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Deep Learning-based Robust Automatic Modulation Classification Using Higher Order Cumulant Features 15

Deep Learning-Based Robust Automatic Modulation Classificati...

引用

15th international conference on Information technology and Electrical Engineering, ICITEE 2023

作者： Suetrong, Nopparuj Taparugssanagorn, Attaphongse Promsuk, Natthanan Chiang Mai University Faculty of Engineering Department of Computer Engineering Chiang Mai Thailand School of Engineering and Technology Asian Institute of Technology Ict Department Pathum Thani Thailand

ISBN: (纸本)9798350304466

The internet of Things (IoT) represents one of the pivotal technologies in our daily lives. Within these systems, wireless communication plays an indispensable role in connecting IoT devices. Consequently, signal modulation emerges as a technique within wireless communication systems, enabling the transmission of baseband signals at higher frequencies. Given the array of modulation types, modulation classification comes into play to differentiate the modulation type of the received signal. Due to the presence of noise and attenuation, automatic modulation classification (AMC) stands as the primary method for such classification. AMC methods can operate without requiring any prior knowledge regarding the received signal. Multiple research groups have extended the depth of deep learning (DL) models or augmented the neuron count in each layer to enhance performance, albeit at the expense of increased model complexity. To tackle this challenge, we present model that merges the gated recurrent unit (GRU) and long short-term memory (LSTM) architectures. Furthermore, data pre-processing techniques, specifically Min-Max normalization and fourth-order cumulant (FOC), have been employed to enhance classification accuracy. The outcomes underscore that our proposed model surpasses both LSTM and GRU models. Moreover, the classification accuracy of normalized data outperforms that of data without normalization. © 2023 IEEE.

关键词： internet of things

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Visual group target tracking algorithm based on MeanShift-PCA-PF 8

Visual group target tracking algorithm based on MeanShift-PC...

引用

8th international conference on Intelligent Computing and signal Processing, ICSP 2023

作者： Li, Jianing Tian, Yan Guo, Min Zuo, Kaige Wang, Xin Xi'an Institute of Optics and Precision Mechanics University of Chinese Academy of Sciences Key Laboratory of Space Precision Measurement Technology Xi'an China Xi'an Institute of Optics and Precision Mechanics Key Laboratory of Space Precision Measurement Technology Xi'an China Xi'an Institute of Optics and Precision Mechanics Shaanxi Normal University Key Laboratory of Space Precision Measurement Technology Xi'an China

ISBN: (纸本)9798350302455

In recent years, target tracking has been widely studied and applied in intelligent video surveillance, human-machine interaction, radar early warning and tracking, and other fields. With the emergence of the new combat concept of unmanned aerial vehicle (UAV) swarm operations, and the maturity of large-area, high-resolution visible light COMS imaging device technology, the imaging detection and visualization of UAV swarm targets in large observation fields will greatly improve the efficiency of command systems. Therefore, the research on the group target detection, tracking, and recognition based on video image is very urgent and crucial. Group targets are collections of densely packed multiple objects with certain group characteristics that meet the given distance criterion. Due to the large number of targets, limited image resolution, and waste of communication resources, multiple target tracking techniques are challenging to implement effectively. In this paper, based on the characteristics of group targets, we propose a group target tracking framework based on optical measurement equipment. By dividing groups according to the density feature of group targets, we obtain the structural properties and spatial geometric features of the group and implement tracking using a particle filter. Finally, we validate the effectiveness of the group target tracking technique through simulation experiments in group target tracking. © 2023 IEEE.

关键词： image resolution

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An Efficient Multilevel Threshold image Segmentation Approach based on Rafflesia Optimization Algorithm 1

引用

18th international conference on Intelligent Information Hiding and Multimedia signal Processing, IIH-MSP 2022

作者： Zhou, Jin-Liang Chu, Shu-Chuan Weng, Shaowei Zheng, Wei-Min College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Science and Engineering Flinders University Adelaide Australia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

Segmentation is an important step from image processing to analysis. The thresholding technique based on entropy has become one of the most commonly used techniques in this field. Among the many thresholding techniques based on entropy, the minimum cross-entropy threshold (MCET) has been dramatically developed. The key to multilevel MCET technology is quickly finding the optimal threshold point. MCET is a relatively time-consuming technique, so we use optimization techniques to search for optimal multilevel thresholds. Rafflesia optimization algorithm (ROA) is an optimization algorithm inspired by rafflesia growth theory to solve real-world multi-parameter optimization problems. In this paper, Rafflesia optimization algorithm, particle swarm optimization, gray wolf optimization, whale optimization algorithm, and multi-version optimizer are used to segment six images and the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) as evaluation metrics. The experimental results show that the ROA algorithm can achieve higher-quality image segmentation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Improved Tuna Optimization-based Energy Efficient Clustering Protocol for internet of Things 16

Improved Tuna Optimization-Based Energy Efficient Clustering...

