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Simulation and Experimental Study of Multi-source Application Layer ARQ for FANET 21st

Simulation and Experimental Study of Multi-source Applicatio...

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21st international conference on Next Generation Teletraffic and Wired/Wireless Advanced networks and systems, NEW2AN 2021 and 14th conference on Internet of things and Smart Spaces, ruSMART 2021

作者： Kaisina, Irina Abilov, Albert Vasiliev, Danil Lamri, Mohammed Amin Nistyuk, Anatoli Department of Communication Networks and Systems Kalashnikov Izhevsk State Technical University ul. Studencheskaya 7 Izhevsk426069 Russia

ISBN: (纸本)9783030977764

this paper discusses a FANET consisting of several source nodes and one destination node in the form of a ground station. As data for transmission, a video stream was considered, which was broadcasted in real time simultaneously by several source nodes. Simulation of the multi-stream data transmission process was carried out in NS-3 environment. Several variations of multi-stream transmission modeling were considered, in the first of which the Application class was used to create several source nodes, in the second the Poisson Pareto Burst Process (PPBP) traffic generator was used. Based on the simulation results, it was concluded that an increase in the number of flying source nodes negatively affects QoS: the requirement for Goodput increases, the destination node buffer is overloaded and video data packets are dropped, and PDR decreases. To solve the above problem, the Multi-Source Application Layer ARQ (MS-AL-ARQ) method is proposed, the main principle of which is to repeat the lost data chunks at the application level. this paper describes the principle of operation of MS-AL-ARQ for working with separate input buffers and a common input buffer at the destination node. Both variations of the MS-AL-ARQ operation are implemented in software, the effectiveness of their application was experimentally proven. © 2022, Springer Nature Switzerland AG.

关键词： Application programs

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Handling 5G Blockage Issue through a MPTCP Proxy Approach

Handling 5G Blockage Issue through a MPTCP Proxy Approach

引用

international conference on the Network of the Future (NOF)

作者： Reza Poorzare Oliver P. Waldhorst Data-Centric Software Systems Research Group at the Institute of Applied Research Karlsruhe University of Applied Science Karlsruhe Germany

Cellular communication has become an integral part of human life, and 5G, as a new mobile communication technology, is expanding to provide enhanced connectivity for users. Nevertheless, due to flaws such as blockage, it cannot accomplish its goals easily. Meanwhile, with the emergence of MPTCP, the feasibility of using multiple networks is now available for users. MPTCP can indeed help the 5G network by combining it with another network in such a way that the alternative one can be an assistance for the 5G network to overcome existing issues such as blockage; however, most servers do not support this protocol, and thus, its deployment by a user can be pointless. In this paper, by using a proxy, we provide multipathing to the client side to improve the user experience. the paper mainly focuses on answering the question of whether or not it is possible to keep the performance of the 5G network stable by exploiting a secondary network. Furthermore, it elaborates on which scenarios can most benefit from the MPTCP proxy deployment, and which combination of congestion control and schedulers can fulfill it the most and enhance mobile networks’ performance.

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Enhancing Sign Language Recognition Using Deep Convolutional Neural networks

Enhancing Sign Language Recognition Using Deep Convolutional...

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international conference on Information, Intelligence, systems and Applications (IISA)

作者： Athanasios Kanavos Orestis Papadimitriou Phivos Mylonas Manolis Maragoudakis Department of Information and Communication Systems Engineering University of the Aegean Samos Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece Department of Informatics Ionian University Corfu Greece

the advancements in sensing technologies and AI algorithms have opened up a wide range of possibilities for developing applications to meet the needs of individuals who are deaf or hard of hearing. Sign language plays a vital role in the lives of people with hearing and speaking disabilities. this research aims to explore digital image processing and machine learning methods for efficiently building a sign language dataset and creating a sign language interface system. the proposed system utilizes a Convolutional Neural Network (CNN) to analyze and interpret hand gestures and poses, converting them into natural language. the developed CNN model specifically focuses on improving the accuracy of predicting the American Sign Language alphabet. Despite variations in dataset conditions and size, the model achieved an exceptional accuracy rate of 98.73%. Additionally, it demonstrated a low loss value of 0.0539, indicating its robust performance.

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Comparative Evaluation of Machine Learning-Based Controller Area Network Intrusion Detection systems

Comparative Evaluation of Machine Learning-Based Controller ...

