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HETEROGENEOUS NEURONAL AND SYNAPTIC DYNAMICS FOR SPIKE-EFFICIENT UNSUPERVISED LEARNING: THEORY AND DESIGN PRINCIPLES 11

HETEROGENEOUS NEURONAL AND SYNAPTIC DYNAMICS FOR SPIKE-EFFIC...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Chakraborty, Biswadeep Mukhopadhyay, Saibal Department of Electrical and Computer Engineering Georgia Institute of Technology United States

This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction performance, enabling spike-efficient (unsupervised) learning. We analytically show that the diversity in neurons' integration/relaxation dynamics improves an RSNN's ability to learn more distinct input patterns (higher memory capacity), leading to improved classification and prediction performance. We further prove that heterogeneous Spike-Timing-Dependent-Plasticity (STDP) dynamics of synapses reduce spiking activity but preserve memory capacity. The analytical results motivate Heterogeneous RSNN design using Bayesian optimization to determine heterogeneity in neurons and synapses to improve E, defined as the ratio of spiking activity and memory capacity. The empirical results on time series classification and prediction tasks show that optimized HRSNN increases performance and reduces spiking activity compared to a homogeneous RSNN. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Neurons

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A Multilevel Ac-Ac Converter with Input-series and Output-Parallel as Dynamic Voltage Restorer 31

A Multilevel Ac-Ac Converter with Input-series and Output-Pa...

引用

31st International Conference on electrical engineering, ICEE 2023

作者： Mortazavi, Seyed Mohesen Beiranvand, Reza Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350312560

In this paper, a bidirectional multilevel ac-ac converter is presented, which is suitable for high-power applications. The structure of the proposed multilevel converter with series input and parallel output has reduced the input voltage and output current of each level, which reduces the stress on the elements of the proposed converter. Reducing the current and voltage of each level has reduced the dimensions, cost, and losses. Due to the output parallel of the multilevel converter, the ripple of the output current has been reduced. A structure of a multilevel converter as a dynamic voltage restorer (DVR) was proposed which is transformerless. The multilevel converter is examined and analyzed using theoretical relationships in different working modes. Then, to verify the performance of the multilevel converter was simulated as a DVR in Matlab/Simulink and its results were presented. In addition to confirming the performance of the multilevel converter in working modes, the results showed the reduction of voltage and current of each switch for each level. © 2023 IEEE.

关键词： Simulink

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Assessing the Viability of AC to DC Microgrid Conversion: A Comprehensive Analysis of Technical and Economic Factors 59

Assessing the Viability of AC to DC Microgrid Conversion: A ...

引用

59th International Universities Power engineering Conference, UPEC 2024

作者： Charalambous, Chrysanthos Polycarpou, Alexis Efthymiou, Venizelos Georghiou, George E. University of Cyprus Pv Technology Laboratory Department Of Electrical and Computer Engineering Cyprus Phaethon Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Frederick University Department of Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350379730

In the face of global warming and the acceleration of climate changes, urgent and decisive measures must be taken to mitigate these phenomena. This study addresses this urgent need by exploring the transition from traditional AC distribution systems to more efficient DC and hybrid AC/DC systems, recognizing the utmost importance of both technical developments and economic considerations. Through a thorough overview of existing literature and empirical knowledge gathered from the nanogrid of FOSS Research Centre for Sustainable Energy of University of Cyprus, this research clarifies the inherent advantages of DC and AC/DC hybrid systems. In addition, it presents a detailed case study for the comprehensive assessment of the feasibility and effectiveness of these systems. The preliminary economic analysis carried out in this study demonstrates the cost-effectiveness and performance improvements offered by hybrid AC/DC microgrids compared to conventional AC configurations. According to the findings of this work it is obvious that the ROI increases with the increase of DC loads over AC loads until the case where DC loads reach the 75% of the total load (ROI=13.9%). However, even in the case of Full DC microgrid, the ROI is higher by 1% compared to the case of Full AC microgrid thus significantly reducing the payback period. These findings not only underline the urgency of the transition to more efficient and sustainable energy distribution systems, but also provide valuable information for policymakers, industry stakeholders and researchers. By embracing these innovative technologies, a more resilient and environmentally responsive energy infrastructure can be promoted that is in line with the demands of a rapidly changing global climate. © 2024 IEEE.

关键词： Clean energy

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Analysis of EDF and RM scheduling algorithms for periodic and aperiodic tasks in multi-core ECU

Analysis of EDF and RM scheduling algorithms for periodic an...

