2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)最新文献_第5页

A Multibeam Antenna for Multi-Orbit LEO Satellites and Terminals with a Very Simple Tracking Technique 基于简单跟踪技术的多轨道LEO卫星和终端多波束天线

2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA) Pub Date : 2022-12-27 DOI: 10.1109/ICCSPA55860.2022.10019125

M. Sanad, N. Hassan

{"title":"A Multibeam Antenna for Multi-Orbit LEO Satellites and Terminals with a Very Simple Tracking Technique","authors":"M. Sanad, N. Hassan","doi":"10.1109/ICCSPA55860.2022.10019125","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019125","url":null,"abstract":"A foldable/deployable multi-beam antenna has been developed for Low-Earth Orbit (LEO) satellites and ground stations. It consists of dual parabolic cylindrical reflectors with multiple resonant feeds. It can generate an arbitrary number of beams with arbitrary vertical and horizontal beam-tilt for each beam. It can be remotely controlled to cover any arbitrary area having any shape/size. It has a very wide frequency band-width. It can even, simultaneously, work at multi wide frequency bands such as 5G-sub-6GHz band (3.3-6.0 GHz), Ku-band (10–16 GHz) and Ka-band (18–31 GHz). A very simple beam tracking technique has been developed without using any adaptive methods. For any specific satellite orbit, the orientation of the ground station (terminal) antenna will always be adjusted such that its generated beams are parallel to the satellite's beams and directed toward them. This automatic orientation of the ground station (terminal) antenna, completely, depends on its location, orientation and elevation with no need for any adaptive beam steering/tracking or any adaptive beam-shaping. Furthermore, the terminal antenna can simultaneously communicate with multiple satellites in different orbits. For example, a penta beam terminal antenna can, simultaneously, communicate with five satellites in five different orbits.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126071951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intrusion Detection Systems Based on Stacking Ensemble Learning in VANET 基于VANET中堆叠集成学习的入侵检测系统

2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA) Pub Date : 2022-12-27 DOI: 10.1109/ICCSPA55860.2022.10019171

Mahshid Behravan, N. Zhang, A. Jaekel, Marc Kneppers

{"title":"Intrusion Detection Systems Based on Stacking Ensemble Learning in VANET","authors":"Mahshid Behravan, N. Zhang, A. Jaekel, Marc Kneppers","doi":"10.1109/ICCSPA55860.2022.10019171","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019171","url":null,"abstract":"Vehicular ad-hoc network (VANET) will play an important role in improving driving safety and efficiency in transport systems. As various attacks arise in VANET, it is essential to design mechanisms that can detect these attacks and then mitigate them. In this paper, we make an effort to detect five different position falsification attacks in VANET, including constant attack, constant offset attack, random attack, random offset attack, and eventual stop attack. Two detection systems based on ensemble machine learning algorithms, including stacking ensemble learning algorithms for classification and stacking ensemble learning for neural network, are proposed. We extracted the most important features by performing feature importance techniques. Then, we train the proposed learning algorithms on VeReMi dataset which includes five different position falsification attacks with three traffic densities and three attacker densities. Extensive experimental results are provided to evaluate the proposed solutions' effectiveness. Based on our results, stacking ensemble learning for classification algorithm can achieve the best performance in terms of accuracy and recall. In low density traffic, accuracy and recall of stacking ensemble learning for classification algorithms are 1 for the constant attack, constant offset attack, and random attack. Accuracy and recall for the random offset attack are 0.999 and 0.996, respectively. For the eventual stop attack, accuracy and recall are 0.995 and 0.985, respectively. In medium density, accuracy and recall of stacking ensemble learning also achieve the best performance.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121716647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Robotic System for Automatic Identification and Collection of Recyclable Plastic Bottles 一种自动识别和收集可回收塑料瓶的机器人系统

2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA) Pub Date : 2022-12-27 DOI: 10.1109/ICCSPA55860.2022.10018969

Uzma Ahmed Din, S. Mukhopadhyay, Usman Tariq

引用次数: 0

Enhanced Deep Learning for Massive MIMO Detection Using Approximate Matrix Inversion 基于近似矩阵反演的大规模MIMO检测增强深度学习

2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA) Pub Date : 2022-12-27 DOI: 10.1109/ICCSPA55860.2022.10019100

