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作者： Zaib, Muhammad Hassan Bashir, Faisal Qureshi, Kashif Naseer Kausar, Sumaira Rizwan, Muhammad Jeon, Gwanggil Department of Computer Science Bahria University Islamabad Pakistan Lab Bahria University Islamabad Pakistan Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Cyber-bullying has been on the rise especially after the explosive widespread of various cyber-attacks. Various types of techniques have been used to tackle cyber-bullying. These techniques focused primarily on data traffic for monitoring malicious activities. This research proposes a methodology where we can detect early Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks. First, we formulate the problem in a practical scenario by comparing flow and non-flow-based datasets using Mann Whitney U statistical test. Flow and non-flow-based datasets and Artificial Neural Network (ANN) and Support Vector Machine (SVM) is used for classification. To keep original features, we use variance, correlation, ½ quartile method to eliminate the unimportant features. The forward selection wrapper method for feature selection is used to find out the best features. To validate the proposed methodology, we take multiple DoS and DDoS single flow and validate it on 10%, 20%, 30%, 40%, and 50%. For validation, the experimental results show + 90% accuracy on the early 10% flow. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Network security

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Sustainable electric vehicles fault detection based on monitoring by deep Learning Architectures in feature extraction and classification

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2023年 57卷

作者： Wongchai, Anupong Aoudni, Yassine Yesubabu, Mannava Reegu, Faheem Ahmad Gowri, N. Vasantha Vijayakumar, P. [a]The Independent researcher and Social Worker Thailand [b]Department of Computers and Information Technology Faculty of Sciences and Arts Turaif Northern Border University Arar 91431 Saudi Arabia [c]Computer & Embedded Systems Laboratory National School of Engineers of Sfax (ENIS) University of Sfax BP 1173 3038 Sfax Tunisia [d]Department Of Computer Science & Engineering Vardhaman College Of Engineering Shamshabad Hyderabad 501218 India [e]College of Computer Science And Information Technology Jazan University Jazan 45142 Saudi Arabia [f]Department of EEE Chaitanya Bharathi Institute of Technology (A) Hyderabad India [g]School of Electronics Engineering Vellore Institute of Technology (VIT) Chennai Tamil Nadu India

Numerous industrial sector paradigms have been altered by the necessity to produce more competitive ma-chinery and the introduction of digital technologies from so-called Industry 4.0. This research proposes novel technique in electric vehicle fault detection based on monitoring data classification and feature extraction using deep learning architectures. The real Proton exchange membrane fuel cell (PEMFC) experiment dataset has been collected as sustainable electric vehicles data using multi-cell parallel electric vehicle and it has been processed for noise removal, dimensionality reduction and extraction using deep stacking auto gradient descent for-classifyingthrough monte Carlo regressive Gaussian naive bayes *** of experiments demonstrate that suggested model achieves over 99% accuracy in identifying flooding fault of fuel cell under load-varying situations. The experimental analysis has been carried out in terms of accuracy, robustness, reliability, preci-sion, recall. The proposed technique attained 99% of accuracy, 89% of robustness, 85% of Reliability, 95% of precision and 81% of recall.

关键词： Electric vehicles Fault detection Monitoring Data classification Feature extraction Deep learning

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Design and Experimental Investigation of Trikarenos: A Fault-Tolerant 28nm RISC-V-based SoC

arXiv

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arXiv 2024年

作者： Rogenmoser, Michael Wiese, Philip Forlin, Bruno Endres Gürkaynak, Frank K. Rech, Paolo Menicucci, Alessandra Ottavi, Marco Benini, Luca The Integrated Systems Laboratory ETH Zurich Switzerland The Computer Architecture for Embedded Systems The University of Twente Netherlands The Department of Industrial Engineering The University of Trento Italy The Department of Space Engineering The Delft University of Technology Netherlands The Computer Architecture for Embedded Systems The University of Twente Netherlands The Department of Electronic Engineering The University of Rome Tor Vergata Italy The Integrated Systems Laboratory ETH Zurich Switzerland The Department of Electrical Electronic and Information Engineering The University of Bologna Italy

We present a fault-tolerant by-design RISC-V SoC and experimentally assess it under atmospheric neutrons and 200 MeV protons. The dedicated ECC and Triple-Core Lockstep countermeasures correct most errors, guaranteein... 详细信息

关键词： Fault tolerance

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Near-Optimal Coverage Path Planning of Distributed Regions for Aerial Robots with Energy Constraint

Near-Optimal Coverage Path Planning of Distributed Regions f...

