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About 10,000 papers for “Test vector”

VIT: View-Vector-Isolations-Test

Jens Deutschmann · Zenodo (CERN European Organization for Nuclear Research) · 2026

We present the View-Vector-Isolations-Test (VIT), a deterministic primality test derived from Fractal Uncertainty Theory (FUT) and Coherent Mathematics (coMath). VIT characterizes primes as isolated resonances in a φ-scaled relational number space, achieving 100% accuracy on tested ranges. Unlike classical approaches based on divisibility, VIT interprets primality as the absence of non-trivial coherence relations in a view-vector network. The test employs a candidate set determined by the golden ratio φ with no free parameters, validating the deep connection between prime numbers and φ-structure predicted by FUT. While computationally equivalent to trial division, VIT provides a novel theoretical perspective on the nature of primes as fundamental resonance modes.

Cited by 0Open access

VIT: View-Vector-Isolations-Test

Jens Deutschmann · Zenodo (CERN European Organization for Nuclear Research) · 2026

We present the View-Vector-Isolations-Test (VIT), a deterministic primality test derived from Fractal Uncertainty Theory (FUT) and Coherent Mathematics (coMath). VIT characterizes primes as isolated resonances in a φ-scaled relational number space, achieving 100% accuracy on tested ranges. Unlike classical approaches based on divisibility, VIT interprets primality as the absence of non-trivial coherence relations in a view-vector network. The test employs a candidate set determined by the golden ratio φ with no free parameters, validating the deep connection between prime numbers and φ-structure predicted by FUT. While computationally equivalent to trial division, VIT provides a novel theoretical perspective on the nature of primes as fundamental resonance modes.

Cited by 0Open access

Terminal Area Test Vector Development

Sean Calhoun, Conrad Rorie, Todd Lewis et al. · NASA Technical Reports Server (NASA) · 2019

This presentation provides an overview of work being done to develop test vectors for the terminal area, in coordination with RTCA Special Committee 228, an organization developing the Minimum Operational Performance Standards (MOPS) for Unmanned Aircraft Systems (UAS) Detect and Avoid (DAA) systems. The work leverages existing encounter data to develop a set of encounters to be used to define and refine both performance-based and functionally-based terminal area MOPS requirements. The encounter set would be used across various organizations supporting MOPS terminal area requirements development to provide some level of consistency in terms of terminal area assumptions. The work will investigate other potentially applicable data sets and make any similarly needed adjustments to arrive at a consolidated set of terminal area encounters.

Cited by 0

Low power testing by test vector ordering with vector repetition

M. Bellos, D. Bakalis, D. Nikolos et al. · 2004

Test vector ordering with vector repetition has been presented as a method to reduce the average as well as the peak power dissipation of a circuit during testing. Based on this method, in this paper we present some techniques that can be used to further reduce the average power dissipation. Experimental results validate that the proposed techniques achieve considerable savings in energy and average power dissipation while reducing the length of the resulting test sequences compared to the original method.

Cited by 6

Control vector by Entrance Test score.

Lex Borghans (771437), Ron Diris (7548302), Wendy Smits (7548305) et al. · Figshare · 2019

<p>The figure shows the average values of a control vector for every score on the Entrance Test, separately for all cohorts. The control vector is constructed by regressing either years of schooling (left half) or log wage (right half) on a set of controls and then fitting the predictive values. Controls are: gender, month of birth, urbanization, ethnicity, dummies for parental social status (six categories) and dummies for parental education (three categories).</p>

Cited by 0Open access

Standard Test Interface Language (STIL) for Digital Test Vector Data

1999

Standard test interface language (STIL) provides an interface between digital test generation tools and test equipment. A test description language is defined that: (a) facilitates the transfer of digital test vector data from CAE to ATE environments; (b) specifies pattern, format, and timing information sufficient to define the application of digital test vectors to a DUT; and (c) supports the volume of test vector data generated from structured tests.

Cited by 1

Reduction of power consumption during test applicationby test vector ordering

Patrick Girard, C. Landrault, S. Pravossoudovitch et al. · Electronics Letters · 1997

The authors address the problem of testing VLSI circuits without exceeding their power ratings during testing. The proposed approach is based on re-ordering test vectors in a test sequence to minimise the switching activity of the circuit during test application. Results of experiments are presented which show a power reduction in the range 7.5–55.8% during test application.

