Open Access
ARTICLE
Ke Jin, Shunfeng Wang*
Journal on Internet of Things, Vol.2, No.1, pp. 1-11, 2020, DOI:10.32604/jiot.2020.09071
Abstract In recent years, image restoration has become a huge subject, and
finite hybrid model has been widely used in image denoising because of its easy
modeling and strong explanatory results. The gaussian mixture model is the most
common one. The existing image denoising methods usually assume that each
component of the natural image is subject to the gaussian mixture model (GMM).
However, this approach is not entirely reasonable. It is well known that most
natural images are complex and their distribution is not entirely gaussian. As a
result, there are still many problems that GMM cannot solve. This paper tries… More >
Open Access
ARTICLE
Peng Xu, Jianwei Zhang*
Journal on Internet of Things, Vol.2, No.1, pp. 13-21, 2020, DOI:10.32604/jiot.2020.09073
Abstract Nonlocal property is an important feature of natural images, which
means that the patch matrix formed by similar image patches is low-rank.
Meanwhile, learning good image priors is of great importance for image
denoising. In this paper, we combine the image self-similarity with EPLL
(Expected patch log likelihood) method, and propose an EPLL denoising model
based on internal and external image similarity to improve the preservation of
image details. The experiment results show that the validity of our method is
proved from two aspects of visual and numerical results. More >
Open Access
ARTICLE
Lei Sun1, Ling Tan1,*, Wenjie Ma1, Jingming Xia2
Journal on Internet of Things, Vol.2, No.1, pp. 23-35, 2020, DOI:10.32604/jiot.2020.09075
Abstract In recent years, WiFi indoor positioning technology has become a hot
research topic at home and abroad. However, at present, indoor positioning
technology still has many problems in terms of practicability and stability, which
seriously affects the accuracy of indoor positioning and increases the complexity of
the calculation process. Aiming at the instability of RSS and the more complicated
data processing, this paper proposes a low-frequency filtering method based on fast
data convergence. Low-frequency filtering uses MATLAB for data fitting to filter
out low-frequency data; data convergence combines the mean and multi-data
parallel analysis process to achieve a good balance… More >
Open Access
ARTICLE
Caifeng Cheng1,2, Deshu Lin3,*
Journal on Internet of Things, Vol.2, No.1, pp. 37-45, 2020, DOI:10.32604/jiot.2020.09116
Abstract Compressive sensing theory mainly includes the sparsely of signal
processing, the structure of the measurement matrix and reconstruction
algorithm. Reconstruction algorithm is the core content of CS theory, that is,
through the low dimensional sparse signal recovers the original signal accurately.
This thesis based on the theory of CS to study further on seismic data
reconstruction algorithm. We select orthogonal matching pursuit algorithm as a
base reconstruction algorithm. Then do the specific research for the
implementation principle, the structure of the algorithm of AOMP and make the
signal simulation at the same time. In view of the OMP algorithm reconstruction… More >
Open Access
ARTICLE
Caifeng Cheng1,2, Deshu Lin3,*
Journal on Internet of Things, Vol.2, No.1, pp. 47-54, 2020, DOI:10.32604/jiot.2020.09117
Abstract In this paper, the observation matrix and reconstruction algorithm of
compressed sensing sampling theorem are studied. The advantages and
disadvantages of greedy reconstruction algorithm are analyzed. The
disadvantages of signal sparsely are preset in this algorithm. The sparsely
adaptive estimation algorithm is proposed. The compressed sampling matching
tracking algorithm supports the set selection and culling atomic standards to
improve. The sparse step size adaptive compressed sampling matching tracking
algorithm is proposed. The improved algorithm selects the sparsely as the step
size to select the support set atom, and the maximum correlation value. Half of
the threshold culling algorithm supports the… More >
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