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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Melinda, Melinda Purnamasari, Prima Dewi Fahmi, Fahmi Sinulingga, Emerson Muliyadi, Muliyadi Yamin, Imam Ashabul Universitas syiah kuala Dept. electrical and computer engineering Banda Aceh Indonesia Universitas Indonesia Dept. electrical engineering Depok Indonesia Universitas sumatera utara Dept. electrical engineering Medan Indonesia Medan Indonesia Universitas syiah kuala Doctoral program in engineering dept. electrical engineering Banda Aceh Indonesia

ISBN: (纸本)9798350363104

Electroencephalography (EEG) is a non-invasive brain imaging technique essential for neuroscience research and clinical applications. Conventional EEG devices are often expensive and impractical for use outside clinical or laboratory settings. This study reports the experience of acquiring EEG data using the Cyton board biosensing, an open-source and portable biosensor device. Detailed procedures for subject preparation, electrode placement, device configuration, and EEG data acquisition are provided. A Butterworth filter was used to eliminate noise from the EEG signals, with results visualized using the Fast Fourier Transform (FFT). Quantitative EEG (qEEG) analysis was conducted using the Power Spectral Density (PSD) method. The highest PSD values were 428.04 μV2/Hz at 5.9 Hz on channel C4 for subject 1,329.77 μV2/Hz at 5.9 Hz on channel C4 for subject 2, and 385.65 μ V2/Hz at 5.9 Hz on channel Fp1 for subject 3, all indicating increased brain activity in the theta wave range. This study provides insights for researchers and practitioners using the Cyton board for EEG data acquisition and contributes to developing portable, affordable EEG methods. © 2024 IEEE.

关键词： Butterworth filters

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Rule-Based Dialect of Tulang Bawang Stemmer

Rule-Based Dialect of Tulang Bawang Stemmer

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Abidin, Zaenal Junaidi, Akmal Wamiliana Lumbanraja, Favorisen Rosyking Kurniasari, Dian Borman, Rohmat Indra Doctoral Program of Mathematics and Natural Sciences Universitas Lampung Bandar Lampung Indonesia Universitas Lampung Department of Computer Science Bandar Lampung Indonesia Universitas Lampung Department of Mathematics Bandar Lampung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandar Lampung Indonesia

ISBN: (纸本)9798331513320

Stemming, an essential procedure in natural language processing (NLP), diminishes words to their base forms, facilitating tasks such as information retrieval and sentiment analysis. Although stemming techniques for highresource languages are well-developed, numerous low-resource languages, including dialect of Tulang Bawang, suffer from inadequate solutions owing to a scarcity of linguistic data and resources. Existing systems, including rule-based stemmers, have demonstrated efficacy in processing low-resource languages such as Indonesian and Javanese by utilizing established morphological rules. Nonetheless, these methods encounter considerable obstacles, such as restricted adaptability, inability to accommodate unusual root structures, and excessive dependence on fixed rules that might result in over- or understemming. Rule-based methodologies frequently misidentify roots when faced with intricate affixes or unconventional word forms. We introduce an improved rule-based Tulang Bawang Stemmer aimed at overcoming these constraints by enhancing current linguistic rules and integrating new patterns specific to the language's morphology. Assessed on 500 test samples and 200 independent test samples, our improved stemmer attained gold standard evaluation metrics of 96.2% and 94%, respectively, surpassing prior implementations in both precision and generalization. The findings demonstrate the potential of enhanced rule-based techniques to improving NLP for lowresource languages. Improved stemming performance enables better downstream applications, promotes more efficient text analysis, and advances research in underrepresented languages. © 2025 IEEE.

