Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibilit...Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.展开更多
Phased array radar has been applied broadly because of its sound performance.But signal of phased array radar is of a wide variety of types.Therefore,recognition of phased array radar is the most puzzling aspect of th...Phased array radar has been applied broadly because of its sound performance.But signal of phased array radar is of a wide variety of types.Therefore,recognition of phased array radar is the most puzzling aspect of the whole emitter identification domain.To solve the problem,the article proposes the method that identifies phased array radar by pulse amplitude information,and studies the phased array radar,models transmit signal of them,and receiving signal by radar countermeasure reconnaissance receiver.From constructing template of pulse train's amplitude vector of mechanical scanning radar,computing distance of samples and standard template,finding threshold of the template matching arithmetic,the article puts forward the template matching algorithm of radar beam scan type recognition to identify phased array radar automatically.展开更多
Template matching is a useful method to detect seismic events through waveform similarity between two signals.The traditional template matching method works well in detecting small tectonic earthquakes.However,the met...Template matching is a useful method to detect seismic events through waveform similarity between two signals.The traditional template matching method works well in detecting small tectonic earthquakes.However,the method has some difficulty when the signals have relatively low signal-to-noise ratios(SNRs)and simple shapes,e.g.a sinusoidal function.In this study,we modify the traditional template matching approach for this situation.We first construct a virtual three-component seismic station using vertical-component waveforms recorded by three stations.Next,we select a template event from the virtual station,and apply the traditional template matching.We then verify this method by detecting icequakes with simple waveforms on the Urumqi Glacier No.1 and compare the results with those from the short-term-averages over long-term-average(STA/LTA),the REST method,and traditional template matching method.It can be concluded that the modified template matching method using virtual stations has some advantages for seismic data with low SNRs.展开更多
In this paper, a template matching and location method, which has been rapidly adopted in microseismic research in recent years, is applied to laboratory acoustic emission(AE) monitoring. First, we used traditional me...In this paper, a template matching and location method, which has been rapidly adopted in microseismic research in recent years, is applied to laboratory acoustic emission(AE) monitoring. First, we used traditional methods to detect P-wave first motions and locate AE hypocenters in three dimensions. In addition, we selected events located with sufficient accuracy(normally corresponding AE events of relatively larger energy, showing clear P-wave first motion and a higher signal-to-noise ratio in most channels) as template events. Then, the template events were used to scan and match other poorly located events in triggered event records or weak events in continuous records. Through crosscorrelation of the multi-channel waveforms between the template and the event to be detected, the weak signal was detected and located using a grid-searching algorithm(with the grid centered at the template hypocenter). In order to examine the performance of the approach, we calibrated the proposed method using experimental data of different rocks and different types of experiments. The results show that the proposed method can significantly improve the detection capability and location accuracy, and can be applied to various laboratory and in situ experiments, which use multi-channel AE monitoring with waveforms recorded in either triggering or continuous mode.展开更多
NIST(National Institute of Standards and Technology) statistical test recognized as the most authoritative is widely used in verifying the randomness of binary sequences. The Non-overlapping Template Matching Test as ...NIST(National Institute of Standards and Technology) statistical test recognized as the most authoritative is widely used in verifying the randomness of binary sequences. The Non-overlapping Template Matching Test as the 7 th test of the NIST Test Suit is remarkably time consuming and the slow performance is one of the major hurdles in the testing process. In this paper, we present an efficient bit-parallel matching algorithm and segmented scan-based strategy for execution on Graphics Processing Unit(GPU) using NVIDIA Compute Unified Device Architecture(CUDA). Experimental results show the significant performance improvement of the parallelized Non-overlapping Template Matching Test, the running speed is 483 times faster than the original NIST implementation without attenuating the test result accuracy.展开更多
Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious individuals.The accuracy of HAR systems is completely reliant on the extraction of promi...Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious individuals.The accuracy of HAR systems is completely reliant on the extraction of prominent features.Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition performance.In this paper,we propose a robust feature extraction method for HAR systems based on template matching.Essentially,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette.In this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the frame.This process is replicated for the whole frame,and the pixel is directed to the optimum match.The best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the frame.In this way,the feature vector is generated.After feature vector generation,the hiddenMarkovmodel(HMM)has been utilized to label the incoming activity.We utilized different publicly available standard datasets for experiments.The proposed method achieved the best accuracy against existing state-of-the-art systems.展开更多
In robot binaural sound source localization(SSL),locating the direction of the sound source accurately in the shortest time is important.It refers to the algorithm complexity,but even more to the shortest duration of ...In robot binaural sound source localization(SSL),locating the direction of the sound source accurately in the shortest time is important.It refers to the algorithm complexity,but even more to the shortest duration of the required signal.A novel binaural SSL method based on feature and frequency weighting is proposed.More specifically,in the training stage,the direction-related interaural cross-correlation function(CCF)and interaural intensity difference(IID)in each frequency band are calculated under noiseless conditions,which are considered the templates.In the testing stage,first the cosine similarities between the CCF and IID of the test signal and templates are calculated in all features and frequency bands.Then,the direction likelihood can be obtained by weighting the similarities.Finally,the direction with maximum likelihood is specified as the direction of the sound source.Experiments were carried out on CIPIC dataset subject 003 with different noises in the noisex-92 dataset and demonstrated that the method can accurately locate the sound source with a short signal duration.展开更多
For non-cooperative communication, the symbol-rate estimation of digital communication signal is an important problem to be solved. In this letter, A new algorithm for the symbol-rate estimation of single-tone digital...For non-cooperative communication, the symbol-rate estimation of digital communication signal is an important problem to be solved. In this letter, A new algorithm for the symbol-rate estimation of single-tone digitally modulated signal (i.e. MPSK/QAM) is proposed. Firstly a section from the received signal is cut as the template, and then the signal is matched sectionwise by making use of the signal selfsimilarity. So a signal con- taining the information of symbol jumping is got, and the symbol-rate can be estimated by DFT (Discrete Fou- rier Transformation). The validity of the new method has been verified by experiments.展开更多
To tackle the problem of severe occlusions in visual tracking, we propose a hierarchical template-matching method based on a layered appearance model. This model integrates holisticand part-region matching in order to...To tackle the problem of severe occlusions in visual tracking, we propose a hierarchical template-matching method based on a layered appearance model. This model integrates holisticand part-region matching in order to locate an object in a coarse-to-fine manner. Furthermore, in order to reduce ambiguity in object localization, only the discriminative parts of an object's appearance template are chosen for similarity computing with respect to their cornerness measurements. The similarity between parts is computed in a layer-wise manner, and from this, occlusions can be evaluated. When the object is partly occluded, it can be located accurately by matching candidate regions with the appearance template. When it is completely occluded, its location can be predicted from its historical motion information using a Kalman filter. The proposed tracker is tested on several practical image sequences, and the experimental results show that it can consistently provide accurate object location for stable tracking, even for severe occlusions.展开更多
A spatiotemporal atlas refers to a standard image sequence that represents the general motion pattern of the targeted anatomy across a group of subjects. Recent years have witnessed an increasing interest in using spa...A spatiotemporal atlas refers to a standard image sequence that represents the general motion pattern of the targeted anatomy across a group of subjects. Recent years have witnessed an increasing interest in using spatiotemporal atlas for scientific research and clinical applications in image processing, data analysis and medical imaging. However, the generation of spatiotemporal atlas is often time-consuming and computationally expensive due to the nonlinear image registration procedures involved. This research targets at accelerating the generation of spatiotemporal atlas by formulating the atlas generation procedure as a multi-level modulation (M-ary) classification problem. In particular, we have implemented a fast template matching method based on singular value decomposition, and applied it to generate high quality spatiotemporal atlas with reasonable time and computational complexity. The performance has been systematically evaluated on public accessible data sets. The results and conclusions hold promise for further developing advanced algorithms for accelerating generation of spatiotemporal atlas.展开更多
