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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Masoud Jafaripour Vivian Mushahwar Mahdi Tavakoli Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering and Department of Biomedical Engineering University of Alberta Department of Medicine Division of Physical Medicine and Rehabilitation University of Alberta

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper introduces a novel hybrid torque allocation method for improving wearability and mobility in integrated functional electrical stimulation (FES) of the quadriceps muscles and powered exoskeleton systems. Our proposed approach leverages a hierarchical closed-loop controller for knee joint position tracking while addressing limitations of powered exoskeletons and FES systems by reducing power consumption and battery size and by mitigating FES-induced muscle fatigue, respectively. The core component is a model-free optimization algorithm that dynamically distributes torque between FES and the exoskeleton by considering tracking error, effort, and the prediction of muscle fatigue in the cost function, computing allocation gain in an online manner. The online optimization approach interactively changes the optimal allocation gain by taking into account the instantaneous value of error and effort and also penalizing FES-induced fatigue, a common challenge in long-duration experiments. The results demonstrate that this dynamic allocation significantly improves system wearability by reducing power consumption without increasing muscle fatigue during the extension phase of walking. This hybrid control approach contributes to improving exoskeleton wearability and rehabilitation outcomes for individuals with SCI and mobility impairments, enhancing assistive technology and quality of life.

关键词： Knee Torque Power demand Exoskeletons Wearable robots Muscles Fatigue Iron Trajectory Resource management

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Long-term radiofrequency electromagnetic fields exposure attenuates cognitive dysfunction in 5×FAD mice by regulating microglial function

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Neural Regeneration Research 2023年第11期18卷 2497-2503页

作者： Yeonghoon Son Hye-Jin Park Ye Ji Jeong Hyung-Do Choi Nam Kim Hae-June Lee Division of Radiation Biomedical Research Korea Institute of Radiological&Medical SciencesSeoulKorea Department of EMF Research Team Radio and Broadcasting Technology LaboratoryElectronics and Telecommunications Research InstituteDaejonKorea School of Electrical and Computer Engineering Chungbuk National UniversityCheongjuKorea

We have previously found that long-term effects of exposure to radiofrequency electromagnetic fields in 5xFAD mice with severe late-stage Alzheimer’s disease reduced both amyloid-βdeposition and glial activation,including *** examine whether this therapeutic effect is due to the regulation of activated microglia,we analyzed mic roglial gene expression profiles and the existence of microglia in the brain in this study.5xFAD mice at the age of 1.5 months were assigned to sham-and radiofrequency electromagnetic fields-exposed groups and then animals were exposed to 1950 MHz radiofrequency electromagnetic fields at a specific absorption rate of 5 W/kg for 2 hours/day and 5 days/week for 6 *** conducted behavioral tests including the object recognition and Y-maze tests and molecular and histopathological analysis of amyloid precursor protein/a myloid-beta metabolism in brain *** confirmed that radiofrequency electromagnetic field exposure for 6 months ameliorated cognitive impairment and amyloid-β*** expression levels of Iba1(pan-microglial marker)and colony-stimulating factor 1 receptor(CSF1R;regulates microglial prolife ration)in the hippocampus in 5xFAD mice treated with radiofrequency electromagnetic fields were significantly reduced compared with those of the sham-exposed ***,we analyzed the expression levels of genes related to mic rogliosis and microglial function in the radiofrequency electromagnetic fields-exposed group compared to those of a CSF1R inhibitor(PLX3397)-treated *** radiofrequency electromagnetic fields and PLX3397 suppressed the levels of genes related to microgliosis(Csf1r,CD68,and Ccl6)and pro-inflammatory cytokine interleukin-1β.N otably,the expression levels of genes related to mic roglial function,including Trem2,Fcgr1α,Ctss,and Spi1,were decreased after long-term radiofrequency electromagnetic field exposure,which was also observed in response to microglial suppression by *** res

关键词： 5×FAD Alzheimer’s disease CSF1R long term exposure microglial function neuroinflammation radiofrequency electromagnetic fields therapeutic effect

