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The performance of measuring moving vehicle targets with onboard millimeter-wave (MMW) radar can be affected by environmental noise, as well as the dynamic changes in the number of these moving vehicles and the problem of interference of measurement data between adjacent vehicles. In this article, we propose a new possibilistic clustering algorithm for this problem. The radar measurement dataset will contain a lot of information about stationary targets prior to cluster analysis. To obtain a moving target measurement dataset, a method for recognizing stationary and moving target measurements was developed. Then, based on the moving vehicle target measurement dataset, it is initialized with an improved fast density peaks (IFDPs) clustering algorithm, and an enhanced adaptive possibilistic c-means (EAPCM) clustering algorithm is implemented. Because the EAPCM algorithm has the ability to delete redundant clusters but does not increase the clusters, the number of clusters obtained by the IFDP algorithm will be greater than that obtained by the fast density peak (FDP) algorithm. Furthermore, the EAPCM algorithm improved the clustering center point of the adaptive probabilistic c-Means (APCM) algorithm, and replaced it with an adaptive line segment, which makes the algorithm adapt to the vehicle-mounted MMW radar measurement dataset. Two different experiments are carried out in this article, the results show the stability and reliability of the stationary and moving vehicle target recognition method in extracting the measurement values of moving targets, and when compared to other algorithms, the EAPCM algorithm has a higher classification rate (CR) and matching accuracy.

For the high-performance computing in a WAN environment, the geographical locations of national supercomputing centers are scattered and the network topology is complex, so it is difficult to form a unified view of resources. To aggregate the widely dispersed storage resources of national supercomputing centers in China, we have previously proposed a global virtual data space named GVDS in the project of “High Performance Computing Virtual Data Space”, a part of the National Key Research and Development Program of China. The GVDS enables large-scale applications of the high-performance computing to run efficiently across WAN. However, the applications running on the GVDS are often data-intensive, requiring large amounts of data from multiple supercomputing centers across WANs. In this regard, the GVDS suffers from performance bottlenecks in data migration and access across WANs. To solve the above-mentioned problem, this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation strategy. In a WAN environment, the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources, guaranteeing lower average migration latency when multiple data migration tasks are running in parallel. In addition, it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests (the IO proxy is a module of GVDS), so as to improve the application’s performance across WANs. The experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.

Fixed polarity Reed-Muller (RM) expression (FPRM) has several practical applications due to its multitude of properties. In order to generate an FPRM with minimum power, based on a genetic algorithm, we propose a Power optimization approach (POA-FPRMs) of Fixed Polarity RM expressions for incompletely specified Boolean functions. Simulation results on MCNC benchmark circuits show that POAFPRMs can effectively reduce power, compared with the traditional polarity optimization approach, where the don't care terms are neglected.

Delay optimization has recently attracted significant attention. However, few studies have focused on the delay optimization of mixed-polarity Reed-Muller (MPRM) logic circuits. In this paper, we propose an efficient delay optimization approach (EDOA) for MPRM logic circuits under the unit delay model, which can derive an optimal MPRM logic circuit with minimum delay. First, the simplest MPRM expression with the fewest number of product terms is obtained using a novel Reed-Muller expression simplification approach (RMESA) considering don’t-care terms. Second, a minimum delay decomposition approach based on a Huffman tree construction algorithm is utilized on the simplest MPRM expression. Experimental results on MCNC benchmark circuits demonstrate that compared to the Berkeley SIS 1.2 and ABC, the EDOA can significantly reduce delay for most circuits. Furthermore, for a few circuits, while reducing delay, the EDOA incurs an area penalty.

The fair division theory has emerged as a promising approach for the throughput allocation of hybrid storage system. While most of them cause the unfair allocation of the throughput servicing time due to taking no consideration of clients' request IO latencies. We propose one Servicing time allocation method based on the client grouping mechanism(STCG), STCG establishes fair allocation policies based on clients' workload weights, which can achieve a fair throughput servicing time allocation, and can maximize the resource utilization. The experimental result shows that STCG's allocation enjoys fair properties in each client group, and compared with the method proposd recently, STCG can improve the resource utilization about 12%-330%.

Although the genetic algorithm has been widely used in the polarity optimization of mixed polarity Reed-Muller (MPRM) logic circuits, few studies have taken into account the polarity conversion sequence. In order to improve the efficiency of polarity optimization of MPRM logic circuits, we propose an efficient and fast polarity optimization approach (FPOA) considering the polarity conversion sequence. The main idea behind the FPOA is that, firstly, the best polarity conversion sequence of the polarity set waiting for evaluation is obtained by using the proposed hybrid genetic algorithm (HGA); secondly, each of polarity in the polarity set is converted according to the best polarity conversion sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been presented for MCNC benchmark circuits. The experimental results show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of polarity optimization of MPRM logic circuits compared with the traditional polarity optimization approach which neglects the polarity conversion sequence and the improved polarity optimization approach with heuristic technique.

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing Center in Guangzhou, National Supercomputing Center in Jinan, National Supercomputing Center in Changsha, Shanghai Supercomputing Center, and Computer Network Information Center in Chinese Academy of Sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

Power optimization can reduce heat dissipation costs and has become an important step of circuit logic synthesis. Because the power optimization for mixed polarity Reed–Muller (MPRM) circuits is a combinatorial optimization problem, in this paper, we first propose a multilevel adaptive memetic algorithm (MAMA), which includes global exploration optimizer, local heuristic optimizer, and initial population optimizer. We use the proposed differential evolution optimization, simulated annealing optimization, and data matching algorithm to make the population evolve. Moreover, based on the proposed matrix decomposition strategy and parallel polarity conversion algorithm, we propose a power optimization approach (POA) for MPRM circuits, which searches for an MPRM circuit with a minimum power using the MAMA. Experimental results demonstrated the effectiveness and superiority of the POA in optimizing the power of MPRM circuits.