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2nd Asia Conference on Electronics engineering, ACEE 2024

作者： Ullah, Najeeb Lee, Lini Islam, Md. Shabiul Islam, Mohammad Tariqul Cyberjaya Malaysia BUITEMS Faculty of ICT Quetta Pakistan Universiti Kebangsaan Bangi Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Malaysia

ISBN: (纸本)9798350375251

This paper presents the construction and investigation of a compact electromagnetic metamaterial (MM) absorber that is polarization-insensitive and provides wide-Angle coverage. The absorber is designed specifically for energy harvesting purposes. The proposed MM absorber is created on an FR4 dielectric substrate and consists of three split-ring resonators (SRRs). The proposed structure demonstrates a significant absorption rate of 98% and 99% at two distinct resonance frequencies: 4.63 GHz and 5.827 GHz, respectively. Furthermore, the absorber can provide a broad absorption range for various polarization angles, extending up to 45°. Most incident EM energy is primarily dissipated through resistors rather than the dielectric substrate. The device is characterized by negative permittivity, permeability, and refractive indexes within the frequency range of 4.5 GHz to 5 GHz, which classifies it as a Double Negative material (DNG). The proposed device can potentially enhance the conversion of incident energy into usable electrical power, resulting in increased efficiency. © 2024 IEEE.

关键词： Refractive index

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Exploring Player's Body as a Game Controller for Fitness Activity Promotion 6

Exploring Player's Body as a Game Controller for Fitness Act...

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6th Experiment at International Conference, exp.at 2023

作者： Simoes, Diogo Menezes, Paulo University of Coimbra Institute of Systems and Robotics Electrical and Computer Engineering Department Coimbra Portugal

ISBN: (纸本)9798350314403

In recent years, there has been a tendency for people to resort to a more sedentary lifestyle, which has only been aggravated by the COVID-19 pandemic and the evermore common remote work policies. This lack of physical activity brings negative health outcomes and can generally be difficult to overcome due to how challenging it can be for a person to find the motivation to adopt a more physically active routine. To address this issue, we propose the use of serious games as an engaging, fun, and highly motivational tool that can aid people in their quest for a healthier lifestyle. We focus on three problem areas: motor control improvement and rehabilitation, post-stroke rehabilitation, and physical activity for general health. There exist multiple solutions for these problems which often require specific equipment that may not be available to the common user. Therefore, we propose a different approach requiring solely a laptop equipped with a camera, thus making it more practical and accessible to the wider public. © 2023 IEEE.

关键词： Serious games

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Graphon Pooling for Reducing Dimensionality of Signals and Convolutional Operators on Graphs

Graphon Pooling for Reducing Dimensionality of Signals and C...

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作者： Parada-Mayorga, Alejandro Wang, Zhiyang Ribeiro, Alejandro University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States

In this article we propose a pooling approach for convolutional information processing on graphs relying on the theory of graphons and limits of dense graph sequences. We present three methods that exploit the induced graphon representation of graphs and graph signals on partitions of [0,1]2 in the graphon space. As a result we derive low dimensional representations of the convolutional operators, while a dimensionality reduction of the signals is achieved by simple local interpolation of functions in L2([0,1]). We prove that those low dimensional representations constitute a convergent sequence of graphs and graph signals, respectively. The methods proposed and the theoretical guarantees that we provide show that the reduced graphs and signals inherit spectral-structural properties of the original quantities. We evaluate our approach with a set of numerical experiments performed on graph neural networks (GNNs) that rely on graphon pooling. We observe that graphon pooling performs significantly better than other approaches proposed in the literature when dimensionality reduction ratios between layers are large. We also observe that when graphon pooling is used we have, in general, less overfitting and lower computational cost. © 1991-2012 IEEE.

关键词： Signal processing

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A Novel Technology for Semiautomatic and Automatic Stem–Stake Coupling of Seedlings and Plants

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 254-262页

作者： Shirzi, Moteaal Asadi Kermani, Mehrdad R. Western University Advanced Robotics and Mechatronic Systems Laboratory Electrical and Computer Engineering Department LondonN6A 5B9 Canada Western University Department of Electrical and Computer Engineering LondonN6A 3K7 Canada Advanced Robotics and Mechatronic Systems Laboratory LondonN6A 5B9 Canada

