Testbed results of an opportunistic routing for multi-robot wireless networks

Opportunistic routing is a candidate for multihop wireless routing where the network topology and radio channels vary rapidly. However, there are not many opportunistic routing algorithms that can be implemented in a real multihop wireless network while exploiting the node mobility. It motivates us to implement an opportunistic routing, random basketball routing (BR), in a real multi-robot network to see if it can enhance the capacity of the multihop network as mobility increases. For implementation purposes, we added some features, such as destination RSSI measuring, a loop-free procedure and distributed relay probability updating, to the original BR. We carried out the experiments on a real multi-robot network and compared BR with AODV combined with CSMA/CA (routing + MAC protocol). We considered both static and dynamic scenarios. Our experiments are encouraging in that BR outperforms AODV + CSMA/CA, particularly in dynamic cases; the throughput of BR is 6.6 times higher than that of AODV + CSMA/CA. BR with dynamic networks shows 1.4 times higher throughput performance than BR with static networks. We investigate the performance of BR in the large-scale network using NS-2 simulation. We verify the effect of node density, speed, destination beacon signal and loop-free procedure. According to the large-scale simulation, the end-to-end throughput grows with the node speed.     

Simulations, models, and testbeds: A mutual catalysis

Recent developments in the area of decentralized and infrastructureless systems opened avenues for novel applications. Along with these new technologies, new questions regarding their operational bounds in terms of e.g. scalability and security arose. Due to the sparse presence of real-world decentralized and infrastructureless systems, new protocols and applications have to be scrutinized by means of simulation, in (small-scale) testbeds, and by analytical models. In this paper, we discuss challenges of evaluating security mechanisms for mobile ad hoc networks and peer-to-peer systems. We focus on harmonizing predictions of analytical models and results obtained from simulation studies and testbed experiments.     

Scheduling gain for frequency-selective Rayleigh-fading channels with application to self-organizing packet scheduling

This paper investigates packet scheduling in the context of Self-Optimizing Networks, and demonstrates how to improve coverage dynamically by adjusting the scheduling strategy. We focus on α-fair schedulers, and we provide methods for calculating the scheduling gain, including several closed form formulas. Scheduling gain is analyzed for different fading models, with a particular focus on the frequency-selective channel. We then propose a coverage-capacity self-optimization algorithm based on α-fair schedulers. A use case illustrates the implementation of the algorithm and simulation results show that important coverage gains are achieved at the expense of very little computing power.     

Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws

Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective functions of the orthopaedic screws should be accurately developed. To our knowledge, there is no study to evaluate the strengths and limitations of the surrogate methods in developing the objective functions of the orthopaedic screws. Three-dimensional finite element models for both the tibial locking screws and the spinal pedicle screws were constructed and analyzed. Then, the learning data were prepared according to the arrangement of the Taguchi orthogonal array, and the verification data were selected with use of a randomized selection. Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network. The applicability and accuracy of those surrogate methods were evaluated and discussed. The multiple linear regression method could successfully construct the objective function of the tibial locking screws, but it failed to develop the objective function of the spinal pedicle screws. The artificial neural network method showed a greater capacity of prediction in developing the objective functions for the tibial locking screws and the spinal pedicle screws than the multiple linear regression method. The artificial neural network method may be a useful option for developing the objective functions of the orthopaedic screws with a greater structural complexity. The surrogate objective functions of the orthopaedic screws could effectively decrease the time and effort required for the design optimization process.     

面向高速工业无线网络的TDMA MAC协议设计与实现

随着无线技术的发展,在工业自动化中引入无线技术已经成为一种潮流,同时TDMA机制由于可以完全避免冲突,因此在大规模高实时要求的面向车间的无线技术研究中受到了广泛关注,但现在面向车间的高速实时无线技术的研究大都局限于软件仿真。通过在商用802.11硬件上设计并实现面向车间级工业应用的高速工业无线TDMAMAC层协议,建立了一个基于TDMA的面向工业应用的高速无线网络原型系统,实验结果表明实验平台完全能够支撑毫秒级时隙调度的TDMA应用。     

基于资源优化的QoS路径选择模糊算法

文中讨论了在ＩＰ网络中支持ＱｏＳ路径时优化网络资源的模糊方法,对ＱｏＳ路径选择中出现的多目标路径费用函数进行了合理的描述。该方法综合考虑了径路中的带宽、时延和路径条数,从初选出来的若干条路径中选择更安全,又节约网络资源的路径。仿真结果表明,模糊算法既兼容普通方法,又比普通方法更合理。同时,模糊方法计算简单迅速,用ＶＬＳＩ（ｖｅｒｙ ｌａｒｇｅ ｓｃａｌｅ ｉｎｔｅｇｒａｔｉｏｎ）硬件很容易实现。     

关于高校计算机基础课程实验的研究

计算机技术的迅速发展与广泛应用,已使其成为每位高校学生的必修课。计算机基础课程的实验教学具有专业性强、应用性广的重要特点。由于各院系专业多、实验内容、要求不同,怎样加强实验教学,如何科学的设计和组织实验课,将直接影响至计算机实验教学效果和学生动手能力的培养。经过多年的实践,这里总结出一套适应院系计算机基础课程的实验教学的模式,并取得较好的教学效果。     

BP神经网络在剩余油分布预测中的应用研究

我国多数油田经过一次、二次采油后,仅能采出地下总储量的30%左右,这意味着有60%～70%的剩余石油仍然残留在地下成为剩余油。加强剩余油分布规律研究、提高石油采收率不仅有着可观的经济效应,而且关系到国家石油战略的安全。本研究应用神经网络的原理,基于BP网络使用MATLAB语言建立一个剩余油分布的预测系统。该系统通过学习在地理坐标和孔隙度之间建立一个非线性函数关系,以此来预测任何区域的孔隙度,再通过孔隙度与剩余油饱和度之间的关系达到剩余油分布预测的目的。     

Expansion of a matrix in terms of external products of configuration vectors

The paper proposes a neural-net iterative algorithm that allows us to represent any random symmetrical N×N matrix as a weighted Hebbian series of configuration vectors with a given accuracy. The iterative algorithm is shown to demonstrate the fastest convergence when the vectors of expansion are stable nods of the N-dimensional space corresponding to the extremums of the neural-net energy functional. It so proves that all conclusions about neural networks and optimization algorithms that are based on Hebbian matrices are true for any other type of matrix. The text was submitted by the author in English.