In this paper, we present a robust subneighborhoods selection technique for feature detection on point clouds scattered over a piecewise smooth surface. The proposed method first identifies all potential features usin...In this paper, we present a robust subneighborhoods selection technique for feature detection on point clouds scattered over a piecewise smooth surface. The proposed method first identifies all potential features using covariance analysis of the local- neighborhoods. To further extract the accurate features from potential features, Gabriel triangles are created in local neighborhoods of each potential feature vertex. These triangles tightly attach to underlying surface and effectively reflect the local geometry struc- ture. Applying a shared nearest neighbor clustering algorithm on ~ 1 reconstructed normals of created triangle set, we classify the lo- cal neighborhoods of the potential feature vertex into multiple subneighborhoods. Each subneighborhood indicates a piecewise smooth surface. The final feature vertex is identified by checking whether it is locating on the intersection of the multiple surfaces. An advantage of this framework is that it is not only robust to noise, but also insensitive to the size of selected neighborhoods. Ex- perimental results on a variety of models are used to illustrate the effectiveness and robustness of our method.展开更多
Feature detection and matching play important roles in many fields of computer vision, such as image understanding, feature recognition, 3D-reconstruction, video analysis, etc. Extracting features is usually the first...Feature detection and matching play important roles in many fields of computer vision, such as image understanding, feature recognition, 3D-reconstruction, video analysis, etc. Extracting features is usually the first step for feature detection or matching, and the gradient feature is one of the most used selections. In this paper, a new image feature-absence importance (AI) feature, which can directly characterize the local structure information, is proposed. Greatly different from the most existing features, the proposed absence importance feature is mainly based on the consideration that the absence of the important pixel will have a great effect on the local structure. Two absence importance features, mean absence importance (MAI) and standard deviation absence importance (SDAI), are defined and used subsequently to construct new algorithms for feature detection and matching. Experiments demonstrate that the proposed absence importance features can be used as an important complement of the gradient feature and applied successfully to the fields of feature detection and matching.展开更多
The shapes of speakers' vocal organs change under their different emotional states, which leads to the deviation of the emotional acoustic space of short-time features from the neutral acoustic space and thereby t...The shapes of speakers' vocal organs change under their different emotional states, which leads to the deviation of the emotional acoustic space of short-time features from the neutral acoustic space and thereby the degradation of the speaker recognition performance. Features deviating greatly from the neutral acoustic space are considered as mismatched features, and they negatively affect speaker recognition systems. Emotion variation produces different feature deformations for different phonemes, so it is reasonable to build a finer model to detect mismatched features under each phoneme. However, given the difficulty of phoneme recognition, three sorts of acoustic class recognition—phoneme classes, Gaussian mixture model(GMM) tokenizer, and probabilistic GMM tokenizer—are proposed to replace phoneme recognition. We propose feature pruning and feature regulation methods to process the mismatched features to improve speaker recognition performance. As for the feature regulation method, a strategy of maximizing the between-class distance and minimizing the within-class distance is adopted to train the transformation matrix to regulate the mismatched features. Experiments conducted on the Mandarin affective speech corpus(MASC) show that our feature pruning and feature regulation methods increase the identification rate(IR) by 3.64% and 6.77%, compared with the baseline GMM-UBM(universal background model) algorithm. Also, corresponding IR increases of 2.09% and 3.32% can be obtained with our methods when applied to the state-of-the-art algorithm i-vector.展开更多
Spectrum sensing is an important part of cognitive radio systems to find spectrum hole for transmission which enables cognitive radio systems coexist with the authorized radio systems without harmful interference.In t...Spectrum sensing is an important part of cognitive radio systems to find spectrum hole for transmission which enables cognitive radio systems coexist with the authorized radio systems without harmful interference.In this paper,an improved cyclostationary feature detection method is proposed to reduce computational complexity without loss of good performance based on the optimal parameter selection strategy for choosing detection parameters of cyclic frequency and lag.Taking binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals as examples,the theoretical analyses are presented for choosing the optimal parameters.Simulation results are given to certify the correctness of the proposed parameter selection strategy and show the performance of the proposed method.展开更多
