scholarly journals Hybrid Optimization-Based Robust Watermarking Using Denoising Convolutional Neural Network

2021 ◽  
Author(s):  
Dhiran Kumar Mahto ◽  
Amit Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optimal Scaling ◽  
Scaling Factor ◽  
Hybrid Optimization ◽  
Robust Watermarking ◽  
Computational Overhead ◽  
Copyright Violation ◽  
Additional Level ◽  
Transform Domains

Abstract Colour images have been widely used in many aspects of life; however, copyright violation issues related to these images motivate research efforts. This paper aims to develop an enhanced watermarking algorithm for producing a watermarked image using hybrid optimisation with high imperceptibility and robustness. The algorithm is based on spatial and transform domains and begins by embedding multiple secret marks into cover media using an optimal scaling factor. The multi-type mark contributes an additional level of authenticity to the proposed algorithm. Furthermore, the marked image is encrypted using an improved encryption scheme, and the denoising convolutional neural network (DnCNN) is employed to enhance the robustness of the proposed algorithm. The results reveal that the proposed watermarking algorithm yields low computational overhead, excellent watermark capacity, imperceptibility, and robustness to common filtering attacks. Moreover, the comparison shows that the proposed algorithm outperforms other competing methods.

scholarly journals Artificial Neural Network Assisted Variable Step Size Incremental Conductance MPPT Method with Adaptive Scaling Factor

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 43
Author(s):  
Song-Pei Ye ◽  
Yi-Hua Liu ◽  
Chun-Yu Liu ◽  
Kun-Che Ho ◽  
Yi-Feng Luo
Keyword(s):  
Neural Network ◽  
Golden Section ◽  
Characteristic Curve ◽  
Optimal Scaling ◽  
Scaling Factor ◽  
Variable Step Size ◽  
Step Size ◽  
Point Tracking ◽  
Incremental Conductance ◽  
Variable Step

In conventional adaptive variable step size (VSS) maximum power point tracking (MPPT) algorithms, a scaling factor is utilized to determine the required perturbation step. However, the performance of the adaptive VSS MPPT algorithm is essentially decided by the choice of scaling factor. In this paper, a neural network assisted variable step size (VSS) incremental conductance (IncCond) MPPT method is proposed. The proposed method utilizes a neural network to obtain an optimal scaling factor that should be used in current irradiance level for the VSS IncCond MPPT method. Only two operating points on the characteristic curve are needed to acquire the optimal scaling factor. Hence, expensive irradiance and temperature sensors are not required. By adopting a proper scaling factor, the performance of the conventional VSS IncCond method can be improved, especially under rapid varying irradiance conditions. To validate the studied algorithm, a 400 W prototyping circuit is built and experiments are carried out accordingly. Comparing with perturb and observe (P&O), α-P&O, golden section and conventional VSS IncCond MPPT methods, the proposed method can improve the tracking loss by 95.58%, 42.51%, 93.66%, and 66.14% under EN50530 testing condition, respectively.

scholarly journals Human Action Recognition using Scaled Convolutional Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 1820-1826
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Action Recognition ◽  
Gradient Descent ◽  
Human Action Recognition ◽  
Human Action ◽  
Scaling Factor ◽  
Classification Error ◽  
Statistical Measures ◽  
Gradient Descent Algorithm

Deep learning is current buzz word in domain of computer vision. In this work, a method for human action recognition based on a variation of General Convolutional Neural Network (GCNN), called Scaled CNN (SCNN) is proposed. In GCNN, weights of the network are updated in every epoch of training to minimize the classification error. In SCNN, the weighs are first computed using gradient descent algorithm as in GCNN, and then multiplied by scaling factor. Scaling factor is calculated using statistical measures, mean and standard deviation of the frames. Since statistical measures vary from video to video, scaling factor adapts to these changes. As the statistical information from the frames is directly used to alter the weights, it results in minimizing the error faster as compared to GCNN. Results of the proposed method prove that higher accuracy can be achieved with less number of epochs if scaling is used.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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