引用

2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Lim, Se Jeong Kim, Jong Hun Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350326413

As the development of innovative automotive software continues to progress, the architecture and solutions required for automotive software become more complex. It is challenging to cope with these issues with a single-core Electronic Control Unit (ECU), and therefore a multi-core ECU is being utilized as a solution. When tasks enter the multi-core ECU, it is necessary to properly divide the processes into multiple cores to execute the tasks. Tasks in a vehicle consist of periodic tasks and aperiodic tasks, which are mainly handled through Rate-monotonic (RM) and Earliest Deadline First (EDF) scheduling, utilizing a Server to manage aperiodic tasks. Polling Server (PS) is employed in RM scheduling, and Total Bandwidth Server is used in EDF scheduling to handle aperiodic tasks. The present study aims to conduct a comparative analysis of the performance of two distinct scheduling algorithms through experiments consisting of periodic and aperiodic tasks by examining several variables. © 2023 IEEE.

关键词： Scheduling algorithms

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Resource Allocation in NR-V2X Mode 2 Using Multi Agent DQN 14

Resource Allocation in NR-V2X Mode 2 Using Multi Agent DQN

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Lee, Insung Kim, Duk Kyung Inha University Department of Electrical and Computer Engineering Incheon Korea Republic of

ISBN: (纸本)9798350335385

V2X communication has been studied to increase road safety and traffic efficiency, and the most recently standardized technology is NR-V2X developed by third generation partnership project (3GPP). NR-V2X supports two modes of communication mode 1 and mode 2. In mode 2, vehicles reserve the resources based on their local observations using semi-persistent scheduling (SPS). In this method, if two or more vehicles select the same resource, a continuous resource collision occurs, and it makes communication performance greatly degraded. To overcome this, we propose a resource allocation method using multi-agent reinforcement learning (MARL). As agents, vehicles that transmit periodic cooperative awareness messages (CAM) are modeled. The state is composed of the received signals strength indicator (RSSI) that the agents received from each resource, and we set the total sum rate of all agents as the shared reward. The proposed method is compared with the random resource allocation in a highway scenario. The results show that the proposed method outperforms in terms of throughput performance. © 2023 IEEE.

关键词： Resource allocation

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Fast and Accurate Modelling of Laminated Cores in electrical Machines using Homogenization and Neural Networks

Fast and Accurate Modelling of Laminated Cores in Electrical...

引用

2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Purnode, Florent Henrotte, François Louppe, Gilles Geuzaine, Christophe University of Liège Department of Electrical Engineering and Computer Science Belgium

ISBN: (纸本)9798350338362

Eddy currents induced in the individual laminations of ferromagnetic laminated cores are usually disregarded in conventional electrical machine simulations, and magnetic losses are then only evaluated a posteriori, by means of Steinmetz-like formulas. This approach yields however seriously inaccurate computed fields and losses whenever the operating frequency increases, or in the presence of higher harmonics, which is an issue of growing importance in industrial R&D. In this contribution we introduce a significantly improved and effective approach based on homogenization and neural networks. The method can be easily implemented in standard finite element packages. © 2023 IEEE.

关键词： Laminating

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Optimization Technique for Indoor Localization: A Multi-Objective Approach to Sampling Time and Error Rate Trade-off 3

Optimization Technique for Indoor Localization: A Multi-Obje...

引用

3rd IEEE International Conference on Signal, Control and Communication, SCC 2023

作者： Rafique, Hamaad Patti, Davide Palesi, Maurizio La Delfa, Gaetano Carmelo Catania, Vincenzo Electronics and Computer Engineering University of Catania Department of Electrical Catania Italy

ISBN: (纸本)9798350326390

Magnetic fingerprint-based indoor localization systems are a promising alternative to GPS in indoor environments by using magnetic fields (MFs) to determine accurate indoor locations. However, the optimal number of MFs required for precise location prediction on the client side (CS), i.e., mobile phone, remains a critical yet unanswered question. Hence, resource-constrained devices face limitations in terms of computational power and energy supply. Therefore, this study aims to optimize prediction error and prediction time at CS with minimal MF samples during the online location prediction phase. This study tackles this challenge by employing efficient clustering techniques and machine learning classifiers to minimize prediction error and prediction time on client devices using multi-object optimization instead of single-objective optimization. Our research findings reveal an optimal sample size of 14 per second that provides an ideal balance, offering precise accuracy while reducing the computational burden and energy consumption, thus contributing to the advancement of efficient indoor positioning systems. © 2023 IEEE.

