Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer charact...Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft.In this paper,the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed,and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed.By modeling the spatial distribution of the disturbance density field,the scattered echo signal images of active light towards the disturbance field at long distance are simulated.On this basis,the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed.The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images.The disturbance signal can be further highlighted by differential processing of the adjacent scattering images.As the distance behind the aircraft increases,the diffusion range of the disturbance signal increases,and the signal intensity and contrast with the background decrease.Under the ground-based observation conditions of the aircraft at a height of 10000 m,a Mach number of1.6,and a detection distance of 100 km,the contrast between the disturbance signal and the back-ground was 30 d B at a distance of one time from the rear of the fuselage,and the diffusion diameter of the disturbance signal was 50 m.At a distance eight times the length of the aircraft,the contrast decreased to 10 dB,and the diameter increased to 290 m.The contrast was reduced to 3 dB at a distance nine times the length of the aircraft,and the diameter was diffused to 310 m.These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.展开更多
The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and ...The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and robust approach is joint device activity detection and channel estimation.In this paper,we present an approach utilizing score-based generative models to address the underdetermined nature of channel estimation,which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems.Our experimental results,based on a comprehensive dataset generated through Monte-Carlo sampling,demonstrate the high precision of our channel estimation approach,with errors reduced to as low as-45 d B,and exceptional accuracy in detecting active devices.展开更多
This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and m...This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.展开更多
Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an al...Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus.Limited studies have,however,reported on COVID-19 transmission pattern analysis,and using geography features for prediction of potential outbreak sites.Predicting the next most probable outbreak site is crucial,particularly for optimizing the planning of medical personnel and supply resources.To tackle the challenge,this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude,when would the outbreak likely to happen and the duration of the outbreak.The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia.The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19.Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases,next outbreak location,and the time interval between start dates of two similar sites.Such findings provided valuable insights for policymakers to perform Active Case Detection.展开更多
Active tamper detection using watermarking technique can localize the tampered area and recover the lost information. In this paper, we propose an approach that the watermark is robust to legitimate lossy compression,...Active tamper detection using watermarking technique can localize the tampered area and recover the lost information. In this paper, we propose an approach that the watermark is robust to legitimate lossy compression, fragile to malicious tampering and capable of recovery. We embed the watermark bits in the direct current value of energy concentration transform domain coefficients. Let the original watermark bits be content dependent and apply error correction coding to them before embedded to the image. While indicating the tam- pered area, the extracted bits from a suspicious image can be further decoded and then used to roughly recover the corresponding area. We also theoretically study the image quality and bit error rate. ExperimentM results demonstrate the effectiveness of the proposed scheme.展开更多
Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detect...Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detection.In addition,multi-inverter networks connecting the distribution system point of common coupling(PCC)create islanding at paralleling inverters,which adds the vulnerability of islanding detection.Furthermore,available islanding detection must overcome more challenges from non-detection zones(NDZs)under reduced power mismatches.Therefore,in this study,a new method called parameter self-adapting active islanding detection was utilized to minimize the dilution effect and reduce NDZs in multi-inverter power systems.The method utilizes an active frequency drift(AFD)method and applies a positive feedback gain of adoption parameters,which significantly minimizes NDZs at parallel inverters.The simulation and experimental outcomes indicate that the proposed method can effectively weaken the dilution effect in multi-inverter networks connecting the distribution system PCC.展开更多
To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resul...To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.展开更多
