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健康数据科学（英文） 2023年第4期 42-43页

作者： Weibin Liao Yanfeng Liao Zhimin Fan Jiexuan Zhang Shuochen Li Jiarui Yang Liantao Ma National Engineering Research Center for Software Engineering Peking UniversityBeijingChina Computer Science and Technology Beijing Institute of TechnologyBeijingChina Department of Ophthalmology Peking University Third HospitalBeijing Key Laboratory of Restoration of Damaged Ocular NervePeking University Third HospitalBeijingChina National Engineering Research Center for Software Engineering Peking UniversityBeijingChina

Background:Retinal vein occlusion(RVO)is one of the most common retinal vascular diseases leading to vision loss if not diagnosed and treated in *** diagnosis of central and branch RVO(CRVO and BRVO)can alleviate the workload of ophthalmologists while facilitating early detection and treat-ment of RVO and laying the foundation for subsequent symptom grading,treatment planning,and ***,the development of a fast,high-performance,and robust diagnostic model is a crucial step toward achieving quantitative and accu-rate assessment of RVO *** there is an extensive research using fundus visual images for clinical assisted diagno-sis with deep learning models,there is a lack of focus on incor-porating doctors'text reports to further enhance the models. Objectives:In this study,we propose a multi-modal medical visual-and-language learning model that utilizes fundus fluo-rescein angiography(FFA)images and physician analysis reports to classify BRVO and CRVO cases. Methods:As shown in Fig.1,the proposed model utilizes an advanced convolutional neural network to extract visual features from FFA images for patient visual representation *** language representation learning,the model first extracts basic patient features such as gender,age,vision,and blood pressure,and then uses regular expression matching to obtain typical patient symptoms from expert text ***,we cre-ated a sign vocabulary for RVO patients,including exudate,macular edema,and intraretinal hemorrhage,among *** on this vocabulary,the proposed model analyzes patient symptom manifestations and learns symptom presentation(such as exudate stage and location).Finally,based on the patient's visual and text representation,the proposed model uses a fully connected(FC)layer for classification tasks. Results:We evaluated the proposed model on a private dataset consisting of 101 patients and 1,265 FFA images,composed of 58 BRVO patients and 43 CRVO patie

关键词： language vision learning classification medical multi-modal occlusion retinal vein

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Experience NECTEC's Virtual 3D Mini-Exhibition Hall: A Digital Showcase for Online Visitors

Experience NECTEC's Virtual 3D Mini-Exhibition Hall: A Digit...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Tulatorn Yongprayoon Mazhar Ali Taweesak Sanpechuda Laor Kovavisaruch Lunchakorn Wuttisittikulkij Pisit Vanichchanunt Department of Electrical Engineering Wireless Communication Ecosystem Research Unit Chulalongkorn University Bangkok Thailand National Electronics and Computer Technology Center Thailand Pathum Thani Thailand Department of Electrical & Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

This study proposes a novel way to improve the online visitor experience at the national Electronics and computer technology center (NECTEC) by providing virtual access to its mini exhibit space and facilities. To enable visitors to explore from a first-person perspective, the main goal is to transform NECTEC's vast 2D indoor location system into an immersive 3D world. Using Unity, a desktop program communicates in realtime with NECTEC's indoor location database to let users find the shortest route to designated locations inside the virtual environment. Furthermore, the study explores sophisticated functionalities like customized avatar tracking and interior navigation. The system is unique because it can dynamically customize avatars using uploaded photos, enabling customized tracking experiences. This study shows that combining virtual environments with real-world indoor location systems is feasible and can improve visitor engagement and interaction through testing and implementation.

关键词： Three-dimensional displays Navigation Heuristic algorithms Avatars Pipelines Merging Virtual environments

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The Development of Remotely Operated Underwater Vehicle for Plastic Waste Detection with Raspberry Pi 13

The Development of Remotely Operated Underwater Vehicle for ...

