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Electrical Engineering, Computer Engineering, Computer Science, & Electrical Engineering Technology Courses at the University of Cincinnati, Cincinnati, Ohio

Electrical Engineering, Computer Engineering, Computer Science, & Electrical Engineering Technology Courses at the University of Cincinnati

 

computer & electrical engineering classOn this page you will find a complete list of all courses offered within the Department of Electrical Engineering & Computing Systems.

 

 

 

electrical & computer engineering class

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Computer Science Courses
Electrical and Computer Engineering Courses
Electrical and Computer Engineering Technology Courses

 

 

 

Computer Science Courses

  • CS1021C | Computer Science 1 | Credits: 4
    • Introduction to computer science with emphasis upon structured and object-oriented programming, algorithm design, and problem-solving. Currently, C++ is used as the computer language.
  • CS1022C | Computer Science 2 | Credits: 4
    • 1.Introduction to classes and objects, multi-dimensional arrays, stacks and queues, and abstract data types. 2. Implementation of data types using pointers and arrays. 2. Complexity analysis.
  • CS2011 | Introduction to Computer Systems | Credits: 3
    • Assembly language programming. Organization of the hardware including registers, memory, and microprocessors and I/O ports. Binary and hexadecimal arithmetic, assembler instructions, processor status (flags), program control, addressing modes.
  • CS2071 | Discrete Computational Structures | Credits: 3
    • Three undergraduate credits. Introduces discrete mathematical topics in the context of CS: mathematical induction, sets, propositional logic, relations and functions, algorithm analysis, graph theory.
  • CS2928 | Data Structures | Credits: 2
    • Data structures such as queues, trees, and graphs. Introduction to hashing, sorting, searching, and complexity of algorithms. Practice implementing concepts of data structures, encapsulation of structure, and behavior of data types.
  • CS3092 | Database Design | Credits: 3
    • Database design with the Entity Relationship model. Relational data model and database design. Physical storage techniques. Reliability and Recovery. Additional topics.
  • CS4001 | Networks | Credits: 3
    • The objective of this course is to develop the ideas and principles behind network phenomena. An understanding of commonalities and differences between small-scale versus large-scale systems is required. The course will cover social networks, game theory and strategic interaction, information networks and networks dynamics.
  • CS4003 | Programming Languages | Credits: 3
    • Concepts and features in design, compilation, and implementation of modern programming languages. Introduction to functional and logic-programming.
  • CS4071 | Design and Analysis of Algorithms | Credits: 3
    • An introduction to the study of sequential algorithms. Analysis of computing time, asymptotic notation, introduction to lower bound theory. Induction, correctness proofs, and recurrence relations. Major design strategies: the greedy method, divide-and-conquer, dynamic programming. Graph and network algorithms.
  • CS4092 | Database Design and Development | Credits: 3
    • Database design with the Entity Relationship model. Relational data model and database design. Physical storage techniques. Reliability and recovery. Concurrency.
  • CS5001 | Computer Science Senior Design I | Credits: 3
    • First of two semester laboratory series in which students create a significant product capitalizing on their prior four years of computer science education. Each student selects a project advisor, and works with him/her to select a design problem, identify the requirements and specifications of the product, and design the product. Team projects are strongly encouraged.
  • CS5002 | Computer Science Senior Design II | Credits: 3
    • Second of two semester laboratory series in which students create a significant product capitalizing on their prior four years of computer science education. Completion of detailed design and implementation of project.
  • CS5005 | Special Studies | Credits: 1
    • Requirements individually arranged including 700-level courses for graduate school preparation. Available only to seniors with high academic rank and upon approval of petition to faculty. Offered each quarter. Credits arranged.
  • CS6015 | Computer Arithmetic | Credits: 3
    • 1.Numeric data representation. 2.Review of Boolean algebra, logic gates, elementary integer and floating point arithmetic. 3.Advanced methods of addition, subtraction, multiplication and division. 4.Rounding methods for floating arithmetic. 5.Fast and parallel hardware evaluation of transcendental functions. 6.Theoretical limits. 7.Pipelined arithmetic. 8.Low power arithmetic. 9.Fault tolerant memory and arithmetic and the neutron problem. 10.Is Intel's implementation of AES safe against side-channel attacks?
  • CS6021 | Mathematical Logic | Credits: 3
    • Propositional and first order logic. Formal proof systems, compactness, completeness, and an introduction to model-theoretic techniques. Goedel's first incompleteness theorem. Coverage will be mathematically precise. Students will be expected to understand the proofs and prove related results.
  • CS6023 | Computability and Complexity | Credits: 3
    • An introduction to theory of computable functions and of computational complexity. Turing machines. Computable and partially computable functions, diagonalization, and the halting problem. Universal machines. Recursive and recursively enumerable sets. Oracles, reductions, and the arithmetic hierarchy. Space and time complexity models. Cook's theorem. P, NP, co-NP, and the polynomial-time hierarchy. Collapses in the space hierarchy.
  • CS6025 | Data Encoding | Credits: 3
    • This is an introduction course to the fields of data compression, data encryption, and error detection at the senior undergraduate and beginning graduate level. Topics include variable-length data coding, dictionary data compression, arithmetic coding, wavelet-based image and video compression, message digests, digital signatures, cyclic redundancy code, block and stream ciphers, and public key encryption. Data types to be covered include textual and multimedia. This course is heavily project-oriented but also covers necessary mathematics behind various transforms, modular arithmetic, information and entropy, probability, and finite fields.
  • CS6026 | Formal Methods | Credits: 3
    • An introduction to formal hardware and software verification. Two approaches are introduced: model checking and logical inference. Tools and representations based on these techniques are applied to specific formal verification problems. Tools discussed are: SAT solvers, SMT solvers, ACL2, ABC, and Cryptol. Representations discussed are CNF propositional formulas, first order logic, and and-inverter graphs. Examples of problems verified range from sorting algorithms such as mergesort to encryption algorithms such as AES.
  • CS6033 | Artificial Intelligence | Credits: 3
    • The course will cover in detail the topics of state space search, game tree search, constraint satisfaction, logic based knowledge representation and reasoning, first order predicate calculus, uncertainty handling using Bayesian probability theory, and some applications of these techniques. Applications may be selected from the area s of automated planning, natural language processing, or machine learning.
  • CS6037 | Machine Learning | Credits: 3
    • The goal of this course is to introduce students to the field of Machine Learning. The course covers traditional machine learning algorithms, and their implementations along with discussions of concrete problems where these algorithms are suitable. Topics covered by course include: Concept Learning and the General-to-Specific Ordering Decision Tree Learning Artificial Neural Networks Evaluating Hypotheses/Bayesian Learning Computational Learning Theory Instance-Based Learning Genetic Algorithms Learning Sets of Rules Analytical Learning Combining Inductive and Analytical Learning Reinforcement Learning
  • CS6043 | Computer Networking | Credits: 3
    • Need for layering, Application Layer, Transport Layer, Network Layer, Link Layer and Local Area Networks, Multimedia Networking, Security in Computer Networks, Network Management.
  • CS6051 | Database Theory | Credits: 3
    • A discussion of databases, with emphasis upon theory. Formal models for database system architectures. Relational, Entity/Relationship, Object-Oriented, and Semistructured models. Methodologies for manipulating and specifying models. Foundations of query languages.
  • CS6052 | Intelligent Data Analysis | Credits: 3
    • This course will introduce students to the theoretical and practical aspects of the field of data mining. Algorithms for data mining will be covered and their relationships with statistics, mathematics, and algorithm design foundations will be explored in detail.
  • CS6053 | Network Security | Credits: 3
