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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).

Normal life can be ensured for schizophrenic patients if diagnosed early. Electroencephalogram (EEG) carries information about the brain network connectivity which can be used to detect brain anomalies that are indicative of schizophrenia. Since deep learning is capable of automatically extracting the significant features and make classifications, the authors proposed a deep learning based model using RNN-LSTM to analyze the EEG signal data to diagnose schizophrenia. The proposed model used three dense layers on top of a 100 dimensional LSTM. EEG signal data of 45 schizophrenic patients and 39 healthy subjects were used in the study. Dimensionality reduction algorithm was used to obtain an optimal feature set and the classifier was run with both sets of data. An accuracy of 98% and 93.67% were obtained with the complete feature set and the reduced feature set respectively. The robustness of the model was evaluated using model performance measure and combined performance measure. Outcomes were compared with the outcome obtained with traditional machine learning classifiers such as Random Forest, SVM, FURIA, and AdaBoost, and the proposed model was found to perform better with the complete dataset. When compared with the result of the researchers who worked with the same set of data using either CNN or RNN, the proposed model's accuracy was either better or comparable to theirs.

This paper describes a microcontroller based emergency lighting system, which can early detect fire and send the alarm message through a mobile network. This is achieved via smoke and gas detector technology added with integrated microcontroller, mobile communication and a LED emergency light. First five Minutes of fire is more important than last five hours [1]. Hence, it is important to have early detection of fire and start fire fighting in its inception. In many hazardous areas where flammable materials are handled, any leak or spillage may give rise to an explosive atmosphere. In this situation, early detection of leaking gas or smoke plays an important role in reducing fire deaths and injuries. In fact, immediately after detection of fire, fire fighting should be started by means of portable fire extinguishers or by informing the fire brigade. This developed system can initiate these functions by detecting the fire hazard, establishing the communication to dwellers and turning on the emergency light to show the exit route.

The present work deals with performance study and stability analysis of an LED driver system. An LED driver based on buck-boost topology is designed and simulated in MATLAB Simulink environment. The driver satisfactorily operates LED modules having power rating in the range of 6 W to 24 W. The power factor and Total Harmonic Distortion comply with standard recommended values. The mathematical model of the LED driver is formulated and the stability analysis of the designed driver is carried out during its operation.