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Content available An Improved Greedy Forwarding Scheme in MANETs
EN
Position-based routing protocols are widely accepted efficient solution for routing in MANETs. The main feature of position-based routing protocols is to use greedy forwarding methods to route data. The greedy forwarding methods select a node, either having maximum progress towards destination (distance-based principle) or minimum deviation with line between source and destination (direction-based strategy). The first method minimizes the hopcount in a path and on the other hand, second method minimizes the spatial distance between source and destination. The distance-based routing has a great impact on the selection of reliable node and the direction based routing plays a major role to increase the stability of route towards destination. Therefore, in this paper authors propose a weighted forwarding method, which combines both the selection, schemes to select an optimal next forwarding node in a range. The simulation results show that the proposed scheme performs better than existing position-based routing protocols.
EN
In literature, varieties of topology and geographical routing protocols have been proposed for routing in the MANETs. It is widely accepted that the geographical routings are a superior decision than topological routings. Majority of geographical routing protocols assume an ideal network model and choose the route that contains minimum number of hops. However, in reality, nodes have limited battery power and wireless links are additionally unreliable, so they may highly affect the routing procedure. Thus, for reliable data transmission, condition of the network such as link quality and residual energy must be considered. This paper aims to propose a novel multi-metric geographical routing protocol that considers both links-quality and energy metric along with progress metric to choose the next optimal node. The progress is determined by utilizing greedy as well as compass routing rather than pure greedy routing schemes. To combine these metrics, fuzzy logics are used to get the optimal result. Further, the protocol deals with “hole” problem and proposes a technique to overcome it. Simulations show that the proposed scheme performs better in terms of the packet delivery ratio, throughput and residual energy than other existing protocols.
3
Content available remote Effect of Ag on Sn-Cu and Sn-Zn lead free solders
EN
Lead and lead-containing compounds are considered as toxic substances due to their detrimental effect on the environment. Sn-based soldering systems, like Sn-Cu and Sn-Zn are considered as the most promising candidates to replace the eutectic Sn-Pb solder compared to other solders because of their low melting temperature and favorable properties. Eutectic Sn-0.7 wt.% Cu and near eutectic composition Sn-8 wt.% Zn solders have been considered here for study. For the Sn-Cu system, besides the eutectic Sn-0.7 wt.% Cu composition, Sn-1Cu and Sn-2Cu were studied. Three compositions containing Ag: Sn-2Ag-0.7Cu, Sn-2.5Ag-0.7Cu and Sn-4.5Ag-0.7Cu were also developed. Ag was added to the eutectic Sn-0.7 wt.% Cu composition in order to reduce the melting temperature of the eutectic alloy and to enhance the mechanical properties. For the Sn-Zn system, besides the Sn-8 wt.% Zn near eutectic composition, Sn-8Zn-0.05Ag, Sn-8Zn-0.1Ag and Sn-8Zn-0.2Ag solder alloys were developed. The structure and morphology of the solder alloys were analyzed using a scanning electron microscope (SEM), filed emission scanning electron microscope (FESEM), electron diffraction X-ray spectroscopy (EDX) and X-ray diffraction (XRD). Thermal analysis of the alloys was also done using a differential scanning calorimeter (DSC). Trace additions of Ag have been found to significantly reduce the melting temperature of the Sn-0.7 wt.% Cu and Sn-8 wt.% Zn alloys.
EN
This paper describes a computationally inexpensive approach to learning and identification of maneuverable terrain to aid autonomous navigation. We adopt a monocular vision based framework, using a single consumer grade camera as the primary sensor, and model the terrain as a Mixture of Gaussians. Self-supervised learning is used to identify navigable terrain in the perception space. Training data is obtained using pre-filtered pixels, which correspond to near-range traversable terrain. The scheme allows for on-line, and in-motion update of the terrain model. The pipeline architecture used in the proposed algorithm is made amenable to real-time implementation by restricting computations to bit-shifts and accumulate operations. Color based clustering using dominant terrain texture is then performed in perception sub-space. Model initialization and update follows at the coarse scale of an octave image pyramid, and is back projected onto the original fine scale. We present results of terrain learning, tested in heterogeneous environments, including urban road, suburban parks, and indoors. Our scheme provides orders of magnitude improvement in time complexity, when compared to existing approaches reported in literature.
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