A classical time-varying signal, the multi-component Chirp signal has been widely used and the ability to estimate its instantaneous frequency （IF） is very useful. But in noisy environments, it is hard to estimate t...A classical time-varying signal, the multi-component Chirp signal has been widely used and the ability to estimate its instantaneous frequency （IF） is very useful. But in noisy environments, it is hard to estimate the 1F of a multi-component Chirp signal accurately. Wigner distribution maxima （WDM） are usually utilized for this estimation. But in practice, estimation bias increases when some points deviate from the true IF in high noise environments. This paper presents a new method of multi-component Chirp signal 1F estimation named Wigner Viterbi fit （WVF）, based on Wigner-Ville distribution （WVD） and the Viterbi algorithm. First, we transform the WVD of the Chirp signal into digital image, and apply the Viterbi algorithm to separate the components and estimate their IF. At last, we establish a linear model to fit the estimation results. Theoretical analysis and simulation results prove that this new method has high precision and better performance than WDM in high noise environments, and better suppression of interference and the edge effect. Compared with WDM, WVF can reduce the mean square error （MSE） by 50% when the signal to noise ration （SNR） is in the range of-15dB to -11dB. WVF is an effective and promising 1F estimation method.展开更多
To study the measurement of distance under the condition of the frequency modulation (FM) multi component signal of a short range radar, the multi points scattering model of a target, the TLS ESPRIT (total least sq...To study the measurement of distance under the condition of the frequency modulation (FM) multi component signal of a short range radar, the multi points scattering model of a target, the TLS ESPRIT (total least square estimation of signal parameters via rotational invariance techniques) and the mathematical statistics methods were used. The method of computing single frequency signal's instantaneous frequency (IF) is unsuitable to the multi component signal. By using the method of the TLS ESPRIT combined with the mathematical statistics, the multi component signal's IF can be obtained. The computer simulation has shown that the method has the high accuracy for measuring the distance.展开更多
Distinguishing close chirp-rates of different linear frequency modulation （LFM） signals under concentrated and complicated signal environment was studied. Firstly, detection and parameter estimation of multi-compone...Distinguishing close chirp-rates of different linear frequency modulation （LFM） signals under concentrated and complicated signal environment was studied. Firstly, detection and parameter estimation of multi-component LFM signal were used by discrete fast fractional Fourier transform （FrFT）. Then the expression of chirp-rate resolution in fractional Fourier domain （FrFD） was deduced from discrete normalize time-frequency distribution, when multi-component LFM signal had only one center frequency. Furthermore, the detail influence of the sampling time, sampling frequency and chirp-rate upon the resolution was analyzed by partial differential equation. Simulation results and analysis indicate that increasing the sampling time can enhance the resolution, but the influence of the sampling frequency can he omitted. What＇s more, in multi-component LFM signal, the chirp-rate resolution of FrFT is no less than a minimal value, and it mainly dependent on the biggest value of chirp-rates, with which it has an approximately positive exponential relationship.展开更多
Chirp signals show energy aggregation in the fractional Fourier domain(FrFD) w hich can be used to estimate the parameter of the signals. In this paper,a parameter estimation method for multi-component chirp signal w ...Chirp signals show energy aggregation in the fractional Fourier domain(FrFD) w hich can be used to estimate the parameter of the signals. In this paper,a parameter estimation method for multi-component chirp signal w hich corrupted by w hite Gaussian noise is proposed based on the discrete fractional Fourier transform(DFrFT) and the differential evolution( DE) algorithm. The proposed algorithm uses the DE algorithm instead of the conventional fine search algorithm to detect the peak of the signals in the FrFD. The paper simulated the influence of the noise and the resolution of the proposed algorithm. The results of the simulation show the proposed method does not only improve the estimation accuracy of the peak coordinate,but also reduces time consuming.展开更多
In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrabilit...In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrability.By focusing on single-component decompositions within the potential BKP hierarchy,it has been observed that specific linear superpositions of decomposition solutions remain consistent with the underlying equations.Moreover,through the implementation of multi-component decompositions within the potential BKP hierarchy,successful endeavors have been undertaken to formulate linear superposition solutions and novel coupled Kd V-type systems that resist decoupling via alterations in dependent variables.展开更多
Multi-component transition group metal borides(MMB_(2))have become a research hotspot due to their new composition design concepts and superior properties compared with conventional ceramics.Most of the current method...Multi-component transition group metal borides(MMB_(2))have become a research hotspot due to their new composition design concepts and superior properties compared with conventional ceramics.Most of the current methods,however,are complicated and time-consuming,the mass production remains a chal-lenge.Herein,we proposed a new high-efficiency strategy for synthesis of MMB_(2)using molten aluminum as the medium for the first time.The prepared Al-containing multi-component borides(TiZrHfNbTa)B_(2)microcrystals had a homogeneous composition with a hexagonal AlB_(2)structure and ultra-high hardness value of∼35.3 GPa,which was much higher than data reported in the literature and the rule of mix-ture estimations.Furthermore,combined with the First-principles calculation results,we found that the Poisson’s ratio(v)values exhibit a clearly ascending trend from 0.17 at VEC=3.5 to 0.18 at VEC=3.4,then to 0.201 at VEC=3.2 with the increasing of Al content.This indicates that the intrinsic toughness of multi-component boride microcrystals is obviously enhanced by the trace-doped Al elements.Besides,the fabricated Al-containing multi-component boride microcrystals have superior oxidation activation en-ergy and structural stability.The enhanced oxidation resistance is mainly attributed to the formation of a protective Al2 O3 oxide layer and the lattice distortion,both of which lead to sluggish diffusion of O_(2).These findings propose a new unexplored avenue for the fabrication of MMB_(2)materials with supe-rior comprehensive performance including ultra-hardness and intrinsically improved thermo-mechanical properties.展开更多
