Multichannel Signal Processing Research Articles

PSO driven designing of robust and computation efficient 1D-CNN architecture for transmission line fault detection

In spite of the amazing success of deep learning in various complex classification tasks, its real-time applications have been greatly hindered due to the enormous computational burden and extensive processing time. In this context, the present investigation is a path-breaking idea and journey toward designing robust and computationally efficient One-dimensional Convolutional Neural Networks (1D-CNN) for the classification of transmission line faults, which demands accurate decisions within 20 ms. Following the multi-channel signal processing approach, sampled three-phase line currents of each half-cycle duration are concatenated to form a one-dimensional array and fed to the 1D-CNN model for exploring unique patterns of line currents associated with various faults. A Particle Swarm Optimization (PSO) driven multi-objective optimization technique is exclusively used to select an optimum number of kernels and kernel sizes for designing a less computation-intensive but robust CNN architecture, making it suitable for fast fault detection and classification without compromising the performance. The efficacy of this holistic design approach has been demonstrated in the simulated environment of an IEEE 5-bus system with significant penetration of distributed generation. With excellent fault pattern extraction through the convolution process, the optimally designed 1D-CNN has proven its strength for accurate transmission line fault classification by combating bidirectional power flow due to renewable energy penetration. The emulation time of the optimally designed 1D-CNN architecture for low-cost, low-power FPGA demonstrates its fastness and suitability for time-critical real-time transmission line fault classification. In general the proposition in the paper explains the candidate Deep Learning (DL) algorithm for multi-channel signal processing focusing on low-computing edge devices.

FPGA-based multi-channel thermocouple temperature measurement system

Aiming at the high and low temperature test requirements of spacecraft before being sent into space, this paper designs and implements a multi-channel thermocouple temperature measurement system based on FPGA.The system adopts thermocouple as temperature sensor, and ADT7310 is used as thermocouple cold junction temperature compensation, which solves the problem of thermocouple cold junction compensation better.The front end adopts AD7193, a 24-bit analog-to-digital converter with low noise, multi-channel and low power consumption, to amplify and A/D convert the signal at measuring junction of thermocouple.The control core is a field programmable logic gate array FPGA, which can better solve the problem of parallel processing of multi-channel temperature signals. At the same time, it uses the transposition method of the thermocouple standard graduation table, the interpolation look-up table algorithm and the cold junction compensation reverse look-up table algorithm, which is effective The temperature measurement accuracy of each channel is improved.The experimental results show that the system can be connected to 24 K-type or T-type thermocouple sensors, the measurement range can reach -200°C∼220°C (73.15K∼493.15K), and the relative error is less than ±0.3%F.S.

The Effect of Multichannel and Channel-Free Hearing Aids on Spectral-Temporal Resolution and Speech Understanding in Noise.

Identifying and understanding speech is difficult for individuals with sensorineural hearing loss, especially in noisy environments. Possible causes include less audibility of the signal, impaired temporal resolution, and low selectivity of frequency. The hearing aid is the most common option used to minimize the problems faced by individuals with sensorineural hearing loss. This article investigates the effects of multichannel and channel-free hearing aid signal processing techniques on spectral-temporal resolution and speech understanding in noise. An experimental study was used in which the determined tests were applied to the participants. Thirty-four individuals with bilateral symmetrical sensorineural hearing loss between the ages of 18 and 70 were included in our study. Spectral-temporally modulated ripple test, random gap detection test (RGDT), and Turkish matrix test were applied to the participants using multichannel and channel-free hearing aids. All the data obtained were compared statistically in terms of the performances of the hearing aids. There was no significant difference between multichannel and channel-free hearing aids for spectral resolution and speech understanding in noise tests (p > 0.05). While there was no significant difference between the two hearing aids for 500 and 4,000 Hz RGDT in temporal resolution measurement (p > 0.05), for 1,000 Hz (p = 0.045), 2,000 Hz (p = 0.046), and composite RGDT (p = 0.001), statistically significant better performances were obtained with the channel-free hearing aids. It is thought that faster processing of the incoming signal in the channel-free hearing aids improves the temporal resolution performance. It is predicted that our study findings might help to determine the signal processing technique that will maximize the communication skills of the patients in various conditions.

