One of the issues when attempting to verify security properties of a protocol is how to model the protocol. We introduce a method for verifying event freshness in tools which use the applied π-calculus and are able to verify secrecy. Event freshness can be used to prove that a protocol never generates the same key twice. In this work we encode state in the applied π-calculus and perform bounded verification of freshness for MiniDC by using the ProVerif tool. MiniDC is a trivial protocol that for each iteration of a loop generates a unique key and outputs it to a private channel. When verifying freshness, the abstractions of ProVerif cause false attacks. We describe methods which can be used to avoid false attacks that appear when verifying freshness. We show how to avoid some false attacks introduced by private channels, state and protocols that disclose their secret. We conclude that the method used to verify freshness in MiniDCis impractical to use in more complicated protocols with state. / Ett av problemen som uppstår vid verifiering av säkerhetsprotokoll är hur protokoll ska modelleras. Vi introducerar en metod för att verifiera att skapde termer inte har använts förr. Denna metod kan användas i program som använder applicerad π-kalkyl som input och kan verifiera sekretess. I detta arbete visar vi hur protokoll med persistenta variabler kan modelleras i applicerad π-kalkyl. Vi verifierar även MiniDC för ett begränsat antal iterationer med hjälp av ProVerif. MiniDC är ett enkelt protokoll som för varje iteration av en loop skapar en nyckel och skickar den över en privat kanal. När man verifierar att skapade termer inte har använts förr så introducerar ProVerifs abstraktioner falska attacker. Vi beskriver metoder som kan användas för att undvika dessa falska attacker. Dessa metoder kan användas för falska attacker introducerade av privata kanaler, persistenta variabler eller protokoll som avslöjar sin krypteringsnyckel. Vår slutsats är att metoden som används för att verifiera MiniDC är opraktisk att använda i mer komplicerade protokoll med persistenta variabler.

The development of innovative applications of Artificial Intelligence (AI) is inseparable from the sharing of public data. However, as people strengthen their awareness of the protection of personal data privacy, it is more and more difficult to collect data from multiple data sources and there is also a risk of leakage in unified data management. But neural networks need a lot of data for model learning and analysis. Federated learning (FL) can solve the above difficulties. It allows the server to learn from the local data of multiple clients without collecting them. This thesis mainly deploys FL on the Raspberry Pi (RPi) and achieves federated averaging (FedAvg) as aggregation method. First in the simulation, we compare the difference between FL and centralized learning (CL). Then we build a reliable communication system based on socket on testbed and implement FL on those devices. In addition, the Paillier encryption algorithm is configured for the communication in FL to avoid model parameters being exposed to public network directly. In other words, the project builds a complete and secure FL system based on hardware. / Utvecklingen av innovativa applikationer för artificiell intelligens (AI) är oskiljaktig från delning av offentlig data. Men eftersom människor stärker sin medvetenhet om skyddet av personuppgiftsskydd är det allt svårare att samla in data från flera datakällor och det finns också risk för läckage i enhetlig datahantering. Men neurala nätverk behöver mycket data för modellinlärning och analys. Federated learning (FL) kan lösa ovanstående svårigheter. Det gör det möjligt för servern att lära av lokala klientdata utan att samla in dem. Denna avhandling använder huvudsakligen FL på Raspberry Pi (RPi) och uppnår federerad genomsnitt (FedAvg) som aggregeringsmetod. Först i simuleringen jämför vi skillnaden mellan FL och CL. Sedan bygger vi ett pålitligt kommunikationssystem baserat på uttag på testbädd och implementerar FL på dessa enheter. Dessutom är Paillier -krypteringsalgoritmen konfigurerad för kommunikation i FL för att undvika att modellparametrar exponeras för det offentliga nätverket direkt. Med andra ord bygger projektet ett komplett och säkert FL -system baserat på hårdvara.