This paper represents a template matching using statistical model and parametric template for multi-template. This algorithm consists of two phases: training and matching phases. In the training phase, the statistical...This paper represents a template matching using statistical model and parametric template for multi-template. This algorithm consists of two phases: training and matching phases. In the training phase, the statistical model created by principal component analysis method (PCA) can be used to synthesize multi-template. The advantage of PCA is to reduce the variances of multi-template. In the matching phase, the normalized cross correlation (NCC) is employed to find the candidates in inspection images. The relationship between image block and multi-template is built to use parametric template method. Results show that the proposed method is more efficient than the conventional template matching and parametric template. Furthermore, the proposed method is more robust than conventional template method.展开更多
Template matching is a fundamental problem in pattern recognition, which has wide applications, especially in industrial inspection. In this paper, we propose a 1-D template matching algorithm which is an alternative ...Template matching is a fundamental problem in pattern recognition, which has wide applications, especially in industrial inspection. In this paper, we propose a 1-D template matching algorithm which is an alternative for 2-D full search block matching algorithms. Our approach consists of three steps. In the first step the images are converted from 2-D into 1-D by summing up the intensity values of the image in two directions horizontal and vertical. In the second step, the template matching is performed among 1-D vectors using the similarity function sum of square difference. Finally, the decision will be taken based on the value of similarity function. Transformation template image and sub-images in the source image from 2-D grey level information into 1-D information vector reduce the dimensionality of the data and accelerate the computations. Experimental results show that the computational time of the proposed approach is faster and performance is better than three basic template matching methods. Moreover, our approach is robust to detect the target object with changes of illumination in the template also when the Gaussian noise added to the source image.展开更多
The following article has been retracted due to special reason of the authors. This paper published in Vol.5 No. 2, 2014, has been removed from this site. Title: Template Matching from 2-D into 1-D Author: Yasser
Purpose-Steady-state visual evoked potential(SSVEP)has been widely used in the application of electroencephalogram(EEG)based non-invasive brain computer interface(BCI)due to its characteristics of high accuracy and in...Purpose-Steady-state visual evoked potential(SSVEP)has been widely used in the application of electroencephalogram(EEG)based non-invasive brain computer interface(BCI)due to its characteristics of high accuracy and information transfer rate(ITR).To recognize the SSVEP components in collected EEG trials,a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years.In this paper,a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.Design/methodology/approach-To survey and compare the recently proposed recognition algorithms for SSVEP,this paper regarded the conventional canonical correlated analysis(CCA)as the baseline,and selected individual template CCA(ITCCA),multi-set CCA(MsetCCA),task related component analysis(TRCA),latent common source extraction(LCSE)and a sum of squared correlation(SSCOR)for comparison.Findings-For the horizontal comparative of the six surveyed recognition algorithms,this paper adopted the“Tsinghua JFPM-SSVEP”data set and compared the average recognition performance on such data set.The comparative contents including:recognition accuracy,ITR,correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation.Based on the optimal time duration of stimulus presentation,the author has also compared the efficiency of the six compared algorithms.To measure the influence of different parameters,the number of training trials,the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.Originality/value-Based on the comparative results,this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes,realtime and computational complexity.Finally,the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.展开更多
Analysis and recognition of ancient scripts is a challenging task as these scripts are inscribed on pillars,stones,or leaves.Optical recognition systems can help in preserving,sharing,and accelerate the study of the a...Analysis and recognition of ancient scripts is a challenging task as these scripts are inscribed on pillars,stones,or leaves.Optical recognition systems can help in preserving,sharing,and accelerate the study of the ancient scripts,but lack of standard dataset for such scripts is a major constraint.Although many scholars and researchers have captured and uploaded inscription images on various websites,manual searching,downloading and extraction of these images is tedious and error prone.Web search queries return a vast number of irrelevant results,and manually extracting images for a specific script is not scalable.This paper proposes a novelmultistage system to identify the specific set of script images from a large set of images downloaded from web sources.The proposed system combines the two most important pattern matching techniques-Scale Invariant Feature Transform(SIFT)and Template matching,in a sequential pipeline,and by using the key strengths of each technique,the system can discard irrelevant images while retaining a specific type of images.展开更多
