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A Robust Pilot Decontamination Scheme in Massive MIMO Systems: Integrating Rateless Orthogonal STBC, Weighted Graph Coloring, and Channel Estimation

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IEEE Access 2025年 13卷 94453-94463页

作者： Habib M. Hussien Zelalem A. Kelem Department of Electrical and Computer Engineering Artificial Intelligence and Robotics Center of Excellence Addis Ababa Science and Technology University Addis Ababa Ethiopia

Even though Multiple Input Multiple Output (MIMO) systems are one of the greatest innovations in wireless cellular network technology, they still face many challenges as we progress to 6G, hindering their ability to achieve the best throughput. In cellular networks, channel estimations are made using pilot sequences. Deploying these orthogonal time-frequency limited resources leads to pilot contamination, causing incorrect channel estimation due to mixed signals from neighboring cells and vice versa. Previous research has proven that this significantly decreases system performance. To reduce the impact and improve the output of Multiuser MIMO systems, this paper proposes a hybrid model combining Rateless Orthogonal Space-Time Block Codes (ROSTBC), Weighted Graph Coloring Pilot Allocation (WGCPA), and Maximum Likelihood Estimation (MLE). While ROSTBC has the ability to adapt to channel conditions, allowing the system to perform well despite pilot contamination, WGCPA and MLE address the problems of pilot contamination and the accuracy of channel estimation, respectively. The results showed that this hybrid scheme has proven to perform better compared to previously proposed schemes, providing a valuable contribution to reliable Massive MIMO wireless communication.

关键词： Channel estimation Contamination Interference Maximum likelihood estimation Resource management Spectral efficiency Accuracy Signal to noise ratio Massive MIMO Encoding

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Traffic Signs Detection System Using YOLO (You Only Look Once) That Provides Notification

Traffic Signs Detection System Using YOLO (You Only Look Onc...

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Yosua Setiawan Muhammad Nurul Puji Winda Astuti Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

Road accidents have become a problem for many road users in many countries. It may affect human daily activity and tasks, potentially influencing productivity and safety. Therefore, reducing road accidents as swiftly and effectively as possible is critical. For this reason, a Traffic signs detection system has been developed in this work to improve road safety. The detection system can detect traffic signs on the road such as traffic lights, speed limits, and no U-turn signs, and then it will provide users with the corresponding text on an LCD and vibration feedback using a motor. Throughout this work, the You Only Look Once (YOLO) algorithm is proposed for detecting traffic signs on the road. Moreover, a camera is used to extract live videos which serve as an input for the Raspberry Pi 4 model B containing custom YOLOv5 and YOLOv8 models. The system then sends the output to an LCD, a vibration motor, and a radio frequency (RF) module as notifications for users. In the end, the performance of the proposed method has been collected and it is found to produce good accuracy within an inconsiderable amount of 1.677 images and 2.263 annotations. Therefore, the accuracy of 0.69 for YOLOv5 and 0.7 for YOLOv8 were obtained from the training and validation process. Furthermore, the system accurately detects the traffic signs on the road.

关键词： YOLO Vibrations Training Road accidents Accuracy Motors Liquid crystal displays Road safety Safety Videos

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Hybrid Encryption Based on Fernet and Rivest Shamir Adleman (RSA)

Hybrid Encryption Based on Fernet and Rivest Shamir Adleman ...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Hisbullah, Muhammad Dafi Sari, Christy Atika Rachmawanto, Eko Hari Handoko, Lekso Budi Umam, Chaerul Isinkaye, Folasade University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia Ekiti State University Department of Computer Science Ekiti Nigeria

ISBN: (纸本)9798350339215

RSA is an asymmetric encryption algorithm that uses two different keys, a public key to encrypt the plain text and a private key to decrypt the cipher text. Fernet is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. This study uses Fernet and RSA which is the combination of symmetric and asymmetric encryption called hybrid encryption. In addition, the cipher text will be hidden inside an image using Stepic. Hybrid Encryption uses asymmetric encryption to encrypt the symmetric encryption secret key, it will secure the symmetric encryption. The result of this study is the lowest error that we got as the MSE is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche (AVA) with 79.00% and 42.34% in order. Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 46.48% for UACI and 99.86% for the NPCR. © 2023 IEEE.

关键词： Signal to noise ratio

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Towards Collaborative Simultaneous Localization and Mapping: A Survey of the Current Research Landscape

Field Robotics

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Field robotics 2025年 2卷 971-1000页

作者： Pierre-Yves Lajoie Benjamin Ramtoula Fang Wu Giovanni Beltrame Department of Computer and Software Engineering Polytechnique Montréal Canada Oxford Robotics Institute Department of Engineering Science University of Oxford United Kingdom Desay SV Automotive Singapore

Motivated by the tremendous progress, we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area's current trends and promising research avenues.

