Multichannel Algorithm Research Articles

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images.

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID19) is life-saving important for both patients and doctors. This research proposes a multi-channel feature deep neural network (MFDNN) algorithm to screen people infected with COVID19. The algorithm integrates data over-sampling technology and MFDNN model to carry out the training. The oversampling technique reduces the deviation of the prior probability of the MFDNN algorithm on unbalanced data. Multi-channel feature fusion technology improves the efficiency of feature extraction and the accuracy of model diagnosis. In the experiment, Compared with traditional deep learning models (VGG19, GoogLeNet, Resnet50, Desnet201), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Furthermore, through ablation experiments, we proved that a multi-channel convolutional neural network (CNN) is superior to single-channel CNN, additional layer and PSN module, and indirectly proved the sufficiency and necessity of each step of the MFDNN classification method. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

Noise-robust interferometric ISAR imaging of 3-D maneuvering motion targets with fine image registration

Low signal-to-noise ratio (SNR) and three-dimensional (3-D) maneuvering motion of targets present enormous challenges to the interferometric inverse synthetic aperture radar (InISAR) imaging. In this paper, we propose a noise-robust InISAR imaging algorithm to acquire high-precision InISAR images of 3-D maneuvering motion targets at low SNR. With respect to time-variant wave path difference (TVWPD) and spatial-variant wave path difference (SVWPD), a novel algorithm, named non-coherent fusion image registration algorithm (NCFIR), is developed to realize fine image registration. By implementing the non-coherent fusion processing on the multi-look ISAR images, it improves the image SNR and therefore makes possible the joint compensation of TVWPD and SVWPD at low SNR. In addition, underpinned by the Bayesian compressive sensing (BCS) theory, a joint multi-channel BCS ISAR imaging algorithm (JMC-BCS) is also proposed. Its capability for suppressing the strong noise and high side lobes of ISAR imaging results effectively improves the SNRs of imaging results. Through iterative processing of JMC-BCS and NCFIR, high-quality 3-D InISAR images of 3-D maneuvering motion targets at low SNR can be obtained. The proposed algorithm improves the SNR of image twice through JMC-BCS and NCFIR, rendering it more robust against noise. Extensive experimental results from both simulated and real data corroborate the effectiveness and robustness of the proposed algorithm which outperforms other available InISAR imaging approaches at low SNR.

Design of Multichannel Resource Adaptive Scheduling Algorithm for English Online Translation Teaching

In order to optimize the multichannel resource scheduling of English online translation teaching and realize the goal of adaptive scheduling algorithm, a multichannel resource adaptive scheduling algorithm for English online translation teaching is designed. The clustering algorithm is used to sort out various English online translation teaching multichannel resources and remove redundancy. Build a unified standard specification; establish an English online translation teaching resource scheduling model with three modules of foundation layer, environment layer, and application layer; form an easy-to-find data set; build an adaptive scheduling algorithm function module of system management, standard management, media materials, online courses, courseware library, and test question library; and optimize the integration speed under multiobjective scheduling algorithm; this paper analyzes the multichannel resource scheduling process of English online translation teaching and realizes the research on the designed adaptive scheduling algorithm. The experimental results show that the test results of each functional module of the scheduling platform under the designed algorithm meet the functional requirements, and the update amount of resources is close to the update upper limit, which can reasonably manage teaching resources and improve students’ interest and learning ability.

A New Multichannel Parallel Real-time FFT Algorithm for a Solar Radio Observation System Based on FPGA

