.

4

Understand the steps required to use real-time brainsensing data as input to an interactive system.

5understand the domains and contexts in which brain-computer interfaces may be effective;

6

Understand the open questions and challenges inbrain-computer interaction research today.

Course outcome

Descriptions

CO1Apply the knowledge of mathematics science and engineeringfundamentals to understand the Brain Organization.

CO2Apply the knowledge of mathematics science and engineeringfundamentals to understand the brain anatomy and Function.

CO3Analyze and process the brain signals for artifact reduction.

CO4Understand types of BCI, principles and its applications which are present state of art in the Neurosciences domain.


,Independent Component Analysis (ICA) , Common Spatial Patterns(CSP) 73 Artifact Reduction Techniques: Thresholding, Band-Stop andNotch Filtering, Linear Modeling, Principal Component Analysis (PCA),Independent Component Analysis (ICA).

III

Building a BCI: Major Types of BCIs: Brain Responses Useful forBuilding BCIs:Conditioned Responses, Population Activity, ImaginedMotor and Cognitive Activity, Stimulus-Evoked Activity. Invasive BCIs: Two Major Paradigms in Invasive Brain-ComputerInterfacing: BCIs Based on Operant Conditioning, BCIs Based onPopulation Decoding.

08

IV

Invasive BCIs in Humans: Cursor and Robotic Control Using aMultielectrode Array Implant, Cognitive BCIs in Humans, Long-TermUse of Invasive BCIs, Long-Term BCI Use and Formation of a StableCortical Representation, Long-Term Use of a Human BCI ImplantSemi-Invasive BCIs:Electrocorticographic (ECoG) BCIs -ECoG BCIs inAnimals, ECoG BCIs in Humans, BCIs Based on Peripheral NerveSignals Nerve-Based BCIs, Targeted Muscle Innervations (TMR). Non-Invasive BCIs:Oscillatory Potentials and ERD, Slow CorticalPotentials, Movement Related Potentials, Stimulus Evoked Potentials;BCIs Based on Cognitive Tasks, Error Potentials in BCIs, Co-adaptiveBCIs, Hierarchical BCIs. Other Noninvasive BCIs: fMRI, MEG, and fNIR: Functional MagneticResonance Imaging Based BCIs, Magneto encephalography BasedBCIs, Functional Near Infrared and Optical BCIs. BCIs that Stimulate: Sensory Restoration, Restoring Hearing:Cochlear Implants, Restoring Sight: Cortical and Retinal Implants,Motor Restoration, Deep Brain Stimulation (DBS), SensoryAugmentation.

09

V Medical Applications: Sensory Restoration, Motor Restoration,Cognitive Restoration, Rehabilitation, Restoring Communication withMenus, Cursors, and Spellers, Brain Controlled WheelchairsNonmedical Applications: Web Browsing and Navigating VirtualWorlds, Robotic Avatars, High Throughput Image Search Lie Detectionand Applications in Law , Monitoring Alertness, Estimating CognitiveLoad, Education and Learning, Security, Identification, andAuthentication, Physical Amplification with Exoskeletons, Mnemonicand Cognitive Amplification , Applications in Space, Gaming andEntertainment, Brain-Controlled Art. Ethics of Brain-Computer Interfacing: Medical, Health, and SafetyIssues, Balancing Risks versus Benefits, Informed Consent, Abuse ofBCI Technology, BCI Security and Privacy, Legal Issues, Moral and

06

Department


Social-Justice Issues.


Text Books:

Sl No


1 Brain-Computer InterfacingRajesh P. N. Rao An Introduction (1

Edition)

2Brain-ComputerInterfacesRevolutionizing Human Bernhard Graimann

(Editor), Brendan Z. Allison (Editor), GertPfurtscheller (Editor)

Computer Interaction(The Frontiers Collection) Hardcover – (13 Dec 2010)

3

Reference Book:

Sl No


Department


1

2



Department


Semester: VII



Course Objectives:

CourseOutcomes

Department


1

This course aims for students to obtain the background to understand brain-computer interaction and human-computerinteraction;

2 understand the literature in the field of brain sensing for human-computer interaction research;

3

Understand the various tools used in brain sensing,with a focus on functional near-infrared spectroscopy(fNIRS) research at Drexel.

4Understand the steps required to use real-time brainsensing data as input to an interactive system.

5 understand the domains and contexts in which brain-computer interfaces may be effective;

6Understand the open questions and challenges inbrain-computer interaction research today.

Course outcome

Descriptions

CO1Apply the knowledge of mathematics science and engineeringfundamentals to understand the Brain Organization.

CO2Apply the knowledge of mathematics science and engineeringfundamentals to understand the brain anatomy and Function.

CO3Analyze and process the brain signals for artifact reduction.

CO4Understand types of BCI, principles and its applications which are present state of art in the Neurosciences domain.



I

Basic Neurosciences: Basic Neuroscience: Neurons, Action Potentialsor Spikes, Dendrites and Axons, Synapses, Spike Generation,Adapting the Connections: Synaptic Plasticity – (LTP, LTD, STDP,Short-Term Facilitation and Depression), Brain Organization,Anatomy, and Function.Recording and Stimulating the Brain: Recording Signals from theBrain: Invasive Techniques &Noninvasive Techniques. Stimulating theBrain - Invasive Techniques & nonTechniques. SimultaneousRecording and Stimulation: Multi-electrode Arrays, Neurochip.

08

II

Signal Processing for BCI's: Spike Sorting, Frequency DomainAnalysis: Fourier analysis, Discrete Fourier Transform (DFT), FastFourier Transform (FFT), Spectral Features, Wavelet Analysis. TimeDomain Analysis: Hjorth Parameters , Fractal Dimension ,Autoregressive (AR) Modeling, Bayesian Filtering, Kalman Filtering,Particle Filtering), Spatial Filtering : (Bipolar, Laplacian, and CommonAverage Referencing ,Principal Component Analysis (PCA),Independent Component Analysis (ICA) , Common Spatial Patterns(CSP) 73 Artifact Reduction Techniques: Thresholding, Band-Stop andNotch Filtering, Linear Modeling, Principal Component Analysis (PCA),Independent Component Analysis (ICA).

08

III

Building a BCI: Major Types of BCIs: Brain Responses Useful forBuilding BCIs:Conditioned Responses, Population Activity, ImaginedMotor and Cognitive Activity, Stimulus-Evoked Activity. Invasive BCIs: Two Major Paradigms in Invasive Brain-ComputerInterfacing: BCIs Based on Operant Conditioning, BCIs Based onPopulation Decoding.

