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Design of MIMO CSI Feedback with Deep Learning 2

Design of MIMO CSI Feedback with Deep Learning

引用

2nd international conference on Artificial Intelligence and intelligent Information processing, AIIIP 2023

作者： Xie, Bin Dai, Bangqiang College of Information Engineering Jiangxi University of Science and Technology Ganzhou China

ISBN: (纸本)9798350371451

In MIMO systems, effective channel state information (CSI) feedback is crucial for optimizing signal transmission and improving system performance. However, with the increasing complexity of CSI, traditional CSI feedback methods are limited in terms of computational error and complexity, making the feedback performance has become a bottleneck. In this paper, we propose a new CSI feedback network based on deep learning approach, which aims to utilize the powerful properties of neural networks to achieve better feedback performance in a more accurate and efficient manner. The network is divided into two parts: encoder and decoder. In the encoding part, it utilizes the coordinated attention mechanism to extract richer CSI features;in the decoding part, it achieves better CSI recovery through different numbers of convolutional channels with multiple receptive fields. Experimental results show that the feedback method proposed in this paper achieves better CSI feedback performance in different scenarios and at different compression rates. © 2023 ieee.

关键词： MIMO systems

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Proceedings - 2023 11th ieee international conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2023

Proceedings - 2023 11th IEEE International Conference on Mob...

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11th ieee international conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2023

ISBN: (纸本)9798350322798

The proceedings contain 8 papers. The topics discussed include: a fault detection mechanism for database management systems on mobile edge computing;distress signal recognition using pose estimation and neural networks;intelligent emergency notification mobile service via multi-task BERT models;surveillance drone cloud and intelligence service;aqua colony for fully automated aquaculture;TransScale: combined-approach elasticity for stream processing in fog environments;efficient cloud-based video summarization using deep learning and cloud computing for reduced storage costs and improved accessibility;and enhancing virtual sensors to deal with missing values and low sampling rates.

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Analysis of Knocking Potential Based on Vibration on a Gasoline Engine for Pertalite and Pertamax Turbo Using signal processing Methods 1

引用

international conference on Innovative Technologies in intelligent systems and Industrial Applications, CITISIA 2022

作者： Qadri, Munzir Astuti, Winda Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Knocking is one of the problems in internal combustion engines with many influencing factors, one of which is the use of fuel octane values that are not in accordance with the recommended use or engine specifications. Knocking can lead to poor performance of an engine and shorten its lifetime. To prevent this from happening, a study was conducted on how the octane rating can affect the knocking of an engine by comparing two types of fuel that have different octane rating levels. The most appropriate signal processing method was also determined to carry out this research. The engine speed was predetermined in each experiment. The achieved results prove that the engine has the potential of knocking to occur when using Pertalite-type fuel, caused by misfires. signal processing is carried out using the Fast Fourier Transform (FFT) and Low Pass Filter (LPF) methods to obtain clear data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Gasoline

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A PARAMETRIC APPROACH OF COMPUTING TAGUCHI signal-TO-NOISE RATIO FROM INCOMPLETE LIFE DATA 14

A PARAMETRIC APPROACH OF COMPUTING TAGUCHI SIGNAL-TO-NOISE R...

引用

14th international conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Jiang, Renyan Xue, Wei Cao, Yu China International Science & Technology Cooperation Base for Laser Processing Robotics Wenzhou University Wenzhou China Zhejiang Provincial Innovation Center of Laser Intelligent Equipment Technology Wenzhou China

ISBN: (纸本)9781837242672

The Taguchi method for experimental design is widely utilized to improve product reliability through the systematic arrangement of multiple runs or treatments, each representing a unique combination of factor levels. In such experiments, lifetime data of sample units are gathered via life tests, which are typically monitored periodically and terminated at a pre-determined endpoint. The resultant data, characterized by small sample sizes, high censorship, and heterogeneity, preclude direct computation of the Taguchi signal-to-noise ratio (SNR), a crucial measure of robustness used to evaluate the performance of a run. This paper addresses this issue by introducing a parametric approach that begins by constructing a life distribution model for each run through a three-step parameter estimation procedure. It then calculates the SNR from the fitted distribution using SNR expressions derived from typical life distributions discussed herein. The effectiveness and superiority of the proposed method are demonstrated through two real-world examples, underscoring its applicability and potential benefits in enhancing the reliability analysis of complex products. © The Institution of Engineering & Technology 2024.

