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This letter proposes using intermittent chaotic clocks, generated from chaotic maps, to drive cryptographic chips running the advanced encryption standard as a countermeasure against correlation power analysis (CPA) attacks. Five different chaotic maps, namely, the logistic map, the Bernoulli shift map, the Henon map, the tent map, and the Ikeda map, are used in this letter to generate chaotic clocks. The performance of these chaotic clocks is evaluated in terms of timing overhead and the resilience of the driven chip against CPA attacks. All proposed chaotic clocking schemes successfully protect the driven chip against attacks, with the clocks produced by the optimized Ikeda, Henon, and logistic maps achieving the lowest-timing overhead. These optimized maps, due to their intermittent chaotic behavior, exhibit lower-timing overhead compared to previous work. Notably, the chaotic clock generated by the optimized Ikeda map approaches the theoretical limit of timing overhead, i.e., half the execution time of a reference periodic clock. [ABSTRACT FROM AUTHOR]

The increasing popularity of LLM-based chatbots combined with their reliance on power-hungry GPU infrastructure forms a critical challenge for providers: minimizing energy consumption under Service-Level Objectives (SLOs) that ensure optimal user experience. Traditional energy optimization methods fall short for LLM inference due to their autoregressive architecture, which renders them incapable of meeting a predefined SLO without energy overprovisioning. This autoregressive nature however, allows for iteration-level adjustments, enabling continuous fine-tuning of the system throughout the inference process. In this letter, we propose a solution based on iteration-level GPU Dynamic Voltage Frequency Scaling (DVFS), aiming to reduce the energy impact of LLM serving, an approach that has the potential for more than 22.8% and up to 45.5% energy gains when tested in real world situations under varying SLO constraints. Our approach works on top of existing LLM hosting services, requires minimal profiling and no intervention to the inference serving system. [ABSTRACT FROM AUTHOR]

Affective computing is an important subfield of artificial intelligence, and with the rapid development of brain-computer interface technology, emotion recognition based on EEG signals has received broad attention. It is still a great challenge to effectively explore the multi-dimensional information in the EEG data in spite of a large number of deep learning methods. In this article, we propose a deep learning model called Attention-based Multiple Dimensions EEG Transformer (AMDET), which can leverage the complementarity among the spectral-spatial-temporal features of EEG data by employing the multi-dimensional global attention mechanism. We first transform the original EEG data into 3D temporal-spectral-spatial representations and then the AMDET would use spectral-spatial transformer blocks to extract effective features in the EEG signal and focus on the critical time frame with the temporal attention block. We conduct extensive experiments on the DEAP, SEED, and SEED-IV datasets to evaluate the performance of AMDET and the results outperform the state-of-the-art baseline on three datasets. Accuracy rates of 97.48%, 96.85%, 97.17%, 87.32% were achieved in the DEAP-Arousal, DEAP-Valence, SEED, and SEED-IV datasets, respectively. Based on AMDET, we can achieve over 90% accuracy with only eight channels, significantly improving the possibility of practical applications. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) foster the development of multifield applications and also present opportunities for malicious activities, and thus, comprehensive surveillance of UAVs is indispensable. Taking advantage of rapidly developing communication technology, perceptive mobile networks (PMNs) are emerging to integrate sensing capability into cellular networks, which paves the way for ubiquitous networked sensing with cooperation among interconnected base stations (BSs). This article explores key issues in designing PMNs for reliable UAV surveillance, e.g., sensing coverage, network clutter, and the performance tradeoff between sensing and communication. Procedures and key strategies of cooperative sensing in PMNs are also presented, highlighting the method of conducting cooperative sensing by multiple BSs to reap spatial diversity gain. Finally, we depict our vision for future research directions of cooperative sensing in PMNs. [ABSTRACT FROM AUTHOR]

This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method is evaluated for its neurophysiological interpretation and effectiveness in motor-related brain-computer interface (BCI) applications. Specifically, we assess the ability of the tracked EEG poles to discriminate between rest, movement execution (ME) and movement imagination (MI) in healthy subjects, as well as movement attempts (MA) in individuals with spinal cord injury (SCI). Our results show that pole tracking effectively captures broad changes in EEG dynamics, such as transitions between rest and movement-related states. It outperformed traditional EEG-based features, increasing detection accuracy for ME by an average of 4.1% (with individual improvements reaching as high as 15%) and MI by an average of 4.5% (up to 13.8%) compared to time-domain low-frequency EEG features. Similarly, compared to alpha/beta band power, the method improved ME detection by an average of 5.9% (up to 10.4%) and MI by an average of 4.3% (up to 10.2%), with results averaged across 15 healthy participants. In one participant with SCI, pole tracking improved MA detection by 12.9% over low-frequency EEG features and 4.8% over alpha/beta band power. However, its ability to distinguish finer movement details within specific movement types was limited. Additionally, the temporal evolution of the extracted pole tracking features revealed event-related desynchronization phenomena, typically observed during ME, MA and MI, as well as increases in frequency, which are of neurophysiological interest.

Time-frequency analysis (TFA) can provide relevant information about subsurface reservoirs, which is essential in reservoir identification. Traditional TFA methods are limited by resolution, resulting in unreliable seismic interpretation. The time-reassigned synchrosqueezing transform (TSST) is a practical TFA approach for processing non-stationary signals. However, for a frequency-domain signal with a strong-varying group delay (GD), the TSST provides a blurred time-frequency representation (TFR). We propose a novel TFA method called high-order TSST (HTSST) to address this problem. First, to describe the signal with GD variations, we develop a frequency domain signal model. Next, the high-order partial derivative of the short-time Fourier transform (STFT) is obtained based on the high-order Taylor expansion of the signal, and a matrix determinant is constructed according to the results. The matrix determinant is then used to deduce an explicit GD estimation formula in the time-frequency domain. Finally, the high-resolution TFR of the HTSST is generated by squeezing the energy to the estimated GD. With a strong-varying GD, frequency-domain signals can thus benefit from a high-resolution TFR provided by HTSST. Numerical simulations are performed to investigate this method’s efficiency. Through the analysis of seismic field data, the efficacy of this method for tight sandstone gas reservoir identification is verified.

