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IEEE International Conference on RFID

作者： Tuan Dinh Le Seong Hoon Kim Minh Hoang Nguyen Daeyoung Kim Seung Young Shin Kyung Eun Lee Rodrigo da Rosa Righi Deparment of Computer Science KAIST Daejeon South Korea Samsung Electronics South Korea Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos São Leopoldo Brazil

With the emergence of the Internet of Things (IoT) vision, RFID technology has attracted more and more interests from many industries. In the EPCglobal standard which standardizes RFID architectural framework and interfaces, the Electronic Product Code Information System (EPCIS) - the repository in the RFID network - plays a pivotal role in storing dynamic and historical RFID trajectory data, well-tailored to supply chain applications. However, in the IoT era, EPCIS should be capable of storing a large amount of realtime and continuous time-series RFID data in a scalable manner. Therefore, in this paper, we analyze the requirements and current design of EPCIS and report current limitations of SQL-based approaches for IoT applications. To cope with these limitations, we explore a NoSQL-based approach for EPCIS in the IoT and propose a column-family-based data model and customization for Cassandra-based EPCIS. Using real-world implementation, we also conduct a performance comparison between MySQL and Cassandra in different deployment scenarios (i.e., physical and virtual machines). As a result, we show the benefits of using Cassandra datastore for EPCIS in the IoT in terms of response time, throughput, and flexibility.

关键词： Radiofrequency identification Data models Indexes Relational databases Standards Containers

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Machine Learning for Prediction of Adverse Drug Reactions: Application to Neonatal Intensive Care Units

SSRN

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SSRN 2023年

作者： do Nascimento, Amanda Roseane Farias Greati, Victor Rodrigues Leopoldino, Ramon Weyler Duarte Martins, Rand Randall Barbosa, Euzebio Guimarães Graduate Program in Pharmaceutical Sciences Health Sciences Center Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil Graduate Program in Systems and Computing Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil

Introduction: The intensive care of neonates is associated with the entry of a large volume of data in their medical records. The treatment of this data can be done through Machine Learning: a tool capable of assisting in the detection and decision-making of a wide range of medical conditions, including adverse drug reactions (ADR). Objective: To train a prediction model to assist in the detection of adverse drug reactions in neonates admitted to an ICU. Methodology: observational study developed in the Neonatal Intensive Care Unit of a teaching hospital in Brazil. Clinical data were collected from daily pharmacotherapeutic follow-up, processed and analyzed by machine learning through libraries written in Python language. Results: Eight hundred and three neonates were included in the study, with a mean gestational age of 32.2 ± 4.2 weeks and mean birth weight of 1807.2 ± 936.6g. The incidence of ADR was 10.8%. Antimicrobials, especially aminoglycosides, were the most prescribed drugs for this population. An algorithm was trained and tested to predict ADR in the NICU, in which the metrics were precision (0.35) and recall (0.823), with specificity (80%) and sensitivity (67%). Conclusion: There is a high potential in the machine learning method to predict ADR in neonates admitted to an ICU. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Multi-Instance Task in Swarm Robotics: Sorting Groups of Robots or Objects into Clusters with Minimalist Controllers

Multi-Instance Task in Swarm Robotics: Sorting Groups of Rob...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Adilson Krischanski Yuri K. Lopes Andre B. Leal Ricardo F. Martins Roberto S.U. Rosso Department of Computer Science Santa Catarina State University (UDESC) Joinville Brazil Graduate Program in Applied Computing (PPGCAP) UDESC Brazil Department of Electrical Engineering Santa Catarina State University (UDESC) Joinville Brazil

