Aim of this thesis is to implement system for translation words from source language into the target language with pair input texts. There are descriptions  of terms and methods used in machine translation and machine build dictionary. The thesis also contains a concept and specification of each part created system including final evaluation. There is analysed options which make extension of existing dictionatry.

The goal of the master´s thesis is to design testing unit for small batch production. The background research part introduces testing principles, we are also discussed different ways of realization of testing connectors and choice of control unit. The work also describes the design of the hardware wiring and mechanical design. The result of this thesis is a device implemented according to the proposal described in the work. No significant fault was found during operation of testing unit, which verified its reliability and full functionality. The work also includes a description of the software implementation in Python.

Presented diploma thesis is focused on the implementation of Wireless M-Bus protocol to embedded device RaspberryPi with expansion board UniPi. The protocol is implemented in Python with Wireless M-Bus devices communicating via IQRF transceiver connected to the UART bus. The theoretical part is focused on an overview of embedded devices for the IoT, the possibility of their expansion. Further, the UniPi expansion board and Wireless M-Bus transceiver are detailed. First part of the thesis focuses on the Wireless M-bus layers, which provides a basic knowledge for understanding the practical part. The theoretical part concludes overview of captured devices including a description of their data units. In the practical part is the implementation of the sample application for receriving data from a Wireless M-Bus sensors. The application is able to read data from devices transmitting encrypted communication.

This study investigates synthetic schematic generation using conditional generative adversarial networks, specifically the Pix2Pix algorithm was implemented for the experimental phase of the study. With the increase in deep neural network’s capabilities and availability, there is a demand for verbose datasets. This in combination with increased privacy concerns, has led to synthetic data generation utilization. Analysis of synthetic images was completed using a survey. Blueprint images were generated and were successful in passing as genuine images with an accuracy of 40%. This study confirms the ability of generative neural networks ability to produce synthetic blueprint images.

Denna kandidatrapport är skriven av åtta studenter i kursen Kandidatprojekt i mjukvaruutveckling, TDDD96, vid Linköpings universitet under våren 2020. Kandidatrapporten inkluderar en sammanfattning av det arbete som utförts i projektet. Till projektet tillhörde ett uppdrag på beställning av Digitala resursenheten vid Linköpings universitet. Uppdraget var att skapa en webbapplikation där examinatorer och handledare för kurser vid Linköpings universitet på ett enkelt och intuitivt sätt kan beställa mjukvara till sina respektive kurser. Projektgruppen jobbade agilt och byggde projektet i Python samt JavaScript. Applikationen nådde alla mål satta tillsammans mellan kunden och projektgruppen och resulterade i en fungerande produkt som kunde utökas och modifieras enligt kundens behov. Till kandidatrapporten inkluderas även individuella bidrag skrivna av vardera gruppmedlem som djupdykt inom ett specifikt ämne eller område.

Consider a situation where you need to identify each and every person in a room and categorize each of the persons as present or absent. To mark the presence of hundreds of people in a room takes a lot of time, which means you can eventually be left out with less time to explain the main aim of that meeting, class, or some other things. In such cases, either we may need a lot of time for a meeting or we may need more people to accomplish the tasks. In the present world, time is considered equivalent to money. If you lose your time you will lose your money. Many education institutions and offices see this as a very important consideration to maintain their busy schedule.  In this study, we came to know that many institutions and offices are facing the same issue, so as engineers we decided to take this as a challenge and try to find a solution to it. Our main objective of this project is to make a working model, which helps in marking the attendances of the person automatically and saves precious time. We can say that human effort is simply replaced by our end product which helps the person to spend more time efficiently.  To replace the human effort with an easy and cost-efficient system that has accurate results, we are using a Raspberry Pi 4 Model B as a microcontroller and along with it, we are using a Raspberry Pi camera module to capture the image of the person. To control and give the commands to the microcontroller, we are using MATLAB as a programming language that interprets the commands given by the user.   Using this Raspberry Pi system with a camera module attached to the Raspberry Pi, We can capture the image of the person who comes in front of the camera. Once the person images are captured, the microcontroller starts running the program which can predict the face of a person from the pre-trained database that we have previously stored in the system. Once the prediction is done, the face of the person is automatically marked as present in an excel sheet under the predicted name.  We are concluding that by using the automated attendance system one can avoid the manual marking of persons and save time. For further future works, one can use a good resolution camera module to capture the images clearly and can use the python coding method to access face recognition. We can also develop a mobile application, that can access the camera present in the smartphone which can be used to capture the images and mark the attendances automatically.