This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We syste...This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We systematically evaluate key deep learning architectures including convolutional neural networks(CNNs),recurrent neural networks(RNNs),transformer-based models,and hybrid systems across critical tasks such as arrhythmia classification,seizure detection,and anomaly segmentation.The study dissects preprocessing techniques(e.g.,wavelet denoising,spectral normalization)and feature extraction strategies(time-frequency analysis,attention mechanisms),demonstrating their impact on model accuracy,noise robustness,and computational efficiency.Experimental results underscore the superiority of deep learning over traditional methods,particularly in automated feature extraction,real-time processing,cross-modal generalization,and achieving up to a 15%increase in classification accuracy and enhanced noise resilience across electrocardiogram(ECG),electroencephalogram(EEG),and electromyogram(EMG)signals.Performance is rigorously benchmarked using precision,recall,F1-scores,area under the receiver operating characteristic curve(AUC-ROC),and computational complexitymetrics,providing a unified framework for comparing model efficacy.Thesurvey addresses persistent challenges:synthetic data generationmitigates limited training samples,interpretability tools(e.g.,Gradient-weighted Class Activation Mapping(Grad-CAM),Shapley values)resolve model opacity,and federated learning ensures privacy-compliant deployments.Distinguished from prior reviews,this work offers a structured taxonomy of deep learning architectures,integrates emerging paradigms like transformers and domain-specific attention mechanisms,and evaluates preprocessing pipelines for spectral-temporal trade-offs.It advances the field by bridging technical advancements with clinical needs,such as scalability in real-world settings(e.g.,wearable devices)and regulatory alignment with theHealth Insurance Portability and Accountability Act(HIPAA)and General Data Protection Regulation(GDPR).By synthesizing technical rigor,ethical considerations,and actionable guidelines for model selection,this survey establishes a holistic reference for developing robust,interpretable biomedical artificial intelligence(AI)systems,accelerating their translation into personalized and equitable healthcare solutions.展开更多
Ca^(2+)signaling plays crucial roles in plant stress responses,including defense against insects.To counteract insect feeding,different parts of a plant deploy systemic signaling to communicate and coordinate defense ...Ca^(2+)signaling plays crucial roles in plant stress responses,including defense against insects.To counteract insect feeding,different parts of a plant deploy systemic signaling to communicate and coordinate defense responses,but little is known about the underlying mechanisms.In this study,micrografting,in vivo imaging of Ca^(2+)and reactive oxygen species(ROS),quantification of jasmonic acid(JA)and defensive metabolites,and bioassay were used to study how Arabidopsis seedlings regulate systemic responses in leaves after hypocotyls are wounded.We show that wounding hypocotyls rapidly activated both Ca^(2+)and ROS signals in leaves.RBOHD,which functions to produce ROS,along with two glutamate receptors GLR3.3 and GLR3.6,but not individually RBOHD or GLR3.3 and GLR3.6,in hypocotyls regulate the dynamics of systemic Ca^(2+)signals in leaves.In line with the systemic Ca^(2+)signals,after wounding hypocotyl,RBOHD,GLR3.3,and GLR3.6 in hypocotyl also cooperatively regulate the transcriptome,hormone jasmonic acid,and defensive secondary metabolites in leaves of Arabidopsis seedlings,thus controlling the systemic resistance to insects.Unlike leaf-to-leaf systemic signaling,this study reveals the unique regulation of wounding-induced hypocotyl-to-leaf systemic signaling and sheds new light on how different plant organs use complex signaling pathways to modulate defense responses.展开更多
Non-cooperative communication detection is a key technology for locating radio interfer-ence sources and conducting reconnaissance on adversary radiation sources.To meet the require-ments of wide-area monitoring,a sin...Non-cooperative communication detection is a key technology for locating radio interfer-ence sources and conducting reconnaissance on adversary radiation sources.To meet the require-ments of wide-area monitoring,a single interception channel often contains mixed multi-source sig-nals and interference,resulting in generally low signal-to-noise ratio(SNR)of the received signals;meanwhile,improving detection quality urgently requires either high frequency resolution or high-time resolution,which poses severe challenges to detection techniques based on time-frequency rep-resentations(TFR).To address this issue,this paper proposes a fixed-frame-structure signal detec-tion algorithm that integrates image enhancement and multi-scale template matching:first,the Otsu-Sauvola hybrid thresholding algorithm is employed to enhance TFR features,suppress noise interference,and extract time-frequency parameters of potential target signals(such as bandwidth and occurrence time);then,by exploiting the inherent time-frequency characteristics of the fixed-frame structure,the signal is subjected to multi-scale transformation(with either high-frequency resolution or high-time resolution),and accurate detection is achieved through the corresponding multi-scale template matching.Experimental results demonstrate that under 0 dB SNR conditions,the proposed algorithm achieves a detection rate greater than 87%,representing a significant improvement over traditional methods.展开更多
A convenient photocatalytic multi-component reaction of alkenes,quinoxalin-2(1H)-ones,and diazo compounds has been developed in the presence of water.A number of ester-containing quinoxalin-2(1H)-ones could be efficie...A convenient photocatalytic multi-component reaction of alkenes,quinoxalin-2(1H)-ones,and diazo compounds has been developed in the presence of water.A number of ester-containing quinoxalin-2(1H)-ones could be efficiently obtained in moderate to good yields at room temperature.This metal-free visiblelight-driven tandem reaction was conducted through proton-coupled electron transfer(PCET)process using water as the hydrogen donor and 1,2,3,5-tetrakis(carbazol-9-yl)-4,6-dicyanobenzene(4CzIPN)as the photocatalyst.展开更多
