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

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 163 ◽  
Author(s):  
Emir Turajlic ◽  
Alen Begović ◽  
Namir Škaljo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Noise Level ◽  
Low Noise ◽  
Singular Value ◽  
Fine Tuning ◽  
Noise Levels ◽  
Noise Level Estimation ◽  
Artificial Neural ◽  
Block Based

The blind additive white Gaussian noise level estimation is an important and a challenging area of digital image processing with numerous applications including image denoising and image segmentation. In this paper, a novel block-based noise level estimation algorithm is proposed. The algorithm relies on the artificial neural network to perform a complex image patch analysis in the singular value decomposition (SVD) domain and to evaluate noise level estimates. The algorithm exhibits the capacity to adjust the effective singular value tail length with respect to the observed noise levels. The results of comparative analysis show that the proposed ANN-based algorithm outperforms the alternative single stage block-based noise level estimating algorithm in the SVD domain in terms of mean square error (MSE) and average error for all considered choices of block size. The most significant improvements in MSE levels are obtained at low noise levels. For some test images, such as “Car” and “Girlface”, at σ = 1 , these improvements can be as high as 99% and 98.5%, respectively. In addition, the proposed algorithm eliminates the error-prone manual parameter fine-tuning and automates the entire noise level estimation process.

scholarly journals Adaptive Block-Based Approach to Image Noise Level Estimation in the SVD Domain

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 397 ◽  
Author(s):  
Emir Turajlic
Keyword(s):  
Noise Level ◽  
Additive White Gaussian Noise ◽  
Block Size ◽  
Tail Length ◽  
Low Noise ◽  
Singular Value ◽  
Computational Time ◽  
Noise Levels ◽  
Noise Level Estimation ◽  
Block Based

Estimation of additive white Gaussian noise levels in images has a variety of image processing applications including image enhancement, segmentation and feature extraction. Designing an algorithm with a consistent performance across a range of noise levels and image contents is a challenging problem; without any prior information, it is difficult to differentiate the noise signal from the underlying image signal. In this paper, an adaptive block-based noise level estimation algorithm in the singular value decomposition domain is proposed. The algorithm has the ability to change the singular value tail length according to the observed noise levels. A number of different choices of block size are considered and, for each choice, a mathematical model is proposed to describe how to adjust the singular value tail length as a function of the initial noise level estimates. In comparison with a seminal fixed singular value tail length algorithm, the proposed algorithm significantly improves the noise level estimation accuracy at low noise levels at the expense of a small increase in computational time; for example, for the block size of 64 × 64 and AWGN level σ = 1 , the MSE is reduced by 65%, whilst the computational time is increased by less than 1.3%.

scholarly journals Vehicular Traffic Noise Modelling of Urban Area- A Contouring & Artificial Neural Network-Based Approach

2021 ◽  
Author(s):  
Abhijit Debnath ◽  
Prasoon Kumar Singh ◽  
Sushmita Banerjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Noise Level ◽  
Urban Areas ◽  
Traffic Noise ◽  
Traffic Volume ◽  
Noise Levels ◽  
Noise Characteristics ◽  
Artificial Neural

Abstract Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causing health risks among the urban populations. In this study we have explored noise descriptors (L10, L90, Ldn, LNI, TNI, NC), contour plotting and finds the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, Speed, traffic flow, road gradient, pavement, road side carriageway distance factors taken as input parameter, whereas LAeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59% 2-wheelers and different vehicle specifications with varying speeds also effects driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicates high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 & .029 in training and 0.458 & 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ±0.6 dB(A) and the R2 linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.

scholarly journals Comparing the Prediction Capabilities of Artificial Neural Network (ANN) and Nonlinear Regression Models in Pet-Poy Yarn Characteristics and Optimization of Yarn Production Conditions

2017 ◽  
Vol 12 (3) ◽  
pp. 155892501701200 ◽  
Author(s):  
Kenan Yıldirimm ◽  
Hamdi Ogut ◽  
Yusuf Ulcay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Nonlinear Regression ◽  
Fine Tuning ◽  
Draw Force ◽  
Non Linear ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Production Conditions

