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2019 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2019

作者： Militaru, Gabriel Popescu, Dan Ichim, Loretta University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (纸本)9789994906116

Integrating innovative technologies in terrestrial-satellite networks are needed to enable Unmanned Aerial Vehicles (UAV) application developers to achieve better results and optimize solutions. Managing a complex UAV - Satellite modem integration process, the authors of this study have performed a series of measurement of throughput and latency time to evaluate the feasibility of the Satellite communication system for remote control and command of a distant UAV. © 2019 Radio Society.

关键词： Satellite communication systems

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computer Vision Based Framework For Detecting Phishing Webpages

Computer Vision Based Framework For Detecting Phishing Webpa...

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Roedunet International Conference (RoEduNet)

作者： Ionuţ Cernica Nirvana Popescu Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania

ISBN: (数字)9780738112657

ISBN: (纸本)9781665418850

One of the most dangerous threats on the internet nowadays is phishing attacks. This type of attack can lead to data breaches, and with it to image and financial loss in a company. The most common technique to exploit this type of attack is by sending emails to the target users to trick them to send their credentials to the attacker servers. If the user clicks on the link from the email, then good detection is needed to protect the user credentials. Many papers presented computer Vision as a good detection technique, but we will explain why this solution can generate lots of false positives in some important environments. This paper focuses on challenges of the computer Vision detection technique and proposes a combination of multiple techniques together with computer Vision technique in order to solve the challenges we have shown. We also will present a methodology to detect phishing attacks that will work with the proposed combination techniques.

关键词： Phishing Servers computer vision Tools Machine learning Browsers Blacklisting

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Fine-tuning calibration of deterministic propagation models in UHF band 6

Fine-tuning calibration of deterministic propagation models ...

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2018 6th IEEE Radio and Antenna Days of the Indian Ocean, IEEE RADIO 2018

作者： Militaru, G. Popescu, D. Ichim, L. Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania

A global characterization of the propagation behaviour that will allow a general approach of the Radio Frequency (RF) prediction is difficult to achieve. In this paper, based on field strength values collected in typical rural environment, the authors are providing their findings after the calibration of a deterministic propagation model in Ultra High Frequency (UHF) frequency band. An evaluation of the error sources, and methods to validate RF antenna pattern, are described. The proposed methodology was used to obtain the measurements that allow the correlation with prediction data. © Published under licence by IOP Publishing Ltd.

关键词： Calibration

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Ethical Considerations in Explainable AI: Balancing Transparency and User Privacy in English Language-based Virtual Assistants

Ethical Considerations in Explainable AI: Balancing Transpar...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Franciskus Antonius Alijoyo S. Sandya Sneha Sri Purnachandra Rao Alapati Dilyorjon Yuldashev Ponni Valavan M Center for Risk Management and Sustainability Indonesia School of Business and Information Technology STMIK LIKMI Bandung Indonesia Krishna University Andhra Pradesh Prasad V Potluri Siddhartha Institute of Technology Kanuru Vijayawada Andhra Pradesh India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy Tamil Nadu India

English Language-Based Virtual Assistants (ELB-VAs) are AI-powered systems designed to comprehend and respond to user queries in the English language, exemplified by virtual assistants like Siri or Alexa. The need for balancing transparency and user privacy in ELB-VAs is paramount due to their pervasive integration into daily life. Ensuring transparency imbues user trust, while safeguarding privacy addresses ethical concerns associated with personal data. Existing methods involve clear privacy policies, user-controlled data sharing settings, and encryption. However, drawbacks include user confusion and potential biases. To address these limitations, this study proposes a novel approach. Methodologically, it integrates pre-processing techniques such as lowercasing and tokenization, coupled with a Natural Language Understanding model. This model undergoes intent and entity recognition training, enhancing accuracy, and incorporates privacy-aware response generation, ensuring informative yet privacy-conscious interactions. The implementation of the study's results is carried out using Python tools, showcasing improved metrics and response times. This approach contributes to a more transparent and privacy-respecting user experience, aligning with evolving ethical norms and setting the stage for advancements in ELB-VA technology. This comprehensive exploration bridges existing gaps, emphasizing the ethical imperative of user-centric and privacy-aware AI interactions in ELB- VAs. The proposed NLU model exhibits a substantial increase in accuracy compared to other methods, with an impressive accuracy value of 99.1 % • On average, it outperforms the Random Forest and Decision Tree models by 15.7 percentage points, highlighting its superior predictive capabilities in the evaluated task. This comprehensive exploration aligns with evolving ethical norms and establishes a foundation for future advancements in ELB-VA technology.