Quaternion empirical wavelet transform and its applications in rolling bearing fault diagnosis

In view of the fact that it is difficult to predict the location and size of the failure of rotating machinery, the sensors sometimes cannot collect the fault information. When the fault location differs from the sensor direction by 180°, the fault information in the signal is weak and may be masked by noise. Multi-channel signal acquisition is one of the methods to solve the problem. Although existing algorithms can extract fault information from a single-channel signal, they are inefficient and sometimes fail to extract weak fault information. Therefore, the information in each channel needs to be associated and combined in advance. This paper proposes a multi-channel signal processing method called quaternion empirical wavelet transform (QEWT). QEWT based on the quaternion Fourier transform can process multiple groups of vibration signals at the same time, which realizes synchronous spectral division, fusion and modal decomposition. The modal separation method based on spectral trend avoids the problems of modal aliasing and low efficiency of scale-space representation. The proposed method combines spectral negentropy with the identification of the fault characteristics of the outer ring and the inner ring, and successfully realizes the fault diagnosis of the rolling bearing.

On the asymptotic behavior of linearly constrained filters for robust multi-channel signal processing

The Kalman filter (KF) is known to loose its optimality properties when the model used does not perfectly match the true system. Extending the use of linear constraints to this filter recently proved to be efficient to mitigate a large class of parametric model mismatch, through the linearly constrained Kalman filter and minimum variance filter (LCKF and LCMVF). However, the asymptotic performances of these new filters are still an open question. In this work, we bring a first answer to the latter problem in the case of measurement model mismatch. We show that both LCKF and LCMVF are equivalent to unconstrained filters in which the directions of the constraints are cancelled by projection, allowing a better understanding of their asymptotic properties. In particular, the steady-state mean square error, when it exists, is derived. The consistency of the filters with respect to nonlinear mismatches is also improved via new constraints. An array processing example is provided to assess the derived formulas, the consistency and performance of the filters.

Multi-Channel and Multi-Agent Signal Processing

Multi-channel and array signal processing is a well-established field with fundamental applications in wireless communications, radar/sonar, remote sensing, medical imaging, and more [...]

A Hybrid Deep Learning Scheme for Multi-Channel Sleep Stage Classification

Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG). Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods. Traditional hand-crafted feature extraction methods choose features manually from raw data, which is tedious, and these features are limited in their ability to balance efficiency and accuracy. Moreover, most of the existing works on sleep staging are either single channel (a single-lead EEG may not contain enough information) or only EEG signal based which can not reveal more complicated physical features for reliable classification of various sleep stages. This study proposes an approach to combine Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) that can discover hidden features from multi-biological signal data to recognize the different sleep stages efficiently. In the proposed scheme, the CNN is designed to extract concealed features from the multi-biological signals, and the GRU is employed to automatically learn the transition rules among different sleep stages. After that, the softmax layers are used to classify various sleep stages. The proposed method was tested on two publicly available databases: Sleep Heart Health Study (SHHS) and St. Vincent's University Hospital/University College Dublin Sleep Apnoea (UCDDB). The experimental results reveal that the proposed model yields better performance compared to state-of-the-art works. Our proposed scheme will assist in building a new system to deal with multi-channel or multi-modal signal processing tasks in various applications.

Kronecker-structured covariance models for multiway data

Many applications produce multiway data of exceedingly high dimension. Modeling such multi-way data is important in multichannel signal and video processing where sensors produce multi-indexed data, e.g. over spatial, frequency, and temporal dimensions. We will address the challenges of covariance representation of multiway data and review some of the progress in statistical modeling of multiway covariance over the past two decades, focusing on tensor-valued covariance models and their inference. We will illustrate through a space weather application: predicting the evolution of solar active regions over time.

A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology

The goal of this paper is to present a theoretical and practical introduction to generalized eigendecomposition (GED), which is a robust and flexible framework used for dimension reduction and source separation in multichannel signal processing. In cognitive electrophysiology, GED is used to create spatial filters that maximize a researcher-specified contrast. For example, one may wish to exploit an assumption that different sources have different frequency content, or that sources vary in magnitude across experimental conditions. GED is fast and easy to compute, performs well in simulated and real data, and is easily adaptable to a variety of specific research goals. This paper introduces GED in a way that ties together myriad individual publications and applications of GED in electrophysiology, and provides sample MATLAB and Python code that can be tested and adapted. Practical considerations and issues that often arise in applications are discussed.

