The Internet of Things （IoT） is aimed at enabling the interconnection and integration of the physical world and the cyber space. It represents the trend of future networking, and leads the third wave of the IT indus...The Internet of Things （IoT） is aimed at enabling the interconnection and integration of the physical world and the cyber space. It represents the trend of future networking, and leads the third wave of the IT industry revolution. In this article, we first introduce some background and related technologies of IoT and discuss the concepts and objectives of IoT. Then, we present the challenges and key scientific problems involved in IoT development. Moreover, we introduce the current research project supported by the National Basic Research Program of China （973 Program）. Finally, we outline future research directions.展开更多
The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a se...The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.展开更多
The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectiv...The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing paradigms.Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities.Incorporating RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field exposure.Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation.We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks.Finally,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.展开更多
Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-att...Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.展开更多
In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medica...In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical tests.This datum is sensitive,and hence security is a must in transforming the sensational contents.In this paper,an Evolutionary Algorithm,namely the Memetic Algorithm is used for encrypting the text messages.The encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 levels.The reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded letter.To show its precision,equivalent to five RGB images and five Grayscale images are used to test the proposed algorithm.The results of the proposed algorithm were analyzed using statistical methods,and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable environment.In the future,to embed the privacy-preserving of medical data,it can be extended with blockchain technology.展开更多
With the rapid advancement of the Internet of Things(IoT),the typical application of wireless body area networks(WBANs)based smart healthcare has drawn wide attention from all sectors of society.To alleviate the press...With the rapid advancement of the Internet of Things(IoT),the typical application of wireless body area networks(WBANs)based smart healthcare has drawn wide attention from all sectors of society.To alleviate the pressing challenges,such as resource limitations,low-latency service provision,mass data processing,rigid security demands,and the lack of a central entity,the advanced solutions of fog computing,software-defined networking(SDN)and blockchain are leveraged in this work.On the basis of these solutions,a task offloading strategy with a centralized low-latency,secure and reliable decision-making algorithm having powerful emergency handling capacity(LSRDM-EH)is designed to facilitate the resource-constrained edge devices for task offloading.Additionally,to well ensure the security of the entire network,a comprehensive blockchain-based two-layer and multidimensional security strategy is proposed.Furthermore,to tackle the inherent time-inefficiency problem of blockchain,we propose a blockchain sharding scheme to reduce system time latency.Extensive simulation has been conducted to validate the performance of the proposed measures,and numerical results verify the superiority of our methods with lower time-latency,higher reliability and security.展开更多
Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides withi...Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within datacenters.The conventional pluggable optics increases at a much slower rate than that of datacenter trafc.The gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is unsustainable.Copackaged optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and photonics.CPO is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale integration.Leading international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and standardization.This review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.展开更多
In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication systems.Existing research ha...In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication systems.Existing research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy consumption.However,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the network.The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high availability.We propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS attacks.Given a number of input parameters,our model has two distinct capabilities.First,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions.Second,it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks.The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters.Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.展开更多
The studypresents theHalfMax InsertionHeuristic (HMIH) as a novel approach to solving theTravelling SalesmanProblem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) a...The studypresents theHalfMax InsertionHeuristic (HMIH) as a novel approach to solving theTravelling SalesmanProblem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) andNearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics,focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative thatminimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting withan initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half Max routine.The paper thoroughly examines and compares HMIH with FIH and NNH via rigorous testing on standard TSPbenchmarks. The results indicate that HMIH consistently delivers superior performance, particularly with respectto tour cost and computational efficiency. HMIH’s tours were sometimes 16% shorter than those generated by FIHand NNH, showcasing its potential and value as a novel benchmark for TSP solutions. The study used statisticalmethods, including Friedman’s Non-parametric Test, to validate the performance of HMIH over FIH and NNH.This guarantees that the identified advantages are statistically significant and consistent in various situations. Thiscomprehensive analysis emphasizes the reliability and efficiency of the heuristic, making a compelling case for itsuse in solving TSP issues. The research shows that, in general, HMIH fared better than FIH in all cases studied,except for a few instances (pr439, eil51, and eil101) where FIH either performed equally or slightly better thanHMIH. HMIH’s efficiency is shown by its improvements in error percentage (δ) and goodness values (g) comparedto FIH and NNH. In the att48 instance, HMIH had an error rate of 6.3%, whereas FIH had 14.6% and NNH had20.9%, indicating that HMIH was closer to the optimal solution. HMIH consistently showed superior performanceacross many benchmarks, with lower percentage error and highe展开更多
