Master's in Computer Engineering

8040
Master of Science
Classroom
No
Graduate
Main Campus - Melbourne
Major Code: 8040 Degree Awarded: Master of Science
Age Restriction: N Admission Status: graduate
Delivery Mode/s: classroom only Location/s: main campus
Admission Materials: GRE

The computer engineering program is committed to excellence in teaching, innovative and challenging research programs, and providing opportunities for the student's development of professional engineering competence and scholarly achievement. A commitment to innovative research stimulates an excellent teaching and research program that allows graduates to use imaginative solutions to engineering problems. The program offers opportunities for graduates to pursue positions in research and development laboratories, industry and government.

Degree Requirements

The Master of Science in Computer Engineering is offered with both thesis and nonthesis options. Each requires a minimum of 30 semester credit hours of approved graduate study; however, course choices vary considerably depending on the student's area of interest. Before completing nine semester credit hours, a student must submit for approval a master's degree program plan to indicate the specialization chosen and the specific courses to be taken. Up to six credit hours of thesis may be included in the 30-credit-hour requirement. A nonthesis candidate must successfully pass the master's final program examination and complete six credit hours of elective courses. The master's final program examination measures the student's understanding of topics in the core courses.

The curriculum includes areas of specialization in embedded systems, machine intelligence, mobile and wireless networking and speech recognition.

Curriculum

To earn the Master of Science in Computer Engineering, students are required to complete three core courses (nine credit hours), five specialization courses for 15 semester credit hours (may substitute approved electives), and six semester credit hours of thesis or approved electives.

A student may choose courses from one of the specializations below or meet with a faculty advisor to create a program plan.

Core Courses (9 Credits)
  • ECE 5520 Computer Architecture
    Credit Hours: 3
    Covers the evolution of computer architecture and the factors influencing the design of hardware and software elements of computer systems. Introduces several methods to build general-purpose processors and computer systems and analyze CPU performance to identify bottlenecks.
  • ECE 5534 Computer Networks 1
    Credit Hours: 3
    Theory, design and analysis of computer communications systems. Topics include TCP/IP, Internet, the World Wide Web, ISO-OSI network architecture, LANs (Ethernet, Fast Ethernet, Token Ring, Token Bus, etc.), ATM, SONET, wireless communications, satellite networks, network modeling and simulation.
  • ECE 5565 Embedded and Real-Time Systems
    Credit Hours: 3
    Introduces embedded and real-time systems including resource-constrained systems, real-time scheduling including multiprocessor scheduling, real-time operating systems (kernels), real-time communication, real-time programming languages, performance analysis, reliability and fault-tolerance, and real-time system requirements and design methods.
Embedded Systems (9 Credits)

The embedded systems specialization focuses on engineering of hardware/software systems. The educational program provides students with the necessary skills to unify hardware and software engineering, spanning microprocessor-based systems, system-on-chip design, parallel and distributed systems and resource-constrained devices. Special emphasis is placed on development of large-scale, secure and dependable real-time systems. The following courses must be taken in addition to the core courses.

Specialization Courses
  • ECE 5540 Cloud Computing
    Credit Hours: 3
    Introduces the fundamental principles, technology and current developments in cloud computing. Includes basic principles of emerging cloud computing paradigms, the history of cloud computing and how to implement algorithms in the cloud.
  • ECE 5575 Programmable Gate Arrays
    Credit Hours: 3
    Covers digital design techniques using field programmable gate arrays (FPGAs) that incorporate both hardware and software components. Covers FPGA architecture, digital design flow and other technologies associated with FPGAs. Includes laboratories for hands-on experience in designing digital systems on FPGA platforms.
  • MTH 5051 Applied Discrete Mathematics
    Credit Hours: 3
    Logic fundamentals, induction, recursion, combinatorial mathematics, discrete probability, graph theory fundamentals, trees, connectivity and traversability. Applications from several fields of science and engineering, including computer science, operations research, and computer and electrical engineering.
  • Restricted Electives Credit Hours: 6
Machine Intelligence (9 Credits)

The machine intelligence (Ml) specialization focuses on models and computational methods for automated inference and reasoning, and applications of Ml in real-world application domains. The specialization area is structured in a manner that provides the necessary theoretical foundations of models, such as support vector machines, neural networks and other probabilistic models, and their associated learning algorithms; and the applications of such models and techniques in a variety of domains with a particular emphasis on speech recognition, among others. The following courses must be taken in addition to the core courses.

