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is an effective way of systematically exploring the search space of a program, and is often used for automatic software testing and bug finding. The program to be analyzed is usually compiled into a binary or an intermediate representation, on which is carried out. During this process, s influence the effectiveness and efficiency of . However, to the best of our knowledge, there exists no work on recommendation for with respect to (w.r.t.) , which is an important testing coverage criterion widely used for mission-critical software. This study describes our use of a state-of-the-art tool to carry out extensive experiments to study the impact of s on w.r.t. MC/DC. The results indicate that instruction combining (IC) optimization is the important and dominant optimization for w.r.t MC/DC. We designed and implemented a support vector machine based method w.r.t. IC (denoted as auto). The experiments on two standard benchmarks (Coreutils and NECLA) showed that auto achieves the best MC/DC on 67.47% of Coreutils programs and 78.26% of NECLA programs.

(GPGPUs) can be used to improve computing performance considerably for regular applications. However, irregular memory access exists in many applications, and the benefits of graphics processing units (GPUs) are less substantial for irregular applications. In recent years, several studies have presented some solutions to remove static irregular memory access. However, eliminating dynamic irregular memory access with software remains a serious challenge. A pure software solution without hardware extensions or offline profiling is proposed to eliminate dynamic irregular memory access, especially for indirect memory access. and index redirection are suggested to reduce the number of memory transactions, thereby improving the performance of GPU kernels. To improve the efficiency of , an operation to reorder data is offloaded to a GPU to reduce overhead and thus transfer data. Through concurrently executing the compute unified device architecture (CUDA) streams of and the data processing kernel, the overhead of can be reduced. After these optimizations, the volume of memory transactions can be reduced by 16.7%–50% compared with CUSPARSE-based benchmarks, and the performance of irregular kernels can be improved by 9.64%–34.9% using an NVIDIA Tesla P4 GPU.

The weighting subspace fitting (WSF) algorithm performs better than the multi-signal classification (MUSIC) algorithm in the case of low signal-to-noise ratio (SNR) and when signals are correlated. In this study, we use the (RMT) to improve WSF. RMT focuses on the asymptotic behavior of eigenvalues and eigenvectors of random matrices with dimensions of matrices increasing at the same rate. The approximative first-order perturbation is applied in WSF when calculating statistics of the eigenvectors of sample covariance. Using the asymptotic results of the norm of the projection from the sample covariance matrix onto the real signal in the , the method of calculating WSF is obtained. Numerical results are shown to prove the superiority of RMT in scenarios with few snapshots and a low SNR.

In dense traffic unmanned aerial vehicle (UAV) ad-hoc networks, traffic congestion can cause increased delay and packet loss, which limit the performance of the networks; therefore, a strategy is required to control the traffic. In this study, we propose TQNGPSR, a traffic-aware enhanced protocol based on greedy perimeter stateless routing (GPSR), for UAV ad-hoc networks. The protocol enforces a strategy using the congestion information of neighbors, and evaluates the quality of a wireless link by the algorithm, which is a algorithm. Based on the evaluation of each wireless link, the protocol makes routing decisions in multiple available choices to reduce delay and decrease packet loss. We simulate the performance of TQNGPSR and compare it with AODV, OLSR, GPSR, and QNGPSR. Simulation results show that TQNGPSR obtains higher packet delivery ratios and lower end-to-end delays than GPSR and QNGPSR. In high node density scenarios, it also outperforms AODV and OLSR in terms of the packet delivery ratio, end-to-end delay, and throughput.

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