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.

The design of functional materials with complex properties is very important for different applications, such as coatings, microelectronics, biotechnologies and medicine. It is also crucial that such kinds of materials have a long service lifetime. Unfortunately, cracks or other types of damages may occur during everyday use and some parts of the material should be changed for the regeneration of the initial properties. One of the approaches to avoid the replacement is utilization of self-healing materials. The aim of this thesis was to design a self-repairable material with superhydrophobic and switchable properties using colloidal particles. Specific goals were the synthesis of colloidal particles and the preparation of functional surfaces incorporated with the obtained particles, which would exhibit a repairable switching behavior and repairable superhydrophobicity. In order to achieve these goals, first, methods of preparation of simple and functional colloidal particles were developed. Second, the behavior of particles at surfaces of easy fusible solid materials, namely, paraffin wax or perfluorodecane, was investigated.

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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Ögonkontakt-sensorer skapar möjligheten att tolka användarens uppmärksamhet, vilketkan användas av system på en mängd olika vis. Dessa inkluderar att skapa nya möjligheterför människa-dator-interaktion och mäta mönster i uppmärksamhet hos individer.I den här uppsatsen gör vi ett försök till att konstruera en ögonkontakt-sensor med hjälpav en Raspberry Pi, med målet att göra den praktisk i verkliga scenarion. För att fastställaatt den är praktisk satte vi upp ett antal kriterier baserat på tidigare användning avögonkontakt-sensorer. För att möta dessa kriterier valde vi att använda en maskininlärningsmetodför att träna en klassificerare med bilder för att lära systemet att upptäcka omen användare har ögonkontakt eller ej. Vårt mål var att undersöka hur god prestanda vikunde uppnå gällande precision, hastighet och avstånd. Efter att ha testat kombinationerav fyra olika metoder för feature extraction kunde vi fastslå att den bästa övergripandeprecisionen uppnåddes genom att använda LDA-komprimering på pixeldatan från varjebild, medan PCA-komprimering var bäst när input-bilderna liknande de från träningen.När vi undersökte systemets hastighet fann vi att nedskalning av bilder hade en stor effektpå hastigheten, men detta sänkte också både precision och maximalt avstånd. Vi lyckadesminska den negativa effekten som en minskad skala hos en bild hade på precisionen, mendet maximala avståndet som sensorn fungerade på var fortfarande relativ till skalan och iförlängningen hastigheten. / Eye contact detection sensors have the possibility of inferring user attention, which can beutilized by a system in a multitude of different ways, including supporting human-computerinteraction and measuring human attention patterns. In this thesis we attempt to builda versatile eye contact sensor using a Raspberry Pi that is suited for real world practicalusage. In order to ensure practicality, we constructed a set of criteria for the system basedon previous implementations. To meet these criteria, we opted to use an appearance-basedmachine learning method where we train a classifier with training images in order to inferif users look at the camera or not. Our aim was to investigate how well we could detecteye contacts on the Raspberry Pi in terms of accuracy, speed and range. After extensivetesting on combinations of four different feature extraction methods, we found that LinearDiscriminant Analysis compression of pixel data provided the best overall accuracy, butPrincipal Component Analysis compression performed the best when tested on imagesfrom the same dataset as the training data. When investigating the speed of the system,we found that down-scaling input images had a huge effect on the speed, but also loweredthe accuracy and range. While we managed to mitigate the effects the scale had on theaccuracy, the range of the system is still relative to the scale of input images and byextension speed.