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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Gabriel Fernandes Andre G. S. Conceicao Humberto X. De Araújo Postgraduate Program in Electrical Engineering Federal University of Bahia Salvador Brazil Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

In this study, a robust Linear Quadratic Regulator (LQR) controller was developed and evaluated for the Bebop 2 quadrotor drone. Polytopic uncertainties in the dynamic model were taken into account in the control design, ensuring robustness. The drone's localization was achieved using fiducial markers and a Kalman Filter for state estimation, integrating visual and positional data. The controller design utilized Linear Matrix Inequalities (LMIs) to optimize performance while accounting for system constraints. A circular trajectory in three-dimensional space was employed to assess the controller's effectiveness in simulation. Results demonstrated precise trajectory tracking, stability, and robust performance despite parameter uncertainties. These findings highlight the applicability of the proposed framework for real-world scenarios involving underactuated UAVs.

关键词： Location awareness Visualization Uncertain systems Uncertainty Trajectory tracking Stability analysis Trajectory Vehicle dynamics Quadrotors Drones

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Comparative Analysis of Clusterization Methods Applied to a Solid-State LiDAR

Comparative Analysis of Clusterization Methods Applied to a ...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Henrique N. Poleselo Andre G. S. Conceicao Postgraduate Program in Electrical Engineering Federal University of Bahia Salvador Brazil Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Object segmentation is one of the main activities for the robot to create a sense of its environment. This task is a precursor to other activities, such as autonomous navigation in a given environment. Through sensors such as LiDAR, it's possible to generate high-resolution three-dimensional maps of the environment in which the robot is located, thus enabling their interpretation so that tasks such as object segmentation can be performed. In this article, the DBSCAN and HDBSCAN unsupervised clustering methods are explored. Results in a simulated environment in Gazebo together with Robot Operating System ROS framework for capturing sensory data from LiDAR Livox Mid-70 coupled to a mobile robot show the performance of such techniques through comparisons.

关键词： Laser radar Operating systems Clustering methods Object segmentation Robot sensing systems Sensors Mobile robots Autonomous robots

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Evaluation of an artificial intelligence-based model in diagnosing periodontal radiographic bone loss

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Clinical Oral Investigations 2025年第4期29卷 1-8页

作者： Cassiano, Luanny de Brito Avelino da Silva, Jordão Paulino Cassiano Martins, Agnes Andrade Barbosa, Matheus Targino Rodrigues, Katryne Targino Barbosa, Ádylla Rominne Lima da Silva Gomes, Gabriela Ellen Maia, Paulo Raphael Leite de Oliveira, Patrícia Teixeira de Sousa Lopes, Maria Luiza Diniz da Silva, Ivanovitch Medeiros Dantas de Aquino Martins, Ana Rafaela Luz Department of Dentistry Federal University of Rio Grande do Norte - UFRN Rio Grande do Norte Natal Brazil Postgraduate Program in Electrical and Computer Engineering Center of Technology Rio Grande do Norte Natal Brazil Department of Dentistry Federal University of Rio Grande do Norte Av. Senador Salgado Filho 1787 Rio Grande do Norte Natal 59056-000 Brazil

Objective: To develop an artificial intelligence model based on convolutional neural network for detecting and measuring periodontal radiographic bone loss (RBL). Materials and methods: Keypoint annotations were carried out in 595 digital bitewing radiographic images using a computer Vision Annotation Tool. The dataset was splitted: 416 of these images were trained using the You Only Look Once version 8 architecture with pose estimation (YOLO-v8-pose), 119 images were destined for the validation set, and 60 images were used for the test set, resulting in a model capable of detecting keypoints related to the cementoenamel junction (CEJ) and alveolar bone crest (ABC). In order to evaluate the performance of the obtained model, the following metrics were analyzed: F1-Score, precision, sensitivity and mean average precision (mAP). Then, an algorithm was implemented to measure the RBL by calculating the Euclidean distance between CEJ and ABC. Results: The model achieved an F1-Score of 66,89%, precision of 61,1%, a sensitivity of 73,9% and an mAP of 73.8%. Conclusions: The developed model and its algorithm for identifying and measuring periodontal radiographic bone loss demonstrated promising performance, thereby presenting a potential tool for assisting in periodontal diagnosis. Further studies comparing the developed model with manual measurements performed by specialists are necessary for its validation. Clinical relevance: Applying artificial intelligence in clinical dental practice can support diagnosis, streamline clinical workflows, and inform treatment planning, representing a significant advancement in dental automation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Alveolar bone loss computer neural networks Deep learning Periodontitis Radiography

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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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Rate maximization with QoS guarantees in IRS-Assisted WPCN-NOMA systems 7

Rate maximization with QoS guarantees in IRS-Assisted WPCN-N...