Undoubtedly,uncooperative or malicious nodes threaten the safety of Internet of Vehicles(IoV)by destroying routing or data.To this end,some researchers have designed some node detection mechanisms and trust calculatin...Undoubtedly,uncooperative or malicious nodes threaten the safety of Internet of Vehicles(IoV)by destroying routing or data.To this end,some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication,data,energy,etc.,to detect and evaluate vehicle nodes.However,it is difficult to effectively assess the trust level of a vehicle node only by message forwarding,data consistency,and energy sufficiency.In order to resolve these problems,a novel mechanism and a new trust calculating model is proposed in this paper.First,the four tuple method is adopted,to qualitatively describing various types of nodes of IoV;Second,analyzing the behavioral features and correlation of various nodes based on route forwarding rate,data forwarding rate and physical location;third,designing double layer detection feature parameters with the ability to detect uncooperative nodes and malicious nodes;fourth,establishing a node correlative detection model with a double layer structure by combining the network layer and the perception layer.Accordingly,we conducted simulation experiments to verify the accuracy and time of this detection method under different speed-rate topological conditions of IoV.The results show that comparing with methods which only considers energy or communication parameters,the method proposed in this paper has obvious advantages in the detection of uncooperative and malicious nodes of IoV;especially,with the double detection feature parameters and node correlative detection model combined,detection accuracy is effectively improved,and the calculation time of node detection is largely reduced.展开更多
ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the ...ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the performances of these methods in detecting oceanic features for both noise free and noise contaminated AVHRR (Advanced Very High Resolution Radiometer) IR image with Kuroshio. Also, practical experiments in detecting the eddy of Kuroshio with these methods are carried out for comparison. Results show that the ICSED algorithm has more advantages than other methods in detecting mesoscale features of ocean. Finally, the effectiveness of window size of ICSED method to oceanic features detection is quantitatively discussed.展开更多
Reconfigurable modular robots feature high mobility due to their unconstrained connection manners.Inspired by the snake multi-joint crawling principle,a chain-type reconfigurable modular robot(CRMR)is designed,which c...Reconfigurable modular robots feature high mobility due to their unconstrained connection manners.Inspired by the snake multi-joint crawling principle,a chain-type reconfigurable modular robot(CRMR)is designed,which could reassemble into various configurations through the compound joint motion.Moreover,an illumination adaptive modular robot identification(IAMRI)algorithm is proposed for CRMR.At first,an adaptive threshold is applied to detect oriented FAST features in the robot image.Then,the effective detection of features in non-uniform illumination areas is achieved through an optimized quadtree decomposition method.After matching features,an improved random sample consensus algorithm is employed to eliminate the mismatched features.Finally,the reconfigurable robot module is identified effectively through the perspective transformation.Compared with ORB,MA,Y-ORB,and S-ORB algorithms,the IAMRI algorithm has an improvement of over 11.6%in feature uniformity,and 13.7%in the comprehensive indicator,respectively.The IAMRI algorithm limits the relative error within 2.5 pixels,efficiently completing the CRMR identification under complex environmental changes.展开更多
With the continuous development of wireless communication technology,the number of access devices continues to soar,which poses a grate challenge to the already scarce spectrum resources.Meanwhile,6G will be an era of...With the continuous development of wireless communication technology,the number of access devices continues to soar,which poses a grate challenge to the already scarce spectrum resources.Meanwhile,6G will be an era of air-space-terrestrial-sea integration,and satellite spectrum resources are also very tight in the context of giant constellations.In this paper,we propose a Non-Orthogonal Multiple Access(NOMA)based spectrum sensing scheme for the future satellite-terrestrial communication scenarios,and design the transceiver from uplink and downlink scenarios,respectively.In order to better identify the user's transmission status,we obtain the feature values of each user through feature detection to make decision.We combine these two technologies to design the transceiver architecture and deduce the threshold value of feature detection in the satellite-terrestrial communication scenario.Simulations are performed in each scenario,and the results illustrate that the proposed scheme combining NOMA and spectrum sensing can greatly improve the throughput with a similar detection probability as Orthogonal Multiple Access(OMA).展开更多
