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Implementing SLAM for Robot Navigation with Lidar in ROS Platform

Implementing SLAM for Robot Navigation with Lidar in ROS Pla...

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Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), International Conference on

作者： Gaurav Kumar Verma Surya Narayan Swar Ranjit Ray Gagan Deep Meena Dept. of Electrical Engineering National Institute of Technology Patna India Underwater Robotics & Autonomous Group CSIR-Central Mechanical Engineering Research Institute Durgapur India Department of Computer Science & Engineering Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (数字)9798331528614

ISBN: (纸本)9798331528621

This research covers the implementation of SLAM (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency capabilities of the ouster, this method works towards mapping and localization in structured and unstructured environments. The paper explains the Ouster sensor integration in the ROS1 Noetic environment, providing information on the sensor calibration, data acquisition, and pre-processing. The ROS ecosystem in Ubuntu 20.04 is set to process the data streams coming from the ouster LiDAR sensor which is further utilised to create map and localize the robot. The Ouster LiDAR sensor scans every object in its field of view (in 3D), the data is then used for the SLAM. SLAM is executed using the Direct Lidar Odometry (DLO) algorithm for mapping and simultaneously helping the robot with localization and enabling it to navigate through the environment smoothly and precisely.

关键词： Location awareness Point cloud compression Laser radar Simultaneous localization and mapping Three-dimensional displays Navigation Throughput Robot localization Real-time systems Streams

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Enhanced Traffic Sign Recognition Using Deep Learning Techniques

Enhanced Traffic Sign Recognition Using Deep Learning Techni...

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International Conference on Computing and Networking technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Dilshad Islam Mohammad Tarek Aziz Mohammad Shahadat Hossain Karl Andersson Dept. of CSE Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Physical and Mathematical Sciences Chattogram Veterinary and Animal Sciences University Chittagong Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Traffic sign recognition plays a pivotal role in modern intelligent transportation systems, contributing significantly to traffic management and road safety. This thesis presents a comprehensive investigation into the utilization of deep learning techniques for enhanced traffic sign recognition. The research delves into edge detection methodologies, deep learning architectures, and classification techniques specifically tailored for the identification of traffic and road signs. A diverse range of deep learning models, including Convolutional Neural Networks (CNN), VGG19, ResNet50, ResNet101, and ResNet152, are scrutinized for their effectiveness in traffic sign classification. The evaluation is conducted on a comprehensive dataset encompassing various road signs captured under diverse environmental conditions. The classification pipeline integrates edge detection algorithms such as Canny, Sobel, and Prewitt in conjunction with the selected neural network models. Experimental results exhibit notable performance disparities among the evaluated architectures. CNN achieves the highest accuracy of 96% when combined with the Prewitt edge detection method, while VGG19 attains 95% accuracy under the same conditions. ResNet50 achieves a peak accuracy of 96% with the Prewitt edge detection technique, while ResNet101 demonstrates the capability to achieve 97% accuracy when utilizing the Canny edge detection method. Remarkably, ResNet152 emerges as the top-performing model, achieving an impressive accuracy rate of 98% when employing the Sobel edge detection method, along with an exceptional F1-Score.

关键词： Deep learning Accuracy Image edge detection Pipelines Neural networks computer architecture Road safety Convolutional neural networks Intelligent transportation systems Residual neural networks

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Attention model-based and multi-organism driven gene recognition from text: application to a microbial biofilm organism set.

Attention model-based and multi-organism driven gene recogni...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Bomgni, Alain Bertrand Fotseu, Ernest Basile Fotseu Wambo, Daril Raoul Kengne Sani, Rajesh K. Lushbough, Carol Zohim, Etienne Gnimpieba University of South Dakota Dept. of Biomedical Engineering Vermillion United States University of Dschang Dept. of Computer Science Dschang Cameroon SD School of Mines and Technology Dept. of Chem. and Bio. Engineering Rapid city United States

