Big lighting data are required for evaluation of lighting performance and impacts on human beings, environment, and ecology for smart urban lighting. However, traditional approaches of measuring road lighting cannot achieve this aim. We propose a rule-of-thumb model approach based on some feature points to reconstruct road lighting in urban areas. We validated the reconstructed illuminance with both software simulated and real road lighting scenes, and the average error is between 6 and 19%. This precision is acceptable in practical applications. Using this approach, we reconstructed the illuminance of three real road lighting environments in a block and further estimated the mesopic luminance and melanopic illuminance performance. In the future, by virtue of Geographic Information System technology, the approach may provide big lighting data for evaluation and analysis, and help build smarter urban lighting.

In this work, we investigated 39 façade lighting displays, all of which consisted of tri-chromatic light sources, namely blue-, green-, and red- light units, in Shenzhen, China. We extracted the spectral characteristics of the mean peak wavelength/full-width at half-maximum, and proposed universal spectral models. We further established the ‘chromaticity-performance’ relation to quantitatively assess the impact of light pollution on typical species based on corresponding action spectra. The findings provide a low-cost, fast and precise approach to assess light pollution of complicated light environment, and may help reduce energy waste and adverse environmental consequences associated with light pollution.

Popularizing community and home early caries screening is essential for caries prevention and treatment. However, a high-precision, low-cost, and portable automated screening tool is currently lacking. This study constructed an automated diagnosis model for dental caries and calculus using fluorescence sub-band imaging combined with deep learning. The proposed method is divided into two stages: the first stage collects imaging information of dental caries in different fluorescence spectral bands and obtains six-channel fluorescence images. The second stage employs a 2-D-3-D hybrid convolutional neural network combined with the attention mechanism for classification and diagnosis. The experiments demonstrate that the method has competitive performance compared to existing methods. In addition, the feasibility of transferring this approach to different smartphones is discussed. This highly accurate, low-cost, portable method has potential applications in community and at-home caries detection.

This paper studies spectrum optimization of four-package LEDs to improve performance of light sources with respect to properties such as luminous efficacy and colour rendering as well as luminous efficacy of radiation under photopic and mesopic conditions and circadian efficacy of radiation for non-visual biological effects. For colour mixing of four-package LEDs, the proportion of each LED in the package follows a linear relationship as does luminous efficacy. For optimization of luminous efficacy of radiation and circadian efficacy of radiation, an inverse proportion function model is proposed. An example of spectrum optimization for a four-package LED shows that by using the proposed model, high performance can be achieved with different weighting coefficients for different applications.