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Data-Driven Autonomous System for Lane Keeping in a 1:10 Scale Vehicle for Autonomous Driving 4th

Data-Driven Autonomous System for Lane Keeping in a 1:10 Sca...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Reveles-Martinez, Rafael Celaya-Padilla, Jose-Maria Gamboa-Rosales, Hamurabi Mendoza-Saldivar, Uziel A. Dominguez-Jaramillo, Diego A. Chavez-Almaraz, Marlon Mireles-Delgado, Flabio Quezada-Villalobos, Alan C. Saldivar, Javier Inst Politecn Nacl Unidad Profes Interdisciplinaria Ingn Campus Zaca Calle Circuito Cerro Gato 202 Zacatecas 98160 Zacatecas Mexico Univ Autonoma Zacatecas Ctr Invest & Innovac Automotriz Mexico CIIAM Jardin Juarez 147Ctr Hist Zacatecas 98000 Zacatecas Mexico

ISBN: (纸本)9783031832093;9783031832109

This work focuses on the assembly of a 1:10 scale autonomous driving vehicle to develop and implement a data-driven navigation methodology for a controlled environment. The objective is that the vehicle can Lane-keeping, overtake and pass other vehicles also at 1:10 scale. Necessary components were selected to autonomously drive such a platform. The vehicle was tested at the Mexican Robotics Tournament 2024, in the Automodel Car category, held at the Universidad Autonoma de Nuevo Leon in Monterrey, Mexico. The competition included, for a given circuit, lane keeping, traffic sign recognition, passing other vehicles, and automatic parking. The lane keeping test on the test track was addressed by image processing, with all communication managed from ROS and Jetson Xavier. The lane detection algorithm achieved 97% accuracy, evaluated on 2967 images captured per lap of the track with an Intel D457 camera. The vehicle completed the course in four out of six possible attempts. Real-time tuning of the adaptive PID controller implemented in the ESP significantly improved departure angle (steering) accuracy. However, there were approximately 79 false detections per lap, indicating the need for additional adjustments especially at zebra crossings. The next phase will focus on optimizing the algorithm for higher speeds and improving robustness to variations in lighting and traffic signals.

关键词： Autonomous Driving Lane-keeping Edge Computing Jetson Xavier ROS Image Processing Sensor Fusion Espressif ESP32-C3 Adaptive PID Control

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Enhancing Forest Fire Classification with Feature Selection and Machine Learning Based on PRISMA Hyperspectral Data 4th

Enhancing Forest Fire Classification with Feature Selection ...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Meroufel, Hanifi Berrichi, Faouzi Amri, Mohammed Bilel Larabi, Mohammed El Amin Ctr Tech Spatiale Agence Spatiale Algerienne Arzew Algeria Algerian Space Agcy Algiers Algeria

ISBN: (纸本)9783031821554;9783031821561

Forest fires, a significant global threat causing vast annual devastation where each year millions of hectares are destroyed over the world. Burned areas necessitate early and accurate detection for effective response. This study investigates the application of Feature Selection (FS) to address data complexity in forest fire detection utilizing PRISMA hyperspectral imagery (HSI). FS is employed to select the most relevant information, thereby reducing complexity and facilitating the interpretation of results. The effectiveness of Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Support Vector Machine (SVM) algorithms is investigated. Analysis reveals that while the raw 229-band data yielded a reasonable accuracy with RF, applying FS through wrapper methods only select 13 bands and improve the performance of LGBM, XGBoost, and SVM. Notably, the best model achieves a classification precision of 94% and recall of 91%, demonstrating the effectiveness of FS in enhancing forest fire detection and mapping.

关键词： Forest fires Feature selection XGBoost LGBM Random Forest Support Vector Machine

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Enhancing Differential Evolution for Neural Network Optimization Through Boundary Individual Consideration 8th

Enhancing Differential Evolution for Neural Network Optimiza...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Angchekar, Mandar G. Mohan, Chilukuri K. Syracuse Univ Dept EECS Syracuse NY 13244 USA

ISBN: (纸本)9783031856273;9783031856280

Differential Evolution (DE) algorithm applications encounter exploration challenges at the boundaries of the convex hull of the population, hindering the discovery of optimal solutions. To address this obstacle, we propose a novel modification of DE that explicitly utilizes "Boundary Individuals" to ensure a thorough exploration of the boundary regions. We applied the improved algorithm, DEBI, to the problem of optimizing neural networks' hyperparameters. Our results, with three healthcare datasets, show that this modification of DE resulted in superior performance, with more consistent and higher fitness scores across generations, as well as improved precision and recall. These findings support the hypothesis that boundary exploration can significantly benefit learning.

