- Minimum total credit - 48
- Minimum course credit - 24
- Minimum research credit - 12
- Minimum no. of courses - 08
- Minimum residency - 04 semesters
- Maximum duration - 03 years
- Minimum CPI (for graduation) - 2.2
- Minimum CPI (for continuation in program) - 1.7
- Minimum CPI (TA/RAship eligibility) - 2.5
The program curriculum includes four specializations tracks that provide a strong foundation and advanced courses in each track. This program tried to leverage the strength and diversity of our faculty and have created the following specializations tracks which are likely to be unique in their output profile.
- VLSI and Embedded Systems (VLSI Sp.)
- Communication Systems (Comm. Sp.)
- Machine Intelligence (MI Sp.)
- Computer Networks (NW Sp.)
Student is admitted to specific specialization according to his/her performance and choice at the time of admissions. Apart from specialization core courses the curriculum includes three important core courses and a wide range of elective courses. The specializations offered by us are different in a qualitative way than the specializations offered in other institutions.
This Program is focused to provide a domain capability to each student that allows them to have a meaningful career in industry or academics. Financial support in the form of Teaching Assistantships or Research Assistantships is provided to majority of the students.
[Numbers within brackets against each course indicate Lecture hrs – Tutorial hrs – Practical hrs : Total credit hrs per week]
Essential Mathematics [3 – 0 – 0 : 3]
Algorithms [1.5 – 0 – 0: 1.5]
Probability Theory [1.5 – 0 – 0: 1.5]
Technical Communication [Pass / Fail]
VLSI Design (VLSI Sp.) [3 – 0 – 0 : 3]
System and Signal Theory (Comm. Sp.) [3 – 0 – 0 : 3]
Pattern Recognition (MI Sp.) [3 – 0 – 0 : 3]
Advanced Computer Networks (NW Sp.) [3 – 0 – 0 : 3]
Analog CMOS IC Design (VLSI Sp.) [3 – 0 – 0 : 3]
VLSI Subsystem Design / [3 – 0 – 0 : 3]
Digital System Architecture / [3 – 0 – 2 : 4]
Embedded Systems Programming (VLSI Sp. - any two) [3 – 0 – 3 : 4.5]
Wireless System Design (Comm. Sp.) [3 – 0 – 0 : 3]
Statistical Estimation Theory (Comm. Sp.) [3 – 0 – 0 : 3]
Digital Image Processing (MI Sp.) [3 – 0 – 0 : 3]
Information Retrieval (MI Sp.) [3 – 0 – 0 : 3]
Multimedia Security with DRM (MI Sp.) [3 – 0 – 0 : 3]
Service Oriented Computing (NW Sp.) [3 – 0 – 0 : 3]
Topics in Wireless Ad Hoc Networks (NW Sp.) [3 – 0 – 2 : 4]
Introduction to Cryptography / [3 – 0 – 0 : 3]
System and Network Security (NW Sp. - any one) [3 – 0 – 2 : 4]
VLSI Design Lab (VLSI Sp.) [3 – 0 – 0 : 3]
Advanced Analog Design (VLSI Sp.) [3 – 0 – 0 : 3]
Advanced Digital Communication (Comm. Sp.) [3 – 0 – 0: 3]
Advanced Wireless Communication (Comm. Sp.) [3 – 0 – 0: 3]
Computer Vision (MI Sp.) [3 – 0 – 0 : 3]
Dynamic Networks (NW Sp.) [3 – 0 – 0 : 3]
This course is designed to provide students with the knowledge and understanding of engineering mathematics. Students would be able to recap their under grad mathematics courses, and strengthen the foundation with some advanced topics. Topics include: elementary algebra, linear algebra, calculus and complex analysis.
This course is designed to provide students how to analyze and design computer algorithms. Topics include: Basic data types arrays, linked lists, stacks, queues, trees, divide and conquer, sorting and searching techniques, hashing, greedy algorithms, and algorithmic complexity.
This course is designed to provide students with the knowledge and understanding of Probability Theory, both theory and application. Topics include: classical definition of probability, axiomatic definition, conditional probability, independence, random variables, distribution function, probability distributions, transformation of random variables, joint distribution of random variables, central limit theorem, Markov and Chebyshev’s inequality, and weak law of large numbers.
This course is designed to provide students with (a) the skills to communicate ideas effectively – verbally and in writing, as well as through formal presentation; (b) skills for job interviews; (c) self-motivation, self-determination and measurable goal-setting; (d) professional behavior standards; (e) self-assessment skills; and (f) principles of consultation as an appropriate tool for relating to others. The course will also focus on ethical issues in professional settings and in the workplace.
