The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks....The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks.This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data.This architecture leverages and combines existing frequent itemset discovery over data streams,association rule deduction,frequent sequential pattern mining,and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.展开更多
Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other...Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business envir-onments.However,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective information.Second,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass differences.These challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective information.To solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images.Moreover,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature images.In addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of strokes.We respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification methods.This is more helpful to the task of signature authentication in justice and finance.展开更多
In the field of quantum communication,quantum steganography is an important branch of quantum information hiding.In a realistic quantum communication system,quantum noises are unavoidable and will seriously impact the...In the field of quantum communication,quantum steganography is an important branch of quantum information hiding.In a realistic quantum communication system,quantum noises are unavoidable and will seriously impact the safety and reliability of the quantum steganographic system.Therefore,it is very important to analyze the influence of noise on the quantum steganography protocol and how to reduce the effect of noise.This paper takes the quantum steganography protocol proposed in 2010 as an example to analyze the effects of noises on information qubits and secret message qubits in the four primary quantum noise environments.The results show that when the noise factor of one quantum channel noise is known,the size of the noise factor of the other quantum channel can be adjusted accordingly,such as artificially applying noise,so that the influence of noises on the protocol is minimized.In addition,this paper also proposes a method of improving the efficiency of the steganographic protocol in a noisy environment.展开更多
In the current dire situation of the corona virus COVID-19,remote consultations were proposed to avoid cross-infection and regional differences in medical resources.However,the safety of digital medical imaging in rem...In the current dire situation of the corona virus COVID-19,remote consultations were proposed to avoid cross-infection and regional differences in medical resources.However,the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical industry.To ensure the integrity and security of medical images,this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest(ROI)and integer wavelet transform(IWT).First,the medical image is divided into two different parts,regions of interest and non-interest regions.Then the integrity of ROI is verified using the hash algorithm,and the recovery data of the ROI region is calculated at the same time.Also,binary images with the basic information of patients are processed by logistic chaotic map encryption,and then the synthetic watermark is embedded in the medical carrier image using IWT transform.The performance of the proposed algorithm is tested by the simulation experiments based on the MATLAB program in CT images of the lungs.Experimental results show that the algorithm can precisely locate the distorted areas of an image and recover the original ROI on the basis of verifying image reliability.The maximum peak signal to noise ratio(PSNR)value of 51.24 has been achieved,which proves that the watermark is invisible and has strong robustness against noise,compression,and filtering attacks.展开更多
Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints,which are made of common fingerprint materials,such as silicon,latex,etc.Thus,to protect our privacy,many ...Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints,which are made of common fingerprint materials,such as silicon,latex,etc.Thus,to protect our privacy,many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint.Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features,but these methods normally destroy or lose spatial information between pixels.Different from existing methods,convolutional neural network(CNN)can generate high-level semantic representations by learning and concatenating low-level edge and shape features from a large amount of labeled data.Thus,CNN is explored to solve the above problem and discriminate true fingerprints from fake ones in this paper.To reduce the redundant information and extract the most distinct features,ROI and PCA operations are performed for learned features of convolutional layer or pooling layer.After that,the extracted features are fed into SVM classifier.Experimental results based on the LivDet(2013)and the LivDet(2011)datasets,which are captured by using different fingerprint materials,indicate that the classification performance of our proposed method is both efficient and convenient compared with the other previous methods.展开更多
Two schemes are proposed to realize the controlled remote preparation of an arbitrary four-qubit cluster-type state via a partially entangled channel. We construct ingenious measurement bases at the sender’s and the ...Two schemes are proposed to realize the controlled remote preparation of an arbitrary four-qubit cluster-type state via a partially entangled channel. We construct ingenious measurement bases at the sender’s and the controller’s locations, which play a decisive role in the proposed schemes. The success probabilities can reach 50% and 100%, respectively. Compared with the previous proposals, the success probabilities are independent of the coefficients of the entangled channel.展开更多
