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The physical appearance of the nostril is important in the objective assessment of a cleft-lip patient while an objective quantitative evaluation is necessary to improve the result of the surgical procedure. The use of Kendall's coefficient of concordance (W) to identify consistency between several raters is proposed in this paper. Linear regression method was then compared with the Neural Network method to find out which is better in determining the consistency of data. The feature factors were extracted from a digital image of the nostril taking into consideration symmetry as the basis. Statistical and Neural Network methods were utilized to process and analyze the deformity assessment data. Two groups of raters were chosen to evaluate the deformity of the cleft lip/ cleft nose based on photos shown to them. The angles and distance were measured with respect to the symmetrical aspect and the elementary reference score and factors were obtained through statistical analysis. Linear regression equations describing the relationship between the selected factors and the elementary score were formulated in order to obtain a more reliable reference data. The target data was pre-processed to achieve a more consistent and stable performance. A Neural Network was used to predict the evaluation score and it performed better than the linear regression method under certain conditions. The proposed method can give an objective evaluation to help surgeons evaluate their performance after a surgical procedure and find out if there is a need for further procedures to be done with lesser computational requirement over other existing three-dimensional algorithms.

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This paper propose a novel objective function for the Dynamic Window Approach (DWA). We prove the convergence property of the objective function using Lyapunov stability criteria. Unlike previous studies which concentrate on implementing the DWA in nonholonomic vehicles, in this paper we have considered a quadrotor whose motion is limited to a fixed X-Y plane just like a holonomic vehicle. Simulation result shows our method guides the vehicle in avoiding obstacles and converge to the goal even in situations where conventional method failed.

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