A combined approach to research and graduate-level teaching of multidimensional signal processing, circuits and systems
Bibliographic record
Abstract
Multidimensional signal processing (MDSP) is the extension to conventional signals and systems to multiple dimensions. The theoretical treatment of multidimensional systems is important for broad areas such as image/video processing, array processing, biomedical imaging, radio-astronomy, space imaging, radar imaging, and multimedia signal processing. MDSP is typically realized in circuitry using analog, digital and mixed-signal devices. The teaching of MDSP theory is typically attempted at the graduate level where students build on fundamentals of digital and analog signal processing and filter design, which they learn as part of standard undergraduate material. An advanced graduate course is described that covers both theory and circuit realization of MDSP algorithms, aimed at the entry-level graduate student audience. The course overview and summarized inventory of concepts is provided. The teaching philosophy and our vision of combined approach to research and teaching and the transfer of scientific findings from research program to mainstream education is discussed. Our combined experience at the University of Akron and University of Calgary is shared for benefit of the circuits and systems community.
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 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".