This diploma work describes how an FPGA is designed to control a CAN controller. It describes the different tools used when working with Actel’s design tools and the sequence of work applied. It gives a short overview of a multiplexer, the CAN bus, an analog/digital-converter and some more information on the actual FPGA. It also brings up the design process of the FPGA, planning, coding, simulating, testing and finally programming the FPGA. The different parts implemented in the FPGA are a shift-register and two state- machines that are connected with each other. They work together to control the SJA1000 CAN controller made by Philips. They also receive data from the analog/digital-converter that they forward onwards to the CAN controller that forward the data on the CAN bus.

<p>This diploma work describes how an FPGA is designed to control a CAN controller. It describes the different tools used when working with Actel’s design tools and the sequence of work applied. It gives a short overview of a multiplexer, the CAN bus, an analog/digital-converter and some more information on the actual FPGA. It also brings up the design process of the FPGA, planning, coding, simulating, testing and finally programming the FPGA. The different parts implemented in the FPGA are a shift-register and two state- machines that are connected with each other. They work together to control the SJA1000 CAN controller made by Philips. They also receive data from the analog/digital-converter that they forward onwards to the CAN controller that forward the data on the CAN bus.</p>

The name ”smart homes” gives a promise of intelligent behavior. Today automation of the home environment is a manual task, with the creation of rules controlling devices relying on the user. For smart homes this tedious manual task can be automated. The purpose of this thesis is development of a prototype that will help users in smart homes create rules. The rules should be automatically created by the use of a machine learning solution. A learning classifier system algorithm is found as a suitable machine learning solution. A learning classifier system is used to find and create rules from sensor data. In the prototype a Raspberry Pi is used to collect the data. This data is processedby the learning classifier system, generating a set of rules. These rules predict actions for controlling a smart lighting system. The rules are continuously updated with new sensory information from the environment constantly reevaluating the previous found rules. The learning classifier system prototype solves the problem of how rules can be generated automatically by the use of machine learning. / Uttrycket ”smarta hem” utlovar ett intelligent beteende. Idag är automatiseringen av hemmiljön en manuell uppgift, där användaren formulerar regler som styr systemet. I smarta hem kan denna uppgift bli automatiserad. Syftet med denna kandidatuppsats är att utveckla en prototyp som ska hjälpa användare i smarta hem att skapa regler. Reglerna ska skapas automatiskt med hjälp av en maskininlärningslösning. Ett självlärande klassificeringssystem bedöms uppfylla den kravställning som görs. Det självlärande klassificeringssystemet används för att skapa regler från sensordata. I prototypen används en Raspberry Pi för att samla in data. Insamlad data behandlas av det självlärande klassificeringssystem som genererar en uppsättning regler. Dessa regler används för att kontrollera ett smart ljussystem. Reglerna uppdateras kontinuerligt med ny sensorinformation från omgivningen och utvärderar de tidigare funna reglerna. Den självlärande klassificeringssystemprototypen löser problemet om hur regler kan skapas automatiskt med hjälp av maskininlärning.