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IEEE International Conference on Consumer Electronics (ICCE)

作者： Xiang-Rui Huang Huai-Jhan Ke Szu-Yu Kuo Liang-Bi Chen Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Shipping and Transportation Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Lilies are popular in the global flower market, but consumers often lack information about specific varieties. To address this issue, this paper proposes a computer recognition platform based on the Vision Transformer (ViT) architecture. The proposed platform uses an improved vision transformer (ViT) architecture to classify different types of lilies, allowing consumers to access information and names of various Lilium species. The experimental results show that the proposed lily classification model achieved a 96.4% accuracy rate in classifying six lily species.

关键词： computer vision Electric potential Accuracy Computational modeling computer architecture computer applications Flowering plants Network architecture Transformers Consumer electronics

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Hierarchical B-frame Video Coding Using Two-Layer CANF without Motion Coding

arXiv

引用

arXiv 2023年

作者： Alexandre, David Hang, Hsueh-Ming Peng, Wen-Hsiao Electrical Engineering and Computer Science International Graduate Program Institute of Electronics Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Typical video compression systems consist of two main modules: motion coding and residual coding. This general architecture is adopted by classical coding schemes (such as international standards H.265 and H.266) and deep learning-based coding schemes. We propose a novel B-frame coding architecture based on two-layer Conditional Augmented Normalization Flows (CANF). It has the striking feature of not transmitting any motion information. Our proposed idea of video compression without motion coding offers a new direction for learned video coding. Our base layer is a low-resolution image compressor that replaces the full-resolution motion compressor. The low-resolution coded image is merged with the warped high-resolution images to generate a high-quality image as a conditioning signal for the enhancement-layer image coding in full resolution. One advantage of this architecture is significantly reduced computational complexity due to eliminating the motion information compressor. In addition, we adopt a skip-mode coding technique to reduce the transmitted latent samples. The rate-distortion performance of our scheme is slightly lower than that of the state-of-the-art learned B-frame coding scheme, B-CANF, but outperforms other learned B-frame coding schemes. However, compared to B-CANF, our scheme saves 45% of multiply–accumulate operations (MACs) for encoding and 27% of MACs for decoding. The code is available at https://***. Copyright © 2023, The Authors. All rights reserved.

关键词： Image coding

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Analysis of the mechanism of degradation of p-type silicon solar cell

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Transactions of the Korean Institute of electrical Engineers 2021年第8期70卷 1181-1187页

作者： Cha, Yewon Jeong, Sungjin Kim, Sungheon Kim, Youngkuk Yi, Junsin Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Korea Republic of

Two main factors that limiting the efficiency of the p-type solar cells are LID(Light Induced Degradation) and LeTID(Light and elevated temperature induced degradation). The major cause of LID is the formation of boron-oxygen defects in boron doped silicon. The LeTID is similar kinds as LID, but also occurs in the dark condition and by the recombination of metals and impurities or the reaction between the hydrogen and arbitrary substance. An overall 7% of LeTID degradation rate occurs over long-term scales. Unexpectedly, these degradations have been recently found to also arises in high purity materials such as float-zone(Fz-Si) and Czochralski-grown(Cz-Si). The proposed method to reduce these two degradations is to use appropriate firing process and annealing conditions. © 2021 Korean Institute of electrical Engineers. All rights reserved.

关键词： Degradation

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads Data models Reliability Image classification

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Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context

arXiv

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arXiv 2024年

作者： Yu, Sangwon Kim, Ik-Hwan Song, Jongyoon Lee, Saehyung Park, Junsung Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Multi-hop reasoning, which requires multi-step reasoning based on the supporting documents within a given context, remains challenging for large language models (LLMs). LLMs often struggle to filter out irrelevant documents within the context, and their performance is sensitive to the position of supporting documents within that context. In this paper, we identify an additional challenge: LLMs' performance is also sensitive to the order in which the supporting documents are presented. We refer to this as the misordered context problem. To address this issue, we propose a simple yet effective method called context repetition (CoRe), which involves prompting the model by repeatedly presenting the context to ensure the supporting documents are presented in the optimal order for the model. Using CoRe, we improve the F1 score by up to 30%p on multi-hop QA tasks and increase accuracy by up to 70%p on a synthetic task. Additionally, CoRe helps mitigate the well-known "lost-in-the-middle" problem in LLMs and can be effectively combined with retrieval-based approaches utilizing Chainof- Thought (CoT) reasoning. Copyright © 2024, The Authors. All rights reserved.

关键词： Context sensitive languages

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Leveraging IoT and TinyML for Smart Battery Management in Electric Bicycles

Leveraging IoT and TinyML for Smart Battery Management in El...

