Multichannel Algorithm Research Articles

A Novel Temporal Fusion Channel Network with Multi-Channel Hybrid Attention for the Remaining Useful Life Prediction of Rolling Bearings

The remaining useful life (RUL) prediction of rolling bearings is crucial for optimizing maintenance schedules, reducing downtime, and extending machinery lifespan. However, existing multi-channel feature fusion methods do not fully capture the correlations between channels and time points in multi-dimensional sensor data. To address the above problems, this paper proposes a multi-channel feature fusion algorithm based on a hybrid attention mechanism and temporal convolutional networks (TCNs), called MCHA-TFCN. The model employs a dual-channel hybrid attention mechanism, integrating self-attention and channel attention to extract spatiotemporal features from multi-channel inputs. It uses causal dilated convolutions in TCNs to capture long-term dependencies and incorporates enhanced residual structures for global feature fusion, effectively extracting high-level spatiotemporal degradation information. The experimental results on the PHM2012 dataset show that MCHA-TFCN achieves excellent performance, with an average Root-Mean-Square Error (RMSE) of 0.091, significantly outperforming existing methods like the DANN and CNN-LSTM.

Partial discharge noise comparison and recognition method based on multi-channel smooth decoherence algorithm applied to GIS

Abstract In order to further improve the accuracy of the online monitoring spectra of partial discharge in gas insulated switchgear, a partial discharge distributed monitoring system is studied, and distributed sensors are used to simultaneously obtain ultra-high frequency electromagnetic wave signals. Based on the accumulated partial discharge spectra of each sensor, the smooth decoherence noise recognition algorithm is proposed on a long-term cumulative spectral pattern, which is used to analyse the correlation between the spectra of each sensor. Furthermore, a method for determining whether partial discharge is an internal signal is proposed, and its effectiveness is verified by practice, the results confirmed that the algorithm can avoid the misjudgement of partial discharge results caused by distributed sensor interference.

ATSDetector: An Android Trojan spyware detection approach with multi-features

With the widespread popularity of Android Trojan spyware, detection technology for Android Trojan spyware is very necessary to prevent financial loss. However, when considering the comprehensive behaviors of Android Trojan spyware, the existing approaches based on a single feature (static information, internal behavior, and external behavior) have low accuracy and even errors. In this paper, we propose a multi-features-based Android Trojan spyware detection approach (hereafter referred to as ATSDetector). Specifically, we first define a multi-channel detection algorithm supported by heterogeneous information. And then, devise a weight-size sharing mechanism to establish the correlation between different behavioral features. At last, we then conduct real-world experiments to prove the effectiveness and stability of ATSDetector. The results show that the assessment accuracy can achieve almost 96.81%, and its kappa coefficient is about 93.62%.

Airborne Multi-Channel Forward-Looking Radar Super-Resolution Imaging Using Improved Fast Iterative Interpolated Beamforming Algorithm

Radar forward-looking imaging is critical in many civil and military fields, such as aircraft landing, autonomous driving, and geological exploration. Although the super-resolution forward-looking imaging algorithm based on spectral estimation has the potential to discriminate multiple targets within the same beam, the estimation of the angle and magnitude of the targets are not accurate due to the influence of sidelobe leakage. This paper proposes a multi-channel super-resolution forward-looking imaging algorithm based on the improved Fast Iterative Interpolated Beamforming (FIIB) algorithm to solve the problem. First, the number of targets and the coarse estimates of angle and magnitude are obtained from the iterative adaptive approach (IAA). Then, the accurate estimates of angle and magnitude are achieved by the strategy of iterative interpolation and leakage subtraction in FIIB. Finally, a high-resolution forward-looking image is obtained through non-coherent accumulation. The simulation results of point targets and scenes show that the proposed algorithm can distinguish multiple targets in the same beam, effectively improve the azimuthal resolution of forward-looking imaging, and attain the accurate reconstruction of point targets and the contour reconstruction of extended targets.