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

作者： Rani, S. Jamuna Santhakumar, D. Saveetha University Department of Computer Science and Engineering TamilNadu Chennai602105 India

ISBN: (纸本)9798331505264

In smart applications, internet of Things (IoT) serves as a backbone to facilitate a minimized cost monitoring system due to recent advancements in the technology of sensing and networking. Smart sensors are utilized in diversified number of real-time applications over the recent years to control and monitor physical objects deployed over the implementation environment. This adoption of smart sensors over the real-time applications generates a huge amount of data in the form of text, number, image, and video, which in turn incurs massive amount of energy during the data transmission process. In this context, clustering is ideal for achieving data collection and exchange over the network, and majority of the researchers utilized optimization algorithms for selecting optimized cluster head (CHs) in the network. In this paper, an Improved Tuna Swarm Optimization Algorithm (HSITSOA)-based Clustering Scheme is proposed for CH rotation in the cluster with the objective to reduce algorithmic convergence time which is considered as one of the predominant limitations of the existing clustering approaches. This proposed HSITSOA approach comprises of the processes of cluster construction and CH selection which is established through the balanced trade-off attained during the exploration and exploitation. The simulation conducted using MATLAB R2019a confirmed its efficacy in determining the best CHs on the cluster. It was also identified to improve the packet delivery rate and network lifetime by 23.49% and 27.98%, better than the benchmarked approaches. © 2024 IEEE.

关键词： MATLAB

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Fault line selection for small current grounding system based on ICEEMDAN and improved VGG 8

Fault line selection for small current grounding system base...

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8th IEEE international conference on Energy internet, ICEI 2024

作者： Liu, Yadong Liu, Naiyu Zhang, Hao Cui, Yunhai Shi, Zhe Li, Chenggang Hu, Xiaochen Zhang, Dahai State Grid Jilin Electric Power Co. Ltd. Electric Power Research Institute Changchun China Beijing Jiaotong University School of Electrical Engineering Beijing China State Grid Jilin Electric Power Co. Ltd. Changchun China

ISBN: (纸本)9798331523558

In order to solve the problem of low accuracy of single-phase grounding fault line selection method in traditional low-current grounding system, this paper proposes a single-phase grounding fault line selection method for small-current grounding distribution network based on improved VGG. Firstly, the original signal is transformed into an Intrinsic Mode Function component by using the ICEEMDAN algorithm, and then the entropy value of each IMF component is calculated by using the distributed entropy algorithm and differentiated processing, and the processed IMF component is reconstructed to highlight the frequency domain characteristics and amplitude characteristics of the original data, and the influence of noise is removed to a certain extent. The processed zero-sequence current data is converted into a two-dimensional image by the Gramian Angular Field algorithm, and on the basis of retaining the data features, a single-phase grounding fault dataset is prepared, and the fault characteristics are more prominent, which is convenient for neural network processing. Then, the line selection results were obtained by improving the training iterative optimization parameters. Finally, an experimental platform was built by using Simulink, and the proposed algorithm was compared with two line selection methods. Experimental results show that the proposed algorithm has higher line selection accuracy and better noise immunity. © 2024 IEEE.

关键词： Electric grounding

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6th international conference on Information and Communication technology for Intelligent systems, ICTIS 2021

6th International Conference on Information and Communicatio...

