Timing-sequence testing of parallel programs

Testing of parallel programs involves two parts-testing of control flow within the processes and testing of timing-sequence.This paper focuses on the latter,particularly on the timing-sequence of message-passing paradigms.Firstly the coarse-grained SYN-sequence model is built up to describe the execution of distributed programs.All of the topics discussed in this paper are based on it .The most direct way to test a program is to run it.A fault-free parallel program should be of both correct computing results and propoer SYN-sequence.In order to analyze the validity of observed SYN-sequence,this paper presents the formal specification (Backus Normal Form) of the valid SYN-sequence.Till now there is little work about the testing coverage for distributed programs.Calculating the number of the valid SYN-sequences is the key to coverage problem,while the number of the valid SYN-sequences is terribly large and it is very hard to obtain the combination law among SYN-events.In order to resolve this problem,this paper proposes an efficient testing strategy-atomic SYN-event testing,which is to linearize the SYN-sequence (making it only consist of serial atomic SYN-events)first and then test each atomic SYN-event independently.This paper particularly provides the calculating formula about the number of the valid SYN-sequences for tree-topology atomic SYN-event(broadcast and combine),Furthermore,the number of valid SYN-sequences also,to some degree,mirrors the testability of parallel programs.Taking tree-topology atomic SYN-event as an example,this paper demonstrates the testability and communication speed of the tree-topology atomic SYN-event under different numbers of branches in order to achieve a more satisfactory tradeoff between testability and communication efficiency.     

Self-orienting wireless multimedia sensor networks for occlusion-free viewpoints

Wireless multimedia sensor networks (WMSN) are formations of a large number of compact form-factor computing devices that can capture multimedia content, such as video and audio, and communicate them over wireless channels. The efficiency of a WMSN heavily depends on the correct orientation (i.e., view) of its individual sensory units in the field. In this paper, we study the problem of self-orientation in WMSN, that is finding the most beneficial orientation for all multimedia sensors to maximize multimedia coverage. We propose a new algorithm to determine a node’s multimedia coverage and find the sensor orientation that minimizes the negative effect of occlusions and overlapping regions in the sensing field. Our approach enables multimedia sensor nodes to compute their directional coverage leading to an efficient and self-configurable sensor orientation calculation. By using simulations, we show that the occlusion-free viewpoint approach increases the multimedia coverage significantly. The self-orientation methodology is designed in the form of a distributed algorithm, making it a suitable candidate for deployment in practical systems.     

Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference

Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, non-stationarity, and non-linearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent difficulties when using neural networks for the processing of high noise, small sample size signals. We introduce a new intelligent signal processing method which addresses the difficulties. The method proposed uses conversion into a symbolic representation with a self-organizing map, and grammatical inference with recurrent neural networks. We apply the method to the prediction of daily foreign exchange rates, addressing difficulties with non-stationarity, overfitting, and unequal a priori class probabilities, and we find significant predictability in comprehensive experiments covering 5 different foreign exchange rates. The method correctly predicts the directionof change for the next day with an error rate of 47.1%. The error rate reduces to around 40% when rejecting examples where the system has low confidence in its prediction. We show that the symbolic representation aids the extraction of symbolic knowledge from the trained recurrent neural networks in the form of deterministic finite state automata. These automata explain the operation of the system and are often relatively simple. Automata rules related to well known behavior such as tr end following and mean reversal are extracted.     

内建自测试中多输入特征寄存器的硬件开销的减少

在内建自测试中,针对随机向量测试,本文提出了一种通过输出信号分组压缩来减少多输入特征寄存器MISR的硬件开销的方法。该方法是在分析输出信号之间相关性的基础上,根据给定的MISR阶数构造具有最小相关度的输出信号集合组,以此来减少输出信号分组压缩时的故障覆盖率损失。该方法不需附加任何辅助电路。     

Modeling and analysis of effects of precipitation and vegetation coverage on runoff and sediment yield in Jinsha River Basin

This paper focuses on the effects of precipitation and vegetation coverage on runoff and sediment yield in the Jinsha River Basin. Results of regression analysis were taken as input variables to investigate the applicability of the adaptive network-based fuzzy inference system (ANFIS) to simulating annual runoff and sediment yield. Correlation analysis indicates that runoff and sediment yield are positively correlated with the precipitation indices, while negatively correlated with the vegetation indices. Furthermore, the results of stepwise regression show that annual precipitation is the most important factor influencing the variation of runoff, followed by forest coverage, and their contributions to the variation of runoff are 69.8% and 17.3%, respectively. For sediment yield, rainfall erosivity is the most important factor, followed by forest coverage, and their contributions to the variation of sediment yield are 49.3% and 24.2%, respectively. The ANFIS model is of high precision in runoff forecasting, with a relative error of less than 5%, but of poor precision in sediment yield forecasting, indicating that precipitation and vegetation coverage can explain only part of the variation of sediment yield, and that other impact factors, such as human activities, should be sufficiently considered as well.     

