Automatisering av testprocesser är viktigt eftersom manuella tester är komplicerade och tidskrävande. Testarbetet effektiviseras och kvaliteten kan höjas genom att automatisera testprocesserna. Arbetet som beskrivs i det här examensarbetet har utförts på Phoniro AB i Halmstad och syftet med projektet är att identifiera och konstruera en helautomatiserad lösning för skanningen av NFC-taggar och QR-koder. I arbetet konstrueras en mekanisk testrigg som är en lämplig lösning baserat på framtagna krav. För att välja den mest lämpliga mjukvaru- och hårdvaruplattformen för testriggen utifrån kraven användes utvärderingsmatriser. Testriggen består av tre delar, en 3D-modell av testriggen, ett kretskort som är testriggens kontroller och mjukvarudel för att programmera testriggen och integrera den med företagets testramverk. Därefter har testriggen byggts upp, alla delar kopplats ihop och programmerats för att testas mot de krav som ställts upp. Testriggen som har tagits fram är en prototyp som har en stor utvecklingspotential med tanke på framtida utveckling / Automation of test processes is essential because manual tests are complicated and time-consuming. Automating the test processes makes the test work more efficient and increases quality. The work described in this diploma thesis was performed at Phoniro AB in Halmstad, and the purpose of the project is to identify and construct a fully automated solution for the scanning of NFC tags and QR codes. The report describes a design that meets the requirements and needs established for the development models used in the project. A mechanical test rig was constructed as a suitable solution, based on developed requirements during the project. Evaluation matrices were used to select the most suitable software and hardware platforms for the test rig based on the project needs. The test rig consists of a 3D-model, a circuit board, and a software component to program the test rig and integrate it with Phoniro’s test framework. The test rig developed is a prototype with excellent potential for future development.

Rubber granulates, used at artificial grass pitches, pose a threat to the environment when leaking into the nature. As the granulates leak to the environment through rain water and snow clearances, they can be transported by rivers and later on end up in the marine life. Therefore, reducing the snow clearances to its minimum is of importance. If the snow clearance problem is minimized or even eliminated, this will have a positive impact on the surrounding nature. The object of this project is to propose a method for deciding when to remove snow and automate the information dispersing upon clearing or closing a pitch. This includes finding low powered sensors to measure snow depth, find a machine learning model to predict upcoming snow levels and create an application with a clear and easy-to-use interface to present weather information and disperse information to the responsible persons. Controlled experiments is used to find the models and sensors that are suitable to solve this problem. The sensors are tested on a single snow quality, where ultrasonic and infrared sensors are found suitable. However, fabricated tests for newly fallen snow questioned the possibility of measuring snow depth using the ultrasonic sensor in the general case. Random Forest is presented as the machine learning model that predicts future snow levels with the highest accuracy. From a survey, indications is found that the web application fulfills the intended functionalities, with some improvements suggested.