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Overview of Machine Learning for Bioengineering EEG signal processing 2

Overview of Machine Learning for Bioengineering EEG Signal P...

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2nd international conference on intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Dhande, Siddhi Kamble, Aahash Gundewar, Swapnil Babu, Naathi Naresh Kumar, Praveen Datta Meghe Inst Higher Educ & Res Dept Comp Sci & Med Engn Wardha India Datta Meghe Inst Higher Educ & Res DU Dept Artificial Intelligence & Data Sci Wardha Maharashtra India Datta Meghe Inst Higher Educ & Res Dept Artificial Intelligence & Machine Learning Wardha India

ISBN: (纸本)9798331540661;9798331540678

Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG recordings, which necessitate more advanced analysis techniques. Machine learning has emerged as an asset in EEG signal processing, enabling novel approaches to comprehension and utilization of brain activity. This article provides an in-depth analysis of the intersection of machine learning and EEG signal processing in bioengineering applications. This text presents a comprehensive overview of EEG data analysis techniques, focusing on the key steps of acquisition, preprocessing, and feature extraction. It highlights the challenges and strategies used to extract valuable information from raw EEG recordings. Additionally, it surveys various machine learning algorithms, including classic and modern deep learning methods, demonstrating their effectiveness in analyzing EEG signals and opening new frontiers in the field of bioengineering. This paper explores the growing connection between machine learning and EEG signal processing, examining how they work together to enhance healthcare, neurotechnology, and our knowledge of the brain. By studying existing research and cutting-edge developments, this study intends to focus on this synergistic relationship and its importance in these fields.

关键词： EEG signal processing Machine learning algorithms Bioengineering applications Neurotechnology

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A Simulation-Driven Exploration of Coil Design and WPT System Integration Strategies for High Quality Factor and Reduced Power Reflections

A Simulation-Driven Exploration of Coil Design and WPT Syste...

引用

2024 ieee international conference on Modeling, Simulation and intelligent Computing, MoSICom 2024

作者： Hassan, Mohamed Hesham Zaghloul, Yasmine Abdullah German International University Robotics & Automation Engineering Cairo Egypt German International University Electrical Engineering Cairo Egypt

ISBN: (纸本)9798331533311

Wireless power transfer (WPT) systems have been widely used in autonomous mobile robots. However, the performance of the WPT system is limited due to the low efficiency of the transfer. This research aims to explore the impact of coil design on performance, demonstrating promising improvements in power transfer efficiency and range. Specifically, this research uses high-frequency electrical stimulation to reduce signal reflections in the WPT system. The proposed system, simulated with its three variations and results, shows a significant reduction in signal reflection over a long range compared to previous literature. Furthermore, a derived equation to optimize coil turns improves the quality factor. The results of the proposed system show that using a double-layer coil with intermediate coils is a reliable and effective way to transfer power wirelessly over a long range. © 2024 ieee.

关键词： Inductive power transmission

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2024 ieee international conference on intelligent signal processing and Effective Communication Technologies, INSPECT 2024

2024 IEEE International Conference on Intelligent Signal Pro...

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2024 ieee international conference on intelligent signal processing and Effective Communication Technologies, INSPECT 2024

ISBN: (纸本)9798350379525

The proceedings contain 157 papers. The topics discussed include: a reversible data hiding scheme for ECG signals using CNN-PEE;design and development of in-memory-compute SRAM cell using 45nm technology;stand alone or non-standalone 5G tactical edge network architecture for military and use case scenarios;secrecy capacity optimization in RF/FSO systems: impact of mixed Rayleigh and log-normal fading with atmospheric turbulence;real-time eye-tracking mouse control system using OpenCV and facial landmark detection;a time-efficient path navigating landmine detection robot;optimizing GPS positioning: a deep learning approach to improve accuracy;a novel structure of on-chip multilayered half-turn inductor for RF applications;and low profile wideband 3 element parasitic hexagonal patch antenna.

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Channel Estimation-Based Rate Maximization for IRS-Aided UAV Wireless Communication systems 15

Channel Estimation-Based Rate Maximization for IRS-Aided UAV...

