Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep infor...Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep information set features from ResNet by modifying its kernel functions to yield Type-1 HanmanNets and then AlexNet, GoogLeNet and VGG-16 by changing their feature maps to yield Type-2 HanmanNets. The two types of HanmanNets exploit the final feature maps of these architectures in the generation of deep information set features from mammograms for their classification using the Hanman Transform Classifier. In this work, the characteristics of the abnormality present in the mammograms are captured using the above network architectures that help derive the features of HanmanNets based on information set concept and their performance is compared via the classification accuracies. The highest accuracy of 100% is achieved for the multi-class classifications on the mini-MIAS database thus surpassing the results in the literature. Validation of the results is done by the expert radiologists to show their clinical relevance.展开更多
The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabli...The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.展开更多
Research on chip-scale atomic clocks （CSACs） based on coherent population trapping （CPT） is reviewed. The back- ground and the inspiration for the research are described, including the important schemes proposed t...Research on chip-scale atomic clocks （CSACs） based on coherent population trapping （CPT） is reviewed. The back- ground and the inspiration for the research are described, including the important schemes proposed to improve the CPT signal quality, the selection of atoms and buffer gases, and the development of micro-cell fabrication. With regard to the re- liability, stability, and service life of the CSACs, the research regarding the sensitivity of the CPT resonance to temperature and laser power changes is also reviewed, as well as the CPT resonance＇s collision and light of frequency shifts. The first generation CSACs have already been developed but its characters are still far from our expectations. Our conclusion is that miniaturization and power reduction are the most important aspects calling for further research.展开更多
Cognitive radio and cooperative communication can greatly improve the spectrum efficiency in wireless communications.We study a cognitive radio network where two secondary source terminals exchange their information w...Cognitive radio and cooperative communication can greatly improve the spectrum efficiency in wireless communications.We study a cognitive radio network where two secondary source terminals exchange their information with the assistance of a relay node under interference power constraints.In order to enhance the transmit rate and maintain fairness between two source terminals,a practical 2-phase analog network coding protocol is adopted and its optimal power allocation algorithm is proposed.Numerical results verify the superiority of the proposed algorithm over the conventional direct transmission protocol and 4-phase amplify-and-forward relay protocol.展开更多
We have studied the optical and magnetic properties of ytterbium implanted GaN epilayer grown on (0001) sapphire by metalorganic chemical vapor by deposition (MOCVD). Samples were implanted at room temperature with Yb...We have studied the optical and magnetic properties of ytterbium implanted GaN epilayer grown on (0001) sapphire by metalorganic chemical vapor by deposition (MOCVD). Samples were implanted at room temperature with Yb ions at dose 4 1015 cm-2 and energy of 150 keV. The implanted samples were annealed at 1000 C in N2 at atmospheric pressure to recover implantation damages. The photoluminescence (PL), PL excitation (PLE), and PL kinetics have been studied with continuous and pulse photo-excitations in 360-1100 nm spectral range at different temperatures. The characteristic Yb3+ ion emission spectra were observed in the spectral range between 970-1050 nm. Theoretical fittings of the experimental PL temperature and PL kinetics data suggest that Yb3+ ions are involved in at least two major luminescence centers. The PLE spectra indicate that excitation of the Yb3+ ion occurs via electron-hole pair generation and complex processes. Magnetization versus magnetic field curves shows an enhancement of magnetic order for Yb-implanted samples in 5 K to 300 K temperature range. The Yb-implanted GaN sample showing weak ferromagnetic behavior was compared with the ferromagnetic in situ doped GaYbN material.展开更多
Light fields are vector functions that map the geometry of light rays to the corresponding plenoptic attributes.They describe the holographic information of scenes by representing the amount of light flowing in every ...Light fields are vector functions that map the geometry of light rays to the corresponding plenoptic attributes.They describe the holographic information of scenes by representing the amount of light flowing in every direction through every point in space.The physical concept of light fields was first proposed in 1936,and light fields are becoming increasingly important in the field of computer graphics,especially with the fast growth of computing capacity as well as network bandwidth.In this article,light field imaging is reviewed from the following aspects with an emphasis on the achievements of the past five years:(1)depth estimation,(2)content editing,(3)image quality,(4)scene reconstruction and view synthesis,and(5)industrial products because the technologies of lights fields also intersect with industrial applications.State-of-the-art research has focused on light field acquisition,manipulation,and display.In addition,the research has extended from the laboratory to industry.According to these achievements and challenges,in the near future,the applications of light fields could offer more portability,accessibility,compatibility,and ability to visualize the world.展开更多
