Showing papers in "Computers & Electrical Engineering in 2020"

TL;DR: A new and clear definition of IIoT is presented, which can help the readers to understand the concept of IIeT, and the state-of-the-art research efforts inIIoT are described.

TL;DR: The PKC-based McEliece secondary key provides an error correction capacity, which can remove the noise during secondary user allocation and enhance the effectiveness of the spectrum management, which collaborate effectively over the noise channel management.

TL;DR: In this study, price prediction is performed with two machine learning methods, namely linear regression (LR) and support vector machine (SVM) by using a time series consisting of daily ether cryptocurrency closing prices.

TL;DR: This paper analyzed and discussed the threat classification pertaining to autonomous vehicles (AV) by using the authentication, accountability, and service availability, and highlighted the countermeasures for AV cyberattacks along with their implementation issues and explored how blockchain can overcome these issues.

TL;DR: An overview on using blockchain for cloud exchange in aspects of security, privacy, reputation systems and transaction management is presented and the open research issues in this promising area are presented.

TL;DR: The security and the performance analysis demonstrate that the proposed scheme supports better assistance to BSHs while maintaining security.

TL;DR: This paper proposes a data sharing incentive model based on evolutionary game theory using blockchain with smart contract that can dynamically control the excitation parameters and continuously encourages users to participate in data sharing.

TL;DR: The aim of this survey is to provide directions for future work in the area of Blockchain-based vehicular networks, and existing research works aiming to overcome vehicular challenges using the Blockchain technology are presented and compared.

TL;DR: A blockchain-based decentralized vehicular ad-hoc network that supports data immutability property is designed and an authentication protocol for the vehicle’s user and a consensus mechanism to validate transactions is proposed.

TL;DR: This paper proposes a framework to compare and characterize existing blockchain oracles mechanisms from industry and uses Fault Tree Analysis to identify weak links that affect the overall reliability of a blockchain-based system.

TL;DR: A comprehensive review on the state-of-the-art literature known as medical image registration using deep neural networks is presented, which allows a deep understanding and insight for the readers active in the field who are investigating the state of theart and seeking to contribute the future literature.

TL;DR: This research presents a bibliometric analysis of articles in BIoT domain, covering papers published in top journals and conferences, and finds research trends and explores diverse research areas, the most influential publications, top publication venues, top funding agencies and future research direction.

TL;DR: This paper presents various applications of blockchain for smart homes and discusses the most commonly used methods in the literature and presents two case studies to show how blockchain empowers smart home transactions by eliminating the middleman.

TL;DR: This paper proposes a near real-time SDN security system that both prevents DDoS attacks on the source-end network and protects the sources SDN controller against traffic impairment, and applies and test a Convolutional Neural Network for DDoS detection and describes how the system could mitigate the detected attacks.

TL;DR: An overview of the VSN concept in a vehicular IoT-based smart city paradigm is overview, focusing on the security aspects, and concludes that VSNs play a key role in developing efficient ITS.

TL;DR: Two methods based on ensemble learning and big data for improving the performance of malware detection at a large scale are designed and implemented using Apache Spark, a popular big data processing framework.

TL;DR: The proposed CNN model for the detection of apple defects achieves a higher recognition rate, the accuracy is better than conventional machine learning algorithms, and the idea and theoretical basis for efficient, non-destructive, and online detection of fruits quality is provided.

TL;DR: The concept of the IoE in the Industrial Internet of Things perspective is emphasized in order to ensure productivity, control, reduced cost, real-time decision making and monitoring, customer satisfaction, and innovative experience.

TL;DR: A comprehensive open-access database containing more than 500 highresolution images categorized into different pathological conditions, including Normal, Macular Hole, Age-related Macular Degeneration, Central Serous Retinopathy, and Diabetic retinopathy is described.

TL;DR: This study focuses on demonstrating a powerful end-to-end lane detection method using contemporary computer vision techniques for self-driving cars and proposes an improved lane detection technique based on perspective transformations and histogram analysis.

TL;DR: An automatic diagnostic methodology for clinical heart disease is presented and computes the most relevant feature subset by taking advantage of feature selection and extraction techniques.

TL;DR: The key idea of the spGEMM algorithm, tSparse, is to multiply sparse rectangular blocks using the mixed precision mode of TCUs, the first time that TCUs are used in the context of spGemM.

TL;DR: A novel extreme-learning-machine (ELM)-based robust adaptive integral terminal sliding mode (AITSM) control strategy is developed for the precise tracking control of a steer-by-wire system with uncertain dynamics and the stability of the closed-loop control system is proved in Lyapunov sense.

TL;DR: The proposed platform comprehensively covers charity collection process using crypto wallets, Initial Coin Offering (ICO), economic model, and introduces CharityCoin (CC) as a digital currency and performance evaluation shows that proposed architecture scales well for large data size.

TL;DR: A literature review of DIF is presented, covering active and passive methods as well as those based on deep learning, and a vast and updated set of references synthesized in textual, tabular, and graphic form are presented.

TL;DR: The time-domain results of the rotor dynamics and deviation in generator voltage for various test cases reveal the potential of the proposed controller in alleviating the overall power system oscillations.

TL;DR: The proposed approach is used to reduce the controller workload, response time, and manage Quality of Services (QoS) and enhanced energy efficiency among controllers and improves the green energy utilization in smart grid systems.

TL;DR: The experiment results show that the proposed deep learning method achieves the state-of-the-art AP in arm fracture detection and it has strong potential application in real clinical environments.

TL;DR: The proposed hybrid model integrated with Ni-MH battery is designed in MATLAB software and shows that the proposed model provides alternative incentives to the consumers and it is economically feasible.

TL;DR: This proposal contains the active and reactive power capabilities of the voltage source converters that interface with the BESS independently, which implies that the Besss have the possibility to work as dynamic energy compensators over all the four quadrants.