Detection Algorithm for Copy-Move Forgery Based on Circle Block

2016 ◽  
pp. 355-364 ◽  
Author(s):  
Choudhary Shyam Prakash ◽  
Sushila Maheshkar
Keyword(s):  
Detection Algorithm

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

Closed Summation Expressions for PD and PFA of Adaptive Sidelobe Blanker Detection Algorithm

2012 ◽  
Vol E95-B (2) ◽  
pp. 676-679 ◽  
Author(s):  
Guolong CUI ◽  
Lingjiang KONG ◽  
Xiaobo YANG ◽  
Jianyu YANG
Keyword(s):  
Detection Algorithm

Mode and Frame Detection Algorithm for DAB OFDM Receiver

2015 ◽  
Vol E98.A (9) ◽  
pp. 1995-1997
Author(s):  
Won-Jae SHIN ◽  
Ki-Won KWON ◽  
Yong-Je WOO ◽  
Hyoungsoo LIM ◽  
Hyoung-Kyu SONG ◽  
...  
Keyword(s):  
Detection Algorithm

Distributed Fast Radio Burst Detection: Algorithm and Application

2018 ◽  
Vol 2018 (16) ◽  
pp. 224-1-224-5
Author(s):  
Stephen Itschner ◽  
Kevin Bandura ◽  
Xin Li
Keyword(s):  
Radio Burst ◽  
Detection Algorithm ◽  
Burst Detection ◽  
Fast Radio Burst

Development of Failure Detection Algorithm for a Slope Using Sensor Data

2018 ◽  
Vol 6 (1) ◽  
pp. 93-101
Author(s):  
Hyun Ki Kim ◽  
Chan Yong Choi ◽  
Ho Suk Bae
Keyword(s):  
Failure Detection ◽  
Detection Algorithm ◽  
Sensor Data
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