Ali J. Almasadeh, Khawla A. Alnajjar, M. Albreem

{"title":"Enhanced Deep Learning for Massive MIMO Detection Using Approximate Matrix Inversion","authors":"Ali J. Almasadeh, Khawla A. Alnajjar, M. Albreem","doi":"10.1109/ICCSPA55860.2022.10019100","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019100","url":null,"abstract":"Massive multiple-input multiple-output (MIMO) is a crucial technology in fifth-generation (5G) and beyond 5G (B5G). However, the huge number of antennas used in massive MIMO systems causes a high computational complexity during signal detection. In this paper, we propose an efficient massive MIMO detection technique which is based on approximate matrix inversion methods and deep learning to enhance the system performance while keeping computational complexity low. Three approximate methods which are Gauss–Seidel (GS), successive over-relaxation (SOR), and conjugate gradient (CG) are exploited for the initialization of a modified version of the MM network (MMNet) algorithm. The performance of the proposed technique is validated under both Gaussian and realistic channel scenarios, i.e., Quadriga channels models. Simulation results show that the proposed technique outperforms MMNet, minimum mean square estimation (MMSE), detection network (DetNet), and orthogonal approximate message passing deep net (OAMP-Net) in terms of symbol error rate (SER) during offline training. It also provides a significant SER improvement of up to 87% when compared to MMNet in the online training scenario.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125644468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Smart Monitoring of Outdoor Insulators 室外绝缘子智能监控

2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA) Pub Date : 2022-12-27 DOI: 10.1109/ICCSPA55860.2022.10019102

Abdulla Lutfi, Ayman H. El-Hag

引用次数: 0

On the Design of a High Data Rate Underwater Acoustic Receiver 一种高数据速率水声接收机的设计

2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA) Pub Date : 2022-12-27 DOI: 10.1109/ICCSPA55860.2022.10019198

Amal Samir, B. ElDiwany, Ahmad Elmoslimany, M. Nafie, Sherif ElAzzouni, T. Elbatt

引用次数: 0

Network Security Challenges in SDN Environments SDN环境下的网络安全挑战

2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA) Pub Date : 2022-12-27 DOI: 10.1109/ICCSPA55860.2022.10019074

Rolan Khalifa, Minar El-Aasser

引用次数: 0

Capacity of the Beaulieu-Xie Fading Channel under Adaptive Transmission Strategies 自适应传输策略下的波谢衰落信道容量

2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA) Pub Date : 2022-12-27 DOI: 10.1109/ICCSPA55860.2022.10019068

Mohamed Mostafa, M. H. Ismail

引用次数: 0

Secrecy Analysis Over Correlated Generalized Gamma Fading Channels 相关广义衰落信道的保密性分析

2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA) Pub Date : 2022-12-27 DOI: 10.1109/ICCSPA55860.2022.10019241

Youssef M. Eldokmak, M. H. Ismail, Mohamed S. Hassan

引用次数: 0

A Hybrid Deep-learning/Fingerprinting for Indoor Positioning Based on IEEE P802.11az 基于IEEE P802.11az的混合深度学习/指纹室内定位

2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA) Pub Date : 2022-12-27 DOI: 10.1109/ICCSPA55860.2022.10019071

Nader G. Rihan, M. Abdelaziz, Samy S. Soliman

{"title":"A Hybrid Deep-learning/Fingerprinting for Indoor Positioning Based on IEEE P802.11az","authors":"Nader G. Rihan, M. Abdelaziz, Samy S. Soliman","doi":"10.1109/ICCSPA55860.2022.10019071","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019071","url":null,"abstract":"Many different technologies were proposed in the past few years for enhancing indoor positioning: WiFi, Radio Frequency Identification (RFID), Ultra Wide Band (UWB), and Bluetooth to mention some. This study followed the recent IEEE positioning standard (P802.11 az). The standard was developed to enhance indoor navigation by minimizing the consumption power with low hardware complexity. Therefore, this standard enables the usage of artificial intelligence algorithms with relatively high complexity. Also, the usage of this standard will enhance indoor localization and positioning for different commercial purposes. We proposed two methods: Time Of Arrival (TOA) and fingerprinting-deep learning, considering a simple Single Input-Single Input (SISO) system at five Gigahertz with the highest standard allowable bandwidth. The behavior of TOA had very low performance considering a realistic multi-path case. On the other hand, the deep learning algorithm achieved ultra-high indoor positioning resolution (around twelve centimeters). Although TOA is a technique that relies on a simple hardware algorithm relative to deep learning, this paper proved the failure of TOA in a simple indoor environment even using the latest IEEE positioning standard compared with the deep learning method.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130623693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1