The gravitational memory effect manifests gravitational nonlinearity,degenerate vacua,and asymptotic symmetries;its detection is considered challenging.We propose using a space-borne interferometer to detect memory si...The gravitational memory effect manifests gravitational nonlinearity,degenerate vacua,and asymptotic symmetries;its detection is considered challenging.We propose using a space-borne interferometer to detect memory signals from stellar-mass binary black holes(BBHs),typically targeted by ground-based detectors.We use DECIGO detector as an example.Over 5 years,DECIGO is estimated to detect approximately 2,036 memory signals(SNRs>3)from stellar-mass BBHs.Simulations used frequency-domain memory waveforms for direct SNR estimation.Predictions utilized a GWTC-3 constrained BBH population model(Power law+Peak mass,DEFAULT spin,Madau-Dickinson merger rate).The analysis used conservative lower merger rate limits and considered orbital eccentricity.The high detection rate stems from strong memory signals within DECIGO’s bandwidth and the abundance of stellar-mass BBHs.This substantial and conservative detection count enables statistical use of the memory effect for fundamental physics and astrophysics.DECIGO exemplifies that space interferometers may better detect memory signals from smaller mass binaries than their typical targets.Detectors in lower frequency bands are expected to find strong memory signals from∼10^(4)M⊙binaries.展开更多
Tumor stroma,or tumor microenvironment(TME),has been in the spotlight during recent years for its role in tumor development,growth,and metastasis.It consists of a myriad of elements,including tumor-associated macropha...Tumor stroma,or tumor microenvironment(TME),has been in the spotlight during recent years for its role in tumor development,growth,and metastasis.It consists of a myriad of elements,including tumor-associated macrophages,cancer-associated fibroblasts,a deregulated extracellular matrix,endothelial cells,and vascular vessels.The release of proinflammatory molecules,due to the inflamed microenvironment,such as cytokines and chemokines is found to play a pivotal role in progression of cancer and response to therapy.This review discusses the major key players and important chemical inflammatory signals released in the TME.Furthermore,the latest breakthroughs in cytokine-mediated crosstalk between immune cells and cancer cells have been highlighted.In addition,recent updates on alterations in cytokine signaling between chronic inflammation and malignant TME have also been reviewed.展开更多
There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are di...There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are difficult to discover unknown signals while recognizing known ones.In this paper,a compact manifold mixup feature-based open-set recognition approach(OR-CMMF)is proposed to address the above problem.First,the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space.Second,the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals.Then,these constructed representations can occupy the extended low-confidence space.Finally,the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals.The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time,as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.展开更多
Terahertz(THz)and millimeter Wave(mmWave)have been considered as potential frequency bands for 6G cellular systems to meet the need of ultra-high data rates.However,indoor communications could be blocked in THz/mmW ce...Terahertz(THz)and millimeter Wave(mmWave)have been considered as potential frequency bands for 6G cellular systems to meet the need of ultra-high data rates.However,indoor communications could be blocked in THz/mmW cellular systems due to the high free-space propagation loss.Deploying a large number of small base stations indoors has been considered as a promising solution for solving indoor coverage problems.However,base station dense deployment leads to a significant increase in system energy consumption.In this paper,we develop a novel ultra-e˙icient energy-saving mechanism with the aim of reducing energy consumption in 6G distributed indoor base station scenarios.Unlike the existing relevant protocol framework of 3GPP,which operates the cellular system based on constant system signaling messages(including cell ID,cell reselection information,etc.),the proposed mechanism eliminates the need for system messages.The intuition comes from the observation that the probability of having no users within the coverage area of an indoor base station is high,hence continuously sending system messages to guarantee the quality of service is unnecessary in indoor scenarios.Specifically,we design a dedicated beacon signal to detect whether there are users in the coverage area of the base station and switch offthe main communication module when there are no active users for energy saving.The beacon frame structure is carefully designed based on the existing 3GPP specifications with minimal protocol modifications,and the protocol parameters involved are optimized.Simulation results show that the proposed mechanism can reduce the system energy from the order of tens of watts to the order of hundreds of milliwatts.Compared to traditional energy-saving schemes,the proposed mechanism achieves an average energy-saving gain of 58%,with a peak energy-saving gain of 90%.展开更多
Atrial Fibrillation(AF)is a cardiac disorder characterized by irregular heart rhythms,typically diagnosed using Electrocardiogram(ECG)signals.In remote regions with limited healthcare personnel,automated AF detection ...Atrial Fibrillation(AF)is a cardiac disorder characterized by irregular heart rhythms,typically diagnosed using Electrocardiogram(ECG)signals.In remote regions with limited healthcare personnel,automated AF detection is extremely important.Although recent studies have explored various machine learning and deep learning approaches,challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification.In this study,we propose a novel framework that integrates robust preprocessing,comprehensive feature extraction,and an ensemble classification strategy.In the first step,ECG signals are divided into equal-sized segments using a 5-s sliding window with 50%overlap,followed by bandpass filtering between 0.5 and 45 Hz for noise removal.After preprocessing,both time and frequency-domain features are extracted,and a custom one-dimensional Convolutional Neural Network—Bidirectional Long Short-Term Memory(1D CNN-BiLSTM)architecture is introduced.Handcrafted and automated features are concatenated into a unified feature vector and classified using Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM)models.A Quantum Genetic Algorithm(QGA)optimizes weighted averages of the classifier outputs for multi-class classification,distinguishing among AF,noisy,normal,and other rhythms.Evaluated on the PhysioNet 2017 Cardiology Challenge dataset,the proposed method achieved an accuracy of 94.40%and an F1-score of 92.30%,outperforming several state-of-the-art techniques.展开更多
Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,th...Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges.Higher sampling rates increase the data volume,data processing,and storage hardware costs.Compressed sampling can address these challenges,but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals.This paper proposes a revolutionary deep learning-based compressed sampling(DL-CS)algorithm for reconstructing neutron ToF signals that outperform traditional compressed sampling methods.This approach comprises four modules:random projection,rising dimensions,initial reconstruction,and final reconstruction.Initially,the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers,replacing random measurement matrices in traditional compressed sampling theory.Subsequently,the signals are reconstructed using a modified inception module,long short-term memory,and self-attention.The performance of this deep compressed sampling method was quantified using the percentage root-mean-square difference,correlation coefficient,and reconstruction time.Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods.This is evidenced by a percentage root-mean-square difference,correlation coefficient,and reconstruction time results of 5%,0.9988,and 0.0108 s,respectively,obtained for sampling rates below 10%for the neutron ToF signal generated using an electron-beam-driven photoneutron source.The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods,exhibiting excellent reconstruction accuracy and speed.展开更多
In this paper,we focus on the recovery of piecewise sparse signals containing both fast-decaying and slow-decaying nonzero entries.In order to improve the performance of classic Orthogonal Matching Pursuit(OMP)and Gen...In this paper,we focus on the recovery of piecewise sparse signals containing both fast-decaying and slow-decaying nonzero entries.In order to improve the performance of classic Orthogonal Matching Pursuit(OMP)and Generalized Orthogonal Matching Pursuit(GOMP)algorithms for solving this problem,we propose the Piecewise Generalized Orthogonal Matching Pursuit(PGOMP)algorithm,by considering the mixed-decaying sparse signals as piecewise sparse signals with two components containing nonzero entries with different decay factors.The algorithm incorporates piecewise selection and deletion to retain the most significant entries according to the sparsity of each component.We provide a theoretical analysis based on the mutual coherence of the measurement matrix and the decay factors of the nonzero entries,establishing a sufficient condition for the PGOMP algorithm to select at least two correct indices in each iteration.Numerical simulations and an image decomposition experiment demonstrate that the proposed algorithm significantly improves the support recovery probability by effectively matching piecewise sparsity with decay factors.展开更多
Sound contains mechanical signals that can promote physiological and biochemical changes in plants.Insects produce different sounds in the environment,which may be relevant to plant behavior.Thus,we evaluated whether ...Sound contains mechanical signals that can promote physiological and biochemical changes in plants.Insects produce different sounds in the environment,which may be relevant to plant behavior.Thus,we evaluated whether signaling cascades are regulated differently by ecological sounds and whether they trigger molecular responses following those produced by herbivorous insects.Soybean plants were treated with two different sounds:chewing herbivore and forest ambient.The responses were markedly distinct,indicating that sound signals may also trigger specific cascades.Enzymes involved in oxidative metabolism were responsive to both sounds,while salicylic acid(SA)was responsive only to the chewing sound.In contrast,lipoxygenase(LOX)activity and jasmonic acid(JA)did not change.Soybean Kunitz trypsin inhibitor gene(SKTI)and Bowman-Birk(BBI)genes,encoding for protease inhibitors,were induced by chewing sound.Chewing sound-induced high expression of the pathogenesis-related protein(PR1)gene,confirming the activation of SA-dependent cascades.In contrast,the sound treatments promoted modifications in different branches of the phenylpropanoid pathway,highlighting a tendency for increased flavonols for plants under chewing sounds.Accordingly,chewing sounds induced pathogenesis-related protein(PR10/Bet v-1)and gmFLS1 flavonol synthase(FLS1)genes involved in flavonoid biosynthesis and flavonols.Finally,our results propose that plants may recognize herbivores by their chewing sound and that different ecological sounds can trigger distinct signaling cascades.展开更多
Large dynamic range and ultra-wideband receiving abilities are significant for many receivers. With these abilities, receivers can obtain signals with different power in ultra-wideband frequency space without informat...Large dynamic range and ultra-wideband receiving abilities are significant for many receivers. With these abilities, receivers can obtain signals with different power in ultra-wideband frequency space without information loss. However, conventional receiving scheme is hard to have large dynamic range and ultra-wideband receiving simultaneously because of the analog-to-digital converter(ADC) dynamic range and sample rate limitations. In this paper, based on the modulated sampling and unlimited sampling, a novel receiving scheme is proposed to achieve large dynamic range and ultra-wideband receiving. Focusing on the single carrier signals, the proposed scheme only uses a single self-rest ADC(SR-ADC) with low sample rate, and it achieves large dynamic range and ultra-wideband receiving simultaneously. Two receiving scenarios are considered, and they are cooperative strong signal receiving and non-cooperative strong/weak signals receiving. In the cooperative receiving scenario, an improved fast recovery method is proposed to obtain the modulated sampling output. In the non-cooperative receiving scenario, the strong and weak signals with different carrier frequencies are considered, and the signal processing method can recover and estimate each signal. Simulation results show that the proposed scheme can realize large dynamic range and ultra-wideband receiving simultaneously when the input signal-to-noise(SNR) ratio is high.展开更多
The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibratio...The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No. 60572098.