The real-time fast Fourier transform (FFT) is the essential algorithm for signal processing in a solar radio receiver. However, field-programmable gate array (FPGA) computation resources have become the limitation of real-time processing of signals with increasing time and spectral resolutions. It is necessary to design a real-time parallel FFT algorithm with reduced resource occupation in the development of future receiving systems. In this paper, we developed a multichannel parallel FFT algorithm named the multichannel parallel real-time fast Fourier transform (MPR-FFT), which can greatly reduce FPGA resource occupation while increasing the real-time processing speed. In this algorithm, the 4L simultaneous N-point FFTs are first converted into L simultaneous 4N-point FFTs. Fusion processing is then performed to obtain the 4 ∗ L ∗ N-point spectrum. This method has been used in developing a solar radio spectrometer, which works in the frequency range of 0.5–15 GHz in the Chashan Observatory. In this spectrometer, 16 channel MPR-FFT with 8k-point data is realized in a Xilinx UltraScale KU115 FPGA. The MPR-FFT algorithm reduced the computational resources to a large extent compared to the Cooley-Tukey-based parallel FFT method; for instance, the Look-Up-Table, Look-Up-Table RAM, Flip-Flop, and Digital Signal Process slices were reduced by 37%, 50%, 17%, and 2.48%, respectively. Although the MPR-FFT consumes 14 block RAM resources more than the Cooley-Tukey-based parallel FFT, the MPR-FFT algorithm presents an overall reduction in resource usage.

Application of Multichannel Active Vibration Control in a Multistage Gear Transmission System

Gears are the most important parts of rotating machinery and power transmission devices. When gears are engaged in meshing transmission, vibration will occur due to factors such as gear machining errors, meshing rigidity, and meshing impact. The traditional FxLMS algorithm, as a common active vibration algorithm, has been widely studied and applied in gear transmission system active vibration control in recent years. However, it is difficult to achieve good performance in convergence speed and convergence precision at the same time. This paper proposes a variable-step-size multichannel FxLMS algorithm based on the sampling function, which accelerates the convergence speed in the initial stage of iteration, improves the convergence accuracy in the steady-state adaptive stage, and makes the modified algorithm more robust. Simulations verify the effectiveness of the algorithm. An experimental platform for active vibration control of the secondary gear transmission system is built. A piezoelectric actuator is installed on an additional gear shaft to form an active structure and equipped with a signal acquisition system and a control system; the proposed variable-step-size multichannel FxLMS algorithm is experimentally verified. The experimental results show that the proposed multichannel variable-step-size FxLMS algorithm has more accurate convergence accuracy than the traditional FxLMS algorithm, and the convergence accuracy can be increased up to 123%.

Multichannel Speech Enhancement With Own Voice-Based Interfering Speech Suppression for Hearing Assistive Devices

Enhancementof a desired speech signal in the presence of competing or interfering speech remains an unsolved problem, as it can be hard to determine which of the speech signals is the one of interest. In this paper, we propose a multichannel noise reduction algorithm which uses the presence of the user’s own voice signal, e.g. during conversations with the target speaker, as an asset to efficiently identify interfering speech and noise. Specifically, following the typical speech pattern in natural conversations, the presence of an own voice may indicate the absence of the target speech, hence undesired speech and noise can be identified and estimated during own voice presence. In contrast to conventional noise reduction systems, the proposed noise reduction systems use the user’s own voice to identify interfering speech that otherwise could be confused with the target speech. We demonstrate the performance of the proposed noise reduction systems in a comparison against state-of-the-art noise reduction systems in terms of beamforming performance for hearing assistive devices. The results show that the proposed beamforming scheme in particular outperforms state-of-the-art methods in terms of ESTOI and PESQ in situations with a target speaker and a strong interfering speaker.

