基于PXI总线的导弹角速度编码器组合测试系统设计

针对某型导弹角速度编码器组合测试快速化、模块化、自动化的要求,通过对角速度编码器组合进行分析,结合角速度编码器组合输入信号的模拟、输出信号的采集、测试系统功能和性能要求,设计了基于PXI总线的导弹角速度编码器组合测试系统;介绍了测试系统的测试原理、硬件结构和软件组成、主要设计思想;实验表明,该测试系统工作性能稳定,能有效地进行组合测试和分组件测试并运用层次故障树分析法定位故障,提高了武器系统检测维修保障能力.     

基于软件无线电的基带调制系统设计

针对通信系统中基带调制的多模式的需求,基于软件无线电原理,用FPGA设计了一种可实现2ASK、2FSK、BPSK三种调制模块,在Quartus II环境中利用VHDL语言完成了芯片程序的设计,并对其进行了仿真和功能测试,实验结果验证了用FPGA可快速实现2ASK、2FSK和BPSK基带调制功能,调制信号稳定可靠,可以用软件来控制调制方式,系统方案正确可行,而且具有良好的稳定性和灵活性.     

无线鼠标电路设计及算法实现

文中设计的无线鼠标包含发射电路和接收电路两部分,发射电路先采用光传感器ADNS5030检测鼠标的移动信息,然后输出到内置单片机的无线模块CC2430的I/O口,通过CC2430控制并将此信息发射到接收电路,接收电路接收到移动信息后,根据鼠标位移状态机解码算法,得到移动信息及按键信息,通过HT82M98A处理后将信息传给计算机,计算机自动完成相应动作;然后不断的循环;经测试,该电路不仅具备普通有线鼠标的全部功能,还可远距离灵活操纵鼠标,在大礼堂等开阔场所可达80 m以上;计算机自动识别无线鼠标接收电路,不需要额外安装程序,每一次鼠标信号处理总时间不大于1 ms,无线鼠标移动灵活,无停滞现象.     

一种基于特征点匹配的图像拼接方法研究

针对图像间因具有旋转及光线强度差异等现象而导致的拼接效果不佳及拼接速度慢的问题,提出一种基于特征点的配准算法;在特征点提取阶段,尺度不变的特征变换方法 (SIFT)具有对图像尺度缩放、旋转、放射变换以及亮度变化保持不变的优点,文章采用了改进的SIFT特征点提取算法;在特征点匹配阶段,采用改进的RANSAC算法对特征点匹配对提纯;最后用加权平均法实现拼接图像的融合;实验证明,该算法有效提高了图像拼接的效率和准确性,拼接精度可以达到亚像素级。     

可编程脉冲产生器的设计及应用

文中针对脉冲产生器的市场供应与实际应用情况,给出了一种以单片机为CPU核心的可编程脉冲产生器的设计思路,阐述了完成该产生器的硬件结构和软件设计方法,并给出相应的硬件电路和软件设计流程;为了验证设计的合理性,将其应用于某测控系统的步进电机控制器中,并进行实验测试,结果表明该设计具有低成本、高可靠性等特点,特别是其脉冲宽度与周期可编程预置,不仅增加了设备的使用灵活性,而且扩大了应用范围,可推广应用于多种功能的测控系统中。     

Influence of base resistance on extracting thermal resistance for SiGe HBTs

In this article, the influence of base resistance on extracting thermal resistance for SiGe heterojunction bipolar transistors is studied and an improved approach for determining the junction temperature and thermal resistance is presented. The proposed method for extracting thermal resistance is based on the temperature sensitivity of the base–emitter (B–E) voltage when the device is biased with a fixed emitter current density. This approach not only takes into account the self‐heating during the different ambient temperature measurement but also revises the empirical equation of B–E voltage due to the influence of base resistance during the power dissipation increment measurement. Results are obtained for devices with different emitter lengths and fingers. Compared with the conventional method, the thermal resistance is about up to 15% improvement for the device with 0.3 × 1.9 μm² emitter area and 13.8% for the device with 0.3 × 13.9 μm² emitter area. The accurate thermal resistance implemented in HICUM model has resulted in better fit for transistor output characteristics. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

