A non-iterative blind signal separation algorithm based on transmit diversity and coding

This paper investigates the performance of blind signal separation (BSS) algorithms that exploit the temporal predictability of speech. Specifically, the investigation considers how the separation performance of two BSS algorithms will be affected when the length of the AR process (used in the algorithms to model speech) is varied. The investigation concludes that the length of the AR process (prediction order) has a significant impact on separation performance. In particular, the separation performance of both algorithms is degraded, if the AR model’s prediction order, over fits, or under fits, the temporal structure of the speech. It is revealed that a prediction order of 3050 provides maximum separation performance for natural speech, however a prediction order of 10 is more applicable if computational cost is a consideration.

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.

2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

Electroencephalography (EEG) has been used extensively for classifying cognitive tasks. Many investigators have demonstrated classification accuracies well over 90% for some combinations of cognitive tasks, signal transformations, and classification methods. Unfortunately, EEG data is prone to significant interference from a wide variety of artifacts, particularly eye blinks. Most methods for classifying cognitive tasks with EEG data simply discard time windows containing eye blink artifacts. However, future applications of EEG-based cognitive task classification should not be hindered by eye blinks. The value of an EEG-controlled human-computer interface, for instance, would be severely diluted if it did not work in the presence of eye blinks. Fortunately, recent advances in blind signal separation algorithms and their applications to EEG data mitigate the artifact contamination issue. In this paper, we show how independent components analysis (ICA) and its extension for sub-Gaussian sources, extended ICA (eICA), can be applied to accurately classify cognitive tasks with eye blink contaminated EEG recordings.

The role of the intelligent optimization algorithm in the blind separation algorithm is to make the independence decision function enter the global optimal position, but the performance of many optimization algorithms depends on the choice of control parameters. In this paper, we use the simplex neighborhood convex set approximation idea, combined with the full random search and multi-role state of the search operator, we use a single-parameter surface-simplex swarm evolution (SSSE), which has a single parameter and pure randomness. The characteristics of the search can overcome the shortcomings of the algorithm’s dependence on the control parameters and easily fall into the local optimum, and improve the application effectiveness of the blind separation algorithm. In the experiment, the fourth-order cumulant (kurtness) is used as the criterion function in ICA, and the improved blind separation algorithm is applied to the blind separation of speech and background music signals. Experimental results show that the improved algorithm effectively separates speech components from the background. The music component and the algorithm have better performance in terms of convergence speed, algorithm stability, and separation effect.

Non-parametric data representation can be done by means of a potential function. This paper introduces a methodology for finding modes of the potential function. Two different methods are considered for the potential function representation: by using summations of Gaussian kernels, and by employing quantum clustering. In the second case each data sample is associated with a quantum physics particle that has a radial energy field around its location. Both methods use a scaling parameter (bandwidth) to model the strength of the influence around each data sample. We estimate the scaling parameter as the mean of the Gamma distribution that models the variances of K-nearest data samples to any given data. The local Hessian is used afterwards to find the modes of the resulting potential function. Each mode is associated with a cluster. We apply the proposed algorithm for blind signal separation and for the topographic segmentation of radar images of terrain.

A new algorithm for blind signal separation that does not require pre-whitening is proposed in this paper. The algorithm is based on an iterative inversion of the mixing matrix. The algorithm is capable of working on-line and provides improved convergence speed and steady state error compared to the popular natural gradient algorithm, with a low additional computational cost.

Under-determined blind separation becomes poorer with increasing number of tags, to the point where it cannot separate source tag signals, reducing overall system performance. This paper proposes a parallelizable identification anti-collision algorithm based on non-negative matrix factorization and adaptive ID sequence grouping of binary tree slots. The number of tags in each group can be controlled within the optimum range by selecting a reasonable number to retained source signal separation in the RFID system, which will greatly improve system performance. With the Matlab software numerical calculation and simulation, the results show that tag identification rate improves from 152.8% to 359.2% compared with the blind separation and dynamic bit-slot group algorithm using the same multi-antenna technology for 4–16 antennas, while increasing tag identification speed from 60% to 78.4%. Thus, the proposed algorithm provides high efficiency and low cost and will have very good application for fast identification of large numbers of tags.

