Design of a Low-Cost UWB Time-Domain Radar System for Subcentimeter Image Resolution
Bibliographic record
Abstract
The design and development of a hybrid Gaussian pulse radar transceiver, including transmitter and receiver, is presented. The transmitter is realized using a transistor-based square pulse generator, a tunable step recovery diode (SRD) Gaussian pulse-shaping network, and a differentiator. The SRD circuit design is modified using a high-pass filter to have a low ringing and narrow pulsewidth. The output pulse with a broadband amplifier has a power of 500 mW, at a pulse repetition frequency (PRF) of 10 MHz, and tunable pulsewidth ranges from 56 to 300 ps. A 90° coupler is configured to convert the Gaussian pulse into a monopulse. Then, a transmitter antenna is used to radiate the pulse, and a separate receiving antenna is used to receive the reflected pulse. The receiver is comprised of a local Gaussian pulse generator, a radio frequency mixer (RF-mixer) accompanied by a low-pass filter (LPF), a low-frequency amplifier (LFA), and an analog-to-digital converter (ADC). The RF-mixer is used for expanding the receiving pulse from the receiving antenna by a factor of 100000. The LPF and LFA are used to detect the envelope of the output of the RF-mixer and amplify it. The ADC is interposed between the LFA and a processor to digitize the expanded pulse for data processing. The validity and reliability of the digitized expanded pulse compared to those received using a sampling oscilloscope are 93% in agreement, which is remarkable. A version of the system is also developed for a single-antenna operation by utilizing an ultrawideband (UWB) coupler as a semicirculator. Both the systems are compared, and the benefits of each are illustrated. The flexibility and performance of the designs are analyzed. The proposed system demonstrates subcentimeter imaging capabilities.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".