Signal Characteristics Research Articles

A time-frequency fusion model for multi-channel speech enhancement

Multi-channel speech enhancement plays a critical role in numerous speech-related applications. Several previous works explicitly utilize deep neural networks (DNNs) to exploit tempo-spectral signal characteristics, which often leads to excellent performance. In this work, we present a time-frequency fusion model, namely TFFM, for multi-channel speech enhancement. We utilize three cascaded U-Nets to capture three types of high-resolution features, aiming to investigate their individual contributions. To be specific, the first U-Net keeps the time dimension and performs feature extraction along the frequency dimension for the high-resolution spectral features with global temporal information, the second U-Net keeps the frequency dimension and extracts features along the time dimension for the high-resolution temporal features with global spectral information, and the third U-Net downsamples and upsamples along both the frequency and time dimensions for the high-resolution tempo-spectral features. These three cascaded U-Nets are designed to aggregate local and global features, thereby effectively handling the tempo-spectral information of speech signals. The proposed TFFM in this work outperforms state-of-the-art baselines.

Radio frequency fingerprint recognition method based on prior information

The open wireless communication environment is vulnerable to various malicious attacks. Wireless communication hardware devices have unique physical layer characteristics. As an inherent unique feature of wireless signals, radio frequency fingerprints provide a guarantee for the identification and verification of wireless signals. Most of the existing radio frequency fingerprint identification methods only extract fingerprints from one of the steady-state signals or transient signals. Neglecting the connection between the two wireless communication signals results in low identification accuracy of the radio frequency fingerprint identification method under the condition of a low signal-to-noise ratio. Aiming at the respective characteristics of these two signals, a radio frequency fingerprinting method combining transient and steady-state signals based on prior information of wireless signals is proposed. This method combines the characteristic stability of steady-state signals and the integrity characteristics of transient signals, which can effectively identify and classify wireless signals and achieve excellent recognition under low signal-to-noise ratio conditions. The effectiveness of the proposed method is verified by experimental comparison with the traditional radio frequency fingerprinting method on the LFM signal dataset.

Fault Diagnosis Method for Vacuum Contactor Based on Time-Frequency Graph Optimization Technique and ShuffleNetV2.

This paper presents a fault diagnosis method for a vacuum contactor using the generalized Stockwell transform (GST) of vibration signals. The objective is to solve the problem of low diagnostic performance efficiency caused by the inadequate feature extraction capability and the redundant pixels in the graph background. The proposed method is based on the time-frequency graph optimization technique and ShuffleNetV2 network. Firstly, vibration signals in different states are collected and converted into GST time-frequency graphs. Secondly, multi-resolution GST time-frequency graphs are generated to cover signal characteristics in all frequency bands by adjusting the GST Gaussian window width factor λ. The OTSU algorithm is then combined to crop the energy concentration area, and the size of these time-frequency graphs is optimized by 68.86%. Finally, considering the advantages of the channel split and channel shuffle methods, the ShuffleNetV2 network is adopted to improve the feature learning ability and identify fault categories. In this paper, the CKJ5-400/1140 vacuum contactor is taken as the test object. The fault recognition accuracy reaches 99.74%, and the single iteration time of model training is reduced by 19.42%.

A novel anomaly detection method for magnetic flux leakage signals via a feature-based unsupervised detection network

High-precision anomaly detection, as the key technology of magnetic flux leakage (MFL) signal detection, is a challenging task. It is difficult to detect anomalies in MFL signals due to the variety of anomalies and the characteristics of the anomalies are easily submerged in the variation of the natural signals. To address the above issues, a feature-based unsupervised detection network (FUDet) is designed, which accomplishes the unsupervised anomaly detection task through feature discrimination and feature reconstruction. Firstly, a bidirectional discrimination module is proposed, which can input normal and anomaly feature distributions to mine the characteristics of samples, so as to enhance the ability of the model to recognize anomaly signals. Secondly, a dynamic noise generation module is designed to generate different feature distributions for each input that are consistent with the characteristics of MFL signals. This module creates an adversarial effect with the discriminator, allowing it to identify more subtle feature differences through training. Finally, a reconstruction classification module is designed to naturally reconstruct the non-normal features and normal features into normal signals, which can be used to detect anomalies by comparing the difference between the input signals and the reconstructed signals. Experimentally, the method is proved to outperform the P-AUROC of the state-of-the-art method by 3.1% under MFL signals and achieves outstanding results in MFL signal anomaly detection.