关键词： Linguistics

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RoboRuggles: Design of a Combat Robot

RoboRuggles: Design of a Combat Robot

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Belinda Truong Brian Nolasco-Ramirez Sophia Sistachs Sophia Puga Ryan Ballard Mackeen Al-Hiraky Shauib Ibraheem Isaiah Alleyne Saurav Basnet Program of Electrical and Computer Engineering Wentworth Institute of Technology Boston MA Program of Electromechanical Engineering Wentworth Institute of Technology Boston MA Program of Mechanical Engineering Wentworth Institute of Technology Boston MA

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

With constant access to resources like research papers, LinkedIn, and YouTube videos in the $21^{\text {st }}$ century people are constantly met with images of new and evolving technologies that inspire them to expand their knowledge. An example of this is the TV show BattleBots which inspired this team of students to develop their own 3-pound robot to compete in the National Havoc Robot League (NHRL) in Norwalk, Connecticut against other teams. This paper follows the process in which the team developed a robot, RoboRuggles. The team had a chance to independently work through complex design challenges and use critical thinking skills to select components that met qualifications and constraints of the NHRL robotics competition.

关键词： Video on demand TV Social networking (online) Mobile communication Web sites Robots Videos Qualifications

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MODELING THE HODGKIN-HUXLEY NEURON TO DETERMINE NEURONAL ENERGY CONSUMPTION EFFICIENCY AND OXYGEN CONSUMPTION VALUES

Biomedical Sciences Instrumentation

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Biomedical Sciences Instrumentation 2022年第2期58卷 50-58页

作者： Enochson, Avery Ewert, Daniel Mitar, Dipankar Striker, Ryan Cleveland, Jerika Wolf, Henry Gieser, Anna Braaten, Ben Allen, Jeffery Allen, Monica Department of Integrated Engineering at Iron Range Engineering Program of Minnesota State University Mankato MN United States Department of Computer Science and Computer Engineering University of Wisconsin – La Crosse La CrosseWI United States Department of Electrical and Computer Engineering North Dakota State University FargoND United States Air Force Research Laboratory Munitions Directorate Eglin Air Force Base Eglin AFB FL United States

Understanding neuronal structure and function is essential to studying the human brain. The goal of this project was to create a model of human brain neurons that accurately reflects neuronal function, energy consumption, and oxygen consumption. Extensive work has been performed on the Hodgkin-Huxley model of neurons to accurately model neuronal firing. This study focuses on the creation of a model of the Hodgkin-Huxley neuron in MATLAB with the assistance of the DynaSim toolbox. This model was used to compute values and using another model the energy efficiency of the neuron was calculated and related to oxygen consumption, which then corresponds to Blood Oxygenation Level Dependent (BOLD) imaging in fMRI. The results of this study provide detailed visual and technical information about how the brain neurons function, which is crucial to the further development in brain imaging techniques and to further our understanding of why and how our complex brains function. © 2022 IAE All rights reserved.

关键词： Neurons

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

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computers, Materials & Continua 2023年第6期75卷 5947-5964页

作者： Ibrahim Aliyu Tai-Won Um Sang-Joon Lee Chang Gyoon Lim Jinsul Kim Department of ICT Convergence System Engineering Chonnam National UniversityGwangju61186Korea Graduate School of Data Science Chonnam National UniversityGwangju61186Korea Interdisciplinary Program of Digital Future Convergence Service Chonnam National UniversityGwangju61186Korea Department of Computer Engineering Chonnam National UniversityYeosu59626Korea

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest ***,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become *** this vein,efforts have been made to predict the HL and CL using a univariate ***,this approach necessitates two models for learning HL and CL,requiring more computational ***,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware *** this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D *** the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and *** the 1D data are not affected by excessive parameters,the pooling layer is not applied in this ***,the use of pooling has been questioned by recent *** performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

关键词： Artificial intelligence(AI) convolutional neural network(CNN) cooling load deep learning energy energy load energy building performance heating load prediction

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Design of an Area-Efficient and Error-Reduced CMOS Approximate Adder

Design of an Area-Efficient and Error-Reduced CMOS Approxima...