Data security plays a vital role in the current scenario due to the advanced and sophisticated data access techniques. Present development in data access is always a threat to data that are stored in electronic device...Data security plays a vital role in the current scenario due to the advanced and sophisticated data access techniques. Present development in data access is always a threat to data that are stored in electronic devices. Among all the forms of data, image is an important aspect that still needs methodologies to be stored securely. This work focuses on a novel technique to secure images using inter block difference and advanced encryption standard (AES). The AES algorithm is chosen for encryption since there is no prevalent attack that is successful in analyzing it. Instead of encrypting the entire image, only a part of the image is encrypted. The proposed work is found to reduce the encryption overhead in a significant way and at the same time preserves the safety of the image. It is also observed that the decryption is done in an efficient and time preserving manner.展开更多
Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the Metaverse.Metaverse object...Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the Metaverse.Metaverse objects can be at the micro-,meso-,or macroscale.The Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain states.Additionally,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social relations.Therefore,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.展开更多
Federated Learning(FL)enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles(IoV)realm.While FL effectively tackles privacy concerns,it also imposes significan...Federated Learning(FL)enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles(IoV)realm.While FL effectively tackles privacy concerns,it also imposes significant resource requirements.In traditional FL,trained models are transmitted to a central server for global aggregation,typically in the cloud.This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server.The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments.These include diverse and distributed data sources,varying data quality,and limited communication resources.By employing dynamic client selection,we can prioritize relevant and high-quality data sources,enhancing model accuracy.To address this issue,we propose an FL framework that selects global aggregation nodes dynamically rather than a single fixed aggregator.Flexible global aggregation ensures efficient utilization of limited network resources while accommodating the dynamic nature of IoV data sources.This approach optimizes both model performance and resource allocation,making FL in IoV more effective and adaptable.The selection of the global aggregation node is based on workload and communication speed considerations.Additionally,our framework overcomes the constraints associated with network,computational,and energy resources in the IoV environment by implementing a client selection algorithm that dynamically adjusts participants according to predefined parameters.Our approach surpasses Federated Averaging(FedAvg)and Hierarchical FL(HFL)regarding energy consumption,delay,and accuracy,yielding superior results.展开更多
Organic electrode materials have high capacity,and environmentally friendly advantages for the next generation lithium-ion batteries(LIBs).However,organic electrode materials face many challenges,such as low reduction...Organic electrode materials have high capacity,and environmentally friendly advantages for the next generation lithium-ion batteries(LIBs).However,organic electrode materials face many challenges,such as low reduction potential as cathode materials or high reduction potential as anode materials.Here,the influence of chemical functionalities that are capable of either electron donating or electron withdrawing groups on the reduction potential and charge-discharge performance of anthraquinone(AQ)based system is studied.The cyclic voltammetry results show that the introduction of two-OH groups,two-NO2 groups and one-CH3 group on anthraquinone structure has a little impact on the reduction potential,which is found to be 2.1 V.But when three or four-OH groups are introduced on AQ structure,the reduction potential is increased to about 3.1 V.The charge-discharge tests show that these materials exhibit moderate cycling stability.展开更多
