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IEEJ Transactions on Electronics, information and Systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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A Novel Inception Deep Learning model for Mobile AI Stroke Prediction system: AI Research and Industry Perspectives for Connected Health 6

A Novel Inception Deep Learning model for Mobile AI Stroke P...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Elbagoury, Bassant M. Maskeliuans, Rytis Zaghow, Marwa Kamel, Nabil Faculty of Computer and Information Sciences Ain Shams University Computer Science and Artificial Intelligence Programs Egypt Faculty of Computer Science and Engineering King Salman International University KSIU Egypt Kaunas University of Technology Faculty of Informatics Department of Multimedia Engineering Lithuania Dell Technologies EMEA Egypt

ISBN: (纸本)9798350373875

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed. © 2024 IEEE.

关键词： Learning systems

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Implementation and Performance Evaluation of Collective Communications Using CIRCUS on Multiple FPGAs

Implementation and Performance Evaluation of Collective Comm...

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2023 International Conference on High Performance Computing in Asia-Pacific Region Workshops, HPC Asia 2023

作者： Kikuchi, Kohei Fujita, Norihisa Kobayashi, Ryohei Boku, Taisuke Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781450399890

In the high-performance computing domain, Field Programmable Gate Array (FPGA) is a novel accelerator that exhibits high flexibility and performance characteristics distinct from other accelerators such as the Graphics Processing Unit (GPU). Recent advanced high-end FPGA is equipped with multiple channels of high speed optical link up to 100Gbps performance for each. This is a crucial feature when we construct PC clusters with FPGAs as accelerators, however it is not easy to utilize from user kernels because this feature is implemented in low level and simple direct communication between neighboring FPGAs. In order to provide the communication feature between FPGAs for accelerated PC clusters, we developed a communication system named CIRCUS which implies a user-friendly API from OpenCL and is equipped with routing function over multi-hop communication on multi-dimensional torus network of FPGAs. However, current CIRCUS only provides a point-to-point communication between source and destination FPGAs. In ordinary parallel processing environment such as MPI, the user program the message passing with various collective communication functions for parallel algorithm, for instance Allreduce, Allgather, etc. In this paper, we implement the collective communication function over CIRCUS for user-friendly programming of ordinary parallel algorithms on FPGAs. As the first target, we implement Allreduce function which is the most essential and important function. The paper describes the CIRCUS system briefly followed by the design, implementation and preliminary performance evaluation on Intel Stratix10 FPGAs. © 2023 ACM.

关键词： Field programmable gate arrays (FPGA)

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Comparative Analysis of COVID-19 Detection Methods Based on Neural Network

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computers, Materials & Continua 2023年第7期76卷 1127-1150页

作者： Inès Hilali-Jaghdam Azhari A Elhag Anis Ben Ishak Bushra M.Elamin Elnaim Omer Eltag Mohammed Elhag Feda Muhammed Abuhaimed S.Abdel-Khalek Computer Sciences and Information Technology Programs Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Quantitative Methods Higher Institute of ManagementUniversity of Tunis1073Tunisia Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam Bin Abdulaziz UniversityAl KharjSaudi Arabia Department of Computer Science Faculty of Science and ArtsKing Khalid UniversityAbha61421Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt

In 2019,the novel coronavirus disease 2019(COVID-19)ravaged the *** of July 2021,there are about 192 million infected people worldwide and 4.1365 million *** present,the new coronavirus is still spreading and circulating in many places around the world,especially since the emergence of Delta variant strains has increased the risk of the COVID-19 pandemic *** symptoms of COVID-19 are diverse,and most patients have mild symptoms,with fever,dry cough,and fatigue as the main manifestations,and about 15.7%to 32.0%of patients will develop severe *** are screened in hospitals or primary care clinics as the initial step in the therapy for *** transcription-polymerase chain reaction(PCR)tests are still the primary method for making the final diagnosis,in hospitals today,the election protocol is based on medical imaging because it is quick and easy to use,which enables doctors to diagnose illnesses and their effects more *** to this approach,individuals who are thought to have COVID-19 first undergo an X-ray session and then,if further information is required,a CT-scan *** methodology has led to a significant increase in the use of computed tomography scans(CT scans)and X-ray pictures in the clinic as substitute diagnostic methods for identifying *** provide a significant collection of various datasets and methods used to diagnose COVID-19,this paper provides a comparative study of various state-of-the-art *** impact of medical imaging techniques on COVID-19 is also discussed.

关键词： Neural networks frameworks intelligent systems COVID-19 CT

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The Implementation of Octave-S in the Risk Management Measurement of information System in the Department of Communication, Informatics, and Statistics 4th

The Implementation of Octave-S in the Risk Management Measur...

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4th International Conference on Inventive Computation and information Technologies, ICICIT 2022

作者： Sukardi, Oggsa Gaol, Ford Lumban Matsuo, Tokuro Master of Information System Management Binus Graduate Programs Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Binus Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789811974014

An information system is an important part in an organization to support business processes and to achieve its vision and mission. The information system nowadays has been one of the assets that ought to be protected by an organization. The Department of Communication, Informatics, and Statistics has the information system supporting its various operational activities. In order to protect the information system, OCTAVE-S method as the management method is used. It is expected that the OCTAVE-S method can manage the risks to the information system as the asset by specifying the possible risks and developing countermeasures and mitigation plans for each of the risks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk management

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, information and Communication Engineers.

