TMS320 F206定点DSP芯片开发实践

以TMS320F206为例,着重探讨了DSP系统开发过程中的硬件设计与调试、软件设计中的流水线冲突、等待状态设置以及如何利用闪速存储器等相关问题.     

Optimal Solutions for the Temporal Precedence Problem

Abstract. In this paper we refer to the Temporal Precedence Problem on Pure Pointer Machines . This problem asks for the design of a data structure, maintaining a set of stored elements and supporting the following two operations: insert and precedes . The operation insert (a) introduces a new element a in the structure, while the operation precedes (a,b) returns true iff element a was inserted before element b temporally. In [11] a solution was provided to the problem with worst-case time complexity O (log log n ) per operation and O(n log log n) space, where n is the number of elements inserted. It was also demonstrated that the precedes operation has a lower bound of Ω (log log n ) for the Pure Pointer Machine model of computation. In this paper we present two simple solutions with linear space and worst-case constant insertion time. In addition, we describe two algorithms that can handle the precedes (a,b) operation in O (log log d ) time, where d is the temporal distance between the elements a and b .     

单词排版的两种算法

在对英文排版时，我们既应考虑单词之间的间隔，以便清晰可读，又应考虑节省版面，以便降低费用，本文通过详细地讨论两种算法可以解决这一矛盾。     

导弹引信动态数据存储仪的研制

本文提供了适用于导弹滑轨与机载试验引信动态数据测试的一种大容量存储测试系统:导弹引信动态数据存储仪,论文论述了系统的构成,工作原理、工作状态图、电路设计及使用存储测试系统在导弹滑轨及特定机载测试环境下的必要性及优越性,该仪器在存储测试系统中首次采用了准静态存储器658512,使存储测试仪的存储容量较先前有了提高,文中阐述了该芯片的使用方法,由于该芯片在出现时间较晚,其使用方法在一股资料中较少涉及,希望本文能起到抛砖引玉的作用。     

最佳基数排序

基数排序由于其效率高而被广泛应用。通常,基数排序所用的基数是10,然而,如果求得一个基数r_(best),并且用r_(best)为基数进行基数排序使排序时间达到最小,则这将具有非常重要的意义。本文给出了求r_(best)的方法,分析了以r_(best)为基数进行基数排序的时间复杂度,提出了进一步提高效率的措施,并将以r_(best)为基数的基数排序速度与以10为基数的基数排序进行了比较。     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

离散Hopfield神经网络和联想记忆的开关电流技术实现

研究了离散Ｈｏｐｆｉｅｌｄ神经网络（ＤＨＮＮ）和联想记忆神经网络的开关电流技术实现，利用多权输入跨导，开关电流延迟器（ＳＩＤ）和可编程电流比较器（ＰＣＣ）实现了离散Ｈｏｐｉｅｌｄ神经网络，并提出了利用离散Ｈｏｐｆｉｅｌｄ神经网络实现自联想记忆时相应的开关电流电路，所提出了开关电流神经网络适宜于超大规模集成，能在低电压（如３．３Ｖ）下工作。     

Drugs, memory, and metamemory: A dose-effect study with lorazepam and scopolamine.

This experiment was designed to use the graded dose-related amnesia produced by the benzodiazepine lorazepam (1.0, 2.0 mg/70 kg, oral) and the anticholinergic scopolamine (0.3, 0.6 mg/70 kg, subcutaneous) as a tool to explore the cognitive and neurochemical mechanisms underlying metamemory in the judgment of learning paradigm, with a placebo-controlled independent groups design in healthy volunteers (n=12/group). Results provide evidence for a pharmacological dissociation between effects on memory versus metamemory (relative accuracy of item-by-item monitoring) across a range of levels of memory performance and suggest that the drugs selectively impair those aspects of metamnemonic monitoring that require participants' awareness of their overall current state of functioning (absolute accuracy of prospective item-by-item monitoring, prospective global monitoring) but not those that rely solely on assessment of individual item characteristics (relative accuracy of item-by-item monitoring). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.     

Network traffic prediction based on a new time series model

Fast and accurate methods for predicting traffic properties and trend are essential for dynamic network resource management and congestion control. With the aim of performing online and feasible prediction of network traffic, this paper proposes a novel time series model, named adaptive autoregressive (AAR). This model is built upon an adaptive memory‐shortening technique and an adaptive‐order selection method originally developed by this study. Compared to the conventional one‐step ahead prediction using traditional Box–Jenkins time series models (e.g. AR, MA, ARMA, ARIMA and ARFIMA), performance results obtained from actual Internet traffic traces have demonstrated that the proposed AAR model is able to support online prediction of dynamic network traffic with reasonable accuracy and relatively low computation complexity. Copyright © 2005 John Wiley & Sons, Ltd.