ЗАСТОСУВАННЯ ПРОСТОРОВОЇ ОБРОБКИ В СИСТЕМАХ ЗВ’ЯЗКУ Й ПЕРЕДАЧІ ДАНИХ КОМПЛЕКСІВ РАДІОМОНІТОРИНГУ ДЛЯ ЗАБЕЗПЕЧЕННЯ ЗАВАДОЗАХИЩЕНОСТІ

The article investigates the issue of improving the level of environmental in communication systems and data transmission of radio monitoring complexes. The relevance of this task is due to the complexity of the modern electronic environment, which is characterized by high dynamics and saturation with electronic means that work on radiation and have a mutual impact, as well as the importance of tasks performed by military radio monitoring units. The paper proposes the use of the algorithm of multichannel weight spatial processing of signals against the background of interference to improve the obstruction of communication systems and data transmission on the basis of the radio relay station "R-415V". This approach is the most rational, since the compensation of existing obstacles is carried out subject to the presence of spatial differences between the directions of the arrival of a useful signal and the obstacle that it is typical for radio relay stations. In addition, the technical prerequisite for the implementation of the proposed solution is the presence in the radio relay station "P 415V" of the directional antenna "DB11", consisting of two identical z-emitters and is the simplest two-element antenna grille that can be used to implement multichannel weight treatment. The article presents the spatial processing algorithm, selected performance indicators and investigated the effectiveness of its work. The calculations were carried out using the computer algebra system – Mathcad 15. Were obtained visual, quantitative and qualitative results of performance indicators depending on the selected electronic situation for certain tactical and technical characteristics of the means of transmitting information without taking into account the influence of destabilizing factors on the quality of processing.

A novel Lanczos quaternion singular spectrum analysis method and its application to bevel gear fault diagnosis with multi-channel signals

Multi-channel signals collected by multiple sensors contain more operating information than single-channel signal, so multi-channel signal processing method can improve confidence level and accuracy of fault diagnosis. Multivariate empirical mode decomposition (MEMD) is the most widely used multi-channel signal method, however, it has the problem of mode mixing. Quaternion singular spectrum analysis (QSSA) is an effective multi-channel signal denoising method with three defects. It needs huge calculation time, the contribution of irrelevant components exists in selected singular values, and the obtained denoising signal derives from certain single-channel signal. Hence, a novel multi-channel signal processing method called Lanczos quaternion singular spectrum analysis (LQSSA) is proposed in this paper. First, LQSSA uses Lanczos method during the decomposition of the proposed method, which reduces the calculation time greatly. Then, filter value factor is obtained by introducing Lagrange multiplier to suppress the contribution of the irrelevant components and improve the purity of required signal. Finally, periodic similarity is used to obtain Lanczos quaternion singular spectrum components (LQSSCs) by taking the signal components as a whole, so it breaks the restriction between different channels. The proposed method is applied to simulated signals and experimental signals of bevel gear, and the analysis results show that the proposed method can extract the fault characteristic frequency from the multi-channel signals effectively.

BeamLearning: An end-to-end deep learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data.

Sound source localization using multichannel signal processing has been a subject of active research for decades. In recent years, the use of deep learning in audio signal processing has significantly improved the performances for machine hearing. This has motivated the scientific community to also develop machine learning strategies for source localization applications. This paper presents BeamLearning, a multiresolution deep learning approach that allows the encoding of relevant information contained in unprocessed time-domain acoustic signals captured by microphone arrays. The use of raw data aims at avoiding the simplifying hypothesis that most traditional model-based localization methods rely on. Benefits of its use are shown for real-time sound source two-dimensional localization tasks in reverberating and noisy environments. Since supervised machine learning approaches require large-sized, physically realistic, precisely labelled datasets, a fast graphics processing unit-based computation of room impulse responses was developed using fractional delays for image source models. A thorough analysis of the network representation and extensive performance tests are carried out using the BeamLearning network with synthetic and experimental datasets. Obtained results demonstrate that the BeamLearning approach significantly outperforms the wideband MUSIC and steered response power-phase transform methods in terms of localization accuracy and computational efficiency in the presence of heavy measurement noise and reverberation.

Ambiguity Suppression Based on Joint Optimization for Multichannel Hybrid and ±π/4 Quad-Pol SAR Systems

Hybrid and ±π/4 quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) systems operating from space can obtain all polarimetric components simultaneously but suffer from severe azimuth ambiguities in the cross-polarized (cross-pol) measurement channels. In this paper, the hybrid and ±π/4 quad-pol SAR systems with multiple receive channels in azimuth are widely investigated to suppress the azimuth ambiguities of the cross-pol components. We first provide a more thorough analysis of the multichannel hybrid and ±π/4 quad-pol SAR systems. Then, the multichannel signal processing is briefly discussed for the reconstruction of the quad-pol SAR signal from the aliased signals, in which the conventional reconstruction algorithm causes extremely severe azimuth ambiguities. To this end, an improved reconstruction method is proposed based on a joint optimization, which allows for the minimization of ambiguities from the desired polarization and the simultaneous power of undesired polarized signal. This method can largely suppress azimuth ambiguities compared with the conventional reconstruction algorithm. Finally, to verify the advantages and effectiveness of the proposed approach, the azimuth ambiguity-to-signal ratio (AASR), the range ambiguity-to-signal ratio (RASR) and signal-to-noise ratio (SNR) of all polarizations, as well as a set of imaging simulation results, are given to describe the effects of reconstruction on the multichannel hybrid and ±π/4 quad-pol SAR systems.