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international conference on Information and communication systems (ICICS)

作者： Omar Y. Al-Jarrah Omar A. Ramadan Baha Alsaify Suboh Alkhushayni Dept. Network Engineering and Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Dept. Mechanical Engineering Faculty of Engineering Jordan University of Science and Technology Irbid Jordan Dept. Information Systems Yarmouk University Irbid Jordan

the controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). these IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. this study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN.

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OPEES: Online Proctored Entrance Examination System with Degree Program Recommender for Colleges and Universities 14

OPEES: Online Proctored Entrance Examination System with Deg...

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14th IEEE international conference on Humanoid, Nanotechnology, Information Technology, communication and Control, Environment, and Management, HNICEM 2022

作者： Bulauitan, Joriz Caezar B. De Jesus, Ashley L. Lopez, Katherine Mega F. Villarama, Darren Joshua A. Goh, Marie Luvett I. Raguro, Ma. Corazon F. Lagman, Ace C. FEU Institute of Technology P. Paredes Manila Sampaloc Philippines

ISBN: (纸本)9781665464932

the college entrance examination is vital for program admission. Typically, entrance examinations are conducted onsite using paper and pens. When the COVID-19 pandemic hit, the entrance examination was lifted and physical gatherings were prohibited. Since many schools cannot offer an online admissions exam, they rely on grades and interviews to admit and qualify students for degree programs. However, academic standards differ between schools, and grades may not be enough to assess students' capacity. thus, this study aims to develop an Online Proctored Entrance Examination System (OPEES) with Degree Program Recommender for colleges and universities to help institutions administer onsite or online entrance tests and generate course suggestions using a rulebased algorithm. the study employed the scrum methodology in software development. OPEES allows applicants to submit applications online, and institutions can manage user accounts, tailor exams and degree programs' criteria, manage exam dates, and assign proctors. Online proctoring using Jitsi, an opensource multiplatform voice, video, and instant messaging tool with end-to-end encryption, ensures exam integrity. the system's features were evaluated by 102 respondents, comprised of end-users (students and school personnel) and IT professionals, using the FURPS (Functionality, Usability, Reliability, Performance, and Supportability) software quality model. In the software evaluation, the overall system proved to be functional as perceived by the respondents, as manifested by the mean rating of 4.61. In conclusion, the system's architecture was deemed feasible and offers a better way to streamline admission examinations and determine a student's applicable degree program by enabling institutions to customize their exams and degree program requirements. It will be beneficial to look into recommendation system algorithms and historical enrollment data to improve the system's use case. © 2022 IEEE.

关键词： Recommender systems

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Collaborative Optimization Algorithm of Deep Learning and Compliant Control in Online Trajectory Planning

Collaborative Optimization Algorithm of Deep Learning and Co...

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international conference on communication systems and Network Technologies (CSNT)

作者： Junting Liao Chunjian Zhao Hui Xie School of Computer and Artificial Intelligence Xiangnan University Chenzhou China China Telecom Hunan Chenzhou Branch Chenzhou China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

With the rapid development of robotics and automation, online trajectory planning faces problems such as poor adaptability to dynamic environments and low computational efficiency. this study aims to propose a collaborative optimization algorithm that combines deep learning and compliant control to improve the efficiency and accuracy of online trajectory planning. First, CNN is used for feature extraction and a prediction model is established. then a compliant control strategy is designed to deal with dynamic obstacles to ensure the flexibility and stability of the robot in trajectory planning. the trained CNN model is then used to generate real-time trajectory predictions. the robot motion is adjusted through a fuzzy logic controller to optimize the relative position of the trajectory and obstacles. the average obstacle avoidance success rate of the algorithm in dynamic environments reaches 97%. the collaborative optimization algorithm of deep learning and compliant control effectively improves the real-time and accuracy of online trajectory planning, providing a new solution for the application of intelligent robots in complex environments.

关键词： Deep learning Trajectory planning Heuristic algorithms Collaboration Predictive models Prediction algorithms Real-time systems Trajectory Convolutional neural networks Optimization

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Role of Edge Computing in IoT

Role of Edge Computing in IoT

引用

international conference on communication systems and Network Technologies (CSNT)

作者： Pragya Singh Preeti Gautam Namita Arya MITS Gwalior Centre of Internet of Things Madhya Pradesh India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Computing stands at a crossroads between speed and scale. the cloud promises vast computing power and storage, yet shuffling data to and from those distant servers takes time - often too much. Edge computing looks to resolve that dilemma by shifting processing closer to the devices generating all that data. Rather than relying solely on gargantuan centralized cloud servers, edge computing utilizes nearby devices and micro data centers to enable real-time, location-aware services and this distributed approach brings quicker responses and leaner loads by leveraging local gadgets to handle localized computing chores. However fully activating edge computing's potential requires surmounting a few obstacles first. Efficiently coordinating myriad devices for data storage and number-crunching poses logistical challenges. Integrating these decentralized nodes into the centralized cloud framework demands care and forethought. And securing expansive networks of hardware and software is no simple feat. Still, the promised gains in speed and adaptability compel this revolution in computing.

