The most important equipment utilized by power systems are transformers, which are passive electrical devices suitable for the transfer of electrical energy from one circuit to another which is associated with Electromagnetic (EM) induction. These equipment are important to help maintain network stability and reliability but despite these advantages, they still exhibit problems due to numerous factors such as overloading, poor dielectric strength, bad insulation, thermal degradation which in turn cause abrupt power outages or results in a major electrical system failure. Unfortunately, transformer users are having difficulty keeping an eye on how distribution transformers (DT) are performing when they are being used which contributes to outages. This paper focused on the performance analysis of a DT and the approach which helps to mitigate the difficulties of identifying load imbalance or overloaded DT to provide long-lasting and trouble-free services for power consumers, using Mansard place 1000 KVA, 11/0.415 KV Distribution Transformer (DT) as a case study to obtain the on-load parameters. The (Fluke 435 series II) Power Quality and Energy Analyzer (PQEA) was the equipment utilized to ascertain the reliability measurements of the DT and the experimental method was carried out on the second terminal connection as regards to IS 1180 standard.

Video summarization special field of signal processing which includes pre-processing of video sets, their contextual segmentation, application-specific feature extraction & selection, and identification of dissimilar frame sets. Various variety of machine learning models are proposed by researchers to design such summarization methods, and each of them varies in terms of their functional nuances, application-specific advantages, deployment specific limitations, and contextual future scopes. Moreover, these models also vary in terms of quantitative & qualitative measures including accuracy of summarization, computational complexity, delay needed for summarization, precision during the summarization process, etc. Due to such a wide variation in performance levels, it is difficult for researchers to identify optimal models for their functional-specific &performance-specific use cases. Because of this, researchers and summarization-system-designers are required to validate individual models, which increases the delay & cost needed for final model deployments. To overcome these delays & reduce deployment costs, this paper initially discusses a multiple variety of video summarization models in terms of their working characteristics. Based on this discussion, researchers shall be able to identify optimum models for their functionality-specific use cases. This paper also analyzes and compares the reviewed models in terms of their performance metrics including summarization accuracy, delay, complexity, scalability and fMeasure, which will further allow readers to identify performance-specific models for their deployments. A novel Summarization Rank Metric (SRM) is calculated based on these evaluation metrics, which will assist readers to identify models that can perform optimally w.r.t. multiple evaluation parameters & different use cases. This metric is calculated by combining all the comparison metrics, which will assist in identification of models that have high accuracy, low delay, low complexity, high scalability & fMeasure levels.

To meet the ever-growing demand of this power hungry world, wasted energy can play a significant role. Wasted energy is the one which is not used in any part of a transferred or transformed process. Footstep energy is such a type that can be utilized properly in potential areas like crowded places. Every time people walk, a system captures this energy as pressure or stress and then converts it into electrical energy. This paper presents the design of a prototype of pressure transduced intelligent power management system for the street lighting system that can be self-enabled automatically from dawn to dusk. A way of avoiding the risk of battery damage is introduced in this design. A priority based power connection scheme is also shown to make an effective power management system for this system. The output results are also in accord with the design scheme that indicate almost linear relationships between stress in piezo transducers and voltage or power produced from it.

The systematic and scientific study of the lifestyle and culture of earlier peoples is known as archaeology. The Indian history of archaeology spans from the 19th century to the present status, it includes the region's history investigated by a wide variety of archaeologists. They don’t have any such authentic digital methods to be quoted in research. Manual Recognition of ancient temple structural elements is quite difficult to recognize, has archaeologists face many complex problems because they don’t have any automated Recognition method. Recognition is helpful for archaeologists to get more detailed information of ancient temples which is very important for further research. Thus, in this work it is proposed to develop automated method for Recognition ancient temple structural elements (vimana & pillars) for further archaeological research purpose. The proposed method extracts Genetic programming evolved spatial descriptor and classifies the temple structural elements visited by archaeologists based on linear discriminant method [LDA]. The proposed method is divided into 3 main phases: pre-processing, genetic programming evolution and Recognition. The Generalized Co-Occurrence Matrix is used in the pre-processing phase to change images into a format that genetic programming systems may use to process them. The second stage produces the best spatial descriptor to date in the form of a programme that is based on fitness Utilizing LDA, the Fitness is determined. Once the program's output has been received, it can be used for Recognition. Experimental results shows, it demonstrates relatively high accuracy in Recognizing both vimana(gopura) & pillars of different temples. The proposed method is implemented in MATLAB. These results will play very significant role in identification of temple architecture, which in-turn helps in conservation and reconstruction of temples. The proposed methodology will give 98.8% accuracy in pillars recognition and 98.4% accuracy in vimana recognition.

Nowadays, as transportation is increasing day by day and the probability of occurance of it in future is also very high. There are so many people travel an hours together every day, due to lack of rest the driver may feel tired or drowsy and may fall asleep. This may lead to several highway calamities causing to severe injuries, loss of human life etc. So solve this issue we propose a driver drowsiness monitoring system that helps in avoiding major accidents. The proposed method detects the status of the driver of the vehicle using the Eye Aspect Ratio (EAR) and Mouth Opening Ratio (MOR) techniques. The developed system includes a Pi camera, Raspberry Pi module and is used to detect and analyze continuously the eye closure status in real-time. When drowsiness is detected buzzer sound will alert the driver which significantly helps in reducing the percentage of highway calamities by alerting.