Aerosol Optical Depth Retrieval Over South Asia Using FY-4A/AGRI Data

The Advanced Geosynchronous Radiation Imager (AGRI) is one of the main imaging sensors onboard the Fengyun-4A (FY-4A) satellite. Because of its high observation frequency, AGRI is suitable for continuous monitoring of atmospheric aerosols. In this study, we propose an aerosol optical depth (AOD) retrieval algorithm called the multichannel (MC) algorithm, which uses four channels (0.65, 0.83, 1.61, and <inline-formula> <tex-math notation="LaTeX">$2.25~\mu \text{m}$ </tex-math></inline-formula>) of AGRI. The algorithm assumes that the ratios between surface reflectance of different channels remain unchanged within two weeks, and the ratios are calculated by using Moderate-Resolution Imaging Spectroradiometer (MODIS)-combined AOD data to perform atmospheric correction on AGRI data under low pollution conditions (AOD at 550 nm less than 0.5). Since this algorithm is not developed for specific surface types, AOD retrieval can be achieved over both dark targets and bright surfaces. This algorithm has been applied to aerosol retrieval in South Asia. The accuracy assessment of the AGRI AOD dataset in 2019 and 2020 using the ground-based data from 11 aerosol robotic network (AERONET) sites shows that the AGRI AOD dataset has a high accuracy, and the statistical parameters of AGRI AOD dataset are slightly better than those of MODIS-combined AOD dataset. The root-mean-square error (RMSE), mean absolute error (MAE), relative mean bias (RMB), and percentage of data with errors within the expected error <inline-formula> <tex-math notation="LaTeX">$\pm (0.05+0.15 \times {{\text {AOD}}}_{{\text {AERONET}}})$ </tex-math></inline-formula> (EE15) of AGRI AOD dataset are 0.16, 0.12, 0.23, and 63.71&#x0025;, respectively. The RMSE, MAE, RMB, and EE15 of MODIS-combined AOD dataset are 0.18, 0.13, 0.24, and 61.06&#x0025;, respectively.

Block Storage Optimization and Parallel Data Processing and Analysis of Product Big Data Based on the Hadoop Platform

The traditional distributed database storage architecture has the problems of low efficiency and storage capacity in managing data resources of seafood products. We reviewed various storage and retrieval technologies for the big data resources. A block storage layout optimization method based on the Hadoop platform and a parallel data processing and analysis method based on the MapReduce model are proposed. A multireplica consistent hashing algorithm based on data correlation and spatial and temporal properties is used in the parallel data processing and analysis method. The data distribution strategy and block size adjustment are studied based on the Hadoop platform. A multidata source parallel join query algorithm and a multi-channel data fusion feature extraction algorithm based on data-optimized storage are designed for the big data resources of seafood products according to the MapReduce parallel frame work. Practical verification shows that the storage optimization and data-retrieval methods provide supports for constructing a big data resource-management platform for seafood products and realize efficient organization and management of the big data resources of seafood products. The execution time of multidata source parallel retrieval is only 32% of the time of the standard Hadoop scheme, and the execution time of the multichannel data fusion feature extraction algorithm is only 35% of the time of the standard Hadoop scheme.

The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4

The detection of cotton impurity rates can reflect the cleaning effect of cotton impurity removal equipment, which plays a vital role in improving cotton quality and economic benefits. Therefore, several studies are being carried out to improve detection accuracy. Image processing technology is increasingly used in cotton impurity detection, in which deep learning technology based on convolution neural networks has shown excellent results in image classification, segmentation, target detection, etc. However, most of these applications focus on detecting foreign fibers in lint, which is of little significance to the parameter adjustment of cotton impurity removal equipment. For this reason, our goal was to develop an impurity detection system for seed cotton. In image segmentation, we propose a multi-channel fusion segmentation algorithm to segment the machine-picked seed cotton image. We collected 1017 images of machine-picked seed cotton as a dataset to train the detection model and tested and recognized 100 groups of samples, with an average recognition rate of 94.1%. Finally, the image segmented by the multi-channel fusion algorithm is input into the improved YOLOv4 network model for classification and recognition, and the established V–W model calculates the content of all kinds of impurities. The experimental results show that the impurity content in machine-picked cotton can be obtained effectively, and the detection accuracy of the impurity rate can increase by 5.6%.