Image tampering detection based on RDS-YOLOv5 feature enhancement transformation

Malicious image tampering has gradually become another way to threaten social stability and personal safety. Timely detection and precise positioning can help reduce the occurrence of risks and improve the overall safety of society. Due to the limitations of highly targeted dataset training and low-level feature extraction efficiency, the generalization and actual performance of the recent tampered detection technology have not yet reached expectations. In this study, we propose a tampered image detection method based on RDS-YOLOv5 feature enhancement transformation. Firstly, a multi-channel feature enhancement fusion algorithm is proposed to enhance the tampering traces in tampered images. Then, an improved deep learning model named RDS-YOLOv5 is proposed for the recognition of tampered images, and a nonlinear loss metric of aspect ratio was introduced into the original SIOU loss function to better optimize the training process of the model. Finally, RDS-YOLOv5 is trained by combining the features of the original image and the enhancement image to improve the robustness of the detection model. A total of 6187 images containing three forms of tampering: splice, remove, and copy-move were used to comprehensively evaluate the proposed algorithm. In ablation test, compared with the original YOLOv5 model, RDS-YOLOv5 achieved a performance improvement of 6.46%, 5.13%, and 3.15% on F1-Score, mAP50 and mAP95, respectively. In comparative experiments, using SRIOU as the loss function significantly improved the model’s ability to search for the real tampered regions by 2.54%. And the RDS-YOLOv5 model trained by the fusion dataset further improved the comprehensive detection performance by about 1%.

An Ultrawide Bandwidth Digital Backend System Based on PFB Algorithm for QTT

For the planning of the QiTai radio Telescope ultrawide bandwidth low-frequency pulsar receiving system, we designed and implemented a Field-Programmable Gate Array (FPGA)+CPU/GPU hybrid architecture digital backend system based on the Polyphase FilterBank (PFB) channeling algorithm. We used the FPGA signal acquisition and processing platform to implement ultrawide bandwidth signal sampling and designed the PFB algorithm to realize the digital channelization of multiple analog bandwidth signals. We also developed data encapsulation and multichannel parallel distribution firmware algorithms and realized the real-time parallel transmission of high-speed astronomical data streams based on the VLBI Data Interchange Format. We developed the Ultra Wide bandwidth Low-frequency pulsar data process PIPEline, which realized the real-time processing and data packaging of massive pulsar signals. Using the L-band (964–1732 MHz bandwidth) receiving system of the Nanshan 26 m radio telescope, we conducted a systematic test on the designed digital backend system and obtained high-quality observation data. By using the professional pulsar data processing software DSPSR to process the observation data, we obtained high signal-to-noise ratio pulse profiles.

New Multi-Channel VSMFxLMS Algorithm for Vibration Reduction of Gear Systems

At present, the active control of gear vibration mostly relies on existing algorithms. In order to achieve effective vibration reduction of the gear system, particularly during the vibration process, this paper proposes a multi-channel VSMFxLMS algorithm based on the FxLMS algorithm. This novel approach takes into account the time-varying nature of the vibration signal during gear vibration. Adaptive filter power coefficients are updated in a skip-tongue variable-step manner using momentum factors. Firstly, the paper establishes the dynamics model of the gear system and analyzes the nonlinear dynamic characteristics of the system. It then examines the vibration damping effect of the FxLMS algorithm and analyzes its performance under different gear system motion states, considering different step lengths and momentum factors. Lastly, the proposed VSMFxLMS algorithm is compared with the FxLMS algorithm, highlighting the superiority of the former. Overall, this research highlights the potential of a multi-channel VSMFxLMS algorithm in reducing vibrations in gear systems. The study optimizes the performance of gear systems while using advanced control strategies.

A class of regularized adaptive multichannel equalization algorithms for speech dereverberation

The multiple input/output inverse theorem (MINT) is a typical speech dereverberation technique based on acoustic channel equalization. Although the adaptive MINT (A-MINT) method has been developed to meet the requirements of real-time application systems, its performance often deteriorates due to the effect of the identification errors of room impulse responses (RIRs). In this paper, we present a regularized A-MINT approach to reduce the sensitivity of the adaptive equalizer to the RIR identification errors. According to the regularized MINT criterion, Newton's and quasi-Newton methods are used to establish two adaptive speech dereverberation algorithms, respectively, which obtain faster convergent rate and better dereverberation performance. The effectiveness of the proposed algorithms is validated through numerical experiments with the RIRs measured in real acoustic environments.

Multichannel algorithm for ambiguity resolution of combined code and phase measurements in a task of angular attitude estimation using signals of Global Navigation Systems

Navigation apparatus of Global Positioning Systems are wildly used in many applications one of which is attitude determination using global satellite navigation signals. For decision of this task there are used code and phase measurements for radiosignals received at space distribute points. It is known that phase measurements are characterized by ambiguity for resolution of which there are used different methods. Using more perfect method of ambiguity resolution one can get better angel estimation accuracy. In the article there is synthesized effective ambiguity resolution method of combined code and phase measurements using multichannel approach. Objective. To get more effective algorithm of code and phase ambiguity resolution in a task of attitude determination using global satellite navigation signals.

