In order to improve the data throughput of the advanced encryption standard （AES） IP core while reducing the hardware resource consumption and finally achieving a tradeoff between speed and area, a mixed pipeline ar...In order to improve the data throughput of the advanced encryption standard （AES） IP core while reducing the hardware resource consumption and finally achieving a tradeoff between speed and area, a mixed pipeline architecture and reconfigurable technology for the design and implementation of the AES IP core is proposed. The encryption and decryption processes of the AES algorithm are achieved in the same process within the mixed pipeline structure. According to the finite field characterizations, the Sbox in the AES algorithm is optimized. ShiftRow and MixColumn, which are the main components in AES round transformation, are optimized with the reconfigurable technology. The design is implemented on the Xilinx Virtex2p xc2vp20-7 field programmable gate array （FPGA） device. It can achieve a data throughput above 2.58 Gbit/s, and it only requires 3 233 slices. Compared with other related designs of AES IP cores on the same device, the proposed design can achieve a tradeoff between speed and area, and obtain satisfactory results in both data throughput and hardware resource consumption.展开更多
AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,com...AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,combining transformer[2]models,3DCNN[3],and diffusion[4]generative models.展开更多
In today’s digital world,the Internet of Things(IoT)plays an important role in both local and global economies due to its widespread adoption in different applications.This technology has the potential to offer sever...In today’s digital world,the Internet of Things(IoT)plays an important role in both local and global economies due to its widespread adoption in different applications.This technology has the potential to offer several advantages over conventional technologies in the near future.However,the potential growth of this technology also attracts attention from hackers,which introduces new challenges for the research community that range from hardware and software security to user privacy and authentication.Therefore,we focus on a particular security concern that is associated with malware detection.The literature presents many countermeasures,but inconsistent results on identical datasets and algorithms raise concerns about model biases,training quality,and complexity.This highlights the need for an adaptive,real-time learning framework that can effectively mitigate malware threats in IoT applications.To address these challenges,(i)we propose an intelligent framework based on Two-step Deep Reinforcement Learning(TwStDRL)that is capable of learning and adapting in real-time to counter malware threats in IoT applications.This framework uses exploration and exploitation phenomena during both the training and testing phases by storing results in a replay memory.The stored knowledge allows the model to effectively navigate the environment and maximize cumulative rewards.(ii)To demonstrate the superiority of the TwStDRL framework,we implement and evaluate several machine learning algorithms for comparative analysis that include Support Vector Machines(SVM),Multi-Layer Perceptron,Random Forests,and k-means Clustering.The selection of these algorithms is driven by the inconsistent results reported in the literature,which create doubt about their robustness and reliability in real-world IoT deployments.(iii)Finally,we provide a comprehensive evaluation to justify why the TwStDRL framework outperforms them in mitigating security threats.During analysis,we noted that our proposed TwStDRL scheme achieves an average performance of 99.45%across accuracy,precision,recall,and F1-score,which is an absolute improvement of roughly 3%over the existing malware-detection models.展开更多
Reconfigurable computing has grown to become an important and large field of research, it offers advantages over traditional hardware and software implementations of computational algorithms. The Advanced Encryption S...Reconfigurable computing has grown to become an important and large field of research, it offers advantages over traditional hardware and software implementations of computational algorithms. The Advanced Encryption Standard （AES） algorithm is widely applied in government department and commerce. This paper analyzed the AES algorithms with different cipher keys, adopted a novel key scheduler that generated the round key real-time, proposed a dynamically reconfigurable encryption system which supported the AES algorithm with different cipher keys, and designed the architecture of the reconfigurable system. The dynamically reconfigurable AES system had been realized on FPGA. The result proves that the reconfigurable AES system is flexible, lower cost and high security level.展开更多
In this work,we present a method that enables a mobile robot to hand over objects to humans efficiently and safely by combining mobile navigation with visual perception.Our robotic system can map its environment in re...In this work,we present a method that enables a mobile robot to hand over objects to humans efficiently and safely by combining mobile navigation with visual perception.Our robotic system can map its environment in real time and locate objects to pick up.It uses advanced algorithms to grasp objects in a way that suits human preference and employs path planning and obstacle avoidance to navigate back to the human user.The robot adjusts its movements during handover by analyzing the human’s posture and movements through visual sensors,ensuring a smooth and collision-free handover.Tests of our system show that it can successfully hand over various objects to humans and adapt to changes in the human’s hand position,highlighting improvements in safety and versatility for robotic handovers.展开更多
