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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Jawhar Ghommam Maarouf Saad Ibrahim Al Naimi Mohammed H. Rahman Faisal Mnif Dept. of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Dept. of Electrical Engineering Ecole de Technologie Superieure Montreal Canada Mechanical Engineering Department University of Wisconsin-Milwaukee Wisconsin USA

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Automatic towing operations for non-powered ships at sea constitute a fundamental step toward automating the towing mission at harbor. This paper is devoted to solve one of the sea maneuvering problems that uses an automatic leader tug. The proposed maneuver involves implementing of robust escorting system (also called towing system) of partially non-powered towed cargo ship for an automatic berthing. To enhance the maneuverability of the towed ship, its rudder system is made active throughout the mission. To address the problem of rejecting the time-varying disturbances induced by wind, current and system parametric uncertainties, an adaptive finite-time sliding mode disturbance observer is first proposed. Then, a composite controller with flexible prescribed performance is developed based on a novel unified towing tracking error, which can ensure the geometric state variables and its corresponding velocity meet the prescribed performance constraints at the same time. Stability analysis is conducted to prove finite-time convergence of the towing tracking errors. Finally, numerical simulations validate the proposed approach.

关键词： Performance evaluation Feedback loop Uncertainty Numerical simulation Disturbance observers Stability analysis Velocity measurement Marine vehicles Time-varying systems System analysis and design

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Internet of Medical Things for Noninvasive and Noncontact Dehydration Monitoring Away From the Hospital: State of the Art, Challenges, and Prospects

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Siyoucef, Soumia Al-Aslani, Rose Adnane, Mourad Mahboob Ur Rahman, Muhammad Laleg-Kirati, Taous-Meriem Al-Naffouri, Tareq Y. Thuwal23955 Saudi Arabia LDCCP Laboratory Ecole Nationale Polytechnique Electronics Engineering Department Algiers16200 Algeria College of Engineering Electrical Engineering Department Riyadh11671 Saudi Arabia National Institute for Research in Digital Science and Technology Paris-Saclay Gif-sur-Yvette75012 France

The dehydration occurs when the body loses more water than it takes in. The mild dehydration can lead to fatigue, cognitive impairments, and physical complications, while severe dehydration can cause life-threatening conditions, such as heat stroke, kidney damage, and hypovolemic shock. Traditional biochemistry-based clinical gold standard methods are expensive, time-consuming, and invasive. Thus, there is a pressing need to design novel noninvasive methods that could do in situ, early and accurate detection of dehydration, which will in turn allow timely intervention. This article presents a methodological review of the literature on a range of innovative Internet of Medical Things (IoMT)-based techniques for dehydration monitoring. We begin by briefly describing the pathophysiology of the dehydration problem, its clinical significance, and current clinical gold-standard methods for assessing hydration level. Subsequently, we critically examine a number of noninvasive and noncontact hydration assessment studies. We also discuss multimodal sensing methods and assess the impact of dehydration among specific population groups (e.g., elderly, infants, and athletes) and on different organs. We also provide a list of existing public and private datasets which make the backbone of machine learning (ML)-driven research on dehydration monitoring. Finally, we provide our opinion statement on the challenges and future prospects of noninvasive and noncontact hydration monitoring. © 1963-2012 IEEE.

关键词： Noninvasive medical procedures

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Electromagnetic Modeling of Transformers in EMT-type Software by a Circuit-Based Method

Electromagnetic Modeling of Transformers in EMT-type Softwar...

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IEEE General Meeting Power& Energy Society

作者： Sadegh Rahimi Pordanjani Mohammed Naïdjate Nicolas Bracikowski Mircea Fratila Jean Mahseredjian Afshin Rezaei-Zare Polytechnique Montreal Nantes University EDF-R&D ERMES Department Department of Electrical Engineering and Computer Science York University

This work proposes a fully circuit-based method for modelling electrical transformers. This method not only offers the advantages of circuit-based methods and can be implemented in electromagnetic transient (EMT) type software, but it can also provide a detailed representation of transformers, comparable to the finite element method (FEM). The proposed method enables a detailed geometrical modelling, as well as representation of magnetic flux paths and consideration of iron core saturation. It can be implemented in EMT-type software to see the effect of power networks on transformers. In addition, the proposed method can represent internal faults in transformers. The problem is constrained to a 2-D domain, which is often used in FEMs to represent the magnetic behavior of power equipment. Finite element analysis based on ANSYS Maxwell is used to verify the proposed method.