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Exploring the Synergy: A Review of Dual-Functional Radar Communication Systems

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IEEE Aerospace and Electronic Systems Magazine 2025年

作者： Hanif, Ali Ahmed, Sajid Alouini, Mohamed-Slim Al-Naffouri, Tareq Y. King Abdullah University of Science and Technology Department of Electrical and Computer Engineering CEMSE Division Thuwal23955 Saudi Arabia

This review paper examines the concept and advancements in the evolving landscape of Dual-functional Radar Communication (DFRC) systems. Traditionally, radar and communication systems have functioned independently, but current research is actively investigating the integration of these functionalities into a unified platform. The paper discusses the motivations behind the development of DFRC systems, the challenges involved, and the potential benefits they offer. A discussion on the performance bounds for DFRC systems is also presented. The paper provides a comprehensive review of the state-of-the-art research on DFRC systems covering various design aspects, resource allocation strategies, and future research directions. Additionally, we explore potential application scenarios for these joint communication and sensing systems, offering a comprehensive perspective on the multifaceted landscape of DFRC technology. This paper also presents a tutorial-style signal model for integrated sensing and communication tailored for newcomers to the field. © 2025 IEEE. All rights reserved.

关键词： Resource allocation

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Optimized Runge-Kutta Methods with Automatic Step Size Control for Compressible Computational Fluid Dynamics

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Communications on Applied Mathematics and Computation 2022年第4期4卷 1191-1228页

作者： Hendrik Ranocha Lisandro Dalcin Matteo Parsani David I.Ketcheson Applied Mathematics Münster University of MünsterMunsterGermany Computer Electrical and Mathematical Science and Engineering Division(CEMSE) Extreme Computing Research Center(ECRC)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Computer Electrical and Mathematical Science and Engineering Division(CEMSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We develop error-control based time integration algorithms for compressible fluid dynam-ics(CFD)applications and show that they are efficient and robust in both the accuracy-limited and stability-limited *** on discontinuous spectral element semidis-cretizations,we design new controllers for existing methods and for some new embedded Runge-Kutta *** demonstrate the importance of choosing adequate controller parameters and provide a means to obtain these in *** compare a wide range of error-control-based methods,along with the common approach in which step size con-trol is based on the Courant-Friedrichs-Lewy(CFL)*** optimized methods give improved performance and naturally adopt a step size close to the maximum stable CFL number at loose tolerances,while additionally providing control of the temporal error at tighter *** numerical examples include challenging industrial CFD applications.

关键词： Explicit Runge-Kutta methods Step size control Compressible Euler equations Compressible Navier-Stokes equations hp-adaptive spatial discretizations

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Cyber-Physical Vulnerabilities and NIST Mitigation Framework for Power Sector

Cyber-Physical Vulnerabilities and NIST Mitigation Framework...

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Cyber Physical Systems, Power Electronics and Electric Vehicles (ICPEEV), International Conference on

作者： Dheeraj Verma Praveen Kumar Agrawal K. R. Niazi Nikhil Gupta Vipin Chandra Pandey Department of Electrical Engineering Malaviya National Institute of Technology Jaipur India Computer Electrical and Mathematical Sciences and Engineering Division KAUST Saudi Arabia

ISBN: (数字)9798350388664

ISBN: (纸本)9798350388671

As power systems evolve and increasingly rely on digital technology, they become more vulnerable to both cyber and physical threats. This paper explores the growing chal-lenges faced by modern power grids, particularly regarding the cybersecurity risks. It takes a closer look at the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) and how it helps address these issues. By guiding organizations through steps such as identifying risks, protecting systems, detecting attacks, responding effectively, and recovering quickly, the NIST CSF provides a strong foundation for securing critical power infrastructure. In particular, this paper highlights the improvements from NIST CSF 1.0 to 2.0, including stronger focus on zero trust security, supply chain risks, and emerging technologies like the Internet of Things (IoT) and distributed energy resources (DERs). The analysis shows how these frameworks are essential for maintaining the stability, reliability, and security of critical systems against evolving threats.”

