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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Shafee, Ahmed Awaad, Tasneem A. Moro, Ahmed Adams State University Department of Engineering and Computer Science AlamosaCO United States Ain Shams University Faculty of Engineering Department of Computer and Systems Engineering Cairo Egypt Siemens Eda Cairo Egypt Siemens Digital Industries Software Integrated Electrical Systems Segment Cairo Egypt

ISBN: (纸本)9798350364910

Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic Control Units (ECUs) have evolved to become the central control units for critical car functions such as engines and brakes, experiencing rapid technological advancements. However, this swift progression in ECU technology has also made them prime targets for cyber attacks. This vulnerability has spurred researchers to focus on securing ECUs. Numerous studies have proposed intrusion detection systems (IDS) to protect against attacks on ECUs in vehicles. Yet, these IDSs are not impenetrable;attackers can exploit them by launching evasion attacks, which can trigger numerous false positive alarms. Such false alarms can be disruptive and potentially hazardous for drivers. Additionally, attackers can evade IDSs from detecting malicious data that can cause harm to the vehicle. Accordingly, in this paper, we propose a novel training framework to train a robust in-vehicle IDS that can encounter evasion attacks. Our methodology is based on implementing two rounds of mimic learning technique for training Random Forest (RF) based IDS. RF has been chosen to incorporate the randomness of the RF architecture to enhance the robustness of the model. Additionally, in each round of the two rounds of the mimic learning technique, an RF model with a different architecture is chosen to improve the resilience of the model against evasion attacks without affecting its accuracy. Our Experimental results have shown the effectiveness of our framework against evasion attacks. © 2024 IEEE.

关键词： Robustness (control systems)

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Powering Wire-Mesh Circuits through MEMS Fiber-Grippers 5

Powering Wire-Mesh Circuits through MEMS Fiber-Grippers

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5th IEEE International Conference on Flexible and Printable Sensors and systems, FLEPS 2023

作者： Song, Nathan Wei, Danming Harnett, Cindy K. University of California Berkeley Department of Physics United States University of Louisville Department of Electrical and Computer Engineering United States

ISBN: (纸本)9781665457330

Integrating MEMS devices with electronic textiles (E-Textiles) and fabrics requires an understanding of how these devices interact with circuit components in different operating conditions. In this paper, we characterize the electronic properties of MEMS 'grippers' in contact with conductive wire. We discuss general guidelines for optimizing the design of said 'grippers' and potential MEMS-based circuits. We then demonstrate how these grippers can act as non-rigid circuit components that effectively transfer power to devices such as LEDs. Analysis shows that our 'grippers' are suitable conductors (under 150 Ohms) under standard operating temperatures (25-100 deg. C) with potential for use as sensors for current overflow or temperature. Methods such as parylene deposition and silver epoxy 'glue' to stabilize MEMS performance are briefly discussed and explored. © 2023 IEEE.

关键词： Electronic properties

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Guided Mode Evolution and Ionization Injection in Meter-Scale Multi-GeV Laser Wakefield Accelerators

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Physical Review Letters 2024年第4期133卷 045002-045002页

作者： J. E. Shrock E. Rockafellow B. Miao M. Le R. C. Hollinger S. Wang A. J. Gonsalves A. Picksley J. J. Rocca H. M. Milchberg Institute for Research in Electronics and Applied Physics and Department of Physics Department of Electrical and Computer Engineering Berkeley California 94720 USA Department of Physics

We show that multi-GeV laser wakefield electron accelerators in meter-scale, low density hydrodynamic plasma waveguides operate in a new nonlinear propagation regime dominated by sustained beating of lowest order modes of the ponderomotively modified channel; this occurs whether or not the injected pulse is linearly matched to the guide. For a continuously doped gas jet, this emergent mode beating effect leads to axially modulated enhancement of ionization injection and a multi-GeV energy spectrum of multiple quasimonoenergetic peaks; the same process in a locally doped jet produces single multi-GeV peaks with <10% energy spread. A three-stage model of drive laser pulse evolution and ionization injection characterizes the beating effect and explains our experimental results.

