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real-time Object Detection using an Ultra-High-Resolution Camera on embedded systems

Real-Time Object Detection using an Ultra-High-Resolution Ca...

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ieee international conference on Imaging systems and Techniques (IST) / ieee international School on Imaging

作者： Antonakakis, Marios Tzavaras, Aimilios Tsakos, Konstantinos Spanakis, Emmanouil G. Sakkalis, Vangelis Zervakis, Michalis Petrakis, Euripides G. M. Tech Univ Crete Sch Elect & Comp Engn Khania Crete Greece Fdn Res & Technol FORTH Inst Comp Sci Iraklion Crete Greece

ISBN: (纸本)9781665481021

Unnamed Aerial Vehicle (UAV) - based remote sensing is a promising technology that is being applied for inspecting live scenes from high altitudes (e.g., for surveillance and recognizing emergencies)..he evolution of hardware and software technologies in the last few years has generated additional interest in embedded systems research and its implementation in energy-independent UAVs for remote sensing. Alongside, ultra-high-resolution optical sensors are mandatory for acquiring high-resolution images which are necessary for accurate object detection from a distance (e.g., 1,000 meters). The processing of ultra-high-resolution images (e.g., 4K or 8K) is beyond the typical resolutions which are used for object detection (e.g., < 2K) emerging a necessity for special treatment in order to succeed a fast object detection. We propose a three-step approach deployed on a Docker runtime environment in an Nvidia Jetson AGX Xavier board. To support fast object detection, the captured images are split into K parts processed in parallel in separate containers running the YOLOv5 object detection algorithm. A final detection is constructed based on each one of the K detections. The experimental results are a good support to our claims of efficiency: the method can achieve close to real-time object detection for ultra-high (i.e., 8K) resolution images (i.e., in less than 1 second per frame).

关键词： ultra-high-resolution images real-time object detection YOLOv5 embedded systems remote sensing

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Tweeting from Left to Right: Tracing the Dynamics of Emotional Messaging and Public Engagement During the 2018 vs. 2022 US Campaigns 21

Tweeting from Left to Right: Tracing the Dynamics of Emotion...

引用

21st ieee international Symposium on Parallel and Distributed Processing with applications, 13th ieee international conference on Big Data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Meng-Jie University of Canterbury School of Psychology Speech and Hearing Christchurch New Zealand

ISBN: (纸本)9798350329223

Despite numerous studies delving into social media politicking, there is, thus far, little understanding of how distinct emotions spread online. Much of the prior work has focused on positive vs. negative diffusion or measured emotional rhetoric without considering public engagement on such sites. Since elected officials increasingly turn to social media, even less is known about the impact of distinct emotional messaging on mobilizing users, attracting supporters, or disseminating political speeches. Moving beyond merely a valence-based approach, the study here seeks to address this gap by analyzing over 23,000 tweets posted by Senate candidates during the 2018 and 2022 US midterm elections through natural language processing. Specifically, leveraging the IBM Tone Analyzer, the present work implements a cross-sectional examination to explore: a) whether certain emotions - joy, confidence, anger, fear, and sadness - conveyed in candidates' tweets might be more likely to garner public attention;and b) if so, does this pattern vary across the political spectrum, illuminating the ways in which Democratic vs. Republican candidates adopt to expand their online reach over time? © 2023 ieee.

关键词： Natural language processing systems

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Evaluation of embedded Devices for real-time Video Lane Detection 29

Evaluation of Embedded Devices for Real-Time Video Lane Dete...

引用

29th international conference on Mixed Design of Integrated Circuits and System (MIXDES)

作者： Podbucki, Kacper Suder, Jakub Marciniak, Tomasz Dabrowski, Adam Poznan Univ Tech Fac Automat Control Robot & Elect Engn Inst Automat Control & Robot Div Signal Proc & Elect Syst Jana Pawla II 24 PL-60965 Poznan Poland

ISBN: (数字)9788363578220

ISBN: (纸本)9781665461764

This paper presents a comparison of the performance of embedded systems processing video sequences in real time. As part of the work, practical programs for detecting lanes located on airport areas, which allow autonomous vehicles to move around the airport, were tested. The following modules were used during the tests: Raspberry Pi 4B, NVIDIA Jetson Nano, NVIDIA Jetson Xavier AGX. For modules from the NVIDIA Jetson family, the maximum performance of video stream processing depending on the resolution and the selected power mode has been checked. The results of the experiment show that NVIDIA Jetson modules have sufficient computing resources to effectively track lines based on the camera image, even in low power modes.