关键词： Prevention and mitigation Supply chains Stability criteria NIST Power system stability Threat assessment Power system reliability Zero Trust Reliability Internet of Things

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DFRC Signaling Strategies based on MIMO Beampattern Invariance

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IEEE Transactions on Communications 2025年

作者： Mazahir, Sana Ahmed, Sajid Alouini, Mohamed-Slim King Abdullah University of Science and Technology CEMSE Division Department of Electrical and Computer Engineering Thuwal23955 Saudi Arabia

The Dual-function radar communication (DFRC) design aims to exploit the tractability and reusability of both radar and communication systems' components, parameters, and spectrum to achieve an integrated system. In this work, we propose a novel DFRC signal design by leveraging the phenomenon of the MIMO beampattern invariance, which was comprehensively explored in our previous work [1]. It is demonstrated that the flexibility afforded by this beampattern invariance can be used to construct an integrated waveform while maintaining the required beam. A beampattern-preserving unitary transformation of the MIMO weight matrix is proposed to realize a desired information-bearing symbol at the communication receiver. The proposed method has the following promising features: 1) low-complexity, closed-form solution for the transmitted signals;2) flexibility of modulation scheme;3) independence from the type of orthonormal MIMO waveforms;4) independence from the beampattern design;5) adaptability for multi-target and multi-user scenarios. Using this generic signal design method, a number of modulation schemes are implemented. Furthermore, the impact of this operation on the estimation accuracy of the target parameters is analyzed by computing their respective Cramer-Rao Lower Bounds (CRLBs). Simulation results show that modulation schemes yielding a range of symbol error rate (SER) performances can be implemented while keeping the radar parameter estimation unaffected. © 2025 IEEE.

关键词： Cramer-Rao bounds

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Evaluating Different Methods for Semantic Reasoning Over Ontologies 1

Evaluating Different Methods for Semantic Reasoning Over Ont...

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1st Scholarly QALD Challenge 2023 and 4th SeMantic Answer Type, Relation and Entity Prediction Tasks Challenge, Scholarly QALD 2023 and SemREC 2023

作者： Zhapa-Camacho, Fernando Hoehndorf, Robert Computational Bioscience Research Center Computer Electrical & Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology 4700 KAUST Thuwal23955 Saudi Arabia

Reasoning over knowledge bases such as Semantic Web ontologies enables the discovery of new facts from existing knowledge. Knowledge-enhanced machine learning has motivated the development of neuro-symbolic reasoners, which enable faster but approximate computation of new facts or entailments. Neuro-symbolic methods generate vector representations (embeddings) of entities in a knowledge base. We analyze some ontology embedding methods, by implementing them as neuro-symbolic reasoners and evaluating their predictive performance on the datasets and tasks provided by the Semantic Reasoning Evaluation Challenge 2023. We explore two types of embedding methods: graph-based and model-theoretic. Regarding graph-based embeddings, we evaluated the impact of different combinations of graph representation of ontologies with knowledge graph embedding methods. For model-theoretic embeddings, which create models for theories, we evaluate the impact of using several models, enabling approximate semantic entailment. © 2023 CEUR-WS. All rights reserved.

关键词： Ontology

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Advanced Intelligent deep learning-based system for Robust Driving Assistance

Advanced Intelligent deep learning-based system for Robust D...

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2023 AEIT International Conference on electrical and Electronic Technologies for Automotive, AEIT AUTOMOTIVE 2023

作者： Rundo, Francesco Pino, Carmelo Castagnolo, Giulia Spampinato, Concetto STMicroelectronics ADG Power and Discretes R and D Division Catania Italy University of Catania PerCeiVe Lab Group Department of Electrical Electronics and Computer Engineering Catania Italy

