Software Defined Radio Subsampling Receiver for Wireless Monitoring and Sensing Medical Applications
Bibliographic record
Abstract
In hospitals, telemetry is utilized by physicians to provide accurate diagnosis for their patients. The increase penetration of wireless technology in healthcare environment for powering, monitoring and sensing purposes of implanted or attached devices to human patients' triggered new challenges in designing reconfigurable wireless transceiver intended for healthcare that can operate in multi-frequency mode. To minimize the number of RF receivers needed for these kind of applications, a broadband subsampling receiver is proposed. Recently, this technique has been proposed for software defined radio (SDR) applications to allow multiple and concurrent RF signal digitization directly from the RF stage by using a sampling frequency much lower than the RF frequency. In this paper, band-pass sub-sampling technique widely used in many applications, such as radar, sonar, data acquisition, measurement and instrumentation is reviewed. An efficient subsampling algorithm is proposed to determine the sampling frequency needed such that interferences between the different monitoring and sensing medical signals is mitigated. Measurement results are provided with RF instrumentation equipment and a low speed ADC to validate the proposed architecture for multi- frequency monitoring and sensing applications.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".