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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Obe, Ruth Kaufmann, Brandt Baird, Kaelen Kadel, Sam Soltani, Yasmin Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Jiang, Zhuoran Sharma, Vijay R. Ren, Lei Peterson, Stephen W. Poif, Jerimy C. U. of Houston-Clear Lake Dept. of Computer Science and of Software Engineering United States U. of San Francisco Dept. of Mathematics and Statistics United States Skidmore College Dept. of Computer Science and of Mathematics United States Mount Holyoke College Dept. of Computer Science and of Psychology United States U. of Houston Dept. of Biomedical Engineering United States U. of Maryland Dept. of Information Systems Baltimore County United States U. of Maryland Dept. of Mathematics and Statistics Baltimore County United States Medical Physics Graduate Program Duke University United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Maryland School of Medicine Department of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa H3D Inc. United States

ISBN: (纸本)9798350345346

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy. © 2023 IEEE.

关键词： Clas-sification Compton camera Distributed Data Parallelism Proton beam therapy PyTorch Recurrent neural network

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A study on estimation of traveling direction for pedestrian dead reckoning by using inertial sensors

A study on estimation of traveling direction for pedestrian ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Takumi Matsumoto Masahiro Fujii Graduate Program in Information Electrical and Electronics Systems Engineering Utsunomiya University 7–1–2 Yoto Utsunomiya Tochigi JAPAN

ISBN: (数字)9781728198026

ISBN: (纸本)9781728198033

In this paper, we propose a new method to estimate the traveling direction for pedestrian by applying IMU (Inertial Measurement Unit) and PCA (Principal Component Analysis) to sensor information from a smartwatch. Because a result of PCA do not have orientation information, an estimate of the traveling direction includes an indeterminacy problem. We show that the proposed method can solve this problem and provide high accuracy in the traveling direction estimation by walking experiments.

关键词： Legged locomotion Measurement units Dead reckoning Conferences Estimation Consumer electronics Principal component analysis

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Deep-Learning-based MR Compressed Sensing using Non-randomly Under-sampled Signal in Nonlinear Phase Encoding Imaging

Deep-Learning-based MR Compressed Sensing using Non-randomly...

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Asia-Pacific Signal and information Processing Association Annual Summit and Conference (APSIPA)

作者： Satoshi ITO Shohei OUCHI Graduate Program in Information Electrical and Electronic Systems Engineering Graduate School of Regional Development and Creativity Utsunomiya University Tochigi Japan

ISBN: (数字)9789881476883

ISBN: (纸本)9781728181301

Optional image scaling, and hence aliasless image reconstruction, is feasible using a signal that violates the sampling theorem in MR phase scrambling Fourier transform imaging. In this method, the main and aliased image components are separated in the scaled space when a large scaling factor is selected. In the present study, a new fast imaging method, in which aliasing artifacts caused by undersampling of the signal, are removed in two steps: in the downscaled space introduced by aliasless reconstruction and through de-aliasing using a deep convolution neural network. The proposed method is shown to provide higher PSNR images compared to random sampling compressed sensing and has an advantage in terms of lowsampling-rate image acquisition.

关键词： Image reconstruction Spatial resolution Magnetic resonance imaging Fourier transforms Image coding Convolutional neural networks Acceleration

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A study on indoor 3D modeling by SOKUIKI sensor and omnidirectional camera 8

A study on indoor 3D modeling by SOKUIKI sensor and omnidire...

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8th IEEE Global Conference on Consumer Electronics, GCCE 2019

作者： Inoue, Shoma Fujii, Masahiro Graduate Program in Information Electrical and Electronics Systems Engineering Utsunomiya University 7-1-2 Yoto Tochigi Utsunomiya Japan

ISBN: (纸本)9781728135755

In this paper, we propose a new 3D space modeling method for a SLAM (Simultaneous Localization and Mapping) by using distance measurement information on the horizontal plane by a SOKUIKI sensor and a 360-degree panoramic image by an omnidirectional camera. The proposed method can suppress the measurement time and the amount of the measurement data in comparison with the conventional SLAM. We evaluate the proposed method by some measurement experiments. We show that it is possible to measure the size of simultaneous objects with a high degree of accuracy. © 2019 IEEE.

