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IEEE International Conference on Automation Science and engineering (CASE)

作者： Allan S. Almeida Tiago T. Ribeiro 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)9798350358513

ISBN: (纸本)9798350358520

One of the most important tasks in autonomous driving and autonomous vehicle navigation is detecting a path or trajectory that the vehicle should follow. Over the past few years, some learning-based works have stood out more than traditional computer vision techniques in detecting such lanes. In this paper we present an approach to solve the lane line detection problem in the context of visual path following by using a residual factorized convolutional neural network. Experimental results show a promising model that can detect lane lines even under severe lighting conditions and in the presence of occlusions and shadows. The path detection system was tested along with a visual path following formulation based on Nonlinear Model Predictive Control. Still, it can be used for any controller in the context of visual navigation for autonomous vehicles. Nonetheless, the proposed model architecture strikes a remarkable balance between accuracy and efficiency, making the system suitable for real-time applications.

关键词： Measurement Visualization Accuracy Navigation Computational modeling Lighting computer architecture Trajectory Convolutional neural networks Autonomous vehicles

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Trajectory Planning with Obstacle Avoidance using APF and COLREGS for USVs

Trajectory Planning with Obstacle Avoidance using APF and CO...

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

作者： Kelvin K. C. F. Nunes Tiago T. Ribeiro 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)9798350352344

ISBN: (纸本)9798350352351

This paper presents a trajectory planning and obstacle avoidance system for Unmanned Surface Vehicles (USV) in complex and dynamic navigation environments. The developed system employs a modified Artificial Potential Field (APF) algorithm and the International Regulations for Preventing Collisions at Sea (COLREGS) to ensure safe and efficient navigation. Modifications were implemented to the original algorithm to deal with obstacles approaching the autonomous vehicle, including adding vectors to determine the direction of deviation based on the cross-product. The proposed system was validated in the Gazebo simulation environment within a dynamic scenario featuring static and moving obstacles.

关键词： Trajectory planning Navigation Heuristic algorithms Vectors Regulation Safety Vehicle dynamics

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State of the Art: Fragility Function of electrical Power System 6

State of the Art: Fragility Function of Electrical Power Sys...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Sahnur Nasution, Elvy Syahrial, Syahrial Devi Sara, Ira Away, Yuwaldi Postgraduate School of Engineering Universitas Syiah Kuala Universitas Muhammadiyah Sumatera Utara Doctoral Program Banda Aceh23111 Indonesia Universitas Syiah Kuala Dept. Electrical and Computer Engineering Banda Aceh23111 Indonesia

ISBN: (纸本)9798350358346

Natural disasters, particularly earthquakes, can cause the electric power system to collapse, which can be brought on by one of the infrastructure breakdowns in the power system. Damaged power system can cause losses to electricity providers, electricity users, so it is necessary to understand the vulnerability of electric power system assets to earthquakes. The research fragility function presents the concept of fragility function in power system of the entire power system, showed fragility of power plant, transmission line, tower transmission, transformers, generator, and substation. Substation is more fragile than the others because substation consist of circuit breaker and disconnecting switch the most fragile asset. In addition, this paper also presents how to creating the fragility function and several solutions to increase the resilience of the electric power system to earthquakes. © 2023 IEEE.

关键词： Earthquakes

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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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ATSC 3.0 Constellation Analysis using computer Vision Combined with AI Decision Tree

ATSC 3.0 Constellation Analysis using Computer Vision Combin...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Jefferson Jesus Hengles Almeida Paulo Batista Lopes Cristiano Akamine Postgraduate Program in Electrical and Computer Engineering (PPGEEC) Mackenzie Presbyterian University São Paulo Brazil

This paper presents a proposal of a computer vision tool based on Open CV that allows the pre-visual identification of non-uniform constellation levels, channel coding, and Signal-to-Noise Ratio (SNR) estimation on ATSC. Nowadays, the challenge of digital television systems is to transmit high quality videos employing the new technologies, such as Ultra High Definition (UHD). Thus, the new standards as ATSC 3.0 have incorporated several modifications on their physical layers. Among them, it is possible to highlight the use of non-uniform constellations, advanced channel error coding and layer division multiplexing (LDM). However, the pre-visual understanding of the received constellations has become hard since the complex symbols are not distributed evenly in the complex plane and there are different layers to be seen simultaneously. So, the techniques of computer vision have a great potential to analyze and to extract an initial information from the images related to the received constellation, to identify the modulation level, channel coding rate and SNR to each layer, without the necessity of complete demodulation.

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

作者： Angelo Leite Medeiros De Góes Adrião Duarte Dória Neto Matheus Gomes Diniz Andrade Thiago Theiry De Oliveira Electrical and Computer Engineering Postgraduate Program Federal University of Rio Grande do Norte Natal Brazil Computer Engineering Federal University of Rio Grande do Norte Natal Brazil

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.

