Research on Image Signal Identification Based on Adaptive Array Stochastic Resonance

Zhao, Jingjing; Ma, Yajie; Pan, Zhenkuan; Zhang, Huage

doi:10.1007/s11424-021-0133-1

Cited by 12 publications

(6 citation statements)

References 37 publications

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“…Principle. e development of evaluation models offers the possibility to solve a number of scientific problems [18][19][20][21][22][23][24][25][26]. Chen et al [27] first proposed the TOPSIS model, and they evaluated the advantages and disadvantages of the decision-making unit by establishing positive and negative ideal solutions and using the parameters of each decision unit in the actual problem and the relative distance of the two solutions.…”

Section: Topsis Methodsmentioning

confidence: 99%

Evaluation of College Students’ Emergency Response Capability Based on Questionnaire‐TOPSIS Innovative Algorithm

2021

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With the development of our society, the diversity of the university environment has been increased. The complexity of the diversified university environment also greatly increases the frequency of campus crisis incidents. Therefore, how to evaluate the emergency response ability of college students and how to take effective response measures have become problems that need urgent attentions. In this study, the evaluation of college students’ emergency response capability based on the questionnaire-TOPSIS innovative algorithm is conducted. Firstly, the questionnaire method is used to analyze the current situation of college students’ ability of emergency response, and then a comprehensive evaluation is made using the TOPSIS innovative algorithm. The results of the ranking of emergency response ability are as follows: excellent > good > undergraduate > graduate > inferior. Secondly, the reasons for the lack of ability of emergency response to students based on the evaluation results are studied intensively. Finally, the development countermeasures for college students’ emergency response ability training are put forward.

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Section: Topsis Methodsmentioning

confidence: 99%

Evaluation of College Students’ Emergency Response Capability Based on Questionnaire‐TOPSIS Innovative Algorithm

2021

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“…The precise detection and localization of specific anatomical structures on medical images, e.g., object segmentation, serves as the first step in identifying pathology and the key issue for an automated diagnostic system [2] . Image signal provides one potential way to analyze the situation [3] . Dental caries, periapical lesions, periodontal disease, odontogenic cysts and tumors all occur in the tooth tissue or around the tooth.…”

Section: Introductionmentioning

confidence: 99%

Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs

et al. 2022

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Panoramic radiographs can assist dentist to quickly evaluate patients' overall oral health status. The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology, and also plays a key role in an automatic diagnosis system. However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. Therefore, it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs. In this study, SWin-Unet, the transformer-based Ushaped encoder-decoder architecture with skip-connections, is introduced to perform panoramic radiograph segmentation. To well evaluate the tooth segmentation performance of SWin-Unet, the PLAGH-BH dataset is introduced for the research purpose. The performance is evaluated by F1 score, mean intersection and Union (IoU) and Acc, Compared with U-Net, Link-Net and FPN baselines, SWin-Unet performs much better in PLAGH-BH tooth segmentation dataset. These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation, and is valuable for the potential clinical application.

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“…For example, in the field of image processing, Chen proposed a low quality video target detection algorithm combining Gaussian model and stochastic resonance model in 2013, and applied it to detect binary image targets; In 2015, Han proposed a stochastic resonance reconstruction technology for nanosecond pulse noise image, and applied it to restore optical pulse image; In 2021, Zhao proposed an adaptive array stochastic resonance image signal recognition method to solve the problem that image signals are difficult to identify under strong noise background, effectively reducing the error rate of signal recognition. These methods reflect the great potential of stochastic resonance in the application of image [ [14] , [15] , [16] , [17] , [18] , [19] ].…”

Section: Introductionmentioning

confidence: 99%

A novel image noise reduction method for composite multistable stochastic resonance systems

Jiao¹,

Shi²,

Wang³

et al. 2023

Heliyon

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Research on Image Signal Identification Based on Adaptive Array Stochastic Resonance

Cited by 12 publications

References 37 publications

Evaluation of College Students’ Emergency Response Capability Based on Questionnaire‐TOPSIS Innovative Algorithm

Evaluation of College Students’ Emergency Response Capability Based on Questionnaire‐TOPSIS Innovative Algorithm

Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs

A novel image noise reduction method for composite multistable stochastic resonance systems

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