In industrial settings, analyzing vibration signals from rolling bearings is a key method for diagnosing faults in rotating machinery. However, these faults often appear as a series of transient events with quickly changing frequencies, which makes it difficult for traditional time-frequency analysis techniques to accurately capture these characteristics. Methods like synchrosqueezing transmute and multi-synchrosqueezing transmute struggle with this task. In this study, we introduce a new method called the Wavelet-Enhanced Transient-Extracting Transform (WTET). Unlike traditional approaches that rely on instantaneous frequency in a time-domain model, WTET uses a group delay (GD) estimator in a frequency-domain model. This allows for more accurate characterization of bearing and rub impact fault signals. Additionally, WTET is better at resisting noise compared to the traditional wavelet transform because it isolates only the central coefficients in the time-frequency domain. WTET also retains the ability to reconstruct the original signal, which makes it effective at extracting different signal modes. Our analysis shows that WTET offers improved diagnostic capability for detecting bearing faults.

We propose a Transfer learning-enabled electroencephalography-based intuitive brain-computer interface system by utilizing phase-based brain functional connectivity methods such as phase lag index (PLI) and Intersite phase clustering (ISPC) along with power features to explore both phase and power-based information from electroencephalography (EEG) signals. Time-frequency decomposition using a complex morlet wavelet is applied to analyze the signal components in both the time and frequency domains and extract phase connectivity and power features. Functional connectivity methods aim to recognize functional interactions and statistical mutuality among signals acquired across various brain areas. The phase-based connectivity features are extracted simultaneously for multiple channels to investigate the phase synchronization among EEG signals across the entire brain. Next, Graph theory is adopted to trace connectivity between brain regions by calculating the connectivity degree of extracted PLI and ISPC features with other electrodes. In Parallel, Discrete wavelet convolution is performed to calculate the time variable frequency band’s specific power from the imagined speech EEG data. Finally, Time-frequency images of the above-mentioned PLI, ISPC, and EEG power features are fed as input to DenseNet-121 architecture for classification. Dense Net architecture overcomes the problem of ‘vanishing gradient’ by connecting each layer directly with other layers, making the network densely connected. The maximum classification accuracy achieved is 100%, 99.14%, and 98.72% for binary, three-class, and four-class classifications, respectively. The experimental results indicate that the proposed phase-based connectivity features, EEG power, and the DenseNet-121 model have achieved excellent accuracy for two public datasets, outperforming the state-of-the-art methods. The outstanding results strengthen the possibility of real-time EEG-based intuitive brain-computer interface communication.

Radar signal recognition plays a crucial role in modern electronic reconnaissance systems. With the increasing complexity of electromagnetic environments, radar signals are susceptible to noise interference under low signal-to-noise ratio (SNR) conditions, posing a challenge to accurate radar signal recognition. To address this issue, we propose a multilayer decomposition denoising empowered convolutional neural network (CNN) for radar signal recognition. Specifically, the original radar signals are first processed by multilayer decomposition denoising, which consists of variational mode decomposition (VMD), local mean decomposition (LMD), and wavelet thresholding (WT) in sequence, termed as VMD-LMD-WT. Then we use the Choi-Williams distribution (CWD) to convert the denoised signals into time-frequency images (TFIs). Finally, an improved CNN with dilated convolution is employed for radar signal recognition. Experiments demonstrate that the multilayer decomposition denoising method effectively improves the SNR of the original signal, which is beneficial for the subsequent recognition task. The recognition accuracy is improved by about 11% compared to that directly using the original signal. Furthermore, compared to the current network models, the proposed CNN network can efficiently extract the signal features and improve the detection accuracy of low probability of intercept (LPI) radar signals. The recognition accuracy reaches 75.3% for 12 signals when the SNR is low to −14dB.

Supporting high mobility is an important feature of the 6G mobile communication systems, which puts forward a great challenge for conventional orthogonal frequency division multiplexing (OFDM) systems due to the severe Doppler effects. Recently, orthogonal time frequency space (OTFS) has been proposed as a promising waveform for high-mobility scenarios, due to its excellent capability of exploiting delay-Doppler (DD) diversity to significantly improve the system performance. However, in multicell OTFS systems, intercell interference (ICI) is one of the major challenges, especially for cell-edge users (CEUs). In this article, the impacts of different types of interference on OTFS systems are first studied. Then, interference coordination and cooperative communications-based multicell OTFS systems are studied to overcome ICI, respectively. Next, simulation results are presented for the performance evaluation of these schemes. Finally, the main challenges are discussed and future research directions for multicell OTFS systems are highlighted. The performance evaluations demonstrate the effectiveness of interference coordination and cooperative communications on multicell OTFS systems. [ABSTRACT FROM AUTHOR]

Sixth generation promises to have a variety of applications, as well as network entities, in terms of power, range, data rates, latency, and propagation environment. The overall heterogeneity of the network poses significant challenges in terms of physical (PHY) layer design. This article first identifies some of the areas where the 5G multinumerology orthogonal frequency division multiplexing (OFDM) falls short before discussing the possible approaches toward PHY design. In particular, we discuss a backward-compatible, forward-looking, and extendable PHY layer framework that can be extended as new requirements arise to enable a graceful and sustainable network evolution. This is illustrated with a simple case study, where users with different requirements are allowed to use the same time-frequency (T-F) resources in a cohesive manner. The article concludes with the discussion of some critical research problems/areas pertaining to the said framework. [ABSTRACT FROM AUTHOR]

Dynamic power management (DPM) techniques on mobile systems are indispensable for deep learning (DL) inference optimization, which is mainly performed on battery-based mobile and/or embedded platforms with constrained resources. To this end, we characterize CNN workloads using object detection applications of YOLOv4/-tiny and YOLOv3/-tiny, and then propose integrated CPU–GPU DVFS governor policies that scale integrated pairs of CPU and GPU frequencies to improve energy–delay product (EDP) with negligible inference execution time degradation. Our results show up to 16.7% EDP improvements with negligible (mostly less than 2%) performance degradation using object detection applications on NVIDIA Jetson TX2. [ABSTRACT FROM AUTHOR]

