基于Labview与单片机的LED电子屏幕

目前,LED点阵显示文字图形的控制系统,价格昂贵,功能难以扩展,使用不便。文中对LED电子屏幕的主要控制技术及其应用领域作了简要介绍,针对实际应用需要设计出一种基于Labview与单片机控制的LED电子屏幕。在提出系统整体设计方案基础上详细阐述了由Labview实现UCDOS下字库点阵数据提取方法,并设计出信息编辑与管理系统,论述了LED点阵显示原理及单片机控制系统硬件与软件设计思想。     

Combining spatial filters for the classification of single-trial EEG in a finger movement task

Brain-computer interface (BCI) is to provide a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In recent years, the event-related desynchronization (ERD) and movement-related potentials (MRPs) are utilized as important features in motor related BCI system, and the common spatial patterns (CSP) algorithm has shown to be very useful for ERD-based classification. However, as MRPs are slow nonoscillatory EEG potential shifts, CSP is not an appropriate approach for MRPs-based classification. Here, another spatial filtering algorithm, discriminative spatial patterns (DSP), is newly introduced for better extraction of the difference in the amplitudes of MRPs, and it is integrated with CSP to extract the features from the EEG signals recorded during voluntary left versus right finger movement tasks. A support vector machines (SVM) based framework is designed as the classifier for the features. The results show that, for MRPs and ERD features, the combined spatial filters can realize the single-trial EEG classification better than anyone of DSP and CSP alone does. Thus, we propose an EEG-based BCI system with the two feature sets, one based on CSP (ERD) and the other based on DSP (MRPs), classified by SVM.     

Active contour external force using vector field convolution for image segmentation.

Snakes, or active contours, have been widely used in image processing applications. Typical roadblocks to consistent performance include limited capture range, noise sensitivity, and poor convergence to concavities. This paper proposes a new external force for active contours, called vector field convolution (VFC), to address these problems. VFC is calculated by convolving the edge map generated from the image with the user-defined vector field kernel. We propose two structures for the magnitude function of the vector field kernel, and we provide an analytical method to estimate the parameter of the magnitude function. Mixed VFC is introduced to alleviate the possible leakage problem caused by choosing inappropriate parameters. We also demonstrate that the standard external force and the gradient vector flow (GVF) external force are special cases of VFC in certain scenarios. Examples and comparisons with GVF are presented in this paper to show the advantages of this innovation, including superior noise robustness, reduced computational cost, and the flexibility of tailoring the force field.     

Strong compound-risk factors: efficient discovery through emerging patterns and contrast sets.

Odds ratio (OR), relative risk (RR) (risk ratio), and absolute risk reduction (ARR) (risk difference) are biostatistics measurements that are widely used for identifying significant risk factors in dichotomous groups of subjects. In the past, they have often been used to assess simple risk factors. In this paper, we introduce the concept of compound-risk factors to broaden the applicability of these statistical tests for assessing factor interplays. We observe that compound-risk factors with a high risk ratio or a big risk difference have an one-to-one correspondence to strong emerging patterns or strong contrast sets-two types of patterns that have been extensively studied in the data mining field. Such a relationship has been unknown to researchers in the past, and efficient algorithms for discovering strong compound-risk factors have been lacking. In this paper, we propose a theoretical framework and a new algorithm that unify the discovery of compound-risk factors that have a strong OR, risk ratio, or a risk difference. Our method guarantees that all patterns meeting a certain test threshold can be efficiently discovered. Our contribution thus represents the first of its kind in linking the risk ratios and ORs to pattern mining algorithms, making it possible to find compound-risk factors in large-scale data sets. In addition, we show that using compound-risk factors can improve classification accuracy in probabilistic learning algorithms on several disease data sets, because these compound-risk factors capture the interdependency between important data attributes.     

