Fletcher's error detection algorithm: how to implement it efficiently and how toavoid the most common pitfalls

1988 ◽  
Vol 18 (5) ◽  
pp. 63-88 ◽  
Author(s):  
Anastase Nakassis
Keyword(s):  
Error Detection ◽  
Detection Algorithm

scholarly journals Novel Error Detection Algorithm for LZSS Compressed Data

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 8940-8947 ◽  
Author(s):  
Beom Kwon ◽  
Myongsik Gong ◽  
Sanghoon Lee
Keyword(s):  
Error Detection ◽  
Detection Algorithm ◽  
Compressed Data

Analysis of Dynamic Method of Line Scales Detection

2009 ◽  
Vol 147-149 ◽  
pp. 576-581
Author(s):  
A. Barakauskas ◽  
Albinas Kasparaitis ◽  
Saulius Kausinis ◽  
R. Lazdinas
Keyword(s):  
Error Detection ◽  
Dynamic Method ◽  
Optical Microscope ◽  
Detection Algorithm ◽  
Uncertainty In Measurement ◽  
Line Scale ◽  
Scale Calibration ◽  
Calibration Program ◽  
Long Stroke ◽  
Scale Detection

The main causes of uncertainty in measurement regarding long-stroke line scales are line detection errors and external factors, especially temperature effects. The number of calibration errors of this sort increases with the extension of calibration time. Therefore, a dynamic method of line scale detection for modern long-stroke line scale comparators is used [1, 2, 3]. The article discusses the dynamic method of line scale detection by means of an optical microscope equipped with a photosensitive cell matrix and a line scale detection algorithm. Advantages of the dynamic method of scale calibration in terms of rate, accuracy and throughput are presented. The method’s error (detection parameters) correlations with detection rate, number of nominal lines, measuring rate, exposition delay are analyzed and mathematical models are described. The optimal values of these parameters are estimated. We are particularly interested in the improvement of the dynamic calibration program algorithm and minimization of uncertainty in measurement. The method was implemented and tested on the long-stroke line scale comparator, which has been developed and realized by JSC Precizika Metrology [3, 4, 5] in cooperation with VGTU and KUT.

An H.264/AVC Error Detection Algorithm Based on Syntax Analysis

2011 ◽  
pp. 215-234
Author(s):  
Luca Superiori ◽  
Olivia Nemethova ◽  
Markus Rupp
Keyword(s):  
Performance Evaluation ◽  
Error Detection ◽  
Detection Algorithm ◽  
Video Sequences ◽  
Video Streams ◽  
Syntax Analysis ◽  
Standard Methods ◽  
Free Data ◽  
Error Probabilities ◽  
Low Rate

In this chapter, we present the possibility of detecting errors in H.264/AVC encoded video streams. Standard methods usually discard the damaged received packet. Since they can still contain valid information, the localization of the corrupted information elements prevents discarding of the error-free data. The proposed error detection method exploits the set of entropy coded words as well as range and significance of the H.264/AVC information elements. The performance evaluation of the presented technique is performed for various bit error probabilities. The results are compared to the typical packet discard approach. Particular focus is given on low-rate video sequences.

Logic Error Detection Algorithm Based on RNN with Threshold Selection

2020 ◽  
Author(s):  
Taku Matsumoto ◽  
Yutaka Watanobe ◽  
Keita Nakamura ◽  
Yunosuke Teshima
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Error Detection ◽  
Language Model ◽  
Source Code ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Educational Systems ◽  
Determination Method ◽  
Logical Errors

Logical errors in source code can be detected by probabilities obtained from a language model trained by the recurrent neural network (RNN). Using the probabilities and determining thresholds, places that are likely to be logic errors can be enumerated. However, when the threshold is set inappropriately, user may miss true logical errors because of passive extraction or unnecessary elements obtained from excessive extraction. Moreover, the probabilities of output from the language model are different for each task, so the threshold should be selected properly. In this paper, we propose a logic error detection algorithm using an RNN and an automatic threshold determination method. The proposed method selects thresholds using incorrect codes and can enhance the detection performance of the trained language model. For evaluating the proposed method, experiments with data from an online judge system, which is one of the educational systems that provide the automated judge for many programming tasks, are conducted. The experimental results show that the selected thresholds can be used to improve the logic error detection performance of the trained language model.

A method for real-time error detection in low-cost environmental sensors data

2019 ◽  
Vol 8 (4) ◽  
pp. 338-350
Author(s):  
Mauricio Loyola
Keyword(s):  
Real Time ◽  
Error Detection ◽  
Measurement Errors ◽  
Low Cost ◽  
Detection Algorithm ◽  
Environmental Sensors ◽  
Content Type ◽  
Building Monitoring ◽  
Learning Techniques

Purpose The purpose of this paper is to propose a simple, fast, and effective method for detecting measurement errors in data collected with low-cost environmental sensors typically used in building monitoring, evaluation, and automation applications. Design/methodology/approach The method combines two unsupervised learning techniques: a distance-based anomaly detection algorithm analyzing temporal patterns in data, and a density-based algorithm comparing data across different spatially related sensors. Findings Results of tests using 60,000 observations of temperature and humidity collected from 20 sensors during three weeks show that the method effectively identified measurement errors and was not affected by valid unusual events. Precision, recall, and accuracy were 0.999 or higher for all cases tested. Originality/value The method is simple to implement, computationally inexpensive, and fast enough to be used in real-time with modest open-source microprocessors and a wide variety of environmental sensors. It is a robust and convenient approach for overcoming the hardware constraints of low-cost sensors, allowing users to improve the quality of collected data at almost no additional cost and effort.

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