Frequency modulation continuous wave (FMCW) synthetic aperture ladar (SAL) system can support the generation and processing of large bandwidth signals, which has strong capabilities in target detection, description, and identification. However, frequency modulation nonlinearity of the transmitted signal has become a bottleneck problem that restricts the high-resolution ranging of FMCW SAL. This letter establishes an echo model with nonlinearity and proposes a nonlinear phase estimation and compensation method based on Wigner–Ville distribution (WVD) with gradient rotation enhancement. First, we establish the nonlinearity model of the internal calibration signal in the time–frequency (TF) domain and use WVD to calculate the TF distribution (TFD). Second, the TFD is enhanced by rotating gradient vector, which can also eliminate noises and false components in the TFD. Then, the nonlinear phase of the transmitted signal is estimated by establishing a nonlinearity model in the TF domain. Finally, the residual video phase (RVP) filtering is introduced to eliminate the range-dependent nonlinearity and achieve nonlinearity correction of echoes at any range. Experiments and real data tests show that the method proposed in this letter can accurately estimate and correct the nonlinearity of the dechirp signal, which effectively improves the range resolution of FMCW SAL. [ABSTRACT FROM AUTHOR]

The distributed multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) system composed of numerous separated transmitters and receivers can realize multiangle imaging, cross- and along-track interferometry through their high-precision cooperation. However, phase synchronization is a major factor affecting the cooperation of distributed MIMO-SAR. As such, this letter proposes a Link-Free Permanent-C (LFPC) phase synchronization scheme for the first time. The designed system framework, basic principle, and error model of the scheme are described, and some simulations are performed to evaluate the synchronization accuracy of the LFPC scheme. The results demonstrate that the LFPC scheme has a frequency stability of $10^{-15}$ when $\rm SNR \ge 50$ dB, and it is a candidate for the future distributed SAR mission. [ABSTRACT FROM AUTHOR]

The Doppler frequency of the echo signal exhibits time-varying and high-order characteristics due to the high-speed relative motion between the satellites in spaceborne inverse synthetic aperture radar (ISAR) imaging of space target, even during a short coherent processing interval (such as 1 s). Therefore, the selection of the optimal imaging time interval (OITI) is crucial to obtain a well-focused image. The existing methods do not consider the fact that the OITIs of scatterers on the target can also vary significantly. In this letter, an OITI selection method for spaceborne ISAR imaging of space targets based on the time–frequency analysis is proposed. First, the differences in optimal imaging instants (OIIs) of scatterers are analyzed. Then, based on the combination of the Margenau–Hill–spectrogram (MHS) and the S-method, the time–frequency curves can be obtained after extracting the time–frequency ridge and polynomial fitting. The OII of the entire target can be estimated after eliminating the differences in the OIIs by multiplying the constructed third-order phase terms with the signal. Finally, a more suitable OITI can be obtained because of the elimination of the differences in the OIIs. The effectiveness and advancement of the proposed method are verified by simulation and real data results. [ABSTRACT FROM AUTHOR]

Time-frequency (TF) analysis is an important tool for seismic signal analysis, and through traditional methods, it is difficult to achieve high TF resolution and energy aggregation. In this letter, we propose the adaptive time-synchroextracting S transform (ATSEST) for seismic data processing and interpretation. The method first calculates the scale parameter of the window function through the Fourier spectrum of the signal to obtain the adaptive S transform spectrum. After that, the final result is obtained by extracting the TF coefficients at the group delay (GD) and removing a large amount of fuzzy energy in the TF spectrum. The results of the synthetic example TF analysis show that the method can improve the localization ability of transient features of seismic signals. We apply the method to the fault identification of field data, and the results show that the coherent attribute slices extracted by using ATSEST can well characterize the fault. [ABSTRACT FROM AUTHOR]

Accurate recognition of the types of mainlobe active deception jamming is essential for radar systems to take antijamming countermeasures. A bunch of deep-learning (DL)-based recognition methods that require large-scale datasets for training have shown promising results. However, capturing a sufficient number of diverse deception jamming samples is particularly intricate in actual dynamic and complex battlefields, the yielding limited or unbalanced datasets presents a significant challenge in training and generalizing DL models. This letter proposes a deep generative model, called the label-guided denoising diffusion probabilistic model (LG-DDPM), to address the issue of limited or class-imbalanced active deception jamming samples through data augmentation. By embedding label information into the diffusion process, the proposed model can generate and expand the active deception jamming samples specific to a predefined class even under low jamming-to-noise ratio (JNR) scenarios. The proposed method demonstrates superior performance in terms of both the fidelity and diversity of the generated jamming samples as well as the recognition accuracy of the DL recognizer when compared to state-of-the-art methods. Experimental results demonstrate the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a nondestructive detection technology. It uses high frequency electromagnetic energy to locate targets underground. In the direction of motion, GPR can produce Bscan with good performance. The lateral image is typically constructed using the results of many detections. By integrating beamforming with stepped frequency continuous wave (SFCW), we propose a method that increases the lateral detection performance. The performances of two beamforming methods in GPR are compared. We designed a $1\times4$ multistatic GPR and did two experiments. The findings demonstrate that the beamforming improves the lateral detection performance in comparison with single-static GPR. Compared with delay-and-sum methods, the minimum variance distortionless response (MVDR) method has a better performance in suppressing the sidelobe level and avoiding false targets. [ABSTRACT FROM AUTHOR]

