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Node flip ambiguity is a key problem that needs to be addressed for the range-based node localization in wireless sensor networks. A Robust Node Localization Method (RNLM) is proposed in this paper. It is a two phase methods. In the first phase, the orthogonal projection detection method is adopted to detect the unknown nodes needed to be positioned. For the unknown nodes that will not occur flip...
Node flip ambiguity is a key problem that needs to be addressed for range-based node localization in wireless sensor networks. In this paper we have implemented robustness analysis for three-dimensional node localization in wireless sensor networks. A robustness criterion to detect flip ambiguity for range-based three-dimensional nodes localization is proposed. We have proposed that flip ambiguity...
In the design of mobile ad hoc networks, a challenging problem is how to route information reliably and efficiently from one node to another in moderate to high level of mobility. In this paper, we propose a new routing protocol, called link effective available time (LEAT) routing. First, we propose a method to find the link available time during an epoch by measuring the distances between the two...
While Two-Way Ranging (TWR) is generating interest as a accurate and low-cost indoor localization technique, most TWR research focuses primarily on algorithms or error reduction, with little regard to a fundamental system-level issue: deciding which Base Stations (BS) should perform the TWR. While most researchers acknowledge the need for a scanning phase to select BSs before the ranging phase is...
This paper presents a FPGA-based auto focusing system for object ranging. It overcomes the disadvantage of manual focusing wise in traditional method of objects ranging. In our system, the distance of objects can be measured automatically by the auto focusing algorithm. We have experimentally demonstrated the effectiveness of this method. The best performance of our system is about 3% relative errors...
In this work, we described a new two-stage hierarchical framework for mammogram retrieval. We tested the proposed approach on the reference library from USF-DDSM. For each query ROI (region of interest), the proposed scheme first computes its 14 texture and shape features, then the voting method based on five classifiers is used to classify the ROIs in the reference library, this phase eliminates...
In this paper, we study semisupervised linear dimensionality reduction. Beyond conventional supervised methods which merely consider labeled instances, the semisupervised scheme allows to leverage abundant and ample unlabeled instances into learning so as to achieve better generalization performance. Under semisupervised settings, our objective is to learn a smooth as well as discriminative subspace...
Floorplanning is becoming more and more important in VLSI design flows, especially for system-on-chip (SoC) designs where IP blocks dominate standard cells. Moreover, in deep sub-micron technologies, where process variations can introduce extra signal skew, it is desirable to have floorplans with balanced net delays to increase the safety margins of the design. In this paper, we investigate the properties...
This paper considers a circular high-density WSNs, which is divided into several homogeneous sectors. For a sector, the route nodes transmit data to base station (BS) along the sides of the sector and the radio of nodes can cover all areas of target area. The paper presents an energy-balance solution to balance the energy consumption, by adjusting transmission range with quality coverage under a FSCTA...
This study applies a hybrid MCDM model for risk evaluation in EC development. First, the risk in EC development is formulated by the combined use of the MCDM (multi-criteria decision making) approach and a proposed five-step hybrid process, which incorporates the technique of ANP (analytic network process) and TOPSIS (technique for order performance by similarity to idea solution). The newly developed...
Typical content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap challenge. Hence, relevance feedback has been adopted as a promising approach to improve the search performance. In this paper, we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called ldquoCollaborative...
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