A Digital Architecture for Direct Digital-to-RF Converters
Bibliographic record
Abstract
This paper presents a novel technique for direct conversion of digital complex time series into radio frequency (RF) band. Most of the operations in this method are implemented by software and/or digital circuits. The proposed method, is composed of some signal processing procedures, some switching circuits, a phase shifter (all-digital phase-locked loop), and an analog RF filter. The switching technique, which is used for a, joint amplitude and phase modulation, makes the method highly power efficient. The signal processing procedure results in a highly linear converter and, shapes the power spectrum of the output in order to satisfy the required power masking properties in a given application. The all-digital phase-locked loop or the phase shifter along with some simple digital circuits control the switching times of the output. The complex input sequence is first converted into phase and amplitude after over-sampling using a CORDIC processor. The phase sequence controls the amplitude and phase of a six-step waveform which has three-levels at the output, i.e., zero and plusmnA(t), where plusmnA(t) is controlled by the amplitude sequence. In this paper, the theoretical and some practical aspects of the proposed technique are presented
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".