Signal Information Research Articles

An enhanced direct position determination of non-circular sources via sparse Bayesian inference and grid refinement strategy

In this study, an off-grid sparse Bayesian inference (OGSBI) based direct position determination (DPD) algorithm is investigated. Existing SBI-based DPD algorithms are confronted with the challenge of excessive computational loads and lack of consideration for non-circular (NC) signals. To address these limitations, we present an enhanced OGSBI-based DPD algorithm for multiple non-circular sources. By utilizing the conjugate information of the NC signals, we expand the dimensionality of the data matrix to achieve a significant improvement in the localization performance. Additionally, a grid refinement strategy is developed to alleviate the computational loads, which involves an initial search to determine the approximate source locations, followed by fine localization using a denser grid. Moreover, the computational complexity and Cramér-Rao lower bound are derived to provide a comprehensive analysis of the proposed algorithm. Numerical simulations demonstrate the superiority of the proposed algorithm in terms of both localization accuracy and computational efficiency.

Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection.

Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroencephalography (EEG) to achieve information control and interaction between the brain and external devices. In this paper, firstly, we apply Riemannian geometry to the covariance matrix extracted by spatial filtering to obtain robust and distinct features. Then, a multiscale temporal-spectral segmentation scheme is developed to enrich the feature dimensionality. In order to determine the optimal feature configurations, we utilize a linear learning-based temporal window and spectral band (TWSB) selection method to evaluate the feature contributions, which efficiently reduces the redundant features and improves the decoding efficiency without excessive loss of accuracy. Finally, support vector machines are used to predict the classification labels based on the selected MI features. To evaluate the performance of our model, we test it on the publicly available BCI Competition IV dataset 2a and 2b. The results show that the method has an average accuracy of 79.1% and 83.1%, which outperforms other existing methods. Using TWSB feature selection instead of selecting all features improves the accuracy by up to about 6%. Moreover, the TWSB selection method can effectively reduce the computational burden. We believe that the framework reveals more interpretable feature information of motor imagery EEG signals, provides neural responses discriminative with high accuracy, and facilitates the performance of real-time MI-BCI.

Low frequency signal detection via correlated Ramsey measurements

The low frequency region of the spectrum is a challenging regime for quantum probes. We support the idea that, in this regime, performing Ramsey measurements carefully controlling the time at which each measurement is initiated is an excellent signal detection strategy. We use the Fisher information to demonstrate a high quality performance in the low frequency regime, compared to more elaborated measurement sequences, and to optimize the correlated Ramsey sequence according to any given experimental parameters, showing that correlated Ramsey rivals with state-of-the-art protocols, and can even outperform commonly employed sequences such as dynamical decoupling in the detection of low frequency signals. Contrary to typical quantum detection protocols for oscillating signals, which require adjusting the time separation between pulses to match the half period of the target signal, and consequently see their scope limited to signals whose period is shorter than the characteristic decoherence time of the probe, or to those protocols whose target is primarily static signals, the time-tagged correlated Ramsey sequence simultaneously tracks the amplitude and the phase information of the target signal, regardless of its frequency, which crucially permits correlating measurements in post-processing, leading to efficient spectral reconstruction.

DA-VICReg: a data augmentation-free self-supervised learning approach for diesel engine fault diagnosis

Self-supervised learning (SSL) aims to extract useful representations from unlabeled data by maximizing the agreement between positive pairs. However, traditional SSL relies on carefully designed data augmentation methods to generate positive pairs. When dealing with 1D vibration signals, data augmentation prone to potentially compromise the fault information in the original signals. Therefore, this paper proposes a data augmentation-free SSL framework for diesel engine fault diagnosis called Domain Adaptation Variance Invariance Covariance Regularization (DA-VICReg). The DA-VICReg uses cyclic angular vibrations collected within the same time period as positive pairs and extracts useful features from unlabeled data using a loss function composed of three terms: Variance, Invariance, and Covariance. We found that when positive pairs originate from different operating conditions, such as varying speeds and torques, the model can develop feature extraction capabilities that remain unaffected by changes in operating conditions. In addition, a spatial pyramid pooling layer and a trilinear attention module are used to extract vibration features at different scales and focus on critical spatial locations and channels. Finally, the proposed approach was validated through experiments on two types of diesel engines, and a comparison with prominent SSL methods confirms the superiority of the proposed approach. In engineering practice, this method can utilize a large amount of signals stored in different time periods for self-supervised training and learn useful features for downstream fault diagnosis tasks.