关键词： Electron beams & optics Laser driven electron acceleration Laser wakefield acceleration Laser-plasma interactions Light-matter interaction Nonlinear optics Plasma acceleration & new acceleration techniques Plasma optics

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Dynamic and Private Recommendation System

Dynamic and Private Recommendation System

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Lo, Gee Yen Tan, Terence Peng Lian Curtin University Malaysia Computer Systems and Networking Miri Malaysia Curtin University Malaysia Department of Electrical & Computer Engineering Miri Malaysia

ISBN: (纸本)9798350310689

The amount of data that we retrieve and use every day is increasing rapidly. As data naturally exist in different data domains, the number of data domains that we retrieve and use every day has also increased as a result. As different recommendation systems are needed for different data domains, more and more recommendation systems will need to be built. Furthermore, users will lose their privacy as many recommendation systems collect information about their users. This paper aims to solve the aforementioned issues by creating a recommendation system that will work with any data domain and upholds the privacy of its users. The Dynamic Implementation Of Privacy Based Recommendation System Application is created in this paper. The objective of this thesis is achieved as the Dynamic Implementation Of Privacy Based Recommendation System Application developed in this paper upholds the privacy of its users as it only stores users' data in their device's local database and the application has the ability to adapt to different data domains. © 2023 IEEE.

关键词： Recommender systems

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Clustering-based heuristic approaches for the Euclidean Leaf-constrained Minimum Spanning Tree Problem 20

Clustering-based heuristic approaches for the Euclidean Leaf...

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20th IEEE India Council International Conference, INDICON 2023

作者： Neha Agarwal, Kajal Yadav, Priyanka Lakshmi, C. Vasantha Vuppuluri, Prem Prakash Patvardhan, C. Agra Department of Physics & Computer Science Agra282005 India Agra Department of Electrical Engineering Agra282005 India

ISBN: (纸本)9798350305593

Given a graph G (V, E) having vertices as points in the two-dimensional Euclidean space and edge-weights representing the Euclidean distances between those vertices, the Euclidean Leaf-Constrained Minimum Spanning Tree (LCMST) problem searches for a minimum cost spanning tree that has at least a specified number of leaf vertices L, 2 ≤ L ≤ |V| - 1. The problem is NP-hard and finds applications in facilities location, circuit, network design, and other domains also. The Euclidean LCMST (e-LCMST) problem arises when the domain is restricted to the Euclidean space. A well-known greedy spanning tree construction-based heuristic for the problem takes O(|V|4) time to compute low-cost LCMST but is restricted in its applicability to only small-sized instances. A recent less greedy heuristic for the e-LCMST problem obtains superior performance in O(|V|3) time. This paper proposes four clustering-based heuristics that obtain good solutions for the e-LCMST problem in O(|V|3) time. In the course of several experiments on standard and large complete graph instances, the proposed heuristics are shown to outperform the existing heuristics for the problem. © 2023 IEEE.

关键词： clustering Euclidean heuristic Leaf-constrained NP-hard spanning tree

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Measurement of the ratio of the scalar polarizability to the vector polarizability for the 6sS1/22→7sS1/22 transition in cesium

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Physical Review A 2024年第6期109卷 062809-062809页

作者： Jonah A. Quirk Carol E. Tanner D. S. Elliott Department of Physics and Astronomy Purdue Quantum Science and Engineering Institute School of Electrical and Computer Engineering

We report measurements of the ratio of the scalar polarizability α to the vector polarizability β for the 6sS1/22→7sS1/22 transition in atomic cesium. These measurements are motivated by a discrepancy between the values of the vector transition polarizability as determined using two separate methods. In the present measurement, we use a two-pathway, coherent-control technique in which we observe the interference between a two-photon interaction driven by infrared light at 1079 nm and a linear Stark-induced interaction driven by the mutually coherent second harmonic of this infrared beam at 540 nm. The result of our measurements is α/β=−9.902(9), in good agreement with the previous determination of this ratio. This measurement, critical to the study of atomic parity violation in cesium, does not reduce the discrepancy between the two methods for the determination of the vector polarizability β for this transition.

关键词： Coherent control Electronic structure of atoms & molecules Stark effect

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OUT-OF-DOMAIN UNLABELED DATA IMPROVES GENERALIZATION 12

OUT-OF-DOMAIN UNLABELED DATA IMPROVES GENERALIZATION

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12th International Conference on Learning Representations, ICLR 2024

作者： Saberi, Amir Hossein Movasaghinia, Mohammad Hosein Najafi, Amir Motahari, Seyed Abolfazl Heidari, Alireza Khalaj, Babak H. Department of Electrical Engineering Department of Computer Engineering Sharif Center for Information Systems and Data Science Iran Sharif Institute for Convergence Science & Technology Sharif University of Technology Tehran Iran