关键词： Taguchi methods

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Active Disturbance Rejection Control of Gait For Coupled System of Biped Robot 7

Active Disturbance Rejection Control of Gait For Coupled Sys...

引用

7th international conference on intelligent Computing and signal processing, ICSP 2022

作者： Zhao, Yongyong Cao, Guohua Yuan, Yi Wang, Jinghua Changchun University of Science and Technology College of Mechanical and Electric Engineering Changchun China Changchun University of Science and Technology Chongqing Research Institute Chongqing China

ISBN: (纸本)9781665478571

Footed robots have a wide range of application scenarios, in bipedal robots have strongly coupled, nonlinear, and complex dynamical systems. In this paper, we use the proper function of the joint angle variation of the lower limb motion of a bipedal robot as the dynamic gait law input of the system, which can better simulate the movement of the robot's more inadequate limb system. For the representation of the dynamical system obtained based on the Lagrangian method, according to the coupled system decoupling and self-anti-disturbance control theory, this paper derives the decoupling process. The design of the self-anti-disturbance controller to realise the step-stable tracking of the joint angle of the biped robot is similar to the human gait in the presence of sinusoidal and step perturbations. The simulation results verify the validity of the theoretical analysis and provide experimental simulation support and a theoretical basis for the design of decoupling and self-anti-disturbance control of robot lower limb motion. © 2022 ieee.

关键词： Dynamical systems

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Machine Learning Algorithm Comparison for Traffic signal: A Design Approach 8

Machine Learning Algorithm Comparison for Traffic Signal: A ...

引用

8th international conference on Communication and Electronics systems, ICCES 2023

作者： Deshmukh, Saloni Parwekar, Aishwarya Danej, Bhagyashri Chavhan, N.A. Agrawal, Rahul Dhule, Chetan G H Raisoni College of Engineering Department of Data Science IOT Cyber Security Nagpur India

ISBN: (纸本)9798350396638

The number of vehicles is increasing significantly every day, especially in major cities. To control traffic flow on extensive road networks and facilitate crossing traffic flow on extensive road networks and facilitate crossing crossings, intersection n traffic signal coordination is required. It is therefore a new technology in the present world for intelligent traffic control and administration systems due to the number of vehicles with so many communication restrictions in arrays. The traffic signal strives to boost the effectiveness of transportation networks, increase safety, and reduce congestion. Technological advances enable better monitoring and management of traffic flow, and traffic signal design and operation are continually developing. The employment of some algorithms for traffic light control has improved the intelligence of traffic light controllers. This research study has developed data instruction strategies using data mining and image processing techniques. By comparing the analysis of several machine learning algorithms for predicting traffic flow and patterns, such as KNN, SVM, DNN, and Random Forest, the accuracy can be increased by lowering the overfitting. © 2023 ieee.

关键词： Support vector machines

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Self-Transriber: Few-Shot Lyrics Transcription With Self-Training 48

Self-Transriber: Few-Shot Lyrics Transcription With Self-Tra...

引用

48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Gao, Xiaoxue Yue, Xianghu Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The current lyrics transcription approaches heavily rely on supervised learning with labeled data, but such data are scarce and manual labeling of singing is expensive. How to benefit from unlabeled data and alleviate limited data problem have not been explored for lyrics transcription. We propose the first semi-supervised lyrics transcription paradigm, Self-Transcriber, by leveraging on unlabeled data using selftraining with noisy student augmentation. We attempt to demonstrate the possibility of lyrics transcription with a few amount of labeled data. Self-Transcriber generates pseudo labels of the unlabeled singing using teacher model, and augments pseudo-labels to the labeled data for student model update with both self-training and supervised training losses. This work closes the gap between supervised and semi- supervised learning as well as opens doors for few-shot learning of lyrics transcription. Our experiments show that our approach using only 12.7 hours of labeled data achieves competitive performance compared with the supervised approaches trained on 149.1 hours of labeled data for lyrics transcription. © 2023 ieee.