Based on the large-scale penetration of electric vehicles(EV)into the building cluster,a multi-objective optimal strategy considering the coordinated dispatch of EV is proposed,for improving the safe and economical op...Based on the large-scale penetration of electric vehicles(EV)into the building cluster,a multi-objective optimal strategy considering the coordinated dispatch of EV is proposed,for improving the safe and economical operation problems of distribution network.The system power loss and node voltage excursion can be effectively reduced,by taking measures of time-of-use(TOU)price mechanism bonded with the reactive compensation of energy storage devices.Firstly,the coordinate charging/discharging load model for EV has been established,to obtain a narrowed gap between load peak and valley.Next,a multi-objective optimization model of the distribution grid is also defined,and the active power loss and node voltage fluctuation are chosen to be the objective function.For improving the efficiency of optimization process,an advanced genetic algorithm associated with elite preservation policy is used.Finally,reactive compensation capacity supplied by capacitor banks is dynamically determined according to the varying building loads.The proposed strategy is demonstrated on the IEEE 33-node test case,and the simulation results show that the power supply pressure can be obviously relieved by introducing the coordinated charging/discharging behavior of EV;in the meantime,via reasonable planning of the compensation capacitor,the remarkably lower active power loss and voltage excursion can be realized,ensuring the safe and economical operation of the distribution system.展开更多
A novel model on dynamic resource allocation in the WDM optical networks is proposed, basing on the integrated considerations of the impacts of transmission impairments and service classification on dynamic resource a...A novel model on dynamic resource allocation in the WDM optical networks is proposed, basing on the integrated considerations of the impacts of transmission impairments and service classification on dynamic resource allocation in the optical layer. In this model, the priorities of optical connection requests are mapped into different thresholds of transmission impairments, and a uniform method which is adopted to evaluate the virtual wavelength path (VWP) candidates is defined. The Advanced Preferred Wavelength Sets Algorithm (A-PWS) and the heuristic Dynamic Min-Cost & Optical Virtual Wavelength Path Algorithm (DMC-OVWP) are presented addressing the routing and wavelength assignment (RWA) problem based on dynamic traffic and multi priorities in wavelength-routed optical networks. For a received optical connection request, DMC-OVWP is employed to calculate a list of the VWP candidates, and an appropriate VWP which matches the request's priority is picked up to establish the lightpath by analyzing the transmission qualities of the VWP candidates.展开更多
文摘In order to improve the data throughput of the advanced encryption standard （AES） IP core while reducing the hardware resource consumption and finally achieving a tradeoff between speed and area, a mixed pipeline architecture and reconfigurable technology for the design and implementation of the AES IP core is proposed. The encryption and decryption processes of the AES algorithm are achieved in the same process within the mixed pipeline structure. According to the finite field characterizations, the Sbox in the AES algorithm is optimized. ShiftRow and MixColumn, which are the main components in AES round transformation, are optimized with the reconfigurable technology. The design is implemented on the Xilinx Virtex2p xc2vp20-7 field programmable gate array （FPGA） device. It can achieve a data throughput above 2.58 Gbit/s, and it only requires 3 233 slices. Compared with other related designs of AES IP cores on the same device, the proposed design can achieve a tradeoff between speed and area, and obtain satisfactory results in both data throughput and hardware resource consumption.
基金supported by the Key Project of International Cooperation of Qilu University of Technology(Grant No.:QLUTGJHZ2018008)Shandong Provincial Natural Science Foundation Committee,China(Grant No.:ZR2016HB54)Shandong Provincial Key Laboratory of Microbial Engineering(SME).
文摘AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,combining transformer[2]models,3DCNN[3],and diffusion[4]generative models.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘In today’s digital world,the Internet of Things(IoT)plays an important role in both local and global economies due to its widespread adoption in different applications.This technology has the potential to offer several advantages over conventional technologies in the near future.However,the potential growth of this technology also attracts attention from hackers,which introduces new challenges for the research community that range from hardware and software security to user privacy and authentication.Therefore,we focus on a particular security concern that is associated with malware detection.The literature presents many countermeasures,but inconsistent results on identical datasets and algorithms raise concerns about model biases,training quality,and complexity.This highlights the need for an adaptive,real-time learning framework that can effectively mitigate malware threats in IoT applications.To address these challenges,(i)we propose an intelligent framework based on Two-step Deep Reinforcement Learning(TwStDRL)that is capable of learning and adapting in real-time to counter malware threats in IoT applications.This framework uses exploration and exploitation phenomena during both the training and testing phases by storing results in a replay memory.The stored knowledge allows the model to effectively navigate the environment and maximize cumulative rewards.(ii)To demonstrate the superiority of the TwStDRL framework,we implement and evaluate several machine learning algorithms for comparative analysis that include Support Vector Machines(SVM),Multi-Layer Perceptron,Random Forests,and k-means Clustering.The selection of these algorithms is driven by the inconsistent results reported in the literature,which create doubt about their robustness and reliability in real-world IoT deployments.(iii)Finally,we provide a comprehensive evaluation to justify why the TwStDRL framework outperforms them in mitigating security threats.During analysis,we noted that our proposed TwStDRL scheme achieves an average performance of 99.45%across accuracy,precision,recall,and F1-score,which is an absolute improvement of roughly 3%over the existing malware-detection models.