Isogeometric analysis(IGA)is introduced to establish the direct link between computer-aided design and analysis.It is commonly implemented by Galerkin formulations(isogeometric Galerkin,IGA-G)through the use of nonuni...Isogeometric analysis(IGA)is introduced to establish the direct link between computer-aided design and analysis.It is commonly implemented by Galerkin formulations(isogeometric Galerkin,IGA-G)through the use of nonuniform rational B-splines(NURBS)basis functions for geometric design and analysis.Another promising approach,isogeometric collocation(IGA-C),working directly with the strong form of the partial differential equation(PDE)over the physical domain defined by NURBS geometry,calculates the derivatives of the numerical solution at the chosen collocation points.In a typical IGA,the knot vector of the NURBS numerical solution is only determined by the physical domain.A new perspective on the IGAmethod is proposed in this study to improve the accuracy and convergence of the solution.Solving the PDE with IGA can be regarded as fitting the load function defined on the NURBS geometry(right-hand side)with derivatives of the NURBS numerical solution(left-hand side).Moreover,the design of the knot vector has a close relationship to theNURBS functions to be fitted in the area of data fitting in geometric design.Therefore,the detected feature points of the load function are integrated into the initial knot vector of the physical domainto construct thenewknot vector of thenumerical solution.Then,they are connected seamlessly with the IGA-C framework for its great potential combining the accuracy and smoothness merits with the computational efficiency,which we call isogeometric collocation by fitting load function(IGACL).In numerical experiments,we implement our method to solve 1D,2D,and 3D PDEs and demonstrate the improvement in accuracy by comparing it with the standard IGA-C method.We also verify the superiority in the accuracy of our knot selection scheme when employed in the IGA-G method,which we call isogeometric Galerkin by fitting load function(IGA-GL).展开更多
Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median...Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median filtering, binary processing and image edge extraction are used to pretreat the seam image. In the post-processing of seam image, the feature points of the target image are succesfully detected by using center line extraction and feature points detection algorithm based on slope analysis. The whole processing time is less than 150 ms, and the real-time processing of seam image can be implemented.展开更多
As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle dr...As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers' fatigue driving by using the human eye tracking technology.展开更多
The safety of rail is very important for the development of high speed railway, and it is necessary to investigate the features of inner cracks in rail. In order to obtain the features of Acoustic Emission (AE) sour...The safety of rail is very important for the development of high speed railway, and it is necessary to investigate the features of inner cracks in rail. In order to obtain the features of Acoustic Emission (AE) sources of inner cracks in rail, AE sources with different types, depths and propagation distances are examined for crack in rail. The finite element method is utilized to model the rail with cracks and the results of experiment demonstrate the effectiveness of this model. Wavelet transform and Rayleigh-Lamb equations are utilized to extract the features of crack AE sources. The results illustrate that the intensity ratio among AE modes can identify the AE source types and the AE sources with different frequencies in rail. There are uniform AE mode features existing in the AE signals from AE sources in rail web, however AE signals from AE sources in rail head and rail base have the complex and unstable AE modes. Different AE source types have the different propagation features in rail. It is helpful to understand the rail cracks and detect the rail cracks based on the AE technique.展开更多
For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed...For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed. Firstly, it combines certain number of con- tinuous audio frames to be an "acoustic feature image", secondly, uses AdaBoost.MH or fast Random AdaBoost feature selection algorithm to select high representative 2D-Haar pattern combinations to construct super feature vectors; thirdly, analyzes the commonality and differ- ences between subcategories, then extracts common features and reduces different features to obtain a generic audio event template, which can support the accurate identification of multi- ple sub-classes and detect and locate the specific audio event from the audio stream accurately. Experimental results show that the use of 2D-Haar acoustic feature super vector can make recog- nition accuracy 5% higher than ones that MFCC, PLP, LPCC and other traditional acoustic features yielded, and can make tile training processing 7 20 times faster and the recognition processing 5-10 times faster, it can even achieve an average precision of 93.38%, an average recall of 95.03% under the optimal parameter configuration found by grid method. Above all, it can provide an accurate and fast mass-data processing method for audio event detection.展开更多
The traditional space target detection methods mainly use the spatial characteristics of the star map to detect the targets, which can not make full use of the time domain information. This paper presents a new space ...The traditional space target detection methods mainly use the spatial characteristics of the star map to detect the targets, which can not make full use of the time domain information. This paper presents a new space moving target detection method based on time domain features. We firstly construct the time spectral data of star map, then analyze the time domain features of the main objects(target, stars and the background) in star maps, finally detect the moving targets using single pulse feature of the time domain signal. The real star map target detection experimental results show that the proposed method can effectively detect the trajectory of moving targets in the star map sequence, and the detection probability achieves 99% when the false alarm rate is about 8×10^(-5), which outperforms those of compared algorithms.展开更多
