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5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Xia, Chun Gutierrez, Jesus J. Kuebler, Stephen M. Rumpf, Raymond C. Touma, Jimmy University of Central Florida The College of Optics and Photonics Creol OrlandoFL32816 United States University of Texas at El Paso Em Lab Department of Electrical and Computer Engineering El PasoTX79968 United States Materials Science and Engineering University of Central Florida Creol The College of Optics and Photonics Chemistry Department OrlandoFL32816 United States Computational Science Program University of Texas at El Paso Em Lab Department of Electrical and Computer Engineering El PasoTX79968 United States Eglin Afb AFRL/Munitions Directorate Integrated Sensing and Processing Branch FL32542 United States

Independent optical power and phase control is experimentally demonstrated in a self-collimating PC based on rod-into-wall unit cell and fabricated in a low-refractive-index medium by multiphoton lithography. © 2... 详细信息

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

关键词： Refractive index

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation 11

A Genetic Algorithm to Optimize Cargo Stability in Container...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Hsieh, Chi-Jung Wu, Kuang-Sung Wu, Tin-Yu Tey, Fu Jie Chen, Yi-Kai Ho, Jye Yi National Ilan University Department of Computer Science and Information Engineering Ilan County Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Pingtung County Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9781665492324

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method. © 2022 IEEE.

关键词： Genetic algorithms

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Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study

arXiv

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arXiv 2023年

作者： Zhang, Ruichen Xiong, Ke Du, Hongyang Niyato, Dusit Kang, Jiawen Shen, Xuemin Poor, H. Vincent The Engineering Research Center of Network Management Technology High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Design and fabrication of a metalens with a hexagonal array of intersecting-wall meta-atoms for operation in the near-infrared 5

Design and fabrication of a metalens with a hexagonal array ...

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5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Horton, Chad L. Kuebler, Stephen M. Martinez, Manuel Bustamante, Edgar Rumpf, Raymond C. Touma, Jimmy University of Central Florida Chemistry Department OrlandoFL32816 United States Creol The College of Optics and Photonics Chemistry Department University of Central Florida Department of Materials Science and Engineering OrlandoFL32816 United States University of Texas at El Paso Em Lab Department of Electrical and Computer Engineering El PasoTX79968 United States University of Texas at El Paso Em Lab Department of Electrical and Computer Engineering Computational Science Program El PasoTX79968 United States AFRL/Munitions Directorate Integrated Sensing and Processing Branch Eglin AFB FL32542 United States

A metalens with a hexagonal array of intersecting-wall meta-atoms was designed for operation at a wavelength of 1.550 μm with a new meta-optics design tool (MODT) and fabricated in the photopolymer IP-Dip by multi-ph... 详细信息

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

关键词： Photons

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Design and Development of a Low-Power Wireless MEMS Lead-Free Piezoelectric Accelerometer System

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IEEE Transactions on Instrumentation and Measurement 2023年 72卷

作者： Li, Cheng-Ying Chen, Ze-Hui Kao, Hsueh-Yu Chang, Sheng-Kai Hsiao, Po-Yu Huang, Yen-Hsiang Huang, Yu-Chieh Chu, Sheng-Yuan Wei, Chia-Ling Liu, Yun-Hui Cheng, Kuang-Wei Tsai, Cheng-Che National Cheng Kung University Department of Electrical Engineering Tainan701 Taiwan National Cheng Kung University Nano-Integrated-Circuit Engineering Program Tainan701 Taiwan Southern Taiwan University of Science and Technology Department of Mechanical Engineering Tainan71005 Taiwan National Cheng Kung University College of Electrical Engineering & Computer Science Department of Electrical Engineering Tainan701 Taiwan Tung Fang Design University School of Application Design Department of Digital Game and Animation Design Kaohsiung82941 Taiwan

In this article, a complete 3-D integrated sensing system intended for practical applications was presented on a printed circuit board (PCB) board, starting with lead-free piezoelectric films deposited using an RF sputtering system, an MEMS structure design/simulation, device fabrication, and a signal processing chip design, including charge- to-voltage transformation, signal amplification, 60-Hz noise rejection, and low-power RF wireless transmission chip for remote monitoring. Both the signal processing chips and the RF wireless transmitter chips were manufactured using the Taiwan Semiconductor Manufacturing Company (TSMC) CMOS process. The piezoelectric coefficient d33 was measured as 27.21 pm/V of the proposed ZnO:Li doped Mg (MLZO) films. To meet the frequency specifications of industrial applications, the ring-structure devices with a 7730-Hz resonance frequency were designed using ANSYS simulation software. There was only a 0.39% deviation compared to the simulation results with the actual experimental data. The measurement results of the proposed system showed that the acceleration detection range was approximately 137 mV/ g (p-p). The voltage-controlled oscillator (VCO) adopted in the wireless transmitter chip had a four-leaf clover structure inductor with two symmetrical axes resulting in better suppression of magnetic field radiation. Finally, this system was applied to industrial spindles, and the normal and abnormal operation information of spindles was successfully and wirelessly monitored. © 1963-2012 IEEE.