In this letter, the clutter range dependence issue caused by crab angle is investigated in an airborne multichannel radar system. A new method is proposed to accomplish the crab angle estimation and the nonstationary error compensation. First, from the center-biased and spectrum-broadened characteristics of the clutter space-time spectrum in the case of nonneglected crab angle, a 1-D cost function based on space-time spectrum broadening degree is constructed to achieve the accurate crab angle estimation. Then, the clutter nonstationary errors can be effectively compensated in the post-Dopplerdomain,significantly improving the subsequent space-time adaptive processing (STAP) performance. Simulation results are presented to validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Surface wave tomography is performed by first estimating surface wave dispersion curves and then inverting them. The objective of this letter is the efficient and reliable estimation of the group velocity dispersion curves of surface waves. In this letter, group velocity dispersion curves of surface waves recorded at a single station with a known location of seismic source or between a pair of stations are estimated using continuous wavelet transform (CWT). The advantage of CWT is its multiresolution property and flexible choice of analyzing function. In the proposed method, the number of CWT filters used is almost half of the total number of filters used in the conventional frequency–time analysis (FTAN) method. The CWT coefficients of the seismogram are the functions of time and scale (analogs to wave period). The arrival time to reach the peak of the envelope function of CWT coefficients is estimated to calculate the group velocity of the surface waves. The group velocity dispersion curve of the surface waves is the plot of the change of group velocity with the period. Synthetic test data and ambient noise recordings of MesoAmerica seismic experiment (MASE) stations are acquired to investigate the performance of the proposed method. It is observed that the proposed method effectively retrieves the weaker surface waves and results in the broader band group-velocity dispersion curve when compared to the conventional FTAN method. The proposed method is also found to be computationally efficient. [ABSTRACT FROM AUTHOR]

In target tracking applications by Doppler through-wall radar (TWR), the frequency ambiguity issue is a notable drawback, which can severely degrade the target localization performance. To overcome this drawback, a convolutional neural networks (CNNs) based target tracking algorithm is proposed in this letter. First, the short-time Fourier transform (STFT) is used to acquire an echo spectrogram. Then, the spectrogram resolution is enhanced by introducing a modified CNN module. Compared with the traditional CNN module, an additional convolutional weight block with a residual structure is designed to help extract deeper time–frequency features. Finally, the target instantaneous frequency (IF) curves are extracted from the enhanced spectrogram and the target positions are obtained. Experimental results prove that the proposed method can effectively enhance the spectrogram resolution and suppress the frequency ambiguity issue, which would lead to higher target localization accuracy and better robustness than traditional target tracking approaches. [ABSTRACT FROM AUTHOR]

The separation of up-going and down-going wavefields is a significant step in vertical seismic profiling (VSP) data processing. The traditional frequency-wavenumber (FK) transform method maps the VSP signal from the time-space domain to the FK domain for separation. However, the FK transform method cannot separate the wavefields well near the reflecting surface. In this letter, we propose a novel method called FK vector classification (FKVC) to separate up-going and down-going wavefields from 2-D VSP data. First, we use the curvelet transform (CT) to extract the direction information of the VSP signal. CT is a tight frame and has multi-direction characteristics, so it can sparsely represent the signal in the curvelet domain. Although the shape of the curvelet window will cause some energy diffusion, synchrosqueezed CT (SSCT) can reassign the curvelet coefficients at the 2-D reference instantaneous frequency (IF) to improve the resolution. Then we extract the FK vector (FKV) of the up-going and down-going wavefields respectively based on SSCT. Finally, the up-going and down-going wavefields can be separated by the angular difference of FKV. Both the synthetic example and real zero-offset VSP data example demonstrate the effectiveness of the proposed method. Compared with the FK transform method, the FKVC has a better separation result. [ABSTRACT FROM AUTHOR]

High-precision time–frequency (TF) analysis (TFA) can accurately detect anomalies in oil and gas reservoirs. However, conventional TFA methods cannot effectively identify fractured-vuggy carbonate reservoirs, which are deep and strongly heterogeneous. In this letter, a TFA method based on analytical mode decomposition (AMD) and the Hilbert–Huang transform (HHT) is proposed. Specifically, seismic signals are decomposed by empirical mode decomposition (EMD) and then by AMD. Finally, the TF distribution (TFD) of the seismic signals is obtained after Hilbert spectrum analysis (HSA). Synthetic seismic data demonstrate that the AMD-HHT method can effectively eliminate mode mixing and provide higher time and frequency resolutions and energy focus than conventional TFA methods. In addition, model and field seismic data also verify that the AMD-HHT method can effectively detect hydrocarbon-related energy anomalies hidden in broadband seismic data. [ABSTRACT FROM AUTHOR]

In inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, traditional high-order phase compensation methods are usually limited by the heavy computational burden of parameter estimation, making it hard to apply in real-time cases. In this letter, an efficient ISAR motion compensation method is proposed based on fast parameter estimation. A novel analytic expression of the image entropy is derived with the help of a compensation matrix based on sinc function interpolation, which can eliminate the high-order phase term. Hence, the parameter estimation is transformed into an optimization problem where the gradient descent algorithm (GDA) can be adopted to accelerate the computation speed. Compared with other recently proposed methods, our method is superior in high robustness and low computing cost. Experiments with simulated and real data have verified that without affecting the image quality, the computing time of our method can be reduced to about 1/4 of the traditional search algorithm. [ABSTRACT FROM AUTHOR]

As an effective time–frequency (TF) analysis method, S-transform (ST) has an extensive application in signal processing. However, for broadband seismic signals, the peaks of the frequency distribution in the TF spectrum of ST biases the actual Fourier spectrum. Besides, the TF resolution of ST is affected by the relatively fixed window function and the Heisenberg uncertainty principle. In order to correct the frequency bias and improve the TF resolution of ST, and at the same time, to increase the flexibility of the window function, we propose a new TF analysis method, called the deconvolutive frequency corrected three-parameter ST (DFC-TPST). The DFC-TPST includes two steps: modifying the window function to achieve the frequency-corrected three-parameter ST (FC-TPST) and deconvoluting FC-TPST to achieve DFC-TPST. The synthetic example proves the superiority of the method in characterizing seismic signals. Through comparison of field data testing, we find that the proposed method can be well applied to hydrocarbon detection in tight sandstone reservoirs. [ABSTRACT FROM AUTHOR]

