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Medical Imaging 2025: Physics of Medical Imaging

作者： Xia, Shao-Jun Vancoillie, Liesbeth Sotoudeh-Paima, Saman Zarei, Mojtaba Ho, Fong Chi Tushar, Fakrul Islam Chen, Xiaoyang Dahal, Lavsen Lafata, Kyle J. Abadi, Ehsan Lo, Joseph Y. Samei, Ehsan Center for Virtual Imaging Trials Carl E. Ravin Advanced Imaging Labs Department of Radiology Durham United States Department of Biomedical Engineering Durham United States Department of Electrical & Computer Engineering Durham United States Medical Physics Graduate Program Durham United States Department of Physics Durham United States Department of Radiation Oncology Duke University Durham United States

ISBN: (纸本)9781510685888

In medical imaging, harmonization plays a crucial role in reducing variability arising from diverse imaging devices and protocols. Patient images obtained under different computed tomography (CT) scan conditions may show varying performance when analyzed using an artificial intelligence model or quantitative assessment. This necessitates the need for harmonization. Virtual imaging trial (VIT) through digital simulation can be used to develop and assess the effectiveness of harmonization models to minimize data variability. The purpose of this study was to assess the utility of a VIT platform for harmonization across a range of lung imaging scenarios. To ensure consistent and reliable analysis across different virtual imaging datasets, we conducted a multi-objective assessment encompassing three typical task-based scenarios: lung structure segmentation, chronic obstructive pulmonary disease (COPD) quantification, and lung nodule quantification. A physics-informed deep neural network was applied as the unified harmonization model for all three tasks. Evaluation results before and after harmonization reveal three findings: 1) modestly improved Dice scores and reduced Hausdorff Distances at 95th Percentile in lung structure segmentation;2) decreased variation in biomarkers and radiomics features in COPD quantification;and 3) increased number of radiomics features with high intraclass correlation coefficient in lung nodule quantification. The results demonstrate the significant potential of harmonization across various task-based scenarios and provide a benchmark for the design of efficient harmonizers. © 2025 SPIE.

关键词： Lung cancer

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Optical Fiber Coiled Sensors for Acoustic Oblique Wave Detection using Distributed Acoustic Sensing

Optical Fiber Coiled Sensors for Acoustic Oblique Wave Detec...

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IEEE SENSORS

作者： Frederico A. Jahnert Beatriz Brusamarello Danilo F. Gomes Sérgio T. de Camargo Manoel F. da Silva Jean C. Cardozo da Silva Cicero Martelli Jucélio T. Pereira Carlos A. Bavastri Graduate Program in Mechanical Engineering Federal University of Parana (UFPR) Curitiba Brazil Graduate Program in Electrical and Computer Engineering Federal University of Technology-Paraná (UTFPR) Curitiba Brazil Petrobras Research and Development Program Rio de Janeiro Brazil

Distributed Acoustic Sensors (DAS) are able to measure dynamic strain and temperature signals quantitatively along several kilometers of optical fiber using the properties of backscattering light. A drawback of DAS is the sensing resolution of around 1 meter long. This paper analyses the use of DAS as a vibration sensor for a thin plate structure using an acoustic excitation. A coiled method of arranging the sensing optical fibers enhances the measured signal and using a calibration method, the strain in the sensed region is obtained. The method can be used to characterize structures acoustically allowing the structure to detect the direction and reconstruct nearby acoustic events. Acoustic excitations were performed using a speaker at 1 meter distance to a 1 millimeter thick free-free 40 centimeters by 73 centimeter instrumented stainless steel plate inside an anechoic chamber. Results measured using reference microphones and accelerometers were compared to the optical fiber sensor through a Finite Element Model. The results demonstrate that the optical fiber system is able to measure the frequency response with an average 54 dB sound pressure level sound source chirp signal for flexural modes with a clear differentiation between angles of incidence up to 500 Hz.