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6th international conference on Information and Communication technology for Intelligent systems, ICTIS 2021

ISBN: (纸本)9789811935701

The proceedings contain 149 papers. The special focus in this conference is on Information and Communication technology for Intelligent systems. The topics include: COVID-19 Triggers a Paradigm Shift in technology for Insurance Industry;internet of Things in Saudi Arabia Universities: State of the Art, Future Opportunities, and Open Challenges;study of Security in Cryptocurrency;study and Analysis of Network Steganography Methods;preface;Review of Different ML Techniques Used for Heart Disease Prediction;survey on Driver Fatigue Detection Using Sensors, Big Data Analytics and Machine Learning Techniques;on Characteristics of κ–Q–Fuzzy Translation and κ–Q–Multiplication in T-Ideal in T-Algebras;blockchain-driven Smart Healthcare System: Challenges, Technologies and Future Research;a Future to the Blockchain technology and Its Concepts;Impacts of DDoS Attacks in Software-Defined Networks;ioT-based Agriculture Trend Prediction based on Weather Data;EEG in Optic Nerves Disorder based on FSVM Using Kernel Membership Function;DDoS Detection in ONOS SDN Controller Using Snort;classification of Keratoconus Using Corneal Topography Pattern with Transfer Learning Approach;intelligent Heuristic Keyword-based Search Methodologies Applied to Cryptographic Cloud Environment;image Enhancement by Various Segmentation Techniques Using Machine Learning;Surakhsha Kavach: ML-based Cross-platform Application for COVID-19 Vulnerability Detection;Malware Family Categorization Using Genetic Algorithm-CNN-based image Classification Technique;master Data Management Maturity Evaluation: A Case Study in Educational Institute;malware Family Classification Using Music Information Retrieval Techniques;text-based Prediction of Heart Disease Doctor Chatbot Using Machine Learning;intelligent Proctoring System;project-based Learning: A Contemporary Approach to Blend Theory and Practical Knowledge of Database Management Course;student’s Employability Concern for Colleges and Universities.

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Edge-based Intrusion Detection using Machine Learning over the IoT Network 11

Edge-Based Intrusion Detection using Machine Learning over t...

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11th international conference on Emerging Trends in Engineering and technology - signal and Information Processing, ICETET - SIP 2023

作者： Wadate, Akash J. Deshpande, S.P. DCPE HVPM PG Department of Computer Science Maharashtra Amravati India

ISBN: (纸本)9798350348422

Edge systems supported & assisted using IoT at a large scale called edge-assisted IoT. For large computational tasks, the need for edge servers has come into existence. Security of IoT is a difficult task to secure IoT information transmission to the edge. The proposed method shows an attack intrusion detection system (IDS) using back propagation (BP) as well as a basis function Radial (BFR) neural network. Attack intrusion detection function found using BP network and for more attack detection BFR has been used. © 2023 IEEE.

关键词： internet of things

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6th international conference on Information and Communication technology for Intelligent systems, ICTIS 2021

6th International Conference on Information and Communicatio...

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6th international conference on Information and Communication technology for Intelligent systems, ICTIS 2021

ISBN: (纸本)9789811935749

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Wireless-Assisted Automatic Online Spatial Calibration based on 5G TSN for Sensor Fusion

Wireless-Assisted Automatic Online Spatial Calibration based...

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2023 IEEE international conference on signal Processing, Communications and Computing, ICSPCC 2023

作者： Lei, Ming Rahman, Mahib Mathaikutty, Deepak Alvarez, Ignacio Intel Corporation

ISBN: (纸本)9798350316728

In the field of artificial intelligence (AI), sensor fusion [1] is becoming an increasingly important technology for many internet of things (IoT) applications. For intelligent transportation system (ITS), we propose a wireless-assisted automatic online spatial calibration for sensor fusion. Due to the mobility of traffic objects such as vehicles, it is critical to make sure ground truth (GT) position and corresponding sample positions are measured based on raw data collected by different sensors at the same time. We use 5G time sensitive networking (TSN) to achieve this time alignment. Additionally, we illustrate how to perform automatic miscalibration detection and correction based on correctly grouped GT position and sample positions. There are many advantages of using our solution in practical sensor fusion systems that can significantly improve traffic safety and efficiency. © 2023 IEEE.

关键词： Calibration

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Extended State Observer based Fault-Tolerant Predictive Control for a Four-Mecanum Wheels Mobile Robot 14

Extended State Observer Based Fault-Tolerant Predictive Cont...

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14th IEEE international conference on Cyber technology in Automation, Control, and Intelligent systems, CYBER 2024

作者： Yang, Binghao Wang, Dongliang Wen, Ziling Wei, Wu Li, Wenji Fan, Zhun Shantou University Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong 515063 China Shantou University School of Department of Electronic and Information Engineering Guangdong 515063 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Guangzhou510640 China School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China

ISBN: (纸本)9798331506056

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment. © 2024 IEEE.

关键词： Mobile robots

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