Capillary array electrophoresis with confocal fluorescence rotary scanner

A capillary array electrophoresis system with rotary corifocal fluorescence scanner was reported. High speed direct current rotary motor combined with a rotary encoder and the reflection mirror has been designed to direct exactly the excitation laser beam. to the array of capillaries, which are symmetrically distributed around the motor. The rotary encoder is introduced to accurately orient the position of each capillary and its output signal triggers the data acquiring system to record. the fluorescence signal corresponding to each capillary. Separations of several amino acids are demonstrated by eight-channel capillary array electrophoresis built by ourselves.

高性能全流水线可控参数JPEG-LS编码器实现

提出了一种高性能的JPEG-LS无损/近无损图像压缩算法VLSI实现结构.通过对JPEG-LS算法瓶颈的分析,针对算法中不利于流水线实现的场景缓存部分,采用了一种信号量集机制避免流水线等待.全流水线结构保证了算法实现可以满足高速图像传感器系统的吞吐量需求.同时通过高度参数化的设计,系统可以动态调整和优化算法参数,使压缩效果和效率适应不同的运行环境.算法在FPGA平台通过验证,并得到了接近甚至超过其他A-SIC实现的性能.

电机转速自适应测量方法及其在DSP上的实现

本文在分析几种常用的基于编码器测速方法的基础上,提出了一种高性能的自适应速度测量方法。该方法选择一个可变的时间周期和编码器脉冲数来测量单位时间内的编码器脉冲数,再通过简单的计算得到转速的测量值。数字信号处理器(DSP)芯片集成有正交脉冲编码电路,并且数据处理速度快,实时性强。本文中提出的方法在电机控制专用DSP芯片TMS320 LF2407A上进行了实现。实验研究表明,可以在提高低速时的测速准确度的同时,提高系统的响应时间。该方法已经在自主研发的全数字伺服驱动系统中得到了成功应用。

一种FPGA的嵌入式可重构机器人控制系统

提出一种基于FPGA的可重构嵌入式微处理器控制系统.在FPGA中嵌入两个NiosⅡ软核,用VHDL语言编写用户自定义组件.在一个由NiosⅡ软核组成的处理器上实现PWM信号生成、编码器信号处理以及多电机同步伺服运算等,在另一个处理器实现机器人任务管理.该控制系统针对微小型爬壁机器人的控制系统设计,不仅具有良好的实时多任务处理能力,而且具有可重构的特点,因而可应用于一类微小型机器人控制系统以提高其设计的灵活性.

基于多传感器的可变形机器人自主控制方法研究

将GPS、电子罗盘、倾角仪、码盘传感器等应用到可变形机器人自主运动控制中.针对可变形机器人自身结构特点,提出了一种基于多传感器信息融合的可变形机器人在野外环境中自主控制的方法.该方法主要实现了在非结构环境中机器人的自主变形、自主避障和自主导航定位等功能.实验验证了该方法的有效性.

无锁存功能脉冲计数器的改进读取方法

在电机伺服控制系统中,需要一个脉冲计数器对电机码盘输出的脉冲进行计数。但是如果脉冲计数器没有数据锁存功能,单片机读出的数值可能不准确,进而影响伺服控制系统的性能。针对没有锁存功能的脉冲计数器,提出了一种改进的读取方法,有效的避免了在读取过程中由于计数器进位或借位造成的读数偏差。

无锁存功能脉冲计数器的改进读取方法

在电机伺服控制系统中,需要一个脉冲计数器对电机码盘输出的脉冲进行计数。单片机根据脉冲的个数和电机旋转方向计算出电机的转角,进而实现对电机的伺服控制。如果脉冲计数器没有数据锁存功能,且单片机读取数值时,脉冲计数器恰好发生了进位或者借位,则读取的数值可能不准确,进而影响伺服控制系统的性能。针对没有锁存功能的脉冲计数器,提出了一种改进的读取方法,有效地避免了在读取过程中由于计数器进位或借位造成的读数偏差。

神经网络用于多模式分类

本文叙述一种改进型HAMMING网在印刷汉字文本识别实用系统中作为粗分类的应用.给出了以3755印刷汉字为多模式分类对象的神经网络分类器的结构及其相应的算法.该方法在微型机上用软件仿真得以实现.取得令人满意的结果.