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A Novel Resource Allocation in Software-Defined Networks for IoT Application 31

A Novel Resource Allocation in Software-Defined Networks for...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： De Sousa, Alexandre Ladeira Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Santo Andre Graduate Program in Information Engineering Sao Paulo Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

The rapid proliferation of Internet of Things (IoT) devices has posed significant challenges for network resource allocation and management. In this paper, we propose a novel methodology for efficient resource allocation in Software-Defined Networks (SDNs) to meet the unique requirements of IoT applications. Our approach leverages the flexibility and programmability of SDN to dynamically allocate network resources based on the specific needs of IoT devices. Our proposal combines reinforcement learning with dynamic resource allocation, demonstrating its effectiveness in optimizing resource allocation and enhancing network performance. The results highlight the potential of our approach to addressing the resource challenges in SDN-based IoT environments. The proposed method obtains results superior to 30% of throughput in comparison to a traditional resource allocation in SDN to IoT applications. © 2023 University of Split, FESB.

关键词： Internet of things

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Unified Approach to Video-Based AI Inference Tasks in Augmented Reality Systems Assisted by Mobile Edge Computing 31

Unified Approach to Video-Based AI Inference Tasks in Augmen...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： Sousa, Alexandre Ladeira De Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Graduate Program in Information Engineering Sao Paulo Santo Andre Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

In the rapidly evolving field of Augmented Reality (AR), delivering real-time, immersive experiences places a significant demand on computational resources, particularly in the context of video-based Artificial Intelligence (AI) inference tasks. Some solutions explorate optimize the tasks using the potential of Mobile Edge Computing (MEC), in which MEC brings the advantages of reduced latency and network congestion, which are critical for AR systems. In this work, we designed a novel algorithm named AR-MEC Optimization for the distribution of AI inference tasks among edge servers while considering several factors including task complexity, server capacity, and network conditions. Our approach not only ensures low-latency responses but also improves overall system efficiency and the user's Quality of Experience (QoE). Experimental results demonstrate that our unified approach significantly outperforms traditional cloud-based methods in terms of response time and computational efficiency, thereby highlighting the potential of MEC-assisted AR systems for real-world applications. © 2023 University of Split, FESB.

关键词： Augmented reality

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using Random Forest, KNN, Decision Tree and Naïve Bayes 7

Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using ...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Pohan, Husni Iskandar Suparta, Wayan Heryadi, Yaya Wibowo, Antoni Lukas Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

Dengue Hemorrhagic Fever is an acute viral infectious disease caused by the dengue virus. Transmitted through the bite of Aedes Mosquitoes and divided into 4 severity. Severity 1 and 2 are characterized by a decrease in platelet levels below normal and there is a change in hemoconcentration, while Severity 3 and 4 are included in Dengue Shock Syndrome (DSS). The purpose of the study was to classify the severity of dengue, based on age, gender, platelets, body temperature, bleeding, and also doctor's recommendation. The advancement of information technology is the reason for the development of medical science, especially with artificial intelligence. Abundant medical data becomes a source that can be processed in improving the quality of medical information. The method chosen is Random Forest, kNN, Decision Tree and Naïve Bayes. The dataset used is the medical records of all DHF patients for 30 months in the period Jan 2019 to June 2021, at the Nadhifa Al Ghiffari Clinic in Bojongsoang District, Bandung Regency. © 2022 IEEE.

关键词： Decision trees

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FPGA-based Implementation of Random Forest Classifier for sEMG Signal Classification

FPGA-based Implementation of Random Forest Classifier for sE...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Wagner Martins Leia B. Bagesteiro Tiago O. Weber Alexandre Balbinot Post-Graduate Program in Electrical Engineering Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Department of Kinesiology San Francisco State University San Francisco CA USA

This paper proposes a method that combines a hardware description language and Random Forests theory, to develop a motion recognition embedded system. To validate this system, the Ninapro database DB2 was used as trai... 详细信息

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

关键词： Training Accuracy VHDL Software algorithms Pattern classification Forestry Hardware Random forests Optimization Software development management

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UAV-Based Optimization for Fruit Counting in Greenhouses