ISBN: (纸本)9781665468190

Nowadays, online databases such as PUBMED and PMC are experiencing an explosion of publications in the field of biomedical sciences. With so much information available online, one of the biggest challenges is managing all that raw, unstructured data and making it machine-readable. Name entity recognition is nowadays a prerequisite for data identification and extraction in biosciences. One of the areas that allows automatic extraction of information from biomedical literature today is Name Entity Recognition. Indeed, it makes it possible to simplify the workflow analysis and automatic extraction of name entities, thus improving the various existing models. There is in the literature a lot of tools for this purpose, but they are unable to extract microbial genes accurately. Moreover, current goal standard corpora such as BIOCREATIVE I to IV have limited representation of microbial knowledge. In this paper, we proposed a new method to recognize biofilm gene mentions from free text. This method relies on a context-specific dictionary to annotate a consistent corpus necessary to train an efficient recognition model. Indeed, this method provides a new workflow for dataset collection generation for microbial biofilm gene. Trained on a set of biofilm organisms our method achieves a score of up to 94%, outperforming state-of-the-art frameworks. © 2022 IEEE.

关键词： Extraction

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Random Access Process Enhancements for Cellular Internet of Things (CIoT) 12

Random Access Process Enhancements for Cellular Internet of ...

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12th IEEE Annual Information technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： John Dian, F. Vahidnia, R. British Columbia Institute of Technology Dept. of Electrical and Computer Engineering Burnaby Canada

ISBN: (纸本)9781665400664

Random Access (RA) is an important process in Cellular Internet of Things (CIoT), which enables the IoT devices to connect to the network for set-up and data transmission. As the number of IoT devices in the network is increasing, the competition for accessing the network increases as well. Additionally, since each IoT application may have different requirements in terms of parameters such as data size, latency, and data rate, it is essential to optimize the random access process to address the requirements of different applications. Therefore, there is a need to design the uplink transmission mechanism to reduce the signaling overhead being exchanged between the User Equipment (UE) and the base station. The 3rd Generation Partnership Project (3GPP) has introduced several random access process enhancements in Release 13 through 16, for IoT devices that have small data size or the ones that not only send small amount of data, but also have a deterministic traffic or a predicated traffic pattern. In this paper, we first discuss the random access process and its advancements and then, explain the applicability of the existing features for some practical IoT use cases. The results of this study shows that RA process developed in release 15 and 16 under certain conditions can increase the efficiency of IoT applications by reducing the latency and power consumption. © 2021 IEEE.

关键词： Internet of things

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A Smart Robotic Design to Identify and Diffuse Bombs in Defence Environments Using Sensors Assisted Internet of Things (IoT) technology

A Smart Robotic Design to Identify and Diffuse Bombs in Defe...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Vijayakumari, P. Mary, S. Jeya Deepa, N. Swarna, M. Narkhede, Alok Al-Safarini, Maram Y. Sairam Engineering College Dept of Ece Chennai India Einstein College of Engineering Dept of Ece Tirunelveli India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India Dr. M.G.R. Educational and Research Institute University Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Numerous news media outlets cover incidents in which trained personnel or combatants are injured or perish while defusing bombs. The military robot that conducts intelligence operations. While it has been beneficial to the Army, firefighters, and police officers in critical situations, many of the current systems are inefficient due to their lack of accuracy and technology. Our objective is to develop the most efficient robot possible in order to achieve accuracy and technology. Robotic limbs equipped with metal detectors are being installed to provide manual assistance. The wireless explosive disposal device will enhance our nation's defense against terrorists, suicide bombers, and other similar activities. Experts are required to operate the explosives detectors and disposal system. However, this method of analysis necessitates additional time and poses a risk to the lives of experts. A novel robot, the IoT Defense Robotic Model (IoTDRM), is developed in this paper with the assistance of Internet of Things (IoT) technology. The proposed model is cross-validated against the conventional logic of the Smart Bomb Diffusing Robot (SBDR) to assess its performance. The signals are transmitted from the sensor end to the IoT controller in this robot, and they are retransmitted to the server end via a wireless system. The user controls the automaton manually using a personal computer or mobile phone. When the sensor detects a metal, a buzzer alarm will sound. The metal is examined using a wireless camera to determine whether it is a weapon. Since the identified metal is a bomb, the user instructs the robot to detonate it via the computer. © 2024 IEEE.

