Signal Fusion Research Articles

TdCCA with Dual-Modal Signal Fusion: Degenerated Occipital and Frontal Connectivity of Adult Moyamoya Disease for Early Identification.

Cognitive impairment in patients with moyamoya disease (MMD) manifests earlier than clinical symptoms. Early identification of brain connectivity changes is essential for uncovering the pathogenesis of cognitive impairment in MMD. We proposed a temporally driven canonical correlation analysis (TdCCA) method to achieve dual-modal synchronous information fusion from electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) for exploring the differences in brain connectivity between MMD and normal control groups. The dual-modal fusion features were extracted based on the imaginary part of coherence of the EEG signal (EEG iCOH) and the Pearson correlation coefficients of the fNIRS signal (fNIRS COR) in the resting and working memory state. The machine learning model showed that the accuracy of TdCCA method reached 97%, far higher than single-modal features and feature-level fusion CCA method. Brain connectivity analysis revealed a significant reduction in the strength of the connections between the right occipital lobe and frontal lobes (EEG iOCH: p = 0.022, fNIRS COR p = 0.011) in MMD. These differences reflected the impaired transient memory and executive function in MMD patients. This study contributes to the understanding of the neurophysiological nature of cognitive impairment in MMD and provides a potential adjuvant early identification method for individuals with chronic cerebral ischemia.

Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization

The permanent magnet synchronous motor (PMSM) plays an important role in the power system of agricultural machinery. Inter-turn short circuit (ITSC) faults are among the most common failures in PMSMs, and early diagnosis of these faults is crucial for enhancing the safety and reliability of motor operation. In this article, a multi-source data-fusion algorithm based on convolutional neural networks (CNNs) has been proposed for the early fault diagnosis of ITSCs. The contributions of this paper can be summarized in three main aspects. Firstly, synchronizing data from different signals extracted by different devices presents a significant challenge. To address this, a signal synchronization method based on maximum cross-correlation is proposed to construct a synchronized dataset of current and vibration signals. Secondly, applying a traditional CNN to the data fusion of different signals is challenging. To solve this problem, a multi-stream high-level feature fusion algorithm based on a channel attention mechanism is proposed. Thirdly, to tackle the issue of hyperparameter tuning in deep learning models, a hyperparameter optimization method based on Bayesian optimization is proposed. Experiments are conducted based on the derived early-stage ITSC fault-severity indicator, validating the effectiveness of the proposed fault-diagnosis algorithm.

A noninvasive blood glucose detection method with strong time adaptability based on fuzzy operator decision fusion and dynamic spectroscopy characteristics of PPG signals.

PPG signals are a new means of non-invasive detection of blood glucose, but there are still shortcomings of poor time adaptability and low prediction accuracy of blood glucose quantitative models. Few studies discuss prediction accuracy in the case of a large time interval span between modeling and prediction. This paper proposes an automatic optimal threshold baseline removal algorithm based on variational mode decomposition (AOT-VMD), which can adaptively eliminate high-frequency noise and baseline interference for each decomposed IMF modal component and reduce the baseline difference of PPG signals from different days. Furthermore, a fuzzy integral multi-model decision fusion algorithm based on error weight is proposed. The fuzzy integral operator is introduced to make the features with large contributions in each sub-model maintain a high-weight value in the overall prediction mechanism, which improves the prediction accuracy of blood glucose. In this paper, a self-developed portable PPG glucose meter is used to collect PPG signals, and the true blood glucose values for 8 consecutive days are collected by CGM. The proposed algorithm is used to build a model with the first day's data and predict the blood glucose values for the remaining 7 days. The experimental results show that the AOT-VMD preprocessing algorithm and the quantitative regression algorithm of the fuzzy integral multiple model decision fusion algorithm proposed in this paper perform well in measurement accuracy and time adaptability compared with the traditional methods. In addition, the proposed method requires less invasive calibration samples in the modeling stage, achieving high-precision prediction for a long period. 100% of the samples are located in areas A and B of the Clarke area in this experiment, and the algorithm has strong time generalization ability. This innovative method can promote the development of a home blood glucose noninvasive detector.

