Microwave Analog Real-Time Spectrum Analyzer (RTSA) Based on the Spectral–Spatial Decomposition Property of Leaky-Wave Structures
Bibliographic record
Abstract
A novel analog real-time spectrum analyzer (RTSA) for the analysis of complex nonstationary signals (such as radar, security and instrumentation, and electromagnetic interference/compatibility signals) is presented, demonstrated, and characterized. This RTSA exploits the space-frequency mapping (spectral-spatial decomposition) property of the composite right/left-handed (CRLH) leaky-wave antenna (LWA) to generate the real-time spectrograms of arbitrary testing signals. Compared to digital RTSAs, it exhibits the advantages of instantaneous acquisition, low computational cost, frequency scalability, and broadband or ultra-wideband operation. The system is demonstrated both theoretically by a commercial full-wave simulator and an efficient Green's function approach and experimentally by a parallel-waveguide prototype including a metal-insulator-metal CRLH LWA, 16 patch antenna probe detectors circularly arranged around the LWA, and a digital oscilloscope performing analog/digital conversion and time-domain acquisition before the postprocessing and displaying of the spectrogram. The system is tested for a large diversity of nonstationary signals and generates, in all cases, spectrograms that are in excellent agreement with theoretical predictions. The fundamental tradeoff between time and frequency resolutions inherent to all RTSA systems is also discussed, and an interchangeable multi-CRLH LWA solution is proposed to handle signals with different time durations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".