Mobile technology is developing significantly.Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners.Typically,computer vision models focus on image detection and cla...Mobile technology is developing significantly.Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners.Typically,computer vision models focus on image detection and classification issues.MobileNetV2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to users.This leads to increased latency.Processing biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational speed.Hence,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is required.Quantizing pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory requirement.This proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and memory.Our contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable models.The model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class Normal.From the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is compressed.The testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,and 89.76%respectively while MobileNetV2-SVM,Ince展开更多
Due to rapid growth in wireless communication technology,higher bandwidth requirement for advance telecommunication systems,capable of operating on two or higher bands with higher channel capacities and minimum distor...Due to rapid growth in wireless communication technology,higher bandwidth requirement for advance telecommunication systems,capable of operating on two or higher bands with higher channel capacities and minimum distortion losses is desired.In this paper,a compact Ultra-Wideband(UWB)V-shaped monopole antenna is presented.UWB response is achieved by modifying the ground plane with Chichen Itzia inspired rectangular staircase shape.The proposed V-shaped is designed by incorporating a rectangle,and an inverted isosceles triangle using FR4 substrate.The size of the antenna is 25 mm×26 mm×1.6 mm.The proposed V-shaped monopole antenna produces bandwidth response of 3 GHz Industrial,Scientific,and Medical(ISM),Worldwide Interoperability for Microwave Access(WiMAX),(IEEE 802.11/HIPERLAN band,5G sub 6 GHz)which with an additional square cut amplified the bandwidth response up to 8 GHz ranging from 3.1 GHz to 10.6 GHz attaining UWB defined by Federal Communications Commission(FCC)with a maximum gain of 3.83 dB.The antenna is designed in Ansys HFSS.Results for key performance parameters of the antenna are presented.The measured results are in good agreement with the simulated results.Due to flat gain,uniform group delay,omni directional radiation pattern characteristics and well-matched impedance,the proposed antenna is suitable for WiMAX,ISM and heterogeneous wireless systems.展开更多
Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are d...Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.展开更多
With advanced prediction modes of intra prediction, intra coding of H.264/AVC offers significant coding gains compared with previous video coding standards. It uses an important tool called Lagrangian rate-distortion ...With advanced prediction modes of intra prediction, intra coding of H.264/AVC offers significant coding gains compared with previous video coding standards. It uses an important tool called Lagrangian rate-distortion optimization (RDO)technique to decide the best coding mode for a block, but the computational burden is extremely high. In this paper, we proposed an improved fast intra prediction algorithm including block type selection and mode decision algorithm based on analysis of edge feature of a block. Our algorithm filters out unlikely block type and candidate modes to reduce the RDO calculations. Experimental results showed that the proposed algorithm can reduce the computation complexity of intra prediction from 52.90% to 56.31%, with 0.04 dB PSNR degradation and 2% increase of bit rate.展开更多
Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols...Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent features.In the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,B展开更多
Quantum entanglement is a key resource for achieving superiority of quantum computing.Currently,scientists are extensively focusing on how to integrate quantum entanglement into various components of quantum machine l...Quantum entanglement is a key resource for achieving superiority of quantum computing.Currently,scientists are extensively focusing on how to integrate quantum entanglement into various components of quantum machine learning(QML)models,aiming to surpass the performance of traditional machine learning models.Notable successes include the use of entangled measurements^([1-3])and entangled channels^([4]),which have been shown to reduce query complexity or improve the prediction precision for specified QML tasks.Quantum entangled data,capable of encoding more information compared to classical data of the same size,is recognized for its potential to achieve quantum advantages.Nevertheless,the impact of the entanglement degree in quantum data on model performance remains a challenging and unresolved research question.展开更多
基金Supported by National Natural Science Foundation of China （60496322）, Natural Science Foundation of Beijing （4083034）, and Scientific Research Common Program of Beijing Municipal Commission.of Education （KM200610005020）_ _ _
基金supported by the National Basic Research 973 Program of China under Grant No.2011CB302701the National Natural Science Foundation of China under Grant No.60833009,the National Natural Science Foundation for Distinguished Young Scientists of China under Grant No.60925010
文摘The Internet of Things （IoT） is aimed at enabling the interconnection and integration of the physical world and the cyber space. It represents the trend of future networking, and leads the third wave of the IT industry revolution. In this article, we first introduce some background and related technologies of IoT and discuss the concepts and objectives of IoT. Then, we present the challenges and key scientific problems involved in IoT development. Moreover, we introduce the current research project supported by the National Basic Research Program of China （973 Program）. Finally, we outline future research directions.