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15th international conference on signal processing and Communications (SPCOM)

作者： Choudhary, Neha Sankirthana, K. N. Joshi, Sandeep Chaubey, V. K. Birla Inst Technol & Sci Pilani Dept Elect & Elect Engn Pilani 333031 Rajasthan India

ISBN: (纸本)9798350350463;9798350350456

In this paper, we provide a channel estimation-based optimization technique for maximizing the weighted sum rate (WSR) of an intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV). The model considers an IRS mounted on a UAV to predict the channel between the multi-input base station (BS) for the multi-user system using mobile IRS-aided wireless channels. We optimize the UAV position and then employ a compressive sensing-based technique that exploits the sparsity of the wireless channel, specifically, we utilize the Bayesian iterative group approximate message passing (BIG-AMP) algorithm. The strategy involves mobile IRS for efficient message passing and estimation of the channel state information between the BS and users. Using these channel estimates, we maximize the WSR for all the users by optimizing the precoding matrix at the BS and also the phase matrix at the mobile IRS.

关键词： Bayesian iterative group approximate message passing (BIG-AMP) channel state information intelligent reflecting surface (IRS) unmanned aerial vehicle (UAV) weighted sum rate (WSR)

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RECONFIGURABLE intelligent SURFACE ASSISTED INTEGRATED SENSING, COMMUNICATION AND COMPUTATION systems 49

RECONFIGURABLE INTELLIGENT SURFACE ASSISTED INTEGRATED SENSI...

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49th ieee international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wan, Jiahua Ken, Hong Pan, Cunhua Yu, Zhiyuan Zhang, Zhenkun Zhang, Yang Southeast Univ Natl Mobile Commun Res Lab Nanjing Peoples R China

ISBN: (纸本)9798350374520;9798350374513

This paper investigates a reconfigurable intelligent surface (RIS)assisted integrated sensing, communication, and computation (ISCC) system. In this paradigm, the integrated sensing and communication (ISAC)-enabled user equipments (UEs) simultaneously detect the target and offload the computational tasks of radar sensing to the edge computing server (ECS) through their communication functionality. To enhance the efficiency of computation offloading, we deploy an RIS to mitigate the high attenuation between UEs and the ECS. A latency minimization problem is investigated with constraints on UE's transmit power, radar signal-to-interference-plus-noise ratio (SINR), RIS phase shift, and computation capability. We propose an algorithm based on the block coordinate descent (BCD) method to decouple the original problem into two subproblems, and then the computational and beamforming variables are optimized alternately utilizing efficient iterative algorithms. Simulation results demonstrate the effectiveness of our proposed algorithm.

关键词： Integrated sensing communication and computation reconfigurable intelligent surface integrated sensing and communication mobile edge computing

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Machine Learning for signal processing in 5G and Beyond: Review 16

Machine Learning for Signal Processing in 5G and Beyond: Rev...

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16th ieee international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Kurt, Mustafa Efe Saoud, Bilal Shayea, Ibraheem Leila, Rzayeva Faculty of Electrical and Electronics Engineering Dept. Electronics & Communications Engineering Istanbul34469 Turkey University of Bouira Faculty of Sciences and Applied Sciences Lisea Laboratory Dept. Electrical Engineering Bouira10000 Algeria Astana It University Dept. Intelligent Systems and Cybersecurity Astana Kazakhstan

ISBN: (纸本)9798331505264

To meet the demand of modern application, modern machine learning and adaptive signal processing techniques are needed. With the help of revolutionary advancements in mobile communication such as 5G and 6G, integration of machine learning in signal processing has emerged as a popular research field nowadays. In this survey paper, researches on machine learning for signal processing in 5G and 6G are considered. Many papers are examined carefully and the revolutionary ideas and applications will be mentioned in advance. Included subjects are channel estimation, beamforming, resource allocation, interference management and more. By using the adaptability and capabilities of machine learning, how these techniques can enhance the performance, efficiency, and reliability of nextgeneration wireless networks will be discussed in this paper. © 2024 ieee.

关键词： 5G mobile communication systems

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Observer-based global adaptive consensus tracking control for nonlinear multi-agent systems 7

Observer-based global adaptive consensus tracking control fo...

