A Deep Learning based Scene Recognition Algorithm for Indoor Localization

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

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Design of Desktop Audiovisual Entertainment System with Deep Learning and Haptic Sensations

Symmetry ◽

10.3390/sym12101718 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1718

Author(s):

Chien-Hsing Chou ◽

Yu-Sheng Su ◽

Che-Ju Hsu ◽

Kong-Chang Lee ◽

Ping-Hsuan Han

Keyword(s):

Deep Learning ◽

Object Detection ◽

User Experience ◽

Recognition System ◽

Scene Recognition ◽

Single Shot ◽

Auditory Signals ◽

Hot Weather ◽

Viewing Experience ◽

At Home

In this study, we designed a four-dimensional (4D) audiovisual entertainment system called Sense. This system comprises a scene recognition system and hardware modules that provide haptic sensations for users when they watch movies and animations at home. In the scene recognition system, we used Google Cloud Vision to detect common scene elements in a video, such as fire, explosions, wind, and rain, and further determine whether the scene depicts hot weather, rain, or snow. Additionally, for animated videos, we applied deep learning with a single shot multibox detector to detect whether the animated video contained scenes of fire-related objects. The hardware module was designed to provide six types of haptic sensations set as line-symmetry to provide a better user experience. After the system considers the results of object detection via the scene recognition system, the system generates corresponding haptic sensations. The system integrates deep learning, auditory signals, and haptic sensations to provide an enhanced viewing experience.

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Wireless Channel Scene Recognition Method Based on an Autocorrelation Function and Deep Learning

IEEE Access ◽

10.1109/access.2020.3044167 ◽

2020 ◽

Vol 8 ◽

pp. 226324-226336

Author(s):

Shuguang Ning ◽

Yigang He ◽

Lifen Yuan ◽

Yuan Huang ◽

Shudong Wang ◽

...

Keyword(s):

Deep Learning ◽

Autocorrelation Function ◽

Wireless Channel ◽

Scene Recognition ◽

Recognition Method

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Research on Automatic Dish Recognition Algorithm Based on Deep Learning

2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL) ◽

10.1109/cvidl51233.2020.00-26 ◽

2020 ◽

Author(s):

Hongmin Shao ◽

Jiong Mu ◽

Rong Tang ◽

Xiao Chen ◽

Mingxin Liu

Keyword(s):

Deep Learning ◽

Recognition Algorithm

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Street Sign Recognition Algorithm Based on Deep Learning

Proceedings of the 2020 3rd International Conference on Image and Graphics Processing ◽

10.1145/3383812.3383819 ◽

2020 ◽

Author(s):

Jiawei Wang ◽

Cen Chen ◽

Chongwen Wang

Keyword(s):

Deep Learning ◽

Recognition Algorithm ◽

Street Sign ◽

Sign Recognition

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An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Mathematical Problems in Engineering ◽

10.1155/2020/2940286 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yuanyuan Xu ◽

Genke Yang ◽

Jiliang Luo ◽

Jianan He

Keyword(s):

Deep Learning ◽

Characteristic Curve ◽

Area Under The Curve ◽

Principal Component ◽

Recognition Algorithm ◽

Electronic Component ◽

Support Vector ◽

Learning Networks ◽

Component Recognition ◽

And Performance

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6 level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

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