Salient object detection on hyperspectral images in wireless network using CNN and saliency optimization

Huang, Chen; Xu, Tingfa; Zhang, Yuhan; Pan, Chenguang; Hao, Jianhua; Li, Xiangmin

doi:10.1016/j.adhoc.2020.102369

Cited by 17 publications

(9 citation statements)

References 26 publications

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“…Analysis of Wireless Network Technology. As the name suggests, the wireless network is a network that can achieve network signal interworking of different communication equipment without wiring [12]. Wireless network technology has a relatively broad scope.…”

Section: Methodsmentioning

confidence: 99%

The Use of Wireless Network Combined with Artificial Intelligence Technology in the Reform of Music Online Teaching System

Chen

Meng

2022

Wireless Communications and Mobile Computing

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The purpose is to respond to the call of the government on the artificial intelligence (AI) policy of music online education. First, wireless network technology and AI technology are described in detail; the application principle of AI technology in traditional music teaching mode and online teaching mode is analyzed; then, the combination model of AI technology and music online education system in 5th-generation (5G) wireless network environment is proposed. These models include music online composition teaching model, music online teaching scoring model, and music online lyric writing teaching model. Finally, the models are verified in the application of AI technology. The results show that AI technology has good application performance in the models above. Moreover, the direction of music online teaching reform and innovation along the combination of 5G network environment and AI is correct under the background of the new era. This exploration expands the application scenario of AI technology and provides ideas for the reform and innovation of music online education in the context of the new era.

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Section: Methodsmentioning

confidence: 99%

The Use of Wireless Network Combined with Artificial Intelligence Technology in the Reform of Music Online Teaching System

Chen

Meng

2022

Wireless Communications and Mobile Computing

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“…"5G Internet+" has had a significant impact on academics' interest in "teacher learning communities," and scholars have linked them to the growth of teacher learning and investigated new paths for teacher learning [23]. When it comes to the concept of "community," literature suggests that it is a group of people who work together to build a better world for everyone [24]. Taking part in group activities helps the learner develop an understanding of their own identity within the context of the community's social culture of reflection [25].…”

Section: Introductionmentioning

confidence: 99%

Remote Music Teaching Classroom Based on Machine Learning and 5G Network Station

Seng

2022

Wireless Communications and Mobile Computing

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Most recently, the technological impacts are increasing day by day. Artificial intelligence, machine learning, deep learning, and big data are used to acquire enough application feasibility to reduce work pressure for humans. The way information society is being developed in this world will result in some changes in the development of the education system. The impact will result in some changes in the development of the education system. This research focuses on improving remote music teaching following the presence of a 5G network. Data perception handling is considered the central issue in remote communication, which creates some inabilities to achieve better communication between the systems. To avoid this kind of risk management, most schools and colleges have planned to create a separate network working under wireless connections while delivering the course content to a minimum number of students. Another effective integration of this evolution is efficiently building the remote music education system. Here the compatibility should match both the cases, be they students or the teachers. In this case, network speed is considered one of the most significant impacts on students listening to online classes. Therefore, a 5G network might be the best path to take by increasing the network speed and giving a strong impetus. The proposed system utilizes convolutional neural network (CNN) algorithm to train the intelligent system to provide remote music education through wireless 5G networks. The parameters used for analyzing the proposed system with the existing system are compline, uncertainty, unconfirmed, and out-of-place. The proposed algorithm has achieved an overall accuracy of 99.13% than the existing system.

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“…To validate the efficiency of our model, we perform several experiments, in which we find that our rich hierarchical model explore representative potential at pixel and semantic level for learning visual saliency strategies which can be utilized for recovering the local details. The visual saliency finding results from the above experiments show that visual saliency maps generated by benefiting saliency optimization process with better quality [45]. Commonly, the accuracy and superior performance of the model have been improved after multi-level feature fusion between low and high scales and performed analysis on ECSSD [31], HKU-IS [20], PASCAL-S [37] and DUT-OMRON [14] datasets.…”

Section: Evaluation Metricsmentioning

confidence: 83%

Visualization of Salient Object With Saliency Maps Using Residual Neural Networks

et al. 2021

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Visual saliency techniques based on Convolutional Neural Networks (CNNs) exhibit an excessive performance for saliency fixation in a scene, but it is harder to train a network in view of their complexity. The imparting Residual Network Model (ResNet) that is more capable to optimize features for predicting salient area in the form of saliency maps within the images. To get saliency maps, an amalgamated framework is presented that contains two streams of Residual Network Model (ResNet-50). Each stream of Reset-50 that is used to enhance the low level and high level semantics features and build a network of 99 layers at two different image scales for generating the normal saliency attention. This model is trained with transfer learning for initialization that is pre-trained on ImageNet for object detection and with some modification to minimize prediction error. At the end, the two streams integrate the features by fusion at low and high scale dimensions of images. This model is fine-tuned on four commonly used datasets and examines both qualitative and quantitative evaluation metrics with state of the art deep saliency models outcomes.

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Salient object detection on hyperspectral images in wireless network using CNN and saliency optimization

Cited by 17 publications

References 26 publications

The Use of Wireless Network Combined with Artificial Intelligence Technology in the Reform of Music Online Teaching System

The Use of Wireless Network Combined with Artificial Intelligence Technology in the Reform of Music Online Teaching System

Remote Music Teaching Classroom Based on Machine Learning and 5G Network Station

Visualization of Salient Object With Saliency Maps Using Residual Neural Networks

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