文摘The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.
基金supported by the EU H2020 Industrial Leadership Project(No.101017011)the Scientific and Technological Research Council of Turkey(TUBITAK)(No.120E401).
文摘The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing paradigms.Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities.Incorporating RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field exposure.Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation.We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks.Finally,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.
文摘Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.
基金Supported by National Basic Research Program of China （973 Program） （2005CB321902） National Natural Science Foundation of China （60727002 60774003 60921001 90916024）+2 种基金 the Commission on Science Technology and Industry for National Defense （A2120061303） the Doctoral Program Foundation of Ministry of Education of China （20030006003） the Innovation Foundation of BUAA for Ph.D. Graduates
文摘In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical tests.This datum is sensitive,and hence security is a must in transforming the sensational contents.In this paper,an Evolutionary Algorithm,namely the Memetic Algorithm is used for encrypting the text messages.The encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 levels.The reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded letter.To show its precision,equivalent to five RGB images and five Grayscale images are used to test the proposed algorithm.The results of the proposed algorithm were analyzed using statistical methods,and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable environment.In the future,to embed the privacy-preserving of medical data,it can be extended with blockchain technology.
基金supported by the National Natural Science Foundation of China(No.61761007)the Scientific Research Project of Guangxi University Xingjian College of Science and Liberal Arts(No.Y2021ZK03)。
文摘With the rapid advancement of the Internet of Things(IoT),the typical application of wireless body area networks(WBANs)based smart healthcare has drawn wide attention from all sectors of society.To alleviate the pressing challenges,such as resource limitations,low-latency service provision,mass data processing,rigid security demands,and the lack of a central entity,the advanced solutions of fog computing,software-defined networking(SDN)and blockchain are leveraged in this work.On the basis of these solutions,a task offloading strategy with a centralized low-latency,secure and reliable decision-making algorithm having powerful emergency handling capacity(LSRDM-EH)is designed to facilitate the resource-constrained edge devices for task offloading.Additionally,to well ensure the security of the entire network,a comprehensive blockchain-based two-layer and multidimensional security strategy is proposed.Furthermore,to tackle the inherent time-inefficiency problem of blockchain,we propose a blockchain sharding scheme to reduce system time latency.Extensive simulation has been conducted to validate the performance of the proposed measures,and numerical results verify the superiority of our methods with lower time-latency,higher reliability and security.
基金supported by the National Key Research and Development Program of China(No.2019YFB2203004).
文摘Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within datacenters.The conventional pluggable optics increases at a much slower rate than that of datacenter trafc.The gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is unsustainable.Copackaged optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and photonics.CPO is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale integration.Leading international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and standardization.This review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.
基金This research work was funded by TMR&D Sdn Bhd under project code RDTC160902.
文摘In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication systems.Existing research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy consumption.However,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the network.The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high availability.We propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS attacks.Given a number of input parameters,our model has two distinct capabilities.First,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions.Second,it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks.The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters.Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.
基金the Centre of Excellence in Mobile and e-Services,the University of Zululand,Kwadlangezwa,South Africa.
文摘The studypresents theHalfMax InsertionHeuristic (HMIH) as a novel approach to solving theTravelling SalesmanProblem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) andNearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics,focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative thatminimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting withan initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half Max routine.The paper thoroughly examines and compares HMIH with FIH and NNH via rigorous testing on standard TSPbenchmarks. The results indicate that HMIH consistently delivers superior performance, particularly with respectto tour cost and computational efficiency. HMIH’s tours were sometimes 16% shorter than those generated by FIHand NNH, showcasing its potential and value as a novel benchmark for TSP solutions. The study used statisticalmethods, including Friedman’s Non-parametric Test, to validate the performance of HMIH over FIH and NNH.This guarantees that the identified advantages are statistically significant and consistent in various situations. Thiscomprehensive analysis emphasizes the reliability and efficiency of the heuristic, making a compelling case for itsuse in solving TSP issues. The research shows that, in general, HMIH fared better than FIH in all cases studied,except for a few instances (pr439, eil51, and eil101) where FIH either performed equally or slightly better thanHMIH. HMIH’s efficiency is shown by its improvements in error percentage (δ) and goodness values (g) comparedto FIH and NNH. In the att48 instance, HMIH had an error rate of 6.3%, whereas FIH had 14.6% and NNH had20.9%, indicating that HMIH was closer to the optimal solution. HMIH consistently showed superior performanceacross many benchmarks, with lower percentage error and highe
文摘Mobile technology is developing significantly.Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners.Typically,computer vision models focus on image detection and classification issues.MobileNetV2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to users.This leads to increased latency.Processing biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational speed.Hence,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is required.Quantizing pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory requirement.This proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and memory.Our contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable models.The model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class Normal.From the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is compressed.The testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,and 89.76%respectively while MobileNetV2-SVM,Ince
基金This work was supported by the Research Program through the National Research Foundation of Korea,NRF-2019R1A2C1005920,S.K.
