复合材料手动扫描超声特征成像检测

研究了复合材料手动扫描超声特征成像检测，通过对超声检测信号的全波列采集与处理，实现了A，B，C扫描成像；相位特征成像；当量特征成像；超声层析技术和超声频谱分析技术。该系统已经在复合材料的检测上得到了良好的应用。     

Implementation of RNS-Based Distributed Arithmetic Discrete Wavelet Transform Architectures Using Field-Programmable Logic

Currently there are design barriers inhibiting the implementation of high-precision digital signal processing (DSP) objects with field programmable logic (FPL) devices. This paper explores overcoming these barriers by fusing together the popular distributed arithmetic (DA) method with the residue number system (RNS) for use in FPL-centric designs. The new design paradigm is studied in the context of a high-performance filter bank and a discrete wavelet transform (DWT). The proposed design paradigm is facilitated by a new RNS accumulator structure based on a carry save adder (CSA). The reported methodology also introduces a polyphase filter structure that results in a reduced look-up table (LUT) budget. The 2C-DA and RNS-DA are compared, in the context of a FPL implementation strategy, using a discrete wavelet transform (DWT) filter bank as a common design theme. The results show that the RNS-DA, compared to a traditional 2C-DA design, enjoys a performance advantage that increases with precision (wordlength).     

无纵向电场分量离子波纹激光的小信号增益

在离子波纹摆动器中，改变电子束的入射方向，保证了离子波纹场的纵向分量为零，从而消除了纵向电场对离子波纹激光的不利影响。给出了束电子的运动轨迹，运用Madey定理计算了小振幅条件下该离子波纹激光的小信号增益。     

1.8 V千兆以太网接收器模拟前端预均衡电路

介绍了一种用于1.8 V电源电压下的千兆以太网接收器的模拟前端预均衡电路。电路分为三个部分:预处理电路、基带漂移补偿电路和可变增益放大电路,主要实现回波消除、基带漂移补偿和电路增益自动控制等功能。为了与百兆模式兼容,提出了一种预处理电路。仿真结果表明,该电路可以很好地实现回波消除的功能,能够对由于基带漂移引起的信号失真给以补偿,可以提供16级不同的增益,并进行频率补偿。电路采用0.18μm标准CMOS工艺实现。     

基于多抽样率信号处理的无源雷达MTD方法

以数字电视信号为机会照射源的雷达系统研究为背景 ,提出用以多抽样率信号处理为基础的多级抽取信号处理算法解决运动目标检测中海量数据处理问题。文中叙述了数字电视广播信号回波的数学模型、多级抽取基本原理、目标检测算法 ,以数字电视信号为反射回波做了仿真 ,对运动目标进行检测 ,并对仿真结果做了分析。     

Study on the Complexity of the Bullwhip Effect

Recent years have witnessed a rapid growth of interest in the study of the dynamic behavior of replenishment rules of bullwhip effect. We prove that bullwhip effect and butterfly effect share a same the self-oscillation amplifying mechanism that is the ordering decisions the supplier self-oscillation amplify the perturbations brought by the errors in the processing of retailers' demand information. This results as an explicit self-similar structure of the sensitivity of the system to the initial values duty to the nonlinear mechanism. In this paper, the causes process of the bullwhip effect is described as the internal nonlinear mechanism and study on the complexity of bullwhip effect for order-up-to policy under demand signal processing. The methodology is based on fractal and chaotic theory and allows important insights to be gained about the complexity behavior of bullwhip effect.     

基于AT89C2051的单户可视对讲门铃室内分机的设计

本文介绍一种基于单片机AT89C2501的单户可视对讲门铃室内分机的工作原理及软、硬件的设计与实现方法。     

基于WVD的防空雷达直升机信号特征分析

基于直升机目标旋转部件的调制模型，本文针对防空雷达提出了分析直升机信号特征的一种新方法——WVD法，并对直升机目标旋转部件调制回波信号在不同噪声情况下加以仿真分析，结果表明：用WVD分析雷达回波中直升机信号特征是一种非常有效的方法。     

Signal Integrity: Fault Modeling and Testing in High-Speed SoCs

As we approach 100 nm technology the interconnect issues are becoming one of the main concerns in the testing of gigahertz system-on-chips. Voltage distortion (noise) and delay violations (skew) contribute to the signal integrity loss and ultimately functional error, performance degradation and reliability problems. In this paper, we first define a model for integrity faults on the high-speed interconnects. Then, we present a BIST-based test methodology that includes two special cells to detect and measure noise and skew occurring on the interconnects of the gigahertz system-on-chips. Using an inexpensive test architecture the integrity information accumulated by these special cells can be scanned out for final test and reliability analysis.     

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.