Journal of Electrical Engineering & Technology 

About: Journal of Electrical Engineering & Technology is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Electric power system & Control theory. It has an ISSN identifier of 1975-0102. Over the lifetime, 3590 publications have been published receiving 22511 citations. The journal is also known as: Journal of electrical engineering and technology & Journal of Electrical Engineering and Technology.

Electric Vehicles Charging Technology Review and Optimal Size Estimation

Abstract: Many different types of electric vehicle (EV) charging technologies are described in literature and implemented in practical applications. This paper presents an overview of the existing and proposed EV charging technologies in terms of converter topologies, power levels, power flow directions and charging control strategies. An overview of the main charging methods is presented as well, particularly the goal is to highlight an effective and fast charging technique for lithium ions batteries concerning prolonging cell cycle life and retaining high charging efficiency. Once presented the main important aspects of charging technologies and strategies, in the last part of this paper, through the use of genetic algorithm, the optimal size of the charging systems is estimated and, on the base of a sensitive analysis, the possible future trends in this field are finally valued.

Vision‑Based Human Activity Recognition System Using Depth Silhouettes: A Smart Home System for Monitoring the Residents

Abstract: The increasing number of elderly people living independently needs especial care in the form of smart home monitoring system that provides monitoring, recording and recognition of daily human activities through video cameras, which offer smart lifecare services at homes. Recent advancements in depth video technologies have made human activity recognition (HAR) realizable for elderly healthcare applications. This study proposes a depth video-based HAR system to utilize skeleton joints features which recognize daily activities of elderly people in indoor environments. Initially, depth maps are processed to track human silhouettes and produce body joints information in the form of skeleton, resulting in a set of 23 joints per each silhouette. Then, from the joints information, skeleton joints features are computed as a centroid point with magnitude and joints distance features. Finally, using these features, hidden Markov model is trained to recognize various human activities. Experimental results show superior recognition rate, resulting up to the mean recognition rate of 84.33% for nine daily routine activities of the elderly.

A Wrist Worn Acceleration Based Human Motion Analysis and Classification for Ambient Smart Home System

Abstract: In recent years, health-care industry has received a major boost due to sensors i.e., accelerometers, magnetometers etc., which allow its user to get instant updates about their current health status in indoor/outdoor environments. The real driving force behind the usage of accelerometer has been the fitness industry but it also holds a prominent place in ambient smart home to monitor resident’s life-style. In this paper, we proposed a novel triaxial accelerometer-based human motion detection and recognition system using multiple features and random forest. Triaxial signals have been statistically processed to produce worthy features like variance, positive and negative peaks, and signal magnitude features. The proposed model was evaluated over HMP recognition data sets and achieved satisfactory recognition accuracy of 85.17%. The proposed system is directly applicable to any elderly/children health monitoring system, 3D animated games/movies and examining the indoor behaviors of people at home, malls and offices.

Depth Images-based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM

Abstract: Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject"s body parts rotation and body parts missing which provide major contributions in human activity recognition.

New Medical Image Fusion Approach with Coding Based on SCD in Wireless Sensor Network

Abstract: The technical development and practical applications of big-data for health is one hot topic under the banner of big-data. Big-data medical image fusion is one of key problems. A new fusion approach with coding based on Spherical Coordinate Domain (SCD) in Wireless Sensor Network (WSN) for big-data medical image is proposed in this paper. In this approach, the three highfrequency coefficients in wavelet domain of medical image are pre-processed. This pre-processing strategy can reduce the redundant ratio of big-data medical image. Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on the multi-scale edge of medical image, it can be fused and reconstructed. Experimental results indicate the novel approach is effective and very useful for transmission of big-data medical image(especially, in the wireless environment).