In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marqu...In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marquardt algorithm and enrichment function.The model is based on the element-free Galerkin method,in which Kelvin viscoelastic model and adjustment function are integrated.Marquardt algorithm is applied to fit the relation between force and displacement caused by surface deformation,and the enrichment function is applied to deal with the discontinuity in the meshless method.To verify the validity of the model,the Sensable Phantom Omni force tactile interactive device is used to simulate the deformations of stomach and heart.Experimental results show that the proposed model improves the real-time performance and accuracy of soft tissue deformation simulation,which provides a new perspective for the application of the meshless method in virtual surgery.展开更多
Transfer alignment is used to initialize SINS(Strapdown Inertial Navigation System)in motion.Lever-arm effect compensation is studied existing in an AUV(Autonomous Underwater Vehicle)before launched from the mother sh...Transfer alignment is used to initialize SINS(Strapdown Inertial Navigation System)in motion.Lever-arm effect compensation is studied existing in an AUV(Autonomous Underwater Vehicle)before launched from the mother ship.The AUV is equipped with SINS,Doppler Velocity Log,depth sensor and other navigation sensors.The lever arm will cause large error on the transfer alignment between master inertial navigation system and slave inertial navigation system,especially in big ship situations.This paper presents a novel method that can effectively estimate and compensate the flexural lever arm between the main inertial navigation system mounted on the mother ship and the slave inertial navigation system equipped on the AUV.The nonlinear measurement equation of angular rate is derived based on three successive rotations of the body frame of the master inertial navigation system.Nonlinear filter is utilized as the nonlinear estimator for its capability of non-linear approximation.Observability analysis was conducted on the SINS state vector based on singular value decomposition method.State equation of SINS was adopted as the system state equation.Simulation experiments were conducted and results showed that the proposed method can estimate the flexural lever arm more accurately,the precision of transfer alignment was improved and alignment time was shortened accordingly.展开更多
Puncture is a common operation in surgery,which involves all kinds of tissue materials with different geometry and mechanical properties.As a new cross-disciplinary research area,Virtual Surgery(VS)makes simulation of...Puncture is a common operation in surgery,which involves all kinds of tissue materials with different geometry and mechanical properties.As a new cross-disciplinary research area,Virtual Surgery(VS)makes simulation of soft tissue in puncture operation possible in virtual environment.In this paper,we introduce a VS-based puncture system composed by three-layer soft tissue,simulated with spherical harmonic function(SHF),which is covered with a force mesh,constructed by mass spring model(MSM).The two models are combined together with a parameter of SHF named surface radius,which provides MSM with real-time deformation data needed in force calculation.Meanwhile,force calculation,divided into the surface spring force and the puncture damping force,makes the force presentation better accord to the corresponding tissue characteristics.Moreover,a deformation resumption algorithm is leveraged to simulate the resumption phenomenon of the broken tissue surface.In evaluation experiment,several residents are invited to grades our model along with other four mainstream soft tissue models in terms of 7 different indicators.After the evaluation,the scores are analyzed by a comprehensive weighted grading method.Experiment results show that the proposed model has better performance during puncture operation than other models,and can well simulate surface resumption phenomenon when tissue surface is broken.展开更多
Real-time performance and accuracy are two most challenging requirements in virtual surgery training.These difficulties limit the promotion of advanced models in virtual surgery,including many geometric and physical m...Real-time performance and accuracy are two most challenging requirements in virtual surgery training.These difficulties limit the promotion of advanced models in virtual surgery,including many geometric and physical models.This paper proposes a physical model of virtual soft tissue,which is a twist model based on the Kriging interpolation and membrane analogy.The proposed model can quickly locate spatial position through Kriging interpolation method and accurately compute the force change on the soft tissue through membrane analogy method.The virtual surgery simulation system is built with a PHANTOM OMNI haptic interaction device to simulate the torsion of virtual stomach and arm,and further verifies the real-time performance and simulation accuracy of the proposed model.The experimental results show that the proposed soft tissue model has high speed and accuracy,realistic deformation,and reliable haptic feedback.展开更多
Coronaviruses are a well-known family of viruses that can infect humans or animals.Recently,the new coronavirus(COVID-19)has spread worldwide.All countries in the world are working hard to control the coronavirus dise...Coronaviruses are a well-known family of viruses that can infect humans or animals.Recently,the new coronavirus(COVID-19)has spread worldwide.All countries in the world are working hard to control the coronavirus disease.However,many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system,which leads to the mass spread of diseases.As a powerful tool,artificial intelligence(AI)has been successfully applied to solve various complex problems ranging from big data analysis to computer vision.In the process of epidemic control,many algorithms are proposed to solve problems in various fields of medical treatment,which is able to reduce the workload of the medical system.Due to excellent learning ability,AI has played an important role in drug development,epidemic forecast,and clinical diagnosis.This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.展开更多
