Image Target Recognition Model of Multi- Channel Structure Convolutional Neural Network Training Automatic Encoder

Zhang, Sen; Cheng, Qingqing; Chen, Dengxi; Zhang, Haijun

doi:10.1109/access.2020.3003059

Cited by 12 publications

(14 citation statements)

References 49 publications

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“…[136] At present, convolutional neural networks are fruitful in image recognition, denoising and segmentation, [137] and autoencoders are used to design predictive models and to analyze and denoise large amounts of data. [138] For multiparameter sensitive problems, deep learning-based dimensionality reduction algorithms such as Principal Component Analysis is used for classification problems with different sensor states. [136] These deep learning-based techniques are powerful and superior when it comes to signal processing, however the accuracy and wide applicability of the models for different sensing structures is still to be proven.…”

Section: Innovative Signal Demodulation Technologymentioning

confidence: 99%

Microfiber Knot Resonators: Structure, Spectral Properties, and Sensing Applications

Zhang,

Gao,

Xia

et al. 2023

Laser & Photonics Reviews

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In recent years, compact micro‐/nanofiber sensors with fast response and high resolution have become a hot field in sensing and nanotechnology. In addition to the unique properties of microfibers such as strong evanescent field, low optical loss, and easy integration, microfiber knot resonators further improve the sensing capability due to their resonance properties. The potential high‐quality factor allows higher sensing sensitivity and resolution, which is very attractive for optical sensing at the micro‐/nano scale. More application potential can be effectively stimulated by improving the structure shape or principle or combining with functional sensitive materials. This paper first introduces the preparation process and spectral characteristics of conventional microfiber knots and then focuses on three new types of devices for enhancing the sensing performance, namely material‐coated, shape‐improved, and principle‐extended, and briefly analyzes their spectral properties. In addition, the research progress and applications of these sensors are summarized. Finally, the existing problems in this field are analyzed, and the directions for further development are discussed and prospected.

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Section: Innovative Signal Demodulation Technologymentioning

confidence: 99%

Microfiber Knot Resonators: Structure, Spectral Properties, and Sensing Applications

Zhang,

Gao,

Xia

et al. 2023

Laser & Photonics Reviews

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show abstract

“…In order to extract the features of the image for classification, a CNN and an automated encoder were combined in a multi-based integrated network structure technique (Zhang et al 2020). In order to enhance the CNN model's prototype nature, the method first pre-trains the convolution kernel with a sparse auto-encoder by expanding the network branch.…”

Section: Related Workmentioning

confidence: 99%

“…Deep learning is a neural network structure with numerous hidden layers, to put it simply. Deep learning, in contrast to typical neural networks, is an unsupervised feature learning technique that can automatically extract target features and avoid problems brought on by manually selecting features (Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Dual- Stream Convolutional Neural Network for Synthetic Aperture Radar Automatic Target Recognition of Ground Military Vehicle

Awujoola¹,

Odion²,

Evwiekpaefe³

et al. 2022

JSR

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Synthetic Aperture Radar (SAR) target classification is one of the largest branches of SAR image analysis. Despite the remarkable achievements of deep learning-based SAR target prediction algorithms, current object recognition algorithms are limited in terms of military applications. Acquisition and labeling of SAR target images are time-consuming and cumbersome. Obtaining adequate training data is also challenging in many cases. Deep learning-based models are always susceptible to overfitting because of insufficient training data. This limitation prevents them from being widely used to classify SAR targets. To overcome the problem of insufficient sampling and to learn more accurate representations for SAR image recognition, we propose a two way input of SAR images into a dual stream of DCNN. Concatenating two input SAR images representations was done using the restricted raw SAR data in order to extract the integral features from the 2 input SAR images representations for classification. The proposed methodology addressed the problem of insufficient sample in SAR target classification and improved classification accuracy without overfitting. Experimental results confirmed that the proposed method is effective in addressing the problem of insufficient sample in SAR target classification. This technique can be integrated into any SAR classification model based on convolutional neural networks (CNNs). The model MCDS-CNN results in a 99.1% recognition accuracy. Despite the limited availability of SAR image data from MSTAR, this approach provides good recognition results.

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“…4 The scheduling structure model of image information resources in college sports multimedia teaching The feature matching model of image information resources in college sports multimedia teaching is constructed. Based on resource transmission technology [25] , mass storage technology and dynamic scheduling technology, the fuzzy correlation feature quantity of college sports multimedia teaching and dynamic analysis of moving images is obtained as follows:…”

Section: Multimedia Usermentioning

confidence: 99%

Realization of college sports multimedia teaching platform based on cloud computing motion image analysis

2022

Preprint

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In order to improve the ability of college sports multimedia teaching, dynamic image analysis and data management, a design scheme of college sports multimedia teaching platform based on cloud computing mode was proposed. The method of dynamic integrated allocation and fuzzy scheduling of multimedia teaching resources is adopted. The characteristic values and dynamic analysis of moving images of college sports multimedia teaching are extracted. By using hierarchical structure and image integration scheduling method, this paper analyzes the characteristic values of image analysis in college sports multimedia teaching and dynamic analysis of moving images. By using the methods of adaptive cooperative control and resource matching scheduling, the control objective allocation function of college sports multimedia teaching and dynamic analysis of moving images is constructed. The correlation of image information resources in college sports multimedia teaching is mined by using the ultra micro pixel feature matching method, and a big data mining and information fusion model for college sports multimedia education and dynamic analysis of motion images is constructed. Use cloud computing technology to provide users with various services, and use machine learning technology to analyze teaching data. Through priority scheduling and dynamic allocation methods, the dynamic allocation of teaching resources and dynamic analysis of moving images in the process of college sports multimedia teaching are realized. In ASP NET technology, cloud computing and Silverlight technology have realized the software development and design of college sports multimedia teaching platform under the motion image analysis mode. The simulation results show that using this method to design the college sports multimedia teaching platform under the motion image analysis mode can improve the dynamic distribution and mining ability of image information resources in college sports multimedia education. The platform has a high level of data fusion and a strong ability to analyze motion images.

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Image Target Recognition Model of Multi- Channel Structure Convolutional Neural Network Training Automatic Encoder

Cited by 12 publications

References 49 publications

Microfiber Knot Resonators: Structure, Spectral Properties, and Sensing Applications

Microfiber Knot Resonators: Structure, Spectral Properties, and Sensing Applications

Multi-Class Dual- Stream Convolutional Neural Network for Synthetic Aperture Radar Automatic Target Recognition of Ground Military Vehicle

Realization of college sports multimedia teaching platform based on cloud computing motion image analysis

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