Denoising by Singular Value Decomposition and Its Application to Electronic Nose Data Processing

Jha, Sunil Kumar; Yadava, R. D. S.

doi:10.1109/jsen.2010.2049351

Cited by 123 publications

(56 citation statements)

References 31 publications

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“…This is needed as suggested in [19] to compensate for the effects of 'dirty RF' . To elaborate on this matter, in [45][46][47] the SVD as denoising technique is considered which can operate in combination with MTM and this has been referred to as MTM-SVD. It has been demonstrated [45,47] that MTM-SVD improves the decision process for spectrum sensing due to its noise reduction scheme, however, the improvement comes at the cost of unwanted bias problems where the number of tapers being used is critical in this respect.…”

Section: Related Studymentioning

confidence: 99%

Removing non-stationary noise in spectrum sensing using matrix factorization

Bloem

Schiphorst

Slump

2013

EURASIP J. Adv. Signal Process.

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Spectrum sensing is key to many applications like dynamic spectrum access (DSA) systems or telecom regulators who need to measure utilization of frequency bands. The International Telecommunication Union (ITU) recommends a 10 dB threshold above the noise to decide whether a channel is occupied or not. However, radio frequency (RF) receiver front-ends are non-ideal. This means that the obtained data is distorted with noise and imperfections from the analog front-end. As part of the front-end the automatic gain control (AGC) circuitry mainly affects the sensing performance as strong adjacent signals lift the noise level. To enhance the performance of spectrum sensing significantly we focus in this article on techniques to remove the noise caused by the AGC from the sensing data. In order to do this we have applied matrix factorization techniques, i.e., SVD (singular value decomposition) and NMF (non-negative matrix factorization), which enables signal space analysis. In addition, we use live measurement results to verify the performance and to remove the effects of the AGC from the sensing data using above mentioned techniques, i.e., applied on block-wise available spectrum data. In this article it is shown that the occupancy in the industrial, scientific and medical (ISM) band, obtained by using energy detection (ITU recommended threshold), can be an overestimation of spectrum usage by 60%.

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Section: Related Studymentioning

confidence: 99%

Removing non-stationary noise in spectrum sensing using matrix factorization

Bloem

Schiphorst

Slump

2013

EURASIP J. Adv. Signal Process.

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“…Often, different combination strategies work best in different application domains. In SAW sensor array-based vapour recognition system, it has been found recently [41][42][43] that a preprocessor comprised data normalisation wrt polymer thickness and vapour concentration, then logarithmic scaling followed by denoising by singular value decomposition (SVD) in combination with the principal component analysis (PCA) for feature extraction and the neural network classification yields substantially enhanced classification rate. The validation data used in these analyses were collected from the published literature, and pertained to the sensing of a number of volatile organic compounds including nerve agents and environmental hazards.…”

Section: Pattern Recognition Systemmentioning

confidence: 99%

“…The procedure implicitly assumes that the rank of the data matrix is lower than the number of sensors in the array. The details of SVD denoising are presented 43 . The data matrix regenerated on the basis of truncated SVD approximates the original data with reduced noise.The preprocessed data matrix as explained above is then PCA processed, and the first few principal components are taken to define the set of features to represent vapour identities.…”

Section: Pattern Recognition Systemmentioning

confidence: 99%

Development of Surface Acoustic Wave Electronic Nose

Jha

Yadava

2010

DSJ

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The paper proposes an effective method to design and develop surface acoustic wave (SAW) sensor array-based electronic nose systems for specific target applications. The paper suggests that before undertaking full hardware development empirically through hit and trial for sensor selection, it is prudent to develop accurate sensor array simulator for generating synthetic data and optimising sensor array design and pattern recognition system. The latter aspects are most time-consuming and cost-intensive parts in the development of an electronic nose system. This is because most of the electronic sensor platforms, circuit components, and electromechanical parts are available commercially-off-the-shelve (COTS), whereas knowledge about specific polymers and data analysis software are often guarded due to commercial or strategic interests. In this study, an 11-element SAW sensor array is modelled to detect and identify trinitrotoluene (TNT) and dinitrotoluene (DNT) explosive vapours in the presence of toluene, benzene, dimethylmethylphosphonate (DMMP) and humidity as interferents. Additive noise sources and outliers were included in the model for data generation. The pattern recognition system consists of: (i) a preprocessor based on logarithmic data scaling, dimensional autoscaling, and singular value decomposition-based denoising, (ii) principal component analysis (PCA)-based feature extractor, and (iii) an artificial neural network (ANN) classifier. The efficacy of this approach is illustrated by presenting detailed PCA analysis and classification results under varied conditions of noise and outlier, and by analysing comparative performance of four classifiers (neural network, k-nearest neighbour, naïve Bayes, and support vector machine).

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“…SVD gets better de-noising results than normal PCA methods through the bilateral decomposition method. Since 2004, SVD has also been developed for bearing fault signal processing [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Zhao et al [21] provided an algorithm to search for the effective singular values based on the maximum peak of the curvature spectrum, which improves the accuracy of the location regarding bearing damage. The same method was used by Jha et al in [16] to distill the position of demarcation; Banerjee et al in [22] proposed a supervised feature selection algorithm based on SVD-entropy. However, SVD-entropy based methods have a limitation.…”

Section: Introductionmentioning

confidence: 99%

Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Gao

Wang

et al. 2016

Applied Sciences

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Singular value decomposition (SVD) is a widely used and powerful tool for signal extraction under noise. Noise attenuation relies on the selection of the effective singular value because these values are significant features of the useful signal. Traditional methods of selecting effective singular values (or selecting the useful components to rebuild the faulty signal) consist of seeking the maximum peak of the differential spectrum of singular values. However, owing to the small number of selected effective singular values, these methods lead to excessive de-noised effects. In order to get a more appropriate number of effective singular values, which preserves the components of the original signal as much as possible, this paper used a difference curvature spectrum of incremental singular entropy to determine the number of effective singular values. Then the position was found where the difference of two peaks in the spectrum declines in an infinitely large degree for the first time, and this position was regarded as the boundary of singular values between noise and a useful signal. The experimental results showed that the modified methods could accurately extract the non-stationary bearing faulty signal under real background noise.

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Denoising by Singular Value Decomposition and Its Application to Electronic Nose Data Processing

Cited by 123 publications

References 31 publications

Removing non-stationary noise in spectrum sensing using matrix factorization

Removing non-stationary noise in spectrum sensing using matrix factorization

Development of Surface Acoustic Wave Electronic Nose

Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

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