Software Defined Radars for Low-Cost Healthcare Monitoring and Imaging Systems: A Comprehensive Review
Bibliographic record
Abstract
Technological advancements have enabled the implementation of software-defined radars (SDRadar) as low-cost, reconfigurable radar systems using software processing. The adaptability and reusability of SDRadar have expanded their application in many healthcare applications. An SDRadar is usually designed with a basic architecture that includes a transmitter, receiver, and a digital signal processor. The transmitter sends out radio waves, which are reflected, or penetrated and scattered, from the targeted object. Those reflected or scattered signals are captured by the received and processed using a digital signal processor to extract useful information. This flexibility allows SDRadar to be easily reprogrammed for different tasks without changing the hardware. To support and motivate researchers and practitioners of various scientific and engineering expertise, a state-of-the-art review of SDRadar, focusing on the healthcare applications of continuous waves, frequency-modulated continuous waves, and stepped-frequency continuous-wave modes, is presented. The review focuses on heart rate and respiration monitoring, as well as medical radar imaging, over a broad frequency range from 0.2 GHz to 20 GHz. Future research trends and potential advancements are also discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".