    • Treats current concerns, trends, and techniques to insure security and safety of data on computers and over networks. There are three parts: 1. Basic tools and assembly: Secret Key and Public Key block ciphers such as DES, 3DES, AES, RSA, Diffie-Hellman Key Exchange, zero-knowledge authentication, and Elliptic Curve Cryptography; hash algorithms such as SHA variants; stream ciphers such as RC4 variants; message integrity and authentication algorithms such as HMAC. Output Feedback Mode, One-time Pads, Cipher Block Chaining are discussed as the means to put many of these algorithms to practical use; 2. Systems using these tools: Kerberos, IPSec, Internet Key Exchange, SSL, PGP, Email Security. Certification authorities, certificates, and key distribution centers to support these systems. Vulnerabilites in protocols specified for these systems and ways they can be fixed; 3. Well known attacks and how to prevent them. This includes denial of service, side-channel, attacks that exploit existing network IP and TCP protocols, offline and online password attacks, stateless cookies. Students will form teams of three to produce systems written in Java that will compete in a contest spanning four days just before finals week.
  • CS6054 | Information Retrieval | Credits: 3
    • This is an introductory course to the field of information retrieval at the senior undergraduate and beginning graduate level. Topics include bag-of-words model and term frequency matrix, tf-idf vector space representation and cosine similarity, vector space-based and graph-theoretical ranking and clustering, latent semantics and latent topic models. Four programming projects with real-world document collections for indexing, ranking, and clustering are designed for both undergraduate and graduate students and an additional project is required for graduate students. This course also covers necessary mathematics in Bayesian statistics and machine learning.
  • CS6060 | Computer Graphics I | Credits: 3
    • 3 credit hours dual level. To introduce both the key topics and the latest implementation of computer graphics. The major topics include: 1. Computer graphics applications 2. Basic graphics primitives, attributes 3. Graphics systems and display hardware 4. Interactive input devices 5. Basic rendering algorithms 6. Graphics standards 7. Graphics libraries: OpenGL 8. Mathematical elements for computer graphics 9. Basic geometric transformations 10. Graphical object modeling 11. Curves, surfaces, and solids 12. 3D concepts 13. 3D viewing 14. Lighting, shading, texture mapping 15. Hidden line/surface removal algorithms 16. Shaders
  • CS6062 | Virtual Reality and Visualization | Credits: 3
    • To study the basic elements for building an immersive visualization environment. To explore the use of the computer graphics and solid modeling to generate tools for modeling and rendering of virtual objects and worlds and creating character animation and dynamics. To introduce the 3D user interface techniques used in immersive platforms.
  • CS6063 | Distributed Systems | Credits: 3
    • This course presents an in-depth analysis of the issues affecting the design, deployment and utilization of systems composed of computing nodes that communicate by means of general-purpose computing networks. Topics include communications protocols, timing & synchronization, error detection/recovery, security, data modeling, and data retrieval strategies.
  • CS6067 | User Interface I | Credits: 3
    • This course introduces the basic concepts of human computer interaction and the latest development of the technology for developing interactive systems. Major topics cover the role of computer technology, human users and human factors for designing windows-based applications, and design methodologies for building software applications
  • CS6068 | Parallel Computing | Credits: 3
    • This course is designed as a dual level and senior undergraduate level course introducing the theory and practice of parallel computing. The course seeks to empower students with the computational thinking and practical programming skills needed to achieve terascale and petascale computing performance in all science and engineering disciplines. Students will study and gain experiences with several parallel algorithmic design patterns. Student will study the critical system and architectural design issues associated with parallel computing. Students will gain experience with parallel programming development environments and learn programming methodologies using a chosen platform. Students will learn analytical techniques for understanding the scalability and portability of of parallel computing software. The course is lab and project oriented.
  • CS6069 | Collaborative Computing | Credits: 3
    • This course covers the basics elements of human and computer interaction for computer-supported cooperative work (CSCW), the latest computing technology of distributed systems, and the software design methodologies that are used to implement collaborative strategies in Internet-based groupware applications.
  • CS6070 | The Theory of Formal Languages and Automata | Credits: 3
    • Finite state automata. Regular languages and grammars. Context-free grammars and their normal forms. Pushdown automata. Turing machines. Nondeterministic Turing machines. Universal Turing Machines. The Chomsky hierarchy. The limits of computation. Complexity theory. Finite-state transducers. Moore machines and Mealy machines.
  • CS6086 | Computational Biology | Credits: 3
    • This is a course on computational biology at the senior undergraduate and beginning graduate level. No biology background is required. Topics include biological measurements and the gathering and storing of data; data formats, labeling, and important databases and web servers; Genomes, transcriptomes, proteomes, and interactomes; spectral analysis and cluster analysis; gene networks and their topology and dynamics. Four programming projects with real biological data are designed for both undergraduate and graduate students and an additional one is required for graduate students.
  • CS6094 | Advanced Programming Techniques | Credits: 3
    • Treats sophisticated ways to use advanced programming constructs which are now or are beginning to be commonplace in modern general purpose programming languages. For example, considers the advanced use of exceptions, threads, reflection, streams, sockets, remote method invocation, virtual functions, continuations, and monads, among other concepts. In addition, modern security features including provisions for public-key crytosystems and class encoding will be discussed. The programming languages Haskell and Java will be used to illustrate the concepts discussed. A team project will be assigned at the end of the semester.
  • CS6097 | Introduction to Wireless and Mobile Networking | Credits: 3
    • Introduction to Wireless and Mobile Systems, Probability Theory, Statistics, and Traffic Theories, Mobile Radio Propagation, Channel Coding, Cellular Concept, Multiple Radio Access, Multiple Division Techniques, Channel Allocation, Mobile Communication Systems, Existing Wireless Systems, Network Protocols, Ad Hoc Networks, Sensor Networks, Wireless LANs and PANs, Recent Advances.
  • CS7001 | CS Seminar I | Credits: 1
    • Course shall cover presentations of skills needed to perform research and also a number of presentations by faculty, visitors, and other graduate students describing their ongoing research.
  • CS7002 | CS Seminar II | Credits: 1
    • There will be frequent presentations by faculty and visitors describing their ongoing research projects.
  • CS7010 | Practical Experience | Credits: 1
    • This course is designed to provide the EECS graduate student with research experience either on campus or off campus in industry. It is required for all MS and PhD students. The advising faculty member aproves the suitability of the work experience and the employment period and may also waive this requirement.
  • CS7035 | Cryptography 1 | Credits: 3
    • Conventional and public key cryptography. Digital signatures. Secure voting schemes. Provably secure cryptosystems. Protocols using zero-knowledge proofs. Using multiprecision arithmetic for implementing and attacking cryptosystems based on large integers--e.g. 300 digit numbers. The analysis and cracking of cryptosystem algorithms and protocols. Examples of cracked systems.
  • CS7036 | Advanced Topics in Machine Learning | Credits: 3
    • The objective of this course is to introduce students to the latest most advanced topics in the field of Machine Learning. This is a fast moving field which recently has brought together theoretical computer science, statistical methods and nonlinear optimization as well as concept from differential geometry. Topics to be covered: Linear Models for Regression and Classification Linear Basis Function Models ML and least squares Regularized least squares Bayesian Linear Regression Equivalent kernel Bayesian Model Comparison Discriminant Functions Least squares for classification Fisher's linear discriminant Probabilistic Generative Models Logistic regression / Multiclass logistic regression Bayesian Information Criterion /Bayesian Logistic Regression Neural Networks,Feed-forward Networks Gradient descent optimization, Backpropagation Bayesian Neural Networks Kernel Methods Constructing Kernels Radial Basis Function Networks SVMs for regression and classification RVM for regression and classification Graphical Models Learning the graph structure Mixture Models and EM, general, for Gaussian mixtures, for Bayesian linear regression The EM Algorithm in General K-means Clustering Maximum likelihood
  • CS7037 | Cryptography 2 | Credits: 3
    • Multiparty Zero-knowledge Protocols. Shannon Entropy and Renyi Entropy applied to cryptography. The New Generation of Message Digests. Anonymity Networks. Undeniable Signatures. Group Signatures. Side Channel Cryptanalysis. Digital Cash.
  • CS7069 | Computational Geometry | Credits: 3
    • The Computation of 1. Convex hulls in the plane, space and hyperspace. 2. Voronoi Diagrams and Delaunay Configurations in the plane and space. 3. Arrangements in the Plane and Higher Dimensions. 4. Geometric Duals of configurations. The Detection of 4. Lines, Circles and Curves in the affine plane and the projective plane. 5. Planes, spheres and quadric surfaces in space.