基于边缘颜色对特征及笔画穿越双层检测车牌定位算法

针对现有车牌定位算法鲁棒性不够、准确度不高以及参数设置困难等问题,提出基于边缘颜色对特征以及笔画穿越双层检测车牌定位算法,不但充分利用车牌边缘颜色搭配信息,而且有效利用了车牌字符结构信息。粗检测阶段：首先进行边缘检测,人工收集所有搭配的彩色边缘特征数据,利用机器学习模型建立车牌边缘颜色对覆盖分类学习模型,然后利用车牌边缘颜色对覆盖分类学习模型,并利用先验信息进行形态学处理形成车牌候选区域。验证阶段：针对粗检测车牌候选区域,扫描车牌边缘穿越信息,最后利用车牌区域整体边缘分布覆盖分类模型进行候选区域验证处理。该方法利用车牌背景与字符具有固定颜色搭配的重要特点,综合利用了车牌的结构特征和纹理特征,提高了车牌定位的可靠性。实验采用100幅含有不同颜色搭配的车牌图像进行实验,定位准确率达到96%以上。     

A mobility constraint model to infer sensor behaviour in forest fire risk monitoring

Wireless sensor networks (WSNs) play an important role in forest fire risk monitoring. Various applications are in operation. However, the use of mobile sensors in forest risk monitoring remains largely unexplored. Our research contributes to fill this gap by designing a model which abstracts mobility constraints within different types of contexts for the inference of mobile sensor behaviour. This behaviour is focused on achieving a suitable spatial coverage of the WSN when monitoring forest fire risk. The proposed mobility constraint model makes use of a Bayesian network approach and consists of three components: (1) a context typology describing different contexts in which a WSN monitors a dynamic phenomenon; (2) a context graph encoding probabilistic dependencies among variables of interest; and (3) contextual rules encoding expert knowledge and application requirements needed for the inference of sensor behaviour. As an illustration, the model is used to simulate the behaviour of a mobile WSN to obtain a suitable spatial coverage in low and high fire risk scenarios. It is shown that the implemented Bayesian network within the mobility constraint model can successfully infer behaviour such as sleeping sensors, moving sensors, or deploying more sensors to enhance spatial coverage. Furthermore, the mobility constraint model contributes towards mobile sensing in which the mobile sensor behaviour is driven by constraints on the state of the phenomenon and the sensing system.     

浅析CDMA山区及交通干线覆盖方案

文章结合不同的用户分布和山区丘陵地形的特点进行站址选择和设备选型的分析,通过系统仿真来比较高山站与平地站的覆盖范围,综合考虑覆盖目标、建设成本和投资收益对二者进行取舍。直放站和射频拉远(RRU)是山区交通干线优化时的主力军,着重分析直放站对施主基站低噪抬升的影响。     

无源雷达探测覆盖能力柔性评估建模研究

针对无源雷达作战效能量化评估问题,将有效探测距离、探测覆盖度指数等作为表征无源雷达探测覆盖能力的典型指标,建立了相应的指标体系和指标解算模型;采用基于算子的柔性评估建模方法,设计实现了指标对应的评估算子,并以这些评估算子为基础建模单元,构建了与指标体系完全一致、灵活、适用的算子树评估模型.评估实验结果表明,该模型能够支持无源雷达探测覆盖能力评估解算和影响因素分析,可用于无源雷达作战部署决策等时的多方案优选,对于同类装备作战效能量化评估也具有一定的借鉴和参考价值.     

漓江流域沿岸风光带植被覆盖度的遥感动态监测与估算

以1990、2008年TM影像和2001年ETM＋影像为数据源,研究漓江精华段沿岸风光带植被覆盖度的动态变化规律,采用归一化植被指数（NDVI）像元二分模型,半定量估算了研究区的植被覆盖度,生成了不同时相的植被覆盖度等级图及面积转移矩阵,并结合植被覆盖度的监测结果对研究区植被覆盖度的变化趋势进行建模分析.结果表明,研究区1990、2001和2008年均以高植被覆盖度等级为主,估算的面积分别为207.058、216.538和232.795 km2.1990-2008年间,研究区内植被覆盖度稳定不变的区域占总面积的44.887％,减少和增加的区域分别占总面积的22.077％和33.036％,说明植被覆盖度总体上呈现增加的趋势;结合1990-2001年和2001-2008年的变化分析结果,植被覆盖度变化趋势是先略微增加,后显著增加,与植被覆盖度均值走势一致,从定量遥感的角度证明了漓江精华段沿岸风光带的绿色生态环境得到了快速的改善.