The unique radiative, photometric and colorimetric characteristic of a light-emitting diode is derived from its spectral power distribution. Modeling such characteristics with respect to the forward current, temperature or operating time has been subject of various studies. Deriving a simple analytical model, however, is not trivial due to the unique emission pattern varying with different types and technologies of light emitting diodes. For this purpose, curve fitting multiple superimposed Gaussian probability density functions to the spectral power distribution is a common approach. Despite excellent $R^{2}$ goodness of fit results, significant deviations within the photometric and colorimetric parameters, such as luminous flux or chromaticity coordinates, are observed. In addition, most studies were conducted on a small sample set of very few different spectral power distributions. This work provides a comprehensive comparison and evaluation of 19 different (superimposed) probability density function based models provided by the literature tested on a total of 15 different spectral power distributions of monochromatic blue, green and red light-emitting diode as well as phosphor-converted spectra of lime, purple and white samples with different correlated color temperatures. All models were evaluated by means of their coefficient of determination, radiant flux, chromaticity coordinate deviation and Bayesian Information Criterion. This study shows that a superimposed (split) Pearson VII model is able to outperform the commonly used Gaussian model approach by far. In addition, an application example in regard of forward current dependence is given to prove the proposed approach.

Lighting is not only a key mediator for the perception of the architectural space but also plays a crucial role regarding the long-term well-being of its human occupants. Future lighting solutions must therefore be capable of monitoring lighting parameters to allow for a dynamic compensation of temporal changes from the optimal or intended conditions. Although mostly based on synthetic data, previous studies adopting small, low-cost, multi-band color sensors for this kind of parameter estimation have reported some promising preliminary results. Building up on these findings, the present work introduces a new methodology for estimating the absolute spectral irradiances of real-world lighting scenarios from the responses of a 10-channel spectral sensor by using a convolutional neural network approach. The lighting scenarios considered here are based on a tunable white floor lamp system set up at three different indoor locations and comprise combinations of LED, fluorescent, tungsten, and daylight lighting conditions. For white light mixtures of the various spectral components, the proposed reconstruction methodology yields estimates of the spectral power distribution with an average root-mean-square error of 1.6%, an average Δu′v′ of less than 0.001, and an average illuminance accuracy of 2.7%. Sensor metamerism is discussed as a limiting factor for the achievable spectral reconstruction accuracy with certain light mixtures.

Photosynthetic activity can be monitored using pulse amplitude modulated (PAM) fluorescence or gas exchange. While PAM provides insight into the light-dependent reactions, gas exchange reflects CO₂ fixation and water balance. Accurate, non-invasive prediction of photosynthetic performance under varying conditions is highly relevant for phenotyping and stress diagnostics. Despite their physiological link, data from both methods do not always correlate. To systematically investigate this relationship, photosynthetic parameters were measured in maize (Zea mays, C4) and basil (Ocimum basilicum, C3) under different photon densities and spectral compositions. Maize showed the highest CO₂ assimilation rate of 30.99 ± 1.54 µmol CO₂/(m²s) under 2000 PAR green light (527 nm), while basil reached 10.56 ± 0.92 µmol CO₂/(m²s) under red light (630 nm). PAM-derived electron transport rates (ETR) increased with light intensity in a pattern similar to CO₂ assimilation, but did not reliably reflect its absolute values under all conditions. To improve prediction accuracy, we applied a machine learning model. XGBoost, a gradient-boosted decision tree algorithm, efficiently captures nonlinear interactions between physiological and environmental parameters. It achieved superior performance (R² = 0.847; MSE = 5.24) compared to the Random Forest model. Our model enables accurate photosynthesis prediction from PAM data across light intensities and spectral conditions in both C3 and C4 plants.

Thermopile sensor arrays provide a sufficient counterbalance between person detection and localization while preserving privacy through low resolution. The latter is especially important in the context of smart building automation applications. Current research has shown that there are two machine learning-based algorithms that are particularly prominent for general object detection: You Only Look Once (YOLOv5) and Detection Transformer (DETR). Over the course of this paper, both algorithms are adapted to localize people in 32 × 32-pixel thermal array images. The drawbacks in precision due to the sparse amount of labeled data were counteracted with a novel generative image generator (IIG). This generator creates synthetic thermal frames from the sparse amount of available labeled data. Multiple robustness tests were performed during the evaluation process to determine the overall usability of the aforementioned algorithms as well as the advantage of the image generator. Both algorithms provide a high mean average precision (mAP) exceeding 98%. They also prove to be robust against disturbances of warm air streams, sun radiation, the replacement of the sensor with an equal type sensor, new persons, cold objects, movements along the image frame border and people standing still. However, the precision decreases for persons wearing thick layers of clothes, such as winter clothing, or in scenarios where the number of present persons exceeds the number of persons the algorithm was trained on. In summary, both algorithms are suitable for detection and localization purposes, although YOLOv5m has the advantage in real-time image processing capabilities, accompanied by a smaller model size and slightly higher precision.

Pulse width modulation for dimming the light output of LEDs has become common. When pulse width modulation is used at low frequencies unwanted visual artefacts including flicker perception and stroboscopic effects may occur. These artefacts need to be avoided or at least reduced to a minimum in order to obtain high user acceptance. In this paper, an optimized phase-shifted pulse width modulation method is described, implemented and validated in a visual experiment. The method is intended to minimize the stroboscopic effect on a reference surface by first optimizing the LED units of a single LED luminaire and then co-optimizing several of these luminaires. The optimized pulse width modulation waveforms are then compared to standard pulse width modulation dimming methods. In the visual experiment, 13 subjects rated the extent of the stroboscopic effect of standard and optimized waveforms in a white painted experimental room. The results indicate that the optimized waveforms are indistinguishable from constant light.