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7th international conference on intelligent robotics and Control Engineering, IRCE 2024

作者： Chen, Xiao Li, Jinsha Li, Junmin Xidian University Xi'an China

ISBN: (纸本)9798350352399

This article delves into the global leader-following consensus issue for second-order nonlinear multi-agent systems(NMASs), which are characterized by the presence of unknown nonlinear functions, unmeasured states, unknown disturbances and unknown control directions. To address these nonideal factors, a method integrating fuzzy logic systems and observers are deployed to manage the uncertainties arising from nonlinear functions and unmeasurable states. A signal substitution approach is adopted, utilizing the leader's information to determine the approximation domain, while Nussbaum gain functions are incorporated to address the unknown control direction. Ultimately, a fuzzy adaptive control protocol is tailored for each follower agent, ensuring that all signals in the closed-loop system achieve global uniform ultimate boundedness(GUUB), with the consensus error converging to a small neighborhood of the origin. Finally, the effectiveness of this approach is validated through simulation examples. © 2024 ieee.

关键词： Multi agent systems

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Early Fault Detection in Rolling Element Bearings Using signal processing Techniques 21

Early Fault Detection in Rolling Element Bearings Using Sign...

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21st ieee international conference on Smart Communities: Improving Quality of Life using AI, robotics and IoT, HONET 2024

作者： Saeed, Ali Akram, M. Usman Khan, Muazzam A. Afsar, M. Belal Quaid-i-Azam University ICESCO Big Data Analytics and Edge Computing Department of Computer Science Islamabad Pakistan National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Otto-von-Guericke University Faculty of Electrical Engineering and Information Technology Maddeburg Germany

ISBN: (纸本)9798350378078

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-To-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method. © 2024 ieee.

关键词： signal modulation

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RIME-SVM for Electromagnetic Radiation signal Identification in Coal Mining Environment 6

RIME-SVM for Electromagnetic Radiation Signal Identification...

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6th ieee international conference on Power, intelligent Computing and systems, ICPICS 2024

作者： Zhu, Pengxue Huang, Shiqi Liu, Le Zhang, Zheng China West Normal University Nanchong China

ISBN: (纸本)9798350374315

In this study, an innovative signal classification and recognition system is proposed for the early warning needs of disasters such as impact ground pressure in coal mining. The RIME-SVM model is introduced, which combines the Rime Optimization Algorithm and Support Vector Machine to optimize the classifier parameter configuration and significantly improve the classification performance. Experiments have demonstrated that the model has excellent signal classification ability in industrial environments, which is especially suitable for the identification of electromagnetic radiation signals in coal mine environments. Compared with the traditional method, RIME-SVM significantly improves the classification accuracy, demonstrates the efficiency and accuracy of processing complex industrial signal data, and brings important progress to the field of coal mine safety monitoring technology. © 2024 ieee.

关键词： Coal mines

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Research on the Human-Machine Collaborative Optimisation Algorithm for intelligent Traffic signal Control systems

Research on the Human-Machine Collaborative Optimisation Alg...

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2024 international conference on Computing, robotics and System Sciences, ICRSS 2024

作者： Li, Zhuoxun Chen, Junju Wu, Kefan Wu, Zhanyi Liu, Qiqi School of Swinburne Shandong University of Science and Technology Shandong Jinan China

ISBN: (纸本)9798331511296

With the worsening of urban traffic congestion, improving traffic flow and resource utilisation efficiency has become an important challenge in the field of intelligent transportation. Traditional traffic signal control systems lack dynamic adaptability and are difficult to cope with complex and ever-changing traffic environments. To address this problem, this paper proposes a bionic ant path planning algorithm based on human-machine collaborative optimisation. The system obtains real-time road traffic data, including information such as signal status, traffic flow and obstacle distribution, through a data acquisition module, and uses an improved ant colony algorithm to achieve global path search and dynamic optimisation. At the same time, a human-machine collaborative interaction mechanism is designed to allow users to adjust path weight parameters in real time according to actual needs, enhancing the flexibility and adaptability of the system. Through simulation experiments, this paper verifies the performance of the proposed algorithm in different scenarios and compares it with traditional path planning algorithms. The results show that the ant colony algorithm based on human-machine collaborative optimisation has significant advantages in terms of convergence speed, path optimisation effect and dynamic environmental adaptability. This study provides an efficient, real-time and flexible path optimisation solution for intelligent traffic signal control systems, which can effectively improve traffic scheduling capabilities in complex dynamic environments. © 2024 ieee.

关键词： Traffic congestion

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