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Thresholding of Curvelet Coefficients, for image denoising, drains out subtle signal component in noise subspace. In effect, it also produces ringing artifacts near edges. We found that the noise sensitivity of Curvelet phases — in contrast to their magnitude — reduces with higher noise level. Thus, we preserved the phase of the coefficients below threshold at coarser scale and estimated the corresponding magnitude by Joint Bilateral Filtering (JBF) technique. In contrast to the traditional hard thresholding, the coefficients in the finest scale is estimated using Bilateral Filtering (BF). The proposed filtering approach in the finest scale exhibits better connectedness among the edges, while removing the granular artifacts in the denoised image due to hard thresholding. Finally, the use of Guided Image Filter (GIF) on the Curvelet-based reconstructed image (initial denoised image in spatial domain) ensures the preservation of small image information with sharper edges and textures detail in the final denoised image. The lower noise sensitivity of Curvelet phase at higher noise strength accelerates the performance of proposed method over several state-of-the-art techniques and provides comparable outcome at lower noise levels.

Nonlinear control laws often need to be implemented with digital hardware. Use of digital control systems leads to communication/processing delays which are widely neglected in control of mechanical systems. This paper proposes a discrete approach to feedback linearization that considers these commonly overlooked delays in design. The proposed approach is shown to both improve the performance and remove the need for continuous derivative terms. In feedback linearization control systems, designed in the continuous domain, derivative terms are required to speed up the control response of mechanical systems, but disadvantageously cause high sensitivity to noise. The proposed approach was used to design a feedback linearization control system for a common turning maneuver of an unmanned helicopter in yaw. At this maneuver, the helicopter centroid motion and pitch rotational speed are almost zero. Governing differential equations of the helicopter at this maneuver are nonlinear and coupled. A feedback linearization law was proposed to curb nonlinearity and, a discrete control system, considering the inevitable delay due to the use of digital control systems, was adopted to complete the control law. This innovative approach resulted in less sensitivity to noises and performance boost. Practical limits in terms of control input, rotor speed, sampling frequency and noises of the gyroscope, the tachometer and the acceleration sensor were taken into account in this research. The results show that the proposed control system leads to fast and smooth yaw turns even at a high pitch angle (close to vertical) or in the case of being hit by external objects.

Our understanding of the input-output function of single neurons has been advanced by biophysically accurate multi-compartmental models. The large number of free parameters in these models requires the use of automated approaches for finding their optimal values in a very multi dimensional parameter-plane. Due to inherent noise in measuring equipment and surroundings determination of the accuracy of the obtained parameters is near impossible. Here we show that finding the parameters distribution via Monte Carlo approach simulations reveals the sensitivity of each parameter to noise. This method allows for the reduction of the complexity of the parameter-plane which in turn enables finding the sensitivity of the model to each parameter and the sensitivity of each parameter to noise.