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An Application-Aware Clustering Strategy in Vehicular Ad Hoc Network

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An Application-Aware Clustering Strategy in Vehicular Ad Hoc...

International Conference on Computing, Electronics & Communications Engineering (iCCECE)

作者： Chi-Hung Wei Chin-Chih Chang Chien-Hao Chu Sean Hsiao Ph.D. Program in Engineering Science Chung Hua University Hsinchu City Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu City Taiwan Bachelor Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu City Taiwan

VANET (Vehicular Ad Hoc Network) was developed to address the unique challenges of communication in a mobile vehicular environment. This technology can enhance driving safety and traffic control. Moreover, similar principles can be applied to unmanned aerial vehicles and robotics. When transmitting data in moving vehicles, it is challenging as access points and base stations keep changing, requiring handovers between different access points and base stations. Clustering is a popular approach used to address this challenge. Common clustering strategies are based on the proximity, communication range, the density, the application requirements of the moving vehicles, or hybrid of them. In this paper, we propose a clustering strategy based on the application sublayer in the vehicle communication protocol. If two computing devices on the move run the same application, they can be grouped together. The contribution of this research is to develop a clustering mechanism based on the application with very low overhead. We also validate the proposed method through simulations. The simulation results indicate that the proposed method is feasible.

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A Square Patch Partially Reflective Surface for Improving the Antenna Gain Characteristic of Low-Cost Dual Polarized Microstrip Antenna

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A Square Patch Partially Reflective Surface for Improving th...

2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Chudpooti, Sukanya Sangpet, Patchadaporn Chudpooti, Nonchanutt Sathitanon, Nontakorn Somjit, Nutapong Akkaraekthalin, Prayoot King Mongkut's University of Technology North Bangkok Facutly of Appied Science Department of Industrial Physics and Medical Instrumentation Bangkok Thailand Faculty of Engineering King Mongkut's University of Technology North Bangkok Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand University of Leeds School of Electronic and Electrical Engineering Leeds United Kingdom

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

This paper presents an improvement gain of a dual-polarized microstrip patch antenna by using the partially reflective surface (PRS). The PRS is designed from a 10 × 10 array patches structure, which is patterned on a 0.8-mm FR-4 substrate. A-dual-port fed by two SMA connectors, which is selected to use as the source of an experiment. The square metallic patch is used to design the PRS layer. The dimension of the square patch is 5 mm × 5 mm. The length of the square patch is varied from 3.0mm to 4.5 mm by step of 0.5 mm to investigate the reflection coefficients, e.g., magnitude response and phase response. Based on the simulated results, a 4.5-mm square patch provides the highest reflection coefficient of 0.53. To prove the design concept, the size of a square patch of 4.5 mm is selected to fabricate and measure the radiation pattern. At an operating frequency of 3.5 GHz, the maximum gain at the zero degrees is increased from 1.2 dBi to 4.96 dBi when compared without and with PRS. © 2022 IEEE.

关键词： Reflection

A large language model-assisted education tool to provide feedback on open-ended responses

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arXiv 2023年

作者： Matelsky, Jordan K. Parodi, Felipe Liu, Tony Lange, Richard D. Kording, Konrad P. Department of Bioengineering University of Pennsylvania United States Research & Exploratory Development Department Johns Hopkins University Applied Physics Laboratory United States Department of Neuroscience University of Pennsylvania United States Department of Computer Science University of Pennsylvania United States Department of Computer Science Rochester Institute of Technology United States CIFAR LMB Program

Open-ended questions are a favored tool among instructors for assessing student understanding and encouraging critical exploration of course material. Providing feedback for such responses is a time-consuming task that can lead to overwhelmed instructors and decreased feedback quality. Many instructors resort to simpler question formats, like multiple-choice questions, which provide immediate feedback but at the expense of personalized and insightful comments. Here, we present a tool that uses large language models (LLMs), guided by instructor-defined criteria, to automate responses to open-ended questions. Our tool delivers rapid personalized feedback, enabling students to quickly test their knowledge and identify areas for improvement. We provide open-source reference implementations both as a web application and as a Jupyter Notebook widget that can be used with instructional coding or math notebooks. With instructor guidance, LLMs hold promise to enhance student learning outcomes and elevate instructional methodologies. © 2023, CC BY-NC-SA.

关键词： Students

Maximal Quantum Interaction between Free Electrons and Photons

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Physical Review Letters 2025年第4期134卷 043803-043803页

作者： Zetao Xie Zeling Chen Hao Li Qinghui Yan Hongsheng Chen Xiao Lin Ido Kaminer Owen D. Miller Yi Yang Department of Physics and HK Institute of Quantum Science and Technology The University of Hong Kong Pokfulam Hong Kong China Z. X. and Z. C. contributed equally to this Letter. Department of Applied Physics and Energy Sciences Institute Yale University New Haven Connecticut 06520 USA Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China Solid State Institute and Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology 32000 Haifa Israel

The emerging field of free-electron quantum optics enables electron-photon entanglement and holds the potential for generating nontrivial photon states for quantum information processing. Although recent experimental studies have entered the quantum regime, rapid theoretical developments predict that qualitatively unique phenomena only emerge beyond a certain interaction strength. It is thus pertinent to identify the maximal electron-photon interaction strength and the materials, geometries, and particle energies that enable one to approach it. We derive an upper limit to the quantum vacuum interaction strength between free electrons and single-mode photons, which illuminates the conditions for the strongest interaction. Crucially, we obtain an explicit energy selection recipe for electrons and photons to achieve maximal interaction at arbitrary separations and identify two optimal regimes favoring either fast or slow electrons over those with intermediate velocities. We validate the limit by analytical and numerical calculations on canonical geometries and provide near-optimal designs indicating the feasibility of strong quantum interactions. Our findings offer fundamental intuition for maximizing the quantum interaction between free electrons and photons and provide practical design rules for future experiments on electron-photon and electron-mediated photon-photon entanglement. They should also enable the evaluation of key metrics for applications such as the maximum power of free-electron radiation sources and the maximum acceleration gradient of dielectric laser accelerators.

