Artificial Inteligens and machine learning has started to get established as reco- gnizable terms to the general masses in their daily lives. Applications such as voice recognicion and image recognicion are used widely in mobile phones and autonomous systems such as self-drivning cars. This study examines how one can utilize this technique to classify sound as a complement to videosurveillan- ce in different settings, for example a busstation or other areas that might need monitoring. To be able to do this a technique called Convolution Neural Ne- twork has been used since this is a popular architecture to use when it comes to image classification. In this model every sound has a visual representation in form of a spectogram that showes frequencies over time. One of the main goals of this study has been to be able to apply this technique on so called IOT units to be able to classify sounds in real time, this because of the fact that these units are relativly affordable and requires little resources. A Rasberry Pi was used to run a prototype version using tensorflow & keras as base api ́s. The studys re- sults show which parts that are important to consider to be able to get a good and reliable system, for example which hardware and software that is needed to get started. The results also shows what factors is important to be able to stream live sound and get reliable results, a classification models architecture is very important where different layers and parameters can have a large impact on the end result. / Termer som Artificiell Intelligens och maskininlärning har under de senaste åren börjat etablera sig hos den breda massan och är numera någonting som på- verkar nästan alla människors vardagliga liv i någon form. Vanliga använd- ningsområden är röststyrning och bildigenkänning som bland annat används i mobiltelefoner och autonoma system som självkörande bilar med mera. Den här studien utforskar hur man kan använda sig av denna teknik för att kunna klassi- ficera ljud som ett komplement till videoövervakning i olika miljöer, till exem- pel på en busstation eller andra övervakningsobjekt. För att göra detta har en teknik kallad Convolution Neural Network använts, vilket är en mycket populär arkitektur att använda vid klassificering av bilder. I denna modell har varje ljud fått en visuell representation i form av ett spektogram som visar frekvenser över tid. Ett av huvudmålen med denna studie har varit att kunna applicera denna teknik på så kallade IOT-enheter för att klassificera ljud i realtid. Dessa är rela- tivt billiga och resurssnåla enheter vilket gör dem till ett attraktivt alternativ för detta ändamål. I denna studie används en Raspberry Pi för att köra en prototyp- version med Tensorflow & Keras som grund APIer. Studien visar bland annat på vilka moment och delar som är viktiga att tänka på för att få igång ett smidigt och pålitligt system, till exempel vilken hårdvara och mjukvara som krävs för att starta. Den visar också på vilka faktorer som spelar in för att kunna streama ljud med bra resultat, detta då en klassifikationsmodells arkitektur och upp- byggnad kan ha stor påverkan på slutresultatet.

Saab want to examine Hardware In the Loop method as a concept, and how an infrastructure of Hardware In the Loop would look like. Hardware In the Loop is based upon continuously testing hardware, which is simulated. The software Saab wants to use for the Hardware In the Loop method is Jenkins, which is a Continuous Integration, and Continuous Delivery tool. To simulate the hardware, they want to examine the use of an Application Programming Interface between a Raspberry Pi, and the programming language Robot Framework. The reason Saab wants this examined, is because they believe that this method can improve the rate of testing, the quality of the tests, and thereby the quality of their products.The theory behind Hardware In the Loop, Continuous Integration, and Continuous Delivery will be explained in this thesis. The Hardware In the Loop method was implemented upon the Continuous Integration and Continuous Delivery tool Jenkins. An Application Programming Interface between the General Purpose Input/Output pins on a Raspberry Pi and Robot Framework, was developed. With these implementations done, the Hardware In the Loop method was successfully integrated, where a Raspberry Pi was used to simulate the hardware. / Saab vill undersöka metoden Hardware In the Loop som ett koncept, dessutom hur en infrastruktur av Hardware In the Loop skulle se ut. Hardware In the Loop baseras på att kontinuerligt testa hårdvara som är simulerad. Mjukvaran Saab vill använda sig av för Hardware In the Loop metoden är Jenkins, vilket är ett Continuous Integration och Continuous Delivery verktyg. För attsimulera hårdvaran vill Saab undersöka användningen av ett Application Programming Interface mellan en Raspberry Pi och programmeringsspråket Robot Framework. Anledning till att Saab vill undersöka allt det här, är för att de tror att det kan förbättra frekvensen av testning och kvaliteten av testning, vilket skulle leda till en förbättring av deras produkter. Teorin bakom Hardware In the Loop, Continuous Integration och Continuous Delivery kommer att förklaras i den här rapporten. Hardware In the Loop metoden blev implementerad med Continuous Integration och Continuous Delivery verktyget Jenkins. Ett Application Programming Interface mellan General Purpose Input/output pinnarna på en Raspberry Pi och Robot Framework blev utvecklat. Med de här implementationerna utförda, så blev Hardware Inthe Loop metoden slutligen integrerat, där Raspberry Pis användes för att simulera hårdvaran.

This thesis introduces the reader into RespiHeart, which is a product under development. RespiHeart is an complement/alternative to the regular Pulse Oximeter and is intended to be used within the health care sector for combined measurements and communication on open inexpensive platforms. This thesis evaluates interaction between RespiHeart and a Raspberry Pi 3 Model B to evaluate if the computer is capable of handling the data from RespiHeart and make signal processing. Python is used throughout the whole project and is a suitable language for interaction and signal processing in real time. / Detta examensarbete introducerar läsaren till RespiHeart, en ny trådlös produkt som är under utveckling. RespiHeart är ett komplement/alternativ till den nuvarande Pulsoximetern och är tänkt att användas inom sjukvården för analys, kommuniakation och kombinerade mätningar på öppna billiga plattformar. Detta project utvärderar interaktionen mellan RespiHeart och en Raspberry Pi 3 Model B för att undersöka om datorn är kapabel till att hantera datan från RespiHeart samt göra signal processing i real tid. Programmeringsspråket Python användes under hela projektet och är ett lämpligt språk att använda för interation och signal processing i real tid.