  • CS7080 | Self Study and Research | Credits: 1
    • Student shall select some reading text, possibly research papers, book chapters, or acquire research skills such as proficiency at LaTex, and then complete its reading and/or practice and understanding during the quarter. The proficiency shall be demonstrated at the end of the semester.
  • CS7081 | Advanced Algorithms I | Credits: 3
    • Advanced treatment of fundamental topics in algorithms that every graduate student should know and have some sophistication in. Knowledge and ability to apply the fundamental design strategies: the greedy method, divide-and-conquer, dynamic programming, to solve important problems in data encryption, efficient polynomial, integer, matrix multiplication, computing the Discrete Fourier transform, using the celebrated FFT algorithm, and so forth. In addition this course will introduce students to lower bound theory and NP-completeness.
  • CS7082 | Advanced Algorithms II | Credits: 3
    • Coverage of advanced topics in algorithms, including multi-core computing, Internet algorithms, randomized algorithms, algorithms for social networks and social media, distributed algorithms, flow theory algorithms, NP-hard, approximation and probabilistic algorithms. In particular, topics will be covered such as the traveling salesperson problem, k-center problems, facility and server location problems, Steiner tree problem, Monte Carlo algorithms, Las Vegas algorithms, Marriage Problem, Isolating Lemma, Internet ranking algorithms such as PageRank and HITS, document security algorithms, web caching and consistent hashing, graphs and social networks, properties of large graphs, matching and network flow theory, ranking, recommendation and trust in social networks, paired comparison ranking models such as Bradley-Terry-Luce and handicap ranking, parallel prefix, parallel matrix multiplication, pointer jumping, leader election.
  • CS7083 | Experimental Combinatorics | Credits: 3
    • Advanced coverage of topics in combinatorial generation. Coverage of methods and algorithms for generating a variety of combinatorial objects. Applications to mathematical identities, symmetries, graphs, game theory, complex social networks, AI applications, crypto, security, and Monte Carlo applications. Topics include: generating classical mathematical objects, such as permutations, combinations, partitions, and prime numbers, generating important computing structures, such as random numbers, graphs, cycles and codes, and generating networks with important properties, such as small-world and power-law properties.
  • CS7092 | Sensor-based Embedded Systems Design | Credits: 3
    • Concepts in sensor system design, coverage, connectivity, Poisson distribution, regular topology, localization, synchronization, data aggregation, location of base station, sensor operating system design, security issues, project utilizing sensor boards.
  • CS7097C | Introduction to Functional Genomics | Credits: 3
    • 'Introduction to Functional Genomics' course provides a general introduction to genome science, covering genome-scale sequencing, analysis of variation, gene expression profiling, gene ontology, proteomics and metabolomics. Lectures, hands-on exercises and projects are used to familiarize students with biological data sets that are being generated by each of these approaches to interrogate biological systems on genome-wide scales, and to introduce statistical, bioinformatic and system biology approaches required to analyze and interpret such data streams. Student projects involve analysis of publicly available data sets with gene expression, sequence and other data pertaining to breast cancer as case studies. The course is addressed to students in both basic science and interdisciplinary programs. At present, the course id cross-listed in a number of graduate programs. The course will be taught jointly by instructors with complementary expertise in different subfields of genomic science, and in bioinformatics and system biology.
  • CS7098C | Introduction to Functional Genomics | Credits: 4
    • 'Introduction to Functional Genomics' course provides a general introduction to genome science, covering genome-scale sequencing, analysis of variation, gene expression profiling, gene ontology, proteomics and metabolomics. Lectures, hands-on exercises and projects are used to familiarize students with biological data sets that are being generated by each of these approaches to interrogate biological systems on genome-wide scales, and to introduce statistical, bioinformatic and system biology approaches required to analyze and interpret such data streams. Student projects involve analysis of publicly available data sets with gene expression, sequence and other data pertaining to breast cancer as case studies. The course is addressed to students in both basic science and interdisciplinary programs. At present, the course id cross-listed in a number of graduate programs. The course will be taught jointly by instructors with complementary expertise in different subfields of genomic science, and in bioinformatics and system biology.
  • CS7099 | Introduction to Bioinformatics | Credits: 3
    • Introduction to Bioinformatics is a multidisciplinary, entry level graduate course, which is an extension of current BME643 course and aims at achieving a deeper understanding of central algorithmic problems and current computational methods used in the context of data rich biomedical research. Subjects covered include: deep sequencing, biological sequence analysis, statistical models for gene expression profiling, prediction of protein and macromolecular complexes structure and function, systems biology. Analysis of algorithmic aspects will be accompanied by projects and case studies to provide a direct illustration of computational issues and to provide knowledge and practical command of standard bioinformatic tools and protocols that are being used to analyze complex biological data.
  • CS8021 | Pattern Recognition | Credits: 3
    • The topics covered will include Statistical Pattern Recognition - its basics and applications, algorithms for clustering and their analysis. A flavour of different types of clustering algorithms will be given and a few algorithms will be studied in great depth. Relevance of all the above techniques for pattern discovery, classifier design, and dimensionality reduction will be investigated. A number of examples from real-life datasets will be examined in depth during the class presentations and by students during their homework assignments.
  • CS8042 | Graph Theory and Networks | Credits: 3
    • Advanced coverage of myriad important applications of graphs in computer science and engineering. Analysis of large graphs, graph models applied to communication and social networks, parallel architectures and interconnection networks, transportation and electrical networks. Analysis of flows in graphs and networks, classical graph problems such the shortest path problem, the minimum-cost spanning problem, Steiner tree problem, the dominating set problem, the traveling salesman problem. Applications to network reliability, routing, connectivity, survivability, and security.
  • CS8046 | Advanced Mobile Computing | Credits: 3
    • Mobile Computing, Selfishness in Ad hoc networks, Data Aggregation in Sensor Networks, Regularly deployed Sensors' coverage and connectivity, Mobile Sensor Network Deployment, Heterogeneous wireless networks, Secured Key Generation in Sensor Networks, Wireless Mesh Networks, Selfishness in Mesh Networks, Secured and Efficient Authentication in Wireless Mesh Networks, followed by student presentations and project.
  • CS8060 | Master of Engineering Captstone Project | Credits: 1
    • Individual projects under supervision of departmental faculty n partial fulfillment of the Master of Engineering degree.
  • CS8080 | Doctoral Dissertation Proposal | Credits: 1
    • Individual research under the supervision of EECS faculty directed towards the completion of the PhD dissertation proposal. Open to doctoral students ONLY who have passed the qualifying examination. Only 9 credits of 20 CS8080 will be counted toward course work requirements.
  • CS8089 | Thesis Research | Credits: 1
    • Research tasks as advised by the thesis/dissertation adviser shall be completed.
  • CS8092 | Independent Study | Credits: 1
    • Individualized study under the direction of a faculty member. This must be arranged between a student and a faculty member with mutual consent and agreement on the requirements for earning the credits. With the approval of the Graduate Program Director, a student may register for a maximum of 9 credits per quarter in order to maintain full-time status. These credits do not count towards MS or PhD degree course requirements.
  • CS8099 | Advanced Computational Biology | Credits: 3
    • The course covers in depth analysis of several problems and subfields in computational biology, including: applications of machine learning and pattern recognition methods to protein structure and function predictions; physical simulation and modeling approaches such as molecular dynamics, homology modeling and docking; combinatorial and optimization-based algorithms for macromolecular structure prediction. The course involves literature-based presentations, short projects and one major research project as well as lectures on the respective subjects. Access to a computational cluster is provided via Protein Modeling Lab and Protein Informatics Core jointly operated by UC and CCHMC.