引用

International Workshop on Secure Internet of Things (SIoT)

作者： Thommas Flores Matheus Andrade Morsinaldo Medeiros Miguel Amaral Marianne Silva Ivanovitch Silva Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte - Natal Brazil

Ahstract-Urban mobility and environmental sustainability are critical challenges intensified by population growth, urbanization, and increased vehicle presence, leading to issues like congestion, pollution, and climate change. To counter this, the urgency for efficient and eco-friendly transportation solutions is evident. Battery Management Systems (BMS) play a central role, overseeing batteries in electronics, vehicles, and energy storage systems for safety and reliability. Focusing on electric bicycles (e-Bikes), the article highlights BMS's role in enhancing cyclist experiences and delves into techniques utilizing energy-efficient hardware and deep learning, notably TinyML, to predict Lithium-Ion battery State of Health (SoH). It examines diverse architectures and parameters, unveiling relationships to guide machine learning model construction, emphasizing delayed feedback integration based on rigorous statistics. The article navigates through data segregation and model training processes, presenting a comprehensive approach that balances sustainability, efficiency, and technological progress in predicting e-Bike battery SoH.

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Electro-Polymerization Process with Double-Sided Electrodes for Supercapacitors 5

Electro-Polymerization Process with Double-Sided Electrodes ...

引用

5th IEEE International Flexible Electronics Technology Conference, IFETC 2023

作者： Koripally, Nandu Yao, Lulu Eedugurala, Naresh Azoulay, Jason D. Ng, Tse Nga University of California San Diego Department of Electrical and Computer Engineering La Jolla CA United States University of California San Diego Materials Science Engineering Program La Jolla CA United States Georgia Institute of Technology Chemisty Department AtlantaGA United States

ISBN: (纸本)9798350332094

Supercapacitors are known for longer cycle life and faster charging rate compared to batteries. However, the energy density of supercapacitors requires improvement to expand their application space. To raise the energy density of redox supercapacitors, this work demonstrates electro-polymerization on both sides of electrodes to increase material loading, reduce overall device mass, and consequently increase the capacitance, power, and energy density of supercapacitors. We achieved an energy density of 1823 mWh kg-1 and a power density of 16.5 W kg-1 with a wide potential window of 3 V. © 2023 IEEE.

关键词： Supercapacitor

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Saccadic Detection in Virtual Gaming for Dyslexia Classification

Saccadic Detection in Virtual Gaming for Dyslexia Classifica...

引用

IEEE International Symposium on Biomedical Imaging

作者： Yih-Choung Yu Haki Shyntassov Padmanabh Kaushik Lisa Gabel Department of Electrical and Computer Engineering Lafayette College Easton PA USA Department of Psychology and the Neuroscience Program Lafayette College Easton PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Dyslexia (aka reading disability) is the most common cause of learning disabilities. It affects children across language orthographies, despite adequate intelligence and educational opportunity. Studies have shown that identifying children with dyslexia at young age is crucial to provide effective intervention to improve learning outcomes. Recently we demonstrated that children with reading disability exhibit impaired performance on a virtual maze learning task across language orthographies. Using a machine learning algorithm, we have achieved a classification accuracy up to 80%. This paper presents an algorithm, including image segmentation, synchronization of image and text data, saccade detection, and event alignment, using the eye-gazing data recorded from an eye tracker during maze-solving tasks. The saccadic events detected by this algorithm showed good correlation with the incorrect decisions participants made during maze-solving, which could be an additional variable to be used in the machine learning algorithm to enhance the accuracy for dyslexia classification.

关键词： Image segmentation Machine learning algorithms Correlation Accuracy Signal processing algorithms Predictive models Dyslexia

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Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach

arXiv

引用

arXiv 2024年

作者： Lee, Saehyung Yu, Sangwon Park, Junsung Yi, Jihun Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

In this paper, we primarily address the issue of dialogue-form context query within the interactive text-to-image retrieval task. Our methodology, PlugIR, actively utilizes the general instruction-following capability of LLMs in two ways. First, by reformulating the dialogue-form context, we eliminate the necessity of fine-tuning a retrieval model on existing visual dialogue data, thereby enabling the use of any arbitrary black-box model. Second, we construct the LLM questioner to generate non-redundant questions about the attributes of the target image, based on the information of retrieval candidate images in the current context. This approach mitigates the issues of noisiness and redundancy in the generated questions. Beyond our methodology, we propose a novel evaluation metric, Best log Rank Integral (BRI), for a comprehensive assessment of the interactive retrieval system. PlugIR demonstrates superior performance compared to both zero-shot and fine-tuned baselines in various benchmarks. Additionally, the two methodologies comprising PlugIR can be flexibly applied together or separately in various situations. Our codes are available at https://***/Saehyung-Lee/PlugIR. © 2024, CC BY.

关键词： Content based retrieval

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