Fourier series-based optimization of LED angular intensity profiles for displays and backlighting
Bibliographic record
Abstract
A method using a Fourier series is demonstrated to optimize an LED array for local dimming applications in liquid crystal display backlighting. The same optimization method is also suitable for LED displays in which the Moiré effect must be suppressed during photography with a minimum loss of spatial resolution. Initially, the angular intensity profile of a Lambertian LED is modelled when backlighting a Lambertian rear projection screen and compared to experimental data. An array of optimized LEDs and the resulting screen intensity pattern is then derived such that an intensity distribution with an intensity deviation of less than 2% is achieved. The angular intensity profile of the LED is modified using adjustable Fourier coefficients optimized according to an algorithm. The algorithm is designed to achieve an illuminated screen area of maximum size for a bounded LED backlight array to appear uniform in intensity to an observer. This Fourier series approach provides an elegant method to optimize the intensity profile of LED backlight arrays without the use of ray tracing. A lens was designed in order to provide this optimized intensity profile as well as created and tested.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".