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN-BiLSTM Architecture.

The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model's generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model's discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely "Abdominal and Direct Fetal ECG Database" and "Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations", resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper's model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols.

Enhancing the Secure Transmission of Data Over Optical Fiber Networks from Source to Destination

ABSTRACT The quantum key distribution (QKD) method allows for the safe creation of encryption keys between trusted entities for secure communication. The suggested system is designed to enable secure communication between multiple parties (a single sender, Alice, and multiple receivers, Bobs) connected via a public channel susceptible to interception. It enables Alice to privately share encryption keys with each Bob over dedicated quantum channels, which can then be used to decrypt messages sent over the public network. The procedure involves Alice generating optical signals with randomly assigned properties such as intensity, phase, polarization, and frequencies. Alice randomly sends either signal states carrying information or decoy states with different frequencies to the receivers (Bobs) over the quantum channels. She also sends a synchronization signal for timing alignment with the quantum signals over the optical links. At the receiving end, the synchronization pulse timestamps the incoming quantum signals. Each Bob then randomly alters the frequencies of the received quantum signals and randomly measures their phase or polarization. The key is created from the frequency, phase, or polarization information of the quantum signals, resulting in longer keys. Key management agents manage the securely QKD-generated keys to encrypt information before sending it to the intended receivers. Essentially, this framework establishes authenticated, private communication by using QKD over dedicated ultra-secure quantum links to distribute decryption keys, ensuring that only intended users can access the content. The approach is designed to ensure end-to-end secure communication across the network.

How to mine the abnormal information of power transformers: An efficient tool for quantifying the fault characteristics via multi-vibration signals

The new power system puts forward higher requirements for the reliability of power equipment. Mining the intrinsic information of equipment operation from massive data is the key challenge to determining fault early warning. The transformer fault diagnosis through vibration signal analysis has gained increasing prominence. However, the vibration signals produced by power transformers exhibit complex nonlinear and multivariate fluctuations due to the interaction of electromagnetic and mechanical factors. Unfortunately, the traditional fault diagnosis models lack the capability to capture extensive global fault information, resulting in inadequate performance in fault identification. This paper proposes the enhanced hierarchical multivariate multiscale fuzzy entropy (EHMvMFE) as an effective method for quantifying defect information in multivariate transformer signals to overcome the challenge. Firstly, the enhanced hierarchical decomposition method and time series coarse-graining theory are introduced into multivariate fuzzy entropy to develop EHMvMFE, which can comprehensively reflect the feature information of multivariate signals from the full range of frequencies. Then, EHMvMFE is utilized to extract the features via multivariate vibration signals of the transformer. Finally, the extreme learning machine is conducted to achieve effective identification of abnormal conditions. The proposed method is applied to two categories of transformer fault cases, and the diagnostic indicators are at least higher than 99.70 % and 91.82 %, which indicates a strong advantage in comparison with other popular models. The approach could be flexibly extended to the fault diagnosis of other power equipment, which is a potentially effective tool for improving the reliability of power systems.

Deep learning neural networks with input processing for vibration-based bearing fault diagnosis under imbalanced data conditions

Deep learning (DL) networks, such as convolutional neural networks (CNNs) and long short-term memory (LSTM), have gained popularity for bearing fault diagnosis utilizing raw vibration signals. However, their accuracy and stability are compromised when facing imbalanced real-world datasets. This research investigates the impact of imbalanced datasets and explores the potential of signal processing techniques on network inputs compared to the direct use of raw vibration signals. The DL techniques studied include LSTM, one-dimensional CNN, and two-dimensional (2D) CNN, and a novel hybrid 2DCNNLSTM algorithm, incorporating signal processing methods such as Fourier transform and continuous wavelet transform while maintaining nearly equal parameters and the same base architecture. The proposed hybrid 2DCNNLSTM algorithm combines the strengths of LSTM and CNN, allowing for improved bearing diagnosis by capturing both spatial and temporal information in vibration signals. The proposed 2DCNNLSTM algorithm also considers multi-channel input augmenting raw vibration signal, mean, and variance channels to extract meaningful features and enhance classification efficiency. The publicly available Case Western Reserve University benchmark-bearing test rig dataset with ten fault classes, the Paderborn University dataset with three fault classes, and NASA Centre for Intelligent Maintenance Systems bearing datasets with five fault classes are utilized to test the proposed deep learning networks’ accuracy, effectiveness, robustness, and stability. The studies reveal that the hybrid 2DCNNLSTM-based networks outperform both CNN and LSTM networks, even without input processing. Further, utilizing multi-channel input by augmenting the 2D raw signal with mean and variance value channels proves to be more efficient in handling imbalanced and complex datasets while employing a 2DCNNLSTM-based network.

