Multichannel Data Research Articles

Research and Development of an Embedded Multi-Channel Sensor Data Acquisition Device for Reservoir Dams

The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.

Enterprise asset management and accounting using the Internet of Things and big data

With the rapid development of information technology, the Internet of Things (IoT) and big data have gradually become the core tools of modern enterprise management and decision-making. This study explores the application and potential value of IoT and big data in the field of enterprise assets and accounting. Through strategic questionnaire design and multi-channel data collection, a corresponding data model is constructed, and the effectiveness of the model is further verified. The results show that these technologies can not only improve the management efficiency of enterprises, but also provide more accurate and timely data support for corporate decision-making. However, in practical applications, enterprises also need to face data integrity, technology updates, and a host of other challenges. This study not only provides enterprises with an opportunity to gain an in-depth understanding of these technologies, but also provides valuable implications for future research and applications.

Attempted movement classification of spinal cord injured patients using hybrid CNN‐LSTM networks

AbstractIndividuals who are suffering from the most severe of motor disabilities can improve their quality of life by controlling and directing mechanical and electronic devices. As for Spinal Cord Injured (SCI) patients', attempted hand movements can be classified using electroencephalography (EEG). The research aims to develop a hybrid CNN‐LSTM (Convolutional Neural Network—Long Short Term Memory) architecture for multichannel EEG signal classification. It is a challenging task to classify real‐world multichannel EEG data from SCI patients. The proposed research preprocessed the EEG data to improve the signal‐to‐noise ratio and arranged for them to extract additional information from the data. The preprocessing step includes filtering, downsampling, and artifact removal, while the postprocessing step includes time‐frequency representation and spatial information encoding. A hybrid CNN‐LSTM is used for feature extraction and classification. The proposed method has been implemented on a dataset consisting of 5 different classes of attempted hand movements from 10 SCI patients. The average classification accuracy of 92.36% is achieved for 5‐class classification. To check the global validity of the proposed network, the BCI competition IV data is classified by the proposed method and has found 92.70% overall accuracy.

Late Quaternary Surface Displacements on Accretionary Wedge Splay Faults in the Cascadia Subduction Zone: Implications for Megathrust Rupture

Because splay faults branch at a steep dip angle from the plate-boundary décollement in an accretionary wedge, their coseismic displacement can potentially result in larger tsunamis with distinct characteristics compared to megathrust-only fault ruptures, posing an enhanced hazard to coastal communities. Elsewhere, there is evidence of coseismic slip on splay faults during many of the largest subduction zone earthquakes, but our understanding of potentially active splay faults and their hazards at the Cascadia subduction zone remains limited. To identify the most recently active splay faults at Cascadia, we conduct stratigraphic and structural interpretations of near-surface deformation in the outer accretionary wedge for the ~400 km along-strike length of the landward vergence zone. We analyze recently acquired high-frequency sparker seismic data and crustal-scale multi-channel seismic data to examine the record of deformation in shallow slope basins and the upper ~1 km of the surrounding accreted sediments and to investigate linkages to deeper décollement structure. We present a new fault map for widest, most completely locked portion of Cascadia from 45 to 48°N latitude, which documents the distribution of faults that show clear evidence of recent late Quaternary activity. We find widespread evidence for active splay faulting up to 30 km landward of the deformation front, in what we define as the active domain, and diminished fault activity landward outside of this zone. The abundance of surface-deforming splay faults in the active outer wedge domain suggests Cascadia megathrust events may commonly host distributed shallow rupture on multiple splay faults located within 30 km of the deformation front.

Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier

Objective. Although emotion recognition has been studied for decades, a more accurate classification method that requires less computing is still needed. At present, in many studies, EEG features are extracted from all channels to recognize emotional states, however, there is a lack of an efficient feature domain that improves classification performance and reduces the number of EEG channels. Approach. In this study, a continuous wavelet transform (CWT)-based feature representation of multi-channel EEG data is proposed for automatic emotion recognition. In the proposed feature, the time-frequency domain information is preserved by using CWT coefficients. For a particular EEG channel, each CWT coefficient is mapped into a strength-to-entropy component ratio to obtain a 2D representation. Finally, a 2D feature matrix, namely CEF2D, is created by concatenating these representations from different channels and fed into a deep convolutional neural network architecture. Based on the CWT domain energy-to-entropy ratio, effective channel and CWT scale selection schemes are also proposed to reduce computational complexity. Main results. Compared with previous studies, the results of this study show that valence and arousal classification accuracy has improved in both 3-class and 2-class cases. For the 2-class problem, the average accuracies obtained for valence and arousal dimensions are 98.83% and 98.95%, respectively, and for the 3-class, the accuracies are 98.25% and 98.68%, respectively. Significance. Our findings show that the entropy-based feature of EEG data in the CWT domain is effective for emotion recognition. Utilizing the proposed feature domain, an effective channel selection method can reduce computational complexity.

Structural Damage Identification Based on Preprocessing Samples Mixed Training of Multi-Channel and Multi-Source Dynamic Response Data

Previous studies have shown that sampling processing and identification methods for neural networks damage identification in multi-channel and multi-source structural dynamic response data exhibit diversity, and the robustness and generalization issues of the model have not been effectively solved. This paper proposes a sample preprocessing technique and mixed training method suitable for multi-channel and multi-source dynamic response data to optimize the current structural damage identification methods based on neural networks. Through multi-dimensional discrete autocorrelation processing and Fourier transform, the preprocessed datasets from multiple sensors with multi-channel can access multi-channel CNN. Furthermore, the measured datasets from the actual structures are mixed with numerical simulation datasets before being used for neural network model training to address the model calibration and the imbalanced sample size under each classification label. The results of extensive model experiments and finite element verification of specimens show that the method performs outstandingly in detecting and identifying damage to simply supported beam structures using multi-channel CNN. The neural network model trained with preprocessed samples exhibits excellent robustness. The model using the mixed training method still performs well in identifying the accuracy of damage location and degree in simply supported beam structures after changing beam section and initial excitation. The method has certain generalization ability in detecting unknown damage in simply supported beam structures.

MultiWaveNet: A long time series forecasting framework based on multi-scale analysis and multi-channel feature fusion

Long time series forecasting is widely used in areas such as power dispatch, traffic control, and weather forecasting. The pattern of seasonality and trends in long time series are often complex, especially when they are presented at different time scales. Existing methods typically focus on only one scale or randomly select scales, which leads to a significant loss of valuable information. Additionally, current methods often transform multi-channel data into a single-channel format, ignoring interactions and complex relationships between channels. The paper proposes MultiWaveNet, a novel long time series forecasting framework that addresses seasonality as well as trends separately. For the seasonal component, the framework uses multi-scale wavelet decomposition to generate subseries at multiple scales. A learnable optimization factor is introduced simultaneously to separate high-frequency components mixed in low-frequency series after wavelet decomposition. In order to reduce information redundancy and model complexity, the paper develops a wavelet domain sampling encoder that consists of just one Transformer encoder, ensuring effective modeling of long-term dependencies while maintaining feature extraction effectiveness. As for the trend component, unlike previous research, the weights of channels are adjusted based on their importance, allowing the more crucial channels to have a greater impact and thereby addressing the limitations of individual processing methods. The paper performs extensive experiments on nine standard datasets, demonstrating that MultiWaveNet is the most competitive method.

Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived from Pose Data

This study introduces an innovative multichannel approach that focuses on the features and configurations of fingers in isolated sign language recognition. The foundation of this approach is based on three different types of data, derived from finger pose data obtained using MediaPipe and processed in separate channels. Using these multichannel data, we trained the proposed MultiChannel-MobileNetV2 model to provide a detailed analysis of finger movements. In our study, we first subject the features extracted from all trained models to dimensionality reduction using Principal Component Analysis. Subsequently, we combine these processed features for classification using a Support Vector Machine. Furthermore, our proposed method includes processing body and facial information using MobileNetV2. Our final proposed sign language recognition method has achieved remarkable accuracy rates of 97.15%, 95.13%, 99.78%, and 95.37% on the BosphorusSign22k-general, BosphorusSign22k, LSA64, and GSL datasets, respectively. These results underscore the generalizability and adaptability of the proposed method, proving its competitive edge over existing studies in the literature.

Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex.

Magnetic Resonance Imaging (MRI) datasets from epidemiological studies often show a lower prevalence of motion artifacts than what is encountered in clinical practice. These artifacts can be unevenly distributed between subject groups and studies which introduces a bias that needs addressing when augmenting data for machine learning purposes. Since unreconstructed multi-channel k-space data is typically not available for population-based MRI datasets, motion simulations must be performed using signal magnitude data. There is thus a need to systematically evaluate how realistic such magnitude-based simulations are. We performed magnitude-based motion simulations on a dataset (MR-ART) from 148 subjects in which real motion-corrupted reference data was also available. The similarity of real and simulated motion was assessed by using image quality metrics (IQMs) including Coefficient of Joint Variation (CJV), Signal-to-Noise-Ratio (SNR), and Contrast-to-Noise-Ratio (CNR). An additional comparison was made by investigating the decrease in the Dice-Sørensen Coefficient (DSC) of automated segmentations with increasing motion severity. Segmentation of the cerebral cortex was performed with 6 freely available tools: FreeSurfer, BrainSuite, ANTs, SAMSEG, FastSurfer, and SynthSeg+. To better mimic the real subject motion, the original motion simulation within an existing data augmentation framework (TorchIO), was modified. This allowed a non-random motion paradigm and phase encoding direction. The mean difference in CJV/SNR/CNR between the real motion-corrupted images and our modified simulations (0.004±0.054/-0.7±1.8/-0.09±0.55) was lower than that of the original simulations (0.015±0.061/0.2±2.0/-0.29±0.62). Further, the mean difference in the DSC between the real motion-corrupted images was lower for our modified simulations (0.03±0.06) compared to the original simulations (-0.15±0.09). SynthSeg+ showed the highest robustness towards all forms of motion, real and simulated. In conclusion, reasonably realistic synthetic motion artifacts can be induced on a large-scale when only magnitude MR images are available to obtain unbiased data sets for the training of machine learning based models.

Triboelectric nanogenerator for Self-Powered musical instrument sensing based on the Ion-Electricfield-Migration Nylon/Na2SO4 nanofiber film

The advanced sensing technologies have a significantly important influence in promoting the design and research of intelligent musical instruments. Herein, we propose an ion-electricfield-migration enhanced triboelectric nanogenerator (IE-TENG) with the features of low-cost, lightweight and high-sensitive for the sensors of intelligent musical instruments. Compared to pure nylon nanofiber films, the voltage of IE-TENG with the nylon/Na2SO4 nanofiber films has been improved by 4.5 times, which is benefiting from the improved electrostatic induction of anions and cations migration. The fabricated IE-TENG sensor can achieve an excellent sensing performance with a sensitivity of 7.29 V N−1, a signal-to-noise ratio of 68.61 dB and a response-time of 10 ms, which are several times higher than the previous research works. Importantly, combined with the array structure design, multi-channel data acquisition and artificial intelligence digital analysis, an intelligent musical instrument system is constructed to gather and analyze the performer's playing information in real-time and quickly give the corresponding evaluation and improvement suggestions. Our finding not only give a good way for fabricating excellent performance TENG, but also promotes the application of TENG in intelligent musical instruments.

ASL MRI Denoising via Multi Channel Collaborative Low-Rank Regularization.

