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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Suresh, Helina Rajini Neelaveni, R. Kumar, Bonda Kiran Arunadevi, R. Indhumathi, G. Varun, M. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of ECE Tamilnadu Chennai India Srm Institute of Science and Technology Department of Cse Tamilnadu Chennai India Kl School of Architecture Kl Deemed to Be University Andhrapradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Rajalakshmi Engineering College Department of ECE Tamilnadu Chennai India Saveetha Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798350300888

The proliferation of IoT devices is primarily responsible for the data deluge that has engulfed multiple industries. Nevertheless, due to the high dimensionality and complexity of IoT data streams, anomaly detection remains a significant challenge. This suggests that an unsupervised deep learning strategy could be used to identify anomalies in IoT data streams. Instead of relying on labeled data, this method employs deep neural networks, which can be trained to recognize anomalies and comprehend complex patterns. Experiments are conducted on a real-world IoT dataset to evaluate the efficacy and accuracy of the proposed method. The results demonstrate its proficiency in detecting anomalies in IoT data streams, paving the way for a safer, more reliable Internet of Things. Using unsupervised deep learning techniques, the method provides a practicable solution to the age-old problem of IoT anomaly identification. © 2023 IEEE.

关键词： Internet of things

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Design and Implementation of an EMG-based System for Monitoring and Help Request

Design and Implementation of an EMG-based System for Monitor...

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2022 International Conference on Smart Systems and Power Management, IC2SPM 2022

作者： Zeine, Carla Daou, Roy Abi Zeid Telawi, Samer Boercsoek, Josef Hayek, Ali Haidar, Gaby Abou University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Kassel Germany Lebanese German University Faculty of Public Health Jounieh Department of Computer Architecture and System Programming Biomedical Technologies Department Jounieh Lebanon Université la Sagesse Faculty of Engineering Furn El Chebbak Mechatronics Department Beirut Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany American University of Science and Technology Faculty of Engineering Zahle Lebanon

ISBN: (数字)9781665452915

ISBN: (纸本)9781665452915

Integrating electrophysiological signals in larger systems is getting wider due to the fact that such modalities facilitate the daily tasks of users. However, the high cost of monitoring systems (e.g., oscilloscopes) limits the testing and analysis of the acquired signal from such noisy systems (i. e., electrophysiological devices). The proposed system is a part of a larger proj ect that deals with the identification of sign language movements through the use of Muscle-computer Interfaces. On one hand, the user is able to display the signal acquired by the electrodes on an LCD screen. On the other hand, the system helps a mute patient to issue a special message to his surrounding environment that does not know the sign language to ask for help. The second feature is not unique, as it can be replaced by any other application based on the user's needs. The system was tested on several persons, and the results are promising. © 2022 IEEE.

关键词： Liquid crystal displays

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DESIGNING EV CHARGING STATIONS DEPLOYMENT THROUGH HOLISTIC SIMULATIONS: THE SANEVEC PROJECT 5

DESIGNING EV CHARGING STATIONS DEPLOYMENT THROUGH HOLISTIC S...

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5th Conference on Code of Practice for Electric Vehicle Charging Equipment Installation: Charging Ahead, EVI 2023

作者： Guisado-Lizar, José-Luis Jiménez-Moreno, Gabriel Diaz-Del-Rio, Fernando Sevillano-Ramos, J.L. Cagigas-Muñiz, Daniel Morón-Fernández, María José Cascado-Caballero, Daniel Jiménez-Morales, Francisco Cárdenas-Montes, Miguel Wainer, Gabriel A. Ragel-Díaz-Jara, David Rodríguez-Leal, J. Cerezuela-Escudero, E. Montes-Sánchez, J.M. Casanueva-Morato, Daniel Ayuso-Martínez, Alvaro Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain Department of Computer Architecture and Technology Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain Department of Condensed Matter Physics Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain Department of Basic Research Centro de Investigaciones Energéticas Medioambientales y Tecnológicas Madrid Spain Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9781839539848

To determine the optimal location of the large number of urban public charging stations that will have to be deployed in the following years to transition to electric vehicles (EVs) is a challenging task, as it is intimately interconnected with the complexity of urban traffic and electric grid operation. The city is a complex system in which emerging patterns such as traffic jams, and instabilities or even outages in the electric grid are difficult to predict. The layout of charging stations can alter those patterns in traffic and electric grid operation, with consequences to mobility, air quality, and electric system. The present article presents the concept under the research project SANEVEC. It will build a framework for the optimal determination of the location of EV charging stations in a city based on simulation, which will be able to reproduce the feedback effects between all the variables involved: locations of the stations, traffic patterns, characteristics, and operation of the electricity grid, charging fees and air quality in the city. It will employ artificial intelligence methods to find high-quality station location solutions from the simulations and to predict the air quality in the city. © The Institution of Engineering & technology 2023.