文摘A classical time-varying signal, the multi-component Chirp signal has been widely used and the ability to estimate its instantaneous frequency （IF） is very useful. But in noisy environments, it is hard to estimate the 1F of a multi-component Chirp signal accurately. Wigner distribution maxima （WDM） are usually utilized for this estimation. But in practice, estimation bias increases when some points deviate from the true IF in high noise environments. This paper presents a new method of multi-component Chirp signal 1F estimation named Wigner Viterbi fit （WVF）, based on Wigner-Ville distribution （WVD） and the Viterbi algorithm. First, we transform the WVD of the Chirp signal into digital image, and apply the Viterbi algorithm to separate the components and estimate their IF. At last, we establish a linear model to fit the estimation results. Theoretical analysis and simulation results prove that this new method has high precision and better performance than WDM in high noise environments, and better suppression of interference and the edge effect. Compared with WDM, WVF can reduce the mean square error （MSE） by 50% when the signal to noise ration （SNR） is in the range of-15dB to -11dB. WVF is an effective and promising 1F estimation method.
基金Doctoral Programme Foundation of Institution of Higher Education of China.
文摘To study the measurement of distance under the condition of the frequency modulation (FM) multi component signal of a short range radar, the multi points scattering model of a target, the TLS ESPRIT (total least square estimation of signal parameters via rotational invariance techniques) and the mathematical statistics methods were used. The method of computing single frequency signal's instantaneous frequency (IF) is unsuitable to the multi component signal. By using the method of the TLS ESPRIT combined with the mathematical statistics, the multi component signal's IF can be obtained. The computer simulation has shown that the method has the high accuracy for measuring the distance.
基金Sponsored by the National Natural Science Foundation of China (60232010 ,60572094)the National Science Foundation of China for Distin-guished Young Scholars (60625104)
文摘Distinguishing close chirp-rates of different linear frequency modulation （LFM） signals under concentrated and complicated signal environment was studied. Firstly, detection and parameter estimation of multi-component LFM signal were used by discrete fast fractional Fourier transform （FrFT）. Then the expression of chirp-rate resolution in fractional Fourier domain （FrFD） was deduced from discrete normalize time-frequency distribution, when multi-component LFM signal had only one center frequency. Furthermore, the detail influence of the sampling time, sampling frequency and chirp-rate upon the resolution was analyzed by partial differential equation. Simulation results and analysis indicate that increasing the sampling time can enhance the resolution, but the influence of the sampling frequency can he omitted. What＇s more, in multi-component LFM signal, the chirp-rate resolution of FrFT is no less than a minimal value, and it mainly dependent on the biggest value of chirp-rates, with which it has an approximately positive exponential relationship.
文摘Chirp signals show energy aggregation in the fractional Fourier domain(FrFD) w hich can be used to estimate the parameter of the signals. In this paper,a parameter estimation method for multi-component chirp signal w hich corrupted by w hite Gaussian noise is proposed based on the discrete fractional Fourier transform(DFrFT) and the differential evolution( DE) algorithm. The proposed algorithm uses the DE algorithm instead of the conventional fine search algorithm to detect the peak of the signals in the FrFD. The paper simulated the influence of the noise and the resolution of the proposed algorithm. The results of the simulation show the proposed method does not only improve the estimation accuracy of the peak coordinate,but also reduces time consuming.
基金sponsored by the National Natural Science Foundations of China under Grant Nos.12301315,12235007,11975131the Zhejiang Provincial Natural Science Foundation of China under Grant No.LQ20A010009。
文摘In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrability.By focusing on single-component decompositions within the potential BKP hierarchy,it has been observed that specific linear superpositions of decomposition solutions remain consistent with the underlying equations.Moreover,through the implementation of multi-component decompositions within the potential BKP hierarchy,successful endeavors have been undertaken to formulate linear superposition solutions and novel coupled Kd V-type systems that resist decoupling via alterations in dependent variables.
基金financially supported by the National Natural Science Foundation of China(Nos.52271033 and 52071179)the Key program of National Natural Science Foundation of China(No.51931003)+2 种基金Natural Science Foundation of Jiangsu Province,China(No.BK20221493)Jiangsu Province Leading Edge Technology Basic Research Major Project(No.BK20222014)Foundation of“Qinglan Project”for Colleges and Universities in Jiangsu Province.
文摘Multi-component transition group metal borides(MMB_(2))have become a research hotspot due to their new composition design concepts and superior properties compared with conventional ceramics.Most of the current methods,however,are complicated and time-consuming,the mass production remains a chal-lenge.Herein,we proposed a new high-efficiency strategy for synthesis of MMB_(2)using molten aluminum as the medium for the first time.The prepared Al-containing multi-component borides(TiZrHfNbTa)B_(2)microcrystals had a homogeneous composition with a hexagonal AlB_(2)structure and ultra-high hardness value of∼35.3 GPa,which was much higher than data reported in the literature and the rule of mix-ture estimations.Furthermore,combined with the First-principles calculation results,we found that the Poisson’s ratio(v)values exhibit a clearly ascending trend from 0.17 at VEC=3.5 to 0.18 at VEC=3.4,then to 0.201 at VEC=3.2 with the increasing of Al content.This indicates that the intrinsic toughness of multi-component boride microcrystals is obviously enhanced by the trace-doped Al elements.Besides,the fabricated Al-containing multi-component boride microcrystals have superior oxidation activation en-ergy and structural stability.The enhanced oxidation resistance is mainly attributed to the formation of a protective Al2 O3 oxide layer and the lattice distortion,both of which lead to sluggish diffusion of O_(2).These findings propose a new unexplored avenue for the fabrication of MMB_(2)materials with supe-rior comprehensive performance including ultra-hardness and intrinsically improved thermo-mechanical properties.