关键词： Differential Evolution Neural Network Optimization Boundary Individuals Hyperparameter Tuning Mutation Evolutionary Algorithms Machine Learning Population-Based Algorithms Classification Accuracy

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A Study on Image Reconstruction Based on Decoding fMRI Through Extracting Image Depth Features 5th

A Study on Image Reconstruction Based on Decoding fMRI Throu...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Deng, Xin Bao, Feiyang Liu, Bin Li, Yijia Zhang, Lianhua Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Visualizing perceived content through functional magnetic resonance imaging (fMRI) analysis is a captivating research area in brain decoding. Previous studies have primarily focused on restoring either high-level semantic features or low-level semantic features from fMRI data, but rarely achieved effective restoration of both. This study proposes a novel approach for decoding the visual cortex activity measured by fMRI into the layered visual features that share the hierarchical information from the corresponding images. By iteratively optimizing the relationship between the layered visual features and the image's depth features extracted by a visual transformer, the method in this research significantly improves the reconstruction of the image's deep features. Furthermore, by incorporating the prior natural image information through a deep generator network, this work enhances the reconstruction process, resulting in richer semantic details. Experimental results verify the effectiveness of our methodology in restoring both high-level and low-level semantic features of the perceived images, ultimately enhancing the overall visual fidelity of the reconstructed image. Importantly, our model demonstrates successful generalization to reconstruct artificial shapes, indicating that the performance of our model is not simply achieved by relying on extensive sample datasets. These findings prove the efficacy of the method in effectively reconstructing the perceived content based on the hierarchical neural representations, providing a new method to study the brain's underlying mechanisms.

关键词： Image reconstruction Neural decoding Functional magnetic resonance imaging Feature extraction Visual Transformer

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Impact of Real-Time Linux for Industrial Edge AI 5th

Impact of Real-Time Linux for Industrial Edge AI

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Fernandez De Barrena, Telmo Garcia, Ander Franco, Javier Luis Ferrando, Juan Fdn Vicomtech Basque Res & Technol Alliance BRTA Mikeletegi 57 Donostia San Sebastian Spain Univ Deusto Fac Engn Unibertsitate Etorb Bilbao 24006 Spain

ISBN: (纸本)9783031807749;9783031807756

Traditionally hard real-time operating systems (RTOS) were reserved for applications with very restrictive requirements, such as aviation, industrial control or safety, where upper bounds of jitter and latency were guaranteed. However, requirements of Industry 4.0 and Industrial Edge Artificial Intelligence (AI) are different. Nowadays Industrial Edge AI does not control safety critical tasks, it analyses data and applies AI models to optimize industrial processes. Thus, they present soft real-time requirements: the sooner a result is returned the better, but no critical harm for operators or industrial assets is introduced by delays on the results from the AI services. Thus, Industrial Edge AI applications have been usually deployed as software containers or on general purpose Operating Systems (OS). However, latest Linux kernel versions include a preemption option to transform general Linux distributions into soft RTOS. This paper focuses on the effect of this option for Industrial Edge AI. In order to measure its impact, three different experiments have been defined, where Raspberry Pis (RPis) and a PLC send data using MQTT and OPC UA Pub/Sub, under different sampling frequencies and computational load conditions. Then, Java and Python clients have been deployed on a different RPi running two versions of the Linux Kernel, the regular one and the soft real-time one. Finally, latency, jitter and packet loss measures have been taken in several variations of these setups in order to identify the response of each Linux Kernel for different use cases. Results of the experiments have been used to generate general guidelines for kernel selection for different use cases.

关键词： Edge AI Industry 4.0 Real-Time Linux Preemption

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Design and Implementation of a High Performance Domain Name Service on Commodity Hardware 18th

Design and Implementation of a High Performance Domain Name ...

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18th Symposium and Summer School on Service Oriented Computing

作者： Heinz, Florian Kluge, Martin Ostbayerische Tech Hsch D-93053 Regensburg Germany Vautron Rechenzentrum AG Regensburg Germany

ISBN: (纸本)9783031725777;9783031725784

The internet Domain Name System (DNS) is one of the essential components of the World Wide Web and the whole internet. The core concept is a large distributed and hierarchical database that translates internet domain names into host addresses and other supplemental information. Name servers can serve millions of domains and usually have to answer thousands of requests per second. Because of their vital function for any internet service, domain name servers are regularly target of Denial-of-Service attacks, where millions of queries per second are used to overload the database system. This work describes an approach on how to achieve answer rates in this dimension with a single server built from low-cost commodity hardware.

