Sciweavers
Explore
Publications
Books
Software
Tutorials
Presentations
Lectures Notes
Datasets
Labs
Conferences
Community
Upcoming
Conferences
Top Ranked Papers
Most Viewed Conferences
Conferences by Acronym
Conferences by Subject
Conferences by Year
Tools
Sci2ools
International Keyboard
Graphical Social Symbols
CSS3 Style Generator
OCR
Web Page to Image
Web Page to PDF
Merge PDF
Split PDF
Latex Equation Editor
Extract Images from PDF
Convert JPEG to PS
Convert Latex to Word
Convert Word to PDF
Image Converter
PDF Converter
Community
Sciweavers
About
Terms of Use
Privacy Policy
Cookies
Free Online Productivity Tools
i2Speak
i2Symbol
i2OCR
iTex2Img
iWeb2Print
iWeb2Shot
i2Type
iPdf2Split
iPdf2Merge
i2Bopomofo
i2Arabic
i2Style
i2Image
i2PDF
iLatex2Rtf
Sci2ools
24
click to vote
VLSID
2006
IEEE
favorite
Email
discuss
report
94
views
VLSI
»
more
VLSID 2006
»
On the Size and Generation of Minimal N-Detection Tests
14 years 3 months ago
Download
www.eng.auburn.edu
The main result of this paper, proved as a theorem, is that a lower bound on the number of test vectors that detect each fault at least N times is N
Kalyana R. Kantipudi
Real-time Traffic
Computer Architecture
|
Lower Bound
|
VLSID 2006
|
claim paper
Related Content
»
WorstCase and AverageCase Analysis of nDetection Test Sets
»
Test Generation for ModelBased Diagnosis
»
On test generation for transition faults with minimized peak power dissipation
»
An analysis of rule coverage as a criterion in generating minimal test suites for grammarb...
»
Generating test data for killing SQL mutants A constraintbased approach
»
Novel Test Pattern Generators for PseudoExhaustive Testing
»
A New ATPG Algorithm to Limit Test Set Size and Achieve Multiple Detections of All Faults
»
Parallel computation of the minimal elements of a poset
»
A scalable method for the generation of small test sets
more »
Post Info
More Details (n/a)
Added
12 Jun 2010
Updated
12 Jun 2010
Type
Conference
Year
2006
Where
VLSID
Authors
Kalyana R. Kantipudi
Comments
(0)
Researcher Info
VLSI Study Group
Computer Vision