08

IV Invasive BCIs in Humans: Cursor and Robotic Control Using aMultielectrode Array Implant, Cognitive BCIs in Humans, Long-TermUse of Invasive BCIs, Long-Term BCI Use and Formation of a StableCortical Representation, Long-Term Use of a Human BCI ImplantSemi-Invasive BCIs:Electrocorticographic (ECoG) BCIs -ECoG BCIs inAnimals, ECoG BCIs in Humans, BCIs Based on Peripheral NerveSignals Nerve-Based BCIs, Targeted Muscle Innervations (TMR). Non-Invasive BCIs:Oscillatory Potentials and ERD, Slow CorticalPotentials, Movement Related Potentials, Stimulus Evoked Potentials;BCIs Based on Cognitive Tasks, Error Potentials in BCIs, Co-adaptiveBCIs, Hierarchical BCIs. Other Noninvasive BCIs: fMRI, MEG, and fNIR: Functional MagneticResonance Imaging Based BCIs, Magneto encephalography BasedBCIs, Functional Near Infrared and Optical BCIs.

09

Department


BCIs that Stimulate: Sensory Restoration, Restoring Hearing:Cochlear Implants, Restoring Sight: Cortical and Retinal Implants,Motor Restoration, Deep Brain Stimulation (DBS), SensoryAugmentation.

V

Medical Applications: Sensory Restoration, Motor Restoration,Cognitive Restoration, Rehabilitation, Restoring Communication withMenus, Cursors, and Spellers, Brain Controlled WheelchairsNonmedical Applications: Web Browsing and Navigating VirtualWorlds, Robotic Avatars, High Throughput Image Search Lie Detectionand Applications in Law , Monitoring Alertness, Estimating CognitiveLoad, Education and Learning, Security, Identification, andAuthentication, Physical Amplification with Exoskeletons, Mnemonicand Cognitive Amplification , Applications in Space, Gaming andEntertainment, Brain-Controlled Art. Ethics of Brain-Computer Interfacing: Medical, Health, and SafetyIssues, Balancing Risks versus Benefits, Informed Consent, Abuse ofBCI Technology, BCI Security and Privacy, Legal Issues, Moral andSocial-Justice Issues.

06


Text Books:

Sl No


1Text Books: Brain-Computer Interfacing:

Rajesh P. N. Rao An Introduction (1 Edition)

2Brain-ComputerInterfacesRevolutionizing Human- by Bernhard

Graimann (Editor), Brendan Z. Allison

Computer Interaction (The Frontiers Collection)

Department


(Editor), GertPfurtscheller (Editor)

Hardcover – (13 Dec 2010)

Reference Book:

Sl No


1

2

Department




Semester: VII



Course Objectives:

Department


1

Pattern recognition techniques are used to designautomated systems that improve their own performancethrough experience..

2

This course covers the methodologies, technologies, and algorithms of statistical pattern recognition from a variety of perspectives

3

Topics including Bayesian Decision Theory, EstimationTheory, Linear Discrimination Functions, NonparametricTechniques, Decision Trees, and Clustering Algorithms etc.will be presented.


Course Outcomes

Department

Course outcome

Descriptions

CO1

CO2

CO3

CO4



I

Introduction: Machine perception, pattern Recognition systems,Design cycles, learning and adaptation.Probability: Random variable, joint distribution and densities,moments of random variable, Estimation of parameters from sample.

08

II

Statistical decision making: Introduction, Baye’s theorem, multiplefeatures, conditionally independent features, decision bounderies,unequal costs of error, estimation of error rates, characteristic curves,problems. (3.1-3.7, 3.9 from text 1). 08

III

Non parametric Decision making: Introduction, Histograms, kerneland window estimators, nearest neighbor classification techniques,adaptive decision boundaries, adaptive discriminate functions,minimum squared error discriminant functions. (4.1-4.7text 1)

08

IV

Clustering: Introduction, Hierarchical clustering, partitionalclustering, Unsupervised Bayesion learning, Hierarchical clustering,partitional clustering, problems.

07

V

Processing of waveforms and images: Introduction, gray levelscaling transformations, equalization, geometric image scaling andinterpolation, edge detection, laplacian and sharpening operators, linedetection and template matching, logarithmic gray level scaling. (7.1-7.9 text 1) 08


Department


Text Books:

Sl No


1Pattern Recognition and ImageAnalysis Earl Gose, Richard

Johnson Baugh and Steve

PHI

Reference Book:

Sl No


1Pattern classification

Richard O.Duda, Peter E.Herd and David & Stork

john Wiley and sons, Inc 2nd

Ed.2001.

2Pattern Recognition: Statistical Structural and Neural Approaches,

Robert SchlkoffJohn Wiley and sons, Inc, 1992.

Department



Department: Medical ElectronicsSemester: VII

Subject Name: Biometrics


Course Objectives:

Course Outcomes

Department


1

Course Objectives:To understand the state-of-the-art in biometrictechnologies;

2To survey the currently available biometric systems;

3

To explore ways to improve some of the currenttechniques;

4

To learn and implement some of the biometricsauthentication;

5To explore new techniques


Department

Course outcome

Descriptions

CO1Understand the fundamentals and the need of biometrics.

CO2Learn the deployment, strength & weakness of the types ofBiometrics.

CO3Learn the uncommon biometrics and its usage.

CO4Understand the applications of Biometrics and learn the risks, standards and testing / Evaluation process of Biometrics.



I

Introduction – Benefits of biometric security – Verification andidentification – Basic working of biometric matching – Accuracy –False match rate – False non-match rate – Failure to enroll rate –Derived metrics – Layered biometric solutions.

08

II

Finger scan – Features – Components – Operation (Steps) – Competingfinger Scan technologies – Strength and weakness. Types of algorithmsused for interpretation. Voice Scan - Features – Components –Operation (Steps) – Competing voice Scan (facial) technologies –Strength and weakness.

08

III

Iris Scan - Features – Components – Operation (Steps) – Competingiris Scan technologies – Strength and weakness. Facial Scan -Features – Components – Operation (Steps) – Competing facial Scantechnologies – Strength and weakness.

08

IV

Other physiological biometrics – Hand scan – Retina scan – AFIS(Automatic Finger Print Identification Systems) – BehavioralBiometrics – Signature scan- keystroke scan.

07

V

Biometrics Application – Biometric Solution Matrix – Bio privacy –Comparison of privacy factor in different biometrics technologies –Designing privacy sympathetic biometric systems. Biometricstandards – (BioAPI , BAPI) – Biometric middleware. Biometrics forNetwork Security: Statistical measures of Biometrics. BiometricTransactions.

08


Department


Text Books:

Sl No


1 Biometrics–Identity Verification in aNetworked World

Samir Nanavati, Michael Thieme, Raj Nanavati

Wiley India Pvt Ltd,2002 .

2Biometrics for Network Security Paul Reid Pearson Education,

2004.