Vortex electromagnetic (EM) wave radar can obtain more accurate rotation parameters for target classification and recognition. However, the existing rotation parameter extraction methods are difficult to obtain the acceleration and initial phase (IP) of composite moving target in the case of multiple scattering points. In this letter, the estimation error of accelerated motion target features in a situation with numerous scattering points is examined for the first time, along with a method for extracting accelerated motion target parameters based on vortex EM wave radar. First, the relationship between the characteristics of nonderivable points (NDPs) in the rotating Doppler frequency shift and the characteristics of the target’s spin motion is analyzed, the method of estimating the number of scattering points is studied, and the formula for extracting the spin parameters is derived. After that, the time-frequency analysis of the echo signal is combined to extract the target translational motion characteristics. Simulation results show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

Simultaneous measurements of multitarget vital signs have been quite challenging for radar-based noncontact vital signs monitoring. This letter presents a novel cumulative phase gradient approach (CPGA) for accurate estimates of multitarget respirations without either range or beam discrimination in a single-input single-output (SISO) radar sensor. The signal model of multitarget respiration is first provided, and then a CPGA is developed to accurately estimate multitarget respirations from the observed mixtures by exploiting the underlying periodicity of the respiration waveforms. The proposed method provides average estimated error rates around 3% in the experiment with closely located three human targets within range resolution, and demonstrates its significant superiorities over other methods. [ABSTRACT FROM AUTHOR]

Forward scatter radar (FSR) can achieve target detection by capturing the signal disturbance caused by targets crossing the baseline, which provides a countermeasure to stealth/small targets. First, the FSR net model based on the Global Navigation Satellite System (GNSS) is constructed. Then, the statistical features in the time and frequency domains are used to describe the fluctuation characteristics of the forward scatter signal. Moreover, the influences of the relative position of the target and baseline on the statistical features are analyzed through dynamic simulations, and the change regulation of time and frequency features is revealed. Finally, an FSR experiment is carried out in an anechoic chamber and the results validate the theoretical analysis. This work provides a meaningful reference for the detection and parameter estimation of targets crossing the baseline in an FSR. [ABSTRACT FROM AUTHOR]

Matching pursuit (MP) is an algorithm, which can reconstruct signal accurately, and is widely used in signal processing. However, MP algorithm has efficiency problem in processing large amount of data, such as five-dimensional (5-D) seismic data. Generalized orthogonal MP with singular value decomposition (SVD_GOMP) is an algorithm, which can improve the calculation efficiency a lot, and keeps the advantage of high accuracy. In this study, a redundant atom dictionary includes incident angles, and azimuth is built. Then, the 5-D seismic data are reconstructed efficiently and accurately by the SVD_GOMP algorithm. Compared with the traditional MP method, the proposed method decomposes the 5-D seismic data at the same time and recovers the angle information efficiently. The reconstructed results of synthetic and field data examples are utilized to demonstrate the feasibility, computational efficiency, and precision of the proposed method. [ABSTRACT FROM AUTHOR]

The dispersion induced by fluid flow is a useful characteristic of hydrocarbon reservoirs and can be deduced from frequency-dependent amplitude versus offset (FAVO) inversion. However, FAVO inversion strongly depends on the spectral decomposition method, which directly affects the resolution of inversion results. In this letter, we propose a new time–frequency analysis method, named modified window-parameter-optimized S-transform (MWPOST), for improving the resolution of FAVO inversion. In MWPOST, the optimized scale parameter of the window function can be adaptively obtained according to the amplitude spectra of actual signals. Synthetic signal analysis indicates that the method has excellent performance with higher time–frequency resolution. Then, we present a high-precision FAVO inversion method using MWPOST for high gas-saturation reservoir delineation. The field seismic data application illustrates that the dispersion factor inverted by the proposed method has a higher time resolution and exhibits better performance for delineating the reservoir boundary with high gas saturation. The proposed method can improve the precision of FAVO and it is feasible to detect the location and spatial distribution of high gas-saturation reservoirs. [ABSTRACT FROM AUTHOR]

Otoacoustic emission (OAE) biometrics are inherently robust to replay and falsification attacks. The widely studied transient-evoked OAE (TEOAE) is non-stationary and offers biometric value only in normal-hearing individuals since it is more susceptible to hearing loss. To address these issues, this paper presents a novel yet promising OAE biometric modality-stimulus-frequency OAE (SFOAE). Unlike TEOAE, SFOAE is a highly stationary signal whose fine structures are idiosyncratic to an individual, and relatively stable over time, making it easier to be a biometric without additional complex feature extraction. Moreover, SFOAE is even present in ears with 50 dB HL hearing loss, applicable to hearing-impaired users. In this paper, SFOAE spectra in response to three stimulus levels are fused in the feature level to consolidate different information, followed by a linear discriminant analysis or a multi-kernel convolutional neural network to further reduce the intra-subject variability and increase the inter-subject variability. Tested on a large cohort of subjects containing varying levels of deafness, the SFOAE-based biometric system yields an equal error rate of 0.541% and 1.364% in closed-set and open-set verification scenarios, respectively. In an identification mode, 99.43% and 97.37% accuracies are attained for closed-set and open-set protocols, respectively. In particular, we observe perfect performance in a population restricted to normal hearing in closed-set scenarios. The reason why the system performs well has been examined based on several comparative tests. Although there are implementation issues to be resolved before SFOAE can be applied in the field of biometric, this paper preliminarily demonstrates the basis and excellent potential of SFOAE as a biometric. [ABSTRACT FROM AUTHOR]

The primary objective of anti-jamming techniques is to ensure that the transmitted data arrives at the intended receiver without being disturbed or jammed with by any jamming signal or other hostile activities to ensuring the security of the communication system. Deep reinforcement learning (DRL) has been extensively utilized in solving the dynamic spectrum anti-jamming problem. However, most of existing DRL-based algorithms require lots of training time, which fails to adapt the fast-channging jamming environment. Our objective is to find a practical and fast-convergence anti-jamming learning solution. To achieve this, we redesign the DRL algorithm in the following two ways. First, we split the cycle of reinforcement learning into two parts: applying process and training process. Second, we use soft labels instead of rewards which bring more information. We further theoretically show that the information gain can help our proposed algorithm converge faster. Moreover, we also show that our labeled DRL algorithm is better than the idealized DRL-based scheme which can obtain the same information as the soft labels. Simulation results demonstrate that compared with existing DRL-based algorithms, our proposed algorithm reduces the number of iterations by up to 90%. [ABSTRACT FROM AUTHOR]