Transition metal phosphides(TMPs)have been regarded as alternative hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)catalysts owing to their comparable activity to those of noble metal-based catalysts...Transition metal phosphides(TMPs)have been regarded as alternative hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)catalysts owing to their comparable activity to those of noble metal-based catalysts.TMPs have been produced in various morphologies,including hollow and porous nanostructures,which are features deemed desirable for electrocatalytic materials.Templated synthesis routes are often responsible for such morphologies.This paper reviews the latest advances and existing challenges in the synthesis of TMP-based OER and HER catalysts through templated methods.A comprehensive review of the structure-property-performance of TMP-based HER and OER catalysts prepared using different templates is presented.The discussion proceeds according to application,first by HER and further divided among the types of templates used-from hard templates,sacrificial templates,and soft templates to the emerging dynamic hydrogen bubble template.OER catalysts are then reviewed and grouped according to their morphology.Finally,prospective research directions for the synthesis of hollow and porous TMP-based catalysts,such as improvements on both activity and stability of TMPs,design of environmentally benign templates and processes,and analysis of the reaction mechanism through advanced material characterization techniques and theoretical calculations,are suggested.展开更多
Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier ...Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.展开更多
Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle(UAV)edge computing.However,the heterogeneity of UAV computation resource,and the task re-allocating between UAVs have not be...Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle(UAV)edge computing.However,the heterogeneity of UAV computation resource,and the task re-allocating between UAVs have not been fully considered yet.Moreover,most existing works neglect the fact that a task can only be executed on the UAV equipped with its desired service function(SF).In this backdrop,this paper formulates the task scheduling problem as a multi-objective task scheduling problem,which aims at maximizing the task execution success ratio while minimizing the average weighted sum of all tasks’completion time and energy consumption.Optimizing three coupled goals in a realtime manner with the dynamic arrival of tasks hinders us from adopting existing methods,like machine learning-based solutions that require a long training time and tremendous pre-knowledge about the task arrival process,or heuristic-based ones that usually incur a long decision-making time.To tackle this problem in a distributed manner,we establish a matching theory framework,in which three conflicting goals are treated as the preferences of tasks,SFs and UAVs.Then,a Distributed Matching Theory-based Re-allocating(DiMaToRe)algorithm is put forward.We formally proved that a stable matching can be achieved by our proposal.Extensive simulation results show that Di Ma To Re algorithm outperforms benchmark algorithms under diverse parameter settings and has good robustness.展开更多
Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same g...Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.展开更多
基金supported by a grant from the Basic Science Research Program through the National Research Foundation(NRF)(2021R1F1A1063634)funded by the Ministry of Science and ICT(MSIT),Republic of KoreaThe authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Group Funding Program Grant Code(NU/RG/SERC/13/40)+2 种基金Also,the authors are thankful to Prince Satam bin Abdulaziz University for supporting this study via funding from Prince Satam bin Abdulaziz University project number(PSAU/2024/R/1445)This work was also supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R54)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.
基金Supported by the National Science and Technology Supported Program of China(No.2011BAH24B06)
文摘Phased array radar has been applied broadly because of its sound performance.But signal of phased array radar is of a wide variety of types.Therefore,recognition of phased array radar is the most puzzling aspect of the whole emitter identification domain.To solve the problem,the article proposes the method that identifies phased array radar by pulse amplitude information,and studies the phased array radar,models transmit signal of them,and receiving signal by radar countermeasure reconnaissance receiver.From constructing template of pulse train's amplitude vector of mechanical scanning radar,computing distance of samples and standard template,finding threshold of the template matching arithmetic,the article puts forward the template matching algorithm of radar beam scan type recognition to identify phased array radar automatically.
基金financially supported by the National Key R&D Program of China(No.2018YFC1504200)the LU JIAXI International Team Program from the KC Wong Education Foundation and CAS(No.GJTD-2018-12)National Natural Science Foundation of China(Nos.41661164035 and 41704066).