VLSI and Embedded Systems Specialization
This course is an overview to modern CMOS VLSI Design, mostly digital logic. The course will start with a basic introduction of MOS transistors as switches and CMOS inverter characteristics. Topics include: introduction to VLSI design, MOSFET transistor, scaling, CMOS process technology and design rules, physical layout of CMOS ICs, stick diagram, CMOS inverter characteristics, power dissipation, pass transistor logic, static logic gates, logical effort and delay estimation of logic gates, delay estimation in long wire, transmission gate circuits, dynamic logic circuits, clock distribution, physical design automation and VLSI testing.
Analog CMOS IC Design
This course is designed to equip students with the knowledge and understanding of analog CMOS circuit design. Topics include: MOSFET models for analog circuit design, layout, matching, amplifiers, references, biasing, amplifier, and op-amp design.
VLSI Subsystem Design
Topics include: Modules in a simple processor - ALUs, ROMS, registers; Adders - half and full adder implementations; Some common adders - Ripple Carry, Carry Save, Carry Look-ahead adders, Manchester Carry chains Multipliers; Common Multipliers - Braun Multipliers, Baugh Wooley Multipliers, modified Booths algorithms; Memory Cells and Arrays - ROMS and SRAMs, Clock Distribution and clock skews, 2-phase clock based module designs, power and testability considerations in chip design.
Digital System Architecture
This course is designed to provide students with the knowledge and understanding of using Verilog HDL for designing HDL based digital systems on FPGAs. Topics include: introduction to digital design methodology, logic design with Verilog, sequential logic design, behavioral modeling of combinational and sequential logic, synthesis of combinational and sequential logic, arithmetic processing, programmable logic device platforms, design of processors, filters and networks using HDL, and communication protocol handling.
Embedded Systems Programming
This course introduces the students to the world of embedded systems. The course will focus on developing embedded devices based on commercially available components. The course aims to cover basic components of Embedded Systems and also to provide an understanding on how to integrate these components to develop a complete system. Significant emphasis will be given on 8051 Architecture, Assembly Language, and Embedded C.
Communication Systems Specialization
System and Signal Theory
This course introduces rigorous mathematical concepts in linear systems and signals theory. Topics include: signal spaces- metric, normed linear spaces, inner product spaces, convexity, continuity, convergence; Discrete Signal representation- subspaces of L2(T), Gram-Schmidt orthogonalization; Signal representation- continuous representations, basis kernels and reciprocal kernels, fourier transforms, Hilbert and other integral transforms, representation of bandpass signals; Linear operators- linear transformations representation, spectral resolution of operators; quadratic functionals and variational problem matrices, least squares problem, diagonalization, singular value decomposition, eigen values and eigen vectors.
Wireless System Design
This course combines theory and applications to provide students wireless system design. Topics include: wireless communication system concepts and performance limitations, analyze system degradation due to RF components, develop wireless communication system budget profiles, calculate propagation losses and link budgets, assess cost vs performance issues, evaluate the performance of differing RF wireless system architectures.
Statistical Estimation Theory
This course aims to introduce the students to the concepts of statistical estimation theory. It deals with parameter estimation in discrete time. Topics include:
Probability space and random variables, Convergence and limit theorems of random variables, Bayesian parameter estimation (minimum mean square error (MMSE), minimum mean absolute error (MMAE), maximum a-posterior probability (MAP) estimation methods), Non-random parameter estimation (sufficient statistics, Fisher's factorization theorem, minimum variance unbiased estimators (MVUE), Cramer-Rao lower bound (CRLB), etc), Kalman-Bucy filtering, Hidden Markov Model (HMM) fliters and Expectation-Maximization (EM) Algorithm.
Advanced Digital Communication
This course is designed to provide students with the knowledge and understanding of advanced digital telecommunications systems with special emphases on digital, wireless communications. Topics include: time and space diversity, code diversity, optimum receiver principles, carrier and timing recovery, equalization, cyclic and convolution coding principles, fiber-optic communications.
Advanced Wireless Communications
The aim of the course is to introduce the students to some of the latest advances in wireless communications and networking. The course focuses its attention on the physical and MAC layer design aspects of wireless networks. The course will also introduce the students to some of the optimization theory related concepts, which are used extensively in the analysis of wireless networks. The course aims
to keep a right balance between theory and practice. Topics include: Wireless channel models, latest multiple access technologies (OFDM, CDMA), wireless standards (2g, 3G, IEEE 802.11, IEEE 802.16), convex optimization, information theory, Information Capacity of single and multiple antenna systems.
Machine Intelligence Specialization
The concept of pattern recognition has been recognized as an important factor in the design and analysis of modern computerized information system. This course is aiming towards the study of automatic pattern recognition and classification techniques. Topics include: Bayesian decision theory, classifier, supervised and unsupervised, feature evaluation and indexing, Genetic algorithms, goal programming for pattern classification, and graphical model for pattern classification.
