Transmitting Encrypted Data by Wavelet Transform and Neural Network
Bibliographic record
Abstract
With development of information and communication technology, data transmission becomes more critical day by day. Higher security for transmitting data is especially required. Therefore; we designed a new method to transmit data on the phone line where there is no speech signal on it. Statistic investigations in one communication center in Iran show that there is about 57% non-speech signal on the phone line. Because a person on one side of the phone line speaks and then waits to hear the voice of the other person on the other side and, therefore; this non-speech signal has a good capacity for transmitting data. This project can be divided in four parts. The first part is the automatic classification of speech signal from non-speech using feature vectors derived from the wavelet analysis. The second part is the classification of speech and nonspeech signals using neural network. For recognizing this two cluster (speech signal and non-speech signal), we used NN. The third part is encrypting data and forth part is transmitting encrypted data on non-speech signal on the phone line.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".