关键词： Air quality

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THE EFFECT OF LIMITED MOBILITY ON THE EXPERIENCED SEGREGATION OF FOREIGN-BORN MINORITIES

arXiv

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arXiv 2024年

作者： Liao, Yuan Gil, Jorge Yeh, Sonia Pereira, Rafael H.M. Alessandretti, Laura Department of Space Earth and Environment Chalmers University of Technology Gothenburg Sweden Department of Architecture and Civil Engineering Chalmers University of Technology Gothenburg Sweden - Brazil Data Science Division Brazil Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

Segregation is a key challenge in promoting more diverse and inclusive cities. Research based on large-scale mobility data indicates that segregation between majority and minority groups persists in daily activities beyond residential areas, like visiting shops and restaurants. Aspects including lifestyle differences, homophily, and mobility constraints have been proposed as drivers of this phenomenon, but their contributions remain poorly quantified. Here, we elucidate how different mechanisms influence segregation outside home, looking at the distinctive segregation experienced by native and foreign-born individuals. Our study is based on the movement of ∼320,000 individual smartphone devices collected in Sweden, where immigration creates profound divides. We find that while day-to-day activities lead to mixing for native-born individuals, foreign-born individuals remain segregated in their out-of-home activities. Using counterfactual simulations, we show that this heterogeneous effect of mobility on experienced segregation results mainly from two mechanisms: homophily and limited travel, i.e. foreign-born individuals (i) tend to visit destinations visited by similar individuals, and (ii) have limited mobility ranges. We show that homophily, as represented by destination preference, plays a minor role, while limited mobility, associated with reduced transport access, limits opportunities for foreign-born minorities to diversify their encounters. Our findings suggest that enhancing transport accessibility in foreign-born concentrated areas could reduce social segregation. © 2024, CC BY-SA.

关键词： Smartphones

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The Seeker’s Dilemma: Realistic Formulation and Benchmarking for Hardware Trojan Detection

The Seeker’s Dilemma: Realistic Formulation and Benchmarkin...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Amin Sarihi Ahmad Patooghy Abdel-Hameed A. Badawy Peter Jamieson Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM MIPS Architecture and Performance Austin TX Department of Computer Systems Technology North Carolina A&T State University Greensboro NC Department of Electrical and Computer Engineering Miami University Oxford OH

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

This work focuses on advancing the security field in the hardware design space by formally defining the problem of Hardware Trojan (HT) detection. The goal is to model HT detection more closely to the real world, i.e., describing the problem as "The Seeker’s Dilemma" (an extension of Hide&Seek on a graph), where a detecting agent is unaware of whether HTs infect circuits or not. Using this problem formulation, we create a benchmark that consists of a mixture of HT-free and HT-infected restructured circuits while preserving their original functionalities. The restructured circuits are randomly infected by HTs, causing a situation where the defender is uncertain if a circuit is infected. Our innovative dataset will help the community better judge the detection quality of different methods by comparing their success rates in circuit classification. We use our benchmark to evaluate three state-of-the-art HT detection tools to show baseline results for this approach. We use Principal Component Analysis to assess the strength of our benchmark, where we observe that some restructured HT-infected circuits are mapped closely to HT-free circuits, leading to significant label misclassification by detectors.

关键词： Accuracy Circuits Detectors Benchmark testing Hardware Quality assessment Trojan horses Security Integrated circuit modeling Principal component analysis

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Application of Digital Twin in Smart Battery Electric Vehicle: Industry 4.0

Application of Digital Twin in Smart Battery Electric Vehicl...

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2022 International Conference on IT and Industrial Technologies, ICIT 2022

作者： Shoukat, Muhammad Usman Yan, Lirong Du, Changqing Mair Raza, Muhammad U Adeel, Muhammad Khan, Tauqeer Wuhan University of Technology School of Automotive Engineering Wuhan430070 China Shenzhen University College of CS and SE Shenzhen518060 China Punjab Tianjin Univ. of Technology Department of Architecture Lahore53720 Pakistan Islamia College University Peshawar Department of Computer Science Peshawar25120 Pakistan

ISBN: (数字)9781665489454

ISBN: (纸本)9781665489454

It is believed that using electric vehicles (EVs) for transportation is essential for addressing environmental and sustainable development challenges. Current ecofriendly concerns, such as the fast depletion of fossil fuels, rising air pollution, rising energy demand, global warming, and climate change, have made it possible to electrify the transportation industry. EVs can address all of the aforementioned issues. For electric vehicles (EVs), and particularly those powered by lithium-ion (Li-ion) batteries, portable power supplies have become indispensable. Although Li- ion batteries have been the focus of EV research for a long time, issues like battery aging and safety have yet to be fully understood. According to our current understanding of smart technology, we have the ability to use the digital twin (DT) to overcome a problem that has been holding back battery development, as well as the preliminary DT applications in complicated systems such as industry 4.0. This research focuses on the characteristics of batteries and how they relate to their modeling, state estimation, remaining usable life prediction, safety, and control. We put together an analysis of some of the most current achievements in battery prognostics and health monitoring. Finally, we provide prospects for the development of DTs in the field of EV batteries. © 2022 IEEE.