Robotics will be a dominant area in society throughout future generations. Nowadays its presence is increasing in the majority of contexts of daily life, with devices and mechanisms which facilitate the accomplishment of diverse daily tasks; as well as at labor level, where machines occupy more and more jobs. This increase in the presence of autonomous robotic systems in society is due to the great efficiency and security they offer compared to human capacity, thanks mainly to the enormous precision of their sensor and actuator systems. Among these, vision sensors are of utmost importance. Humans and many animals enjoy powerful perception systems in a natural way, but which in Robotics constitutes a constant line of research. The main problem lies in the correct interpretation of visual data and the extraction of relevant information from camera images. Thus, Robotics becomes something beyond an scientific are, but also a social and cultural topic. Therefore, it is essential to raise an early awareness and train younger students to acquire the skills which will be most demanded in the short and mid-term future. In doing so, we will be ensuring their integration into a labor market dominated by intelligent robotic systems. In addition to having a high capacity for reasoning and decision-making, these robots incorporate important advances in their perceptual systems, allowing them to interact effectively in the working environments of this new industrial revolution. Since a few years ago, there are different Educational Robotics kits available in the market which are designed to be used in pre-university education. To use them as a learning tool, a correct teacher training is necessary, as well as a change in the teaching-learning methodology and in the educational environment in general. In addition, taking into account that young people live immersed in a constant environment of technological learning, most of these kits usually have a short period of interest for students, who demand motivating intellectual challenges. This thesis aims to provide several solutions to some classic problems inherent to Robotics, such as navigation and localization, but using a camera as the main sensor. In addition, a learning framework for teaching of Robotics with Vision as a subject is presented. Using it the students at pre-university curricular level learn the principles of Science and Engineering and the computer programming skills demanded in today's society. The use of Python language and its exercises about robots with vision makes this learning framework unique and more powerful than other existing frameworks. This teaching framework has been successfully used in several secondary education schools during the last two academic years (2016/2017 and 2017/2018), which includes: its software infrastructure, its hardware platform, an academic curriculum with theoretical and practical content, as well as a constructivist pedagogical methodology. The performance and satisfaction of more than 2,000 students and teachers using it, in curricular subjects such as Programming, Robotics and Technology and ICTs of Secondary Education (CSO) and extracurricular activities, have been evaluated.

Ansiktsigenkänning med hjälp av maskininlärning är ett växande område och används i många sammanhang i dagens samhälle, till exempel som autentiseringsmetod i mobiltelefoner. De flesta system för ansiktsigenkänning har haft stor budget och starka utvecklare bakom sig, men går det att skapa ett fungerande system med begränsade resurser och datamängd? Det här projektet undersöker hur mycket data som krävs för att producera en fungerande ansiktsautentiseringssmodul för kontorsmiljö baserad på bildklassificering. I projektet används ett förtränat Convolutional Neural Network (ResNet34), data som är insamlad med hjälp av uppdragsgivaren samt en bilddatabas från NVIDIA. Resultaten visar att mängden data som krävs för att producera en tillförlitlig modell troligtvis överstiger den mängd som är rimlig att samla in från användaren. / Face recognition using machine learning is a changing field and is used in many contexts in today’s society, for example as an authentication method in mobile phones. Most face recognition systems have had large budgets and strong developers behind them, but is it possible to create a working system with a limited amount of resourses and data? This project investigates how much data is required to produce a working face recognition module for an office environment based on image classification. This project used a pretrained Convolutional Neural Network (ResNet34), data collected with the help of the client, and an image database from NVIDIA. The results show that the amount of data required to produce and reliable model probably exceeds the amount that is reasonable to collect from the user.

Syftet med detta arbete var att få en djupare förståelse om fysiken och tekniken inom området analog elektronik och mikrokontrollerkort. Via simulering, utveckling och konstruktion av olika kretslösningar transformerades ljudsignaler efter önskad effekt. Projektet innebar att teoretiskt framtaga dessa kretsar med önskad utsignal och eftersökt effekt, däribland wah och fuzz. Dessa kretslösningar realiserades sedan praktiskt till den grad att dessa kopplades mellan instrument och förstärkare.Dessa effektpedaler producerades i både analog och digital tappning, vid den digitala med hjälp av mikrokontrollerkortet Raspberry Pi Pico och vidare digital ljudbehandling. Ena analoga pedalen, med fuzzeffekt, fungerade inte vid konstruktion. Andra analoga pedalen, wahpedalen, fungerade begränsat och den digitala pedalen uppfyllde de efterfrågade specifikationerna och var fullt användbar vid hopkoppling med elgitarr.

