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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

引用

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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Leveraging Language Models for Human Activity Recognition for Intelligent Lighting

Leveraging Language Models for Human Activity Recognition fo...

引用

Sustainable Smart Lighting World Conference & Expo (LS18), IEEE

作者： Ali Mahmoudi Tanir Ozcelebi Nirvana Meratnia Math. and Computer Science Dept. Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (数字)9798331540197

ISBN: (纸本)9798331540203

Recognizing human activities is necessary to achieve fully context-aware lighting in a smart home. Ambient sensors suit this task as they are ubiquitous, non-intrusive, and non-obstructive. However, each home has a unique floor plan and a unique number and placement of these sensors. This leads to heterogeneity of input data spaces for machine learning models, which hinders their use in different homes. We propose that converting the data into the semantic input space of a language model can not only unify the input space across different houses but also improve the prediction performance thanks to the model’s understanding of semantics. We compared a pre-trained BERT model with one trained from scratch to test the proposed semantic conversion on three datasets from CASAS. The results show that the pre-trained model performs better on accuracy and F1-score.

关键词： Training Semantics Smart homes Predictive models Prediction algorithms Data models Reliability Smart lighting Intelligent sensors Testing

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Centralized and Decentralized Federated Learning in Autonomous Swarm Robots: Approaches, Algorithms, Optimization Criteria and Challenges : The Sixth Edition of International Conference on Pattern Analysis and Intelligent Systems (PAIS’24)

Centralized and Decentralized Federated Learning in Autonomo...

引用

International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Aicha Hafid Riadh Hocine Lahcene Guezouli Mohamed. R Abdessemed Dept. Math and Computer Science University of Batna2 Batna Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

Fault tolerance and energy consumption optimization are critical issues in swarm robotics. This study examines recent approaches to address these challenges, focusing on a comparative analysis between Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL). CFL requires a centralized access point for model aggregation, while DFL eliminates the need for a central server, enabling aggregation at each node according to a specific architecture. The analysis of the results reveals that other approaches, such as Hybrid Federated Learning (HFL), more effectively meet the needs of intelligent agents (swarm robots). This effectiveness is particularly enhanced when HFL is combined with Deep Reinforcement Learning (DRL), resulting in Deep Hybrid Federated Reinforcement Learning (DHFRL). The results demonstrate that, although DFL eliminates the necessity of a central server, hybrid approaches are more efficient, especially when combined with Deep Reinforcement Learning (DRL), thus forming Deep Hybrid Federated Reinforcement Learning (DHFRL).

关键词： Federated learning Swarm robotics Focusing Deep reinforcement learning Servers Intelligent agents Pattern analysis

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Benchmarking Deep Learning Algorithms for Intrusion Detection IoT Networks

Benchmarking Deep Learning Algorithms for Intrusion Detectio...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Hoummady Enzo Fehmi Jaafar Dept. of Math and Computer Science Université du Quebec àChicoutimi Saguenay Canada

ISBN: (数字)9798331522728

ISBN: (纸本)9798331522735

With the increasing popularity of Internet of Things (IoT) and its connected devices, security has become a major concern. In this paper, we conducted a benchmark to evaluate performance of different deep learning algorithm device based on its network traffic. We developed our own dataset for our pilot study that included three different types of cyberattacks: reverse shell, keylogger, and *** diversity and scope of our research has been enhanced by the incorporation of the CIC IoT dataset, which has been added to our initial work. We conducted a systematic evaluation of the performance of various deep learning models, which included CNNs and LSTM networks. Our benchmarking efforts on the CIC IoT dataset resulted in a significant improvement, with all models achieving an accuracy of over 99% and more than 93% on our custom dataset.

