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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Salama, Ramiz Al-Turjman, Fadi Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Mersin 10 Nicosia Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin 10 Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin 10 Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

Blockchain and artificial intelligence are two technologies that, when combined, have the ability to help each other realize their full potential. Blockchains can guarantee the accessibility and consistent admittance to integrity safeguarded big data indexes from numerous areas, allowing AI systems to learn more effectively and thoroughly. Similarly, artificial intelligence (AI) can be used to offer new consensus processes, and hence new methods of engaging with Blockchains. When it comes to sensitive data, such as corporate, healthcare, and financial data, various security and privacy problems arise that must be properly evaluated. Interaction with Blockchains is vulnerable to data credibility checks, transactional data leakages, data protection rules compliance, on-chain data privacy, and malicious smart contracts. To solve these issues, new security and privacy-preserving technologies are being developed. AI-based blockchain data processing, either based on AI or used to defend AI-based blockchain data processing, is emerging to simplify the integration of these two cutting-edge technologies. © 2022 IEEE.

关键词： Internet of things

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Photon-Counting Integral Imaging Using Bayesian Estimation with Depth Camera Information 23

Photon-Counting Integral Imaging Using Bayesian Estimation w...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Uchino, Hideaki Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose a method for visualizing object information under low light conditions using photon-counting integral imaging and depth images as prior information. To visualize 3D objects under low-light conditions, computational photon-counting imaging may be used. It is possible to estimate photons using statistical methods such as the Poisson distribution. Therefore, photon-counting integral imaging can visualize 3D images under low-light conditions by maximum likelihood estimation (MLE). However, MLE does not consider prior information so it may not be an accurate estimation. In addition, it is difficult to obtain accurate depth information as the distance increases due to rounding errors caused by computational reconstruction. Therefore, we propose Bayesian estimation such as a maximum a posteriori estimation method that uses as a priori information images from a depth camera, which can be acquired even in low-light conditions, to improve the quality of 3D images. To evaluate image quality of the proposed method, optical experiments were conducted by calculating image quality metrics. This technology can be utilized for night vision applications, such as object recognition in security camera systems and autonomous driving, using RGB cameras and depth cameras. © 2023 ICROS.

关键词： Maximum likelihood estimation

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Improvement of Depth Mapping and Occlusion Removal Method Using Integral Imaging 23

Improvement of Depth Mapping and Occlusion Removal Method Us...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Sakanoue, Haruka Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose the depth mapping and occlusion removal method using integral imaging (InIm). InIm is a method to obtain 3D information by capturing and reconstructing an object from multiple viewpoints. InIm can remove occlusion using parallax obtained by photographing an object from multiple viewpoints. However, occlusion removal uses a unit matrix to calculate the number of overlaps, which results in an overall darkening of the image. To solve this problem, this paper aims to improve occlusion removal by using the matrix of the mask image to calculate the number of overlaps. InIm can provide depth mapping that shows the distance from an object as pixel values. However, when there is an occlusion in front of the object, it is difficult to provide depth mapping object due to the influence of the occlusion. To solve this problem, we propose a method of depth mapping and occlusion removal method using InIm. Our method is expected to make great progress in improving safety in the field of autonomous driving. © 2023 ICROS.

关键词： Mapping

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A Study on Coherence Length and Interferometry in Digital Holographic Microscopy 23

A Study on Coherence Length and Interferometry in Digital Ho...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Koga, Takahiro Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

Digital Holographic Microscopy (DHM) can display 3D profiles of microscopic objects and is expected to be used in a wide range of fields such as microbiology, microstructural investigation, and disease diagnosis. Since these fields require detailed information of the object, acquiring an accurate 3D profile is the most important. However, DHM requires several experimental equipments, and it does not define appropriate lasers for interferometers. Therefore, in this paper, we implement an experiment and find the optimum laser for the most accurate 3D profiles of interferometers by comparing 3D profiles using two types of interferometers and three types of lasers with different wavelengths. The results show that the relationship between the laser's coherence length and the optical path length of the interferometer is related to the accuracy of the 3D profiles. They also show that the accuracy of the 3D profile is higher when the coherence length is longer than the optical path length plus twice the height of the object. © 2023 ICROS.