Feature Fusion of Pulse Current, Ultrahigh Frequency, and Photon Count Signal: A Novel Discharge Pattern Recognition Method of Metal Particles in GIS/GIL

Feature Fusion of Pulse Current, Ultrahigh Frequency, and Photon Count Signal: A Novel Discharge Pattern Recognition Method of Metal Particles in GIS/GIL

Feedback projection synchronization of discrete chaotic systems and its application to speech encryption

Abstract Chaotic systems are widely used in secure communication due to their sensitivity to initial values, unpredictability, and complex motion trajectories. In this paper, we study the encryption method of chaotic synchronization and introduce a scaling factor based on traditional feedback control synchronization to achieve more accurate projection synchronization. The effectiveness and robustness of the method in chaotic systems are verified through theoretical proofs and numerical simulations. A chaotic masked speech encryption system utilizing bit similarity is designed; the structural similarity index (SSIM) of the decrypted signal with the original signal is as high as 0.992866, while the SSIM value of the encrypted signal with the original signal is only 0.000030, proving the efficiency and security of the encryption process. Additionally, we analyzed the data transmission process of the encryption system. The fusion of the control signal and the encryption sequence into one transmission sequence in the channel not only saves hardware and software design resources but also reduces inter-channel interference and conflict, improving the reliability and stability of the transmission. Experimental results show that the system performs well in terms of data transmission security and anti-interference capability.

Synchronization-based fusion of EEG and eye blink signals for enhanced decoding accuracy

Decoding locomotor tasks is crucial in cognitive neuroscience for understanding brain responses to physical tasks. Traditional methods like EEG offer brain activity insights but may require additional modalities for enhanced interpretative precision and depth. The integration of EEG with ocular metrics, particularly eye blinks, presents a promising avenue for understanding cognitive processes by combining neural and ocular behaviors. However, synchronizing EEG and eye blink activities poses a significant challenge due to their frequently inconsistent alignment. Our study with 35 participants performing various locomotor tasks such as standing, walking, and transversing obstacles introduced a novel methodology, pcEEG+, which fuses EEG principal components (pcEEG) with aligned eye blink data (syncBlink). The results demonstrated that pcEEG+ significantly improved decoding accuracy in locomotor tasks, reaching 78% in some conditions, and surpassed standalone pcEEG and syncBlink methods by 7.6% and 22.7%, respectively. The temporal generalization matrix confirmed the consistency of pcEEG+ across tasks and times. The results were replicated using two driving simulator datasets, thereby confirming the validity of our method. This study demonstrates the efficacy of the pcEEG+ method in decoding locomotor tasks, underscoring the importance of temporal synchronization for accuracy and offering a deeper insight into brain activity during complex movements.

Simulation of Space Charge Effects in Fourier Transform Quadrupole Ion Traps (FT-QITs).

We present ion dynamics simulations regarding the effect of space charge on the performance of Fourier transform quadrupole ion traps (FT-QITs) with special attention to signal stability, mass resolving power, and sensitivity. Ion trajectory simulations within an idealized QIT geometry are performed by applying a dedicated application (QITSim) using an in-house developed open simulation framework (IDSimF). Image current detection transients are generated by the application and are subsequently transformed into frequency spectra of ion secular motion. Such frequency spectra are the basis for the calculation of mass spectra in FT-QIT instruments. The simulation results are used to assess the extent of space charge induced effects regarding the analytical performance of FT-QIT systems. The simulation results for two Cl+ and seven Xe+ isotope ions exhibit diverse space charge induced phenomena. Most prominently, complete isotope signal fusion, quantitative individual signal suppression, and severe signal distortions are observed even at low absolute numbers of trapped ions. Furthermore, significant shifts of the secular oscillation frequencies occur, even when the signal shape appears to be mostly unaffected and when the frequency separation for trapped ions with different masses is large. This significantly limits the applicability of FT-QIT systems for high resolution mass spectrometry. An increase of the RF amplitude of the trapping field as well as increasing the extent of ion excitation partly mitigate these adverse space charge effects; nevertheless, the usable operational range of the simulated FT-QIT system for analytical applications remains rather narrow.

Multilevel attention mechanism for motion fatigue recognition based on sEMG and ACC signal fusion.

