Speckle hinders information in synthetic aperture radar (SAR) images and makesautomatic information extraction very difficult. The Bayesian approach allows us to perform the despeckling of an image while preserving its texture and structures. This model-based approach relies on a prior model of the scene. This paper presents an evaluation of two despeckling and texture extraction model-based methods using the two levels of Bayesian inference. Thefirst method uses a Gauss-Markov random field as prior, and the second is based on an auto-binomial model (ABM). Both methods calculate a maximum a posteriori and determine the best model using an evidence maximization algorithm. Our evaluation approach assesses the quality of the image by means of the despeckling and texture extraction qualities. The proposed objective measures are used to quantify the despeckling performances of these methods. The accuracy of modeling and characterization of texture were determined usingboth supervised and unsupervised classifications, and confusionmatrices. Real and simulated SAR data were used during the validation procedure. The results show that both methods enhance the image during the despeckling process. The ABM is superior regarding texture extraction and despeckling for real SAR images.
COBISS.SI-ID: 15493398
This paper presents a model-based despeckling (MBD) of synthetic aperture radar (SAR) images using Bayesian analysis. The SAR image is despeckled using first-order Bayesian inference. The novelty in this paper is an autobinomial model (ABM), which models a prior probability density function (pdf); meanwhile, the likelihood pdf is modeled as a gamma distribution. Analytically, a solution for a maximum a posteriori estimate using an autobinomial prior cannot be computed; therefore, an approximation is introduced using differential. The best ABM for approximating the texture parameters in SAR images is found by using second-order Bayesian inference. The edges in the SAR images are detected using region borders, which have statistically different properties. Coefficient of variation is used to distinguish between homogeneous and heterogeneous areas. The experimental results show that the proposed method preserves the textural features and removes noise significantly in the homogeneous and heterogeneous regions. The proposed despeckling method is good regarding objective measures for synthetic images and better despeckles the real SAR images, when compared with the state-of-the-art MBD methods.
COBISS.SI-ID: 13292566
This paper proposes a new-wavelet-based synthetic aperture radar (SAR) image despeckling algorithm using the sequential Monte Carlo method. A model-based Bayesian approach is proposed. This paper presents two methods for SAR image despeckling. The first method, called WGGPF, models a prior with Generalized Gaussian (GG) probability density function (pdf) and the second method, called WGMPF, models prior with a Generalized Gaussian Markov random field (GGMRF). The likelihood pdf is modeled using a Gaussian pdf. The GGMRF model is used because it enables texture parameter estimation. The prior is modeled using GG pdf, when texture parameters are not needed. A particle filter is used for drawing particles from the prior for different shape parameters of GG pdf. When the GGMRF prior is used, the particles are drawn from prior in order to estimate noise-free wavelet coefficients and for those coefficients the texture parameter is changed in order to obtain the best textural parameters. The texture parameters are changed for a predefined set of shape parameters of GGMRF. The particles with the highest weights represents the final noise-free estimate with corresponding textural parameters. The despeckling algorithms are compared with the state-of-the-art methods using synthetic and real SAR data. The experimental results show that the proposed despeckling algorithms efficiently remove noise and proposed methods are comparable with the state-of-the-art methods regarding objective measurements. The proposed WGMPF preserves textures of the real, high-resolution SAR images well.
COBISS.SI-ID: 13420310
Synthesis of a simple robust controller with a pole placement technique and a H. metrics is the method used for control of a servo mechanism with BLDC and BDC electric motors. The method includes solving a polynomial equation on the basis of the chosen characteristic polynomial using the Manabe standard polynomial form and parametric solutions. Parametric solutions are introduced directly into the structure of the servo controller. On the basis of the chosen parametric solutions the robustness of a closedloop system is assessed through uncertainty models and assessment of the norm . ... The design procedure and the optimization are performed with a genetic algorithm differential evolution - DE. The DE optimization method determines a suboptimal solution throughout the optimization on the basis of a spectrally square polynomial and Šiljakćs absolute stability test. The stability of the designed controller during the optimization is being checked with Lipatovćs stability condition. Both utilized approaches: Šiljakćs test and Lipatovćs condition, check the robustness and stability characteristics on the basis of the polynomial's coefficients, and are very convenient for automated design of closed-loop control and for application in optimization algorithms such as DE.
COBISS.SI-ID: 14949398
Over recent years, in order to assist the evaluation, construction, and upgrade of communication networks, the need for the simulation of complex communication networks has increased. Traffic modeling has had a very large impact on network simulation reliability, which is usually statistically described for simulation purposes. Most often, the network traffic analysis isbased on the captured packets. However, modeling of network traffic is usually described by statistics of data sources, from higher layers of a Transmission Control Protocol/Internet Protocol (TCP/IP) model. For these reasons, we have developed a method that allows distribution parameter estimation of the data-source process from captured packets. This method is based on the algorithm, which mimics defragmentation as opposite to the TCP/IPfragmentation and encapsulation processes. The proposed method achieves an accurate description of network traffic.
COBISS.SI-ID: 14478358