Probability-confidence-kernel-based localized multiple kernel learning with lp norm

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features in terms of their discriminative power for each individual sample. However, models excessively fitting to a specific sample would obstacle the extension to unseen data, while a more general form is often insufficient for diverse locality characterization. Hence, both learning sample-specific local models for each training datum and extending the learned models to unseen test data should be equally addressed in designing LMKL algorithm. In this paper, for an integrative solution, we propose a probability confidence kernel (PCK), which measures per-sample similarity with respect to probabilistic-prediction-based class attribute: The class attribute similarity complements the spatial-similarity-based base kernels for more reasonable locality characterization, and the predefined form of involved class probability density function facilitates the extension to the whole input space and ensures its statistical meaning. Incorporating PCK into support-vectormachine-based LMKL framework, we propose a new PCK-LMKL with arbitrary l(p)-norm constraint implied in the definition of PCKs, where both the parameters in PCK and the final classifier can be efficiently optimized in a joint manner. Evaluations of PCK-LMKL on both benchmark machine learning data sets (ten University of California Irvine (UCI) data sets) and challenging computer vision data sets (15-scene data set and Caltech-101 data set) have shown to achieve state-of-the-art performances.     

Detection of sudden pedestrian crossings for driving assistance systems

In this paper, we study the problem of detecting sudden pedestrian crossings to assist drivers in avoiding accidents. This application has two major requirements: to detect crossing pedestrians as early as possible just as they enter the view of the car-mounted camera and to maintain a false alarm rate as low as possible for practical purposes. Although many current sliding-window-based approaches using various features and classification algorithms have been proposed for image-/video-based pedestrian detection, their performance in terms of accuracy and processing speed falls far short of practical application requirements. To address this problem, we propose a three-level coarse-to-fine video-based framework that detects partially visible pedestrians just as they enter the camera view, with low false alarm rate and high speed. The framework is tested on a new collection of high-resolution videos captured from a moving vehicle and yields a performance better than that of state-of-the-art pedestrian detection while running at a frame rate of 55 fps.     

Fuzzy local maximal marginal embedding for feature extraction

In graph-based linear dimensionality reduction algorithms, it is crucial to construct a neighbor graph that can correctly reflect the relationship between samples. This paper presents an improved algorithm called fuzzy local maximal marginal embedding (FLMME) for linear dimensionality reduction. Significantly differing from the existing graph-based algorithms is that two novel fuzzy gradual graphs are constructed in FLMME, which help to pull the near neighbor samples in same class nearer and nearer and repel the far neighbor samples of margin between different classes farther and farther when they are projected to feature subspace. Through the fuzzy gradual graphs, FLMME algorithm has lower sensitivities to the sample variations caused by varying illumination, expression, viewing conditions and shapes. The proposed FLMME algorithm is evaluated through experiments by using the WINE database, the Yale and ORL face image databases and the USPS handwriting digital databases. The results show that the FLMME outperforms PCA, LDA, LPP and local maximal marginal embedding.     

A Polynomial Algorithm for Computing Elementary Siphons in a Class of Petri Nets

A set of elementary siphons plays a key role in the development of deadlock prevention policies for automated manufacturing systems. This paper addresses the computation problem for elementary siphons in a subclass of Petri nets which are basic systems of simple sequential processes with resources (BS³PR) and can model many automated manufacturing systems. An algorithm for enumerating elementary siphons is established by the one‐to‐one relationship between maximal perfect resource‐transition circuits (MPCs) and strict minimal siphons. A set of MPCs is first computed, followed by a set of elementary siphons in a BS³PR. The presented algorithm is proved to have polynomial‐time complexity. An example is used to illustrate the algorithm.