Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been proposed to overcome the limitations of RRT*. The goal of the proposed method is to accelerate the rate of convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*-- Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*- Smart algorithm

Shape from focus (SFF), which relies on image focus as a cue within sequenced images, represents a passive technique in recovering object shapes in scenes. Although numerous methods have been recently proposed, less attention has been paid to particular factors affecting them. In regard to SFF, one such critical factor impacting system application is the total number of images. A large data set requires a huge amount of computation power, whereas decreasing the number of images causes shape reconstruction to be crude and erroneous. The total number of images is inversely proportional to interframe distance or sampling step size. In this paper, interframe distance (or sampling step size) criteria for SFF systems have been formulated. In particular, light ray focusing is approximated by the use of a Gaussian beam followed by the formulation of a sampling expression using Nyquist sampling. Consequently, a fitting function for focus curves is also obtained. Experiments are performed on simulated and real objects to validate the proposed schemes.

Urine tests are performed by using an off-the-shelf reference sheet to compare the color of test strips. However, the tabular representation is difficult to use and more prone to visual errors, especially when the reference color-swatches to be compared are spatially apart. Thus, making it is difficult to distinguish between the subtle differences of shades on the reagent pads. This manuscript represents a new arrangement of reference arrays for urine test strips (urinalysis). Reference color swatches are grouped in a doughnut chart, surrounding each reagent pad on the strip. The urine test can be evaluated using naked eye by referring to the strip with no additional sheet necessary. Along with this new strip, an algorithm for smartphone based application is also proposed as an alternative to deliver diagnostic results. The proposed colorimetric detection method evaluates the captured image of the strip, under various color spaces and evaluates ten different tests for urine. Thus, the proposed system can deliver results on the spot using both naked eye and smartphone. The proposed scheme delivered accurate results under various environmental illumination conditions without any calibration requirements, exhibiting performances suitable for real-life applications and an ease for a common user.

In regard to Shape from Focus, one critical factor impacting system application is mechanical vibration of the translational stage causing jitter noise along the optical axis. This noise is not detectable by simply observing the image. However, when focus measures are applied, inaccuracies in the depth occur. In this article, jitter noise and focus curves are modeled by Gaussian distribution and quadratic function, respectively. Then Kalman filter is designed and applied to eliminate this noise in the focus curves, as a post-processing step after the focus measure application. Experiments are implemented with simulated objects and real objects to show usefulness of proposed algorithm.

In this article, we introduce a novel shape from focus method to compute 3D shape of microscopic objects, based on modified-pixel intensities and Bezier surface approximations. A new and simple but effective focus measure is proposed. In our focus measure, the original intensities of a sequence of small neighborhood are modified by subtracting the maximum of the values of first and last frames. An initial depth map is calculated by finding the maximum of the pixel's focused energy and its corresponding frame number. Missing information between two consecutive frames, false depth detection, and enhancement of noise related intensities may provide inaccurate depth map. To overcome these problems and to produce an accurate depth map, we proposed Bezier surface approximation. The proposed method is tested using synthetic and real image sequences. The comparative analysis demonstrates the effectiveness of the proposed method.

The specific characteristics and operations of microgrid cause protection problems due to high penetration of distributed energy resources. To resolve these issues, the proposed scheme employs the Hilbert transform and data mining approach to protect the microgrid. First, the Hilbert transform is used to preprocess the faulted voltage and current signals to extract the sensitive fault features. Then, the obtained data set of the extracted features is input to the logistic regression classifier for fault detection. Later, fault classification is done by training the AdaBoost classifier. In the proposed scheme, the simulation results for feature extractions are evaluated on a standard International Electrotechnical Commission (IEC) medium voltage microgrid, compatible with MATLAB/SIMULINK software environment, whereas, Python is used for training and testing of data mining model. The results are evaluated under grid-connected and islanded modes for both looped and radial configurations by simulating various fault and no-fault cases. The results show that the accuracy of the proposed logistic regression and AdaBoost classifier is higher when compared to decision tree, support vector machine, and random forest methods. The results further validate the robustness of the proposed method against the measurement noise.

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