We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially robust or ii) non-robust loss functions have been considered. Notably, we allow the unlabeled samples to deviate slightly (in total variation sense) from the in-domain distribution. The core idea behind our framework is to combine Distributionally Robust Optimization (DRO) with self-supervised training. As a result, we also leverage efficient polynomial-time algorithms for the training stage. From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in Rd, where in addition to the m independent and labeled samples from the true distribution, a set of n (usually with n m) out of domain and unlabeled samples are given as well. Using only the labeled data, it is known that the generalization error can be bounded by ∝ (d/m)1/2. However, using our method on both isotropic and non-isotropic Gaussian mixture models, one can derive a new set of analytically explicit and non-asymptotic bounds which show substantial improvement on the generalization error compared to ERM. Our results underscore two significant insights: 1) out-of-domain samples, even when unlabeled, can be harnessed to narrow the generalization gap, provided that the true data distribution adheres to a form of the "cluster assumption", and 2) the semi-supervised learning paradigm can be regarded as a special case of our framework when there are no distributional shifts. We validate our claims through experiments conducted on a variety of synthetic and real-world datasets. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Polynomial approximation

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α - Mutual Information: A Tunable Privacy Measure for Privacy Protection in Data Sharing 22

α - Mutual Information: A Tunable Privacy Measure for Priva...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Asl, MirHamed Jafarzadeh Shateri, Mohammadhadi Labeau, Fabrice McGill University Department of Electrical and Computer Engineering QC Canada Ecole de Technologie Supérieure Department of Systems Engineering QC Canada

ISBN: (纸本)9798350345346

This paper adopts Arimoto's α -Mutual Information as a tunable privacy measure, in a privacy-preserving data release setting that aims to prevent disclosing private data to adversaries. By fine-tuning the privacy metric, we demonstrate that our approach yields superior models that effectively thwart attackers across various performance dimensions. We formulate a general distortion-based mechanism that manipulates the original data to offer privacy protection. The distortion metrics are determined according to the data structure of a specific experiment. We confront the problem expressed in the formulation by employing a general adversarial deep learning framework that consists of a releaser and an adversary, trained with opposite goals. This study conducts empirical experiments on images and time-series data to verify the functionality of α -Mutual Information. We evaluate the privacy-utility trade-off of customized models and compare them to mutual information as the baseline measure. Finally, we analyze the consequence of an attacker's access to side information about private data and witness that adapting the privacy measure results in a more refined model than the state-of-the-art in terms of resiliency against side information. © 2023 IEEE.

关键词： adversarial learning Arimoto's -mutual information data sharing privacy-utility trade-off Tunable privacy measure

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Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

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computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Javad Hassannataj Joloudari Senthil Kumar Jagatheesaperumal Kandala N.V.P.SRajesh Silvia Gaftandzhieva Sadiq Hussain Rahimullah Rabih Najibullah Haqjoo Mobeen Nazar Hamed Vahdat-Nejad Rositsa Doneva School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)*** functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic *** these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle *** paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection systems in the Internet of Vehicles(IDS-IoV)based on anomaly ***-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV *** examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of vehicles intrusion detection system

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Robust Wireless Power Transfer by Self-Oscillating Controlled Inverter and Circular Pads 3

Robust Wireless Power Transfer by Self-Oscillating Controlle...

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3rd International Conference on electrical Machines and Drives, ICEMD 2023

作者： Eikani, Alireza Amirkhani, Mohammad Jafari, Hossein Vaez-Zadeh, Sadegh Mirsalim, Mojtaba Khaburi, Davood Arab University of Tehran Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering Tehran Iran Department of Electrical Engineering Tehran Iran Iran University of Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350393743

Inductive wireless power transfer (WPT) faces significant challenges in achieving high efficiency and stable output power for charging mobile devices like EVs. Using circular pads (CP) as the transmitter (Tx) and receiver (Rx) and a self-oscillating controlled inverter, this paper demonstrates a robust WPT system that maintains high constant transfer efficiency and output power in a defined region regardless of coupling coefficient variations without communication between Tx and Rx or control circuits for the receiver side. In contrast to couplers with non-identical Tx and Rx pads and multiple decoupled coils in the Rx pad, the compact CP-CP magnetic coupler with a single identical coil on the Tx and Rx is more robust at a wider transfer distance (air gap) and lateral and rotational misalignment between the Tx and Rx. The proposed 1-kW robust WPT system with up to a 20 cm air gap between the Tx and Rx and up to 12 cm lateral misalignment in all directions at a 15 cm air gap and all degrees of rotational misalignment can tolerate coupling variations and achieve constant output power with 93% constant transfer efficiency. © 2023 IEEE.

关键词： Energy transfer

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