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13th International Physics Seminar 2024, IPS 2024

作者： Delina, M. Taryudi Amin, M.F. Hussaan, A.M. Trismidianto Arifin, F.I. Syah, S.M.I. Physics Department Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta13220 Indonesia Electrical Engineering Department Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta13220 Indonesia Department of Computer Science IQRA University Rd. Shaheed-e-Millat Sindh75500 Pakistan Center of Atmospheric Research and Technology National Research and Innovation Agency Bandung40173 Indonesia

Underwater Plastic waste has had a serious impact on the environment. Unfortunately, Detecting and collecting underwater plastic waste is still a challenge, especially in Indonesia because of its position in the water, and the water turbidity. In previous research, a computer algorithm for detecting underwater plastic waste has been developed with Yolov3. To improve the research, we developed a Remotely Operated Underwater Vehicle (ROUV) with Raspberry Pi through the research and Development (R&D) method. This ROUV is like a submarine that operates in the water and is integrated with a camera and computer algorithm to detect plastic waste. To get close to the real condition, we created an artificial water environment in a pond with various turbidity (from 20 to 200 Nephelometric Turbidity Unit). We use 30 pieces of plastic waste in several colors: white, red, black, and transparent. The plastic waste dipped in the water and was then detected by the ROUV camera. The result shows that the effective threshold for object detection is around 100 Nephelometric Turbidity Unit (NTU). Below 100 NTU, although the number of detected plastic images decreases as the turbidity increases, the visibility and contrast still allow for reasonably good object detection. The average confidence value for detection at turbidity levels below 100 NTU is 77%. At high turbidity conditions, the detection model may require adjustment or retraining with data that reflects those turbidity conditions. © Published under licence by IOP Publishing Ltd.

关键词： Remotely operated vehicles

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CorcPUM: Efficient Processing Using Cross-Point Memory via Cooperative Row-Column Access Pipelining and Adaptive Timing Optimization in Subarrays 23

CorcPUM: Efficient Processing Using Cross-Point Memory via C...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Chengning Wang Dan Feng Wei Tong Jingning Liu Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China (School of Computer Science and Technology Huazhong University of Science and Technology) Wuhan China

ISBN: (纸本)9798350323481

Emerging cross-point memory can in-situ perform vector-matrix multiplication (VMM) for energy-efficient scientific computation. However, parasitic-capacitance-induced row charging and discharging latency is a major performance bottleneck of subarray VMM. We propose a memory-timing-compliant bulk VMM processing-using-memory design with row access and column access co-optimization from rethinking of read access commands and μ-op timing. We propose row-level-parallelism-adaptive timing termination mechanism to reduce tail latency of tRCD and tRP by exploiting row nonlinear charging and bulk-interleaved row-column-cooperative VMM access mechanism to reduce tRAS and overlap CL without increasing column ADC precision. Evaluations show that our design can achieve 5.03× performance speedup compared with an aggressive baseline.

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MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal

MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal

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IEEE International Conference on Big Data

作者： Kuo-Hsuan Hung Kuan-Chen Wang Kai-Chun Liu Wei-Lun Chen Xugang Lu Yu Tsao Chii-Wann Lin Department of Biomedical Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academic Sinica Taiwan College of Information and Computer Sciences University of Massachusetts Amherst USA National Institute of Information and Communications Technology Japan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Electrocardiogram (ECG) is an important non-invasive method for diagnosing cardiovascular disease. However, ECG signals are susceptible to noise contamination, such as electrical interference or signal wandering, which reduces diagnostic accuracy. Various ECG denoising methods have been proposed, but most existing methods yield suboptimal performance under very noisy conditions or require several steps during inference, leading to latency during online processing. In this paper, we propose a novel ECG denoising model, namely Mamba-based ECG Enhancer (MECG-E), which leverages the Mamba architecture known for its fast inference and outstanding nonlinear mapping capabilities. Experimental results indicate that MECG-E surpasses several well-known existing models across multiple metrics under different noise conditions. Additionally, MECG-E requires less inference time than state-of-the-art diffusion-based ECG denoisers, demonstrating the model’s functionality and efficiency.