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DRL-based Dynamic Channel Access and SCLAR Maximization for Networks under Jamming

DRL-based Dynamic Channel Access and SCLAR Maximization for ...

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IEEE Conference on Wireless Communications and Networking

作者： Abdul Basit Muddasir Rahim Georges Kaddoum Tri Nhu Do Nadir Adam Department of Electrical Engineering Ecole de Technologie Supérieure (ETS) Université du Québec Montreal QC Canada Artificial Intelligence & Cyber Systems Research Center Lebanese American University Department of Electrical Engineering Polytechnique Montreal QC Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

This paper investigates a deep reinforcement learning (DRL)-based approach for managing channel access in wireless networks. Specifically, we consider a scenario in which an intelligent user device (iUD) shares a time-varying uplink wireless channel with several fixed transmission schedule user devices (fUDs) and an unknown-schedule malicious jammer. The iUD aims to harmoniously coexist with the fUDs, avoid the jammer, and adaptively learn an optimal channel access strategy in the face of dynamic channel conditions, to maximize the network's sum cross-layer achievable rate (SCLAR). Through extensive simulations, we demonstrate that when we appropriately define the state space, action space, and rewards within the DRL frame-work, the iUD can effectively coexist with other UDs and optimize the network's SCLAR. We show that the proposed algorithm outperforms the tabular Q-learning and a fully connected deep neural network approach.

关键词： Training Schedules Q-learning Wireless networks Simulation Heuristic algorithms Artificial neural networks

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The Use of Anomaly Detection for the Detection of Different Types of DDoS Attacks in Cloud Environment

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Journal of Hardware and Systems Security 2021年第3期5卷 208-222页

作者： Abbasi, Hossein Ezzati-Jivan, Naser Bellaiche, Martine Talhi, Chamseddine Dagenais, Michel R. Department of Computer and Software Engineering Ecole Polytechnique Montreal Canada

In most scenarios, distributed denial of service (DDoS) attacks can be categorized into three distinct groups: (1) attacks targeting and consuming bandwidth, (2) attacks targeting selected applications and (3) attacks targeting connection-layer exhaustion. This study discusses in depth our proposal of a unique, inclusive model that has the ability to precisely detect and categorize DDoS attacks with the help of comparing normal traffic and resource usage against the traffic and resource utilization reported during potential attack situations. Since the features from all three attack categories are dependent upon each other, we based the metrics of our detection model on data collected from all three types during each attack. Additionally, we utilized the cumulative sum algorithm for the sake of change detection in traffic and resource usage patterns.

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Instantaneous Frequency based estimation of time varying Directions of Arrival

Instantaneous Frequency based estimation of time varying Dir...

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International Symposium on Signals, Circuits and Systems (ISSCS)

作者： Chafaa Lyes Abbassen Adel Belouchrani Karim Abed-Meraim Electrical Engineering Department LDCCP lab. Ecole Nationale Polytechnique Algiers Algeria University of Orléans PRISME Lab. France

This paper addresses the problem of estimating time-varying directions of arrival. It demonstrates how the concept of instantaneous frequency can be employed for this purpose. The proposed approach can localize more sources than the number of available sensors. It also theoretically allows the estimation of any angular time variation. Herein, we consider linear and hyperbolic time variations, which, in practice, take into account velocity and acceleration, respectively. Numerical experiments are conducted to validate the effectiveness of the proposed method.

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Revealing Brain and Spinal Cord Functional Networks by Block-Structured Graph Laplacian Mixture Models

Revealing Brain and Spinal Cord Functional Networks by Block...