基金The Natural Sciences and Engineering Research Council of Canada(NSERC)funded this review study.
文摘This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We systematically evaluate key deep learning architectures including convolutional neural networks(CNNs),recurrent neural networks(RNNs),transformer-based models,and hybrid systems across critical tasks such as arrhythmia classification,seizure detection,and anomaly segmentation.The study dissects preprocessing techniques(e.g.,wavelet denoising,spectral normalization)and feature extraction strategies(time-frequency analysis,attention mechanisms),demonstrating their impact on model accuracy,noise robustness,and computational efficiency.Experimental results underscore the superiority of deep learning over traditional methods,particularly in automated feature extraction,real-time processing,cross-modal generalization,and achieving up to a 15%increase in classification accuracy and enhanced noise resilience across electrocardiogram(ECG),electroencephalogram(EEG),and electromyogram(EMG)signals.Performance is rigorously benchmarked using precision,recall,F1-scores,area under the receiver operating characteristic curve(AUC-ROC),and computational complexitymetrics,providing a unified framework for comparing model efficacy.Thesurvey addresses persistent challenges:synthetic data generationmitigates limited training samples,interpretability tools(e.g.,Gradient-weighted Class Activation Mapping(Grad-CAM),Shapley values)resolve model opacity,and federated learning ensures privacy-compliant deployments.Distinguished from prior reviews,this work offers a structured taxonomy of deep learning architectures,integrates emerging paradigms like transformers and domain-specific attention mechanisms,and evaluates preprocessing pipelines for spectral-temporal trade-offs.It advances the field by bridging technical advancements with clinical needs,such as scalability in real-world settings(e.g.,wearable devices)and regulatory alignment with theHealth Insurance Portability and Accountability Act(HIPAA)and General Data Protection Regulation(GDPR).By synthesizing technical rigor,ethical considerations,and actionable guidelines for model selection,this survey establishes a holistic reference for developing robust,interpretable biomedical artificial intelligence(AI)systems,accelerating their translation into personalized and equitable healthcare solutions.
基金National Natural Science Foundation of China(U23A20199)Yunnan Revitalization Talent Support Program“Yunling Scholar”and Yunnan Fundamental Research Projects(202201AS070056)。
文摘Ca^(2+)signaling plays crucial roles in plant stress responses,including defense against insects.To counteract insect feeding,different parts of a plant deploy systemic signaling to communicate and coordinate defense responses,but little is known about the underlying mechanisms.In this study,micrografting,in vivo imaging of Ca^(2+)and reactive oxygen species(ROS),quantification of jasmonic acid(JA)and defensive metabolites,and bioassay were used to study how Arabidopsis seedlings regulate systemic responses in leaves after hypocotyls are wounded.We show that wounding hypocotyls rapidly activated both Ca^(2+)and ROS signals in leaves.RBOHD,which functions to produce ROS,along with two glutamate receptors GLR3.3 and GLR3.6,but not individually RBOHD or GLR3.3 and GLR3.6,in hypocotyls regulate the dynamics of systemic Ca^(2+)signals in leaves.In line with the systemic Ca^(2+)signals,after wounding hypocotyl,RBOHD,GLR3.3,and GLR3.6 in hypocotyl also cooperatively regulate the transcriptome,hormone jasmonic acid,and defensive secondary metabolites in leaves of Arabidopsis seedlings,thus controlling the systemic resistance to insects.Unlike leaf-to-leaf systemic signaling,this study reveals the unique regulation of wounding-induced hypocotyl-to-leaf systemic signaling and sheds new light on how different plant organs use complex signaling pathways to modulate defense responses.
文摘Non-cooperative communication detection is a key technology for locating radio interfer-ence sources and conducting reconnaissance on adversary radiation sources.To meet the require-ments of wide-area monitoring,a single interception channel often contains mixed multi-source sig-nals and interference,resulting in generally low signal-to-noise ratio(SNR)of the received signals;meanwhile,improving detection quality urgently requires either high frequency resolution or high-time resolution,which poses severe challenges to detection techniques based on time-frequency rep-resentations(TFR).To address this issue,this paper proposes a fixed-frame-structure signal detec-tion algorithm that integrates image enhancement and multi-scale template matching:first,the Otsu-Sauvola hybrid thresholding algorithm is employed to enhance TFR features,suppress noise interference,and extract time-frequency parameters of potential target signals(such as bandwidth and occurrence time);then,by exploiting the inherent time-frequency characteristics of the fixed-frame structure,the signal is subjected to multi-scale transformation(with either high-frequency resolution or high-time resolution),and accurate detection is achieved through the corresponding multi-scale template matching.Experimental results demonstrate that under 0 dB SNR conditions,the proposed algorithm achieves a detection rate greater than 87%,representing a significant improvement over traditional methods.
基金supported by Sichuan Science and Technology Program(No.2023NSFSC0101)the 2024 Provincial platform project of Chengdu Normal University(No.GNFZ202404)+1 种基金Natural Science Foundation of Shandong Province(No.ZR2021MB065)National Natural Science Foundation of China(No.22101237)。
文摘A convenient photocatalytic multi-component reaction of alkenes,quinoxalin-2(1H)-ones,and diazo compounds has been developed in the presence of water.A number of ester-containing quinoxalin-2(1H)-ones could be efficiently obtained in moderate to good yields at room temperature.This metal-free visiblelight-driven tandem reaction was conducted through proton-coupled electron transfer(PCET)process using water as the hydrogen donor and 1,2,3,5-tetrakis(carbazol-9-yl)-4,6-dicyanobenzene(4CzIPN)as the photocatalyst.