In order to solve the problem of real-time soft tissue torsion simulation in virtual surgeries,a torsion model based on coil spring is proposed to actualize real-time interactions and applications in virtual surgeries...In order to solve the problem of real-time soft tissue torsion simulation in virtual surgeries,a torsion model based on coil spring is proposed to actualize real-time interactions and applications in virtual surgeries. The proposed model is composed of several connected coil springs in series. The sum of torsion deformation on every coil is equivalent to the soft tissue surface deformation. The calculation of the model is simple because the method for calculating the torsion deformation for each coil spring is the same. The virtual surgery simulation system is established on PHANTOM OMNI haptic device based on the Open GL 3 D graphic interface and VC + + software,and it is used to simulate the torsion deformation of virtual legs and arms. Experimental results show that the proposed model can effectively simulate the torsion deformation of soft tissue while being of real-time performance and simplicity,which can well meet requirements of virtual operation simulations.展开更多
Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) ma...Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) matching,which is the prototype of many advanced information hiding methods.Many existing algorithms deal with steganalysis problems by using the dependencies between adjacent pixels.From another aspect,this paper calculates the differences among pixel pairs and proves that the histogram of difference values will be smoothed by stego noises.We calculate the difference histogram characteristic function(DHCF) and deduce that the moment of DHCFs(DHCFM) will be diminished after stego bits are hidden in the image.Accordingly,we compute the DHCFMs as the discriminative features.We calibrate the features by decreasing the influence of image content on them and train support vector machine classifiers based on the calibrated features.Experimental results demonstrate that the DHCFMs calculated with nonadjacent pixels are helpful to detect stego messages hidden by LSB matching.展开更多
Due to the huge difference of noise distribution,the result of a mixture of multiple noises becomes very complicated.Under normal circumstances,the most common type of mixed noise is to add impulse noise(IN)and then w...Due to the huge difference of noise distribution,the result of a mixture of multiple noises becomes very complicated.Under normal circumstances,the most common type of mixed noise is to add impulse noise(IN)and then white Gaussian noise(AWGN).From the reduction of cascaded IN and AWGN to the latest sparse representation,a great deal of methods has been proposed to reduce this form of mixed noise.However,when the mixed noise is very strong,most methods often produce a lot of artifacts.In order to solve the above problems,we propose a method based on residual learning for the removal of AWGN-IN noise in this paper.By training,our model can obtain stable nonlinear mapping from the images with mixed noise to the clean images.After a series of experiments under different noise settings,the results show that our method is obviously better than the traditional sparse representation and patch based method.Meanwhile,the time of model training and image denoising is greatly reduced.展开更多
feature representations from a large amount of data,and use reinforcement learning to learn the best strategy to complete the task.Through the combination of deep learning and reinforcement learning,end-to-end input a...feature representations from a large amount of data,and use reinforcement learning to learn the best strategy to complete the task.Through the combination of deep learning and reinforcement learning,end-to-end input and output can be achieved,and substantial breakthroughs have been made in many planning and decision-making systems with infinite states,such as games,in particular,AlphaGo,robotics,natural language processing,dialogue systems,machine translation,and computer vision.In this paper we have summarized the main techniques of deep reinforcement learning and its applications in image processing.展开更多
Cyber security lacks comprehensive theoretical guidance. General security theory, as a set of basic security theory concepts, is intended to guide cyber security and all the other security work. The general theory of ...Cyber security lacks comprehensive theoretical guidance. General security theory, as a set of basic security theory concepts, is intended to guide cyber security and all the other security work. The general theory of security aims to unify the main branches of cyber security and establish a unified basic theory. This paper proposal an overview on the general theory of security, which is devoted to constructing a comprehensive model of network security. The hierarchical structure of the meridian-collateral tree is described. Shannon information theory is employed to build a cyberspace security model. Some central concepts of security, i.e., the attack and defense, are discussed and several general theorems on security are presented.展开更多
Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in t...Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in that process.One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,online.This paper presents a novel chronic disease prediction system based on an incremental deep neural network.The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner.With time,the system can predict diabetes more and more accurately by processing the feedback information.Many diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input attributes.In order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was created.Users’data collected by different sensors were used to train the network model.We evaluated our system using a real-world diabetes dataset to confirm its effectiveness.The experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.展开更多