Electrical and Computer Engineering Courses

  • EECE1080C | Programming for ECE | Credits: 4
    • Introduction to software methods for solving engineering problems. Emphasis is on the software development process including creating problem requirements, systematic software design, correct and maintainable C++ coding, and efficient software testing methods.
  • EECE1975 | Digital Design | Credits: 1
    • Theory and practice of digital system design; combinational logic, synchronous sequential circuits, components and technologies, digital design processes including requirements generation, design and verification using Hardware Description Language (HDL) modeling and simulation, hardware synthesis, digital test design, digital circuit design labs. This course is a continuation of 20EECE175.
  • EECE2040 | Data Structures Programming | Credits: 2
    • Introduction to intermediate and advanced programming methods. Objects, pointers, proper use of dynamic data structures, files.
  • EECE2040L | Data Structures Programming Lab | Credits: 2
    • Introduction to intermediate and advanced programming methods. Objects, pointers, proper use of dynamic data structures, files.
  • EECE2050C | Network Analysis | Credits: 5
    • Voltage, current, and power in electrical networks. Resistive networks: Ohm's law, Kirchhoff's Laws, node voltage and mesh current analysis. Network theorems: superposition, source transformations, Th?venin's theorem. Transient and steady-state solutions for RL, RC and RLC networks with constant forcing functions. Use of phasors in steady-state analysis of networks with sinusoidal forcing functions. AC power circuit analysis. Polyphase circuit analysis. Magnetically coupled networks. Frequency response, resonance, bandwidth, and quality factor.
  • EECE2060C | Digital Design | Credits: 4
    • Theory and practice of digital system design; combinational logic, synchronous sequential circuits, components and technologies, digital design processes including requirements generation, design and verification using Hardware Description Language (HDL) modeling and simulation, hardware synthesis, digital test design, digital circuit design labs included.
  • EECE2070 | Electronics I | Credits: 3
    • Operational amplifiers, nonlinear circuits, linear amplifiers, bipolar and field-effect stages, differential and multistage amplifiers; use of MATLAB and PSpice.
  • EECE2070L | Electronics Laboratory I | Credits: 1
    • Computer controlled use of oscilloscope and spectrum analyzer, circuit simulation, operational amplifiers, transistor biasing and single-stage amplification, multi-stage and feedback amplifiers, active filters.
  • EECE2075 | Signals and Systems I | Credits: 3
    • An introduction to the fundamentals of signals and systems, including the mathematical and computational methods for system analysis and design. Continuous-time and discrete-time systems using time-domain methods.
  • EECE2077 | Semiconductor Devices | Credits: 3
    • Fundamentals of semiconductor diodes and transistors, static characteristics, biasing, carrier flow and small-signal models. Light emission and detection with semiconductor junctions.
  • EECE2940 | Data Structures Programming Lab | Credits: 3
    • Introduction to intermediate and advanced programming methods. Objects, pointers, proper use of dynamic data structures, files.
  • EECE3026 | Introduction to Computer Architecture & Organization | Credits: 3
    • Fundamentals of computers. The stored program concept. Addressing modes, instruction formats, and instruction sets. Data path and control unit design. Hardwired and microprogrammed control. Memory components and the memory hierarchy.
  • EECE3071 | Electronics II | Credits: 3
    • Frequency response of amplifiers, feedback systems, active filters, oscillators, tuned amplifiers, field-effect and bipolar digital circuits, use of MATLAB, PSPICE.
  • EECE3071L | Electronics Laboratory II | Credits: 1
    • Multi-stage, tuned, large-signal, and power amplifiers, emitter-coupled oscillators, active filters, and transistor-transistor and emitter-coupled logic.
  • EECE3076 | Signals and Systems II | Credits: 3
    • Fundamentals of signals and systems, including the mathematical and computational methods for system analysis and design. Frequency-domain methods for continuous-time and discrete-time systems.
  • EECE3080 | Engineering Electromagnetics | Credits: 4
    • Definition of field vectors. Coulomb's law: Fields and potentials; Magnetostatics: Biot-Savart law, Ampere's law. Electric and magnetic properties of materials. Dynamical fields: Faraday's and Lenz' laws, displacement current. Uniform plane waves: The wave equation, Poynting vector, waves in lossy media and in good conductors, skin effect. Reflection and refraction: Fresnel equations, Brewster angle and critical angle. Waveguides: Rectangular waveguides, planar transmission lines, dielectric slab waveguides, optical fibers. Transmission Lines: Transmission line equations, impedance matching and Smith chart. Antennas: Radiating fields of Hertzian dipole, planar antennas, simple arrays.
  • EECE3093 | Software Engineering | Credits: 3
    • Introduction to software engineering and formal methods. General course topics include software life-cycle models, requirements analysis, specification, design, testing and maintenance. The course introduces students to formal methods, global software engineering, and ethics for software engineering.
  • EECE3093C | Software Engineering | Credits: 4
    • Introduction to software engineering and formal methods. General course topics include software lifecycle models, requirements analysis, specification, design, testing and maintenance. The course introduces students to formal methods, global software engineering, and ethics for software engineering. In the lab sessions, students apply concepts taught in lecture, and develop a large group project.
  • EECE3093L | Software Engineering Lab | Credits: 1
    • Lab to accompany 20-EECE 3093, Software Engineering. Students apply concepts taught in Software Engineering lecture, and develop a large group project.
  • EECE3943 | Signal & Systems I | Credits: 3
    • An introduction to the fundamentals of signals and systems and the computational methods for computer-aided system analysis and design.
  • EECE3951 | Electronics I | Credits: 3
    • Study of analog circuit analysis for bipolar and FET transistors and circuit design including single, differential and multistage amplifiers and use of operational amplifiers.
  • EECE4005 | Special Studies | Credits: 1
    • Requirements individually arranged, including 700-level courses for graduate school preparation.
  • EECE4029 | Operating Systems & Systems Programming | Credits: 3
    • Introduction to concepts of modern operating systems and systems programming. Emphasis is on the concepts, algorithms and architectures of modern operating systems. Students will also learn Unix system programming such as synchronizing, inter-process communication, and networking.
  • EECE4038C | Embedded System Design | Credits: 3
    • Introduction to microprocessors and their uses. Review of gate level digital fundamentals. Architecture and register set of CPU, types of memory, interfacing using FPGA technology. Use of assembly language to create binary machine code. Link to high level languages.
  • EECE4040 | Advanced Data Structures and Algorithms | Credits: 3
    • Introduction to data abstraction; problem-solving with abstract data types, algorithm analysis and space/time complexity.
  • EECE4090 | Control Systems | Credits: 3
    • Modeling, transfer functions, state space, transients and steady state, stability and performance characteristics of closed loop systems. Analysis and design of feedback systems. Use of classical frequency domain and state space methods.
  • EECE4092 | Database Design and Development | Credits: 3
    • Database design with the Entity Relationship model. Relational data model and database design. Physical storage techniques. Reliability and recovery. Concurrency.
  • EECE4965 | Electronics Laboratory | Credits: 3
    • Design and breadboarding of small signal discrete and linear integrated electronic devices and circuits, experimental measurement of BJT and FET device characteristics, and verification of circuit gain, bandwidth, and feedback circuit performance employing laboratory instrumentation.
  • EECE4966 | Electronic Design Laboratory | Credits: 2
    • Analog and digital circuit design projects with theoretical and experimental work to meet design specifications.
  • EECE4974 | Electromagnetic Fields II | Credits: 2
    • Applications of Maxwell's equations to electromagnetic fields problems. Calculation of fields associated with static and moving charges, and electric currents. Electric and magnetic properties of materials. Motion of charges in electric and magnetic fields, and basic electromechanisms.
  • EECE4982 | Solid State Electronics II | Credits: 2
    • Fundamental device operation of MOS capacitor, bipolar, field effect and junction FET transistors. Biasing, carrier flow and control, static characteristics, secondary effects, switching, small signal models.
  • EECE5001 | Electrical Engineering Senior Design I | Credits: 3
    • First of two semester laboratory series in which students create a significant product capitalizing on their prior four years of electrical/computer engineering education. Each student selects a project advisor, and works with him/her to select a design problem, identify the requirements and specifications of the product, and design the product.
  • EECE5002 | Electrical Engineering Senior Design II | Credits: 3
    • Second of two semester laboratory series in which students create a significant product capitalizing on their prior four years of electrical/computer engineering education. Completion of detailed design and implementation of project.
  • EECE5031 | Computer Engineering Senior Design I | Credits: 3
    • First of two semester laboratory series in which students create a significant product capitalizing on their prior four years of electrical/computer engineering education. Each student selects a project advisor, and works with him/her to select a design problem, identify the requirements and specifications of the product, and design the product.
  • EECE5032 | Computer Engineering Senior Design II | Credits: 3
    • Second of two semester laboratory series in which students create a significant product capitalizing on their prior four years of electrical/computer engineering education. Completion of detailed design and implementation of project.
  • EECE6007 | Biomedical Microsystems | Credits: 3
    • Principles of biomedical microsystems will be discussed, including medical instrumentation, microsurgical tools, nucleic acid structure and analysis, cell structure and culture, biosensors, point-of-care systems, and microfluidic lab-on-a-chip. Examples and case studies of these microsystems will be included.
  • EECE6008 | Fundamentals of MEMS | Credits: 3