Effects of Spatial Correlation on MIMO Adaptive Antenna System With Optimum Combining

In this paper, we investigate the effects of the spatial fading correlation on the performance of a multiple-input multiple-output (MIMO) adaptive antenna system with optimum combining (OC) in the presence of multiple cochannel interferers over a correlated Rayleigh fading channel. Based on the Khatri's distribution functions of quadratic forms in complex Gaussian random matrices, we develop a unified determinant representation of those joint eigenvalue distributions. Taking into account the spatial correlation among the antenna elements at the transmitter or receiver, we derive the closed-form formulas for the probability density function and outage probability of the maximum output signal-to-interference ratio (SIR) in an interference-limited MIMO-OC system. Furthermore, the average output SIR and error probability are also investigated. From numerical examples, we show that a new theoretical approach gives a simple and accurate way to assess the performance of the MIMO-OC system over arbitrarily correlated fading channels     

A Network Calculus With Effective Bandwidth

This paper establishes a link between two principal tools for the analysis of network traffic, namely, effective bandwidth and network calculus. It is shown that a general version of effective bandwidth can be expressed within the framework of a probabilistic version of the network calculus, where both arrivals and service are specified in terms of probabilistic bounds. By formulating well-known effective bandwidth expressions in terms of probabilistic envelope functions, the developed network calculus can be applied to a wide range of traffic types, including traffic that has self-similar characteristics. As applications, probabilistic lower bounds are presented on the service given by three different scheduling algorithms: static priority, earliest deadline first, and generalized processor sharing. Numerical examples show the impact of specific traffic models and scheduling algorithms on the multiplexing gain in a network.     

Power-aware routing based on DSR for Mobile Ad hoc NETworks

Energy consumption is a crucial design concern in Mobile Ad hoc NETworks （MANETs） since nodes are powered by batteries with limited energy, whereas Dynamic Source Routing （DSR） protocol does not take the energy limitation of MANET nodes into account. This paper proposes an energy-saving routing algorithm based on DSR： Power Aware Routing protocol based on DSR （PAR-DSR）. The design objective of PAR-DSR is to select energy-efficient paths. The main features of PAR-DSR are： （1） Nodes use the Signal Attenuation Rate （SAR） to conduct power control operations; （2） Minimum path cost as metric to balance the traffic and energy consumption of wireless nodes. The simulation results show that PAR-DSR can greatly reduce the energy consumption of MANET nodes. The average node lifetime of PAR-DSR is 50%-77% longer than that of DSR.     

高分辨率波长位移解调技术研究进展

简要阐述了在光纤传感技术中实现高分辨率波长位移解调技术的基本原理,重点介绍了几种典型解调方法的结构和原理以及波分复用的方案,最后对各种解调方案的优缺点进行了比较分析.     

Energy Aware Signal Processing for Software Defined Radio Baseband Implementation

The fast pacing diversity and evolution of wireless communications require a wide variety of baseband implementations within a short time-to-market. Besides, the exponentially increased design complexity and design cost of deep sub-micron silicon highly desire the designs to be reused as much as possible. This yields an increasing demand for reconfigurable/ programmable baseband solutions. Implementing all baseband functionalities on programmable architectures, as foreseen in the tier-2 SDR, will become necessary in the future. However, the energy efficiency of SDR baseband platforms is a major concern. This brings a challenging gap that is continuously broadened by the exploding baseband complexity. We advocate a system level approach to bridge the gap. Specifically, we fully leverage the advantages (programmability) of SDR platforms to compensate its disadvantages (energy efficiency). Highly flexible and dynamic baseband signal processing algorithms are designed and implemented to exploit the abundant dynamics in the environment and the user requirement. Instead of always performing the best effort, the baseband can dynamically and autonomously adjust its work load to optimize the average energy consumption. In this paper, we will introduce such baseband signal processing techniques optimized for SDR implementations. The methodology and design steps will be presented together with 3 representative case studies in HSDPA, WiMAX and 3GPP LTE.     

提高电气自动化控制设备可靠性的方法

本文简单阐述电气自动化控制设备可靠性的重要性,深入分析如何测试电气自动化控制设备可靠性以及对其产生影响的各方面因素,最终总结出提高电气自动化控制设备可靠性的方法.