Navigating Unknowns: Deep Learning Robustness for Gravitational-wave Signal Reconstruction

We present a rapid and reliable deep-learning-based method for gravitational-wave (GW) signal reconstruction from elusive, generic binary black hole mergers in LIGO data. We demonstrate that our model, AWaRe, effectively recovers GWs with parameters it has not encountered during training. This includes features like higher black hole masses, additional harmonics, eccentricity, and varied waveform systematics, which introduce complex modulations in the waveform’s amplitudes and phases. The accurate reconstructions of these unseen signal characteristics demonstrate AWaRe's ability to handle complex features in the waveforms. By directly incorporating waveform reconstruction uncertainty estimation into the AWaRe framework, we show that for real GW events, the uncertainties in AWaRe's reconstructions align closely with those achieved by benchmark algorithms like BayesWave and coherent WaveBurst. The robustness of our model to real GW events and its ability to extrapolate to unseen data open new avenues for investigations in various aspects of GW astrophysics and data analysis, including tests of general relativity and the enhancement of current GW search methodologies.

Fusion-driven fault diagnosis based on adaptive tuning feature mode decomposition and synergy graph enhanced transformer for bearings under noisy conditions

Bearing fault diagnosis is critical for maintaining the reliability of health monitoring in electromechanical systems. However, traditional feature extraction methods often struggle to accurately capture fault information in complex environments. This paper proposes an adaptive tuning feature mode decomposition (ATFMD) method, based on the intrinsic signal characteristics, to effectively extract robust features. ATFMD dynamically adjusts to complex fault signals, providing more representative feature inputs and pruning redundant features induced by noise. Additionally, given the limitations of single-dimensional feature domains in fully revealing fault information, this study constructs feature topology graphs by mapping spatial phase and time–frequency characteristics, offering a comprehensive representation of fault information. To achieve complementary fusion of fault information within the feature topological graph, this paper proposes the synergy graph enhanced transformer (SGET). SGET optimizes the fusion process by reinforcing feature interactions through its synergy graph representation module. Additionally, a hierarchical cross-attention mechanism is employed to modulate attention distribution across feature dimensions, enhancing the sensitivity to critical features during fusion. Experimental validation was conducted on two distinct rotating machinery transmission systems. The results demonstrate that the proposed method maintains exceptional robustness and generalization, even in the presence of severe noise and complex fault conditions. Compared to the leading methods, including MS-DGCNs, CapsFormer, TFT, and ConvFormer, the proposed method achieves notable accuracy improvements of 8.13 %, 8.71 %, 11.94 %, and 6.25 %, respectively, under challenging conditions.

Analytic Hierarchy Process (AHP)-Based Evaluation of Extremely-Low-Frequency Magnetic Field Contribution Rates

The extremely-low-frequency (ELF) magnetic fields of submarines serve as key characteristics for target detection, with their formation mechanisms being complex and diverse. Effectively manipulating a submarine to reduce its magnetic signature is crucial for enhancing its magnetic stealth capabilities. However, current research on the impact of various causative factors is insufficient. This study proposes a contribution rate assessment method based on the Analytic Hierarchy Process (AHP) model, aiming to provide a theoretical basis for effective manipulation. Initially, a thorough analysis of the threat causes of a submarine’s ELF magnetic fields is conducted, and a corresponding hierarchical threat structure model is established. Subsequently, magnetic field signal characteristics generated by different causes are obtained through simulation, and threat matrices and characteristic matrices are constructed. Finally, the contribution rates of different causative magnetic fields to the total magnetic field are calculated, and the simulation results validate the effectiveness of the method. At the stern detection line, the contribution rate of the wake magnetic field is the highest, reaching 0.7649. Along the radial detection line, the contribution rate of the shaft frequency magnetic field is the highest and gradually decays, eventually falling below the wake magnetic field at 150 m and remaining at an approximately 0.5 contribution rate. This study calculates the contribution rates under different operational conditions and detection scenarios, laying a technical foundation for research on the comprehensive active control strategies of submarine ELF magnetic fields in different scenarios.

Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations

A Novel simulation framework using self-governing drones is used to locate and reduce unauthorized drones in interior environments. The recommended method uses Received Signal Strength Indicator (RSSI) to identify an alien agent drone, which has different signal characteristics than the approved swarm of UAVs. Real-time threat detection is possible with this technology. After detecting the drone, the swarm organizes itself to encircle and besiege it for 10 seconds, making it inert before returning to their original positions. This unique solution uses RSSI to quickly identify and mitigate enclosed area concerns. It provides a reliable and effective indoor drone security solution. The simulation results show that the approach works in delicate environments including warehouses, laboratories, and other indoor facilities. This study advances unmanned aerial system (UAS) autonomous swarm intelligence and security procedures.