关键词： Wireless sensor networks

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Brain Tumor Detection and Segmentation Using Deep Learning Models

Brain Tumor Detection and Segmentation Using Deep Learning M...

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Artificial Intelligence (WSAI), World Symposium on

作者： Moses Kazeem Abiodun Abidemi Emmanuel Adeniyi Joseph Bamidele Awotunde Halleluyah Aworinde George Tinuoye Agbotiname Lucky Imoize Chun-Ta Li Cheng-Chi Lee Department of Computer Science Landmark University Omu-Aran Kwara State Nigeria Department of Computer Science Bowen University Iwo Osun State Nigeria Department of Computer Science University of Ilorin Ilorin Kwara State Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Nigeria Bachelor's Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City Taiwan Department of Library and Information Science Fu Jen Catholic University New Taipei City Taiwan

ISBN: (数字)9798350374162

ISBN: (纸本)9798350374179

This study focuses on brain tumor detection and segmentation using Convolutional Neural Networks (CNN) with architectures of Fully Convolutional Net-work (FCN) and VGG16. The dataset imported for this study consists of brain MRI images, which were preprocessed to improve the overall image resolution. Data augmentation techniques were applied to heighten the variability and volume of the training data. For feature extraction, CNN models were utilized, specifically the VGG16 architectures. This model was trained on a preprocessed dataset to learn the important features associated with the presence of brain tumor. Image segmentation was performed to precisely outline and identify the tumor region within the brain images. FCN architecture was employed for this task, which allowed pixel-level classification of tumor regions. Finally, image classification was carried out to group brain images based on the absence or presence of a tumor. The trained models were evaluated using accuracy, precision, recall and F1score as the performance metrics. The results of the study demonstrated promising outcomes, achieving an accuracy score of 93% and a precision, recall and f1score ranging from 75% to 100% based on the specific type of tumor enlisted. This indicates that the developed CNN models, with the FCN and VGG16 architectures, effectively detect and segment brain tumors from MRI images. The findings of this study contribute to the field of medical image analysis and gives a foundation for further research and development in brain tumor diagnosis and treatment. The study recommends the use of more deep learning algorithms for the process of detecting brain tumors.

关键词： Deep learning Image segmentation Accuracy Magnetic resonance imaging Computational modeling Brain tumors Brain modeling Distance measurement Classification algorithms Convolutional neural networks

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Exploring 3D Reconstruction with Drone Images: Advances and Challenges in Urban Environments

Exploring 3D Reconstruction with Drone Images: Advances and ...

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Annual Conference of Industrial Electronics Society

作者： Thiago João Miranda Baldivieso Taise Grazielle Da Silva Batista Fabio Suim Chagas Luiz Carlos Pacheco Rodrigues Velho Paulo Fernando Ferreira Rosa Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil State Institute of the Environment Inea/RJ Rio de Janeiro Brazil Department of Science and Industry Systems University of South-Eastern Norway Kongsberg Norway Department of Computer Graphics Institute for Pure and Applied Mathematics Rio de Janeiro Brazil Electrical Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software—both commercial and open-source—and concluding with a qualitative evaluation of the results.

关键词： Industrial electronics Three-dimensional displays Urban areas Aircraft Image reconstruction Photogrammetry Drones

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BRAIN-TO-TEXT BENCHMARK’24: LESSONS LEARNED

arXiv

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arXiv 2024年

作者： Willett, Francis R. Li, Jingyuan Le, Trung Fan, Chaofei Chen, Mingfei Shlizerman, Eli Chen, Yue Zheng, Xin Okubo, Tatsuo S. Benster, Tyler Lee, Hyun Dong Kounga, Maxwell Buchanan, E. Kelly Zoltowski, David Linderman, Scott W. Henderson, Jaimie M. Department of Neurosurgery Stanford University United States Department of Electrical & Computer Engineering University of Washington United States Department of Computer Science Stanford University United States Department of Applied Mathematics University of Washington United States China Neurosciences Phd Program Stanford University United States Wu Tsai Neurosciences Institute Department of Statistics Stanford University United States

Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-to-Text Benchmark’24 and associated competition was created to foster the advancement of decoding algorithms that convert neural activity to text. Here, we summarize the lessons learned from the competition ending on June 1, 2024 (the top 4 entrants also presented their experiences in a recorded webinar). The largest improvements in accuracy were achieved using an ensembling approach, where the output of multiple independent decoders was merged using a fine-tuned large language model (an approach used by all 3 top entrants). Performance gains were also found by improving how the baseline recurrent neural network (RNN) model was trained, including by optimizing learning rate scheduling and by using a diphone training objective. Improving upon the model architecture itself proved more difficult, however, with attempts to use deep state space models or transformers not yet appearing to offer a benefit over the RNN baseline. The benchmark will remain open indefinitely to support further work towards increasing the accuracy of brain-to-text algorithms. © 2024, CC BY.

关键词： Recurrent neural networks

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Synthesis of a magnetic iron oxide/zinc oxide engineered nanocatalyst for enhanced visible-light photodegradation of Cartasol brilliant violet 5BFN in aqueous solution

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Nano-Structures and Nano-Objects 2021年 26卷

作者： Algarín, Milagros Amaya, María Solano, Ricardo Patiño-Ruiz, David Herrera, Adriana Chemical Engineering Program. Nanomaterials and Computer-Aided Process Engineering Research Group. Universidad de Cartagena Cartagena Colombia Doctorate in Engineering Program. Nanomaterials and Computer-Aided Process Engineering Research Group. Universidad de Cartagena Cartagena Colombia

Engineered nanocomposites combine the physicochemical properties of different materials, increasing the applicability for environmental remediation. Cartasol brilliant violet (5BFN) is an organic and toxic compound that pollutes water sources due to the widespread use in the industry. In this study, a novel engineered nanocomposite was fabricated using magnetic iron oxide (IO-NPs) and zinc oxide (ZnO-NPs) nanoparticles and used for 5BFN photodegradation in aqueous solutions. The IO-NPs and ZnO-NPs were synthesized via the co-precipitation and green methods, respectively. A crystalline structure was confirmed for both nanoparticles with an average crystallite size of about 50nm for IO-NPs and 29nm for ZnO-NPs. The IO-NPs’ surface was modified using a silica matrix, allowing the encapsulation, accumulation, and formation of the ZnO-NPs shell. Although a superparamagnetic behavior is predominant, the ZnO-NPs promoted low spin coordination and decreased the saturation magnetization from 66.8 to 17.5 emu/g. A sterical stabilization with the siloxane and silanol groups of the silica matrix also decreases the magnetic response. The 5BFN photodegradation was evaluated varying the IO:ZnO weight ratio, initial pH, and nanocatalyst dose. A maximum photodegradation performance of 89.2% was achieved using 0.4 g/L of IO@ZnO-NPs and pH 6.8. The IO:ZnO weight ratio plays an important role, increasing the photocatalytic performance through the reduction of the bandgap. This enhances the visible light adsorption on the IO@ZnO-NPs, allowing a higher 5BFN photodegradation and extending their application for other organic dyes in water sources. © 2021 Elsevier B.V.

关键词： Cartasol brilliant violet 5BFN Iron oxide nanoparticles Nanocomposite Photodegradation Silica matrix Zinc oxide nanoparticles

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Dementia Detection by Shopping Refugees Support Robot 2

Dementia Detection by Shopping Refugees Support Robot

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2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020

作者： Kishimoto, Yuta Arai, Kenichi Imai, Tetsuo Kobayashi, Toru Nagasaki University Computer and Information Science Program Graduate School of Engineering Nagasaki Japan Nagasaki University Division of Electrical Engineering and Computer Science Graduate School of Engineering Nagasaki Japan

ISBN: (纸本)9781728170633

We have developed the Shopping Refugees Support Robot that enables the elderly to order items through Social Networking Service (SNS) by voice conversation without relying on smartphones. This paper proposes a detection method of dementia signs using this Robot. Concretely, deterioration of cognitive function is detected by the evaluation of short-term memory utilizing order history of the Robot. We expect detection of dementia signs on a daily basis without making the elderly person conscious of having a dementia test, by information that only the person knows. We implemented a prototype and confirmed its functional validity. © 2020 IEEE.

关键词： Internet of things

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