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7th International Conference on Future Data and Security Engineering, FDSE 2020

作者： Huynh, Hiep Xuan Tran, Toan Bao Pham, Quyen Ngoc Nguyen, Hai Thanh College of Information and Communication Technology Can Tho University Can Tho900100 Viet Nam Center of Software Engineering Duy Tan University Da Nang550000 Viet Nam Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

ISBN: (纸本)9789813343696

An approach, namely genome-wide association study (GWAS), is usually deployed in genetics research to associate specific genetic variations with particular diseases. This method can be scanning the genomes from a vast of sources and looking for genetic markers that can be used to discriminate phenotypes. In recent years, advancements in computational resources have proposed and published to provide robust tools supporting studies on GWAS. This study aims to propose a method based on machine learning on Oat sequences to provide insights into the genetic architectures to discriminate phenotypes comparing to a statistical analysis on Genome-Wide Association Analysis provided in a R package. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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research on Modeling of Reconfiguration for Integrated Electronic System

Research on Modeling of Reconfiguration for Integrated Elect...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Dianjun Lv Gongwang Qiu Yu Cai Yizhi Lou Tao Zhang Research and Development Center China Academy of Launch Vehicle Technology Beijing China School of Software and Microelectronics Northwestern Polytechnical University Xi’an China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

Integrated electronic system technology has been widely used in aircraft to reduce costs and space and increase flexibility. However, with the increase of aircraft function and mission requirements, integrated electronic system becomes more and more complicated. Especially, under the requirement of system reconfigurable, the reconfiguration decisions' blueprint lacks unified modeling description, can not be quickly mapped diversified system architecture, and reconstruction configuration blueprint lacks flexible resource allocation mapping. Therefore, this paper proposes an integrated electronic system reconfigurable modeling technology based on AADL scalable architecture. By analyzing system resources and system requirements, this method constructs AADL models such as system execution platform, application program and so on, and combines AADL extensible attachments to illustrate the logical implementation architecture of the system under different blueprints. Finally, the effectiveness of this method in modeling reconfigurable integrated electronic systems is proved by experiments, and it can support the verification and analysis of reconstruction strategy.

关键词： research and development System implementation software Microelectronics Computer architecture

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You Can Wash Hands Better: Accurate Daily Handwashing Assessment with Smartwatches

arXiv

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arXiv 2021年

作者： Wang, Fei Wu, Xilei Zhang, Tingting Wang, Xin Wang, Pengcheng Ding, Han Shi, Jingang Han, Jinsong Huang, Dong School of Software Engineering Xi’an Jiaotong University Xi’an China Local Life Research and Development Center Kuaishou Technology Inc. Beijing China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China College of Computer Science and Technology Zhejiang University Hangzhou China Robotics Institute Carnegie Mellon University Pittsburgh United States

Hand hygiene is among the most effective daily practices for preventing infectious diseases such as influenza, malaria, and skin infections. While professional guidelines emphasize proper handwashing to reduce the risk of viral infections, surveys reveal that adherence to these recommendations remains low. To address this gap, we propose UWash, a wearable solution leveraging smartwatches to evaluate handwashing procedures, aiming to raise awareness and cultivate high-quality handwashing habits. We frame the task of handwashing assessment as an action segmentation problem, similar to those in computer vision, and introduce a simple yet efficient two-stream UNet-like network to achieve this goal. Experiments involving 51 subjects demonstrate that UWash achieves 92.27% accuracy in handwashing gesture recognition, an error of Copyright © 2021, The Authors. All rights reserved.

关键词： Diseases

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Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

arXiv

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arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

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Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients

arXiv

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arXiv 2022年

作者： Nalepa, Jakub Kotowski, Krzysztof Machura, Bartosz Adamski, Szymon Bozek, Oskar Eksner, Bartosz Kokoszka, Bartosz Pekala, Tomasz Radom, Mateusz Strzelczak, Marek Zarudzki, Lukasz Krason, Agata Arcadu, Filippo Tessier, Jean Future Processing Healthcare Gliwice Poland Department of Algorithmics and Software Silesian University of Technology Gliwice Poland Department of Radiodiagnostics and Invasive Radiology School of Medicine in Katowice Medical University of Silesia in Katowice Katowice Poland Department of Radiology and Nuclear Medicine ZSM Chorzów Chorzów Poland Department of Radiodiagnostics Interventional Radiology and Nuclear Medicine University Clinical Centre Katowice Poland Department of Radiology and Diagnostic Imaging Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Gliwice Poland Roche Pharmaceutical Research & Early Development Early Clinical Development Oncology Roche Innovation Center Basel Basel Switzerland

Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimensional measurements that follow the current Response Assessment in Neuro-Oncology (RANO) criteria. Also, we introduce a rigorous manual annotation process which was followed to delineate the tumor sub-regions by the human experts, and to capture their segmentation confidences that are later used while training the deep learning models. The results of our extensive experimental study performed over 760 pre-operative and 504 post-operative adult patients with glioma obtained from the public database (acquired at 19 sites in years 2021-2020) and from a clinical treatment trial (47 and 69 sites for pre-/post-operative patients, 2009-2011) and backed up with thorough quantitative, qualitative and statistical analysis revealed that our pipeline performs accurate segmentation of pre- and post-operative MRIs in a fraction of the manual delineation time (up to 20 times faster than humans). The bidimensional and volumetric measurements were in strong agreement with expert radiologists, and we showed that RANO measurements are not always sufficient to quantify tumor burden. Copyright © 2022, The Authors. All rights reserved.

