Most existing flocking algorithms assume one single virtual leader and rely on information on both relative positions and relative velocities among neighboring agents.In this paper,the problem of controlling a flock o...Most existing flocking algorithms assume one single virtual leader and rely on information on both relative positions and relative velocities among neighboring agents.In this paper,the problem of controlling a flock of mobile autonomous agents to follow multiple virtual leaders is investigated by using only position information in the sense that agents with the same virtual leader asymptotically attain the same velocity and track the corresponding virtual leader based on only position measurements.A flocking algorithm is proposed under which every agent asymptotically attains its desired velocity,collision between agents can be avoided,and the final tight formation minimizes all agents' global potentials.A simulation example is presented to verify and illustrate the theoretical results.展开更多
A head-related transfer function (HRTF) model for fast and real-time synthesizing multiple virtual sound sources is proposed. A head-related impulse response (HRIR, time- domain version of HRTF) is first decompose...A head-related transfer function (HRTF) model for fast and real-time synthesizing multiple virtual sound sources is proposed. A head-related impulse response (HRIR, time- domain version of HRTF) is first decomposed by a two-level wavelet packet and then represented by a model composed of subband filters and reconstruction filters. The coefficients of the subband filters are the zero interpolation of the wavelet coefficients of the HRIR. The coefficients of the reconstruction filters can be calculated from the wavelet function. The model is simplified by applying a threshold method to reduce the wavelet coefficients. The calculated results indicate that for a model with 30 wavelet coefficients, the error of reconstructed HRIR is about 1%. And the result of a psychoacoustic test shows that a model with 35 wavelet coefficients is perceptually indistinguishable from the original HRIR. When multiple virtual sound sources are synthesized simultaneously, the computational cost of the proposed model is much less than the traditional HRTF filters.展开更多
Virtual Machine(VM) allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and th...Virtual Machine(VM) allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant's VMs may considerably impact the tenant's Quality of Service(Qo S). In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs' network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem(LP-MKP). The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the Qo S among tenants, and improves tenants' overall Qo S in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No.61104140the Fundamental Research Funds for the Central Universities HUST under Grant Nos.2011JC055 and 20112292+1 种基金the Research Fund for the Doctoral Program of Higher Education (RFDP) under Grant No.20100142120023Natural Science Foundation of Hubei Province of China under Grant No.2011CDB042
文摘Most existing flocking algorithms assume one single virtual leader and rely on information on both relative positions and relative velocities among neighboring agents.In this paper,the problem of controlling a flock of mobile autonomous agents to follow multiple virtual leaders is investigated by using only position information in the sense that agents with the same virtual leader asymptotically attain the same velocity and track the corresponding virtual leader based on only position measurements.A flocking algorithm is proposed under which every agent asymptotically attains its desired velocity,collision between agents can be avoided,and the final tight formation minimizes all agents' global potentials.A simulation example is presented to verify and illustrate the theoretical results.
基金supported by the National Nature Science Fund of China(50938003,10774049)State Key Lab of Subtropical Building Science,South China University of Technology
文摘A head-related transfer function (HRTF) model for fast and real-time synthesizing multiple virtual sound sources is proposed. A head-related impulse response (HRIR, time- domain version of HRTF) is first decomposed by a two-level wavelet packet and then represented by a model composed of subband filters and reconstruction filters. The coefficients of the subband filters are the zero interpolation of the wavelet coefficients of the HRIR. The coefficients of the reconstruction filters can be calculated from the wavelet function. The model is simplified by applying a threshold method to reduce the wavelet coefficients. The calculated results indicate that for a model with 30 wavelet coefficients, the error of reconstructed HRIR is about 1%. And the result of a psychoacoustic test shows that a model with 35 wavelet coefficients is perceptually indistinguishable from the original HRIR. When multiple virtual sound sources are synthesized simultaneously, the computational cost of the proposed model is much less than the traditional HRTF filters.
基金supported in part by the National Key Basic Research and Development (973) Program of China (No. 2011CB302600)the National Natural Science Foundation of China (No. 61222205)+1 种基金the Program for New Century Excellent Talents in Universitythe Fok Ying-Tong Education Foundation (No. 141066)
文摘Virtual Machine(VM) allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant's VMs may considerably impact the tenant's Quality of Service(Qo S). In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs' network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem(LP-MKP). The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the Qo S among tenants, and improves tenants' overall Qo S in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.