Arterial spin labeling (ASL) perfusion MRI is the only non-invasive imaging technique for quantifying regional cerebral blood flow (CBF), which is a fundamental physiological variable. ASL MRI has a relatively low signal-to-noise-ratio (SNR). In this study, we proposed a novel ASL denoising method by simultaneously exploiting the inter- and intra-receive channel data correlations. MRI including ASL MRI data have been routinely acquired with multi-channel coils but current denoising methods are designed for denoising the coil-combined data. Indeed, the concurrently acquired multi-channel images differ only by coil sensitivity weighting and random noise, resulting in a strong low-rank structure of the stacked multi-channel data matrix. In our method, this matrix was formed by stacking the vectorized slices from different channels. Matrix rank was then approximately measured through the logarithm-determinant of the covariance matrix. Notably, our filtering technique is applied directly to complex data, avoiding the need to separate magnitude and phase or divide real and imaginary data, thereby ensuring minimal information loss. The degree of low-rank regularization is controlled based on the estimated noise level, striking a balance between noise removal and texture preservation. A noteworthy advantage of our framework is its freedom from parameter tuning, distinguishing it from most existing methods. Experimental results on real-world imaging data demonstrate the effectiveness of our proposed approach in significantly improving ASL perfusion quality. By effectively mitigating noise while preserving important textural information, our method showcases its potential for enhancing the utility and accuracy of ASL perfusion MRI, paving the way for improved neuroimaging studies and clinical diagnoses.

Performance testing of gas-tight portable RPC for muography applications

This paper reports the latest developmental efforts for a position-sensitive glass-based Resistive Plate Chamber (RPC) and a multi-channel Data AcQuisition (DAQ) system tailored for muon tracking in muography applications. The designed setup prioritizes portability, aiming for field applications where both the detector and the DAQ operate effectively in external environmental conditions. Comprehensive discussions on hardware development activities and signal processing techniques are included, incorporating noise filtering to enhance the accurate detection of real muons. A muon absorption measurement has also been carried out to understand the behavior of these detectors from an application perspective.

Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech Representation

Self-supervised speech pre-training methods have developed rapidly in recent years, which show to be very effective for many near-field single-channel speech tasks. However, far-field multichannel speech processing is suffering from the scarcity of labeled multichannel data and complex ambient noises. The efficacy of self-supervised learning for far-field multichannel and multi-modal speech processing has not been well explored. Considering that visual information helps to improve speech recognition performance in noisy scenes, in this work we propose the multichannel multi-modal speech self-supervised learning framework AV-wav2vec2, which utilizes video and multichannel audio data as inputs. First, we propose a multi-path structure to process multi-channel audio streams and a visual stream in parallel, with intra-, and inter-channel contrastive as training targets to fully exploit the rich information in multi-channel speech data. Second, based on contrastive learning, we use additional single-channel audio data, which is trained jointly to improve the performance of multichannel multi-modal representation. Finally, we use a Chinese multichannel multi-modal dataset in real scenarios to validate the effectiveness of the proposed method on audio-visual speech recognition (AVSR), automatic speech recognition (ASR), visual speech recognition (VSR) and audio-visual speaker diarization (AVSD) tasks.

Vision transformer-based electronic nose for enhanced mixed gases classification

The classification of mixed gases is one of the major functions of the electronic nose. To address the challenges associated with complex feature construction and inadequate feature extraction in gas classification, we propose a classification model for gas mixtures based on the vision transformer (ViT). The whole-process signals of the sensor array are taken as input signals in the proposed classification model, and self-attention mechanism is employed for the fusion of global information and adaptive feature extraction to make full use of the dependence of responses at different stages of the whole-process signals to improve the model’s classification accuracy. Our model exhibited a remarkable accuracy (96.66%) using a dataset containing acetone, methanol, ammonia, and their binary mixtures. In comparison, experiments conducted by support vector machine and a one-dimensional deep convolutional neural network model demonstrated classification accuracy of 90.56% and 92.75%, respectively. Experimental results indicate that the ViT gas classification model can be effectively combined with multi-channel time series data from the sensor array using the self-attention mechanism, thereby improving the accuracy of mixed gases classification. This advancement can be expected to become a standard method for classifying mixed gases.

