The diploma thesis deals with the industrial inspection of correctly made connectors based on computer recognition, and the detection and recognition application is implemented in Python on the Raspberry Pi platform.The work uses empirically known OpenCV library for recognition. The work also deals with the selection of suitable hardware devices, which are a camera with a lens and an illuminator, from which is created one compact device together with the Raspberry Pi microcomputer. The compact device is further mounted on the designed mechanical structure under which is created inspection zone. Finally, Raspberry Pi has a web-based user interface to check the inspection and the interface to write the data to the database.

The development of smart ID cards necessitates the development of secure methods for verifying the authenticity of these cards by organizations, businesses, and government authorities. This motivated the development of such a scanner capable of reading several ID cards from various countries. 365id presented its solution in 2016 by introducing a unique scanner that can verify the authenticity of ID cards. The verification process works by capturing three images using the scanner's three distinct techniques to validate the ID cards' hidden features. After many years of the company's success and the services it provides, including the 365id scanner, it is now the time to take the scanner a step further and improve its services. Thus, this project was founded by the 365id company to research and generate various suggestions for improving the quality of the scanner's photo environment. Either by modifying the way light is reflected inside the scanner or by modifying the parameters that control the camera's operation and investigating the possibility of replacing the camera with a better alternative. This is to increase the image quality and accuracy of the verification procedure. The results proved that the desired effect could be achieved at the lowest costs by adjusting the camera settings and adding new materials to reduce and isolate the light's reflection inside the scanner. In comparison, the results proved that it is not feasible to replace the camera at present. Nevertheless, it is an option that may be available soon.

This thesis describes the design and subsequent implementation of a unit for traffic monitoring using Raspberry PI. First section provides a quick overview of assistance systems, which use a road lane detection techniques. Next there is a description of two diferent methods for road lane detection. Follow the description of monitoring scene. Then the work describe the practical part including the design and realization of supporting electronics, selecting of each components, including the modifying of cameras, mechanical design and creating of unit. Another section is about selection and installation of appropriate software components necessary for running of the unit and the selection of development tools for creating user application. After description of graphical user interafce, there is a description of road lanes detection algorithm. At the end of the thesis is summarized a reliability of unit in real traffic situation. At the appendix there are technical drawings, describing the unit.

<p>There is a growing demand for indoor farm management systems that can track plant growth, allow automatic control and aid in real-time decision making. Internet of Thing (IoT)-based solutions are being applied to meet these needs and numerous researchers have created prototypes for meeting specific needs using sensors, algorithms, and automations. However, limited studies are available that report on comprehensive large-scale experiments to test various aspects related to availability, scalability and reliability of sensors and actuators used in low-cost indoor farms. The purpose of this study was to develop a low-cost, IoT devices driven indoor farm as a testbed for growing microgreens and other experimental crops. The testbed was designed using off-the-shelf sensors and actuators for conducting research experiments, addressing identified challenges, and utilizing remotely acquired data for developing an intelligent farm management system. The sensors were used for collecting and monitoring electrical conductivity (EC), pH and dissolved oxygen (DO) levels of the nutrient solution, light intensity, environmental variables, and imagery data. The control of light emitting diodes (LEDs), irrigation pumps, and camera modules was carried out using commercially available components. All the sensors and actuators were remotely monitored, controlled, and coordinated using a cloud-based dashboard, Raspberry Pis, and Arduino microcontrollers. To implement a reliable, real-time control of actuators, edge computing was used as it helped in minimizing latency and identifying anomalies.</p> <p>Decision making about overall system performance and harvesting schedule was accomplished by providing alerts on anomalies in the sensors and actuators and through installation of cameras to predict yield of microgreens, respectively. A split-plot statistical design was used to evaluate the effect of lighting, nutrition solution concentration, seed density, and day of harvest on the growth of microgreens. This study complements and expands past efforts by other researchers on building a low cost IoT-based indoor farm. While the experience with the testbed demonstrates its real-world potential of conducting experimental research, some major lessons were learnt along the way that could be used for future enhancements.</p>