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IEEE Latin American Robotics Symposium, LARS

作者： Wilson Eduardo Barioni Igor Pardal Latini André Lazzaretti Marco Teixeira Flavio Neves Lucia Valeria Ramos De Arruda Automation and Advanced Control System Laboratory (LASCA) Graduate Program in Electrical and Computer Engineering (CPGEI) Universidade Tecnologica Federal do Parana (UTFPR) Curitiba Parana Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

Automated Guided Vehicles (AGVs) are usual in industrial settings, with an increasing economic impact on processes. They move through numerous environments inside factories, commonly navigating long distances, while performing several activities. The constant detection of such vehicles, especially in cases of maintenance and safety, is a main issue in an industry setting. Usually, this information could be provided by a supervisory system, but many applications are not so large as to make such a system viable. Thus, a solution via machine learning and computer vision is developed in this work, by using simple cameras, such as the factories' security cameras. As the industrial environment is a scenario with a lot of variation and noise, the Transfer Learning technique is used to improve the training step of the developed AGV detection system. Finally, a database with 1067 images is used to build and validate the model, achieving a result greater than 80% of F1-score for various confidence values.

关键词： Training computer vision Remotely guided vehicles Computational modeling Transfer learning Cameras Production facilities

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A Robust Subtree-Based Authentication Scheme for TMIS Using Chaotic Hash Function

A Robust Subtree-Based Authentication Scheme for TMIS Using ...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Chandrashekhar Meshram Mohammad S. Obaidat Balqies Sadoun Agbotiname Lucky Imoize Vishesh P. Gaikwad Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post Graduate College College of Chhindwara University Betul M.P. India King Abdullah II School of Information Technology University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China SRM University SRM Nagar Kattankulathur TN India Amity University Noida UP India College of Engineering Al-Balqa’ Applied University Al-Salt Jordan Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Surat Gujarat India

ISBN: (数字)9798350359091

ISBN: (纸本)9798350359107

The widespread availability of broadband telecommunication and the relatively affordable cost of vital signs monitoring devices have enabled telemedicine to bring its benefits directly to patients' homes. In contemporary healthcare, Telecare Medicine Information System (TMIS) facilitates remote disease diagnosis, treatment, and healthcare delivery through the capabilities of wireless communication and information technology. To protect patient confidentiality, TMIS must develop robust authentication systems since it operates on the public Internet, which exposes its operations to security vulnerabilities. To ensure patient privacy, we present a novel Subtree-based Authentication technique for TMIS. An evaluation of the proposed scheme reveals that it is resistant to known potential attacks, and offers more security effectiveness and resource efficiency than existing schemes, making it an excellent solution for enhancing the security of TMIS implementations.

关键词： Privacy Chaotic communication Telemedicine Authentication Passwords Internet Security

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer Science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning computer science Image recognition Gesture recognition computer architecture Auditory system Assistive technologies

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Terahertz Time-Domain Imaging for Radiotherapy Treatment Monitoring in Pancreatic Ductal Adenocarcinoma

Terahertz Time-Domain Imaging for Radiotherapy Treatment Mon...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Debamitra Chakraborty Bradley N. Mills Jing Cheng Ivan Komissarov Scott Gerber Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York Laboratory for Laser Energetics University of Rochester Rochester New York Department of Surgery University of Rochester Medical Center Rochester New York Department of Electrical and Computer Engineering University of Rochester Rochester New York

ISBN: (纸本)9798350369311

Terahertz time-domain imaging was performed of stereotactic body radiotherapy-treated murine pancreatic ductal adenocarcinoma (PDAC) with a high spatial resolution. To generate 2D maps of the tissue samples, the refractive index and absorption coefficient were used as imaging markers extracted with the maximum a-posteriori probability (MAP) estimation algorithm. The results were then compared with the terahertz maps of untreated murine PDAC tissue samples and demonstrated that terahertz imaging can probe physical changes with high sensitivity in the tissue post treatment.

关键词： Maximum a posteriori estimation Terahertz wave imaging Laser theory Sensitivity Refractive index Radiation therapy Time-domain analysis Spatial resolution Probes Monitoring

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Design and exploration of vertically stacked complementary tunneling field-effect transistors

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Applied Physics Express 2024年第1期17卷 014001-014001页

作者： Thoti, Narasimhulu Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

The purpose of this letter is to study the design and explore vertically stacked complementary tunneling field-effect transistors (CTFETs) using CFET technology for emerging technology nodes. As a prior work, the CTFET’s device-level simulations are implemented and deliberated in strict compliance with the experimental settings. This work comprises the study of physical and DC analyses by scaling the p- to n-CTFET separation (D pn), being a significant factor in CFET/CTFET design for its process difficulty. By utilizing the 50% benefit in footprint, the work is further extended to CTFET static random access memory implementation and characterization with hold/read noise margin analysis. © 2023 The Author(s). Published on behalf of The Japan Society of Applied Physics by IOP Publishing Ltd

关键词： Si-Ge alloys

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A Distributed Task Allocation Protocol for Cooperative Multi-UAV Search and Rescue Systems

A Distributed Task Allocation Protocol for Cooperative Multi...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： De Freitas, Edison Pignaton Basso, Maik Da Silva, Antonio Arlis Santos Vizzotto, Marcos Rodrigues Correa, Mateus Schein Cavalheiro Federal University of Rio Grande Do sul Graduate Program in Electrical Engineering Porto Alegre Brazil Federal University of Rio Grande Do sul Graduate Program in Computer Science Porto Alegre91.501-970 Brazil Federal University of Rio Grande Do sul Department of Electric Automation and Energy Porto Alegre Brazil

