The detection of driving space is the most fundamental step in intelligent vehicle control. This research paper proposes a generic vision based algorithm for identifying driving surfaces in various indoor and outdoor environments. In this paper, instead of relying on a static model for demarcating the boundaries of the driving surfaces, we propose a novel algorithm that provides an adaptive method to detect a drivable surface in any environment. The uniqueness of the proposed algorithm lies in the robustness of the adaptive model that caters for changes in the environment. These changes may be in the form of light composition, off road disturbances, on road static and dynamic objects, shadows and variations in texture for indoor environment. It basically provides a highly dynamic online mechanism for changing the parameters of the Canny Edge Enhancement algorithm. This enables us to accurately determine the starting point and orientation of the driving surface boundary. Subsequently weighted average is used on the candidate edges to optimize the edge detection results. Experiments were carried out on our university's Intelligent DRIving System (IDRIS) for outdoor environments and on P3AT for indoor purposes. The experimentation results show that the proposed method can detect the driving surface boundaries in real-time for various different environments.

In this paper comparative flow field analysis of two intake configuration i.e. Boundary Layer Diverter Intake and Diverterless Supersonic Intake is carried out based on dimensionless parameters under various flow conditions. Numerical analysis of aircraft intake is a complex phenomenon which involves both external and internal flow analysis. In this research, both external and internal flow characteristics of intake duct are analyzed in detail. A comprehensive mesh scheme is devised and implemented to accurately capture the flow behavior in external surrounding of intake duct and flow passing through the intake duct. The analysis is carried out at different flow conditions to analyze the flow behavior in subsonic and supersonic regimes. Engine design mass flow rate is used for accurate intake analysis and results are validated with available literature. Boundary layer diversion and pressure recovery are examined for each intake configuration and comparative analysis based on pressure recovery is carried out subsequently. The analysis reveals that at subsonic and transonic regimes, Boundary Layer Diverter intake is much more effective than Diverter less Supersonic Intake, however, in supersonic regime Diverter less Supersonic Intake is found be to more effective. The research can further help in modifying/ improving the design of an existing intake configuration for enhanced intake efficiency.

Abstract-Through the study of pigmented skin lesions risk factors, the appearance of malignant melanoma turns the anomalous occurrence of these lesions to annoying sign. The difficulty of differentiation between malignant melanoma and melanocytic naive is the error-bone problem that usually faces the physicians in diagnosis. To think through the hard mission of pigmented skin lesions diagnosis different clinical diagnosis algorithms were proposed such as pattern analysis, ABCD rule of dermoscopy, Menzies method, and 7-points checklist. Computerized monitoring of these algorithms improves the diagnosis of melanoma compared to simple naked-eye of physician during examination. Toward the serious step of melanoma early detection, aiming to reduce melanoma mortality rate, several computerized studies and procedures were proposed. Through this research different approaches with a huge number of features were discussed to point out the best approach or methodology could be followed to accurately diagnose the pigmented skin lesion. This paper proposes automated system for diagnosis of melanoma to provide quantitative and objective evaluation of skin lesion as opposed to visual assessment, which is subjective in nature. Two different data sets were utilized to reduce the effect of qualitative interpretation problem upon accurate diagnosis. Set of clinical images that are acquired from a standard camera while the other set is acquired from a special dermoscopic camera and so named dermoscopic images. System contribution appears in new, complete and different approaches presented for the aim of pigmented skin lesion diagnosis. These approaches result from using large conclusive set of features fed to different classifiers. The three main types of different features extracted from the region of interest are geometric, chromatic, and texture features. Three statistical methods were proposed to select the most significant features that will cause a valuable effect in diagnosis; Fisher score method, t-test, and F-test. The selected features of high-ranking score based on the statistical methods are used for the diagnosis of the two lesion groups using Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) as three different classifiers proposed. The overall System performance was then measured in regards to Specificity, Sensitivity and Accuracy. According to the different approaches that will be mentioned later the best result was showen by the ANN designed with the feature selected according to fisher score method enables a diagnostic accuracy of 96.25% and 97% for dermoscopic and clinical images respectively.

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In this paper we present the first comparative study of evolutionary classifiers for the problem of road detection. We use seven evolutionary algorithms (GAssist-ADI, XCS, UCS, cAnt, EvRBF,Fuzzy-AB and FuzzySLAVE ) for this purpose and to develop better understanding we also compare their performance with two well-known non-evolutionary classifiers (kNN, C4.5 ). Further we identify vision based features that enable a single classifier to learn to successfully classify a variety of regions in various roads as opposed to training a new classifier for each type of road. For this we collect a real-world dataset of road images of various roads taken at different times of the day. Then, using Information Gain (I.G) and CfsSubsetMerit values we evaluate the efficacy of our features in facilitating the detection. Our results indicate that intelligent features coupled with right evolutionary technique provides a promising solution for the domain of road detection.