A novel feature mode decomposition method and its application for gear fault detection

Fault detection can promptly reveal the potential hazards of mechanical equipment, guaranteeing the safety, stability, and reliability of their operation. Although many advanced fault detection methods, such as spectral kurtosis, deconvolution and decomposition, have been developed, most of them still suffer from insufficient utilization of fault features and incomplete extraction of diagnostic information. Given this, we propose a novel method named feature mode decomposition (FMD). Firstly, to coarsely steer the decomposition direction, a finite impulse response (FIR) filter bank is set up with a window initialization. The, correlated kurtosis (CK) is taken to evaluate the latent fault-related information in mode signals, thereby guides the updating process of all adaptive filters, assisted with period estimation. Ultimately, the unnecessary and intermingled modes are weeded out by mode selection. Experimental cases verified that the proposed FMD can adaptively decompose the fault mode of gear fault signal with excellent inspection ability. Comparison between the results and those of the classical variational mode decomposition (VMD) further highlights that FMD is more robust to other interference and noise.

MEET: A Multi-Band EEG Transformer for Brain States Decoding.

Electroencephalography (EEG) is among the most widely used and inexpensive neuroimaging techniques. Compared to the CNN or RNN based models, Transformer can better capture the temporal information in EEG signals and focus more on global features of the brain's functional activities. Importantly, according to the multiscale nature of EEG signals, it is crucial to consider the multi-band concept into the design of EEG Transformer architecture. We propose a novel Multi-band EEG Transformer (MEET) to represent and analyze the multiscale temporal time series of human brain EEG signals. MEET mainly includes three parts: 1) transform the EEG signals into multi-band images, and preserve the 3D spatial information between electrodes; 2) design a Band Attention Block to compute the attention maps of the stacked multi-band images and infer the fused feature maps; 3) apply the Temporal Self-Attention and Spatial Self-Attention modules to extract the spatiotemporal features for the characterization and differentiation of multi-frame dynamic brain states. The experimental results show that: 1) MEET outperforms state-of-the-art methods on multiple open EEG datasets (SEED, SEED-IV, WM) for brain states classification; 2) MEET demonstrates that 5-bands fusion is the best integration strategy; and 3) MEET identifies interpretable brain attention regions. MEET is an interpretable and universal model based on the multiband-multiscale characteristics of EEG. The innovative combination of band attention and temporal/spatial self-attention mechanisms in MEET achieves promising data-driven learning of the temporal dependencies and spatial relationships of EEG signals across the entire brain in a holistic and comprehensive fashion.

Impact of speech rate on perception of vowel and consonant duration by bilinguals and monolinguals.

The perceptual boundary between short and long categories depends on speech rate. We investigated the influence of speech rate on perceptual boundaries for short and long vowel and consonant contrasts by Spanish-English bilingual listeners and English monolinguals. Listeners tended to adapt their perceptual boundaries to speech rates, but the strategy differed between groups, especially for consonants. Understanding the factors that influence auditory processing in this population is essential for developing appropriate assessments of auditory comprehension. These findings have implications for the clinical care of older populations whose ability to rely on spectral and/or temporal information in the auditory signal may decline.

Determination of impact parameter for CEE with digi-input neural networks

The impact parameter characterizes the centrality in nucleus-nucleus collision geometry. The determination of impact parameters in real experiments is usually based on the reconstructed particle attributes or the derived event-level observables. For the scheduled Cooler-storage-ring External-target Experiment (CEE), the low beam energy reduces correlation between the impact parameter and charged particle multiplicity, which decreases the validity of the explicit determination methods. This work investigates a few neural network-based models that directly take the digitized signals from the external Time-of-flight detectors as input. The model with the best performance shows a mean absolute error of 0.479 fm with simulated U-U collisions at 0.5 AGeV. The performances of the models implemented with digi inputs are compared with reference models with phase space inputs, showing the capability of neural networks to handle the original but potentially interrelated digitized signal information.

