Rasanjalee Rathnayake

Monitoring electrical appliances in residential buildings is essential for minimizing energy waste and enhancing safety through the early detection of abnormal conditions. While researchers have investigated both intrusive and nonintrusive load monitoring approaches, the non-intrusive approach has emerged as preferred due to its cost-effectiveness and noninvasive implementation. Despite considerable progress in appliance monitoring and fault detection systems over the past two decades, critical challenges and limitations persist. This paper proposes a low-complexity appliance identification and monitoring solution to overcome those issues. Furthermore, the proposed solution is integrated with an abnormal condition detection mechanism for critical appliances, aiming to save energy and ensure the safety of the power system. Furthermore, the solution incorporates user feedback via a dedicated mobile application, enhancing adaptability and performance. The proposed solution has been validated in real-time environments using both custom and publicly available datasets, demonstrating improved accuracy in energy monitoring and increased consumer safety.

Iris extraction has gained prominence due to its application versatility across many domains. However, achieving real-time iris extraction poses challenges due to several factors. While non-learning-based iris extraction methods are cost-effective and resource-efficient, they are outperformed by learning-based algorithms, which deliver superior accuracy and performance. In response, this article proposes a Convolutional Neural Networks (CNN)-based accurate direct iris extraction mechanism for a broad spectrum of eye images. The innovation of our approach lies in its proficiency with varied image types, including those where the iris is partially obscured by the eyelid. We enhance the method’s reliability by introducing a modified Circular Hough Transform (CHT). Extensive testing demonstrates our method’s excellent real-time performance across diverse image types, even under challenging conditions. These findings underscore the proposed method’s potential as a cost-effective and computationally efficient solution for real-time iris extraction in varied application domains.

Pupil localization extracts pupil center coordinates from images and videos of the human eye along with the pupillary boundary. Pupil localization essentially plays a major role in identity verification, disease recognition, visual focus of attention (VFOA) tracking, dementia cognitive assessment, and human fatigue detection. However, the process of pupil localization still remains challenging due to various factors, such as poor-quality images, eye makeup, contact lenses, eyelashes, hair strips, eyebrows, closed eyes, and eye saccades. The pupil localization strategies are essentially divided into learning-based and non-learning-based approaches and discussed in detail with the relevant techniques used. This article aims to deliver the essence of current trends in pupil localization and critically discusses the advantages and disadvantages of each method. Hence, this article can be useful to a broad spectrum of readers as a guide to analyze the latest trends in pupil localization.