关键词： Bombs (ordnance)

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Comparative Analysis of Machine Learning Algorithms and Datasets for Detecting Cyberbullying on Social Media Platforms

Comparative Analysis of Machine Learning Algorithms and Data...

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Computing and Intelligent Reality Technologies (ICCIRT), International Conference on

作者： Anusha Palagati Santhosh Kumar Balan S Arun Joe Babulo Laxmi Raja K.K. Natarajan Rajkumar Kalimuthu Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering SMIEEE Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications K.S.R College of Arts and Science for Women Tiruchengode Tamilnadu India Department of Cyber Security Karpagam Academy of Higher Education Coimbatore Tamilnadu India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Sultanate of Oman Department of Cyber Security Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

Cyberbullying is defined as sharing of undesirable material in the cyberspace, primarily- the use of the social sites that instigate hostility and create feelings of hatred among the individuals. Another increasing threat due to increase use of internet-based communication is cyberbullying especially on the site such as Twitter. Different ML and NLP approaches including support vector machine (SVM), Naϊve bayes, Random Forest, and logistic regression is employed to detect cyberbul Bullivating. These methods for supervised learning mine patterns in text and image, allowing computers to promptly flag risky interactions. Some of these approaches also utilizes OCR for the identification of image-based bullying. Consequently, the objective of this research is to identify the correct algorithm and dataset for improving the identification of cyberbullying and solving the problem that manifests itself in the steady increase in cases of this type of bullying.

关键词： Support vector machines computers Logistic regression Machine learning algorithms Supervised learning Optical character recognition Cyberbullying Bayes methods Object recognition Random forests

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A Deep Learning-Based Coyote Detection System Using Audio Data

A Deep Learning-Based Coyote Detection System Using Audio Da...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Heesun Jung Bokyung Kwon Youngbin Kim Yejin Lee Jihyeon Park Griffin Pegg Yaqin Mia Wang Anthony H. Smith Dept. Computer Science Hallym University Chuncheon South Korea Dept. Computer Information Engineering Kwangwoon University Seoul South Korea Dept. Computer Science and Statistics Jeju National University Jeju South Korea Dept. Computer and Information Technology Purdue University West Lafayette IN USA

The attacks on livestock, human, and crops by coyotes are occurring over the United States, while traditional simple management such as public education about the method of avoiding coyotes and coyote hunting contests to reduce their numbers are executed. There are not sufficient cases of technical approaches or research about the preventing coyotes. The method of a coyote howling sound classification using machine learning and feature extraction to reduce the damage from the coyotes is needed. This paper applies the optimal model created by comparing and analyzing various combinations of machine learning and feature extraction methods to the coyote detection platform. It is expected that an additional technical approach to current coyote damage prevention can improve the accuracy and make the previous management more practical.

关键词： Analytical models Education Crops Machine learning Learning (artificial intelligence) Feature extraction Data models

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An Adaptive Hierarchical Method for Anytime Set-wise Clustering of Variable and High-Speed Data Streams

An Adaptive Hierarchical Method for Anytime Set-wise Cluster...

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IEEE International Conference on Big Data

作者： Aarti Jagat Sesh Challa Utkarsh Darolia Mridul Chandak Poonam Goyal Navneet Goyal Dept. of Computer Science Pilani Campus ADAPT Lab Birla Institute of Technology & Science Pilani India

