"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:c3bce984-730b-495b-9ebf-077f80b895ea","http://resolver.tudelft.nl/uuid:c3bce984-730b-495b-9ebf-077f80b895ea","Indisputable GDPR compliant signatures in ContractChain","van den Hoek, Martijn (TU Delft Electrical Engineering, Mathematics and Computer Science); Houwing, Krijn (TU Delft Electrical Engineering, Mathematics and Computer Science); Vollebregt, Frank (TU Delft Electrical Engineering, Mathematics and Computer Science)","Erkin, Zekeriya (mentor); Visser, Otto (graduation committee); Wang, Huijuan (graduation committee); Delft University of Technology (degree granting institution)","2019","Nowadays, entering into a contract with an overseas company still relies on postal services to send a printed contract, which is signed on paper. Lizard Global is developing an online platform for constructing, reviewing and signing digital contracts for one of their clients. In the original system, when a signee signed a contract, his personal information was used as a signature and stored in blockchain. However, this way of signing a contract does not enjoy the same degree of legal validity as a written signature. Moreover, the implications on privacy legislation, specifically the European Data Protection Regulation (GDPR) had not yet been taken into account by Lizard Global. This project describes how agile development was used to construct a high quality software solution to the problem, thereby implementing firstly an advanced e-signature to make signing a contract legally binding and secondly functionality to store this signature in blockchain such that it is compliant with the GDPR legislation. This is done by only storing hashed values in the blockchain and adding a user panel. In this panel, signees are able to control their personal data. High quality is obtained by testing thoroughly (100 per cent branch coverage), using the static analysis tool ESLint and requesting, receiving and implementing feedback from the software improvement group.
Science, the workload for teaching assistants and instructors has skyrocketed. To
reduce this workload, automated tools can be used to make the grading process easier. This paper describes the development of AuTA (Automatic Teaching Assistant), a tool that will help instructors and teaching assistants analyze and grade programming assignments and provide useful feedback to the student.","Thesis; Education; Bachelor; Code Quality; Feedback","en","bachelor thesis","","","","","","","","","","","","","Labrador",""
"uuid:00aac32f-e154-4181-baea-c7c00994da12","http://resolver.tudelft.nl/uuid:00aac32f-e154-4181-baea-c7c00994da12","Feasibility Study of LUFAR","Liefaard, Maxim (TU Delft Electrical Engineering, Mathematics and Computer Science); Bruens, Raoul (TU Delft Electrical Engineering, Mathematics and Computer Science); van Hassel, Dana (TU Delft Electrical Engineering, Mathematics and Computer Science); Noorthoek, Sterre (TU Delft Electrical Engineering, Mathematics and Computer Science)","Abeel, Thomas (graduation committee); Verma, Maneesh (mentor); Verhoeven, Chris (mentor); Visser, Otto (graduation committee); Wang, Huijuan (graduation committee); Delft University of Technology (degree granting institution)","2019","With the steady increase in space missions, enabled through technological advances and increase of commercialisation within the space flight industry, both more and increasingly complex missions can be designed for space. To this end, the Lunar Zebro project competes within this field through its small lunar rover design, drastically decreasing deployment costs and risk of the mission. The road map of Lunar Zebro aims to have a multitude of rovers deployed on the Moon, being able to complete several tasks like exploring, observing, and mapping. Since this concept of rover cooperation adds a novel level of complexity to the mission, a feasibility study is required to look into the difficulties of navigating the Moon with a larger group of rovers. LunarSim is the software package developed during this project. LunarSim aims to facilitate a simulation environment in which Lunar Zebro rovers and space mission designs can be tested and validated. To legitimise the workings of the simulation, a few scenarios have been developed to test the core functionalities of the software product. These scenarios are based on phases in a practical mission plan that consists out of navigating to and observing a crater location. The scenarios is evaluated through examination of a set of defined fitness criteria. In this report, the reader will find documentation on the development process of LunarSim: the simulation in Unity, the ROS back-end, and the bridge between these two systems. Additionally, the report elaborates how the developed software was used to aid in the feasibility study of LUFAR. First, initial research and requirements are formulated to define the scope of the simulation, after which the software architecture is introduced. Then, the systems implemented for the simulation are explained. Subsequently, the implemented rover behaviour algorithm that was used for testing is explained, with additional resources on how to develop a new custom rover behaviour. After this, an evaluation is given of the simulation based on the initial requirements and research with future research and concluding remarks. At the end of the report, the technical specifications in terms of software architecture, simulation environment, and rover behaviour are defined to give an in-depth view of LunarSim.","Space; Moon; Rover; Mission design; Simulation; Multi-Agent System; ROS; C#; Unity; C++; Systems Engineering","en","bachelor thesis","","","","","","","","","","","","Computer Science and Engineering","Lunar Zebro",""