关键词： Indoor positioning systems

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SADA: Unsupervised Domain Adaptation for Reliable Scene Awareness 59

SADA: Unsupervised Domain Adaptation for Reliable Scene Awar...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Maghsoumi, Hossein Fallah, Yaser P. Atia, George University of Central Department of Electrical and Computer Engineering OrlandoFL United States University of Central Department of Computer Science OrlandoFL United States

ISBN: (纸本)9798331513269

Although deep neural networks are extensively employed in autonomous driving, they must be adjusted to handle unforeseen environmental conditions that were not included in their initial training. Unsupervised domain adaptation (UDA) techniques have proven effective in transferring learned knowledge from one domain to another when the labels in the target domain are unavailable. However, their potential application in scene awareness remains underexplored. This paper introduces SADA (Scene Awareness with Unsupervised Domain Adaptation), a novel approach designed to enhance the reliability of drivable area and lane detection tasks in autonomous driving systems. We leverage UDA techniques in unified multi-task networks to address domain shift challenges, ensuring robust feature extraction under diverse and complex conditions. The proposed technique is evaluated on different real-world datasets and the results demonstrate that the proposed methods outperform the baseline scheme in terms of detection accuracy and consistency. © 2025 IEEE.

关键词： Deep neural networks

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FedSaw: Communication-Efficient Cross-Silo Federated Learning with Adaptive Compression 20

FedSaw: Communication-Efficient Cross-Silo Federated Learnin...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ying, Chen Li, Baochun Li, Bo University of Toronto Department of Electrical and Computer Engineering Canada Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350324334

Cross-silo federated learning (FL) is an emerging approach for institutions to collaboratively train a machine learning model without sharing their siloed data. However, it conventionally requires institutions to centrally store their clients' data, posing a threat to clients' privacy. This paper thus studies a preferable setting to leave data distributed to clients, where traditional FL is used not only across institutions but also among their clients. As this new setting inherits or even exacerbates the major problem of communication inefficiency in the traditional setting, we explore the feasibility of leveraging two compression techniques, pruning and quantization, to improve communication efficiency. Starting by applying off-the-shelf pruning and quantization mechanisms, we observe that they could largely reduce communication overhead with a negligible reduction, sometimes even a slight increase, in training performance. By mathematically analyzing the impact of compression on the performance of the trained model, we find that pruning and quantizing with a proper amount can offset possible performance degradation due to non-i.i.d data. Based on this finding, we propose FedSaw, a new cross-silo FL framework that can improve communication efficiency by adaptively tuning the pruning amount and quantizing updates throughout training. In our extensive evaluation with six benchmark datasets, FedSaw consistently outperformed its state-of-the-art competitors. It decreased the wall-clock training time and communication overhead used for converging to the target accuracy by up to 86.7% and 91.5%, respectively. © 2023 IEEE.

关键词： Efficiency

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Wi-Six: Precise Positioning in the Metaverse via Optimal Wi-Fi Router Deployment in 6G Networks

Wi-Six: Precise Positioning in the Metaverse via Optimal Wi-...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Famili, Alireza Atalay, Tolga Stavrou, Angelos Wang, Haining Virginia Tech Department of Electrical and Computer Engineering VA United States

ISBN: (纸本)9798350333336

Mobile networks are evolving at a rapid pace to accommodate the growing versatility of use cases. 5G deployments have been designed in a way to offer services in the form of end-to-end logically isolated fragments called network slices. An increasingly popular service type in this delivery model is the augmented and virtual reality (AR/VR) wearables which will play a key role in the delivery of the Metaverse. In addition to rigorous latency control, accurate Metaverse realization depends highly on high-precision localization of such user equipment (UE) in a three-dimensional environment. Being a successor to 5G, 6G networks will operate at higher frequency ranges in denser deployments. In such environments, maintaining high-accuracy localization for indoor deployments will become a challenging task considering the signal characteristics at higher frequencies. To that end, we propose the Wi-Six framework, which relies on well-defined (third-generation partnership project) 3GPP principles to model a hybrid radio access network (RAN) composed of Wi-Fi routers that offload positioning from base stations (BSs) for indoor environments. Our contribution in this paper is to demonstrate that the placement geometry of Wi-Six transmission and reception points (TRPs) plays a significant role in the positioning accuracy of AR/VR Metaverse wearable equipment. We formulate and solve an NP-Hard problem of TRP placement using a novel optimization framework. We show that by using our optimal placement solution, positioning accuracy improves significantly compared to arbitrary anchor placements. © 2023 IEEE.

关键词： Optimization

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