A Differentiator with a Difference

Lyons, Richard G.

doi:10.1002/9781118316948.ch28

Cited by 33 publications

(43 citation statements)

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“…Based on those results, we derive accurate formulas for estimating the optimal DFT lengths, which have not been reported before and differ from other works where optimal design refers to optimal overall filtering performance for fixed DFT and filter lengths [17], [18]. It will also be shown that, using optimal DFT lengths, that minimize the computational complexities (multiplication rates), the frequency-domain implementations become more efficient than the corresponding time-domain implementations for filter lengths that are shorter than those reported earlier in the literature [1], [3], [20].…”

Section: A Contributionsmentioning

confidence: 66%

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On frequency-domain implementation of digital FIR filters

Johansson

Gustafsson

2015

2015 IEEE International Conference on Digital Signal Processing (DSP)

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Section: A Contributionsmentioning

confidence: 66%

“…Consequently, each F kn (z M ) is here a constant, viz. F kn (z M ) = f k (n), and it then follows from ( 19), (20), and (29), that d n (q) can be written as…”

Section: B Overlap-savementioning

confidence: 99%

On frequency-domain implementation of digital FIR filters

Johansson

Gustafsson

2015

2015 IEEE International Conference on Digital Signal Processing (DSP)

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“…The main step of the proposed method of determining the envelope is to use a moving average, i.e., by replacing the samples in a defined time window with the local average calculated in this window. The window width was empirically selected and set to 5000, which gives the equivalent of 1.25 s, taking into account the sampling frequency of 4 kHz [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

New Methods for the Acoustic-Signal Segmentation of the Temporomandibular Joint

Kajor

Kucharski

Grochala

et al. 2022

JCM

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(1) Background: The stethoscope is one of the main accessory tools in the diagnosis of temporomandibular joint disorders (TMD). However, the clinical auscultation of the masticatory system still lacks computer-aided support, which would decrease the time needed for each diagnosis. This can be achieved with digital signal processing and classification algorithms. The segmentation of acoustic signals is usually the first step in many sound processing methodologies. We postulate that it is possible to implement the automatic segmentation of the acoustic signals of the temporomandibular joint (TMJ), which can contribute to the development of advanced TMD classification algorithms. (2) Methods: In this paper, we compare two different methods for the segmentation of TMJ sounds which are used in diagnosis of the masticatory system. The first method is based solely on digital signal processing (DSP) and includes filtering and envelope calculation. The second method takes advantage of a deep learning approach established on a U-Net neural network, combined with long short-term memory (LSTM) architecture. (3) Results: Both developed methods were validated against our own TMJ sound database created from the signals recorded with an electronic stethoscope during a clinical diagnostic trail of TMJ. The Dice score of the DSP method was 0.86 and the sensitivity was 0.91; for the deep learning approach, Dice score was 0.85 and there was a sensitivity of 0.98. (4) Conclusions: The presented results indicate that with the use of signal processing and deep learning, it is possible to automatically segment the TMJ sounds into sections of diagnostic value. Such methods can provide representative data for the development of TMD classification algorithms.

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“…An important part of the demodulators is the discrete-time differentiators, which are normally FIR differentiators, to avoid distortion. A discrete-time differentiator structure suggested by Lyons (2007) was used in the implementation of the demodulator. This differentiator is described by the difference equation d ]…”

Section: Methodsmentioning

confidence: 99%

Algorithm Development of a Sampled Data Frequency Modulation Demodulator for the Implementation of Software Defined Radios

Yesufu¹

2015

CSSP

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The study developed and evaluated the performance of an improved algorithm to demodulate sampled data frequency modulation (FM) signals in typical field programmable gate array (FPGA)-based software defined radios (SDR). An algorithm, based on the modification of a standard differentiate-divide FM demodulator was developed. The hardware resources requirement and the input noise suppression ability of the developed algorithm were investigated. The demodulator developed requires a quarter of the hardware resources needed by conventional differentiate-divide FM demodulators. The output signal to noise ratio (SNR) of the developed demodulator is lower than that of the standard differentiate dividedemodulator when the input carrier to noise ratio (CNR) is less than 45 decibels. The SNR plot of the developed demodulator is more linear, compared to that of the original differentiate-divide demodulator. This implies that the developed demodulator can be implemented using smaller sized FPGAs, thus reducing cost and the power dissipated. The small silicon area occupied by the demodulator gives room for instantiation of more demodulators and other signal processing units on the same FPGA chip.

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A Differentiator with a Difference

Cited by 33 publications

References 0 publications

On frequency-domain implementation of digital FIR filters

On frequency-domain implementation of digital FIR filters

New Methods for the Acoustic-Signal Segmentation of the Temporomandibular Joint

Algorithm Development of a Sampled Data Frequency Modulation Demodulator for the Implementation of Software Defined Radios

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