关键词： Quantum optics

Strategies of Collaborative Filtering Recommendation Mechanism Using a Deep Learning Approach 3

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Strategies of Collaborative Filtering Recommendation Mechani...

3rd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2021

作者： Chang, Chin-Chih Chang, Huan-Ming Chung Hua University Becholar Program in Applied Foreign Language and Smart Commerce Hsinchu30012 Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu30012 Taiwan

ISBN: (纸本)9781665445160

Nowadays, recommendation systems are widely used to help users locate the items they want. Collaborative filtering (CF) is a commonly used method for the recommendation. CF techniques use user-item ratings for prediction but suffer from the problems of data sparsity, cold start, and scalability. Though the Matrix Factorization (MF) techniques like Singular Value Decomposition (SVD) or Principal Component Analysis (PCA) overcome the above-mentioned problems, these methods are possible to deliver unmeaningful results in the condition of a low ranked approximation and denser singular vectors. In this paper, we review strategies of collaborative filtering recommendation mechanisms and propose an approach based on an autoencoder of convolutional neural network. Autoencoders are unsupervised learning methods in which neural networks are supported for the task of representation learning. We identify the user's features through learning, and then use these features to combine the collaborative filtering algorithm to recommend items. The experimental results show that the convolutional autoencoder can effectively reduce the computations when the amount of data is huge and benefited from the performance of its convolutional neural network. © 2021 IEEE.

关键词： Recommender systems

Empirical influence functions to understand the logic of fine-tuning

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arXiv 2024年

作者： Matelsky, Jordan K. Ungar, Lyle Kording, Konrad P. Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States Research & Exploratory Development Department Johns Hopkins University Applied Physics Laboratory LaurelMD United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Department of Bioengineering Department of Neuroscience University of Pennsylvania PhiladelphiaPA United States CIFAR LMB Program Canada

Understanding the process of learning in neural networks is crucial for improving their performance and interpreting their behavior. This can be approximately understood by asking how a model's output is influenced when we fine-tune on a new training sample. There are desiderata for such influences, such as decreasing influence with semantic distance, sparseness, noise invariance, transitive causality, and logical consistency. Here we use the empirical influence measured using fine-tuning to demonstrate how individual training samples affect outputs. We show that these desiderata are violated for both for simple convolutional networks and for a modern LLM. We also illustrate how prompting can partially rescue this failure. Our paper presents an efficient and practical way of quantifying how well neural networks learn from fine-tuning stimuli. Our results suggest that popular models cannot generalize or perform logic in the way they appear to. © 2024, CC BY.

关键词： Sampling

Modeling Atomistically Assembled Diffractive Optics in Solids

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arXiv 2024年

作者： Kling, Trevor Na, Dong-Yeop Hosseini, Mahdi Department of Electrical and Computer Engineering Applied Physics Program Northwestern University EvanstonIL60208 United States Department of Electrical Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

We develop a model describing long-range atom-atom interactions in a two-dimensional periodic or a-periodic lattice of optical centers inside a solid-state host material. We consider realistic environmental and technical conditions such as frequency and position broadening. Even when considering a significant frequency broadening in the ensemble (∼300 GHz), we observe up to a three-fold increase in directional scattering from the resonant lattice in a system. The model can be used to scalably design quantum optical elements, e.g. a quantum lens, harnessing atomistic engineering (e.g. via ion implantation) of collective interactions in materials to enhance quantum properties. © 2024, CC BY.

关键词： Nanocrystals

DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance 11

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DCGAN-based Medical Image Augmentation to Improve ELM Classi...

11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Rando Setiawan, Noor Akhmad Permanasari, Adhistya Erna Rulaningtyas, Riries Suksmono, Andriyan Bayu Sitanggang, Imas Sukaesih Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Biomedical Engineering Program Dept. of Physics Universitas Airlangga Surabaya Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Ipb University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9781665460309

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%. © 2022 IEEE.

关键词： Generative adversarial networks

Semantic Data Representation for Explainable Windows Malware Detection Models

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arXiv 2024年

作者： Švec, Peter Balogh, Štefan Homola, Martin Kľuka, Ján Bisták, Tomáš Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava Slovakia Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská dolina Bratislava Slovakia

Ontologies are a standard tool for creating semantic schemata in many knowledge intensive domains of human interest. They are becoming increasingly important also in the areas that have been until very recently dominated by subsymbolic knowledge representation and machine-learning (ML) based data processing. One such area is information security, and specifically, malware detection. We thus propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE – the Windows binary format) malware files. This ontology is inspired by the structure of the EMBER dataset, which focuses on the static malware analysis of PE files. With this proposal, we hope to provide a unified semantic representation for the existing and future PE-malware datasets and facilitate the application of symbolic, neuro-symbolic, or otherwise explainable approaches in the PE-malware-detection domain, which may produce interpretable results described by the terms defined in our ontology. In addition, we also publish semantically treated EMBER data, including fractional datasets, to support the reproducibility of experiments on EMBER. We supplement our work with a preliminary case study, conducted using concept learning, to show the general feasibility of our approach. While we were not able to match the precision of the state-of-the-art ML tools, the learned malware discriminators were interesting and highly interpretable. Copyright © 2024, The Authors. All rights reserved.

关键词： Ontology