"uuid:c2c6ee21-de0d-4a6b-8d78-7ee7de1f1e00","http://resolver.tudelft.nl/uuid:c2c6ee21-de0d-4a6b-8d78-7ee7de1f1e00","Estimatic","Rietveld, Jip (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Software Technology); de Vries, Rolf (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Software Technology); de Boer, Jaap (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Software Technology); Hondelink, Dieuwer (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Software Technology)","Bozzon, Alessandro (mentor); Visser, Otto (graduation committee); Wang, Huijuan (graduation committee); Janssen, Richard (graduation committee); Delft University of Technology (degree granting institution)","2019","Amsterdam Airport Schiphol has 5 runways, each of which can be used for take-off or landing of aeroplanes. The weather heavily influences which runway configuration air traffic control might pick. Airport Forecasting Service (AFOS) predicts which configuration of runways works most efficiently given a set of expected weather conditions and the standard deviations of wind components. These standard deviations give the system an indication of the accuracy of the weather forecasts.
Currently, the KNMI (Royal Netherlands Meteorological Institute) is the only meteorological institute that provides these standard deviations along with the weather forecast. This raises the main research question of this report: Is it possible to make accurate enough estimations of the standard deviation of wind direction and wind speed using historical data and future weather expectations. Estimating these standard deviations has been researched with two different approaches: a statistical method approach and a machine learning approach.
Statistical Methods Four fitting methods have been researched in search of the best statistical model to estimate the standard deviation of wind direction and speed: the Maximum Likelihood Method (MLM) and three Least Square Method implementations of a Weibull, Minimum Weibull and Double Weibull distribution. The performance of aggregates on the outcome of these four methods was also researched. One case takes the minimum standard deviation of the four, the other takes the mean.
MLM not only performs the best but also performs most consistently of the four fitting methods. Taking into account aggregates, MLM is more consistent than the minimum method but the minimum method outperforms it. Neither of these methods managed to meet the success criteria.
Machine Learning In regards to machine learning, the problem of estimating the standard deviations of wind direction and wind speed is a regression problem. The following machine learning models have been researched for Estimatic: MLPN, LSTM RNN, ERNN and RBFN.
LSTM RNNs outperform MLPNs, RBFNs and ERNNs for both wind direction and speed standard deviation estimation. LSTM RNN performance did not meet the success criteria.
The research concludes that it is not possible to make accurate enough estimations of the standard deviation of wind components using the historical data and future weather expectations available for Amsterdam Airport Schiphol.","Wind speed; Wind direction; Wind; Schiphol; Weather forecast; Standard deviation; KNMI; Machine learning; Statistical methods; Statistics","en","bachelor thesis","","","","","","","","","","","","Computer Science and Engineering","","52.3105386, 4.7682744"
"uuid:041533eb-6010-418e-b3d1-80ff7cc4996b","http://resolver.tudelft.nl/uuid:041533eb-6010-418e-b3d1-80ff7cc4996b","Server Program for Retail RFID System with advanced message handling","Beijen, Mike (TU Delft Electrical Engineering, Mathematics and Computer Science); Chong, Kevin (TU Delft Electrical Engineering, Mathematics and Computer Science); Holland, Callum Robert (TU Delft Electrical Engineering, Mathematics and Computer Science); Keller, Glenn (TU Delft Electrical Engineering, Mathematics and Computer Science)","Aniche, Maurício (graduation committee); Pawelczak, Przemek (graduation committee); Visser, Otto (graduation committee); Wang, Huijuan (graduation committee); Delft University of Technology (degree granting institution)","2019","Our challenge was to create a server program for retail RFID system with advanced message handling. However, RFID software solutions are heavily dependent on the requirements and use cases of the system. The developed solution allows for convenient interaction with RFID tags through different components of the designed system. The complete system has been developed with scalability and maintainability in mind and is thoroughly tested using unit testing, integration testing and end-to-end testing.","","en","bachelor thesis","","","","","","","","2024-07-03","","","","Computer Science and Engineering","",""
