Advanced Signal Processing and Digital Noise Reduction

Vaseghi, Saeed

doi:10.1007/978-3-322-92773-6

Cited by 302 publications

(149 citation statements)

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“…The proposed method can be used to enhance the sensitivity in such applications by removing or decreasing the effect of undesired distortions, and optimally estimate I(ω) from the provided formulations. In detection of the absorption lines, for example, it may be possible to achieve further resolution by an accurate estimation of I(ω) from (14). If X g (ω) is assumed to be smooth (which is a practically reasonable assumption), X g (ω) can be approximated by a locally-smoothed version ofX g (ω), and U (ω) is estimated.…”

Section: Extraction Of the Sensing Informationmentioning

confidence: 99%

“…If X g (ω) is assumed to be smooth (which is a practically reasonable assumption), X g (ω) can be approximated by a locally-smoothed version ofX g (ω), and U (ω) is estimated. Because H 1 (ω) and H 2 (ω) are already identified, we can tap into (14) and find I(ω) by solving a second degree polynomial at each ω. We will show the results for both methods in the following section.…”

Section: Extraction Of the Sensing Informationmentioning

confidence: 99%

“…Deconvolution, which is a well-known problem in the processing of telecommunication signals, seismic signals, etc [10- 13], uses mathematical methods for estimation and removal of the convolutional effect of the medium or instrument from the altered signal. Echo Cancellation [14] is a popular branch of deconvolution methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Signal Recovery in Pulsed Terahertz Integrated Circuits

Heidari¹,

Neshat²,

Saeedkia³

et al. 2010

PIER

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Abstract-In this article, a time-domain calibration procedure is proposed for pulsed Terahertz Integrated Circuits (TIC) used in onchip applications, where the conventional calibration methods are not applicable. The proposed post-detection method removes the unwanted linear distortions, such as interfering echoes and frequency dispersion, by using only one single-port measurement. The method employs a wave-transfer model for analysis of the TIC, and the model parameters are obtained by a proposed blind estimation algorithm. A complete implementation of the method is demonstrated for a fabricated TIC, when used in an on-chip sensing application. The features of interest in the measured signal, such as absorption lines, can be masked or weakened by the distortion of the THz signal happening in a TIC. The proposed signal recovery approach improves the detection of those otherwise hidden features, and can significantly enhance the performance of existing TICs. To show the effectiveness of the proposed de-embedding method, numerical results are presented for simulated and measured signals. The method presented in this article is enabling for accurate TIC applications, and can be utilized to optimally design novel TIC structures for specific purposes.

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Section: Extraction Of the Sensing Informationmentioning

confidence: 99%

Section: Extraction Of the Sensing Informationmentioning

confidence: 99%

See 1 more Smart Citation

Signal Recovery in Pulsed Terahertz Integrated Circuits

Heidari¹,

Neshat²,

Saeedkia³

et al. 2010

PIER

View full text Add to dashboard Cite

show abstract

“…However, these approaches improve the speech signal by eliminating or reducing the impact of noise on the speech spectrum immediately before extracting features from it. The best examples of speech enhancement techniques are Spectral Subtraction (Boll, 1979), Nonlinear Spectral Subtraction (Yapanel et al, 2001), Wiener filter (Vaseghi, 2008), Kalman filter (Paliwal, Basu, 1987), and Adaptive noise cancellation (Sambur, 1978;Jie, Zhenli, 2009). The second group is the robust feature extraction which improves the feature extraction algorithm by changing or modifying some inner processes to obtain the feature vectors.…”

Section: Introductionmentioning

confidence: 99%

Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition

Majeed¹,

Husain²,

Samad³

2015

Archives of Acoustics

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In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions.

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“…If the data are assumed to be i.i.d. stationary Gaussian, the denoising will coincide with a classical Wiener filter [18]. This solution is the simplest one but it is not the best according to the output image quality.…”

Section: Introductionmentioning

confidence: 99%

Image denoising based on the edge-process model

2005

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In this paper a novel stochastic image model in the transform domain is presented and its performance in image denoising application is experimentally validated. The proposed model exploits local subband image statistics and is based on geometrical priors. Contrarily to models based on local correlations, or mixture models, the proposed model performs a partition of the image into non-overlapping regions with distinctive statistics. A close form analytical solution of the image denoising problem for an additive white Gaussian noise (AWGN) is derived and its performance bounds are analyzed. Despite being very simple, the proposed stochastic image model provides a number of advantages in comparison to the existing approaches: (a) simplicity of stochastic image modeling; (b) completeness of the model, taking into account multiresolution, spatially adaptive image behavior, geometrical priors and providing an accurate fit to the global image statistics; (c) very low complexity of the algorithm; (d) tractability of the model and of the obtained results due to the closed-form solution and to the existence of analytical performance bounds; (e) extensibility to different transform domains, such as orthogonal, biorthogonal and overcomplete data representations.

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Advanced Signal Processing and Digital Noise Reduction

Cited by 302 publications

References 0 publications

Signal Recovery in Pulsed Terahertz Integrated Circuits

Signal Recovery in Pulsed Terahertz Integrated Circuits

Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition

Image denoising based on the edge-process model

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