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Packet Delivery Ratio Assessment and Analysis of OLSR Protocol for FANETs

Packet Delivery Ratio Assessment and Analysis of OLSR Protoc...

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2024 international conference on Telecommunications and intelligent systems, ICTIS 2024

作者： Hameurlaine, Hicham Mehallegue, Noureddine Benssalah, Mustapha Ecole Militaire Polytechnique Signal Processing Laboratory Alger Algeria Research and Development Centre Alger Algeria

ISBN: (纸本)9798331527396

This study explores Flying Ad Hoc Networks (FANETs) comprised of Unmanned Aerial Vehicles (UAVs), which play a vital role in applications such as aerial surveillance, disaster response, and environmental monitoring. A notable challenge associated with the Optimized Link State Routing (OLSR) protocol is packet loss, an issue that arises from its proactive nature. While this characteristic ensures low end-to-end delay making OLSR advantageous in critical situations, it can adversely affect overall reliability. Utilizing the NS-2.35 simulator, this research evaluates the performance of OLSR by analyzing how various factors, such as operational area size, mobility models, node speed, and traffic rate, impact the Packet Delivery Ratio (PDR). Several mobility models, including time, random and group-based movements, are employed to simulate realistic UAV behaviors. The study underscores OLSR's robustness in these contexts, particularly noting that the Pursue Mobility Model (PMM) is particularly effective in sustaining a high PDR. Moreover, the findings emphasize the significant impact of area size on OLSR's performance, providing valuable insights for optimizing FANET deployments and enhancing the reliability and efficiency of these systems in critical applications. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Networked systems Diagnostics: A Fusion of Failure Mode and Effects Analysis and a Delphi Expert Study

Networked Systems Diagnostics: A Fusion of Failure Mode and ...

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2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Ren, Yongxu Deichsel, Felix Hopf, Valentin Seiler, Jurgen Kaup, Andre Beckerle, Philipp Friedrich-Alexander-Universität Erlangen-Nürnberg Chair of Autonomous System and Mechatronics Erlangen Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Chair of Multimedia Communications and Signal Processing Erlangen Germany Friedrich-Alexandcr-Universität Erlangen-Nürnberg Chair of Automatic Control Erlangen Germany Friedrich-Alexander-Universitat Erlangen-Nurnberg Chair of Autonomous System and Mechatronics Erlangen Germany

ISBN: (纸本)9781665410205

Networked devices, especially those comprising multiple identical devices, are extensively utilized in industrial scenarios. However, their complexity poses unique challenges in diagnostic processes, demanding efficient methodologies to identify and assess risks. The application of Failure Mode and Effects Analysis (FMEA) for analyzing complex systems, especially those consisting of networked devices, appears to be limited, particularly in identifying critical risk factors. In this paper, we propose a novel pipeline to diagnose networked systems by fusing FMEA with a Delphi (expert) Study. Our approach leverages the collective knowledge of a group of experts through a structured Delphi Study enabling them to contribute individually but also to interact. We demonstrate the applicability of our approach through a case study involving a system of networked mobile robots. Based on a Fault Tree Analysis (FTA), Risk Priority Numbers (RPN) of minimal fault tree cut sets are calculated to identify the most critical mechatronic failures. Our findings show that our methodology provides an RPN ranking that closely aligns with expert insights, highlighting its efficacy in accurately assessing risk in complex networked systems. © 2024 ieee.

关键词： Failure modes

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Lightweight Micro Aerial Vehicles (MAVs) Application for Indoor Environment Mapping using Ultrasonic Sensors

Lightweight Micro Aerial Vehicles (MAVs) Application for Ind...