In this paper,a recurrent neural network(RNN)is used to estimate uncertainties and implement feedback control for nonlinear dynamic systems.The neural network approximates the uncertainties related to unmodeled dynami...In this paper,a recurrent neural network(RNN)is used to estimate uncertainties and implement feedback control for nonlinear dynamic systems.The neural network approximates the uncertainties related to unmodeled dynamics,parametric variations,and external disturbances.The RNN has a single hidden layer and uses the tracking error and the output as feedback to estimate the disturbance.The RNN weights are online adapted,and the adaptation laws are developed from the stability analysis of the controlled system with the RNN estimation.The used activation function,at the hidden layer,has an expression that simplifies the adaptation laws from the stability analysis.It is found that the adaptive RNN enhances the tracking performance of the feedback controller at the transient and steady state responses.The proposed RNN based feedback control is applied to a DC–DC converter for current regulation.Simulation and experimental results are provided to show its effectiveness.Compared to the feedforward neural network and the conventional feedback control,the RNN based feedback control provides good tracking performance.展开更多
Adding a reputation incentive system to peer-to-peer(P2P)energy transactions can encourage prosumers to regulate their trading behavior,which is important for ensuring the efficiency and reliability of P2P transaction...Adding a reputation incentive system to peer-to-peer(P2P)energy transactions can encourage prosumers to regulate their trading behavior,which is important for ensuring the efficiency and reliability of P2P transactions.This study proposed a P2P transaction mechanism and game optimization model for prosumers involved in distributed energy sources considering reputation-value incentives.First,the deviation of P2P transactions and the non-consumption rate of distributed renewable energy in P2P transactions were established as indicators to quantify the influencing factors of the reputation value,and a reputation incentive model of P2P transactions for prosumers was constructed.Then,the penalty coefficient was applied to the cost function of the prosumers,and a non-cooperative game model of P2P transactions based on the complete information of multi-prosumers was established.Furthermore,the Nash equilibrium problem was transformed into a nonlinear optimization problem by constructing the modified optimal reaction function,and the Nash equilibrium solution of the game was obtained via a relaxation algorithm.Finally,the modified IEEE 33-node test system based on electricity market P2P and an IEEE 123-node test system were used to analyze and verify the cost and P2P participation of prosumers considering the reputation value.The results show that the addition of the reputation incentive system can encourage prosumers to standardize their interactive transaction behavior and actively participate in P2P transactions.It can also improve the operation efficiency of the power grid and promote the perfection of the P2P transaction mechanism.展开更多
文摘Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep information set features from ResNet by modifying its kernel functions to yield Type-1 HanmanNets and then AlexNet, GoogLeNet and VGG-16 by changing their feature maps to yield Type-2 HanmanNets. The two types of HanmanNets exploit the final feature maps of these architectures in the generation of deep information set features from mammograms for their classification using the Hanman Transform Classifier. In this work, the characteristics of the abnormality present in the mammograms are captured using the above network architectures that help derive the features of HanmanNets based on information set concept and their performance is compared via the classification accuracies. The highest accuracy of 100% is achieved for the multi-class classifications on the mini-MIAS database thus surpassing the results in the literature. Validation of the results is done by the expert radiologists to show their clinical relevance.
基金support from the National Science Foundation under Grants 1443894,1560437,and 1731017Louisiana Board of Regents under Grant LEQSF(2017-20)-RD-A-29a research gift from Intel Corporation
文摘The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.
基金Project support by the National Natural Science Foundation of China(Grant No.11074012)
文摘Research on chip-scale atomic clocks （CSACs） based on coherent population trapping （CPT） is reviewed. The back- ground and the inspiration for the research are described, including the important schemes proposed to improve the CPT signal quality, the selection of atoms and buffer gases, and the development of micro-cell fabrication. With regard to the re- liability, stability, and service life of the CSACs, the research regarding the sensitivity of the CPT resonance to temperature and laser power changes is also reviewed, as well as the CPT resonance＇s collision and light of frequency shifts. The first generation CSACs have already been developed but its characters are still far from our expectations. Our conclusion is that miniaturization and power reduction are the most important aspects calling for further research.
基金Acknowledgements The work was supported by National Natural Science Foundation of China （Grant No.60972008）. The corresponding author is Jiang Wei.