Forest Fire Detection Based on Spatial Characteristics of Surface Temperature

Amidst the escalating threat of global warming, which manifests in more frequent forest fires, the prompt and accurate detection of forest fires has ascended to paramount importance. The current surveillance algorithms employed for forest fire monitoring—including, but not limited to, fixed threshold algorithms, multi-channel threshold algorithms, and contextual algorithms—rely primarily upon the degree of deviation between the pixel temperature and the background temperature to discern pyric events. Notwithstanding, these algorithms typically fail to account for the spatial heterogeneity of the background temperature, precipitating the consequential oversight of low-temperature fire point pixels, thus impeding the expedited detection of fires in their initial stages. For the amelioration of this deficiency, the present study introduces a spatial feature-based (STF) method for forest fire detection, leveraging Himawari-8/9 imagery as the main data source, complemented by the Shuttle Radar Topography Mission (SRTM) DEM data inputs. Our proposed modality reconstructs the surface temperature information via selecting the optimally designated machine learning model, subsequently identifying the fire point through utilizing the difference between the reconstructed surface temperatures and empirical observations, in tandem with the spatial contextual algorithm. The results confirm that the random forest model demonstrates superior efficacy in the reconstruction of the surface temperature. Benchmarking the STF method against both the fire point datasets disseminated by the China Forest and Grassland Fire Prevention and Suppression Network (CFGFPN) and the Wild Land Fire (WLF) fire point product validation datasets from Himawari-8/9 yielded a zero rate of omission errors and a comprehensive evaluative index, predominantly surpassing 0.74. These findings show that the STF method proposed herein significantly augments the identification of lower-temperature fire point pixels, thereby amplifying the sensitivity of forest surveillance.

A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature.

Objective: The sorting of neural spike data recorded by multichannel and high channel neural probes such as Neuropixels, especially in real-time, remains a significant technical challenge. Most neural spike sorting algorithms focus on sorting neural spikes post-hoc for high sorting accuracy-but reducing the processing delay for fast sorting, potentially even live sorting, is generally not possible with these algorithms.Approach: Here we report our Graph nEtwork Multichannel sorting (GEMsort) algorithm, which is largely based on graph network, to allow rapid neural spike sorting for multiple neural recording channels. This was accomplished by two innovations: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing. In addition, the channel from which the representative neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features.Main results: Synthetic and experimentally recorded multichannel neural recordings were used to evaluate the sorting performance of GEMsort. The sorting results of GEMsort were also compared with two other state-of-the-art sorting algorithms (Kilosort and Mountainsort) in sorting time and sorting agreements.Significance: GEMsort allows rapidly sort neural spikes and is highly suitable to be implemented with digital circuitry for high processing speed and channel scalability.

An online decoupling-whitening frequency domain filtered-error least mean square algorithm for active road noise control.

Active road noise control (ARNC) systems have been widely investigated for low-frequency road noise attenuation in vehicle cabins. Multiple reference and error sensors are usually required to ensure noticeable noise reduction. However, this tends to slow down the convergence speed of adaptive algorithms due to the coupling of secondary paths and the cross correlation of reference signals. Furthermore, the high computational burden of normally utilized multichannel control algorithms exacerbates the difficulty of practical implementations. In this paper, an online decoupling-whitening frequency domain filtered-error least mean square (ODW-FDFeLMS) algorithm is proposed to address the aforementioned problems. Secondary path decoupling through inner-outer product decomposition and online reference whitening through spectral factorization effectively accelerate the convergence rate. Additionally, the utilization of the filtered-error algorithm based on frequency domain processing mitigates the computational complexity. Simulations with measured road noise data confirm the superiority of the ODW-FDFeLMS algorithm over existing algorithms in terms of convergence speed and computational complexity. Real-time experiments in a vehicle cabin further confirm the effectiveness of the proposed algorithm.

