Real-Time Implementation of Signal Processing Techniques for Disturbances Detection

Singh, Himanshu; Mohanty, Soumya R.; Kishor, Nand; Thakur, Kiran

doi:10.1109/tie.2018.2851968

Cited by 51 publications

(13 citation statements)

References 23 publications

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“…The second-pipelined stage computes the transform (UWT/DTCWT) operation on the pre-processed signal of the first stage. Real-time implementations of different transforms are reported in the literature [54,55,56,57]. Addition and average mathematical operations are needed to be executed in the third-pipelined stage of the proposed algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…In order to obtain the denoised image, the thresholded multiscale coefficients from Equation (25) are reconstructed by employing the transform T−1(·) as follows trueS^=T−1(trueu^k(i)), where sfalse^ an estimate of the true signal (or image) s, or simply stated the denoised signal or image. Implementation of various forward and inverse wavelet transforms is reported in [54,55,56,57].…”

Section: Proposed Denoising Framework Using Detection Theorymentioning

confidence: 99%

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A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors

Naveed

Ehsan

McDonald-Maier

et al. 2019

Sensors

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Output from imaging sensors based on CMOS and CCD devices is prone to noise due to inherent electronic fluctuations and low photon count. The resulting noise in the acquired image could be effectively modelled as signal-dependent Poisson noise or as a mixture of Poisson and Gaussian noise. To that end, we propose a generalized framework based on detection theory and hypothesis testing coupled with the variance stability transformation (VST) for Poisson or Poisson–Gaussian denoising. VST transforms signal-dependent Poisson noise to a signal independent Gaussian noise with stable variance. Subsequently, multiscale transforms are employed on the noisy image to segregate signal and noise into separate coefficients. That facilitates the application of local binary hypothesis testing on multiple scales using empirical distribution function (EDF) for the purpose of detection and removal of noise. We demonstrate the effectiveness of the proposed framework with different multiscale transforms and on a wide variety of input datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Proposed Denoising Framework Using Detection Theorymentioning

confidence: 99%

A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors

Naveed

Ehsan

McDonald-Maier

et al. 2019

Sensors

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show abstract

“…This chapter is targeting a real time application, so hardware solution is best recommended. Initially, the proposed audio watermarking algorithm is implemented on the MATLAB; however MATLAB provides only the simulation platform to validate the performance [13]. The real-time implementation of the proposed audio watermarking is achieved in Xilinx ISE software and simulated result of the audio watermarking is discussed.…”

Section: Hardware Solutionmentioning

confidence: 99%

Hardware Implementation of Audio Watermarking Based on DWT Transform

Joshi¹

2020

Security and Privacy From a Legal, Ethical, and Technical Perspective

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Presently, the duplicate copy of an audio can be generated with great ease using some smart devices, and transmitted over the internet which raises concern over copyright and privacy. Digital audio watermarking is a procedure to insert some data bits known as watermark into audio signal. Then the audio with watermark is to be transmitted to end user or made public. The proposed algorithm is used to insert a binary watermark image into a detailed coefficient of the Daubechies 9/7based DWT transform. A watermark is dispersed consistently in low frequencies, which builds the robustness and inaudibility of the watermark data. Further, the watermark is embedded into an audio signal to have robust system against audio attacks and inaudible performance. The algorithm is verified using MATLAB and subsequently implemented on FPGA hardware to verify the real-time performance. Hardware implementation helps to embed the watermark at the same instance when audio is being captured. The results show promising application for real-time audio applications.

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“…Various research studies based on the orthogonal or approximate orthogonal decomposition methods of EMD [12,[14][15][16][17] are not convincing. References [18,19] use Schmidtʼs formula to make IMFs strictly orthogonal, but it cannot guarantee that the IMF is obtained from the orthogonal decomposition of the signal. Mode-mixing between IMFs is another major problem that limits EMD applications.…”

Section: Introductionmentioning

confidence: 99%

An Improved Empirical Mode Decomposition Based on Local Integral Mean and Its Application in Signal Processing

Xiao-dong

Wang

et al. 2021

Mathematical Problems in Engineering

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Empirical mode decomposition (EMD) is an effective method to deal with nonlinear nonstationary data, but the lack of orthogonal decomposition theory and mode-mixing are the main problems that limit the application of EMD. In order to solve these two problems, we propose an improved method of EMD. The most important part of this improved method is to change the mean value by envelopes of signal in EMD to the mean value by the definite integral, which enables the mean value to be mathematically expressed strictly. Firstly, we prove that the signal is orthogonally decomposed by the improved method. Secondly, the Monte Carlo method of white noise is used to explain that the improved method can effectively alleviate mode-mixing. In addition, the improved method is adaptive and does not need any input parameters, and the intrinsic mode functions (IMFs) generated from it is robust to sifting. We have carried out experiments on a series of artificial and real data, the results show that the improved method is the orthogonal decomposition method and can effectively alleviate mode-mixing, and it has better decomposition performance and physical meaning than EMD, ensemble EMD (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In addition, the improved method is generally more time-consuming than EMD, but far less than EEMD and CEEMDAN.

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Real-Time Implementation of Signal Processing Techniques for Disturbances Detection

Cited by 51 publications

References 23 publications

A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors

A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors

Hardware Implementation of Audio Watermarking Based on DWT Transform

An Improved Empirical Mode Decomposition Based on Local Integral Mean and Its Application in Signal Processing

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