Angular velocity dynamics identification of small unmanned helicopter using fuzzy model

Attitude identification method for unmanned helicopter based on fuzzy model at hovering is presented. The dynamical attitude model is considered as basis for attitude control and it is very complex. To reduce the complexity of model, nonlinear model of unmanned helicopter with unknown parameters are to be determined by fuzzy system first and then derivative based gradient method is used to identify unknown parameters of model. Gradient method is used due to ability that fuzzy system is not necessarily to be linear in parameters, therefore all fuzzy sets for input and output can be adjusted. The validity of the proposed model was verified using experimental data obtained by the commercially available flight simulator X-Plane. The simulation results showed high accuracy of the modeling method and attitude dynamics data matched well with experimental data.

Input series out putparallel based multi-modular boost half bridge DC-DC converter with a dynamic control scheme

Multi-modular system plays an important role in power system architecture because low voltage and low power converters can be connected in any combination parallel or series at input/ output side in order to obtained any given power system specifications. Multi-modular boost haft bridge DC-DC converter in the configuration of input series output parallel has been investigated in this paper. The boost half bridge DC-DC converters are connected in input series output parallel con- figuration in order to achieve equal input voltage sharing and output current sharing between the con- verters. This can be achieved with the help of dynamic control scheme which consists of two loops, a voltage loop and a current loop, for each module. Dynamic behavior of multi-modular converter configuration has been observe by varying the load condition. Moreover, the results obtained through multi-modular converter describe that the system has good dynamic and steady state response. Al- though two converter modules are focused in this paper but it can be modified to any number of modules.

Denoising of Medical Images Using Multiwavelet Transforms and Various Thresholding Techniques

The problem of estimating an image corrupted by additive white Gaussian noise has been of interest for practical reasons. Non-linear denoising methods based on wavelets, have become popular but Multiwavelets outperform wavelets in image denoising. Multiwavelets are wavelets with several scaling and wavelet functions, offer simultaneously Orthogonality, Symmetry, Short support and Vanishing moments, which is not possible with ordinary (scalar) wavelets. These properties make Multiwavelets promising for image processing applications, such as image denoising. The aim of this paper is to apply various non-linear thresholding techniques such as hard, soft, universal, modified universal, fixed and multivariate thresholding in Multiwavelet transform domain such as Discrete Multiwavelet Transform, Symmetric Asymmetric (SA4), Chui Lian (CL), and Bi-Hermite (Bih52S) for different Multiwavelets at different levels, to denoise an image and determine the best one out of it. The performance of denoising algorithms and various thresholding are measured using quantitative performance measures such as, Mean Square Error (MSE), and Root Mean Square Error (RMSE), Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR). It is found that CL Multiwavelet transform in combination with modified universal thresholding has given best results.