Upload
others
View
4
Download
0
Embed Size (px)
Citation preview
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Department of Medical Electronics
VII SEMESTER SCHEME
(2019-20 Scheme)
Sl Sub.Code Name of Subject LH T PR S C
1 ML7T01 Biomedical Digital Signal
Processing 4 0 0 0 4
2 ML7T02 Principles of Medical Imaging 4 0 0 0 4
3 ML7T03 IoT and smart sensors 3 0 0 1 4
4 ML7PE2X Elective II 3 0 0 0 3
5 ML7PE3X Elective III 3 0 0 0 3
6 ML7L01 BMDSP Lab 0 0 3 0 1.5
7 ML7L02 C++ and Python Lab 0 0 3 0 1.5
8 ML7PW01 Project Work 0
8 0 4
Total 18 00 14 01 25
LH=Lecture Hour; T = Tutorial Hour; XX = CV/ ME/ EE/ EC/ CS/ML…;
PR= Practical Hour; OE= Open Elective; S=Self-study Hour; Q = 1/2/3/…;
C= Credit; R = 1/2/3/ ….; L = Laboratory; PW = Project Work.
Elective –II Credits 3-0-0-3
Sub.Code Name of the Subject Sub.Code Name of the Subject
ML7PE21 Artificial Organs and
Biomaterials ML7PE23
Linear Algebra and its
applications in medicine
ML7PE22 Adaptive Signal Processing ML7PE24 Brain Computer Interface
Elective –III Credits 3-0-0-3
Sub.Code Name of the Subject Sub.Code Name of the Subject
ML7PE31 Pattern Recognition in Medicine ML7PE33 Ergonomics and
Rehabilitation Engineering
ML7PE32 Biometrics ML7PE34 Artificial Intelligence
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Department of Medical Electronics
VIII SEMESTER SCHEME
(2019-20 Scheme)
Sl.
No.
Sub.Code Name of Subject LH T PR S C
1 ML8T01 Neural Networks 4 0 0 0 4
2 ML8T02 Biomedical Therapeutic Equipments 4 0 0 0 4
3 ML8PE3X Elective IV 3 0 0 0 3
4 ML8PE4X Elective V 3 0 0 0 3
5 ML8PW02 Project Work 2 4 12 0 10
6 ML8TS01 Technical Seminar 0 0 0 1 1
Total 16 04 12 01 25
LH=Lecture Hour; T = Tutorial Hour; XX = CV/ ME/ EE/ EC/ CS/ML…;
PR= Practical Hour; OE= Open Elective; S=Self-study Hour; Q = 1/2/3/…;
C= Credit; R = 1/2/3/ ….; L = Laboratory; PW = Project Work;
TS=Technical Seminar.
Elective –IV Credits 3-0-0-3
Sub.Code Name of the Subject
ML8PE311 Speech Signal Processing
ML8PE312 Machine Learning
ML8PE313 Smart Wearable Systems
ML8PE314 Clinical Data Analytics
Elective –V Credits 3-0-0-3
Sub.Code Name of the Subject
ML8PE411 ARM Processors
ML8PE412 Robotics and Automation
ML8PE413 Medical Device Development
ML8PE414 Virtual BMI
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
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
Sl. No Course Objectives
1
This course helps to understand the nature and difficulties to acquire bio-
signal and its processing concepts for analysis.
2
It also helps to bring out the concepts related Neurological signal processing
and Sleep disorder.
3 Explains the concept of data compression techniques.
4 Emphasizes on Signal averaging, adaptive filers and its applications.
Course outcome
Descriptions
CO1
On completion of the course the student can recall Understand the origin of EEG signals and their characteristics.
CO2 Understand the origin of ECG signals and their characteristics
CO3
Understand the processing techniques required to analyze the bio
medical signals
CO4 Understand data reduction techniques for ECG signal.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
1
Introduction to Biomedical Signals: The nature of biomedical signals, 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
2
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
3
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
4
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
5
Signal Averaging: Basics of signal averaging, signal averaging as a
digital filter, a typical averager.
Adaptive Filters: Principle of an adaptive filter, the steepest descent algorithm, adaptive noise canceller: (a)cancellation of 60 Hz interference in electrocardiography, (b) Canceling donor-heart
interference in heart-transplant electrocardiography, (c)Cancellation of ECG signal from the electrical activity of the chest muscles, (d)canceling of maternal ECG in fetal ECG, (e)Cancellation of High frequency noise in Electro-surgery.
12
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY, TUMKUR. (A Constituent College of Sri Siddhartha Academy of Higher Education, Agalakote, Tumkur.)
Model Question Paper
ML7T01 : BIOMEDICAL DIGITAL SIGNAL PROCESSING
TIME: 3.00 Hours SEM: VII MAX MARKS: 100
NOTE: Answer any five full questions.
1.a) With block diagram explain computer aided diagnosis and therapy based
upon biomedical signal analysis.
10
b) List and explain the difficulties encountered in Biomedical signal acquisition
and analysis.
10
OR
2.a) With neat diagram of neuron explain function of different parts of neuron. 6
b) What is action potential? Discuss genesis and propagation of action
potential.
8
c) List different types of EEG signals with frequency range. 6
3.a) Explain high speed QRS detection algorithm/Pan Tompkins algorithm. 10
b) Draw neat diagram of ECG. Mention different parts and functions of ECG. 6
c) What is template matching technique of QRS detection? 4
OR
4.a) Discuss ECG lead system in detail. 10
b) Explain various methods used for estimation of R-R interval. 10
5.a) Explain important Characteristics of EEG in various sleep stages. 10
b) Discuss Markov model and Morkov chains with an example. 10
OR
6.a) Discuss Dynamics of sleep wake transitions. 10
b) What is a hypnogram? Explain with an example. Explain how hypnogram
parameters used in sleep EEG analysis.
10
7.a) Explain how ECG data reduction is done by AZTEC algorithm. 10
b) Explain data compression by DPCM technique with neat block diagram. 10
OR
8.a) Explain how ECG data reduction is done by Turning point algorithm. 10
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
b) With an example explain ECG data compression by FAN algorithm. 10
9.a) Explain the principle of adaptive filters with neat diagram and equations. 10
b) Explain Widrow-Hoff Least-Mean-Sqaure adaptive algorithm in detail. 10
OR
10.a) Explain cancellation of 60 Hz interference in electrocardiography. 10
b) Explain the procedure involved in cancelling of maternal ECG in fetal
electrocardiography.
10
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Biomedical Digital Signal Processing
Willis J. Tompkins PHI.
2 Biomedical Signal Processing principles and techniques D. C. Reddy Tata McGraw-Hill,
2005
3 Biomedical Signal Analysis
Rangaraj M. Rangayyan,
IEEE Press, 2001.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Biomedical Signal Processing
Akay M Academic: Press 1994
2 Biomedical Signal Processing Cohen.A Vol. I Time &
Frequency Analysis, CRC Press, 1986.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VII Subject Name: Principles of Medical Imaging
Subject Code: ML7T02 L-T-P-C: 4-0-0-4
Course Objectives:
Sl.No Course Objectives
1
Build the physics background of interaction of radiation with matter,
enabling participants to understand projection radiography,
mammography, and fluoroscopy and train them to assess image
distortions, image attenuation for X-ray radiography systems.