关键词： Computational Imaging

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Autoencoder-XGBoost Classifier (AeXGB) for Predicting Severity Level of Parkinson's Disease from Spontaneous Speech

Autoencoder-XGBoost Classifier (AeXGB) for Predicting Severi...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wai, Thiri Liao, Yu-Shan Liao, Ting-Yun Lin, Chin-Hsien Hung, Chi-Sheng Fu, Li-Chen National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Biomedical Engineering College of Medical Science and Technology Taipei Medical University National Taiwan University Hospital Bei-Hu Branch Department of Neurology Taipei Taiwan Institute of Molecular Medicine College of Medicine National Taiwan University Taipei Taiwan National Taiwan University Hospital and National Taiwan University College of Medicine Department of Internal Medicine Taipei Taiwan and Advanced Robotics Taiwan

ISBN: (纸本)9781665410205

Parkinson's disease (PD) is the cause of the gradual decline of nerve cells that control movement disorder disease, which is most common among the elderly in the US after Alzheimer's disease. There are several studies on detecting Parkinson's disease from speech using machine learning techniques;however, most of them focus on classifying healthy patients (HC) against Parkinson's disease (PD). This paper focuses on developing a screening system that could detect healthy (HC) vs. mild Parkinson's (MP) vs. severe Parkinson's (SP) from spontaneous speech. Four acoustic feature sets were compared for the screening system. Our proposed AeXGB was also compared with six different classifying approaches. The result has shown that extracting the phonation features and using AeXGB could achieve an accuracy of 92% for classifying HC vs. MP vs. SP, which outperforms the traditional machine learning approach for three class classifications of Parkinson's severity level from spontaneous speech. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Dynamic models for Planar Peristaltic Locomotion of a Metameric Earthworm-like Robot

arXiv

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arXiv 2023年

作者： Zhou, Qinyan Fang, Hongbin Bi, Zhihai Xu, Jian Institute of AI and Robotics MOE Engineering Research Center of AI & Robotics State Key Laboratory of Medical Neurobiology Fudan University Shanghai20043 China

The development of versatile robots capable of traversing challenging and irregular environments is of increasing interest in the field of robotics, and metameric robots have been identified as a promising solution due to their slender, deformable bodies. Inspired by the effective locomotion of earthworms, earthworm-like robots capable of both rectilinear and planar locomotion have been designed and prototyped. While much research has focused on developing kinematic models to describe the planar locomotion of earthworm-like robots, the authors argue that the development of dynamic models is critical to improving the accuracy and efficiency of these robots. A comprehensive analysis of the dynamics of a metemeric earthworm-like robot capable of planar motion is presented in this work. The model takes into account the complex interactions between the robot's deformable body and the forces acting on it, and draws on the methods previously used to develop mathematical models of snake-like robots. The proposed model represents a significant advancement in the field of metameric robotics and has the potential to enhance the performance of earthworm-like robots in a variety of challenging environments, such as underground pipes and tunnels, and serves as a foundation for future research into the dynamics of soft-bodied robots. Copyright © 2023, The Authors. All rights reserved.

关键词： Robots

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Depth-Guided Vision Transformer With Normalizing Flows for Monocular 3D Object Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 673-689页

作者： Cong Pan Junran Peng Zhaoxiang Zhang the Center for Research on Intelligent Perception and Computing(CRIPAC) National Laboratory of Pattern Recognition(NLPR)Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 the School of Future Technology University of Chinese Academy of Sciences(UCAS)Beijing 100049China the Huawei Inc. Beijing 100085China IEEE the Institute of Automation Chinese Academy of Sciences(CASIA)Beijing 100190 the University of Chinese Academy of Sciences(UCAS) Beijing 100049 the Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science&Innovation Chinese Academy of Sciences(HKISI CAS)Hong Kong 999077China

Monocular 3D object detection is challenging due to the lack of accurate depth *** methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB ***-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion ***,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with *** from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth *** we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each ***,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input *** method is the first Swin-Transformer-based backbone architecture for monocular 3D object *** experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.