Reference Book:

Sl No


1Biometrics- The Ultimate Reference John D.

Woodward, Jr.Wiley Dreamtech.

2Biometric Systems Technology,Design and Performance Evaluation

James Wayman,Anil Jain, DavideMaltoni and DarioMaio

SpringerPublications.

3Personal Identification in NetworkedSociety

Jain, A.K.; R Bolle,Ruud M.; SPankanti, Sharath

1st ed. 1999.2ndprinting, 2006,SpringerPublications.

4Handbook of Biometrics Jain, Anil K.;

Flynn, Patrick;Ross, Arun A

Springer, 2008.

Department




Semester: VII

Subject Name: Ergonomics and Rehabilitation Engineering


Course Objectives:

CourseOutcomes

Department


1

This course covers the use of ergonomicprinciples to recognize, evaluate, and controlworkplace conditions that cause or contribute tomusculoskeletal and nerve disorders. Coursetopics include work physiology, anthropometry,musculoskeletal disorders, use of video displayterminals, and risk factors such as vibration,temperature, material handling, repetition, andlifting and patient transfers in health care.

2

Course emphasis is on industrial case studies covering analysis and design of work stations and equipment workshops in manual lifting, andcoverage of current OSHA compliance policies and guidelines.


Department

Course outcome

Descriptions

CO1

CO2

CO3

CO4



I

Introduction : Focus of ergonomics & its applications, Bodymechanics: Basics, Anatomy of Spine & pelvis related to posture,postural stability & adaptation, Low back pain, risk factorsformusculo skeletal disorders in workplaces, Anthropometricprinciples in workspace: Designing for a population of users, Humanvariability sources, applied anthropometry in ergonomics & design,anthropometry & personal space.

08

II

Design of Repetitive Tasks: Work related musculoskeletal disorders,injuries to upper body at work, neck disorders, carpal tunnelsyndrome, tennis elbow, shoulder disorder, ergonomic interventions.Design of physical environment: human thermoregulation, thermalenvironment, working in hot & cold climates, skin temperature,protection against extreme climates, comfort & indoor climate, ISOstandards.

07

III

Engineering Concepts in Rehabilitation Engineering: Anthropometry:Methods for Static and dynamic Measurements: Area Measurements,Measurement of characteristics and movement, Ergonomic aspects indesignating devices: Introduction to Models in Process Control, Designof Information Devices, and Design of Controls Active Prostheses:Active above knee prostheses. Myoelectric hand and arm prostheses-different types block diagram, signal flow diagram and functions. TheMARCUS intelligent Hand prostheses.

08

IV

Engineering concepts in sensory rehabilitation engineering: Sensoryaugmentation and substitution: Visual system: Visual augmentation,Tactual vision substitution, and Auditory vision substitution. Auditorysystem: Auditory augmentation, Audiometer, Hearing aids, cochlearimplantation, visual auditory substitution, tactual auditorysubstitution, Tactual system: Tactual augmentation, Tactualsubstitution.

08

V

Orthopedic Prosthetics and Orthotics in rehabilitation: Engineeringconcepts in motor rehabilitation, applications. Computer AidedEngineering in Customized Component Design. Intelligent prostheticknee, A hierarchically controlled prosthetic and A self-aligning orthoticknee joint. Externally powered and controlled Orthotics andProsthetics. FES systems- Restoration of hand function, restoration ofstanding and walking, Hybrid Assistive Systems (HAS).

08


Department


Text Books:Sl No


1Introduction to Ergonomics

R S Bridger Rout ledge Taylor &Francis group, London,2008

2 Handbook of biomedical engineering.

Bronzino, Joseph 2nd edition, CRC Press, 2000. 24

3 Rehabilitation engineering. Robinson C.J CRC press 1995.

Reference Book:

Sl No


1 Fitting the task to human, A textbook of occupational ergonomics

Taylor &Francis 5th edition, ACGIHpublications , 2008

2 Work study & Ergonomics by, DhanpatRai& sons 1992

3 Intelligent systems and technologies in rehabilitation engineering;.

Horia- NocholaiTeodorecu,L.C.Jain

CRC; December 2000

4 Fitting the task to the man, Etienne Grandjean,Harold Oldroyd,

Taylor & Francis,1988.


Department



Department: Medical ElectronicsSemester: VII

Subject Name: Subject Code: ML7PE34 L-T-P-C: 3-0-0-3

Course Objectives:

Course Outcomes

Department


1

To create appreciation and understanding of both theachievements of AIand the theory underlying thoseachievements.

2

To impart basic proficiency in representing real lifeproblems in a state space representation so as tosolve them using different AI techniques.

3

To create an understanding of the basic issues of knowledge representation and heuristic search techniques.

Course outcome

Descriptions

CO1On completion of this course, the students shall be able to Demonstrate the knowledge of building blocks of AI.

CO2Analyze and formalize the problem as a state space tree, designheuristics and solve using different search techniques.

CO3Analyze and demonstrate knowledge representation using varioustechniques.

CO4 Develop AI solutions for a given problem.


Department



I

Introduction: What is Artificial Intelligence?, AI Problems, The underlying Assumption, What is an AI Technique, Problems, problem spaces, and search Defining the problem as a State Space Search, Production Systems, Problem Characteristics, Production System Characteristics, Issues in the Design of search programs, Additional Problems.

08

IIHeuristic and Search Techniques: Generate-and-Test, Hill Climbing, Best-First Search, Problem Reduction, Constraint satisfaction, Means-Ends Analysis

08

III

Knowledge Representation Issues: Representation and Mappings,Approaches to knowledge Representation, Issues in knowledgeRepresentation, Weak Slot Filler Structures: Semantic Nets, Frames

08

IVUsing Predicate Logic: Representing the simple facts in logic,Representing Instance and ISA Relationships, Computable functionsand predicates, Resolution, Natural Deduction

08

V

Strong slot-and-Filter Structures: Conceptual Dependency, Scripts,CYC Expert Systems Representation and Using Domain Knowledge,Expert Systems shells, Explanation, Knowledge Acquisition.

07


Text Books:

Sl No


1Artificial Intelligence Elaine Rich, Kevin

Knight,ShivashankarB Nair

3rd Edition, Tata McGraw Hill, 1991.

Department


Reference Book:

Sl No


1 Artificial Intelligence A ModernApproach

Stuart Russel,Peter Norvig

2nd Edition,Pearson Education,2003.

2 Principles of Artificial Intelligence Nils J. Nilsson Elsevier, 1980.

Department



Semester: VII

Subject Name: Biomedical Digital Signal Processing LabSubject Code: ML7L01 L-T-P-C: 0-0-3-1.5

Course Objectives:

Course Outcomes

Department


1To understand the basic signals in the field ofbiomedical.