关键词： Robots Simultaneous localization and mapping Robot kinematics Robot sensing systems Collaboration Surveys Multi-robot systems Sensors Market research Trajectory

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Overview of Energy Harvesting and Sustainability in Smart Cities 3

Overview of Energy Harvesting and Sustainability in Smart Ci...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Altrjman, Chadi Al-Turjman, Fadi Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Sustainability is a key factor in the context of smart cities. In smart cities, promoting sustainable energy is one strategy for achieving sustainability and lowering greenhouse gas emissions. Different energy sources are needed to meet the need for affordable and dependable electrical energy, where the environmental impact typically outweighs the cost of generation. In order to increase sustainability in the electrical industry, smart cities are examined in this article. The methodological strategy for this involved conducting a systematic literature review utilizing the PRISMA procedure. 154 journal articles were examined in-depth for this review. Energy efficiency, renewable energy, and energy and urban planning were the themes that the results were organized into. The results of the study showed that: (a) there is a global academic publication landscape for research on smart cities and energy sustainability;(b) publications are unbalanced when comparing the results of studies on the energy use intensity of different continents to those on the energy sustain ability of smart cities;and (c) there is a strong focus on technology-related issues related to energy sustainability, efficiency, and renewables topics in the literature, but much less attention is given to these issues. Urban and energy authorities are informed by the insights produced, and scholars are given ideas for future research directions. © 2023 IEEE.

关键词： Smart city

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Heterogeneous Wireless Access Networks for IoT and E-Health-A Survey 3

Heterogeneous Wireless Access Networks for IoT and E-Health-...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

ISBN: (纸本)9798350330724

In a heterogeneous wireless environment, there are many different radio access technologies (RATs), such as Wi-Fi and 2G, 3G, 4G, and 5G, which each have a different degree of coverage and processing power to meet various service requirements. A mobile user may have access to various access networks in such a situation. On the PC of each user, numerous programs with varied Quality of Service (QoS) requirements can operate simultaneously. It would be advantageous for a multi-interface terminal to use two or more interfaces simultaneously in order to improve performance. However, using multiple networks at once could use up more energy than just using one interface. Therefore, energy use must to be taken into account when analyzing the Flow/Interface Relationship (FIA). This paper presents a novel method for choosing the ideal FIA that achieves the best trade-off between all the characteristics taken into account, dubbed Smart Tabu Search (STS). STS considers user preferences, network circumstances, network expenses, application QoS specifications, and battery life of mobile devices. We use simulations and testbed experiments to validate our concept. Professionals are now able to diagnose and monitor patients remotely because to the growing use of healthcare monitoring devices. One of the most frequent occurrences that affects the reliability of information transmission in any network is congestion, which is defined as the unchecked increase in traffic relative to network capacity. With the use of a mesh network, wireless sensors, and some of the most significant models for vital signs, the article aims to provide a realistic simulation environment for a healthcare system. The simulator environment is a helpful tool for assessing the dependability and effectiveness of the healthcare system in a realistic setting. However, the system is not appropriate for real-time applications due to the sluggish network adaption caused by the end-to-end based traffic regulation deci

关键词： Electrocardiograms

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Degradable Tracking System based on Hardware Multi-Model Estimators

Degradable Tracking System based on Hardware Multi-Model Est...

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2022 Resilience Week, RWS 2022

作者： Kiriakidis, Kiriakos Croteau, Brien Severson, Tracie Rodriguez-Seda, Erick Robucci, Ryan Islam, Riadul Rahman, Saad United States Naval Academy Department of Weapons Robotics and Control Engineering Department of Cyber Science United States University of Maryland Department of Computer Science and Electrical Engineering United States

ISBN: (数字)9781665488198

ISBN: (纸本)9781665488198

Sensing systems onboard unmanned vehicles operate in an environment of constrained computational resources. A cyber-Attack may primarily aim to degrade these computing devices and, ultimately, incapacitate the sensing system itself. To prepare a prototype tracking system for degradation, this paper proposes distributed hardware implementation of a Multiple Model estimator on two FPGA units and, after an attack, adaptation of the estimator by leveraging Dynamic Partial Reconfiguration of the single surviving FPGA. The method ensures that the most likely models of the estimator are loaded on to the fabric of the surviving FPGA with minimal interruption. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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EDM10: A Polyphonic Stereo Dataset with Identical BGM for Musical Instrument Identification 27th