关键词： Lyrics transcription music information retrieval singing processing and self-training

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Interpretable analysis of feature importance and implicit correlation based on sEMG grayscale images 6

Interpretable analysis of feature importance and implicit co...

引用

ieee 6th international conference on Industrial Cyber-Physical systems (ICPS)

作者： Ao, Xiaohu Wang, Feng Zhao, Juan She, Jinhua China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Tokyo Univ Technol Sch Engn Hachioji Tokyo 1920982 Japan

ISBN: (纸本)9798350311259

For patients requiring upper limb rehabilitation, the hand rehabilitation robot assists the patient in completing movements within a certain training trajectory to achieve therapeutic results. There have been studies based on deep learning to convert surface electromyography (sEMG) signals into sEMG images for motion intention analysis. Although good recognition accuracy has been achieved, the working principle of neural networks and the processing of image features by the networks are not well explained. The interpretability of deep neural networks determines human confidence in neural network decisions. In this paper, we design a method based on feature importance and implicit correlation for hand motion intention recognition, experimentally explored that convolutional neural networks have implicit definitions for sEMG grayscale images of the same hand gesture action, and verified the effectiveness of the designed method.

关键词： sEMG signal grayscale images implicit correlation interpretability

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Investigation on Deep Learning and Machine Learning Approaches for Antenna Design Based on Radar signal processing 4

Investigation on Deep Learning and Machine Learning Approach...

引用

4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Govindasamy, Santhakumar Bhushan, Kavi Nijhawan, Ginni Kumar, B Santhosh Chandrashekar, Rakesh Issa, Ali Ashoor Sri Krishna College of Technology and Tamilnadu Electronics and Communication Engineering India Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

This paper gives a complete review of the use of Deep Learning (DL) and Machine Learning (ML) approaches in the area of antenna design for radar data processing. It analyzes the potential of DL and ML to overcome the constraints of conventional antenna design methodologies, especially in the face of complicated environmental variables and the necessity for interference reduction. By employing sophisticated computational techniques, the research reveals how these AI-based approaches may considerably boost the design and performance optimization of radar antennas. The integration of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning into the design process offers promise for constructing adaptable and efficient radar systems. Empirical findings from the research illustrate the durability of ML models, notably Support Vector Machines (SVMs), in forecasting antenna performance, stressing its resilience even in high-noise environments. This investigation's results are crucial for the progress of intelligent radar systems and lay the way for future improvements in the industry. © 2024 ieee.

关键词： Support vector machines

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Research on multi-step short-term power load forecasting based on optimized VMD signal processing and PSO-SVR 7

Research on multi-step short-term power load forecasting bas...

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7th ieee international conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Tan, Xuebiao Wang, Jing Ding, Dan Kang, Lun Fu, Wentian Wang, Haiwen Ran, Yunzheng Zhong, Jianwei Enshi Power Supply Company State Grid Hubei Electric Power Co Ltd Enshi China School of Intelligent Science and Engineering Hubei Minzu University Enshi China

ISBN: (纸本)9798350350760

Accurate load forecasting is beneficial for the rational scheduling of power generation energy, meeting people's daily electricity needs, and facilitating the steady development of the Chinese economy. It is difficult to predict short-term load demand using historical load data, mainly due to the difficulties caused by its nonlinear characteristics. Therefore, this article proposes a VMD-PSO-SVR short-term forecasting model for the power load of an industrial park. This model uses Variational Mode Decomposition (VMD) to split the original historical load data into several nonlinear subsequences. Then, selective reconstruction is performed based on the degree of correlation between the subsequences and the original sequence to form several new subsequences. Next, the easily predictable subsequences are input into a support vector regression prediction model optimized by particle swarm optimization (PSO) for prediction. Finally, the predicted results of each component are reconstructed to obtain the final prediction result. A case study in an industrial park concludes that the average absolute percentage error (MAPE%) and goodness of fit test (R2%) evaluation indicators of the VMD-PSO-SVR prediction model are 0.6415% and 99.7476%, respectively. Compared with other models, it can more accurately predict future short-term load demand. ©2024 ieee.

关键词： Variational mode decomposition

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