Specialization Courses
  • ECE 5258 Pattern Recognition
    Credit Hours: 3
    Includes Bayes decision theory; optimal pattern recognition algorithms; feature extraction criteria and algorithms; adaptive pattern recognition; supervised and unsupervised learning; applications to failure detection; and target, image and speech recognition.
  • ECE 5268 Theory and Applications of Neural Networks
    Credit Hours: 3
    Includes learning in a single neuron, single- and multi-layer perceptrons, recurrent neural networks, structured neural networks, neural networks to perform principal component analysis, principal component regression and partial least squares regression.
    Requirement(s):
    Instructor approval or prerequisite course
  • MTH 5411 Mathematical Statistics 1
    Credit Hours: 3
    Covers discrete and continuous random variables, generating and moment generating functions, multivariate distributions, covariance and correlation, sums of independent random variables, conditional expectation, Central Limit Theorem, Markov and Chebyshev inequalities and the Law of Large Numbers.
Mobile and Wireless Networking (9 Credits)

The mobile and wireless networking specialization focuses on computer networking and communications for a variety of systems including next-generation wireless local- and wide-area networks, ad hoc and sensor networks, and specialty networks (satellite, avionic). Particular emphasis is placed on networking aspects including medium access control, routing and transport protocols; quality of service, energy efficiency and mobility management; and security and applications for intelligent and resource-constrained embedded network devices. The following courses must be taken in addition to the core courses.

Specialization Courses
  • ECE 5113 Wireless Local Area Networks
    Credit Hours: 3
    Provides the basics of wireless networking and WLAN technologies, the leading WLAN standards, WLAN configurations, WLAN implementation considerations, the benefits and applications of WLANs, WLAN trends and case studies.
  • ECE 5118 Wireless Sensor Networks
    Credit Hours: 3
    Pervasive networks and network embedded systems, power-aware issues in wireless sensor networks, collaborative signal and information processing, routing and MAC protocols in sensor networks, clustering and coordination in sensor networks, sensor networks applications.
    Requirement(s):
    Graduate standing
  • MTH 5411 Mathematical Statistics 1
    Credit Hours: 3
    Covers discrete and continuous random variables, generating and moment generating functions, multivariate distributions, covariance and correlation, sums of independent random variables, conditional expectation, Central Limit Theorem, Markov and Chebyshev inequalities and the Law of Large Numbers.
Speech Recognition (9 Credits)

The speech recognition (SR) specialization introduces students to techniques for speech recognition and language modeling used in the rapidly developing field of automatic speech recognition and natural language processing. Advanced SR topics include acoustic-phonetic modeling, robust speech recognition, processing paralinguistic information and multimodal processing. In addition, the specialization area examines most modern aspects of modern natural language theory, such as probabilistic modeling of pronunciation and spelling, probabilistic parsing and semantic analysis, among others. The following courses must be taken in addition to the core courses.

Specialization Courses
  • ECE 5526 Speech Recognition
    Credit Hours: 3
    Basic approaches in speech recognition, dynamic time warping, hidden Markov models and neural networks.
  • ECE 5527 Search and Decoding in Speech Recognition
    Credit Hours: 3
    Issues with searching for best answers from recognition hypotheses generated by the recognizer, including lattice networks, dictionaries, language modeling and its use in speech recognition, network search algorithms, word networks and standard lattice format, finite state grammars, Bi-grams, N-grams and other language modeling techniques.
  • MTH 5102 Linear Algebra
    Credit Hours: 3
    Linear algebra, systems of linear equations and Gauss elimination method; inverses, rank and determinants; vector spaces; linear transformations, linear functional and dual spaces; eigenvalues, eigenvectors; symmetric, Hermitian and normal transformations; and quadratic forms.
Recommended Electives

For all computer engineering options, courses in the remaining specializations and those listed below are recommended. Students opting for the thesis option may include six credit hours of thesis (ECE 5999):

