MétaCan
Menu
Back to cohort
Record W3113300757 · doi:10.1002/dac.4702

Design of quantum‐dot cellular automata‐based communication system using modular N‐bit binary to gray and gray to binary converters

2020· article· en· W3113300757 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Communication Systems · 2020
Typearticle
Languageen
FieldComputer Science
TopicQuantum-Dot Cellular Automata
Canadian institutionsUniversity of Saskatchewan
FundersCancer Research Society
KeywordsConvertersComputer scienceGray codeQuantum dot cellular automatonBinary numberElectronic engineeringModular designCMOSDissipationPower (physics)AlgorithmCellular automatonArithmeticMathematicsEngineering

Abstract

fetched live from OpenAlex

Summary In the current digital era, there is a need of secure and efficient nano communication systems with ultra‐low power consumption. One technology that can be used for designing these systems is quantum‐dot cellular automata (QCA). In the nano regime, QCA is able to operate with higher speed and lower power dissipation along with high density compared to CMOS technology. This work explores the applicability and feasibility of designing nano communication systems using code converters. In this paper, efficient 4‐bit, 8‐bit, and 16‐bit designs of binary to gray (B2G) and gray to binary (G2B) converters which can be scaled up to N‐bits are proposed. The N‐bit B2G and G2B converters can be designed using 33 + 38 (0.25N − 1) and 63 + 76 (0.25N − 1) cells with a latency of 0.5 and 0.25N clock cycles, respectively. The converters are then used to design 4‐bit, 8‐bit, 16‐bit, and 32‐bit communication systems for efficient data transmission and reception. Based on the performance comparison, it is observed that the proposed B2G and G2B designs achieve up to 90.03% and 99.64% improvement in terms of cost of the circuit thereby making them most cost efficient QCA designs. In addition to this, exhaustive energy dissipation analysis of the proposed designs is also presented. It is observed that the proposed designs can be efficiently utilized in designing nano communication systems requiring minimal area and ultra‐low power consumption.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0050.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.282
Teacher spread0.229 · 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