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ARSlice:Head-Mounted Display Augmented with Dynamic Tracking and Projection

Journal of computer science & technology 2022年第3期37卷 666-679页

作者： Yu-Ping Wang Sen-Wei Xie Li-Hui Wang Hongjin Xu Satoshi Tabata Masatoshi Ishikawa Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Institute of Semiconductors Guangdong Academy of SciencesGuangzhou 510651China Data Science Research Division Information Technology CenterThe University of TokyoTokyo 113-8656Japan Core Network Department Advanced Operations Development DivisionKDDI CooperationTokyo 206-0034Japan Tokyo University of Science Tokyo 113-8601Japan

Computed tomography(CT)generates cross-sectional images of the *** CT images has been a challenging *** emergence of the augmented and virtual reality technology has provided promising ***,existing solutions suffer from tethered display or wireless transmission *** this paper,we present ARSlice,a proof-of-concept prototype that can visualize CT images in an untethered manner without wireless transmission *** ARSlice prototype consists of two parts,the user end and the projector *** employing dynamic tracking and projection,the projector end can track the user-end equipment and project CT images onto it in real *** user-end equipment is responsible for displaying these CT images into the 3D *** main feature is that the user-end equipment is a pure optical device with light weight,low cost,and no energy *** experiments demonstrate that our ARSlice prototype provides part of six degrees of freedom for the user,and a high frame *** interactively visualizing CT images into the 3D space,our ARSlice prototype can help untrained users better understand that CT images are slices of a body.

关键词： augmented and virtual reality computed tomography(CT)image visualization interactive visualization

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Bioinspired Quantum Oracle Circuits for Biomolecular Solutions of the Maximum Cut Problem

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2024年第3期23卷 499-506页

作者： Chang, Weng-Long Wong, Renata Chen, Yu-Hao Chung, Wen-Yu Chen, Ju-Chin Vasilakos, Athanasios V. National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City Taiwan National Taiwan University Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Chang Gung University Department of Artificial Intelligence Taoyuan33302 Taiwan National Taiwan University Department of Physics Taipei10617 Taiwan Grimstad4630 Norway

Given an undirected, unweighted graph with n vertices and m edges, the maximum cut problem is to find a partition of the n vertices into disjoint subsets V1 and V2 such that the number of edges between them is as large as possible. Classically, it is an NP-complete problem, which has potential applications ranging from circuit layout design, statistical physics, computer vision, machine learning and network science to clustering. In this paper, we propose a biomolecular and a quantum algorithm to solve the maximum cut problem for any graph G. The quantum algorithm is inspired by the biomolecular algorithm and has a quadratic speedup over its classical counterparts, where the temporal and spatial complexities are reduced to, respectively, O √ 2n/r ) and O (m2 ). With respect to oracle-related quantum algorithms for NP-complete problems, we identify our algorithm as optimal. Furthermore, to justify the feasibility of the proposed algorithm, we successfully solve a typical maximum cut problem for a graph with three vertices and two edges by carrying out experiments on IBM's quantum simulator. © 2002-2011 IEEE.

关键词： Approximation algorithms

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Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors

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Intelligent Automation & Soft Computing 2023年第11期38卷 139-151页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering Image Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road *** a result,reckless driving behaviour can cause congestion and *** vision and multimodal sensors have been used to study driving behaviour categorization to lessen this *** research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s *** the other hand,driving a car is a complicated action that requires a wide range of body *** this work,we proposed a ResNet-SE model,an efﬁcient deep learning classiﬁer for driving activity clas-siﬁcation based on signal data obtained in real-world trafﬁc conditions using smart ***-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning *** data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this *** performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its *** to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%.

关键词： Smart glasses human activity recognition deep learning wearable sensors driving activity

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Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor

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Intelligent Automation & Soft Computing 2023年第9期37卷 2669-2686页

作者： Sakorn Mekruksavanich Narit Hnoohom Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok10800Thailand

Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical *** evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor ***,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many *** study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection *** proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body *** experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor ***,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and ***,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.

关键词： Human activity recognition deep learning wearable sensors indoor and outdoor activity deep pyramidal residual network

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Human Activity Recognition for People with Knee Abnormality Using Surface Electromyography and Knee Angle Sensors 8

Human Activity Recognition for People with Knee Abnormality ...

引用

Joint 8th International Conference on Digital Arts, Media and technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350396546

With the onset of technological advancements, biosensors are being effectively used in a variety of contexts, including the diagnosis of diseases, the promotion of their prevention and rehabilitation, the monitoring of patient health, and human activity recognition (HAR). Recently, HAR research results have been obtained and applied in many commercial applications such as fall detection using smart watch sensors, activity classification using smart home sensors. However, previous HAR models utilizing wearable sensors primarily utilized data from healthy individuals, and such models are frequently inaccurate when applied to individuals with medical mobility impairments. In this work, surface electromyography and goniometer sensors were used to categorize various types of rehabilitation activities based on HAR models developed for individuals with knee abnormalities. To achieve the goal of our study, a deep residual network called ResNeXt was proposed to detect human activities with high performance. To investigate the classification performance of deep learning models, a 5-fold cross-validation technique was applied for both training and testing. Based on the results of our study, the combination of features extracted from the goniometer and electromyograph signals resulted in the highest F1-score (93.31%) and the best accuracy (93.52%). © 2023 IEEE.

