361499
Energy Efficient Broadcast in Wireless Ad hoc Networks with Hitch-hiking
In this paper, we propose a novel concept called Hitch-hiking in order to reduce the energy consumption of broadcast application for wireless networks. Hitch-hiking takes advantage of the physical layer design that facilitates the combining of partial signals to obtain the complete information. The concept of combining partial signals using maximal ratio combiner [15] has been used to improve the reliability of the communication link but has never been exploited to reduce energy consumption in broadcasting over wireless ad hoc networks. We study the advantage of Hitch-hiking for the scenario when the transmission power level of nodes is fixed as well as the scenario when the nodes can adjust their power level. For both scenarios, we show that Hitch-hiking is advantageous and have proposed algorithms to construct broadcast tree with Hitch-hiking taken into consideration. For fixed transmission power case, we propose and analyze a centralized heuristic algorithm called SPWMH (Single Power Wireless Multicast with Hitch-hiking) to construct a broadcast tree with minimum forwarding nodes. For the latter case, we propose a centralized heuristic algorithm called Wireless Multicast with Hitch-hiking (WMH) to construct an energy efficient tree using Hitch-hiking and also present a distributed version of the heuristic. We also evaluate the proposed heuristics through simulation. Simulation results show that Hitch-hiking can reduce the transmission cost of broadcast by as much as 50%. Further, we propose and evaluate a protocol called Power Saving with Broadcast Tree (PSBT) that reduces energy consumption of broadcast by eliminating redundancy in receive operation. Finally, we propose an algorithm that takes advantage of both Hitch-hiking and PSBT in conserving energy.
17371165
QoS Routing in Hypercube Multicomputers
In this paper, we present a coding method to capture QoS information in hypercube multicomputers. The coding method is based on a localized algorithm where each node interacts with its neighbors to gather QoS information. Specifically, each node maintains a QoS vector where the k-th element represents the guaranteed QoS performance to a destination that is k hops away. The localized algorithm exhibits desirable properties of self-stabilizing, self-optimizing, and self-healing. Simulation results show that this coding method provides a good approximation of the minimum QoS value to a k-hop destination and, at the same time, uses a relatively small number of packets to propagate a change in link state (QoS value) compared with the classical distributed Bellman-Ford method.
7582647
On self-similarity and hamiltonicity of dual-cubes
The dual-cube is a newly proposed topology for interconnection networks, which uses low dimensional hypercubes as building blocks. The primary advantages of the dual-cube over the hypercube are that, with the same node degree n, the dual-cube contains 2/sup n-1/ times more nodes than the hypercube and, with the same amount of nodes, the dual-cube has approximately 50% less links than the hypercube. This paper was focused on the investigations of the structural self-similarity and the Hamiltonian property of the dual-cube. It was shown that a dual-cube can be recursively constructed from lower dimensional dual-cubes and, conversely, a dual-cube can be recursively decomposed into lower dimensional dual-cubes. It was also proved that all dual-cubes are Hamiltonian. There exist multiple Hamiltonian cycles on a dual-cube, among which, [(n-1)/2] cycles are edge-disjoint. It was illustrated that rings and linear arrays can be effectively emulated on dual-cubes. Some strategies for track sharing in efficient VLSI layout design were also discussed.
16257632
XYZ: a scalable, partially centralized lookup service for large-scale peer-to-peer systems
Peer-to-peer (P2P) systems are characterized by direct access between peer computers, rather than through a centralized server. File sharing is the dominant P2P application on the Internet, allowing users to easily contribute, search and obtain content. P2P systems can be categorized by the degrees of centralization. For fully centralized systems, the lookup service will not be available when the central directory server is down. For purely decentralized and unstructured systems, since there is no information about which nodes are likely to have the relevant files, searching essentially amounts to random search. This makes the lookup service unscalable and unpredictable. The objective of this paper is to design a partially centralized, scalable and self-organizing lookup service (XYZ) for wide area P2P systems. A clustering method is used to create the system backbone by connecting the cluster heads together and a color clustering method is adopted to create color overlays and minimize the searching space. Simulations and analysis are also provided. Extensions are proposed to achieve better performance
2254959
Local-Safety-Information-Based Broadcasting in Hypercube Multicomputers with Node and Link Faults
This paper presents a method to cope with fault-tolerant broadcasting in hypercube multicomputers with both node and link faults. The local safety concept is extended to faulty hypercubes with both node and link faults. The local-safety-based algorithm is used in a fully unsafe hypercube, where there is no safe node. A fully unsafe hypercube can be split into a set of maximal safe subcubes. We show that if these maximal safe subcubes meet certain requirements given in the paper, broadcasting can still be carried out successfully and in some case optimal broadcast is still possible. The method is extended to fault-tolerant routing and multicasting when the system contains both node and link faults.
27746153
Broadcasting in Faulty Cube-Connected-Cycles with Minimum Recovery Time
We propose a broadcasting algorithm for a faulty Cube-Connected-Cycles multiprocessor. The broadcasting algorithm delivers a message to all the nodes non-redundantly using a binomial tree. The traffic generated because of the recovery is guaranteed to be minimum.
2718321
Face recognition with one training image per person
At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. projection-combined principal component analysis, (PC)2A, is proposed. (PC)2A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results show that on a gray-level frontal view face database where each person has only one training image, (PC)2A achieves 3-5% higher accuracy than the standard eigenface technique through using 10-15% fewer eigenfaces.
59008197
A cycle-based rerouting scheme for wavelength-routed WDM networks
In this paper, we propose a cycle-based rerouting scheme (CRS) for link restoration in wavelength-routed WDM networks. The CRS is based on a scalable set of Pcycles covering a mesh network. Instead of using these Pcycles as enhanced self-healing rings as in traditional approaches, the CRS decomposes these P-cycles into rerouting paths for the links that the P-cycles cover. In this way, flexible and cost-effective link restoration is achieved. Simulation results show that the rerouting performance of the CRS can be very close to the theoretical upper bound.
45978330
Two sufficient conditions for a graph to be type 1
204989501
On clique partitions of split graphs
Abstract Split graphs are graphs formed by taking a complete graph and an empty graph disjoint from it and some or all of the possible edges joining the two. We prove that the problem of deciding the clique partition number is NP-complete, even when restricted to the class of split graphs.
412675
Linear Asymmetric Classifier for cascade detectors
The detection of faces in images is fundamentally a rare event detection problem. Cascade classifiers provide an efficient computational solution, by leveraging the asymmetry in the distribution of faces vs. non-faces. Training a cascade classifier in turn requires a solution for the following subproblems: Design a classifier for each node in the cascade with very high detection rate but only moderate false positive rate. While there are a few strategies in the literature for indirectly addressing this asymmetric node learning goal, none of them are based on a satisfactory theoretical framework. We present a mathematical characterization of the node-learning problem and describe an effective closed form approximation to the optimal solution, which we call the Linear Asymmetric Classifier (LAC). We first use AdaBoost or AsymBoost to select features, and use LAC to learn a linear discriminant function to achieve the node learning goal. Experimental results on face detection show that LAC can improve the detection performance in comparison to standard methods. We also show that Fisher Discriminant Analysis on the features selected by AdaBoost yields better performance than AdaBoost itself.