Compressive Sensing Ece 8th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective IV) detail syllabus for Electronics And Communication Engineering (Ece), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (EC8009), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other ece 8th sem syllabus for be 2017 regulation anna univ you can visit Ece 8th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective IV subjects do refer to Professional Elective IV. The detail syllabus for compressive sensing is as follows.
Course Objective:
- 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
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Unit II
Sparsity and Signal Recovery
Signal Representation; Basis vectors; Sensing matrices; Restricted Isometric Property; Coherence; Stable recovery; Number of measurements.
Unit III
Recovery Algorithms
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
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Unit V
Applications of Compressive Sensing
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.
Course Outcome:
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:
- Radha S, Hemalatha R, Aasha Nandhini S, Compressive Sensing for Wireless Communication: Challenges and Opportunities, River publication, 2016. (Unit I-V)
- 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:
- 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.
- 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.
- 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.
- 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.
For detail syllabus of all other subjects of BE Ece, 2017 regulation do visit Ece 8th Sem syllabus for 2017 Regulation.
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