ISBN: (纸本)9788887237573

The monitoring of the driver's state of concentration and the risk assessment of driving scenarios have received the majority of research interest in the area of automotive applications. In that situation, physiological tracking of the driver of the vehicle has shown to be a great non-invasive method of providing reliable and intelligent vehicle driving assistance. The authors of this work propose an advanced driving assistance system based on the usage of ad-hoc designed bio-inspired sensing platform which samples the driver's PhotoPlethysmoGraphic (PPG) signal, properly correlated with the respective level of subject attention. The perceptual analysis of the driver's attention level is certainly a robust and non-invasive approach for drowsiness monitoring. An innovative downstream deep architecture will be used to adequately process the so-collected driver's PPG signal by reconstructing the corresponding attention level. Extensive scientific literature confirmed that the PPG signal can be adequately used to reconstruct the activation of the autonomic nervous system of the monitored subject and therefore of the correlated level of attention managed by the activation of the sympathetic or parasympathetic nerve. Additionally, a video saliency analysis-based external intelligent automotive-grade vision-based system will be in charge of determining the amount of danger associated with the driving situation. The gathered experiment results attested to the usefulness of the whole pipeline proposal. © 2023 AEIT.

关键词： Chemical activation

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DrugEL: Ensemble Learning Model for Identification of Druggable Proteins

DrugEL: Ensemble Learning Model for Identification of Drugga...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Md. Mamun Ali Kawsar Ahmed Francis M. Bui Fang-Xiang Wu Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada Department of Mechanical Engineering and Department of Computer Science Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Druggable proteins are defined as proteins that can interact with drugs to modulate certain biological activity. The identification of druggable proteins holds significant clinical importance, directly impacting the development of targeted therapies for diseases like cancer and metabolic disorders. Identifying druggable proteins involves various methods, including computational prediction models, mass spectrometry (MS), and biochemical assays, but achieving high accuracy remains a challenge. This study proposes DrugEL, an ensemble learning model that uses Bayesian inference to integrate predictions from multiple algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), LightGBM (LGBM), and Decision Tree (DT) with seven feature extraction methods (LSA, AAC, PAAC, GAAC, NMBroto, AAIndex, and KNN). The results show that DrugEL outperforms existing models in terms of accuracy (0.9758), MCC (0.9515), AUC (0.9758), sensitivity (0.9742) and specificity (0.9774), particularly excelling with the LSA method.

关键词： Proteins Analytical models Accuracy Sensitivity Biological system modeling Computational modeling Nearest neighbor methods Predictive models Ensemble learning Random forests

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Efficient Service Differentiation and Energy Management in Hybrid WiFi/LiFi Networks

arXiv

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arXiv 2025年

作者： Ihsan, Asim Asif, Muhammad Safi, Hossein Tavakkolnia, Iman Haas, Harald Electrical Engineering Division Department of Engineering University of Cambridge Cambridge United Kingdom School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

In this paper, we propose an innovative predict-and-optimize algorithm designed for hybrid WiFi/LiFi networks, aiming to achieve service differentiation while maximizing energy efficiency (EE). The proposed framework utilizes multi-access technology real-time intelligent controller (mATRIC) to dynamically predict the appropriate network slice for each user based on historically monitored key performance indicators (KPIs). This prediction is facilitated by a deep learning model trained using the resilient backpropagation algorithm, with training conducted on KPIs data at the universal non-real time RAN intelligent controller (non-RT RIC). This trained model enables real-time slice selection by mATRIC. In the subsequent phase, the algorithm focuses on optimizing EE of hybrid network as a function of precoding vectors for the predicted slices by employing techniques from sequential convex approximation and the inner approximation method. We introduce novel approximations to convert non-convex objective functions and constraints into convex forms, and develop an iterative algorithm to achieve sub-optimal solutions. Additionally, the EE maximization problem, ensures alignment with end-to-end latency requirements. It also addresses the various constraints inherent to hybrid systems, such as input signal limitations for LiFi LEDs, data rate restrictions, and power budget considerations. Simulation results validate the effectiveness of the proposed algorithm, demonstrating significant improvements in EE while ensuring service differentiation within hybrid network environments. © 2025, CC BY.

关键词： Budget control

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