Relying only on behaviors that emerge from simple responsive controllers; swarms of robots have been shown capable of autonomously aggregate themselves or objects into clusters without any form of communication. We push these controllers to the limit, requiring robots to sort themselves or objects into different clusters. Based on a responsive controller that maps the current reading of a line-of-sight sensor to a pair of speeds for the robots' differential wheels, we demonstrate how multiple tasks instances can be accomplished by a robotic swarm. Using the dividing rectangles approach and physics simulation, a training step optimizes the parameters of the controller guided by a fitness function. We conducted a series of systematic trials in physics-based simulation and evaluate the performance in terms of dispersion and the ratio of clustered robots/objects. Across 20 trials where 30 robots cluster themselves into 3 groups, an average of 99.83% of them were correctly clustered into their group after 300 s. Across 50 trials where 15 robots cluster 30 objects into 3 groups, an average of 61.20%, 82.87%, and 97.73% of objects were correctly clustered into their group after 600 s, 900 s, and 1800 s, respectively. The object cluster behavior scales well while the aggregation does not, the latter due to the requirement of control tuning based on the number of robots.

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Experience sharing between cooperative reinforcement learning agents

arXiv

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arXiv 2019年

作者： Souza, Lucas Oliveira de Oliveira Ramos, Gabriel Ralha, Celia Ghedini Numenta Redwood City United States Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos São Leopoldo Brazil Computer Science Department University of Brasilia Brasilia Brazil

The idea of experience sharing between cooperative agents naturally emerges from our understanding of how humans learn. Our evolution as a species is tightly linked to the ability to exchange learned knowledge with one another. It follows that experience sharing (ES) between autonomous and independent agents could become the key to accelerate learning in cooperative multiagent settings. We investigate if randomly selecting experiences to share can increase the performance of deep reinforcement learning agents, and propose three new methods for selecting experiences to accelerate the learning process. Firstly, we introduce Focused ES, which prioritizes unexplored regions of the state space. Secondly, we present Prioritized ES, in which temporal-difference error is used as a measure of priority. Finally, we devise Focused Prioritized ES, which combines both previous approaches. The methods are empirically validated in a control problem. While sharing randomly selected experiences between two Deep Q-Network agents shows no improvement over a single agent baseline, we show that the proposed ES methods can successfully outperform the baseline. In particular, the Focused ES accelerates learning by a factor of 2, reducing by 51% the number of episodes required to complete the task. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Stroke Lesion Detection Using Convolutional Neural Networks

Stroke Lesion Detection Using Convolutional Neural Networks

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International Joint Conference on Neural Networks

作者： Danillo Roberto Pereira Pedro P. Reboucas Filho Gustavo Henrique de Rosa Joao Paulo Papa Victor Hugo C. de Albuquerque Department of Computing São Paulo State University Bauru SP Brazil Federal Institute of Education Science and Technology of Ceará CE Brazil Graduate Program in Applied Informatics University of Fortaleza Fortaleza CE Brazil

Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm Optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results.

关键词： Computed tomography Hemorrhaging Brain Attenuation Blood Image segmentation Cranium

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A Quantitative Analysis on Different Carbonate Indicators Based on Spaceborne Data in a Controlled Karst Area

A Quantitative Analysis on Different Carbonate Indicators Ba...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： M. Muller V. F. Sales D. C. Zanotta A. Marques T. T. Guimarães L. Bachi E. M. Souza D. Brum L. Gonzaga M. R. Veronez C. L. Cazarin Vizlab X-Reality & GeoInformatics Lab - Graduate Program in Applied Computing UNISINOS University Rio Grande do Sul Brazil State University of Maringa Parana Brazil CENPES - PETROBRAS Rio de Janeiro Brazil

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

New sensors aboard recently launched satellites have induced the development of several measures aimed to indicate the presence of many materials over the Earth. Karsts are places rich in carbonate rocks and present large economic and environmental importance. This paper aimed at assessing the performance and consistency of different carbonate estimators derived from orbital images acquired over a controlled karst area. Experiments were assisted by a multi-scaled reference data built through a high spatial resolution Unmanned Aerial Vehicle (UAV) image acquired over the selected area. Results show a considerable unconformity among selected measures and better performance presented by indices exploiting measures along visible and infrared spectral regions.

