Bibliographic record
Abstract
Over the past few years a significant growth of research involving the utilization of RF/microwave technologies in healthcare applications has been taking place. This "Special Issue on Biomedical Applications of RF/Microwave Technologies" is proof. Encouraged by funding from government agencies and private sources, and recognizing the emerging opportunities offered by high-frequency electronics and novel sensing technologies for advancing healthcare, researchers from various engineering disciplines have directed their interests to applications involving biological science and clinical medicine. The increase in RF/microwave-related activities targeting medical or biological problems is noticeable within the IEEE Microwave Theory and Techniques Society (IEEE MTT-S) community and beyond. These activities are broad in their scopes and involve multiple disciplines. They range from therapeutic, diagnostic, remote monitoring, and imaging applications of microwave technologies in clinical settings, as well as those involving sensing and communication over or through body tissues, where physiological or biochemical information are transmitted wirelessly, to the biological effects of these applications, in which the interaction of microwaves with tissues and living systems should be understood and manipulated. In addition, RF/microwave now serves as one of the key enabling technologies in innovative healthcare delivery and telemedicine. Three papers are on exposure systems for bioelectromagnetic research. Nine papers present radar-based systems for vital sign monitoring and object imaging. The remaining two radar papers focus on cancer detection by imaging. The Special Issue features papers from the US, UK, Japan, Korea, Australia, Canada, Saudi Arabia, Italy, Malaysia, Belgium, Germany, China, and France, indicating global efforts utilizing RF/microwave innovation for healthcare.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.249 | 0.162 |
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".