2
Expose students to the developments in X-ray Computed Tomography
leading to modern day multi-slice, helical CT scanners and introduce
the concept of computed tomography reconstruction
3
Divulge the image formation, image quality, and imaging hardware for
ultrasound scanning. Explain the imaging principles and derive the
fundamental equation of MRI.
4
Expose the participants to advanced MR techniques including fast spin
echoes, MR angiography, echo planar imaging, magnetization prepared
sequences, diffusion and perfusion theory and sequences.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Course Outcomes
Course outcome
Descriptions
CO1 On 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.
CO4 Understand the principles of Radionuclide imaging and Magnetic resonance imaging.
UNIT Description Hours
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 and Detection of Ultrasound, Ultrasonic Diagnostics Methods, New Developments, Image Characteristics, Biological effects of Ultrasounds.
10
IV
Radionuclide Imaging: Fundamentals of Radioactivity, Generation and Detection of nuclear emission, Diagnostic methods using radiation detector probes, Radionuclide Imaging Systems, New Radionuclide Imaging methods, Characteristics of Radionuclide Images, Internal radiation dosimeter and biological effects.
11
V
Magnetic Resonance Imaging
Fundamentals of nuclear magnetic resonance, Generation and Detection of NMR signal, Imaging Methods, In vivo NMR Spectroscopy, Characteristics of MRI, Biological Effects of Magnetic Fields.
10
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Principles of Medical Imaging
Shung K. Kirk, Tsui Benjamin, Smith.B.Michael
2 Fundamentals of Medical Imaging
Suetens Paul Cambridge University Press, 2002
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Handbook of Biomedical
Instrumentation
Khandpur R.S. 2nd Ed., Tata-
McGRaw Hill, 2003.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Semester: VII Subject Name: IoT and SMART SENSORS
Subject Code: ML7T03 L-T-P-C: 3-0-1-4
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
Understand the purpose of measurement, the methods of measurements,
errors associated with measurements.
2
Know the principle of transduction, classifications and the characteristics
of different transducers and study its biomedical applications.
Course outcome
Descriptions
CO1
On the completion of this course the student will be able to Explain the basic design and requirement of IoT.
CO2
Identify the importance of different types of protocols and models
used with IoT.
CO3 Analyze the requirements of components of smart sensors.
CO4 Determine the importance of communication protocol and standards that is used with smart sensors and improve the functionality of conventional systems using IoT.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
I
Introduction to IoT: Definition & Characteristics of IoT, Physical Design of IoT, Logical Design of IoT, IoT Enabling Technologies, IoT Levels.
08
II
IoT System Management: Introduction, Machine-to-Machine (M2M), Difference between IoT and M2M, SDN and NFV for IoT, Need for IoT System Management, SNMP, Network Operator Requirements, NETCONF, YANG, IoT Systems Management with NETCONF-YANG.
08
III
Domain 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 20 mA Signal Transmitter, Analog to Digital Converter, MCU control, Modular MCU Design, DSP control.
08
V
Protocols and Standards for Smart Sensors: CAN protocol, CAN Module, Neuron Chips, MCU Protocols, IEEE 1451 working relationship, IEEE 1451.1, IEEE 1451.2, IEEE P1451.3, IEEE P1451.4.
07
Question paper Pattern:
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Internet of Things – A hands-on
approach, Arshdeep Bahga and Vijay Madisetti
Universities Press
(India) Private Ltd.,
2015
2 Understanding Smart Sensors
Randy Frank 2nd Edition, Artech House Publications, 2000.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Rethinking the Internet of Things: A
Scalable Approach to Connecting
Everything
Francis daCosta and Byron Henderson
Apress Open,
Intel Publication.
2014
2 Learning Internet of Things, Smart Peter Waher, PACKT Publishing,
2015
3 Sensor Systems Gerard Meijer,
John – Wiley and Sons
2008.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Semester: VII Subject Name: Artificial Organs and Biomaterials
Subject Code: ML7PE21 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
Course Objectives: To create awareness to the student with modern artificial organs devices
and methods used to partially support or completely replace pathological
organ
2 Understand the design and working of artificial heart, kidney, and blood.
3 To know about working of heart valve. Design of artificial heart valve
4
Study about biomaterial which is used for design of artificial organ.
Understand the characteristics of polymeric and metallic biomaterial.
Course outcome
Descriptions
CO1
on the completion of this course the student will be able to Understand the need of artificial organs.
CO2
Understand the function of various organs in your body.
CO3
Learn about the design of the various artificial organs using biomaterial.
CO4
Understand the various biomaterials. Learn composite,
biodegradable polymeric and tissue derived materials.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
I
ARTIFICIAL ORGANS: INTRODUCTION: Substitutive medicine, outlook for organ replacement, design consideration, evaluation process. ARTIFICIAL HEART AND CIRCULATORY ASSIST DEVICES: Engineering design, Engg design of artificial heart and circulatory assist devices.
07
II
ARTIFICIAL KIDNEY: Functions of the kidneys, kidney disease, renal failure, renal transplantation, artificial kidney, dialyzers, and membranes for haemodialysis, haemodialysis machine, peritoneal dialysis equipment-therapy format, fluid and solute removal. ARTIFICIAL BLOOD: Artificial oxygen carriers, fluorocarbons, hemoglo bin for oxygen carrying plasma expanders, hemoglobin based artificial blood.
07
III
ARTIFICIAL LUNGS: Gas exchange systems, Cardiopulmonary bypass (heart-lung machine)-principle, block diagram and working, artificial lung versus natural lung. CARDIAC VALVE PROSTHESES: Mechanical valves, tissue valves, current types of prostheses, tissue versus mechanical, engineering concerns and hemodynamic assessment of prosthetic heart valves, implications for thrombus deposition, durability, current trends in valve design.
08
IV
CERAMIC BIOMATERIALS: Introduction, non absorbable/relatively bioinert bioceramics, biodegradable/restorable ceramics, bioreactive ceramics, deterioration of ceramics, bioceramic-manufacturing techniques POLYMERIC BIOMATERIALS: Introduction, polymerization and basic structure, polymers used as biomaterials, sterilization, surface
modifications 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, Dental metals, Corrosion of metallic implants, Manufacturing of implants.
09
Question paper Pattern:
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Biomedical Engineering Handbook- J.D.Bronzino Volume1(2nd Edition)
(CRC Press / IEEE Press, 2000).
2 Biomedical Engineering Handbook J.D.Bronzino Volume 2 (2nd Edition)
(CRC Press / IEEE Press, 2000)
3 Handbook of Biomedical Instrumentation
R.S.Khandpur (2nd Edition) by (Tata McGraw Hill, 2003)
Reference Books:
Sl No
Text Book title Author Volume and Year of Edition
1
2
3
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VII Subject Name: Adaptive Signal Processing
Subject Code: ML7PE22 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
2
3
4
Course outcome
Descriptions
CO1 Describe optimal minimum mean square estimators and in particular linear estimators.
CO2 Hypothesize Wiener filters (FIR, non-causal, causal) and evaluate their performance.
CO3 Apply combination of theory and software implementations to solve adaptive signal problems.
