A Secure Proposed Method for Real-Time Preserving Transmitted Biomedical Signals Based on Virtual Instruments
Bibliographic record
Abstract
Biomedical signals, encompassing electrocardiograms (ECG), electroencephalograms (EEG), electrooculograms (EOG), and other physiological data, are subjected to collection, preprocessing, and information extraction to discern patterns and trends.Given the transmission of these physiological activities over the Internet, the potential for unauthorized access necessitates stringent scrutiny.To mitigate data loss and theft, encryption of biomedical signals is implemented, with a particular emphasis on preserving the confidentiality of biomedical data.Commonly encrypted data include .datsignal files, images, confidential emails, user data, and directories.This paper proposes a robust method for encrypting and decrypting .datfiles, specifically for ECG signals, utilizing the discrete fourier transform (DFT) and its inverse (IDFT).Through the use of LabVIEW software, the encryption module accepts the .datinput, converts it into ASCII values, and then performs DFT on them.The encrypted data is subsequently stored for transmission with the support of a security key.Data is decrypted using the security key and IDFT is applied.A transformation is performed so that the ASCII values are returned to the original string format.In addition to demonstrating enhanced security for signals and information transmitted over long distances, the proposed encryption method is also able to achieve significant savings in data transmission costs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".