M-estimate based diffusion active noise control algorithm over distributed networks and its performance analysis

Multi-channel active noise control (ANC) systems can achieve effective attenuation of low-frequency noise in spatial areas and have been extensively studied. Recent works have focused on the application of multitask diffusion strategies in ANC systems based on wireless acoustic sensor and actuator networks (WASAN), which show the advantages of saving computational burden and enhancing flexibility. However, these works all adopt the mean-square error minimization as the optimization criterion, which leads to severe performance degradation in the presence of impulsive noise interference. Therefore, in this paper, we exploit the M-estimate function and the augmented weight vector of the control filter to formulate a robust minimization problem and propose a distributed augmented diffusion filtered-x least mean M-estimate (DADFxLMM) algorithm for multi-channel ANC systems. The proposed algorithm leverages the M-estimate function to overcome the influence of outliers in the error signal and achieves robustness against impulsive noise, and also eliminates the dependence on the symmetry of the acoustic paths by integrating the augmented weight vectors. Moreover, we replace the score function with the update probability of the weight vector to avoid the need for integration and Price’s theorem, and analyze the mean and mean-square performance of the proposed DADFxLMM algorithm under the contaminated Gaussian (CG) noise model. Simulation results under different system configurations demonstrate that the proposed algorithm outperforms the existing multi-channel ANC algorithms in the presence of impulsive noise interference.

Noise suppression for an aeromagnetic measurement system on an unmanned helicopter.

An unmanned helicopter is one of the main platforms for conducting unmanned aerial vehicle aeromagnetic measurements and combines the advantages of rotary-wing and fixed-wing unmanned aerial vehicles. However, unmanned helicopter-based aeromagnetic measurement systems face complex static magnetic noise and maneuvering magnetic interference, which limit their practical performance. To address this issue, an improved multi-channel frequency measurement algorithm for the optically pumped magnetic sensor is proposed to suppress the static magnetic noise proportional to the frequency noise generated by the random quantization error and the airborne electromagnetic interference. A novel aeromagnetic compensation method for the maneuvering magnetic interference is then proposed to weaken the negative effects of the strong multicollinearity of the attitude parameters of the unmanned helicopter on the compensation accuracy and stability by introducing a regularization term and weight matrix. In addition, dedicated software is developed for the real-time calculation and compensation of magnetic interference fields. A dedicated unmanned-helicopter-based aeromagnetic measurement system is developed, and ground and flight experiments are carried out. The ground test results indicate that the static noise of the proposed system is only 0.000 82 nT. In the flight experiments, the system achieves an improvement ratio of 8.33, which is higher than the improvement ratio of 4.37 for a state-of-the-art commercial compensator. Furthermore, the dynamic noise after compensation decreases by 37.6% from 0.0157 to 0.0098 nT.

A study on multi-channel active sound profiling algorithm for hybrid control of broadband and narrowband noise inside vehicles

The vehicle interior noise consists of both broadband road noise and narrowband engine order noise. Currently, the road noise control (RNC) system and engine noise control (EOC) system operate independently in engineering applications. This paper introduces a novel multi-channel hybrid broadband and narrowband active noise control (HBNANC) algorithm, designed to simultaneously attenuate road noise and engine order noise inside vehicles. On this basis, by integrating an error signal design (ESD) subsystem and a sinusoidal error separation (SES) subsystem, a multi-channel hybrid broadband and narrowband active sound profiling (HBNASP) algorithm is further proposed, enabling specific attenuation or amplification of individual narrowband components to meet subjective acoustic comfort requirements. The paper validates the algorithms’ effectiveness and robustness through simulations, and real vehicle experimental measurements under various conditions. The research offers a comprehensive approach to enhancing the auditory experience inside vehicles, contributing to the advancement of active noise control technology.

Permittivity tensor imaging: modular label-free imaging of 3D dry mass and 3D orientation at high resolution

The dry mass and the orientation of biomolecules can be imaged without a label by measuring their permittivity tensor (PT), which describes how biomolecules affect the phase and polarization of light. Three-dimensional (3D) imaging of PT has been challenging. We present a label-free computational microscopy technique, PT imaging (PTI), for the 3D measurement of PT. PTI encodes the invisible PT into images using oblique illumination, polarization-sensitive detection and volumetric sampling. PT is decoded from the data with a vectorial imaging model and a multi-channel inverse algorithm, assuming uniaxial symmetry in each voxel. We demonstrate high-resolution imaging of PT of isotropic beads, anisotropic glass targets, mouse brain tissue, infected cells and histology slides. PTI outperforms previous label-free imaging techniques such as vector tomography, ptychography and light-field imaging in resolving the 3D orientation and symmetry of organelles, cells and tissue. We provide open-source software and modular hardware to enable the adoption of the method.