关键词： lane detection embedded systems video analysis microprocessor power modes NVIDIA Jetson Raspberry Pi

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The Application of Virtual Fencing in Home Security systems

The Application of Virtual Fencing in Home Security Systems

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2024 international conference on Computer and applications, ICCA 2024

作者： Ignatius, Arnav Neil Jain, Anuj Chauhan, Parth Sandeep Upadhyaya, Vikas Niit University Department of Computer Science Neemrana India Niit University Ece Department Neemrana India

ISBN: (纸本)9798350367560

To increase the security of homes and bring relief to homeowners when they are away from it, Security systems that may be able to perform real-time monitoring and detection of intruders breaching the bounds of their property could be solved by implementing the concept of Virtual Fences in Computer Vision using Machine Learning. This paper covers previous implementations of Virtual Fencing while emphasizing Object Detection methods that are required to perform such a task. Explores the dichotomy between single stage and two stage detectors and compares their architecture, performance, and applications. It examines various datasets that may be used to train object detection models and performance metrics that could be used to analyse and benchmark the capabilities of such models. A proof of concept is developed using the YOLOv8 model and a custom Virtual Fencing Logic along with a comparison between performance of YOLOv8 and YOLOv9 variants. © 2024 ieee.

关键词： Adversarial machine learning

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The Role of IoT in Optimizing Urban Infrastructure in Smart Cities

The Role of IoT in Optimizing Urban Infrastructure in Smart ...

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2024 international conference on Decision Aid Sciences and applications, DASA 2024

作者： Shatat, Abdallah Shatat, Ahmad Mobin Akhtar, Mohammad Al Dweiri, Mohammad Management Information Systems Applied Science University Al Eker Bahrain Basic Sciences Riyadh Elm University Riyadh Saudi Arabia Business Administration Al-Zaytoonah University Amman Jordan

ISBN: (纸本)9798350369106

This research explores the role of the Internet of Things (IoT) in optimizing urban infrastructure in smart cities. Specifically, it examines how IoT enhances energy management, waste management, and public safety through real-time data collection and automated systems. The study adopts a descriptive-analytical approach, assessing existing smart city applications across several cities globally and their implications for improving urban services. The paper concludes that while IoT can transform urban environments, national-level strategies must consider local contexts to address infrastructure limitations, legal challenges, and privacy concerns. Recommendations are provided for developing a national IoT strategy tailored to specific urban needs. © 2024 ieee.

关键词： Sustainable city

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Research on an embedded-based mask wearing detection system 4

Research on an embedded-based mask wearing detection system

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4th international conference on Computer Vision, Image and Deep Learning, CVIDL 2023

作者： Chen, Yong School of Computer and Information Technology Hefei University of Technology Hefei China

ISBN: (纸本)9798350326444

Since the outbreak of the new crown epidemic began worldwide, the virus has been spreading very fast, and the most convenient and effective way to deal with the spread of the virus is to wear a mask properly. Up to now, the new crown is still in epidemic, not only the new crown virus, but also the current outbreak of influenza A virus. In order to reduce the speed of virus spread and protect our own health, in some public places, we have to wear masks according to the epidemic prevention and control requirements. In this paper, we propose a mask wearing detection system based on embedded Linux, using the optimized and improved YOLOv5 network model, and deploy it to the embedded system to achieve real-time detection of mask wearing. © 2023 ieee.

关键词： embedded systems

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Towards Broad Learning Networks on Unmanned Mobile Robot for Semantic Segmentation 39

Towards Broad Learning Networks on Unmanned Mobile Robot for...

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ieee international conference on Robotics and Automation (ICRA)

作者： Li, Jiehao Dai, Yingpeng Wang, Junzheng Su, Xiaohang Ma, Ruijun Beijing Inst Technol Sch Automat State Key Lab Intelligent Control & Decis Beijing 100081 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

ISBN: (纸本)9781728196817

This article investigates the real-time semantic segmentation in robot engineering applications based on the Broad Learning System (BLS), and a novel Multi-level Enhancement Layers Network (MELNet) based on BLS framework is proposed for real-time vision tasks in a complex street scene on the unmanned mobile robot. This network mainly solves two problems: (1) mitigating the contradiction between accuracy and speed while maintaining low model complexity, and (2) accurately describing objects based on their shape despite their different sizes. Firstly, the BLS architecture is expanded to the deep network with trainable parameters. This trainable network could adjust its weights in a complex environment, and mitigate the adverse impact of the environment on the complex tasks. Secondly, enhancement layers with the extended enhancement layers could extract both detailed information and semantic information. Moreover, an Upsampling Atrous Spatial Pyramid Pooling (UPASPP) is designed to fuse detail and semantic information to describe object features properly. Finally, in the case of the MNIST dataset and Cityscapes dataset, we get high accuracy with 8.01M parameters and quicker inference speed on a single GTX 1070 Ti card. At the same time, the unmanned mobile robot (BIT-NAZA) is employed to evaluate semantic performance in real-world situations. This reveals that MELNet could be run adequately on the embedded device and effectively operate in the real-robot system.

