An Efficient FPGA Implementation of MUSIC Processor Using Cyclic Jacobi Method: LiDAR Applications
Bibliographic record
Abstract
LiDAR is a technology that uses lasers to measure the position of elements. Measuring the laser travel time and calculating the distance between the LiDAR and the surface requires the calculation of eigenvalues and eigenvectors of the convergence matrix. SVD algorithms have been proposed to solve an eigenvalue problem, which is computationally expensive. As embedded systems are resource-constrained hardware, optimized algorithms are needed. This is the subject of our paper. The first part of this paper presents the methodology and the internal architectures of the MUSIC processor using the Cyclic Jacobi method. The second part presents the results obtained at each step of the FPGA processing, such as the complex covariance matrix, the unitary and inverse transformation, and the value and vector decomposition. We compare them to their equivalents in the literature. Finally, simulations are performed to select the way that guarantees the best performance in terms of speed, accuracy and power consumption.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".