植入式片上系统的发展与现状

对目前国际上植入式片上系统的最新研究现状进行分析,总结了植入式生物信号检测与微电刺激系统的基本结构模型;介绍了系统的工作原理,讨论了系统实现的关键技术、存在的问题及可能的解决方法;最后,对植入式系统的发展趋势进行了展望.     

雷达动态多目标模拟系统的设计

雷达动态多目标模拟系统旨在解决如何检测相控阵测量雷达的多目标跟踪性能这一技术难题.系统采用程控延时技术、数字控制技术及中断处理技术,可模拟10个以上运动目标,目标距离不受雷达重复频率的限制;采用数字存贮技术对地物杂波信号进行模拟,杂波分布可随场地和天线波束指向变化;对多台雷达同时工作时的同频干扰信号进行了模拟.系统可选择高频和视频两种接口将目标信号输入雷达,以适应各种检测要求.该系统可以有效检测相控阵雷达多目标跟踪性能,同时为雷达操作人员提供了实用的模拟训练器材.     

一种实用性强的MC-CDMA盲信道估计方法

基于子空间分解的MC-CDMA盲信道估计算法运算量大,实用性差.本文根据在MC-CDMA中信号子空间维数远小于噪声子空间维数的特点提出从信号子空间的角度进行MC-CDMA盲信道估计,并采用PASTd算法对信号子空间及其维数进行动态跟踪.本文还通过对子空间跟踪的结果进行正交化处理的方法,显著提高了盲信道估计的性能.本文的盲信道估计方法运算量低,实用性强,仿真结果表明其盲信道估计性能接近特征值分解法.     

微弱信号在数字信号处理器上的检测与实现

本文首先介绍了微弱信号的检测理论,包括自相关检测和互相关检测.虽然模拟电路对于信号处理的实现比较简单,但是对于容易被噪声掩盖的微弱的有用信号的检测并不是十分理想,而对于数字电路来说,设计和实现方式上相对于模拟电路的实现比较复杂,但是对于提取微弱信号效果较为明显,容易在噪声干扰下检测出微弱信号.针对模拟电路处理微弱信号存在的问题,本文提出了采用数字电路进行微弱信号处理的过程以及在DSP上的软件、硬件的数字实现过程.     

Frequency estimation of sinusiode from wideband using sub-Nyquist sampling

A novel frequency estimation algorithm for wideband signal with sub-Nyquist sampling is proposed in this paper. With the aid of information provided by the auxiliary delayed sampling channel and the aliased frequency estimation for wideband signal with sub-Nyquist sampling, the frequency aliasing due to sub-Nyquist sampling can be solved. This method can reduce the complexity of the overall hardware at the cost of an auxiliary sampling channel. Furthermore, in order to alleviate the computation burden for its practicability, a more simplified algorithm is put forward and its validity is proved by our numerical simulation results. The Cramer-Rao Lower Bound （CRLB） of the frequency estimation is also derived at the end of this paper.     

卫星导航欺骗信号检测技术探究

在卫星导航系统已全面应用于国防、社会民生等各方面的今天,保证系统的应用安全已变得越来越重要.通过研究卫星导航欺骗信号的产生原理和类型,对当前各种检测技术的针对性原理和优劣进行了分析比较,从而得出了不同的检测技术应根据应用场景不同、欺骗信号的类型不同、应用领域的不同而有所选择.     

Maritime filtering for images and videos

Maritime signal processing technologies have emerged as an important area of study because of the increasing popularity of autonomous ships and automatic maritime surveillance systems. However, the various techniques developed for detecting or tracking objects remain unable to address various maritime noise challenges that cause several types of false positives in maritime visual surveillance. Maritime signal processing is challenging because of the prevalence of noise sources such as severe dynamic backgrounds, wakes, and reflections, owing to the complex, unconstrained, and diverse nature of such scenes caused by the surface properties of water. Moreover, few studies have investigated specific maritime noise filtering as a general integrated processing approach with image and video technologies in the context of maritime visual surveillance. In this study, we propose a novel maritime noise prior (MNP) based on a dark channel prior and observations of the characteristics of the sea. A general maritime filtering technique is developed to suppress noise originating from the properties of water in maritime images and videos. The proposed method employs a noniterative, nonlinear, and simple maritime filtering approach based on MNP that does not require specialized knowledge of application scene conditions or structure. We conducted image and video experiments by applying our approach to three publicly available databases. In experiments with color images, our method successfully filtered related background noise and water, i.e., severe boat wakes and reflections, while preserving objects other than water in color images. In the experiments with video sequences, the results demonstrated that the proposed filter improved the overall performance of state-of-the-art background subtraction (BS) algorithms from 36.60%–50.63%. By combining BS algorithms and filtering to enhance foreground detection in video sequences, the proposed method ensures the universal applicability and flexibility required to eliminate noise from images and videos obtained in challenging maritime environments. The results indicate that the proposed method is appropriate for maritime surveillance applications implementing image segmentation and foreground detection, and it can potentially increase the accuracy of maritime visual surveillance.     

用DSP实现多目标实时跟踪

介绍了在 TMS3 2 0 C62 0 3上实现 4个目标的快速跟踪技术。为了提高实时性和精确度 ,采用线性汇编优化实现。1个 3 2 0× 160主窗口的 DSP平均处理时间为 2 m s,3个 60× 60副窗口的 DSP平均处理时间各为 0 .4ms,实现了复杂背景下低对比度目标的快速跟踪。实验结果证明跟踪效果良好 ,解决了以前窗口开不大、跟踪速度慢的问题     

Design of a Temporal Learning Chip for Signal Generation and Classification

The design and analog VLSI implementation of a recurrent neural network with integrated temporal learning is presented. The learning algorithm is forward in time, and is implemented strictly as instantaneous, local weight updates. PSpice simulations of networks with 4 to 6 neurons demonstrate robust learning of trajectory generation and classification tasks. A scalable 2-D VLSI architecture is described and a prototupe 4-neuron recurrent neural network with learning has subsequently been fabricated in MOSIS TinyChip 2 micron technology. Experimental results of the chip validate the learning performance with convergence in the millisecond range. Specific experimental results of learning circular and figure-8 dynamic trajectories are included.     

A Low Power Dissipation Technique for a Low Voltage OTA

This paper proposes a novel low power dissipation technique for a low voltage OTA. A conventional low power OTA with a class AB input stage is not suitable for a low voltage operation (±1.5 V supply voltages), because it uses composite transistors (referred to CMOS pair) which has a large threshold voltage. On the other hand, the tail-current type OTA needs a large tail-current value to obtain a sufficient input range at the expense of power dissipation. Therefore, the conventional tail-current type OTA has a trade-off between the input range and the power dissipation to the tail-current value. The trade-off can be eliminated by the proposed technique. The technique exploits negative feedback control including a current amplifier and a minimum current selecting circuit. The proposed technique was used on Wang's OTA to create another OTA, named Low Power Wang's OTA. Also, SPICE simulations are used to verify the efficiency of Low Power Wang's OTA. Although the static power of Low Power Wang's OTA is 122 W, it has a sufficient input range, whereas conventional Wang's OTA needs 703 W to obtain a sufficient input range. However, we can say that as the input signal gets larger, the power of Low Power Wang's OTA becomes larger.