Principles underlying the design and analysis of efficient algorithms. Topics to be covered include: divide-and-conquer algorithms, graph algorithms, matroids and greedy algorithms, randomized algorithms, NP-completeness, approximation algorithms, linear programming.

0An in-depth introduction to the main models and concepts of the mathematical theory of computation, including: Computability: What problems can be solved in principle? How might you prove that a problem can’t be solved? Complexity: What problems can be solved within given resource constraints? How do constraints on different resources (e.g., time, space, or parallel time) relate? Logic: What are the best ways to formally specify a problem, and how do these specifications relate to the difficulty of the problem?

The course will primarily cover the design and analysis of parallel algorithms, computational models and complexity classes. Parallel algorithms for various problems including: basic arithmetic, sorting, searching, selection, graph theory, matrix computations, combinatorial enumeration, optimization, computational geometry, and numerical analysis will also be extensively studied in the course.

This course will give an introduction to formal languages and automata theory. Automata and formal languages appear (possibly in various disguises) in almost every branch of computer science. A formal language is a set of strings where a string is a finite sequence of symbols. An example of a formal language is the set of all “syntactically correct” Pascal programs (accepted by a certain compiler). A main problem that will be discussed is how to define an infinite language in a finite way. A related problem is to construct an algorithm that can decide whether a string is in the language or not. Both problems are of practical importance, for instance for constructing compilers and design of programming languages. At the end of the course, students will be introduced to the theory of computability.

This course starts with the basics of graphs, digraphs, and networks. It covers spanning trees, connectivity, traversal, planarity, coloring, network flows, algebraic specification of networks, and layouts on surfaces. Drawings and concrete examples abound. Applications concentrate on graphs as models for computer science, operations research, and sociology, including special attention to software design and to parallel architectures.

Origins, computer arithmetic and complexity- what is cryptography, a history of factoring and primality testing, computer arithmetic and complexity, Symmetric-key cryptosystems- an introduction to congruences, block ciphers, DES cryptanalysis, successor AES, stream ciphers, Public-key crypto-systems- exponentiation, discrete logs, public key cryptography, authentication, knapsacks, Primality Testing- an introduction to primitive roots, true primality tests, probabilistic primality tests, Agrawal algorithm,

Factoring- three algorithms, the number field sieve, Advanced topics – elliptic curves and cryptography, zero knowledge, quantum cryptography.

In this course, students will learn different distributed database management algorithms to support concurrency, transaction management, query optimization, replication, recovery, distributed database design and security; implement a client-server DBMS and distributed database applications. Distributed databases – various contemporary issues including data model partitioning, fragmentation, replication issues, query optimization, concurrency control, restart and recovery, distributed database design, client-server and distributed database applications will be discussed in details. Students will build a distributed system with Oracle DBMS. Particular attention will be paid to detailed consideration of distributed database management issues.

This course provides an in-depth examination of principles of distributed operating systems. Covered topics include processes and threads, concurrent programming, distributed interprocess communication, distributed process scheduling, shared virtual memory, distributed file systems. In-depth examples will be taken from current operating systems such as UNIX and MACH. Some coverage of operating system principles for multiprocessors will also be included.

This course examines the structure of modern computer systems. We explore hardware and technology trends that have led to current machine organizations, then consider specific features and their impact on software and performance. These may include superscalar issue, caches, pipelines, branch prediction, and parallelism. Midterm and final exams, team project, homework, in-class exercises

Review of CMOS logic circuits; impact of fabrication issues on design; high speed switching circuits; high performance memory structures; advanced clocking strategies and clock distribution; performance optimization; deep sub-micron design issues; ASIC design flow: logic synthesis, placement and routing; design verification; low power design. Students will learn and participate in the process of design, simulation and layout of a complex digital system.