2

To study origins and characteristics of some of themost commonly used biomedical signals, includingECG, EEG, evoked potentials, and EMG.

3To understand Sources and characteristics of noiseand artifacts in bio signals.

4To understand use of bio signals in diagnosis, patient monitoring and physiological investigation.

Course outcome

Descriptions

CO1

CO2

CO3

CO4



I

1. Computation of Convolution and Correlation Sequences.2. Signal Averaging to Improve the SNR3. Read and plotting of ECG data, spectrum of ECG with 50 HZ

noise.4. Design of FIR Filter for ECG.5. Integer filters for ECG6. QRS detection and Heart rate determination.7. Correlation and Template matching.8. Realization of Notch filter for removal of line interference9. Data Compression Techniques using AZTEC algorithm.10. Data Compression Techniques using TP algorithm.11. Data Compression Techniques using FAN algorithm.

Note: The above experiments are to be conducted using Matlab/ Lab VIEW/ “C” language.


Text Books:

Sl No


1Bioelectrical Signal Processing inCardiac & Neurological Applications

Leif SSrnmo ,Pablo Laguna -Elsevier -

Academic Press.

Reference Book:

Sl No


1 Biomedical Digital Signal Processing Willis J. Tompkins PHI.

Department


2 Biomedical Signal Processing-principles and techniques by

D. C. Reddy Tata McGraw-Hill,2005

3 Biomedical Signal Analysis by M.RangayyanRangaraj

IEEE Press, 2001.

Department: Medical Electronics Semester: VIISubject Name: C++ and Python Lab

Subject Code: ML7L02 L-T-P-C: 0-0-3-1.5

Course Objectives:

Course Outcomes

Department


1

2

3

4

Course outcome

Descriptions

CO1By the completion of this course, the student will be able to: know how to use data types based on the programs and declare variables.

CO2 Learn the concepts and importance of functions, arrays, classes &objects.

CO3 Understand the concept of Operator Overloading and inheritancefor effective programming.

CO4 Learn the basic concepts of python.


UNIT Description HoursI C++ Lab:

1. Write a C++ program to calculate the sum of the series i) 1+x+x2+x3+...+xn

ii) -1+2-4+8-16+...10242. Write a C++ program to sort the elements of an array using i) Selection sort ii) Bubble sort3. Write a C++ program to accept two arrays of different lengths. Merge the two accepted arrays.4. Write a C++ program to accept two 2-dimensional arrays and perform addition, subtraction and multiplication.5. Write a C++ program to find the LCM and GCD of 2 given numbers using functions.6. Write a C++ program to find the factorial of a given number using recursive function.7. Write a C++ program to find the largest, smallest and their averagesusing functions.8. Write a C++ program to accept the information about an employee and calculate the following and display using structure.i) Accept the basic salary, name, id_no of an employee.ii) Calculate DA, HRA, PF, LIC, Gross and net salary.

DA: 45% of basic salary HRA: If basic is >=2000 and <3000, HRA=800 If basic is >=3000 and <4000, HRA=1000

If basic is >=4000 and <6000, HRA=1200 If basic is >=6000, HRA=1500 PF: 11.5% of basic salaryLIC: 17% of basic salaryGross=basic salary+DA+HRANet salary=Gross-PF-LIC

9. Write a C++ program to find the sum of two complex numbers using classes by overloading operator +.10. Write a C++ program to multiply two numbers using Multiple Inheritance.Python Lab:2.Basic programs using python:

i) Display of a word/ sentence.

ii) Performing calculations.

iii) Use of variables and objects.

Department


iv)Use of loops, arrays, functions, plots.


Text Books:

Sl No


1Object Oriented programming inTURBO C++ ,

Robert Lafore,Galgotia

Publications.2002.

2.Classic Data Structures, Debasis Samanta, Second Edition,

PHI, 2009.

Reference Book:Sl No


1 Object Oriented Programming withC++

E.Balaguruswamy, third edition, TMH2006

2 C++ the complete reference, Herbert Schildt, fourth edition,TMH, 2003.

Department


Department: Medical ElectronicsSemester: VIII

Subject Name: Neural Networks


Course Objectives:

Course Outcomes

Department


1This course gives an introduction to basic neuralnetwork architectures and learning rules.

2

Emphasis is placed on the mathematical analysis ofthese networks, on methods of training them and ontheir application to practical engineering problems insuch areas as pattern recognition, signals processingand control systems.


Department

Course outcome

Descriptions

CO1

on the completion of this course the students will be able toThe fundamental concepts of artificial neural network.

CO2Network architectures and its principles.

CO3Different learning algorithms and its applications.

CO4 Information representation in biological system and its models.



IIntroduction: The classic neuron, Membrane potential, Actionpotential, Neuronal electrical behavior, Cable Equation, SynapticIntegration. Models of Neuron, Synaptic Electrical Events, slowpotential theory of neuron, two state neurons, Feedback.

10

IINetwork Architectures: Single layer feed forward networks; Multilayerfeed forward networks, Recurrent Networks, Knowledgerepresentation.

11

III Learning processes: Introduction Error correction learning, Memorybased learning, Hebbian Learning, Competitive learning.

10

IV Learning paradigms: Learning with a teacher, Learning without ateacher, Learning tasks, Memory, Adaptation Artificial intelligence andNeural networks.

10

V Information representation in biological Systems, Distributed,Map, layered structures, Visual system, Auditory System.

11


Department


Text Books:

Sl No


1 An Introduction to neural networks James A. Anderson 2e PHI 1995

2Neural Networks Simon Haykin Pearson education

PHI 2001

Reference Book:

Sl No


1 Fundamentals of Artificial Neural Networks

Mohammad Hasan PHI, 1999

2

Department


Department: Medical ElectronicsSemester: VIII

Subject Name: Biomedical Therapeutic Equipments


Course Objectives:

Course Outcomes

Department


1

The objective of this course is to introduce thestudents to the application of biomedicalinstrumentation used in surgery.

2

This course is to familiarize the students withphysiotherapy and electrotherapy instruments andvarious machines used in ICU.

3

It includes brief study of different types of ventilatorsand how to design a automated drug delivery unitdepends on the requirement of patient.

Course outcome

Descriptions

CO1 Learn the working principle of Instruments for surgery andphysiotherapy, electrotherapy instruments

CO2 Understand the working of kidney, design of artificial kidney.Advantages and need of anesthesia machine.

CO3 Understand the principles of ventilators, study about differenttypes of ventilators

CO4 Analyzing the concepts of Automated Drug delivery Systems.


Department



I

Instruments for Surgery: Principles of surgical diathermy, surgicaldiathermy Machine, safety aspects in electro- surgical units, surgicaldiathermy Analyzer.