Multi-step prediction of ship heave motion using transformer-enhanced multi-scale CNN

To achieve precise multi-step heave motion prediction for active compensation control in marine equipment, an innovative approach that integrates an attention-fused multi-scale CNN with a Transformer encoder is introduced in this study. The dual-path CNN and point-wise convolution-enhanced Transformer encoder are designed to capture local features across various scales and the global characteristics of heave motion signals, respectively. Furthermore, a novel optimization objective which combines a slope factor-optimized Huber loss function with a maximum square distance loss function is proposed to better approximate the original signals at extreme points. The proposed model is trained and tested on simulated data under multiple sea conditions. In typical active compensation scenarios (Hs=3.5m), the proposed model demonstrates superior performance compared to baseline methods, achieving an RMSE of 0.0083 m, an MAE of 0.0066 m, and the upper whisker of the box plot for the prediction error is less than 0.02 m. These outcomes effectively satisfy the accuracy requirements for active compensation systems. The generalization test on real-ship collected data demonstrates excellent performance after fine-tuning. Additionally, the model is considered suitable for deployment in real-world applications due to its memory efficiency and fast inference speed.

Fatigue Driving State Detection Based on Spatial Characteristics of EEG Signals

Fatigue Driving State Detection Based on Spatial Characteristics of EEG Signals

Magnetic resonance imaging characteristics of musculoskeletal hydatid disease

Background Hydatid disease is a parasitic infection seen in endemic areas. Musculoskeletal hydatid disease is rarely reported. Purpose To describe the magnetic resonance imaging (MRI) features of musculoskeletal hydatid disease and to highlight the specific findings in the diagnosis of hydatid cysts. Material and Methods The MRI scans of 29 cases diagnosed as musculoskeletal hydatid disease between 2000 and 2022 were retrospectively analyzed. The localization, size, appearance (unilocular or multilocular), signal characteristics, rim sign, presence of internal septa and membrane, and gadolinium enhancement pattern of hydatid cysts were evaluated. Results A total of 29 patients diagnosed with hydatid cyst were included in the study. Of the lesions, 18 were localized in bone and 11 were in soft tissue. The bone hydatid cysts on MRI showed heterogeneous low to intermediate signal intensity on T1-weighted images and high signal intensity on T2-weighted images in the medullary bone. In 15/18 patients, there was also cortical destruction and extension into the adjacent soft tissue planes. In 8/11 cases where the cyst was located in muscle tissue, the “cyst or cysts within a cyst” appearance was observed on MRI. The rim sign was observed in 7/11 cases and the “water lily” sign was noted in 2/11 cases. Conclusion MRI provides valuable information for the diagnosis of hydatid disease with its distinctive imaging features. Knowledge of the different patterns of hydatid cysts on MRI may be helpful in the diagnosis of this disease.

Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis

Background and objectiveMotor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance. MethodsThe idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test. ResultsThe recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, p < 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (p < 0.001). ConclusionsThe research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.

The many faces of myxopapillary ependynomas.

Myxopapillary ependymomas (MPE), classified as grade 2 tumors by the WHO, are rare spinal neoplasms. Despite their slow growth and generally benign nature, MPE have a high recurrence rate and potential for cerebrospinal fluid dissemination. This study aims to identify the MRI characteristics and pathological patterns of MPE and investigate potential correlations between the MRI characteristics and specific histopathological patterns. We assessed 13 patients (7 men; mean age, 45.1 years) with pathologically proven MPE. MRI images were reviewed for tumor location, size, T1 and T2 signal characteristics, contrast enhancement, hemosiderin cap presence, vertebral scalloping, drop metastasis, and prominent intradural flow voids. Four histopathological patterns (microcystic, solid, hemorrhagic, and high hyalin content) were defined and segmented, with surface areas measured and percentages calculated relative to the total tissue surface. Most tumors were in the lumbar region (84.61%), with MRI revealing typical features such as T2 hyperintensity (100%) and contrast enhancement (92.3%). A rare non-enhancing MPE was noted. Large tumors exhibited a microcystic pathology pattern, with two cases with this pattern showing drop metastasis on MRI. Smaller tumors typically presented a solid pathology pattern with homogenous MRI signals. This study underscores the diverse MRI presentations of MPE and suggests a potential link between microcystic patterns in pathology and large MPE with drop metastasis.ABBREVIATIONS: MPE= myxopapillary ependymoma.

