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Experimental Assessment of the Effects of Building Materials on Wi-Fi Signal 2.4 GHz and 5 GHz

Journal of computer and Communications 2024年第5期12卷 1-10页

作者： Sylhane Bytyqi Besfort Jashari Department of Telecommunication Faculty of Electrical and Computer Engineering University of Pristina Pristina Kosovo

We are all witnesses to the widespread use of wireless LANs (WLAN) and their easy implementation in indoor environments. Wi-Fi is the most popular technology for the WLAN. However, interference caused by building materials is a common, yet often overlooked, contributor to poor Wi-Fi performance. This interference occurs due to the nature of radio wave propagation and the characteristics of the wireless communication system. Therefore, during the implementation of these networks, one must consider the quasi-static nature of the Wi-Fi signal and its dependence on the influence of various building materials on the propagation of these waves. This paper presents the effects of building materials and structures on indoor environments for Wi-Fi 2.4 GHz and 5 GHz. To establish the interdependencies between factors influencing electric field levels, measurements were conducted in an experimental Wi-Fi network at different distances from the access point (AP). The results obtained show that the electric field strength of the Wi-Fi signal decreases depending on the distance, the building materials, and the transmitted frequency. Concrete material had the most significant impact on the strength of the electric field in Wi-Fi, while glass had a relatively minor effect on reducing it. Wi-Fi operates within the radio frequency spectrum, typically utilizing frequencies in the 2.4 GHz and 5 GHz bands. Additionally, measurements revealed that Wi-Fi signal penetration is more pronounced at lower frequencies (2.4 GHz) as opposed to the Wi-Fi signal 5 GHz. The findings can be used to address the impact of building materials and structures on indoor radio wave propagation, ultimately ensuring seamless Wi-Fi signal coverage within buildings.

关键词： WLAN Electric Field AP Building Materials Frequency Wi-Fi

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A study of the leverage effect in Bitcoin Conditional Heteroskedasticity prediction 10

A study of the leverage effect in Bitcoin Conditional Hetero...

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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Abarghouie, Masoud Omidvari Alizadeh, Sasan H. Khademzadeh, Ahmad Department of Computer Engineering Science and Research branch Islamic Azad University Tehran Iran Faculty of Information Technology Iran Telecommunication Research Center Tehran Iran Iran Telecommunication Research Center Tehran Iran

ISBN: (纸本)9798350348873

In today's world, countries establish large companies specifically designed to effectively manage the risks associated with volatility exchange rates and leveraged assets. Bitcoin is widely recognized as the leading cryptocurrency, making it highly appealing to investors and traders. The recorded data reveals a growing number of transactions conducted using Bitcoin cryptocurrency. Consequently, accurately predicting the volatility of Bitcoin's conditional heteroskedasticity holds significant importance for the financial markets. Cryptocurrencies present a challenging situation due to their complex dynamics, extreme observations, asymmetry, and various other non-linear features. Modeling these cryptocurrencies accurately becomes difficult using statistical models, as they fail to consider the leverage effect and provide precise predictions. In this paper, we have employed a hybrid modeling approach by utilizing MLP neural network and GARCH statistical model. The experiments conducted on Bitcoin time series data have yielded promising results, showcasing a notable reduction in the prediction error of conditional heteroskedasticity volatility. ©2024 IEEE.

关键词： Forecasting

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Hybrid Reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s Disease Detection

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Journal of Bionic engineering 2025年第2期22卷 884-900页

作者： Digambar Puri Pramod Kachare Smith Khare Ibrahim Al-Shourbaji Abdoh Jabbari Abdalla Alameen Department of Electronics and Telecommunication Ramrao Adik Institute of TechnologyD.Y.Patil CampusNavi-MumbaiMaharashtra400706India Maersk Mc-Kinney Moller Institute Faculty of EngineeringUniversity of Southern DenmarkOdenseDenmark Department of Electrical and Electronics Engineering Jazan UniversityJazan45142Saudi Arabia Department of Computer Science University of HertfordshireHatfieldUK Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam Bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia

The global incidence of Alzheimer's Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of AD using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition *** Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible AD EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG ***,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.

关键词： Alzheimer's Disease Brain disorder Electroencephalogram Reptile Search Algorithm Snake Optimizer Optimization

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Design of Ultra-Wideband Inverted U-Shaped Slot Antenna with Reflector for GPR Applications 19

Design of Ultra-Wideband Inverted U-Shaped Slot Antenna with...