关键词： Monocular 3D object detection normalizing flows Swin Transformer

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PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Gao, Ning Zhou, Sanping Wang, Le Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

Semi-supervised learning has emerged as a widely adopted technique in the field of medical image segmentation. The existing works either focuses on the construction of consistency constraints or the generation of pseudo labels to provide high-quality supervisory signals, whose main challenge mainly comes from how to keep the continuous improvement of model capabilities. In this paper, we propose a simple yet effective semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation, whose goal is to generate high-fidelity pseudo labels by learning robust and diverse features in the training process. Specifically, our PMT employs a standard mean teacher to penalize the consistency of the current state and utilizes two sets of MT architectures for co-training. The two sets of MT architectures are individually updated for prolonged periods to maintain stable model diversity established through performance gaps generated by iteration differences. Additionally, a difference-driven alignment regularizer is employed to expedite the alignment of lagging models with the representation capabilities of leading models. Furthermore, a simple yet effective pseudo-label filtering algorithm is employed for facile evaluation of models and selection of high-fidelity pseudo-labels outputted when models are operating at high performance for co-training purposes. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches across various dimensions. The code is available at https://***/Axi404/PMT. © 2024, CC BY.

关键词： Self-supervised learning

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Noise Removal in Scattering Media Enviorment Using Peploaraphy and DCLGAN 23

Noise Removal in Scattering Media Enviorment Using Peploarap...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Numata, Riki Kim, Hyun-Woo Ono, Seiya Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

Noise removal in scattering media environments such as fog and smoke are an important task in reality. For example, it can be applied in many fields such as searching under turbid water and rescuing in smoke. Therefore, the purpose of this research is to remove the noise under the scattering medium environment and to visualize the object. As a conventional method for removing noise, there is a technique called peplography. Peplograhy enables the denoising of dense fog and reconstructing the image in scattering media environments. However, peplography has a problem that complex noise exists after removing particularly deep noise, and as a result, it becomes difficult to visualize the object. This research aims to solve this problem. We propose a new denoising technology that combines peplography with DCLGAN. DCLGAN is an image transformation algorithm using deep learning and an improved model of CycleGAN which is also used in the field of noise reduction. In this study, we evaluate the conventional method and the proposed method for scattering media images. As the evaluation method, visual evaluation and numerical evaluation using the PSNR index are performed to demonstrate the effectiveness of the proposed method. We believe that the proposed method can be applied in various fields such as object visualization under turbid water and automatic driving technology with fog removed. © 2023 ICROS.

关键词： Smoke

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Creation of a Mobile App for engineering Students' Online Lab 2

Creation of a Mobile App for Engineering Students' Online La...

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2nd IEEE International Conference on Artificial Intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Salama, Ramiz Al-Turjman, Fadi Ai and Robotics Institute Near East University Research Center for Ai and IoT Department of Computer Engineering Mersin 10 Nicosia Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin 10 Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin 10 Kyrenia Cyprus

ISBN: (纸本)9798350334104

This app helps you access your study materials on your mobile phone. It is an application that allows you to easily communicate with the instructor. Instructors can provide reading materials such as text, images, videos, and links to other sources such as YouTube. It can also be an online video session between instructor and student. Furthermore, this app is a miniature/similar app that adds a laboratory simulation to the university's current online education system (UZEM SYSTEM). This is because the app is aimed at engineering students, and most engineering courses require practicality, so lab internships are required. This allows students to perform laboratory simulations with the help of instructors. The student will need to connect into the app in order to access the contents. Both the teacher and the student will need to select a course from a selection of courses after logging into the site. The chosen course will offer a list of alternatives for learning/teaching techniques, including face-to-face instruction, lab simulations, presentations on a screen, messaging, etc. © 2022 IEEE.

关键词： Students

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