ISBN: (纸本)9780738131153

Nowadays, applications involving multiple robotic systems connected through a wireless network - to perform tasks together - have an increased research interest. Unmanned Aerial Vehicles (UAVs) are excellent tools for evaluating and surveying distributed robotic systems applications due to their flexibility and three-dimensional mobility capability. Cooperative systems composed of multiples UAVs may have a centralized coordination which assists the members of a team to make their decisions in a distributed manner regarding the tasks to be performed. This paper proposes a system composed of embedded hardware and a high-level communication protocol that serves as a basis for identifying, distributing, and allocating tasks in a simple and distributed way to be used by groups of UAVs. The proposal is evaluated in a search and rescue scenario. The setup is configured with two types of UAVs - namely, searchers (search for new tasks) and workers (execution of tasks). Also, a set of nine simulation experiments covering three different setups were performed. Then the proposal is evaluated by two sets of field experiments with real vehicles. The obtained results in the proposed experiments demonstrate that the system can be successfully used in the proposed scenario for task distribution and allocation. © 2021 IEEE.

关键词： Antennas

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Estimating the 3D center point of an object with Kinect sensor RGB-D images

Estimating the 3D center point of an object with Kinect sens...

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IEEE Latin American Robotics Symposium, LARS

作者： Gustavo Fardo Armênio João Alberto Fabro Renzo De Rosa Tognella Felipe Pierre Conter Marlon Vaz De Oliveira Everson De Souza Silva Computer Engineering Course - UTFPR Curitiba PR Brazil PPGCA-Graduate Program on Applied Informatics UTFPR-Federal University of Technology - Paraná Curitiba PR Brazil Information System Course - UTFPR Curitiba PR Brazil UNILA - University of Latin American Integration Foz do Iguaçu PR Brazil CPGEI-Graduate Program on Electrical Engineering and Industrial Informatics/UTFPR IFPR- Federal Institute of Technology - Paraná Pinhais PR Brazil PPGCA-Graduate Program on Applied Informatics UTFPR Curitiba Brazil

This article describes the estimation of a 3D point using a Kinect sensor and the Robot Operating System (ROS) along with You Only Look Once (YOLO) for object detection. The Kinect sensor provides RGB-D images, which are used to create a Point Cloud representing the geometry of the environment. ROS is used as a robotics development framework, while YOLO is employed to identify objects in the scene. The article presents the packages used, the datasets used for measurement, and the configuration of ROS and YOLO. Additionally, the functionalities of RViz, a 3D visualization tool used in the tests, are explored. Furthermore, it covers the methods employed, the acquired data, and an analysis of the error margin in relation to the measurement of the distance between the Kinect and the object. The findings and techniques presented in this study contribute to addressing the challenges faced in the RoboCup@Home competition, specifically in the context of object manipulation tasks.

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A Large Reservoir Computing Forecasting Method Based on Randomized Fuzzy Cognitive Maps

A Large Reservoir Computing Forecasting Method Based on Rand...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Omid Orang Fabricio J. Erazo-Costa Petrônio C. L. Silva Guilherme de Alencar Barreto Frederico Gadelha Guimarães Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte Brazil Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Federal Institute of Northern Minas Gerais Januária Campus Brazil Department of Teleinformatics Engineering Universidade Federal do Ceará Fortaleza Brazil

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

This paper introduces a novel forecastings technique based on randomized fuzzy cognitive maps (FCM), called LRHFCM (or large reservoir of randomized high-order FCM) for predicting univariate time series. LR-HFCM is a hybrid method combining fuzzy time series (FTS), FCMs, and reservoir computing. It is a type of echo state network (ESN) consisting of the input layer, intermediate (or large reservoir) layer, and output layer, where LASSO regression is applied to train the output layer. The novelty of this approach is that the internal layer includes a very large reservoir, considering different combinations from the sets of concepts and order using a certain number of sub-reservoirs to capture different dynamics of input time series. It is important to highlight that the weights within each sub-reservoir are chosen randomly and remain constant throughout the training process. The validity of the LR-HFCM approach is evaluated across 15 different time series datasets. The results highlight the outperformance of the LR-HFCM technique in comparison to various baseline models.

关键词： Training Adaptation models Adaptive systems Computational modeling Time series analysis Fuzzy cognitive maps Reservoirs

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Aldian Nurcahyo Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Compuer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia Compuer Science Departement Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices.

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electrical power quality improvement by using electricity treatment system

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AIP Conference Proceedings 2024年第1期3039卷

作者： Heddy Darmawan Charles O. P. Marpaung Periaman Sirumapea Post Graduate Program of Electrical Engineering Universitas Kristen Indonesia (UKI) Jakarta Indonesia Electrical Engineering Department Universitas Kristen Indonesia (UKI) Jakarta Indonesia

This publication aims to provide the most significant knowledge for advancing power quality using an electricity treatment system. The voltage between neutral and ground (NEV) has been confirmed to be zero volts for safety reasons. The NEV must be zero volts, especially concerning modern computer hardware or the time of 4-O. Regulator Sed device regulators will receive inaccurate data if the NEV is more significant than zero volts or higher than the value set in NEC 2020, which differs from what the industrial factory wants. Additionally, a human exposed to high NEV may risk an electric shock. The Electricity Treatment System was developed to improve the electrical power quality, including NEV and other electrical characteristics.

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