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Computer vision-based real-time applications demand robust image matching approaches due to disparity in images. This can be achieved using descriptor vector with scale and rotation invariance capability. This paper presents a rotation invariant descriptor vector formation based on line point duality. The proposed descriptor uses a simple consistent method of key point detection. For obtaining the descriptor vector, line segments present in the input image are used. These line segments are located within a region of interest around obtained key points in the input image. The obtained descriptor vector is used for matching of disparate images. Experiments are carried out for four different image sets with rotation at the range of angles to validate the performance of the proposed descriptor in real-time. For comparative study, normalized match ratio is computed using multi-layered neural network with two hidden layers.

Unit test cases have become an essential tool to test application code. Several applications make use of SQL queries in order to retrieve or update information from a database. Database queries for these applications are written natively in SQL using JDBC or using ORM frameworks like Hibernate. Unit testing these applications is typically done by loading a fixed dataset and running unit tests. However with fixed datasets, errors in queries may be missed. In this demonstration, we present a system that takes as input a database application program, and generates datasets and unit tests using the datasets to test the correctness of function with queries in the application. Our techniques are based on static program analysis and mutation testing. We consider database applications written in Java using JDBC or Hibernate APIs. The front-end of our system is a plugin to the IntelliJ IDEA IDE. We believe that such a system would be of great value to application developers and testers.

This paper proposes and analyzes two different control techniques to remove the effect of lumped uncertainties and disturbances for trajectory tracking by robotic manipulator. These techniques are a) Adaptive backstepping sliding mode control and b) Nonlinear disturbance observer based backstepping sliding mode control. Adaptive backstepping sliding mode control estimates the system uncertainties and disturbance using an adaptive law. Lyapunov theory is used to define the adaptive law for the convergence of tracking error. The second technique initially estimates the unknown external disturbances using non-linear disturbance observer and then generates control input using beckstepping sliding mode controller. Backstepping sliding mode ensures the sliding surface to be chattering free and improves convergence rate in finite-time. The stability of system is analyzed using Lyapunov theory for both the techniques. Simulation results show the effectiveness and robustness of proposed techniques FOR trajectory tracking.

H 2 O 2 and O 3 when used in water under UV light is particularly well suited for the breakdown of organic molecules as the cleavage of the O-O bond gives OH radicals or “O” atoms. The OH radical is known to be extremely reactive with most organic molecules with H-atom donor properties and promptly attacks such species to produce new types of radicals which may subsequently initiate several radical chain reactions. Also OH radicals are more effective than OR and OOR radicals (R = aryl group) in producing carbon radicals from organics and hence the use of H 2 O 2 as the source of free radicals is very attractive. This photo-oxidation is also called advanced oxidation or enhanced oxidation.

This paper proposes a heuristically optimized version of Improved Mountain Clustering (IMC) Technique referred to as IMC-2. IMC-2 provides better quality clusters measured in terms of Global Silhouette and Separation indices as measures of information. The IMC-2 based color segmentation approach has been applied to various categories of images including face, stripes and grayscale images and compared with some extensively used clustering techniques such as K-means and FCM. The color segmentation performance has been compared on widely used and accepted validation indices, Global Silhouette Index and Separation Index. The color segments or clusters obtained have been verified visually and validated quantitatively.

This paper presents a novel fuzzy rule based gene ranking algorithm for extracting salient genes from a large set of microarray data which helps us to reduce computational efforts towards model building process. The proposed algorithm is an unsupervised approach and does not require class information for gene ranking and Microarray data has been used to form a set of robust fuzzy rule base which helps us to find salient genes based on its average relevance with already formed fuzzy rules in rule base. Fuzzy rule based ranking has been carried out to select salient genes based on their average firing strength in order of high relevancy and only top ranked genes are utilized to classify normal and cancerous tissues for a carcinoma dataset [1]. Result validate the effectiveness of our gene ranking method as for the same no. of genes, our ranking scheme helps to improve the classifier performance by selecting better salient genes.

This paper presents improved mountain clustering technique based MRI (magnetic resonance imaging) brain image segmentation for spotting tumors. The proposed technique is compared with some existing techniques such as K-Means and FCM, clustering. The performance of all these clustering techniques is compared in terms of cluster entropy as a measure of information and also is visually compared for image segmentation of various brain tumor MRI images. The cluster entropy is heuristically determined, but is found to be effective in forming correct clusters as verified by visual assessment.

This paper presents Improved Mountain Clustering (IMC) based medical image segmentation. Proposed technique is a more powerful approach for X-Ray image based diagnosing diseases like lung cancer and tuberculosis. The IMC based segmentation approach was applied on lung X-Ray images and compared with some existing techniques such as K-Means and FCM based segmentation approaches. The performance of all these segmentation approaches is compared in terms of cluster entropy as a measure of information. The segments obtained from the methods have been verified visually.