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7th Workshop on Communication Networks and Power Systems, WCNPS 2022

作者： De Pinho, Carlos Augusto Melo Lima, Francisco Rafael Marques Federal University of Ceará Postgraduate Program in Computer and Electrical Engineering campus Sobral Brazil Brazil

ISBN: (数字)9781665474566

ISBN: (纸本)9781665474566

In this article, Intelligent Reflecting Surface (IRS) technology is used to assist a Wireless Powered Communication Network (WPCN) system composed of a Hybrid Access Point (HAP) and multiple devices during downlink and uplink phases. In the downlink, devices charge their batteries through Wireless Power Transfer (WPT) from the power signals coming directly from the HAP and from the signals reflected by the IRS. In the uplink, the devices transmit their information signals to the HAP by employing Non-Orthogonal Multiple Access (NOMA). In the HAP, information is decoded through Successive Interference Cancellation (SIC). Based on this system model, an optimization problem is formulated so as to maximize the total uplink data rate subject to individual Quality of Service (QoS) guarantees for devices where IRS's phase shifts, uplink/downlink time duration and device ordering in SIC are optimized. Motivated by the high complexity of the optimization problem, we employ alternate optimization where Genetic Algorithm (GA) is used to define IRS's phase shifts in uplink/downlink and a heuristic solution is employed to optimize the remaining variables. Simulation results show that the proposed solution is able to satisfy different levels of QoS demands with higher spectral efficiency compared to baseline solutions. © 2022 IEEE.

关键词： Energy harvesting

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A Software Process Simulator Machine for software engineering simulation games

A Software Process Simulator Machine for software engineerin...

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作者： Chaves, Rafael O. Favero, Elói L. Da Tavares, Emanuel M. C. Oliveira, Sandro R.B. Postgraduate Program in Electrical Engineering ITEC UFPA Belém Pará Brazil Postgraduate Program in Computer Science ICEN UFPA Belém Pará Brazil

ISBN: (纸本)9780769543598

Develop new games without having to start from scratch has been made possible by using game engines, since they offer a number of specialized components and optimized functions which are common in games. However, one realizes that process simulation games have not taken advantage of these technologies. This paper aims to demonstrate the results of the development of a software process simulation component called Software Process Simulator Machine (SPSM), which focuses on help and motivate software process educational game development through the addressing of main generic requirements for process simulation software. © 2010 IEEE.

关键词： Software design

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Parallel processing in a cluster of microcomputers with application in contingency analysis

Parallel processing in a cluster of microcomputers with appl...

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2004 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America

作者： Balduino, Liliane Alves, Antônio César Baleerio Electrical and Computer Engineering in the Postgraduate Program School of Electrical and Computer Engineering Federal University of Goiás Brazil School of Electrical and Computer Engineering Federal University of Goiás Brazil Catholic University of Goiás

ISBN: (纸本)0780387759

This work is about the use of a microcomputer network as a parallel processing environment. In order to allow the communication, the synchronization and the distribution of the tasks among processes, the PVM (Parallel Virtual Machine) and MPI (Message Passing Interface) systems, were used. These computational systems allowed for parallel processing in the area of electric power systems, namely the contingency analysis. This application, usually found in Energy Management Systems, requires the use of a large quantity of resources in a short time. The aim is to analyze thousands of disturbance scenarios, in a preventive mode in real time. Two parallel programming paradigms classified as Master/Slave, in the synchronous and asynchronous modes, were implemented. Case studies with sequential and parallel implementations were carried out to validate the algorithms used to analyze elaborated contingencies, including the use of data from the Interconnected Brazilian Electric Power System, allowing the analysis of single and multiple disturbances. ©2004 IEEE.

关键词： Microcomputers

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Classification and tracking of items on a moving conveyor belt using convolutional networks and image processing

Classification and tracking of items on a moving conveyor be...