In the simulation of acupuncture manipulation,it is necessary to accurately capture the information of acupuncture points and particles around them.Therefore,a soft tissue modeling method that can accurately track mod...In the simulation of acupuncture manipulation,it is necessary to accurately capture the information of acupuncture points and particles around them.Therefore,a soft tissue modeling method that can accurately track model particles is needed.In this paper,a soft tissue acupuncture model based on the mass-spring force net is designed.MSM is used as the auxiliary model and the SHF model is combined.SHF is used to establish a three-layer soft tissue model of skin,fat,and muscle,and a layer of the MSM based force network is covered on the surface of soft tissue to realize the complementary advantages and disadvantages of spherical harmonic function and MSM.In addition,a springback algorithm is designed to simulate the springback phenomenon of soft tissue skin during acupuncture.The evaluation results show that the soft tissue acupuncture modeling method based on mass-spring force net can effectively simulate the springback phenomenon of soft tissue surface during acupuncture surgery,and has good comprehensive performance in the application of virtual acupuncture surgery simulation.展开更多
In order to solve the problem that the existing meshless models are of high computational complexity and are difficult to express the biomechanical characteristics of real soft tissue, a local high-resolution deformat...In order to solve the problem that the existing meshless models are of high computational complexity and are difficult to express the biomechanical characteristics of real soft tissue, a local high-resolution deformation model of soft tissue based on element-free Galerkin method is proposed. The proposed model applies an element-free Galerkin method to establish the model, and integrates Kelvin viscoelastic model and adjustment function to simulate nonlinear viscoelasticity of soft tissue. Meanwhile, a local high-resolution algorithm is applied to sample and render the deformed region of the model to reduce the computational complexity. To verify the effectiveness of the model,liver and brain tumor deformation simulation experiments are carried out. The experimental results show that compared with the existing meshless models, the proposed model well reflects the biomechanical characteristics of soft tissue, and is of high authenticity, which can provide better visual feedback to users while reducing computational cost.展开更多
The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image.Studies have shown that certain local regions are more likely to inspire an...The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image.Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image.However,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset.Therefore,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation.To perform more accurate emotional region prediction,this study designs a multilayer attentional interaction module.The module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention module.Through layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal scale.To complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation module.The module uses global average pooling to compress spatial information and connect channel information from all layers.Then,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each layer.Eventually,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost information.To reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small dataset.The experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods,with accuracies of 90.27%,84.66%and 84.96%.展开更多
基金funded by the Enterprise Ireland Innovation Partnership Programme with Ericsson under grant agreement IP/2011/0135[6]supported by the National Natural Science Foundation of China(No.61373131,61303039,61232016,61501247)+1 种基金the PAPDCICAEET funds
文摘The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks.This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data.This architecture leverages and combines existing frequent itemset discovery over data streams,association rule deduction,frequent sequential pattern mining,and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.
基金This work was supported,in part,by the National Nature Science Foundation of China under grant numbers 62272236in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business envir-onments.However,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective information.Second,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass differences.These challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective information.To solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images.Moreover,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature images.In addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of strokes.We respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification methods.This is more helpful to the task of signature authentication in justice and finance.
基金This work was supported by the National Natural Science Foundation of China(Nos.61373131,61303039,61232016,61501247)the Six Talent Peaks Project of Jiangsu Province(Grant No.2015-XXRJ-013)+3 种基金Natural Science Foundation of Jiangsu Province(Grant No.BK20171458)the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(China under Grant No.16KJB520030)Sichuan Youth Science and Technique Foundation(No.2017JQ0048)NUIST Research Foundation for Talented Scholars(2015r014),PAPD and CICAEET funds.
