To present the basic theory and ideas showing when it is possible to reconstruct sparse or nearly sparse signals from undersampled data
To expose students to recent ideas in modern convex optimization allowing rapid signal recovery
To give students a sense of real time applications that might benefit from compressive sensing ideas
UNIT I INTRODUCTION TO COMPRESSED SENSING 9
Introduction; Motivation; Mathematical Background; Traditional Sampling; Traditional Compression; Conventional Data Acquisition System; Drawbacks of Transform coding; Compressed Sensing (CS).
UNIT II SPARSITY AND SIGNAL RECOVERY 9
Signal Representation; Basis vectors; Sensing matrices; Restricted Isometric Property; Coherence; Stable recovery; Number of measurements.
UNIT III RECOVERY ALGORITHMS 9
Basis Pursuit algorithm: L1 minimization; Matching pursuit: Orthogonal Matching Pursuit(OMP), Stagewise OMP, Regularized OMP, Compressive Sampling Matching Pursuit (CoSaMP); Iterative Thresholding algorithm: Hard thresholding, Soft thresholding; Model based : Model based CoSaMP, Model based HIT.
UNIT IV COMPRESSIVE SENSING FOR WSN 9
Basics of WSN; Wireless Sensor without Compressive Sensing; Wireless Sensor with Compressive Sensing; Compressive Wireless Sensing: Spatial compression in WSNs, Projections n WSNs, Compressed Sensing in WSNs.
UNIT V APPLICATIONS OF COMPRESSIVE SENSING 9
Compressed Sensing for Real-Time Energy-Efficient Compression on Wireless Body Sensor odes; Compressive sensing in video surveillance; An Application of Compressive Sensing for Image Fusion; Single-Pixel Imaging via Compressive Sampling.
At the end of the course, the student should be able to:
Appreciate the motivation and the necessity for compressed sensing technology.
Design a new algorithm or modify an existing algorithm for different application areas in wireless sensor network.
1. Radha S, Hemalatha R, Aasha Nandhini S, ―Compressive Sensing for Wireless Communication: Challenges and Opportunities‖, River publication, 2016. (UNIT I-V)
2. Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar and Gitta Kutyniok, ―Introduction to Compressed Sensing,‖ in Compressed Sensing: Theory and Applications, Y. Eldar and G. Kutyniok, eds., Cambridge University Press, 2011 (UNIT I)
1. Duarte, M.F.; Davenport, M.A.; Takhar, D.; Laska, J.N.; Ting Sun; Kelly, K.F.; Baraniuk, R.G.; , “Single-Pixel Imaging via Compressive Sampling,” Signal Processing Magazine, IEEE, vol.25, no.2, pp.83-91, March 2008.
2. Tao Wan.; Zengchang Qin.; , ―An application of compressive sensing for image fusion‖, CIVR ’10 Proceedings of the ACM International Conference on Image and Video Retrieval, Pages 3-9.
3. H. Mamaghanian , N. Khaled , D. Atienza and P. Vandergheynst “Compressed sensing for real-time energy-efficient ecg compression on wireless body sensor nodes”, IEEE Trans. Biomed. Eng., vol. 58, no. 9, pp.2456 -2466 2011.
4. Mohammadreza Balouchestani.; Kaamran Raahemifar.; and Sridhar Krishnan.;, ―COMPRESSED SENSING IN WIRELESS SENSOR NETWORKS: SURVEY‖ , Canadian Journal on Multimedia and Wireless Networks Vol. 2, No. 1, February 2011.