Alladi Ramakrishnan Hall

Digital Signal Processing (DSP): From Plato to Looking Forward.

Prof. V K Madan

School of Electrical and Communication Sciences , Kalasalingam University , Krishnankoil, Virudhunagar

Digital Signal Processing is a modern and dynamic discipline. However the origin of digital signal processing (DSP), and many other disciplines too, can be traced back to Plato. The Plato dogma from astronomy to physics to electrical engineering to DSP has helped in the development of many disciplines. However it has been a prominent psychological force to have lopsided development of disciplines. DSP has progressed a lot by merging tools from many disciplines like physics, statistics, number theory, and DSP has found applications in possibly in all scientific and engineering fields including proving Ramanujam’s formula for π. The speaker has given a new classification of signals based on fundamental problems of aliasing and quantization noise, and it has enhanced the role of DSP in many disciplines. The speaker has also developed many DSP techniques in esoteric areas like nuclear spectral processing and population sciences, and developed applications in other areas too. For the acquisition of nuclear spectra, DSP is being increasingly used to replace analog front end in pulse processing. However, for the digital processing of the acquired spectrum, there is hardly any literature. DSP techniques have helped in better understanding of nuclear spectra, in refinement, extension, and consolidation of existing algorithms, and in developing new algorithms. It includes analysis of real complex spectrum using direct approach while all other approaches in the literature are “art-forms”. It describes simple solution to known “least successful area” of using Walsh functions to restore spectra, and it is a reverse problem. To the speaker’s knowledge successful Walsh restoration in any field, there is probably only one other paper in open literature. The lecture also describes Fourier and GF(p) based transformation of spectra for restoration, and DSP smoothing interval criterion superior to the earlier proposed criteria. The lecture would cover briefly other DSP applications, both developedDigital Signal Processing is a modern and dynamic discipline. However the origin of digital signal processing (DSP), and many other disciplines too, can be traced back to Plato. The Plato dogma from astronomy to physics to electrical engineering to DSP has helped in the development of many disciplines. However it has been a prominent psychological force to have lopsided development of disciplines. DSP has progressed a lot by merging tools from many disciplines like physics, statistics, number theory, and DSP has found applications in possibly in all scientific and engineering fields including proving Ramanujam’s formula for π. The speaker has given a new classification of signals based on fundamental problems of aliasing and quantization noise, and it has enhanced the role of DSP in many disciplines. The speaker has also developed many DSP techniques in esoteric areas like nuclear spectral processing and population sciences, and developed applications in other areas too. For the acquisition of nuclear spectra, DSP is being increasingly used to replace analog front end in pulse processing. However, for the digital processing of the acquired spectrum, there is hardly any literature. DSP techniques have helped in better understanding of nuclear spectra, in refinement, extension, and consolidation of existing algorithms, and in developing new algorithms. It includes analysis of real complex spectrum using direct approach while all other approaches in the literature are “art-forms”. It describes simple solution to known “least successful area” of using Walsh functions to restore spectra, and it is a reverse problem. To the speaker’s knowledge successful Walsh restoration in any field, there is probably only one other paper in open literature. The lecture also describes Fourier and GF(p) based transformation of spectra for restoration, and DSP smoothing interval criterion superior to the earlier proposed criteria. The lecture would cover briefly other DSP applications, both developedDigital Signal Processing is a modern and dynamic discipline. However the origin of digital signal processing (DSP), and many other disciplines too, can be traced back to Plato. The Plato dogma from astronomy to physics to electrical engineering to DSP has helped in the development of many disciplines. However it has been a prominent psychological force to have lopsided development of disciplines. DSP has progressed a lot by merging tools from many disciplines like physics, statistics, number theory, and DSP has found applications in possibly in all scientific and engineering fields including proving Ramanujam’s formula for π. The speaker has given a new classification of signals based on fundamental problems of aliasing and quantization noise, and it has enhanced the role of DSP in many disciplines. The speaker has also developed many DSP techniques in esoteric areas like nuclear spectral processing and population sciences, and developed applications in other areas too. For the acquisition of nuclear spectra, DSP is being increasingly used to replace analog front end in pulse processing. However, for the digital processing of the acquired spectrum, there is hardly any literature. DSP techniques have helped in better understanding of nuclear spectra, in refinement, extension, and consolidation of existing algorithms, and in developing new algorithms. It includes analysis of real complex spectrum using direct approach while all other approaches in the literature are “art-forms”. It describes simple solution to known “least successful area” of using Walsh functions to restore spectra, and it is a reverse problem. To the speaker’s knowledge successful Walsh restoration in any field, there is probably only one other paper in open literature. The lecture also describes Fourier and GF(p) based transformation of spectra for restoration, and DSP smoothing interval criterion superior to the earlier proposed criteria. The lecture would cover briefly other DSP applications, both developed, and developing., and developing., and developing.