Comparing synthetic imagery with real imagery for visible signature analysis: human observer results

2017 ◽  
Author(s):  
Neal Winter ◽  
Joanne B. Culpepper ◽  
Noel J. Richards ◽  
Christopher S. Madden ◽  
Vivienne Wheaton
Keyword(s):  
Human Observer ◽  
Signature Analysis ◽  
Real Imagery

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

Using watermark visibility measurements to select an optimized pair of spot colors for use in a binary watermark

2020 ◽  
Vol 2020 (15) ◽  
pp. 197-1-197-7
Author(s):  
Alastair Reed ◽  
Vlado Kitanovski ◽  
Kristyn Falkenstern ◽  
Marius Pedersen
Keyword(s):  
Food Packaging ◽  
Red Light ◽  
Human Observer ◽  
Viewing Distance ◽  
Point Of Sale ◽  
Complementary Color ◽  
Low Visibility ◽  
Packaging Industry ◽  
Spot Color ◽  
Selection Of

Spot colors are widely used in the food packaging industry. We wish to add a watermark signal within a spot color that is readable by a Point Of Sale (POS) barcode scanner which typically has red illumination. Some spot colors such as blue, black and green reflect very little red light and are difficult to modulate with a watermark at low visibility to a human observer. The visibility measurements that have been made with the Digimarc watermark enables the selection of a complementary color to the base color which can be detected by a POS barcode scanner but is imperceptible at normal viewing distance.

Signature Analysis: Statistical Models and Their Application to FA

1996 ◽  
Author(s):  
C.K. Lakshminarayan ◽  
S. Pabbisetty ◽  
O. Adams ◽  
F. Pires ◽  
M. Thomas ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Failure Analysis ◽  
Statistical Models ◽  
Essential Elements ◽  
Assessment Model ◽  
Signature Analysis ◽  
Risk Assessment Model ◽  
Sample Sizes ◽  
Reliability Engineering ◽  
Semiconductor Chips

Abstract This paper deals with the basic concepts of Signature Analysis and the application of statistical models for its implementation. It develops a scheme for computing sample sizes when the failures are random. It also introduces statistical models that comprehend correlations among failures that fail due to the same failure mechanism. The idea of correlation is important because semiconductor chips are processed in batches. Also any risk assessment model should comprehend correlations over time. The statistical models developed will provide the required sample sizes for the Failure Analysis lab to state "We are A% confident that B% of future parts will fail due to the same signature." The paper provides tables and graphs for the evaluation of such a risk assessment. The implementation of Signature Analysis will achieve the dual objective of improved customer satisfaction and reduced cycle time. This paper will also highlight it's applicability as well as the essential elements that need to be in place for it to be effective. Different examples have been illustrated of how the concept is being used by Failure Analysis Operations (FA) and Customer Quality and Reliability Engineering groups.

Signature Analysis of Package Delamination Using Scanning Acoustic Microscope

1996 ◽  
Author(s):  
S.X. Li ◽  
K. Lee ◽  
J. Hulog ◽  
R. Gannamani ◽  
S. Yin
Keyword(s):  
Failure Analysis ◽  
Early Stage ◽  
Signature Analysis ◽  
Acoustic Microscope ◽  
Reflow Soldering ◽  
Moisture Expansion ◽  
Scanning Acoustic Microscope ◽  
Package Reliability

Abstract Package delaminations are often associated with electrical and package reliability problems in IC devices. Delaminations caused by electrical-over-stress (EOS) and moisture expansion during reflow soldering have shown different delamination patterns. A Scanning Acoustic Microscope (SAM) can be used to detect package delaminations. Understanding these delamination signatures can help us quickly identify the failure cause at an early stage of the failure analysis.

Mutational Signature Analysis Reveals NTHL1 Deficiency to Cause a Multi-Tumor Phenotype Including a Predisposition to Colon and Breast Cancer

2018 ◽  
Author(s):  
Judith E. Grolleman ◽  
Richarda M. de Voer ◽  
Fadwa A. Elsayed ◽  
Maartje Nielsen ◽  
Robbert D. A. Weren ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Signature Analysis ◽  
Mutational Signature ◽  
Tumor Phenotype
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