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Printed circuit boards are used for various product manufacturing and assisting purposes. This paper deals with a multifaceted trainer board for integrating various modules for Internet of Things (IoT) applications. As IoT plays a major role in the upcoming technological growth, the next generation of students need to learn about the hardware and software involved in a more effective manner. Also hardware is vital for hands-on training. To guide the students and aspirants to apply various ideas onto one board requires a perfect trainer board. Moreover, this trainer board should be well flexible to handle a vast variety of modules. Arduino Mega, is one example, where programming and interfacing of modules are easy and well-suited to develop IoT applications. This novel trainer board is designed for interfacing with the Arduino Mega, HC-05 (Bluetooth module) and Node MCU (Wi-Fi module) that are embedded on the board. The main purpose is to interface various Analog/Digital/PWM pins with various devices. Each Digital or analog pins are connected with 3-port or 4-port female headers to enable various modules to be linked with the board. Also, each 3 or 4-port header pins are connected with power supply and ground by default and there are interlinks between Analog/digital I/O. The board and external modules can be powered with 5V, 9V and 12V which can be selected based on the application.

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Geospatial data differ accurately and precisely in the attributes as well as their temporal and spatial dimensions. The two approaches proposed for are road extraction based on Normalized Difference Vegetation Index (NDVI) and Fuzzy c means clustering. Image-based and vector-based algorithms are integrated for conflation. Road Intersections and Terminations of different types of are automatically detected by spatial contextual measure extraction algorithm. Iterative Relaxation Algorithm (IRA) is especially used point matching based at the comparative distance records in among the points. The Vector Road Intersections that is coordinated to removed factor sets by way of a Relaxation-Labeling Algorithm. A Rubber-sheeting Transformation is a neighborhood affined ameliorations, which splits the map parts into small sections and implemented nearby modifications on every piece, also preservative topology in the route. At the end of Rubber-Sheeting Transform there can be misalignment that's befell inside the Road segments. In order to clear up this trouble an energetic Contour Model (snake) that is used to address the outstanding dislocation mistakes. Road network extraction is analyzed and compared based on NDVI and Fuzzy C means clustering .This method can be extended for more information.