The emergency departments in Region Östergötland use pen and paper to a large extent when recording emergency care procedures and measurements. During treatment the patient should be the main focus. Because of this, recording of measurements done could be delayed or in worst case forgotten during stressful situations. The proposal of this project is the development of a prototype that tries to make the administrative work a passive procedure rather than an active one. The system developed uses a Raspberry Pi, along with Node-Red, which connects predefined patient data and medical records, with the clinical staff tending the patient. All these connections are initiated by mainly using RFID technology. The conclusion made with the developed system is that it should unload the staff with the recording of data and that it helps make a data logging a more passive work than today’s used methods. Along with a process that is easier to operate, the time spent on administrative work could be reduced with the proposed system.

This thesis deals with creating hardware and software equipment of intelligent beehive for data collection purpose. Measurement characteristics are temperature, humidity, weight of beehive and audio record of bees. They are measured by available IoT technology. They are simple single-chip component with a clear focus. This thesis also include creation of software for measurements this characteristics and communications with server. The software featured of the remote server are also documented. This software analyze incoming measurement and also provides their displays. Software in this thesis is developed in language Python.

Motivation behind this master dissertation is to introduce a novel study called " A fuzzy logic micro-controller enabled system for the monitoring of micro-climatic parameters of a greenhouse" which is capable of intelligently monitoring and controlling the greenhouse climate conditions in a preprogrammed manner. The proposed system consists of three stations: Sensor Station, Coordinator Station, and Central Station. To allow for better monitoring of the climate condition in the greenhouse, fuzzy logic controller is embedded in the system as the system becomes more intelligent with fuzzy decision making. The sensor station is equipped with several sensor elements such as MQ-7 (Carbon monoxide sensor), DHT11 (Temperature and humidity sensor), LDR (light sensor), grove moisture sensor (soil moisture sensor). The communication between the sensor station and the coordinator station is achieved through XBee wireless modules connected to the Arduino Mega and the communication between coordinator station and the central station is also achieved via XBee wireless modules connected to the Arduino Mega. The experiments and tests of the system were carried out at one of IKHALA TVET COLLEGE’s greenhouses that is used for learning purposes by students studying agriculture at the college. The purpose of conducting the experiments at the college’s green house was to determine the functionality and reliability of the designed wireless sensor network using ZigBee wireless technology. The experiment result indicated that XBee modules could be used as one solution to lower the installation cost, increase flexibility and reliability and create a greenhouse management system that is only based on wireless nodes. The experiment result also showed that the system became more intelligent if fuzzy logic was used by the system for decision making. The overall system design showed advantages in cost, size, power, flexibility and intelligence. It is trusted that the results of the project will give the chance for further research and development of a low cost greenhouse monitoring system for commercial use. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

This thesis deals with the issue of attendance recording. The aim of the thesis was to design a solution and then create a hardware terminal for the electronic attendance system. The whole system is based on the latest web technologies using the Raspberry Pi hardware, for the client terminal. Future use of the device is possible for both home and commercial use. The whole system was distributed globaly as Open Source project. The result of thesis is a physical prototype of a client terminal, based on the Raspberry Pi platform, Electron based application for terminal and a web application communicating with the server solution.

Internet of Things is the latest phenomenon in the computing industry. Even if it has not been completely defined yet, we are already surrounded by various devices connected to the Internet. This thesis project focuses on low cost and low-power wireless solutions and on the on-line backend behind the architecture. At the same time the present work also deals with Cloud Computing which can provide a highly scalable runtime environment for this backend without building an infrastructure. To handle the huge amount of data collected by billions of devices, BigData services could be used in the same cloud space. The project is a collection of the theoretical background of the Internet of Things; so as a result, it provides the reader with an overview of the concept. It also provides a walktrough of the design, implementation and testing process of a complex agricultural Internet of Things solution.

In this paper is described creation of a robot in Robot Operating system (ROS) withAckermann steering. It contains the principle of Ackermann steering geometry, search ofcontroller boards and basics of ROS structure. A RC car with connected PixHawk controlleris used as a basis of the robot. On the robot is placed an onboard computer Raspberry Pi3 with running ROS. This computer is connected to a laptop through Wi-Fi network. Theprocedure of starting up the robot and ROS is also described in this paper, as well asdesign of the graphical user interface (GUI) that will display sensory data and allow otherfunctionality. Another part of thesis explains principle of an optical encoder and how tocreate your own encoder which can detect rotation of a wheel. This is used to implementrobot odometry. The structure of ROS navigation library is analyzed with regards to itscommissioning. Implementation of the GUI and navigation library will follow in the masterthesis.

Thisdiplomathesisdealswiththedesignofamonitoringsystemforsolidfuelboilers.The first describes the sensors most commonly used in boilers room including a description of the principle of operation. The main content of the thesis is the design of the own boiler monitoring system. First the appropriate structure of the entire system is selected followed by the detailed design of individual parts of the device electronic wiring and selection of suitable sensors. The result of the thesis is the basis for the production of the proposed device and created service program.