基金Supported by the National Natural Science Foun-dation of China (60374008)
文摘Reconfigurable computing has grown to become an important and large field of research, it offers advantages over traditional hardware and software implementations of computational algorithms. The Advanced Encryption Standard （AES） algorithm is widely applied in government department and commerce. This paper analyzed the AES algorithms with different cipher keys, adopted a novel key scheduler that generated the round key real-time, proposed a dynamically reconfigurable encryption system which supported the AES algorithm with different cipher keys, and designed the architecture of the reconfigurable system. The dynamically reconfigurable AES system had been realized on FPGA. The result proves that the reconfigurable AES system is flexible, lower cost and high security level.
基金supported by the National Natural Science Foundation of China(grant nos.62306185 and 62073274)the Guangdong Basic and Applied Basic Research Foundation(grant no.2023B1515020089)the Shenzhen Science and Technology Program(grant no.JSGGKQTD20221101115656029).
文摘In this work,we present a method that enables a mobile robot to hand over objects to humans efficiently and safely by combining mobile navigation with visual perception.Our robotic system can map its environment in real time and locate objects to pick up.It uses advanced algorithms to grasp objects in a way that suits human preference and employs path planning and obstacle avoidance to navigate back to the human user.The robot adjusts its movements during handover by analyzing the human’s posture and movements through visual sensors,ensuring a smooth and collision-free handover.Tests of our system show that it can successfully hand over various objects to humans and adapt to changes in the human’s hand position,highlighting improvements in safety and versatility for robotic handovers.
基金supported by Natural Science Foundation of Hunan Province(2017JJ5044).
文摘Based on the large-scale penetration of electric vehicles(EV)into the building cluster,a multi-objective optimal strategy considering the coordinated dispatch of EV is proposed,for improving the safe and economical operation problems of distribution network.The system power loss and node voltage excursion can be effectively reduced,by taking measures of time-of-use(TOU)price mechanism bonded with the reactive compensation of energy storage devices.Firstly,the coordinate charging/discharging load model for EV has been established,to obtain a narrowed gap between load peak and valley.Next,a multi-objective optimization model of the distribution grid is also defined,and the active power loss and node voltage fluctuation are chosen to be the objective function.For improving the efficiency of optimization process,an advanced genetic algorithm associated with elite preservation policy is used.Finally,reactive compensation capacity supplied by capacitor banks is dynamically determined according to the varying building loads.The proposed strategy is demonstrated on the IEEE 33-node test case,and the simulation results show that the power supply pressure can be obviously relieved by introducing the coordinated charging/discharging behavior of EV;in the meantime,via reasonable planning of the compensation capacitor,the remarkably lower active power loss and voltage excursion can be realized,ensuring the safe and economical operation of the distribution system.
基金supported in part by the National Natural Science Foundation of China(Grant No.60272048).
文摘A novel model on dynamic resource allocation in the WDM optical networks is proposed, basing on the integrated considerations of the impacts of transmission impairments and service classification on dynamic resource allocation in the optical layer. In this model, the priorities of optical connection requests are mapped into different thresholds of transmission impairments, and a uniform method which is adopted to evaluate the virtual wavelength path (VWP) candidates is defined. The Advanced Preferred Wavelength Sets Algorithm (A-PWS) and the heuristic Dynamic Min-Cost & Optical Virtual Wavelength Path Algorithm (DMC-OVWP) are presented addressing the routing and wavelength assignment (RWA) problem based on dynamic traffic and multi priorities in wavelength-routed optical networks. For a received optical connection request, DMC-OVWP is employed to calculate a list of the VWP candidates, and an appropriate VWP which matches the request's priority is picked up to establish the lightpath by analyzing the transmission qualities of the VWP candidates.