A new authentication algorithm for grid identity trusted computing unlimited by hardware is presented;the trusted root is made as an image data.The grid entity is trusted in the soft platform when its feature of image...A new authentication algorithm for grid identity trusted computing unlimited by hardware is presented;the trusted root is made as an image data.The grid entity is trusted in the soft platform when its feature of image root is entirely matched with that from the other entities' feature database in a scale space process.To recognize and detect the stable image root feature,the non-homogeneous linear expandable scale space is proposed.Focusing on relations between the scale parameter of the inhomogeneous Gaussian function terms and the space evolution of thermal diffusion homogeneous equations,three space evolution operators are constructed to exact and mark the feature from image root.Analysis and verification are carried on the new scale space,operators and the core of making decisions for grid entities certifications.展开更多
An optimization scheme for choosing the optimum number of secondary users in cooperative spectrum sensing based on the cyclostationary feature detection with Neyman-Pearson criterion is proposed in this paper.The opti...An optimization scheme for choosing the optimum number of secondary users in cooperative spectrum sensing based on the cyclostationary feature detection with Neyman-Pearson criterion is proposed in this paper.The optimal soft combination test statistic for the cooperative spectrum sensing based on cyclostationary feature detection is derived according to the generalized likelihood ratio test and its corresponding detection performance is deduced.A target function,considering two important parameters as the resource use efficiency and the number of samples employed by each cooperative secondary user in the system design,is constructed to obtain the optimum number of cooperative secondary users.It can be found that the selection scheme is to make a tradeoff between the system complexity of the cognitive radio network and the global sensing performance of the cooperative spectrum sensing.展开更多
Through the supply chain,the quality or quality change of the products can generate important losses.The quality control in some steps is made manually that supposes a high level of subjectivity,controlling the qualit...Through the supply chain,the quality or quality change of the products can generate important losses.The quality control in some steps is made manually that supposes a high level of subjectivity,controlling the quality and its evolution using automatic systems can suppose a reduction of the losses.Testing some automatic image analysis techniques in the case of tomatoes and zucchini is the main objective of this study.Two steps in the supply chain are considered,the feeding of the raw products into the handling chain(because low quality generates a reduction of the chain productivity)and the cool storage of the processed products(as the value at the market is reduced).It was proposed to analyze the incoming products at the head the processing line using CCD cameras to detect low quality and/or dirty products(corresponding to specific farmers/suppliers,it should be asked to improve to maintain the productivity of the line).The second stage is analyzing the evolution of the products along the cool chain(storage and transport),the use of an App developed to be use under Android was proposed to substitute the“visual”evaluation used in practice.The algorithms used,including stages of pre-treatment,segmentation,analysis and presentation of the results take account of the short time available and the limited capacity of the batteries.High performance techniques were applied to the homography stage to discard some of the images,resulting in better performance.Also threads and renderscript kernels were created to parallelize the methods used on the resulting images being able to inspect faster the products.The proposed method achieves success rates comparable to,and improving,the expert inspection.展开更多
基金Supported by National Natural Science Foundation of China(No.u0935004,61173102)the Fundamental Research Funds for the Central Unibersities(DUT11SX08)
文摘In this paper, we present a robust subneighborhoods selection technique for feature detection on point clouds scattered over a piecewise smooth surface. The proposed method first identifies all potential features using covariance analysis of the local- neighborhoods. To further extract the accurate features from potential features, Gabriel triangles are created in local neighborhoods of each potential feature vertex. These triangles tightly attach to underlying surface and effectively reflect the local geometry struc- ture. Applying a shared nearest neighbor clustering algorithm on ~ 1 reconstructed normals of created triangle set, we classify the lo- cal neighborhoods of the potential feature vertex into multiple subneighborhoods. Each subneighborhood indicates a piecewise smooth surface. The final feature vertex is identified by checking whether it is locating on the intersection of the multiple surfaces. An advantage of this framework is that it is not only robust to noise, but also insensitive to the size of selected neighborhoods. Ex- perimental results on a variety of models are used to illustrate the effectiveness and robustness of our method.