This study aims to develop a cost-effective and reliable motion monitoring device capable of comprehensive fatigue analysis. It achieves this objective by integrating surface electromyography (sEMG) and accelerometer (ACC) signals through a feature fusion strategy. The study introduces a multi-level attention mechanism for classification, leveraging convolutional neural networks (CNNs). The preprocessing phase involves a local feature attention mechanism that enhances local waveform features using the amplitude envelope. A dual-scale attention mechanism, operating at both channel and neuron levels, is employed to enhance the model's learning from high-dimensional fused data, improving feature extraction and generalization. The local feature attention mechanism significantly improves the model's classification accuracy and convergence, as demonstrated in ablation experiments. The model, optimized with multi-level attention mechanisms, excels in accuracy and generalization, particularly in handling data with pseudo-artifacts. Computational analysis indicates that the proposed optimization algorithm has minimal impact on CNN's training and testing times. The study achieves recognition accuracies of 92.52%, 92.38%, and 92.30%, as well as F1-scores of 91.92%, 92.13%, and 92.29% for the three fatigue states, affirming its reliability. This research provides technical support for the development of affordable and dependable wearable motion monitoring devices.

Signal Fusion of Multi-Frequency Continuous Wave Ground Penetrating Radar

Abstract Carrier-less pulse radar, with its short pulse duration, low average power, and weak anti-interference capability, poses significant limitations to the application of ground penetrating radar (GPR). Fusing continuous wave signals from multiple frequency components forms the basis for the design and data interpretation of continuous wave radar. We firstly introduce two fusion methods: the application of the Inverse Fast Fourier Transform (IFFT) to multi-frequency signals and direct superposition. And we demonstrate these methods by synthesizing three basic sinusoidal signals and analyse their time-frequency relationship. Furthermore, we perform Fast Fourier Transform (FFT) on the transmit waveform of pulse radar to obtain a set of frequency components. By superimposing three sets of frequency components with different numbers of frequency points, we obtain the fused waveform and compare it with the original Ricker wavelet. The results show that while reducing the number of frequency points may increase the error in the synthesized signal slightly, it remains capable of accurately synthesizing the Ricker wavelet. However, this reduction also decreases the time window of the synthesized signal and may result in temporal overlapping. These findings offer insights into selecting parameters for frequency components in multi-frequency continuous wave radar and lay a foundation for signal fusion in such radar systems.

Recognition of Heat-Damaged Corn Seeds Based on Fusion of Laser Ultrasonic Signal and Infrared Image Features

Corn is widely cultivated on a global scale. However, high temperatures during storage and transportation can lead to thermal damage to the kernels, negatively impacting their quality. Traditional methods for identifying heat-damaged grains primarily rely on manual inspection, which is characterized by low efficiency and accuracy. This study proposes a novel identification method that integrates laser ultrasonic signals with infrared image texture features. A pulsed laser stimulates the seeds to generate laser ultrasonic signals, while an infrared camera captures infrared images of the seeds. We extract time-domain, frequency-domain, and Hilbert-domain features from the laser ultrasonic signals, in addition to texture features from the infrared images. These features are combined using Canonical Correlation Analysis (CCA). Subsequently, the fused features are classified using a Backpropagation (BP) neural network, Support Vector Machine (SVM), and Particle Swarm Optimization–Support Vector Machine (PSO–SVM). The results indicate that the recognition rate achieved with the fused ‘signal-image’ features reaches 99.17%, providing a novel approach for detecting heat-damaged corn seeds.

PV-LaP: Multi-sensor fusion for 3D Scene Understanding in intelligent transportation systems

Intelligent transportation systems are pivotal in modern urban development, aiming to enhance traffic management efficiency, safety, and sustainability. However, existing 3D Visual Scene Understanding methods often face challenges of robustness and high computational complexity in complex traffic environments. This paper proposes a Multi-Sensor Signal Fusion method based on PV-RCNN and LapDepth (PV-LaP) to improve 3D Visual Scene Understanding. By integrating camera and LiDAR data, the PV-LaP method enhances environmental perception accuracy. Evaluated on the KITTI and WHU-TLS datasets, the PV-LaP framework demonstrated superior performance. On the KITTI dataset, our method achieved an Absolute Relative Error (Abs Rel) of 0.079 and a Root Mean Squared Error (RMSE) of 3.014, outperforming state-of-the-art methods. On the WHU-TLS dataset, the method improved 3D reconstruction precision with a PSNR of 19.15 and an LPIPS of 0.299. Despite its high computational demands, PV-LaP offers significant improvements in accuracy and robustness, providing valuable insights for the future development of intelligent transportation systems.