关键词： 3D modeling

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Citizen science in environmental and ecological sciences

NATURE REVIEWS METHODS PRIMERS

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NATURE REVIEWS METHODS PRIMERS 2022年第1期2卷 1-20页

作者： Morneau, Dominique Novel Data Ecosystems for Sustainability Research Group Advancing Systems Analysis Program International Institute for Applied Systems Analysis (IIASA) Laxenburg Austria Centre d’écologie et des sciences de la conservation Muséum national d’Histoire naturelle Paris France Faculté de droit Université de Namur Namur Belgium Citizen Science Lab Leiden University Leiden Netherlands Department of Mechanical Engineering University of Bath Bath UK Nordic Foundation for Development and Ecology (NORDECO) Copenhagen Denmark Department of Biology Brandeis University Waltham MA USA Ornamental Plant Pathology Department of Plant Pathology Puyallup Research and Extension Center Washington State University Puyallup WA USA Environmental and Sustainability Participatory Information Systems Group Institute of Marine Sciences (ICM-CSIC) Barcelona Spain Electron Microscopy Science Technology Platform The Francis Crick Institute London UK Departamento de Biologia Marina Facultad Ciencias del Mar Universidad Católica del Norte Coquimbo Chile Millennium Nucleus Ecology and Sustainable Management of Oceanic Island (ESMOI) Coquimbo Chile Centro de Estudios Avanzados en Zonas Áridas (CEAZA) Coquimbo Chile Department of Geography University College London (UCL) London UK

This PrimeView highlights the utility of citizen science across a range of ecological and environmental science research.

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Using Online Grocery Applications during the COVID-19 Pandemic: Their Relationship with Open Innovation

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第2期8卷

作者： Gumasing, Ma. Janice J. Prasetyo, Yogi Tri Persada, Satria Fadil Ong, Ardvin Kester S. Young, Michael Nayat Nadlifatin, Reny Redi, Anak Agung Ngurah Perwira School of Industrial Engineering and Engineering Management Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Malang 65154 Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Industrial Engineering Department BINUS Graduate Program—Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

This present research examines the behavioral intentions of Filipinos to use online grocery applications during the novel COVID-19 pandemic. The study proposes an integration of the health belief model (HBM) and the Unified Theory of Acceptance and Use of Technology (UTAUT2) to identify the factors affecting the acceptance and usage of Filipinos of online grocery applications in terms of the impact of health risk for COVID-19. To accurately measure the factors and their relationship to behavioral intentions and usage behavior, a questionnaire was developed and distributed to 373 residents in the Philippines. Partial least squares structural equation modeling (PLS-SEM) was applied as an analytical method for this study. The results revealed that performance expectancy, perceived benefits, perceived severity, and cues to action significantly influenced the behavioral intentions and usage of online grocery apps during the COVID-19 pandemic. The study’s findings can be utilized as a theoretical framework for future researchers of consumer behavior;e-commerce developers;and grocery industry retailers, to enhance the innovation and services of online grocery applications. The results of this study may also be used and capitalized on by investors and managers to apply in strategizing when developing and marketing online grocery applications among con-sumers. Moreover, the framework of this study may be adopted and utilized by other online markets, even in different counties. Further theoretical and practical aspects are discussed in this paper. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： health belief model online grocery applications structural equation modeling UTAUT2

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Terahertz Magnetospectroscopy of Gadolinium Gallium Garnet in Fields up to 25 T

Terahertz Magnetospectroscopy of Gadolinium Gallium Garnet i...

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JSAP-OSA Joint Symposia, JSAP 2021

作者： Bao, J. Peraca, N. Marquez Tay, F. Kritzell, T.E. Li, X. Noe, G.T. Katayama, I. Takeda, J. Nojiri, H. Yamahara, H. Tabata, H. Baydin, A. Kono, J. Department of Physics and Astronomy Rice University HoustonTX United States Department of Electrical and Computer Engineering Rice University HoustonTX United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX United States Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama Japan Institute for Materials Research Tohoku University Sendai Japan Department of Electrical Engineering and Information Systems Graduate School of Engineering University of Tokyo Japan Smalley-Curl Institute Rice University HoustonTX United States Department of Materials Science and NanoEngineering Rice University HoustonTX United States