CO4 Identify applications in which it would be possible to use the different adaptive filtering approaches.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
I
ADAPTIVE SYSTEMS: Definition and characteristics, Areas of application, general properties, open and close loop adaptation, Application closed loop adaptation, examples of adaptive systems. The adaptive linear combiner: General description, input signal and weight vectors, desired response and error, the performance function gradient and minimum mean square error. Example of a performance surface, alternative expression of the gradient, De correlation of error and input components.
08
II
PROPERTIES OF QUADRATIC PERFORMANCE SURFACE: Normal form of input correlation Matrix, Eigen and eigen vectors of the input correlation matrix. An example with two weights, geometrical significance of Eigen vectors and Eigen values.
08
III
SEARCHING THE PERFORMANCE SURFACE: Methods of searching the performance surface. Basic idea of gradient search methods, A simple gradient search algorithm and its solution.
08
IV
SEARCHING THE PERFORMANCE SURFACE: Methods of searching the performance surface. Basic idea of gradient search methods, A simple gradient search algorithm and its solution. Stability and rate of convergence, the learning curve, Gradient search by Newton’s method in multi dimensional space, gradient search by the method of steepest descent, comparison of learning curves.
09
V
GRADIENT ESTIMATION AND EFFECTS ON ADAPTATION: Gradient component estimation by derivatives measurements, the performance penalty, derivative measurement and performance penalties with multiple weights.
06
Question paper Pattern:
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1
Adaptive signal Processing
B. Widrow & S D
Streans,
Pearson Education
1985.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Adaptive filters C F N Cowan & P M
Grant
Prentice Hall, 1985.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VII Subject Name: Linear Algebra and Its Applications In Medicine
Subject Code: ML7PE23 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
Solve systems of linear equations using various methods including
Gaussian and GaussJordan elimination and inverse matrices.
2
Perform 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.
4 Find the dimension of spaces such as those associated with matrices and linear transformations.
Course outcome
Descriptions
CO1 Understand 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.
CO3 Understand concepts in Eigen spaces and its applications
CO4
Understand the concept of probability, distributions and its application in Biology and medical Science.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
I
Linear equations: Fields; system of linear equations, and its solution sets;
elementary row operations and echelon forms Matrix operations; invertible
matrices, LU-factorization.
Vector spaces: Vector spaces; subspaces; bases and dimension; coordinates;
summary of row-equivalence; computations concerning subspaces.
09
II
Linear Transformations: Linear transformations; algebra of linear
transformations; isomorphism; representation of transformations by matrices;
transpose of a linear transformation.
08
III
Canonical Forms: Characteristic values; invariant subspaces; direct-sum
decompositions; invariant direct sums; primary decomposition theorem; cyclic
bases; Jordan canonical form.
07
IV
Inner Product Spaces: Inner products; inner product spaces; orthogonal sets
and projections.
08
V
Gram-Schmidt process; QR-factorization; least-squares problems; unitary
operators Symmetric Matrices and Quadratic Forms: Digitalization; quadratic
forms; constrained Optimization; singular value decomposition.
07
Question paper Pattern:
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
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
Text Book title Author Volume and Year of Edition
1
2
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VII Subject Name: Brain Computer Interface
Subject Code: ML7PE24 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
This course aims for students to obtain the background to understand brain-computer interaction and human-computer interaction;
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.
4
Understand the steps required to use real-time brain sensing data as input to an interactive system.
5 understand the domains and contexts in which brain-computer interfaces may be effective;
6
Understand the open questions and challenges in brain-computer interaction research today.
Course outcome
Descriptions
CO1
Apply the knowledge of mathematics science and engineering
fundamentals to understand the Brain Organization.
CO2
Apply the knowledge of mathematics science and engineering
fundamentals to understand the brain anatomy and Function.
CO3 Analyze and process the brain signals for artifact reduction.
CO4 Understand types of BCI, principles and its applications which are present state of art in the Neurosciences domain.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
I
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 the Brain: Invasive Techniques &Noninvasive Techniques. Stimulating the Brain - Invasive Techniques & nonTechniques. Simultaneous Recording and Stimulation: Multi-electrode Arrays, Neurochip.
08
II
Signal Processing for BCI's: Spike Sorting, Frequency Domain Analysis: Fourier analysis, Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), Spectral Features, Wavelet Analysis. Time Domain Analysis: Hjorth Parameters , Fractal Dimension , Autoregressive (AR) Modeling, Bayesian Filtering, Kalman Filtering, Particle Filtering), Spatial Filtering : (Bipolar, Laplacian, and Common Average Referencing ,Principal Component Analysis (PCA) ,Independent Component Analysis (ICA) , Common Spatial Patterns (CSP) 73 Artifact Reduction Techniques: Thresholding, Band-Stop and Notch Filtering, Linear Modeling, Principal Component Analysis (PCA), Independent Component Analysis (ICA).
08
III
Building a BCI: Major Types of BCIs: Brain Responses Useful for Building BCIs:Conditioned Responses, Population Activity, Imagined Motor and Cognitive Activity, Stimulus-Evoked Activity. Invasive BCIs: Two Major Paradigms in Invasive Brain-Computer Interfacing: BCIs Based on Operant Conditioning, BCIs Based on Population Decoding.
08
IV
Invasive BCIs in Humans: Cursor and Robotic Control Using a Multielectrode Array Implant, Cognitive BCIs in Humans, Long-Term Use of Invasive BCIs, Long-Term BCI Use and Formation of a Stable
Cortical Representation, Long-Term Use of a Human BCI Implant Semi-Invasive BCIs:Electrocorticographic (ECoG) BCIs -ECoG BCIs in Animals, ECoG BCIs in Humans, BCIs Based on Peripheral Nerve Signals Nerve-Based BCIs, Targeted Muscle Innervations (TMR). Non-Invasive BCIs:Oscillatory Potentials and ERD, Slow Cortical Potentials, Movement Related Potentials, Stimulus Evoked Potentials; BCIs Based on Cognitive Tasks, Error Potentials in BCIs, Co-adaptive BCIs, Hierarchical BCIs. Other Noninvasive BCIs: fMRI, MEG, and fNIR: Functional Magnetic Resonance Imaging Based BCIs, Magneto encephalography Based BCIs, 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), Sensory Augmentation.
09
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
V
Medical Applications: Sensory Restoration, Motor Restoration, Cognitive Restoration, Rehabilitation, Restoring Communication with Menus, Cursors, and Spellers, Brain Controlled Wheelchairs Nonmedical Applications: Web Browsing and Navigating Virtual Worlds, Robotic Avatars, High Throughput Image Search Lie Detection and Applications in Law , Monitoring Alertness, Estimating Cognitive Load, Education and Learning, Security, Identification, and
Authentication, Physical Amplification with Exoskeletons, Mnemonic and Cognitive Amplification , Applications in Space, Gaming and Entertainment, Brain-Controlled Art. Ethics of Brain-Computer Interfacing: Medical, Health, and Safety Issues, Balancing Risks versus Benefits, Informed Consent, Abuse of BCI Technology, BCI Security and Privacy, Legal Issues, Moral and Social-Justice Issues.
