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Record W1976362254 · doi:10.1142/s0218126613500291

LOW POWER CONSUMPTION SECURITY PLATFORM FOR INDUSTRIAL COMMUNICATIONS USING AN MPSOC

2013· article· en· W1976362254 on OpenAlexfundno aff
José M. Granado-Criado, Miguel Á. Vega-Rodríguez, José M. Chaves-González, Juan M. Sánchez‐Pérez, Juan A. Gómez‐Pulido

Bibliographic record

VenueJournal of Circuits Systems and Computers · 2013
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsnot available
FundersEuropean Regional Development FundMinisterio de Economía y CompetitividadBritish Columbia Institute of Technology
KeywordsMicroBlazeMPSoCComputer sciencePower consumptionEmbedded systemEncryptionAdvanced Encryption StandardCryptographyPower (physics)Field-programmable gate arraySystem on a chipComputer networkComputer security

Abstract

fetched live from OpenAlex

This work presents a novel security platform for industrial communications using a nine-MicroBlaze MPSoC. This platform has low power consumption and cost, therefore, it is very appropriate for embedded systems, where restrictions on cost and power consumption have to be fulfilled. This system uses the RSA asymmetric algorithm combined with the AES symmetric algorithm, which was developed using two encryption modes, ECB and CBC. In this way, the platform makes possible to combine different algorithms and modes in function of the necessities of speed and security required. Furthermore, due to the implementation of standard algorithms (AES and RSA) and modes (ECB and CBC), this platform can be connected to the Internet, and can even use secure protocols as SSL.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.104
GPT teacher head0.314
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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