A novel monitoring method based on the fusion of sound and image signals for laser welding penetration status

Accurately identifying penetration status is crucial for ensuring high-quality welding. However, relying on a single sensor to monitor the welding status falls short in accuracy. Thus, a novel monitoring method that fuses sound and image is proposed for assessing the laser welding penetration status. First, a microphone and a high-speed camera are employed to collect sound and image signals during welding. Second, to overcome noise challenges in sound signal, a denoising method is proposed that utilizes Grey Wolf Optimization (GWO) to find the optimal parameters for Variational Mode Decomposition (VMD). The time and frequency domain features from the denoised sound and Local Binary Pattern (LBP) features from the image are extracted. These features constitute a high-dimensional dataset, which is reduced using Principal Component Analysis (PCA). Finally, a back propagation neural network (BPNN) is constructed for fusion monitoring. The results demonstrate that fusion monitoring achieves higher accuracy (97.9%) compared to using sound (79.9%) or image (92.3%) features independently. In comparison to other methods like K-Nearest Neighbor (KNN), Naive Bayesian (NB), Support Vector Machine (SVM), and Decision Tree, BPNN fusion monitoring achieves higher accuracies, with increases of 18.09%, 12.79%, 6.53%, and 25.84% respectively. The proposed method helps to promote the development of welding intelligence.

A compressor blade crack detection method based on the multilevel information fusion of acoustic and vibration signals

Single-signal crack detection methods perform unsatisfactorily in the presence of noise, prompting the need for alternative approaches. Leveraging multisource signals, which contain rich and complementary information, is a promising direction. In light of this, a compressor blade crack detection method based on the multilevel information fusion of acoustic and vibration signals is proposed. First, a multiscale data-level fusion convolutional neural network is designed, which fuses multisource homogeneous signals for crack detection. Second, multiple networks are trained with acoustic and vibration signals as inputs, respectively. Softmax layer of each network provides preliminary probabilities for each category, while the precision for each category in the validation set is calculated. Finally, an improved Dempster–Shafer theory approach is proposed to derive the final probability for each category according to the preliminary probabilities and precision, after which crack detection is realized. The proposed method is validated with experimental data from five categories of blades.

Analysis of Motor Functions of Hemiplegic Patients Based on Dual-Mode Signal Fusion

Analysis of Motor Functions of Hemiplegic Patients Based on Dual-Mode Signal Fusion

A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model’s superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles.

A Novel Defect Quantification Method Utilizing Multi-Sensor Magnetic Flux Leakage Signal Fusion

In the assessment of pipeline integrity using magnetic flux leakage (MFL) detection, it is crucial to quantify defects accurately and efficiently using MFL signals. However, in complex detection environments, traditional defect inversion methods exhibit low quantification accuracy and efficiency due to the complexity of their algorithms or excessive reliance on a priori knowledge and expert experience. To address these issues, this study presents a novel defect quantification method based on multi-sensor signal fusion (MSSF). The method employs a multi-sensor probe to fuse the MFL signals under multiple lift-off values, enhancing the diversity of defect information. This enables defect-opening profile recognition using the characteristic approximation approach (CAA). Subsequently, the MSSF method is based on a 3D magnetic dipole model and integrates the structural features of multi-sensor probes to develop an algorithm. This algorithm iteratively determines the defect depth at multiple data acquisition points within the defect region to obtain the maximum defect depth. The feasibility of the MSSF quantification method is validated through finite element simulation and physical experiments. The results demonstrate that the proposed method achieves accurate defect quantification while enhancing efficiency, with the number of iterations for each defect depth calculation point consistently requiring fewer than 15 iterations. For rectangular metal loss, perforation, and conical defects, quantification errors are less than 10%, meeting practical inspection requirements.

Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach.

