Blind Source Separation Schemes for Mono-sensor and Multi-sensor Systems with Application to Signal Detection

Ali, Sadiq; Khan, Nabeel Ali; Haneef, Muhammad; Luo, Xiliang

doi:10.1007/s00034-017-0533-6

Cited by 15 publications

(6 citation statements)

References 41 publications

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“…There are various approaches to extracting signal from a mixture of signal and noise. This includes independent component analysis, blind signal separation and blind signal extraction methods [ 25 , 26 , 27 , 28 ]. The blind signal extraction method has an advantage over other known methods for reducing noise in the LED certification system.…”

Section: Led Efficiency Measurement Problemmentioning

confidence: 99%

Intelligent LED Certification System in Mass Production

Malykhina

Tarkhov

Shkodyrev

et al. 2021

Sensors

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It is impossible to effectively use light-emitting diodes (LEDs) in medicine and telecommunication systems without knowing their main characteristics, the most important of them being efficiency. Reliable measurement of LED efficiency holds particular significance for mass production automation. The method for measuring LED efficiency consists in comparing two cooling curves of the LED crystal obtained after exposure to short current pulses of positive and negative polarities. The measurement results are adversely affected by noise in the electrical measuring circuit. The widely used instrumental noise suppression filters, as well as classical digital infinite impulse response (IIR), finite impulse response (FIR) filters, and adaptive filters fail to yield satisfactory results. Unlike adaptive filters, blind methods do not require a special reference signal, which makes them more promising for removing noise and reconstructing the waveform when measuring the efficiency of LEDs. The article suggests a method for sequential blind signal extraction based on a cascading neural network. Statistical analysis of signal and noise values has revealed that the signal and the noise have different forms of the probability density function (PDF). Therefore, it is preferable to use high-order statistical moments characterizing the shape of the PDF for signal extraction. Generalized statistical moments were used as an objective function for optimization of neural network parameters, namely, generalized skewness and generalized kurtosis. The order of the generalized moments was chosen according to the criterion of the maximum Mahalanobis distance. The proposed method has made it possible to implement a multi-temporal comparison of the crystal cooling curves for measuring LED efficiency.

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Section: Led Efficiency Measurement Problemmentioning

confidence: 99%

Intelligent LED Certification System in Mass Production

Malykhina

Tarkhov

Shkodyrev

et al. 2021

Sensors

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“…Fusion of multimodal data has achieved more attention in various fields of application, including multi-media analysis [13], biometric score fusion [12], footstep detection [15], automatic target recognition [14]. A multi-source signal can either be a single channel obtained from single sensor or a multi-channel obtained from multi-sensors [8]. In case of multi-sensor recording, when the number of sensors is greater than that of the number of sources termed as overdetermined case, then the recovery of the original signal can be done using the linear combination of obtained mixed signals [8].…”

Section: Introductionmentioning

confidence: 99%

“…In case of multi-sensor recording, when the number of sensors is greater than that of the number of sources termed as overdetermined case, then the recovery of the original signal can be done using the linear combination of obtained mixed signals [8]. In order to model the complex dependencies over multivariate data for the evaluation of joint pdf, copula theory is used in [2][3][4][5][6][7][8]. Hence, the advantages of using the copula theory for LR based detection in the presence of multi modal data requires more computational price [5].…”

Section: Introductionmentioning

confidence: 99%

“…CS is a new signal front-end processing method that is developed in the recent years. It overcomes the signal bandwidth limits using the Shannon's theoretical signal recovery, and has the capability to complete the signal acquisition at a sampling rate less than twice the signal frequency [8]. This can reduce the computation needed for WSNs to obtain the signals, with the preservation of storage space and consumption of energy [10].…”

Section: Introductionmentioning

confidence: 99%

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Tree Copula Theory Based Fusion And Compressive Sensing For Activity Detection using Multi-Modal Data

Badiger¹,

M²

2019

IJITEE

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Activity detection based on likelihood ratio in the presence of high dimensional multimodal data acts as a challenging problem as the estimation of joint probability density functions (pdfs) with intermodal dependence is tedious. The existing method with above expectations fails due to poor performance in the presence of strongly dependent data. This paper proposes a Compressive Sensing Based Detection method in the Multi-sensor signal using the deep learning method. The proposed Tree copula- Grasshopper optimization based Deep Convolutional Neural Network (TC-GO based DCNN) detection method comprises of three main steps, such as compressive sensing, fusion and detection. The signals are initially collected from the sensors in order to subject them under tensor based compressive sensing. The compressed signals are then fused together using tree copula theory, and the parameters are estimated with the Grasshopper optimization algorithm (GOA). The activity detection is finally performed using DCNN, which is trained with the Stochastic Gradient Descent (SGD) Optimizer. The performance of the proposed method is evaluated based on the evaluation metrics, such as probability of detection and probability of false alarm. The highest probability of detection and least probability of false alarm are obtained as 0.9083, and 0.0959, respectively using the proposed method that shows the effectiveness of the proposed method in activity detection.

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“…For example, its recent applications include astronomical imaging, remote sensing, medical imaging, biological data analysis, image and speech signal processing, etc. [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Blind image separation using pyramid technique

Abbass

Kim

2018

J Image Video Proc.

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Signal and image separation is an important processing step for accurate image reconstruction, which is increasingly applied to many medical imaging applications and communication systems. Most of the conventional separation approaches are based on frequency domain and time domain. These approaches, however, are sensitive to noise and thus often produce undesirable results. In this paper, we propose a novel method of image separation. It incorporates the property of pyramid component extracted from the image and a finite ridgelet transform (FRT) to obtain a precise analysis of the images and thus correctly separate the images even in a highly noisy environment. We obtain the multiple components of the target images by employing a pyramid processing, which operates in the various domains and thus can decompose the image into multiple components. In addition, the pyramid decomposition in the proposed method can eliminate information redundancy in the target image and thus can substantially enhance the quality of image separation. We have conducted extensive simulations, which demonstrate that the proposed pyramid structure with FRT outperforms the conventional methods based on time domain and trigonometric transforms.

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Blind Source Separation Schemes for Mono-sensor and Multi-sensor Systems with Application to Signal Detection

Cited by 15 publications

References 41 publications

Intelligent LED Certification System in Mass Production

Intelligent LED Certification System in Mass Production

Tree Copula Theory Based Fusion And Compressive Sensing For Activity Detection using Multi-Modal Data

Blind image separation using pyramid technique

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