scispace - formally typeset
Search or ask a question
JournalISSN: 1450-5843

Electronics 

University of Banja Luka
About: Electronics is an academic journal published by University of Banja Luka. The journal publishes majorly in the area(s): Control theory & Deep learning. It has an ISSN identifier of 1450-5843. It is also open access. Over the lifetime, 7899 publications have been published receiving 93133 citations. The journal is also known as: Electronics & Electronic devices.


Papers
More filters
Journal Article
TL;DR: Integrated circuits will lead to such wonders as home computers or at least terminals connected to a central computer, automatic controls for automobiles, and personal portable communications equipment as discussed by the authors. But the biggest potential lies in the production of large systems.
Abstract: The future of integrated electronics is the future of electronics itself. The advantages of integration will bring about a proliferation of electronics, pushing this science into many new areas. Integrated circuits will lead to such wonders as home computers—or at least terminals connected to a central computer—automatic controls for automobiles, and personal portable communications equipment. The electronic wristwatch needs only a display to be feasible today. But the biggest potential lies in the production of large systems. In telephone communications, integrated circuits in digital filters will separate channels on multiplex equipment. Integrated circuits will also switch telephone circuits and perform data processing. Computers will be more powerful, and will be organized in completely different ways. For example, memories built of integrated electronics may be distributed throughout the machine instead of being concentrated in a central unit. In addition, the improved reliability made possible by integrated circuits will allow the construction of larger processing units. Machines similar to those in existence today will be built at lower costs and with faster turnaround.

6,077 citations

Journal ArticleDOI
TL;DR: This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network and goes on to cover Convolutional Neural Network, Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL).
Abstract: In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

922 citations

Journal ArticleDOI
TL;DR: A review of the current state of the research field on machine learning interpretability while focusing on the societal impact and on the developed methods and metrics is provided.
Abstract: Machine learning systems are becoming increasingly ubiquitous. These systems’s adoption has been expanding, accelerating the shift towards a more algorithmic society, meaning that algorithmically informed decisions have greater potential for significant social impact. However, most of these accurate decision support systems remain complex black boxes, meaning their internal logic and inner workings are hidden to the user and even experts cannot fully understand the rationale behind their predictions. Moreover, new regulations and highly regulated domains have made the audit and verifiability of decisions mandatory, increasing the demand for the ability to question, understand, and trust machine learning systems, for which interpretability is indispensable. The research community has recognized this interpretability problem and focused on developing both interpretable models and explanation methods over the past few years. However, the emergence of these methods shows there is no consensus on how to assess the explanation quality. Which are the most suitable metrics to assess the quality of an explanation? The aim of this article is to provide a review of the current state of the research field on machine learning interpretability while focusing on the societal impact and on the developed methods and metrics. Furthermore, a complete literature review is presented in order to identify future directions of work on this field.

813 citations

Journal ArticleDOI
TL;DR: A review of wearable pulse rate sensors with green LEDs can be found in this paper. But, the authors do not discuss the application of these sensors in the medical field. But, they briefly present the history of wearable PPG and recent developments in wearable pulse-rate sensors.
Abstract: Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.

700 citations

Journal ArticleDOI
TL;DR: In this article, the authors conduct a survey on several published valence change resistive switching memories with a particular interest in the I-V characteristic and the corresponding conduction mechanism.
Abstract: Resistive switching effect in transition metal oxide (TMO) based material is often associated with the valence change mechanism (VCM). Typical modeling of valence change resistive switching memory consists of three closely related phenomena, i.e., conductive filament (CF) geometry evolution, conduction mechanism and temperature dynamic evolution. It is widely agreed that the electrochemical reduction-oxidation (redox) process and oxygen vacancies migration plays an essential role in the CF forming and rupture process. However, the conduction mechanism of resistive switching memory varies considerably depending on the material used in the dielectric layer and selection of electrodes. Among the popular observations are the Poole-Frenkel emission, Schottky emission, space-charge-limited conduction (SCLC), trap-assisted tunneling (TAT) and hopping conduction. In this article, we will conduct a survey on several published valence change resistive switching memories with a particular interest in the I-V characteristic and the corresponding conduction mechanism.

474 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
2023158
202255
20212,894
20202,217
20191,590
2018485