This study explores the diagnostic value of combining the Padua score with the thrombotic biomarker tissue plasminogen activator inhibitor-1(tPAI-1)for assessing the risk of deep vein thrombosis(DVT)in patients with p...This study explores the diagnostic value of combining the Padua score with the thrombotic biomarker tissue plasminogen activator inhibitor-1(tPAI-1)for assessing the risk of deep vein thrombosis(DVT)in patients with pulmonary heart disease.These patients often exhibit symptoms similar to venous thrombosis,such as dyspnea and bilateral lower limb swelling,complicating differential diagnosis.The Padua Prediction Score assesses the risk of venous thromboembolism(VTE)in hospitalized patients,while tPAI-1,a key fibrinolytic system inhibitor,indicates a hypercoagulable state.Clinical data from hospitalized patients with cor pulmonale were retrospectively analyzed.ROC curves compared the diagnostic value of the Padua score,tPAI-1 levels,and their combined model for predicting DVT risk.Results showed that tPAI-1 levels were significantly higher in DVT patients compared to non-DVT patients.The Padua score demonstrated a sensitivity of 82.61%and a specificity of 55.26%at a cutoff value of 3.The combined model had a significantly higher AUC than the Padua score alone,indicating better discriminatory ability in diagnosing DVT risk.The combination of the Padua score and tPAI-1 detection significantly improves the accuracy of diagnosing DVT risk in patients with pulmonary heart disease,reducing missed and incorrect diagnoses.This study provides a comprehensive assessment tool for clinicians,enhancing the diagnosis and treatment of patients with cor pulmonale complicated by DVT.Future research should validate these findings in larger samples and explore additional thrombotic biomarkers to optimize the predictive model.展开更多
After detecting a target object,a service robot must approach the target object to perform the associated service task.In active object detection(AOD)tasks,effective feature information representation and comprehensiv...After detecting a target object,a service robot must approach the target object to perform the associated service task.In active object detection(AOD)tasks,effective feature information representation and comprehensive action execution strategies are crucial.Currently,most AOD tasks are accomplished by traditional reinforcement learning algorithms,but there are still problems such as high task failure rates and model training efficiency.To solve these problems,this paper proposes a combined data-driven and knowledge-guided solution.First,semantic information features,depth information features and target object bounding box information are used as inputs to comprehensively represent feature information.Second,a policy network is constructed based on the proximal policy optimizaton(PPO)algorithm.The reward value is set according to the robot′s action,the position of the bounding box,and the distance to the target object,and then applied to the robot′s training process.Finally,the knowledge of the path experience in the task,the robot′s collision avoidance ability and the prediction of target object loss are combined to guide the robot′s behavior,and a comprehensive decision model is proposed to enable the robot to make the best decision.Relevant experiments were conducted on an active vision dataset.The robot achieves an average success rate of 91.36%and an average step size of 9.3631 in performing the AOD task in the test scenes,which verifies the effectiveness of the proposed scheme.展开更多
Active object detection(AOD)is a crucial task in the field of robotics.A key challenge in household environments for AOD is that the target object is often undetectable due to partial occlusion,which leads to the fail...Active object detection(AOD)is a crucial task in the field of robotics.A key challenge in household environments for AOD is that the target object is often undetectable due to partial occlusion,which leads to the failure of traditional methods.To address the occlusion problem,this paper first proposes a novel occlusion handling method based on the large multimodal model(LMM).The method utilises an LMM to detect and analyse input RGB images and generates adjustment actions to progressively eliminate occlusion.After the occlusion is handled,an improved AOD method based on a deep Q-learning network(DQN)is used to complete the task.We introduce an attention mechanism to process image features,enabling the model to focus on critical regions of the input images.Additionally,a new reward function is proposed that comprehensively considers the bounding box of the target object and the robot's distance to the object,along with the actions performed by the robot.Ex-periments on the dataset and in real-world scenarios validate the effectiveness of the proposed method in performing AOD tasks under partial occlusion.展开更多