    • Introduction to MEMS principles, MEMS materials, fundamental MEMS microstructures, microsensors and microactuators, MEMS-based sensors, microsystems and control, circuit integration and system partitioning, packing, assembly, and testing.
  • EECE6010 | Database Management Theory | Credits: 3
    • Database formal architectures emphasizing modeling and theory. Formal methods for database architectures; relational, hierarchical, object, object-relational and network; data dependencies, normalization, integrity constraints, concurrency, heterogeneous systems.
  • EECE6011 | RF and Microwave Wireless Communications | Credits: 3
    • RF and microwave components, circuits and systems for wireless communications including microwave frequencies. Introduction to basic concepts of microwave communication including satellite communications, cellular phones and radar. Microwave components such as transmission lines, wave guides, microwave passive and active devices, microwave circuits, antennas and antenna arrays.
  • EECE6015C | Instrumentation & Industrial Control | Credits: 4
    • This course covers topics related to selection and utilization of analog, digital and piezoelectric sensors in electric energy, power system, manufacturing and other industrial plants. Students will learn how to connect these sensors to digital controllers and program the controllers for electrometrical device monitoring and protection.
  • EECE6016C | Electric Machines and Drives | Credits: 4
    • The course first covers 3-phase power systems, including phase sequence, wye and delta loads, and 3-phase transformers. Next induction and synchronous motors are covered, followed by DC motors and generators. Finally, synchronous generators are covered.
  • EECE6017C | Embedded Systems | Credits: 4
    • Embedded system design and development. High-level design tools, system-level design, and designing for testability will be emphasized.
  • EECE6018 | Microfabrication of Semiconductor Devices | Credits: 3
    • The fabrication of semiconductor devices will be described, starting with basic properties (such as crystal structure, particle motion, phase diagrams, defects) and continuing to the main microfabrication processes (growth, oxidation, diffusion, etc.). Both fundamental and practical aspects of microfabrication will be covered. Examples of microfabrication processes relevant to both bipolar and MOS transistors will be included.
  • EECE6019 | Probability and Random Processes | Credits: 3
    • The course will provide an introduction to random variables, stochastic processes and stochastic modeling techniques. The first part of the course will cover the fundamentals of random variables. The second part will introduce stochastic processes and methods for their analysis and estimation, leading into issues of modeling in the context of linear systems and Markov chains.
  • EECE6022C | Quantum Systems | Credits: 3
    • This course will introduce the fundamental principles of quantum mechanics but the main focus of the class will be on practical applications.
  • EECE6024 | Introduction to Digital Signal Processing | Credits: 3
    • Representation and processing of signals using digital techniques; time and frequency domain analyses, sampling, the DFT and the FFT, design and implementation of digital filters.
  • EECE6026 | Introduction to Communication Systems | Credits: 3
    • Digital modulation and demodulation, coding and decoding, equalization, and their applications in modern communication systems.
  • EECE6028 | Introduction to Nanoelectronics | Credits: 3
    • Emerging technologies in nanofabrication and nanoscale electronic devices. Nanoscale silicon MOSFETs, multi-gate MOSFETs, SiGe bipolar transistors, III-V FETs, carbon nanotubes, spintronics, nanowires and device applications, organic and molecular electronics, nanolithography, nanofabrication, nanoscale imaging.
  • EECE6029 | Operating Systems | Credits: 3
    • Function, design, and integration of the parts of an operating system. Policies for scheduling and page-replacement. Non-preemptable resource allocation, deadlock, starvation, livelock, access control, mutual exclusion and concurrency.
  • EECE6033 | Artificial Intelligence | Credits: 3
    • The course will cover in detail the topics of state space search, game tree search, constraint satisfaction, logic based knowledge representation and reasoning, first order predicate calculus, uncertainty handling using bayesian probability theory, and some applications of these techniques. Applications may be selected from the areas of automated planning, natural language processing, or machine learning.
  • EECE6036 | Intelligent Systems | Credits: 3
    • Intelligent systems can be characterized by the ability to (i) extract pertinent information from irrelevant details, (ii) learn from examples and generalize this knowledge, and (iii) draw inferences from incomplete information. This course focuses on the underlying theory of intelligent systems.
  • EECE6038C | Advanced Microsystem Design | Credits: 4
    • Examination of microprocessors and their uses. Microprocessor architectures and register sets, types of memory, interfacing using FPGA technology. Use of assembly language to create binary machine code. Link to high level languages. Development of a microsystem from application-specific requirements.
  • EECE6041C | Microfabrication Lab for Semiconductor Devices and MEMS | Credits: 3
    • Silicon semiconductor devices, NMOS and MEMS devices, surface or bulk micromachining techniques, photolithography, oxidation, thin film deposition, boron doping, ion implantation, isotropic and anisotropic etching, wafer bonding, and NMOS and MEMS device characterization.
  • EECE6042 | Digital Image Processing | Credits: 3
    • Digital image foundation and characterization, discrete transforms, image enhancement, encoding, compression and restoration. Prerequisite: senior or graduate standing.
  • EECE6043 | Optimization Methods and Models | Credits: 3
    • This course presents the fundamental methods and modeling techniques for optimization methods applicable to deterministic systems models. Topics and methods in functional, constrained and unconstrained optimization are covered, as well as various mathematical programming paradigms.
  • EECE6048C | Optics for Engineers | Credits: 3
    • Fundamental topics in optics and optical materials used frequently in engineered devices and systems. Theory of light propagation and interaction with materials, geometrical optics, absorption/fluorescence, interference, polarization/birefringence, diffraction, Fourier optics, fiber optics, light sources/detectors, biophotonics and imaging.
  • EECE6050 | Compound and Organic Semiconductor Physics | Credits: 3
    • This course will introduce the fundamental principles and concepts of solid state physics with application to metals, elementary and compound semiconductors and organic materials. Those principles will be applied to the study of a wide variety of devices, including an introduction to the concept of bandgap engineering using heterostructures.
  • EECE6052 | Semiconductor Photonics | Credits: 3
    • Photonic devices utilizing semiconductors will be described including light emitting devices, light detecting devices, and light waveguiding devices. Both device and material considerations will be covered. Applications of photonic devices to communications, computing, sensing, and energy generation will be included.
  • EECE6053 | Intelligent Data Analysis | Credits: 3
    • This course will introduce students to the theoretical and practical aspects of the field of data mining. Algorithms for data mining will be covered and their relationships with statistics, mathematics, and algorithm design foundations will be explored in detail.
  • EECE6061 | Computer Network Exploitation | Credits: 3
    • This course will familiarize students with principles of computer network attack and defense including attacker motivations, common categories of exploits, and methods to defend against attacks. Topics include historical overview of notable attacks, network enumeration, shellcode construction, exploitation of vulnerabilities including printf, stack and heap overflows; defense mechanisms including input validation, stack canaries, DEP and ASLR.
  • EECE6063 | Distributed Systems | Credits: 3
    • This course presents an in-depth analysis of the issues affecting the design, deployment and utilization of systems composed of computing nodes that communicate by means of general-purpose computing networks. Topics include communications protocols, timing & synchronization, error detection/recovery, security, data modeling, and data retrieval strategies.
  • EECE6078C | Biomicrofluidic Systems | Credits: 4
    • Principles of microfluidic systems design and fabrication will be described. The labs will be focused on development of a passive microfluidic mixer. Each student group will develop a micromixer design, simulate and optimize it in CFD software, fabricate it, and characterize it. Lectures will focus on operation of microfluidic systems and their applications to biology and medicine.
  • EECE6080C | Introduction to VLSI Design | Credits: 4
    • Introduces techniques and tools for scalable VLSI design and analysis. Emphasis is on circuit and physical design and on performance analysis. Includes lab experiments with hands-on usage of CAD tools.
  • EECE6082C | VLSI Design for Test and Power | Credits: 4
    • Fault modeling, automatic test pattern generation, fault simulation, design for testability, built-in self testing, memory testing, delay testing. Power sources, power estimation and analysis, power optimization for different levels (layout, gate, register-transfer, architectural), low-leakage design, low-power memory design.
  • EECE6083 | Compiler Theory and Practice | Credits: 3
    • Scanning, parsing, semantics analysis. Runtime organization and code generation.
  • EECE6086C | VLSI Design Automation | Credits: 4
    • VLSI design automation, layout synthesis and logic synthesis, partitioning, placement and routing algorithms. Logic synthesis, technology mapping and retiming algorithms. Introduction to formal verification. Course includes laboratory sessions and software development.
  • EECE7001 | EECS Seminar | Credits: 1
    • Discussion of current research topics in the field and of ongoing faculty research. Introduction to essential research skills such as researching a topic, writing a technical paper, and making a technical presentation.
  • EECE7002 | EECS Seminar | Credits: 1
    • Discussion of current research topics in the field and of ongoing faculty research. Introduction to essential research skills such as researching a topic, writing a technical paper, and making a technical presentation.
  • EECE7004 | Practical Experience | Credits: 1
    • This course is designed to provide the EECS graduate student with research experience either on campus or off-campus in industry. This course is required for all MS and PhD students. The advising faculty member approves the suitability of the work experience and the employment period and may also waive this requirement.
  • EECE7011 | Electromagnetic Systems | Credits: 3
