A Review of the Teaching and Learning on Power Electronics Course

—In this review, we describe various kinds of problem and solution related teaching and learning on power electronics course all around the world. The method was used the study of literature on journal articles and proceedings published by reputable international organizations. Thirtynine papers were obtained using Boolean operators, according to the specified criteria. The results of the problems generally established that student learning motivation was low, teaching approaches that are still teacher-centered, the scope of the curriculum extends, and the physical limitations of laboratory equipment. The solutions offered are very diverse ranging from models, strategies, methods and learning techniques supported by information and communication technology

ReSCon '09, Research Student Conference: Book of Abstracts

The second SED Research Student Conference (ReSCon2009) was hosted over three days, 22-24 June 2009, in the Lecture Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School of Engineering and Design at Brunel University. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Incorporating Modular Arrangement of Predetermined Time Standard with a Wearable Sensing Glove

“Performance” – a common watchword in the present age, and that which is optimized through the most functional methodology of investigating the work procedure. This encompassed the auditing, updating of the tasks, while at the same time, applied automation and mechanization. The Modular Arrangement of Predetermined Time Standard (MODAPTS) is a useful application of a work measurement technique that allow a greater variety of work for manufacturing, engineering, and administrative service activities to be measured quickly with ease and accuracy. The MODAPTS, however, made it extremely difficult for engineers to use because it required an ample amount of time to analyze and code the raw data. A new design was proposed to help resolve the conventional system\u27s inadequacy because in MODAPTS, each task cycle of a minute required about 2 hours to calculate and document, and also, the judgment of the analysts varied for the same task. This study aimed to reduce the time taken for the traditional MODAPTS documentation usually took and produced unified results by integrating MODAPTS with a Sensing Wearable Glove while maintaining the same performance. The objective was to introduce an easy, cost-effective solution, and to compare the accuracy of coding between manual and automated calculated MODAPTS while maintaining consistent performance. This study discusses the glove and accompanying software design that detected movements using flex sensors, gyroscopes, microcontrollers, and pressure sensors. These movements were translated into analog data used to create MODAPTS codes as an output, which then sent the data wirelessly using the Bluetooth module. The device designed in this study is capable of sensing gestures for various operations, and the traditional method was compared to the proposed method. This was in turn, validated using the two-way ANOVA analysis. It was observed that the sensor-based glove provided efficient and reliable results, just like the traditional method results while maintaining the same performance

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

A survey on artificial intelligence-based acoustic source identification

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions

Automatic Emotion Recognition From Multimodal Information Fusion Using Deep Learning Approaches

During recent years, the advances in computational and information systems have contributed to the growth of research areas, including a ective computing, which aims to identify the emotional states of humans to develop di erent interaction and computational systems. For doing so, emotions have been characterized by speci c kind of data including audio, facial expressions, physiological signals, among others. However, the natural response of data to a single emotional event suggests a correlation in different modalities when it achieves a maximum peak of expression. This fact could lead the thinking that the processing of multiple data modalities (multimodal information fusion) could provide more learning patterns to perform emotion recognition. On the other hand, Deep Learning strategies have gained interest in the research community from 2012, since they are adaptive models which have shown promising results in the analysis of many kinds of data, such as images, signals, and other temporal data. This work aims to determine if information fusion using Deep Neural Network architectures improves the recognition of emotions in comparison with the use of unimodal models. Thus, a new information fusion model based on Deep Neural Network architectures is proposed to recognize the emotional states from audio-visual information. The proposal takes advantage of the adaptiveness of the Deep Learning models to extract deep features according to the input data type. The proposed approach was developed in three stages. In a rst stage, characterization and preprocessing algorithms (INTERSPEECH 2010 Paralinguistic challenge features in audio and Viola Jones face detection in video) were used for dimensionality reduction and detection of the main information from raw data. Then, two models based on unimodal analysis were developed for processing audio and video separately. These models were used for developing two information fusion strategies: a decision fusion and a characteristic fusion model, respectively. All models were evaluated using the eNTERFACE database, a well-known public audiovisual emotional dataset, which allows compare results with state of the art methods. Experimental results showed that Deep Learning approaches that fused the audio and visual information outperform the unimodal strategies.Magister en Automatización y Contro