Stream clustering is a fundamental problem in many streaming data analysis applications. Comparing to classical batchmode clustering, there are two key challenges in stream clustering: (i) Given that input data are changing continuously, how to incrementally update clustering results efficiently? (ii) Given that clusters continuously evolve with the evolution of data, how to capture the cluster evolution activities? Unfortunately, most of existing stream clustering algorithms can neither update the cluster result in real time nor track the evolution of clusters.In this paper, we propose an stream clustering algorithm EDMStream by exploring the Evolution of Density Mountain. The density mountain is used to abstract the data distribution, the changes of which indicate data distribution evolution. We track the evolution of clusters by monitoring the changes of density mountains. We further provide efficient data structures and filtering schemes to ensure the update of density mountains in real time, which makes online clustering possible. The experimental results on synthetic and real datasets show that, comparing to the state-of-the-art stream clustering algorithms, e.g., D-Stream, DenStream, DBSTREAM and MR-Stream, our algorithm can response to a cluster update much faster (say 7-15x faster than the best of the competitors) and at the same time achieve comparable cluster quality. Furthermore, EDMStream can successfully capture the cluster evolution activities.

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Opportunistic array radar (OAR) is a new generation radar system based on the stealth of the platform, which can improve the modern radar performance effectively. Designing the orthogonal code sets with low autocorrelation and cross-correlation is a key issue for OAR. This paper proposes a novel hybrid genetic algorithm (HGA) and designs the polyphase orthogonal code sets with low autocorrelation and cross-correlation properties, which can be used in the OAR system. The novel algorithm combines with simulated annealing (SA) and genetic algorithm (GA), adds in keeping best individuals and competition in small scope, and introduces grey correlation evaluation to evaluate fitness function. These avoid the premature convergence problem existed in GA and enhance the global searching capability. At last, the genetic results are optimized to obtain the best solution by using greedy algorithm. The simulation results show that the proposed algorithm is effective for the design of orthogonal phase signals used in OAR systems.

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In this paper, the optical and electrical characteristics of ultra-thin GaAs solar cell with front-surface pyramid array are investigated systematically. The results show that the cell with pyramid array could significantly improve the photon absorption efficiency due to synergistic effects of antireflection and diffraction compared with reference solar cells with single and double anti-reflective coating (ARC), and the maximal integrated quantum efficiency of 0.88 is obtained for pyramid cells, much higher than that of 0.75 and 0.71 for double and single ARC solar cells, respectively. Electric simulation demonstrates the total recombination rate of solar cell with pyramid array is higher than that of reference solar cells due to higher carrier concentration. The radiative recombination dominated in the base and emitter region, while the SRH recombination becomes the primary recombination mechanism in the space charge region, both which are much higher than Auger recombination. Surface recombination has little impact on the photovoltaic performance due to the presence of wide bandgap window layer. For pyramid cell, the J_sc,V_oc,η of 22.65mA/cm2, 1.120V and 21.84% are obtained when considering photon recycling effect, respectively, are higher than those of 19.52mA/cm2, 1.106V and 18.72% for double ARC solar cell, and those of 18.32mA/cm2, 1.106V and 17.60% for single ARC solar cell.