A metasurface-loaded 1×2 patch array antenna assisted by a deep-learning optimization method is proposed to realize port and radiation pattern decoupling simultaneously to enhance the isolation among elements in multi-input multi-output (MIMO) systems. The deep-learning-assisted optimization method uses an artificial neural network (ANN) and a particle swarm optimization (PSO) algorithm to seek the optimal structure of the antenna to achieve port decoupling with undistorted radiation patterns. The ANN is trained to describe the nonlinear relationship between the geometric parameters and the responses of the antenna. The PSO algorithm, guided by the cost function and number of iterations, is used to optimize the structure of the antenna according to the cost function combined with the trained ANN. Finally, by constraining the cost function, we obtain a 1×2 patch array antenna with a metasurface fixed above by studs, which achieves port and radiation pattern decoupling simultaneously. To validate the principle and design method, we designed, fabricated, and measured an antenna prototype with dimensions of 0.88 λ 0 ×0.47 λ 0 ×0.21 λ 0 ( λ 0 is the wavelength in free space at the center frequency). The measured fractional bandwidth is 8% (4.8–5.2 GHz). The isolation of the two-element patch antenna increases from 7.6 dB to 24.3 dB with an envelope correlation coefficient (ECC) of <0.0005 at 0.35 λ 0 . Moreover, the H-plane radiation pattern of each element is consistent and symmetric in the broadside direction. These characteristics make the proposed antenna suitable for MIMO antenna systems with close spacing.

In this paper, we present FlexBFS, a parallelism-aware implementation for breadth-first search on GPU. Our implementation can adjust the computation resources according to the feedback of available parallelism dynamically. We also optimized our program in three ways: (1)a simplified two-level queue management,(2)a combined kernel strategy and (3)a high-degree vertices specialization approach. Our experimental results show that it can achieve 3~20 times speedup against the fastest serial version, and can outperform the TBB based multi-threading CPU version and the previous most effective GPU version on all types of input graphs.

Many applications meet certain programming patterns like pipeline, fork-join, do-all etc. While tools such as OS threads and OpenMP allow programmers only to express task or data parallelism, special support for programming patterns is distinctly lacking. Intel threading building blocks (TBB) is developed to address this problem, but its scheduler is general and not optimized for any of its parallel algorithms which include pipeline specially. In this paper, we provide a non-blocking framework for pipeline application on multi-core platform. We target linear pipeline in which each filter has one entrance and one exit. We design a novel work-stealing scheduler optimized specially for pipeline application: first, priority based stealing, priority is calculated for each filter in pipeline so that a worker can find the optimal "victim" easily when it needs to steal, second, multiple tasks can be stolen at a time so that much stealing time is reduced. A non-block queue is used to store intermediate result to reduce lock overhead and increase scalability. We apply our framework to four case studies, including text filter, two fish, ferret, ded up. And our framework reduces execution time of TBB by 72% in best case and 20% on average on an 8 core machine.

The Cell Broadband Engine is a typical heterogeneous chip multiprocessor which provides potential high performance for computing-intensive applications. Our researches focus on how to use Cell to speed up block cryptography applications. In this paper, we propose a mapping framework for block cryptography working in ECB mode and corresponding optimizing strategy. We take four algorithms(RC5, 3DES, AES, and Twofish) as benchmark and implement these four algorithms using Cell programming language. In order to enhance the performance, we present an optimizing strategy and evaluate the effects of the optimizing methods including compiler optimization, dual buffering, vectorization, and loop unrolling. The experiments indicate that all these four algorithms can obtain 5-20 times speedup compared with traditional processors, which shows that our mapping framework and optimizing strategy are effective for the block cryptography algorithms.