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International SoC Design Conference, ISOCC

作者： Novi Prihatiningrum Jungu Kang Chanyeong Choi Yeongkyo Seo Electrical and Computer Engineering / Program in Semiconductor and Device Inha University Incheon Korea

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

Approximate adders have emerged as promising solutions in facilitating error-tolerant computing applications where a degree of imprecision is acceptable. Approximate adders offer significant improvements in terms of area utilization and power consumption compared to their exact counterparts. This paper proposes a novel design of an 8-bit CMOS approximate adder that utilizes a 6-bit least significant inaccurate sub-adder using approximate full adder (AFA) and copy adder with an error reduction unit (ERU). The implementation results demonstrate that our proposed approximate adder improves area, power, and delay by up to 20%, 9%, and 24%, respectively compared to the conventional AFA-based 8-bit adder while maintaining a low MED of 9.57.

关键词： Deep learning Power demand Image processing CMOS technology Delays Adders

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Improved IP lookup technology for trie-based data structures

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Journal of computer and System Sciences 2023年 133卷 41-55页

作者： Lin, Yen-Heng Hsieh, Sun-Yuan Master Degree Program on Artificial Intelligence National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering Institute of Manufacturing Information Systems Institute of Medical Informatics National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou Puli Township54561 Taiwan

Many Internet protocol (IP) lookup algorithms have been formulated to improve network performance. This study reviewed and experimentally evaluated technologies for trie-based methods that reduce memory access, memory consumption and IP lookup time. Experiments involving binary tries (for simplicity) were conducted on four real-world data sets, two of which were on IPv4 router tables (855,997 and 876,489 active prefixes) and two of which were on IPv6 router tables (155,310 and 157,579 active prefixes). Technologies designed to reduce time taken (at the expense of memory consumption) worked well for IPv4, and those designed to reduce memory consumption worked well for IPv6. Various combinations of these technologies were applied together in the experiments. © 2022 Elsevier Inc.

关键词： Table lookup

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Localization and Perception for Control of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment

arXiv

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arXiv 2024年

作者： Wen, Bowen Graduate Program in Electrical and Computer Engineering The Ohio State University United States

Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. Accurate localization is essential for self driving vehicle navigation in GPS inaccessible environments. This thesis concentrates on low speed autonomous shuttles that are mainly utilized for university campus intelligent transportation systems and presents initial results of ongoing work on developing solutions to the localization and perception challenges of a university planned pilot deployment orientated application. The paper treats autonomous driving with real time kinematics GPS (Global Positioning Systems) with an inertial measurement unit (IMU), combined with simultaneous localization and mapping (SLAM) with three-dimensional light detection and ranging (LIDAR) sensor, which provides solutions to scenarios where GPS is not available or a lower cost and hence lower accuracy GPS is desirable. The in-house automated low speed electric vehicle from the Automated Driving Lab is used in experimental evaluation and verification. An improved version of Hector SLAM was implemented on ROS and compared with high resolution GPS aided localization framework in the same hardware architecture. The overall configuration that combines ROS with DSpace controller can be easily transplantable prototype in other hardware architectures for future similar research. Real-world experiments that are reported here have been conducted in a small test area close to the Ohio State University AV pilot test route. are used for demonstrating the feasibility and robustness of this approach to developing and evaluating low speed autonomous shuttle localization and perception algorithms for control and decision making. © 2024, CC BY.

关键词： Global positioning system

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A Case Study on Energy Consumption Trends and Demand Reduction Strategies at an Urban Campus

A Case Study on Energy Consumption Trends and Demand Reducti...

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Annual North-American Power Symposium

作者： Nicole Mejia Arias Gianna Cameron Saurav Basnet Program in Electrical and Computer Engineering Wentworth Institute of Technology Boston MA USA

ISBN: (数字)9798331521035

ISBN: (纸本)9798331521042

This study examines peak energy demands across campus buildings, assesses demand charges, and explores strategies to reduce energy consumption. Despite increased energy usage in 2023, demand charges were lower than in 2022. This decrease is attributed to advanced energy management strategies, the adoption of efficient technologies such as LED lighting, and participation in demand response programs. These initiatives have resulted in significant cost savings and enhanced energy management at Wentworth Institute of technology.

关键词： Energy consumption Costs Buildings Lighting Market research Light emitting diodes Demand response Energy management North America

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