Information Systems Engineering

Main block

Detail Information Systems Engineering Elective Course Description


CPEN 661 Information Security

The information security course examines the design and implementation of information security system that assures content confidentiality. Topics include introduction to confidentiality, integrity, availability, authentication techniques and models, controls and protection models, security kernels, secure programming, audit, intrusion, detection and response, operational security issues, physical security issues, personal security, policy formation and enforcement, access controls, information flow, legal, privacy and social issues, identification and authentication in local and distributed systems, classification and trust modeling, risks and vulnerabilities, risk assessment, database security, encryption, host based and network based security issues, secure network design, implementation and transition issues, and techniques for responding to security breaches.

Reference books and materials [1] Bidgoli, H., Handbook of Information Security Vol-3, John Wiley & Sons, 2006 [2] Stamp, M., Information Security: Principles and Practice, John Wiley & Sons, 2006 [4] Anderson, R., Security Engineering: Guide to Building Dependable Distributed Systems, 2nd Ed., John Wiley & Sons, 2008 [5] Kizza, J. M., and Kizza, F. M., Securing the Information Structure, IGI Global Snippet, 2008


CPEN 662 Pattern Recognition

The pattern recognition course describes various methods and techniques that are used in pattern recognition. Topics include Bayes decision theory, description of patterns, feature extraction and classification, classification models, non-parametric pattern classification techniques, parameter estimation, pattern classification using linear discriminant functions, uncertainty in pattern recognition, fuzzy sets, inductive learning of rules for pattern recognition, learning discriminates, unsupervised learning and clustering, feature extraction, neural network techniques, structural recognition techniques, and other forms of pattern recognition methods and applications.

Reference books and materials [1] Bishop, C. M., Pattern Recognition and Machine Learning, Springer, 2007 [2] Ripley, B. D., Pattern Recognition and Neural Network, Cambridge University Press, 2008 [3] Looney, C., Pattern Recognition Using Neural Network, Oxford University Press, 1997


CPEN 663 Cryptography and Analysis

The cryptography course focuses on the mathematical concepts and techniques behind modern information encryption and network technologies. Topics include survey of classical and modern encryption techniques and algorithms, encryption theory and foundations, cryptographic and crypto-analysis techniques, one-way functions, pseudo-random function, encryption system modeling, authentication protocols, public-key cryptosystems, notions of security, zero-knowledge proofs, multi-party cryptographic protocols, security policies, legal and ethical issues, practical applications of encryption.

Reference books and materials [1] Stallings, W., Cryptography and Network Security: Principles and Practice, 5th Ed., Prentice Hall, 2011 [2] Mao, W., Modern Cryptography: Theory and Practice, Prentice Hall, 2003 [3] Schneier, B., Applied Cryptography, 2nd Ed., Prentice Hall, 1999 [4] Swenson, C., Modern Cryptanalysis: Techniques for Advanced Code Breaking, John Wiley and Sons, 2008


CPEN 664 Artificial Intelligence

The artificial intelligence course focuses on the issues and principles and techniques of artificial intelligence. Topics include knowledge representation, organization and manipulation of the world and how to reason logically with the knowledge, concepts of inconsistency, uncertainty, probabilistic reasoning, structured knowledge, logic programming, computational and statistical learning theory, machine learning including supervised, unsupervised and reinforcement learning, decision making including search oriented problem solving, planning, games, Markov and decision processes, planning and temporal reasoning, inference and theorem proving, reasoning under uncertainty, search and information retrieval, principles of intelligent agents, speech and natural language processing involving parsing, machine translation, and information extraction, speech recognition, computer vision, and robotics

Reference books and materials [1] Jones, T. M., Artificial Intelligence: A Systems Approach, Jones & Bartlett Learning, 2008 [2] Nilsson, N. J., Artificial Intelligence: A New Synthesis, Morgan Kaufmann, 1998 [3] Luger, G. F., Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Addison Wesley, 2009 [4] Russell, S., and Norvig, P., Artificial Intelligence: A Modern Approach, 3rd Ed., Prentice Hall, 2009


