Spatial structure and landscape associations of SRTM error

This paper evaluates the spatial structure of Shuttle Radar Topography Mission (SRTM) error and its associations with globally available topographic and land cover variables across a wide range of landscapes. Two continental-scale SRTM elevation data samples were extracted, along with collocated National Elevation Dataset (NED) elevations, MODIS composite forest cover percentage, and global ecoregion major habitat type codes. The larger punctual sample contained nearly 247,000 sites on a regular grid across the conterminous United States, while the smaller areal sample consisted of 37,500 45″ × 45″ rectangular regions on a regular grid. Sub-pixel positional mismatch was accounted for by finding and using the best local fit between the 1 arc sec horizontal resolution NED product and the 3 arc sec (3″) horizontal resolution SRTM product. Slope and aspect were calculated for all samples. Using the larger point sample, we identified associations between SRTM error, defined as NED-SRTM 3″ differences, with these land cover and terrain derivative variables. Using the areal sample, we developed semivariograms of elevation error for tens of thousands of small regions across the United States, as well as for sets of these regions with common slope and landcover properties. This facilitated a more comprehensive evaluation of the spatial structure of SRTM error than has previously been done. The punctual sample RMSE was 8.6 m, conforming to previous estimates of SRTM error, but many errors in excess of 50 m were identified. Nearly 90% of these large errors were positive and correlated with high forest cover percentage. Overall, SRTM elevations consistently overestimated the surface. Forest cover and slope were positively correlated with positive bias. A strong association of aspect with SRTM error was noted, with positive error magnitudes peaking for aspects oriented to the northwest and negative error magnitudes peaking for slopes facing southeast. Error bias, standard deviation, and semivariograms differed substantially across ecoregion types. These variables were incorporated in a regression model to predict SRTM error: this model explained nearly 60% of the total error variation and has the potential to substantially improve the SRTM data product worldwide using globally available datasets.     

On the relationships among the all-uses, all-DU-paths, andall-edges testing criteria

The all-du-paths data flow testing criterion was designed to be more demanding than the all-uses criterion, which itself was designed to be more demanding than the all-edges criterion. However, formal comparison metrics developed within the testing community have failed to validate these relationships, without requiring restrictive or undecidable assumptions regarding the universe of programs to which the criteria apply. We show that the formal relationships among these criteria can be made consistent with their intended relative strengths, without making restrictive or undecidable assumptions     

Fault detection effectiveness of mutation and data flow testing

We report results from an experiment to compare the fault detection effectiveness of mutation, its variants and the all-uses data flow criteria. Adequate test sets were generated randomly, as opposed to by human testers as in some previous studies. We view our results in the light of those from earlier studies comparing mutation with path-oriented testing strategies. We identify and discuss factors that one might consider while evaluating an adequacy criterion for use in practice. Results from our experiments strengthen a hypothesis that an adequacy criterion based on one of the two variants of mutation has superior fault detection effectiveness than that of the all-uses criterion.     

An empirical, path-oriented approach to software analysis and testing

Error flow analysis and testing techniques focus on the introduction of errors through code faults into data states of an executing program, and their subsequent cancellation or propagation to output. The goals and limitations of several error flow techniques are discussed, including mutation analysis, fault-based testing, PIE analysis, and dynamic impact analysis. The attributes desired of a good error flow technique are proposed, and a model called dynamic error flow analysis (DEFA) is described that embodies many of these attributes. A testing strategy is proposed that uses DEFA information to select an optimal set of test paths and to quantify the results of successful testing. An experiment is presented that illustrates this testing strategy. In this experiment, the proposed testing strategy outperforms mutation testing in catching arbitrary data state errors.     

