An improved geometric overcomplete blind source separation algorithm

As more data-intensive applications emerge, advanced retrieval semantics, such as ranking and skylines, have attracted attention. Geographic information systems are such an application with massive spatial data. Our goal is to efficiently support skyline queries over massive spatial data. To achieve this goal, we first observe that the best known algorithm VS 2, despite its claim, may fail to deliver correct results. In contrast, we present a simple and efficient algorithm that computes the correct results. To validate the effectiveness and efficiency of our algorithm, we provide an extensive empirical comparison of our algorithm and VS 2 in several aspects.

Blind single-channel source separation is a long-standing machine learning and signal processing problem. Traditional blind source separation (BSS) algorithms were proposed to solve this task utilizing multiple signal constraints. Generative adversarial network (GAN) are free from statistical constraints and samples, but the role of adversarial training in the task of BSS has not been fully demonstrated. Therefore, a new separation network model that enables to learn the known separated signal distribution from the stepwise fine estimation of the unknown mixture distribution was presented in this paper, and a self-attention mechanism was introduced to solve the problem of the blurring details of the generated image by the generator which preserves image details in the process of image separation. Compared with the existing single-channel blind source separation algorithm based on generative adversarial network-neural egg separation (NES), the detailed information of this new separation algorithm is more prominent, and the source signal in the mixed image has been separated more effectively, and has better separation performance than the classic blind source separation algorithms.

The problem of blind separation of signals in a post-nonlinear mixture is addressed in this paper. A post-nonlinear mixture is formed by a component-wise nonlinear distortion of a linear mixture. Hence, a nonlinear adjusting part, placed in front of the linear separation structure, is needed to compensate for the distortion in separating such signals. The learning rules for the post-nonlinear separation structure are derived by a maximum likelihood approach. An algorithm for the blind separation of post-nonlinearly mixed suband super-Gaussian signals is proposed, based on some previous works. Multilayer perceptrons are used in this algorithm to model the nonlinear part of the separation structure. The algorithm switches between sub- and super-Gaussian probability models during learning according to a stability condition, and it operates in a block-adaptive manner. The effectiveness of the algorithm is verified by experiments on artificial and natural signals.

The question of how to separate individual brain and non-brain signals, mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings, is a significant problem in contemporary neuroscience. This study proposes and evaluates a novel EEG Blind Source Separation (BSS) algorithm based on a weak exclusion principle (WEP). The chief point in which it differs from most previous EEG BSS algorithms is that the proposed algorithm is not based upon the hypothesis that the sources are statistically independent. Our first step was to investigate algorithm performance on simulated signals which have ground truth. The purpose of this simulation is to illustrate the proposed algorithm's efficacy. The results show that the proposed algorithm has good separation performance. Then, we used the proposed algorithm to separate real EEG signals from a memory study using a revised version of Sternberg Task. The results show that the proposed algorithm can effectively separate the non-brain and brain sources.

In this paper, a time-domain blind source separation algorithm for non-stationary convolutive mixtures is proposed by reprogramming the vectors of convolutive mixture model and generalizing the joint approximate diagonalization method. Firstly the sampling convolutive mixture signals are reseted for matching instantaneous mixture model, then considering non-stationarity of the sources, space whitening and joint block-diagonalization method is exploited to obtain the original signals. This algorithm simplifies the convolutive mixture problem into the instantaneous mixture problem from a new point of view, so it avoids domain transformation and convolution operation, as well as decreases the complexity. Computer simulation verifies its effectiveness and gives the analysis results about the effect on the signal to interference ratio as its parameter changes.

Different standards to measure the sound power of a machine can be chosen. Historically, in a place with extremely loud background noise, the intensity standard (UNE-EN ISO 9614-X) has been used because there is not enough signal-to-noise relation for the pressure method (UNE-EN ISO 374x). Nevertheless, in some cases, the interfering intensity will make the measurement impossible. To solve this problem for both techniques, a new methodology based on the use of blind source separation algorithms is proposed in order to allow an estimation of the signals of the primary sources from some mixed signals and, therefore, to calculate the sound power of the machine of interest. To make the estimated signals as similar as possible to the real signals of the machine under test, different preprocessing techniques (FFT, DCT, cepstrum or wavelets) have been used before the blind source separation algorithms. Once the tests have been conducted for every machine, it is possible to establish at least one process, or combination to obtain results similar to those obtained using the international standards. Finally, since the signals can be scaled at the output of the blind source separation algorithms, one possible signal correction scheme is analyzed.

In the recent past, blind source separation (BSS) algorithms using multivariate statistical data analysis technique have been successfully used for source identification and separation in the field of biomedical and statistical signal processing. Recently numbers of different BSS techniques have been developed. With BSS methods being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper presents the performance of five BSS algorithms (SOBI, TDSEP, FastICA, JADE and Infomax) for decomposition of sEMG to identify subtle finger movements. It is observed that BSS algorithms based on second-order statistics (SOBI and TDSEP) gives better performance compared to algorithms based on higher-order statistics (FastICA, JADE and infomax).

