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Purpose Good illumination creates an aesthetic environment that may positively influence patients’ well-being and provide comfort to the hospital staff. This study aims to focus on exploring the energy efficiency of lighting and subjective perception of the lit environment in a hospital ward to assess quality indicators of ambient lighting conditions. Design/methodology/approach The existing conventional tubular fluorescent lamp–based lighting system in the surveyed patients’ ward was retrofitted with light-emitting diode (LED) luminaires to explore illumination and energy parameters. Thereafter, a software lighting model was created, simulated and analyzed. A Web-based survey with five bipolar adjective pairs in a semantic differential scale was conducted with 48 participants to record and analyze their subjective responses pertaining to the variations in lamp types and surface reflectance combinations. Findings The findings imply that the LED tubular lamp–based illumination was deemed more adequate compared to other lamp types and the effects of variations in room surface reflectance combinations on the participants’ responses were statistically significant at α = 0.05 level. The simulated horizontal work plane average illuminance level varied from 131 to 171 lx, mean room surface exitance (MRSE) levels remained between 30 and 90 lm/m2 and overall uniformity of illuminance remained between 0.5 and 0.7. Originality/value In a hospital ward illuminated by LED tubular lamps, variations in room surface reflectance combinations for a constant luminous flux package output from the lamps may affect the subjective perception of users and the correlation between horizontal work plane average illuminance and MRSE is found to be highly linear (coefficient of determination > 0.97).

High Scintillation is a major problematic issue for Free Space Optical Communication (FSOC) system. Consequently, in this case, the Single Input & Single Output (SISO) optical channel performance has been degraded. To minimise this problem, the FSO system has integrated a number of mitigating strategies. Two methodologies have been discussed in this manuscript: receiver diversity scheme (Single Input Multiple Output) and aperture averaging. In this challenging environmental setting, both methods work effectively. However, the experimental analysis shows that the aperture averaging technique outperforms SIMO and SISO approaches in terms of signal eye pattern, BER, and SNR. Compared to the SISO link, the average Signal to Noise Ratio has improved by 6.67%, 26.7%, and 33.9% for 1 × 2, 1 × 4-based SIMO and Aperture averaging systems, respectively. Reduction of average Bit Error Rate (BER) has been possible 6.19%, 21.2%, and 32.33% for 1 × 2, 1 × 4 based SIMO channels and aperture averaging scheme, respectively, compared with the SISO channel.

Commissioning, maintenance and revamping of energy-efficient, economical and controllable road lighting systems that satisfactorily cater to the visual requirements of pedestrians and motorists are preferable in the wake of the current global energy crisis. Installation of new road lighting systems or retrofitting of the existing ones would require careful consideration of the design configurations that would maintain road surface average illuminance, uniformity of illuminance, and diversity of illuminance levels in accordance with applicable regional, national, or international standards and guidelines. To facilitate rapid approximation of pertinent road surface illuminance parameters with a view of the prevailing road lighting practices involving common heuristic configurations, photometric simulations of road lighting were conducted in a created software model with photometric data tables of high-pressure sodium (HPS) and light-emitting diode (LED) luminaires possessing different power ratings for a specified set of luminaire mounting height, road width, pole spacing, overhang and tilt values, and a mathematical model consisting of six equations derived by multiple linear regression was propounded (coefficient of determination > 0.90) with relevant predictor variables. Moreover, a framework for LED luminaire power rating selection for retrofitting operations was proposed to assist manufacturers, electrical contractors and utility operators entrusted with such work.

An outdoor luminaire based on light-emitting diodes (LED), including its design, development, and performance study, is discussed in this article. Illuminance on exterior surface, correlated color temperature (CCT), color rendering index (CRI), and spectral power of the light source can be controlled wirelessly. Arrays of cool white (CW) and warm white (WW) LEDs make up light sources. To adjust the light output as well as the correlated color temperature (CCT) and other parameters, algorithm for regulating lighting has been established. The circuitry for controlling the light has been programmed into a microcontroller, and it uses a pulse width modulation (PWM) technique to independently adjust the brightness of the light emitted by the WWLED array and the CWLED array that make up a given light source. The planned system links to the lighting system through Wi-Fi, and it is controlled by a smart phone. The smart phone has been used as a handheld device (HHD). The developed system's performance has been demonstrated by the experimental findings. It offers a wide range of color temperature options, increased spectral power with increased illumination, enhanced color rendering index, and the ability to create a calming nocturnal external lighting ambiance on demand.

Energy-efficient road illumination systems are an indispensable part of any modern society to ensure safe nocturnal transportation of freight and passengers, assure better navigation, promote improved visibility of surroundings, and diminish the probability of occurrence of accidents and crimes. Simulative estimation of pertinent photometric parameters and probable energy expenditure before commissioning of projects is very desirable to project managers, transportation engineers, local authorities, and other stakeholders for envisaging a complete picture of the deliverables and ensuring total conformity with applicable lighting and energy codes. This simulation-based work was conducted with luminaire luminous intensity distribution data tables of different light-emitting diode luminaires having type II luminous intensity distribution patterns in the simulation environment of an industry-standard lighting simulation software. Thereafter, the multiple regression analysis technique was utilized upon the simulatively obtained substantial dataset to propound a model of twenty-four bivariate quadratic polynomial equations for estimating the changes in road surface illuminance parameters, and three general equations were derived with the same dataset, two for estimating road surface illuminance parameters and one for estimating energy efficiency. The propounded model and general equations can be readily followed by various stakeholders to rapidly estimate pertinent photometric parameters and installation energy efficiency and the approach expatiated in this work can prove to be useful for exploring inter-variable relationships, providing objective functions in optimization tools, and proffering neoteric, heuristic perspectives to lighting design.

Illuminance measurement is a salient feature to evaluate the quality of lighting and its precise measurement using appropriate sensor is of utmost importance for any real-time scenario. Here we present, the estimation of lux value on the acquired dataset using different types of machine learning regression models viz. Multiple Linear Regression, Support Vector Machine Regression, General Regression Neural Network, and Gaussian Process Regression. We have carried out extensive comparative performance assessment of the evaluating parameters: Percentage Absolute Error, R-Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and Root Mean Squared Error to predict the illuminance values with respect to a calibrated meter. The RGB values are obtained from a sensor that is integrated with a microcontroller and the lux data are obtained from a standard chroma meter at the client end. Now, the RGB data are transmitted in a wireless network and four different types of machine learning regression techniques are applied at the server end for proper estimation of the illuminance values. Thus, an accurate RGB sensor model is developed that can predict the lux values and also comparable to a standard lux meter. Gaussian Process Regression has given the best possible response for prediction of illuminance values in comparison with other regression methods on a real-time captured dataset.