Media Access Control for Narrowband Internet of Things: A Survey

Sun, Yuyi; He, Shibo; Tong, Fei

doi:10.1007/978-3-319-78262-1_161

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…In addition, for common features of interest in high-resolution remote sensing images, such as residential areas, airports, aircraft, and ships, a detection algorithm based on joint multi-image saliency (JMS) is proposed in the literature [ 12 ], which processes multiple multispectral remote sensing images with similar spatial structure and spectral details by jointly using the correlation information between this set of remote sensing images to simultaneously detect the features of interest in this set of multispectral remote sensing images. The literature [ 13 ] proposes a region of interest detection algorithm based on superpixel segmentation and statistical significance analysis, which detects the region of interest in remote sensing images accurately based on the final generated significance map by fusing the statistical significance feature map based on histogram statistics and the information significance feature map based on information entropy analysis. The literature [ 14 ] proposes that candidate regions containing feature targets can be predicted by supervised learning models constructed from various salient features.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things

Shi

2022

Computational Intelligence and Neuroscience

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Computer vision is one of the hottest research directions in artificial intelligence at present, and its research goal is to give computers the ability to perceive and cognize their surroundings from a single image. Image recognition is an important research direction in the field of computer vision, which has important research significance and application value in industrial applications such as video surveillance, biometric identification, unmanned vehicles, human-computer interaction, and medical image recognition. In this article, we propose an end-to-end, pixel-to-pixel IoT-oriented fuzzy support tensor product adaptive image classification method. Considering the problem that traditional support tensor product classification methods are difficult to directly produce pixel-to-pixel classification results, the research is based on the idea of inverse convolution network design, which directly outputs dense pixel-by-pixel classification results for images to be classified of arbitrary size to achieve true end-to-end and pixel-to-pixel high-score image classification and improve the efficiency of support tensor product models for high-score image classification on a pixel-by-pixel basis. Moreover, considering that network supervised classification training using deep learning requires a large amount of labeled data as true values and obtaining a large number of labeled data sources is a difficult problem in the field of image classification, this article proposes using a large amount of unlabeled high-resolution remote sensing images for learning generic structured features through unsupervised to assist the labeled high-resolution remote sensing images for better-supervised feature extraction and classification training. By finding a balance between generic structural feature learning of images and differentiated feature learning related to the target class, the dependence of supervised classification on the number of labeled samples is reduced, and the network robustness of the support tensor product algorithm is improved under a small number of labeled training samples.

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Section: Related Workmentioning

confidence: 99%

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things

Shi

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Media Access Control for Narrowband Internet of Things: A Survey

Cited by 1 publication

References 12 publications

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things

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