Bibliographic record
Abstract
Traditional fast Discrete Cosine Transform (DCT)/ Inverse DCT (IDCT) algorithms have focused on reducing arithmetic complexity and have fixed run-time complexities regardless of the input. Recently, data-dependent signal processing has been applied to the DCT/IDCT. These algorithms have variable run-time complexities. A new two-dimensional 8 x 8 low-power DCT/IDCT design is implemented using VHDL by applying the data-dependent signal-processing concept onto the traditional fixed-complexity fast DCT/IDCT algorithm. To reduce power, the design is based on Loeffler's fast algorithm, which uses a low number of multiplications. On top of that, zero bypassing, data segmentation, input truncation, and hardwired canonical sign-digit (CSD) multipliers are used to reduce the run-time computation, hence reduce the switching activities and the power. When synthesized using Canadian Microelectronic Corporation 3-V 0.35 om CMOSP technology, this FDCT/IDCT design consumes 122.7/124.9 mW with clock frequency of 40MHz and processing rate of 320M sample/sec. With technology scaling to 0.35 om technology, the proposed design features lower switching capacitance per sample, i.e. more power-efficient, than other previously reported high-performance FDCT/IDCT designs.* *This work is supported by National Sciences and Engineering Research Council of Canada (NSERC) post-graduate scholarship, and NSERC research grants.
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 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".