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2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Khanam, Zeba McDonald-Maier, Klaus Ehsan, Shoaib Embedded and Intelligent Systems Lab School of Computer Science and Electronics Engineering University of Essex United Kingdom

ISBN: (纸本)9780738131153

Unmanned Aircraft Vehicles (UAVs) have gained immense popularity for area coverage having applications such as environmental monitoring, demining, search and rescue, among others. Most of the existing studies exploring area coverage have considered only a single region, however, few recent studies have considered coverage of multiple distributed regions. One of the limitations which UAV suffers while covering distributed multiple regions is energy constraints where complete area coverage is not possible. From a strategical point of view, we propose a novel algorithm which solves a variant of area coverage problem where the UAV aims to achieve near-optimal area coverage due to path length limitation caused by the energy constraint. In this paper, a preliminary study is conducted by first formulating the problem and later on presenting a solution. The solution has been partitioned into two inter-dependent sub-problems: i) inter-region coverage, ii) intra-region coverage. The performance of the algorithm has been evaluated by analysing its properties over an exhaustive set of test case scenarios and comparing it against two state-of-the-art area coverage approaches. © 2021 IEEE.

关键词： Motion planning

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Joint Optimization of Throughput and Energy Consumption in Microservices-Based UAV Networks

Joint Optimization of Throughput and Energy Consumption in M...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： José Gómez-DelaHiz Aymen Fakhreddine Juan M. Murillo Jaime Galán-Jiménez Dept. of Computer Systems and Telematics Engineering University of Extremadura Spain Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria Technology Innovation Institute Abu Dhabi UAE

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

The application of Unmanned Aerial Vehicle (UAV) networks to the coverage problem in rural and low-income areas is actively studied nowadays by the research community. A UAV-based network infrastructure is different from traditional cellular networks relying on Base Stations. By mounting small cells on top of UAVs, coverage can be enhanced in regions where network operators avoid to invest due to the low Return on Investment. In case there is a requirement for an enhanced throughput in a rural area (e.g., users requesting IoT applications with strict QoS requriements), a potential solution is to place several UAV s over the same area, thus maximizing the offered throughput. However, this situation would lead to an increase in the energy consumption. To tackle this problem, this paper proposes an optimal solution to the problem of jointly maximizing the offered throughput in rural scenarios where users request microservice-based IoT applications, while minimizing the energy consumption of the swarm of UAV s. A Mixed Integer Linear Programming (MILP)-based formulation is defined and evaluated over realistic scenarios. Experimental results demonstrate how the solution is able to perform UAV placement in a way to maximize the offered throughput in the highest number of areas, while minimizing the total number of deployed UAVs.

关键词： Energy consumption Costs Microservice architectures Quality of service Throughput Autonomous aerial vehicles Mixed integer linear programming

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Throughput-Energy Efficiency Trade-off in Microservices-Based UAV Networks

Throughput-Energy Efficiency Trade-off in Microservices-Base...

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IEEE Symposium on computers and Communications (ISCC)

作者： José Gómez-delaHiz Andrés García-López Santiago García-Gil Diego Ramos-Ramos Aymen Fakhreddine Juan Manuel Murillo Jaime Galán-Jiménez Dept. of Computer Systems and Telematics Engineering University of Extremadura Spain Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria Technology Innovation Institute Abu Dhabi UAE

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

Rural areas broadband access suffers from the limited investment of network operators, due to the forecasted return on investment. As a consequence, digital services such as eHealth, remote education, or smart agriculture cannot be offered to the rural population. In this context, research on Unmanned Aerial Vehicles (UAV) networks has emerged, which aims to solve the coverage problem by relying on small cells mounted on UAVs to provide coverage. From the network Quality of Service (QoS) point of view, i.e., the performance offered to users according to certain parameters such as delay, reliability, and throughput provisioning can be identified as one of the weak points. For the problem of maximizing throughput, the main solution is to group several UAVs in the same area. However, as the offered throughput increases, the power consumption will also increase. In this context, this paper proposes a genetic algorithm to solve the problem of jointly maximizing the offered throughput in rural scenarios where users request microservice-based IoT applications while minimizing the energy consumption of the swarm of UAVs. The algorithm is defined and evaluated in realistic scenarios, demonstrating its effectiveness on increasing the throughput while decreasing the number of UAVs that are required.

关键词： Smart agriculture Energy consumption Microservice architectures Quality of service Throughput Autonomous aerial vehicles Telecommunications Telecommunication network reliability Investment Genetic algorithms

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Cost-Effective Network Packet Manipulation and Error-Detection Device

Cost-Effective Network Packet Manipulation and Error-Detecti...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Alexander F. K. Janus Steffen Moser Frank Slomka Faculty of Engineering Computer Science and Psychology Institute of Embedded System/Real-Time Systems School of Advanced Professional Studies Ulm University Ulm Germany

Real-time multimedia communication applications like video conferencing and screen sharing tools are an integral part of our daily communications. These time-critical services are handled via the Internet, a packet-switched and essentially best-effort network, not explicitly designed for robust real-time communication. From a video conferencing service provider's perspective, network issues cause a lot of user complaints due to various reasons: lossy WLAN channels, poorly configured home routers, or crowded carrier-grade network address translation devices. All of these issues have in common that they are often time-variant and non-deterministic. Furthermore, the end-to-end transmission in these highly inhomogeneous networks often suffers from a mixture of more than one error source. To enable a detailed and impartial analysis of multimedia service quality, it is necessary to analyze packets in defective online scenarios and introduce a broadly similar error pattern in an otherwise stable lab network. In this paper, we present SEDER, a hardware-software design of a layer two transparent Ethernet bridge that can be used for two purposes: error detection and error recreation. Implemented on low-cost hardware, the bridge can be easily deployed anywhere to allow debugging, even at a user's site. It is possible to parameterize our bridge to imitate almost all possible network effects, including complex combinations of more than one effect. In addition, we present two case studies, firstly emphasizing the capabilities of SEDER and secondly showing the need to use SEDER to harden open source Video Conferencing Tools against errors.