Azimuth Multichannel Reconstruction Based on Advanced Hyperbolic Range Equation

To acquire high-resolution wide-swath (HRWS) imaging capacity, the displaced phase center multichannel azimuth beam (DPCMAB) technology is usually adopted in spaceborne synthetic aperture radar (SAR), while multichannel reconstruction must be carried out before imaging process due to azimuth nonuniform sampling. Up to now, almost all azimuth multichannel reconstruction algorithms have been mainly based on conventional hyperbolic range equation (CHRE), but the accuracy of the CHRE model is usually not suitable for the HRWS mode, especially for high resolution and large squint observation cases. In this study, the azimuth multichannel signal model based on the advanced hyperbolic range equation (AHRE) is established and analyzed. The major difference between multichannel signal models based on CHRE and AHRE is the additional time-varying phase error between azimuth channels. The time-varying phase error is small and can be ignored in the monostatic DPCMAB SAR system, but it must be considered and compensated in the distributed DPCMAB SAR system. In addition to the time-varying phase error, additional Doppler spectrum shift and extended Doppler bandwidth should be considered in the squint case during azimuth multichannel reconstruction. The azimuth multichannel reconstruction algorithm based on AHRE is proposed in this paper. Before multichannel reconstruction and combination, time-varying phase errors between azimuth channels were first compensated, and the range-frequency-dependent de-skewing function was derived to remove the two-dimension (2D) spectrum tilt to avoid azimuth under-sampling. Then, azimuth multichannel data were reconstructed according to the azimuth multichannel impulse response based on AHRE. Finally, the range-frequency dependent re-skewing function was introduced to recover the tilted 2D spectrum. Simulation results on both point and distributed targets validated the proposed azimuth multichannel reconstruction approach.

Underwater Target Localization Using Opportunistic Ship Noise Recorded on a Compact Hydrophone Array

In this research, a new application using broadband ship noise as a source-of-opportunity to estimate the scattering field from the underwater targets is reported. For this purpose, a field trial was conducted in collaboration with JASCO Applied Sciences at Duncan’s Cove, Canada in September 2020. A hydrophone array was deployed in the outbound shipping lane at a depth of approximately 71 m to collect broadband noise data from different ship types and effectively localize the underwater targets. In this experiment, a target was installed at a distance (93 m) from the hydrophone array at a depth of 25 m. In this study, a matched field processing (MFP) algorithm is utilized for localization. Different propagation models are presented using Green’s function to generate the replica signal; this includes normal modes in a shallow water waveguide, the Lloyd-mirror pattern for deep water, as well as the image model. We use the MFP algorithm with different types of underwater environment models and a proposed estimator to find the best match between the received signal and the replica signal. Finally, by applying the scatter function on the proposed multi-channel cross correlation coefficient time-frequency localization algorithm, the location of target is detected.

Technology of locating loose particles inside sealed electronic equipment based on Parameter-Optimized Random Forest

Due to the complex internal structure and uneven composition material of sealed electronic equipment, traditional acoustic emission source localization methods are unsuitable for the research of locating loose particles inside sealed electronic equipment. Aiming at this problem, the author analyzed the essence of locating loose particles from another angle, considered converting the loose particle localization problem into the multi-classification problem, and sought for solving. Based on the existing loose particle detection system, the multi-channel signal synchronous acquisition circuit was designed to synchronously collect loose particle signals. The two-stage dual-threshold pulse extraction algorithm and the multi-channel pulse-matching algorithm were designed to preprocess the collected loose particle signals. Multiple features with good localization performance were extracted and selected from the processed loose particle signals, and the localization data set was eventually established. The localization performances of different classification algorithms were analyzed and compared by performing them on the localization data set, respectively, then the parameters of better-performed Random Forest were optimized. Finally, the loose particle localization model based on parameter-optimized Random Forest was used to perform localization tests on the aerospace power supply. Test results show that the average localization accuracy is 84.08%, which is the highest localization accuracy achieved in aerospace engineering area of the world currently. It is an important supplement to the loose particle detection research and has important application value for improving the reliability of aerospace system. Theoretically, it can be applied to the research of collision signal localization with similar generation mechanism.

Multichannel Approach for Arrayed Waveguide Grating-Based FBG Interrogation Systems.