Optimization of aero-engine condition monitoring by autoregressive model analysis based on wireless communication

The high value of aero-engines makes it necessary to extend their life in terms of econ-omy of use. Health management is an important basis for this work. Background: The vibration signal is an important representation of the working state of an aero-engine. The current usual method is based on wired communication with simple waveform presentation. The many incon-veniences of this method have seriously affected the further development of this work. Methods: This research team acquires engine vibration signals in real time through a designed wireless signal sensor, and then analyzes and visualizes the signals in depth. Results: Through this method, it can visually and effectively demonstrate signal characteristics for engineers, effectively identify fault signals, and monitor engine operating conditions in real time. Conclusions: The method enables the optimization of aero-engine work-state monitoring.

Scale reduction Transformer-based soft measurement of oil–water two-phase flow

In addressing the challenge of parameter measurement in high-water-content oil–water two-phase flow, we independently develop a dual-helix microwave sensor measurement system. Using this system, we conduct vertically upward experiments on oil–water two-phase flow and collect time-series signals characterizing microwave attenuation. Based on signal characteristics, we design a scale reduction Transformer soft measurement model (SRSM-Transformer). The time filter module effectively facilitates the aggregation and embedding of channel features with diverse physical significance. Additionally, the scale reduction temporal module models global information from a multi-resolution perspective, enabling the model to flexibly extract multi-scale feature information from fluid signals. Demonstrating pronounced superiority over existing models, our proposed model achieves recognition accuracies of 93.63% for total flow rate and 95.73% for water content. This research provides a new direction for modeling complex industrial oil–water two-phase flow systems and measuring multi-coupled key parameters.

CNS Embryonal Tumor with PLAGL Amplification, a New Tumor Type in Children and Adolescents: Insights from a Comprehensive MRI Analysis.

CNS embryonal tumor with PLAGL1/PLAGL2 amplification (ET, PLAGL) is a newly identified, highly malignant pediatric tumor. Systematic MRI descriptions of ET, PLAGL are currently lacking. MRI data from 19 treatment-naïve patients with confirmed ET, PLAGL were analyzed. Evaluation focused on anatomical involvement, tumor localization, MRI signal characteristics, DWI behavior, and the presence of necrosis and hemorrhage. Descriptive statistics (median, interquartile range, percentage) were assessed. Ten patients had PLAGL1 and nine PLAGL2 amplifications. The solid components of the tumors were often multinodular with heterogeneous enhancement (mild to intermediate in 47% and intermediate to strong in 47% of cases). Non-solid components included cysts in 47% and necrosis in 84% of the cases. The tumors showed heterogeneous T2WI hyper-and isointensity (74%), relatively little diffusion restriction (ADC values < contralateral normal-appearing WM in 36% of cases with available DWI), and tendencies towards hemorrhage/calcification (42%). No reliable distinction was found between PLAGL1-and PLAGL2-amplified tumors or compared to other embryonal CNS tumors. The study contributes to understanding the imaging characteristics of ET, PLAGL. It underscores the need for collaboration in studying rare pediatric tumors and advocates for the use of harmonized imaging protocols for better characterization. ATRT= atypical teratoid/rhabdoid tumor; ETMR= embryonal tumor with multilayered rosettes; ET, PLAGL= CNS embryonal tumor with PLAGL amplification; EVD= external ventricular drain; IQR: interquartile range; PLAGL1= pleomorphic adenoma gene-like 1; PLAGL2= pleomorphic adenoma gene-like 2; WHO= World Health Organization.

DOA estimation of noncircular signals with direction-dependent mutual coupling

In this paper, a reweighted sparse recovery algorithm based on the optimal weighted subspace fitting (WSF) for non-circular signals in direction-dependent mutual coupling (MC) is proposed. Firstly, a new augmented model is constructed by leveraging the characteristics of non-circular signals. Next, a new direction matrix without mutual coupling coefficients is obtained by a novel transformation method. Then, two sparse recovery models are constructed by utilizing the WSF technique, and the sparsity of the solution is increased by constructing a weighted matrix. Finally, the direction of arrival (DOA) is achieved by a sparse recovery approach. For both coherent and incoherent signals, the developed approach can achieve precise DOA estimation in the case of direction-dependent MC. The robustness and advantage of the developed approach are testified by various experiments.