Computer Networks is a graduate course that introduces fundamental concepts in the design and implementation of computer communication networks and their protocols. Topics include: layered network architectures, applications, transport and routing, IP version 6, mobile IP, multicasting, session initiation protocol, quality of service, network security, network management, and TCP/IP in wireless networks. An emphasis will be placed on the protocols used in the Internet

Probability, random variables and their properties, mathematical expectation, specific discrete and continuous random variates (Poisson, exponential, etc.). Simulation tools, random number and variate generation, event serialisation and time advance algorithms; process and resource classes, Performance measures, model instrumentation and result presentation. Simple stochastic processes – discrete time Markov chains, continuous time Markov processes; Poisson process, Birth and Death process and their application to the simple (e.g. M/M/1) queues. More advanced queuing theory – multi-server queues, non-Markovian queues, networks of queues. mean value analysis (analytic derivation of throughput, utilisation, mean queue size and delay). Applications – case studies in computer systems and networks using analysis and simulation, advanced simulation software

This course will review recent developments in transparent data embedding and watermarking for audio, image, and video. Data-embedding and watermarking algorithms embed text, binary streams, audio, image, or video in a host audio, image, or video signal. The embedded data are perceptually inaudible or invisible to maintain the quality of the source data. The embedded data can add features to the host multimedia signal, e.g., multilingual soundtracks in a movie, or provide copyright protection. The course will also discuss the reliability of data-embedding procedures and their ability to deliver new services such as viewing a movie in a given rated version from a single multicast stream. Other topics will cover issues and problems associated with copy and copyright protections and assess the viability of current watermarking algorithms as a means for protecting copyrighted data

In-depth introduction to Artificial Intelligence focusing on techniques that allow intelligent systems to operate in real-time and cope with missing information, uncertainty, and limited computational resources. Topics include: advanced search and problem-solving techniques, resource-bounded search, principles of knowledge representation and reasoning, meta-reasoning, reasoning under uncertainty, Bayesian networks and influence diagrams, decision theory and the value of information, planning and scheduling, intelligent agents architectures, and learning

This is a research course tailored to the needs of the current students enrolled, there is no strictly set syllabus. Following is a list of the topics which will be covered in depth in this course: Extended ASOCS coverage, Hamming Networks, Learning by a critic and the Associate Search Network, Genetic Algorithms, Self Organizing Topological Feature Maps, Counterpropagation networks, BAM’s (bidirectional associative memories), Boolean Networks, RCE’s (Restricted Coloumb Energy Networks), Implementation of actual applications in neural networks.

This course covers a variety of methods that enable a machine to learn. We will cover as much of Duda, Hart, & Stork’s ÔPattern RecognitionÕ as time permits. Topics will include Bayesian decision theory, maximum-likelihood estimation, expectation maximization, nearest-neighbor methods, linear discriminants, support vector machines, artificial neural networks, classification and regression trees, ensemble classifiers, clustering, and self-organizing feature maps. There will be weekly problem sets including some programming. There will be a midterm, and a final exam

In addition to traditions rooted in mechanics and dynamics, geometrical reasoning, and artificial intelligence, the study of robot systems is growing to include many issues traditionally part of the computing sciences; distributed and adaptive control, architecture, software engineering, real-time systems, information processing and learning. In robotics, processing and its relationship to mechanical function are dependent on the target platform and the world in which it is situated. Designing an embedded computational system for sensory and motor processes requires that designers appreciate and understand all of these disciplines. This course is concerned with the design and analysis of adaptive, closed-loop physical systems. The focus will be sensory and motor systems that interpret and manipulate their environments. Toward this end, we will study mechanisms (kinematics and dynamics), actuators, sensors (with a focus on active vision), signal processing, associative memory, feedback control theory, supervised and unsupervised learning, and task planning. Interesting examples of integrated sensory, motor, and computational systems can be found in nature, so occasionally we will relate the subject matter to biological systems. Students will experiment with system identification and control, image processing, path planning, and learning on simulated platforms to reinforce the material presented in class.

People are able to infer the characteristics of a scene or object from an image of it. In this course, we will study what is involved in building artificial systems which try to infer such characteristics from an image. Topics include: Basics of image formation – the effect of geometry, viewpoint, lighting and albedo on image formation. Basic image operations such as filtering, convolution and correlation. Frequency representations of images. The importance of scale in images. Measurements of image properties such as color, texture, appearance and shape. Inference of motion and structure from moving objects and images. Detecting and recognizing objects in images.

Introductory material to Genetic Algorithm, Classical and Steady state Genetic Agorithm, Genetic operators, schemata and Genetic algorithm, Genetic programming, Evolution Strategies and Evolution Programming, Learning Classifiers System, Multimodal function optimization, Multi-objective problems, combinatorial optimization problems, Biology & Chemistry Applications

Introduction to Neuro-Fuzzy and Soft Computing, Soft Computing and AI, Neural Networks, Fuzzy Set Theory, MF Formulation and Parameterization, Fuzzy Union, Intersection, and Complement, Fuzzy Rules and Fuzzy Reasoning, Fuzzy Inference Systems, Regression and Optimization, Supervised Learning Neural Networks, Neuro-Fuzzy Modeling, ANFIS, Neuro-Fuzzy Control, ANFIS Applications

Introduction; Point operations; Histograms; Spatial operations; Affine transformations; Image rectification; Interpolation and other transformations; Contrast enhancement; Convolution operation, Magnification and Zooming; Fourier transform; Edge detection; Boundary extraction and representation; Mathematical morphology.

