Real-time Spectrum Analysis with Convolutional Neural Network on Embedded/Edge Hardware Platforms
Bibliographic record
Abstract
This paper presents a comprehensive approach to combining well-known technologies, readily available hardware, and open-source software to investigate and define the vector of parameters for describing the radio-frequency spectrum, which should be considered when designing practical, real-time applications. Spectrum analysis is vital in various applications, not only in wireless communication but also in numerous other essential aspects of human life, including security and surveillance, autonomous vehicles, medicine, and more. Advances in software-defined radio (SDR) technology allow unprecedented control over the entire radio-frequency processing chain. Advanced embedded systems, built with sophisticated hardware and software, enable devices to perform complex tasks at the edge. Convolutional Neural Networks (CNNs) have revolutionized image recognition by allowing computers to understand and interpret visual data with high accuracy. Bringing all of that together, we have developed a working system and compiled the vector of parameters that impact the trade-offs between latency, frame rate (FPS), energy consumption, network training efforts, and hardware throughput constraints that developers face when tackling real-world applications, such as cognitive radio.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".