In this manuscript, an optically passive fiber Bragg grating (FBG) interrogation system able to perform high-frequency measurement is proposed. The idea is mainly based on the use of an arrayed waveguide grating (AWG) device which is used to discriminate the fiber optic sensor (FOS) wavelength encoded response under test in function of its output channels. As made clear by the theoretical model studied in the proposed manuscript, the Bragg wavelength shift can be detected as in linear dependence with the proposed interrogation function which changes with the voltage produced by two (or more) adjacent AWG output channels. To prove the feasibility of the system, some experimental analyses are conducted with a custom electrical module characterized by high-speed and low-noise operational amplifiers. As static measurements, three FBGs with different full width at half maximum (FWHM) have been monitored under wide-range wavelength variation; while, as dynamic measurement, one FBG, glued onto a metal plate, in order to sense the vibration at low and high frequency, was detected. The output signals have been processed by a digital acquisition (DAQ) board and a graphical user interface (GUI). The presented work highlights the characteristics of the proposed idea as competitor among the entire class of interrogation systems currently used. This is because here, the main device, that is the AWG, is passive and reliable, without the need to use modulation signals, or moving parts, that affect the speed of the system. In addition, the innovative multi-channel detection algorithm allows the use of any type of FOS without the need to have a perfectly match of spectra. Moreover, it is also characterized by a high dynamic range without loss of sensitivity.

Comparison of multichannel signal deconvolution algorithms in airborne LiDAR bathymetry based on wavelet transform

Airborne LiDAR bathymetry offers low cost and high mobility, making it an ideal option for shallow-water measurements. However, due to differences in the measurement environment and the laser emission channel, the received waveform is difficult to extract using a single algorithm. The choice of a suitable waveform processing method is thus of extreme importance to guarantee the accuracy of the bathymetric retrieval. In this study, we use a wavelet-denoising method to denoise the received waveform and subsequently test four algorithms for denoised-waveform processing, namely, the Richardson–Lucy deconvolution (RLD), blind deconvolution (BD), Wiener filter deconvolution (WFD), and constrained least-squares filter deconvolution (RFD). The simulation and measured multichannel databases are used to evaluate the algorithms, with focus on improving their performance after data-denoising and their capability of extracting water depth. Results show that applying wavelet denoising before deconvolution improves the extraction accuracy. The four algorithms perform better for the shallow-water orthogonal polarization channel (PMT2) than for the shallow horizontal row polarization channel (PMT1). Of the four algorithms, RLD provides the best signal-detection rate, and RFD is the most robust; BD has low computational efficiency, and WFD performs poorly in deep water (< 25 m).

A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis.

Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize the correlations between the projections. To this end, we seek a multichannel CCA algorithm that can be implemented in a biologically plausible neural network. For biological plausibility, we require that the network operates in the online setting and its synaptic update rules are local. Starting from a novel CCA objective function, we derive an online optimization algorithm whose optimization steps can be implemented in a single-layer neural network with multicompartmental neurons and local non-Hebbian learning rules. We also derive an extension of our online CCA algorithm with adaptive output rank and output whitening. Interestingly, the extension maps onto a neural network whose neural architecture and synaptic updates resemble neural circuitry and non-Hebbian plasticity observed in the cortex.

Channel allocation optimization algorithm for hybrid wireless mesh networks for information physical fusion system

Reasonable channel selection and allocation is the basis and premise of fair routing protocol design in information physical fusion system. In this paper, a channel allocation optimization algorithm for hybrid wireless mesh networks is proposed for the information physical fusion system. A new channel allocation model is designed, which uses greedy algorithm to select the channel with the least interference for the wireless network link. Based on the spatiotemporal multi interface and multi-channel allocation optimization algorithm, a joint channel allocation algorithm based on greedy algorithm is proposed in this paper. The communication between nodes is artificially divided into continuous sub time periods, but the communication between nodes cannot be guaranteed to exist in a certain period of time. The communication lines between nodes can be determined to avoid the shortcoming because the shortest path is used as the index to allocate the communication path.