文摘Cognitive radio and cooperative communication can greatly improve the spectrum efficiency in wireless communications.We study a cognitive radio network where two secondary source terminals exchange their information with the assistance of a relay node under interference power constraints.In order to enhance the transmit rate and maintain fairness between two source terminals,a practical 2-phase analog network coding protocol is adopted and its optimal power allocation algorithm is proposed.Numerical results verify the superiority of the proposed algorithm over the conventional direct transmission protocol and 4-phase amplify-and-forward relay protocol.
基金Project supported by the 1804 Fund grant of Ohio University and the US Department of Energy (DE-AC02-05CH11231)
文摘We have studied the optical and magnetic properties of ytterbium implanted GaN epilayer grown on (0001) sapphire by metalorganic chemical vapor by deposition (MOCVD). Samples were implanted at room temperature with Yb ions at dose 4 1015 cm-2 and energy of 150 keV. The implanted samples were annealed at 1000 C in N2 at atmospheric pressure to recover implantation damages. The photoluminescence (PL), PL excitation (PLE), and PL kinetics have been studied with continuous and pulse photo-excitations in 360-1100 nm spectral range at different temperatures. The characteristic Yb3+ ion emission spectra were observed in the spectral range between 970-1050 nm. Theoretical fittings of the experimental PL temperature and PL kinetics data suggest that Yb3+ ions are involved in at least two major luminescence centers. The PLE spectra indicate that excitation of the Yb3+ ion occurs via electron-hole pair generation and complex processes. Magnetization versus magnetic field curves shows an enhancement of magnetic order for Yb-implanted samples in 5 K to 300 K temperature range. The Yb-implanted GaN sample showing weak ferromagnetic behavior was compared with the ferromagnetic in situ doped GaYbN material.
基金The last author was supported by the National Key R&D Program of China,No.2019YFB1405703.
文摘Light fields are vector functions that map the geometry of light rays to the corresponding plenoptic attributes.They describe the holographic information of scenes by representing the amount of light flowing in every direction through every point in space.The physical concept of light fields was first proposed in 1936,and light fields are becoming increasingly important in the field of computer graphics,especially with the fast growth of computing capacity as well as network bandwidth.In this article,light field imaging is reviewed from the following aspects with an emphasis on the achievements of the past five years:(1)depth estimation,(2)content editing,(3)image quality,(4)scene reconstruction and view synthesis,and(5)industrial products because the technologies of lights fields also intersect with industrial applications.State-of-the-art research has focused on light field acquisition,manipulation,and display.In addition,the research has extended from the laboratory to industry.According to these achievements and challenges,in the near future,the applications of light fields could offer more portability,accessibility,compatibility,and ability to visualize the world.
文摘Data security plays a vital role in the current scenario due to the advanced and sophisticated data access techniques. Present development in data access is always a threat to data that are stored in electronic devices. Among all the forms of data, image is an important aspect that still needs methodologies to be stored securely. This work focuses on a novel technique to secure images using inter block difference and advanced encryption standard (AES). The AES algorithm is chosen for encryption since there is no prevalent attack that is successful in analyzing it. Instead of encrypting the entire image, only a part of the image is encrypted. The proposed work is found to reduce the encryption overhead in a significant way and at the same time preserves the safety of the image. It is also observed that the decryption is done in an efficient and time preserving manner.
文摘Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the Metaverse.Metaverse objects can be at the micro-,meso-,or macroscale.The Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain states.Additionally,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social relations.Therefore,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.
基金supported by the UAE University UPAR Research Grant Program under Grant 31T122.
文摘Federated Learning(FL)enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles(IoV)realm.While FL effectively tackles privacy concerns,it also imposes significant resource requirements.In traditional FL,trained models are transmitted to a central server for global aggregation,typically in the cloud.This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server.The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments.These include diverse and distributed data sources,varying data quality,and limited communication resources.By employing dynamic client selection,we can prioritize relevant and high-quality data sources,enhancing model accuracy.To address this issue,we propose an FL framework that selects global aggregation nodes dynamically rather than a single fixed aggregator.Flexible global aggregation ensures efficient utilization of limited network resources while accommodating the dynamic nature of IoV data sources.This approach optimizes both model performance and resource allocation,making FL in IoV more effective and adaptable.The selection of the global aggregation node is based on workload and communication speed considerations.Additionally,our framework overcomes the constraints associated with network,computational,and energy resources in the IoV environment by implementing a client selection algorithm that dynamically adjusts participants according to predefined parameters.Our approach surpasses Federated Averaging(FedAvg)and Hierarchical FL(HFL)regarding energy consumption,delay,and accuracy,yielding superior results.