关键词： Electric potential Noise reduction Interference Electrocardiography Real-time systems Data models Noise measurement Cardiovascular diseases Contamination Signal to noise ratio

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An Efficient GDPR-Compliant Data Management for IoHT Applications

An Efficient GDPR-Compliant Data Management for IoHT Applica...

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IEEE International Conference on Communications Workshops, ICC

作者： I-Hsun Chuang ShihHao Huang Wan-Hsuan Hong Yau-Hwang Kuo Department of Computer Science and Information Engineering Center for Research of E-life DIgital Technology (CREDIT) National Cheng Kung University Tainan Taiwan Center for Research of E-life DIgital Technology (CREDIT) Graduate Institute of Electrical Engineering National Taiwan University Taipei Taiwan

The rapid rise of Internet of Health Things (IoHT) makes preserving data privacy become a critical task. In 2016, European Union has published General Data Protection Regulation (GDPR) which urges service providers to protect user privacy. Existing fine-grained encryption methods, such as key-policy attribute-based encryption (KP-ABE), belong to pairing-based cryptography which is impractical in resource-constraint IoHT devices. Also, habitual data-sharing systems fail to satisfy the right to access, the right to data portability, and the right to erasure claimed by GDPR at the same time. Thus, this paper proposes the GDPR-compliant Data Management (GCDM) composed of pairing-free KP-ABE (PF-KP-ABE) and GDPR-compliant Revocable Blockchain (GRBC) to provide IoHT applications efficient and secure data-sharing service. PF-KP-ABE exploits the pairing-free mechanism to improve the performance of fine-grained access control on IoHT devices. GRBC applies the special-designed Create/ Read/ Update/ Delete (CRUD) operations to comply with GDPR. Security analysis demonstrates the GDPR compliance of GRBC as well as the correctness of PF-KP-ABE. Then, the Perfect Forward Secrecy of PF-KP-ABE is also proved. Experiment results show that the proposed PF-KP-ABE outperforms existing KP-ABE on both Control Centre and IoHT devices. Hence, GCDM is the most practical data management for various IoHT applications.

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Solar Image Cloud Removal based on Improved Pix2Pix Network

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computers, Materials & Continua 2022年第12期73卷 6181-6193页

作者： Xukun Zhang Wei Song Ganghua Lin Yuxi Shi School of Information Engineering Minzu University of ChinaBeijing100081China Key Laboratory of Solar Activity Chinese Academy of Sciences(KLSACAS)Beijing100101China National Language Resource Monitoring and Research Center of Minority Languages Minzu University of ChinaBeijing100081China National Astronomical Observatories Chinese Academy of Sciences(NAOCCAS)Beijing100101China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNew Jersey07102USA

In ground-based observations of the Sun,solar images are often affected by appearance of thin clouds,which contaminate the images and affect the scientific results from data *** this paper,the improved Pixel to Pixel Network(Pix2Pix)network is used to convert polluted images to clear images to remove the cloud shadow in the solar *** adding attention module to the model,the hidden layer of Pix2Pix model can infer the attention map of the input feature vector according to the input feature *** then,the attention map is multiplied by the input feature map to give different weights to the hidden features in the feature map,adaptively refine the input feature map to make the model pay attention to important feature information and achieve better recovery *** order to further enhance the model’s ability to recover detailed features,perceptual loss is added to the loss *** model was tested on the full disk H-alpha images datasets provided by Huairou Solar Observing Station,national Astronomical *** experimental results show that the model can effectively remove the influence of thin clouds on the picture and restore the details of solar *** peak signal-to-noise ratio(PSNR)reaches 27.3012 and the learned perceptual image patch similarity(LPIPS)reaches 0.330,which is superior to the existed dehaze algorithms.