关键词： Cloud computing Technological innovation Standards organizations Real-time systems Software Hardware Security Servers Internet of things Edge computing

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Machine Learning Models for Intrusion Detection System

Machine Learning Models for Intrusion Detection System

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international conference on communication systems and Network Technologies (CSNT)

作者： Sourabh Hiremath Rohit D B Shudhanshu Singh Sheela S V Radhika K R Dept of ISE BMSCE Bengaluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Protecting networks from mischievous attacks in the scenario of cybersecurity require Intrusion Detection System (IDS). Leveraging machine learning algorithms to develop IDS has gathered substantial consideration due to its capacity to adapt and study from vast datasets. In this research paper, an extensive analysis of intrusion detection has been conducted utilizing various machine learning models trained on the CICIDS2018 dataset. Fifteen distinct models have been explored, ranging from traditional methods to sophisticated deep learning architectures. Subsequently, Random Forest has been identified as the optimal classifier based on its superior performance metrics of F1 score 99.79%, precision 99.79%, and recall 99.81%. Moreover, a framework has been proposed for real-time intrusion detection using the Random Forest classifier, employing CICFlowMeter for data collection, which aligns with the dataset used for model training.

关键词： Training Analytical models Adaptation models Machine learning algorithms Intrusion detection Telecommunication traffic Network security Real-time systems Computer security Random forests

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Distributed Photovoltaic communication Latency Optimization Method Based on Sliding Window and CNN

Distributed Photovoltaic Communication Latency Optimization ...

引用

international conference on Electronics Information and Emergency communication

作者： Longmei Geng Bin Li Yi Sun Gang Zhang Tao Chen School of Electric and Electronic Engineering North China Electric Power University Beijing China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

With the growing demand for energy and the increasing global attention to environmental protection, the utilization of renewable energy such as distributed photovoltaic has received more and more attention. Distributed photovoltaic is developing rapidly. the problem of communication latency is also becoming more and more prominent. In this paper, the current situation of distributed photovoltaic communication is analyzed. Secondly, a latency optimization mechanism based on sliding window is proposed to compress packets and reduce latency. Finally, the CNN model is established and trained to predict the distributed photovoltaic communication latency, and the experimental results show that the established CNN model has a good effect on the distributed photovoltaic communication latency prediction, which provides technical support for the grid integration of renewable resources.

关键词： Photovoltaic systems Analytical models Renewable energy sources Uncertainty Neural networks Predictive models Power system stability

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A wavelet scattering convolutional network for magnetic resonance spectroscopy signal quantitation 14

A wavelet scattering convolutional network for magnetic reso...

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14th international conference on Bio-Inspired systems and Signal Processing, BIOSIGNALS 2021 - Part of the 14th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2021

作者： Shamaei, Amirmohammad Starčuková, Jana Zenon Starčuk, Z.S. Institute of Scientific Instruments of the CAS Královopolská 147 Brno612 64 Czech Republic Department of Biomedical Engineering Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno616 00 Czech Republic

ISBN: (纸本)9789897584909

Magnetic resonance spectroscopy (MRS) can provide quantitative information about local metabolite concentrations in living tissues, but in practice the quantification can be difficult. Recently deep learning (DL) has been used for quantification of MRS signals in the frequency domain, and DL combined with time-frequency analysis for artefact detection in MRS. the networks most widely used in previous studies were Convolutional Neural networks (CNN). Nonetheless, the optimal architecture and hyper-parameters of the CNN for MRS are not well understood;CNN has no knowledge about the nature of the MRS signal and its training is computationally expensive. On the other hand, Wavelet Scattering Convolutional Network (WSCN) is well-understood and computationally cheap. In this study, we found that a wavelet scattering network could hopefully be also used for metabolite quantification. We showed that a WSCN could yield results more robust than QUEST (one of quantitation methods based on model fitting) and the same as a CNN while being faster. We used wavelet scattering transform to extract features from the MRS signal, and a superficial neural network implementation to predict metabolite concentrations. Effects of phase, noise, and macromolecules variation on the WSCN estimation accuracy were also investigated. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Magnetic resonance spectroscopy

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