Weak signal reception is a very important and challenging problem for communication systems especially in the presence of non-Gaussian noise,and in which case the performance of optimal linear correlated receiver degr...Weak signal reception is a very important and challenging problem for communication systems especially in the presence of non-Gaussian noise,and in which case the performance of optimal linear correlated receiver degrades dramatically.Aiming at this,a novel uncorrelated reception scheme based on adaptive bistable stochastic resonance(ABSR)for a weak signal in additive Laplacian noise is investigated.By analyzing the key issue that the quantitative cooperative resonance matching relationship between the characteristics of the noisy signal and the nonlinear bistable system,an analytical expression of the bistable system parameters is derived.On this basis,by means of bistable system parameters self-adaptive adjustment,the counterintuitive stochastic resonance(SR)phenomenon can be easily generated at which the random noise is changed into a benefit to assist signal transmission.Finally,it is demonstrated that approximately 8dB bit error ratio(BER)performance improvement for the ABSR-based uncorrelated receiver when compared with the traditional uncorrelated receiver at low signal to noise ratio(SNR)conditions varying from-30dB to-5dB.展开更多
Because of its strong ability to solve problems,evolutionary multitask optimization(EMTO)algorithms have been widely studied recently.Evolutionary algorithms have the advantage of fast searching for the optimal soluti...Because of its strong ability to solve problems,evolutionary multitask optimization(EMTO)algorithms have been widely studied recently.Evolutionary algorithms have the advantage of fast searching for the optimal solution,but it is easy to fall into local optimum and difficult to generalize.Combining evolutionary multitask algorithms with evolutionary optimization algorithms can be an effective method for solving these problems.Through the implicit parallelism of tasks themselves and the knowledge transfer between tasks,more promising individual algorithms can be generated in the evolution process,which can jump out of the local optimum.How to better combine the two has also been studied more and more.This paper explores the existing evolutionary multitasking theory and improvement scheme in detail.Then,it summarizes the application of EMTO in different scenarios.Finally,according to the existing research,the future research trends and potential exploration directions are revealed.展开更多
In recent years,gesture recognition has been widely used in the fields of intelligent driving,virtual reality,and human-computer interaction.With the development of artificial intelligence,deep learning has achieved r...In recent years,gesture recognition has been widely used in the fields of intelligent driving,virtual reality,and human-computer interaction.With the development of artificial intelligence,deep learning has achieved remarkable success in computer vision.To help researchers better understanding the development status of gesture recognition in video,this article provides a detailed survey of the latest developments in gesture recognition technology for videos based on deep learning.The reviewed methods are broadly categorized into three groups based on the type of neural networks used for recognition:two stream convolutional neural networks,3D convolutional neural networks,and Long-short Term Memory(LSTM)networks.In this review,we discuss the advantages and limitations of existing technologies,focusing on the feature extraction method of the spatiotemporal structure information in a video sequence,and consider future research directions.展开更多
The rapid development of online social network has attracted a lot of research attention. On online social network, people can discuss their ideas, express their interests and opinions, all of which are demonstrated b...The rapid development of online social network has attracted a lot of research attention. On online social network, people can discuss their ideas, express their interests and opinions, all of which are demonstrated by information propagation. So how to model the information propagation cascade accurately has become a hot topic. In this paper, we firstly incorporate the retweet probability into the traditional propagation models. To find the accurate retweet probability, we introduce the logistic regression model for every user based on the extracted features. With the crawled real dataset, simulation is conducted on the real online social network and moreover some novel results have been obtained. The homogenous retweet probability in the original model has underestimated the speed of information propagation, despite the scale of information propagation is almost at the same level. Besides, the initial information poster is really important for a certain propagation, which enables us to make effective strategies to prevent epidemics of rumor on social network.展开更多
Aimed at the problems of small gradient, low learning rate, slow convergence error when the DBN using back-propagation process to fix the network connection weight and bias, proposing a new algorithm that combines wit...Aimed at the problems of small gradient, low learning rate, slow convergence error when the DBN using back-propagation process to fix the network connection weight and bias, proposing a new algorithm that combines with multi-innovation theory to improve standard DBN algorithm, that is the multi-innovation DBN(MI-DBN). It sets up a new model of back-propagation process in DBN algorithm, making the use of single innovation in previous algorithm extend to the use of innovation of the preceding multiple period, thus increasing convergence rate of error largely. To study the application of the algorithm in the social computing, and recognize the meaningful information about the handwritten numbers in social networking images. This paper compares MI-DBN algorithm with other representative classifiers through experiments. The result shows that MI-DBN algorithm, comparing with other representative classifiers, has a faster convergence rate and a smaller error for MNIST dataset recognition. And handwritten numbers on the image also have a precise degree of recognition.展开更多
基金supported in part by the National Natural Science Foundation of China(62001356)in part by the National Natural Science Foundation for Distinguished Young Scholar(61825104)+1 种基金in part by the National Key Research and Development Program of China(2022YFC3301300)in part by the Innovative Research Groups of the National Natural Science Foundation of China(62121001)。
文摘Weak signal reception is a very important and challenging problem for communication systems especially in the presence of non-Gaussian noise,and in which case the performance of optimal linear correlated receiver degrades dramatically.Aiming at this,a novel uncorrelated reception scheme based on adaptive bistable stochastic resonance(ABSR)for a weak signal in additive Laplacian noise is investigated.By analyzing the key issue that the quantitative cooperative resonance matching relationship between the characteristics of the noisy signal and the nonlinear bistable system,an analytical expression of the bistable system parameters is derived.On this basis,by means of bistable system parameters self-adaptive adjustment,the counterintuitive stochastic resonance(SR)phenomenon can be easily generated at which the random noise is changed into a benefit to assist signal transmission.Finally,it is demonstrated that approximately 8dB bit error ratio(BER)performance improvement for the ABSR-based uncorrelated receiver when compared with the traditional uncorrelated receiver at low signal to noise ratio(SNR)conditions varying from-30dB to-5dB.
