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Research interests: current emphasis is on sparse sampling in one and multiple dimensions, network signal processing, number theoretic signal processing, and applications in digital communications, array processing, radar signal processing, genomic signal processing, and multirate systems and filter banks.
Considered to be one of the pioneering contributors to multirate signal processing research, Prof. Vaidyanathan has heavily influenced the research directions in filter banks and multirate systems. He is most well known for developing the general theory of filter banks with perfect reconstruction, as well as orthonormal filter banks, which have impacted digital communications, audio, and image coders. One of his earliest contributions was in the area of low-sensitivity digital filter structures. He showed how such structures can be designed directly in discrete time, without the need for transforming electrical circuits into the digital domain. An early proponent of applying signal processing methods to genomics, Vaidyanathan developed methods to computationally predict the location of protein coding genes and noncoding genes. His recent work on Ramanujan-sums, and his introduction of Ramanujan subspaces have resulted in new representations and efficient algorithms for signals with hidden patterns such as periodicities. His work on coprime and nested sampling and spatial arrays has had a major impact in array processing research, leading to new directions in this area.
Considered to be one of the pioneering contributors to multirate signal processing research, Prof. Vaidyanathan has heavily influenced the research directions in filter banks and multirate systems. He is most well known for developing the general theory of filter banks with perfect reconstruction, as well as orthonormal filter banks, which have impacted digital communications, audio, and image coders. One of his earliest contributions was in the area of low-sensitivity digital filter structures. He showed how such structures can be designed directly in discrete time, without the need for transforming electrical circuits into the digital domain. An early proponent of applying signal processing methods to genomics, Vaidyanathan developed methods to computationally predict the location of protein coding genes and noncoding genes. His recent work on Ramanujan-sums, and his introduction of Ramanujan subspaces have resulted in new representations and efficient algorithms for signals with hidden patterns such as periodicities. His work on coprime and nested sampling and spatial arrays has had a major impact in array processing research, leading to new directions in this area.
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ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)pp.12891-12895, (2024)
IEEE Transactions on Signal Processingno. 99 (2024): 1-16
IEEE SIGNAL PROCESSING LETTERS (2024): 171-175
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)pp.1-5, (2023)
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ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)pp.1-5, (2023)
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Asilomar Conference on Signals, Systems and Computerspp.1293-1297, (2023)
Pranav Kulkarni,P. P. Vaidyanathan
Asilomar Conference on Signals, Systems and Computerspp.1334-1338, (2023)
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)pp.1-5, (2023)
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IEEE Transactions on Signal Processing (2023): 494-511
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