This course uses a detailed examination of the Java and ML programming languages as a basis for studying fundamental principles underlying the design and implementation of modern programming languages. The course addresses a wide range of programming language concepts and issues from both a practical and a theoretical perspective. Some attention is given to such traditional topics as control constructs, type systems and type checking, since these are the foundations for all subsequent developments. The bulk of the course, however, is devoted to more contemporary language features such as object orientation, modularity, polymorphism and concurrency. Our study of both traditional topics and contemporary features is driven by first exploring their realization in Java and ML, then comparing and contrasting with the realizations found in other modern languages such as C++, C#, Ada 95 and Modula-3. In addition, we consider some emerging concepts and directions for programming languages such as orthogonal persistence, reflection, interoperability and open implementation. While the predominant paradigm for contemporary programming languages — the imperative, object-oriented paradigm — is our primary focus, and the functional paradigm is our secondary focus, other paradigms such as the logic programming paradigm are also discussed. Homework problems, programming exercises and projects reinforce the material covered in lectures and readings.

This course introduces students to the principal activities involved in developing high-quality software systems. The course stresses the use of defined, systematic processes in the creation of carefully defined and engineered software products. Among the topics covered are requirements analysis, software architecture, formal specification methods, process definition, software design methods, and test planning. Issues specific to the development of software by teams and groups will also be addressed. Students will be required to read selected papers from the literature and complete homework projects

This course will survey current research in developing tools and techniques for assuring software quality. As computing technology continues to permeate every aspect of personal and public life, the need for assuring the reliability of our computing infrastructure is increasing steadily. Driven by these societal needs, software quality research has become very active in the last few years. This course will survey current work in this area. While research in software engineering is as old as programming, recent approaches have broken new ground and there is currently a great deal of ferment. Thus, this course will necessarily take in a broad selection of topics, including research in testing, monitoring of running systems, capturing and querying program traces, several variations on extending type systems and other static analyses, theorem proving systems, and software model checking. The syllabus will be entirely research papers.

This course exposes students to issues of professional practice, ethical behaviour, and computer law. Topics included may be history of computing, impact of computer on the society, computing careers, legal and ethical responsibilities, intellectual property rights management, copyrights, patents law, trade secrets, software piracy, laws related to information security and ethical responsibilities of computing profession

The objective of this course is to summarize the rational for antimonopoly efforts, describe several ways in which the information technology industry is affected by shortages of labor supply, suggest and defend ways to address limitations on access to computing, and outline the evolution pf pricing strategies for computing goods and services. Suggested topics to be covered are monopolies and their economic implications, effect of skilled labor supply and demand on the quality of computing products, pricing strategies in the computing domain, and differences in access to computing resources and the possible effects thereof.

This course provides an in-depth understanding about the management of technology and innovation. Covered topics are: management of dynamic changes in R&D, integration of technology planning, product planning, business planning and the market demands, human, social and environmental concerns associated with technological change, case studies of global technology firms, business case development for deployment of new technologies critical decision analysis methods, new challenges and responses in technology management, technology transfer, and business and technology strategy

Telecommunications: Point-to-Point Systems and Networks, Information Carrying Capacity, The Need for Fiber-Optic Communications Systems, A Fiber-Optic Communications System: The Basic Blocks, Worldwide Submarine Networks, Electromagnetic Spectrum, Radiation & Absorption, Optical Fibers-Basics: Step-Index Fiber, Numerical Aperture, Attenuation, Calculation of Total Attenuation, Intermodal and Chromatic Dispersion, Graded-Index Fiber, The Structure of a Singlemode Fiber, Bit Rate and Bandwidth, Cutoff Wavelength, V-Number, Attenuation Constant, Dispersion in Multimode Fibers, Electrical & Optical Bandwidth, Spectral Width, Singlemode Fibers, Gaussian Beam, Bit Rate of a Singlemode Fiber, Fabrication, Cabling, and Installation, Fiber Cable Connectorization and Testing, Power Budget, Light Sources, Transmitters and Receivers; Transmitter Modules, Receiver Units, Components of Fiber-Optic Networks; Passive Components, Switches, Transceivers, TDM, WDM and DWDM systems, Add/Drop Problem, Multiplexing Hierarchy in Telecommunications, SONET & SDH Systems, FDDI, and Functional Modules of Fiber-Optic Networks-Telephone and Computer Networks, Networks, Protocols, and Services, OSI, ATM Networks and Layers; Broadband Communication System, Network Management and Future of Fiber-Optic Networks

