Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain

Turajlić, Emir; Begović, Alen; Skaljo, Namir

doi:10.3390/electronics8020163

Cited by 9 publications

(5 citation statements)

References 44 publications

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“…The generative model is a model that can learn the potential distribution of data and generate new sensor samples. Traditional generative models include the Gaussian model (GM), Bayesian network (BN) [3], S-type reliability network (SRN) [4], Gaussian mixture model (GMM) [5], multinomial mixture model (MMM) [6], hidden Markov model (HMM) [7] and hidden Markov random field (HMRF) [8]. Goodfellow et al [9] proposed generative adversarial networks (GAN) by summarizing the advantages and disadvantages of traditional generative networks.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

Chen

Tao

Wang

et al. 2019

Sensors

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To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more effective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

Chen

Tao

Wang

et al. 2019

Sensors

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“…These prediction methods are mainly classified into three groups: physical propagation models, traditional statistical methods and machine learning methods [13], [14]. Deep learning derives from the study of ANNs, which in many areas of data science have demonstrated a remarkable ability to learn complex, non-linear relationships between sets of variables [15], [16]. The ANN is inspired by the nature of real dynamic systems emulating the human brain, where neurons are layered and interconnected by mathematical functions.…”

Section: Introductionmentioning

confidence: 99%

Noise estimation using an artificial neural network in the urban area of Jaen, Cajamarca

et al. 2023

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Jaen is a city in constant urban growth which generates an increase in vehicular traffic and active noise pollution. The research presents the development of an artificial neural network (ANN) to estimate the noise produced by vehicular traffic in the urban area of the city. Consequently, information was collected from two investigations coded as T1 and T2, for which a matrix of 10 variables was elaborated with 210 and 273 data respectively. Random random sampling was performed to divide the data matrix into 80% (training) and 20% (validation). Weka software and the multi-layer perceptron (MLP) training algorithm were used to model the ANN. An ANN for T1 with 6-19-1 architecture and an ANN for T2 with 6-15-1 architecture were obtained. The performance of the ANNs was evaluated using the correlation coefficient (R), coefficient of determination (R2) and root mean square error (RMSE). The results show that the MLP networks are able to estimate the sound pressure level with values of R=0.9927, R2=0.9854 and RMSE=0.7313 for T1, R=0.9989, R2=0.9978, and RMSE=0.1515 for T2.

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“…The method was firstly established by Beltrami and Jordan [1] and successively generalized by Autonne [2], Eckart and Young [3]. Since then SVD has successfully been applied on a huge number of different application fields, such as biomedical signal processing [4][5][6][7][8][9][10], image processing [11][12][13][14][15][16][17][18][19], Kalman filtering [20][21][22], array signal processing [23], dynamic networks [24], speech processing [25], simultaneous localization and mapping (SLAM) systems [26], and variable digital filter design [27], to cite just a few.…”

Section: Introductionmentioning

confidence: 99%

Singular Value Decomposition in Embedded Systems Based on ARM Cortex-M Architecture

et al. 2020

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Singular value decomposition (SVD) is a central mathematical tool for several emerging applications in embedded systems, such as multiple-input multiple-output (MIMO) systems, data analytics, sparse representation of signals. Since SVD algorithms reduce to solve an eigenvalue problem, that is computationally expensive, both specific hardware solutions and parallel implementations have been proposed to overcome this bottleneck. However, as those solutions require additional hardware resources that are not in general available in embedded systems, optimized algorithms are demanded in this context. The aim of this paper is to present an efficient implementation of the SVD algorithm on ARM Cortex-M. To this end, we proceed to (i) present a comprehensive treatment of the most common algorithms for SVD, providing a fairly complete and deep overview of these algorithms, with a common notation, (ii) implement them on an ARM Cortex-M4F microcontroller, in order to develop a library suitable for embedded systems without an operating system, (iii) find, through a comparative study of the proposed SVD algorithms, the best implementation suitable for a low-resource bare-metal embedded system, (iv) show a practical application to Kalman filtering of an inertial measurement unit (IMU), as an example of how SVD can improve the accuracy of existing algorithms and of its usefulness on a such low-resources system. All these contributions can be used as guidelines for embedded system designers. Regarding the second point, the chosen algorithms have been implemented on ARM Cortex-M4F microcontrollers with very limited hardware resources with respect to more advanced CPUs. Several experiments have been conducted to select which algorithms guarantee the best performance in terms of speed, accuracy and energy consumption.

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Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain

Cited by 9 publications

References 44 publications

RETRACTED: The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

RETRACTED: The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

Noise estimation using an artificial neural network in the urban area of Jaen, Cajamarca

Singular Value Decomposition in Embedded Systems Based on ARM Cortex-M Architecture

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