A Combined Antenna Array and Multi-User Detection DS-CDMA Receiver in Single-Path and Multi-Path Fading Channels

An adaptive antenna array is incorporated into a decorrelatingmulti-user detector to effectively increase the DS-CDMA system capacity.Capacity improvement is due to beamforming gain, spatial diversity gain(assuming large angle spread), and the decorrelating effect. Thereceiver has been analyzed for the cases of sufficiently andinsufficiently spaced receiving antennas. The receiver consists of afront-end beamformer for every user in the cell and has knowledge of allusers' signature sequences. The beamformer estimates the desired userchannel vector, enhancing its signal and reducing the co-channelinterference from other directions. The multi-user detection, exploitingknowledge of other users, rejects those interferers whose arrival anglesare close to that of the desired user. The average uncoded Bit ErrorRate (BER) as a function of the number of in-beam active users, theaverage antenna Signal-to-Noise Ratio (SNR), and the number of receivingantennas is examined in both single-path and multi-path Rayleigh fadingchannels. Analysis shows an increase in system capacity proportional tothe number of receiving antennas.     

一种新颖的表面等离子谐振效应光波导水质传感技术

光学表面等离子谐振（ＳＰＲ）效应是一种很有效的水质检测技术，经过详细的理论推导，同了ＳＰＲ反射光强的普遍适用公式。在纯水与受污染水两种情况下，根据一些已知参数计算出结果。由计算结果数据一出的曲线比较表明，ＳＰＲ效应在两种情况下有明显差别，这也正是ＳＰＲ效应用于水质检测的理论根据。     

Outlier-resistant methods for estimation and model fitting

Least squares is perhaps the most widely used technique for model fitting. In this article, we illustrate the poor performance of least squares when there are spurious values, or outliers, in a sequence of measurements. A brief overview of three well-known classes of robust alternatives to the least-squares mean is presented. For robust regression, a recent proposal called least median squares (LMS) is decribed. LMS regression is compared to least-squares regression in an example involving the estimation of optical fiber geometry. References are provided for software that is available for robust estimation techniques surveyed in this article.     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong

At the moment, weather forecasting is still an art — the experience and intuition of forecasters play a significant role in determining the quality of forecasting. This paper describes the development of a new approach to rainfall forecasting using neural networks. It deals with the extraction of information from radar images and an evaluation of past rain gauge records to provide shortterm rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Preprocessing procedures were essential for this neural network rainfall forecasting. The forecast of the rainfall was performed every half an hour so that a storm warning signal can be delivered to the public in advance. The network architecture is based on a recurrent Sigma-Pi network. The results are very promising, and this neural-based rainfall forecasting system is capable of providing a rain storm warning signal to the Hong Kong public one hour ahead.     

基于信息的智能控制理论研究

本文介绍的基于信息的智能控制理论的发展过程，主要分为智能控制的信息指标和智能控制的信息结构分析两部分，文中在最后对这套理论可能的发展方向进行了探讨。     

地面反射波谱与卫星磁带记录数据间关系模拟的初步探讨──以冬小麦和土壤为例

根据冬小麦和土壤地面反射波谱测试数据，计算了在卫星高度上与卫星磁带数据相对应波段的辐亮度值，对ＮＯＡＡＡＶＨＲＲ和ＴＭ某些通道的差值绿度植被指数ＤＶＩ、归一化绿度植被指数ＮＤＶＩ和比值绿度植被指数ＲＶＩ的分析，从理论上证明了目前采用ＴＭＤＶＩ＿（４，３）提取冬小麦种植面积和ＮＯＡＡＮＤＶＩ＿（２，１）区分植被和土壤背景的有效性。同时在冬小麦种植面积和长势监测方面提出了一些新建议。     

Management of multidimensional discrete data

Spatial database management involves two main categories of data: vector and raster data. The former has received a lot of in-depth investigation; the latter still lacks a sound framework. Current DBMSs either regard raster data as pure byte sequences where the DBMS has no knowledge about the underlying semantics, or they do not complement array structures with storage mechanisms suitable for huge arrays, or they are designed as specialized systems with sophisticated imaging functionality, but no general database capabilities (e.g., a query language). Many types of array data will require database support in the future, notably 2-D images, audio data and general signal-time series (1-D), animations (3-D), static or time-variant voxel fields (3-D and 4-D), and the ISO/IEC PIKS (Programmer's Imaging Kernel System) BasicImage type (5-D). In this article, we propose a comprehensive support ofmultidimensional discrete data (MDD) in databases, including operations on arrays of arbitrary size over arbitrary data types. A set of requirements is developed, a small set of language constructs is proposed (based on a formal algebraic semantics), and a novel MDD architecture is outlined to provide the basis for efficient MDD query evaluation.     

低频电脉冲采油工程设备的开发与应用

低频电脉冲采油技术是通过升压、整流、充电等电路将电能蓄积在电容器中,通过触发电路,使能量瞬时释放,在井内液体中产生震动波,作用于地层,改善地层的渗流状况,达到提高采收率的目的。Ⅰ型设备在双河油田应用11井次,成功率72%;Ⅱ型设备在古城油田应用10井次,成功率90%。主要是由于Ⅰ型设备关键部件在处理油井时要放在井下,受高温、高压的影响及井下空间的限制,设备稳定性差、寿命短、能量小、效果差;Ⅱ型设备解决了上述问题,从而提高了试验成功率。