Program Slicing-Based Regression Testing Techniques

After changes are made to a previously tested program, a goal of regression testing is to perform retesting based on the modifications while maintaining the same testing coverage as completely retesting the program. This paper presents a novel approach to data flow based regression testing that uses slicing algorithms for the explicit detection of definition-use associations that are affected by a program change. An important benefit of this slicing technique is that, unlike previous techniques, neither data flow history nor recomputation of data flow for the entire program is required to detect affected definition-use associations. The program changes drive the recomputation of the required partial data flow through slicing. Another advantage is that the technique achieves the same testing coverage with respect to the affected definition-use associations as a complete retest of the program, without maintaining a test suite. Thus, the overhead of maintaining and updating a test suite is eliminated.     

Error-control coding in computers

In this article, intended for readers with basic knowledge in coding, the codes used in actual systems are surveyed. Error control in high-speed memories is examined, including bit-error-correcting/detecting codes, byte-error-correcting/detecting codes, and codes to detect single-byte errors as well as correct single-bit errors and detect double-bit errors. Tape and disk memory codes for error control in mass memories are discussed. Processor error control and unidirectional error-control codes are covered, including the application of the latter to masking asymmetric line faults     

Mood and achievement in a difficult driving task

Ratings of mood, degree of involvement and probability of success were made before and after two varieties of a difficult driving task on a slippery road. Error scores were also obtained. Involvement and probability of success were related to mood, and error scores were consistently and negatively correlated with mood: subjects who felt more unpleasant, tired, tense and uncertain also made more errors. These trends were confirmed also when rated probability of success was held constant in partial correlation analyses.     

Mapping simulated error due to terrain slope in airborne lidar observations

Quantification of geo-location error in light detection and ranging (lidar) observations is typically limited to empirical assessments, commonly quantified as the fundamental vertical accuracy (FVA). Methodological recommendations indicate that validation observations used for quantifying the FVA should not be collected in sloped terrain; however, terrain slope has been shown to contribute to the lidar error budget. Therefore, users of lidar information generally do not have adequate information to characterize error in sloped conditions. This study proposes a novel geometric terrain-based error propagation algorithm for simulating error bounds of individual lidar observations in the presence of terrain slope. A steep, glacierized, alpine test site in the Canadian Rockies was used to evaluate the algorithm. Error simulations were modelled from the terrain-based error propagation algorithm as well as a pre-existing sensor hardware error propagation algorithm and validated with high-accuracy GPS observations. Simulated versus observed errors showed that terrain-based error simulations provided a reasonable ‘worst-case scenario’ simulation of potential error and were superior to hardware-only simulated errors. Results were separated into three individual flight lines, and terrain-based error simulations were greater than the observed errors in 82%, 89%, and 100% of the tested points in each respective flight line, or 90%, overall. This contrasted hardware-only error simulations, which were greater than observed errors in 32%, 42%, and 84% of tested points in each respective flight line, or 50% overall. This work provides a comprehensive understanding of the distribution of errors within lidar point clouds over complex terrain types and provides a new methodology for propagating lidar observational errors into derived products.     

Training Novice Users in Developing Strategies for Responding to Errors When Browsing the Web

Novice users frequently make errors when learning a new computer task and spend a large portion of their time trying to recover from errors. Three methods for helping novice users respond to errors have been presented in the literature: error management training, exploratory training, and conceptual models. In error management training, errors are presented as opportunities for learning, and users are instructed in strategies for coping with errors. In exploration, users are given an overview of their environment and are taught how to navigate through their task environment. Conceptual models are graphical or mathematical representations of a system that correspond closely to the real-world system. This experiment tested the effectiveness of these different approaches on training novice users to use the Internet. In this experiment, users received 3 hr of training on the World Wide Web and then were asked to perform a set of information retrieval tasks. Performance was measured in two ways: task performance and performance time. Participants who received exploratory training had significantly higher task performance. Participants who received exploration and conceptual models, both individually and together, were able to complete the tasks in less time. Error management had no significant effect on the performance of participants. In the task application of Web browsing, exploration seems to be the most appropriate training method for novice users.     

