17604686
Enhanced high-performance distributed coordination function for IEEE 802.11 multi-rate LANs
To compensate for the effects of fading in wireless channels, IEEE 802.11 systems utilize a rate-adaptation mechanism to accomplish a multi-rate capability. However, the IEEE 802.11 distributed coordination function results in a fundamental performance anomaly in multi-rate networks; namely, when stations with different transmission rates collide, the throughput performance of the high-rate station is significantly degraded by the relatively longer channel occupancy time of the low-rate station. This study resolves this problem through the use of an enhanced high-performance distributed coordination function (EHDCF) protocol. While most existing solutions to the multi-rate performance anomaly problem have the form of simple contention-based protocols, EHDCF has two modes, namely a contending mode and an active mode. In the proposed protocol, new stations joining the network are assigned a contending mode, but switch to an active node (and are therefore permitted to transmit data packets) as soon as they have gained access to the channel. Having transmitted a data packet, the active node then selects the next transmission station in accordance with a probability-based rule designed such that the high-rate stations within the network receive a greater number of transmission opportunities than the low-rate stations. The simulation results show that the EHDCF protocol not only yields a significant improvement in the network throughput but also guarantees the temporal fairness of all the stations. Copyright q 2009 John Wiley & Sons, Ltd.
16607650
Performance Analysis of Multi-channel DCA MAC Protocol in Cognitive Wireless Mesh Networks
The spectrum resource gets scared with the development of wireless communication. Users in cognitive wireless mesh network need to switch among multi-channels to communicate. In this paper, we propose a mathematical model of multi-channel DCA protocol in cognitive wireless mesh network. This model is based on Bernoulli and M/M/K queuing model. From the validation by compare simulation with analytical result, we can see our model and analysis is simple and accurate.
10205775
Kernel-based Global Folley-Sammon Discriminate Analysis
Kernel trick is a useful method dealing with the nonlinearity of patterns ' distribution, and widely used for non-linear pattern classification problem. Recently, many kernel-based Fisher discriminate methods were proposed, the features extracted by those methods are statistically uncorrelated, usually ineffectively when used in so called "small sample problem "(SSS), which exists in most FR tasks. To overcome this shortcoming, in this paper we present Kernel-based Global Folley-Sammon Discriminate Analysis (KGFS), it can be realized in three steps: firstly by combing kernel function, map data onto an implicit high-dimensional feature space, secondly construct orthogonal feature space there, finally calculate iterative global orthogonal discriminate vectors. Experiments have been done on ORL dataset. In terms of classification error rate performance, proposed kernel based method KGFS has a better performance than other three commonly used kernel-based methods, such as GDA, MGDAandKDDA.
45662594
Neural Network Based Kinematic Control of the Hyper-Redundant Snake-Like Manipulator
In a sinusoid like curve configuration, the snake-like manipulator (also called snake arm) has a wide range of potential applications for its redundancy to overcome conventional industrial robot's limitation when carrying out a complex task. It can perform many kinds of locomotion like the nature snake or the animal's tentacle to avoid obstacles, follow designated trajectories, and grasp objects. Effectively control of the snake-like manipulator is difficult for its redundancy. In this study, we propose an approach based on BP neural network to kinematic control the hyper-redundant snake-like manipulator. This approach, inspired by the Serpenoid curve and the concertina motion principle of the nature snake, is completely capable of solving the control problem of a planar snake-like manipulator with any number of links following any desired direction and trajectory. With shape transformation and base rotation, the manipulator's configuration changes accordingly and moves actively to perform the designated tasks. By using BP neural networks in modeling the inverse kinematics, this approach has such superiorities as few control parameters and high precision. Simulations have demonstrated that this control technique for the snake-like manipulator is available and effective.
15225105
Development of an OpenGL based multi-robot simulating platform
Simulating platform plays a crucial role in multi-robot research, as a tool to quickly and efficiently test new concepts, strategies, and algorithms. In this paper, OpenGL and Visual C++ based modeling method has been discussed in detail to establish a platform for the 3D simulating of multi-link mobile robots and their collaboration. The cooperation strategies, the control methods and the communication mechanisms of multi-robot system can be explored and verified by this platform. A planetary robot system is used to verify the validity of the developed platform. The effectiveness and the practicability of the platform are successfully demonstrated by a four-robot motion coordination simulating results.
34344940
A Distributed Hebb Neural Network for Network Anomaly Detection
One of the most challenging problems in anomaly detection is to develop scalable algorithms which are capable of dealing with large audit data, network traffic data, or alter data. In this paper a distributed neural network based on Hebb rule is presented to improve the speed and scalability of inductive learning. The speed is improved by randomly splitting a large data set into disjoint subsets and each subset data is presented to an independent neural network, these networks can be trained in distributed and each one in parallel. The analysis of completeness and risk bounds of competitive Hebb learning proof that the distributed Hebb neural network can avoid the accuracy being degraded as compared to running a single algorithm with the entire data. The experiments are performed on the KDD'99 Data set, which is a standard intrusion detection benchmark. Comparisons with other approaches on the same benchmark demonstrate the effectiveness and applicability of the proposed method.
62585345
An effective collision resolution mechanism for wireless LAN
Based on the current standardized IEEE 802.11 distributed coordination function (DCF) protocol, this paper proposes a new efficient collision resolution mechanism, called GDCF (gentle DCF). Our main motivation is based on the observation that 802.11 DCF decreases the contention window to the initial value after each success transmission, which essentially assumes that each successful transmission is an indication that the system is under low traffic loading. GDCF takes a more conservative measure by halving the contention window size after c consecutive successful transmissions. This "gentle" decrease lowers the collision probability, especially when the competing node number is large. We compute the optimal value for c, and the numerical results from both analysis and simulation demonstrate that GDCF significantly improve the performance of 802.11 DCF including throughput, fairness, and energy efficiency. In addition, GDCF is flexible for supporting priority access by selecting different values of c for different traffic types; it is fully compatible with the original 802.11 DCF, and simple to implement, as it does not requires changes in control message structure and access procedures in DCF.