change of blood glucose (BG) level stimulates the autonomic nervous system leading to variation in both human's electrocardiogram (ECG) and photoplethysmogram (PPG). In this article, we aimed to construct a novel multimodal framework based on ECG and PPG signal fusion to establish a universal BG monitoring model. This is proposed as a spatiotemporal decision fusion strategy that uses weight-based Choquet integral for BG monitoring. Specifically, the multimodal framework performs three-level fusion. First, ECG and PPG signals are collected and coupled into different pools. Second, the temporal statistical features and spatial morphological features in the ECG and PPG signals are extracted through numerical analysis and residual networks, respectively. Furthermore, the suitable temporal statistical features are determined with three feature selection techniques, and the spatial morphological features are compressed by deep neural networks (DNNs). Lastly, weight-based Choquet integral multimodel fusion is integrated for coupling different BG monitoring algorithms based on the temporal statistical features and spatial morphological features. To verify the feasibility of the model, a total of 103 days of ECG and PPG signals encompassing 21 participants were collected in this article. The BG levels of participants ranged between 2.2 and 21.8 mmol/L. The results obtained show that the proposed model has excellent BG monitoring performance with a root-mean-square error (RMSE) of 1.49 mmol/L, mean absolute relative difference (MARD) of 13.42%, and Zone A + B of 99.49% in tenfold cross-validation. Therefore, we conclude that the proposed fusion approach for BG monitoring has potentials in practical applications of diabetes management.

Adaptive signal fusion for swashplate pump fault detection using bidirectional long short-term memory and wavelet scattering transform

In construction equipment, swashplate pumps are vital for uninterrupted operations. However, these pumps often experience multiple simultaneous faults. Current detection methods typically rely on a single signal, insufficient for accurately identifying multiple faults in this hydraulic system. To improve accuracy, we utilize multiple signals that provide comprehensive information, using an adaptive signal-level fusion technique. This method involves analyzing pressure, shaft torque, and swashplate torque signals to identify potential faults. The study employs a Bidirectional Long Short-Term Memory (BiLSTM) model to analyze each type of signal, assessing the model's ability to classify different faults. Once detection accuracy for each fault type is assessed, we propose using a weight matrix to adaptively fuse signals based on their assigned weights. Additionally, the Wavelet Scattering Transform (WST) is utilized for feature extraction from signals with low variance, and Bayesian optimization is applied to find the optimal settings for the BiLSTM model. The proposed approach is tested under various scenarios, including the impact of white Gaussian noise, to evaluate its effectiveness and stability. The results show that the adaptive signal-level fusion approach outperforms traditional methods and individual signal analyses in accurately and robustly detecting swashplate pump faults, highlighting its potential to significantly improve the reliability of construction equipment.

A Wide-Bandwidth Inexpensive Current Sensor Based on the Signal Fusion of Tunneling Magnetoresistance and a Current Transformer.

In technology and industrial production, many applications require wide-bandwidth current measurements. In this paper, a signal fusion scheme for a current sensor comprising tunneling magnetoresistance and a current transformer is proposed, achieving a flat frequency response in the DC to MHz range. The measurement principles in different cases of the scheme are introduced, and the total transfer function of the entire scheme is derived by analyzing each section separately. Furthermore, the feasibility and selected parameters of the scheme are verified through a systematic simulation utilizing the MATLAB software. Based on the proposed scheme, a group of principal prototypes are built to experimentally evaluate the bandwidth, amplitude and phase flatness, accuracy, sensitivity, and impulse response. The relative amplitude variation in the passband of the fusion sensor is less than 4%, and the estimated bandwidth of the fusion sensor is close to 17 MHz. The accuracy is better than 0.6%, even when measuring the current at 1 MHz, and the relative standard deviation is 5% when measuring the impulse signal. The sensors developed using this scheme, with a low financial cost, have advantages in many wide-bandwidth current measuring scenarios.

Adaptive variable sampling T-SSD algorithm

Addressing the challenges of non-unique decomposition outcomes and prolonged decomposition durations in the fault feature adaptive extraction algorithm based on tensor decomposition, this paper presents a novel algorithm called the adaptive variable sampling tensor singular spectrum decomposition (T-SSD) algorithm. The proposed approach centers on decomposing multichannel time series with adaptive sampling frequency, leveraging tensor singular value decomposition. Initially, the embedding dimension and the number of resampling points were optimized by power spectral density analysis and adaptive sampling algorithm. Subsequently, a third-order tensor is constructed based on the principle of tensor-tensor-preserving order multiplication, combining trajectory tensor construction and embedding dimension. Finally, the signal decomposition and reconstruction of multichannel component signals are achieved through adaptive sampling, interpolation, and complementary back steps. Experimental signal analysis indicates that this algorithm can better extract fault characteristics from signals compared to common signal processing algorithms. In comparison with the traditional T-SSD algorithm, this method significantly improves decomposition efficiency, particularly for low-frequency components. It effectively tackles the efficiency challenge caused by data redundancy, enabling the organic fusion and adaptive decomposition of multichannel signals.