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2021 International Symposium on Intelligent Signal Processing and Communication systems, ISPACS 2021

作者： Putra, Dody Ichwana Pratama, Muhammad Harry Bintang Lanante, Leonardo Grad, Hiroshi Ochi Kyushu Institute of Technology School of Computer Science and System Engineering Iizuka Japan

ISBN: (纸本)9781665419512

This paper proposes a method to detect the packet format and classify signal modulation type of wireless LAN signals using the distributed model of Convolutional Neural Network (CNN). The main advantages of this method compared to the conventional are the high accuracy of the model and the flexibility for lowering the complexity. These are achieved because the CNN models are trained to perform multiple small classification tasks instead of a single big classification task. This method makes retraining much easier. The five-fold cross-validation is applied to assess the performance for training and testing the model. To validate the model, actual Wi-Fi signals are used to test the model using Software Define Radio (SDR). The result shows the proposed method can accurately classify different packet formats and signal modulation types above 90%, even when different timing offsets occur. © 2021 IEEE.

关键词： Orthogonal frequency division multiplexing

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Study on Prediction of Melt Pool Shape in SLM Method for Maraging Steel 10

Study on Prediction of Melt Pool Shape in SLM Method for Mar...

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10th International Conference on Leading Edge Manufacturing Technologies in 21st Century, LEM 2021

作者： Fukunaga, Taiichiro Narahara, Hiroyuki Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

The Selective Laser Melting (SLM) of the powder bed fusion is a method of laminating metal powder that are selectively melted and solidified by laser beam to build a three-dimensional object. In recent years, its use has expanded in various fields such as the automotive industry, medical industry, and aerospace industry. In this study, we propose a molten pool formation model that uses only a small number of parameters that dominate melt pool shape formation. The formula was derived and compared with the experimental values. © 2021 The Japan Society of Mechanical Engineers.

关键词： Laser beams

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Ultimatelink Between Characters Having a Certain Meaning in Physical Space to URL in Cyberspace with Robust Print and Scan

Ultimatelink Between Characters Having a Certain Meaning in ...

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

作者： Keiji Yamadera Michiharu Niimi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

We propose a method that links character string having a certain semantic meaning on papers in real world to the digital information in cyberspace, and call it Ultimatelink. Ultimatelink adds additional information to characters by superimposing a circular color mark on the characters without changing the shape of the characters. A URL (Universal Resource Locator) can be generated by connecting additional information of semantically organized characters which are come from captured images by digital devices. In the experiment, we investigated the behavior of Ultimatelink for the five type of Japanese characters. The target characters are multiple katakana characters, multiple kanji characters only, multiple kanji and hiragana characters, kanji with a few strokes, and kanji with many strokes. As a result of experiments, it was confirmed that information superimposition and information extraction are possible in most cases, but there are problems such as failure to extract a single character or the ability to extract color markers appropriately depending on the character shape.

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Crafting Compelling Argumentative Writing for Undergraduates: Exploring the Nexus of Digital Annotations, Conversational Agents, and Collaborative Concept Maps

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Journal of Educational Computing Research 2024年第5期62卷 1327-1357页

作者： Sandra, Randi Proska Hwang, Wu-Yuin Zafirah, Afifah Hariyanti, Uun Engkizar, Engkizar Hadi, Ahmaddul Fauzan, Ahmad Universitas Negeri Padang West Sumatra Indonesia Graduate Institute of Network Learning Technology National Central University Taoyuan Taiwan College of Science and Engineering National Dong Hwa University Hualien Taiwan Department of Information System Faculty of Computer Science Universitas Brawijaya East Java Indonesia

Argumentative writing is a fundamental aspect of undergraduate students’ academic and scientific writing related to critical thinking and problem-solving skills. However, previous studies have shown that students face various difficulties with argumentative writing, such as unclear and illogical ideas, less-structured arguments, and unbalanced interpretation of issues, data, and evidence. This study aims to improve the argumentative writing skills of undergraduate students by integrating computer-supported argumentative writing tools, such as annotation, conversational agents (CAs), and collaborative concept maps, into an online learning management system. Since the study was conducted during the COVID-19 pandemic, these tools can support meaningful learning activities and investigation in argumentative writing. The researchers divided sixty participants into the experimental group (N = 30) and the control group (N = 30). The results showed that the experimental group’s writing achievements outperformed the control group, and the three tools effectively improved the five elements of argumentative writing, including claims, grounds, warrants, backings, and rebuttal. Furthermore, a deep analysis found that the number of annotations, valid CAs’ responses, and argument nodes on collaborative concept maps can significantly predict students’ argumentative writing development. Moreover, students perceived that the incorporated tools could effectively improve their argumentative writing skills. © The Author(s) 2024.