基金supported by National Natural Science Foundation of China(Nos.61201395,61272394,61472119 and 61472373)the program for Science & Technology Innovation Talents in Universities of Henan Province(No.13HASTIT039)the Program for Young Backbone Teachers in Universities of Henan Province(Nos.2012GGJS-057 and 2013GGJS-052)
文摘Feature detection and matching play important roles in many fields of computer vision, such as image understanding, feature recognition, 3D-reconstruction, video analysis, etc. Extracting features is usually the first step for feature detection or matching, and the gradient feature is one of the most used selections. In this paper, a new image feature-absence importance (AI) feature, which can directly characterize the local structure information, is proposed. Greatly different from the most existing features, the proposed absence importance feature is mainly based on the consideration that the absence of the important pixel will have a great effect on the local structure. Two absence importance features, mean absence importance (MAI) and standard deviation absence importance (SDAI), are defined and used subsequently to construct new algorithms for feature detection and matching. Experiments demonstrate that the proposed absence importance features can be used as an important complement of the gradient feature and applied successfully to the fields of feature detection and matching.
基金Project Supported by the National Basic Research Program(973) of China(No.2013CB329504)the National Natural Science Foundation of China(No.60970080)the National HighTech R&D Program(863)of China(No.2006AA01Z136)
文摘The shapes of speakers' vocal organs change under their different emotional states, which leads to the deviation of the emotional acoustic space of short-time features from the neutral acoustic space and thereby the degradation of the speaker recognition performance. Features deviating greatly from the neutral acoustic space are considered as mismatched features, and they negatively affect speaker recognition systems. Emotion variation produces different feature deformations for different phonemes, so it is reasonable to build a finer model to detect mismatched features under each phoneme. However, given the difficulty of phoneme recognition, three sorts of acoustic class recognition—phoneme classes, Gaussian mixture model(GMM) tokenizer, and probabilistic GMM tokenizer—are proposed to replace phoneme recognition. We propose feature pruning and feature regulation methods to process the mismatched features to improve speaker recognition performance. As for the feature regulation method, a strategy of maximizing the between-class distance and minimizing the within-class distance is adopted to train the transformation matrix to regulate the mismatched features. Experiments conducted on the Mandarin affective speech corpus(MASC) show that our feature pruning and feature regulation methods increase the identification rate(IR) by 3.64% and 6.77%, compared with the baseline GMM-UBM(universal background model) algorithm. Also, corresponding IR increases of 2.09% and 3.32% can be obtained with our methods when applied to the state-of-the-art algorithm i-vector.
基金the National Natural Science Founda-tion of China (Nos. 60802058 and 60832009)the SMC Young Teacher Sponsorship of Shanghai JiaotongUniversity
文摘Spectrum sensing is an important part of cognitive radio systems to find spectrum hole for transmission which enables cognitive radio systems coexist with the authorized radio systems without harmful interference.In this paper,an improved cyclostationary feature detection method is proposed to reduce computational complexity without loss of good performance based on the optimal parameter selection strategy for choosing detection parameters of cyclic frequency and lag.Taking binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals as examples,the theoretical analyses are presented for choosing the optimal parameters.Simulation results are given to certify the correctness of the proposed parameter selection strategy and show the performance of the proposed method.
基金This research is supported by the National Natural Science Foundations of China under Grants Nos.61862040,61762060 and 61762059The authors gratefully acknowledge the anonymous reviewers for their helpful comments and suggestions.