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Classification of Small Region of Interest from Remote Images Using Neural Networks

Classification of Small Region of Interest from Remote Image...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Loretta Ichim Dan Popescu Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania

ISBN: (数字)9781728198095

ISBN: (纸本)9781728198101

The classification and segmentation of small region of interest from ground images is important in many domains: flood detection and evaluation, crop monitoring, environment monitoring, defense, etc. The paper uses a fusion scheme to classify small regions from remote images in four classes (different for satellite and unmanned aerial vehicle -UAVs). As novelty, two neural networks, considered as primary classifiers, AlexNet and Perceptron (the last based on ten textural image features) are combined in a convolutional scheme to make up the global classifier. The weights from the convolutional layer are calculated according to the performances of the primary classifiers in a validation phase. Two datasets with different images are used for classifier learning, validation, and testing, one consisting in images from satellite and another consisting in images from UAVs. The global system performances were better than those of the individual neural network (system components) accuracies.

关键词： Neural networks Satellites Unmanned aerial vehicles Image segmentation Testing Feature extraction Monitoring

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Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach

Real-Time Object Detection and Robotic Manipulation for Agri...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Hongyu Zhao Zezhi Tang Zhenhong Li Yi Dong Yuancheng Si Mingyang Lu George Panoutsos The Department of Physics Imperial College London United Kingdom The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom The Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom The Department of Electronics and Computer Science University of Southampton Southampton United Kingdom The Department of Economics Fudan University Shanghai China The Center for Nondestructive Evaluation (CNDE) Iowa State University Ames IA United States

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.

关键词： YOLO Visualization Crops Grasping Production Robustness Real-time systems

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An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

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Sensors 2024年第10期24卷 3098页

作者： Chi, Tsun-Kuang Chen, Tsung-Yi Lin, Yu-Chen Lin, Ting-Lan Zhang, Jun-Ting Lu, Cheng-Lin Chen, Shih-Lun Li, Kuo-Chen Abu, Patricia Angela R. Department of Electronic Engineering Chung Yuan Christian University Taoyuan320314 Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Information Management Chung Yuan Christian University Taoyuan320317 Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Quezon1108 Philippines

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems. © 2024 by the authors.

关键词： Deep learning

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Self-triggered adaptive control for multi-agent systems with timed constraints and connectivity maintenance ⁎

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IFAC-PapersOnLine 2020年第2期53卷 4007-4013页

作者： Maria Guinaldo Dimos V. Dimarogonas Department of Computer Science and Automatic Control UNED Madrid Spain Division of Decision and Control School of EECS KTH Royal Institute of Technology Stockholm Sweden

This paper presents a distributed control strategy for a multi-agent system commanded by a set of leaders that has to accomplish a high-level plan consisting of a sequence of tasks specified by a state-space region and a timed constraint. The agents are also subject to relative-distance constraints with its neighbors. The solution consists in an adaptive distributed mechanism to update the feedback gains for the leader agents, which is executed following a self-triggered algorithm. The results show how the proposed approach provides less conservative results than if feedback gains are held constant, and are illustrated with a simulation example.

关键词： Multi-agent systems distributed control self-triggering adaptive control

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The Search for Assembly Line Balance Oriented on the Allocation of Quality control Points

The Search for Assembly Line Balance Oriented on the Allocat...

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International Conference on control, Automation and Robotics (ICCAR)

作者： Sara Bysko Jolanta Krystek Szymon Bysko Krzysztof Majchrowicz Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland

ISBN: (数字)9781728161396

ISBN: (纸本)9781728161402

In the paper important aspects of the proper use of quality inspection in production process were presented. The paper deals with the problem of assembly line balance analysis taking into account occurrence of quality checkpoints. It is very important for producers, because the earlier the detection of a defect, the greater the possibility of reducing the costs of manufacturing a faulty product. In order to present the real assembly line balancing problem taking into account occurrence of checkpoints in strictly defined places, simplified process of USB flash drive production was analyzed. The assembly line balancing was performed and the indicators evaluating quality of the solutions were obtained. The calculations were made for different conditions regarding the type and location of quality inspections. Approaches like classical assembly line balancing without and with quality inspection, and fragmentary assembly line balancing with quality inspection were verified. All results and discussion are presented in the paper.

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Skinny: A Lightweight U-Net For Skin Detection And Segmentation

Skinny: A Lightweight U-Net For Skin Detection And Segmentat...

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IEEE International Conference on Image Processing

作者： Tomasz Tarasiewicz Jakub Nalepa Michal Kawulok Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

The use of deep-learned features has recently allowed for improving the performance of skin detection and segmentation. In particular, fully-convolutional U-Nets, proposed for segmenting medical images, occurred to be extremely effective here. However, the spatial context, which is rather narrow for U-Nets, may be more important for skin segmentation than for segmenting other image structures. We propose Skinny-a lightweight U-Net-based architecture that extends the range of multi-scale analysis. The results of our experiments indicate that Skinny outperforms the state-of-the-art skin segmentation techniques, rendering the F-score of 92.3% and 94.9% for the ECU and HGR datasets, respectively.

关键词： Skin Image segmentation Feature extraction Training Bayes methods Kernel computer architecture

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