This Master's Thesis deals with the controlling of robotic car by Raspberry Pi and the ca- mera. Theoretical part describes individual steps of image processing and probabilistic plan- ning for searching path in the work space. In particular, algorithm RRT (Rapidly-exploring Random Tree) is discussed and the balanced bidirectional RRT is further introduced and used for nonholonomic planning in configuration space. Next chapter speaks about propo- sed solution and there is the accurate description of connection Raspberry Pi to the robotic car. Rest of the work provides look at implemetation details and evaluation. In the end, conclusion was given and some improvements were suggested.

This thesis deals with complete design and manufacturing of autonomous two wheeled self-balancing robot. The goal of this thesis is to maintain the robot in up-right position and to follow black line using camera. The robot is controlled using Raspberry Pi and driven by DC motors. This thesis includes the design and implementation of hardware and software parts. Subsequently there was created the dynamic model in Matlab/Simulink. Based on this model, the LQR and PID controller was designed.

This thesis solves design of functional device that will be able to automatically positioning back view mirrors according to the positio of driver face. Measuring position of the face provides stereovision of two webacams. The device is based on a computer Raspberry Pi 2 with designed expansion board. The created prototype can follow set view int the mirror with accuracy up to 7 cm (horizontally up to 5cm) in level of rear corner of a car. The results of this project validate design of automatic positioning mirror and it can be basis for specific implementations of the device in car.

This diploma thesis deals with the modification of an existing device for controlling the heating system of the house. The original proposal builds on my bachelor thesis Terminal for visualization and control conditions in a house. Adjustments are based on the findings of the test operation and the deficiencies found in everyday operations. Here, the emphasis is on simple design, low acquisition cost and durability of the resulting device. Newly designed wireless units will replace the original wired sensors, as well as the control unit of the existing device based on the prototype system will be replaced with a new wireless central unit. The alphanumeric display with control buttons will remain as the control panel of this unit. The wireless central unit is connected via a serial communication interface to the visualization and control unit, which extends the offered options of the heating control system.

The aim of this master thesis is to propose a system for monitoring and regulating the climate in groups of terrariums using the Raspberry Pi platform and subsequent transparent display through the web server. Each group of terrariums has its own control device that wirelessly communicates with the Raspberry Pi control computer. The measured values are stored in the MySQL database on the control computer. The measured values are graphically displayed on the web page.

Context. During the usage of a product, evaluating its performance quality is a crucial procedure. Web servers are one of the most used technological products in today’s modern world[1]. Thus, in this thesis we will evaluate and compare the performances of two web servers. The servers that are tested in the experiment are a raspberry pi 3B and a personal HP laptop. Objectives. The main objective of the study is to evaluate the performance of a raspberry pi 3B as a web server. In order to give a clearer image of how the raspberry pi performs, the laptop will also be evaluated and its performance will be used as a contrast during the study. Realization. To fulfill our objective, an experiment was conducted with the help of a performance testing tool called apache bench. To provide comprehensive performance results, the served content and the server software were altered throughout the experiment. The number of simulated users sending the requests were also altered. Results. The results were gathered by sending more than 1000 HTTP-requests to the two servers that served static and dynamic websites. The number of served requests per second and the CPU consumption of the servers were the recorded results. The raspberry pi exhibited response times as high as 1164 requests per second and CPU consumption that varied between ≈6% and ≈40%. In comparison to the laptop, on one occasion it exhibited a better processor utilization serving HTTPrequests of one user. Conclusions. Regardless of the used server software, although the laptop was slightly performing better, the raspberry pi had a closer response time in comparison to the laptop when both of them were serving a static website. On the contrary, when both were serving dynamic content the raspberry pi had a very low response time in the comparison. Out of the two used server software, nginx gave it a better CPU consumption in contrast to the laptop that had a better processor. That is irrespective of the served content type.

