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A High-Performance 220-290 GHz Micromachined Waveguide Switch Based on Interference Between MEMS Reconfigurable Surfaces

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IEEE Transactions on Terahertz Science and Technology 2024年第2期14卷 188-198页

作者： Karimi, Armin Shah, Umer Yu, Suxian Oberhammer, Joachim Kth Royal Institute of Technology Micro and Nanosystems Technology Division School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden

This article presents a highly integrated novel silicon micromachined single-pole-single-throw waveguide switch based on two microelectromechanically reconfigurable switching surfaces (MEMS-RSs), which allows optimizing the switching performance by tuning the interference between the two such MEMS-RSs utilizing integrated electrostatic comb-drive actuators. The switch prototype is implemented with axially aligned standard WR-3.4 waveguide ports with a total footprint of 3 mm×3.5 mm×1.2 mm. The measured blocking (off) state insertion loss (isolation) and return loss, measured between two standard WR-3.4 waveguide flanges, are 28.5-32.5 dB and better than 0.7 dB, and the propagating (on) state insertion and return losses are 0.7-1.2 dB and better than 17 dB in the 220-290 GHz frequency band, respectively. The measured results were in excellent agreement with the simulation data, implying 27.5% fractional bandwidth, which is very close to a full waveguide band performance. For further investigations, two variants of the switching circuit with only a single MEMS-RS and without any MEMS-RSs have also been fabricated. The single MEMS-RS switch achieved the off-state isolation, on-state insertion loss, and return loss of only 11.5-12.5 dB, 0.8-1.3 dB, and better than 12 dB from 220 to 274 GHz, respectively, which clearly indicates the drastic performance improvement of the interference-based double MEMS-RS switch design. Moreover, measurement of the waveguide-only reference structure showed that the waveguide section alone attributed to 0.2-0.5 dB of the measured on-state insertion loss of the double MEMS-RS switch, and the rest is due to the introduction of the MEMS-RSs inside the waveguides. © 2011-2012 IEEE.

关键词： Millimeter waves

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Cache Allocation in Multi-Tenant Edge Computing: An Online Model-Based Reinforcement Learning Approach

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IEEE Transactions on Cloud Computing 2025年第2期13卷 459-472页

作者： Ben-Ameur, Ayoub Araldo, Andrea Chahed, Tijani Dán, György Institut Polytechnique de Paris SAMOVAR Telecom SudParis Palaiseau91120 France KTH Royal Institute of Technology Division of Network and Systems Engineering School of Electrical Engineering and Computer Science Stockholm11428 Sweden

We consider a Network Operator (NO) that owns Edge Computing (EC) resources, virtualizes them and lets third party Service Providers (SPs) run their services, using the allocated slice of resources. We focus on one specific resource, i.e., cache space, and on the problem of how to allocate it among several SPs in order to minimize the backhaul traffic. Due to confidentiality guarantees, the NO cannot observe the nature of the traffic of SPs, which is encrypted. Allocation decisions are thus challenging, since they must be taken solely based on observed monitoring information. Another challenge is that not all the traffic is cacheable. We propose a data-driven cache allocation strategy, based on Reinforcement Learning (RL). Unlike most RL applications, in which the decision policy is learned offline on a simulator, we assume no previous knowledge is available to build such a simulator. We thus apply RL in an online fashion, i.e., the model and the policy are learned by directly perturbing and monitoring the actual system. Since perturbations generate spurious traffic, we thus need to limit perturbations. This requires learning to be extremely efficient. To this aim, we devise a strategy that learns an approximation of the cost function, while interacting with the system. We then use such an approximation in a Model-Based RL (MB-RL) to speed up convergence. We prove analytically that our strategy brings cache allocation boundedly close to the optimum and stably remains in such an allocation. We show in simulations that such convergence is obtained within few minutes. We also study its fairness, its sensitivity to several scenario characteristics and compare it with a method from the state-of-the-art. © 2013 IEEE.