文摘Template matching is a useful method to detect seismic events through waveform similarity between two signals.The traditional template matching method works well in detecting small tectonic earthquakes.However,the method has some difficulty when the signals have relatively low signal-to-noise ratios(SNRs)and simple shapes,e.g.a sinusoidal function.In this study,we modify the traditional template matching approach for this situation.We first construct a virtual three-component seismic station using vertical-component waveforms recorded by three stations.Next,we select a template event from the virtual station,and apply the traditional template matching.We then verify this method by detecting icequakes with simple waveforms on the Urumqi Glacier No.1 and compare the results with those from the short-term-averages over long-term-average(STA/LTA),the REST method,and traditional template matching method.It can be concluded that the modified template matching method using virtual stations has some advantages for seismic data with low SNRs.
基金funding support from Grant-in-Aid for Scientific Research(Grant No.19H00722)by Japan Society for the Promotion of Science(JSPS)。
文摘In this paper, a template matching and location method, which has been rapidly adopted in microseismic research in recent years, is applied to laboratory acoustic emission(AE) monitoring. First, we used traditional methods to detect P-wave first motions and locate AE hypocenters in three dimensions. In addition, we selected events located with sufficient accuracy(normally corresponding AE events of relatively larger energy, showing clear P-wave first motion and a higher signal-to-noise ratio in most channels) as template events. Then, the template events were used to scan and match other poorly located events in triggered event records or weak events in continuous records. Through crosscorrelation of the multi-channel waveforms between the template and the event to be detected, the weak signal was detected and located using a grid-searching algorithm(with the grid centered at the template hypocenter). In order to examine the performance of the approach, we calibrated the proposed method using experimental data of different rocks and different types of experiments. The results show that the proposed method can significantly improve the detection capability and location accuracy, and can be applied to various laboratory and in situ experiments, which use multi-channel AE monitoring with waveforms recorded in either triggering or continuous mode.
基金supported in part by Shanxi Scholarship Council of China(Grant No.2017-key-2)the Natural Science Foundation of Shanxi Province(Grant No.201801D121145)+1 种基金the Natural Science Foundation of China(NSFC)(Grant No.61731014,61705157,61927811)the Program for Guangdong Introducing Innovative and Entrepreneurial Teams。
文摘NIST(National Institute of Standards and Technology) statistical test recognized as the most authoritative is widely used in verifying the randomness of binary sequences. The Non-overlapping Template Matching Test as the 7 th test of the NIST Test Suit is remarkably time consuming and the slow performance is one of the major hurdles in the testing process. In this paper, we present an efficient bit-parallel matching algorithm and segmented scan-based strategy for execution on Graphics Processing Unit(GPU) using NVIDIA Compute Unified Device Architecture(CUDA). Experimental results show the significant performance improvement of the parallelized Non-overlapping Template Matching Test, the running speed is 483 times faster than the original NIST implementation without attenuating the test result accuracy.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this work through the Project Number“375213500”.
文摘Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious individuals.The accuracy of HAR systems is completely reliant on the extraction of prominent features.Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition performance.In this paper,we propose a robust feature extraction method for HAR systems based on template matching.Essentially,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette.In this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the frame.This process is replicated for the whole frame,and the pixel is directed to the optimum match.The best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the frame.In this way,the feature vector is generated.After feature vector generation,the hiddenMarkovmodel(HMM)has been utilized to label the incoming activity.We utilized different publicly available standard datasets for experiments.The proposed method achieved the best accuracy against existing state-of-the-art systems.
文摘In robot binaural sound source localization(SSL),locating the direction of the sound source accurately in the shortest time is important.It refers to the algorithm complexity,but even more to the shortest duration of the required signal.A novel binaural SSL method based on feature and frequency weighting is proposed.More specifically,in the training stage,the direction-related interaural cross-correlation function(CCF)and interaural intensity difference(IID)in each frequency band are calculated under noiseless conditions,which are considered the templates.In the testing stage,first the cosine similarities between the CCF and IID of the test signal and templates are calculated in all features and frequency bands.Then,the direction likelihood can be obtained by weighting the similarities.Finally,the direction with maximum likelihood is specified as the direction of the sound source.Experiments were carried out on CIPIC dataset subject 003 with different noises in the noisex-92 dataset and demonstrated that the method can accurately locate the sound source with a short signal duration.