We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (1) frequency-domain ICA with direction-of-arrival (DOA) estimation; (2) null beamforming based on the estimated DOA; (3) integration of (1) and (2) based on the algorithm diversity in both iteration and frequency domain. The inverse of the mixing matrix obtained by ICA is temporally substituted by the matrix based on null beamforming through iterative optimization, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.

Blind Source Separation (BSS) algorithms based on the noise-free model are not applicable when the Signal Noise Ratio (SNR) is low. In view of this situation, our solution is to denoise the mixtures with additive white Gaussian noise firstly, and then use BSS algorithms. This paper proposes a piecewise Empirical Mode Decomposition (EMD) thresholding approach to denoise mixtures with strong noise. This approach can distinguish the noise-dominated IMFs and signal-dominated IMFs, and then respectively apply different thresholdings methods. Simulation results show that compared with the Wavelet denoising, the proposed approach has a better denoising performance, and can remarkably enhance the separation performance of BSS algorithms, especially when the signal SNR is low.

With the increasing construction of high-voltage substation, the distance between resident area and substation is getting closer. Noise of substations has been an unavoidable problem which can be controlled better by analyzing it. Otherwise, the operational conditions of equipment in substation can be predicted by comparing the measured noise signals with the signals during normal operation. Corona noise emitted by high voltage transmission line, environmental quasi-stationary noise and noumenon noise generated by electric appliances are three main types of the noise signals in the substation which can't be measured independently by traditional methods. An Underdetermined Blind Source Separation (UBSS) algorithm based on the Sparse Component Analysis (SCA) was proposed to separate the mixed 220kV substation noise in this paper which can be applied to overcome the shortage of Independent Component Analysis (ICA). The feasibility of the algorithm was verified in the simulation and the experiment before using it to separate the substation noise. An approach to feature extraction based on the wavelet modulus maxima was employed to analyze the separated noise. All the results show that SCA can separat mixed substation noise efficiently.

This work presents the application of Blind Source Separation (BSS) Algorithms on Dual-band IR Spectrogram for breast cancer detection and tracing the effect of long-term chemotherapy for breast-cancer patients. We take Dual-band IR Spectrogram's RAW Data as an input to the BSS algorithms. Integrated into the back-end processor of a Dual-band IR Sensor and Readout Circuit Platform, our Improved Neighbor-based BSS algorithm operated on Dual-band IR Spectrogram provides a better breast cancer detection. Considering the demarcating degree, our Improved Neighbor-based BSS algorithm is approximately 15% better than other algorithms. As to the correctness rate, our improved algorithm approximately increases 10% compared with other algorithms.

This paper describes a novel modification to the well-known natural gradient or INFOMAX algorithm for blind source separation that largely mitigates its divergence problems. The modified algorithm imposes an a posteriori scalar gradient constraint that adds little computational complexity to the algorithm and exhibits fast convergence and excellent performance for fixed step size values that are largely independent of input signal magnitudes and initial separation matrix estimates. Evaluation of the approach for the separation of instantaneous and convolutive source mixtures using both time- and frequency-domain implementations shows its excellent separation behavior.

This paper presented a new blind source separation algorithm using the cyclostationary statistics of source signals. On the basis of the blind source separation model, we first determined several useful assumptions. Next we get an updating equation of separation matrix, making use of the independence and cyclostationary statistics of source signals. Later we discussed the situations of real signals, complex signals and normalized algorithms. Finally, the paper compared the algorithm with other three algorithms and analyzed the convergence and performance index (PI). The simulation results demonstrated that the new presented algorithm had faster convergence speed, better separation effects and robustness, comparing to the other algorithms. Its convergence speed is nearly twice as fast as the speed of normalized EASI algorithm.

We investigate an acoustic echo cancellation method using a blind source separation technique. In a hands-free mobile system, when the large acoustic noise picked up by the microphone is mixed with the echo, the echo cancellation system shows poor performance. We use a blind source separation algorithm to control the acoustic noise which has to be suppressed. The blind source separation algorithm separates the original echo signal from the observation signal which is mixed with acoustic noise. The separated echo signal is applied to the reference signal of the adaptive filter; this approach gives good performance of the adaptive algorithm for echo cancellation. Computer simulation results show the efficiency of the suggested method.

The existence of noise, sound scattering and reverberation in active sonar echo signal makes it very difficult to extract the target echo signal. This paper uses the convolution blind source separation algorithm to process the active sonar echo signal. The signal is transferred to the frequency-domain by sliding the STFT. The JADE blind source separation algorithm is used in the frequency domain, and the signal is finally inverted to the time domain through the ISTFT. The separation effect is compared and evaluated through similarity coefficients, time-domain waveforms, and time-frequency diagrams. The simulation results show that this method has a good effect on the separation of active sonar echo signals.