High-precision dynamic gesture recognition based on microfiber sensor and EMT-Net

Dynamic gesture recognition, which utilizes flexible wearable sensors and deep learning, is invaluable for human–computer interaction. Nevertheless, the primary challenges persist are the rapid detection of intricate gestures and the accurate recognition of dynamic signals. In this study, we suggest utilizing a microfiber sensor to identify the variations in wrist skin and detect dynamic gesture. In order to tackle the issue of insufficient feature extraction in the detected signals, resulting in reduced accuracy in recognition, we introduce a network dubbed EMT-Net (improve multi-head attention transformer network). This network utilizes a transformer encoder to capture and represent the characteristics of dynamic gesture signal and uses a CNN to classify the encoded features. To ensure that the model comprehensively captures the temporal and statistical characteristics of the signals, we enhance the multi-head attention mechanism by restricting certain attention heads to concentrate solely on the statistical features of the signals while allowing others to focus on the temporal features and global dependencies. Furthermore, because of the varying discriminatory abilities of different characteristics, we have developed an attention module to redistribution the attention weights on statistical features. The experimental results demonstrate that the microfiber sensor effectively recognizes ten distinct forms of dynamic gesture signals. Simultaneously, EMT-Net attains proficient identification with an accuracy of 98.80%, precision of 98.81%, recall of 98.80%, and an F1 score of 98.80%. The application value of this dynamic gesture recognition technology, which utilizes microfiber sensors and EMT-Net, is significant. The forthcoming alterations in human–computer interaction, virtual reality, and various other domains are anticipated.

CT and MR imaging characteristics of primary hepatic lymphoma

BackgroundPrimary hepatic lymphoma (PHL) is a rare primary liver tumor. The purpose of this study was to investigate the crucial imaging characteristics of PHL.MethodsCT/MR data and clinical features of 16 patients with pathologically proven PHL were retrospectively reviewed. The assessed imaging characteristics included lesion distribution, growth appearance, density or signal characteristics, and typical signs during the dynamic enhancement.ResultsA total of sixteen patients were included in this study, ranging in age from 27 to 86 years (nine men and seven women; mean age, 62 years). Right upper abdominal pain, malaise, or first detection during physical examination were the chief complaint. Dynamic contrast-enhanced CT and MRI demonstrated focal masses in thirteen patients (13/16, 81%), and multiple nodules in three patients (3/16, 19%). The lesions showed vascular floating sign in 10 patients (10/16, 63%) and biliary pass-through sign in 6 patients (6/16, 37%). Necrosis in PHLs were observed in 10 patients (10/16, 63%) and presented target sign on T2WI in 5 out of 11 patients (5/11, 45%) undergoing MRI. After enhancement, PHLs had multinodular sign in the arterial phase in 10 patients (10/16, 63%), and presented washout of contrast medium in the portal-venous or delayed phase in 12 patients (12/16, 75%), pseudocapsule in the delayed phase in 15 patients (15/16, 94%), and double-ring sign in the delayed phase in 8 patients (8/16, 50%).ConclusionPHL is generally seen in elderly patients with elevated beta-2-microglobulin levels. Its distinctive imaging features including obviously low ADC value, biliary/vascular floating sign, multinodular sign and double-ring sign in the delayed phase, may help to diagnose and differentiate PHL.

Magnetic resonance neurography in the diagnosis of neurological subtypes of thoracic outlet syndrome.

Neurological thoracic outlet syndrome (TOS) can be challenging to diagnose, particularly given its described subtypes of neurogenic TOS (NTOS) and disputed TOS (DTOS) that exhibit variable clinical presentations and etiologies. The diagnostic workup of TOS often includes magnetic resonance neurography (MRN) of the brachial plexus. Specific MRN imaging modifications for TOS evaluation are required to maximize spatial and contrast resolution to increase the conspicuity of nerve segments and their relationships to surrounding osseous structures. Dynamic assessment with arm positioning is used to evaluate outlet narrowing and compression of the plexus. Individual nerve segments are interrogated for their longitudinal and cross-sectional morphologies and signal characteristics. In patients with NTOS, MRN may reveal focal impingement of the C8/T1 nerve roots and/or lower trunk with accompanying abnormal T2-weighted signal hyperintensity. Predisposing anatomical entities include cervical ribs, rib synostoses, hypertrophic callous following clavicular fracture, remnant first thoracic rib from prior incomplete resection, and variable perineural scarring. In comparison, DTOS patients frequently demonstrate signal hyperintensity and enlargement of the mid plexus (trunk and division level), with narrowing of the costoclavicular interval. Following comprehensive diagnostic workup that frequently includes electrodiagnostic testing, patients are directed to different management pathways. Nonsurgical management is considered for all cases of DTOS; all patients with NTOS or DTOS who fail conservative treatment warrant referral for a surgical opinion. If surgery is pursued, MRN can be helpful in preoperative planning.