MDSP:a Modular DSP Architecture for a Real-Time 3D Laser Range Sensor
Bibliographic record
Abstract
By combining a number of embedded digital signal processors (DSPs) within a desktop PC, we have developed a system that offers not only the mass storage, network utilities, user interface and presentation graphics of a regular PC, but also the real-time response rates normally associated only with embedded systems. In this paper we outline the design of such a system that has been built to demonstrate a real-time 3D laser range sensor. The prototype range sensor consists of a custom-built auto-synchronized 3D laser scanner head that is directly interfaced to off-the-shelf computing hardware. The hardware includes a number of PCI bus DSP cards that communicate using dedicated high-speed interprocessor links. Communication between the desktop PC and the embedded DSPs uses the PCI bus. This processing power will be required to achieve real-time data acquisition and 3D geometrical tracking capabilities. This paper outlines the prototype 3D laser range sensor and describes its computing architecture. The embedded DSPs run under a commercial multiprocessor real-time operating system. This combination leads to a highly modular system in which processors may be added or removed with minimal side effects.
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 | 0.000 | 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) | 0.001 | 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".