Agile Blocker and Clock Jitter Tolerant Low-Power Frequency Selective Receiver with Energy Harvesting Capability
Bibliographic record
Abstract
In this article, a novel tunable, blocker and clock jitter tolerant, low power, quadrature phase shift frequency selective (QPS-FS) receiver with energy harvesting capability is proposed. The receiver's design embraces and integrates (i) the baseband to radio frequency (RF) impedance translation concept to improve selectivity over that of conventional homodyne receiver topologies and (ii) broadband quadrature phase shift circuitry in the RF path to remove an active multi-phase clock generation circuit in passive mixer (PM) receivers. The use of a single local oscillator clock signal with a passive clock division network improves the receiver's robustness against clock jitter and reduces the source clock frequency by a factor of N, compared to PM receivers using N switches (N≥4). As a consequence, the frequency coverage of the QPS-FS receiver is improved by a factor of N, given a clock source of maximum frequency; and, the power consumption of the whole receiver system can eventually be reduced. The tunable QPS-FS receiver separates the wanted RF band signal from the unwanted blockers/interferers. The desired RF signal is frequency down-converted to baseband, while the undesired blocker/interferer signals are reflected by the receiver, collected and could be energy recycled using an auxiliary energy harvesting device.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".