The character of transient processes in electrical machines and transformers defines the shape of magnetization curve of the magnetic circuit, i.e. of its sheets. Approximate analytical or numerical methods are used to determine the influence of saturation and hysteresis on transient processes. This paper presents an analytical method for the calculation of transient process in a magnetic circuit with assumed magnetization characteristic, one part of which contains saturation. An operator calculus was used to solve Maxwell's equations that characterize the transient process. The applied method has been verified by the simulation results using the adapted part of psbxfosaturable.mdl of the MATLAB Simulink software package. It is also shown that due to saturation and influence of hysteresis, additional free components appear in the sheets of the magnetic circuit (recognized in obtained values of current, induction and flux). Analysis of the shape of time diagrams of quantities also shows that the time constants of these quantities increase. In the linear part of the magnetization curve, solutions with higher accuracy are obtained, and in the part of saturation in which the accuracy of the method was not in foreground, only a qualitative analysis of the transient process has been achieved. In comparison with other methods, two regimes of transient processes when given are analyzed: magnetic excitation forces and field strengths, and magnetic fluxes and inductions.

The digital signature image is a digital pattern with highly variable features. The pattern recognition of digital signature images aims to build a specific characteristic capable of representing a considerable pattern variation while maintaining the boundary conditions of authentication. The feature as an attribute that describes the characteristics of a pattern becomes a determinant factor of reliability of a method of recognizing digital signature image pattern for Handwritten Signature Verification (HSV). To construct HSV required two types of signature samples that are the original signature samples used as training samples and the guess signature samples (consist of valid and imposter signature) which are used as test samples. This study proposes two unique features of 16-Bits Binary Chain to Decimal (16BCD) and Virtual Center of Gravity (VCG). The 16BCD feature obtained from image segmentation with a 4x4 pixel region. All pixels in each region of the segmentation result rearranged into a 16-bit binary chain. The VCG feature is a virtual representation of the Original Signature Pattern (OSP) gravity center against Pattern Space and Background. The verification mechanism uses criteria: the percent of acceptable correlation coefficients for the acceptable feature of 16BCD feature, Mean Absolute Error (MAE) against 16BCD, and the percent deviation of acceptable distance to the VCG feature prototype. Verification test results obtained Acceptance Rate (AR) 80% (which states the percentage of HSV success based on a number of original signature samples) with an efficiency of 90% (which states the percentage of success of HSV in distinguishing valid or forgery signature based on a sample of guessing signatures).

This paper presents a digital image watermarking scheme in the frequency domain for colour images. The algorithm was developed using the digital wavelet transform together with fractal encryption. A host image was first transformed into a frequency domain using the discrete wavelet transform after which a binary watermark was permuted and encrypted with a fractal generated from the watermark and a random key. The encrypted watermark is then embedded into the host image in the frequency domain to form a watermarked image. The algorithm’s performance was examined based on the image quality of the watermarked image using peak signal to noise ratio. A perceptual metrics called the structural similarity index metric was further used to examine the structural similarity of the watermarked image and the extracted watermark. Again, the normalised cross-correlation was introduced to further assess the robustness of the algorithm. Our algorithm produced a peak signal to noise ratio of 51.1382dB and a structural similarity index of 0.9999 when tested on colour images of Lena, baboon and pepper indicating the quality of the watermarked images produced and hence indicates a higher imperceptibility of the proposed algorithm. The extracted watermark also had a structural similarity of 1 and a normalised cross correlation of 1 indicating a perfect similarity between the original watermark and the extracted watermark hence shows a higher performance of the proposed algorithm. The algorithm also showed a very good level of robustness when various attacks such as Gaussian noise, Poisson noise, salt and pepper noise, speckle noise and filtering were applied.

Underwater Object Detection is one of the most challenging and unexplored domains in this area of Computer Vision. The proposed research refines the image enhancement of under-water imagery by proposing an improvement of already existing tools for underwater Object detection. The comparative study clearly depicts the enhancement of the proposed method with respect to the existing methods for underwater object detection. Moreover, a framework for detection of underwater organisms such as fishes are proposed, which will act as the steppingstone for re- serving the ecosystem of the whole fish community. Mostly the object detection using deep learning has been the prime goal to this research and the comparison between other preexisting methods are compared at the end. As a result, techniques that are already well established will be used for overall enhancement of those images. Through this enhancement and through finding a healthy environment for their breeding ground, the extinction of selected species of fishes is can be diminished and decreased. All this is carried out by overcoming difficulties underwater through a novel technique that can be integrated into an Underwater Autonomous Vehicle and can be classified as robust in nature. Robustness will depend on three important factors in this research, first is accuracy, then fast and lastly being upgradeable. The proposed method is a modified VGGNet-16, which is trained using the ImageCLEF FISH_TS dataset. The overall result provides an accuracy of 96.4% which surpasses all its predecessors.

As a method of processing images, image interpolation has been widely applied to image processing. This paper proposed a new method of image super-resolution algorithm based on bilateral quadratic interpolation. We translate interpolation areas of the pixels to the specified area to construct the bilateral quadratic interpolation surfaces. The constructed surfaces are used to estimate the pixel values of the compensating pixel areas. By replacing each pixel with the corresponding areas, the image is amplified. The amplified images of the algorithm have more details remained than the results of the common algorithms. And this novel algorithm has a better improvement in the fidelity of the images. Moreover, it has a better performance in running speed and the quality of the images such as PSNR and SSIM. It can be used on the amplification of the color images, which can provide better quality amplified images for people. And it makes it convenient for people to study carefully on partial information of images.