文摘Undoubtedly,uncooperative or malicious nodes threaten the safety of Internet of Vehicles(IoV)by destroying routing or data.To this end,some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication,data,energy,etc.,to detect and evaluate vehicle nodes.However,it is difficult to effectively assess the trust level of a vehicle node only by message forwarding,data consistency,and energy sufficiency.In order to resolve these problems,a novel mechanism and a new trust calculating model is proposed in this paper.First,the four tuple method is adopted,to qualitatively describing various types of nodes of IoV;Second,analyzing the behavioral features and correlation of various nodes based on route forwarding rate,data forwarding rate and physical location;third,designing double layer detection feature parameters with the ability to detect uncooperative nodes and malicious nodes;fourth,establishing a node correlative detection model with a double layer structure by combining the network layer and the perception layer.Accordingly,we conducted simulation experiments to verify the accuracy and time of this detection method under different speed-rate topological conditions of IoV.The results show that comparing with methods which only considers energy or communication parameters,the method proposed in this paper has obvious advantages in the detection of uncooperative and malicious nodes of IoV;especially,with the double detection feature parameters and node correlative detection model combined,detection accuracy is effectively improved,and the calculation time of node detection is largely reduced.
文摘ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the performances of these methods in detecting oceanic features for both noise free and noise contaminated AVHRR (Advanced Very High Resolution Radiometer) IR image with Kuroshio. Also, practical experiments in detecting the eddy of Kuroshio with these methods are carried out for comparison. Results show that the ICSED algorithm has more advantages than other methods in detecting mesoscale features of ocean. Finally, the effectiveness of window size of ICSED method to oceanic features detection is quantitatively discussed.
基金supported by the National Key R&D Program of China(Grant No.2018YFB1304600)the National Natural Science Foundation of China(Grant No.62003337)+1 种基金the Open Fund for State Key Laboratory of Robotics(Grant No.2023O03)the Liaoning Province Joint Open Fund for Key Scientific and Technological Innovation Bases(Grant No.2021-KF-12-05).
文摘Reconfigurable modular robots feature high mobility due to their unconstrained connection manners.Inspired by the snake multi-joint crawling principle,a chain-type reconfigurable modular robot(CRMR)is designed,which could reassemble into various configurations through the compound joint motion.Moreover,an illumination adaptive modular robot identification(IAMRI)algorithm is proposed for CRMR.At first,an adaptive threshold is applied to detect oriented FAST features in the robot image.Then,the effective detection of features in non-uniform illumination areas is achieved through an optimized quadtree decomposition method.After matching features,an improved random sample consensus algorithm is employed to eliminate the mismatched features.Finally,the reconfigurable robot module is identified effectively through the perspective transformation.Compared with ORB,MA,Y-ORB,and S-ORB algorithms,the IAMRI algorithm has an improvement of over 11.6%in feature uniformity,and 13.7%in the comprehensive indicator,respectively.The IAMRI algorithm limits the relative error within 2.5 pixels,efficiently completing the CRMR identification under complex environmental changes.
基金supported in part by the National Key Research and Development Program of China(2018YFB1802300)the Science and Technology Commission Foundation of Shanghai(Nos.21511101400 and 22511100600)+2 种基金the Young Elite Scientists Sponsorship Program by CICthe Program of Shanghai Academic/Technology Research Leader(No.21XD1433700)the Shanghai Rising-Star Program(No.21QC1400800)。
文摘With the continuous development of wireless communication technology,the number of access devices continues to soar,which poses a grate challenge to the already scarce spectrum resources.Meanwhile,6G will be an era of air-space-terrestrial-sea integration,and satellite spectrum resources are also very tight in the context of giant constellations.In this paper,we propose a Non-Orthogonal Multiple Access(NOMA)based spectrum sensing scheme for the future satellite-terrestrial communication scenarios,and design the transceiver from uplink and downlink scenarios,respectively.In order to better identify the user's transmission status,we obtain the feature values of each user through feature detection to make decision.We combine these two technologies to design the transceiver architecture and deduce the threshold value of feature detection in the satellite-terrestrial communication scenario.Simulations are performed in each scenario,and the results illustrate that the proposed scheme combining NOMA and spectrum sensing can greatly improve the throughput with a similar detection probability as Orthogonal Multiple Access(OMA).
基金supported by the National Natural Science Foundation of China under Grant Nos.61872316,62272406,61932018the National Key R&D Plan of China under Grant No.2020YFB1708900.