关键词： argumentative writing collaborative concept map conversational agents digital annotation undergraduate students

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Image Reconstruction for Still Image Capture Using Wireless CMOS Imaging Device

Image Reconstruction for Still Image Capture Using Wireless ...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Treepetchkul, Thanaree Siwadamrongpong, Ronnakorn Tan, Renzo Roel P. Takehara, Hironari Sunaga, Yoshinori Haruta, Makito Tashiro, Hiroyuki Sasagawa, Kiyotaka Ohta, Jun Division of Materials Science Graduate School of Science and Technology Nara Institute of Science and Technology 8916-5 Takayama Nara Ikoma630-0192 Japan Division of Information Science Graduate School of Science and Technology Nara Institute of Science and Technology 8916-5 Takayama Nara Ikoma630-0192 Japan Department of Opto-Electronic System Engineering Faculty of Science and Technology Chitose Institute of Science and Technology 758-65 Bibi Hokkaido Chitose366-8655 Japan Department of Health Science Faculty of Medical Sciences Kyushu University 3-1-1 Maidashi Hikashi-ku Fukuoka812-8582 Japan

We introduce a wireless system for lensless imaging devices. By reconstructing the low-resolution image acquired by dividing the image into sub-pixel arrays, a high-resolution still image was acquired on a low-transfe... 详细信息

关键词： CMOS integrated circuits

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Modelling in-device inference and classification of binary digits using nonlinear dynamics of spin Hall oscillator

Modelling in-device inference and classification of binary d...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Mohan, John Rex Yamanaka, Chisato Feng, Ryoyan Mathew, Arun Jacob Nakamura, Yoji Medwal, Rohit Gupta, Surbhi Rawat, Rajdeep Singh Fukuma, Yasuhiro Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Department of Physics and Information Technology Iizuka820-8592 Japan Indian Institute of Technology Kanpur Department of Physics Kanpur208016 India National Institute of Education Nanyang Technological University Natural Sciences and Science Education 637616 Singapore Kyushu Institute of Technology Research Center for Neuromorphic AI Hardware Kitakyushu808-0196 Japan

ISBN: (纸本)9798350338362

In this work, we employ micromagnetic modelling of a spin Hall oscillator for a direct inference and classification of binary digit inputs. The spectral characteristics of the oscillation is utilized for the classification. We observed a direct inference of binary digit inputs up to a sequence of four binary digits. Subsequently, handwritten digit image recognition is tested with the Modified National institute of Standards and Testing (MNIST) handwritten digit database and acquired an accuracy of 88.6% with a linear classifier network. © 2023 IEEE.

关键词： Classification (of information)

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Experimental Evaluation of Rate Adaptation using Deep-Q-Network in IEEE 802.11 WLAN

Experimental Evaluation of Rate Adaptation using Deep-Q-Netw...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Muhammad Harry Bintang Pratama Tomoki Nakashima Yuhei Nagao Masayuki Kurosaki Hiroshi Ochi Grad. School of Computer Science and System Engineering Kyushu Institute of Technology Iizuka Japan

Rate adaptation algorithm has an important role in the Wi-Fi network. It ensures that the nodes transmit at a suitable transmission rate with minimum packet errors on the receiving side. However, there are cases when the existing algorithms fail to adapt to the changes in the communication environment. In this paper, we propose a rate adaptation algorithm using a Deep Q-network (DQN), in which the DQN agent controls the transmission rate of a node in response to the communication environment. We also evaluate the proposed algorithm using the field-programmable gate array (FPGA) and software-defined radio (SDR). The experimental result shows that the proposed algorithm can adaptively select the suitable MCS and maintain the throughput.

关键词： Adaptation models Simulation Throughput Real-time systems Field programmable gate arrays Wireless fidelity IEEE 802.11 Standard

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana Zohair Ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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Active Control of Random Lasing Using the Optical Trapping Technique

Active Control of Random Lasing Using the Optical Trapping T...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

作者： Kaku, Takashi Yokoi, Naomichi Okamoto, Takashi Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Science and Technology Chitose Institute of Science and Technology 758-65 Bibi Chitose Hokkaido066-8655 Japan

A method to control the random laser action of particle-dispersed media using the optical trapping technique is proposed. By focusing a trapping beam into a small region of the scattering medium, some of the scattering particles are trapped in that region, causing the distribution of scatterers inhomogeneous. According to the experimental results, the emission intensity of the spectral peak varies, depending on whether the trapping beam is on or off. Other spectral features caused by the particle trapping are also examined. © IEEE 2022.

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Active Control of Random Lasing Using the Optical Trapping Technique

Active Control of Random Lasing Using the Optical Trapping T...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

ISBN: (纸本)9798350350012

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