This article introduces a novel mechatronic system for coupling the stems of seedlings and plants to wooden stakes or ropes, a crucial process for supporting them during growth, transportation, and fruiting in plant propagation facilities and greenhouses. The stem–stake coupling device utilizes interconnected mechanisms and an impedance control method to adjust motor torque and speed, shaping metallic wire into clips of various shapes and dimensions, effectively securing plant stems to stakes or ropes. In a robotic system, a claw-shaped arm mechanism, a stereo camera, and real-time vision techniques are integrated into the stem–stake coupling device to identify the optimal coupling point and automate the coupling task. This innovation addresses the labor-intensive task of manual coupling, offering a scalable solution for growers through handheld devices or fully automated robotic systems. In the context of increasing labor shortages and rising costs, the technology offers a sustainable and efficient alternative with significant potential to enhance operational efficiency in greenhouses and propagation facilities. © 2023 IEEE.

关键词： Seed

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A Multisensorial Socially Assistive Robot for Therapies with Children with Autism Spectrum Disorder and Down Syndrome Using Serious Games 9th

A Multisensorial Socially Assistive Robot for Therapies wit...

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9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Panceri, J.A.C. Freitas, E.V.S. Schreider, S.L. Souza, J.C. Caldeira, E.M.O. Bastos-Filho, T.F. Postgraduate Program in Electrical Engineering Federal University of Espirito Santo Vitória Brazil Automation and Control Engineering Department Federal Institute of Espirito Santo Linhares Brazil Electrical Engineering Department Federal University of Espirito Santo Vitória Brazil Postgraduate Program in Biotechnology Federal University of Espirito Santo Vitória Brazil

ISBN: (纸本)9783031494062

Much of the data obtained by health professionals for diagnosis and therapies applied to children with Autistic Spectrum Disorder (ASD) and Down Syndrome (DS) depend on visual observations and interviews with family members and clinical assessments. Therefore, the use of sensors has the potential of complementing this process by generating quantitative data. This work demonstrates that the use of sensors embedded in a Socially Assistive Robot (SAR) developed at UFES/Brazil, termed Mobile Autonomous Robot for Interaction with Autistics and Trisomy 21 (MARIA T21), can be used as a centralized tool to quantify more objective measures of children’s spatial, postural and social behavior during the interaction with the robot through Serious Games (SG) projected on the floor by the robot. The information generated can help therapists in the application of more efficient therapies, as well as monitor the evolution of the children submitted to the protocol with the robot. The results demonstrates the feasibility of using these sensors and evidence the requirements to be evolved. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Serious games

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TraiNDSim: A Simulation Framework for Comprehensive Performance Evaluation of Neuromorphic Devices for On-Chip Training 24

TraiNDSim: A Simulation Framework for Comprehensive Performa...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Heo, Donghyeok Bang, Hyeonsu Ko, Jong Hwan Department of Ai Systems Engineering Sungkyunkwan University Suwon Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400706011

The advancement of neuromorphic devices (NDs) for processing deep neural networks has narrowed the accuracy gap with software-trained models. To accurately assess ND performance, reliable simulation frameworks for on-chip training are crucial. However, existing frameworks encounter difficulties accurately reflecting the characteristics of NDs in training simulations. Consequently, we introduce TraiNDSim, a novel framework that comprehensively evaluates the performance of NDs to address these difficulties. Specifically, we propose an advanced conductance normalization strategy called layer-wise normalization, which limits the weight range by taking the initial weight distribution into account. Additionally, our framework integrates three conductance models, notably refining one of the conventional models to depend solely on nonlinearity. Moreover, it features a bi-directional weight representation method with a unique conductance compensation technique. Our comprehensive analysis using TraiNDSim demonstrates its effectiveness in accurately reflecting the impact of ND parameters on training, promising more precise device performance evaluations. Our framework is available at https://***/donghyeokheo/TraiNDSim. © 2024 Copyright held by the owner/author(s).

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Accelerating OTA Circuit Design: Transistor Sizing Based on a Transformer Model and Precomputed Lookup Tables

Accelerating OTA Circuit Design: Transistor Sizing Based on ...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Ghosh, Subhadip Gebru, Endalk Y. Kashyap, Chandramouli V. Harjani, Ramesh Sapatnekar, Sachin S. University of Minnesota Department of Electrical and Computer Engineering MinneapolisMN United States Cadence Design Systems PortlandOR United States