  • CSE 5283 Computer Vision
    Credit Hours: 3
    Develops computational methods that model the capacity of the human vision system. Develops main concepts of computer vision research and its applications including robot navigation and interaction, autonomous exploration, traffic monitoring, biometrics identification and building 3D images.
  • CSE 5290 Artificial Intelligence
    Credit Hours: 3
    Introduces the theoretical foundations of artificial intelligence, focusing on the areas of automated reasoning, search and heuristics. Introduces an AI language to implement concepts.
  • CSE 5656 Theory and Applications of Complex Networks
    Credit Hours: 3
    Explores complex networks by studying theory, developing algorithmic results and investigating applications related to networks' distribution and redistribution of information, water, food and energy; representation of physical pipes, personal relationships or the manifestation of economic interdependencies.
    Requirement(s):
    Instructor approval for undergraduate registration
  • CSE 5694 Robotics and Artificial Intelligence
    Credit Hours: 3
    Introduces the concept of robot and the various algorithms used in robotics. Emphasizes artificial intelligence in robotics. Covers modeling, planning, mapping and localization algorithms. Also includes using robotic arms and teams of mobile robots.
  • ECE 5245 Digital Signal Processing 1
    Credit Hours: 3
    Describes discrete-time signals in the time and frequency domains; z-transform, discrete Fourier transform, FFT algorithms; introduction to classical digital filter design techniques; and filter banks.
  • ECE 5248 Advanced Filtering
    Credit Hours: 3
    Bayesian estimation theory; filtering, smoothing and prediction for linear and nonlinear systems, Gaussian and non-Gaussian models, and for known or unknown models; fast algorithms for filter design and implementation; linear, nonlinear and adaptive filters; applications.
  • ECE 5256 Digital Image Processing
    Credit Hours: 3
    Investigates image processing by machine for such purposes as robotics, biomedicine, remote sensing and photogrammetry. Includes image enhancement and analysis, transform techniques including wavelet transform, feature extraction, segmentation, compression and morphology.
  • ECE 5259 Medical Imaging
    Credit Hours: 3
    Presents the interdisciplinary principles of medical imaging techniques such as diagnostic ultrasound, radiography, x-ray computer tomography (CT) and magnetic resonance imaging (MRI). Includes the physical principles, noise modeling and signal processing for each imaging modality.
  • ECE 5525 Speech Processing
    Credit Hours: 3
    Fundamentals of digital speech processing, digital models for speech signals, acoustic theory of speech production, speech perception, speech analysis, homomorphic speech processing, coding of speech signals, linear predictive coding, methods for speech recognition and digital speech processing for man-machine communication by voice.
  • MTH 5101 Introductory Analysis
    Credit Hours: 3
    Rigorous treatment of calculus. Includes sequences and series of real numbers, limits of functions, topology of the real line, continuous functions, uniform continuity, differentiation, Riemann integration, sequences and series of functions, Taylor's theorem, uniform convergence and Fourier series.
  • MTH 5102 Linear Algebra
    Credit Hours: 3
    Linear algebra, systems of linear equations and Gauss elimination method; inverses, rank and determinants; vector spaces; linear transformations, linear functional and dual spaces; eigenvalues, eigenvectors; symmetric, Hermitian and normal transformations; and quadratic forms.
  • MTH 5412 Mathematical Statistics 2
    Credit Hours: 3
    Includes maximum likelihood and Bayes estimators, confidence intervals, testing hypotheses, uniformly most powerful tests, nonparametric methods (chi-square and Kolmogorov-Smirnov goodness-of-fit tests) and regression analysis.
  • MTH 5425 Theory of Stochastic Signals
    Credit Hours: 3
    Covers univariate and multivariate distributions, generating and moment generating functions; autocorrelation, wide-sense, strict-sense stationary, voltage, Poisson, Wiener, random telegraph signal and white noise processes; Direc delta function, Fourier transform, system response, transfer function and spectral analysis.
    Requirement(s):
    Instructor approval
Program for Graduates from Other Fields

A student admitted to this program is expected to have a bachelor's degree from a regionally accredited institution or the equivalent, with an undergraduate major in an engineering discipline, mathematics or the physical sciences, and an academic and/or professional record indicating a high probability of success in graduate work. Preparatory courses required to provide a student with the background necessary for successful graduate study in computer engineering are listed below. Depending on the individual's background, other courses (e.g., differential equations and linear algebra) may also be required. Proficiency in these areas may be demonstrated by either successful course completion or by passing an equivalency examination. When possible, a student will be notified of deficiencies at the time of acceptance. In addition to the preparatory work described, all degree requirements listed above for the master of science degree must be fulfilled.

  • ECE 1552 Computer Design
    Credit Hours: 4
    Studies design of computer structures and embedded systems. Includes processor units, instruction set architecture, embedded systems organization and control, input/output organization, timer implementation, interrupts and basic computer organization and design. Also includes development of a working knowledge of the process through lab development, interfacing and programming.
  • ECE 2112 Circuit Theory 2
    Credit Hours: 4
    Continues . Includes phasors and steady-state response; AC power and two-port equivalent circuits and transfer functions; Fourier analysis transforms analysis, Laplace transforms; and lab projects.
  • ECE 2551 Software/Hardware Design
    Credit Hours: 3
    Studies software and hardware aspects of computer design and corresponding interdependencies. Includes use of C++ software development environments. Lab includes the application of high-level language concepts to digital signal processing.
  • ECE 3111 Electronics
    Credit Hours: 4
    Introduces diodes, bipolar and field-effect transistors; analysis and design of semiconductor circuits; single and multistage amplifiers; design algorithms; operational amplifiers and oscillators. Includes lab projects.
  • ECE 4112 Digital Electronics
    Credit Hours: 3
    Covers the fundamentals of digital electronics. Emphasizes analytical reasoning and integrated circuits. Discusses logic families and large-scale circuits. Uses electronic design automation tools such as VHDL and Quartus 11.