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Non-invasive oscillatory neuromodulation of cortical vestibular functions: method and future implications

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Brain Stimulation 2023年第1期16卷 266-267页

作者： Giannotta, Alessandro Rossi, Simone Cinti, Alessandra Viberti, Francesca Benelli, Alberto Neri, Francesco De Monte, David Romanella, Sara Smeralda, Carmelo Donniacuo, Aniello Prattichizzo, Domenico Santarnecchi, Emiliano Mandalà, Marco Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab) Unit of Neurology and Clinical Neurophysiology Department of Medicine Surgery and Neuroscience University of Siena Italy Oto-Neuro-Tech Conjoined Lab Policlinico Le Scotte University of Siena Italy Otolaryngology Department of Medicine Surgery and Neuroscience Neurology and Clinical Neurophysiology Section University of Siena Italy Siena Robotics and Systems (SiRS) Lab Department of Information Engineering and Mathematics University of Siena Siena Italy Precision Neuroscience & Neuromodulation Program Massachusetts General Hospital Harvard Medical School Boston MA USA

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OpenCL-enabled Parallel Raytracing for Astrophysical Application on Multiple FPGAs with Optical Links

OpenCL-enabled Parallel Raytracing for Astrophysical Applica...

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IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC)

作者： Norihisa Fujita Ryohei Kobayashi Yoshiki Yamaguchi Taisuke Boku Kohji Yoshikawa Makito Abe Masayuki Umemura Center for Computational Sciences University of Tsukuba Japan Degree Program in Systems and Information Engineering University of Tsukuba Japan Degree Program in Pure and Applied Sciences University of Tsukuba Japan

ISBN: (数字)9781665415927

ISBN: (纸本)9781665415934

In an earlier study, we optimized the Authentic Radiative Transfer (ART) method to solve the space radiative transfer problems in early universe astrophysical simulations using an Intel Arria 10 Field programmable Gate Array (FPGA). In this paper, we optimize this method for use on the latest FPGA, an Intel Stratix 10, and evaluate its performance by comparing the GPU implementation on multiple nodes. For the multi-FPGA computing and communication framework, we apply our original system, called as Communication Integrated Reconfigurable CompUting System (CIRCUS), to realize OpenCL based programming and utilize multiple optical links on an FPGA for parallel FPGA processing, and this study is the first implementation of a real application applied using CIRCUS. The FPGA implementation is 4.54-, 8.41-, and 10.64-times faster than that of a GPU on one, two, and four nodes, respectively, for multi-GPU cases using an InfiniBand HDR100 network. It also achieves 94.2 % parallel efficiency running on four FPGAs. We believe this efficiency is brought about from the low-latency and high-efficiency pipelined communication of CIRCUS, which provide easy programming on multi-FPGAs using OpenCL for high-performance computing applications.

关键词： Field programmable gate arrays Subspace constraints Kernel Graphics processing units Hardware Random access memory Acceleration

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Applying mobility business intelligence concept in analyzing oil palm plantation productivity

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AIP Conference Proceedings 2024年第1期3026卷

作者： Andreas Wahyu Krisdiarto Irya Wisnubhadra Anzaludin Samsinga Perbangsa Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology STIPER Institute of Agriculture Yogyakarta Indonesia Informatics Engineering Department Atma Jaya Yogyakarta University Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia BDSRC Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The palm oil business employs almost 20 million people, generates USD 21 billion in revenue, and plays a vital role in Indonesia’s social economy. The Fresh Fruit Bunches (FFB) to Palm Oil Mills (POM) distribution system is one important aspect of fruit quality. Three steps are involved in getting Oil Palm FFB from the plantation to the POM. The first part of the procedure involves cutting FFB from the tree, the second stage involves gathering the fruit at a fruit collection point (FCP), and the third stage involves transporting the fruit to the palm oil mill (POM). As of now, the cost of the FFB transportation is still considerable, accounting for roughly 15% to 20% of the FFB pricing. The use of the Business Intelligence (BI) idea in the oil palm harvesting system is presented in this study as a foundation for creating web-based applications.

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