Decoder-based Decompression for test sets containing don&amp;#x2019;t cares

Voyiatzis, Ioannis; Antonopoulou, Hera

doi:10.1109/dtis.2007.4449526

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2004, Arslan and Orailoglu [17] presented a scheme based on RAS, although they utilized a different name. In the same year, Sheth and Savir proposed [18] a design for a scan latch that was utilized in [19] to design a latch-based RAS architecture with low hardware overhead. In 2005, Mudlapur, Agrawal and Singh, proposed in [20] a random access scan flip flop, which they subsequently utilized to propose, in [21], a Random Access Scan architecture with reduced overhead.…”

Section: Introductionmentioning

confidence: 99%

Output response compaction in RAS-based schemes

Voyiatzis

Antonopoulou

Efstathiou

2009

2009 4th International Conference on Design &Amp; Technology of Integrated Systems in Nanoscal Era

Self Cite

View full text Add to dashboard Cite

Scan design has been the backbone of design for testability schemes in industry because it provides for controllability and observability of the internal flip flops of the circuit. However, the serial scan causes high switching activity during testing which results in high power dissipation. Another problem that may appear in scan designs is unknown values, which can invalidate the final signature captured in the response verifier. An alternate to serial scan architecture is Random Access Scan (RAS). In RAS, flip-flops operate as addressable memory elements during test mode in a fashion similar to Random Access Memory (RAM). This approach reduces the time needed to set and observe the states of the flip-flops while at the same time the power dissipation is reduced by as much as 99% comparatively to serial scan.In this work we propose a response compaction scheme for RASbased architectures that results in lower hardware overhead compared to the response compaction scheme utilized in previous RAS-based schemes, while at the same time eliminates the problem of unknown values.

show abstract

Section: Introductionmentioning

confidence: 99%

Output response compaction in RAS-based schemes

Voyiatzis

Antonopoulou

Efstathiou

2009

2009 4th International Conference on Design &Amp; Technology of Integrated Systems in Nanoscal Era

Self Cite

View full text Add to dashboard Cite

show abstract

“…{Sex=Female, Age=[18][19][20][21][22][23], self_control=HIGH, emotionality=HIGH} => {N_Neuroticism.Stability=LOW} 0.1341463 1.0000000 1.952381 {Age=[18-23], City=ATHENS, sociability=LOW} => {N_Neuroticism.Stability=HIGH} 0.1341463 0.9166667 1.879167 {City=ATHENS, Father_job=Retired,well_being=HIGH, self_control=HIGH} => {N_Neuroticism.Stability=HIGH} 0.1219512 0.9090909 1.863636 Age=[18-23], City=CEPHALONIA, self_control=HIGH, emotionality=HIGH} => {N_Neuroticism.Stability=LOW} 0.1219512 0.9090909 1.774892…”

mentioning

confidence: 99%

Associations of Personality Traits and Emotional Intelligence In Social Network Consumers via Data Mining Techniques

Stamoulis¹,

Papavasileiou²,

Gkintoni³

et al. 2022

Preprint

View full text Add to dashboard Cite

The present study aims to investigate personality traits and emotional intelligence in young adults’ consumers who use social networks to make shopping. Consumer behavior has long been of interest to researchers. Contemporary research on Consumer Behavior considers a wide range of factors influencing the consumer,and acknowledges a broad range of consumption activities beyond purchasing. Νowadays, since online purchases is a daily practice, it is useful to study the behavior of online consumers who are social network users and make shopping via social media. Two basic components that influence consumer behavior are personality and emotional intelligence. Emotional intelligence is both characteristic of personality and intellectual capacity, which a person inherits from the genetic material of its parents and evolves - develops throughout lifetime. Personality’ refers to the pattern of thoughts, feelings and behavior that makes each individual separate from others. These affect the way of think, feeling and behaving towards itself and others. The data were collected by completion of the self-report questionnaire Trait Emotional Intelligence (TEIQue), as far as emotional intelligence concerned, and the Eysenck Personality Questionnaire (EPQ) as far as personality traits of personality disorders concerned. Specifically, for the collection of data electronic questionnaire TEIQue and EPQ were being created through Google Forms service and posted through the website “http://www.cicos.gr/iccmi2017/aptei”. Then the collected data were selected for analysis, with relevant transformations in order to have a suitable form for the implementation of the respective machine learning algorithms included in the software package R. Furthermore, the parameters of the corresponding set of algorithms were determined depending on the case of application to produce inference rules. Some of the algorithms implemented according to specific research questions that were applied, were the classification algorithms for the production of decision trees, regarding the four more general factors (welfare, self-control, emotionality and sociability) of emotional intelligence and also personality traits of social network consumers. The results obtained, after weighing and criteria basis, present consumers' rates, which in turn analyze the degree of emotional intelligence and personality traits. Personality and emotional intelligence index can play a crucial role in clarifying consumer behavior of social network users.

show abstract