3-D electromagnetic-model-based absolute attitude estimation using a deep neural network

Yang, Xiu; Ni, Weijian; Yan, Weidong; Bu, Hui; Zhang, Han; Jian, Wu; Ma, Long

doi:10.1080/2150704x.2021.1949068

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…When the tag needs to transmit data back to the reader, the transmitted data stream changes the parameters of the resonant circuit of the electronic tag, which changes the impedance and phase of the primary circuit coupled with the magnetic field. This process is a modulation process [29]. The reader obtains the load modulation signal by detecting the voltage of the transformed impedance and then extracts the data returned by the tag through demodulation and related signal processing.…”

Section: Methodsmentioning

confidence: 99%

Pedestrian Attitude Estimation and Recognition Algorithm Based on RF Data

Zhang

Sun

et al. 2022

Wireless Communications and Mobile Computing

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Pedestrian detection plays a vital role in the estimation of human posture, especially the pedestrian detector, which provides the position information of human body in the image. This paper proposes a pedestrian gesture recognition algorithm using radio frequency data based on RFID technology. Its goal is to serve as a starting point for research in the field of human gesture recognition. The position of pedestrians in the input picture can be adaptively adjusted by learning the transformation parameters of samples during network training, and the local features of each block can be trained by multiple cross entropy functions at the network’s end, allowing the sample local area to be fully utilized as the network’s training. More accurate DFL and attitude feature recognition can be achieved using the measured received signal strength information and channel state information. The pedestrian gesture recognition algorithm based on RF data proposed in this paper improves pedestrian recognition accuracy, especially in noisy environments, such as fuzzy or occlusion, according to experimental results.

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Section: Methodsmentioning

confidence: 99%

Pedestrian Attitude Estimation and Recognition Algorithm Based on RF Data

Zhang

Sun

et al. 2022

Wireless Communications and Mobile Computing

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“…Taking the tactical characteristic index data of track and field teaching in colleges and universities as the basic data of initial training in deep neural network, an appropriate loss function is determined to measure the error between the output of deep neural network and the original data [23,24], and the following results are obtained:…”

Section: Evaluation On Teaching Tactics Of Track and Fieldmentioning

confidence: 99%

“…Taking the tactical characteristic index data of track and field teaching in colleges and universities as the basic data of initial training in deep neural network, an appropriate loss function is determined to measure the error between the output of deep neural network and the original data [ 23 , 24 ], and the following results are obtained:

where s i represents the network output value, and y represents the sample label value. The network output with the sample labels was fitted by the algorithm to minimize the error.…”

Section: Design Of Evaluation Model Of Teaching Tactics Characteristi...mentioning

confidence: 99%

Analysis of Teaching Tactics Characteristics of Track and Field Sports Training in Colleges and Universities Based on Deep Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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In the analysis of the teaching tactical characteristics of track and field sports training in colleges and universities, the teaching tactical characteristics are not quantified, which leads to the low key degree of determining the influencing factor indicators in colleges and universities, and the error in the evaluation of the teaching tactical characteristics of track and field sports training is large. Therefore, this paper designs a method to analyze the tactical characteristics of college track and field sports training teaching based on deep neural network. Firstly, by analyzing the current situation of track and field sports training teaching in colleges and universities, it determines the areas that need to be improved in teaching. Then, by determining the factors of teaching environment, the core competitiveness of track and field teams, and the teaching ability of track and field coaches, these factors are determined as the key characteristics, the data basis is analyzed, and the unified data quantitative processing is carried out to determine the key factor indexes affecting the analysis of tactical characteristics. Finally, the deep neural network is introduced to construct the evaluation model of the tactical characteristics of college track and field sports training teaching, and the characteristic analysis results are further modified with the help of cascade noise reduction self-encoder to complete the analysis of the tactical characteristics of college track and field sports training teaching. The experimental results show that the proposed method can effectively analyze the teaching tactical characteristics of track and field sports training in colleges and universities and improve the performance of the evaluation of the teaching tactical characteristics of track and field sports training.

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Image description software generation based on data mining and computer vision

Wang,

Zhu

2024

Measurement: Sensors

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3-D electromagnetic-model-based absolute attitude estimation using a deep neural network

Cited by 3 publications

References 24 publications

Pedestrian Attitude Estimation and Recognition Algorithm Based on RF Data

Pedestrian Attitude Estimation and Recognition Algorithm Based on RF Data

Analysis of Teaching Tactics Characteristics of Track and Field Sports Training in Colleges and Universities Based on Deep Neural Network

Image description software generation based on data mining and computer vision

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