文摘Isogeometric analysis(IGA)is introduced to establish the direct link between computer-aided design and analysis.It is commonly implemented by Galerkin formulations(isogeometric Galerkin,IGA-G)through the use of nonuniform rational B-splines(NURBS)basis functions for geometric design and analysis.Another promising approach,isogeometric collocation(IGA-C),working directly with the strong form of the partial differential equation(PDE)over the physical domain defined by NURBS geometry,calculates the derivatives of the numerical solution at the chosen collocation points.In a typical IGA,the knot vector of the NURBS numerical solution is only determined by the physical domain.A new perspective on the IGAmethod is proposed in this study to improve the accuracy and convergence of the solution.Solving the PDE with IGA can be regarded as fitting the load function defined on the NURBS geometry(right-hand side)with derivatives of the NURBS numerical solution(left-hand side).Moreover,the design of the knot vector has a close relationship to theNURBS functions to be fitted in the area of data fitting in geometric design.Therefore,the detected feature points of the load function are integrated into the initial knot vector of the physical domainto construct thenewknot vector of thenumerical solution.Then,they are connected seamlessly with the IGA-C framework for its great potential combining the accuracy and smoothness merits with the computational efficiency,which we call isogeometric collocation by fitting load function(IGACL).In numerical experiments,we implement our method to solve 1D,2D,and 3D PDEs and demonstrate the improvement in accuracy by comparing it with the standard IGA-C method.We also verify the superiority in the accuracy of our knot selection scheme when employed in the IGA-G method,which we call isogeometric Galerkin by fitting load function(IGA-GL).
基金The work was supported by National Natural Science Foundation of China (No. 50975195).
文摘Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median filtering, binary processing and image edge extraction are used to pretreat the seam image. In the post-processing of seam image, the feature points of the target image are succesfully detected by using center line extraction and feature points detection algorithm based on slope analysis. The whole processing time is less than 150 ms, and the real-time processing of seam image can be implemented.
基金supported by the National Natural Science Foundation of China(61272357,61300074,61572075)
文摘As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers' fatigue driving by using the human eye tracking technology.
基金supported by the China Scholarship Council,the National Natural Science Foundation of China(61171197,61201307,61371045)the Innovation Funds of Harbin Institute of Technology(Grant IDGA18102011)the Promotive Research Fund for Excellent Young and Middle-Aged Scientisits of Shandong Province(BS2010DX001)
文摘The safety of rail is very important for the development of high speed railway, and it is necessary to investigate the features of inner cracks in rail. In order to obtain the features of Acoustic Emission (AE) sources of inner cracks in rail, AE sources with different types, depths and propagation distances are examined for crack in rail. The finite element method is utilized to model the rail with cracks and the results of experiment demonstrate the effectiveness of this model. Wavelet transform and Rayleigh-Lamb equations are utilized to extract the features of crack AE sources. The results illustrate that the intensity ratio among AE modes can identify the AE source types and the AE sources with different frequencies in rail. There are uniform AE mode features existing in the AE signals from AE sources in rail web, however AE signals from AE sources in rail head and rail base have the complex and unstable AE modes. Different AE source types have the different propagation features in rail. It is helpful to understand the rail cracks and detect the rail cracks based on the AE technique.
文摘For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed. Firstly, it combines certain number of con- tinuous audio frames to be an "acoustic feature image", secondly, uses AdaBoost.MH or fast Random AdaBoost feature selection algorithm to select high representative 2D-Haar pattern combinations to construct super feature vectors; thirdly, analyzes the commonality and differ- ences between subcategories, then extracts common features and reduces different features to obtain a generic audio event template, which can support the accurate identification of multi- ple sub-classes and detect and locate the specific audio event from the audio stream accurately. Experimental results show that the use of 2D-Haar acoustic feature super vector can make recog- nition accuracy 5% higher than ones that MFCC, PLP, LPCC and other traditional acoustic features yielded, and can make tile training processing 7 20 times faster and the recognition processing 5-10 times faster, it can even achieve an average precision of 93.38%, an average recall of 95.03% under the optimal parameter configuration found by grid method. Above all, it can provide an accurate and fast mass-data processing method for audio event detection.