In the manufacture of yarn, predicting the effect of changing production conditions is vital to reducing defects in the end product. This study compares, for the first time, non-linear regression and artificial neural network (ANN) models in predicting 10 yarn properties shaped by the influence of winding speed, quenching air temperature and/or quenching air speed during production. A multilayer perceptron ANN model was created by training 81 patterns using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The hyperbolic tangent, or TanH, activation function and logistic activation functions were used for the hidden and output layers respectively. Results showed that the ANN approach exhibited a greater prediction capability over the nonlinear regression method. ANN simultaneously predicted all of the 10 final properties of a yarn; tensile strength, tensile strain, draw force, crystallinity ratio, dye uptake based on the colour strengths (K/S), brightness, boiling shrinkage and yarn evenness, more accurately than the non-linear regression model (R2=0.97 vs. R2=0.92). These results lend support to the idea that the ANN analysis combined with optimization can be used successfully to prevent production defects by fine tuning the production environment.

scholarly journals Development of traffic noise prediction model for major arterial roads of tier-II city of India (Surat) using artificial neural network

Noise Mapping ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 172-184
Author(s):  
Ramesh B. Ranpise ◽  
B. N. Tandel ◽  
Vivek A. Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Prediction Models ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Noise Levels ◽  
Street Traffic ◽  
Artificial Neural

Abstract In the issue of expanding noise levels the world over, road traffic noise is main contributor. The investigation of street traffic noise in urban communities is a significant issue. Ample opportunity has already passed to understand the significance of noise appraisal through prediction models with the goal that assurance against street traffic noise can be actualized. Noise predictions models are utilized in an increasing range of decision-making applications. This study’s main objective is to assess ambient noise levels at major arterial roads of Surat city, compare these with prescribed standards, and develop a noise prediction model for arterial roads using an Artificial Neural Network. The feed-forward back propagation method has been used to train the model. Models have been developed using the data of three roads separately, and one final model has also been developed using the data of all three roads. Among the prediction in three arterial roads, the predicted output result from the model of Adajan-Rander showed a better correlation with a mean squared error (MSE) of 0.789 and R2 value of 0.707. But with the combined model, there is a slight deterioration in mean squared value (MSE) 1.550, with R2 not getting changed much significantly, i.e., 0.755. However, the combined model’s prediction can be adopted due to the variety of data used in its training.

scholarly journals Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis

2021 ◽  
Vol 13 (20) ◽  
pp. 11366
Author(s):  
SeyedAli Ghahari ◽  
Cesar Queiroz ◽  
Samuel Labi ◽  
Sue McNeil
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Global Perspective ◽  
Fine Tuning ◽  
Neural Network Modeling ◽  
Monolithic Group ◽  
Artificial Neural ◽  
Mean Square Errors ◽  
Nonlinear Autoregressive

Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) the global level, where all countries are considered as a monolithic group; and (b) the cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using nonlinear autoregressive recurrent neural network models with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with an objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by fine-tuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSEs) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries.

scholarly journals An Improved Collaborative Algorithm with Artificial Neural Network in Multidisciplinary Design Optimization of AUV

2017 ◽  
Author(s):  
Xiaodong Xu ◽  
Hongkai Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Optimization ◽  
Optimization Algorithm ◽  
Information Exchange ◽  
Multidisciplinary Design Optimization ◽  
Low Noise ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Artificial Neural

Multidisciplinary Design Optimization (MDO) is the most active field in the design of current complex system engineering, which is possessed with such two difficulties as subsystem information exchange and analytical and computational complexity of systems. Therefore, an improved collaborative optimization algorithm based on ANN (artificial neural network) response surface was proposed dependent on the consistency constraint algorithm and concurrent subspace algorithm. As an optimization method with secondary structure, it satisfied only local constraints in discipline layer, but provided a coordinated mechanism for interdisciplinary conflict in system layer. Finally, it was applied in the multidisciplinary design optimization of autonomous underwater vehicle (AUV). As shown from the result, the MDO convergence stability and reliability of low resistance, low noise and high maneuvering performance of the AUV shape can be ensured by the improved collaborative optimization algorithm, thus verifying the effectiveness of the algorithm.

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