Ramanujan and DFT mixed basis representation for removal of PLI in ECG signal

Shah, Shaik Basheeruddin; Chakka, Vijay Kumar; Goli, Srikanth

doi:10.1109/spin.2017.8050003

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Ramanujan filter banks have also been shown to be applicable in the identification of epileptic seizures in patients, which are characterized by sudden appearance of periodic waveforms in the measured EEG records [38]. Other interesting applications have recently been reported by a number of authors such as, for example in source-separation [39], RF communications [40], ECG signal processing [41] and brain-computer interfacing [42,43]. For details, the reader is requested to consult the above papers and references therein.…”

Section: Discussionmentioning

confidence: 99%

Srinivasa Ramanujan and signal-processing problems

Vaidyanathan

Tenneti

2019

Phil. Trans. R. Soc. A.

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The Ramanujan sum c q ( n ) has been used by mathematicians to derive many important infinite series expansions for arithmetic-functions in number theory. Interestingly, this sum has many properties which are attractive from the point of view of digital signal processing. One of these is that c q ( n ) is periodic with period q , and another is that it is always integer-valued in spite of the presence of complex roots of unity in the definition. Engineers and physicists have in the past used the Ramanujan-sum to extract periodicity information from signals. In recent years, this idea has been developed further by introducing the concept of Ramanujan-subspaces. Based on this, Ramanujan dictionaries and filter banks have been developed, which are very useful to identify integer-valued periods in possibly complex-valued signals. This paper gives an overview of these developments from the view point of signal processing. This article is part of a discussion meeting issue ‘Srinivasa Ramanujan: in celebration of the centenary of his election as FRS’.

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Section: Discussionmentioning

confidence: 99%

Srinivasa Ramanujan and signal-processing problems

Vaidyanathan

Tenneti

2019

Phil. Trans. R. Soc. A.

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“…Dictionary approaches were also developed for detecting periods and a generalized framework of nested periodic dictionaries is presented in [5]. Ramanujan periodicity transform (RPT) is shown to be useful in applications such as robust detection of brain stimuli for brain computer interfaces [6]- [8] and removal of interference from ECG signals [9]- [11]. An overview of recent developments can be found in [14].…”

Section: Introductionmentioning

confidence: 99%

On the Zeros of Ramanujan Filters

Kulkarni

Vaidyanathan

2020

IEEE Signal Process. Lett.

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Ramanujan filter banks have been used for identifying periodicity structure in streaming data. This paper studies the locations of zeros of Ramanujan filters. All the zeros of Ramanujan filters are shown to lie on or inside the unit circle in the z-plane. A convenient factorization appears as a corollary of this result, which is useful to identify common factors between different Ramanujan filters in a filter bank. For certain families of Ramanujan filters, further structure is identified in the locations of zeros of those filters. It is shown that increasing the number of periods of Ramanujan sums in the filter definition only increases zeros on the unit circle in z-plane. A potential application of these results is that by identifying common factors between Ramanujan filters, one can obtain efficient implementations of Ramanujan filter banks (RFB) as demonstrated here.

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Signal Representation Using Ramanujan Subspaces Utilizing A Prior Signal Information

Shah

Chakka

2020

2020 International Conference on Signal Processing and Communications (SPCOM)

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Ramanujan and DFT mixed basis representation for removal of PLI in ECG signal

Cited by 4 publications

References 13 publications

Srinivasa Ramanujan and signal-processing problems

Srinivasa Ramanujan and signal-processing problems

On the Zeros of Ramanujan Filters

Signal Representation Using Ramanujan Subspaces Utilizing A Prior Signal Information

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