Department of Electrical Engineering and Computer Science

One or more advanced topics. May be repeated when topic changes. (Credit may not be obtained for both CSEN 5303 and EEEN 5303 courses if the topic is the same.)

Introduces the design principles of modern computers. The topics include RISC and CISC architecture, interconnection networks, multiprocessors and multicomputer systems, dataflow and systolic arrays, future outlook for architectures and the basics of parallel algorithms. (Credit may not be obtained in both CSEN 5304 and EEEN 5304.)

Designed for project option students and requires completion of research project. Prerequisite: departmental approval. May be repeated for a maximum of 6 semester hours.

Designed for thesis option students. The course requires completion of thesis research. Prerequisite: departmental approval. May be repeated for maximum of 6 semester hours.

This course introduces the structure of a compiler and the various techniques used for designing a compiler. Topics include grammars, parsing methods, implementation details and translator writing systems.

Basic concepts and architecture of database systems, ER model, relational model, relational algebra, SQL, ER-to-rational mapping, functional dependencies normalization, database design process, object-oriented database. Distributed database. Prerequisite: graduate standing in computer science or another engineering discipline.

Fundamental concepts of intelligent computer systems, knowledge representation, logical reasoning, search strategies, game theory, Bayesian networks, neural networks, reinforcement learning, natural language processing, and current research in AI area.

Operating systems principles; procedures and their implementation; protection, concurrent, cooperating and communicating processes; storage management; resource allocation; scheduling; file systems; and system design issues.

The International Standards Organization (ISO) Open Systems Interconnection (OSI) model as a framework for the study of computer communication networks. Data communication. Functions and protocols of physical layer, medium access sublayer, link layer, network layer and transport layer. Case studies. ISDN. Prerequisite: graduate standing in computer science or electrical engineering.

Covers development life-cycle models, inspection process, software quality metrics, testing, validation metrics, estimation and scheduling. Prerequisite: graduate standing in engineering.

Types of data, similarity and distance functions, data preprocessing, sampling, feature selection, discretization, attribute transformation, classification and prediction methods, decision trees, support vector machines, regressions, classification rules, Bayesian learning, neural networks, association rule mining, sequential pattern mining, cluster analysis, anomaly detection.

Fundamental concepts of machine learning including supervised and unsupervised learning, deep learning, probabilistic models, and reinforcement learning; covers key algorithms such as decision trees, neural networks, support vector machines, prototype methods and nearest neighbors, ensemble learning, and undirected graphical models; algorithm design, model evaluation, and optimization techniques.

Characteristics of systems and techniques used in real time computer applications. Scheduling theory, verification and design techniques including simulation and probabilistic models. Prerequisite: graduate standing.

Introduction of the design and analysis of computer algorithms. Topics include asymptotic efficiency; a survey of useful algorithms for sorting, information retrieval, and graphs; paradigms for algorithm design; and a brief introduction to complexity classes including NP. Prerequisite: graduate standing.

Principles of parallel algorithm design: task decomposition, synchronization, load balancing, computational dependencies; parallel computing architectures; collective communications; analyzing parallel algorithms; scalability, efficiency, speedup; parallel algorithms for fundamental problems such as sorting; shared-memory programming; distributed-memory programming; hybrid programming; message passing interface; GPU architecture and programming; transactional memory.

Cloud computing, services, and technologies for hosting, storing, and processing data on the cloud. Fundamentals of cloud computing using cloud services, such as cloud servers, databases, and data warehouses, along with principles and architectural foundations upon which cloud computing is based. Software design and implementation strategies that support the integration and exploitation of cloud-based resources, integration of cloud infrastructure into the design of software systems, security considerations associate with cloud computing, such as the use of public, private, and hybrid cloud resources.

Introduction to data science and data analytics programming in Python using libraries such as NumPy for computational array operations, SciPY for scientific and numerical computing, Pandas for data analysis and manipulation; an overview of techniques and methods for loading datasets, formatting data, feature analysis, and processing large amounts of data; collecting, pruning, munging, analyzing, visualizing, and processing data; building machine learning models, training them with data, testing the models to evaluate their performance, and using the models to make decisions or predictions.

Methods and tools used to analyze, visualize, and derive insights from complex data; data wrangling, statistical analysis, predictive modeling, data-driven decision making; applications in machine learning, natural language processing, and big data analytics; data analysis lifecycle, data acquisition and cleaning, model deployment, and result interpretation.

Includes a review of network architectures, perceptron, linear networks, back-propagation and radial basis networks. A real-time laboratory experience in seeing the application of neural networks. Prerequisite: graduate standing in Computer Science. (Credit may not be obtained in both CSEN 5350 and EEEN 5350.)

Computational and statistical algorithms in Python and Biopython for bioinformatics and biological applications. Computational tools for analyzing and modeling biological sequences of genes and proteins stored in databases in the GenBank, FASTA, and PDB formats. Gene and protein modeling, design, visualization, optimization, structure prediction, sequence analysis, sequence similarity search using BLAST, parsing, analyzing, transcribing and translating gene or protein sequences.

Design and development of software for mobile platforms including: user interface design, user experience factors, processes, threading, multitasking, database integration, application lifecycles, resource constraints in mobile environments, app security, cloud and network integration.

Individual or group research on advanced problems conducted under the supervision of a faculty member. Maximum credit 8 semester hours.