Single Versus Multi-channel Dispersion Analysis of Ultrasonic Guided Waves Propagating in Long Bones.

Ultrasonic guided wave techniques have been applied to characterize cortical bone for osteoporosis assessment. Compared with the current gold-standard X-ray-based diagnostic methods, ultrasound-based techniques pose some advantages such as compactness, low cost, lack of ionizing radiation, and their ability to detect the mechanical properties of the cortex. Axial transmission technique with a source-receiver offset is employed to acquire the ultrasound data. The dispersion characteristics of the guided waves in bones are normally analyzed in the transformed domains using the dispersion curves. The transformed domain can be time-frequency map using a single channel or wavenumber-frequency (or phase velocity-frequency) map with multi-channels. In terms of acquisition effort, the first method is more cost- and time-effective than the latter. However, it remains unclear whether single-channel dispersion analysis can provide as much quantitative guided-wave information as the multi-channel analysis. The objective of this study is to compare the two methods using numerically simulated and ex vivo data of a simple bovine bone plate and explore their advantages and disadvantages. Both single- and multi-channel signal processing approaches are implemented using sparsity-constrained optimization algorithms to reinforce the focusing power. While the single-channel data acquisition and processing are much faster than those of the multi-channel, modal identification and analysis of the multi-channel data are straightforward and more convincing.

A Novel Weight Generator in Real-Time Processing Architecture of DBF-SAR

Digital beamforming (DBF) with scan-on-receive (SCORE) technique in elevation is a powerful technique that enables a spaceborne synthetic aperture radar (SAR) to achieve high-resolution wide swath (HRWS) imaging. In the spaceborne DBF-SAR system, sampling signals from each channel are weighted by weights generated by a digital signal processing system in real-time. However, the contradiction between the shortage of spaceborne hardware resources and resource demand of the multichannel real-time signal processing increases the difficulty of system design. In order to solve this problem, a novel weight generator and an improved intermediate frequency (IF) DBF real-time processing architecture are proposed in this article. By taking advantage of the special properties of the SCORE algorithm, the proposed weight generator calculates weights using a linear polynomial algorithm. The simulation result shows that a low-order approximation can achieve high performance. The proposed generator can correct multichannel amplitude and phase error at a low cost on hardware resources. The effectiveness of the proposed method is verified by experiments with a raw data processing instance of an X-band 16 channels DBF-SAR.

Design and Implementation of a Real-Time Multi-Beam Sonar System Based on FPGA and DSP.

Aiming at addressing the contradiction between the high-speed real-time positioning and multi-channel signal processing in multi-beam sonar systems, in this work we present a real-time multi-beam sonar system based on a Field Programmable Gate Array (FPGA) and Digital Signal Processing (DSP) from two perspectives, i.e., hardware implementation and software optimization. In terms of hardware, an efficient high-voltage pulse transmitting module and a multi-channel data acquisition module with time versus gain (TVG) compensation with characteristics such as low noise and high phase amplitude consistency, are proposed. In terms of algorithms, we study three beamforming methods, namely delay-and-sum (D&S), direct-method (DM) and Chirp Zeta Transform (CZT). We compare the computational efficiency of DM and CZT in the digital domain. In terms of software, according to the transmission bandwidth of the Gigabit Ethernet and a serial rapid IO (SRIO) interface, the data transmission paths of the acquired data and the beam pattern between the FPGA, the DSP, and a personal computer (PC) are planned. A master-slave multi-core pipelined signal processing architecture is designed based on DSP, which enhances the data throughput of the signal processor by seven times as compared with that of the single-core operation. The experimental results reveal that the sound source level of the transmitting module is around 190.25 dB, the transmitting beam width is 64° × 64°, the background noise of the acquisition module is less than 4 μVrms, the amplitude consistency error of each channel is less than −6.55 dB, and the phase consistency error is less than 0.2°. It is noteworthy that the beam number of the sonar system is 90 × 90, the scanning angle interval is 0.33°, the working distance ranges from 5 m to 40 m, and the maximum distance resolution is 0.384 m. In the positioning experiment performed in this work; the 3-D real-time position of the baffle placed in the detection sector is realized. Please note that the maximum deviation of azimuth is 2°, the maximum deviation of elevation is 2.3°, and the maximum distance deviation is 0.379 m.

