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Section: Publications & Resources
The Signal Processing Society (SPS) has 13 Technical Committees and 2 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society.
Section: Publications & Resources
The Signal Processing Society (SPS) has 13 Technical Committees and 2 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society.
Section: Publications & Resources
The Signal Processing Society (SPS) has 13 Technical Committees and 2 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society.
Section: Publications & Resources
The Signal Processing Society (SPS) has 13 Technical Committees and 2 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society.
Section: Publications & Resources
The Signal Processing Society (SPS) has 13 Technical Committees and 2 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society.
Section: Publications & Resources
The Signal Processing Society (SPS) has 13 Technical Committees and 2 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society.
Section: Publications & Resources
The Signal Processing Society (SPS) has 13 Technical Committees that support a broad selection of signal processing-related activities defined by the scope of the Society.
Section: Publications & Resources
The Signal Processing Society (SPS) has 12 Technical Committees that support a broad selection of signal processing-related activities defined by the scope of the Society.
Advisor: Sriram Ganapathy
Email: sriram.iisc@gmail.com
Chair: Debarpan Bhattacharya
Email: debarpanbhatta123@gmail.com
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Much effort has been devoted to recovering sparse signals from one-bit measurements in recent years. However, it is still quite challenging to recover signals with high fidelity, which is desired in practical one-bit compressive sensing (1-bit CS) applications. We introduce the notion of Schur-concavity in this paper and propose to construct signals by taking advantage of Schur-Concave functions , which are capable of enhancing sparsity.