文摘In the field of quantum communication,quantum steganography is an important branch of quantum information hiding.In a realistic quantum communication system,quantum noises are unavoidable and will seriously impact the safety and reliability of the quantum steganographic system.Therefore,it is very important to analyze the influence of noise on the quantum steganography protocol and how to reduce the effect of noise.This paper takes the quantum steganography protocol proposed in 2010 as an example to analyze the effects of noises on information qubits and secret message qubits in the four primary quantum noise environments.The results show that when the noise factor of one quantum channel noise is known,the size of the noise factor of the other quantum channel can be adjusted accordingly,such as artificially applying noise,so that the influence of noises on the protocol is minimized.In addition,this paper also proposes a method of improving the efficiency of the steganographic protocol in a noisy environment.
基金This work was supported,in part,by the National Nature Science Foundation of China under grant numbers 61502240,61502096,61304205,61773219in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘In the current dire situation of the corona virus COVID-19,remote consultations were proposed to avoid cross-infection and regional differences in medical resources.However,the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical industry.To ensure the integrity and security of medical images,this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest(ROI)and integer wavelet transform(IWT).First,the medical image is divided into two different parts,regions of interest and non-interest regions.Then the integrity of ROI is verified using the hash algorithm,and the recovery data of the ROI region is calculated at the same time.Also,binary images with the basic information of patients are processed by logistic chaotic map encryption,and then the synthetic watermark is embedded in the medical carrier image using IWT transform.The performance of the proposed algorithm is tested by the simulation experiments based on the MATLAB program in CT images of the lungs.Experimental results show that the algorithm can precisely locate the distorted areas of an image and recover the original ROI on the basis of verifying image reliability.The maximum peak signal to noise ratio(PSNR)value of 51.24 has been achieved,which proves that the watermark is invisible and has strong robustness against noise,compression,and filtering attacks.
文摘Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints,which are made of common fingerprint materials,such as silicon,latex,etc.Thus,to protect our privacy,many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint.Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features,but these methods normally destroy or lose spatial information between pixels.Different from existing methods,convolutional neural network(CNN)can generate high-level semantic representations by learning and concatenating low-level edge and shape features from a large amount of labeled data.Thus,CNN is explored to solve the above problem and discriminate true fingerprints from fake ones in this paper.To reduce the redundant information and extract the most distinct features,ROI and PCA operations are performed for learned features of convolutional layer or pooling layer.After that,the extracted features are fed into SVM classifier.Experimental results based on the LivDet(2013)and the LivDet(2011)datasets,which are captured by using different fingerprint materials,indicate that the classification performance of our proposed method is both efficient and convenient compared with the other previous methods.
基金supported by the National Natural Science Foundation of China(Grant Nos.61201253,61373131,61572246,and 61502147)PAPDCICAEET funds
文摘Two schemes are proposed to realize the controlled remote preparation of an arbitrary four-qubit cluster-type state via a partially entangled channel. We construct ingenious measurement bases at the sender’s and the controller’s locations, which play a decisive role in the proposed schemes. The success probabilities can reach 50% and 100%, respectively. Compared with the previous proposals, the success probabilities are independent of the coefficients of the entangled channel.
基金This work was supported,in part,by the National Nature Science Foundation of China under grant numbers 61502240,61502096,61304205,61773219in part,by the Natural Science Foundation of Jiangsu Province under grant number BK20191401+1 种基金in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundin part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund.
文摘In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marquardt algorithm and enrichment function.The model is based on the element-free Galerkin method,in which Kelvin viscoelastic model and adjustment function are integrated.Marquardt algorithm is applied to fit the relation between force and displacement caused by surface deformation,and the enrichment function is applied to deal with the discontinuity in the meshless method.To verify the validity of the model,the Sensable Phantom Omni force tactile interactive device is used to simulate the deformations of stomach and heart.Experimental results show that the proposed model improves the real-time performance and accuracy of soft tissue deformation simulation,which provides a new perspective for the application of the meshless method in virtual surgery.
基金This work is funded by Natural Science Foundation of Jiangsu Province under Grant BK20160955a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions and Science Research Foundation of Nanjing University of Information Science and Technology under Grant 20110430+1 种基金Open Foundation of Jiangsu Key Laboratory of Meteorological Observation and Information Processing(KDXS1304)Open Foundation of Jiangsu Key Laboratory of Ocean Dynamic Remote Sensing and Acoustics(KHYS1405).