Digital Image Processing
The field of Digital Image Processing (DIP) has seen a significant increase in the level of interest from other disciplines. DIP is continuously enhanced by other areas such as neural nets, wavelet theory, mathematical morphology, data compression and recognition, and artificial intelligence. Digital Image Processing plays a crucial role in the areas of remote sensing, scientific visualization, telecommunications, medical imaging, robotics, biology and environmental engineering among others.
The explosive growth of available digital information (e.g., Web pages, emails, news, scientific literature) demands intelligent information agents that can sift through all available information and find out the most valuable and relevant information. Web search engines, such as Google, Yahoo!, and MSN, are several examples of such tools. This course studies the basic principles and practical algorithms used for information retrieval (IR). The contents includes: Goals and history of IR, Vector-space retrieval model, Language model, Text tokenization, Stemming, Relevance feedback, Query expansion; Ontology, Text entities, Part of Speech tagging, Named Entity Recognition, Word Sense Disambiguation, Language dependent modules for Indexing, IR in linguistic resource constrained situation, Statistical stemming, Dictionary construction, Document alignment, Passage retrieval, Question Answering, Domain specific QA, Text categorization, clustering, summarization, Creation of test cases, Performance metrics: recall, precision, bpref, Evaluations on benchmark text collections form TREC, CLEF, NTCIR, FIRE.
Multimedia Security with DRM
Digital technologies facilitate new experiences for content users in consuming, authoring, replicating and delivery of digital contents. However, prevalence of digital replication, devices and explosive growth of Internet usages also result in serious copyright, infringement problems at the same time. DRM is range of technologies that prevent access to content without authorization and enables the secure exchange of data over internet or other electronic media. This course will discuss robust digital watermarking, information theoretic approaches in watermarking, digital finger printing, designing aspects of watermarking algorithms, detectors and decoders for watermarking, multimedia data authentication, fragile watermarking techniques for multimedia data authentication, digital watermarking and protocol issues, digital watermarking for video signals, digital image forensics.
This course is designed to discuss the different approaches for 3D depth estimation using 2D images and solve the ill-posed problems. Topics include: geometric transformation, pin hole camera model, real aperture camera, bidirectional reflection distribution function, reflectance map, calculus of variation, shape from shading, photometric stereo, shape from stereo, estimation techniques, least squares, motion field and optical flow, Markov random fields, depth from focus and defocus, super-resolution imaging and super-resolution depth estimation.
Computer Networks Specialization
Advanced Computer Networks
This course assumes an exposure to basic knowledge of networking concepts and background, such as basics of Internet protocols, various link layers and framing concepts. However, an in-depth review of undergraduate level networks material will be done in this course. The course will emphasize the concepts and issues underlying the design and implementation of the Internet. The course will also focus on quantitatively analyze the performance of network protocols.
Service Oriented Computing
This course is designed to provide students with the basic concepts, theories, and techniques for service-oriented computing. The course aims to formulate the foundational concepts of Web services and evaluate existing approaches. Topics include: conceptual modeling, ontologies, discovery, matchmaking, messaging, transactions, processes, and Web services.
Topics in Wireless Ad Hoc Networks
This course discusses a wide cross-section of problems including link and mobility models, MAC protocols, routing protocols, quality of service, cooperation, and energy efficiency using cross-layer optimization mechanisms. The course will emphasize distributed algorithms for solving common problems of time synchronization, localization, resource allocation, and data management.
Introduction to Cryptography
Cryptography is concerned with the mathematical, algorithmic, and implementation aspects of information and network security. This course aims to cover classical cryptography, number theory, secret key cryptography, data integrity, public key cryptography, identity-based cryptography, key management, and Provable security.
System & Network Security
This course discusses the cryptographic techniques used to design and analysis of security services used in system and network security. The course covers various known attacks for hosts and networks, and mechanisms to mitigate security threats against systems. Topics include: basics of cryptographic primitives, operating system security, network security, viruses, intrusion detection, firewalls, and database security, attacks, and safeguarding mechanisms.
This course is designed to provide students with the concepts of modelling dynamic behaviour of networks, data structures for dynamic networks, searching and routing in dynamic networks, achieving efficiency in throughput and maintaining it in dynamic networks, comparison of dynamic networks.
List of Electives
Approaches to Semantic Web
Sensor Networks Systems
Advanced Digital Signal Processing
Intro to Graph Theory
Intro to Modern Algebra
Intro to Coding Theory and Appls
Testing of VLSI Circuits
Introduction to Cryptography
Combinatorial Games and Algorithm Design
Sensor Network Devices
- Not more than one F in all the courses taken.
- Not more than two X’s in thesis grades.