EDM10: A Polyphonic Stereo Dataset with Identical BGM for ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mukherjee, Himadri Marciano, Matteo Dhar, Ankita Roy, Kaushik TISA Lab Department of Computer Science West Bengal State University Barasat India Gazelien Records Lab Department of Arts and Humanities Music Program New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science and Engineering Sister Nivedita University New Town India

ISBN: (纸本)9783031784972

Music Signal Processing has significantly evolved in the past decades. One of the major areas of interest in this field has been automatic music transcription. It is a challenging task by itself that aggravates even more when the input audio is polyphonic (multiple instruments and timbres are played simultaneously). This requires the identification of musical instruments in the piece at the outset. The field of music signal processing that deals with this aspect is known as automatic music instrument identification. This field also has the potential of categorizing and recommending music based on instruments. Disparate datasets have been proposed to date for this task but none of them have interclass background similarity to the best of our knowledge. Further, the lead melody being played also varies from class to class in most cases. These aspects can introduce a possible bias for the machine learning models that can get manipulated unfairly by the additional variances in the classes other than the lead instrument itself. This sets the stage for a dataset where the classes are different only in terms of the tone of the lead instrument alone. In this paper, we introduce the first musical instrument dataset of 10 musical instruments with Electronic Dance Music melodies (EDM10) having identical background music (BGM) across instruments. This dataset is the first of its kind wherein synthetic tones have been used that have taken over the Globe. We introduce out-of-mood testing using exotic scales for musical instrument identification. The dataset is composed of 35800 polyphonic clips of 3 s each and a baseline result of 89.73% was obtained using a deep learning-based approach. The dataset is freely available for research purposes. https://***/yV5e36TK1jHMKjpC6. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic musical instruments

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Preference detection of the humanoid robot face based on EEG and eye movement

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Neural Computing and Applications 2024年第19期36卷 11603-11621页

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, Aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI and Robotics Ministry of Education Shanghai Engineering Research Center of AI and Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

The face of a humanoid robot can affect the user experience, and the detection of face preference is particularly important. Preference detection belongs to a branch of emotion recognition that has received much attention from researchers. Most of the previous preference detection studies have been conducted based on a single modality. In this paper, we detect face preferences of humanoid robots based on electroencephalogram (EEG) signals and eye movement signals for single modality, canonical correlation analysis fusion modality, and bimodal deep autoencoder (BDAE) fusion modality, respectively. We validated the theory of frontal asymmetry by analyzing the preference patterns of EEG and found that participants had higher alpha wave energy for preference faces. In addition, hidden preferences extracted by EEG signals were better classified than preferences from participants' subjective feedback, and also, the classification performance of eye movement data was improved. Finally, experimental results showed that BDAE multimodal fusion using frontal alpha and beta power spectral densities and eye movement information as features performed best, with the highest average accuracy of 83.13% for the SVM and 71.09% for the KNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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Maize EfficientNet Fusion: Advancing Maize Disease Detection with MF-NET 25

Maize EfficientNet Fusion: Advancing Maize Disease Detection...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Khalid, Fatima Hanif, Muhammad Ul Ain, Qurat Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Faculty of Computer Science and Engineering KPK Topi Pakistan Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Aerial Robotics and Vision Lab KPK Topi Pakistan Shifa Tameer-E-Millat University Department of Computing Islamabad Pakistan

ISBN: (纸本)9798350379037

Maize is a vital global crop, essential for food security but highly susceptible to diseases that threaten yield and quality. Traditional methods for detecting these diseases are computationally intensive and rely on high-quality images, limiting their practical use in diverse field conditions. This research addresses these challenges by proposing MF-Net, a novel model that leverages two EfficientNet-b0 architectures with ReLU and Swish activation functions. Our approach involves partially training Model R with frozen early layers using ReLU, and fully training Model S with Swish activation for enhanced optimization and robustness. We employ model concatenation and feature fusion techniques to create a lightweight yet powerful model, complemented by additional layers for improved feature extraction and regularization. Experimental results on the Corn Disease and Severity (CD&S) dataset are compelling: MF-Net achieves a remarkable 95.7% accuracy and 96% precision in disease classification, and 88.3% accuracy and 88.46% precision in severity level detection. These results highlight the model's effectiveness under challenging conditions such as cluttered backgrounds and variable lighting, significantly reducing computation time without compromising accuracy. Hence, MF-Net presents an innovative and efficient solution for maize disease detection and severity assessment, offering significant advancements for agricultural practices and crop management. © 2024 IEEE.

关键词： CD&S Dataset EffiecientNet-b0 Fused Truncated EfficientNet Maize Disease Maize Severity Level

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