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Smartphone Acoustic Sensing for Contactless Respiration Monitoring and Gesture-Based Authentication. [PDF]
Sensors (Basel)Nandini R, Mishra A.
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Recent Progress of Photodetectors and Optoelectronic Synapses Based on Metal Oxide Thin-Film Transistors. [PDF]
Materials (Basel)Ren J, Liang L, Cao H.
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Nanoporous gold electrode-assisted CRISPR/Cas12a electrochemical detection of synthetic methylated DNA models for breast cancer liquid-biopsy development. [PDF]
RSC AdvWang X +6 more
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Blind signal separation: statistical principles
Proceedings of the IEEE, 1998Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis that aim to recover unobserved signals or "sources" from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals.
J -F Cardoso
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Analytical method for blind binary signal separation
IEEE Transactions on Signal Processing, 1997 The blind separation of multiple co-channel binary digital signals using an antenna array involves finding a factorization of a data matrix X into X=AS, where all entries of S are +1 or -1. It is shown that this problem can be solved exactly and non-iteratively, via a certain generalized eigenvalue decomposition.
A -J van der Veen
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Blind Source Separation of Graph Signals
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020With a change of signal notion to graph signal, new means of performing blind source separation (BSS) appear. Particularly, existing independent component analysis (ICA) methods exploit the non-Gaussianity of the signals or other types of prior information.
Vorobyov, Sergiy A. +3 more
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On blind separation of nonstationary signals
Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005., 2006In this paper we consider a time-frequency based approach to blind separation of nonstationary signals. In particular, we propose a time-frequency ‘point selection’ algorithm based on multiple hypothesis testing, which allows automatic selection of auto- or cross-source locations on the time-frequency plane.
Luke A. Cirillo, Abdelhak M. Zoubir
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A neural network for blind signal separation
Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 2002An unsupervised neural network is constructed for the problem of blind signal separation. It is designed based on the condition that the outputs of the neural network are independent. A study of the stability of the neural network in the sense of expectation is presented. A stability condition on the system matrix A is obtained. Simulation studies show
Xie-Ting Ling +2 more
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Blind Separation of Cyclostationary Signals
2009In this paper, we propose a new method for the blind source separation with assuming that the source signals are cyclostationarity. The proposed method exploits the characteristics of cyclostationary signals in the Fraction-of-Time probability framework in order to simultaneously separate all sources without restricting the distribution or the number ...
Nhat Anh Cheviet +3 more
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Multichannel blind signal separation and reconstruction
IEEE Transactions on Speech and Audio Processing, 1997The separation of multiple signals from their superposition recorded at several sensors is addressed. The methods employ polyspectra of the sensor data in order to extract the unknown signals and estimate the finite impulse response (FIR) coupling systems via a linear equation based algorithm.
Sanyogita Shamsunder +1 more
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