Cited by 27

Support Vector Machines (SVM) in Test Extraction

Ghazali, Nadirah · UTPedia (Universiti Teknologi Petronas) · 2006

Text categorization is the process of grouping documents or words into predefined categories. Each category consists of documents or words having similar attributes. There exist numerous algorithms to address the need of text categorization including Naive Bayes, k-nearest-neighbor classifier, and decision trees. In this project, Support Vector Machines (SVM) is studied and experimented by the implementation ofa textual extractor. This algorithm is used to extract important points from a lengthy document, by which it classifies each word in the document under its relevant category and constructs the structure of the summary with reference to the categorized words. The performance of the extractor is evaluated using a similar corpus against an existing summarizer, which uses a different kind of approach. Summarization is part of text categorization whereby it is considered an essential part of today's information-led society, and it has been a growing area of research for over 40 years. This project's objective is to create a summarizer, or extractor, based on machine learning algorithms, which are namely SVM and K-Means. Each word in the particular document is processed by both algorithms to determine its actual occurrence in the document by which it will first be clustered or grouped into categories based on parts of speech (verb, noun, adjective) which is done by K-Means, then later processed by SVM to determine the actual occurrence of each word in each of the cluster, taking into account whether the words have similar meanings with otherwords in the subsequent cluster. The corpus chosen to evaluate the application is the Reuters-21578 dataset comprising of newspaper articles. Evaluation of the applications are carried out against another accompanying system-generated extract which is already in the market, as a means to observe the amount of sentences overlap with the tested applications, in this case, the Text Extractor and also Microsoft Word AutoSummarizer. Results show that the Text Extractor has optimal results at compression rates of 10 - 20% and 35 - 45%

Cited by 0

AIRGrav Gravity Vector Observation - Colorado Test Site

Foroughi, Ismael, Ferguson, Stephen · Mendeley Data · 2025

Column Description: 1 Line number 2 Flight number 3 Time UTC seconds 4 Latitude deg 5 Longitude deg 6 Height ellipsoidal m 7 UTMX 8 UTMY 9 terrain attraction east mGal 10 terrain attraction north mGal 11 terrain attraction up mGal 12 terrain attraction east filtered mGal 13 terrain attraction north filtered mGal 14 terrain attraction up filtered mGal 15 g east, Kalman filtered mGal 16 g north, Kalman filtered mGal 17 g up, Kalman filtered mGal 18 g east, Kalman filtered, levelled mGal 19 g north, Kalman filtered, levelled mGal 20 g up, Kalman filtered, levelled mGal

Cited by 0Open access

Comparison of random test vector generation strategies

W.H. Debany, C. Hartmann, Pramod K. Varshney et al. · 2002

Four random test generation strategies are compared to determine their relative effectiveness: equiprobable 0s and 1s; two weighted random pattern generation algorithms; and the maximum output information entropy principle. The test generation strategies are compared at a variety of target fault coverages. Two statistically based metrics are used to evaluate the techniques: a large-sample test of the difference of means and an upper confidence limit. The two weighted random test pattern generation strategies are found to be generally superior to equiprobable 0s and 1s and maximum output entropy. For a given logic circuit, the same technique is not necessarily optimal at every fault coverage.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 3

DCT Compression of Test Vector in SoC

Ch. Shanthi Priya, B.R.K. Singh · International Journal Of Scientific Research And Education · 2016

This paper reports results on studies of the problem and demonstrates the feasibility of the suggested methodology with simulation runs on the International Symposium on Circuits and Systems (ISCAS) 85 combinational and ISCAS 89 full-scan sequential benchmark circuits. When the storage nears it limit, they then try to reduce those files size to minimum by using data compression software. In this project i proposed a new algorithm for data compression, called DISCRETE COSINE transform. This algorithm will manipulates each bit of data inside file to minimize the size without losing any data after decoding to lossless compression. This basic algorithm is intended to be combining with other data compression algorithms to optimize the compression ratio

Cited by 0Open access