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ECE
158. Digital Signal Processing |
Prof. Mitra |
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Prerequisites: ECE 130A-B, or consent of instructor. Mathematics 124A is
recommended but not required.
Open to ECE majors only. Lecture, 3 hours;
laboratory, 3 hours.
Discrete signals and systems, convolution, z-transforms, discrete Fourier
transforms, digital filters. (F) |
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ECE 160. Multimedia Computing |
Prof. Manjunath |
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Prerequisites: ECE 155A, 158, and 178. Not open for credit to
students who have completed Computer Science 182.
Lecture, 3 hours; laboratory,
3 hours.
Introduction to multimedia and applications, including video conferencing,
WWW, digital libraries, video on demand.
Digital video and audio communication
architectures, standards (including JPEG and MPEG2), multimedia storage and
retrieval.
Multimedia computing on the Internet and
digital libraries. (not offered 1999-2000) |
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ECE
178. Fundamentals of Computer Image Processing |
Prof. Manjunath |
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Prerequisites: ECE 15 and ECE 130A-B, or consent of
instructor. Open to ECE majors only.
Lecture, 3 hours; discussion, 1 hour.
Basic concepts in image processing. Techniques, capabilities, and limitations
with emphasis on use of digital computer
but also of optical and analog systems. Image sampling, reconstruction,
enhancement, restoration, data extraction, and coding.
Some hands-on laboratory experience is offered. (W)
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ECE 181B. Introduction to
Computer Vision |
Prof. Manjunath Prof. Wang |
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Same course as Computer Science 181B. Lecture, 3 hours;
discussion, 1 hour.
Overview of image processing, pattern recognition; image
formation, binary images; edge detection, image segmentation,
introduction to
textured image analysis, optical flow, depth from stereo, shape from shading,
shape from motion, shape representation techniques, issues in object
recognition, case study of some vision systems.(S) |
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ECE
258A. Advanced Digital Signal Processing |
Prof. Mitra |
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Prerequisite: ECE 158 or consent of instructor. Lecture, 4
hours.
Digital filter design, discrete random signals, effects of finite word
length arithmetic, fast Fourier transform and applications,
power spectrum estimation. (W) |
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ECE 258B. Multirate Digital
Signal Processing
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Prof. Mitra |
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Prerequisites: ECE 158 and ECE 258A or consent of instructor.
Lecture, 4 hours.
Multirate digital filter theory, polyphase decomposition,
decimator and interpolar design, efficient implementations, orthogonal
transforms, wavelet transform, analysis and synthesis filter banks, quadrature
mirror filter banks, transmultiplexer, subhand decomposition, applications. (S)
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ECE 277A.
Neural Network Theory |
Prof. Manjunath |
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ECE 278A. Digital Image
Processing
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Prof. Manjunath |
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Prerequisites: ECE 158 or equivalent, ECE 178, or consent of
instructor. Lecture, 3 hours; laboratory, 3 hours.
Two-dimensional signals and systems. Two-dimensional Fourier and
z-transforms. Discrete Fourier transform, two-dimensional digital filters. Image
processing basics, image enhancement and restoration. Special image processing
software available for laboratory experimentation. (S) |
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ECE 281B.Advanced Topics in Computer Vision |
Prof. Manjunath Prof. Wang |
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Prerequisite: ECE 181B, or consent of instructor. Lecture, 3 hours; Same course as Computer Science 281B.
Advanced topics in computer vision: image sequence analysis, spatio temporal filtering, camera calibration and hand-eye coordination, robot navigation, shape representation, physically-based modeling, multi-sensory fusion, biological models, expert vision systems, and other topics selected from recent research papers. (F; offered alternate years)
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ECE
594N: Introduction to Data Mining |
Prof. Manjunath |
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Recommended Text: Principles of Data Mining
by Hand, Mannila and Smith, MIT Press, 2001.
Data mining refers to tools and techniques for processing and managing large collections of data with the main objective being able to detect
significant "patterns" or associations in such data sets. As such, it has a wide range of applications to problems in natural and social sciences,
medicine, finance, and marketing. This introductory course will cover some of the basic principles of data mining with emphasis on data mining
tasks and algorithms. These will include, for example, tools for classification
and clustering, data structures for organizing high-dimensional data, association rules for mining, and retrieval by content (mostly chapters
9-14 in the recommended text above, but I will also be using other resources.)
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