基金supported by the National Natural Science Foundation of China(Grant Nos.11633001,11920101003,and 12205222 for S.H.)the Key Program of the National Natural Science Foundation of China(Grant No.12433001)+1 种基金the Strate-gic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB23000000)the National Key Research and Development Program of China(Grant No.2021YFC2203001 for Z.C.Z.).
文摘The gravitational memory effect manifests gravitational nonlinearity,degenerate vacua,and asymptotic symmetries;its detection is considered challenging.We propose using a space-borne interferometer to detect memory signals from stellar-mass binary black holes(BBHs),typically targeted by ground-based detectors.We use DECIGO detector as an example.Over 5 years,DECIGO is estimated to detect approximately 2,036 memory signals(SNRs>3)from stellar-mass BBHs.Simulations used frequency-domain memory waveforms for direct SNR estimation.Predictions utilized a GWTC-3 constrained BBH population model(Power law+Peak mass,DEFAULT spin,Madau-Dickinson merger rate).The analysis used conservative lower merger rate limits and considered orbital eccentricity.The high detection rate stems from strong memory signals within DECIGO’s bandwidth and the abundance of stellar-mass BBHs.This substantial and conservative detection count enables statistical use of the memory effect for fundamental physics and astrophysics.DECIGO exemplifies that space interferometers may better detect memory signals from smaller mass binaries than their typical targets.Detectors in lower frequency bands are expected to find strong memory signals from∼10^(4)M⊙binaries.
文摘Tumor stroma,or tumor microenvironment(TME),has been in the spotlight during recent years for its role in tumor development,growth,and metastasis.It consists of a myriad of elements,including tumor-associated macrophages,cancer-associated fibroblasts,a deregulated extracellular matrix,endothelial cells,and vascular vessels.The release of proinflammatory molecules,due to the inflamed microenvironment,such as cytokines and chemokines is found to play a pivotal role in progression of cancer and response to therapy.This review discusses the major key players and important chemical inflammatory signals released in the TME.Furthermore,the latest breakthroughs in cytokine-mediated crosstalk between immune cells and cancer cells have been highlighted.In addition,recent updates on alterations in cytokine signaling between chronic inflammation and malignant TME have also been reviewed.
基金fully supported by National Natural Science Foundation of China(61871422)Natural Science Foundation of Sichuan Province(2023NSFSC1422)Central Universities of South west Minzu University(ZYN2022032)。
文摘There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are difficult to discover unknown signals while recognizing known ones.In this paper,a compact manifold mixup feature-based open-set recognition approach(OR-CMMF)is proposed to address the above problem.First,the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space.Second,the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals.Then,these constructed representations can occupy the extended low-confidence space.Finally,the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals.The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time,as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.
基金supported by the National Natural Science Foundation of China under Grant No.62201121the Fundamental Fundamental Research Funds for Central Universities under Grant No.ZYGX2024XJ070。
文摘Terahertz(THz)and millimeter Wave(mmWave)have been considered as potential frequency bands for 6G cellular systems to meet the need of ultra-high data rates.However,indoor communications could be blocked in THz/mmW cellular systems due to the high free-space propagation loss.Deploying a large number of small base stations indoors has been considered as a promising solution for solving indoor coverage problems.However,base station dense deployment leads to a significant increase in system energy consumption.In this paper,we develop a novel ultra-e˙icient energy-saving mechanism with the aim of reducing energy consumption in 6G distributed indoor base station scenarios.Unlike the existing relevant protocol framework of 3GPP,which operates the cellular system based on constant system signaling messages(including cell ID,cell reselection information,etc.),the proposed mechanism eliminates the need for system messages.The intuition comes from the observation that the probability of having no users within the coverage area of an indoor base station is high,hence continuously sending system messages to guarantee the quality of service is unnecessary in indoor scenarios.Specifically,we design a dedicated beacon signal to detect whether there are users in the coverage area of the base station and switch offthe main communication module when there are no active users for energy saving.The beacon frame structure is carefully designed based on the existing 3GPP specifications with minimal protocol modifications,and the protocol parameters involved are optimized.Simulation results show that the proposed mechanism can reduce the system energy from the order of tens of watts to the order of hundreds of milliwatts.Compared to traditional energy-saving schemes,the proposed mechanism achieves an average energy-saving gain of 58%,with a peak energy-saving gain of 90%.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2501)。
文摘Atrial Fibrillation(AF)is a cardiac disorder characterized by irregular heart rhythms,typically diagnosed using Electrocardiogram(ECG)signals.In remote regions with limited healthcare personnel,automated AF detection is extremely important.Although recent studies have explored various machine learning and deep learning approaches,challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification.In this study,we propose a novel framework that integrates robust preprocessing,comprehensive feature extraction,and an ensemble classification strategy.In the first step,ECG signals are divided into equal-sized segments using a 5-s sliding window with 50%overlap,followed by bandpass filtering between 0.5 and 45 Hz for noise removal.After preprocessing,both time and frequency-domain features are extracted,and a custom one-dimensional Convolutional Neural Network—Bidirectional Long Short-Term Memory(1D CNN-BiLSTM)architecture is introduced.Handcrafted and automated features are concatenated into a unified feature vector and classified using Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM)models.A Quantum Genetic Algorithm(QGA)optimizes weighted averages of the classifier outputs for multi-class classification,distinguishing among AF,noisy,normal,and other rhythms.Evaluated on the PhysioNet 2017 Cardiology Challenge dataset,the proposed method achieved an accuracy of 94.40%and an F1-score of 92.30%,outperforming several state-of-the-art techniques.