关键词： Tumors

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Automated Dead/Bright Pixel Distance Measurement System on Computer Monitors

Automated Dead/Bright Pixel Distance Measurement System on C...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Victor L.G. Cavalcante Diego A. Amoedo Adriana S. Souza Luciana R. Costa Myke D.M. Valadão Caio F.S. Cruz Agemilson P. Silva Ruan J.S. Belem Anderson S. Jesus Lucas G.C. Evangelista Osmar R.A. Silva Ricardo G. Paula Thiago B. Bezerra Waldir S.S. Júnior Celso B. Carvalho Federal University of Amazonas and Center for R&D in Elec. and Inf. Tech. (UFAM/CETELI) Brazil ENVISION/TPV Technology Limited Brazil ICTS Institute Center of Development Research in Software Technology Brazil

ISBN: (数字)9781728173993

ISBN: (纸本)9781728174006

In this work, conducted by three partners (UFAM/CETELI, ICTS and ENVISION/TPV), we propose an automated dead/bright pixel distance measurement system for use on monitors and TVs screen. The proposed system use Homography transformation and proportion estimation and some simple detection algorithms to detect edge, corners, and contour. As result, the proposed system perform the 700 Euclidean distance in millimeters. As result, the average distance measurement error was 0.44 millimeter.

关键词： TV Image edge detection Estimation Euclidean distance Detection algorithms Monitoring Corner detection

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Dead Pixel Detection on Liquid Crystal Displays using Random Forest, SVM, and Harris Detector

Dead Pixel Detection on Liquid Crystal Displays using Random...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： David A.O. Ferreira Diego A. Amoedo Luciana R. Costa Myke D.M. Valadão Adriana S. Souza Kethilen Y. Ouchi Antônio M.C. Pereira Gustavo M. Torres Mateus O. Silva Caio F.S. Cruz Agemilson P. Silva Ruan J.S. Belem Anderson S. Jesus André S. Costa Lucas G.C. Evangelista Osmar R.A. Silva Thiago B. Bezerra Waldir S.S. Júnior Celso B. Carvalho Federal University of Amazonas and Center for R&D in Elec. and Inf. Tech. (UFAM/CETELI) Brazil ENVISION/TPV Technology Limited Brazil ICTS Institute Center of Development Research in Software Technology Brazil

ISBN: (数字)9781728173993

ISBN: (纸本)9781728174006

Manufacturing TVs and monitors requires an effective method to detect the dead pixels. These defects are usually identified by operators manually. However, manual inspection is susceptible to failure due to human fatigue and generate a high cost for production process. In this work, conducted by three partners (UFAM/CETELI, ICTS and ENVISION/TPV), we propose three methods for automated detection of dead pixels. Two proposed methods were based on machine learning (ML) techniques, random forest (RF) and support vector machine (SVM) algorithms. The third method was based in digital image processing (DIP), Harris algorithm. As result, the SVM obtained the better performance with 92.1% of precision in two of three used image database.

关键词： Databases Support vector machines Radio frequency Liquid crystal displays Measurement Training Monitoring

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A method for constructing reservoir area-storage-elevation curve using sentinel-1 radar remote sensing image 40

A method for constructing reservoir area-storage-elevation c...

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40th Asian Conference on Remote Sensing: Progress of Remote Sensing technology for Smart Future, ACRS 2019

作者： Hiep, Nguyen Quoc Hung, Nguyen Anh Nam, Do Hoai Choi, Hyeoung-Wook Kim, Gi-Ho Center for Water Resources Software Ministry of Agriculture and Rural Development No.269 Chua BocStreet Dong Da District Hanoi Viet Nam Institute of Spatial Information Technology Research GEO C&I Co. Ltd 435 Hwarang-ro Dong-gu Daegu Korea Republic of Korea Water Resource Corporation Korea Republic of