关键词： Lithium-ion batteries

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Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly

arXiv

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arXiv 2024年

作者： Kyaw, Alexander Htet Jeon, Se Hwan Smith, Miana Gershenfeld, Neil Department of Architecture The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Center for Bits and Atoms Massachusetts Institute of Technology CambridgeMA02139 United States

We present a system that transforms speech into physical objects by combining 3D generative Artificial Intelligence (AI) with robotic assembly. The system leverages natural language input to make design and manufacturing more accessible, enabling individuals without expertise in 3D modeling or robotic programming to create physical objects. We propose utilizing discrete robotic assembly of lattice-based voxel components to address the challenges of using generative AI outputs in physical production—such as design variability, fabrication speed, structural integrity, and material waste. The system interprets speech to generate 3D objects, discretizes them into voxel components, computes an optimized assembly sequence, and generates a robotic toolpath. The results are demonstrated through the assembly of various objects, ranging from chairs to shelves, which are prompted via speech and realized within 5 minutes using a 6-axis robotic arm. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic assembly

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Unsupervised Fouling Reconstruction in the Pipe Bend

Unsupervised Fouling Reconstruction in the Pipe Bend

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IEEE Symposium (IUS) Ultrasonics

作者： Denys Iablonskyi Carlos-Omar Rasgado-Moreno Madis Ratassepp Arto Klami Edward Hæggström Ari Salmi Department of Computer Science Department of Physics University of Helsinki Helsinki Finland Department of Civil Engineering and Architecture Tallinn University of Technology Tallinn Estonia Department of Computer Science University of Helsinki Helsinki Finland Department of Physics University of Helsinki Helsinki Finland

Guided wave tomography allows the investigation of extended areas with a limited number of measurements, typically using transducer arrays. Most imaging methods rely heavily on the material properties such as dispersion curves, that in the case of fouling deposition are not always known. In the case of complex shaped structures, geometrical anisotropy can bring additional complexity. Here we present an unsupervised machine learning approach based on the Gaussian process to detect and characterize the fouling in a pipe bend that relies only on the difference between clean/healthy and fouled/damaged measured signals.

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SOCIO-SPATIAL SEGREGATION AND HUMAN MOBILITY: A REVIEW OF EMPIRICAL EVIDENCE

arXiv

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arXiv 2024年

Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation. © 2024, CC BY-SA.

关键词： Leisure

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Enhancing OSN Security: Detecting Email Hijacking and DNS Spoofing Using Energy Consumption and Opcode Sequence Analysis

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第2期49卷 333-354页

作者： Rawat, Romil Borana, Kamal Gupta, Shweta Ingle, Mandakini Dibouliya, Ashish Bhardwaj, Purvee Rawat, Anjali LabGeoInf Research LABoratory in GEOmatics and INFormation systems Rome Italy Department of CSE SVIIT SVVV Indore India Indore India Data Architecture Webster Bank United States Rabindranath Tagore University MP Bhopal India Department of Computer and Communication Technology University of Extremadura Spain

The rapid increase in automation within Online Social Networks (OSNs) has led to a surge in cyber threats, notably Email Hijacking and DNS (Domain Name System) Spoofing, which leverage malicious scripts to manipulate traffic, steal credentials, and evade detection. Traditional security mechanisms fail to effectively identify such automation-based attacks, necessitating an advanced detection framework. Objective & Purpose-This study introduces the Automated Social Network Attack Detection Model (ASNADM), which combines Energy Consumption Footprint (EComp-FP) Analysis and Automated Software Opcode Sequence Analysis (ASOSA-OSM- opcode sequence mining) for high-precision OSN security. EComp-FP detects deviations in power consumption linked to malicious automation tools, while ASOSA-OSM analyzes opcode sequences to differentiate between benign and attack behaviors. The Self-Adaptive Fuzzy Pattern Matching Clustering (SAFPMC) Algorithm enhances classification accuracy, reducing false alarms and improving real-time threat detection. Methodology and Dataset-The model was rigorously evaluated using the SPEMC-15K-E (Spam Email Classification dataset in English) dataset (15,000 samples: 7,500 benign, 7,500 malicious). EComp-FP achieved 99.87% accuracy with a 1.4W power deviation, while ASOSA-OSM attained 99.81% accuracy, detecting automation tools with an Opcode Frequency Variance (OFV) of 8.7 in malicious samples versus 3.5 in benign ones. The hybrid EComp (Energy Consumption) + OSA (Opcode Sequence Analysis) model outperformed both standalone methods, achieving 99.93% accuracy, 99.91% F1-score, a false positive rate of just 0.07%, and a false negative rate of 0.05%. Among classifiers, the Self-Adaptive Soft Fuzzy C-Means (SSFCM) Hybrid model achieved the highest performance, with 99.93% accuracy, 99.85% precision, 99.9% recall, and the lowest misclassification rate of 0.05%, surpassing Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Mach

关键词： Tweets

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