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19th International Conference on Electrical engineering/Electronics, computer, telecommunications and Information Technology, ECTI-CON 2022

作者： Sutham, Thanakorn Thaiwirot, Wanwisa Akkaraekthalin, Prayoot Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani Thailand Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

This paper presents the ultra-wideband inverted U-shaped slot antenna with reflector for ground penetrating radar (GPR) application. The inverted U-shaped slot of the original slot antenna is modified by adding the wide rectangular slot. In addition, the small rectangular slots based on defected ground structure (DGS) are etched on the bottom edge of the radiating slot to improve impedance matching, especially at lower frequencies. Moreover, two corners of ground plane are bended in order to improve the performance of the antenna at frequency below 900 MHz. In addition, the reflector is added behind the proposed antenna in order to improve the directivity. The proposed antenna has a dimension of 260 × 300 × 230 mm3. It is found from measured results that the proposed antenna achieves a very wide impedance bandwidth of 166% (0.33 GHz to 3.59 GHz), covering the whole frequency range of GPR. The proposed antenna has maximum gain at 0 degree around 8.86 dBi at 1.5 GHz. © 2022 IEEE.

关键词： Slot antennas

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Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure Detection

Using Explainable AI for EEG-based Reduced Montage Neonatal ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Udayantha, Dinuka Sandun Weerasinghe, Kavindu Wickramasinghe, Nima Abeyratne, Akila Wickremasinghe, Kithmin Wanigasinghe, Jithangi De Silva, Anjula Edussooriya, Chamira U. S. University of Moratuwa Department of Electronic and Telecommunication Engineering Sri Lanka University of British Columbia Department of Electrical and Computer Engineering Canada University of Colombo Faculty of Medicine Department of Paediatrics Sri Lanka

ISBN: (纸本)9781665410205

The neonatal period is the most vulnerable time for the development of seizures. Seizures in the immature brain lead to detrimental consequences, therefore require early diagnosis. The gold -standard for neonatal seizure detection currently relies on continuous video-EEG monitoring;which involves recording multi-channel electroencephalogram (EEG) alongside real-time video monitoring within a neonatal intensive care unit (NICU). However, video-EEG monitoring technology requires clinical expertise and is often limited to technologically advanced and resourceful settings. Cost-effective new techniques could help the medical fraternity make an accurate diagnosis and advocate treatment without delay. In this work, a novel explainable deep learning model to automate the neonatal seizure detection process with a reduced EEG montage is proposed, which employs convolutional nets, graph attention layers, and fully connected layers. Beyond its ability to detect seizures in real time with a reduced montage, this model offers the unique advantage of real-time interpretability. By evaluating the performance on the Zenodo dataset with 10-fold cross-validation, the presented model achieves an absolute improvement of 8.31% and 42.86% in area under curve (AUC) and recall, respectively. © 2024 IEEE.

关键词： Electroencephalography

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RF-Enhanced Pavement Markings for Mobile Robot Lane Detection 19

RF-Enhanced Pavement Markings for Mobile Robot Lane Detectio...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Suo, Dajiang Li, Heyi Bhattacharyya, Rahul Melià-Seguí, Joan Sarma, Sanjay E. Massachusetts Institute of Technology The Department of Mechanical Engineering CambridgeMA United States Faculty of Computer Science Multimedia and Telecommunication Barcelona Spain

ISBN: (纸本)9798350320695

The ability to detect and keep in lanes is crucial for the safe operation of autonomous mobile robots in construction sites and their coordination with humans in autonomous ports or logistic centers. While computer vision-based lane detection algorithms perform well under normal conditions, their performance may degrade under low visibility conditions and in adverse weather. Since robots are not constrained by human perception limits, this paper proposes a radio frequency (RF) pavement marking system that builds on Radio Frequency Identification (RFID), a short-range communication technology, to provide lane-detection assistance. We present not only the hardware designs for the RFID systems on both vehicles and roads but also a filtering algorithm to mitigate the noise in the backscattered RF signals for lane detection. Experimental results show that the information on lane keeping provided by the RF pavement markings aligns with the visual channel when mobile robots move at a speed of less than 40 miles per hour. © 2023 IEEE.

关键词： Mobile robots

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IFA in Named Data Networking (NDN) : Case Study on INHERENT Topology 9

IFA in Named Data Networking (NDN) : Case Study on INHERENT ...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Tiarama, Ribka Gumelar, Agil Fuad Rayhan, Muhammad Nur Pirusita, Nauroha Shofiyyah Jupriyadi Ahdan, Syaiful Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

Named Data Networking (NDN) is part of an NSF research project that started in 2010 and was created to be implemented as a future Internet architecture. NDN as a network service has evolved from an Internet host-based packet delivery model to an Internet content-based packet delivery model. Attackers design various attacks for NDN networks by exploiting the interaction between interest packets and data packets. IFA is one type of DDoS attack on NDN networks. IFA attackers would overload intermediate routing nodes by requesting a large number of fake Interest packets. In this paper, the interest-flooding attack model is tested by varying the attack time and location of the attacker. The results show that IFA attacks are more effective when the consumer is closer to the producer due to the increased latency resulting from the attack. © 2023 IEEE.