关键词： Deep learning

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MA2-FPN for Tiny Object Detection from Remote Sensing images 15

MA2-FPN for Tiny Object Detection from Remote Sensing Images

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15th International Congress on image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2022

作者： Li, Saiwei Tong, Qiang Liu, Xuhong Cui, Zhanqi Liu, Xiulei College of Computer Beijing Information Science and Technology University Beijing China Beijing Information Science and Technology University Beijing Advanced Innovation Center for Materials Genome Engineering Laboratory of Data Science and Information Studies Beijing China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Tiny object detection has been a challenging topic in computer vision recent years. Moreover, in remote sensing field, smaller and clustered tiny objects make its detection more difficult compared to ground-based images. This makes general detectors fail to achieve good performance when facing tiny objects in remote sensing images. In this paper, we propose a Mask Augmented Attention Feature Pyramid Network(MA2-FPN) to detect tiny objects in remote sensing images, which consists of two modules, Attention Enhancement Module(AEM) and Mask Supervision Module(MSM). Specifically, AEM aggregates tiny target context and spatial feature information by large kernel separable convolutional attention mechanism, and MSM supervises AEM through a segmentation attention loss to aggregate attention information more accurately while suppressing the influence of irrelevant background. Experiments based on the AI-TOD benchmark show that our MA2-FPN achieves state-of-the-art(SOTA) level. © 2022 IEEE.

关键词： Object detection

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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Blockchain-Based Secure and Efficient Secret image Sharing With Outsourcing Computation in Wireless Networks

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IEEE Transactions on Wireless Communications 2024年第1期23卷 423-435页

作者： Zhou, Zhili Wan, Yao Cui, Qi Yu, Keping Mumtaz, Shahid Yang, Ching-Nung Guizani, Mohsen Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Department of Applied Informatics Silesian University of Technology Gliwice44-100 Poland Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom Department of Computer Science and Information Engineering National Dong Hwa University Hualien97401 Taiwan Abu Dhabi United Arab Emirates

Secret image Sharing (SIS) is the technology that shares any given secret image by generating and distributing n shadow images in the way that any subset of k shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the (k,n) threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks. © 2023 IEEE.

关键词： Smart contract

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A Novel Diagnostic Model for Early Detection of Alzheimer’s Disease Based on Clinical and Neuroimaging Features 11th

A Novel Diagnostic Model for Early Detection of Alzheimer’s...

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11th International Conference on Model and Data engineering, MEDI 2022

作者： Gad, Eyad Gamal, Aya Elattar, Mustafa Selim, Sahar School of Engineering and Applied Sciences Nile University Giza Egypt Medical Imaging and Image Processing Research Group Center for Informatics Science Nile University Giza Egypt School of Information Technology and Computer Science Nile University Giza Egypt

ISBN: (纸本)9783031215940

Alzheimer’s Disease (AD) is a dangerous disease that is known for its characteristics of eroding memory and destroying the brain. The classification of Alzheimer's disease is an important topic that has recently been addressed by many studies using Machine Learning (ML) and Deep Learning (DL) methods. Most research papers tackling early diagnosis of AD use these methods as a feature extractor for neuroimaging data. In our research paper, the proposed algorithm is to optimize the performance of the prediction of early diagnosis from the multimodal dataset by a multi-step framework that uses a Deep Neural Network (DNN) as an optimization technique to extract features and train these features by Random Forest (RF) classifier. The results of the proposed algorithm showed that using only demographic and clinical data results in a balanced accuracy of 88% and an area under the curve (AUC) of 94.6. Ultimately, combining clinical and neuroimaging features, prediction results improved further to a balanced accuracy of 92% and an AUC of 97%. This study successfully outperformed other studies for both clinical and the combination of clinical and neuroimaging data, proving that multimodal data is efficient in the early diagnosis of AD. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neuroimaging

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Characterizing and controlling infrared phonon anomaly of bilayer graphene in optical-electrical force nanoscopy

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Light(science & Applications) 2023年第12期12卷 2700-2710页

作者： Junghoon Jahng Sunho Lee Seong-Gu Hong Chang Jun Lee Sergey G.Menabde Min Seok Jang Dong-Hyun Kim Jangyup Son Eun Seong Lee Hyperspectral Nano-imaging Team Korea Research Institute of Standards and ScienceDaejeon 34113Republic of Korea Department of Aerospace Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Multiscale Mechanical Properties Measurement Team Korea Research Institute of Standards and ScienceDaejeon 34113Republic of Korea School of Mechanical Engineering Chonnam National UniversityGwangju 61186Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Functional Composite Materials Research Center Korea Institute of Science and TechnologyJeonbuk 55324Republic of Korea SKKU Advanced Institute of Nanotechnology Sungkyunkwan UniversitySuwon 16419Republic of Korea Division of Nano&Information Technology KIST SchoolUniversity of Science and TechnologySeoul 02792Republic of Korea

We,for the first time,report the nanoscopic imaging study of anomalous infrared(IR)phonon enhancement of bilayer graphene,originated from the charge imbalance between the top and bottom layers,resulting in the enhancement of E1u mode of bilayer graphene near 0.2 *** modified the multifrequency atomic force microscope platform to combine photo-induced force microscope with electrostatic/Kelvin probe force microscope constituting a novel hybrid nanoscale optical-electrical force imaging *** enables to observe a correlation between the IR response,doping level,and topographic information of the graphene *** the nanoscale spectroscopic image measurements,we demonstrate that the charge imbalance at the graphene interface can be controlled by chemical(doping effect via Redox mechanism)and mechanical(triboelectric effect by the doped cantilever)***,we can also diagnosis the subsurface cracks on the stacked few-layer graphene at nanoscale,by monitoring the strain-induced IR phonon *** approach provides new insights into the development of graphene-based electronic and photonic devices and their potential applications.

关键词： electrical force optical

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