Structural Analysis of Network Traffic Flows

Network traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a thorough understanding of OD flows is essential for modeling network traffic, and for addressing a wide variety of problems including traffic engineering, traffic matrix estimation, capacity planning, forecasting and anomaly detection. However, to date, OD flows have not been closely studied, and there is very little known about their properties. We present the first analysis of complete sets of OD flow timeseries, taken from two different backbone networks (Abilene and Sprint-Europe). Using Principal Component Analysis (PCA), we find that the set of OD flows has small intrinsic dimension. In fact, even in a network with over a hundred OD flows, these flows can be accurately modeled in time using a small number (10 or less) of independent components or dimensions. We also show how to use PCA to systematically decompose the structure of OD flow timeseries into three main constituents: common periodic trends, short-lived bursts, and noise. We provide insight into how the various constituents contribute to the overall structure of OD flows and explore the extent to which this decomposition varies over time.

Characterization of Network-Wide Anomalies in Traffic Flows

Detecting and understanding anomalies in IP networks is an open and ill-defined problem. Toward this end, we have recently proposed the subspace method for anomaly diagnosis. In this paper we present the first large-scale exploration of the power of the subspace method when applied to flow traffic. An important aspect of this approach is that it fuses information from flow measurements taken throughout a network. We apply the subspace method to three different types of sampled flow traffic in a large academic network: multivariate timeseries of byte counts, packet counts, and IP-flow counts. We show that each traffic type brings into focus a different set of anomalies via the subspace method. We illustrate and classify the set of anomalies detected. We find that almost all of the anomalies detected represent events of interest to network operators. Furthermore, the anomalies span a remarkably wide spectrum of event types, including denial of service attacks (single-source and distributed), flash crowds, port scanning, downstream traffic engineering, high-rate flows, worm propagation, and network outage.

Improved kinematic sensing for motion control applications

New compensation methods are presented that can greatly reduce the slit errors (i.e. transition location errors) and interval errors induced due to non-idealities in optical incremental encoders (square-wave). An M/T-type, constant sample-time digital tachometer (CSDT) is selected for measuring the velocity of the sensor drives. Using this data, three encoder compensation techniques (two pseudoinverse based methods and an iterative method) are presented that improve velocity measurement accuracy. The methods do not require precise knowledge of shaft velocity. During the initial learning stage of the compensation algorithm (possibly performed in-situ), slit errors/interval errors are calculated through pseudoinversebased solutions of simple approximate linear equations, which can provide fast solutions, or an iterative method that requires very little memory storage. Subsequent operation of the motion system utilizes adjusted slit positions for more accurate velocity calculation. In the theoretical analysis of the compensation of encoder errors, encoder error sources such as random electrical noise and error in estimated reference velocity are considered. Initially, the proposed learning compensation techniques are validated by implementing the algorithms in MATLAB software, showing a 95% to 99% improvement in velocity measurement. However, it is also observed that the efficiency of the algorithm decreases with the higher presence of non-repetitive random noise and/or with the errors in reference velocity calculations. The performance improvement in velocity measurement is also demonstrated experimentally using motor-drive systems, each of which includes a field-programmable gate array (FPGA) for CSDT counting/timing purposes, and a digital-signal-processor (DSP). Results from open-loop velocity measurement and closed-loop servocontrol applications, on three optical incremental square-wave encoders and two motor drives, are compiled. While implementing these algorithms experimentally on different drives (with and without a flywheel) and on encoders of different resolutions, slit error reductions of 60% to 86% are obtained (typically approximately 80%).