As the world population ages, the healthcare system is facing new challenges in treating more patients at a lower cost than today. One trend in addressing this problem is to increase the opportunities of in-home care. To achieve this there is a need for safe and cost-effective monitoring systems. Biosignal PI is an ongoing open-source project created to develop a flexible and affordable platform for development of stand-alone devices able to measure and process physiological signals. This master thesis project, performed at the department of Medical Sensors, Signals and System at the School of Technology and Health, aimed at further develop the Biosignal PI software by constructing a new flexible software framework architecture that could be used for measurement and processing of different types of biosignals. The project also aimed at implementing features for Heart Rate Variability(HRV) Analysis in the Biosignal PI software as well as developing a graphical user interface(GUI) for the Raspberry PI hardware module PiFace Control and Display. The project developed a new flexible abstract software framework for the Biosignal PI. The new framework was constructed to abstract all hardware specifics into smaller interchangeable modules, with the idea of the modules being independent in handling their specific task making it possible to make changes in the Biosignal PI software without having to rewrite all of the core. The new developed Biosignal PI software framework was implemented into the existing hardware setup consisting of an Raspberry PI, a small and affordable single-board computer, connected to ADAS1000, a low power analog front end capable of recording an Electrocardiography(ECG). To control the Biosignal PI software two different GUIs were implemented. One GUI extending the original software GUI with the added feature of making it able to perform HRV-Analysis on the Raspberry PI. This GUI requires a mouse and computer screen to function. To be able to control the Biosignal PI without mouse the project also created a GUI for the PiFace Control and Display. The PiFace GUI enables the user to collect and store ECG signals without the need of an big computer screen, increasing the mobility of the Biosignal PI device.   To help with the development process and also to make the project more compliant with the Medical Device Directive a couple of development tools were implemented such as a CMake build system, integrating the project with the Googletest testing framework for automated testing and the implementation of the document generator software Doxygen to be able to create an Software Documentation.    The Biosignal PI software developed in this thesis is available through Github at https://github.com/biosignalpi/Version-A1-Rapsberry-PI / Allt eftersom världens befolkning åldras, ställs sjukvården inför nya utmaningar i att behandla fler patienter till en lägre kostnad än idag. En trend för att lösa detta problem är att utöka möjligheterna till vård i hemmet.För att kunna göra detta finns det ett ökande behov av säkra och kostnadseffektiva patientövervakningssystem. Biosignal PI är ett pågående projekt med öppen källkod som skapats för att utveckla en flexibel och prisvärd plattform för utveckling av fristående enheter som kan mäta och bearbeta olika fysiologiska signaler. Detta examensarbete genomfördes vid institutionen för medicinska sensorer, signaler och system vid Skolan för Teknik och Hälsa. Projektet syftade till att vidareutveckla den befintliga mjukvaran för Biosignal PI genom att skapa ett nytt flexibelt mjukvaruramverk som kan användas för mätning och bearbetning av olika typer av biosignaler.Projektet syftade också till att utvidga mjukvaran och lägga till funktioner för att kunna genomföra hjärtfrekvensvariabilitets(HRV) analys i Biosignal PIs mjukvara, samt att utveckla ett grafiskt användargränssnitt(GUI) för hårdvarumodulen PiFace Control and Display. Projektet har utvecklat ett nytt flexibelt mjukvaruramverk för Biosignal PI. Det nya ramverket konstruerades för att abstrahera alla hårdvaruspecifika delar in i mindre utbytbara moduler, med tanken att modulerna ska vara oberoende i hur de hanterar sin specifika uppgift. På så sätt ska det vara möjligt att göra ändringar i Biosignal PIs programvara utan att behöva skriva om hela mjukvaran.Det nyutvecklade Biosignal PI ramverket implementerades i det befintliga hårdvaru systemet, som består av en Raspberry PI, liten och prisvärd enkortsdator, ansluten till ADAS1000, en analog hårdvarumodul med möjlighet att registrera ett elektrokardiografi(EKG/ECG). För att kontrollera Biosignal PI programmet har två olika grafiska användargränssnitt skapats.Det ena gränssnitt är en utvidgning av original programvaran med tillagd funktionalitet för att kunna göra HRV-Analys på Raspberry PI, detta gränssnitt kräver dock mus och dataskärm för att kunna användas.För att kunna styra Biosignal PI utan mus och skärm skapades det även ett gränssnitt för PiFace Control and Display. PiFace gränssnittet gör det möjligt för användaren att samla in och lagra EKG-signaler utan att behöva en stor datorskärm, på så sätt kan man öka Biosignal PI systemets mobilitet. För att underlätta utvecklingsprocessen, samt göra projektet mer förenligt med det medicintekniska regelverket, har ett par utvecklingsverktyg integrerats till Biosignal PI projektet såsom CMake för kontroll av kompileringsprocessen, test ramverket Googletest för automatiserad testning samt integrering med dokumentations generatorn Doxygen för att kunna skapa en dokumentation av mjukvaran.

The world is getting busier and more crowded each year. Due to this fact resources such as public transport, available energy, and usable space are becoming congested and require vast amounts of logistical support. As of February 2018, nearly 95% of Americans own a mobile cell phone according to the Pew Research Center. These devices are consistently broadcasting their presents to other devices. By leveraging this data to provide occupational awareness of high traffic areas such as public transit stops, buildings, etc logistic efforts can be streamline to best suit the dynamics of the population. With the rise of The Internet of Things, a scalable low-cost occupancy monitoring system can be deployed to collect this broadcasted data and present it to logistics in real time. Simple IoT devices such as the Raspberry Pi, wireless cards capable of passive monitoring, and the utilization of specialized software can provide this capability. Additionally, this combination of hardware and software can be integrated in a way to be as simple as a typical plug and play set up making system deployment quick and easy. This effort details the development and integration work done to deliver a working product acting as a foundation to build upon. Machine learning algorithms such as k-Nearest-Neighbors were also developed to estimate a mobile device's approximate location inside a building.

Tools that automate processes are always sough after across the entire IT field. This project's aim was to build and evaluate a semi-automated reconnaissance tool based on a Raspberry Pi 4, for use in penetration testing and/or digital forensics. The software is written in Python 3 and utilizes Scapy, PyQt5 and the Aircrack-ng suite along with other pre-existing tools. The device is targeted against wireless networks and its main purpose is to capture what is known as the WPA handshake and thereby crack Wi-Fi passwords. Upon achieving this, the program shall then connect to the cracked network, start packet sniffing and perform a host discovery and scan for open ports. The final product underwent three tests and passed them all, except the step involving port scanning - most likely due to hardware and/or operating system faults, since other devices are able to perform these operations. The main functionalities of this device and software are to: identify and assess nearby network access points, perform deauthentication attacks, capture network traffic (including WPA handshakes), crack Wi-Fi passwords, connect to cracked networks and finally to perform host discovery and port scanning. All of these steps shall be executed automatically after selecting the target networks and pressing the start button. Based on the test results it can be stated that this device is well suited for practical use within cyber security and digital forensics. However, due to the Raspberry Pi's limited computing power users may be advised to outsource the cracking process to a more powerful machine, for the purpose of productivity and time efficiency.

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