Reservoir elevation-area-storage (Z-F-W) curves are used for regulating discharge during the flood season and allocating water supply in the dry season. According to the latest statistics of the Directorate of Water Resources, Vietnam has about 6636 reservoirs, of which the Z-F-W curves of 474 reservoirs have been already identified and the remaining 6162 reservoirs have no information on the Z-F-W relationship. In order to regulate or allocate water in these reservoirs, operators assume a linear relationship between the area or storage and elevation, varying from the dead to normal water level. By mean of this method, calculated area and storage are subject to large errors but they are still used to operate the reservoirs. In fact, even the reservoirs attributed with the Z-F-W relationships, but the relationships may not be stationary as the reservoirs were built a long time ago with continuous sediment deposition and erosion processes that altered the reservoir bathymetry;as a result, these relationships also require corrections. This paper introduces a method for constructing the Z-F-W curves of the reservoir with the capacity larger than one million cubic meters in Ha Tinh province of Vietnam using Synthetic Aperture Radar (SAR) Sentinel-1 remote sensing image. The scope of the paper includes such steps as (i) pre-processing Sentinel-1 image;(ii) developing image interpretation algorithms for automatic processes for determining the reservoir area using backscattering points of radar image polarization on dry, moist and wetland;(iii) eliminating noise and correcting pixel values which represent water but they are interpreted as not water and vice versa;(iv) implementing methods to validate the interpretation results of the reservoir Z-F-W relationships: conducting field survey to determine the boundary of water surface by Global Positioning System (GPS) and comparing with that determined using image interpretation, method of comparing the values of reservoir capa

关键词： Reservoirs (water)

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Super Resolution of Pathology Images with Hierarchical Feature Integration and Local Image Patterns

SSRN

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SSRN 2024年

作者： Gao, Chaofei Xu, Feng Li, Lei Wang, Shuyang Ling, Ren Zhang, Xie Deng, Xi Zhou, Mengzhe Ling, Jin Department of Gastroenterology The Affiliated Lihuili Hospital of Ningbo University Zhejiang Ningbo315040 China Pathological Diagnosis Department Meikang Medicalsystem Laboratory. Ningbo Zhejiang 315105 China Department of Pathology School of Basic Medical Sciences Fudan University Xuhui District Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Xuhui District Shanghai200032 China Department of Research and Development Shanghai Laizi Software Technology Co. LTD. Fengxian District Shanghai201499 China Ningbo Clinical Pathology Diagnosis Center Zhejiang Ningbo315020 China MOE Key Laboratory of Bioinformatics Bioinformatics Division BNRist Department of Automation Tsinghua University Beijing100084 China

The rapid development of artificial intelligence and digital pathology has demonstrated promise in precision diagnosis in clinical. However, pathology analysis at single cell and sub-cellular level is still limited by the image resolution as the high resolution image acquirement is expensive and time-consuming. Additionally, most super resolution models ignored the intrinsic characteristics of pathology images without reaching satisfactory performance. In this paper, we developed a hierarchical super resolution deep learning framework based on local image patterns (HLIP) to reconstruct super resolution images from the low resolution ones with arbitrary input size and magnification. HLIP integrates hierarchical feature maps and conducts reconstruction by considering the local image patterns of neighboring pixels. Benchmark analysis shows HLIP achieves the best performance on both the internal and external validation datasets and the generated super resolution images approximate high resolution ones, which have reached clinical quality. It is revealed that HLIP can be applied in multiple clinical scenes as a versatile tool to improve the performance of other models on the tasks including the gland segmentation, cell segmentation and Helicobacter pylori detection. Moreover, HLIP can be integrated directly into the existing computer-aided pathology diagnostic systems for real-time super resolution. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Synchronous hybrid brain–computer interfaces for recognizing emergency braking intention

Brain-X

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Brain-X 2024年第1期2卷

作者： Ju, Jiawei Feleke, Aberham Genetu Li, Hongqi Li, Haiyang Institute of Neuroscience Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai Shanghai China School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Software Northwestern Polytechnical University Shaanxi Xi'an China The Research & Development Institute of Northwestern Polytechnical University in Shenzhen Guangdong Shenzhen China The Yangtze River Delta Research Institute of Northwestern Polytechnical University in Taicang Jiangsu Suzhou China Chemical Engineering Department Pohang University of Science and Technology Gyeongbuk Pohang South Korea

Hybrid neurophysiological signals, such as the combination of electroencephalography (EEG) and electromyography (EMG), can be used to reduce road traffic accidents by obtaining the driver's intentions in advance and accordingly applying appropriate auxiliary controls. However, whether they can be used in combination and can achieve better results in situations of detecting emergency braking from normal driving and soft braking has not been explored. This study used one feature-level (hybrid BCI-FL) and three classifier-level (hybrid BCIs-CLs) hybrid strategies, the spectral band, and spectral point features to construct recognition models. Offline and pseudo-online experiments were conducted. The recognition performance with the spectral point features showed a better result than that with spectral band features. In all experiments, the two proposed hybrid BCI strategies could achieve a detection accuracy close to or above 95%, while the detection advanced time is less than 300 ms. In particular, for the developed hybrid BCI recognition models, the hybrid BCI-FL and hybrid BCI-CL2 recognition models with spectral point features achieved 4.25% (p < 0.015) and 4.69% (p < 0.006) higher system accuracies, respectively, than that of the current better single EMG-based recognition model. This research promotes the application of hybrid EEG and EMG signals in intelligent driving assistance systems. © 2024 The Authors. Brain-X published by John Wiley & Sons Australia, Ltd on behalf of Ainuohui Medical technology.

关键词： electroencephalography electromyography emergency braking hybrid recognition

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