The main issue in any image ZOOMING techniques is to preserve the structure of the zoomed image. The zoomed image may suffer from the discontinuities in the soft regions and edges; it may contain artifacts, such as image blurring and blocky, and staircase effects. This paper presents a novel image ZOOMING technique using Partial Differential Equations (PDEs). It combines a non-linear Fourth-order PDE method with the LOCALLY ADAPTIVE ZOOMING ((LAZ)) ALGORITHM. The proposed method uses high-resolution image obtained from (LAZ) ALGORITHM to construct zoomed image by Fourth-order PDE. This proposed method preserves edges and minimizes blurring and staircase effects in the zoomed image. In order to evaluate image quality obtained from the proposed method, this paper focuses on both subjective and objective assessments. The results of these measures on a variety of images show that the proposed method is superior over the other image ZOOMING methods.

This article investigates the problem of simultaneous attitude and vibration control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. ADAPTIVE integral sliding mode control is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an active fault tolerant ALGORITHM considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant control led to discontinuous commands in the control signal, resulting in chattering. This issue was resolved by introducing an ADAPTIVE rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback control ALGORITHM has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.

Backpropagation ALGORITHM performs gradient descent only in the weight space of a network with fixed topology. A very small network cannot learn the problem well, and a very larger network will lead to overfitting and poor generalization performance. ALGORITHMs that can find an appropriate network architecture automatically are thus highly desirable. The ALGORITHMs that are introduced by researchers can be classified into five major groups. Pruning ALGORITHMs, constructive ALGORITHMs, hybrid ALGORITHMs, evolutionary ALGORITHMs, and learning automata based ALGORITHMs. Meybodi and Beigy introduced the first learning automata based ALGORITHMs, called survival ALGORITHM. This ALGORITHM produces networks with low complexity and high generalization. Survival ALGORITHM by turning off and on the weights, tries to find the most important weights. At the beginning, all weights of the network are on and contribute to learning. The on weights, whose absolute values are less than a threshold value, are penalized and those, whose absolute value are larger than another threshold value, are rewarded. The on weights, whose absolute values lie between these two threshold values, neither rewarded, nor penalized. The values of these two thresholds are determinative and have considerable effect on the performance of the survival ALGORITHM. Determination of the values of these thresholds is not an easy task and usually is determined by trial and error or using past experience. In this paper, we propose a method for adaptation of these two threshold values. The proposed method have been tested on number of problems and shown through simulations that the network generated by the survival ALGORITHM when threshold values are adapted has lesser number of weights and neurons, comparing to the network generated by the first version of the ALGORITHM reported earlier. Experimentation shows that the ADAPTIVE survival ALGORITHM has nearly the same degree of generalization as the non-ADAPTIVE version.

Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce ADAPTIVE GDD (AGDD), which eliminates the inappropriate effect of clustered samples by ADAPTIVEly updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

Image ZOOMING is one of the important issues of image processing that maintains the quality and structure of image. ZOOMING an image necessitates placing the extra pixels in the image data. Moreover, adding the data to the image must be consistent with the texture in the image in order to prevent artificial blocks. In this study, the required pixels are estimated using barycentric rational interpolation. The proposed method is a non-linear one which can preserve the edges and reduces the blur and block artifacts on the zoomed image. Numerical results are presented using PSNR and SSIM fidelity measures and they are compared to some other methods. The average PSNR of the original image and image ZOOMING was 33. 08 which can prove that image ZOOMING is very similar to the original image. The experimental results reveal that the proposed method has a better performance compared to other methods and can provide good image quality.

Purpose: Multimodal Cardiac Image (MCI) registration is one of the evolving fields in the diagnostic methods of Cardiovascular Diseases (CVDs). Since the heart has nonlinear and dynamic behavior, Temporal Registration (TR) is the fundamental step for the spatial registration and fusion of MCIs to integrate the heart's anatomical and functional information into a single and more informative display. Therefore, in this study, a TR framework is proposed to align MCIs in the same cardiac phase. Materials and Methods: A manifold learning-based method is proposed for the TR of MCIs. The Euclidean distance among consecutive samples lying on the LOCALLY Linear Embedding (LLE) of MCIs is computed. By considering cardiac volume pattern concepts from distance plots of LLEs, six cardiac phases (end-diastole, rapid-ejection, end-systole, rapid-filling, reduced-filling, and atrial-contraction) are temporally registered. Results: The validation of the proposed method proceeds by collecting the data of Computed Tomography Coronary Angiography (CTCA) and Transthoracic Echocardiography (TTE) from ten patients in four acquisition views. The Correlation Coefficient (CC) between the frame number resulted from the proposed method and manually selected by an expert is analyzed. Results show that the average CC between two resulted frame numbers is about 0. 82± 0. 08 for six cardiac phases. Moreover, the maximum Mean Absolute Error (MAE) value of two slice extraction methods is about 0. 17 for four acquisition views. Conclusion: By extracting the intrinsic parameters of MCIs, and finding the relationship among them in a lower-dimensional space, a fast, fully automatic, and user-independent framework for TR of MCIs is presented. The proposed method is more accurate compared to Electrocardiogram (ECG) signal labeling or time-series processing methods which can be helpful in different MCI fusion methods.

In the last two decades, many researchers have focused on the problem of automation of vehicles, and many research has been devoted to solving the challenges posed by this area. One of the important aspects in this area is the problem of localizing the vehicle and mapping the environment simultaneously in an unknown environment, which is briefly referred to as SLAM. So far, many methods have been proposed to solve this problem, but few of these researches have been implemented on the platform of collaborative robots. In this paper, SLAM problem is extended to multi robot platform by employing extended kalman filter. Due to lack of knowledge about the measurement noise covariance, the elements of this matrix adapted according to the actual data received from the sensor by employing particle swarm optimization technique. Then, to solve this problem in the dynamic environment, probability hypothesis density filter is used to track the dynamic objects in the field of view of sensors. Finally, the performance of the ALGORITHM is evaluated in a MATLAB environment.