Duan Zhou

Target Tracking Approximation Algorithms with Particle Filter Optimization and Fault-Tolerant Analysis in Wireless Sensor Networks

In order to process target tracking approximation with unknown motion state models beforehand in a two-dimensional field of binary proximity sensors, the algorithms based on cost functions of particle filters and near-linear curve simple optimization are proposed in this paper. Through moving target across detecting intersecting fields of sensor nodes sequentially, cost functions are introduced to solve target tracking approximation and velocity estimation which is not similar to traditional particle filters that rely on probabilistic assumptions about the motion states. Then a near-linear curve geometric approach is used to simplify and easily describe target trajectories that are below a certain error measure. Because there maybe some sensor nodes invalid in practice, so a fault-tolerant detection is applied to avoid the nodes’ reporting fault and also improve accuracy of tracking at the same time. The validity of our algorithms is demonstrated through simulation results.

Hardware/Software Partitioning Algorithm under Multi-Constraints for the Optimization of Power Consumption

An improved particle swarm algorithm with multiple neighborhood optimizations was proposed for hardware/software partitioning of system on chip under multiple constraints. A method for fitness calculation was designed with the unity of multiple constraint conditions and objective function. The particle position was updated by the average of the best position for all particles, the individual optimal location and the global optimal position. The effect of the average of position on particle position was adjusted adaptively according to the number of iterations. The multiple neighborhood of the optimal solution was searched for a better solution. The variation information of the optimal position was randomly produced by the Gaussian function. An arbitrary particle in population would be replaced by the variation particle whose fitness was better than the optimal fitness. The experimental results show that the proposed algorithm achieves hardware/software partitioning with lower power in a shorter searching time under the same constraints.

System Reliability-Aware Energy Optimization for Network-on-Chip with Voltage-Frequency Islands

In this paper, a novel energy optimization algorithm for Network-on-Chip (NoC) based on voltage-frequency islands (VFIs) is proposed under the system reliability constraints. The proposed algorithm mainly includes the three processes of VFI partitioning, assignment and task mapping to reduce the energy consumption. A system reliability-aware mapping algorithm is proposed which employs a hybrid algorithm based on quantum-behaved particle swarm optimization and multi-neighborhood simulated annealing strategy. The optimum particles upon QPSO are further optimized by annealing operation mentioned above. Besides, the catastrophic operation is executed to enlarge the search scope and avoid getting into the local optimum. It minimizes the communication energy consumption of NoC system on the condition of meeting system reliability constraints. Experimental results show that the proposed algorithm is quite effective in optimizing energy consumption of NoC with voltage-frequency islands under system reliability constraints.

FPGA implementation of network on chip based on Pentacle topology

A novel topology based on pentacle is proposed for network on chip (NoC). Johnson coding and global asynchronous local synchronous (GALS) are applied to improve the performance of NoC and the resource utilization of FPGA. Simulation results show that, compared with 2D Mesh and Octagon, Pentacle achieves average latency reductions of 30.7% and 15.0%, and increases throughput of 17.6% and 8.1%, respectively. Pentacle topology, which focuses on reducing latency and increasing throughput, is validated by simulation and implemented on FPGA.

Target Tracking Approximation Algorithms Based on Particle Filters and near-Linear Curve Simplified Optimization in WSN

In order to process target tracking approximation with unknown motion state models beforehand in a two-dimensional field of binary proximity sensors, the algorithms based on cost functions of particle filters and near-linear curve simple optimization are proposed in this paper. Through moving target across detecting intersecting fields of sensors sequentially, cost functions are introduced to solve target tracking approximation and velocity estimation which is not similar to traditional particle filters that rely on probabilistic assumptions about the motion states. Then a near-linear curve geometric approach is used to simplify and easily describe target trajectories that are below a certain error measure. The validity of our algorithms is demonstrated through simulation results.