10

II

Physiotherapy and Electrotherapy Equipments: High frequencyheat therapy, Shortwave diathermy, microwave diathermy,ultrasound therapy unit, Electro diagnostic therapeutic apparatus,pain relief through electrical Stimulation, bladder and cerebellastimulators.

10

III

Haemodialysis Machine: Artificial kidney, dialyzer, Membranes for haemodialysis. Lithotripters: Stone disease problems, lithotripter machine, extra-corporeal Shock wave therapy.Anesthesia Machine: Need for anesthesia, anesthesia Machine

10

IV

Ventilators: Artificial ventilation, ventilators, types of ventilators, ventilators terms, classification of ventilators. Modern ventilators. Humidifiers,Nebulizers and Aspirators

10

V

Automated Drug Delivery Systems: Infusion pumps, components ofdrugs infusion systems and implantable infusion systems. ClosedLoop Control Infusion Pumps.

12


Department


Text Books:

Sl No


1Handbook of BiomedicalInstrumentation

R.S.Khandpur, McGraw Hill, 2003.

2Biomedical Instrumentation Dr.M. Arumugam- Second Edition-

1994.

Reference Book:

Sl No


1

2

Department


Department: Medical Electronics Semester: VIII

Subject Name: Machine Learning


Course Objectives:

Course Outcomes

Department


1

The main goal of this course is to help studentslearn, understand, and practice big data analyticsand machine learning approaches, which include thestudy of modern computing big data technologiesand scaling up machine learning techniques focusingon industry applications.

2

Mainly the course objectives are: conceptualizationand summarization of big data and machinelearning, trivial data versus big data, big datacomputing technologies, machine learningtechniques, and scaling up machine learningapproaches.

3


Department

Course outcome

Descriptions

CO1

Apply the knowledge of mathematics science and engineeringfundamentals in the understanding of fundamental issues andchallenges of machine learning: data, model selection, modelcomplexity, etc.

CO2Analyze the strengths and weaknesses of many popular machinelearning approaches.

CO3

Comprehend the underlying mathematical relationships withinand across Machine Learning algorithms and the paradigms ofsupervised and un-supervised learning.

CO4Design and implement various machine learning algorithms in arange of real-world applications.



I

Introduction: Introduction to machine learning, Examples of MachineLearning Applications. Parametric regression: linear regression,polynomial regression, locally weighted regression, numericaloptimization, gradient descent, kernel methods.

08

II

Generative learning: Gaussian parameter estimation, maximumlikelihood estimation, MAP estimation, Bayesian estimation, bias andvariance of estimators, missing and noisy features, nonparametricdensity estimation, Gaussian discriminant analysis, naive Bayes.Discriminative learning: linear discrimination, logistic regression, logitand logistic functions, generalized linear models, softmax regression.

08

III

Neural networks: the perceptron algorithm, multilayer perceptrons,backpropagation, nonlinear regression, multiclass discrimination,training procedures, localized network structure, dimensionalityreduction interpretation.Support vector machines: functional and geometric margins, optimummargin classifier, constrained optimization, Lagrange multipliers,primal/dual problems, KKT conditions, dual of the optimum marginclassifier, soft margins, kernels, quadratic programming, SMOalgorithm.

08

IV

Graphical and sequential models: Bayesian networks, conditionalindependence, Markov random fields, inference in graphical models,belief propagation, Markov models, hidden Markov models, decodingstates from observations, learning HMM parameters.

07

V

Unsupervised learning: K-means clustering, expectationmaximization, Gaussian mixture density estimation, mixture of naiveBayes, model selection. Dimensionality reduction: feature selection,principal component analysis, linear discriminant analysis, factoranalysis, independent component analysis, multidimensional scaling,and manifold learning.

08


Department


Text Books:

Sl No


1Elements of Statistical Learning, T.Hastie,

R. Tibshirani andJ. Friedman,

Springer, 2001.

2Machine Learning, EthemAlpaydin, MIT Press, 2010.

Reference Book:

Sl No


1 Pattern Recognition and MachineLearning,

C. Bishop, Springer, 2006.

2 Machine Learning: A ProbabilisticPerspective,

K. Murphy, MIT Press, 2012.

3 Pattern Classification, R. Duda, E. Hart,and D. Stork,

Wiley-Inter science,2000.

4 Machine Learning T. Mitchell, McGraw-Hill, 1997.

Department



Semester: VIII

Subject Name: SMART WEARABLE SYSTEMS


Course Objectives:

Course Outcomes

Department


1

Extensive efforts have been made in both academia andindustry in the research and development of smart wearablesystems (SWS) for health monitoring (HM). Primarilyinfluenced by skyrocketing healthcare costs and supportedby recent technological advances in micro- andnanotechnologies, miniaturisation of sensors, and smartfabrics, the continuous advances in SWS will progressivelychange the landscape of healthcare by allowing individualmanagement and continuous monitoring of a patient’shealth status.

2

Consisting of various components and devices, rangingfrom sensors and actuators to multimedia devices, thesesystems support complex healthcare applications andenable low-cost wearable, non-invasive alternatives forcontinuous 24-h monitoring of health, activity, mobility,and mental status, both indoors and outdoors. Our objectivehas been to examine the current research in wearable toserve as references for researchers and provide perspectivesfor future research


Department

Course outcome

Descriptions

CO1

On completion of this course, the students shall be able to

Understand the basic foundations on biological and artificialneural network and the importance of neuron models for patternclassification

CO2Demonstrate the process of forming association between relatedpatterns through associative networks

CO3Apply the principles of back propagation supervised learning forerror minimization

CO4

Understand and analyze the various competition based learningalgorithms and importance of resonance based network learningalgorithms.



I

Introduction : What is Wearable Systems, Need for Wearable Systems,Drawbacks of Conventional Systems for Wearable Monitoring,Applications of Wearable Systems, Recent developments – Global andIndian Scenario, Types of Wearable Systems, Components of wearableSystems, Physiological Parameters commonly monitored in wearableapplications, Smart textiles, & textiles sensors, Wearable Systems forDisaster management, Home Health care, Astronauts, Soldiers inbattle field, athletes, SIDS, Sleep Apnea Monitoring.

08

II

Smart Sensors& Vital Parameters : Vital parameters monitored andtheir significances, Bio-potential signal recordings (ECG, EEG, EMG),Dry Electrodes design and fabrication methods, Smart Sensors –textile electrodes, polymer electrodes, non-contact electrodes, MEMSand Nano Electrode Arrays, Cuff-less Blood Pressure Measurement,PPG, Galvanic Skin Response (GSR), Body TemperatureMeasurements, Activity Monitoring for Energy Expenditure,Respiratory parameters.

