Career Training: Signal Processing for Sensors,
Image Processing, Multimedia and Music
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Signal processing is the technology that allows us to record and listen to music and speech, to record and play back movies and videos, and transmit photos electronically. It allows us to record, transmit, and extract information about the environment, whether that environment is the air, water, or a tumor in the body. Signal processing is is used in cell and smart phones, and is necessary for “smart” technology, such as smart cars that can sense the traffic around them, or tv’s that can sense when no one is watching and turn themselves off. It is used in weather and economic forecasting, in computer vision, in sonar and radar, as well as applications in security and health.
Primary courses related to your future that involves signal and image processing include EECS 216, 401, 451, and 452. Secondary courses include EECS 442, 455 and a math course on linear algebra such as Math 217 or 419.
ENG 100: Music Signal Processing
Recent Instructors: Jeff Fessler and Andrew E. Yagle
This course will teach you to analyze music the way an engineer would analyze an unfamiliar phenomenon. You will learn the basics of Fourier signal analysis and synthesis, motivated by observation of musical signals. There are two projects: (1) analysis of touch-tone phone signals and design of touch-tone analyzers and synthesizers on a computer; and (2) a simple music synthesizer and music transcriber that produces a musical-staff-like transcription from .wav files of synthesized music, also on a computer. Both of these projects allow you to apply learned signal processing techniques to analyze, design and test a solution to an open-ended problem.
EECS 216: Introduction to Signals and Systems
Recent Instructors: Jessy Grizzle, Stephane Lafortune, Greg Wakefield, Kim A. Winick, and Andrew E. Yagle
This course introduces students to basic concepts in continuous-time linear system theory. The analysis of continuous-time systems is considered in both the time and frequency domains. Topics include linearity, impulse response, convolution, frequency response, filtering, Fourier series, Fourier transforms, sampling theorem, relationship between continuous-time and discrete-time systems (as time permits), Laplace transforms, system transfer function, poles and zeros, stability. Applications of these techniques will be discussed using examples from circuits, signal processing, communication and control.
EECS 401: Probabilistic Methods in Engineering
Recent Instructors: Mingyan Liu, Sandeep Pradhan Sadanandarao, Clay Scott
This course covers basic concepts of probability theory and random processes. Subjects include: set theory, axioms of probability, basic principles of counting, conditional probability, independence, discrete and continuous random variables, functions of random variables, probability distribution functions, joint and conditional distribution, expectation, law of large numbers, introduction to discrete and continuous random processes, power spectral density.
EECS 442: Computer Vision
Recent Instructor: Silvio Savarese
Computational methods for the recovery, representation, and application of visual information. Topics from image formation, binary images, digital geometry, similarity and dissimilarity detection, matching, curve and surface fitting, constraint propagation relaxation labeling, stereo, shading texture, object representation and recognition, dynamic scene analysis, and knowledge based techniques. Hardware, software techniques.
EECS 451: Digital Signal Processing and Analysis
Recent Instructor: Andrew E. Yagle
This course covers the basics of digital signal processing, including: Sampling, linear time-invariant systems, convolution, z-transforms, Discrete-time Fourier transform, Discrete-time Fourier series Discrete Fourier transform, Fast Fourier transform, data windows, FIR and IIR filter design, multirate filtering, image processing, spectrogram.
EECS 452: Digital Signal Processing Design Laboratory
Recent Instructors: Mark Brehob, Al Hero, Mingyan Liu
This course is a senior/graduate design course whose main focus is the application of real-time digital signal processing (including theory, software and hardware) to a multi-week team project. This course satisfies the CoE's major design experience requirement and consists of lectures, structured laboratory exercises and team projects. The lectures and structured laboratory exercises are intended to provide a foundation for the team projects to build on. The lectures and structured laboratory exercises cover: Architectural features of DSP processors (arithmetic, memory organization, pipe lining, and use of special on-chip hardware); Amplitude quantization effects (in A/D and D/A conversion, waveform generation and digital filter implementation); Special on-chip hardware (serial ports, host ports, and timers); Programming of DSP processors; Design and implementation of FIR and IIR filters; FFT usage; Real-time concepts (interrupts, critical sections, threads of execution, etc.).
See also: Video Overview
EECS 455: Digital Communication Signals and Systems
Instructor: Wayne Stark
This course covers many aspects of digital communications systems. First, the fundamental tradeoff between bandwidth efficiency and energy efficiency in communication systems is discussed. Signal design and bandwidth are explored. Principles of optimum receiver/matched filtering are taught. Analysis of performance of digital communications is investigated. A variety of modulation schemes including binary phase shift keying, quadrature amplitude modulation, frequency shift keying are covered. Error control techniques including convolutional codes and block codes are included in the course material. Applications to GPS, digital cellular telephone and wireless local area networks are part of the course as well.
For students interested in more advanced coursework, there are many graduate-level (ie, 500 and above) courses available. Recommended courses include EECS 501, 545, 551, 556 and 564. Please check the online catalog and speak with an undergraduate advisor for more information.
Digital Signal Processing Design Lab (EECS 452)
Video - Course Overview
> Student Profile Video
Rob Moran spent a summer working for a local signal processing company, Quantum Signal LLC, started by U-M graduates.
Summer Undergraduate Research in Engineering (SURE) Projects
> Helical CT Image Reconstruction Video
Undergraduates worked with faculty to develop sophisticated algorithms that provide improved image quality at lower X-ray doses.
> Multiple Target Tracking with Single Minimally Calibrated Camera Video
Undergraduates worked with faculty to develop cutting edge technology in visual recognition.