基金supported by the National Defense Technology Foundation Program of China(No.JSJT2022209A001-3)Sichuan Science and Technology Program(No.2021JDRC0011)+1 种基金Nuclear Energy Development Research Program of China(Research on High Energy X-ray Imaging of Nuclear Fuel)Scientific Research and Innovation Team Program of Sichuan University of Science and Engineering(No.SUSE652A001).
文摘Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges.Higher sampling rates increase the data volume,data processing,and storage hardware costs.Compressed sampling can address these challenges,but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals.This paper proposes a revolutionary deep learning-based compressed sampling(DL-CS)algorithm for reconstructing neutron ToF signals that outperform traditional compressed sampling methods.This approach comprises four modules:random projection,rising dimensions,initial reconstruction,and final reconstruction.Initially,the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers,replacing random measurement matrices in traditional compressed sampling theory.Subsequently,the signals are reconstructed using a modified inception module,long short-term memory,and self-attention.The performance of this deep compressed sampling method was quantified using the percentage root-mean-square difference,correlation coefficient,and reconstruction time.Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods.This is evidenced by a percentage root-mean-square difference,correlation coefficient,and reconstruction time results of 5%,0.9988,and 0.0108 s,respectively,obtained for sampling rates below 10%for the neutron ToF signal generated using an electron-beam-driven photoneutron source.The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods,exhibiting excellent reconstruction accuracy and speed.
基金Supported by the National Key R&D Program of China(Grant No.2023YFA1009200)the National Natural Science Foundation of China(Grant Nos.12271079+1 种基金12494552)the Fundamental Research Funds for the Central Universities of China(Grant No.DUT24LAB127)。
文摘In this paper,we focus on the recovery of piecewise sparse signals containing both fast-decaying and slow-decaying nonzero entries.In order to improve the performance of classic Orthogonal Matching Pursuit(OMP)and Generalized Orthogonal Matching Pursuit(GOMP)algorithms for solving this problem,we propose the Piecewise Generalized Orthogonal Matching Pursuit(PGOMP)algorithm,by considering the mixed-decaying sparse signals as piecewise sparse signals with two components containing nonzero entries with different decay factors.The algorithm incorporates piecewise selection and deletion to retain the most significant entries according to the sparsity of each component.We provide a theoretical analysis based on the mutual coherence of the measurement matrix and the decay factors of the nonzero entries,establishing a sufficient condition for the PGOMP algorithm to select at least two correct indices in each iteration.Numerical simulations and an image decomposition experiment demonstrate that the proposed algorithm significantly improves the support recovery probability by effectively matching piecewise sparsity with decay factors.
文摘Sound contains mechanical signals that can promote physiological and biochemical changes in plants.Insects produce different sounds in the environment,which may be relevant to plant behavior.Thus,we evaluated whether signaling cascades are regulated differently by ecological sounds and whether they trigger molecular responses following those produced by herbivorous insects.Soybean plants were treated with two different sounds:chewing herbivore and forest ambient.The responses were markedly distinct,indicating that sound signals may also trigger specific cascades.Enzymes involved in oxidative metabolism were responsive to both sounds,while salicylic acid(SA)was responsive only to the chewing sound.In contrast,lipoxygenase(LOX)activity and jasmonic acid(JA)did not change.Soybean Kunitz trypsin inhibitor gene(SKTI)and Bowman-Birk(BBI)genes,encoding for protease inhibitors,were induced by chewing sound.Chewing sound-induced high expression of the pathogenesis-related protein(PR1)gene,confirming the activation of SA-dependent cascades.In contrast,the sound treatments promoted modifications in different branches of the phenylpropanoid pathway,highlighting a tendency for increased flavonols for plants under chewing sounds.Accordingly,chewing sounds induced pathogenesis-related protein(PR10/Bet v-1)and gmFLS1 flavonol synthase(FLS1)genes involved in flavonoid biosynthesis and flavonols.Finally,our results propose that plants may recognize herbivores by their chewing sound and that different ecological sounds can trigger distinct signaling cascades.
文摘Large dynamic range and ultra-wideband receiving abilities are significant for many receivers. With these abilities, receivers can obtain signals with different power in ultra-wideband frequency space without information loss. However, conventional receiving scheme is hard to have large dynamic range and ultra-wideband receiving simultaneously because of the analog-to-digital converter(ADC) dynamic range and sample rate limitations. In this paper, based on the modulated sampling and unlimited sampling, a novel receiving scheme is proposed to achieve large dynamic range and ultra-wideband receiving. Focusing on the single carrier signals, the proposed scheme only uses a single self-rest ADC(SR-ADC) with low sample rate, and it achieves large dynamic range and ultra-wideband receiving simultaneously. Two receiving scenarios are considered, and they are cooperative strong signal receiving and non-cooperative strong/weak signals receiving. In the cooperative receiving scenario, an improved fast recovery method is proposed to obtain the modulated sampling output. In the non-cooperative receiving scenario, the strong and weak signals with different carrier frequencies are considered, and the signal processing method can recover and estimate each signal. Simulation results show that the proposed scheme can realize large dynamic range and ultra-wideband receiving simultaneously when the input signal-to-noise(SNR) ratio is high.
基金Supported by the Scientific Research and Technology Development Project of Petrochina Southwest Oil and Gas Field Company(20230307-02)。
文摘The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.