CPEN 665 Neural Networks

The neural networks course examines models of computation that learn tasks from examples of desired input and output behavior for various applications. Topics include basic concepts behind neural network models, biological neuron model, neural network architectures, feed-forward, feed-back, Hopefield models, adaptation and learning in neural networks including perceptron, iterative learning, multilayer network with hidden layers, back-propagation, convergence and speeding up algorithms, supervised Hebbian learning, Widrow-Hoff learning, associative learning, competitive learning, self-organizing neural networks, neural network training and testing techniques including setting of training parameters, preparing training data, training the network, and testing of the network, neural network applications to speech, robotics, face and pattern recognition, implementation process using electronics and optical.

Reference books and materials [1] Rising, L., and Schmidt, D. C., Design Patterns in Communication Software, Cambridge University Press, 2001 [2] Anandalingam, G., and Raghavan, S., Telecommunications Network Design and Management, Kluwer Academic Publisher, 2003 [3] Anandalingam, G., and Raghavan, S., Telecommunications Planning, Kluwer Academic Publisher, 2006


CPEN 618 Digital Communication Systems

The digital communication course investigates the modern trends in generating digital signal for propagation and processing. Topics include basics of stochastic processes, design of baseband and passband digital communication systems, characteristics of channels, channel capacity, modulation techniques, including PAM, QAM, PSK, FSK, and spread spectrum, optimal demodulation techniques and performance, synchronization, inter-symbol interference (ISI) and equalization techniques.

Reference books and materials [1] Haykin, S. S., Neural Networks and Learning Machines, Prentice Hall, 2009 [2] Dreyfus, G., Neural Network: Methodology and Applications, Birkhauser, 2005 [3] Haykin, S. S., Neural Network: A comprehensive Foundation, 2nd Ed., Prentice Hall, 1999 [4] Mehrotra, K., Mohan, C., and Ranka, S., Elements of Artificial Neural Network, MIT Press, 1997 [5] Sivanandam, S. N., Sumathi, and Deepa, Introduction to Neural Network Using Matlab, Tata McGraw Hill, 2006


CPEN 667 Advanced Database Systems

The database system course examines the principles of the design of systems that can manipulate and retrieve data from large databases using high level formal languages. Topics include data base development lifecycle, data modeling, database architectures, database design theory, data acquisition, models for database systems, data integration and cleaning, query processing, concurrency control and transaction management, data search and recovery, distributed and parallel data management in cluster computing and peer-to-peer, web pages, sensor networks and RFID, data storage, inference and data mining, data security and privacy, declarative data-intensive systems, data visualization, query optimization and stream algorithms, current trends in development of database systems.

Reference books and materials [1] Ramakrishnan, R., and Gehrke, J., Database Management Systems, McGraw Hill, 2002 [2] Zaniolo, C., Advanced Database Systems, Morgan Kaufmann, 1997 [3] Ramakrishnan, R., and Gehrke, J., Database Management Systems, McGraw Hill, 2003 [4] O’Neal, P., and O’Neal, E., Database: Principles, Programming, and Performance, Morgan Kaufmann, 2001 [5] Mullins, C., Database Administration: Complete Guide to Practices and Procedures, Addison Wesley, 2002


CPEN 668 Data Compression

The data compression course examines the theoretical foundation of compression techniques and algorithms for lossy and lossless data compression as well as signal modeling and its extensions to data compression and applications to multimedia data compression. Topics include basics of signal encoding and decoding for compression, lossy and lossless compression, communication building blocks, and fixed and variable rates, quantization theory including uniform quantization, distortion and bit rate, high rate quantization theory and elementary distortion rate theory, architecture for data compression including signal models and spectral analysis, coding forms, entropy and variable quantization, lossless coding algorithms such as Huffman, arithmetic, universal lossless, adaptive and predictive coding algorithms, distortion and similarity measures, lossy coding algorithms such as scalar quantization algorithm, and vector quantization coding algorithm, speech and audio compression techniques, image and video compression techniques, compression standards and formats.