关键词： Deep learning Performance evaluation Systematics Intrusion detection Telecommunication traffic Benchmark testing Internet of Things Security computer crime Long short term memory

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SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions 3

SynthBA: Reliable Brain Age Estimation Across Multiple MRI S...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Puglisi, Lemuel Rondinella, Alessia De Meo, Linda Guarnera, Francesco Battiato, Sebastiano Ravi, Daniele University of Catania Dept. of Math and Computer Science Catania Italy University College London Department of Neuroinflammation London United Kingdom School of Physics Engineering and Cs University of Hertfordshire Hatfield United Kingdom

ISBN: (纸本)9798350378009

Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age Difference), has been utilized to investigate neurodegenerative conditions. Brain age can be predicted using MRIs and machine learning techniques. However, existing methods are often sensitive to acquisition-related variabilities, such as differences in acquisition protocols, scanners, MRI sequences, and resolutions, significantly limiting their application in highly heterogeneous clinical settings. In this study, we introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed for predicting brain age. SynthBA utilizes an advanced domain randomization technique, ensuring effective operation across a wide array of acquisition-related variabilities. To assess the effectiveness and robustness of SynthBA, we evaluate its predictive capabilities on internal and external datasets, encompassing various MRI sequences and resolutions, and compare it with state-of-the-art techniques. Additionally, we calculate the brain PAD in a large cohort of subjects with Alzheimer's Disease (AD), demonstrating a significant correlation with AD-related measures of cognitive dysfunction. SynthBA holds the potential to facilitate the broader adoption of brain age prediction in clinical settings, where re-training or fine-tuning is often unfeasible. The SynthBA source code and pre-trained models are publicly available at https://***/LemuelPuglisi/SynthBA. © 2024 IEEE.

关键词： Deep learning

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Measuring the Effects of Document Size on RAM Artifacts: A Memory Forensics Approach 3

Measuring the Effects of Document Size on RAM Artifacts: A M...

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3rd Intelligent Cybersecurity Conference, ICSC 2023

作者： Al-Sharif, Ziad A. Al-Senjalawi, Reema Alzoubi, Omar A. Lewis University Dept. of Engineering Computing and Mathematical Sciences Romeoville United States Jordan University of Sci. and Tech. Irbid Jordan Jordan University of Sci. and Tech. Dept. of Computer Science Irbid Jordan College of Arts & Science Applied Science University Bahrain

ISBN: (纸本)9798350382174

Cybercrimes have significantly increased due to the rapid adoption of software and technology in almost every aspect of our lives. The main memory or RAM of a computing machine is often used to provide critical information about the recent system activities, including the running processes, network connections, used passwords, and encryption keys. Moreover, RAM may contain information about the contents of recently used documents and digital files. Therefore, this paper is focused on studying the RAM-based digital artifacts of different sizes of computer documents. It evaluates the amount of document-based digital artifacts that are recovered from the RAM of a confiscated machine. Our methodology analyzes various memory dumps and learns about the digital artifacts in relation to the contents of a document that might be related to a criminal investigation. Two different usage scenarios are assumed: The first assumes that the RAM dump is created while the document is being opened and viewed whereas the second one assumes the RAM dump is created shortly after the document is being closed. Experiments show that the recovered contents are significantly affected by the used document size;the amount of recovered volatile artifacts of a used document is impacted by the original document size. Results show that the ratio of the recovered contents is very close for various document sizes during the same usage scenario. Additionally, closing the document will reduce the amount of recovered content, but still allow for a significant ratio to be considered as evidence of the actual use of the document on the confiscated machine. © 2023 IEEE.

关键词： Random access storage

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A Face Pre-Processing Approach to Evade Deepfake Detector 1

A Face Pre-Processing Approach to Evade Deepfake Detector

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1st ACM Workshop on Security Implications of Deepfakes and Cheapfakes, WDC 2022, co-located with ACM AsiaCCS 2022

作者： Kim, Taejune Kim, Jeongho Kim, Jeonghyeon Woo, Simon S. Computer Science & Engineering Dept. Sungkyunkwan University Suwon Korea Republic of Dept. of Artificial Intelligence Sungkyunkwan University Suwon Korea Republic of Applied Data Science Dept. Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9781450391788

Recently, various image synthesis technologies have increased the prevalence of impersonation attacks. With the development of such technologies, damages to people such as defamation or fake news have also increased. Deepfakes have already evolved to the point, where people cannot easily distinguish fake from real. This leads to an urgent need for developing detection methods. Currently, in order to detect deepfakes, many deepfake datasets are widely used in deep neural networks. And several methods have been proposed and demonstrated to be effective in detecting deepfakes. In this work, we present pre-processing techniques such as face restoration, edge smoothing, face beautification to mitigate the artifacts of deepfakes and makes them appear more natural to humans, while lowering the deepfake detection performance. Through extensive experiments, our method can significantly lower the performance of the state-of-The-Art deepfake detectors and expose the vulnerability of deployed detectors. © 2022 ACM.