关键词： Contrastive Learning

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Wide bandgap semiconductor-based integrated circuits

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Chip 2023年第4期2卷 105-132页

作者： Saravanan Yuvaraja Vishal Khandelwal Xiao Tang Xiaohang Li Advanced Semiconductor Laboratory(ASL) Electrical and Computer Engineering ProgramComputerElectrical and Mathematical Science&Engineering(CEMSE)DivisionKing Abdullah University of Science andTechnology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

Wide-bandgap semiconductors exhibit much larger energybandgaps than traditional semiconductors such as silicon,rendering them very promising to be applied in the fields of electronics and *** examples of semiconductors include SiC,GaN,ZnO,and diamond,which exhibitdistinctive characteristics such as elevated mobility and *** characteristics facilitate the operation of awide range of devices,including energy-efficient bipolar junctiontransistors(BJTs)and metal-oxide-semiconductor field-effecttransistors(MOSFETs),as well as high-frequency high-electronmobility transistors(HEMTs)and optoelectronic components suchas light-emitting diodes(LEDs)and *** semiconductorsare used in building integrated circuits(ICs)to facilitate theoperation of power electronics,computer devices,RF systems,andother optoelectronic *** breakthroughs includevarious applications such as imaging,optical communication,*** them,the field of power electronics has witnessedtremendous progress in recent years with the development of widebandgap(WBG)semiconductor devices,which is capable ofswitching large currents and voltages rapidly with low ***,it has been proven challenging to integrate these deviceswith silicon complementary metal oxide semiconductor(CMOS)logic circuits required for complex control *** monolithic integration of silicon CMOS with WBG devices increases thecomplexity of fabricating monolithically integrated smart integrated circuits(ICs).This review article proposes implementingCMOS logic directly on the WBG platform as a ***,achieving the CMOS functionalities with the adoption of WBGmaterials still remains a significant *** article summarizesthe research progress in the fabrication of integrated circuitsadopting various WBG materials ranging from SiC to diamond,with the goal of building future smart power ICs.

关键词： Wide bandgap semiconductors Integrated circuits Transistors Power electronics

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Joint Device and Training Scheduling for Wireless Federated Learning

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IEEE Internet of Things Journal 2025年第12期12卷 18806-18819页

作者： Chen, Xiaobing Zhou, Xiangwei Zhang, Hongchao Sun, Mingxuan Zhao, Taibiao Louisiana State University Division of Electrical and Computer Engineering Baton RougeLA70803 United States Louisiana State University Department of Mathematics Baton RougeLA70803 United States Louisiana State University Division of Computer Science and Engineering Baton RougeLA70803 United States

The advent of ubiquitous computing devices in the Internet of Things (IoT) has resulted in an explosion of data. Traditional centralized machine learning models face challenges including limited bandwidth in wireless environments and privacy concerns due to their data aggregation approach. Federated learning addresses these challenges via decentralizing model training across numerous devices, leveraging model updates to enhance privacy and reduce communication overhead. To improve its cost efficiency, current research focuses on minimizing either time or energy costs but rarely both, and does not jointly optimize the parameters of device and training scheduling in the presence of system and data heterogeneity inherent in IoT networks. In our paper, we first introduce a multi-group transmission scheme and propose a comprehensive device scheduling framework, Group Scheduling on Orthogonal Frequency-division Multiple Access (GS-OFDMA), to address time bottlenecks. Then we formulate a joint optimization problem for device and training scheduling that minimizes the total cost of training while ensuring model convergence. To tackle the resulting mixed integer nonlinear programming problem, we develop an iterative algorithm. Experimental results show that our approach significantly reduces the total cost by at least 35% across various real-world datasets and data distributions in comparison with random participant selection. The proposed GS-OFDMA protocol also exhibits higher time efficiency over other device scheduling schemes. © 2014 IEEE.

关键词： Federated learning

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Strain-induced lateral heterostructures: Hole localization and the emergence of flat bands in rippled MoS2 monolayers

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Physical Review Materials 2025年第2期9卷 L021002-L021002页

作者： Meshal Alawein Joel W. Ager Ali Javey D. C. Chrzan Electrical Engineering and Computer Science University of California Berkeley California 94720 USA Materials Sciences Division Lawrence Berkeley National Laboratories Berkeley California 94720 USA Materials Science and Engineering University of California Berkeley California 94720 USA

The formation of 2D lateral heterostructures in rippled MoS2 and similar transition metal dichalcogenides (TMDs) is studied using density functional theory. Compression of rippled TMDs beyond a threshold compression leads to the formation of a flat valence band associated with strongly localized holes. The implications for exciton manipulation and the emergence of one-dimensional heavy fermion behavior are discussed.