Effects of magnetic and Aharonov–Bohm flux fields on information entropy with modified Kratzer plus screened Coulomb potential for selected diatomic molecules

The Shannon information entropy for scandium fluoride (ScF) and hydrogen ( H 2 ) molecules in the presence of magnetic and Aharanov–Bohm (AB) fields with modified Kratzer plus screened Coulomb potential in the position and momentum coordinate is investigated. The wavefunction and probability density are obtained by solving the two-dimensional (2D) Schrödinger wave equation using the Nikiforov–Uvarov functional analysis method and then applied to calculate the Shannon entropy. The graphical behaviour of the 2D wavefunction and probability density is showcased. Our results depict the effects of the external fields on Shannon entropy for ScF and ( H 2 ) , the negative values of the position Shannon entropies obtained imply higher localisation of the particles and molecules indicating more stability of the system. Another interesting part of this work is that Bialynicki–Birula–Mycielski inequality is tested and proven bounded for both diatomic molecules. A few to mention, this research will enhance the apprehension of the dynamics of quantum molecules and systems subjected to external fields, quantum information signal processing and communication.

Research on classification methods for rubber based on terahertz time-domain spectroscopy with data fusion strategy

Rubber, an essential industrial product, plays a crucial role in industry and human life. However, some manufacturers of rubber use inferior, recycled or low-cost rubber materials to replace high-quality rubber products. Therefore, in order to ensure industrial production, protect natural environment and ensure personal safety, it is of great economic and social significance to enable rapid and non-destructive qualitative identification of rubber products. In this paper, eight different types of black rubber, including Nitrile Butadiene Rubber (NBR), Choloroprene Rubber (CR) and Ethylen Propylen Diene Monomer (EPDM), were selected as research objects. Terahertz Time-Domain Spectroscopy (THz-TDS) technology was utilized to get the absorption spectra, refractive index spectra and time-domain spectra of these samples for exploring the differences between different types of rubber in terahertz spectra. To reduce the data volume, three band selection algorithms were employed to extract features from the absorption spectra and refractive index spectra, and temporal features of time-domain spectra were extracted for subsequent data processing. In the selection of classification model algorithms, K-Nearest Neighbor (KNN), Random Forest (RF) and Supervised Kohonen Neural Networks (S-Kohonen) were employed to recognize these spectral data. To fully exploit the latent information within these data and considering the possibility of the loss of partial information in single-type signal, a feature-level data fusion method was applied to different optical parameter signals to obtain more information about terahertz spectra during the process of data modeling. The results of the research indicated that the model with the highest prediction accuracy was the KNN model, with an accuracy of 97.37%, when refractive index spectra and time-domain spectra were fused as inputs, the F1-score reached 99.64. The performance of data fusion was noticeably better than that of a single-type signal. This study validates the feasibility of using terahertz time-domain spectroscopy for qualitative identification of different types of rubber.

Electromagnetic based flexible bioelectronics and its applications

With the increasing demand in seamless interface between artificial devices and biological structures, flexible bioelectronics has been developed rapidly in recent years. Compared with traditional rigid bioelectronics, flexible devices are more adaptable to the integration for various parts both inside and outside of the organism. Significant achievements have been made in biomedical devices, neuroelectronics and wearable devices. With the development of flexible bioelectronics, electromagnetics is becoming a crucial part in signal interference reduction and information transmission or feedback, taking advantages of strong penetration and rapid response in a variety of biological materials. In this review, we focus on the latest developments in electromagnetic based flexible bioelectronics, involving materials, sensation, seamless integration, and power supply, as well as the latest achievements in the fields of external wearables, internal implants, soft robotics and drug delivery system. Based on these, the main challenges facing flexible bioelectronics, are analyzed, including stretchability caused by mismatch between mechanical properties of soft and hard components, biocompatibility, environmental stability, to facilitate the further development of flexible bioelectronics.

A novel residual global context shrinkage network based fault diagnosis method for rotating machinery under noisy conditions

In real industrial environments, vibration signals generated during the operation of rotating machinery are typically accompanied by significant noise. Existing deep learning methods often yield unsatisfactory diagnostic results when dealing with noisy signals. To address this problem, a novel residual global context shrinkage network (RGNet) is proposed in this paper. Firstly, to fully utilize the useful information in the raw vibration signal, a multi-sensor fusion strategy based on dispersion entropy is designed as the input of the deep network. Then, the RGNet is designed, which improves the long-distance modeling capability of the deep network while suppressing noise, optimizes the network gradient and computational performance. Finally, the noise suppression ability and feature extraction ability of the RGNet are intuitively revealed through an interpretability study. The advantages of the proposed method are proved through a series of comparison experiments under noisy backgrounds.