The paper studies the capacities of time hopping ultra wideband (TH-UWB) systems by adopting orthogonal pulse-based modulation schemes in a multipath environment with the consideration of multiple access interference (MAI) and inter symbol interference (ISI). Recently, orthogonal pulse-based modulation schemes such as pulse shape modulation (PSM), biorthogonal PSM (BPSM) and the combination of orthogonal pulse position modulation (OPPM) and BPSM have been proposed for UWB system to reduce ISI and MAI. In this paper, we first investigate the influences of ISI and MAI on the capacities of TH-UWB systems in terms of signal-to-interference and noise ratio (SINR) for high-order modulation schemes based on various sets of orthogonal pulses. How mutual information varies with the number of multipath components (MPCs) is then analyzed. The complete mathematical analysis and simulation results are provided in detail. When maximizing the mutual information, the Gaussian distribution approximate is assumed to describe the characteristics of MAI and ISI. Simulation results are provided to verify the theoretical analysis. Some conclusions are drawn thereafter, such as that capacity depends on the pulse patterns adopted and mutual information varies with inversely proportional to the number of MPCs. We also observed that the capacities of UWB systems for OPPM-BPSM schemes are larger than those of PSM and BPSM schemes.

Choosing a proper Region of Interest (ROI) for Remote Photoplethysmography (rPPG) is essential and a challenging first step, and it has a direct effect on the accuracy and reliability of the overall heart rate (HR) algorithm. Non-skin areas have no contribution to the HR information; however, few works have tackled the issue of non-skin pixels included in the ROI. First, this paper considers improving the quality of the rPPG signal by filtering out non-skin pixels included within the ROI. The feasibility of employing skin segmentation for ROI definition is demonstrated. Then, this technique is compared with our previous real-time rPPG-based method. Moreover, we explore the effect of extracting the HR from three ROIs using signal fusion. Second, we give a comprehensive account of the examined methods in our algorithm for face detection, face tracking, skin detection, and blind signal separation. Finally, we compare our rPPG measurements with ground truth values obtained from a commercial pulse oximeter. Based on the simulation results, the proposed algorrithm significantly improves the quality of the rPPG technique.

When the channel wave propagates in the coal seam and encounters an abnormal structure, the frequency, amplitude and other parameters will change. Receiving the channel wave signal clearly and extracting the time-frequency characteristics accurately are the keys to predict the abnormal structure ahead. In this paper, the phased source and new feature extraction method were employed for channel wave seismic exploration. The controllable phased source is used to generate channel wave. The CEEMDAN algorithm based on the EMD algorithm and the Fast ICA algorithm for blind source signal separation are combined new Fast ICA-CEEMDAN method for decomposing the channel wave signal. The homogeneous medium and the equivalent medium model of the mined-out coal measure stratum are established by the COMSOL multiphysics, the received channel wave signal is decomposed by Fast ICA-CEEMDAN method. The Hilbert marginal spectrum, time spectrum, instantaneous frequency spectrum and instantaneous frequency spectrum of channel wave signal were extracted. There are five characteristic parameters of phase spectrum and instantaneous amplitude spectrum. The results show that the method can accurately reflect the local characteristics of the channel wave signal, and has high time-frequency resolution, which provides favorable reference for coal mining.

For the blind source separation algorithm, choices of nonlinear function and step-size are very important. In this paper, we propose a novel natural gradient speech blind separation algorithm. The main ingredients are the use of a variable step-size technology and a nonlinear function based on Laplacian normal mixture distribution. Simulation results indicate the proposed method ensures steady state error of algorithm and accelerates convergence speed of algorithm.

The problem of noise amplification in the multichannel blind separation of communication signals is researched. A new algorithm and its improved algorithm to seek the separation matrix is proposed based on the idea of maximizing the signal-to-interference-plus-noise power ratio (SINR). When the condition number of the separation matrix is large, the separation matrix sought by this algorithm and its improved algorithm can beautifully solve the problem of separation performance degradation caused by noise amplification. Simulation results show that this algorithm and its improved algorithm can reduce the bit error rate effectively and improve the performance of blind separation, exhibiting practical application value.

A cyclostationary-based blind signal separation (BSS) algorithm is proposed in this paper. The proposed separation algorithm is used for simultaneously received multiple spectrum overlapped BPSK signals in single channel electromagnetic surveillance system. The mixed signal is separated by means of blind adaptive frequency shift (BA-FRESH) filter based on the independent signals’ cyclostationary. This BA-FRESH filtering technique does not require training signals. It can separate the independent signals only by knowing some of their cycle frequencies, which is gained by the second order cycle cumulants in this work. Numerical simulation results proved the effectiveness of this separation method.