文摘Due to rapid growth in wireless communication technology,higher bandwidth requirement for advance telecommunication systems,capable of operating on two or higher bands with higher channel capacities and minimum distortion losses is desired.In this paper,a compact Ultra-Wideband(UWB)V-shaped monopole antenna is presented.UWB response is achieved by modifying the ground plane with Chichen Itzia inspired rectangular staircase shape.The proposed V-shaped is designed by incorporating a rectangle,and an inverted isosceles triangle using FR4 substrate.The size of the antenna is 25 mm×26 mm×1.6 mm.The proposed V-shaped monopole antenna produces bandwidth response of 3 GHz Industrial,Scientific,and Medical(ISM),Worldwide Interoperability for Microwave Access(WiMAX),(IEEE 802.11/HIPERLAN band,5G sub 6 GHz)which with an additional square cut amplified the bandwidth response up to 8 GHz ranging from 3.1 GHz to 10.6 GHz attaining UWB defined by Federal Communications Commission(FCC)with a maximum gain of 3.83 dB.The antenna is designed in Ansys HFSS.Results for key performance parameters of the antenna are presented.The measured results are in good agreement with the simulated results.Due to flat gain,uniform group delay,omni directional radiation pattern characteristics and well-matched impedance,the proposed antenna is suitable for WiMAX,ISM and heterogeneous wireless systems.
基金supported by the National Natural Science Foundation of China (Grant Nos. 62221005, 61936001, and 62376045)the Natural Science Foundation of Chongqing, China (Grant Nos. cstc2021ycjhbgzxm0013)the Project of Chongqing Municipal Education Commission, China (Grant No. HZ2021008)。
文摘Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.
基金Project (No. 60472040) supported by the National Natural Science Foundation of China
文摘With advanced prediction modes of intra prediction, intra coding of H.264/AVC offers significant coding gains compared with previous video coding standards. It uses an important tool called Lagrangian rate-distortion optimization (RDO)technique to decide the best coding mode for a block, but the computational burden is extremely high. In this paper, we proposed an improved fast intra prediction algorithm including block type selection and mode decision algorithm based on analysis of edge feature of a block. Our algorithm filters out unlikely block type and candidate modes to reduce the RDO calculations. Experimental results showed that the proposed algorithm can reduce the computation complexity of intra prediction from 52.90% to 56.31%, with 0.04 dB PSNR degradation and 2% increase of bit rate.
基金supported by the National Natural Science Foundation of China (22176060 and 21822603)Shanghai Municipal Science and Technology Major Project (2018SHZDZX03)+1 种基金the Program of Introducing Talents of Discipline to Universities (B16017)the Science and Technology Commission of Shanghai Municipality (20DZ2250400)。
基金MMU Postdoctoral and Research Fellow(Account:MMUI/230023.02).
文摘Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent features.In the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,B
基金support from the National Natural Science Foundation of China(U23A20318 and 62276195)support from the National Natural Science Foundation of China(12175003,12361161602)NSAF(U2330201)。
文摘Quantum entanglement is a key resource for achieving superiority of quantum computing.Currently,scientists are extensively focusing on how to integrate quantum entanglement into various components of quantum machine learning(QML)models,aiming to surpass the performance of traditional machine learning models.Notable successes include the use of entangled measurements^([1-3])and entangled channels^([4]),which have been shown to reduce query complexity or improve the prediction precision for specified QML tasks.Quantum entangled data,capable of encoding more information compared to classical data of the same size,is recognized for its potential to achieve quantum advantages.Nevertheless,the impact of the entanglement degree in quantum data on model performance remains a challenging and unresolved research question.