In the industry, machinery failure causes catastrophic accidents and destructive damage to the machines. It causes the machinery to stop and reduces production, causing financial losses to the industry. As a result, identifying machine faults at an early stage is critical. With the rapid advancement in artificial intelligence-based methods, developing automated systems that can diagnose machinery faults is necessary and challenging. This paper proposes a multi-channel time-frequency domain deep convolutional neural network (CNN)-based approach for machinery fault diagnosis using multivariate time-series data from multisensors (tachometer, microphone, underhang bearing accelerometer, and overhand bearing accelerometer). The wavelet synchro-squeezed transform (WSST) based technique is used to evaluate the time-frequency images from the multivariate time-series data. The time-frequency images are fed into the multi-channel deep CNN model for automated fault detection. The proposed multi-channel deep CNN model is multi-headed, considering the time-frequency domain information of each channel time-series data for automated fault detection. The proposed model’s performance is compared to benchmark models regarding testing accuracy, total parameters, and model size. Experiments have shown that the proposed model outperforms benchmark models regarding classification accuracy. The proposed multi-channel CNN model has obtained the accuracy and F1-score values of 99.48% and 99% for fault classification using time-frequency images of multi-sensor data. Finally, the proposed model’s performance is measured regarding inference time when deployed on edge computing devices such as the Raspberry Pi and the Nvidia Jetson AGX Xavier.

The study of signals, processes, and systems has motivated the development of different representations that can be used to analyze and understand them. Classical ways of studying the behavior of signals are the time domain and frequency domain representations. For the analysis of non-stationary signals, time-frequency representations have become an essential tool to understand how the frequency content of signals changes with time. A common time-frequency technique employed in the literature is the wavelet transform. Nevertheless, selecting an adequate mother wavelet to perform the wavelet transform has become challenging due to the diverse available wavelet families. This paper reviews the applications and uses of a particular class of wavelet basis known as the Generalized Morse Wavelets. This class of wavelet family provides a systematic framework to choose and generate a wavelet for general-purpose use. This study reviews the application of Generalized Morse Wavelets in biomedical engineering, dynamical systems analysis, electrical engineering, geophysics, and communication systems. Moreover, the parameters of the Generalized Morse Wavelets used in each study are presented. The results of this study reveal that Generalized Morse Wavelets have proven helpful in studying signals, systems, and processes in areas ranging from biomedical engineering to geophysics. Nonetheless, the parameters of the Generalized Morse Wavelets are yet to be chosen through a rigorous methodology and argumentation. Therefore, there is an opportunity to generate methods for selecting the parameters of the Generalized Morse Wavelets based on the characteristics of the signals, systems, or processes under research.

Rotating blades are key turbomachinery components with high failure risk due to harsh working conditions. Thus, monitoring the running state of blades is urgently required. Blade tip timing (BTT) is regarded as a potential technology for blade vibration monitoring owing to its noncontact nature and low cost. However, severe undersampling and strict probe layout hinder the application of BTT. As a result of severe undersampling, aliasing and its suppression are the main research topics in BTT. However, in this article, a biprobes method for BTT based on the utilization of aliasing is proposed to identify the natural frequencies of blades. Active aliasing is used in this method to produce aliasing findings at various equivalent sampling frequencies, and the closed-form robust Chinese remainder theorem is used for dealiasing. Unlike the conventional multirate sampling systems for BTT that use a multiprobe configuration or varied speed, the suggested method utilizes varying delay durations to obtain varying equivalent sample frequencies. Furthermore, to ensure that active aliasing and dealiasing form a closed loop, an included angle coprime layout is proposed. Finally, the robustness and effectiveness of the proposed method are verified through numerical and experimental validation. [ABSTRACT FROM AUTHOR]

In this article, a dual-frequency control method for regulating the output power in class-E resonant dc–dc converters has been introduced. As in the standard on–off control or other recently proposed dual-frequency controls, the approach is based on the ability of the converter to alternately operate in a high- and a low-power state. The proposed solution has a twofold advantage: on the one hand, soft-switching capabilities (i.e., zero-voltage and zero-voltage-derivative switching) are preserved in both operating states; on the other hand, it is possible to reduce to zero the transient time required to switch from one state to the other one. The most straightforward consequence is the possibility to increase to very large values the frequency at which the two operating states are switched, up to the same order of magnitude as the main switching frequency of the converter. In this way, the additional ripple introduced by the proposed dual-frequency control can be decreased to a negligible value. The approach has been validated by measurements on a prototype operating between 4 and 8 MHz and in which it has been possible to increase the control frequency up to 500 kHz. [ABSTRACT FROM AUTHOR]

