Assigned Project Lab Papers

After the 7 structured labs that cover basic digital signal processing (DSP) operations, ECE 420 students will explore in depth a chosen fundamental DSP algorithm using high-level languages (such as MATLAB or Python) for 2 weeks on assigned project labs.

Later, the final projects should be built upon the assigned project lab. Students have to demonstrate their understanding of the algorithm and its implementation through oral quiz during the assigned project lab.

Students have to develop a testing and validation plan to demonstrate that the high-level implementation works. Methodology and results should be included in a short report.

Assigned Project Lab Proposal

Prior to the start of the Assigned Project Lab, a proposal must be submitted outlining the work to be performed. There is not a formal required structure for the Assigned Project Lab proposal, but it should address the following items.

• Overview of the algorithm to be implemented, including citation of sources.
• Plan for testing and validation of the algorithm's implementation.
• Rough idea(s) for Final Project applications of the algorithm.

Recommended Papers

Following is a list of highly common and popular DSP algorithms that are used in many real-time DSP systems. Students should consult with the instructor and TAs in picking a paper that is fundamental to their intended final project.

B. Widrow. Adaptive noise cancelling: Principles and applications. Proc. IEEE, vol. 63, pp.1692 -1716, 1975.

Van Veen, Barry D., and Kevin M. Buckley. Beamforming: A versatile approach to spatial filtering. ASSP Magazine, IEEE 5.2 (1988): 4-24.

Cho, Hojin, Myungchul Sung, and Bongjin Jun. Canny text detector: Fast and robust scene text localization algorithm. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.

L. Rabiner et al.A comparative performance study of several pitch detection algorithms. IEEE Transactions on Acoustics, Speech and Signal Processing, 24.5 (1976): 399-418.

Canny, John. A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on 6 (1986): 679- 698.

T. Callaghan, N. Czink, F. Mani, A. Paulraj and G. Papanicolaou, Correlation-based Radio Localization in an Indoor Environment. EURASIP Journal on Wireless Communications and Networking, 2011, pp. 135.

K. Karplus and A. Strong. Digital Synthesis of Plucked-String and Drum Timbres. Computer Music Journal (1983): 43.

Matthew Turk and Alex Pentland, Eigenfaces for Recognition. Journal of Cognitive Neuroscience 1991 3:1, 71-86

S. Paris and F. Durand. 2009. A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach. Int. J. Comput. Vision 81, 1 (January 2009), 24-52.

Ballard, Dana H. Generalizing the Hough transform to detect arbitrary shapes. Pattern recognition 13.2 (1981): 111-122.

Boykov, Y.Y.; Jolly, M.-P., Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. Proceedings. Eighth IEEE International Conference on Computer Vision (ICCV), vol.1, pp.105-112, 2001.

Auger, Francois, and Patrick Flandrin.Improving the readability of time-frequency and time-scale representations by the reassignment method. Signal Processing, IEEE Transactions on 43.5 (1995): 1068-1089.

S. Baker and I. Matthews, Lukas-Kanade 20 years on: A unifying framework. International Journal of Computer Vision, vol. 56, no. 3, pp. 221-255, Mar. 2004.

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, issue 5, pp. 603 - 619, 2002.

Allen, J. B., D. A. Berkley, and J. Blauert. Multimicrophone signal processing technique to remove room reverberation from speech signals. The Journal of the Acoustical Society of America 62 (1977): 912.

Shi, Jianbo, and Jitendra Malik. Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on 22.8 (2000): 888-905.

Lowe, David G. Object recognition from local scale-invariant features. Computer vision, 1999. The proceedings of the seventh IEEE international conference on. Vol. 2. Ieee, 1999.

Butler, Darren, Sridha Sridharan, and VM Jr Bove. Real-time adaptive background segmentation. Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03). 2003 IEEE International Conference on. Vol. 3. IEEE, 2003.

Islam, Md Zahidul, Chi-min Oh, and Chil-Woo Lee. Real time moving object tracking by particle filter. Computer Science and its Applications, 2008. CSA'08. International Symposium on. IEEE, 2008.

Fujishima, Takuya. Realtime Chord Recognition of Musical Sound: a System Using Common Lisp Music. ICMC. 1999.

Kass, Michael, Andrew Witkin, and Demetri Terzopoulos. Snakes: Active contour models. International journal of computer vision 1.4 (1988): 321- 331.

Klatt, Dennis H.Software for a cascade/parallel formant synthesizer. the Journal of the Acoustical Society of America 67 (1980): 971.

Boll, Steven. Suppression of acoustic noise in speech using spectral subtraction. Acoustics, Speech and Signal Processing, IEEE Transactions on, 27.2 (1979): 113-120.

Chowning, John M. The synthesis of complex audio spectra by means of frequency modulation. Computer Music Journal (1977): 46-54.

M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A tutorial on particle for online nonlinear/non-Gaussian Bayesian trackin. IEEE Transactions on Signal Processing, Volume: 50, Issue: 2, pp. 174 - 188, 2002.