Memristive arrangements of nanofluidic pores.

We demonstrate that nanofluidic diodes in multipore membranes show a memristive behavior that can be controlled not only by the amplitude and frequency of the external signal but also by series and parallel arrangements of the membranes. Each memristor consists of a polymeric membrane with conical nanopores that allow current rectification due to the electrical interaction between the ionic solution and the pore surface charges. This surface charge-regulated ionic transport shows a rich nonlinear physics, including memory and inductive effects, which are characterized here by the current-voltage curves and electrical impedance spectroscopy. Also, neuromorphiclike potentiation of the membrane conductance following voltage pulses (spikes) is observed. The multipore membrane with nanofluidic diodes shows physical concepts that should have application for information processing and signal conversion in iontronics hybrid devices.

Research on inter-shaft bearing fault diagnosis method based on STFT-CNN modeling

Inter-shaft bearings are an essential component of aircraft engines, and their operational status determines the safety of aircraft engine operation. Therefore, to improve the accuracy of fault type prediction and enrich the feature information in vibration signals of aircraft engine inter-shaft bearings, this paper proposes an STFT-CNN model based on the AlexNet architecture, extending its application to the research of aircraft engine inter-shaft bearing fault diagnosis. This approach addresses the common reliance on personnel experience for fault type diagnosis in traditional aircraft engine inter-shaft bearing fault diagnosis. Firstly, real vibration fault signals from inter-shaft bearings are collected through experiments to enrich feature information in non-stationary signals using STFT time-frequency methods. Secondly, utilizing the high interpretability of the STFT-CNN model, fault feature data from inter-shaft bearings under various operating conditions are extracted to refine our understanding of fault feature information. Finally, leveraging the robustness of the STFT-CNN model, fault types are classified and predicted. The training process involves comparative analysis using different pooling algorithms, time-frequency analysis methods, and various deep learning network models. The results demonstrate that the STFT-CNN model, employing the maximum pooling algorithm, outperforms other models in predicting inter-shaft bearing faults, achieving an average fault prediction accuracy of 98.8% .

Research on Communication Signal Modulation Recognition Based on a CCLDNN

In this paper, a new automatic modulation recognition (AMR) method named CCLDNN (complex-valued convolution long short-term memory deep neural network) is proposed. It is designed to significantly improve the recognition accuracy of modulation modes in low signal-to-noise ratio (SNR) environments. The model integrates the advantages of existing mainstream neural networks. The phase and amplitude information of complex signals is effectively captured through a complex module in the input layer. The Squeeze-and-Excitation (SE) attention mechanism, Bi-LSTM layer, and deep convolutional layer are introduced in the feature extraction layer to gradually enhance feature expression. Among these, the introduction of LSTM enables the model to capture the sequence dependence of signals, and the application of the SE attention mechanism further improves the model’s ability to focus on key features. Tests using the RadioML2016.10a dataset show that the model performs well at multiple SNR levels, achieving an average recognition accuracy of more than 80% over an SNR range of 0 dB to 18 dB. However, under the condition of a low SNR from −20 dB to −2 dB, the model still maintains a high recognition ability. The advanced CCLDNN method shows great deep learning potential in solving practical communication problems.

Autonomous integration of TSN-unaware applications with QoS requirements in TSN networks

Modern industrial networks transport both best-effort and real-time traffic. Time-Sensitive Networking (TSN) was introduced by the IEEE TSN Task Group as an enhancement to Ethernet to provide high quality of service (QoS) for real-time traffic. In a TSN network, applications signal their QoS requirements to the network before transmitting data. The network then allocates resources to meet these requirements. However, TSN-unaware applications can neither perform this registration process nor profit from TSN’s QoS benefits.The contributions of this paper are twofold. First, we introduce a novel network architecture in which an additional device acts as a central user configuration (CUC) for TSN-unaware applications and autonomously signals their QoS requirements to the network. Second, we propose a processing method to detect real-time streams in a network and extract the necessary information for the TSN stream signaling. It leverages a Deep Recurrent Neural Network (DRNN) to detect periodic traffic, extracts an accurate traffic description, and uses traffic classification to determine the source application. As a result, our proposal allows TSN-unaware applications to benefit from TSNs QoS guarantees. Our evaluations underline the effectiveness of the proposed architecture and processing method.