文摘For non-cooperative communication, the symbol-rate estimation of digital communication signal is an important problem to be solved. In this letter, A new algorithm for the symbol-rate estimation of single-tone digitally modulated signal (i.e. MPSK/QAM) is proposed. Firstly a section from the received signal is cut as the template, and then the signal is matched sectionwise by making use of the signal selfsimilarity. So a signal con- taining the information of symbol jumping is got, and the symbol-rate can be estimated by DFT (Discrete Fou- rier Transformation). The validity of the new method has been verified by experiments.
基金supported by the Aeronautical Science Foundation of China under Grant 20115169016supported in part by the technique cooperation project of ZTE on Intelligent Video Analysis in 2012
文摘To tackle the problem of severe occlusions in visual tracking, we propose a hierarchical template-matching method based on a layered appearance model. This model integrates holisticand part-region matching in order to locate an object in a coarse-to-fine manner. Furthermore, in order to reduce ambiguity in object localization, only the discriminative parts of an object's appearance template are chosen for similarity computing with respect to their cornerness measurements. The similarity between parts is computed in a layer-wise manner, and from this, occlusions can be evaluated. When the object is partly occluded, it can be located accurately by matching candidate regions with the appearance template. When it is completely occluded, its location can be predicted from its historical motion information using a Kalman filter. The proposed tracker is tested on several practical image sequences, and the experimental results show that it can consistently provide accurate object location for stable tracking, even for severe occlusions.
文摘A spatiotemporal atlas refers to a standard image sequence that represents the general motion pattern of the targeted anatomy across a group of subjects. Recent years have witnessed an increasing interest in using spatiotemporal atlas for scientific research and clinical applications in image processing, data analysis and medical imaging. However, the generation of spatiotemporal atlas is often time-consuming and computationally expensive due to the nonlinear image registration procedures involved. This research targets at accelerating the generation of spatiotemporal atlas by formulating the atlas generation procedure as a multi-level modulation (M-ary) classification problem. In particular, we have implemented a fast template matching method based on singular value decomposition, and applied it to generate high quality spatiotemporal atlas with reasonable time and computational complexity. The performance has been systematically evaluated on public accessible data sets. The results and conclusions hold promise for further developing advanced algorithms for accelerating generation of spatiotemporal atlas.
文摘This paper represents a template matching using statistical model and parametric template for multi-template. This algorithm consists of two phases: training and matching phases. In the training phase, the statistical model created by principal component analysis method (PCA) can be used to synthesize multi-template. The advantage of PCA is to reduce the variances of multi-template. In the matching phase, the normalized cross correlation (NCC) is employed to find the candidates in inspection images. The relationship between image block and multi-template is built to use parametric template method. Results show that the proposed method is more efficient than the conventional template matching and parametric template. Furthermore, the proposed method is more robust than conventional template method.
文摘Template matching is a fundamental problem in pattern recognition, which has wide applications, especially in industrial inspection. In this paper, we propose a 1-D template matching algorithm which is an alternative for 2-D full search block matching algorithms. Our approach consists of three steps. In the first step the images are converted from 2-D into 1-D by summing up the intensity values of the image in two directions horizontal and vertical. In the second step, the template matching is performed among 1-D vectors using the similarity function sum of square difference. Finally, the decision will be taken based on the value of similarity function. Transformation template image and sub-images in the source image from 2-D grey level information into 1-D information vector reduce the dimensionality of the data and accelerate the computations. Experimental results show that the computational time of the proposed approach is faster and performance is better than three basic template matching methods. Moreover, our approach is robust to detect the target object with changes of illumination in the template also when the Gaussian noise added to the source image.
文摘The following article has been retracted due to special reason of the authors. This paper published in Vol.5 No. 2, 2014, has been removed from this site. Title: Template Matching from 2-D into 1-D Author: Yasser
基金supported by National Natural Science Foundation of China(Grant No.62106049).