内容指导的自适应时域错误隐藏算法

为了减小视频传输中的错误对解码端重建视频质量的影响,提出了一种使用于H.264的基于内容的自适应时域错误隐藏算法。该算法首先对受损块邻域进行边缘检测,通过对边缘信息的进一步分析来对受损块的宏块模式进行估计,然后根据估计的宏块模式对受损块进行重建。实验表明,该算法能更好地适应丢失块内存在运动目标的情形,差错隐藏效果明显。     

An Experimental Evaluation of Data Flow and Mutation Testing

Two experimental comparisons of data flow and mutation testing are presented. These techniques are widely considered to be effective for unit-level software testing, but can only be analytically compared to a limited extent. We compare the techniques by evaluating the effectiveness of test data developed for each. We develop ten independent sets of test data for a number of programs: five to satisfy the mutation criterion and five to satisfy the all-uses data-flow criterion. These test sets are developed using automated tools, in a manner consistent with the way a test engineer might be expected to generate test data in practice. We use these test sets in two separate experiments. First we measure the effectiveness of the test data that was developed for one technique in terms of the other. Second, we investigate the ability of the test sets to find faults. We place a number of faults into each of our subject programs, and measure the number of faults that are detected by the test sets. Our results indicate that while both techniques are effective, mutation-adequate test sets are closer to satisfying the data flow criterion, and detect more faults.     

子词驻留特征在电话语音确认中的应用

语速和插入删除错误是导致自动电话转接系统发生错误的重要原因。该文给出一种基于子词似然比(LLR)和子词驻留特征融合的语音确认方法减少上述错误。提出基于最小分类错误准则方法求取子词特征融合参数。电话转接系统实验结果表明,采用子词驻留特征能有效提高语音确认效果,与LLR方法相比,名称关键词的等错误率下降3.35%,数字串关键词的等错误率下降4.05%。     

Error analysis of ticket vending machines: comparing analytic and empirical data

An error analysis was performed on the three ticket vending machines installed at London underground and overground train stations. A brief analytic inspection, resulting in a set of predicted errors, was followed by lengthy empirical observations of successes, failures and errors occurring during machine use. There were two observational phases, 5 years apart. Comparisons were made between the patterns of error-making on the three machines, using error categories derived from the initial analysis. It was found that these comparisons were sufficient to account for most of the between-machine and between-phase differences, although some unattributed errors remained. It was also found that much of the observed pattern of error-making had been predicted by the initial inspection, and it is suggested that, for relatively simple interfaces such as these, the method (Dialogue Error Analysis) is sufficient to identify and prioritize most problems that will occur in use. Attempt was also made to relate the observed error categories to influential taxonomies of human error.     

Estimation of classifier performance

An expression for expected classifier performance previously derived by the authors (ibid., vol.11, no.8, p.873-855, Aug. 1989) is applied to a variety of error estimation methods and a unified and comprehensive approach to the analysis of classifier performance is presented. After the error expression is introduced, it is applied to three cases: (1) a given classifier and a finite test set; (2) given test distributions a finite design set; and (3) finite and independent design and test sets. For all cases, the expected values and variances of the classifier errors are presented. Although the study of Case 1 does not produce any new results, it is important to confirm that the proposed approach produces the known results, and also to show how these results are modified when the design set becomes finite, as in Cases 2 and 3. The error expression is used to compute the bias between the leave-one-out and resubstitution errors for quadratic classifiers. The effect of outliers in design samples on the classification error is discussed. Finally, the theoretical analysis of the bootstrap method is presented for quadratic classifiers     