Multi-Channel Audio Completion Algorithm Based on Tensor Nuclear Norm

Multi-channel audio signals provide a better auditory sensation to the audience. However, missing data may occur in the collection, transmission, compression, or other processes of audio signals, resulting in audio quality degradation and affecting the auditory experience. As a result, the completeness of the audio signal has become a popular research topic in the field of signal processing. In this paper, the tensor nuclear norm is introduced into the audio signal completion algorithm, and the multi-channel audio signals with missing data are restored by using the completion algorithm based on the tensor nuclear norm. First of all, the multi-channel audio signals are preprocessed and are then transformed from the time domain to the frequency domain. Afterwards, the multi-channel audio with missing data is modeled to construct a third-order multi-channel audio tensor. In the next part, the tensor completion algorithm is used to complete the third-order tensor. The optimal solution of the convex optimization model of the tensor completion is obtained by using the convex relaxation technique and, ultimately, the data recovery of the multi-channel audio with data loss is accomplished. The experimental results of the tensor completion algorithm and the traditional matrix completion algorithm are compared using both objective and subjective indicators. The final result shows that the high-order tensor completion algorithm has a better completion ability and can restore the audio signal better.

A joint estimation algorithm for single-input multiple-output underwater acoustic communications

In single-input multiple-output (SIMO) underwater acoustic (UWA) communications, the receiver based on passive time reversal (PTR) combined with decision feedback equalizer (DFE) is widely used but has a limited performance. A multi-channel joint estimation algorithm based on sparse Bayesian learning (MJSBL) is proposed in this paper to exploit the diverse gain from multi-channels, where reasonable prior distribution functions are selected for parameters in the probabilistic model. Afterwards, the algorithm is derived by the mean-field variational inference (VI), iteratively updating the estimation of symbols, channels and noise variation. As a result, the maximum likelihood estimation of the dictionary matrix, as well as the maximum posterior estimation of the channel vectors and noise variance, can be approximated. Simulation and experimental results verify that compared to typical single-channel and multi-channel algorithms, the systems always have lower bit error rates (BERs) and symbol error rates (SERs) with the MJSBL algorithm for different communications distances and symbol block lengths.

Hydraulic fault prediction of integrated transmission system based on LSTM

Integrated drive-line fluids play a role in transmitting torque, providing control pressure, lubrication, and cleaning in the integrated drive-train. The increase of oil abrasive particles will accelerate the wear rate of rotating parts, while insufficient oil will lead to rapid ablation and gluing of rotating parts. According to statistics, more than 75% of hydraulic equipment failure is caused by oil pollution. Oil pollution leads to filter blockage which will cause fatal failure so that the entire transmission system cannot work. Therefore, it is extremely important to predict the fault and health management of the hydraulic system. This paper studies the oil fault of the integrated transmission system, proposes a data preprocessing method, and solves the problem of difficult extraction of deterioration index in the multichannel strong noise signal of the hydraulic system through the information fusion algorithm and the mean smoothing algorithm. According to the abnormal data judgment criterion based on the sliding window, the identification of the early fault starting point of the hydraulic system is realized, which provides a basis for the start-up fault prediction and the division of normal and degraded states. A multi-channel fusion fault prediction algorithm for hydraulic systems based on a Long-Short Term Memory (LSTM) deep network is proposed, which realizes long-term and high-precision fault prediction of hydraulic systems. Finally, the oil failure prediction algorithm of the integrated transmission system is verified by using real vehicle fault data.

A reliable evaluation approach for multichannel signal denoising algorithms based on a novel arterial pulse acquisition system

BackgroundTactile sensors are utilized to measure multichannel pulse signals in pulse wave analysis (PWA). Owing to noise interferences, researchers have applied various denoising algorithms on multichannel pulse signals. To comprehensively assess these algorithms, numerous evaluation metrics have been proposed. However, these studies did not investigate the noise mechanisms in depth and lacked reference pulse signals, thus making the evaluations insufficiently objective. Materials and methodsAn applicable denoising evaluation approach for multichannel pulse signal algorithms based on an arterial pulse acquisition system is established by superimposing real-world multichannel noise to the reference signals. The system, comprising a SphygmoCor and a uniaxial noise acquisition device, allows us to acquire single-reference pulse signals as well as real-world multichannel noise. ResultsWe assess eight popular denoising algorithms with three evaluation metrics, including amplitude relative error (ARE), mean square error (MSE) and increased percentage signal-noise ratio (SNR%). Our proposed approach provides accurate and objective evaluations of multichannel pulse signal denoising. Notably, classic algorithms for single-channel denoising are not recommended for multichannel denoising. Comparatively, RPCA-based algorithms can denoise pulse signals independently for each channel. ConclusionThis study sets the stage for the establishment of accurate and objective pulse signal denoising evaluations and provides insights for data-driven clinical diagnoses in cardiovascular medicine.