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IEEE International Symposium on Biomedical Imaging

作者： Ilaria Ricchi William Cappelletti Ekansh Sareen Pascal Frossard Dimitri Van de Ville MIPLAB Neuro-X Institute Ecole Polytechnique Fédérale de Lausanne (EPFL) Geneva Switzerland Department of Radiology and Medical Informatics University of Geneva Geneva Switzerland LTS4 Electrical Engineering Institute Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The Graph Laplacian Mixture Model (GLMM) allows to infer multiple underlying graph structures from multivariate time series data. Given its effectiveness in identifying brain states from brain activity measured by functional magnetic resonance imaging (fMRI), we adapt GLMM for block-structured datasets, where data is organized into communities with strong intra-community interactions and relatively weak inter-community connections. This modification of the GLMM enables to capture the intricate structure inherent to block-structured datasets, such as fMRI data that simultaneously covers brain and cervical spinal cord, which both come with different properties. By integrating the prior knowledge on community structure into the GLMM framework, we proposed an approach that more accurately reflects the hierarchical organization and interaction patterns present in these complex networks. This enhancement offers a powerful tool for studying networked systems with distinct community structures, improving our ability to interpret patterns and dynamics in neural data with natural sub-structural organization.

关键词： Knowledge engineering Brain Spinal cord Laplace equations Time series analysis Mixture models Organizations Complex networks Functional magnetic resonance imaging Signal processing

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Statistical Collusion by Collectives on Learning Platforms

arXiv

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arXiv 2025年

作者： Gauthier, Etienne Bach, Francis Jordan, Michael I. Inria Ecole Normale Supérieure PSL Research University France Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data. Moreover they need to develop implementable coordination algorithms based on quantities that can be inferred from observed data. We develop a framework that provides a theoretical and algorithmic treatment of these issues and present experimental results in a product evaluation domain. © 2025, CC BY.

关键词： Contrastive Learning

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Fundamental Limits to Enhancing the Local Density of States Through Nanostructuring

Fundamental Limits to Enhancing the Local Density of States ...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Pengning Chao Rodrick Kuate Defo Sean Molesky Alejandro Rodriguez Department of Electrical and Computer Engineering Princeton University Princeton NJ USA Department of Engineering Physics Polytechnique Montréal Montréal Québec Canada

We analyze bounds to the electromagnetic local density of states achievable via material structuring, deriving novel results including finite bound saturation in the infinite susceptibility limit and sub-linear bandwi... 详细信息

ISBN: (纸本)9781957171258

关键词： Electric fields Multiple scattering Photonic devices Polarization Scattering theory Susceptibility

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Sensitivity analysis for linear changes of the constraint matrix of a linear program

arXiv

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arXiv 2024年

作者： Miftari, Bardhyl Louveaux, Quentin Ernst, Damien Derval, Guillaume Department of Electrical Engineering and Computer Science University of Liège Liège Belgium LTCI Telecom Paris Institut Polytechnique de Paris Paris France

Understanding the variation of the optimal value with respect to change in the data is an old problem of mathematical optimisation. This paper focuses on the linear problem f(λ) = min ctx such that (A+λD′)x ≤ b, where λ is an unknown parameter that varies within an interval and D′ is a matrix modifying the coefficients of the constraint matrix A. This problem is used to analyse the impact of multiple affine changes in the constraint matrix on the objective function. The function f(λ) can have an erratic behaviour and computing it for a large number of points is computationally heavy while not providing any guarantees in between the computed points. As a new approach to the problem, we derive several bounding methods that provide guarantees on the objective function’s behaviour. Those guarantees can be exploited to avoid recomputing the problem for numerous λ. The bounding methods are based on approaches from robust optimisation or Lagrangian relaxations. For each approach, we derive three methods of increasing complexity and precision, one that provides constant bounds, one that provides λ-dependant bounds and one that provides envelope bounds. We assess each bounding method in terms of precision, availability and timing. We show that for a large number of problems, on those metrics, the bound approach is faster than the naive sampling approach considered with 100 points while still providing a good precision and stronger guarantees on a large dataset of problems. We also introduce an iterative algorithm that uses these bounds to compute an approximation of the original function within a given error threshold and discuss its performances. © 2024, CC BY-SA.

关键词： Sensitivity analysis

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