06
Question paper Pattern:
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1
Brain-Computer Interfacing
Rajesh P. N. Rao
An Introduction (1
Edition)
2 Brain-ComputerInterfaces
Revolutionizing Human Bernhard Graimann
(Editor), Brendan Z.
Allison (Editor),
GertPfurtscheller
(Editor)
Computer Interaction
(The Frontiers
Collection)
Hardcover – (13 Dec
2010)
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1
2
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VII
Subject Name: Pattern Recognition in Medicine
Subject Code: ML7PE31 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
Pattern recognition techniques are used to design
automated systems that improve their own performance
through 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, Estimation
Theory, Linear Discrimination Functions, Nonparametric
Techniques, Decision Trees, and Clustering Algorithms
etc. will be presented.
Course outcome
Descriptions
CO1
CO2
CO3
CO4
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
UNIT Description Hours
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, multiple
features, 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, kernel and window estimators, nearest neighbor classification techniques, adaptive decision boundaries, adaptive discriminate functions, minimum squared error discriminant functions. (4.1-4.7 text 1)
08
IV
Clustering: Introduction, Hierarchical clustering, partitional clustering, Unsupervised Bayesion learning, Hierarchical clustering, partitional clustering, problems.
07
V
Processing of waveforms and images: Introduction, gray level scaling transformations, equalization, geometric image scaling and interpolation, edge detection, laplacian and sharpening operators, line detection and template matching, logarithmic gray level scaling. (7.1-7.9 text 1)
08
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Pattern Recognition and Image
Analysis Earl Gose, Richard Johnson Baugh and Steve
PHI
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Pattern classification
Richard O.Duda, Peter E.Herd and David & Stork
john Wiley and sons, Inc 2nd Ed.2001.
2 Pattern Recognition: Statistical Structural and Neural Approaches,
Robert Schlkoff John Wiley and sons, Inc, 1992.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VII Subject Name: Biometrics
Subject Code: ML7PE32 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
Course Objectives: To understand the state-of-the-art in biometric technologies;
2 To survey the currently available biometric systems;
3
To explore ways to improve some of the current techniques;
4
To learn and implement some of the biometrics authentication;
5 To explore new techniques
Course outcome
Descriptions
CO1 Understand the fundamentals and the need of biometrics.
CO2
Learn the deployment, strength & weakness of the types of
Biometrics.
CO3 Learn the uncommon biometrics and its usage.
CO4 Understand the applications of Biometrics and learn the risks, standards and testing / Evaluation process of Biometrics.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
UNIT Description Hours
I
Introduction – Benefits of biometric security – Verification and identification – 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) – Competing finger Scan technologies – Strength and weakness. Types of algorithms used for interpretation. Voice Scan - Features – Components – Operation (Steps) – Competing voice Scan (facial) technologies – Strength and weakness.
08
III
Iris Scan - Features – Components – Operation (Steps) – Competing iris Scan technologies – Strength and weakness. Facial Scan - Features – Components – Operation (Steps) – Competing facial Scan technologies – Strength and weakness.
08
IV
Other physiological biometrics – Hand scan – Retina scan – AFIS (Automatic Finger Print Identification Systems) – Behavioral Biometrics – 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. Biometric standards – (BioAPI , BAPI) – Biometric middleware. Biometrics for Network Security: Statistical measures of Biometrics. Biometric Transactions.
08
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1
Biometrics–Identity Verification in a
Networked World
Samir Nanavati,
Michael Thieme, Raj
Nanavati
Wiley India Pvt Ltd,
2002 .
2 Biometrics for Network Security Paul Reid Pearson Education,
2004.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Biometrics- The Ultimate Reference John D.
Woodward, Jr. Wiley Dreamtech.
2 Biometric Systems Technology, Design and Performance Evaluation
James Wayman, Anil Jain, Davide Maltoni and Dario Maio
Springer Publications.
3 Personal Identification in Networked Society
Jain, A.K.; R Bolle, Ruud M.; S Pankanti, Sharath
1st ed. 1999.2nd printing, 2006, Springer Publications.
4 Handbook of Biometrics Jain, Anil K.;
Flynn, Patrick; Ross, Arun A
Springer, 2008.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VII Subject Name: Ergonomics and Rehabilitation Engineering
Subject Code: ML7PE33 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
This course covers the use of ergonomic principles to recognize, evaluate, and control
workplace conditions that cause or contribute to musculoskeletal and nerve disorders. Course topics include work physiology, anthropometry,
musculoskeletal disorders, use of video display terminals, and risk factors such as vibration,
temperature, material handling, repetition, and lifting 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,
and coverage of current OSHA compliance policies and guidelines.
Course outcome
Descriptions
CO1
CO2
CO3
CO4
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
I
Introduction : Focus of ergonomics & its applications, Body mechanics: Basics, Anatomy of Spine & pelvis related to posture, postural stability & adaptation, Low back pain, risk factors formusculo skeletal disorders in workplaces, Anthropometric principles in workspace: Designing for a population of users, Human variability 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 tunnel syndrome, tennis elbow, shoulder disorder, ergonomic interventions. Design of physical environment: human thermoregulation, thermal environment, working in hot & cold climates, skin temperature, protection against extreme climates, comfort & indoor climate, ISO standards.
07
III
Engineering Concepts in Rehabilitation Engineering: Anthropometry: Methods for Static and dynamic Measurements: Area Measurements, Measurement of characteristics and movement, Ergonomic aspects in designating devices: Introduction to Models in Process Control, Design of 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. The MARCUS intelligent Hand prostheses.
08
IV
Engineering concepts in sensory rehabilitation engineering: Sensory augmentation and substitution: Visual system: Visual augmentation, Tactual vision substitution, and Auditory vision substitution. Auditory system: Auditory augmentation, Audiometer, Hearing aids, cochlear
implantation, visual auditory substitution, tactual auditory substitution, Tactual system: Tactual augmentation, Tactual substitution.
08
V
Orthopedic Prosthetics and Orthotics in rehabilitation: Engineering concepts in motor rehabilitation, applications. Computer Aided Engineering in Customized Component Design. Intelligent prosthetic knee, A hierarchically controlled prosthetic and A self-aligning orthotic knee joint. Externally powered and controlled Orthotics and Prosthetics. FES systems- Restoration of hand function, restoration of standing and walking, Hybrid Assistive Systems (HAS).
08
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Introduction 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
Text Book title Author Volume and Year of Edition
1 Fitting the task to human, A textbook of occupational ergonomics
Taylor &Francis 5th edition, ACGIH publications , 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.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VII
Subject Name: Artificial Intelligence
Subject Code: ML7PE34 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
To create appreciation and understanding of both the achievements of AIand the theory underlying those achievements.
2
To impart basic proficiency in representing real life problems in a state space representation so as to solve them using different AI techniques.
3
To create an understanding of the basic issues of knowledge representation and heuristic search techniques.
Course outcome
Descriptions
CO1 On completion of this course, the students shall be able to Demonstrate the knowledge of building blocks of AI.
CO2 Analyze and formalize the problem as a state space tree, design heuristics and solve using different search techniques.
CO3 Analyze and demonstrate knowledge representation using various techniques.
