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Deep Learning-Aided Frequency Offset Estimation Method for 5G System based on Synchronization signal 8

Deep Learning-Aided Frequency Offset Estimation Method for 5...

引用

8th international conference on intelligent Computing and signal processing, ICSP 2023

作者： Li, Xiaolei Zhao, Xu Zhang, Yubing Gan, Jie Li, Dejian Wang, Yubo Beijing Smart-Chip Microelectronics Technology Co. Ltd Beijing China

ISBN: (纸本)9798350302455

We offer an effective frequency offset estimation approach for orthogonal frequency division multiplexing (OFDM) systems in 5G, supported by a deep neural network. The deep fusion architecture was a conventional, fully connected multilayer network divided into two stages: offline training and online inference. Initially, a time-domain cross-correlation frequency offset estimation approach based on PSS and SSS was presented, and the training sample set was created under the initial frequency offset predicted by this method. The deep fusion network is offline trained using the training sample pack to get the ideal network weights. The online inference is employed to execute calculations on the newly selected initial frequency offset group using the trained fusion network to determine the final frequency offset. The simulation results demonstrate that the performance of the suggested approach is significantly better than that of the current methods and that improved performance can be obtained at a relatively low additional cost. The deep learning-assisted frequency offset estimation algorithm also increases the frequency offset estimation range from ±1.752 kHz to ±7 kHz, offering excellent accuracy and stability in frequency offset estimation, particularly concerning having a more vital processing capability of border frequency offset. © 2023 ieee.

关键词： Deep neural networks

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Real-time Lightweight Detection Model on Optimized YOLOv7: Applied to the UAVs in Urban Environments 8

Real-time Lightweight Detection Model on Optimized YOLOv7: A...

引用

8th international conference on intelligent Computing and signal processing, ICSP 2023

作者： Wei, Feng Zhou, Jianping Tan, Xiang Lin, Jing Wang, Hu 830000 China Chinese Academy of Sciences State Key Laboratory of Resources and Environmental Information Systems Institute of Geographic Sciences and Natural Resources Research 100101 China Jiangxi U-Fly Technology Corporation 332020 China

ISBN: (纸本)9798350302455

Considering the conditional constraints in the urban environment, the traditional method of detecting drones is not applicable, and combining target detection with it is an effective solution. We propose a lightweight object detection model based on YOLOv7. The model replaces the Backbone based on the lightweight network MobileViTv3, which reduces the number of parameters of the model;Secondly, the SPD-Conv module is introduced to increase the detection accuracy of small-sized drones;In addition, we integrate the SimAM Module (Simple, Parameter-Free Attention Module) to find important regions in the scene. The experimental results show that the mAP50 of the model on the data set is 92.75%, the accuracy is 90.63%, and the number of parameters is 24.93M. It has the characteristics of high recognition accuracy and low memory usage, and the recognition effect is also better than the original model. © 2023 ieee.

关键词： Drones

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Real-Time Polygonal Semantic Mapping for Humanoid Robot Stair Climbing 23

Real-Time Polygonal Semantic Mapping for Humanoid Robot Stai...

引用

23rd ieee-RAS international conference on Humanoid Robots, Humanoids 2024

作者： Bin, Teng Yao, Jianming Lam, Tin Lun Zhang, Tianwei The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin China Guangdong University of Technology Guangzhou China The Chinese University of Hong Kong - Shenzhen Shenzhen China

ISBN: (纸本)9798350373578

We present a novel algorithm for real-time planar semantic mapping tailored for humanoid robots navigating complex terrains such as staircases. Our method is adaptable to any odometry input and leverages GPU-accelerated processes for planar extraction, enabling the rapid generation of globally consistent semantic maps. We utilize an anisotropic diffusion filter on depth images to effectively minimize noise from gradient jumps while preserving essential edge details, enhancing normal vector images' accuracy and smoothness. Both the anisotropic diffusion and the RANSAC-based plane extraction processes are optimized for parallel processing on GPUs, significantly enhancing computational efficiency. Our approach achieves real-time performance, processing single frames at rates exceeding 30 Hz, which facilitates detailed plane extraction and map management swiftly and efficiently. Extensive testing underscores the algorithm's capabilities in real-time scenarios and demonstrates its practical application in humanoid robot gait planning, significantly improving its ability to navigate dynamic environments. © 2024 ieee.

关键词： Anisotropy

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ChatAN: Interval Adaptive Normalization based on ChatGPT Knowledge Augmentation 8

ChatAN: Interval Adaptive Normalization based on ChatGPT Kno...

