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IET Signal Processing

IET Signal Processing

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Modified quasi-maximum likelihood estimator for polynomial phase signal
Ahmed DallilAbdelaziz Ouldali
Keywords:frequency estimationmaximum likelihood estimationmean square error methodspolynomialsstochastic processesnoisy environmentroot mean square errordeterminist instantaneous frequency estimates selectionestimator performancessignal-to-noise ratiosshort signal observationpolynomial phase signalmodified quasimaximum likelihood estimator
Abstracts:In this study, the authors focus on the selection of instantaneous frequency estimates in the quasi-maximum likelihood estimator for polynomial phase signal. More specifically, they are interested in the case of short signal observations at low signal-to-noise ratios. In the first part of this work, the benefit of instantaneous frequency estimates selection for the above estimator performances is highlighted. Then the authors propose a modified quasi-maximum likelihood estimator based on a determinist instantaneous frequency estimates selection. Numerical studies provided at the end of this study showed clearly that the performances of the proposal in terms of root mean square error and signal to noise ratio threshold compared to the classical approach was better, in the considered case of short signal observation in a noisy environment.
Space-time matched filter design for interference suppression in coherent frequency diverse array
Huake WangGuisheng LiaoJingwei XuShengqi Zhu
Keywords:array signal processingfiltering theoryinterference suppressionmatched filtersquadratic programmingtwo-dimensional range-angle outputsone-dimensional range profilessidelobe interferencerange resolution enhancementoptimal filter parametersquadratic constraintsquadratic programinghybrid coding techniqueSTMFrange resolution scalestemporal transmitted waveformspatial steering vectoradaptive weight vectorstraditional adaptive beamforming algorithmsinterference directionstarget directionsmain lobesfilter structuresequivalent transmittwo-dimensional angle-time matched filtercoherent FDArange-angle dependencystable gainspatial coveragecoherent frequency diverse array radarsingle frequency-shifted waveforminterference suppressionspace-time matched filter design
Abstracts:By transmitting a single frequency-shifted waveform, coherent frequency diverse array (FDA) can provide a simple way to realize full spatial coverages with stable gains. Owing to the range-angle dependency in coherent FDA, the authors implement a two-dimensional angle-time matched filter to perform equivalent transmit beamforming and matched filtering simultaneously. However, such filter structures merely control main-lobes of equivalent transmit beams towards target directions. It fails to form nulls at interference directions. Moreover, traditional adaptive beamforming by designing adaptive weight vectors are no longer applicable. Additionally, they find that the range resolution scales linearly with the element number. To tackle these issues, a space-time matched filter (STMF) in combination with the hybrid coding technique is proposed. Aiming at mitigating interferences, the STMF is designed with a formulation of quadratically constrained quadratic programing. By relaxation of quadratic constraints, the hard non-convex problem can be turned into the second-order cone programing to obtain optimal filter parameters. Furthermore, the hybrid coding technique is devised to jointly improve the range resolution. Numerical experiments of both two-dimensional range-angle outputs and one-dimensional range profiles via filtering are provided, which demonstrate that the STMF with hybrid coding can effectively suppress sidelobe interferences with a range resolution enhancement.
Multi-kernel-based random vector functional link network with decomposed features for epileptic EEG signal classification
Sebamai ParijaPradipta Kishore DashRanjeeta Bisoi
Keywords:electroencephalographyentropyfeature extractionimage classificationmedical signal processingoptimisationsignal classificationsupport vector machineswavelet transformsepileptic EEG signalssensitivitymultikernel-based random vector functional link networkdecomposed featuresepileptic EEG signal classificationimproved hybrid modelempirical mode decompositionmultikernel random vector functional link networkkernel parametersefficient optimisation algorithmwater cycle algorithmepileptic electroencephalogram signalsWCA-EMD-WMKRVFLNkernel functionsintrinsic mode functionsimportant statistical based featuresentropy based features
Abstracts:This study proposes an improved hybrid model built with empirical mode decomposition (EMD) features combined with weighted multi-kernel random vector functional link network (WMKRVFLN) where the kernel parameters are optimised with an efficient optimisation algorithm known as water cycle algorithm (WCA) for diagnosis and classification of epileptic electroencephalogram (EEG) signals. The proposed model with optimisation is known as WCA-EMD-WMKRVFLN. The tanh and wavelet kernel functions are contributing together to the effectiveness of the proposed model. The features generated from EMD in terms of intrinsic mode functions (IMFs) are modulated to find important statistical and entropy based features and these features in a reduced form are employed as inputs to the model to classify epileptic EEG signals. The presented approach is evaluated in terms of percentage correct classification accuracy (ACC), specificity and sensitivity using two datasets and is compared with different classifiers and state-of-the-art techniques. The highest accuracies of 99.69% (five-class) and 100% (three-class) achieved using the Bonn-University dataset and 99.0% ACC (two-class) achieved using the Bern-Barcelona dataset. The achieved results report that the presented approach is a promising approach for EEG signal classification and is superior to several state-of-the-art techniques and is highly comparable to many such techniques.
Statistical inference for the block sparsity of complex-valued signals
Jianfeng WangZhiyong ZhouJun Yu
Keywords:compressed sensingsensitivity analysissignal reconstructionstatistical analysissensitivity analysiscompressive sensingstatistical inferencereal-valued signalsblock-sparse signalsblock sparsitycomplex-valued signal
Abstracts:Block sparsity is an important parameter in many algorithms to successfully recover block-sparse signals under the framework of compressive sensing. However, it is often unknown and needs to be estimated. Recently there emerges a few research works about how to estimate block sparsity of real-valued signals, while there is, to the best of our knowledge, no research that has been done on complex-valued signals. In this study, the authors propose a method to estimate the block sparsity of complex-valued signal. Its statistical properties are obtained and verified by simulations. In addition, the authors demonstrate the importance of accurately estimating the block sparsity through a sensitivity analysis.
