Design of GPU-Based Frequency Domain Multi-Channel Wideband Signal Processing Unit
Bibliographic record
Abstract
Commercial products, such as the National Instrument's USRP (universal software radio peripheral) products and the latest instruments, provide functions to receive and analyze signals. Because the characteristics of the target signal are diverse, these systems are designed to receive a wideband signal to a narrowband signal and provide functions for extracting a desired signal and analyzing it in detail if necessary. In the past, these functions were implemented in the form of hardware, such as FPGA (field programmable gate array) and DSP(digital signal processor), for real-time processes. However, owing to the development of software technology, it is possible to replace the signal-processing part of the acquisition system with software. The existing GPU-based signal processing unit is capable of processing a few channeld only in real-time owing to its excessive computation and memory usage. To overcome this limitation, frequency-domain signal processing is applied and the computation time and memory usage are considerably reduced. Thus, real-time processing of hundreds of channels is possible. In this study, a multi-channel wideband signal processing unit is designed and implemented using programmable equipment, such as CPU and GPU processors, rather than expensive hardware equipment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".