Many objects have repetitive elements, and finding repetitive patterns facilitates object recognition and numerous applications. We devise a representation to describe configurations of repetitive elements. By modeling spatial configurations, visual patterns are more discriminative than local features, and are able to tackle with object scaling, rotation, and deformation. We transfer the pattern discovery problem into finding frequent subgraphs from a graph, and exploit a graph mining algorithm to solve this problem. Visual patterns are then exploited in architecture image classification and product image retrieval, based on the idea that visual pattern can describe elements conveying architecture styles and emblematic motifs of brands. Experimental results show that our pattern discovery approach has promising performance and is superior to the conventional bag-of-words approach.

Algorithm visualization is a unique research topic that integrates engineering skills such as computer graphics, system programming, database management, computer networks, etc., to facilitate algorithmic researchers in testing their ideas, demonstrating new findings, and teaching algorithm design in the classroom. Within the broad applications of algorithm visualization, there still remain performance issues that deserve further research, e.g., system portability, collaboration capability, and animation effect in 3D environments. Using modern technologies of Java programming, we develop an algorithm visualization and debugging system, dubbed GeoBuilder, for geometric computing. The GeoBuilder system features Java's promising portability, engagement of collaboration in algorithm development, and automatic camera positioning for tracking 3D geometric objects. In this paper, we describe the design of the GeoBuilder system and demonstrate its applications.

In this letter, we examine the symbol-error-rate (SER) performance of decode-and-forward (DF) cooperative communications. Our focus is on the scenario in which multiple dual-hop relays are employed and the channel environment is described by Nakagami-m fading. Cooperative diversity is observed from the derived error-rate expressions, and some insight into how channel conditions in the relay nodes affect the SER performance is obtained. In addition, with the knowledge of the partial channel state information (CSI) at the transmitting sides, we derive an optimal power allocation scheme to further enhance the SER performance. Simulation results are finally presented to demonstrate the correctness of our analysis and perception for the considered DF protocol.

In this paper, we analyze the symbol-error-rate (SER) performance of decode-and-forward (DF) cooperative communications over Nakagami-m fading channels. For the classical three-node cooperation model, which includes a source, a relay and a destination, the exact SER expressions for M-phase shift keying (PSK) and M-quadrature amplitude modulation (QAM) are derived. For easy usage, we provide asymptotic approximations for the SER as well. In addition, optimal power allocation is investigated based on the derived asymptotic SER expressions. Simulations are finally provided to verify the correctness of our analysis.

Geometric algorithm visualization techniques are very important to algorithmic research in geometric computing. The relevant applications include geometric algorithm development, testing, demonstration and teaching. Within these topics, there still remain performance issues to improve system portability, animation effect and so on. In this paper, we present a geometric algorithm visualization system that is featured by Java's portability and the "dynamic decision of camera position" for 3D geometric algorithm visualization.