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2023 ieee international conference on Omni-Layer intelligent systems, COINS 2023

作者： Conceicao, Maria Ines Grilo, Antonio Basiri, Meysam INESC-ID Instituto Superior Tecnico UL Lisbon Portugal Institute for Systems and Robotics Instituto Superior Tecnico UL Lisbon Portugal

ISBN: (纸本)9798350346473

Indoor localisation and mapping missions usually require several sensors to correctly calculate the position of the Unmanned Aerial Vehicle (UAV) within the space and detect and localize obstacles. This research paper presents the development of a mapping system for UAVs in unknown indoor environments resorting to ultrasonic sensors. The core of the system is the DJI Ryze Tello UAV, a small 80g Micro Air Vehicle (MAV) with very limited capabilities such as payload and available flight time. A distributed system was developed to ensure the successful execution of the mission without exceeding the capabilities of the MAV. A prototype was developed that consists of two hardware components, one integrated into the MAV and another in a teth-ered configuration. The operation of the system is independent of the environment and supports multiple simultaneous missions. An original autonomous flight and mapping algorithm was created, based on vertical motion patterns, to provide an efficient and effective mapping solution. Data processing procedures were also implemented to complement the initial framing of detected obstacles. The obtained system allows the digital reconstruction of the space. The mapped obstacle distances in the test environment have resulted in more than 90% of accuracy. © 2023 ieee.

关键词： Antennas

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Impact of Electromagnetic Interference on Hardware Impaired RIS assisted FD Communications 18

Impact of Electromagnetic Interference on Hardware Impaired ...

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18th ieee international conference on Advanced Networks and Telecommunications systems, ANTS 2024

作者： Gupta, Priya Mishra, Bishnu Prasad Thomas, Manoj Vankayala, Satya Kumar Yoon, Seungil Vignan's Foundation for Science Technology and Research Andhra Pradesh India Samsung R&d Institute India Bangalore System S/W Group Bengaluru India Oam S/W R&d Group Samsung Electronics Suwon Korea Republic of

ISBN: (纸本)9798350391725

The paper explores full-duplex (FD) physical layer security systems, highlighting the transformative potential of FD Reconfigurable intelligent Surfaces (RIS). Our objective is to optimize the sum rate while accounting for the persistent electromagnetic interference (EMI) from uncontrollable sources and spatial channel correlation. Additionally, we consider the impact of low-cost hardware at both the RIS and user devices resulting in impairments. To achieve this objective, the maximal ratio combiner/maximal ratio transmitter processing technique is employed to design the precoder and combining vector, while the RIS reflection beamforming matrix is designed using a heuristic approach based on channel knowledge between the base station and RIS, and RIS to the user. Simulation results demonstrate that the effects of hardware impairments, EMI, and channel correlation on the sum rate are significant thereby can't be ignored © 2024 ieee.

关键词： signal interference

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Computer Aided Diagnosis of Depression Using EEG signal processing

Computer Aided Diagnosis of Depression Using EEG Signal Proc...

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2023 Annual international conference on Emerging Research Areas: international conference on intelligent systems, AICERA/ICIS 2023

作者： Anand, Abin B. Michael, Alent Biju, Aneesha Chacko, Deryck Joseph Darsana, P. Amal Jyothi College of Engineering Department of E.C.E Kottayam India

ISBN: (纸本)9798350303452

Major depressive disorder (MDD) emerges as a prominent factor leading to disability on a global scale and contributes significantly to the global burden of illness overall. The traditional method of detecting MDD is by continuous medical examination by a psychologist or psychiatrist. Our objective is to develop a non-invasive device which collects brain signals from the head and gets interfaced with a computer. The purpose of this paper is to develop a computer-aided diagnosis system that can identify depression in real time. The proposed system comprises three main components: the ADS1299 Front-End (FE) Printed Development Kit (PDK) evaluation board, a wearable electrode, and a desktop application. The primary approach involves the utilization of electroencephalogram (EEG) signal processing, along with the ADS1299 FE PDK evaluation board and deep learning techniques. This paper involves the utilisation of a publicly available dataset for training the deep learning model. The convolutional Neural Network (CNN) algorithm is used for the classification process. Absolute and relative powers are computed, and an asymmetry image matrix is generated based on the relative power values. By analysing the image matrix, the system can classify a patient as healthy or suffering from major depression based on higher or lower relative power, respectively. This paper seeks to make a valuable contribution to the academic sphere of the study of mental health diagnosis by leveraging advanced signal processing techniques and deep learning models for more accurate and efficient detection of depression. © 2023 ieee.