A high-precision fusion bathymetry of multi-channel waveform curvature for bathymetric LiDAR systems

Airborne LiDAR bathymetry (ALB) system is an attractive and efficient method for nearshore bathymetry and underwater topography mapping. To ensure and improve the measurement accuracy and reliability in various water environments, different receivers using a segmented field of view (FOV) have been designed and are implemented in ALB. These are used to obtain various echo data of multiple channels and perform bathymetry at various water depths. However, in echo waveform processing, detailed information about the water body described by the different echo waveforms is lacking, making it difficult to overcome severe waveform superposition in extremely shallow areas or to separate the weak echo of the water bottom from the noise in extremely deep areas. Therefore, we employed a novel method of multi-channel waveform fusion bathymetry (MWFB) to achieve information complementarity, gain multi-channel waveforms, and detect the robustness of water and bottom echo signals. This method uses an adaptive signal waveform extraction of a multichannel fusion mechanism to eliminate systematic and random noise, and the waveform curvature is used to reconstruct the energy–waveform curves. Iterative decomposition based on waveform curvature and energy curves was conducted to improve the correctness of waveform decomposition and the reliability of waveform components. Furthermore, the water surface and bottom peaks were detected based on the multichannel waveform curvature, which enabled acquisition of the accurate temporal positions of the peaks and achieve high-accuracy bathymetry. According to the experimental results and a comparison with reference datasets, the highest bathymetric accuracy of the MWFB method reached an RMSE of 0.22. For the different study areas, the bathymetric accuracy of the slope reached 95%, 92%, and 97% at Ganquan Island, Lingyang Reef, and Bei Island, respectively. Furthermore, the bathymetric point numbers were effectively increased and bathymetric accuracy was also improved in shallow and deep water, which illustrates the superior bathymetric performance of the MWFB method and its ability to provide a high-efficiency waveform dataset through multi-channel data fusion. The novel LiDAR bathymetric method proposed in this study can effectively achieve high-accuracy nearshore bathymetry and seafloor topographical mapping.

Multichannel consecutive data cross-extraction with 1DCNN-attention for diagnosis of power transformer

Power transformer plays a critical role in grid infrastructure, and its diagnosis is paramount for maintaining stable operation. However, the current methods for transformer diagnosis focus on discrete dissolved gas analysis, neglecting deep feature extraction of multichannel consecutive data. The unutilized sequential data contains the significant temporal information reflecting the transformer condition. In light of this, the structure of multichannel consecutive data cross-extraction (MCDC) is proposed in this article in order to comprehensively exploit the intrinsic characteristic and evaluate the states of transformer. Moreover, for the better accommodation in scenario of transformer diagnosis, one dimensional convolution neural network attention (1DCNN-attention) mechanism is introduced and offers a more efficient solution given the simplified spatial complexity. Finally, the effectiveness of MCDC and the superior generalization ability, compared with other algorithms, are validated in experiments conducted on a dataset collected from real operation cases of power transformer. Additionally, the better stability of 1DCNN-attention has also been certified.

Origin of a high-velocity layer: Insights from seismic reflection imaging (South China Sea)

Magmatism and crustal structural characteristics are well-known phenomena that support understanding the marginal sea's tectonic evolution. The South China Sea (SCS) has become a natural laboratory for researchers to investigate the evolution of the marginal sea because of its complex tectonic background and multiple phases of magmatic activities. However, due to limitations in resolution and depth of observations, the evolutionary process of the SCS is still controversial. We use the latest processed multi-channel seismic reflection profile Line AB to evaluate the sedimentary sequences, crustal tectonics, and the Cenozoic magmatism in the southern SCS. The gravity modeling results indicate a reduction in crustal thickness from 18 km in the proximal domain to 7 km in the Continent-Ocean Transition (COT). The COT is roughly 35 km wide, representing a rapid break up process of the SCS. A high-velocity layer (HVL) of about 3 km thickness was formed in the continental slope crust, interpreted as post-spreading magmatic underplating. In addition, four sedimentary sequences, post-spreading magmatic intrusion, and the possible magma conduits were identified in the profile. Combining with previous ocean bottom seismometer (OBS) data, multi-channel seismic data, fault system data, and petrologic observations, we indicate the deep mantle magmatic material upwelled to the crust and further intruded into sediments along the hyper-extended crust or detachment faults during the post-spreading stage. We contend that cooling and heat shrinking within the SCS oceanic lithosphere have the potential to initiate decompression melt deformation in the continental slope. In summary, we construct a model of the origin and migration mechanism of extensive post-spreading magmatism in the SCS. Our research reveals the Cenozoic evolution and the HVL of the southern SCS, and provides a possible genesis for the anomalously young magmatism observed at the continental margin.

