Computation of Hilbert Transform via Discrete Cosine Transform

Olkkonen, H.; Pesola, P.; Olkkonen, Juuso

doi:10.4236/jsip.2010.11002

Cited by 11 publications

(6 citation statements)

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“…In addition, it is also possible to obtain the envelope of a signal in specified frequency range by taking the absolute value of the analytic signal. The analytic signal x a [ n ] might be estimated from a real signal x [ n ] by…”

Section: Software Descriptionmentioning

confidence: 99%

CudaFilters: A SignalPlant library for GPU‐accelerated FFT and FIR filtering

et al. 2017

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SummarySignal filtering is one of the essential tasks in signal processing. It may become an extremely time-consuming process, as in the case of intracranial electroencephalogram recordings (eg, 30-min records) with a large number of channels (up to 256) and high sampling frequencies (up to 5 kHz in research related to ultra-highfrequency oscillations). The usual way of dealing with time consumption is process parallelization. Moreover, parallelization using graphic processing unit (GPU) allows further shortening of computing times thanks to the large number of GPU cores. This paper describes a library for GPU-accelerated finite impulse response (FIR) and fast Fourier transform (FFT) filtering-"CudaFilters." This library is designed for SignalPlant software-a free tool for signal analysis. The resultant acceleration in computing times was 5× to 40× depending on the task, data, and hardware configuration. The results were also compared to computing speeds in Matlab.

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Section: Software Descriptionmentioning

confidence: 99%

CudaFilters: A SignalPlant library for GPU‐accelerated FFT and FIR filtering

et al. 2017

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“…This was simplified using an architecture based on Fast Hartley transform [7] involving real additions and multiplications, that is computationally less complex compared to the FFT based architecture [6]. Apart from these, various approaches were present for the implementation of DHT such as Winograd Fourier Transform Algorithm, prime factor algorithm and Discrete Cosine Transform based approach [8][9][10]. Architectures in the time domain, on other hand, includes filtering approach [11] and systolic array approach [12].…”

Section: Introductionmentioning

confidence: 99%

Low complexity underdetermined blind source separation system architecture for emerging remote healthcare applications

Mopuri

Reddy

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In this paper, we have proposed a low complexity architecture for the Under-determined Blind Source Separation (UBSS) algorithm targeting remote healthcare applications. UBSS algorithm, departing from the typical BSS convention-equal number of the sources and sensors present, which is of tremendous interest in the field of Biomedical signal processing especially for remote health care applications. Since such applications are constrained by the on-chip area and power consumption limitation due to the battery backup, a low complexity architecture needs to be formulated. In this paper, firstly we have introduced UBSS architecture, followed by the identification of the most computationally intensive module N-point Discrete Hilbert Transform (DHT) and finally proposed a low complexity DHT architecture design to make the entire UBSS architecture suitable for such resource constrained applications. The proposed DHT architecture implementation and experimental comparison results show that the proposed design saves 50.28%, 48.40% and 46.27% on-chip area and 53.25%, 48.01% and 45.95% power consumption when compared to the state of the art method for N = 32, 64 and 128 respectively. Furthermore the proposed DHT architecture works for N = 2m point, but the state of art architecture works for N = 4m point, where m is an integer.

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“…Reference [8] gives more application examples of HT. About the design method of HT, we can find some methods [9][10][11][12][13], but window method is one of the most frequently used methods [2][3][4]. The reason is that window method is the simplest one of them and several windows have good performances.…”

Section: Introductionmentioning

confidence: 99%

An Application of Sinc Sum Function in Hilbert Transformer

Wang¹

2013

ENG

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An application of the sinc sum function in Hilbert transformer (HT) is studied. The expression of the frequency re- sponse of HT is expressed with sinc sum functions. Some properties of sub-amplitude response of HT are proved by using the properties of the sinc sum function. A general HT formula is obtained theoretically and it contains a general window function. As an example three new window functions are obtained. Different from the existing window func- tions obtained from lowpass filters, these window functions are obtained directly from HT. Comparisons show that new windows are better than the Hanning, Hamming, Blackman and Kaiser windows in terms of HT performances.

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Computation of Hilbert Transform via Discrete Cosine Transform

Cited by 11 publications

References 20 publications

CudaFilters: A SignalPlant library for GPU‐accelerated FFT and FIR filtering

CudaFilters: A SignalPlant library for GPU‐accelerated FFT and FIR filtering

Low complexity underdetermined blind source separation system architecture for emerging remote healthcare applications

An Application of Sinc Sum Function in Hilbert Transformer

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