ECE 802 Project Guidelines

ECE 802 Course Project

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General Guidelines

The course project in this class will provide you an opportunity to independently research a topic of your choice, as well as try out your original ideas, in the areas of wavelet and time-frequency analysis. The ideal projects should come from your own research interests, either from your thesis work or an area that you want to explore. In that case, it is important that what you present for evaluation in this course is what you do during the course and specifically for the course. If you do not have a clear research topic that you want to work on, you could consult the following list of suggested projects. Each of these projects would investigate a promising new direction in the wavelet field, which has plenty of room for novel improvements and applications. The project will be graded on the written proposal, the final report, and the quality of your oral presentation. Your project proposal should be no more than 2 pages long, double spaced with no less than 11 point font. If you are going to base your project on a paper or two please include them with the project proposal. Also you should come and see me in advance of the proposal deadline to discuss your project. Your written final project report should be typed in IEEE conference format (will be on the web page), with no less than 11 point font, double column, no more than 4 pages in length including figures and references. Each paper should include an abstract, an introductory section, a methods section, a results section, and a conclusion section. Also you need to include a bibliographical citation index at the end of the report. All figures should be clearly labeled and you should include self contained explanatory figure captions which the reader should be able to understand without having to dig through the main body of the paper. The proposal and written report will be evaluated based on the difficulty of the topic or work undertaken; the organization and clarity of exposition (spell check before submitting!); and the technical contribution (extension of results of a paper, implementation of a new idea, analysis of an algorithm, etc). The oral presentation is a major component of the project. Each project will be allotted a 15 minute time slot for presentation. All presentations should be based on overheads, transparencies, or LCD video displays, which should be carefully prepared before the talk. As a rough guideline, a 15 minute talk can accommodate no more than about 5 major ideas or conclusions and 20 slides or so. Slides should not be cluttered with equations and should be readable. Talks should have the following structure: 1) a brief introduction explaining the nature of the problem and the approaches taken; 2) a concise development of the technical content of your final report which includes only the essential concepts and equations needed to understand the problem and your approach; 3) your conclusions. Do not include lengthy derivations in your talk and make sure that all symbols and concepts are defined before they are shown to the audience. Remember the background of the audience: they know the material covered in this class but they may not be familiar with your problem area. In as far as possible use intuitive explanations and illustrative examples, and explain difficult concepts by using graphical explanations instead of mathematical equations. Practice is 1

highly recommended to ensure that the talk can fit in the allotted amount of time. The oral presentation will be evaluated according to: organization and clarity of exposition; the quality of the transparencies; and the speaker’s enthusiasm.

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Suggested Topics

Here are some topics that you can explore further. You do not have to constrain yourself with these. If you have any other ideas that you want to pursue, please make an appointment with me to discuss the feasibility of the project. • Wavelets in multimedia applications: Recent years have seen an increase in the application of wavelets to multimedia applications (audio, image and video). Some common applications that use wavelets are compression (JPEG), watermarking, contentbased retrieval, object recognition and indexing. Some resources that might be useful are IEEE Trans. on Multimedia, IEEE Trans. on Image Processing and IEEE Workshop on Multimedia Signal Processing. • Signal Denoising using wavelets: An important feature of the representation of a signal in the wavelet domain is the sparsity of the coefficients. Typically, there are very few coefficients with high amplitudes, where most of the energy of the signal is concentrated. The procedure exploits the fact that the wavelet transform maps white noise in the signal domain to white noise in the transform domain. Thus, the signal energy is concentrated into fewer coefficients in the transform domain, while the noise energy isn’t. This principle enables separation of noise from the signal. Some key papers in this area are: – D. L. Donoho and I. M. Johnstone, ’Adapting to Unknown Smoothness via Wavelet Shrinkage’, Journal of American Statistical Association, vol. 90, pp. 1200-1224, Dec 1995. – S. G. Chang, B. Yu and M. Vetterli, ’Adaptive Wavelet Thresholding for Image Denoising and Compression’, IEEE Trans. on Image Processing, vol. 9, no. 9, pp. 1532-1546, Sep. 2000. – D. L. Donoho, ’Denoising by Soft-thresholding’, IEEE Transactions on Information Theory, vol. 41, no. 3, May 1995. – D. L. Donoho, I. M. Johnstone, ’Ideal Spatial Adaptation by Wavelet Shrinkage’, Biometrika, vol. 81, pp. 425-455, Aug. 1994. • Extensions of wavelets: Directional wavelets, ridgelets, contourlets – M. N. Do and M. Vetterli, ’The Finite Ridgelet Transform for Image Representation’, IEEE Trans. on Image Processing, pp.1-14, Jan. 2003 – J.L. Starck, E. Candes, and D.L. Donoho, ”The Curvelet Transform for Image Denoising”, IEEE Transactions on Image Processing , 11, 6, pp 670 -684, 2002 2