关键词： Domain Name System Data Structures information Systems High Performance Computing

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Improved Classification Techniques Using Convolutional Neural Networks for Rice Plant Disease Detection 4th

Improved Classification Techniques Using Convolutional Neura...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Saxena, Swati Gohil, Bhavesh N. Sardar Vallabhbhai Natl Inst Technol SVNIT Surat Gujarat India

ISBN: (纸本)9783031837951;9783031837968

Rice, one of the most important crops globally, serves as a vital nutritional source for over 50% of the world's population. However, it is highly susceptible to various diseases that can severely impact both the quality and quantity of the harvest, sometimes causing a reduction of 20%-40% in crop yield. Timely detection and accurate identification of rice plant diseases are critical for ensuring a healthy harvest. Farmers must be able to recognize these diseases visually, but daily field surveys are labor-intensive, time-consuming and costly, ultimately driving up the price of rice for consumers. Rice leaves are prone to several bacterial, viral and fungal diseases, which significantly affect rice production. To meet the growing global demand for rice, effective disease detection is essential. However, challenges in disease recognition arise due to background noise and varying capture conditions in images. Convolutional Neural Network (CNN)-based models have significantly advanced the detection and identification of rice plant diseases. This paper aims to achieve efficient and timely detection and identification of rice plant diseases by exploring the CNN models, both with and without segmentation techniques, for diseases such as Brown Spot, Leaf Blast and Neck Blast.

关键词： Rice Disease Image Processing Segmentation Techniques Convolutional Neural Networks

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Systematic Review: Augmented Reality and Virtual Reality in Education at Different Academic Levels 4th

Systematic Review: Augmented Reality and Virtual Reality in ...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Chiliquinga, Cesar Arguello, Milton Mayorga, Patricia Univ Fuerzas Armadas ESPE Dept Ciencias Computac Sangolqui Ecuador

ISBN: (纸本)9783031840777;9783031840784

Augmented Reality and virtual reality have revolutionized conventional teaching and learning methods, marked by a significant shift during the events triggered by COVID-19, which pressed for the exploration of different technological options to emulate face-to-face learning. This study considered 41 publications from the last five years across three major platforms: IEEE, Scopus, and scienceDirect. Through content analysis, an objective, systematic, and quantitative review was conducted to assess their impact on education at various educational levels: elementary, secondary, higher education, and special education. Results have been compiled from publications related to the development and evaluation of applications in teachers and students, systematic reviews with different academic approaches, proposals for implementation, and academic areas that have been integrated by these technologies. It has been determined that their evolution and development are increasing over the years and that they have positive effects on learning as they generate motivation among students and facilitate the understanding of abstract concepts. Despite this, certain limitations must still be overcome, such as the high cost of implementation, especially in VR, training for teachers and students, and conducting studies with larger populations and extended trial periods for better outcomes. Therefore, understanding their scope, benefits, challenges, and limitations helps to better comprehend their potential.

关键词： Virtual Reality Augmented Reality Technology Metaverse

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BA-GAN: A Boundary-Aware Generative Adversarial Network for Document Restoration 4th

BA-GAN: A Boundary-Aware Generative Adversarial Network for ...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Nafchi, Amin Ghasemi Cheriet, Mohamed Ecole Technol Super Dept Syst Engn Montreal PQ Canada

ISBN: (纸本)9783031821523;9783031821530

Various degradations available in historical document images pose challenges to automatic document restoration methods. Recent strides in deep learning and generative adversarial networks (GANs) for document analysis encounter challenges in generalization and practical application. Due to the absence of boundary-related supervision, conventional convolutional approaches often struggle to extract stroke edges from documents undergoing binarization consistently. In this study, we present a GAN-based robust end-to-end system to restore heavily damaged document images and tackle the problem of inaccurate stroke edge extraction. In the proposed framework, there is a single generator along with two discriminators. This approach leverages object and contour data concurrently, enhancing binarization results. The auxiliary discriminator is crucial in ensuring the accurate prediction of stroke edges. The solution consistently outperforms state-of-the-art techniques on DIBCO datasets from 2017 and 2018, proving its ability to recover a damaged document image. The results obtained across a diverse set of degradations showcase the flexibility of the proposed approach, making it applicable to various document restoration applications.

关键词： Document analysis Generative adversarial networks Document binarization Deep Neural Network

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Minimial-Risk Training Samples for QNN Training from Measurements 18th

Minimial-Risk Training Samples for QNN Training from Measure...

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18th Symposium and Summer School on Service Oriented Computing

作者： Mandl, Alexander Barzen, Johanna Bechtold, Marvin Leymann, Frank Univ Stuttgart Inst Architecture Applicat Syst Univ Str 38 D-70569 Stuttgart Germany

ISBN: (纸本)9783031725777;9783031725784

By using Quantum Neural Networks (QNNs), the principles of quantum computing can be employed to perform supervised learning on quantum computers. Herein, a unitary transformation is trained using sets of quantum input states and their associated outputs. When the exact output states of the transformation are known, recent results show that entanglement can drastically reduce the approximation error of a QNN, without increasing the number of required training samples. However, the exact output states might not be readily available. In certain scenarios, only the measurement outcomes of these quantum states after measurement with an observable are available. Therefore, this work investigates the effect of entangled training samples when training using measurement outcomes. For observables described by one-dimensional projectors, we specify entangled training samples that minimize the approximation error. Furthermore, we validate our findings on a simulator and show that when using entangled training samples, the approximation error depends on the factorization of the entangled samples.

关键词： Quantum Neural Networks Entanglement Measurement

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