基金Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2022JM-327 and in part by the CAAI-Huawei MindSpore Academic Open Fund.
文摘Because of its strong ability to solve problems,evolutionary multitask optimization(EMTO)algorithms have been widely studied recently.Evolutionary algorithms have the advantage of fast searching for the optimal solution,but it is easy to fall into local optimum and difficult to generalize.Combining evolutionary multitask algorithms with evolutionary optimization algorithms can be an effective method for solving these problems.Through the implicit parallelism of tasks themselves and the knowledge transfer between tasks,more promising individual algorithms can be generated in the evolution process,which can jump out of the local optimum.How to better combine the two has also been studied more and more.This paper explores the existing evolutionary multitasking theory and improvement scheme in detail.Then,it summarizes the application of EMTO in different scenarios.Finally,according to the existing research,the future research trends and potential exploration directions are revealed.
基金the National Key R&D Program of China(2018YFC0807500)the National Natural Science Foundation of China(61772396,61772392,62002271,61902296)+3 种基金the Fundamental Research Funds for the Central Universities(JBF180301,XJS210310,XJS190307)Xi'an Key Laboratory of Big Data and Intelligent Vision(201805053ZD4CG37)the National Natural Science Foundation of Shaanxi Province(2020JQ-330,2020JM-195)the China Postdoctoral Science Foundation(2019M663640).
文摘In recent years,gesture recognition has been widely used in the fields of intelligent driving,virtual reality,and human-computer interaction.With the development of artificial intelligence,deep learning has achieved remarkable success in computer vision.To help researchers better understanding the development status of gesture recognition in video,this article provides a detailed survey of the latest developments in gesture recognition technology for videos based on deep learning.The reviewed methods are broadly categorized into three groups based on the type of neural networks used for recognition:two stream convolutional neural networks,3D convolutional neural networks,and Long-short Term Memory(LSTM)networks.In this review,we discuss the advantages and limitations of existing technologies,focusing on the feature extraction method of the spatiotemporal structure information in a video sequence,and consider future research directions.
基金The work was jointly supported by the National Natural Science Foundations of China under grant No. 61472302,61272280,41271447, and 61272195The Program for New Century Excellent Talents in University under grant No. NCET-12-0919+2 种基金 The Fundamental Research Funds for the Central Universities under grant No.K5051203020,K5051303016,K5051303018, BDY081422, and K50513100006 Natural Science Foundation of Shaanxi Province, under grant No.2014JM8310The Creative Project of the Science and Technology State of xi’an under grant No. CXY1341(6).
文摘The rapid development of online social network has attracted a lot of research attention. On online social network, people can discuss their ideas, express their interests and opinions, all of which are demonstrated by information propagation. So how to model the information propagation cascade accurately has become a hot topic. In this paper, we firstly incorporate the retweet probability into the traditional propagation models. To find the accurate retweet probability, we introduce the logistic regression model for every user based on the extracted features. With the crawled real dataset, simulation is conducted on the real online social network and moreover some novel results have been obtained. The homogenous retweet probability in the original model has underestimated the speed of information propagation, despite the scale of information propagation is almost at the same level. Besides, the initial information poster is really important for a certain propagation, which enables us to make effective strategies to prevent epidemics of rumor on social network.
文摘Aimed at the problems of small gradient, low learning rate, slow convergence error when the DBN using back-propagation process to fix the network connection weight and bias, proposing a new algorithm that combines with multi-innovation theory to improve standard DBN algorithm, that is the multi-innovation DBN(MI-DBN). It sets up a new model of back-propagation process in DBN algorithm, making the use of single innovation in previous algorithm extend to the use of innovation of the preceding multiple period, thus increasing convergence rate of error largely. To study the application of the algorithm in the social computing, and recognize the meaningful information about the handwritten numbers in social networking images. This paper compares MI-DBN algorithm with other representative classifiers through experiments. The result shows that MI-DBN algorithm, comparing with other representative classifiers, has a faster convergence rate and a smaller error for MNIST dataset recognition. And handwritten numbers on the image also have a precise degree of recognition.
