The use of security systems is becoming more prevalent in this modern age. With the advent of machine learning algorithms, there is a unique opportunity to integrate machine learning algorithms into modern camera-based security systems. We present an inexpensive, light-weight computer vision security system with an integrated deep neural network. The security system uses active image classification to detect intruders and provide a notification and a video to the owner over email. In addition, the system maintains an integrated graphical user interface controller as well an android application controller.
A common problem in autonomous route planning and controls to implement efficient motor control, sensor feedback, and route planning algorithms to allow a robot to successfully navigate and traverse a line grid maze from a start point to a different destination point in an efficient manner. We present an E-Puck controller that utilized ground sensor feedback to perform a variety of functions; detect nodes (cross-sections), detect lines just past nodes, provideBraitenberg line tracking functionality, and allow for left and right turns on a line grid. In addition, we present an AStar algorithm that is utilized to efficiently solve for a route from a start node to an end node in a line grid maze. Finally, we present tuned parameters for node detection,