Reference books and materials [1] Salomon, D., Motta, G., and Bryant, D., Handbook of Data Compression, Springer, 2009 [2] Sayood, K., Lossless Compression Handbook, Academic Press, 2003 [3] Gibson, J. D., Berger, T., Lookabaugh, T., Lindbergh, D., and Baker, R. L., Digital Compression for Multimedia: Principles and Standards, Morgan Kauffmann, 1998 [4] Salomon, D., A Concise Introduction to Data Compression, Springer, 2008


CPEN 669 Speech Processing and Recognition

The speech processing and recognition course covers the fundamentals of speech recognition and voice interfacing with machines. Topics include overview of voice interfaces, issues with voice interfacing, recent applications successes and failures, speech technologies like recognition, synthesis, and compressed audio transmission, automated speech processing, models and theoretical concepts in speech recognition, speech articulation and perception processes, digital speech processing and phonetic feature extraction, identifying words from pronunciations, syntax and semantics in speech understanding, task constraints and natural language, alternative speech recognition system structures, voice interfacing projects, and strategies for achieving user satisfaction.

Reference books and materials [1] Altmann, G. T. M., Cognitive Models of Speech Processing, MIT Press, 1995 [2] Benesty, J., Sondhi, M. M., and Huang, Y., Handbook of Speech Processing, Springer, 2008 [3] Sadaoki, Furui, Digital Speech Processing, Synthesis, and Recognition, Dekker Inc., 1989 [4] Jurafski, D., and Martin, J. H., Speech and Language Processing, 2nd Ed., Prentice, 2008


CPEN 672 Data Mining

The data mining course provides background information on the design and use of data mining algorithms and applications in data mining on the web, computational biology and various medical applications. Topics include models, methods and processes of data mining including search and querying, data dredging and fishing, discrete structures involving item-set mining, concept lattices, condensed representation, frequent pattern mining, customized data structures for speeding up data mining algorithms, attribute-value learning techniques including decision tree, decision lists, classification and regression trees, association rules, and rule-based mining, relational mining techniques, probabilistic techniques including conditional independence and its modeling, representational complexity, Bayesian networks, and probabilistic models for query approximation, sequences and order, compositional data mining, mining chains of relations, integrated query and mining languages, paradigms for interfacing with database systems, and application in bi-informatics, personalization, information retrieval, web modeling, filtering and text processing.

Reference books and materials [1] Larose, D. T., Data Mining Methods and Models, Wiley, 2006 [2] Han, J., and Kamber, M., Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001 [3] Wang, J., Data Mining: Opportunities and Challenges, IGI, 2003 [4] Grossman, R. L., Data Mining for Scientific and Engineering Applications, Springer, 2001 [5] Hastie, T., Tibshirani, R., and Friedman, J., Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Ed., Springer, 2009


CPEN 674 Computer Vision

The computer vision course involves the development of algorithms and software that have the potential to mimic a biological organism's ability to see. Topics include the physics of vision and its computational modeling, mathematical techniques for representing and reasoning with curves, surfaces, and volumes, image formation and sensing, camera model, thin lens model, lighting and reflectance, image capture and processing including edge finding, corner detection, image segmentation and texture analysis, image reflectometry involving color, image irradiance, and reflectance map, image analysis techniques such as convolution, filtering, noise, derivatives, and smoothing, scale space and SIFT, motion estimation and optic flow, 3D vision including shape from shading and shape from texture and defocus, geometric camera calibration, homographies, structure from motion, epipolar geometry and estimating of fundamental matrix, and dense stereo correspondence.

Reference books and materials [1] Szeliski, R., Computer Vision: Algorithms and Applications, Springer, 2010 [2] Cipolla, R., Computer Vision: Detection, Recognition, and Reconstruction, Springer, 2010 [3] Kulkarni, A. D., Computer Vision and Fuzzy-Neural Systems, Prentice Hall, 2001 [4] I. Guyon, Feature Extraction: Foundations and Applications, Springer, 2006