关键词： Deep neural networks

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Hyperparameter Learning of Bayesian Context Tree Models

Hyperparameter Learning of Bayesian Context Tree Models

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Nakahara, Yuta Saito, Shota Shimada, Koshi Matsushima, Toshiyasu Waseda University Center for Data Science Tokyo Japan Gunma University Faculty of Informatics Gunma Japan Waseda University Dept. of Pure and Applied Math. Tokyo Japan

ISBN: (纸本)9781665475549

In recent years, Bayesian counterparts of the context tree weighting method are studied for many tasks. All these tasks require a hyperparameter setting of the prior distribution for context tree models. Therefore, we provide a framework for statistically learning these hyperparameters from data. Specifically, we consider a hierarchical Bayesian model that assumes hyperprior distributions behind the hyperparameters and learn them using an empirical variational Bayesian (EVB) method. This is the first study to propose an EVB method on the Bayesian context trees. The derived algorithm has a suggestive form that consists of subroutines partially optimal to each local probabilistic model. © 2023 IEEE.

关键词： Bayesian networks

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Exploration and Evaluation of Machine Learning Methods for Detecting Oil Spills in Open Seas

Exploration and Evaluation of Machine Learning Methods for D...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Sibai, Fadi N. Sibai, Ahmad El-Moursy, Ali GUST Engineering & Applied Innovation Research Center Gulf Univ. for Science and Tech. West Mishref Hawalli Kuwait Univ. of South Florida Computer Science & Engg. Dept. TampaFL United States Univ. of Sharjah Comp. Engg. Dept. Sharjah United Arab Emirates

ISBN: (纸本)9798350369106

Detecting oil spills in open seas is important for the early containment of the pollution diffusion and avoidance of further ecological catastrophes. Herein, we explore and assess the application of several machine learning (ML) methods and image processing techniques for the detection of oil spills in images of open seas captured at the elevation of planes, helicopters, or drones. The image dataset used contains 24 clean sea images and 24 dirty sea images with oil slicks. The machine learning methods experimented with include neural networks, SVM, random forest, decision tree, naïve Bayes, AdaBoost, and logistic regression. We also evaluated these ML methods with and without the application of Sobel Feldman edge detection and Vintage filtering image processing techniques. Results indicate the superiority of the in and neural networks methods over the other explored ML methods, achieving perfect and near-perfect classification, respectively. © 2024 IEEE.

关键词： Oil spills

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Optimization of Vehicular Congestion Control in Traffic Signals using YOLO algorithm

Optimization of Vehicular Congestion Control in Traffic Sign...

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Smart Systems for Electrical, Electronics, Communication and computer Engineering (ICSSEECC), International Conference on

作者： D. Sivaganesan S. Barath Kumar S. Krishnadharani G. Rashmitha Dept. of Computer Science and Engg PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

The increase in the volume of vehicles utilizing our roads has highlighted the seriousness of the ongoing problem of traffic congestion. This problem is even worse at intersections, where a line of cars waits patiently for their turn to move forward. The traditional traffic light systems, which were originally intended to handle a less intense traffic load, are now having difficulty keeping up with the increasing number of vehicles, which further complicates the situation. We have adopted cutting-edge technologies, particularly those found in the fields of computer vision and machine learning, to tackle this modern dilemma. Our methodology is based on the application of a state-of-the-art system powered by a deep Convolutional Neural Network, which we call You Only Look Once (YOLO). With the help of this cutting-edge technology, we can quickly and thoroughly examine real-time data at signalized road crossings to gain a sophisticated understanding of the constantly changing traffic environment. Once we have a thorough understanding of the complex traffic dynamics thanks to YOLO's capabilities, we use this knowledge to make the most out of traffic light phases. We focus on closely examining important data elements, like the average wait time for a car and the number of cars in the line. The main goal is to create a coordinated and efficient traffic flow. This reduces the amount of time that drivers must wait in traffic overall and gives priority to safe and effective vehicle movement through intersections, resulting in a more responsive and fluid urban mobility experience.

关键词： YOLO Roads Computational modeling Real-time systems Classification algorithms Convolutional neural networks Automobiles

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