Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device off...Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.展开更多
Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A...Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.展开更多
A computer-assisted method is presented for optimization of mobile phase compositions and development distance in gradient two-step development HPTLC. The method is based on a system which can predict the final R(f) v...A computer-assisted method is presented for optimization of mobile phase compositions and development distance in gradient two-step development HPTLC. The method is based on a system which can predict the final R(f) values for gradient two-step development from values measured using five preliminary runs. The statistical scanning method is then used for optimization, using R(f) difference as the selection criterion. The method was evaluated using a mixture of eight components. Excellent agreement was obtained between predicted and experimental results.展开更多
A computer-assisted method is presented for optimization for the selection of mobile phase composition in semi-preparative HPLC.The optimization for the expected separation is based on a polynomial estimation from fiv...A computer-assisted method is presented for optimization for the selection of mobile phase composition in semi-preparative HPLC.The optimization for the expected separation is based on a polynomial estimation from five preliminary runs.Statistical scanning technique was used for optimization.Double criteria simulation system (DCSS) is established for chromatographic perfor- mance measurement in this method.The validity of the optimization strategy is confirmed by applying it to a technical Cypermethrin separation.Excellent agreement is obtained between the predicted and experimental results.展开更多
The basic principle of optimal method called “moving overlapping resolution mapping Method” to select the optimal binary mobile phase composition of multi-step linear gradient liquid chromatography is discussed with...The basic principle of optimal method called “moving overlapping resolution mapping Method” to select the optimal binary mobile phase composition of multi-step linear gradient liquid chromatography is discussed with simultaneously considering effects of position of solute inside the column and mobile phase composition on peak resolution and retention value, then a BASIC program based on this principle is developed in IBM-PC computer. The validities of both principle of optimization and BASIC program are confirmed by separation of samples Containing bile acids and PAHs in RP-HPLC.展开更多
The existing radio access network(RAN)is facing many challenges to meet the very strict speed and latency requirements by different mobile applications in addition to the increasing pressure to reduce operating cost.I...The existing radio access network(RAN)is facing many challenges to meet the very strict speed and latency requirements by different mobile applications in addition to the increasing pressure to reduce operating cost.Innovation and development in RAN have been accelerated to tackle these challenges and to define how next generation mobile networks should look like.The role of machine learning(ML)and artificial intelligence(AI)driven innovations within the RAN domain is strengthening and attracting lots of attention to tackle many of the challenging problems.In this paper we surveyed RAN network base stations(BSs)clustering and its applications in the literature.The paper also demonstrates how to leverage community detection algorithms to understand underlying community structures within RAN.Tracking areas(TAs)novel framework was developed by adapting existing community detection algorithm to solve the problem of statically partitioning a set of BSs into TA according to mobility patterns.Finally,live network dataset in dense urban part of Cairo is used to assess how the developed framework is used to partition this part of the network more efficiently compared to other clustering techniques.Results obtained showed that the new methodology saved up to 34.6%of inter TA signaling overhead and surpassing other conventional clustering algorithms.展开更多
基金supported by National Natural Science Foundation of China (Grant No.61261017, No.61571143 and No.61561014)Guangxi Natural Science Foundation (2013GXNSFAA019334 and 2014GXNSFAA118387)+3 种基金Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (No.CRKL150112)Guangxi Key Lab of Wireless Wideband Communication & Signal Processing (GXKL0614202, GXKL0614101 and GXKL061501)Sci.and Tech.on Info.Transmission and Dissemination in Communication Networks Lab (No.ITD-U14008/KX142600015)Graduate Student Research Innovation Project of Guilin University of Electronic Technology (YJCXS201523)
文摘Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.
基金supported by the National Natural Science Foundation of China(61173017)the National High Technology Research and Development Program(863 Program)(2014AA01A701)
文摘Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.
文摘A computer-assisted method is presented for optimization of mobile phase compositions and development distance in gradient two-step development HPTLC. The method is based on a system which can predict the final R(f) values for gradient two-step development from values measured using five preliminary runs. The statistical scanning method is then used for optimization, using R(f) difference as the selection criterion. The method was evaluated using a mixture of eight components. Excellent agreement was obtained between predicted and experimental results.
文摘A computer-assisted method is presented for optimization for the selection of mobile phase composition in semi-preparative HPLC.The optimization for the expected separation is based on a polynomial estimation from five preliminary runs.Statistical scanning technique was used for optimization.Double criteria simulation system (DCSS) is established for chromatographic perfor- mance measurement in this method.The validity of the optimization strategy is confirmed by applying it to a technical Cypermethrin separation.Excellent agreement is obtained between the predicted and experimental results.
文摘The basic principle of optimal method called “moving overlapping resolution mapping Method” to select the optimal binary mobile phase composition of multi-step linear gradient liquid chromatography is discussed with simultaneously considering effects of position of solute inside the column and mobile phase composition on peak resolution and retention value, then a BASIC program based on this principle is developed in IBM-PC computer. The validities of both principle of optimization and BASIC program are confirmed by separation of samples Containing bile acids and PAHs in RP-HPLC.
文摘The existing radio access network(RAN)is facing many challenges to meet the very strict speed and latency requirements by different mobile applications in addition to the increasing pressure to reduce operating cost.Innovation and development in RAN have been accelerated to tackle these challenges and to define how next generation mobile networks should look like.The role of machine learning(ML)and artificial intelligence(AI)driven innovations within the RAN domain is strengthening and attracting lots of attention to tackle many of the challenging problems.In this paper we surveyed RAN network base stations(BSs)clustering and its applications in the literature.The paper also demonstrates how to leverage community detection algorithms to understand underlying community structures within RAN.Tracking areas(TAs)novel framework was developed by adapting existing community detection algorithm to solve the problem of statically partitioning a set of BSs into TA according to mobility patterns.Finally,live network dataset in dense urban part of Cairo is used to assess how the developed framework is used to partition this part of the network more efficiently compared to other clustering techniques.Results obtained showed that the new methodology saved up to 34.6%of inter TA signaling overhead and surpassing other conventional clustering algorithms.