关键词： Floods

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A Multiband Tri-branch Monopole Antenna Base on Step Impedance Technique for WLAN, WiMAX, 5G Technology, and IoT Application 20

A Multiband Tri-branch Monopole Antenna Base on Step Impedan...

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20th International Conference on Electrical engineering/Electronics, computer, telecommunications and Information Technology, ECTI-CON 2023

作者： Tangjitjetsada, Maleeya Suangun, Thanakarn Chanwattanapong, Wanchalerm Mahatthanajatuphat, Chatree Phimthai, Kanyanat Akkaraekthalin, Prayoot Faculty of Engineering and Architecture Department of Electronics and Telecommunication Engineering Nonthaburi Thailand University of Phayao Faculty of Engineering Department of Electrical Engineering Phayao Thailand Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunications Pathum Thani Thailand Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350310467

A multiband tri-branch monopole antenna is presented. The antenna structure is constructed of radiating patches with three branches. It is fed by a microstrip transmission line. Essentially, the antenna is a simple structure created by a step impedance technique. In addition, the antenna can be separately regulated for each operational frequency band of 2.44 GHz, 3.36 GHz, and 5.52 GHz by the height of a central patch, a right patch with a thin strip line, and a small patch on the left, respectively. The antenna is manufactured on a size of 50x35 mm2 FR4 substrate with a thickness of 0.8 mm and a permittivity of 4.3. The suggested antenna is appropriate for network board internet of things (NB-IoT) technology working at 2.4 GHz/5.2 GHz/5.8 GHz (WLAN), 3.5 GHz (WiMAX), and 2.3 GHz/2.6 GHz/3.5 GHz (5G technology). The antenna's radiation pattern is omnidirectional, and its average gain is approximately 2dBi. © 2023 IEEE.

关键词： Internet of things

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Wav2Vn: Scalable Unsupervised Pre-training and Adaptation for Multi-Accent Vietnamese ASR 9

Wav2Vn: Scalable Unsupervised Pre-training and Adaptation fo...

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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Kim, Viet Nguyen Ngoc, Huyen Nguyen Thi Minh, Hieu Dam Khoi, Nguyen Do Hoang Do, Tien-Dung Dung, Cao Truong Posts and Telecommunication Institute of Technology Department of Information Technology Ha Noi Viet Nam Swinburne University Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Faculty of Information Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Electronic Engineering Ha Noi Viet Nam

ISBN: (纸本)9798331505042

Automatic speech recognition (ASR) systems have seen significant advancements in recent years, driven by deep learning and large-scale pre-training methods. However, achieving high accuracy across multiple accents remains a challenging task, especially for languages with rich phonetic diversity like Vietnamese. The Vietnamese language encompasses a wide range of regional accents, each with unique phonetic characteristics that can hinder the performance of conventional ASR. To address this issue, we present a Vietnamese language processing model called Wav2Vn as a novel approach to scalable unsupervised pretraining and adaptation for ASR targeting multiple Vietnamese accents. Our method leverages the power of the Mixture of Experts (MoE) to effectively handle the diverse phonetic variations across different Vietnamese accents. Our results demonstrate a significant performance improvement in terms of word error rate (WER) and robustness to accent variability compared to the current state-of-the-art PhoWhisper. © 2024 IEEE.

关键词： Deep learning

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Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles 10

Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Purnama, Iwan Pane, Rahmadani Sooai, Adri Gabriel Edi, Firman Chamid, Ahmad Abdul Adi, Puput Dani Prasetyo Universitas Labuhanbatu Faculty of Science and Technology Department of Information Technology Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Batam City Indonesia Muria Kudus University Department of Informatics Engineering Kudus Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This article presents a simulation study on the use of LoRaWAN technology in autonomous vehicles. The research focuses on developing an Auto-Handover Gateway for Autonomous Vehicles, utilizing LoRaWAN parameters such as bit rates, spreading factor (SF), packet loss, and received signal strength. These parameters are tested in simulations of autonomous vehicle movements, which include both regular and random motion patterns. The aim is to assess the performance of LoRaWAN in different scenarios and its impact on communication in autonomous vehicle systems. The simulation results show several key trends. As the vehicle moves further, the spreading factor (SF) increases, and bit rates tend to decrease, indicating a drop in communication performance over longer distances. The research also identifies that the gateway with the closest proximity to the vehicle is selected, both in random and non-random movement scenarios. Additionally, packet loss increases as the distance between the vehicle and the gateway grows, highlighting the challenges of maintaining reliable communication over long distances. These findings provide insights into optimizing LoRaWAN parameters for real-time communication in autonomous vehicles, contributing to the development of intelligent vehicle networks. © 2024 IEEE.

关键词： Packet loss

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