Design of multi-channel data acquisition system for real-time perception lidar

Lidar is the most effective means for real-time environmental awareness. Based on the demand of multi-channel perception lidar with large amount of data, real-time data transmission and miniaturization, a multi-channel parallel signal acquisition and processing system was designed based on FPGA and DSP. A self-developed three-dimensional lidar was designed, including mechanical scanning, linear array detection, data acquisition and control system. The system was implemented and verified in the three-dimensional point cloud imaging experiments. In this system, FPGA was responsible for multi-channel lidar data acquisition, control and transmission. DSP was used for data calculation and uploading the point cloud data to the upper computer through the network to realize the real-time display of point cloud. The experiments of lidar show that the proposed real-time data acquisition system could meet the requirements of point cloud calculation at 2 Mpts/s, with real time data acquisition and procession at 20 fps. The rapid calculation of lidar point cloud of the environment and obstacles is realized. It can be applied to automatic driving, navigation obstacle avoidance, perimeter security and etc.

FMCW Radar With Enhanced Resolution and Processing Time by Beam Switching

We present the design of a novel K-band radar architecture for short-range target detection. Applications include direction finding systems and automotive radar. The developed system is compact and low cost and employs substrate-integrated-waveguide (SIW) antenna arrays and a $4\times 4$ Butler matrix (BM) beamformer. In particular, the proposed radar transmits a frequency modulated continuous-wave (FMCW) signal at 24 GHz, scanning the horizontal plane by switching the four input ports of the BM in time. Also, in conjunction with a new processing method for the received radar signals, the architecture is able to provide enhanced resolution at reduced computational burden and when compared to more standard single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) systems. The radar performance has also been measured in an anechoic chamber and results have been analyzed by illuminating and identifying test targets which are 2° apart, while also making comparisons to SIMO and MIMO FMCW radars. Moreover, the proposed radar architecture, by appropriate design, can also be scaled to operate at other microwave and millimeter-wave frequencies, while also providing a computationally efficient multi-channel radar signal processing platform.

Ambisonic Signal Processing DNNs Guaranteeing Rotation, Scale and Time Translation Equivariance

We propose a novel framework to design Ambisonic signal processing deep neural networks (DNNs) that guarantee physical symmetries. In general, spatial acoustic signal processing DNNs for, e.g., sound event detection, ought to perform with the equivalent accuracy regardless of the directions of arrival of sound sources. This property is well known as rotation symmetry in natural science. However, in most conventional multichannel signal processing DNNs, rotation symmetry has not been explicitly incorporated into the model structure, and pseudo rotation symmetry has been acquired by training models with a large amount of signal datasets arriving from various directions. Therefore, the conventional methods will not perform sufficiently when the training dataset is relatively small scale or statistically biased, e.g., the distribution of the arriving directions of the sound events is inhomogeneous. Furthermore, in order to efficiently handle acoustic signals in DNNs, it is necessary to consider several additional symmetries, such as amplitude scaling and time translation of the signals. In this paper, we integratedly formulate these symmetry assumptions, which are called equivariance, in the form of constraints for our targeted DNN design. We propose a new DNN design method called Clebsch-Gordan Nets with Scale and Time translation Symmetry (CGNets-STS), which guarantees to simultaneously satisfy three types of equivariance (3D rotation, amplitude scaling, and time translation). As an instance of this method, we design a DNN model for sound event localization and detection tasks from Ambisonic signals. Experimental results show that this model is highly robust against spatial rotations for input data.

A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm

The modulation recognition method based on deep learning plays a significant role in the intelligent communication system. To further improve the recognition rate, especially in the case of small samples with a low signal-to-noise ratio, this paper proposes a new modulation recognition method based on flying fish swarm algorithm. First, Short-Time Fourier Transform, Choi-Williams Distribution, and Cyclic Spectrum are combined to complete multi-channel signal processing. Second, AlexNet, VGGNet, GoogLeNet, and ResNet are transferred to realize feature extraction. Third, the support vector machine classifies the modulations after dimension reduction and feature fusion. Finally, the flying fish swarm is proposed to optimize the signal processing methods, the types of networks, the layers of networks, the dimensions of features, and the parameters of the support vector machine. The method can accurately recognize BPSK, QPSK, OQPSK, 8PSK, 4ASK, QAM16, QAM32, and QAM64. The simulation results show that the average recognition rate of modulation is 94.5% at SNR of 0 dB and 84.7% at SNR of −4 dB. Besides, the proposed modulation recognition method possesses good robustness under low SNR conditions.