Sparsity-based joint active user detection and channel estimation(JADCE)algorithms are crucial in grant-free massive machine-type communication(mMTC)systems.The conventional compressed sensing algorithms are tailored ...Sparsity-based joint active user detection and channel estimation(JADCE)algorithms are crucial in grant-free massive machine-type communication(mMTC)systems.The conventional compressed sensing algorithms are tailored for noncoherent communication systems,where the correlation between any two measurements is as minimal as possible.However,existing sparsity-based JADCE approaches may not achieve optimal performance in strongly coherent systems,especially with a small number of pilot subcarriers.To tackle this challenge,we formulate JADCE as a joint sparse signal recovery problem,leveraging the block-type row-sparse structure of millimeter-wave(mmWave)channels in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMOOFDM)systems.Then,we propose an efficient difference-of-convex function algorithm(DCA)based JADCE algorithm with multiple measurement vector(MMV)frameworks,promoting the row-sparsity of the channel matrix.To mitigate the computational complexity further,we introduce a fast DCA-based JADCE algorithm via a proximal operator,which allows a low-complexity alternating direction multiplier method(ADMM)to resolve the optimization problem directly.Finally,simulation results demonstrate that the two proposed difference-of-convex(DC)algorithms achieve effective active user detection and accurate channel estimation compared with state-of-the-art compressed sensing based JADCE techniques.展开更多
[Objective] TO study the combined extracting process of porcine blood superoxide dismutase （SOD） and other bioactive substances and thus to provide technical basis for making full use of blood resources and large-sc...[Objective] TO study the combined extracting process of porcine blood superoxide dismutase （SOD） and other bioactive substances and thus to provide technical basis for making full use of blood resources and large-scale production of SOD. [Method] Fibronectin, immunoglobulin, and hemoglobin were isolated from porcine blood, and SOD was extracted. Trace pyrogallol self-oxidation method to determine SOD activity was modified by optimizing the volume of pyrogallol and SOD samples, reaction temperature, and buffer pH. The specific activity of SOD was determined with the optimized extraction conditions. [ Result] The improved experimental conditions of SOD activity detection were as follows： 7 pyrogallol （50 mmol/L）, 3 ml Tris-HCI （50 mmol/L, pH 8.2）, reactive temperature at 25（3, and 10 pl SOD sample solution. The specific activity of extracted SOD was 5 056 U/mg protein. [ Conclusion] Four kinds of bioactive substance can be isolated from porcine blood by modern biological engi- neering integration technology, and the extracted SOD has better activity.展开更多
An electrochemically reduced graphene oxide sample, ERGO_0.8v, was prepared by electrochemical reduction of graphene oxide （GO） at -0.8 V, which shows unique electrocatalytic activity toward tetracycline （TTC） det...An electrochemically reduced graphene oxide sample, ERGO_0.8v, was prepared by electrochemical reduction of graphene oxide （GO） at -0.8 V, which shows unique electrocatalytic activity toward tetracycline （TTC） detection compared to the ERGO-12v （GO applied to a negative potential of-1.2 V）, GO, chemically reduced GO （CRGO）-modified glassy carbon electrode （GC） and bare GC electrodes. The redox peaks of TTC on an ERGO-0.8v-modifled glass carbon electrode （GC/ERGO-0.8v） were within 0-0.5 V in a pH 3.0 buffer solution with the oxidation peak current correlating well with TTC concentration over a wide range from 0.1 to 160 mg/L Physical characterizations with Fourier transform infrared （FT-IR）, Raman, and X-ray photoelectron spectroscopies （XPS） demonstrated that the oxygen-containing functional groups on GO diminished after the electrochemical reduction at -0.8 V, yet still existed in large amounts, and the defect density changed as new sp2 domains were formed. These changes demonstrated that this adjustment in the number of oxygen-containing groups might be the main factor affecting the electrocatalytic behavior of ERGO. Additionally, the defect density and sp2 domains also exert a profound influence on this behavior. A possible mechanism for the TTC redox reaction at the GC/ERGO-0.8v electrode is also presented. This work suggests that the electrochemical reduction is an effective method to establish new catalytic activities of GO by setting appropriate parameters.展开更多
A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spect...A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function(PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.展开更多
In this work, a novel voice activity detection （VAD） algorithm that uses speech absence probability （SAP） based on Teager energy （TE） was proposed for speech enhancement. The proposed method employs local SAP （...In this work, a novel voice activity detection （VAD） algorithm that uses speech absence probability （SAP） based on Teager energy （TE） was proposed for speech enhancement. The proposed method employs local SAP （LSAP） based on the TE of noisy speech as a feature parameter for voice activity detection （VAD） in each frequency subband, rather than conventional LSAP. Results show that the TE operator can enhance the abiTity to discriminate speech and noise and further suppress noise components. Therefore, TE-based LSAP provides a better representation of LSAP, resulting in improved VAD for estimating noise power in a speech enhancement algorithm. In addition, the presented method utilizes TE-based global SAP （GSAP） derived in each frame as the weighting parameter for modifying the adopted TE operator and improving its performance. The proposed algorithm was evaluated by objective and subjective quality tests under various environments, and was shown to produce better results than the conventional method.展开更多