08

III

Wearable Computers : Flexible Electronics, Wearable Computers,Signal Processors, Signal Conditioning circuits design, PowerRequirements, Wearable Systems Packaging, Batteries and charging,Wireless Communication Technologies and Protocols, ReceiverSystems, Mobile Applications based devices.

08

IV

Wireless Body Area Networks: Wireless Body Area Networks –Introduction, Personal Area Networks (PAN), Application in VitalPhysiological Parameter monitoring, Design of Sensor & Sink Nodes,Architecture, Communication & Routing Protocols, Security, Powerand Energy Harvesting.

07

V

Data Processing And Validation : Classification Algorithms, DataMining and Data Fusion, Signal Processing Algorithms in wearableApplications, Issues of wearable physiological monitoring systems,Statistical Validation of Parameters, Certifications of Medical Devicesand Patenting.

08

Department



Text Books:

Sl No


1Wearable Monitoring Systems, Annalisa Bonfiglo,

Danilo De Rossi,Springer, 2011

2Wearable Sensors: Fundamentals, Implementation and Applications,

Edward Sazonov,Micheal R Neuman,

Elseiver, 2014.

Reference Book:

Sl No


1 Wearable Electronics: Design, Prototypeand wear your own interactive garments,

Kate Hartman, Make Maker Media

2 , Wearable Technology, Elijah Hunter Kindle Edition

3 Body Sensor Networks, Guang Zhong Yang, Springer .

Department



Semester: VIII

Subject Name: SPEECH SIGNAL PROCESSING


Course Objectives:

Course Outcomes

Department


1 To understand the characteristics of speech signal,

2 To apply signal processing concepts to speech signal,

3To get an insight into a few applications of speech

processing.

Course outcome

Descriptions

CO1

On completion of the course the student can recallProperties of speech signal and its production and discrimination system

CO2 Design of filter bank and its implementation, and spectrographic display.

CO3 Digital representation of speech signal using different quantizationtechniques.

CO4 LPC algorithms and its applications for speech coding and fundamental algorithms for speech synthesis, coding and recognition.



I

Digital Models For Speech Signals: Process of Speech Production,Lossless tube models, Digital models for Speech signals.

Time Domain Models For Speech Processing: Time dependentprocessing of speech, Short time energy and average magnitude, Shorttime average zero crossing rate, Speech Vs silence discriminationusing energy and zero crossing.

08

II

Short Time Fourier Analysis: Linear filtering interpretation, Filterbank summation method, Design of digital filter banks,Implementation using FFT, Spectrographic displays.

08

III

Digital Representations Of The Speech Waveform: Sampling speechsignals, Review of the statistical model for speech, Instantaneousquantization, Adaptive Quantization, General theory of differentialquantization, Delta modulation.

08

IV

Linear Predictive Coding Of Speech: Basic principles of linearpredictive analysis, Solution of LPC equations, Prediction error signal,Frequency domain interpretation, Relation between the various speechparameters, Applications of LPC parameters.

07

V

Speech Synthesis: Principles of Speech synthesis, Synthesis basedon waveform coding, analysis synthesis method, speech productionmechanism, Synthesis by rule, Text to speech conversion. Speech Recognition: Principles of Speech recognition, Speech perioddetection, Spectral distance measures, Structure of word recognitionsystems, Dynamic time warping (DTW), Word recognition usingphoneme units.

08


Department


Text Books:

Sl No


1Digital Processing of Speech

SignalsL R Rabiner and R W Schafer,

Pearson

Education 2004.

2Digital Speech Processing- Synthesis and Recognition,.

Sadoaki Furui, 2nd Edition, Mercel Dekker 2002.

Reference Book:

Sl No


1 Introduction to Data Compression

Khalid Sayood 3rd Edition, Elsivier

Publications

2 Digital Speech A M Kondoz, 2nd Edition, Wiley Publications

3

Department



Semester: VIII

Subject Name: Clinical Data AnalyticsSubject Code: ML8PE44 L-T-P-C: 3-0-0-3

Course Objectives:

Course Outcomes

Department


1Identify key tools and approaches to improveanalytics capabilities in clinical settings.

2

Describe different governance and operationsstrategies in analytics in clinical settings.

3

Discuss value-based payment systems and the role of data analytics in achieving their potential.

4Analyze data used in population management and

value-based care systems

Course outcome

Descriptions

CO1Ability to apply knowledge of mathematics, science and Engineering to develop the solution using biostatistical concepts.

CO2Ability to analyse a problem and formulate appropriate solution forbiostatistical concepts application.

CO3 An ability to design and perform statistical test and interpret results

CO4 Ability to implement and demonstrate statistical analysis usingmodern tool usage.



I

Introduction to Biostatistics: Introduction, Some basic concepts,Measurement and Measurement Scales, Simple random sample,Computers and biostatistician analysis. Descriptive Statistics:Introduction, ordered array, grouped data-frequency distribution,descriptive statistics – measure of central tendency, measure ofdispersion, measure of central tendency probability distributions ofdiscrete variables, binomial distribution, Poisson distribution,continuous probability distribution, normal distribution.

08

II

Sampling distributions: distribution of sample mean, distribution ofthe difference between two sample means, distribution of sampleproportion, distribution of the difference between two sampleproportions, Estimation: confidence interval for a population mean, t-distribution, confidence interval for differences between twopopulation means, confidence interval for a population proportion,confidence interval for difference between two populationsdetermination of sample size for estimating means, for estimatingproportions , confidence interval for the variance of normallydistributed population, confidence interval for ratio of variances of twonormally distributed populations.

08

III

Hypothesis Testing : Introduction, hypothesis testing – singlepopulation mean, difference between two population means, pairedcomparisons, hypothesis testing-single population proportion,difference between two population proportions, single populationvariance, ratio of two population variances.

07

IV

Analysis of Variance (ANOVA): Introduction, completely randomizeddesign, randomized complete block design, repeated measures design,factorial experiment UNIT-5 8 hours Linear Regression andCorrelation: the regression model, sample regression equation,evaluating and using regression equation, correlation modelcorrelation coefficient Multiple linear regression model, obtainingmultiple regression equation, evaluating multiple regression equation,using the multiple regression equation, multiple correlation model,mathematical properties of Chisquare distribution.

08

V

Linear Regression and Correlation: the regression model, sampleregression equation, evaluating and using regression equation,correlation model correlation coefficient Multiple linear regressionmodel, obtaining multiple regression equation, evaluating multipleregression equation, using the multiple regression equation, multiplecorrelation model, mathematical properties of Chi-square distribution.