文摘Transfer alignment is used to initialize SINS(Strapdown Inertial Navigation System)in motion.Lever-arm effect compensation is studied existing in an AUV(Autonomous Underwater Vehicle)before launched from the mother ship.The AUV is equipped with SINS,Doppler Velocity Log,depth sensor and other navigation sensors.The lever arm will cause large error on the transfer alignment between master inertial navigation system and slave inertial navigation system,especially in big ship situations.This paper presents a novel method that can effectively estimate and compensate the flexural lever arm between the main inertial navigation system mounted on the mother ship and the slave inertial navigation system equipped on the AUV.The nonlinear measurement equation of angular rate is derived based on three successive rotations of the body frame of the master inertial navigation system.Nonlinear filter is utilized as the nonlinear estimator for its capability of non-linear approximation.Observability analysis was conducted on the SINS state vector based on singular value decomposition method.State equation of SINS was adopted as the system state equation.Simulation experiments were conducted and results showed that the proposed method can estimate the flexural lever arm more accurately,the precision of transfer alignment was improved and alignment time was shortened accordingly.
基金This work was supported in part by the National Nature Science Foundation of China(No.61502240,61502096,61304205,61773219)Natural Science Foundation of Jiangsu Province(No.BK20141002,BK20150634).
文摘Puncture is a common operation in surgery,which involves all kinds of tissue materials with different geometry and mechanical properties.As a new cross-disciplinary research area,Virtual Surgery(VS)makes simulation of soft tissue in puncture operation possible in virtual environment.In this paper,we introduce a VS-based puncture system composed by three-layer soft tissue,simulated with spherical harmonic function(SHF),which is covered with a force mesh,constructed by mass spring model(MSM).The two models are combined together with a parameter of SHF named surface radius,which provides MSM with real-time deformation data needed in force calculation.Meanwhile,force calculation,divided into the surface spring force and the puncture damping force,makes the force presentation better accord to the corresponding tissue characteristics.Moreover,a deformation resumption algorithm is leveraged to simulate the resumption phenomenon of the broken tissue surface.In evaluation experiment,several residents are invited to grades our model along with other four mainstream soft tissue models in terms of 7 different indicators.After the evaluation,the scores are analyzed by a comprehensive weighted grading method.Experiment results show that the proposed model has better performance during puncture operation than other models,and can well simulate surface resumption phenomenon when tissue surface is broken.
基金This work was supported in part by the National Nature Science Foundation of China(No.61502240,61502096,61304205,61773219)Natural Science Foundation of Jiangsu Province(BK20150634,BK20141002).
文摘Real-time performance and accuracy are two most challenging requirements in virtual surgery training.These difficulties limit the promotion of advanced models in virtual surgery,including many geometric and physical models.This paper proposes a physical model of virtual soft tissue,which is a twist model based on the Kriging interpolation and membrane analogy.The proposed model can quickly locate spatial position through Kriging interpolation method and accurately compute the force change on the soft tissue through membrane analogy method.The virtual surgery simulation system is built with a PHANTOM OMNI haptic interaction device to simulate the torsion of virtual stomach and arm,and further verifies the real-time performance and simulation accuracy of the proposed model.The experimental results show that the proposed soft tissue model has high speed and accuracy,realistic deformation,and reliable haptic feedback.
基金This work is supported in part by the Jiangsu Basic Research Programs-Natural Science Foundation under Grant Numbers BK20181407in part by the National Natural Science Foundation of China under Grant Numbers U1936118,61672294+3 种基金in part by Six peak talent project of Jiangsu Province(R2016L13)Qinglan Project of Jiangsu Province,and“333”project of Jiangsu Province,in part by the National Natural Science Foundation of China under Grant Numbers U1836208,61702276,61772283,61602253,and 61601236in part by National Key R&D Program of China under Grant 2018YFB1003205in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund,in part by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.Zhihua Xia is supported by BK21+program from the Ministry of Education of Korea.