关键词： Rocks Remote sensing Earth Indexes Artificial satellites Thermal sensors Sensor phenomena and characterization

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Ubiquitous System for Stroke Monitoring and Alert

Ubiquitous System for Stroke Monitoring and Alert

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： Allan Barcelos Sandro J. Rigo Jorge L.V. Barbosa Applied Computing Graduate Program University of Vale do Rio dos Sinos (UNISINOS) São Leopoldo Brazil Appl. Comput. Grad. Program Univ. of Vale do Rio dos Sinos (UNISINOS) Sao Leopoldo Brazil

Research regarding stroke indicates that short elapsed time between accident and treatment can be fundamental to allow saving patient's life and avoid future sequels. This paper describes a model for monitoring and rescue victims in situations of possible stroke occurrence. It uses stroke symptoms that can be monitored by mobile equipment, ambient intelligence and artificial neural networks. The model is independent from human operation and applications or third parties devices, therefore adding facilities to increase the quality of life for people with stroke sequel, due to constant monitoring and follow-up provided, allowing the stroke patient to consider a recovery period with greater autonomy. A prototype based on free software platforms was developed, in order to assess the accuracy and the time elapsed between the prototype to detect and to send an alert. The results indicate a positive outlook for the work continuity.

关键词： Monitoring Biomedical monitoring Artificial neural networks Prototypes Blood pressure Mobile communication Sensors

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Quantum Wrapper Networking: Investigating the Noise Impact of Classical Headers on the Quantum Payload

Quantum Wrapper Networking: Investigating the Noise Impact o...

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2024 Frontiers in Optics, FiO 2024

作者： Gül, Gamze On, Mehmet Berkay Tan, Shannon G. Proietti, Roberto Kanter, Gregory S. Yoo, S.J. Ben Kumar, Prem Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy

We study the impact in time and frequency domains of classical headers on quantum payloads in quantum wrapper networking. We identify and characterize in-fiber scattering processes that produce noise photons degrading...

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Double Noise Filtering in CT: Pre- and Post-Reconstruction

Double Noise Filtering in CT: Pre- and Post-Reconstruction

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Vinicius C. Assis Denis H.P. Salvadeo Nelson D.A. Mascarenhas Alexandre L.M. Levada Department of Statistics Applied Mathematics and Computing State University of São Paulo Rio Claro SP Brazil Faculdade Campo Limpo Paulista Graduate Program in Computer Science SP Brazil Department of Computing Federal University of São Carlos São Carlos SP Brazil

Motivated by the ALARA (As Low As Reasonably Achievable) principle, this paper proposes to denoise Computed Tomography (CT) images by using a double-filtering approach. First, projection data were filtered using methods to filter Poisson noise (pre-filtering step). Then the filtered back projection (FBP) algorithm was applied to image reconstruction. After, the reconstructed images were denoised by using suitable methods for filtering Gaussian noise (post-filtering step). Finally, known metrics of image quality evaluation (such as SSIM and PSNR) were used to compare the filtered images with the ones considered ideal images in various combinations of filters. The results lead to the conclusion that a second filtering applied on image domain can improve the CT denoising quality from pre-filtering step. Thus, CT double-filtering strategy achieved a better balance between noise reduction and details preservation.

关键词： Noise Noise measurement Computed tomography Noise reduction Image reconstruction Correlation Standards

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Electronic-photonic quantum systems on-chip

Electronic-photonic quantum systems on-chip

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Quantum 2.0, QUANTUM 2022

作者： Wang, Imbert Ramesh, Anirudh Kramnik, Danielius Fargas Cabanillas, Josep M. Stojanović, Vladimir Kumar, Prem Popović, Miloš A. Department of Electrical and Computer Engineering Photonics Center Boston University BostonMA02215 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA94720 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We present progress towards realizing electronic-photonic quantum systems on-chip;particularly, entangled photon-pair sources, placing them in the context of previous work, and outlining our vision for mass-producible... 详细信息

ISBN: (纸本)9781957171111

关键词： Quantum entanglement

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