FAD * for Stadsgårdskajen Re-introduction of some of the knowledge from the master builder to the contemporary architect. This project investigates the interaction between designing architecture and building architecture in a computer controlled context. Algorithms have been developed to simulate and control an automated in situ brick stacking process. Brick laying is a well researched topic for robotic processes. It is an ideal material for building with robots (in Stockholm) since it is a generic building block that is cheap and easy to produce locally. Corbeled vaults, instead of keystone vaults, can be built without support and with all identical elements and are therefore also suitable for robotic fabrication.  The algorithms have been incorporated in a parametric model that have been used to develop public building at Stadsgårdskajen, Stockholm. FAD is about creating and explore a different architectural expression as well as reintroducing the brick as a relevant construction material for Stockholm. / FAD* för Stadsgårdskajen Återinförande av några av byggmästarens förmågor till den samtida arkitekten I detta examensarbete undersöks förhållandet mellan att designa arkitektur och att bygga arkitektur i en datorkontrollerad miljö. Algoritmer har utarbetats för att simulera och styra en automatiserad in situ murningsprocess. Inom fabrikation och automatisering med industrirobotar är tegelband ett välundersökt ämne. Tegel är ett idealiskt material vid byggande med robotar. Det är enkelt och billigt att framställa och tegel som byggkloss är en lämplig typologi för addativ fabrikation med robotar. Valv byggda genom utkragning istället för traditionella valvbågar kan byggas utan stöd och med identiska element. Därför är denna byggnadsteknik även lämplig för additiv fabrikation med robotoar. Algoritmerna har intergrerats i en parametrisk modell som har använts för att formge en offentlig byggnad på Stadsgårdskajen i Stockholm. FAD handlar såväl om skapande och utforskande av ett nytt arkitekturspråk som av återinförande av tegel som ett betydelsefullt konstruktionsmaterial i Stockholm.

Climate change is being fought with policies and measures implemented worldwide to reduce Greenhouse Gases (GHG). One of these policies is to reduce the use of fossil fuels because they accelerate climate change and harm human health through pollution. The automotive industry is a significant player in this transition. The automotive industry is shifting from vehicles powered by a fossil energy powertrain to one that consumes clean energy. The industry is moving towards electric mobility, but this technology has a disadvantage compared to traditional mobility. Electric Vehicles (EVs) need more time to regain their range than conventional vehicles (CV) that use fossil fuels, a massive reason why users do not adopt this technology. This thesis focuses on an optimised charging process to make EVs a realistic alternative for user transport and meet the Paris Climate Accords goals. To this end, a study has been carried out analysing various points and technologies such as Electro-Mobility (e-mobility), recharging technologies, or Cloud-based systems that could optimise the charging process and have a scalable, scalable system that all EVs can use. This project has used the Internet of Things (IoT) to enable EVs to communicate with charging stations in a reliable manner with a scalable system. In addition, it has been possible to store data in the cloud, where it is processed in real-time, and mathematical equations have been developed in the python programming language. The user has access to the information through an interface that allows them to check the most optimal charging points (CPs) in different categories such as total charging time, distance from the various stations, charging total price and the CPs services. These technologies used in this thesis will contribute to optimising the charging process, leaving the door open to future developments in the e-mobility field. / <p>Universidad de Granada, Spanien och  Universidad de Málaga, Spanien</p>

Ett problem med ett programmeringsspråk som har många bibliotek är att det kan vara svårt att identifiera vilket bibliotek som är snabbast. Rapporten bygger på ett experiment där olika Pythonbibliotek jämförs baserat på renderingstiden för diagram med olika datamängder. Stapeldiagram, linjediagram och punktdiagram skapas med Pythonbiblioteken Matlablib, Seaborn och Plotly. Mätningarna visar att det finns en signifikant skillnad på renderingstiderna oavsett vilket bibliotek som används. Mätningarna visar även att det finns en signifikant skillnad, olika bibliotek skalar olika beroende på mängden av data.

Denna kandidatrapport behandlar resultatet av ett kandidatarbete i kursen TDDD96:Kandidatprojekt i programvarutvekling som ges av Linköpings universitet. Projektet heter TestCoverage Visualizer (TCV) och beställdes av Saab Aeronautics. Uppdraget handlade omatt skapa ett visualiseringsverktyg för resultaten av deras testfiler.Projektet genomfördes av åtta studenter under våren 2021. Gruppen använde sig avScrum vid utvecklingen och utvecklade i språken JavaScript och Python. Projektet resulterade i en webbapplikation med en backend och frontend samt en tillhörande parser sommed hjälp av reguljära uttryck kan parsa testfiler och sparade dem i databasen. Resultatenvisades grafiskt på frontend:en med hjälp av mätare och grafer.I kandidatrapporten inkluderades även individuella bidrag skrivna av respektive projektmedlem som fördjupar sig i något ämne eller område som har något med projektet attgöra.