关键词： Electroencephalography

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Comparative Analysis of SVM and CNN Classifiers for EEG signal Classification in Response to Different Auditory Stimuli

Comparative Analysis of SVM and CNN Classifiers for EEG Sign...

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2024 international conference on Telecommunications and intelligent systems, ICTIS 2024

作者： Tibermacine, Ahmed Akrour, Djouher Khamar, Rofeida Tibermacine, Imad Eddine Rabehi, Abdelaziz University of Biskra Department of Computer Science Biskra Algeria Sapienza University of Rome Department of Computer Automation and Management Engineering Rome Italy Zian Achour University of Djelfa Laboratory of Telecommunications and Smart Systems Faculty of Sciences and Technology Djelfa17000 Algeria

ISBN: (纸本)9798331527396

This study aims to classify brainwave patterns using electroencephalogram (EEG) signals in response to various auditory stimuli, specifically Quran recitation, participants' favorite music, and Interstellar's main theme. By analyzing the unique EEG responses elicited by these stimuli, the study explores cognitive and emotional processing differences. Two prominent classifiers, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), were utilized. SVM, combined with Power Spectral Density (PSD) features, achieved the highest accuracy of 95% when distinguishing Quran recitation from rest. CNN, particularly with Kernel Density Estimation (KDE) features, exhibited strong performance in distinguishing between different auditory stimuli, with an average accuracy exceeding 90%. These results highlight the critical role of feature extraction techniques, such as PSD and KDE, in improving classification performance and deepening our understanding of EEG-based emotional and cognitive responses to auditory stimuli. © 2024 ieee.

关键词： Convolutional neural networks

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Enhanced Single Remote Sensing Image Dehazing via Vision Transformer with Silencing Map Transmission and advanced Image processing Techniques

Enhanced Single Remote Sensing Image Dehazing via Vision Tra...

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2024 international conference on intelligent systems for Cybersecurity, ISCS 2024

作者： Medasani, Sivasubramanyam Shireesha, B. Hemanth Reddy, B. Teja, C.Charan Sainath Chowdary, E. Venkata School of Engineering Electronics and Communication Engineering Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati India Tirupati India

ISBN: (纸本)9798350375237

- This research provides an improved single remote sensing picture dehazing method using sophisticated image processing techniques and Vision Transformer with Silencing Map Transmission. The dehazing output generation, morphological analysis, dark channel prior, silencing map transmission, recurrent neural networks (RNN), and input image processing are some of the components that are included in the suggested method. The objective is to far outperform current techniques in terms of dehazed image quality. The initial action in the process is getting ready the input image, after which morphological analysis is performed to determine the properties of the haze. The key characteristics of the haze are retrieved by using the dark channel before. Transmitting silencing maps improves our comprehension of scene depth, which is important for precise dehazing. RNN is used to learn from sequential data and propagate information effectively. Clearer and more aesthetically pleasing photos are produced by the dehazing output, which is generated based on the learned attributes. Mean Squared Error (MSE), Peak signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), and Time of Execution (TOE) are among the metrics used to assess the efficacy of the suggested approach. The supremacy of the suggested strategy is shown in contrast to current approaches. The trial results demonstrate considerable improvements in PSNR, SSIM, and TOE measures across a variety of test images. In particular, the suggested approach outperforms current methods in terms of PSNR improvements of up to 4.23 dB and TOE reductions of up to 2.58 seconds. To sum up, the enhanced single remote sensing picture dehazing approach that has been described provides significant improvements in both image quality and processing efficiency. As such, it is a promising solution that might be used for a variety of remote sensing applications. © 2024 ieee.

关键词： Recurrent neural networks

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INS-GNSS-Vision-UWB Navigation for Vertical Taking-Off and Landing in Urban Air Mobility Scenarios 3

INS-GNSS-Vision-UWB Navigation for Vertical Taking-Off and L...