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8th international conference on intelligent Computing and signal processing, ICSP 2023

作者： Yang, Hanyu Zhao, Xiaoyong Wang, Lei Zhang, Jingle Li, Jianhua Beijing Information Science & Technology University School of Information & Management Beijing China Beijing Information Science & Technology University Information Systems Institute Beijing Advanced Innovation Center for Materials Genome Engineering Beijing China City Brain Zhongguancun Science City City Brain Co. Ltd. Beijing China

ISBN: (纸本)9798350302455

Big data streams have massive and complex characteristics. Due to the lack of global static information, the normalization of large data streams is susceptible to outliers and conceptual drift, which leads to incorrect modeling results. In addition, in online anomaly detection applications, it is significant to ensure real-time data processing to prevent severe consequences from missing critical points due to data flow backlogs. In this paper, we propose a method for interval adaptive normalization based on ChatGPT knowledge augmentation. The approach combines domain knowledge obtained by ChatGPT knowledge augmentation with interval adaptive sliding windows. Experimental results on three datasets in the cybersecurity domain show that the anomaly detection algorithm incorporating our proposed normalization algorithm achieves the maximum improvement of 32.46% and 32.68% in AUCROC and AUCPR compared to the traditional normalization method. © 2023 ieee.

关键词： Anomaly detection

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Enhancing Water Resource Management: A GUI-based Approach for Water Body Classification and Change Detection in VHRS Images 3

Enhancing Water Resource Management: A GUI-based Approach fo...

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3rd ieee international conference on intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Vasavi, S. Rajeswari, V. V. S. N Akhila Sree Kalyan, Ch. Venkata Vr Siddhartha Engineering College Computer Science & Engineering Department Andhra Pradesh India

ISBN: (纸本)9798350361186

The accurate classification of water bodies is crucial for effective water resource management, particularly when leveraging remote sensing and deep learning techniques. However, achieving precise classification in Very HighResolution Satellite (VHRS) images poses a significant challenge, necessitating accurate identification and categorization methodologies. Existing methodologies lack userfriendly interfaces and struggle with real-time integration into Geographic Information System (GIS) maps. This proposed system aims to develop a graphical user interface (GUI) that facilitates the classification of water bodies in VHRS images. The GUI employs preprocessing techniques such as Bilateral filtering and false color composites. Water body segmentation is performed using the U-Net model, followed by classification with the Random Forest Classifier. Additionally, the GUI facilitates change detection analysis, enabling to generate suitable vector data from raster images for the identification of temporal variations in water bodies. The detected changes are seamlessly integrated into GIS maps, ensuring the timely and accurate updating of spatial data. The proposed approach is evaluated using an urban dataset from Kolkata, West Bengal, India. © 2024 ieee.

关键词： Geographic information systems

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An improved method for evaluating equivalent acceleration factors of repairable systems in a fleet 5

An improved method for evaluating equivalent acceleration fa...

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5th international conference on System Reliability and Safety Engineering, SRSE 2023

作者： Jiang, Renyan Li, Fengping Xue, Wei Li, Xiao Zhang, Kunpeng Wenzhou University Institute of Laser and Optoelectronic Intelligent Manufacturing Wenzhou China Wenzhou University China International Science & Technology Cooperation Base for Laser Processing Robotics Wenzhou China

ISBN: (纸本)9798350305944

One important issue in reliability modeling of repairable systems is to quantitatively evaluate the heterogeneity of systems in a fleet. Recently, a fleet heterogeneity evaluation method has been proposed in the literature. It first estimates the mean times between failure (MTBFs) of individual systems, then defines the equivalent acceleration factor (EAF) of a specific system as the ratio between the MTBFs of this system and a reference system, and finally a heterogeneity measure is defined as the ratio of the sample average and standard deviation of EAFs of all the systems. The smaller this ratio is, the larger the heterogeneity of the fleet is. It is well-known that the estimated MTBF is inaccurate when the failure number is relatively small. Thus, a main drawback of the above-mentioned method is that the MTBFs of different systems are estimated at different time points. This paper aims to address this issue by proposing an improved method for estimating the MTBFs and EAFs of systems. The proposed method uses an "average process"as the reference process and estimates the MTBFs at a common time point. Two datasets are analyzed to illustrate the proposed method and its appropriateness. © 2023 ieee.

关键词： Mean time between failures

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A Deep Learning Framework for Prognosis Patients with COVID-19 3

A Deep Learning Framework for Prognosis Patients with COVID-...

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3rd ieee international conference on intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Shaik, Mohammad Khaja Vanaparthi, Kiranmai Swarnalatha, Gundala Reddy, Pundru Chandra Shaker Dalai, Rama Prasanna Jayaram, B. St. Ann's College of Engineering and Technology Electronics and Communication Engineering Andhra Pradesh Chirala India Telangana Hyderabad India Telangana Hyderabad India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal506371 India Centurion University of Technology and Management Electrical and Electronics Engineering Odisha India Rmk Engineering College Computer Science and Business Systems Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798350361186

A branch of AI known as 'deep learning' sifts through mountains of data in search of patterns and predictions using a machine learning approach called an artificial neural network. Rapid progress and widespread, successful application across many fields have been its hallmarks in recent years. A significant obstacle to healthcare transformation is the learning of useful information from complicated, high-dimensional, and diverse biological data. This research aims to give a concise synopsis of recent developments in deep learning, a survey of deep learning studies conducted on COVID-19, and some realworld applications of deep-learning(DL) strategies' for COVID19 diagnosis, prognosis, and therapy management. DL has the potential to enhance the quality and speed of drug development by analyzing medicinal imaging data, lab test results, and other pertinent data to make diagnoses, assess the course and prognosis of illnesses, and even provide healing recommendations and drug-utilize regimens. In addition, it may guide policymakers in creating effective control and preventative strategies. Additionally, we evaluate the present state of deep learning's capabilities and shortcomings in terms of COVID-19 treatment accuracy, covering issues such as the dearth of phenotypically plentiful data and the necessity for deeper learning models that are easier to understand and work with. We wrap up by talking about how to get beyond the obstacles that are standing in the way of deep learning's potential future clinical uses. © 2024 ieee.

