An SVD-based image watermarking in wavelet domain using SVR and PSO

In this paper, we proposed a novel watermarking scheme based on adaptive quantization index modulation and singular value decomposition in the hybrid discrete wavelet transform (DWT) and discrete cosine transform (DCT). The secret watermark bits are embedded on the singular values vector of blocks within low frequency subband in host image hybrid DWT-DCT domain. To embed watermark imperceptibly, robustly and securely, we model the adaptive quantization steps by utilizing human visual system (HVS) characteristics and particle swarm optimization (PSO) algorithm. Experimental results demonstrate that the proposed scheme is robust to variety of image processing attacks. In the proposed algorithm the quantized embedding strategy is adopted, so no host image is needed for blind extraction of watermarking image.

Security and authentication of the people information is effectively provided through various watermarking algorithms. Most of the conventional watermarking algorithms have their own drawbacks in maintaining the quality and security of the image against various attacks. In order to overcome the above stated issues, a watermarking technique based on optimization is required for preserving image quality. In this paper, singular value decomposition (SVD) based digital image watermarking (DIW) scheme is proposed in hybrid discrete cosine transform (DCT) and discrete wavelet transform (DWT) using optimization algorithms like particle swarm optimization (PSO) and Jaya algorithm. The watermark image (WI) is embedded into low frequency DWT sub-band of DCT of cover image. PSO and Jaya algorithms are applied to improve the security threats and quality by assessing the fitness function. The work involves in utilizing the feature points in embedding of watermarks and analyzing the impact of various types of attacks with respect to image quality. The performance parameters like peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) and correlation coefficient of fitness function given by PSO and Jaya algorithm.

Blind digital speech watermarking based on Eigen-value quantization in DWT

The state of health and remaining useful life of lithium-ion batteries are key indicators for the normal operation of electrical devices. To address the problem of the capacity of lithium-ion batteries being difficult to measure online, in this paper, we propose an online method based on particle swarm optimization and support vector regression to estimation the state of health and remaining useful life. First, a novel health indicator is extracted from the discharge voltage to characterize the capacity of lithium-ion batteries. Then, based on the capacity degradation characteristics, support vector regression is used to predict the remaining useful life of these batteries, and particle swarm optimization is selected to optimize the parameters of the support vector regression, which effectively enhances the predictive performance of the model. Validated for the NASA battery aging dataset, when training with the first 40% of the dataset, the maximum error of the predicted remaining useful life was four cycles, and when training with the first 50% of the dataset, the maximum error of the predicted remaining useful life was only one cycle. When comparing to a deep neural network, support vector regression, long short-term memory algorithms and existing similar methods in the literature, the particle swarm optimization and support vector regression method can obtain more accurate prediction results.

Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach

According to an experimental dataset on the tensile strength and elongation of TA15 titanium alloy under different hot deformation process parameters including temperature, strain, strain rate and cooling condition, support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, is proposed to establish a model for prediction of the tensile strength and elongation of hot deformed TA15 titanium alloy. For tensile strength, the mean absolute percentage error (MAPE) achieved by SVR is 0.65% and 0.68% for the training and test set, respectively. At the same time, the MAPE for elongation achieved by SVR is 1.51% and 3.36% for the training and test set, respectively. The MAPEs for both tensile strength and elongation achieved by SVR are much smaller than those of BPNN by using identical training and test samples. Accordingly, the established SVR model was adopted to illustrate the relationships among tensile strength, elongation, and the process parameters. From the 3D surface of tensile strength vs. temperature and strain rate, it is found that to reach a higher tensile strength, a strain rate lower than 0.01s-1 is required, and a lower strain will be helpful for achieving the maximum elongation. These suggest that SVR as a novel approach has a theoretical significance and potential practical value in fabrication of TA15 titanium alloy with desired properties.

With the increasing global warming and enormous pollution, it is very obvious to generate power from renewable energy sources. In fact, photo voltaic (PV) power is a main renewable energy source for sustainable power generation. But PV power generation is uncertain and intermittent in nature. Stable and reliable power supply may not be feasible with PV power. Power supply reliability is important as much as its availability. To handle the inconsistency of PV power generation, power sector is highly dependent on forecasting methods and techniques. In this paper the feed forward neural network (FFNN) of artificial neural networks(ANN) with optimization technique particle swarm optimization (PSO) and support vector regression (SVR) are used in short term PV power generation forecasting and their performance is compared with the error calculation in terms of the mean absolute error (MAE) and root mean squared error (RMSE). The PV power and meteorological data of solar irradiation and temperature of Kolkata region of India was used for forecasting. Similar days of the year sunny days, cloudy days and rainy days are clustered using k-means clustering technique. Thereafter, a feed forward neural network is implemented to forecast a day head PV power and week ahead photo voltaic power. PSO is used to optimize the weights of the neural network. The performance of the proposed forecasting method is compared to a data mining approach, support vector regression (SVR).In this paper, the significance of proper selection of input parameters in PV power forecasting is emphasized. With the application of K-means clustering, the accuracy of ANN-PSO approach is improved significantly in a day ahead PV power forecasting and a week ahead PV power forecasting with a good margin. Even the accuracy of ANN-PSO approach after K-means clustering is better than SVR model in a week ahead forecasting