In this study,the theory of minimum detectable activity concentration(MDAC)for airborne gamma-ray spectrometry(AGS)was derived,and the relationship between the MDAC and the intrinsic effi-ciency of a scintillation cou...In this study,the theory of minimum detectable activity concentration(MDAC)for airborne gamma-ray spectrometry(AGS)was derived,and the relationship between the MDAC and the intrinsic effi-ciency of a scintillation counter,volume,and energy res-olution of scintillation crystals,and flight altitude of an aircraft was investigated.To verify this theory,experi-mental devices based on NaI and CeBr 3 scintillation counters were prepared,and the potassium,uranium,and thorium contents in calibration pads obtained via the stripping ratio method and theory were compared.The MDACs of AGS under different conditions were calculated and analyzed using the proposed theory and the Monte Carlo method.The relative errors found via a comparison of the experimental and theoretical results were less than 4%.The theory of MDAC can guide the work of AGS in probing areas with low radioactivity.展开更多
β-Glucosidase activity assays constitute an important indicator for the early diagnosis of neonatal necrotizing enterocolitis and qualitative changes in medicinal plants.The drawbacks of the existing methods are high...β-Glucosidase activity assays constitute an important indicator for the early diagnosis of neonatal necrotizing enterocolitis and qualitative changes in medicinal plants.The drawbacks of the existing methods are high consumption of both time and reagents,complexity in operation,and requirement of expensive instruments and highly trained personnel.The present study provides a simplified,highly selective,and miniaturized glucometer-based strategy for the detection ofβ-glucosidase activity.Single-factor experiments showed that optimumβ-glucosidase activity was exhibited at 50°C and pH 5.0 in a citric acid-sodium citrate buffer when reacting with 0.03 g/mL salicin for 30 min.The procedure for detection was simplified without the need of a chromogenic reaction.Validation of the analytical method demonstrated that the accuracy,precision,repeatability,stability,and durability were good.The linear ranges ofβ-glucosidase in a buffer solution and rat serum were 0.0873–1.5498 U/mL and 0.4076–2.9019 U/mL,respectively.The proposed method was free from interference fromβ-dextranase,snailase,β-galactosidase,hemicellulase,and glucuronic acid released by baicalin.This demonstrated that the proposed assay had a higher selectivity than the conventional dinitrosalicylic acid(DNS)assay because of the specificity for salicin and unique recognition of glucose by a personal glucose meter.Miniaturization of the method resulted in a microassay forβ-glucosidase activity.The easy-to-operate method was successfully used to detect a series ofβ-glucosidases extracted from bitter almonds and cultured by Aspergillus niger.In addition,the simplified and miniaturized glucometer-based assay has potential application in the point-of-care testing ofβ-glucosidase in many fields,including medical diagnostics,food safety,and environmental monitoring.展开更多
Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram o...Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.展开更多
The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,th...The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.展开更多
Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which ar...Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD progression.Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage.The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using wearables.In the proposed work,a publicly available dataset collected using wearables is applied.Currently,no real-world data is available to illustrate the daily activities of ADP.Hence,the proposed method has synthesized the wearables data according to the abnormal activities of ADP.In the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal activities.The results obtained show that the proposed MH architectures achieve improved performance than the conventional methods.Moreover,the MH models for activity recognition perform better than the abnormality detection.展开更多
文摘Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft.In this paper,the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed,and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed.By modeling the spatial distribution of the disturbance density field,the scattered echo signal images of active light towards the disturbance field at long distance are simulated.On this basis,the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed.The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images.The disturbance signal can be further highlighted by differential processing of the adjacent scattering images.As the distance behind the aircraft increases,the diffusion range of the disturbance signal increases,and the signal intensity and contrast with the background decrease.Under the ground-based observation conditions of the aircraft at a height of 10000 m,a Mach number of1.6,and a detection distance of 100 km,the contrast between the disturbance signal and the back-ground was 30 d B at a distance of one time from the rear of the fuselage,and the diffusion diameter of the disturbance signal was 50 m.At a distance eight times the length of the aircraft,the contrast decreased to 10 dB,and the diameter increased to 290 m.The contrast was reduced to 3 dB at a distance nine times the length of the aircraft,and the diameter was diffused to 310 m.These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.