关键词： COVID-19

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4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

4th International conference on Innovative Manufacturing, Me...

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4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

ISBN: (纸本)9789819988181

The proceedings contain 50 papers. The special focus in this conference is on Innovative Manufacturing, Mechatronics and Materials Forum. The topics include: Effect of Crack Length, Depth, and Location on Natural Frequencies of Railway Track;imputation Analysis of Time-Series Data Using a Random Forest Algorithm;harnessing Machine Learning, Blockchain, and Digital Twin Technology for Advanced robotics in Manufacturing: Challenges and Future Directions;humanizing Humanoids: An Extensive Review on the Potential of Prosthetic Robotic Arm with Integrated Monitoring System for Disabled People;intelligent Machining systems for Robotic End-Effectors: State-of-the-Art and Toward Future Directions;artificial Neural Network Analysis in Road Crash Data: A Review on Its Potential Application in Autonomous Vehicles;the Role and Impact of robotics Integration in Precision Machining and Manufacturing: A Comprehensive Review;nanomaterial in robotics: Bridging the Gap Between Current Applications and Future Possibilities;Optimized-ELM Based on Geometric Mean Optimizer for Bearing Fault Diagnosis;detection of Fault Features in Remanufacturing of Automotive Components Using Image processing and Computer Vision Techniques;the Significance of the Thoracic Spinal Multiple Segments During Different Pick-Object Approaches;Vibration Suppression of the Flexible Beam Structure Using PID Controller Tuned by Advanced Firefly Algorithm;Bearing Fault Diagnosis Based on Prominence Peak-Picking IMFs Selection and PSO-SSAE;investigation of Wheel Robot Grouser Width Parameter Effect on Robot Mobility in Soft Sand Terrain Using Sand Test Bed;automated Harmonic signal Removal-Based Image Feature Extraction Technique: A Comparative Study Using Online Databases;Human Mental Stage Interpretation Based on the Analysis of Electroencephalogram (EEG) signals.

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Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions 27

Exploring Domain Shift on Radar-Based 3D Object Detection Am...

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27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Zhang, Miao Abdulatif, Sherif Loesch, Benedikt Altmann, Marco Schwarz, Marius Yang, Bin Robert Bosch GmbH Corporate Research 71272 Germany University of Stuttgart Institute of Signal Processing and System Theory 70569 Germany

ISBN: (纸本)9798331505929

The rapid evolution of deep learning and its integration with autonomous driving systems have led to substantial advancements in 3D perception using multimodal sensors. Notably, radar sensors show greater robustness compared to cameras and lidar under adverse weather and varying illumination conditions. This study delves into the often-overlooked yet crucial issue of domain shift in 4D radar-based object detection, examining how varying environmental conditions, such as different weather patterns and road types, impact 3D object detection performance. Our findings highlight distinct domain shifts across various weather scenarios, revealing unique dataset sensitivities that underscore the critical role of radar point cloud generation. Additionally, we demonstrate that transitioning between different road types, especially from highways to urban settings, introduces notable domain shifts, emphasizing the necessity for diverse data collection across varied road environments. To the best of our knowledge, this is the first comprehensive analysis of domain shift effects on 4D radar-based object detection. We believe this empirical study contributes to understanding the complex nature of domain shifts in radar data and suggests paths forward for data collection strategy in the face of environmental variability. © 2024 ieee.

关键词： Network security

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Data processing model for equipment monitoring and diagnostics systems 5

Data processing model for equipment monitoring and diagnosti...

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5th international Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2023

作者： Sultanov, Mahsud M. Boldyrev, Ilya A. Menshikov, Pavel D. National Research University MPEI Digital Technology Research Laboratory Volzhsky Russia

ISBN: (纸本)9798350399523

In this paper, considers the application of equipment monitoring and diagnostics system on the example of turbine condenser. The analysis of the absolute error of the measuring channel after the whole sequence of signal conversion is performed. The measuring channel was simulated using SimInTech, the results were processed using the Office software package. To analyze the obtained data on the system states it is necessary to apply the methods of signal processing. It should be taken into account that the data are not complete and have a number of errors associated with the method of their acquisition, conversion, averaging, and recording in a database and, if necessary, recovery from this averaged sample to analyze the original signal. © 2023 ieee.

关键词： signal processing

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