One or more advanced topics. May be repeated when topic changes. (Credit may not be obtained in both EEEN 5303 and CSEN 5303 courses if the topic is the same.)

Introduces the design principles of modern computers. The topics include RISC and CISC architectures, interconnection networks, multiprocessors and multicomputer systems, dataflow and systolic arrays, future outlook for architectures and the basics of parallel algorithms. (Credit may not be obtained in both EEEN 5304 and CSEN 5304.)

Designed for project option students and requires completion of research project. Prerequisite: departmental approval. May be repeated for a maximum of 6 semester hours.

Designed for thesis option students. The course requires completion of thesis research. Prerequisite: departmental approval. May be repeated for maximum of 6 semester hours.

Bipolar transistor small and large signal models, single and multiple transistor amplifiers, current mirrors, active loads, output stages, operational amplifier with single-ended outputs.

This course is designed to give students experience in nanofabrication methods such as thin film deposition, etching, implantation, and lithography to manipulate various materials including dielectrics, semiconductors, organics, polymers, metallic materials, and molecular films. In addition, this course will introduce MEMS/NEMS and advanced nanoscale CMOS devices. Prerequisites: graduate standing.

In this course, you will learn about traditional solar cell architectures, 1st and 2nd generation solar cells, nanotubes and nanowires-based solar cells, thin-film organic conjugates solar cells, CIGS solar cells, plasmonic effects, and light trapping. Prerequisite: graduate standing.

Nonlinear systems and stability, linearization, phase plane analysis, Lyapunov stability, feedback linearization, sliding mode control, reinforcement learning, and deep learning for control.

Signal and system norms, Structured and unstructured uncertainty, Robustness (stability and performance) analysis of control systems in time and frequency domains, Linear Matrix Inequalities (LMI), Linear Fractional Transformations (LFT), H-2, H-infinity and mu controller designs. Prerequisite: Graduate standing.

Quantum Mechanics, energy band theory, carrier statistics, recombination-generation, carrier transport.

Array signal processing fundamentals, beamforming techniques, direction of arrival (DOA) estimation algorithms, adaptive algorithms like LMS and RLS, smart antenna system design, interference suppression methods, applications in wireless communications, and simulation and design of antenna arrays.

Register operations, arithmetic operations, control of operations, memory systems, methods of input and output. Examples of commercial systems, system design of a general purpose computer.

Mathematical analysis of engineering, dynamic systems. Modeling, simulation, transfer functions, state variables, stability of linear systems.

Signal and system norms, Lp functions, adaptive parameter identification and control, stability, Model Reference Adaptive Control (MRAC), multi objective evolutionary/genetic algorithms, adaptive backstepping, and robust adaptive control laws.

Principles of electronic system design using Application-Specific Integrated Circuits (ASIC) approach: digital hardware modeling techniques using an HDL, logic simulation, logic synthesis, standard cells, gate arrays, sea of gates, bit serial hardware design methods and analog methods.

Digital processing of signals, z-transform, digital filters, discrete and fast Fourier transforms, power spectrum, autocorrelation, cepstrum analysis.

Principles of design and fabrication of microelectronic circuits via Very Large Scale Integrated circuitry (VLSI), structured design methods for VLSI systems, use of computer-aided design tools, design projects of small to medium scale integrated circuits.

Introduces the computer vision systems. Topics include edge detection, spatial-domain processing, frequency-domain processing, color processing, texture analysis, shape analysis and making movies from a deck of frames.

Classical and modern control analysis and design methods and techniques. Topics include discrete control system analysis, sampled data systems, discrete equivalents of continuous systems, design using transform techniques, design using state-space methods and the real-time control of dynamic systems using digital computers and micro-controllers.

Embedded system architecture and programming. Role of microprocessors, FPGAs, and PLCs; input/output; analog and digital interfacing; sensor networks and peripherals in hardware integration. (Credit may not be obtained for this course and for MHEN 5373).

Fundamentals of digital signal processing, waveform coding, speech spectrum, voice coders, linear predictive coding, speech recognition, adaptive noise cancellation and multirate signal processing.

Advanced concepts of circuit design for digital /analog (mixed signal) Very Large Scale Integrated Circuitry (VLSI) components in state-of-the-art Complementary Metal Oxide Semiconductor (CMOS) technologies. Emphasis is on the design and optimization of high-speed (high performance devices), high density (heterogeneous systems on a chip), low-power (portable applications) and analog (mixed-signa) integrated circuits. Prerequisites: Undergraduate degree in engineering or physical sciences.

This course introduces fundamental concepts and technologies in the area of wireless communication systems such as wireless applications, modulation techniques, wireless channel models, digital communication over wireless channels, multiple access techniques, and wireless standards.

Includes a review of network architectures, perceptron, linear networks, back-propagation and radial basis networks. A real-time laboratory experience in seeing the application of neural networks. Prerequisite: graduate standing in Computer Science. Prerequisite: graduate standing in Electrical Engineering. (Credit may not be obtained in both EEEN 5350 and CSEN 5350.)

Mechanical Processes and Components; Electrical Systems and Sensors; Actuators; PLCs; Data Acquisition; Machine Vision; Noise, Analysis and Design Considerations. Credit may not be obtained in both EEEN 5371 and MHEN 5371.

Individual or group research on advanced problems conducted under the supervision of a faculty member. Maximum credit 8 semester hours.