"uuid:c2458e36-234b-43cc-965e-b5d26f0b8809","http://resolver.tudelft.nl/uuid:c2458e36-234b-43cc-965e-b5d26f0b8809","Material Tracking System","Edixhoven, Tom (TU Delft Electrical Engineering, Mathematics and Computer Science); van Geffen, Hunter (TU Delft Electrical Engineering, Mathematics and Computer Science); Kruit, Bas (TU Delft Electrical Engineering, Mathematics and Computer Science); Smit, Mels (TU Delft Electrical Engineering, Mathematics and Computer Science)","Finavaro Aniche, Mauricio (mentor); Visser, Otto (graduation committee); Wang, Huijuan (graduation committee); Delft University of Technology (degree granting institution)","2019","For a steel company it is advantageous to be able to easily track steel through the production process. At Tata Steel this is currently done with the Material Tracking Table. However, generating this table takes months. Therefore a new system had to be developed. This paper describes the building of such a new system, which generates this Material Tracking Table in less than 1 hour, as well as the related systems concerning the acquisition of the input data and the visualisation of the resulting output data.","clustering; data visualisation; Web application; framework; memory management","en","bachelor thesis","","","","","","","","","","","","","",""
"uuid:1c523b11-c220-43da-b4c2-a712d6fee8d4","http://resolver.tudelft.nl/uuid:1c523b11-c220-43da-b4c2-a712d6fee8d4","Automated Transaction Monitoring","Kostense, Bastijn (TU Delft Electrical Engineering, Mathematics and Computer Science); Hageman, Rico (TU Delft Electrical Engineering, Mathematics and Computer Science); van der Wilk, Hilco (TU Delft Electrical Engineering, Mathematics and Computer Science); van Walraven, Bram (TU Delft Electrical Engineering, Mathematics and Computer Science)","van den Oever, Sander (mentor); Visser, Otto (graduation committee); Wang, Huijuan (graduation committee); Delft University of Technology (degree granting institution)","2019","For the past 10 weeks, we have been tasked with improving the performance of the transaction monitoring system of bunq, an internationally active mobile bank. bunq has requested that we improve this system by automating the training of the machine learning model, providing better input data for this model and creating additional machine learning models. During this project, we have been working at the offices of bunq on this system. This thesis will give an overview of our research, software design process and implementation.","Transaction Monitoring; Fraud; Machine Learning","en","bachelor thesis","","","","","","","","","","","","","",""
"uuid:dfa920b8-613d-4f93-9a1b-7c8c60268308","http://resolver.tudelft.nl/uuid:dfa920b8-613d-4f93-9a1b-7c8c60268308","Computer Vision for Exam Grading: Final Report","Young On, Ruben (TU Delft Electrical Engineering, Mathematics and Computer Science); van de Kuilen, Richard (TU Delft Electrical Engineering, Mathematics and Computer Science); Bijl, Robin (TU Delft Electrical Engineering, Mathematics and Computer Science); Leistra, Hidde (TU Delft Electrical Engineering, Mathematics and Computer Science); Jugariu, Timo (TU Delft Electrical Engineering, Mathematics and Computer Science)","Hugtenburg, Stefan (graduation committee); Akhmerov, Anton (mentor); Wang, Huijuan (graduation committee); Delft University of Technology (degree granting institution)","2019","Grading exams is a time-consuming activity for teachers. Zesje is an open-source tool created to aid teach-ers in exam grading and streamline the grading process. Zesje currently uses computer vision techniques torealign images, and automatically find student numbers. However, teachers can currently only use Zesje tograde questions manually. Moreover the computer vision capabilities of Zesje can be improved. To make iteasier to grade exams, it should be possible for teachers to have multiple choice questions graded automati-cally. This project describes various improvements for Zesje, most notably using computer vision for the auto-matic grading of multiple choice questions, improving the accuracy of aligning scanned submissions, andautomatically detecting blank solutions. The team had to make several choices regarding implementations and choice of technology. Design goalswere also created to serve as a guideline for the project. At the end of the project, with the features imple-mented by the team, Zesje can automatically grade multiple choice questions, identify blank solutions andhas the corresponding front-end changes that allow the user to create multiple choice checkboxes on theexam PDF. These features have been tested extensively. The use of Zesje also poses some ethical challenges. Using automated grading may result in the event thatsome submissions may never be seen by a grader. By using benchmarks to compare the performance of processing scans in Zesje, the team found out thatthe grading time has greatly been reduced.","computer vision; auto grading; digital grading; open source","en","bachelor thesis","","","","","","","","","","","","Computer Science and Engineering","Bachelor Project","52.0021256, 4.3732982"