With the development in computing technologies, fog computing is developing as public and robust computing, which complements cloud computing to provide services, computation, and storage on the edge network. The future growth and support of 5G access networks additional advance the viability and implementation of fog networks and widen the scope of devices that can participate and serve in IoT communication. However, scaling fog computing, the number of end-users increases. Hence, the workload between fog nodes needs to be distributed efficiently. Otherwise, some of the nodes will be overloaded, and others will be under-loaded. Consequently, one of the critical factors for managing resources in fog computing efficiently and avoiding overloaded or under-loaded is load balancing. Therefore, load balancing between these resources is a challenge in fog computing. There are different techniques to balance the load, such as optimization algorithms or machine learning. This paper proposes a load balancing model in fog computing based on a many-objective particle swarm optimization (PSO) algorithm with support vector regression (SVR). The proposed load balancing model considered four metrics to optimize them while distributing the load: response time, energy consumption, resource utilization, and throughput. Besides, It combines SVR with PSO to improve PSO performance. The proposed model has been simulated and tested to evaluate the performance from different aspects. The experiments show that the proposed model efficiently balances the load with optimizing the four metrics. In addition, it improves the performance of PSO, which is used to balance the load.

Particle size is the key technical index of grinding process, but it is difficult to be measured online directly, and the lab analysis has large-time delay. Combined with the advantage of soft-sensor modeling based on support vector regression (SVR), a soft-sensor approach for particle size of grinding process with SVR is proposed. Firstly, the empirical formulae are used to roughly determine the range of the model parameters of the SVR, and then the particle swarm optimization (PSO) algorithm with improved inertia weights is adopted to obtain the optimal parameters of the SVR model to enhance the generalization capability. In this paper the empirical model and BP neural network model are established in order to analysis and prove the superiority of the proposed SVR soft-sensor. Finally, a software package based on the proposed method is developed. The application results show that the proposed method is easy to use and can realize the precise measurement of particle size online.

A novel modeling approach, support vector regression (SVR) combined with particle swarm optimization (PSO), was employed to construct mathematical model for prediction of the purity and average particle size of hydroxyapatite (HA) based on five synthesis process factors, including the P 2 O 5 content, Ca(NO 3 ) 2 ·4H 2 O content, aging time, synthesis temperature, and holding time. The accuracy and reliability of the constructed SVR model are validated through the mean absolute percentage error of the leave-one-out cross validation. Then the SVR model is applied to optimize the process parameters. The maximum purity of HA is found to be 98.06% predicted by the SVR model under the optimal synthesis parameters, i.e., the P 2 O 5 content is 1.98 mol/L, Ca(NO 3 ) 2 ·4H 2 O content is 2.68mol/L, aging time is 17.68h, synthesis temperature is 818.93°C, and holding time is 3.17h. These studies suggest that SVR is an effective and practical methodology to assist the design of experiment, and is helpful to increase the yield and control the average particle size of the synthesized HA via rational process parameters.

The tremendous advancement of digital technology has increased the ease with which digital multimedia signals (image, video, audio) are stored, transmitted, and reproduced. Consequently, the content providers and owners are faced with problems of protection against copyright violation and other forms of abuse to their digital property. Digital watermarking has been proposed in the last decade as a solution to prevent illegal and malicious copying and distribution of digital media by embedding an unnoticeable information into the media content. This chapter describes three imperceptible and robust watermarking algorithms for different types of multimedia objects (image, video, audio). The three algorithms are based on cascading two powerful mathematical transforms; the Discrete Wavelet Transform (DWT), and the Singular Value Decomposition (SVD). The two transforms are different, and thus provide complementary levels of robustness against the same attack. In the proposed dual-transform algorithms, the watermark bits are not embedded directly on the wavelet coefficients, but rather on the elements of singular values of the DWT sub-bands of the multimedia object. Effectiveness of the proposed algorithms is demonstrated through extensive experimentation.

Prediction of maximum pitting corrosion depth in oil and gas pipelines

An enhanced hybrid image watermarking scheme for security of medical and non-medical images based on DWT and 2-D SVD

The purpose of this paper is to improve the robustness of traditional image watermarking based on singular value decomposition (SVD) by using optimization-based quantization on multiple singular values in the wavelet domain. In this work, we divide the middle-frequency parts of discrete-time wavelet transform (DWT) into several square blocks and then use multiple singular value quantizations to embed a watermark bit. To minimize the difference between original and watermarked singular values, an optimized-quality formula is proposed. First, the peak signal-to-noise ratio (PSNR) is defined as a performance index in a matrix form. Then, an optimized-quality functional that relates the performance index to the quantization technique is obtained. Finally, the Lagrange Principle is utilized to obtain the optimized-quality formula and then the formula is applied to watermarking. Experimental results show that the watermarked image can keep a high PSNR and achieve better bit-error rate (BER) even when the number of coefficients for embedding a watermark bit increases.

In this article, a new contrast enhancement approach is presented for quality enhancement of low-contrast satellite images. The proposed technique is based on the Artificial Bee Colony (ABC) algorithm using Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). The method employs the ABC technique to learn the parameters of the adaptive thresholding function required for optimum enhancement. In this approach, the input image is primarily decomposed into four sub-bands through DWT, and then each sub-band of DWT is optimized through the ABC algorithm. After that, a singular value matrix of the low–low thresholded sub-band image is estimated and, finally, the enhanced image is constructed by applying inverse DWT. The results obtained through this method reveal that the proposed methodology gives better performance in terms of peak signal-to-noise ratio (PSNR), mean square error (MSE), and mean and standard deviation as compared to General Histogram Equalization (GHE), Discrete Cosine Transform and Singular Value Decomposition (DCT-SVD), DWT-SVD, Particle Swarm Optimization (PSO), and modified versions of the PSO-based enhancement approach.