基金supported by the National High Technology Research and Development Program of China(No.2011AAXXX2035)the Third Phase of Innovative Engineering Projects Foundation of the Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences(No.065X32CN60)
文摘The traditional space target detection methods mainly use the spatial characteristics of the star map to detect the targets, which can not make full use of the time domain information. This paper presents a new space moving target detection method based on time domain features. We firstly construct the time spectral data of star map, then analyze the time domain features of the main objects(target, stars and the background) in star maps, finally detect the moving targets using single pulse feature of the time domain signal. The real star map target detection experimental results show that the proposed method can effectively detect the trajectory of moving targets in the star map sequence, and the detection probability achieves 99% when the false alarm rate is about 8×10^(-5), which outperforms those of compared algorithms.
基金Foundation item: Supported by the National Natural Science Foundation (61070151,60903203,61103246)the Natural Science Foundation of Fujian Province (2010J01353)+1 种基金the Xiamen University of Technology Scientific Research Foundation (YKJ11024R)Xiamen Scientific Research Foundation (3502Z20123037)
文摘A new authentication algorithm for grid identity trusted computing unlimited by hardware is presented;the trusted root is made as an image data.The grid entity is trusted in the soft platform when its feature of image root is entirely matched with that from the other entities' feature database in a scale space process.To recognize and detect the stable image root feature,the non-homogeneous linear expandable scale space is proposed.Focusing on relations between the scale parameter of the inhomogeneous Gaussian function terms and the space evolution of thermal diffusion homogeneous equations,three space evolution operators are constructed to exact and mark the feature from image root.Analysis and verification are carried on the new scale space,operators and the core of making decisions for grid entities certifications.
基金Supported in part by the National Basic Research Program of China (Grant No. 2004CB318000)the National High-Tech Research & Development Program of China (Grant Nos. 2006AA01Z301, 2006AA01Z302, 2007AA01Z336)Key Grant Project of Chinese Ministry of Education (Grant No. 103001)
基金the National Natural Science Foundation of China (Nos.60832009,60872017 and 60802058)the National High Technology Research and Development Program (863) of China (No.2009AA011505)the Important National Science and Technology Specific Projects (Nos.2010ZX03003-002-03 and 2011ZX03003-001-03)
文摘An optimization scheme for choosing the optimum number of secondary users in cooperative spectrum sensing based on the cyclostationary feature detection with Neyman-Pearson criterion is proposed in this paper.The optimal soft combination test statistic for the cooperative spectrum sensing based on cyclostationary feature detection is derived according to the generalized likelihood ratio test and its corresponding detection performance is deduced.A target function,considering two important parameters as the resource use efficiency and the number of samples employed by each cooperative secondary user in the system design,is constructed to obtain the optimum number of cooperative secondary users.It can be found that the selection scheme is to make a tradeoff between the system complexity of the cognitive radio network and the global sensing performance of the cooperative spectrum sensing.
基金funded by the Controlcrop Project,P10-TEP-6174,project framework,supported by the Andalusian Ministry of Economy,Innovation and Science(Andalusia,Spain)the Spanish Ministry of Science and Innovation as well as the EUERDF funds under grant DPI2014-56364-C2-1-R,by TEAP project included in the Marie Curie Actions(PIRSES-GA-2013-612659)by Young Scientists Fund of National Natural Science Foundation of China(31401683).
文摘Through the supply chain,the quality or quality change of the products can generate important losses.The quality control in some steps is made manually that supposes a high level of subjectivity,controlling the quality and its evolution using automatic systems can suppose a reduction of the losses.Testing some automatic image analysis techniques in the case of tomatoes and zucchini is the main objective of this study.Two steps in the supply chain are considered,the feeding of the raw products into the handling chain(because low quality generates a reduction of the chain productivity)and the cool storage of the processed products(as the value at the market is reduced).It was proposed to analyze the incoming products at the head the processing line using CCD cameras to detect low quality and/or dirty products(corresponding to specific farmers/suppliers,it should be asked to improve to maintain the productivity of the line).The second stage is analyzing the evolution of the products along the cool chain(storage and transport),the use of an App developed to be use under Android was proposed to substitute the“visual”evaluation used in practice.The algorithms used,including stages of pre-treatment,segmentation,analysis and presentation of the results take account of the short time available and the limited capacity of the batteries.High performance techniques were applied to the homography stage to discard some of the images,resulting in better performance.Also threads and renderscript kernels were created to parallelize the methods used on the resulting images being able to inspect faster the products.The proposed method achieves success rates comparable to,and improving,the expert inspection.