    • Advanced theory of electromagnetic phenomena, devices and systems for graduate students. Covers the fundamentals of electromagnetism necessary for other EE graduate required courses.
  • EECE7014 | Electrofluidics and Applications | Credits: 3
    • The effect of electric fields on fluid flow will be described in some detail, including the role of fluid electrical and mechanical properties, ions and molecules in the fluid, geometry effects, etc. The course will focus on two major areas of electrofluidics: electrowetting and electrospinning. Associated applications will be discussed, including electrowetting lenses and displays, electrospinning of polymer nanofibers for textiles, filters, etc.
  • EECE7021 | Plasma Processing of Materials | Credits: 3
    • Introduction to the basic concepts of plasma-assisted methods and technology for microelectronic fabrication. Plasma background, plasma assisted etching, plasma enhanced deposition, current applications.
  • EECE7026 | BioChips and Lab-on-Chips | Credits: 3
    • Fundamentals of micro and nano fabrication of biochips and lab-on-a-chips, on-chip biochemical detection methods, micro/nano fluidics, basic components of lab-on-a-chips, integration of lab-on-a-chips, and micro total analysis systems (uTAS).
  • EECE7032 | BioSensors and Bioelectronics | Credits: 3
    • Fundamentals of biosensors, bioelectronics, physicochemical transduction mechanisms for biotransduction, molecular recognition and bio-immobilization principles and procedures, coupled mass transport kinetics of enzyme-catalyzed and molecular binding reactions. Fundamentals of electrochemistry and electrochemical biosensors, ion-selective field effect transistors (ISFET), electronic noses and tongues, and protein biochips. Bioelectronics for bio-signal conditioning and processing.
  • EECE7033 | Linear Systems Theory | Credits: 3
    • An introduction to the state-space framework for the analysis, design and control of linear systems. The course covers: 1) basic mathematical methods in linear algebra and optimization; 2) fundamental principles including stability, observability and controllability; and 3) techniques for system analysis and design.
  • EECE7042 | Video Processing and Communications | Credits: 3
    • Fundamental theory and techniques of digital video processing with a focus on video coding and communications, video formation, perception and representation, sampling, modeling, 2D motion estimation and coding.
  • EECE7045 | Stochastic Decision and Control Processes | Credits: 3
    • This course presents the fundamental methods and modeling techniques of decision and control problems for discrete-time stochastic systems, Dynamic Programming and its applications, computational algorithms and techniques. Topics in risk-sensitive optimality criteria and Approximate Dynamic Programming are also covered.
  • EECE7065 | Complex Systems and Networks | Credits: 3
    • This course provides a comprehensive introduction to complex systems and networks. The focus is on applying general principles derived from biological and human complex systems to the analysis, design and productive use of artificial systems with the same attributes of autonomy, adaptivity and resilience.
  • EECE7080 | Self-Study Research | Credits: 1
    • Research study not directly related to the thesis or dissertation. Self-study to determine the research area and topic. Credits may not be counted towards MS/PhD program. Credits arranged each quarter.
  • EECE7095 | Introduction to Computer Architecture | Credits: 3
    • Processor design concepts. Instruction set design: general register machines, stack machines, RISC machines. The memory hierarchy: virtual memory management, cache memories, cache coherency and consistency. Pipelining: instruction pipelining, functional unit pipelining, multiple functional units, vector processing. Input/Output processing. Parallel processing architectures: multiprocessors, multicomputers, many-core processors.
  • EECE7097C | Introduction to Functional Genomics | Credits: 3
    • 'Introduction to Functional Genomics' course provides a general introduction to genome science, covering genome-scale sequencing, analysis of variation, gene expression profiling, gene ontology, proteomics and metabolomics. Lectures, hands-on exercises and projects are used to familiarize students with biological data sets that are being generated by each of these approaches to interrogate biological systems on genome-wide scales, and to introduce statistical, bioinformatic and system biology approaches required to analyze and interpret such data streams. Student projects involve analysis of publicly available data sets with gene expression, sequence and other data pertaining to breast cancer as case studies. The course is addressed to students in both basic science and interdisciplinary programs. At present, the course id cross-listed in a number of graduate programs. The course will be taught jointly by instructors with complementary expertise in different subfields of genomic science, and in bioinformatics and system biology.
  • EECE7099 | Introduction to Bioinformatics | Credits: 3
    • Introduction to Bioinformatics is a multidisciplinary, entry level graduate course, which is an extension of current BME643 course and aims at achieving a deeper understanding of central algorithmic problems and current computational methods used in the context of data rich biomedical research. Subjects covered include: deep sequencing, biological sequence analysis, statistical models for gene expression profiling, prediction of protein and macromolecular complexes structure and function, systems biology. Analysis of algorithmic aspects will be accompanied by projects and case studies to provide a direct illustration of computational issues and to provide knowledge and practical command of standard bioinformatic tools and protocols that are being used to analyze complex biological data.
  • EECE8010 | Advanced Materials | Credits: 3
    • Advanced topics in electronic, optical, biological and other emerging materials in electrical engineering.
  • EECE8016 | Advanced Topics in Power Systems | Credits: 3
    • Advanced topics in alternative methods of electric energy generation, power plant instrumentation and control, and electric power management and SmartGrid.
  • EECE8020 | Advanced Devices | Credits: 3
    • Advanced topics in electronic, optical, biological and other emerging devices in electrical engineering.
  • EECE8024 | Advanced Topics in Signal Processing | Credits: 3
    • Advanced topics in signal processing, focusing on recent advances in areas such as real-time data acquisition and adaptive signal processing.
  • EECE8026 | Advanced Topics in Communications | Credits: 3
    • Advanced topics in communications, focusing on recent advances in areas such as wireless communications and secure communications.
  • EECE8030 | Advanced Applications | Credits: 3
    • Advanced topics in electronic, optical, biological and other new applications of electrical engineering research and technology.
  • EECE8036 | Advanced Topics in Intelligent Systems | Credits: 3
    • Advanced topics in distributed, adaptive and biologically-inspired systems and algorithms.
  • EECE8042 | Advanced Topics in Image Analysis and Vision Systems | Credits: 3
    • Advanced topics in image analysis and vision systems, focusing on recent advances in areas such as 3D image analysis, mathematical morphology, facet model, and texture.
  • EECE8043 | Advanced Topics in Optimization | Credits: 3
    • Advanced topics in optimization, focusing on recent advances in optimization techniques and applications.
  • EECE8045 | Advanced Topics in Stochastic Decision Processes | Credits: 3
    • Application of principles of probability and statistics to the design and control of engineering, economic and logistic systems in a random or uncertain environment. Emphasis is placed on Bayesian decision analysis and other advanced topics.
  • EECE8070 | Topics in Data and Knowledge Management | Credits: 3
    • Advanced topics in data and knowledge management, focusing on recent advances in areas such as advanced data models and query optimization, data warehousing, intelligent knowledge representation, data mining, and the semantic web.
  • EECE8075 | Data Warehousing and Mining | Credits: 3
    • Data warehouse design with conceptual data models and physical storage techniques; data mining techniques including clustering, pattern recognition, and data visualization.
  • EECE8080C | Topics in Circuit and System Design | Credits: 4
    • Examination of advanced issues in VLSI circuit and system design, including embedded system design. Emphasis is on understanding design trends for modern chips and how to design circuits and systems in existing and emerging technologies to meet performance goals. Exploration of design tradeoffs for performance, power, and reliability.
  • EECE8085C | Topics in VLSI CAD/Test | Credits: 4
    • Examination of advanced issues in VLSI CAD, power, and test. Emphasis is on understanding design trends for modern chips and how to design CAD, power, test, and simulation tools to meet design trends in industry. Exploration of design tradeoffs for performance, power, and reliability.
  • EECE8090 | Advanced Topics in Control Systems | Credits: 3
    • Advanced topics in control systems, focusing on recent advances in areas such as sensor/control systems and multivariable control.
  • EECE8095 | Topics in Computer Systems | Credits: 3
    • Advanced topics in computer systems, focusing on recent advances in areas such as advanced operating systems and networks, real-time operating systems, embedded systems, parallel and multicore systems, experimental computer systems, and system modeling and simulation.
  • EECE9012 | Final MS Project | Credits: 3
    • Course used by GE-ACE students only for completion of the final MS project.
  • EECE9060 | Master of Engineering Capstone Project | Credits: 1
    • Student works under the direction of a faculty member to complete the MEng capstone project.
  • EECE9080 | Doctoral Dissertation Proposal | Credits: 1
    • Individual research under supervision of EECS faculty directed towards completion of the PhD dissertation proposal. Open only to doctoral students who have passed the qualifying examination. Only 9 credits of 20EECE9080 will be counted towards course work requirements.
  • EECE9088 | MS Project | Credits: 1
    • Individual project under supervision of EECS faculty directed towards completion of the MS project. Open to part-time EECS MS students who are pursuing the project option. The project option is not available to full-time students.
  • EECE9089 | Thesis or Dissertation Research | Credits: 1
    • Individual research under supervision of EECS faculty directed towards completion of the MS thesis or the PhD dissertation. Open to full-time EECS MS students and all EECS PhD students.
  • EECE9092 | Independent Study | Credits: 1
    • Individual advanced study not directly connected with thesis or dissertation. Limits of 3 credits for MS and 6 credits for PhD.