关键词： Performance evaluation Learning systems Shape Fuses Semantics Feature extraction real-time systems

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Poster: Rethinking embedded Sensor Data Processing and Analysis with Large Language Models 23

Poster: Rethinking Embedded Sensor Data Processing and Analy...

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21st Annual international conference on Mobile systems, applications and Services, MobiSys 2023

作者： Sooriya Patabandige, Pramuka Medaranga Waskito, Steven Antya Orvala Li, Kunjun Leow, Kai Jie Chakrabarty, Shantanu Varshney, Ambuj National University of Singapore Singapore NCS Group Singapore

ISBN: (纸本)9798400701108

An important step in the deployment of wireless embedded systems is the analysis of the sensor data. Traditionally, this requires machine learning models tailored to the application use case. However, this step requires significant expertise from the end user and can be less adaptable to the dynamics of real-world deployments. In recent years, large language models have seen significant developments. These models have been shown to be capable of performing general-purpose tasks. In this work, we explore the hypothesis that large language models can be used to aid in sensor data analysis. Our preliminary findings through real-world experiments show significant promise for two tasks: inferring hand gestures through tracking of light and vibration sensor data. We believe these findings highlight the potential of large language models in sensor data analysis, and thus, it warrants further study. © 2023 Owner/Author(s).

关键词： Data handling

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A Fast Iris Liveness Detection for embedded systems using Textural Feature Level Fusion Algorithm

A Fast Iris Liveness Detection for Embedded Systems using Te...

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2023 international conference on Industry Sciences and Computer Science Innovation, iSCSi 2023

作者： Tran, Chung Nguyen Nguyen, Minh Son Castells-Rufas, David Carrabina, Jordi Universitat Autònoma de Barcelona Bellaterra08193 Spain University of Information Technology-VNUHCM Ho Chi Minh City Viet Nam

Iris recognition is a widely used biometric authentication technique due to its high accuracy and uniqueness. However, iris recognition systems are susceptible to attacks using fake or synthetic iris images, causing a serious security threat. To address this issue, this paper presents a fast iris liveness detection method specifically designed for embedded systems. The proposed method utilizes a textural feature level fusion algorithm using Local Binary Pattern (LBP) and Gray-Level Co-Occurrence Matrix (GLCM) to distinguish between live and printed iris images. LBP captures texture information, while GLCM characterizes the statistical properties of the iris images. By combining these complementary features, the proposed method enhances the discrimination capability and robustness against presentation attacks. Furthermore, to enable real-time and efficient implementation, the proposed liveness detection is optimized and implemented for embedded systems. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in accurately detecting iris liveness. The proposed fast iris liveness detection is implemented and optimized on C++ which can be complied and deployed in various embedded devices for iris recognition systems on real-world applications, such as access control, biometric authentication, and surveillance systems. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Local binary pattern

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An efficient Manuka honey purity classification technique with FSVM

An efficient Manuka honey purity classification technique wi...

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2024 ieee international conference on computing, applications and systems, COMPAS 2024

作者： Huma, Shafia Nur Akter, Sabrina Akter, Subrina Nafisa, Nuren Patwary, M. Jamshed Alam International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh Chittagong University of Engineering and Technology Institute of Information and Communication Technology Chittagong Bangladesh

ISBN: (纸本)9798331529765

Manuka honey is renowned for its exceptional medicinal properties in healing wound infections and other conditions. Due to its high cost, this honey is a common target for fraud. Several machine-learning techniques are available for classifying the quality of Manuka honey. However, existing methods often struggle with accurate categorization. Imprecise data, high dimensionality, imbalances in various classes, and lengthy processing times are the root causes of misclassification. This research addresses these issues by providing a viable honey impurity classification method using Fuzzy Support Vector Machine (FSVM). The experiments utilize a freely available pre-processed Manuka honey dataset containing 3300 data points and 128 features. FSVM enhances the classification process by assigning fuzzy membership functions to each class, thereby handling imprecise data and resolving class imbalance problems. Furthermore, this study aims to minimize time and space complexity by reducing dimensions. For this purpose, four feature reduction techniques have been applied to the dataset, and the results have revealed that applying FSVM along with Class Embodiment Autoencoder (CEAE) has yielded 97% accuracy and F1 score. Lastly, a comparative analysis has been performed with other existing SVM methods, and it has been found that the proposed technique has outperformed previous SVMs in terms of accuracy and time and space complexity. The previous method takes (O(N!)) time complexity whereas the proposed method's time complexity is (O(N2)), where N represents the number of data points in the dataset. This significant reduction provides a clear picture of the feasibility of the suggested approach and its potential to enhance the procedures involved in the classification and authentication of honey. © 2024 ieee.

关键词： Support vector machines

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