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3rd international conference on Service robotics, ICoSR 2024

作者： Zhang, Bihui Huang, Tianqi Guo, Pengbin Zuo, Jianhong Chinese Academy of Sciences Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Beijing China College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350367119

In UAM take-off and landing scenarios, there are multipath effects, signal obstruction and interference in relatively confined spaces. To compensate for the limitations of Global Navigation Satellite systems and Inertial Navigation systems in such near-ground environments, we conducted research on the INS-GNSS-Vision-UWB combination navigation technology. Firstly, based on the Error-State Kalman Filter, a fusion navigation framework that combines INS, GNSS, Vision, and UWB modules was designed. Secondly, from an engineering perspective, a series of evaluation criteria for the navigation modules, including power consumption, computational resource usage, latency, and effective range, were discussed. Finally, ground and flight tests were conducted. In the ground tests, the power consumption, CPU/memory usage, data acquisition, and processing latency of different modules were measured. In the flight experiments, the positioning accuracy of different navigation modules were compared, and the effective range as well as the feasibility of yaw angle measurement of AprilTag and UWB were tested. The experimental results show that the INS-GNSS-Vision-UWB combination navigation has significant advantages over single positioning modules in terms of accuracy and stability. UWB outperforms AprilTag in terms of accuracy, resource consumption, and effective range. However, AprilTag can provide additional yaw angle measurements in complex electromagnetic environments. © 2024 ieee.

关键词： Inertial navigation systems

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Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments

Transfer Learning-Based Deep Residual Learning for Speech Re...

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2024 international conference on Telecommunications and intelligent systems, ICTIS 2024

作者： Djeffal, Noussaiba Addou, Djamel Kheddar, Hamza Selouani, Sid Ahmed University of Sciences and Technology USTHB Speech and Signal Processing Laboratory Algiers Algeria University of MEDEA LSEA Laboratory Dept. Electrical Engineering Medea Algeria Universite de Moncton Research Laboratory in Human-System Interaction Shippagan Campus Shippagan Canada

ISBN: (纸本)9798331527396

Addressing the detrimental impact of non-stationary environmental noise on automatic speech recognition (ASR) has been a persistent and significant research focus. Despite advancements, this challenge continues to be a major concern. Recently, data-driven supervised approaches, such as deep neural networks, have emerged as promising alternatives to traditional unsupervised methods. With extensive training, these approaches have the potential to overcome the challenges posed by diverse real-life acoustic environments. In this light, this paper introduces a novel neural framework that incorporates a robust frontend into ASR systems in both clean and noisy environments. Utilizing the Aurora-2 speech database, the authors evaluate the effectiveness of an acoustic feature set for Mel-frequency, employing the approach of transfer learning based on Residual neural network (ResNet). The experimental results demonstrate a significant improvement in recognition accuracy compared to convolutional neural networks (CNN) and long short-term memory (LSTM) networks. They achieved accuracies of 98.94% in clean and 91.21% in noisy mode. © 2024 ieee.

关键词： Clean speech Feature enhancement Noisy speech ResNet Speech recognition Transfer learning

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Poster Abstract: Shallowly Buried Trash Detection in Sandy Land Based on IR-UWB Radar 23

Poster Abstract: Shallowly Buried Trash Detection in Sandy L...

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23rd ACM/ieee international conference on Information processing in Sensor Networks, IPSN 2024

作者： Fan, Guiyun Zhang, Yongkui Jin, Haiming Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350362015

Fast and accurate sand trash detection and localization is extremely important for trash cleaning, and at present, it still mainly depends on sanitation workers to carry out manual detection. Existing computer vision-based methods cannot detect the shallowly-buried trash. Besides, it is difficult and costly to use ground penetrating radar to detect. To overcome these limitations, we design and implement a novel detection system for shallowly buried trash in sandy land, which integrates the commercial IR-UWB radar into the intelligent unmanned vehicle. By controlling the movement of the vehicle, the radar scans the targeted sandy land and synthesizes the signal into the radar heat-map to detect and locate the shallowly buried trash. Experimental results show that the detection accuracy of the system reaches 92.3% with the radar is 75cm from the ground and the angle perpendicular to the ground is 20°. In the direction parallel to the ground, the farthest trash can be detected is 4.2m away from the radar. Within the range of 4.5m2, it can detect and locate up to 9 trash at the same time. © 2024 ieee.

关键词： Ground penetrating radar systems

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