Characterization of indoor wireless channel at 5 GHz U-NII bands

The paper reports on the experiments undertaken at the University of Wollongong to characterise fading profiles and delay parameters of an indoor wireless channels at 5 GHz U-NII bands. The measurements were undertaken at different locations around the campus with results recorded for a post-processing to calculate the Rician K-factor, the level crossing rate and the average fade duration as well as mean excess delay, rms delay spread, and the coherence bandwidth of the channel. The presented measurement results can be useful in developing a Markov chain based model of the transport channel for IEEE802.11a or HYPRLAN-2 networks. The results also indicate scenarios where the coherence bandwidth of the channel is smaller than the width of the sub-carrier OFDM channels in either of the mentioned systems.     

A pulsed neural network model of spectro-temporal receptive fields and population coding in auditory cortex

The existence of spectro-temporal receptive fields and evidence for population coding in auditory cortex motivate the development of such models, that explicitly operate in the time-frequency domain and are based on a pulsed neural network. In presenting such a model, a formal connection of the fields of Time Frequency Analysis and Pulsed Neural Networks is established. The resulting neural time-frequency signal representation is shown to be representable as a signal-dependent overcomplete dictionary. It is derived from neural population coding. Signal decomposition and filtering effects are presented, indicating obvious technical applications of the proposed model. This revised version was published online in June 2006 with corrections to the Cover Date.     

The evidence for neural information processing with precise spike-times: A survey

This paper surveys recent findings in neuroscience regarding the behavioral relevancy of the precise timing with which real spiking neurons emit spikes. The literature suggests that in almost any system where the processing-speed of a neural (sub)-system is required to be high, the timing of single spikes can be very precise and reliable. Additionally, new, more refined methods are finding precisely timed spikes where previously none where found. This line of evidence thus provides additional motivation for researching the computational properties of networks of artificial spiking neurons that compute with more precisely timed spikes. This revised version was published online in June 2006 with corrections to the Cover Date.     

网络化测控设备间的时间同步

为使广域网络范围内的测控设备同步工作，必须准确的计算出网络数据传输的时延。依据排队论，本文提出并证明了准确的网络节点模型和数据延时分布统计模型，以此为基础计算出数据报在互联网上传输的精确时延并给出时延修正方法。实验数据论证了模型的正确性。最后提出了广域自动测试系统内设备的同步解决方案，时间同步精度在10～50μs范围内。     

神经网络在结构损伤模拟中的应用

识别一个结构在震动状态下的变化，在结构监测中是十分重要的，神经网络就非常适用于这种目的。本文研究了使用可分析的学习样本来训练神经网络的可行性问题。神经网络从损伤状态中训练产生，然后用于诊断一个五层钢框架在一系列震动模拟中的状态。结果表明，使用神经网络可使在线结构诊断更加可行。     

A new TDOA algorithm based on Taylor series expansion in cellular networks

Time difference of arrival (TDOA) is the positioning technique with the most potential in cellular mobile telecommunication systems. The Taylor series expansion method has been widely used in solving nonlinear equations for its high accuracy and good robustness. However, the performance of the Taylor’s method depends highly on the initial estimation. Therefore, one new algorithm, hybrid optimizing algorithm (HOA) was proposed, which combines the Taylor series expansion method with the steepest decent method. The steepest decent method features fast convergence at the initial iteration and small computation complexity. HOA takes great advantage of both methods. Simulation results show that HOA achieves better performance on positioning accuracy and efficiency. __________ Translated from Journal on Communications, 2007, 28(6): 7–11 [译自: 通信学报]     

Decentralized adaptive recurrent neural control structure

This paper presents a novel decentralized variable structure neural control approach for large-scale uncertain systems, which is developed using recurrent high-order neural networks (RHONN). It is assumed that each subsystem belongs to a class of block-controllable nonlinear systems whose vector fields includes interconnection terms, which are bounded by nonlinear functions. A decentralized RHONN structure and the respective learning law are proposed in order to approximate online the dynamical behavior of each nonlinear subsystem. The control law, which is able to regulate and to track the desired reference signals, is designed using the well-known variable structure theory. The stability of the whole system is analyzed via the Lyapunov methodology. The applicability of the proposed decentralized identification and control algorithm is illustrated via simulations as applied to an interconnected double inverted pendulum.     

浅议高职高专计算机网络实验室的建设

文章分析了计算机网络实验的建设对于计算机网络及相关课程的必要性,对高职高专计算机网络实验室的建设问题进行了探讨,提出了网络实验室应具备的功能及建设原则,并给出一种较为全面实用的解决方案。     

基于Matlab参数辨识的纯迟延系统预估控制在火电厂的应用

在探讨Smith预传器的控制原理的基础上,针对制约其工程应用的参数估计问题阐述了一种用Matlab软件计算的工程案例.在分析其应用前景的同时,提出将DCS控制系统、Matlab软件和Smith控制有机结合的思路.     

Learning Structure from Data and Its Application to Ozone Prediction

In this paper we propose an algorithm for structure learning in predictive expert systems based on a probabilistic network representation. The idea is to have the simplest structure (minimum number of links) with acceptable predictive capability. The algorithm starts by building a tree structure based on measuring mutual information between pairs of variables, and then it adds links as necessary to obtain certain predictive performance. We have applied this method for ozone prediction in México City, where the ozone level is used as a global indicator for the air quality in different parts of the city. It is important to predict the ozone level a day, or at least several hours in advance, to reduce the health hazards and industrial losses that occur when the ozone reaches emergency levels. We obtained as a first approximation a tree-structured dependency model for predicting ozone in one part of the city. We observe that even with only three parameters, its estimations are acceptable.A causal network representation and the structure learning techniques produced some very interesting results for the ozone prediction problem. Firstly, we got some insight into the dependence structure of the phenomena. Secondly, we got an indication of which are the important and not so important variables for ozone forecasting. Taking this into account, the measurement and computational costs for ozone prediction could be reduced. And thirdly, we have obtained satisfactory short term ozone predictions based on a small set of the most important parameters.