Yield Improvement Using Fail Signature Detection Algorithm (FSDA)

2006 ◽  
Author(s):  
A. Inani ◽  
R. Burch ◽  
B. Stine ◽  
J. Kim
Keyword(s):  
Detection Algorithm ◽  
Yield Improvement ◽  
Signature Detection

scholarly journals Virus signature detection algorithm

2021 ◽  
Vol 2131 (2) ◽  
pp. 022086
Author(s):  
D Zemlyanaya ◽  
N Boldyrikhin ◽  
A Svizhenko ◽  
B Yukhnov
Keyword(s):  
Operating System ◽  
Detection Algorithm ◽  
Practical Significance ◽  
Risk Of Infection ◽  
Signature Detection

Abstract The purpose of this research is to develop an algorithm for finding virus signatures. The method of searching for virus signatures was analyzed to achieve this goal. A brief description of the Boyer-Moore algorithm was also considered. The result of the research is a new algorithm that optimizes the speed of finding virus signatures by scanning the beginning and end of the file, since these are common cases where viruses are located. The practical significance of this research lies in the development of an algorithm for finding virus signatures, which reduces the risk of infection of the operating system with viruses and provides the ability to quickly detect malware.

scholarly journals Ships Matching Based on an Adaptive Acoustic Spectrum Signature Detection Algorithm

2018 ◽  
Vol 9 (4) ◽  
pp. 13-29
Author(s):  
Dahai Cheng ◽  
Huigang Xu ◽  
Ruiliang Gong ◽  
Huan Huang
Keyword(s):  
Detection Algorithm ◽  
Acoustic Spectrum ◽  
Signature Detection

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

Low-Complexity Soft-ML Detection Algorithm for Modified-DCM in WiMedia UWB Systems

2013 ◽  
Vol E96.B (3) ◽  
pp. 910-913 ◽  
Author(s):  
Kilhwan KIM ◽  
Jangyong PARK ◽  
Jihun KOO ◽  
Yongsuk KIM ◽  
Jaeseok KIM
Keyword(s):  
Low Complexity ◽  
Detection Algorithm ◽  
Ml Detection
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