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Computer Engineering MS

Program overview

The master of science degree in computer engineering provides students with a higher level of specialized knowledge in computer engineering, strengthening their ability to successfully formulate solutions to current technical problems, and offering a significant independent learning experience in preparation for further graduate study or for continuing professional development at the leading edge of the discipline. The program accommodates applicants with undergraduate degrees in computer engineering or related programs such as electrical engineering or computer science. (Some additional bridge courses may be required for applicants from undergraduate degrees outside of computer engineering.)

Curriculum

The degree requires 30 semester credit hours and includes Analytical Topics (CMPE-610), two restricted core courses, five graduate electives, two to three semesters of graduate seminar, and six semester credit hours of thesis research. Core courses and graduate electives provide breadth and depth of knowledge to conduct meaningful thesis research.

The Computer Engineering Graduate Seminar (CMPE-795) provides students with exposure to a variety of research topics presented by researchers from within RIT, from industry, and from other universities. Students are expected to conduct graduate level thesis research under the supervision of a primary faculty adviser and thesis committee.

Computer engineering, MS degree, typical course sequence

CourseSem. Cr. Hrs.
First Year
CMPE-610 Analytical Topics in Computer Engineering 3
Choose two of the following restricted core courses: 6
   CMPE-630    Digital Integrated Circuit Design  
   CMPE-655    Multiple Processor Systems  
   CMPE-660    Reconfigurable Computing  
   CMPE-670    Data and Communication Network  
   CMPE-685    Computer Vision  
  Graduate Electives* 15
CMPE-795 Graduate Seminar 0
Second Year
CMPE-796 Thesis Proposal Seminar 0
CMPE-790 Thesis 6
Total Semester Credit Hours 30

* At least two graduate electives must come from the computer engineering department.

Graduate electives

Students select five graduate electives from within the following research tracks. Students are encouraged to choose most of their graduate electives within a single research track. At least two of the electives must be from the computer engineering department (computer engineering department courses begin with the prefix CMPE). Courses outside the lists below may be considered with approval from the department of computer engineering.

Computer architecture

Computer architecture deals with hardware resource management, instruction set architectures and their close connection with the underlying hardware, and the interconnection and communication of those hardware components. Some of the current computer architecture challenges that are being tackled in the computer engineering department include energy efficient architectures, high performance architectures, graphic processing units (GPUs), reconfigurable hardware, chip multiprocessors, and Networks-on-Chips.

Course
CMPE-655 Multiple Processor Systems
CMPE-660 Reconfigurable Computing
CMPE-655 Performance Engineering of Real-time and Embedded Systems
CMPE-731 Design and Testing of Multi-core Chips
CMPE-750 Advanced Computer Architecture
CMPE-755 High Performance Architectures
CSCI-652 Distributed Systems
CSCI-654 Foundations of Parallel Computing
CSCI-742 Compiler Construction
Integrated circuits and systems

Modern processors demand high computational density, small form factors, and low energy dissipation with extremely high performance demands. This is enabled by the nanoscale and heterogeneous integration of transistors and other emerging devices at the massive-scale. Such nanocomputers open unimaginable opportunities as well as challenges to computer engineers. This research focuses on designing computers with emerging novel technologies in the presence of severe physical constraints; investigating dynamic reconfigurability to exploit the power of nano-scale electronics for building reliable computing systems; and studying the applicability of emerging technologies to address challenges in computing hardware of the future.

Course
CMPE-630 Digital Integrated Circuit Design
CMPE-655 Multiple Processor Systems
CMPE-730 Advanced Digital Integrated Circuit Design
CMPE-731 Design and Testing of Multi-core Chips
CMPE-750 Advanced Computer Architecture
EEEE-602 Random Signals and Noise
EEEE-610 Analog Electronics
EEEE-620 Design of Digital Systems
EEEE-712 Advanced Field Effect Devices
EEEE-713 Solid State Physics
EEEE-720 Advanced Topics in Digital Systems Design
EEEE-726 Mixed Signal IC Design
EEEE-730 Advanced Analog IC Design
Networks and security

The prevalence of interconnected computing, sensing, and actuating devices have transformed our way of life. Ubiquitous access to data using/from these devices with reliable performance as well as security assurance presents exciting challenges for engineers and scientists. Resilient to environmental uncertainty, system failures, and cyber attacks requires advances in hardware, software, and networking techniques. The research track in networks and security focuses on intelligent wireless and sensor networks, cryptographic engineering, and predictive cyber situation awareness.