Set-wise Clustering is a clustering technique for data streams that groups sets of objects based on distribution patterns, applicable in contexts like retail chain clustering, text-based community clustering, restaurant categorization, etc. The existing set-wise clustering method cannot handle variable and high-speed streams with reasonable accuracy. This paper presents an Anytime Set-wise Clustering method for data streams known as ANYSETCLUS. The method handles the variable inter-arrival rates of stream objects using a proposed indexing structure called AnySetClusTree, which stores a hierarchy of micro-clusters of multi-set entities at varying granularity. ANYSETCLUS is highly adaptive as it supports incremental model updates, segregates outliers, enables outlier-to-concept transition, and captures concept drift. The method also enables anytime offline clustering wherein it can generate multiple clusterings of varying granularity and purity depending upon the available time allowance for final clustering. The experimental results affirm the superior efficacy of the proposed method in handling variable and high-speed streams compared to the state-of-the-art method. The experimental results also showcase its effectiveness in achieving significantly higher micro-cluster purity for low and high-speed streams. This contrasts with the state-of-the-art method, which is unable to generate valid clustering results for high-speed streams. The experiments further validate the proposed method’s capability for anytime offline clustering.

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Post-Quantum Cryptography Integration to O-RAN

Post-Quantum Cryptography Integration to O-RAN

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Abdullah Aydeger Engin Zeydan Josep Mangues Dept. of Electrical Engineering and Computer Science Florida Institute of Technology Melbourne FL USA Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) Barcelona Spain

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

With its disaggregated and open interfaces, the Open Radio Access Network (O-RAN) architecture promises flexibility, innovation, and cost-effectiveness for future wireless networks. However, the increased reliance on software and open interfaces also introduces new security challenges. As the threat of quantum computing looms, the traditional cryptographic algorithms used in O-RAN will become vulnerable to potential attacks. This paper proposes the integration of Post-Quantum Cryptography (PQC) into O-RAN to enhance its security against post-quantum adversaries. We discuss the specific vulnerabilities of current O-RAN security mechanisms to quantum attacks and identify key areas where PQC can be applied, such as key exchange, authentication, and data protection. We present a framework for evaluating the suitability of different PQC algorithms for O-RAN and outline potential challenges and implementation considerations. By proactively integrating PQC into O-RAN, we aim to ensure the long-term security and resilience of this emerging network architecture in the era of quantum computing.

关键词： Technological innovation Quantum computing Wireless networks Scalability Software algorithms Open RAN Network architecture Software Radio networks Resilience

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Evaluating Deep Learning Architectures for EEG-Based Driver Fatigue Detection: A Comparative Analysis

Evaluating Deep Learning Architectures for EEG-Based Driver ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Kawsar Ahmed Refat Md. Wajed Ali Dipon Roy Robiul Khan Naimur Rahaman Tusher Moumita Kamal Department of Electrical and Electronic Engineering Pabna University of Science and Technology Pabna Bangladesh Dept. of Computer Science Tennessee Tech University Cookeville TN USA

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Driver fatigue is a leading cause of road accidents and fatalities worldwide, highlighting the urgent need for effective and reliable detection systems. Recent advancements in deep learning (DL) have demonstrated superior capabilities in electroencephalogram (EEG)-based driver fatigue detection. However, existing methods often struggle with the accurate classification of both spatial and temporal features of EEG signals, necessitating significant improvements to enhance detection reliability and performance. This study presents a comparative analysis of deep learning models for EEG-based driver fatigue detection. To process EEG data, the Continuous Wavelet Transform (CWT) is used to generate time-frequency images, effectively capturing both spectral and temporal information. These images are then classified using different DL models, including single classifiers—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Vision Transformer (ViT), Visual Geometry Group 16 (VGG-16), and Gated Recurrent Unit (GRU)—as well as hybrid classifiers such as CNN-LSTM, CNN-GRU, and CNN-ViT. The proposed approach is validated using a publicly available EEG dataset specifically collected for driver fatigue detection. Experimental results demonstrate that the single classifier CNN and the hybrid classifier CNN-LSTM achieve superior accuracies of 97.9% and 98.34%, respectively. These findings highlight the potential of these models in building reliable driver fatigue detection systems, contributing to enhanced traffic safety and reduced accident rates.

关键词： Deep learning Continuous wavelet transforms Accuracy Fatigue Brain modeling Transformers Electroencephalography Convolutional neural networks Reliability Vehicles

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