This paper proposes a vision-based guidance strategy for safe navigation of a nonholonomic mobile robot in unknown indoor environments. The proposed switching-based sliding mode control (SMC) law makes the robot follow the desired trajectory as given by the guidance law. The guidance strategy uses the centroid of the depth map of an obstacle as obtained from the Red Green Blue -Depth (RGB-D) sensor to generate the desired angular velocity. The fuzzy rule-based guidance is developed to generate the desired linear velocity command. The analysis of guidance strategy is done for an infinite length obstacle. The proposed SMC is shown to be asymptotically stable using Krasovskii Method. The finite time convergence of robot navigation has been shown using Poincare Map method. The stability of the proposed SMC under burst losses has also been established. Experiments on the Pioneer P3-DX robot in different obstacle scenarios show that the robot safely navigates in presence of communication channel burst losses.

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 a novel adaptive Type-2 fuzzy filter for removing salt and pepper noise from the images. The filter removes noise in two steps. In the first step, the pixels are categorized as good or bad based on their primary membership function (MF) values in the respective filter window. In this paper, two approaches have been proposed for finding threshold between good or bad pixels by designing primary MFs. a) MFs with distinct Means and same Variance and b) MFs with distinct Means and distinct Variances. The primary MFs of the Type-2 fuzzy set is chosen as Gaussian membership functions. Whereas, in the second step, the pixels categorized as bad are denoised. For denoising, a novel Type-1 fuzzy approach based on a weighted mean of good pixels is presented in the paper. The proposed filter is validated for several standard images with the noise level as low as 20% to as high as 99%. The results show that the proposed filter performs better in terms of peak signal-noise-ratio values compared to other state-of-the-art algorithms.