Bibliographic record
Abstract
Introduction Analog input signals are converted into digital signals for digital processing and transmission. The analog-to-digital (A/D) converter (ADC) performs this functionality using two steps: the sample-and-hold (S/H) operation, followed by digital quantization. The ADC is primarily characterized by the sampling rate and resolution. A sampling rate of above twice the Nyquist frequency is a must; otherwise, aliasing occurs and the result is not usable. A higher sample rate leads to a more accurate result, but a more complex system. The successive-approximation ADC successively increases the digital code by digitizing the difference until a match is found. The successive-approximation ADC is the most popular type of ADC. The sigma-delta (Σ-Δ) ADC uses oversampling and noise shaping to significantly attenuate the power of quantization noise in the band of interest. The digital-to-analog (D/A) converter (DAC) is used to convert the processed digital signal back to an analog signal by comparing it to the input voltage. This chapter introduces ADCs and DACs that are used in wireless communication systems. Sampling Ideal and natural sampling An analog signal x(t) , bandlimited to f max , can be transformed into digital form by periodically sampling the signal at time nT , where T is the sampling period.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.129 | 0.096 |
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".