Deep recurrent–convolutional neural network for classification of simultaneous EEG–fNIRS signals
Hamidreza GhonchiMansoor FatehVahid AbolghasemiSaideh FerdowsiMohsen Rezvani
Keywords:brain-computer interfacesconvolutional neural netselectroencephalographyinfrared spectroscopymedical signal processingrecurrent neural netssignal classificationsimultaneous EEG-fNIRS signal classificationdeep recurrent-convolutional neural networkspatial featurestemporal featureshuman braindeep neural network modeladjacent signalscomplex correlationsnear-infrared spectroscopyEEG signalstraditional BCI systemsbrain-computer interface
Abstracts:Brain-computer interface (BCI) is a powerful system for communicating between the brain and outside world. Traditional BCI systems work based on electroencephalogram (EEG) signals only. Recently, researchers have used a combination of EEG signals with other signals to improve the performance of BCI systems. Among these signals, the combination of EEG with functional near-infrared spectroscopy (fNIRS) has achieved favourable results. In most studies, only EEGs or fNIRs have been considered as chain-like sequences, and do not consider complex correlations between adjacent signals, neither in time nor channel location. In this study, a deep neural network model has been introduced to identify the exact objectives of the human brain by introducing temporal and spatial features. The proposed model incorporates the spatial relationship between EEG and fNIRS signals. This could be implemented by transforming the sequences of these chain-like signals into hierarchical three-rank tensors. The tests show that the proposed model has a precision of 99.6%.
Spectrum estimation using frequency shifting and decimation
Raymundo AlbertCecilia Galarza
Keywords:frequency estimationleast squares approximationsmean square error methodsspectral analysisroot mean square errorsignal to noise ratio regimefrequency separationclosely spaced frequenciesnumerical stabilityparametric spectral estimation techniquesfrequency shiftingspectrum estimation
Abstracts:Parametric spectral estimation techniques are widely used to estimate the parameters of sums of complex sinusoids corrupted by noise. In this work, the authors show that the numerical stability of the estimated frequencies not only depends on the size of the amplitudes associated with the real frequencies, but also to the distance among frequencies. Therefore, for closely spaced frequencies, the estimates are vulnerable to large deviate from their true values. To overcome this problem, they propose a strategy to artificially increase the frequency separation by downsampling the baseband equivalent of the noisy signal before applying a spectral estimation technique. This methodology significantly improves the estimation performance especially in the low signal to noise ratio regime. The performance of the technique is assessed in terms of the root mean square error and it is compared to results obtained in previous publications.
Block-online multi-channel speech enhancement using deep neural network-supported relative transfer function estimates
Jiri MalekZbynĕk KoldovskýMarek Bohac
Keywords:array signal processingneural netsspeech enhancementspeech recognitiontransfer functionsenhancement methodbaseline automatic speech recognition systemspeech qualityperceptual evaluationbatch processingprocessing regimeprocessed blockhighly dynamic environmentsrelative transfer functionsdeep neural network-based voice activity detectionvoice assistant scenariosshort utterancesblock-online processingdeep neural network-supported relative transfer function estimatesblock-online multichannel speech enhancementblock lengthtime 250.0 ms
Abstracts:This work addresses the problem of block-online processing for multi-channel speech enhancement. Such processing is vital in scenarios with moving speakers and/or when short utterances are processed, e.g. in voice assistant applications. We consider several variants of a system that performs beamforming supported by deep neural network-based voice activity detection followed by post-filtering. The speaker is targeted through estimating relative transfer functions between microphones. Each block of the input signals is processed independently to make the method applicable in highly dynamic environments. Due to short processed blocks, the statistics required by the beamformer are estimated less precisely. The influence of this inaccuracy is studied and compared to batch processing regime, when recordings are treated as one block. The experimental evaluation is performed on large datasets of CHiME-4 and another dataset featuring moving target speaker. The experiments are evaluated in terms of objective and perceptual criteria. Moreover, word error rate (WER) of a speech recognition system is evaluated, for which the method serves as a front-end. The results indicate that the proposed method is robust for short length of the processed block. Significant improvements in terms of the criteria and WER are observed even for the block length of 250 ms.
S-transform: from main concepts to some power quality applications
Carlos BeuterMario Oleskovicz
Keywords:Fourier transformspower supply qualityspectral analysisStockwell transformSTFTtime-varying harmonicsideal extractionspectral analysis toolspower quality applicationstime-varying signalshigh-frequency informationshort time Fourier transform
Abstracts:Among various spectral analysis tools arisen in the last years, some were more prominent, such as Fourier transform, windowed Fourier transform and wavelet transform (WT). Nevertheless, all of them present implementation restrictions for an ideal extraction for low as well as high frequencies, of variant signals in time, as, for example, signals containing time-varying harmonics. In this sense, this study presents a review of concepts on the S-transform (ST), also known as Stockwell transform, applied in the analysis of some signals in the context of power quality (PQ). ST gathers, in a single function, positive qualities of both short time Fourier transform (STFT) and WT. This study presents a mathematical basis and some considerations regarding ST, referring to published papers, including a comparison between ST and STFT, as well as WT. It is worth emphasising that a final solution for the extraction of the low- and high-frequency information from time-varying signals is not yet available. ST is a satisfactory approach, but it still needs more detailed studies. This study presents a necessary set of steps for a better understanding of ST, reproduction of examples found in correspondent literature, as well as the ones regarding PQ.
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