ISBN: (纸本)9783982674100

Device sizing is crucial for meeting performance specifications in operational transconductance amplifiers (OTAs), and this work proposes an automated sizing framework based on a transformer model. The approach first leverages the driving-point signal flow graph (DP-SFG) to map an OTA circuit and its specifications into transformer-friendly sequential data. A specialized tokenization approach is applied to the sequential data to expedite the training of the transformer on a diverse range of OTA topologies, under multiple specifications. Under specific performance constraints, the trained transformer model is used to accurately predict DP-SFG parameters in the inference phase. The predicted DP-SFG parameters are then translated to transistor sizes using a precomputed look-up table-based approach inspired by the 9m / I d methodology. In contrast to previous conventional or machine-learning-based methods, the proposed framework achieves significant improvements in both speed and computational efficiency by reducing the need for expensive SPICE simulations within the optimization loop;instead, almost all SPICE simulations are confined to the one-time training phase. The method is validated on a variety of unseen specifications, and the sizing solution demonstrates over 90% success in meeting specifications with just one SPICE simulation for validation, and 100% success with 3-5 additional SPICE simulations. © 2025 EDAA.

关键词： Table lookup

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Deep Reinforcement Learning Method for Control of Mixed Autonomy Traffic systems

Deep Reinforcement Learning Method for Control of Mixed Auto...

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2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent systems, MFI 2024

作者： Liu, Xingyu Apriaskar, Esa Mihaylova, Lyudmila University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Semarang50229 Indonesia

ISBN: (纸本)9798350368031

The introduction of autonomous vehicles (AVs) presents a novel approach to regulating and optimising traffic flow through the automated control of AVs. In this context, the AV is defined as the actuator and an optimal control policy is desired to make control decisions. Deep Reinforcement Learning (DRL) is a novel method which aims to maximize the cumulative rewards given by the predefined reward function by making sequential decisions in a stochastic environment. In light of the above, we propose a DRL-based vehicular control method to train an optimal policy for the control of AV in a model-free fashion, and consequently improve the traffic efficiency with the obtained control policy. A single-lane circular road environment with both AV and human-driven vehicles is selected to serve as the mixed autonomy traffic system in the Simulation of Urban MObility (SUMO) [1] traffic simulator, and the Proximal Policy Optimization (PPO) algorithm is applied for the policy improvement. Simulation results demonstrate that our strategy is effective in mitigating the unstable stop-and-go waves, increasing 67.7% of the average driving speed and reducing 19.3% of the average energy consumption in a closed-ring road environment. © 2024 IEEE.

关键词： Deep reinforcement learning

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Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyML 2nd

Edge Intelligence for Wildlife Conservation: Real-Time Hornb...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Hing, Kong Ka Behjati, Mehran Saleh, Vala Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Electrical Engineering Shiraz University of Technology Shiraz Iran Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real-time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real-time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno-canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel-Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real-world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental monitoring

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Neural Network Trained Cold-plate Models for Modular Converter Exploration

Neural Network Trained Cold-plate Models for Modular Convert...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Shabani, Hamed Cuzner, Robert M. Ordonez, Juan Marques, Crístofer Hood University of Wisconsin - Milwaukee Center for Sustainable Electrical Energy Systems Department of Electrical and Computer Engineering MilwaukeeWI United States FAMU-FSU College of Engineering Florida State University Center for Advanced Power Systems Department of Mechanical Engineering TallahasseeFL United States RS Rio Grande Brazil FAMU-FSU College of Engineering Center for Advanced Power Systems TallahasseeFL United States

ISBN: (纸本)9798350376067

Designing power electronics for medium voltage (MV) and applications, such as the Modular Multilevel Converter (MMC) MVac-MVdc converter for Multi-Terminal (MT) MVdc systems, requires a multidisciplinary approach to optimize a highly intricate system. One of the main challenges in this field is exploring various MMC implementations and evaluating them through appropriate metrics to determine the most effective approach. To address this challenge, this paper introduces a Virtual Prototyping Process (VPP) for Power Electronic Building Block (PEBB) based systems that leverages neural network-trained coldplate surrogate models. These coldplate surrogate model is useful in managing the complexity of a single thermal interface point connected to multiple coldplate-cooled elements within an MMC Sub-Module Drawer (SMD) to a system-level thermal management system. This approach enables the correlation of the intrinsic power capability of point of source to point of load Power Trains as a function of coolant approach and thermal management system capability to facilitate solutuion space exploration. This exploration is conducted in an evolutionary environment (within the VPP) that balances competing objectives like power density, efficiency, specific power and specific cost. The shipboard Integrated Power and Energy System (IPES) application is used as a use-case example. © 2024 IEEE.

关键词： Benchmarking

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