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Co-sponsored by: MEFGI, Rajkot The workshop aims at introducing participants the concept of wavelet transform and sparse representation of signals for the analysis of the source of signals in MATLAB environment. Fourier Transform has been a mathematical tool for frequency analysis of the signal with the limitation of signal being assumed as stationary. Besides this, Fourier transform while depicts the frequencies present in a signal it hides the temporal information. In a real life, majority of the signals are found to be nonstationary and therefore for their frequency analysis with greater resolution and frequency localization with respect to time wavelet transform has been proven to be an efficient technique of signal processing. Wavelet transform is used in the analysis of signals acquired from various sources, such as speech, machine vibration, electrical power, biomedical activities, underground movements, underwater movements, etc. The application of wavelet transform or decomposition is widespread in almost every branch of engineering and science.  In signal processing we often have to solve ill-conditioned inverse problems, which means somehow we have to recover the best possible solution amongst the given partial or noisy knowledge of signal. Hence sparse representation helps us to figure out the sparsest possible solution amongst other. The sparse reconstruction can be realized by solving l1 minimization problem. The underline idea of a sparse solution is choosing an  appropriate basis which can represent the given signal with minimal co-efficient. Data Separation and Missing Data Recovery (M.D.R) Problems are the major application of sparse representation in Engineering domain. Speaker(s): Dr Dinesh Kumar, Prof. Abir Bhattacharyya Agenda: PG, PhD students and those who are planning to start their PhD in near future from various engineering streams can be the ideal participants for the workshop. Location: Room: MB111 Bldg: Main Building EE Department MEFGI Rajkot, Gujarat 360003