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Research on the Classification Method of Urolithiasis Composition Based on Machine Learning

Research on the Classification Method of Urolithiasis Compos...

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2022 IEEE International Conference on Advances in Electrical engineering and computer Applications, AEECA 2022

作者： Liao, Xingjiao Liu, Yaohui Guo, Mei Yu, Fang Xiangnan University Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Hunan Chenzhou China Xiangnan University School of Computer and Artificial Intelligence Hunan Chenzhou China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

This paper aims to investigate how to accurately classify urinary calculi components by CT images. The image segmentation is completed by grayscale adjustment of the two-dimensional image and region growth of the three-dimensional image. Grayscale and energy features of the region of interest are extracted in three dimensions. In this paper, the three different models trained by the EasyEnsemble ensemble algorithm are connected in series to achieve layer-by-layer classification of unbalanced samples. The experimental results show that this method can effectively analyze the components of simple calcium oxalate monohydrate, anhydrous uric acid, carbonated apatite and dihydrate calcium oxalate in urinary tract stones, and the AUCs reach AUC=0.93, AUC=1.00, AUC= 0.86, AUC=0.76, respectively, indicating that this method can better perform multi-classification of unbalanced samples. © 2022 IEEE.

关键词： Image segmentation

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Neural NILM Learning Paradigms: From Centralised to Decentralised Learning 5

Neural NILM Learning Paradigms: From Centralised to Decentra...

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5th International Conference on Signal Processing and Information Security, ICSPIS 2022

作者： Bousbiat, Hafsa Klemenjak, Christoph Himeur, Yassine Elmenreich, Wilfried Amira, Abbes Mansoor, Wathiq Atalla, Shadi University of Klagenfurt Institute of Networked and Embedded Systems Klagenfurt Austria University of Dubai College of Engineering and IT Dubai United Arab Emirates University of Sharjah Department of Computer Science United Arab Emirates

ISBN: (纸本)9781665492652

Non-intrusive Load Monitoring (NILM) is becoming a paramount in both industrial and residential sectors to achieve efficient energy consumption. Thus, research on this matter flourished in recent years, where deep neural networks gained the highest interest from the research community, commonly referred to as neural NILM. As a predominant practice, neural NILM models follow a centralised based learning scheme where the energy data is assumed to be available in a central node for training. However, this practice and the enormous amount of data required by these algorithms raise privacy and security concerns from the consumer's side since energy data can reveal in-home activities and occupancy records if intercepted. Federated Learning (FL), also referred to as collaborative learning, is seen as viable solution to address these issues. Nonetheless, its application in neural NILM is still in its infancy and many challenges are yet to be addressed. The current paper presents an overview of neural NILM models following both a centralised and a federated learning paradigm. Furthermore, it identifies the main challenges with regard to both learning paradigms along with potential future research directions for more robust, secure and privacy-preserving models in the neural NILM industry. © 2022 IEEE.

关键词： Deep neural networks

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Character spotting and autonomous tagging: offline handwriting recognition for Bangla, Korean and other alphabetic scripts

Character spotting and autonomous tagging: offline handwriti...

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作者： Majid, Nishatul Smith, Elisa H. Barney Department of Physics and Engineering Fort Lewis College DurangoCO United States Department of Electrical and Computer Engineering Boise State University BoiseID United States Embedded Internet Systems Lab Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

This paper demonstrates a framework for offline handwriting recognition using character spotting and autonomous tagging which works for any alphabetic script. Character spotting builds on the idea of object detection to find character elements in unsegmented word images. An autonomous tagging approach is introduced which automates the production of a character image training set by estimating character locations in a word based on typical character size. Although scripts can vary vividly from each other, our proposed approach provides a simple and powerful workflow for unconstrained offline recognition that should work for any alphabetic script with few adjustments. Here we demonstrate this approach with handwritten Bangla, obtaining a character recognition accuracy (CRA) of 94.8% and 91.12% with precision and autonomous tagging, respectively. Furthermore, we explained how character spotting and autonomous tagging can be implemented for other alphabetic scripts. We demonstrated that with handwritten Hangul/Korean obtaining a Jamo recognition accuracy (JRA) of 93.16% using a tiny fraction of the PE92 training set. The combination of character spotting and autonomous tagging takes away one of the biggest frustrations—data annotation by hand, and thus, we believe this has the potential to revolutionize the growth of offline recognition development. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Character recognition

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