Recent advances in smartphones and affordable open-source hardware platforms have enabled the development of low-cost architectures for Internet-of-Things (IoT)-enabled home automation and security systems. These systems usually consist of sensing and actuating layer that is made up of sensors such as passive infrared sensors, also known as motion sensors; temperature sensors; smoke sensors, and web cameras for security surveillance. These sensors, smart electrical appliances, and other IoT devices connect to the Internet through a home gateway. This paper lays out an architecture for a cost-effective smart door sensor that will inform a user through an Android application, of door open events in a house or office environment. The proposed architecture uses an Arduino-compatible Elegoo Mega 2560 microcontroller board along with the Raspberry Pi 2 board for communicating with a web server that implements a RESTful API. Several programming languages are used in the implementation and further applications of the door sensor are discussed as well as some of its shortcomings such as possible interference from other radio frequency devices.

Recent advances in smartphones and affordable open-source hardware platforms have enabled the development of low-cost architectures for IoT-enabled home automation and security systems. These systems usually consist of a sensing and actuating layer that is made up of sensors such as PIR (Passive Infra-red) sensors, also known as motion sensors; temperature sensors; smoke sensors, and web cameras for security surveillance. These sensors, smart electrical appliances and other IoT devices connect to the Internet through a home gateway. This paper lays out architecture for a cost effective “smart” door sensor that will inform a user through an Android application, of door open events in a house or office environment. The proposed architecture uses an Arduino-compatible Elegoo Mega 2560 microcontroller (MCU) board along with the Raspberry Pi 2 board for communicating with a web server that implements a RESTful API. Several programming languages are used in the implementation and further applications of the door sensor are discussed as well as some of its shortcomings such as possible interference from other RF (Radio Frequency) devices.

A self-driven car is a vehicle that can drive without human intervention by making correct decisions based on the environmental conditions. Since the innovation is in its beginning periods, totally moving beyond the human inclusion is still a long shot. However, rapid technological advancements are being made towards the safety of the driver and the passengers. One such safety feature is a Lane Detection System that empowers vehicle to detect road lane lines in various climate conditions. This research provides a feasible and economical solution to detect the road lane lines while driving in a sunny, rainy, or snowy weather condition. An algorithm is designed to perform real time road lane line detection on a low voltage computer that can be easily powered in a regular auto vehicle. The algorithm runs on a RaspberryPi computer placed inside the car. A camera, attached to the vehicle’s windshield, captures the real time images and passes them to the RaspberryPi for processing. The algorithm processes each frame and determines the lane lines. The detected lane lines can be viewed on a 7 inch display screen connected to the Raspberry Pi. The entire system is mounted inside a Jeep Wrangler to conduct the experiments and is powered by the vehicle’s standard charger of 12V-15V power supply. The algorithm provides approximately 97% accurate detection of road lane lines in all weather conditions.

An algorithm is presented to count bee numbers in images of Langsthroth hive entrances. The algorithm computes approximate bee counts by adjusting the brightness of the image, cropping a white or green area in the image, removing the background and noise from the cropped area, finding the total number of bee pixels, and dividing that number by the average number of pixels in a single bee. On 1005 images with green landing pads, the algorithm achieved an accuracy of 80 percent when compared to the human bee counting. On 776 images with white landing pads, the algorithm achieved an accuracy of 85% compared to the human bee counting.

The essence of this master thesis is design and implementation of sensor system based on robotic framework which is called ROS (Robot Operating System). The main task is to perform detailed analysis and test of capabilities of the framework with final implementation on specific robot application (sensor system) with following evaluation of applicability of the system in mobile robotics. As parallel aim is to create detailed general and practical guide for beginners with ROS which they are also beginners in Linux based operating systems.