"uuid:1f77ead5-58be-4f1d-b176-817e8761d283","http://resolver.tudelft.nl/uuid:1f77ead5-58be-4f1d-b176-817e8761d283","TelaSol: A Coach Cockpit Application","Vijlbrief, Sam (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems); Kroon, Mirco (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems); Janssen, Boris (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems); Gerlach, Laurens (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems)","Migut, Gosia (mentor); Dukalski, Rado (mentor); Wang, Huijuan (graduation committee); Visser, Otto (graduation committee); Delft University of Technology (degree granting institution)","2019","Team Sunweb, a professional cycling team and our client, is constantly looking for innovations to help them win races. They tasked us with creating an application which could assist coaches with determining the strategy during a race. This application, which we dubbed TelaSol, is supposed to run on a tablet that is mounted on the dashboard inside the coach car. For this project we developed an application that allows races to be prepared on a desktop computer and tracked during a race on a tablet-optimized interactive dashboard. On this dashboard, there will be information on the riders, the route and comments that can be added before the race.
During development we have considered existing solutions, relevant literature and useful technologies to get an idea of what was possible and how we could achieve our goal. We used this knowledge to create our initial set of requirements. We then proceeded development of application using an agile approach, which involves regular feedback moments from our client to update the requirements and adjust our focus accordingly. To verify the quality of our product we relied on a combination of automated tests, user testing and validation through the client.
Initially the application was supposed to integrate live data coming from the riders during the race, but due to a regulation change we had to change our focus. Instead, we focused primarily on creating the application for playback purposes, while still keeping it adaptable to live data. The application performs the main tasks that were initially defined properly. After further development on live data and extensive situational testing, the app can be used to its full potential. Using TelaSol, Team Sunweb will improve their ability to analyze races and increase their chances of winning.
After researching state of the art Machine Learning models for price recommendation, the architecture of the system was designed. The supplied data was preprocessed, after which a custom Genetic Algorithm was developed for optimising models and ensembles. After validation on real-life company data, a comparison using empirical metrics was conducted. We use these empirical metrics to show that a bagging ensemble is the most efficient and accurate model for this purpose. This bagging ensemble outperformed the currently implemented functions, whilst adhering to the set boundaries on response times. Lastly, recommendations are made to the company with an overview of potential future work in this subject.
Based on an experimental app developed during the research phase, raw smartphone GPS data was found to be unsuitable for video rendering. To improve this data, a Kalman Filter is used, in combination with a smoothing algorithm. The system has been designed to allow code sharing between iOS and Android where possible. The system has been implemented in Objective-C, Java, and TypeScript. Separating the system in three blocks enables code reuse which improves maintainability of the system. The filter has been integrated as shared code in the TypeScript implementation, which allows filtering to happen on the device. The user of the React Native Module developed has freedom to retrieve the unprocessed and processed data.
The system has been tested by means of unit tests in all three programming languages used. Tests have been executed using a continuous integration server, testing each pull request against the current code base to ensure quality. Part of the testing phase includes the React Native Module to be implemented in the client's smartphone application to demonstrate its use. The application has been sent to a number of test participants to collect data from different routes and activities. The project can be seen as a success since all important requirements have been successfully implemented.
preferences in mind. At the end of the project, we gave a demo to TNO and they were impressed with the results of the project. West IT was also happy with the delivered product and is interested in further developing it","Planning; Scheduling problem; Automated scheduling","en","bachelor thesis","","","","","","","","2017-07-04","","","","","",""