UAV-Based Optimization for Fruit Counting in Greenhouses

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： João Vítor R. Vasconcelos Thayron M. Hudson André G. dos Santos Martin Saska Alexandre S. Brandão Department of Electrical Engineering Núcleo de Especialização em Robótica Graduate Program on Computer Science Federal Univer-sity of Vicosa Vicosa MG Brazil Graduate Program on Computer Science Federal University of Vicosa Vicosa MG Brazil Department of Cybernetics Multi-robot Systems Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a solution for counting fruit in agricultural greenhouses using Unmanned Aerial Vehicles (UAV s). Initially, a heuristic based on Simulated Annealing was used to optimize the UAV's trajectory, ensuring efficient coverage of the beds. Next, digital image processing (DIP) techniques were implemented to count the fruit, including depth segmentation, application of bounding boxes, color filtering, and element counting. The DIP accuracy was evaluated in multiple scenarios and the results indicate high reliability in fruit counting, with the potential to optimize agricultural operations and provide valuable information to producers. Possible future improvements could include further refinements in image processing to increase the accuracy of counting other fruits. Ultimately, this work contributes to the advancement of automation in agriculture by offering a viable and efficient solution for counting fruit in greenhouses using UAV s.

关键词： Image segmentation Accuracy Image color analysis Greenhouses Simulated annealing Autonomous aerial vehicles Trajectory

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Federated Learning for Sleep Detection Problems

Federated Learning for Sleep Detection Problems

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International Conference on Smart Computing (SMARTCOMP)

作者： Guilherme Antonio Borges Julio Cesar Santos dos Anjos Jorge Sá Silva Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal Federal Institute Sul-rio-grandense (IFSUL) Rio Grande Sul Brazil Graduate Program Teleinformatics Engineering Federal University of Ceara Fortaleza Brazil Department of Electrical and Computer Engineering (DEEC) University of Coimbra Coimbra Portugal

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

Human-in-the-loop cyber-physical Systems use data from various sources to provide valuable assistance to users, but privacy concerns arise when sensitive information is shared. Federated Learning is a promising solution that enables the processing of user data without sharing sensitive information. While this method holds great potential, its efficacy in detecting sleep problems remains an open question. In this way, using a real-world dataset application, our study meticulously evaluates and comprehends the impact of incorporating Federated Learning on sleep detection. Our study evaluates the impact of incorporating Federated Learning on sleep detection and compares it with traditional Machine Learning models. Our findings reveal that our approach delivers accurate sleep detection results over 84% on par with conventional techniques. Our results emphasize the critical importance of handling human error inputs, as this factor significantly influences the accuracy of results in both methods.

关键词： Data privacy Accuracy Uncertainty Data analysis Federated learning Flowering plants Data models

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Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision

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Light(Science & Applications) 2022年第11期11卷 2817-2831页

作者： Yueming Li Ying Jiang Lu Lan Xiaowei Ge Ran Cheng Yuewei Zhan Guo Chen Linli Shi Runyu Wang Nan Zheng Chen Yang Ji-Xin Cheng Department of Mechanical Engineering Boston UniversityBostonMA 02215USA Graduate Program for Neuroscience Boston UniversityBostonMA 02215USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Chemistry Boston UniversitBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Division of Materials Science and Engineering Boston UniversityBostonMA 02215USA Department of Biological Engineering Massachusetts Institute of TechnologyCambridgeMA 02142USA

High precision neuromodulation is a powerful tool to decipher neurocircuits and treat neurological *** non-invasive neuromodulation methods offer limited precision at the milimeter ***,we report opticallygenerated focused ultrasound(OFUS)for non-invasive brain stimulation with ultrahigh *** is generated by a soft optoacoustic pad(SOAP)fabricated through embedding candle soot nanoparticles in a curved polydimethylsiloxane *** generates a transcranial ultrasound focus at 15 MHz with an ultrahigh lateral resolution of 83μm,which is two orders of magnitude smaller than that of conventional transcranial-focused ultrasound(tFUS).Here,we show effective OFUS neurostimulation in vitro with a single ultrasound *** demonstrate submillimeter transcranial stimulation of the mouse motor cortex in *** acoustic energy of 0.6 mJ/cm?,four orders of magnitude less than that of tFUS,is suffcient for successful OFUS *** offers new capabilities for neuroscience studies and disease treatments by delivering a focus with ultrahigh precision noninvasively.

关键词： SOAP stimulation precision

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