08

Department



Text Books:

Sl No


1.“Biostatistics-A Foundation for

Analysis in the HealthSciences”

Wayne W. Daniel, John Wiley & Sons Publication, 6th Edition

2Fundamentals of Biiostatistics khan and

khanum,Ukaazpublications, 2ndrevise edition

3“An introduction to statistical Method and data analysis”,

R.Lyman ott..

Reference Book:

Sl No


Department


1

23


Semester: VIII

Subject Name: ARM PROCESSORS


Course Objectives:

Course Outcomes

Department


1

This course introduces the concept of architectureand programming of advanced embeddedmicrocontrollers i.eARMfamily ofmicrocontrollers that are widely used in designof real time sophisticated embedded systemslike tablets, hand held devices, automation andindustrial control systems.

2

It also covers writing Embedded C programming ofLPC2148 for GPIO,ADC,DAC, UART, LCD,Timers and etc.

3It also explains the concepts of embedded system and its components


Department

Course outcome

Descriptions

CO1Describe the ARM processor architecture and its family.

CO2 Develop assembly language programs to perform specific tasksusing ARM instructions.

CO3 Develop ARM microcontroller applications using Embedded Clanguage.

CO4 Design and develop program to interface external hardware withLPC214x microcontroller.



I

ARM EMBEDDED SYSTEMS The RISC Design Philosophy, The ARM Design Philosophy, EmbeddedSystem Hardware, Embedded System Software.ARM PROCESSOR FUNDAMENTALSRegisters, Current Program Status Register, Pipeline, Exceptions,Interrupts, and Vector Table, Core Extensions, Architecture Revisions,ARM Processor Families, LPC2148 Microcontroller Architecture,Memory Mapping, Register Description.

08

II

INTRODUCTION TO THE ARM INSTRUCTIONS SETData Processing Instructions, Branch Instructions, Load-StoreInstructions, Software Interrupt Instructions, Program Status RegisterInstruction, Example Programs.

07

III

INTRODUCTION TO THE ARM INSTRUCTIONS SET contd….Loading Constants, ARMv5E Extensions, Conditional Execution, andExample Programs.EFFICIENT C PROGRAMMINGOverview of C Compilers and Optimization, Basic C Data Types, CLooping Structures, Register Allocation, Function Calls, PointerAliasing, Structure Arrangement, Bit-fields, Unaligned Data andEndianness, Division, Floating Point, Inline Functions and InlineAssembly.

08

IV

InterfacingSensors, Actuators, GPIO, LED, 7 segment display, stepper motor,Keyboard, Push button switch, Data Conversions (ADC, DAC), Timers,Communication Protocols: UART, I2C, SPI, CAN(onboard), Programsusing C.

08

V

Embedded System ComponentsEmbedded v/s General computing system, Classification of Embeddedsystems, Major applications and purpose of Embedded systems. Coreof an Embedded System including all types of processor/controller,Memory.

08

Department



Text Books:

SlNo


1ARM Systems Developer's Guide Designing andOptimizing System Software,

Andrew N. Sloss, Dominic Symes, ChrisWright,

Morgan Kaufmann Publishers, ElseveirInc, 2004.(Chapters 1, 2, 3, 5)

2Introduction to Embedded Systems, Shibu K

V, Secondedition, Tata McGraw Hill Education Private Limited, 2017. (Chapters 1

Department


and 2 selected topics)

3LPC214x User Manual –

http://www.keil.com/dd/docs/datashts/philips/user_manual_lpc214x.pdf

(LPC2148, GPIO, Registers, Embedded components selected)

Reference Book:

Sl No


1 ARM System On Chip Architecture Steve Furber, Second Edition, Pearson Education Limited, 2000.

2 ARM ASSEMBLY LANGUAGE Fundamentals and Techniques

WilliamHohl, Christopher

Hinds, Second Edition, CRC Press, 2015.

3 ARM Assembly Language An Introduction

Gibson, Second Edition,2007.

Department





Semester: VIII

Subject Name: Robotics And Automation


Course Objectives:

Course Outcomes

Department


1This course introduces fundamental concepts in robotics.

2

The objective of the course is to provide an introductoryunderstanding of robotics. Students will be exposedto a broad range of topics in robotics with emphasison basics of manipulators, coordinate transformationand kinematics, trajectory planning, controltechniques, sensors and devices, robot applicationsand economics analysis.


Department

Course outcome

Descriptions

CO1 Understand the fundamental concepts of robot

CO2 Calculate the forward kinematics and inverse kinematics of serial and parallel robots.

CO3Be able to calculate the Jacobian for serial and parallel robot.

CO4 Be able to do the path planning for a robotic system.



I

BASIC CONCEPTS Automation and Robotics – An over view ofRobotics – present and future applications – classification bycoordinate system and control system, Hydraulic, Pneumatic andelectric drivers – Determination HP of motor and gearing ratio.

08

II

MANIPULATORS: Construction of Manipulators, Manipulator Dynamic and Force Control, Electronic and Pneumatic manupulators.ACTUATORS AND GRIPPERS Pneumatic, Hydraulic

Actuators, Stepper Motor ControlCircuits, End Effecter, Various types of Grippers.

08

III

TRANSFORMATION AND DYNAMICS Differential transformationand manipulators,Jacobians – problems.Dynamics: Lagrange – Euler and Newton – Eulerformations.

07

IV

KINEMATICS Forward and Inverse Kinematic Problems, Solutions of Inverse Kinematicproblems,Multiple Solution, Jacobian Work Envelop – Hill ClimbingTechniques.

08

V

PATH PLANNING Trajectory planningand avoidance of obstacles,path planning, skew

motion, joint integrated motion – straight-line motion.

08


Department


Text Books:

SlNo


1Industrial Robotics Groover M P Pearson Edu.

2Robotics control, Sensing, Vision and Intelligence,

Fu, K.S., Gonzalez, R.C., and Lee, C.S.G.,

McGraw-Hill Publishing company, New Delhi, 2003.

3Robot Engineering-An Integrated Approach,

Klafter, R.D., Chmielewski,T.A., and Negin. M,

Prentice Hall of India, New Delhi, 2002.

4Introduction to Robotics Mechanicsand Control,

Craig, J.J., Addison Wesley,1999.

Reference Book:

Sl No


1 Robotics, CSP Rao and V.V. Reddy,

Pearson Publications (In press)

2 An Introduction to Robot Technology,

P. Coiffet and M. Chaironze Kogam

3 Robot Analysis and Intelligence Asada and Slow time Wiley Inter-

Department


Science.

4 Robot Dynamics and Control Mark W. Spong and M. Vidyasagar,

JohnPage Ltd. 1983 London.Wiley & Sons..