文摘The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and robust approach is joint device activity detection and channel estimation.In this paper,we present an approach utilizing score-based generative models to address the underdetermined nature of channel estimation,which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems.Our experimental results,based on a comprehensive dataset generated through Monte-Carlo sampling,demonstrate the high precision of our channel estimation approach,with errors reduced to as low as-45 d B,and exceptional accuracy in detecting active devices.
基金supported by National Natural Science Foundation of China(NSFC)under Grant No.62001190The work of J.Wen was supported by NSFC(Nos.11871248,61932010,61932011)+3 种基金the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019),Guangdong Major Project of Basic and Applied Basic Research(2019B030302008)the Fundamental Research Funds for the Central Universities(No.21618329)The work of P.Fan was supported by National Key R&D Project(No.2018YFB1801104)NSFC Project(No.6202010600).
文摘This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.
文摘Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus.Limited studies have,however,reported on COVID-19 transmission pattern analysis,and using geography features for prediction of potential outbreak sites.Predicting the next most probable outbreak site is crucial,particularly for optimizing the planning of medical personnel and supply resources.To tackle the challenge,this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude,when would the outbreak likely to happen and the duration of the outbreak.The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia.The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19.Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases,next outbreak location,and the time interval between start dates of two similar sites.Such findings provided valuable insights for policymakers to perform Active Case Detection.
基金the National Natural Science Foundation of China(Nos.61071152 and 61271316)the National Basic Research Program (973) of China(Nos.2010CB731406 and 2013CB329605)the National "Twelfth Five-Year" Plan for Science&Technology Support(No.2012BAH38B04)
文摘Active tamper detection using watermarking technique can localize the tampered area and recover the lost information. In this paper, we propose an approach that the watermark is robust to legitimate lossy compression, fragile to malicious tampering and capable of recovery. We embed the watermark bits in the direct current value of energy concentration transform domain coefficients. Let the original watermark bits be content dependent and apply error correction coding to them before embedded to the image. While indicating the tam- pered area, the extracted bits from a suspicious image can be further decoded and then used to roughly recover the corresponding area. We also theoretically study the image quality and bit error rate. ExperimentM results demonstrate the effectiveness of the proposed scheme.
基金supported by the National Natural Science Foundation of China under Grant No.61671109.
文摘Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detection.In addition,multi-inverter networks connecting the distribution system point of common coupling(PCC)create islanding at paralleling inverters,which adds the vulnerability of islanding detection.Furthermore,available islanding detection must overcome more challenges from non-detection zones(NDZs)under reduced power mismatches.Therefore,in this study,a new method called parameter self-adapting active islanding detection was utilized to minimize the dilution effect and reduce NDZs in multi-inverter power systems.The method utilizes an active frequency drift(AFD)method and applies a positive feedback gain of adoption parameters,which significantly minimizes NDZs at parallel inverters.The simulation and experimental outcomes indicate that the proposed method can effectively weaken the dilution effect in multi-inverter networks connecting the distribution system PCC.
基金This work was supported by the National Key R&D Program of China(Nos.2022YFF0709503,2022YFB1902700,2017YFC0602101)the Key Research and Development Program of Sichuan province(No.2023YFG0347)the Key Research and Development Program of Sichuan province(No.2020ZDZX0007).
文摘To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.