基于误差分布估计的机器人手眼标定方法研究

精确的机器人手眼标定对于机器人的视觉环境感知具有重要的意义。现有算法通常采用最小二乘估计或全局非线性优化求解方法对机器人手眼系统的变换参数进行估计。当系统存在测量粗差时直接采用最小二乘估计会导致标定结果精度的下降;基于全局非线性优化策略的标定算法则由于数据粗差的影响,求解过程易过早收敛也会造成标定精度低。为了解决误差粗差敏感的问题,提出了一种基于误差分布估计的加权最小二乘鲁棒估计方法,以提高机器人手眼标定的精度。首先,通过最小二乘估计计算手眼变换矩阵;之后计算每对坐标对应的误差值;根据误差值的分布概率初始化对应坐标数据的权值;最后采用加权的最小二乘估计重新计算机器人手眼标定矩阵。最后引入迭代估计策略进一步提高手眼标定的精度。设计的机器人手眼标定实验及结果证明,所提算法能够在数据粗差影响下保持较高的标定精度,更适用于机器人的手眼标定问题。     

VB处理运行时错误方法的应用

通过分析应用程序运行产生的各种运行时错误,指出了只有通过编写错误处理程序才能解决这些问题;设计了设置错误捕获、编写错误处理程序、退出错误处理程序、错误处理顺序、自动生成错误等具体方法及步骤。     

An empirical comparison of data flow and mutation-based test adequacy criteria

Evaluation of the adequacy of a test set consisting of one or more test cases is a problem oftes encountered in software testing environments. Two test adequacy criiteria are considered, namely the data flow based all-uses criterion and a mutation based criterion. An empirical study was conducted to compare the ‘difficulty’ of satisfying the two criteria and their costs. Similar studies conducted in the past are discussed in the light of this study. A discussion is also presented of how and why the results of this study, when viewed in conjunction with the results of earlier comparisons of testing methods, are useful to a software test team.     

基于字串切分统计词典的繁体中文拼写检错方法*

针对繁体中文拼写检错的问题进行了研究,提出一种基于字串切分统计词典的检错方法。利用语料库中字串出现的频率信息作为检错依据,根据字串及其频率信息来建立统计词典,并设计了基于统计规则评判的检错算法。以SIGHAN7会议中文拼写校验任务中用于检错评测的1000句测试集作为实验测试集,并与此会议提交的结果进行比较,实验结果表明,与基于复杂语言模型的检错方法相比,该方法在实现简单的同时也有很好的检错效果,获得了较高的准确率和精确率以及较低的误报率。     

水下机器人圆概率偏差的有效预报

在水下机器人设计过程中,对于导航精度设计指标无法用物理实验进行验证。如何有效预报圆概率偏差(CEP)成为了亟待解决的问题。采用直线航路模型,对误差源在直线航路上引起的导航误差进行定量分析,推求各误差合成后的纵向误差和横向误差表达式,建立水下机器人圆概率偏差迭代方程,求得圆概率偏差预报结果。推导结果表明横、纵误差之比小于0.7时可保证圆概率偏差不大于前两者之中的大者。该方法与实际实验结果吻合,表明提出的导航误差分析和圆概率估算方法是有效的,可准确预报水下机器人导航精度。     

用于程序循环控制的错误检测算法

为确保安全苛求系统中程序执行的正确性,针对程序循环控制中内存未更新、循环提前结束和循环滞后结束的问题,提出一种基于含签名和时间戳的算术(ANBD)码的循环控制错误检测算法。该算法通过ANBD码,将程序变量编码为含签名的码字。通过校验码字签名,检测循环控制错误。运用ANBD码中的时间标签,可检测内存未更新错误。另外,在ANBD码基础上,通过采用在线语句块签名分配算法、语句块签名函数和变量签名补偿函数,检测循环提前结束错误和循环滞后结束错误。该算法理论错误漏检概率为1/A,其中A为编码素数,选取97~10993的素数进行错误漏检概率测试,得到理论模型与测试结果的归一化均方误差(NMSE)约为-30 dB。测试结果表明,该算法可检测循环控制中出现的各类错误,且编码素数A接近2³²时,错误漏检概率可达10^-9,能够满足安全苛求系统的应用要求。