Semester: VIII

Subject Name: Medical Device DevelopmentSubject Code: ML8PE53 L-T-P-C: 3-0-0-3

Course Objectives:

Course Outcomes

Department


1Understand the processes for medical device development after “design freeze”

2Become familiar with the European regulatory framework for medical devices

3Gain an understanding of manufacturing process validation

4Build on the student’s current understanding of the Quality Management System

5Understand key aspects of Product Management both during and after product launch

6Discuss Good Clinical Practices and regulationssurrounding management of clinical trials


Department

Course outcome

Descriptions

CO1 Identify and analyse unmet clinical need and its requirements tosolve it.

CO2Search, analyse and document clinical practice, engineeringscience and relevant literature in order to determine the need forfurther research and development in a chosen clinical area.

CO3 Develop a sustainable business plan, including market overview,regulation strategies for health & safety of individuals andintellectual property (IP) strategies.

CO4 . Understand medical device design engineering and manufacturing process by avoiding common quality pitfalls in turnlearning project management.



I

MedTech Invention: Needs finding through Observation and ProblemIdentification. Need Statement Development. Need Screening &Selection through Stakeholder Analysis, Market Analysis & NeedsFiltering. Concept Generation, Screening and selection.

10

II

Product Requirements: Define MedTech Device. Classification ofDevice. Role of Requirements in MedTech Product Development.Market Requirements, Customer Requirements, Clinical Workflow.Design Input. ISO 13485. Intended use, Functional / performancerequirements, safety, usability requirements etc.....

07

III

Design Engineering: Design and Development Plan. Design Process.Design Outputs, Intermediate deliverables - System Architecture,Subsystem requirements, Prototype, System Integration. DesignReview. Design Verification.

08

IV

Validation: System Validation. Usability Validation. Safety Validation.Clinical Validation, Regulatory Submission UNIT V [6 hours] ProgramManagement: Program Planning, Stage Gate Process, Milestones.Budgeting, Development Strategy, Risk identification and Mitigationprocess.

07

V

Program Management: Program Planning, Stage Gate Process,Milestones. Budgeting, Development Strategy, Risk identification andMitigation process.

06


Department


Text Books:

SlNo


1“Biodesign: The Process of InnovatingMedical Technologies”,

Stefanos Zenios ,Josh Makower,Paul Yock, Todd J.Brinton, Uday N.Kumar, LynDenend, ThomasM. Krummel

CambridgeUniversity Press; 2ndedition.

Reference Book:

Sl No


1 “Inventing medical devices: Aperspective from India”

Dr JagdishChaturvedi

CreateSpaceIndependentPublishingPlatform; 1stedition,2015.

2 “The Medical Device R&D Handbook”

Theodore R. Kucklick

Second Edition, CRC Press, 2012.

Department



Semester: VIII

Subject Name: Virtual BMISubject Code: ML8PE54 L-T-P-C: 3-0-0-3

Course Objectives:

Course Outcomes

Department


1

The main goal of this course is for students to learn applications of programming, signal transduction, data acquisition, data analysis, and signal processing used in thedesign of medical and laboratory instrumentation.

2

The software package LabVIEW has become a standard in academic and industrial environments for data acquisition, interfacing of instruments and instrumentation control.

3

Students will learn LabVIEW as a tool for the design of computer-based virtual instruments, which add software-based intelligence to sensors and basic laboratory bench devices.

Course outcome

Descriptions

CO1 Describe the Graphical System Design approach & basic features and techniques of Lab VIEW.

CO2 Use the Modular Programming concepts for creation of VIs & employ DAQ assistant for configuration of hardware devices.

CO3 Describe the Lab VIEW and BioBench software for EMG, ECG, andCardiopulmonary system analysis.

CO4 Explain the Medical Device Development Applications for Surgical Video Systems and Healthcare Information Management Systems using Information Science and Technology.



I

Graphical System Design (GSD): Introduction, GSD model, Designflow with GSD, Virtual Instrumentation, Virtual Instrumentation andtraditional instrumentation, Hardware and software in virtualinstrumentation, Virtual Instrumentation for test, control and design,GSD using LabVIEW, Graphical programming and texturalprogramming. Introduction to LabVIEW: Introduction, Advantages ofLabVIEW, Advantages of LabVIEW, Software environment, Creatingand saving a VI, Front panel toolbar, Block diagram toolbar, Palettes,Shortcut menus, Property dialog boxes, Front panel controls andindicators, Block diagram, Data types, Data flow program, LabVIEWdocumentation resources, Keyword shortcuts.

07

II

Modular Programming: Introduction, Modular Programming inLabVIEW, Build a VI front panel and block diagram, ICON andconnector pane, Creating an icon, Building a connector pane,Displaying subVIs and express Vis as icons or expandable nodes,Creating subVIs from sections of a VI, Opening and editing subVIs,Placing subVIs on block diagrams, Saving subVIs, Creating a stand-alone application. Data Acquisition: DAQ software architecture, DAQassistant, Channels and task configurations, Selecting andconfiguring a data acquisition device, Components of computer basedmeasurement system.

08

III

General Goals of Virtual Bio-Instrumentation (VBI): Definition ofVBI and importance, General Goals of VBI applications. BasicConcepts: DAQ basics, LabVIEW basics, BioBench basics.Neuromuscular Electrophysiology (Electromyography): Physiologicalbasis, Experiment set up, Experiment descriptions, Trouble shootingthe nerve –Muscle Preparation. Cardiac Electrophysiology(Electrocardiology):Physiological basis, Experiment descriptions.Cardiopulmonary Applications: Cardiopulmonary measurementsystem, Hiw the Cardiopulmonary measurement system works,Clinical Significance.

08

IV

Medical Device Development Applications: The Endotester – AVirtual Instrument –Based Quality control and Technology,Assessment System for surgicalvideoSystems: Introduction, Materialsand Methods, Endoscope Tests, Results, Discussion. Fluid SenseInnovative IV Pump Testing: Introduction, The test System, TrainingEmulator.

08

V Healthcare Information management Systems: Medical Informatics:Defining medical informatics, Computers in medicine, ElectronicMedical record, Computerized physician order entry, Decision support.Information Retrieval, Medical Imaging, Patient Monitoring, Medical

08

Department


Education, Medical Simulation. Managing Disparate Information:ActiveX, ActiveX Data Objects(ADO), Dynamic Link Libraries,Database Connectivity, Integrated Dashboards.


Text Books:

SlNo


1 Virtual Instrumentation using LabVIEW

Jovitha Jerome PHI Learning Private Limited 2010. (Module 1 & 2)

2“Virtual Bio-Instrumentation” Biomedical Clinical, and Healthcare Applications in Lab VIEW.

Jon B. Olansen and Eric Rosow,

Prentice Hall Publication, 2002.

Reference Book:

Sl No


123

Department


Department

syllabus for the academic year – 2019 - 2020 vii, viii...hardware for ultrasound scanning. explain...

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