基金Sichuan Province Medical Research Project Plan(Project No.S21113)。
文摘This study explores the diagnostic value of combining the Padua score with the thrombotic biomarker tissue plasminogen activator inhibitor-1(tPAI-1)for assessing the risk of deep vein thrombosis(DVT)in patients with pulmonary heart disease.These patients often exhibit symptoms similar to venous thrombosis,such as dyspnea and bilateral lower limb swelling,complicating differential diagnosis.The Padua Prediction Score assesses the risk of venous thromboembolism(VTE)in hospitalized patients,while tPAI-1,a key fibrinolytic system inhibitor,indicates a hypercoagulable state.Clinical data from hospitalized patients with cor pulmonale were retrospectively analyzed.ROC curves compared the diagnostic value of the Padua score,tPAI-1 levels,and their combined model for predicting DVT risk.Results showed that tPAI-1 levels were significantly higher in DVT patients compared to non-DVT patients.The Padua score demonstrated a sensitivity of 82.61%and a specificity of 55.26%at a cutoff value of 3.The combined model had a significantly higher AUC than the Padua score alone,indicating better discriminatory ability in diagnosing DVT risk.The combination of the Padua score and tPAI-1 detection significantly improves the accuracy of diagnosing DVT risk in patients with pulmonary heart disease,reducing missed and incorrect diagnoses.This study provides a comprehensive assessment tool for clinicians,enhancing the diagnosis and treatment of patients with cor pulmonale complicated by DVT.Future research should validate these findings in larger samples and explore additional thrombotic biomarkers to optimize the predictive model.
基金supported in part by the National Natural Science Foundation of China(Nos.62273203 and U1813215)in part by the Special Fund for the Taishan Scholars Program of Shandong Province,China(No.ts2015110005).
文摘After detecting a target object,a service robot must approach the target object to perform the associated service task.In active object detection(AOD)tasks,effective feature information representation and comprehensive action execution strategies are crucial.Currently,most AOD tasks are accomplished by traditional reinforcement learning algorithms,but there are still problems such as high task failure rates and model training efficiency.To solve these problems,this paper proposes a combined data-driven and knowledge-guided solution.First,semantic information features,depth information features and target object bounding box information are used as inputs to comprehensively represent feature information.Second,a policy network is constructed based on the proximal policy optimizaton(PPO)algorithm.The reward value is set according to the robot′s action,the position of the bounding box,and the distance to the target object,and then applied to the robot′s training process.Finally,the knowledge of the path experience in the task,the robot′s collision avoidance ability and the prediction of target object loss are combined to guide the robot′s behavior,and a comprehensive decision model is proposed to enable the robot to make the best decision.Relevant experiments were conducted on an active vision dataset.The robot achieves an average success rate of 91.36%and an average step size of 9.3631 in performing the AOD task in the test scenes,which verifies the effectiveness of the proposed scheme.
基金National Natural Science Foundation of China,Grant No.62273203National Key R&D Program of China,Grant No.2018YFB1307101Taishan Scholars Program of Shandong Province,Grant No.ts201511005.
文摘Active object detection(AOD)is a crucial task in the field of robotics.A key challenge in household environments for AOD is that the target object is often undetectable due to partial occlusion,which leads to the failure of traditional methods.To address the occlusion problem,this paper first proposes a novel occlusion handling method based on the large multimodal model(LMM).The method utilises an LMM to detect and analyse input RGB images and generates adjustment actions to progressively eliminate occlusion.After the occlusion is handled,an improved AOD method based on a deep Q-learning network(DQN)is used to complete the task.We introduce an attention mechanism to process image features,enabling the model to focus on critical regions of the input images.Additionally,a new reward function is proposed that comprehensively considers the bounding box of the target object and the robot's distance to the object,along with the actions performed by the robot.Ex-periments on the dataset and in real-world scenarios validate the effectiveness of the proposed method in performing AOD tasks under partial occlusion.