基金Project(21875076)supported by the National Natural Science Foundation of ChinaProjects(2018A050506077,2017A050506048)supported by the Scientific and Technological Plan of Guangdong Province,ChinaProject(201910574037)supported by the Undergraduates’ Innovating Experimentation Project of China
文摘Organic electrode materials have high capacity,and environmentally friendly advantages for the next generation lithium-ion batteries(LIBs).However,organic electrode materials face many challenges,such as low reduction potential as cathode materials or high reduction potential as anode materials.Here,the influence of chemical functionalities that are capable of either electron donating or electron withdrawing groups on the reduction potential and charge-discharge performance of anthraquinone(AQ)based system is studied.The cyclic voltammetry results show that the introduction of two-OH groups,two-NO2 groups and one-CH3 group on anthraquinone structure has a little impact on the reduction potential,which is found to be 2.1 V.But when three or four-OH groups are introduced on AQ structure,the reduction potential is increased to about 3.1 V.The charge-discharge tests show that these materials exhibit moderate cycling stability.
基金supported in part by Khalifa University of Science and Technology (KUST),United Arab Emirates under Award CIRA-2020-013.
文摘In this paper,a recurrent neural network(RNN)is used to estimate uncertainties and implement feedback control for nonlinear dynamic systems.The neural network approximates the uncertainties related to unmodeled dynamics,parametric variations,and external disturbances.The RNN has a single hidden layer and uses the tracking error and the output as feedback to estimate the disturbance.The RNN weights are online adapted,and the adaptation laws are developed from the stability analysis of the controlled system with the RNN estimation.The used activation function,at the hidden layer,has an expression that simplifies the adaptation laws from the stability analysis.It is found that the adaptive RNN enhances the tracking performance of the feedback controller at the transient and steady state responses.The proposed RNN based feedback control is applied to a DC–DC converter for current regulation.Simulation and experimental results are provided to show its effectiveness.Compared to the feedforward neural network and the conventional feedback control,the RNN based feedback control provides good tracking performance.
基金supported by the National Natural Science Foundation of China(U2066211,52177124,52107134)the Institute of Electrical Engineering,CAS(E155610101)+1 种基金the DNL Cooperation Fund,CAS(DNL202023)the Youth Innovation Promotion Association of CAS(2019143).
文摘Adding a reputation incentive system to peer-to-peer(P2P)energy transactions can encourage prosumers to regulate their trading behavior,which is important for ensuring the efficiency and reliability of P2P transactions.This study proposed a P2P transaction mechanism and game optimization model for prosumers involved in distributed energy sources considering reputation-value incentives.First,the deviation of P2P transactions and the non-consumption rate of distributed renewable energy in P2P transactions were established as indicators to quantify the influencing factors of the reputation value,and a reputation incentive model of P2P transactions for prosumers was constructed.Then,the penalty coefficient was applied to the cost function of the prosumers,and a non-cooperative game model of P2P transactions based on the complete information of multi-prosumers was established.Furthermore,the Nash equilibrium problem was transformed into a nonlinear optimization problem by constructing the modified optimal reaction function,and the Nash equilibrium solution of the game was obtained via a relaxation algorithm.Finally,the modified IEEE 33-node test system based on electricity market P2P and an IEEE 123-node test system were used to analyze and verify the cost and P2P participation of prosumers considering the reputation value.The results show that the addition of the reputation incentive system can encourage prosumers to standardize their interactive transaction behavior and actively participate in P2P transactions.It can also improve the operation efficiency of the power grid and promote the perfection of the P2P transaction mechanism.
