Design of quantum‐dot cellular automata‐based communication system using modular N‐bit binary to gray and gray to binary converters
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".