文摘Coronaviruses are a well-known family of viruses that can infect humans or animals.Recently,the new coronavirus(COVID-19)has spread worldwide.All countries in the world are working hard to control the coronavirus disease.However,many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system,which leads to the mass spread of diseases.As a powerful tool,artificial intelligence(AI)has been successfully applied to solve various complex problems ranging from big data analysis to computer vision.In the process of epidemic control,many algorithms are proposed to solve problems in various fields of medical treatment,which is able to reduce the workload of the medical system.Due to excellent learning ability,AI has played an important role in drug development,epidemic forecast,and clinical diagnosis.This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.
基金Supported by the National Natural Science Foundation of China(No.61502240,61502096,61304205,61773219)the Natural Science Foundation of Jiangsu Province(No.BK20141002,BK20150634)
文摘In order to solve the problem of real-time soft tissue torsion simulation in virtual surgeries,a torsion model based on coil spring is proposed to actualize real-time interactions and applications in virtual surgeries. The proposed model is composed of several connected coil springs in series. The sum of torsion deformation on every coil is equivalent to the soft tissue surface deformation. The calculation of the model is simple because the method for calculating the torsion deformation for each coil spring is the same. The virtual surgery simulation system is established on PHANTOM OMNI haptic device based on the Open GL 3 D graphic interface and VC + + software,and it is used to simulate the torsion deformation of virtual legs and arms. Experimental results show that the proposed model can effectively simulate the torsion deformation of soft tissue while being of real-time performance and simplicity,which can well meet requirements of virtual operation simulations.
基金supported by the NSFC(61173141,61362032,U1536206, 61232016,U1405254,61373133,61502242,61572258)BK20150925+4 种基金the Natural Science Foundation of Jiangxi Province, China(20151BAB207003)the Fund of Jiangsu Engineering Center of Network Monitoring(KJR1402)the Fund of MOE Internet Innovation Platform(KJRP1403)the CICAEET fundthe PAPD fund
文摘Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) matching,which is the prototype of many advanced information hiding methods.Many existing algorithms deal with steganalysis problems by using the dependencies between adjacent pixels.From another aspect,this paper calculates the differences among pixel pairs and proves that the histogram of difference values will be smoothed by stego noises.We calculate the difference histogram characteristic function(DHCF) and deduce that the moment of DHCFs(DHCFM) will be diminished after stego bits are hidden in the image.Accordingly,we compute the DHCFMs as the discriminative features.We calibrate the features by decreasing the influence of image content on them and train support vector machine classifiers based on the calibrated features.Experimental results demonstrate that the DHCFMs calculated with nonadjacent pixels are helpful to detect stego messages hidden by LSB matching.
基金supported in part by the National Natural Science Foundation of China under Grant 61601235,in part by the Natural Science Foundation of Jiangsu Province of China under Grant BK20160972.
文摘Due to the huge difference of noise distribution,the result of a mixture of multiple noises becomes very complicated.Under normal circumstances,the most common type of mixed noise is to add impulse noise(IN)and then white Gaussian noise(AWGN).From the reduction of cascaded IN and AWGN to the latest sparse representation,a great deal of methods has been proposed to reduce this form of mixed noise.However,when the mixed noise is very strong,most methods often produce a lot of artifacts.In order to solve the above problems,we propose a method based on residual learning for the removal of AWGN-IN noise in this paper.By training,our model can obtain stable nonlinear mapping from the images with mixed noise to the clean images.After a series of experiments under different noise settings,the results show that our method is obviously better than the traditional sparse representation and patch based method.Meanwhile,the time of model training and image denoising is greatly reduced.
基金This work was supported in part by the Open Research Project of State Key Laboratory of Novel Software Technology under Grant KFKT2018B23the Priority Academic Program Development of Jiangsu Higher Education Institutions,the 2018 Tiancheng Huizhi Innovation Promotion Education and Scientific Research Innovation Fund of the Ministry of Education under Grant 2018A03038 and the Open Project Program of the State Key Lab of CAD&CG(Grant No.A1916),Zhejiang University.
文摘feature representations from a large amount of data,and use reinforcement learning to learn the best strategy to complete the task.Through the combination of deep learning and reinforcement learning,end-to-end input and output can be achieved,and substantial breakthroughs have been made in many planning and decision-making systems with infinite states,such as games,in particular,AlphaGo,robotics,natural language processing,dialogue systems,machine translation,and computer vision.In this paper we have summarized the main techniques of deep reinforcement learning and its applications in image processing.