文摘Purpose-Steady-state visual evoked potential(SSVEP)has been widely used in the application of electroencephalogram(EEG)based non-invasive brain computer interface(BCI)due to its characteristics of high accuracy and information transfer rate(ITR).To recognize the SSVEP components in collected EEG trials,a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years.In this paper,a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.Design/methodology/approach-To survey and compare the recently proposed recognition algorithms for SSVEP,this paper regarded the conventional canonical correlated analysis(CCA)as the baseline,and selected individual template CCA(ITCCA),multi-set CCA(MsetCCA),task related component analysis(TRCA),latent common source extraction(LCSE)and a sum of squared correlation(SSCOR)for comparison.Findings-For the horizontal comparative of the six surveyed recognition algorithms,this paper adopted the“Tsinghua JFPM-SSVEP”data set and compared the average recognition performance on such data set.The comparative contents including:recognition accuracy,ITR,correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation.Based on the optimal time duration of stimulus presentation,the author has also compared the efficiency of the six compared algorithms.To measure the influence of different parameters,the number of training trials,the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.Originality/value-Based on the comparative results,this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes,realtime and computational complexity.Finally,the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.
文摘Analysis and recognition of ancient scripts is a challenging task as these scripts are inscribed on pillars,stones,or leaves.Optical recognition systems can help in preserving,sharing,and accelerate the study of the ancient scripts,but lack of standard dataset for such scripts is a major constraint.Although many scholars and researchers have captured and uploaded inscription images on various websites,manual searching,downloading and extraction of these images is tedious and error prone.Web search queries return a vast number of irrelevant results,and manually extracting images for a specific script is not scalable.This paper proposes a novelmultistage system to identify the specific set of script images from a large set of images downloaded from web sources.The proposed system combines the two most important pattern matching techniques-Scale Invariant Feature Transform(SIFT)and Template matching,in a sequential pipeline,and by using the key strengths of each technique,the system can discard irrelevant images while retaining a specific type of images.
基金the support from the CIPHER Project(IIID 2018-008)funded by the Commission on Higher Education-Philippine California Advanced Research Institutes(CHED-PCARI)。
文摘Transition metal phosphides(TMPs)have been regarded as alternative hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)catalysts owing to their comparable activity to those of noble metal-based catalysts.TMPs have been produced in various morphologies,including hollow and porous nanostructures,which are features deemed desirable for electrocatalytic materials.Templated synthesis routes are often responsible for such morphologies.This paper reviews the latest advances and existing challenges in the synthesis of TMP-based OER and HER catalysts through templated methods.A comprehensive review of the structure-property-performance of TMP-based HER and OER catalysts prepared using different templates is presented.The discussion proceeds according to application,first by HER and further divided among the types of templates used-from hard templates,sacrificial templates,and soft templates to the emerging dynamic hydrogen bubble template.OER catalysts are then reviewed and grouped according to their morphology.Finally,prospective research directions for the synthesis of hollow and porous TMP-based catalysts,such as improvements on both activity and stability of TMPs,design of environmentally benign templates and processes,and analysis of the reaction mechanism through advanced material characterization techniques and theoretical calculations,are suggested.
基金supported by the National Natural Science Foundation of China (62276192)。
文摘Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.
基金supported by the National Natural Science Foundation of China under Grant 62171465。
文摘Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle(UAV)edge computing.However,the heterogeneity of UAV computation resource,and the task re-allocating between UAVs have not been fully considered yet.Moreover,most existing works neglect the fact that a task can only be executed on the UAV equipped with its desired service function(SF).In this backdrop,this paper formulates the task scheduling problem as a multi-objective task scheduling problem,which aims at maximizing the task execution success ratio while minimizing the average weighted sum of all tasks’completion time and energy consumption.Optimizing three coupled goals in a realtime manner with the dynamic arrival of tasks hinders us from adopting existing methods,like machine learning-based solutions that require a long training time and tremendous pre-knowledge about the task arrival process,or heuristic-based ones that usually incur a long decision-making time.To tackle this problem in a distributed manner,we establish a matching theory framework,in which three conflicting goals are treated as the preferences of tasks,SFs and UAVs.Then,a Distributed Matching Theory-based Re-allocating(DiMaToRe)algorithm is put forward.We formally proved that a stable matching can be achieved by our proposal.Extensive simulation results show that Di Ma To Re algorithm outperforms benchmark algorithms under diverse parameter settings and has good robustness.
基金funded by the Fujian Province Science and Technology Plan,China(Grant Number 2019H0017).
文摘Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.