Electrical and Computer Engineering Technology Courses

  • ELTN1041 | Digital Systems | Credits: 3
    • Number systems, Boolean algebra, and logic concepts. Investigation of gates, latches, decoders, and their interconnection into combinational logic subsystems. Study of CMOS and TTL logic families and characteristics. The application of flip-flops to shift registers and counters. Design of 555 Timer circuits. Interfacing analog and digital systems using A/D and D/A converters.
  • ELTN1042 | Circuit Analysis I | Credits: 3
    • Analysis of linear bilateral networks, DC electric circuits that involve multiple independent sources, using Ohm's Law, Kirchhoff's voltage and current laws, mesh and nodal analysis methods, Thevenin's and Norton's theorems, and the maximum power transfer theorem. Also explored, is the steady state and transient behavior of capacitors and inductors.
  • ELTN1042L | Circuit Analysis I Laboratory | Credits: 1
    • Utilization of electrical measurement instrumentation in the verification and proofing the concepts, methods and laws of DC circuit analysis.
  • ELTN1043 | Circuit Analysis II | Credits: 3
    • Investigation of circuits containing real and reactive components with sinusoidal forcing functions. Problem solution employing phasor analysis, superposition, RC, RL, and RLC parallel circuits including resonance and transient phenomena.
  • ELTN1043L | Circuit Analysis II Laboratory | Credits: 1
    • Experiments are performed to verify and study AC applications of RC, RL and RLC circuits and resonance transient phenomena, and time domain analysis.
  • ELTN1051L | Digital Systems Laboratory | Credits: 1
    • Learn how to connect combinational logic circuits in order to perform specified tasks. Study of CMOS and TTL logic families and characteristics. The application of flip-flops to shift registers and counters. Design of 555 Timer circuits. Interfacing analog and digital systems using A/D and D/A converters.
  • ELTN2003 | Electronics | Credits: 3
    • Introduction to semiconductor concepts and device characterization. The operation of the PN junction and bipolar transistors are introduced. Half- wave and full-wave power supply design and construction are presented. Various transistor bias schemes are introduced. Amplifiers considered are common emitter and common collector circuits. Amplifier characteristics such as gain and input and output impedances are presented in tabular form. Introduction to operational amplifiers to include bias currents and offset voltages. Open and closed loop responses are examined. Basic op-amp circuits are explored. Special purpose op-amp circuits such as instrumentation amplifiers and log amps are examined. Active filter circuits containing op-amps are examined and practical oscillator circuits are compared. Communication circuits are introduced and special purpose linear integrated circuits are used to construct receiver circuits. Three terminal regulators are discussed in detail. A class project is used to reinforce concepts learned in both the lecture and laboratory.
  • ELTN2013L | Electronics Laboratory | Credits: 1
    • Use diode and transistors to build electronics circuits. Use operational amplifiers to built electronics circuits, measure the electronics signals and analyze them.
  • ELTN2047 | Electronic Communication | Credits: 3
    • This course introduces the concepts of thermal and shot noise in communication systems. Amplitude Modulation (AM) and detection concepts are introduced. AM receiver architectures are discussed and a direct conversion receiver is built as a class project. Single-sideband modulation is also introduced. Frequency and angle modulation techniques and introduced and receiver architectures are presented for the detection of FM waveforms. Receiver figures of merit such as noise factor, sensitivity, and dynamic range are explained. Simple antenna structures are introduced and demonstrated in the laboratory.
  • ELTN2057L | Electronic Communication Laboratory | Credits: 1
    • This course introduces sub-system components of an RF receiver. Amplitude Modulation (AM) and detection concepts are introduced. AM receiver architectures are discussed and a direct conversion receiver is built as a class project. Single-sideband modulation is also introduced. Frequency and angle modulation techniques and introduced and receiver architectures are presented for the detection of FM waveforms. Receiver figures of merit such as noise factor, sensitivity, and dynamic range are explained. Simple antenna structures are introduced and demonstrated in the laboratory. The student is also introduced to a spectrum analyzer as a test instrument.
  • ELTN2942 | Digital Systems II | Credits: 3
    • Study of CMOS and TTL logic families and characteristics. The application of flip-flops to shift registers and counters. Design of 555 Timer circuits. Interfacing analog and digital systems using A/D and D/A converters.
  • ELTN2952 | Digital Systems II Lab | Credits: 1
    • Study of CMOS and TTL logic families and characteristics. The application of flip-flops to shift registers and counters. Design of 555 Timer circuits. Interfacing analog and digital systems using A/D and D/A converters.
  • ELTN3009 | Telecommunications I | Credits: 3
    • Introduction to voice and data networks, equipment, protocols, and external factors. The history of telecommunications systems, including technology developments, the impact of commerce and government regulation, the introduction of computers, the transition from analog to digital signalling, and the evolution of data networks to integrated networks. Hardware and software components are presented with emphasis on the integration of components into telecommunications systems.
  • ELTN3010 | Telecommunications II | Credits: 3
    • Advanced concepts of voice and data networks, equipment, protocols, and media with emphasis on technology integration and emerging communications technologies. Advanced protocols used in internetworking. Integrated design and management of complex networks. Emerging technology designs such as Voice over IP (VoIP), presence, and unified messaging.
  • ELTN3019L | Telecommunications I Laboratory | Credits: 1
    • Use of laboratory network equipment and technical tools to design, build, configure, manage, and support basic voice and data telecommunication systems.
  • ELTN3020L | Telecommunications II Laboratory | Credits: 1
    • Use of laboratory network equipment and technical tools to design, build, configure, manage, and support complex, integrated telecommunication systems.
  • ELTN3042 | Microprocessor and Embedded Systems | Credits: 3
    • An introduction to assembly language programming and project development of microprocessor systems. Students will learn how to write, assemble and debug microcontroller programs that interface a variety of hardware circuits, such as displays, keypads, stepping motors, and A/D converters.
  • ELTN3052L | Microprocessor and Embedded Systems Laboratory | Credits: 1
    • A hands on laboratory course that supports and reinforces the concepts presented in the accompanying lecture course. Students will learn how to write, assemble and debug microcontroller programs that interface a variety of hardware circuits, such as displays, keypads, stepping motors, and A/D converters.
  • ELTN4011 | Senior Design I | Credits: 3
    • Senior design is a two-semester sequence that culminates in the presentation of each student's project at the annual College of Engineering and Applied Science(CEAS) Tech-Expo conference in May. During the first semester, student completes a process that results in the selection of a real-world design project combining both theoretical and experimental elements in an area of each student's primary interest. Students will place order for the parts and start building the prototypes of their projects.
  • ELTN4012 | Senior Design II | Credits: 3