CO4 Develop AI solutions for a given problem.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
UNIT Description Hours
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
II
Heuristic 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 knowledge Representation, Weak Slot Filler Structures: Semantic Nets, Frames
08
IV
Using Predicate Logic: Representing the simple facts in logic, Representing Instance and ISA Relationships, Computable functions and 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
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Artificial Intelligence Elaine Rich, Kevin
Knight,Shivashankar B Nair
3rd Edition, Tata
McGraw Hill, 1991.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Artificial Intelligence A Modern Approach
Stuart Russel, Peter Norvig
2nd Edition, Pearson Education, 2003.
2 Principles of Artificial Intelligence Nils J. Nilsson Elsevier, 1980.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VII Subject Name: Biomedical Digital Signal Processing Lab
Subject Code: ML7L01 L-T-P-C: 0-0-3-1.5
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1 To understand the basic signals in the field of biomedical.
2
To study origins and characteristics of some of the most commonly used biomedical signals, including ECG, EEG, evoked potentials, and EMG.
3 To understand Sources and characteristics of noise and artifacts in bio signals.
4 To understand use of bio signals in diagnosis, patient monitoring and physiological investigation.
Course outcome
Descriptions
CO1 To read and plot different biomedical signals
CO2 To add and eliminate noise in signals.
CO3 Realization of Digital filters.
CO4
Understand and apply various data compression techniques on different
ECG signals.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
Text Books:
Sl
No
Text Book title Author Volume and Year
of Edition
1 Bioelectrical Signal Processing in Cardiac & Neurological Applications
Leif SSrnmo , Pablo Laguna - Elsevier -
Academic Press.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Biomedical Digital Signal Processing Willis J. Tompkins PHI.
2 Biomedical Signal Processing-principles and techniques by
D. C. Reddy Tata McGraw-Hill, 2005
3 Biomedical Signal Analysis by M.Rangayyan Rangaraj
IEEE Press, 2001.
UNIT Description Hours
I
1. Computation of Convolution and Correlation Sequences. 2. Signal Averaging to Improve the SNR 3. Read and plotting of ECG data, spectrum of ECG with 50 HZ
noise. 4. Design of FIR Filter for ECG. 5. Integer filters for ECG
6. QRS detection and Heart rate determination. 7. Correlation and Template matching. 8. Realization of Notch filter for removal of line interference 9. 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.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VII Subject Name: C++ and Python Lab
Subject Code: ML7L02 L-T-P-C: 0-0-3-1.5
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
2
3
4
Course outcome
Descriptions
CO1 By 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 inheritance for effective programming.
CO4 Learn the basic concepts of python.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
I
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+...1024 2. Write a C++ program to sort the elements of an array using i) Selection sort ii) Bubble sort
3. 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 averages using 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 salary LIC: 17% of basic salary Gross=basic salary+DA+HRA Net 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.
iv)Use of loops, arrays, functions, plots.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Object Oriented programming in TURBO C++ ,
Robert Lafore, Galgotia
Publications.2002.
2. Classic Data Structures, Debasis Samanta, Second Edition,
PHI, 2009.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Object Oriented Programming with C++
E.Balaguruswamy, third edition, TMH 2006
2 C++ the complete reference, Herbert Schildt, fourth edition, TMH, 2003.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VIII Subject Name: Neural Networks
Subject Code: ML8T01 L-T-P-C: 4-0-0-4
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1 This course gives an introduction to basic neural network architectures and learning rules.
2
Emphasis is placed on the mathematical analysis of these networks, on methods of training them and on their application to practical engineering problems in such areas as pattern recognition, signals processing and control systems.
Course outcome
Descriptions
CO1
on the completion of this course the students will be able to The fundamental concepts of artificial neural network.
CO2 Network architectures and its principles.
CO3 Different learning algorithms and its applications.
CO4 Information representation in biological system and its models.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
UNIT Description Hours
I
Introduction: The classic neuron, Membrane potential, Action potential, Neuronal electrical behavior, Cable Equation, Synaptic Integration. Models of Neuron, Synaptic Electrical Events, slow potential theory of neuron, two state neurons, Feedback.
10
II
Network Architectures: Single layer feed forward networks; Multilayer feed forward networks, Recurrent Networks, Knowledge representation.
11
III
Learning processes: Introduction Error correction learning, Memory based learning, Hebbian Learning, Competitive learning.
10
IV
Learning paradigms: Learning with a teacher, Learning without a teacher, Learning tasks, Memory, Adaptation Artificial intelligence and Neural networks.
10
V
Information representation in biological Systems, Distributed, Map, layered structures, Visual system, Auditory System.
11
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 An Introduction to neural networks
James A. Anderson 2e PHI 1995
2 Neural Networks Simon Haykin Pearson education
PHI 2001
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Fundamentals of Artificial Neural Networks
Mohammad Hasan PHI, 1999
2
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VIII Subject Name: Biomedical Therapeutic Equipments
Subject Code: ML8T02 L-T-P-C: 4-0-0-4
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
The objective of this course is to introduce the students to the application of biomedical instrumentation used in surgery.
2
This course is to familiarize the students with physiotherapy and electrotherapy instruments and various machines used in ICU.
3
It includes brief study of different types of ventilators and how to design a automated drug delivery unit depends on the requirement of patient.
Course outcome
Descriptions
CO1 Learn the working principle of Instruments for surgery and physiotherapy, 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 different types of ventilators
CO4 Analyzing the concepts of Automated Drug delivery Systems.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
UNIT Description Hours
I
Instruments for Surgery: Principles of surgical diathermy, surgical diathermy Machine, safety aspects in electro- surgical units, surgical diathermy Analyzer.
10
II
Physiotherapy and Electrotherapy Equipments: High frequency heat therapy, Shortwave diathermy, microwave diathermy, ultrasound therapy unit, Electro diagnostic therapeutic apparatus, pain relief through electrical Stimulation, bladder and cerebella stimulators.
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 of drugs infusion systems and implantable infusion systems. Closed Loop Control Infusion Pumps.
12
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Handbook of Biomedical Instrumentation
R.S.Khandpur, McGraw Hill, 2003.
2 Biomedical Instrumentation Dr.M. Arumugam- Second Edition-
1994.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1
2
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VIII Subject Name: SPEECH SIGNAL PROCESSING
Subject Code: ML8PE311 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1 To understand the characteristics of speech signal,
2 To apply signal processing concepts to speech signal,
3 To get an insight into a few applications of speech processing.
Course outcome
Descriptions
CO1 On completion of the course the student can recall Properties 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 quantization techniques.
CO4 LPC algorithms and its applications for speech coding and fundamental algorithms for speech synthesis, coding and recognition.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
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 dependent
processing of speech, Short time energy and average magnitude, Short
time average zero crossing rate, Speech Vs silence discrimination
using energy and zero crossing.
08
II
Short Time Fourier Analysis: Linear filtering interpretation, Filter
bank summation method, Design of digital filter banks,
Implementation using FFT, Spectrographic displays.
08
III
Digital Representations Of The Speech Waveform: Sampling speech signals, Review of the statistical model for speech, Instantaneous quantization, Adaptive Quantization, General theory of differential quantization, Delta modulation.
08
IV
Linear Predictive Coding Of Speech: Basic principles of linear predictive analysis, Solution of LPC equations, Prediction error signal, Frequency domain interpretation, Relation between the various speech parameters, Applications of LPC parameters.