In micromotion signature analysis, the radar return signal with missing sampling may cause defocused time–frequency representation (TFR) and thus prevent micromotion characteristics acquisition. To address the issue, we present an adaptive time–frequency distribution reconstruction method based on $L_{1}$ regularization. First, the $L_{1}$ regularization is expressed as a combination of two $L_{2}$ regularizations based on Hadamard product parametrization. Then, the iterated Tikhonov regularization is applied to solve each $L_{2}$ regularization alternatively. Moreover, the regularization parameter is updated adaptively based on the matching pursuit principle at each iteration. Finally, the reconstructed TFR is updated based on least-square-error criterion to eliminate the attenuation of signal amplitude. Simulation and measurement data examples have demonstrated the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Sleep staging is a vital process for evaluating sleep quality and diagnosing sleep-related diseases. Most of the existing automatic sleep staging methods focus on time-domain information and often ignore the transformation relationship between sleep stages. To deal with the above problems, we propose a Temporal-Spectral fused and Attention-based deep neural Network model (TSA-Net) for automatic sleep staging, using a single-channel electroencephalogram (EEG) signal. The TSA-Net is composed of a two-stream feature extractor, feature context learning, and conditional random field (CRF). Specifically, the two-stream feature extractor module can automatically extract and fuse EEG features from time and frequency domains, considering that both temporal and spectral features can provide abundant distinguishing information for sleep staging. Subsequently, the feature context learning module learns the dependencies between features using the multi-head self-attention mechanism and outputs a preliminary sleep stage. Finally, the CRF module further applies transition rules to improve classification performance. We evaluate our model on two public datasets, Sleep-EDF-20 and Sleep-EDF-78. In terms of accuracy, the TSA-Net achieves 86.64% and 82.21% on the Fpz-Cz channel, respectively. The experimental results illustrate that our TSA-Net can optimize the performance of sleep staging and achieve better staging performance than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Over the last decades, conventional transcutaneous electrical nerve stimulation (TENS) has been utilized as an efficient rehabilitation intervention for alleviation of chronic pain, including phantom limb pain (PLP). However, recently the literature has increasingly focused on alternative temporal stimulation patterns such as pulse width modulation (PWM). While the effect of non-modulated high frequency (NMHF) TENS on somatosensory (SI) cortex activity and sensory perception has been studied, the possible alteration following PWM TENS at the SI has not yet been explored. Therefore, we investigated the cortical modulation by PWM TENS for the first time and conducted a comparative analysis with the conventional TENS pattern. We recorded sensory evoked potentials (SEP) from 14 healthy subjects before, immediately, and 60 min after TENS interventions (PWM and NMHF). The results revealed suppression of SEP components, theta, and alpha band power simultaneously associated with the perceived intensity reduction when the single sensory pulses applied ipsilaterally to the TENS side. The reduction of N1 amplitude, theta, and alpha band activity occurred immediately after both patterns remained at least 60 min. However, the P2 wave was suppressed right after PWM TENS, while NMHF could not induce significant reduction immediately after the intervention phase. As such, since PLP relief has been shown to be correlated with inhibition at somatosensory cortex, we, therefore, believe that the result of this study provides further evidence that PWM TENS may also be potential therapeutic intervention for PLP reduction. Future studies on PLP patients with PWM TENS sessions is needed to validate our result. [ABSTRACT FROM AUTHOR]

Steady-state visual evoked potential (SSVEP) is one of the most popular brain-computer interface (BCI) paradigms, with high information transmission rate and signal-to-noise ratio. Many calibration-free and calibration-based approaches have been proposed to improve the performance of SSVEP-based BCIs. This paper considers a quick calibration scenario, where there are plenty of data from multiple source subjects, but only a small number of calibration trials from a subset of stimulus frequencies for the new subject. We propose small data least-squares transformation (sd-LST) to solve this problem. Experiments on three publicly available SSVEP datasets demonstrated that sd-LST outperformed several classical or state-of-the-art approaches, with only about 10 calibration trials for 40-target SSVEP-based BCI spellers. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) based on motor imagery (MI) electroencephalogram (EEG) has become an essential way for rehabilitation, because of the activation and interaction of motor neurons between the brain and rehabilitation devices in recent years. However, due to the discrepancies between individuals, the frequency ranges can be different even for the same rhythm component of EEG recordings, which brings difficulties to the extraction of features for MI classification. Typical algorithms for MI classification such as common spatial patterns (CSP) require multi-channel analysis and lack frequency information. With the development of BCI, the single-channel BCI system has become indispensable for simplicity of use. However, the currently available single-channel detection methods have low classification accuracy. To address this issue, two novel frameworks based on an improved two-dimensional nonlinear FitzHugh–Nagumo (FHN) neuron system are proposed to extract features of the single-channel MI. To evaluate the effectiveness of the proposed methods, this research utilized an open-access database (BCI competition IV dataset 2a), an offline database, and a 10-fold cross-validation procedure. Experimental results showed that the improved nonlinear FHN system can transfer the energy of noise into MI, thereby effectively enhancing the time-frequency energy. Compared with the traditional methods, the proposed methods can achieve higher classification accuracy and robustness. [ABSTRACT FROM AUTHOR]

Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a novel method named Riemannian sparse optimization and Dempster-Shafer fusion of multi-time-frequency patterns (RSODSF) to enhance the decoding efficiency. First, we effectively combine the Riemannian geometry of the spatial covariance matrix with sparse optimization to extract more robust and distinct features. Second, the Dempster-Shafer theory is introduced and used to fuse each time window after sparse optimization of Riemannian features. Besides, the probabilistic values of the support vector machine (SVM) are obtained and transformed to effectively fuse multiple classifiers to leverage potential soft information of multiple trained SVM. The open-access BCI Competition IV dataset IIa and Competition III dataset IIIa are employed to evaluate the performance of the proposed RSODSF. It achieves higher average accuracy (89.7% and 96.8%) than state-of-the-art methods. The improvement over the common spatial patterns (SFBCSP) are respectively 9.9% and 12.4% (p < 0.01, paired t-test). These results show that our proposed RSODSF method is a promising candidate for the performance improvement of MI-BCI. [ABSTRACT FROM AUTHOR]

Rotating machinery is ubiquitous, and its failures constitute a major cause of the failures of transportation infrastructures. Most fault-diagnosis methods for rotating machinery are based on vibration-signal analysis because vibrations directly reflect the transient regime of machinery elements. This article proposes a novel multiscale shared-learning network (MSSLN) architecture to extract and classify the fault features inherent to multiscale factors of vibration signals. The architecture fuses layer-wise activations with multiscale flows, to enable the network to fully learn the shared representation with consistency across multiscale factors. This characteristic helps MSSLN provide more faithful diagnoses than existing single- and multiscale methods. Experiments on bearing and gearbox datasets are used to evaluate the fault-diagnosis performance of transportation infrastructures. Extensive experimental results and comprehensive analyses demonstrate the superiority of the proposed MSSLN in fault diagnosis for bearings and gearboxes, the two foundational elements in transportation infrastructures. [ABSTRACT FROM AUTHOR]