Reappraisal of SMAP inversion algorithms for soil moisture and vegetation optical depth

NASA's Soil Moisture Active Passive (SMAP) satellite mission has been providing high-quality global estimates of soil moisture (SM) and vegetation optical depth (VOD) using L-band radiometry since 2015. To date, a variety of retrieval algorithms as well as surface roughness and scattering albedo have been developed. However, a comprehensive evaluation of different algorithms with the new surface parameters across diverse biomes, climates, and terrain slopes is lacking. To narrow down this knowledge gap, here we examine the performance of various existing algorithms, including V-pol Single Channel Algorithms (SCA-V), H-pol Single Channel Algorithms (SCAH), classic DCA, extended DCA (E-DCA), regularized DCA (RDCA), land parameter retrieval model (LPRM), multi-temporal DCA (MT-DCA), constrained multi-channel algorithm (CMCA), and spatially constrained multi-channel algorithm (S-CMCA). The SM estimates are evaluated against in-situ measurements from the International Soil Moisture Network (ISMN) while VOD estimates are compared with the two-band enhanced vegetation index (EVI2), tree height, and aboveground biomass.This study has led to several important findings: (1) The overall bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE) of SM estimates from different algorithms generally increase with vegetation density while their temporal correlations with in-situ measurements decrease as the terrain slope increases. (2) The divergence between different SM estimates is relatively larger over forested areas than non-forested areas. (3) In terms of temporal correlation with in-situ measurements, the SCA-V and RDCA outperform other algorithms over most land cover types and climates. (4) SCA-H typically underestimates SM more compared to other algorithms across sparsely vegetated areas and most climates. (5) The ubRMSE values demonstrate that all algorithms have close performance when EVI2 is less than 0.3; however, the performance of classic DCA decays notably when EVI2 exceeds 0.3. (6) VOD retrievals from RDCA exhibit improved spatial correlations with EVI2, tree height, and aboveground biomass across the globe compared to other algorithms. Overall, RDCA exhibits a good compromise between the high performance of SM and VOD.

Simplified fast transversal filter algorithms for multichannel active noise control

In recent years, more attention has been paid to the performance of algorithm in active noise control (ANC). Compared with filtered-x LMS (FxLMS) algorithm based on stochastic gradient descent, filtered-x RLS (FXRLS) algorithm has faster convergence speed and better tracking performance at the cost of high computational complexity. In order to reduce the computation, fast transversal filter (FTF) algorithm can be used in ANC system. In this paper, simplified multi-channel FXFTF algorithms are presented, and the convergence speed and noise reduction performance of different multichannel algorithms are simulated and compared, and the numerical stability of the algorithms are analyzed.

Estimating the number of layers for precise wavelength scanning interferometry

Wavelength scanning interferometry (WSI) is a promising imaging technique for measuring the three-dimensional structure of the inner tissues in biomedicine. One of the important problems in WSI is how to estimate the number of layers without prior knowledge of the tissues. In this paper, a subspace decomposition-based virtual multichannel (SDVM) algorithm has been proposed for this problem, in which the interference signal of each pixel is first cast into a virtual multichannel model by the sum-to-product formula of trigonometric functions, and then, by combining the subspace decomposition method of multichannel-signal processing, the layers of the sample can be estimated in an unsupervised manner. The experimental results illustrate that SDVM is a robust algorithm with a success rate greater than 85%.

Pre-test analysis to reproduce random pressure fields with multi-channel acoustic control

Pre-test analysis is proposed as a methodology to design control strategies for environmental acoustic tests. In the space industry these tests are used to assess the survivability of hardware, which is exposed to harsh dynamic loads in service. In this work an array of loudspeakers and microphones is used in a laboratory to reproduce the acoustic environment generated during the launch of a spacecraft. Active control is required to design the signals driving the loudspeakers. Pre-test analysis uses an internal model of the electro-acoustic plant responses to automatically design the control parameters required by multi-channel random control algorithms. The outcome of pre-test analysis is the automatic selection of the control microphones, and a set of realizable test references in the form of a spectral density matrix. The methodology proposed in this work to design the control strategies is validated experimentally on system level tests. Compared with other approaches, proper design of the control strategy for the environmental acoustic test significantly enhances the correlation between the measured and desired pressure responses.