Course
CMPE-661 Hardware and Software Design for Cryptographic Applications
CMPE-670 Data and Communication Networks
CMPE-770 Wireless Networks
CSCI-642 Secure Coding
CSCI-662 Foundations of Cryptography
CSCI-720 Big Data Analytics
CSCI-734 Foundations of Security Measurement and Evaluation
CSCI-735 Foundations of Intelligent Security Systems
CSCI-736 Neural Networks and Machine Learning
CSCI-762 Advanced Cryptography
CSEC-743 Computer Viruses and Malicious Software
CSEC-744 Network Security
EEEE-602 Random Signals and Noise
EEEE-693 Digital Data Communication
EEEE-797 Wireless Communication
NSSA-612 Network Modeling and Analysis
NSSA-711 Advanced Routing Protocols
NSSA-715 Network Design and Performance
Computer vision and machine intelligence

Visual information is ubiquitous and ever more important for applications such as robotics, health care, human-computer interaction, biometrics, surveillance, games, entertainment, transportation, and commerce. Computer vision focuses on extracting information from image and video data for modeling, interpretation, detection, tracking, and recognition. Machine intelligence methods deal with human-machine interaction, artificial intelligence, agent reasoning, and robotics. Algorithm development for these areas spans image processing, pattern recognition, and machine learning, and is intimately related to system design and hardware implementations.

Course
CMPE-680 Digital Image Processing Algorithms
CMPE-685 Computer Vision
CSCI-713 Applied Perception in Graphics and Visualization
CSCI-715 Applications in Virtual Reality
CSCI-719 Topics in Computer Graphics
CSCI-720 Big Data Analytics
CSCI-731 Advanced Computer Vision
EEEE-647 Artificial Intelligence Explorations
EEEE-670 Pattern Recognition
EEEE-685 Principles of Robotics
EEEE-780 Digital Video Processing
EEEE-781 Image and Video Compression
IMGS-756 Advanced Digital Image Processing
Signal processing, control and embedded systems

This research area is concerned with algorithms and devices used at the core of systems that interact with our physical world. As such, this area considers the sensing, analysis, and modeling of dynamic systems with the intent of measuring information about a system, communicating this information, and processing it to adapt its behavior. Application areas are robust feedback-based control where uncertainty in the dynamics and environment must be considered during the design process and signal processing algorithms and devices for system sensing and adaptation.

Course
CMPE-663 Real-time and Embedded Systems
CMPE-664 Modeling of Real-time Systems
CMPE-665 Performance Engineering of Real-Time and Embedded Systems
EEEE-602 Random Signals and Noise
EEEE-610 Analog Electronics
EEEE-661 Modern Control Theory
EEEE-733 Robust Control
EEEE-765 Optimal Control
EEEE-768 Adaptive Signal Processing
EEEE-793 Error Detection and Error Correction
EEEE-794 Information Theory
MATH-781 Wavelets and Applications
Additional graduate-level math courses

These additional math courses also may be used as electives. Students must consult with their adviser and obtain department approval for using these or other graduate-level math courses towards electives. 

Course
ISEE-601 Systems Modeling and Optimization
ISEE-701 Linear Programming
ISEE-702 Integer and Nonlinear Programming
MATH-603 Optimization Theory
MATH-605 Stochastic Processes
MATH-611 Numerical Analysis
MATH-651 Combinatorics and Graph Theory I

Thesis research

An important aspect of graduate study is the student’s preparation to lead challenging, state-of-the-art technical projects. To do this effectively, it is essential that students obtain experiences in reviewing related work of others in the field, as well as conducting meaningful independent research under a faculty mentor.

Thesis work begins by selecting a faculty adviser, identifying a topic, forming a committee (which approves the research topic), and submitting a proposal. The thesis topic, formulated by working closely with a faculty adviser, is related to recent technical developments in the field of computer engineering. Upon completion of the research outlined in the thesis proposal, the work is reported in a document submitted to the faculty committee and a thesis defense presentation. A technical paper resulting from the thesis research is submitted to a refereed conference or journal for publication.

Admission requirements

To be considered for admission to the MS program in computer engineering, candidates must fulfill the following requirements:

  • Hold a baccalaureate degree from an accredited university in computer engineering or a related field,
  • Submit official transcripts (in English) from all previously completed undergraduate and graduate course work,
  • Have an GPA of 3.0 or higher,
  • Submit scores from the Graduate Record Exam (GRE),
  • Submit two letters of reference from individuals well qualified to judge the candidate's ability for graduate study, and
  • Complete a graduate application.
  • International applicants whose native language is not English must submit scores from the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS).

Career Outcomes

Job Titles

Computer engineer, hardware design engineer, software design engineer, software engineer, systems consultant

Functions

VLSI design, computer interfacing, digital systems design, realtime systems, image processing, software engineering

Recent Employers

Adobe Systems, Eastman Kodak Co., Intel, Harris Corp., Motorola, IBM Corp., SUN Microsystems


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