Designing and implementing a system of multiplexing and demultiplexing on FPGA using MATLAB/simulink for the detection of acoustic signals
Bibliographic record
Abstract
In order to locate the source of a sound in the sea, we can use a series of hydrophones. We can go up to a dozen, more we have, the better accuracy. But when descending to depths very significant (1 km), all wiring becomes heavy. Indeed, each hydrophone requires a pair of wire twisted in addition to other wires when they are equipped with a pre-amplification circuit. Ultimately, the power cable becomes very large and very cumbersome when it comes to the wind, especially if it is deployed over several hundred meters. Thus, our mission is to develop a technique to reduce the diameter of the cable carrying the information captured by hydrophones, priority or other sensors plunged into the sea, to a computer to the surface. The proposed solution is to send each hydrophone signals through an optical fiber. The challenge is therefore to develop a technique to convert signals from several hydrophones and multiplex them through an optical fiber. Then, the signals transmitted are converted into electrical signals to store in .wav format on the hard disk of a computer. The use of an optical fiber is justified by the fact that they can transmit a considerable amount of information thus reducing the number and size of transmission cables. In this paper we will explain the general system architecture including optical fiber transmission system and especially we will show the implementation of the multiplexer and de-multiplexer system on FPGA using MATLAB/Simulink with system generator in Xilinx system.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".