关键词： Deep learning

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Deep Learning Approach for Seagrass Species Classification

Deep Learning Approach for Seagrass Species Classification

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2024 International Conference on Green Energy, Computing and Sustainable technology, GECOST 2024

作者： Ozaeta, Mark Anthony A. Fajardo, Arnel C. Brazas, Felimon P. Cantal, Jed Allan M. Graduate Programs Technological Institute of the Philippines-Manila Metro Manila Philippines College of Computing Studies Information and Communication Technology Isabela State University-Cauayan Isabela Philippines College of Science and Computer Studies De la Salle University-Dasmari Ñas Cavite Philippines

ISBN: (纸本)9798350357905

Integrating deep learning algorithms with image-based data presents considerable potential for improving classification tasks, specifically in the context of seagrass monitoring and conservation initiatives. As exceptionally productive ecosystems, seagrasses encounter significant risks from climatic fluctuations and human activities. Furthermore, careful monitoring and conservation approaches are imperative. However, the intricate characteristics of seagrass species present obstacles to developing robust classification models. This research presents a novel methodology for enhancing the identification of seagrass species by curating an extensive image dataset that includes temporal variations, a wide range of geographic locations, water depths, and turbidity levels. A deep learning approach based on differentiable architecture search with early stopping was used to train a neural network. The results showed an impressive 93.72% in classifying five seagrass species sampled in Calatagan, Batangas and Puerto Galera, Oriental Mindoro, Philippines namely, Cymodocea rotundata, Enhalus acoroides, Halophila ovalis, Syringodium isoetifolium, and Thalassia hemprichii. © 2024 IEEE.

关键词： Image classification

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Application of artificial intelligence techniques in modeling attenuation behavior of ionization radiation: a review

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Radiation Detection technology and Methods 2023年第1期7卷 56-83页

作者： Joseph Konadu Boahen Samir A.Elsagheer Mohamed Ahmed S.G.Khalil Mohsen A.Hassan Materials Science and Engineering Department School of Innovative Design EngineeringEgypt-Japan University of Science and Technology(E-JUST)P.O.Box 179New Borg El-Arab CityEgypt Computer Science and Information Technology Programs Egypt-Japan University of Science and Technology(E-JUST)AlexandriaEgypt Faculty of Engineering Aswan UniversityAswanEgypt Institute of Basic and Applied Science Egypt-Japan University of Science and Technology(E-JUST)179 New Borg El-Arab CityAlexandriaEgypt Environmental and Smart Technology Group Faculty of ScienceFayoum UniversityFayoum63514Egypt

Introduction Shielding of ionizing radiations,which are gamma rays,neutrons,and X-rays,can be achieved by attenuating its intensity using different *** is therefore crucial in ensuring the safety of lives and essential equipment in areas such as nuclear power plants,radiotherapy facilities,space exploration,and *** Intelligent technologies have become desirable in modeling shielding materials’attenuation behavior due to their unique *** The overview aims to present the recent application of AI technologies in modeling the radiation attenuation behavior of *** A total of 41 relevant articles were obtained using Scopus and web of science *** search was restricted to articles and conference papers published within the last two *** From the overview,it was realized that AI techniques can predict the attenuation properties of shielding materials and optimize the shield *** methods can be grouped into predictive models which are:fuzzy logic,Support Vector Regression,Neural Networks,and optimization models which include Genetic algorithms,Ant Colony,and Particle Swarm *** networks are the most robust and widely used *** predictive models are used in predicting parameters such as attenuation coefficient,buildup factor,shield thickness,and radiation dose rates,whiles the optimization techniques are employed in single and multi-objective attenuator *** In the overview,the accuracies and complexities of the various AI techniques have been discussed giving insight into their *** AI techniques are easy to model compared to conventional methods and can save computational time when coupled with conventional statistical and deterministic models or employed as a standalone technique.

关键词： Ionization radiation Attenuation behavior Artificial intelligence Modeling

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ArmorNet: Animated Cartoon Pornography Detection Using Transformer Network

ArmorNet: Animated Cartoon Pornography Detection Using Trans...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Mahmoud Taha Abdulwahab Al-Sammak Shady Y. Elmashad Ahmed B. Zaky Faculty of Engineering at Shoubra Egypt Computer Science and Information Technology Programs (CSIT) Benha University Egypt Japan University of Science and Technology

The management of inappropriate video content has become a critical issue in today's society, amplified by the internet's extensive accessibility. The control of sensitive material, such as explicit content and adult scenes, holds significant importance. Although advancements in machine learning and neural networks have enabled the creation of models to filter explicit content in movies, these methods might prove inadequate for efficiently filtering cartoon movies for children. This inadequacy stems from the differing criteria required for filtering content meant for children versus adults, with distinct parameters for age groups. To address this disparity, ArmorNet, a deep neural network model, has been proposed and compared against ResNet in a study that evaluated classification accuracy on Danbooru2020, a recognized dataset of animated cartoon images. This comparison highlights ArmorNet's superior performance and efficiency, despite the substantial computational resources needed for training. Notably, ArmorNet's lightweight design prioritizes classification accuracy, rendering it suitable for low-resource devices like mobile phones and gaming consoles.

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and Systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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