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.     

基于FPGA的雷达状态参数采集系统设计与实现

为使雷达回波模拟系统能够逼真地模拟真实作战环境,有效地对雷达各分系统进行测试,提出了雷达状态参数采集电路的设计方案,并给出了其软硬件实现;该方案以FPGA为核心,解决了不同状态参数的实时性采集问题,并通过制定串行传输协议,避免了数据传输过程中的冲突,保证了各状态参数传输的准确性,输出接口采用LVDS标准接口,确保了各状态参数的高速长距离传输;实验结果表明,该系统工作稳定,能够准确、实时地采集并传输雷达状态参数,且便于与其他数字电路的集成,满足系统设计的要求。     

组网火控雷达抗ARM跟踪技术

为了满足火控雷达抗反辐射导弹的对抗要求,提出了火控雷达脉冲间隔自适应变化的间歇跟踪方法;首先分析了单部雷达自适应变化脉冲间隔的间歇跟踪原理,给出了跟踪性能的评价指标;其次,为克服雷达局限,研究了组网火控雷达间歇跟踪技术;最后对跟踪效果进行了仿真;结果证明:采用组网雷达间歇跟踪方法可以同时实现对目标载机的准确跟踪和对反辐射导弹的有效对抗。     

高性价比雷达回波模拟系统跳频源的研制

针对雷达回波模拟系统的需要,选取具有数字接口、高度集成的DDS电路,实现了200MHz～250 MHz带宽内输出频率数控可变,系统跳频速度高于200 000跳/秒,频率分辨率小于3Hz,输出信号杂散优于70dBc,数据传输速率快,体积约50mm×80mm×30mm;给出了跳频源硬件电路和软件程序的具体设计方法,经过实际测试,验证了该电路便于数字控制,体积小、成本低,输出信号实现了低杂散、快跳变、误差小、频率分辨率高,系统整体性能优异。     

空中交通预警与防撞系统(TCAS)风险及对策研究

空中交通预警与防撞系统TCAS对飞行安全非常重要,它能够通过对附近的飞机进行冲突检测,预测未来可能发生的危险,提示飞行员避免危险,帮助机组维持与其他飞机之间的空中交通安全间隔;但TCAS本身依然存在风险;在此对TCAS进行了半物理仿真测试,包括数字样机开发测试、物理样机开发测试、虚拟试飞以及数据统计分析等,对其各项功能进行测试与验证;在参阅众多相关文献的基础上,结合所做的研究工作,讨论了TCAS的主要风险,并针对这些风险,提出TCAS在设计、测试、操作方面需要改进的地方,从各方面完善TCAS,降低其风险,进一步保障飞行安全。     

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。     

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.