    • This is the second semester senior design sequence course. Completion of established milestones toward completion of the final project. To encourage independent, self directed work on a technical project. Successful completion of the proposed project, meeting stated design specifications. Successful demonstration of the completed design project. Composing and submitting a written final report (Senior Design Thesis). This final report will be bound and placed in the college library.
  • ELTN4015 | Flexible Automation | Credits: 3
    • Introduction to Programmable Logic Controllers (PLCs) architecture and programming based upon the Allen Bradley Small Logic Controller (SLC 503) series family of controllers. Followed by Advance PLC functions and industrial networking based upon the Allen Bradley Small Logic Controller (SLC 503) series family of controllers.
  • ELTN4016 | Electric Machinery | Credits: 3
    • The course first covers three phase power systems, including phase sequence, wye and delta loads and power. Next three phase induction and synchronous motors are covered followed by DC motors and generators. As Time permits single phase motors and synchronous alternators will be covered.
  • ELTN4022 | Topics in Operating Systems | Credits: 3
    • This is an introductory course on the internal operations and fundamental principles of modern operating systems. Topics include processes and threads, CPU scheduling, memory management, file systems, I/O systems, security and protection. Students will use simulators to analyze operating system algorithms. Students will also apply basic mathematical models to operating systems concepts.
  • ELTN4025L | Flexible Automation Laboratory | Credits: 1
    • Ladder logic diagram is used to program Allen Bradley Small Logic Controller (SLC 503) series family of controllers. Advance programming techniques is used to carry out industrial and process control applications.
  • ELTN4026L | Electric Machinery Laboratory | Credits: 1
    • This laboratory course provides experiments with three phase power systems, AC and DC machines in support of the lecture class.
  • ELTN4032L | Topics in Operating Systems Laboratory | Credits: 1
    • Experimentation and simulation of operating systems in support of the lecture material. Topics include processes and threads, deadlocks, memory management, file systems, I/O systems, security and protection mechanisms.
  • ELTN4056 | Computer Networks | Credits: 3
    • To provide a well-rounded understanding of the concepts underlying modern communications networks, with particular emphasis on the protocols, architectures, applications deployed in the Internet. Topics include data communication basics, packet switching and network technologies, Internetworking, Internet applications and network security.
  • ELTN4057 | Wireless Communications and Networks | Credits: 3
    • A study of wireless communications and networking. Topics range from physical layer to application layer in the wireless and mobile networking fields. It introduces wireless transmission fundamentals, antenna basics, wireless/mobile radio propagation, digital modulation techniques, techniques to combat channel fading, radio multiple access techniques, Trunking theory basics, cellular networks, wireless local area networks, wireless personal area networks, WiMax and recent advances in wireless networking. Students will use various software tools to simulate and analyze wireless communication systems.
  • ELTN4066L | Computer Networks Laboratory | Credits: 1
    • Exercises and projects to illustrate lecture concepts. Using a network analyzer to examine packet and protocol details on a live network. Using software tools to model, simulate and evaluate network performance. Hands-on network programming with Socket API.
  • ELTN4067L | Wireless Communications and Networks Laboratory | Credits: 1
    • Simulation and application projects to demonstrate lecture topics on wireless communications and networks.
  • ELTN4069 | Electric Power and Power Electronics Systems | Credits: 3
    • Introduction to AC power Systems and Three-phase electric power distribution. Using transformers in the power distribution system. Converting AC power to DC and vise versa. Using variable DC or AC power inverters/converters to supply electricity to digital, electronics and electromechanical devices.
  • ELTN4072 | Feedback Control | Credits: 3
    • Analysis and modeling of feedback systems using differential equations and transfer functions. Study of transient analysis, stability and tracking error. Design, analysis and simulation of both analog and digital PID controllers. Introduction to microcomputer control.
  • ELTN4079L | Electric Power and Power Electronics Systems Laboratory | Credits: 1
    • This laboratory course provides experiments with three phase power systems, AC and DC machines in support of the lecture class.
  • ELTN4082L | Feedback Control Laboratory | Credits: 1
    • Experimental investigation of modeling, analysis, design and simulation of feedback control systems including both analog, digital and hybrid systems. Measurement of system transient response, steady state error and compensation effects.
  • ELTN4083 | Computer Architecture and Assembly Language | Credits: 3
    • An introduction to computer hardware organization and architecture with an emphasis on the relationship between computing hardware and machine language instruction sets. Topics include performance and cost, machine organization, instruction set architecture, assembly language MIPS, ALU, computer arithmetic, basic processor implementation techniques, cache memory and parallel architectures.
  • ELTN4085 | Digital Signal Processing | Credits: 3
    • Discrete-time signal operations and Z-transforms. Design of finite impulse response (FIR) and infinite impulse response (IIR) digital filters using the Filter Design and Analysis Tool (FDAT) in MATLAB. Introduction to Texas Instruments' TMS320C6713 DSK for signal processing applications including digital filters.
  • ELTN4086 | Graphical Network Programming | Credits: 3
    • This course introduces computer and electrical engineering students to elements of Java programming with an emphasis on graphics and networking. Students will use NetBeans as a development platform to design and program projects that demonstrate OOP (Object Oriented Programming) concepts and techniques. Students will design and program projects that implement Graphical User Interfaces using NetBeans and Java. Students will write and modify projects that implement network communication and data transfer.
  • ELTN4093L | Computer Architecture and Assembly Language Laboratory | Credits: 1
    • Experiments and MIPS programming projects to reinforce computer architecture lecture topics.
  • ELTN4095L | Digital Signal Processing Laboratory | Credits: 1
    • Experiments in aliasing, signal generation, and filter design to support the material covered in lecture using both MATLAB/SIMULINK and Texas Instruments' TMS320C6713.
  • ELTN4096L | Graphical Network Programming Laboratory | Credits: 1
    • This is a lab course in which students are assigned a series of design and programming exercises using NetBeans and Java. Students will write a lab report at the completion of each project that documents the algorithms, and other technical information associated with the project. Students will develop competency in moderately complex Java programming and simple technical writing. Programming exercises will focus on simple graphics, GUIs, data structures, and networking.
  • ELTN4919 | Flexible Automation II | Credits: 3
    • Advance PLC functions and industrial networking based upon the Allen Bradley Small Logic Controller (SLC 503) series family of controllers.
  • ELTN4929 | Flexible Automation II Lab | Credits: 1
    • Advance PLC functions and industrial networking based upon the Allen Bradley Small Logic Controller (SLC 503) series family of controllers.