Performance Analysis of Adaptive Variational Mode Decomposition Approach for Image Encryption

2020 ◽  
Author(s):  
Feng Heng ◽  
Ruru Liu ◽  
Zuo Sun
Keyword(s):  
Performance Analysis ◽  
Image Encryption ◽  
Variational Mode Decomposition ◽  
Decomposition Approach ◽  
Mode Decomposition

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

Color image encryption method based on 2D-variational mode decomposition

2019 ◽  
Vol 78 (13) ◽  
pp. 17719-17738
Author(s):  
Shouqiang Kang ◽  
Yaqi Liang ◽  
Yujing Wang ◽  
Mikulovich V I
Keyword(s):  
Image Encryption ◽  
Color Image ◽  
Variational Mode Decomposition ◽  
Color Image Encryption ◽  
Mode Decomposition ◽  
Encryption Method

scholarly journals Hydrological peaks evaluation at chitral and tarbela stations, Pakistan, using combined Bayesian regularized neural network and signal difference average based variational mode decomposition method: a case study

2021 ◽  
Author(s):  
Mehwish Shafi Khan ◽  
Syed Ahmad Hassan
Keyword(s):  
Neural Network ◽  
River Flow ◽  
Irrigation System ◽  
Moving Average ◽  
Indus Basin ◽  
Gradient Boosting ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Decomposition Approach ◽  
Mode Decomposition

Abstract Pakistan being an agricultural country highly depends on its natural water resources originate from the upper regions of Hindu Kush-Karakoram-Himalaya Mountains and nourish one of the world's largest Indus Basin irrigation system. This paper presents streamflow modelling and forecasting using signal difference average (SDA) based variational mode decomposition (VMD) combined with machine learning (ML) methods at Chitral and Tarbela stations on the Indus River network. For this purpose, VMD based; random forest (VMD-RF), gradient boosting machine (VMD-GBM) and Bayesian regularized neural network (VMD-BRNN) have been opted. Moreover, traditional time series flow model that is seasonal autoregressive integrated moving average (SARIMA) and classical decomposition approach with particle swarm optimization-based support vector regression (PSO-SVR) are considered as benchmark models for comparison. The results show that overall, VMD-BRNN performed best, followed by VMD-GBM and VMD-RF, whereas, SARIMA and PSO-SVR ranked last. Overall, SARIMA and PSO-SVR are failing to capture most of the peaks even during training period. Whereas, hybridization of VMD and ML methods has shown increased robustness of the models. The results show that the influential role of the high dimensional components and robustness on the river flow may be explored by most optimum SDA based VMD signals hybrid with BRNN method.

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