Previous motor task performance impacts phase-based EEG resting-state connectivity states

Abstract The resting human brain cycles through distinct states that can be analyzed using microstate analysis and electroencephalography (EEG) data. This approach classifies multichannel EEG data into spontaneously interchanging microstates based on topographic features. These microstates may be valuable biomarkers in neurodegenerative diseases since they reflect the resting brain’s state. However, microstates do not provide information about the active neural networks during the resting state. This article presents an alternative and complementary method for analyzing resting-state EEG data and demonstrates its reproducibility and reliability. This method considers cerebral connectivity states defined by phase synchronization and measured using the corrected imaginary phase-locking value (ciPLV) based on source-reconstructed EEG recordings. We analyzed resting-state EEG data from young, healthy participants acquired on five consecutive days before and after a motor task. We show that our data reproduce microstates previously reported. Further, we reveal four stable topographic patterns over the multiple recording sessions in the source connectivity space. While the classical microstates were unaffected by a preceding motor task, the connectivity states were altered, reflecting the suppression of frontal activity in the post-movement resting state.

Intelligent fault diagnosis for electro-hydrostatic actuator based on multisource information convolutional residual network

The electro-hydrostatic actuator (EHA), known for its advantages such as minimal throttling loss, high efficiency, and a significant volume-to-power ratio, has found extensive application in the fields of aeronautics and astronautics. However, ensuring the safety of aircraft that utilize EHAs requires efficient fault diagnosis due to the demanding operational conditions and prolonged usage. Traditional diagnostic approaches face challenges such as intricate fault modeling, complex multi-channel monitoring data, and a limited number of fault samples within the electro-hydraulic system. To overcome these challenges, we propose an intelligent diagnosis method based on a multi-source information convolutional residual network. Specifically, a multis-cale kernel is implemented to capture features at different scales, enhancing model expressiveness. The efficiency channel attention mechanism dynamically focuses on relevant channel features to improve feature learning ability. The residual network adaptively recalibrates features at each layer to facilitate fault feature learning. Additionally, the activate or not activation function is introduced to selectively activate shallow features, thereby improving the feature representation and generalization capability of the model. Experimental data from the EHA system validates the superiority of the proposed method, demonstrating a significant enhancement in the diagnostic accuracy of EHAs with limited samples.

Rolling bearing fault diagnosis based on the fusion of sparse filtering and discriminative domain adaptation method under multi-channel data-driven

Currently, the diagnostic performance of many deep learning algorithms may drop dramatically when the distribution of training data is significantly different from that of the test data. Moreover, the fault diagnosis approaches based on single-channel data may suffer problems such as large precision fluctuation, low reliability, and incomplete expression of fault features. To overcome the above deficiencies, a novel multi-channel data-driven fault recognition method based on the fusion of sparse filtering (SF) and discriminative domain adaptation (MSFDDA) is proposed in this article. Firstly, inspired by attention mechanisms and information fusion methods, a spectrum-based weighted multi-channel data fusion strategy is designed to fully utilize the data collected by sensors to obtain a more comprehensive representation of fault features. Then, the joint probability-based discriminative maximum mean discrepancy algorithm is introduced into the SF method to strengthen the capability of extracting the domain invariant features. Finally, two bearing datasets are employed to verify the validity of the MSFDDA method, which proved to be superior to other current domain adaptation methods.