9 research outputs found

    Received Signal Strength Indicator Node Localization Algorithm Based on Constraint Particle Swarm Optimization

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    Because the RSSI value greatly changes, the direct use of RSSI value has more errors in the positioning process as the basis to calculate the position of anchor nodes. This paper proposes a RSSI node localization algorithm based on constraint particle swarm optimization (PSO-RSSI). In the algorithm, particle swarm optimization is used to select anchor nodes set which are near the unknown node. The algorithm takes an element in the set, and measure distance between it and the other elements in the set. Then, the maximum likelihood method is used to calculate the coordinates. According to the difference between the calculated coordinates and the actual coordinates of the anchor node, the obtain coordinate of unknown node is corrected. When all the elements in the set perform such operation, the statistical methods are used to determine the coordinates of the unknown node. The algorithm embodies all the reference points influence on positioning, corrects the error problem on a single reference node positioning in the past. The simulation results show that the effect of the PSO-RSSI algorithm is more excellent

    Embedded Palmprint Recognition System Using OMAP 3530

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    We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the ccentral pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance

    Semantic-Based Requirements Content Management for Cloud Software

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    Cloud Software is a software complex system whose topology and behavior can evolve dynamically in Cloud-computing environments. Given the unpredictable, dynamic, elasticity, and on-demand nature of the Cloud, it would be unrealistic to assume that traditional software engineering can “cleanly” satisfy the behavioral requirements of Cloud Software. In particular, the majority of traditional requirements managements take document-centric approaches, which have low degree of automation, coarse-grained management, and limited support for requirements modeling activities. Facing the challenges, based on metamodeling frame called RGPS (Role-Goal-Process-Service) international standard, this paper firstly presents a hierarchical framework of semantic-based requirements content management for Cloud Software. And then, it focuses on some of the important management techniques in this framework, such as the native storage scheme, an ordered index with keywords, requirements instances classification based linear conditional random fields (CRFs), and breadth-first search algorithm for associated instances. Finally, a prototype tool called RGPS-RM for semantic-based requirements content management is implemented to provide supporting services for open requirements process of Cloud Software. The proposed framework applied to the Cloud Software development is demonstrated to show the validity and applicability. RGPS-RM also displays effect of fine-grained retrieval and breadth-first search algorithm for associated instance in visualization
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