基金supported by the Guangdong Basic and Applied Basic Research Foundation,China(No.2022A1515140074)and the Natural Science Foundation of Liaoning Province,China(No.2023-MS-108)。
文摘Sparsity-based joint active user detection and channel estimation(JADCE)algorithms are crucial in grant-free massive machine-type communication(mMTC)systems.The conventional compressed sensing algorithms are tailored for noncoherent communication systems,where the correlation between any two measurements is as minimal as possible.However,existing sparsity-based JADCE approaches may not achieve optimal performance in strongly coherent systems,especially with a small number of pilot subcarriers.To tackle this challenge,we formulate JADCE as a joint sparse signal recovery problem,leveraging the block-type row-sparse structure of millimeter-wave(mmWave)channels in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMOOFDM)systems.Then,we propose an efficient difference-of-convex function algorithm(DCA)based JADCE algorithm with multiple measurement vector(MMV)frameworks,promoting the row-sparsity of the channel matrix.To mitigate the computational complexity further,we introduce a fast DCA-based JADCE algorithm via a proximal operator,which allows a low-complexity alternating direction multiplier method(ADMM)to resolve the optimization problem directly.Finally,simulation results demonstrate that the two proposed difference-of-convex(DC)algorithms achieve effective active user detection and accurate channel estimation compared with state-of-the-art compressed sensing based JADCE techniques.
文摘[Objective] TO study the combined extracting process of porcine blood superoxide dismutase （SOD） and other bioactive substances and thus to provide technical basis for making full use of blood resources and large-scale production of SOD. [Method] Fibronectin, immunoglobulin, and hemoglobin were isolated from porcine blood, and SOD was extracted. Trace pyrogallol self-oxidation method to determine SOD activity was modified by optimizing the volume of pyrogallol and SOD samples, reaction temperature, and buffer pH. The specific activity of SOD was determined with the optimized extraction conditions. [ Result] The improved experimental conditions of SOD activity detection were as follows： 7 pyrogallol （50 mmol/L）, 3 ml Tris-HCI （50 mmol/L, pH 8.2）, reactive temperature at 25（3, and 10 pl SOD sample solution. The specific activity of extracted SOD was 5 056 U/mg protein. [ Conclusion] Four kinds of bioactive substance can be isolated from porcine blood by modern biological engi- neering integration technology, and the extracted SOD has better activity.
基金supported by the National Natural Science Foundation of China(21007033)the Fundamental Research Funds of Shandong University(2015JC017)~~
文摘An electrochemically reduced graphene oxide sample, ERGO_0.8v, was prepared by electrochemical reduction of graphene oxide （GO） at -0.8 V, which shows unique electrocatalytic activity toward tetracycline （TTC） detection compared to the ERGO-12v （GO applied to a negative potential of-1.2 V）, GO, chemically reduced GO （CRGO）-modified glassy carbon electrode （GC） and bare GC electrodes. The redox peaks of TTC on an ERGO-0.8v-modifled glass carbon electrode （GC/ERGO-0.8v） were within 0-0.5 V in a pH 3.0 buffer solution with the oxidation peak current correlating well with TTC concentration over a wide range from 0.1 to 160 mg/L Physical characterizations with Fourier transform infrared （FT-IR）, Raman, and X-ray photoelectron spectroscopies （XPS） demonstrated that the oxygen-containing functional groups on GO diminished after the electrochemical reduction at -0.8 V, yet still existed in large amounts, and the defect density changed as new sp2 domains were formed. These changes demonstrated that this adjustment in the number of oxygen-containing groups might be the main factor affecting the electrocatalytic behavior of ERGO. Additionally, the defect density and sp2 domains also exert a profound influence on this behavior. A possible mechanism for the TTC redox reaction at the GC/ERGO-0.8v electrode is also presented. This work suggests that the electrochemical reduction is an effective method to establish new catalytic activities of GO by setting appropriate parameters.
基金supported by the KERI Primary Research Program through the Korea Research Council for Industrial Science & Technology funded by the Ministry of Science,ICT and Future Planning (No.15-12-N0101-46)
文摘A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function(PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.
基金Project supported by Inha University Research GrantProject(10031764) supported by the Strategic Technology Development Program of Ministry of Knowledge Economy, Korea
文摘In this work, a novel voice activity detection （VAD） algorithm that uses speech absence probability （SAP） based on Teager energy （TE） was proposed for speech enhancement. The proposed method employs local SAP （LSAP） based on the TE of noisy speech as a feature parameter for voice activity detection （VAD） in each frequency subband, rather than conventional LSAP. Results show that the TE operator can enhance the abiTity to discriminate speech and noise and further suppress noise components. Therefore, TE-based LSAP provides a better representation of LSAP, resulting in improved VAD for estimating noise power in a speech enhancement algorithm. In addition, the presented method utilizes TE-based global SAP （GSAP） derived in each frame as the weighting parameter for modifying the adopted TE operator and improving its performance. The proposed algorithm was evaluated by objective and subjective quality tests under various environments, and was shown to produce better results than the conventional method.