– R. H. Bamberger and M. J. T. Smith, ’A filter bank for the directional decomposition of images: Theory and design,’ IEEE Trans. Signal Proc. 40, pp. 882893, April 1992. • Sparse representations: In recent years, there is a growing interest in the study of sparse representation for signals. Using an overcomplete dictionary that contains signal-atoms, signals are described by sparse linear combinations of these atoms. Some of the current topics of interest are developing fast algorithms for solving this optimization problem, finding dictionaries that adapt to the signal, applications in compression, denoising, and source separation. Some introductory papers in the field include: – Greed is good: algorithmic results for sparse approximation Tropp, J.A.; Information Theory, IEEE Transactions on Volume 50, Issue 10, Oct. 2004 Page(s):2231 - 2242 – S. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Trans. Signal Processing, vol. 41, pp. 3397-3415, Dec. 1993. – R. R. Coifman and M. V. Wickerhauser, Entropy-based algorithms for best-basis selection, IEEE Trans. Inform. Theory, vol. 38, pp. 713-718, Mar. 1992. – S. S. Chen, D. L. Donoho, and M. A. Saunders, Atomic decomposition by basis pursuit, SIAM J. Sci. Comput., vol. 20, no. 1, pp. 33-61, 1999. • Compressed (Compressive) Sensing: Compressed sensing is based on the idea that signals can be sampled at sub-Nyquist rates and still be recovered assuming they satisfy certain conditions. One of these conditions is the sparsity of the signal in a certain basis, such as the wavelet transform. In practice, we often compress the data soon after sensing, trading off signal representation complexity (bits) for some error (consider JPEG image compression in digital cameras, for example). Clearly, this is wasteful of valuable sensing resources. Over the past few years, a new theory of ”compressive sensing” has begun to emerge, in which the signal is sampled (and simultaneously compressed) at a greatly reduced rate. For resources in this area, check http://www.dsp.ece.rice.edu/cs/ • Wavelet based pattern recognition, texture analysis: – Log-polar wavelet energy signatures for rotation and scale invariant texture classification Chi-Man Pun; Moon-Chuen Lee; Pattern Analysis and Machine Intelligence, IEEE Transactions on , Volume: 25 Issue: 5 , May 2003 Page(s): 590 -603 – Texture classification and segmentation using wavelet frames Unser, M.; Image Processing, IEEE Transactions on , Volume: 4 Issue: 11 , Nov. 1995 Page(s): 1549 -1560 – On the selection of an optimal wavelet basis for texture characterization Mojsilovic, A.; Popovic, M.V.; Rackov, D.M.; Image Processing, IEEE Transactions on , Volume: 9 Issue: 12 , Dec. 2000 Page(s): 2043 -2050 3

– Multiscale image segmentation using wavelet-domain hidden Markov models Choi, H.; Baraniuk, R.G.; Image Processing, IEEE Transactions on , Volume: 10 Issue: 9 , Sept. 2001 Page(s): 1309 -1321 • Feature Extraction from Time-Frequency Distributions for Signal Classification: One of the disadvantages of quadratic time-frequency distributions is the amount of data that these transforms yield. In applications such as signal classification, it is necessary to extract features that express these three-dimensional surfaces in a compact way. There have been various different feature extraction methods for timefrequency distributions such as instantaneous frequency, average time and frequency, and principal component analysis. Some recent papers in the area are: – Gillespie, B.W.; Atlas, L.E.; Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on] Volume 49, Issue 3, March 2001 Page(s):485 - 496 – P. M. Bentley, P. M. Grant, and J. T. E. McDonnell, Time-frequency and timescale techniques for the classification of native and bioprosthetic heart valve sounds, IEEE Trans. Biomed. Eng., vol. 45, pp. 125-128, Jan. 1998. – Q. Q. Huynh, L. N. Cooper, N. Intrator, and H. Shouval, Classification of underwater mammals using feature extraction based on time-frequency analysis and BCM theory, IEEE Trans. Signal Processing, vol. 46, pp. 1202-1207, May 1998. – C. Heitz, Optimum time-frequency representations for the classification and detection of signals, Appl. Signal Process., vol. 2, no. 3, pp. 124-143, 1995.

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