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Leveraging IoT and TinyML for Smart Battery Management in Electric Bicycles

Leveraging IoT and TinyML for Smart Battery Management in El...

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International Workshop on Secure Internet of Things (SIoT)

作者： Thommas Flores Matheus Andrade Morsinaldo Medeiros Miguel Amaral Marianne Silva Ivanovitch Silva Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte - Natal Brazil

Ahstract-Urban mobility and environmental sustainability are critical challenges intensified by population growth, urbanization, and increased vehicle presence, leading to issues like congestion, pollution, and climate change. To counter this, the urgency for efficient and eco-friendly transportation solutions is evident. Battery Management Systems (BMS) play a central role, overseeing batteries in electronics, vehicles, and energy storage systems for safety and reliability. Focusing on electric bicycles (e-Bikes), the article highlights BMS's role in enhancing cyclist experiences and delves into techniques utilizing energy-efficient hardware and deep learning, notably TinyML, to predict Lithium-Ion battery State of Health (SoH). It examines diverse architectures and parameters, unveiling relationships to guide machine learning model construction, emphasizing delayed feedback integration based on rigorous statistics. The article navigates through data segregation and model training processes, presenting a comprehensive approach that balances sustainability, efficiency, and technological progress in predicting e-Bike battery SoH.

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A Multi-Layered Methodology for Driver Behavior Analysis Using TinyML and Edge Computing

A Multi-Layered Methodology for Driver Behavior Analysis Usi...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Morsinaldo Medeiros Thommas Flores Marianne Silva Ivanovitch Silva Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte Natal Brazil Federal University of Alagoas Penedo Brazil

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

The rapid increase in vehicular sensor data and advances in Internet of Things (IoT) technologies pose a dual challenge and opportunity for real-time traffic management and driver behavior analysis in the domain of Intelligent Transportation Systems (ITS). The current gap lies in effectively processing this data at the network edge, which is critical for timely and efficient decision-making in ITS. Our study proposes a novel, multi-layered, stream-oriented data processing methodology specifically designed for edge computing environments to address this challenge. This approach integrates soft sensors, the Typicality and Eccentricity Data Analytics (TEDA) framework, and an incremental clustering algorithm to detect and classify driver behavior patterns. The emphasis on using low-energy hardware and TinyML techniques is crucial, aiming to optimize processing efficiency while minimizing the environmental impact. To substantiate the efficacy of our methodology, we conducted a practical case study in Natal-RN, Brazil, utilizing the Freematics One + OBD-II microcontroller device for real-world application and validation. This involved two participants and focused on real-time data analysis for driver profile detection. The preliminary results demonstrate a significant potential of our approach in accurately classifying driving behaviors and patterns, offering insights for enhancing vehicle efficiency and reducing fuel consumption. This study fills a critical gap in ITS. It sets the stage for future research in sustainable and adaptive transportation systems, leveraging the power of edge computing and incremental algorithms in real-time data stream processing.

关键词： Data analysis Soft sensors Image edge detection Heuristic algorithms Clustering algorithms Real-time systems Classification algorithms

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A Comparative Analysis of Deep Learning Models for Detection of Knee Osteoarthritis Disease through Mobile Apps 8

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

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8th International Conference on Wireless and Telematics, ICWT 2022

作者： Kardawi, Muhammad Yusuf Muchtar, Kahlil Nurdin, Yudha Maulina, Novi Muchallil, Sayed Universitas Syiah Kuala Department of Electrical and Computer Engineering Aceh Indonesia Aceh Indonesia Postgraduate Program School of Medicine Universitas Syiah Kuala Microbiology Department Universitas Syiah Kuala Aceh Indonesia

ISBN: (数字)9781665451222

ISBN: (纸本)9781665451222

Osteoarthritis is a progressive degenerative joint disease characterized by damage to joint cartilage and structural joint diarthrodial. As many as 80% of osteoarthritis patients will experience movement limitations, and 25% cannot carry out daily activities. At present, the stages of osteoarthritis diagnosis are through visual observations of the narrowing of joint and osteophyte gaps contained in X-ray images through the observation of radiology and assessing the severity of disorders. This study proposes a CAD (computer-Aided Design) support system to detect knee osteoarthritis based on X-ray images with deep learning (DL) techniques. In the design of the CAD-related architecture evaluation, including ResNet, VggNet, EfficientNet, and InceptionV3 in building a predictive model using the Convolutional Neural Network (CNN) based on Mobile Application and Cloud Computing to be able to classify normal knee and knee osteoarthritis with the percentage of testing accuracy each architecture is 93.17% on ResNet, 95.26% on VggNet, 94.64% in InceptionV3 and 96.59% on EfficientNet. The experimental results can be found through our project channel: https://***/EXj-7Dv6xGM © 2022 IEEE.

关键词： Cloud computing

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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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