07
V
Speech Synthesis: Principles of Speech synthesis, Synthesis based
on waveform coding, analysis synthesis method, speech production
mechanism, Synthesis by rule, Text to speech conversion. Speech Recognition: Principles of Speech recognition, Speech period detection, Spectral distance measures, Structure of word recognition systems, Dynamic time warping (DTW), Word recognition using phoneme units.
08
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY, TUMKUR (An autonomous institution under Visvesvaraya Technological University, Belgaum)
ML8PE311: Speech Signal Processing Model Question Paper
TIME: 3 HOURS SEM: 8TH
MAX MARKS: 100
Answer any five full questions:
1. a) Explain different classes of phonemes. 12M
b) Explain the transfer function of the lossless tube model. 08M
OR
2. a) With the relevant block diagram explain the general discrete-time model for speech
roduction. 10M
b) Explain mechanism of speech production with relevant diagram 10M
3. a) What are the effects of filter bank summation method to the short time spectrum on
the resulting synthesis? 08M
b) Write a note on spectrographic displays. 07M
c) Find the cutoff frequency for all the low pass filters when the input sampling rate is
9.6 kHz and we wish to design a filter bank of 15 equally spaced filters that covers the
range 200Hz to 3200 Hz? 05M
OR
4. a) Explain the methods for implementing synthesis of a single channel in terms of
linear filtering. 10M
b) Explain the implementation of FBS method using FFT by briefing both analysis and
synthesis techniques. 10M
5. a) Briefly explain the 2 common uniform quantizer characteristics. 06M
b) Explain DPCM with adaptive quantization. 08M
c) Explain briefly the adaptive quantization with variable step size and variable gain
representation. 06M
OR
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
6. a) Explain the following:
i) Feed forward adaptive quantizer.
ii) Feedback adaptive quantizer. 12M
b) Explain linear delta modulation. 08M
7. a) Explain the autocorrelation method of linear prediction 08M
b) Explain frequency domain interpretations of-
i) Linear predictive analysis
ii) Mean squared prediction error. 12M
OR
8. a) Explain Durbin’s recursive solution for autocorrelation equations 08M
b) Explain the applications of LPC parameters. 12M
9. a) Give the comparison of the features of three speech synthesis methods along with
the figure of their basic principles. 06M
b) Write a note on Synthesis based on waveform coding 07M
c) Explain briefly Text to speech conversion 07M
OR
10. a) Explain the difficulties in speech recognition. 06M
b) Explain the structure of word recognition systems. 07M
c) Write short note on Word recognition using phoneme units. 07M
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Digital Processing of Speech Signals L R Rabiner and R
W Schafer, Pearson Education 2004.
2 Digital Speech Processing-
Synthesis and Recognition,.
Sadoaki Furui,
2nd Edition,
Mercel Dekker 2002.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Introduction to Data Compression Khalid Sayood 3rd Edition, Elsivier Publications
2 Digital Speech A M Kondoz, 2nd Edition, Wiley Publications
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VIII Subject Name: Machine Learning
Subject Code: ML8PE312 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
The main goal of this course is to help students learn, understand, and practice big data analytics and machine learning approaches, which include the study of modern computing big data technologies and scaling up machine learning techniques focusing on industry applications.
2
Mainly the course objectives are: conceptualization and summarization of big data and machine learning, trivial data versus big data, big data computing technologies, machine learning techniques, and scaling up machine learning approaches.
Course outcome
Descriptions
CO1
Apply the knowledge of mathematics science and engineering fundamentals in the understanding of fundamental issues and challenges of machine learning: data, model selection, model complexity, etc.
CO2 Analyze the strengths and weaknesses of many popular machine learning approaches.
CO3
Comprehend the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and un-supervised learning.
CO4 Design and implement various machine learning algorithms in a range of real-world applications.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
UNIT Description Hours
I
Introduction: Introduction to machine learning, Examples of Machine Learning Applications. Parametric regression: linear regression, polynomial regression, locally weighted regression, numerical optimization, gradient descent, kernel methods.
08
II
Generative learning: Gaussian parameter estimation, maximum
likelihood estimation, MAP estimation, Bayesian estimation, bias and variance of estimators, missing and noisy features, nonparametric density estimation, Gaussian discriminant analysis, naive Bayes. Discriminative learning: linear discrimination, logistic regression, logit and 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, dimensionality reduction interpretation. Support vector machines: functional and geometric margins, optimum margin classifier, constrained optimization, Lagrange multipliers, primal/dual problems, KKT conditions, dual of the optimum margin classifier, soft margins, kernels, quadratic programming, SMO algorithm.
08
IV
Graphical and sequential models: Bayesian networks, conditional independence, Markov random fields, inference in graphical models, belief propagation, Markov models, hidden Markov models, decoding states from observations, learning HMM parameters.
07
V
Unsupervised learning: K-means clustering, expectation maximization, Gaussian mixture density estimation, mixture of naive Bayes, model selection. Dimensionality reduction: feature selection, principal component analysis, linear discriminant analysis, factor analysis, independent component analysis, multidimensional scaling,
and manifold learning.
08
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Elements of Statistical Learning, T. Hastie,
R. Tibshirani and J. Friedman,
Springer, 2001.
2 Machine Learning, EthemAlpaydin, MIT Press, 2010.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Pattern Recognition and Machine Learning,
C. Bishop, Springer, 2006.
2 Machine Learning: A Probabilistic Perspective,
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.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VIII Subject Name: SMART WEARABLE SYSTEMS
Subject Code: ML8PE313 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
Extensive efforts have been made in both academia and industry in the research
and development of smart wearable systems (SWS) for health monitoring (HM).
Primarily influenced by skyrocketing healthcare costs and supported by recent
technological advances in micro- and nanotechnologies, miniaturisation of
sensors, and smart fabrics, the continuous advances in SWS will progressively
change the landscape of healthcare by allowing individual management and
continuous monitoring of a patient’s health status.
2
Consisting of various components and devices, ranging from sensors and
actuators to multimedia devices, these systems support complex healthcare
applications and enable low-cost wearable, non-invasive alternatives for
continuous 24-h monitoring of health, activity, mobility, and mental status, both
indoors and outdoors. Our objective has been to examine the current research in
wearable to serve as references for researchers and provide perspectives for future
research
Course outcome
Descriptions
CO1 Understand the basic foundations on biological and artificial neural network and the importance of neuron models for pattern classification
CO2 Demonstrate the process of forming association between related patterns through associative networks
CO3 Apply the principles of back propagation supervised learning for error minimization
CO4
Understand and analyze the various competition based learning algorithms and importance of resonance based network learning algorithms.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
I
Introduction : What is Wearable Systems, Need for Wearable
Systems, Drawbacks of Conventional Systems for Wearable
Monitoring, Applications of Wearable Systems, Recent developments –
Global and Indian Scenario, Types of Wearable Systems, Components
of wearable Systems, Physiological Parameters commonly monitored
in wearable applications, Smart textiles, & textiles sensors, Wearable
Systems for Disaster management, Home Health care, Astronauts,
Soldiers in battle field, athletes, SIDS, Sleep Apnea Monitoring.