关键词： Layered semiconductors

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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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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

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

ISBN: (纸本)9798350386226

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. © 2024 IEEE.

关键词： Contrastive Learning

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Entanglement-Controlled Quantum Federated Learning

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IEEE Internet of Things Journal 2025年第11期12卷 18318-18330页

作者： Park, Soohyun Lee, Hyunsoo Jung, Soyi Park, Jihong Bennis, Mehdi Kim, Joongheon Sookmyung Women’s University Division of Computer Science Seoul04310 Korea Republic of Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of Pillar Tampines Singapore University of Oulu Centre for Wireless Communications Oulu90014 Finland

According to the advances in quantum computing and distributed learning, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. To successfully opeate QFL under various and dynamic channel conditions in Internet of Things (IoT) environments, this article develops a novel depth-controllable architecture of entangled slimmable QNNs (eSQNNs), and thus, proposes an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eSQNNs. Even though the proposed eSQNN-based eSQFL is superior, training the depth-controllable eSQNN architecture is challenging due to high-entanglement entropy and interdepth interference. Therefore, the proposed method in this article mitigates the interference using entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing interdepth quantum state differences, respectively. Furthermore, the proposed method optimizes the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. The novelty of this work is evaluated via extensive simulations in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions. © 2014 IEEE.

关键词： Federated learning

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Cyber-Physical Vulnerabilities and NIST Mitigation Framework for Power Sector 2

Cyber-Physical Vulnerabilities and NIST Mitigation Framework...

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2nd International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

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

ISBN: (纸本)9798350388664

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 challenges 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". © 2024 IEEE.

关键词： Cyber attacks

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Extended-Attention Applied in Selected Stages 7

Extended-Attention Applied in Selected Stages

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Zeyu Todo, Yuki Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Kanazawa Japan

ISBN: (纸本)9798350350890

The Self-attention mechanism was widely applied in computer Vision (CV) field. However, while Self-attention mechanism brings high accuracy to Transformer, it also brings a huge amount of calculation. To further enhance accuracy and decrease computational load, in this paper, two refinements were implemented. Firstly, two matrices were introduced to enhance the extraction of image information for Extended-attention method. The capacity of Transformer was enhanced by these matrices to capture crucial information in complex tasks, the final accuracy and generalization performance were significantly improved. Secondly, the impact of implementing the aforementioned Self-attention mechanism at various stages of the Transformer is investigated. Intriguingly, our findings reveal that selectively implementing this method at specific stages leads to a significant enhancement in the model's performance. © 2024 IEEE.

关键词： Deep learning Different Stages Self-attention Transformer

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HBC Transmission Policies for Energy Harvesting Empowered ECG Nodes 46

HBC Transmission Policies for Energy Harvesting Empowered EC...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Alamoudi, Abeer Celik, Abdulkadir Eltawil, Ahmed M. Computer Electrical and Mathematical Sciences and Engineering Division Thuwal23955-6900 Saudi Arabia

ISBN: (纸本)9798350371499

The Internet of Bodies is a body-centric network that connects smart devices to enable real-time monitoring of physiological data, including early detection of cardiovascular diseases through electrocardiograms (ECG) monitoring. In contrast to power-intensive RF transceivers in many ECG wearables, Human Body Communication (HBC) offers an energy-efficient (EE) and secure alternative, utilizing the body as a communication channel. This paper explores the functionality of an IoB sensor node designed for ECG monitoring, leveraging HBC and incorporating energy harvesting techniques to ensure sustained power. It introduces a scheme that utilizes ECG signal characteristics to identify sensing and transmission time durations, and it formulates an optimization problem within a Markov Decision Process framework. Given the battery and computational power constraint of IoB nodes, we propose lightweight and EE policies. Simulations confirm their benefits over myopic policies in terms of maximizing node lifetime and optimizing energy utilization, making them a promising solution for IoB systems operating under dynamic energy arrival rates. © 2024 IEEE.

关键词： Markov processes

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