## EC8009 - COMPRESSIVE SENSING (Syllabus) 2017-regulation Anna University

EC8009

COMPRESSIVE SENSING

LPTC

3003

OBJECTIVES:
• 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 in WSNs, Compressed Sensing in WSNs.

UNIT V

APPLICATIONS OF COMPRESSIVE SENSING

9

Compressed Sensing for Real-Time Energy-Efficient Compression on Wireless Body Sensor Nodes; Compressive sensing in video surveillance; An Application of Compressive Sensing for Image Fusion; Single-Pixel Imaging via Compressive Sampling.

TOTAL : 45 PERIODS

OUTCOMES: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.

TEXT BOOKS:
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)

REFERENCES
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.