08
II
Smart Sensors& Vital Parameters : Vital parameters monitored and
their significances, Bio-potential signal recordings (ECG, EEG, EMG),
Dry Electrodes design and fabrication methods, Smart Sensors –
textile electrodes, polymer electrodes, non-contact electrodes, MEMS
and Nano Electrode Arrays, Cuff-less Blood Pressure Measurement,
PPG, Galvanic Skin Response (GSR), Body Temperature
Measurements, Activity Monitoring for Energy Expenditure,
Respiratory parameters.
08
III
Wearable Computers : Flexible Electronics, Wearable Computers,
Signal Processors, Signal Conditioning circuits design, Power
Requirements, Wearable Systems Packaging, Batteries and charging,
Wireless Communication Technologies and Protocols, Receiver
Systems, Mobile Applications based devices.
08
IV
Wireless Body Area Networks: Wireless Body Area Networks –
Introduction, Personal Area Networks (PAN), Application in Vital
Physiological Parameter monitoring, Design of Sensor & Sink Nodes,
Architecture, Communication & Routing Protocols, Security, Power
and Energy Harvesting.
07
V
Data Processing And Validation : Classification Algorithms, Data
Mining and Data Fusion, Signal Processing Algorithms in wearable
Applications, Issues of wearable physiological monitoring systems,
Statistical Validation of Parameters, Certifications of Medical Devices
and Patenting.
08
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Wearable Monitoring Systems, Annalisa Bonfiglo,
Danilo De Rossi, Springer, 2011
2 Wearable Sensors: Fundamentals,
Implementation and Applications,
Edward Sazonov, Micheal R Neuman,
Elseiver, 2014.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 Wearable Electronics: Design, Prototype
and 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 .
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VIII Subject Name: Clinical Data Analytics
Subject Code: ML8PE314 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1 Identify key tools and approaches to improve analytics capabilities in clinical settings.
2
Describe different governance and operations strategies in analytics in clinical settings.
3 Discuss value-based payment systems and the role of data analytics in achieving their potential.
4 Analyze data used in population management and value-based care systems
Course outcome
Descriptions
CO1
Ability to apply knowledge of mathematics, science and
Engineering to develop the solution using biostatistical concepts.
CO2 Ability to analyse a problem and formulate appropriate solution for biostatistical concepts application.
CO3 An ability to design and perform statistical test and interpret results
CO4 Ability to implement and demonstrate statistical analysis using modern tool usage.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
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 of dispersion, measure of central tendency probability distributions of
discrete variables, binomial distribution, Poisson distribution, continuous probability distribution, normal distribution.
08
II
Sampling distributions: distribution of sample mean, distribution of the difference between two sample means, distribution of sample proportion, distribution of the difference between two sample proportions, Estimation: confidence interval for a population mean, t-distribution, confidence interval for differences between two population means, confidence interval for a population proportion, confidence interval for difference between two populations determination of sample size for estimating means, for estimating proportions , confidence interval for the variance of normally distributed population, confidence interval for ratio of variances of two normally distributed populations.
08
III
Hypothesis Testing : Introduction, hypothesis testing – single population mean, difference between two population means, paired comparisons, hypothesis testing-single population proportion, difference between two population proportions, single population variance, ratio of two population variances.
07
IV
Analysis of Variance (ANOVA): Introduction, completely randomized design, randomized complete block design, repeated measures design, factorial experiment UNIT-5 8 hours Linear Regression and Correlation: the regression model, sample regression equation, evaluating and using regression equation, correlation model correlation coefficient Multiple linear regression model, obtaining multiple 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, sample regression equation, evaluating and using regression equation, correlation model correlation coefficient Multiple linear regression model, obtaining multiple regression equation, evaluating multiple regression equation, using the multiple regression equation, multiple correlation model, mathematical properties of Chi-square distribution.
08
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 1. .“Biostatistics-A Foundation for
Analysis in the Health Sciences”
2. Wayne W. Daniel,
John Wiley & Sons Publication, 6th Edition
2 Fundamentals of Biiostatistics khan and
khanum, Ukaaz publications, 2nd revise edition
3 “An introduction to statistical Method and data analysis”,
R.Lyman ott..
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1
2
3
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VIII Subject Name: ARM PROCESSORS
Subject Code: ML8PE411 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1
This course introduces the concept of architecture and programming of advanced embedded microcontrollers i.eARMfamily of microcontrollers that are widely used in design of real time sophisticated embedded systems like tablets, hand held devices, automation and industrial control systems.
2
It also covers writing Embedded C programming of LPC2148 for GPIO,ADC,DAC, UART, LCD, Timers and etc.
3 It also explains the concepts of embedded system and its components
Course
outcome
Descriptions
CO1 Describe the ARM processor architecture and its family.
CO2 Develop assembly language programs to perform specific tasks using ARM instructions.
CO3 Develop ARM microcontroller applications using Embedded C language.
CO4 Design and develop program to interface external hardware with LPC214x microcontroller.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
UNIT Description Hours
I
ARM EMBEDDED SYSTEMS The RISC Design Philosophy, The ARM Design Philosophy, Embedded System Hardware, Embedded System Software. ARM PROCESSOR FUNDAMENTALS Registers, 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 SET Data Processing Instructions, Branch Instructions, Load-Store Instructions, Software Interrupt Instructions, Program Status Register Instruction, Example Programs.
07
III
INTRODUCTION TO THE ARM INSTRUCTIONS SET contd…. Loading Constants, ARMv5E Extensions, Conditional Execution, and Example Programs. EFFICIENT C PROGRAMMING Overview of C Compilers and Optimization, Basic C Data Types, C Looping Structures, Register Allocation, Function Calls, Pointer Aliasing, Structure Arrangement, Bit-fields, Unaligned Data and Endianness, Division, Floating Point, Inline Functions and Inline Assembly.
08
IV
Interfacing Sensors, Actuators, GPIO, LED, 7 segment display, stepper motor, Keyboard, Push button switch, Data Conversions (ADC, DAC), Timers, Communication Protocols: UART, I2C, SPI, CAN(onboard), Programs using C.
08
V
Embedded System Components Embedded v/s General computing system, Classification of Embedded systems, Major applications and purpose of Embedded systems. Core of an Embedded System including all types of processor/controller, Memory.
08
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 ARM Systems Developer's Guide Designing and Optimizing System Software,
Andrew N. Sloss,
Dominic Symes, Chris Wright,
Morgan Kaufmann
Publishers, ElseveirInc, 2004.(Chapters 1, 2, 3, 5)
2 Introduction to Embedded Systems, Shibu K V, Secondedition,
Tata McGraw Hill Education Private Limited, 2017. (Chapters 1 and 2 selected topics)
3 LPC214x User Manual –
http://www.keil.com/dd/docs/datashts/philips/user_manual_lpc214x.pdf
(LPC2148, GPIO, Registers, Embedded
components selected)
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
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.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VIII Subject Name: Robotics And Automation
Subject Code: ML8PE412 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1 This course introduces fundamental concepts in robotics.
2
The objective of the course is to provide an introductory
understanding of robotics. Students will be exposed to a
broad range of topics in robotics with emphasis on basics
of manipulators, coordinate transformation and kinematics,
trajectory planning, control techniques, sensors and
devices, robot applications and economics analysis.