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2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： De Goes, Angelo Leite Medeiros Neto, Adriao Duarte Doria Andrade, Matheus Gomes Diniz De Oliveira, Thiago Theiry Federal University of Rio Grande Do Norte Electrical and Computer Engineering Postgraduate Program Natal Brazil Federal University of Rio Grande Do Norte Computer Engineering Natal Brazil

ISBN: (纸本)9798350394528

The prolonged waiting time at supermarket checkout lines poses a significant challenge to the shopping experience, impacting customer satisfaction and operational efficiency. This paper presents a prototype that addresses this issue by utilizing computer vision and deep learning. The model, incorporating convolutional neural networks such as YOLO v4 tiny and YOLO v5 small, along with tools like OpenCV and Roboflow for dataset management, achieves a remarkable 98% mean average precision for two-class detection. It efficiently detects, classifies, tracks, and counts items on a mobile supermarket conveyor belt. Additionally, we introduce a versatile framework designed for seamless integration into real-world applications. It comprises a customizable monitoring application and simulator that facilitates synthetic image data generation. Managing diverse items in a supermarket presents a major challenge for data gathering, labeling, and training. In that sense, the importance of customizable monitoring and simulation tools is highlighted, emphasizing their practical role. Our findings demonstrate the feasibility of maintaining a minimal 0% to 2.85% precision tradeoff while using half of the data as synthetic for two-class detection, indicating potential practicality in supermarkets with proper scaling. In summary, this study brings tangible benefits to both customers and retailers, offering a potential to streamline, speed up, and cut costs in the supermarket checkout process. © 2024 IEEE.

关键词： Deep learning

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Can adaptive strategies sustain bidirectional LoRaWAN traffic? 6

Can adaptive strategies sustain bidirectional LoRaWAN traffi...

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6th IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd4.0 and IoT 2023

作者： Sisinni, Emiliano Depari, Alessandro Bellagente, Paolo Flammini, Alessandra Silva, Ivanovitvh Flores, Thommas Ferrari, Paolo University of Brescia Dept. of Information Engineering Brescia Italy Ufrn Dept. of Computer Engineering and Automation Natal Brazil Postgraduate Program in Electrical and Computer Enigeneering Ufrn Natal Brazil

ISBN: (纸本)9798350396577

Low Power Wide Area Networks (LPWANs) are a viable solution for addressing the requirements of Internet of Things (IoT) applications that can tolerate sporadic updates, possibly only in the uplink (from the field towards the cloud and the user) direction. LoRaWAN is currently the most diffused example of LPWAN, exploiting LoRa radios. LoRa, an enhanced version of chirp spread spectrum modulation, trades off the radio sensitivity for the data rate, permitting extensive area coverage. However, the price is a relatively long frame duration, leading to high collision probability in dense, asynchronous scenarios;fortunately, polarity inversion minimizes collisions between uplink and downlink traffic. Therefore, adaptive strategies (based on some feedback from the backend) have also been suggested to adapt the data rate depending on the environment condition dynamically. This work compares the ADR (Adaptive Data Rate) and the ADP (Adaptive Data Payload) strategies when bidirectional traffic exists, using a purposely designed simulator. Results considering a different number of gateways show that ADP tries to maximize the amount of data effectively received in both direction but suffer from the adoption of longer frame duration, leading to higher collision probability. In contrast, ADR tries to force the adoption of the fastest data rate, limiting the overall amount of transferred data. © 2023 IEEE.

关键词： Wide area networks

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A Comparative Analysis of Deep Learning Models for Detecting Malaria Disease Through LBP Features 6

A Comparative Analysis of Deep Learning Models for Detecting...

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6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022

作者： Zarima, Nona Muchtar, Kahlil Bintang, Akhyar Oktiana, Maulisa Maulina, Novi Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Aceh Indonesia School of Medicine Universitas Syiah Kuala Postgraduate Program Microbiology Department Aceh Indonesia

ISBN: (数字)9781665497428

ISBN: (纸本)9781665497428

Malaria is a parasitic infection spread by the plasmodium parasite. Malaria continues to be a major threat to world health, with an estimated 200 million cases and over 400,000 fatalities each year. When exposed to this disease, symptoms develop 10-15 days after the parasite enters the body. This disease becomes chronic if it is not treated medically, and it eventually leads to death. Using spatial information collected from microscopic images, several techniques based on image processing and machine learning have been utilized to diagnose malaria. Using the Local Binary Pattern (LBP) texture feature as a feature extraction approach, this study contributes to the development of a predictive and high-accuracy deep learning model by testing multiple Deep Learning models and determining which model delivers the best accuracy. To be specific, we tested frequently used baseline methods, namely ResNet34, VGG16, Inception V3, and EfficientNet. The results demonstrate that EfficientNet has a 91 percent outstanding accuracy rate, compared to 87 percent for VGG16, 81 percent for Resnet34, and 77 percent for InceptionV3, respectively. © 2022 IEEE.

关键词： Local binary pattern

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