关键词： Pix2Pix decontamination full-disk H-alpha images

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SMART: towards pre-trained missing-aware model for patient health status prediction 24

SMART: towards pre-trained missing-aware model for patient h...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhihao Yu Xu Chu Yujie Jin Yasha Wang Junfeng Zhao School of Computer Science Peking University and National Research and Engineering Center of Software Engineering Peking University School of Computer Science Peking University and Center on Frontiers of Computing Studies Peking University Beijing China and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Peking University Information Technology Institute (Tianjin Binhai) National Research and Engineering Center of Software Engineering Peking University and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Peking University Information Technology Institute (Tianjin Binhai) School of Computer Science Peking University and National Research and Engineering Center of Software Engineering Peking University and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Nanhu Laboratory Jiaxing China

ISBN: (纸本)9798331314385

Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obsess difficult-to-interpolate details and may introduce additional noise when making clinical predictions. To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via missing-aware temporal and variable attentions and learns to impute missing values through a novel self-supervised pre-training approach which reconstructs missing data representations in the latent space rather than in input space as usual. By adopting elaborated attentions and focusing on learning higher-order representations, SMART promotes better generalization and robustness to missing data. We validate the effectiveness of SMART through extensive experiments on six EHR tasks, demonstrating its superiority over state-of-the-art methods. Our code is available at https://***/yzhHoward/SMART.

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Development of Business Intelligence Framework for Open Government Data Portal Usage Analysis: A Case Study of Thailand 13

Development of Business Intelligence Framework for Open Gove...

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13th International Conference Knowledge and Systems engineering, KSE 2021

作者： Sripramong, Sahaporn Anutariya, Chutiporn Buranarach, Marut Tumsangthong, Patipat Wutthitasarn, Theerawat School of Engineering and Technology Asian Institute of Technology Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9781665499750

The usage of Open Government Data (OGD) can enable the government transparency for better governance, increase citizen collaboration, and promote innovation through public service improvement. OGD initiatives involve different stakeholders and complex publishing guidelines. In general, governments initiate OGD in their countries and provide the government-run portals to support the provision and consumption process of OGD. Insufficient usage of OGD and limited provision by the government is a great challenge to OGD development. Monitoring the operation and constantly analyzing the usage can be beneficial for OGD policymakers to gain insight into usage, to plan, and to enact the law to support the OGD initiative. This study proposes a Business Intelligence (BI) framework to analyze OGD portal datasets and its usage. The data from Thailand's OGD (ThOGD) portal was derived to validate our framework. The work focuses on designing and implementing the BI framework. We demonstrated the features of the prototyped BI system in supporting the analysis through various data visualization. © 2021 IEEE.

关键词： Open Data

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Dynamically Reconfigurable Polarizer Implemented Using an Energy Harvesting Triggered Metasurface

Dynamically Reconfigurable Polarizer Implemented Using an En...

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Antennas and Propagation & USNC/URSI national Radio Science Meeting

作者： Adnan Nadeem Nosherwan Shoaib Photos Vryonides Symeon Nikolaou Frederick Research Center Nicosia Cyprus Electrical Engineering Department Frederick University Nicosia Cyprus School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

The manuscript describes the implementation of a mm-wave dynamically reconfigurable polarizer, which is triggered by an energy harvester which controls a reconfigurable cells metasurface (MSF). The polarizer is implemented as a 5×5 MSF array operating in K-band (26 GHz) which is able to control the propagation of incident waves and selectively choose to filter out the y-polarization. The proposed MSF transmits both x and y-polarized components simultaneously when the diodes which are used in the reconfigurable geometry cells are in OFF state. However, upon detecting an incident y-polarized wave, the rectifier provides a trigger signal that activates the DC biasing circuit and switches all the diodes simultaneously to ON state. Under this biasing condition the transmitted wave is strictly polarized along the x direction and the incident y-polarized wave is reflected. In both states of the MSF, the simulation results demonstrate transmission efficiency exceeding 95%. As for the rectifier, it exhibits a maximum RF-DC power conversion efficiency of 56%.

关键词： Rectennas PIN photodiodes Simulation K-band Rectifiers Switches Metasurfaces

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