Course outcome
Descriptions
CO1 Understand the fundamental concepts of robot
CO2 Calculate the forward kinematics and inverse kinematics of serial and parallel robots.
CO3 Be able to calculate the Jacobian for serial and parallel robot.
CO4 Be able to do the path planning for a robotic system.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
UNIT Description Hours
I
BASIC CONCEPTS Automation and Robotics – An over view of Robotics – present and future applications – classification by coordinate system and control system, Hydraulic, Pneumatic and electric 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 Control Circuits, End Effecter, Various types of Grippers.
08
III
TRANSFORMATION AND DYNAMICS Differential transformation and manipulators, Jacobians – problems.Dynamics: Lagrange – Euler and Newton – Euler formations.
07
IV
KINEMATICS Forward and Inverse Kinematic Problems, Solutions of Inverse Kinematic problems,Multiple Solution, Jacobian Work Envelop – Hill Climbing Techniques.
08
V
PATH PLANNING Trajectory planning and avoidance of obstacles, path planning, skew motion, joint integrated motion – straight-line motion.
08
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 Industrial Robotics Groover M P Pearson Edu.
2 Robotics control, Sensing, Vision and Intelligence,
Fu, K.S., Gonzalez, R.C., and Lee, C.S.G.,
McGraw-Hill Publishing company, New Delhi, 2003.
3 Robot Engineering-An Integrated Approach,
Klafter, R.D., Chmielewski, T.A., and Negin. M,
Prentice Hall of India, New Delhi, 2002.
4 Introduction to Robotics Mechanics and Control,
Craig, J.J., Addison Wesley, 1999.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
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-Science.
4 Robot Dynamics and Control Mark W. Spong and M. Vidyasagar,
JohnPage Ltd. 1983 London.Wiley & Sons..
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VIII Subject Name: Medical Device Development
Subject Code: ML8PE413 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
1 Understand the processes for medical device development after “design freeze”
2 Become familiar with the European regulatory framework for medical devices
3 Gain an understanding of manufacturing process validation
4 Build on the student’s current understanding of the Quality Management System
5 Understand key aspects of Product Management both during and after product launch
6 Discuss Good Clinical Practices and regulations surrounding management of clinical trials
Course outcome
Descriptions
CO1 Identify and analyse unmet clinical need and its requirements to solve it.
CO2 Search, analyse and document clinical practice, engineering science and relevant literature in order to determine the need for further research and development in a chosen clinical area.
CO3 Develop a sustainable business plan, including market overview, regulation strategies for health & safety of individuals and intellectual property (IP) strategies.
CO4 . Understand medical device design engineering and manufacturing process by avoiding common quality pitfalls in turn learning project management.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
UNIT Description Hours
I
MedTech Invention: Needs finding through Observation and Problem Identification. Need Statement Development. Need Screening & Selection through Stakeholder Analysis, Market Analysis & Needs Filtering. Concept Generation, Screening and selection.
10
II
Product Requirements: Define MedTech Device. Classification of
Device. Role of Requirements in MedTech Product Development. Market Requirements, Customer Requirements, Clinical Workflow. Design Input. ISO 13485. Intended use, Functional / performance requirements, 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. Design Review. Design Verification.
08
IV
Validation: System Validation. Usability Validation. Safety Validation. Clinical Validation, Regulatory Submission UNIT V [6 hours] Program Management: Program Planning, Stage Gate Process, Milestones. Budgeting, Development Strategy, Risk identification and Mitigation process.
07
V
Program Management: Program Planning, Stage Gate Process, Milestones. Budgeting, Development Strategy, Risk identification and Mitigation process.
06
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
1 “Biodesign: The Process of Innovating
Medical Technologies”,
Stefanos Zenios ,
Josh Makower,
Paul Yock, Todd J.
Brinton, Uday N.
Kumar, Lyn
Denend, Thomas
M. Krummel
Cambridge
University Press; 2nd
edition.
Reference Book:
Sl No
Text Book title Author Volume and Year of Edition
1 “Inventing medical devices: A perspective from India”
Dr Jagdish Chaturvedi
CreateSpace Independent Publishing Platform; 1st edition,2015.
2 “The Medical Device R&D Handbook”
Theodore R. Kucklick
Second Edition, CRC Press, 2012.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Syllabus for the Academic Year 2019 - 2020
Department: Medical Electronics Semester: VIII Subject Name: Virtual BMI
Subject Code: ML8PE414 L-T-P-C: 3-0-0-3
Course Objectives:
Course Outcomes
Sl.No Course Objectives
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 the
design 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, and Cardiopulmonary system analysis.
CO4 Explain the Medical Device Development Applications for Surgical Video Systems and Healthcare Information Management Systems using Information Science and Technology.
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
UNIT Description Hours
I
Graphical System Design (GSD): Introduction, GSD model, Design flow with GSD, Virtual Instrumentation, Virtual Instrumentation and traditional instrumentation, Hardware and software in virtual instrumentation, Virtual Instrumentation for test, control and design, GSD using LabVIEW, Graphical programming and textural programming. Introduction to LabVIEW: Introduction, Advantages of
LabVIEW, Advantages of LabVIEW, Software environment, Creating and saving a VI, Front panel toolbar, Block diagram toolbar, Palettes, Shortcut menus, Property dialog boxes, Front panel controls and indicators, Block diagram, Data types, Data flow program, LabVIEW documentation resources, Keyword shortcuts.
07
II
Modular Programming: Introduction, Modular Programming in LabVIEW, Build a VI front panel and block diagram, ICON and connector 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, DAQ assistant, Channels and task configurations, Selecting and configuring a data acquisition device, Components of computer based measurement system.
08
III
General Goals of Virtual Bio-Instrumentation (VBI): Definition of VBI and importance, General Goals of VBI applications. Basic Concepts: DAQ basics, LabVIEW basics, BioBench basics. Neuromuscular Electrophysiology (Electromyography): Physiological basis, Experiment set up, Experiment descriptions, Trouble shooting the nerve –Muscle Preparation. Cardiac Electrophysiology (Electrocardiology):Physiological basis, Experiment descriptions. Cardiopulmonary Applications: Cardiopulmonary measurement system, Hiw the Cardiopulmonary measurement system works, Clinical Significance.
08
IV
Medical Device Development Applications: The Endotester – A Virtual Instrument –Based Quality control and Technology, Assessment System for surgicalvideoSystems: Introduction, Materials and Methods, Endoscope Tests, Results, Discussion. Fluid Sense Innovative IV Pump Testing: Introduction, The test System, Training Emulator.
08
V
Healthcare Information management Systems: Medical Informatics: Defining medical informatics, Computers in medicine, Electronic Medical record, Computerized physician order entry, Decision support. Information Retrieval, Medical Imaging, Patient Monitoring, Medical Education, Medical Simulation. Managing Disparate Information: ActiveX, ActiveX Data Objects(ADO), Dynamic Link Libraries, Database Connectivity, Integrated Dashboards.
08
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)
Medical Electronics and Engineering
Question paper Pattern:
Text Books:
Sl No
Text Book title Author Volume and Year of Edition
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
Text Book title Author Volume and Year of Edition
1
2
3