The individual differences and the dynamic uncertain relationships among different electroencephalogram (EEG) regions are essential factors that limit EEG emotion recognition. To address these issues, in this article, we propose a variational instance-adaptive graph method (V-IAG) that simultaneously captures the individual dependencies among different EEG electrodes and estimates the underlying uncertain information. Specifically, we employ two branches, i.e., instance-adaptive branch and variational branch, to construct the graph. Inspired by the attention mechanism, the instance-adaptive branch generates the graph based on the input so as to characterize the individual dependencies among EEG channels. The variational branch generates the probabilistic graph, which quantifies the uncertainties. We combine these two types of graphs to extract more discriminative features. To present more precise graph representation, we propose a new operation named the multi-level and multi-graph convolution operation, which aggregates the features of EEG channels from different frequencies with different graphs. Furthermore, we design the graph coarsening and employ the sparse constraint to obtain more robust features. We conduct extensive experiments on three widely-used EEG emotion recognition databases, i.e., SJTU emotion EEG dataset (SEED), multi-modal physiological emotion recognition dataset (MPED) and DREAMER. The results demonstrate that the proposed model achieves the-state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Classification of interbeat interval time-series which fluctuates in an irregular and complex manner is very challenging. Typically, entropy methods are employed to quantify the complexity of the time-series for classifying. Traditional entropy methods focus on the frequency distribution of all the observations in a time-series. This requires a relatively long time-series with at least a couple of thousands of data points, which limits their usages in practical applications. The methods are also sensitive to the parameter settings. In this paper, we propose a conceptually new approach called attention entropy, which pays attention only to the key observations. Instead of counting the frequency of all observations, it analyzes the frequency distribution of the intervals between the key observations in a time-series. Attention entropy does not need any parameter to tune, it is robust to the time-series length, and requires only linear time to compute. Experiments show that it outperforms fourteen state-of-the-art entropy methods evaluated by real-world datasets. It achieves average classification accuracy of AUC = 0.71 while the second-best method, multiscale entropy, achieves AUC = 0.62 when classifying four groups of people with a time-series length of 100. [ABSTRACT FROM AUTHOR]

Fetal phonocardiography (fPCG) is receiving attention as it is a promising method for continuous fetal monitoring due to its non-invasive and passive nature. However, it suffers from the interference from various sources, overlapping the desired signal in the time and frequency domains. This paper introduces the state-of-the-art methods used for fPCG signal extraction and processing, as well as means of detection and classification of various features defining fetal health state. It also provides an extensive summary of remaining challenges, along with the practical insights and suggestions for the future research directions. [ABSTRACT FROM AUTHOR]

LLC resonant converters are widely used in high power density and high efficiency applications. Practically, the parasitic capacitors of mosfets and diodes, the stray capacitor of the high frequency transformer, and the dead time will have a great impact not only on the steady state resonant waveforms but also on the zero voltage switching-on (ZVS-on) of the mosfets. This article presents a piecewise-approximated time domain analysis of LLC both considering the parasitic/stray capacitors and the dead time. On this basis, not only the accurate steady state resonant waveforms, but also the ZVS-on boundary can be obtained, which will guide the parameter design, and switching frequency/dead time configuration instead of slow and cumbersome simulations. The major applications of the presented analysis, including the resonant/switching waveform illustration, the critical dead time calculation, and the optimal converter design are verified by a 500 W asymmetric half bridge LLC experimental prototype. [ABSTRACT FROM AUTHOR]

Due to the limitation of existing rectifier, it is difficult to realize the multiscale frequency regulation (FR) of rectifier interfaced large-size (multi MW scale) load. To overcome this issue, a novel coordinative control method of hybrid rectifier is proposed to realize multiscale FR, and it is specifically applied in electrolytic hydrogen production (EHP) load. The auxiliary converter in hybrid rectifier is utilized to realize short-time/low-power/small-energy scale FR, such as inertia support during transient state, smoothing the rate of change of frequency and buying time for the action of silicon controlled rectifier (SCR). SCR in hybrid rectifier takes the responsibility of FR of long-time/high-power/large-energy scale, such as primary or secondary FR. The operation characteristic of large-size EHP load is reconstructed so that it obtains the multiscale FR capability. The effectiveness of proposed method is verified by experimental results. [ABSTRACT FROM AUTHOR]

Winding deformation caused by short-circuit is one of the main causes of transformer damage. Even if the transformer successfully withstands one short-circuit impact, accumulative effect of multiple short-circuit impacts will cause sudden permanent deformation of the winding. Internal winding deformation is a hidden fault due to the visual blocking of the shell of transformer, so it is extremely important to measure the accumulative effect and analyze its influence. In this paper, a SFSZ7-31500/110 transformer was used for short-time multiple short-circuit impact tests. The measurability of accumulative effect was determined by calculating the difference between measured value and reference value of the acceleration of the time-domain vibration acceleration signal, and then a time-frequency matrix was constructed based on Wigner-Ville Distribution (WVD) time-frequency analysis. Based on Fuzzy C-Means (FCM) clustering and the singular values of the matrix, the membership degree of winding mechanical state was calculated, and the influence degree of the accumulative effect was quantitatively analyzed. Results show the method can reflect the deformation trend and degree of winding before the impedance change rate. Research provides some reference for the mechanical state evaluation and fault early warning of windings, and also has a guiding significance for the safe and stable operation of transformers.

As a powerful method in signal processing, time-frequency analysis shows the characteristics of signals in the form of a joint domain distribution of time and frequency. The S-transform is one of the most effective and frequently used algorithms for time-frequency analysis of seismic signals. To address the problems of fixed time window, such as single time-frequency resolution and poor energy aggregation, an adaptive generalized S-transform algorithm is proposed. In this method, we designed a new generalized Gaussian window function with four variable parameters to obtain the optimal time-frequency distribution. The proposed window function uses time-frequency aggregation as the objective function for parameter optimization. Combined with the calculation results of the time-frequency aggregation and time-frequency distribution, we proved that the proposed method has a satisfactory analysis effect and anti-noise performance. In addition, a simulation experiment of the seismic signal is carried out using this algorithm, and the results show that the proposed adaptive generalized S-transform enhances the time-frequency resolution and energy aggregation in seismic signal analysis.