This paper proposes the study and analysis of harmonics, energy consumption and power quality of light emitting diode (LED) lamps equipped in building lighting systems. LED lamps with external (LED MR16) and internal (LED light bulb) drivers are investigated using an experimental setup to compare the results. The power quality of both LED lamps is studied by using a power quality meter to measure the generated harmonic currents from various case studies. The case study is divided into four major cases: one LED lamp is turned on with one driver, two LED lamps are turned on using the two drivers, eight LED lamps are turned on with one driver, and eight LED lamps are turned on with the eight drivers. As harmonics are related to total power factor (PF), which affects the energy savings of the building, hence, a filtering circuit to reduce harmonic current has been designed and implemented to improve power quality and/or power factor of the system. The different cases of harmonic filter insertion at the input of an LED lamp’s driver are discussed and then compared with a lighting standard to show the effectiveness of the passive filtering technique used in the studied system. In addition, the obtained result can be applied to both newly built and retrofitted buildings that aim to use LED technology to increase energy efficiency and decrease energy costs, and could be a helpful guide for end-users and manufacturers in addressing and developing LED issues.

This study investigates the use of an alternative energy source in the production of electric energy to meet the increasing energy requirements, encourage the use of clean energy, and thus reduce the effects of global warming. The alternative energy source used is a mechanical energy by piezoelectric material, which can convert mechanical energy into electrical energy, that can convert mechanical energy from pressure forces and vibrations during activities such as walking and traveling into electrical energy. Herein, a pilot device is designed, involving the modification of a bicycle into a stationary exercise bike with a piezoelectric generator, to study energy conversion and storage generated from using the bike. Secondly, the piezoelectric energy harvesting system is used on bicycles as a micro-mobility, light electric utility vehicle with smart operation, providing a novel approach to smart city design. The results show that the energy harvested from the piezoelectric devices can be stored in a 3200 mAh, 5 V battery and power sensors on the bicycle. Moreover, 13.6 mW power can be generated at regular cycling speed, outputting 11.5 V and 1.2 mA. Therefore, the piezoelectric energy harvesting system has sufficient potential for application as a renewable energy source that can be used with low power equipment.

This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1–3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.

Communication among hard-of-hearing individuals presents challenges, and to facilitate communication, sign language is preferred. Many people in the deaf and hard-of-hearing communities struggle to understand sign language due to their lack of sign-mode knowledge. Contemporary researchers utilize glove and vision-based approaches to capture hand movement and analyze communication; most researchers use vision-based techniques to identify disabled people’s communication because the glove-based approach causes individuals to feel uncomfortable. However, the glove solution successfully identifies motion and hand dexterity, even though it only recognizes the numbers, words, and letters being communicated, failing to identify sentences. Therefore, artificial intelligence (AI) is integrated with the sign language prediction system to identify disabled people’s sentence-based communication. Here, wearable glove-related sign language information is utilized to analyze the recognition system’s efficiency. The collected inputs are processed using progression learning deep convolutional neural networks (PLD-CNNs). The technique known as progression learning processes sentences by dividing them into words, creating a training dataset. The model assists in efforts to understand sign language sentences. A memetic optimization algorithm is used to calibrate network performance, minimizing recognition optimization problems. This process maximizes convergence speed and reduces translation difficulties, enhancing the overall learning process. The created system is developed using the MATLAB (R2021b) tool, and its proficiency is evaluated using performance metrics. The experimental findings illustrate that the proposed system works by recognizing sign language movements with excellent precision, recall, accuracy, and F1 scores, rendering it a powerful tool in the detection of gestures in general and sign-based sentences in particular.

This study examined the electromechanical characteristics of piezoelectric materials, which constitute a compact renewable energy source; these materials can convert mechanical energy (such as pressure or a cumulative impact) in the form of mechanical stress to electricity. This study further explored systems that require moderate energy and utilize piezoelectric materials to create an energy-generating floor. The electrical characteristics of these piezoelectric materials were studied, including the feasibility of installing them as a power source for road lighting, particularly cycling lanes. Furthermore, the effects of riders’ weights and cycling speeds were investigated. The results indicate that the electric power generated is adequate for the installation of these materials and can thus help improve visibility in the event of insufficient lighting.

Sign language plays a crucial role in bridging communication barriers within the Deaf community. Recognizing sign language sentences remains a significant challenge due to their complex structure, variations in signing styles, and temporal dynamics. This study introduces an innovative sign language sentence recognition (SLSR) approach using Hybrid Graph Embedding and Adaptive Convolutional Networks (HGE-ACN) specifically developed for single-handed wearable glove devices. The system relies on sensor data from a glove with six-axis inertial sensors and five-finger curvature sensors. The proposed HGE-ACN framework integrates graph-based embeddings to capture dynamic spatial–temporal relationships in motion and curvature data. At the same time, the Adaptive Convolutional Networks extract robust glove-based features to handle variations in signing speed, transitions between gestures, and individual signer styles. The lightweight design enables real-time processing and enhances recognition accuracy, making it suitable for practical use. Extensive experiments demonstrate that HGE-ACN achieves superior accuracy and computational efficiency compared to existing glove-based recognition methods. The system maintains robustness under various conditions, including inconsistent signing speeds and environmental noise. This work has promising applications in real-time assistive tools, educational technologies, and human–computer interaction systems, facilitating more inclusive and accessible communication platforms for the deaf and hard-of-hearing communities. Future work will explore multi-lingual sign language recognition and real-world deployment across diverse environments.