Communication system model for information rate evaluation of differential detection over time-varying channels

A communication system model for mutual information performance analysis of multiple-symbol differential M-phase shift keying over time-correlated, time-varying flat-fading communication channels is developed. This model is a finite-state Markov (FSM) equivalent channel representing the cascade of the differential encoder, FSM channel model and differential decoder. A state-space approach is used to model channel phase time correlations. The equivalent model falls in a class that facilitates the use of the forward backward algorithm, enabling the important information theoretic results to be evaluated. Using such a model, one is able to calculate mutual information for differential detection over time-varying fading channels with an essentially finite time set of correlations, including the Clarke fading channel. Using the equivalent channel, it is proved and corroborated by simulations that multiple-symbol differential detection preserves the channel information capacity when the observation interval approaches infinity.

Absence of the 7-repeat variant of the DRD4 VNTR is associated with drifting sustained attention in children with ADHD but not in controls

Many genetic studies have demonstrated an association between the 7-repeat (7r) allele of a 48-base pair variable number of tandem repeats (VNTR) in exon 3 of the DRD4 gene and the phenotype of attention deficit hyperactivity disorder (ADHD). Previous studies have shown inconsistent associations between the 7r allele and neurocognitive performance in children with ADHD. We investigated the performance of 128 children with and without ADHD on the Fixed and Random versions of the Sustained Attention to Response Task (SART). We employed timeseries analyses of reaction-time data to allow a fine-grained analysis of reaction time variability, a candidate endophenotype for ADHD. Children were grouped into either the 7r-present group (possessing at least one copy of the 7r allele) or the 7r-absent group. The ADHD group made significantly more commission errors and was significantly more variable in RT in terms of fast moment-to-moment variability than the control group, but no effect of genotype was found on these measures. Children with ADHD without the 7r allele made significantly more omission errors, were significantly more variable in the slow frequency domain and showed less sensitivity to the signal (d') than those children with ADHD the 7r and control children with or without the 7r. These results highlight the utility of time-series analyses of reaction time data for delineating the neuropsychological deficits associated with ADHD and the DRD4 VNTR. Absence of the 7-repeat allele in children with ADHD is associated with a neurocognitive profile of drifting sustained attention that gives rise to variable and inconsistent performance. (c) 2008 Wiley-Liss, Inc.

Modelling the impact of antibiotic use and infection control practices on the incidence of hospital-acquired methicillin-resistant Staphylococcus aureus: a time-series analysis

Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) is a major nosocomial pathogen worldwide. A wide range of factors have been suggested to influence the spread of MRSA. The objective of this study was to evaluate the effect of antimicrobial drug use and infection control practices on nosocomial MRSA incidence in a 426-bed general teaching hospital in Northern Ireland.

Methods: The present research involved the retrospective collection of monthly data on the usage of antibiotics and on infection control practices within the hospital over a 5 year period (January 2000–December 2004). A multivariate ARIMA (time-series analysis) model was built to relate MRSA incidence with antibiotic use and infection control practices.

Results: Analysis of the 5 year data set showed that temporal variations in MRSA incidence followed temporal variations in the use of fluoroquinolones, third-generation cephalosporins, macrolides and amoxicillin/clavulanic acid (coefficients = 0.005, 0.03, 0.002 and 0.003, respectively, with various time lags). Temporal relationships were also observed between MRSA incidence and infection control practices, i.e. the number of patients actively screened for MRSA (coefficient = -0.007), the use of alcohol-impregnated wipes (coefficient = -0.0003) and the bulk orders of alcohol-based handrub (coefficients = -0.04 and -0.08), with increased infection control activity being associated with decreased MRSA incidence, and between MRSA incidence and the number of new patients admitted with MRSA (coefficient = 0.22). The model explained 78.4% of the variance in the monthly incidence of MRSA.

Conclusions: The results of this study confirm the value of infection control policies as well as suggest the usefulness of restricting the use of certain antimicrobial classes to control MRSA.