基金This work was supported by the Sichuan Science and Technology Program(No.2020JDRC0108)the National Science Foundation of China(Nos.41774147 and 41774190).
文摘In this study,the theory of minimum detectable activity concentration(MDAC)for airborne gamma-ray spectrometry(AGS)was derived,and the relationship between the MDAC and the intrinsic effi-ciency of a scintillation counter,volume,and energy res-olution of scintillation crystals,and flight altitude of an aircraft was investigated.To verify this theory,experi-mental devices based on NaI and CeBr 3 scintillation counters were prepared,and the potassium,uranium,and thorium contents in calibration pads obtained via the stripping ratio method and theory were compared.The MDACs of AGS under different conditions were calculated and analyzed using the proposed theory and the Monte Carlo method.The relative errors found via a comparison of the experimental and theoretical results were less than 4%.The theory of MDAC can guide the work of AGS in probing areas with low radioactivity.
基金Project supported by the National Natural Science Foundation of China(No.81173561)the Program of Shanghai Academic/Technology Research Leader(No.18XD1403700)China
文摘β-Glucosidase activity assays constitute an important indicator for the early diagnosis of neonatal necrotizing enterocolitis and qualitative changes in medicinal plants.The drawbacks of the existing methods are high consumption of both time and reagents,complexity in operation,and requirement of expensive instruments and highly trained personnel.The present study provides a simplified,highly selective,and miniaturized glucometer-based strategy for the detection ofβ-glucosidase activity.Single-factor experiments showed that optimumβ-glucosidase activity was exhibited at 50°C and pH 5.0 in a citric acid-sodium citrate buffer when reacting with 0.03 g/mL salicin for 30 min.The procedure for detection was simplified without the need of a chromogenic reaction.Validation of the analytical method demonstrated that the accuracy,precision,repeatability,stability,and durability were good.The linear ranges ofβ-glucosidase in a buffer solution and rat serum were 0.0873–1.5498 U/mL and 0.4076–2.9019 U/mL,respectively.The proposed method was free from interference fromβ-dextranase,snailase,β-galactosidase,hemicellulase,and glucuronic acid released by baicalin.This demonstrated that the proposed assay had a higher selectivity than the conventional dinitrosalicylic acid(DNS)assay because of the specificity for salicin and unique recognition of glucose by a personal glucose meter.Miniaturization of the method resulted in a microassay forβ-glucosidase activity.The easy-to-operate method was successfully used to detect a series ofβ-glucosidases extracted from bitter almonds and cultured by Aspergillus niger.In addition,the simplified and miniaturized glucometer-based assay has potential application in the point-of-care testing ofβ-glucosidase in many fields,including medical diagnostics,food safety,and environmental monitoring.
基金The National Natural Science Foundation of China(No.12174053,91938203,11674057,11874109)the Fundamental Research Funds for the Central Universities(No.2242021k30019).
文摘Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.
基金supported by National Key Research and Development Program of China under Grants 2021YFB1600500,2021YFB3201502,and 2022YFB3207704Natural Science Foundation of China(NSFC)under Grants U2233216,62071044,61827901,62088101 and 62201056+1 种基金supported by Shandong Province Natural Science Foundation under Grant ZR2022YQ62supported by Beijing Nova Program,Beijing Institute of Technology Research Fund Program for Young Scholars under grant XSQD-202121009.
文摘The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.
文摘Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD progression.Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage.The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using wearables.In the proposed work,a publicly available dataset collected using wearables is applied.Currently,no real-world data is available to illustrate the daily activities of ADP.Hence,the proposed method has synthesized the wearables data according to the abnormal activities of ADP.In the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal activities.The results obtained show that the proposed MH architectures achieve improved performance than the conventional methods.Moreover,the MH models for activity recognition perform better than the abnormality detection.
