Abstract-To deal with pattern synthesis of antenna arrays, a chaotic particle swarm optimization (CPSO) is presented to avoid the premature convergence. By fusing with the ergodic and stochastic chaos, the novel algorithm explores the global optimum with the comprehensive learning strategy. The chaotic searching region can be adjusted adaptively. To evaluate the performance of CPSO, several representative benchmark functions are minimized using various optimization algorithms. Numerical results demonstrate that the proposed approach improves the performance of the algorithm significantly, in terms of both the convergence speed and exploration ability. Moreover, CPSO was applied to array synthesis examples, including the equally spaced linear array, unequally spaced linear array and conformal array, compared with other optimization methods. Experimental results show its high performance in the pattern synthesis with low side lobe, multi-nulls and shaped beam.

In this paper we propose a novel aesthetic model emphasizing psychovisual statistics extracted from multiple levels in contrast to earlier approaches that rely only on descriptors suited for image recognition or based on photographic principles. At the lowest level, we determine dark-channel, sharpness and eye-sensitivity statistics over rectangular cells within a frame. At the next level, we extract Sentibank features (1, 200 pre-trained visual classifiers) on a given frame, that invoke specific sentiments such as "colorful clouds", "smiling face" etc. and collect the classifier responses as framelevel statistics. At the topmost level, we extract trajectories from video shots. Using viewer's fixation priors, the trajectories are labeled as foreground, and background/camera on which statistics are computed. Additionally, spatio-temporal local binary patterns are computed that capture texture variations in a given shot. Classifiers are trained on individual feature representations independently. On thorough evaluation of 9 different types of features, we select the best features from each level -dark channel, affect and camera motion statistics. Next, corresponding classifier scores are integrated in a sophisticated low-rank fusion framework to improve the final prediction scores. Our approach demonstrates strong correlation with human prediction on 1, 000 broadcast quality videos released by NHK as an aesthetic evaluation dataset.

Abstract-It is generally believed genetic algorithm (GA) is superior to particle swarm optimization (PSO) while dealing with the discrete optimization problems. In this paper, a suitable mapping method is adopted and the modified PSO can effectively deal with the discrete optimization problems of linear array pattern synthesis. This strategy has been applied in thinned linear array pattern synthesis with minimum sidelobe level, 4-bit digital phase shifter linear array pattern synthesis and unequally spaced thinned array pattern synthesis with minimum sidelobe level. The obtained results are all superior to those in existing literatures with GA, iterative FFT and different versions of binary PSO, that show the effectiveness of this strategy and its potential application to other discrete electromagnetic optimization problems.

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