基金supported by the National Key R&D Program of China (2016YFF0204001)the National Key Technology Support Program (2015BAH08F02)+3 种基金the CCF-Venustech Hongyan Research Initiative (2016-009)the PAPD fundthe CICAEET fundthe Guizhou Provincial Key Laboratory of Public Big Data Program
文摘Cyber security lacks comprehensive theoretical guidance. General security theory, as a set of basic security theory concepts, is intended to guide cyber security and all the other security work. The general theory of security aims to unify the main branches of cyber security and establish a unified basic theory. This paper proposal an overview on the general theory of security, which is devoted to constructing a comprehensive model of network security. The hierarchical structure of the meridian-collateral tree is described. Shannon information theory is employed to build a cyberspace security model. Some central concepts of security, i.e., the attack and defense, are discussed and several general theorems on security are presented.
基金funding from the Humanities and Social Sciences Projects of the Ministry of Education(Grant No.18YJC760112,Bin Yang)the Social Science Fund of Jiangsu Province(Grant No.18YSD002,Bin Yang)Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle Infrastructure Systems(Changsha University of Science and Technology)(Grant No.kfj180402,Lingyun Xiang).
文摘Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in that process.One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,online.This paper presents a novel chronic disease prediction system based on an incremental deep neural network.The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner.With time,the system can predict diabetes more and more accurately by processing the feedback information.Many diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input attributes.In order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was created.Users’data collected by different sensors were used to train the network model.We evaluated our system using a real-world diabetes dataset to confirm its effectiveness.The experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.
基金This work was supported,in part,by the National Nature Science Foundation of China under Grant Numbers 61773219in part,by the Natural Science Foundation of Jiangsu Province under Grant Number BK20201136,BK20191401+2 种基金in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundin part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fundNUIST Students’Platform for Innovation and Entrepreneurship Training Program.
文摘In the simulation of acupuncture manipulation,it is necessary to accurately capture the information of acupuncture points and particles around them.Therefore,a soft tissue modeling method that can accurately track model particles is needed.In this paper,a soft tissue acupuncture model based on the mass-spring force net is designed.MSM is used as the auxiliary model and the SHF model is combined.SHF is used to establish a three-layer soft tissue model of skin,fat,and muscle,and a layer of the MSM based force network is covered on the surface of soft tissue to realize the complementary advantages and disadvantages of spherical harmonic function and MSM.In addition,a springback algorithm is designed to simulate the springback phenomenon of soft tissue skin during acupuncture.The evaluation results show that the soft tissue acupuncture modeling method based on mass-spring force net can effectively simulate the springback phenomenon of soft tissue surface during acupuncture surgery,and has good comprehensive performance in the application of virtual acupuncture surgery simulation.
基金Supported by the National Natural Science Foundation of China(No.61502240,61502096,61304205,61773219)Natural Science Foundation of Jiangsu Province(No.BK20141002,BK20150634)
文摘In order to solve the problem that the existing meshless models are of high computational complexity and are difficult to express the biomechanical characteristics of real soft tissue, a local high-resolution deformation model of soft tissue based on element-free Galerkin method is proposed. The proposed model applies an element-free Galerkin method to establish the model, and integrates Kelvin viscoelastic model and adjustment function to simulate nonlinear viscoelasticity of soft tissue. Meanwhile, a local high-resolution algorithm is applied to sample and render the deformed region of the model to reduce the computational complexity. To verify the effectiveness of the model,liver and brain tumor deformation simulation experiments are carried out. The experimental results show that compared with the existing meshless models, the proposed model well reflects the biomechanical characteristics of soft tissue, and is of high authenticity, which can provide better visual feedback to users while reducing computational cost.
基金This study was supported,in part,by the National Nature Science Foundation of China under Grant 62272236in part,by the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image.Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image.However,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset.Therefore,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation.To perform more accurate emotional region prediction,this study designs a multilayer attentional interaction module.The module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention module.Through layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal scale.To complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation module.The module uses global average pooling to compress spatial information and connect channel information from all layers.Then,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each layer.Eventually,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost information.To reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small dataset.The experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods,with accuracies of 90.27%,84.66%and 84.96%.