Microcomputer principles and Applications. Fundamental characteristics of the software life cycle. Tools. techniques and management controls for development and maintenance of large software systems. Software metrics and models. Human factors and experimental design

Technical survey of the ways and means that voice, data and video traffic are moved long distance. Topics covered include Data Networks (Ethernet and Token Ring Local Area Networks; FDDI and SMDS Metropolitan Networks; Internet, Frame Relay, and ATM Wide Area Networks); The Telephone System (POTs, Network Synchronization and switching, ISDN, SONET, cellular Telephone); and video (NTSC, Switching and Timing, compressed Video standards such as MPEG and Px64, HDTV).

Aspects of radiowave propagation for fixed and mobile communication systems, and cellular system design. Large-scale and small-scale propagation models, multipath fading, link-budget, interference and frequency reuse, multiple access schemes and system capacity. Trunking and grade of service, wireless network planning and operation. Architecture and operation of 2G cellular mobile systems, 2.5 G and 3G technologies. Special techniques/Diversity, Equalization, Interleaving, and Smart Antenna

Telecom services, local, long distance, mobile telephony, voice over IP, business models, operations and maintenance, cost and pricing for service packages, wireless vs wireline telephony, industry dynamics, market competition, regulatory issues, cost of compliance, ITU and telecom policy for local nd global market, industry restructure, privitazation trends in developing markets, spectrum management, licensing and fees, tariff, interconnection, overseas access, business models

The purpose of this course is to give the students of Computer Science/Engineering the basic background in Digital Signal Processing. This course introduces how a computer (a general purpose or special purpose DSP chip) could be used to solve Signal Processing problems digitally. The topics include introduction to discrete signal and systems, difference equations, discrete convolution, Z-transform and Fast Fourier transform techniques

Retrieval and analysis of electronic information are essential in today’s research environment. This course explores the theory and practice of biological database searching and analysis. In particular, students are introduced to integrated systems where a variety of data sources are connected through worldwide web access. Information retrieval as well as interpretation are discussed and many practical examples in a computer laboratory setting enable students to improve their data mining skills. Methods included in the course are searching the biomedical literature, sequence homology searching and multiple alignments, protein sequence motif analysis, and several genome analytical methods

Topics from Genetics: DNA-Structure, function and replication, the polymerase chain reaction, sequencing, RNA-mechanism of synthesis and regulation, organization of eukaryotic/prokaryotic genomes, analyzing complex genomes and mapping, expression/differentiation/regulation of eukaryotic genes, principles of cloning, restriction enzymes, cloning vectors. Topics from Protein: Amino acids, protein structure, protein function (binding and catalysis), function and catalysis, polysaccharides, lipids, membranes, cell biology

Uses of microarrays, sequence databases for microarrays, computer design of oligonucleotide probes, image processing, normalization, measuring and quantifying microarray variability, analysis of differentially expressed genes, analysis of relationships between genes, tissues or treatments, classification of tissues and samples, experimental design, data standards, storage and sharing.

The completion of the human genome sequence is just the latest achievement in genome sequencing. Armed with the complete genome sequence, scientists need to identify the genes encoded within, to assign functions to the genes, and to put these into functional and metabolic pathways. This course will provide an overview of the laboratory and computational techniques beginning with genome sequencing and annotation, extending to bioinformatics analysis and comparative genomics and including functional genomics

Deriving and visualizing macromolecular structures, X-ray crystallography, NMR spectroscopy, electron microscopy, structure visualization, examining and interpreting structural data, Protein Data Bank (PDB), structures classification schemes (CATH, SCOP), structure validation software, secondary structure assignment, structure comparison and alignment, domain assignment, functional assignment from structure, protein docking, comparative assessment of protein structure prediction (CASP), comparative modeling, fold recognition, ab-initio structure prediction

Project