This report describes the process of the Bachelorproject(TI3806) done for ‘De Energiebespaarders’, a startup in Amsterdam striving to make homes more energy efficient through accessible advice and installation of insulation or solar panels. The goal of the project was to apply machine learning to improve their system for identifying house features; windows, doors, and walls, and calculate their surface areas to improve the advice they can give to customers. The old system could produce good results, but was time-consuming to use and sensitive in regard to user input. We chose to implement a new system that makes the process automatic. In the report, our design process and our chosen implementation is described. Our new system makes use of a convolutional neural network to give pixels a label of wall(blue), window(red), door(purple), or nothing(black), without the need for the user to click the individual features beforehand. The results are promising and can save a lot of time, but the results are still inconsistent at times. Therefore, the report also contains recommendations for improvement of this new system, as De Energiebespaarders have shown interest in further developing our system.

Every development roll-out, there needs to be certainty everything works. Especially when it comes to the banking domain. People care about their money and want it to be handled with care. Currently this means some tests, including making a payment using a Maestro debit card, need to be performed manually. In order to further testing, this project aims to automate these Maestro payments. This has been achieved by constructing a robotic device to perform the physical actions associated with creating a payment request, and create a piece of software to verify if the payment was accepted. The main software is based around the public API bunq B.V. offers. Using the API it is possible to retrieve information related to your bank accounts, including payments made. The robotic device is consists of a combination of solenoids and servos to operate the keypad and to present a bunq Maestro debit card. Additionally the system has extra functionality such as self assessment if the Maestro workflow is still functioning. As a result there should no longer be a need to physically take a terminal home and complete payments. Even though the delivered system does not have functionality to work with a phone's NFC, the system should be easily upgraded to support this functionality.

In this report, we present our work on improving the administration software and automating parts of the customer service work for Plugify (https://www.plugify.nl), a young Dutch startup that provides an online music act booking platform. We first analyze Plugify's needs, and then discuss how we design, develop, and iterate the systems they require. We leverage and customize pre-existing administration software named ForestAdmin to provide us with a stable administration panel to create and modify data. Furthermore, we rework Plugify's existing internal notification system to be smarter. Then, we create a small number of custom web pages for Plugify's customer service to more easily process relevant data. Finally, we provide Plugify with a system for metrics in an intelligent way. We also discuss how we perform unit, integration and usability testing, to ensure, among others, the stability, reliability, usability, performance and completeness of the systems we have built.

SeaState5 wants to facilitate the monitoring process for seabed cables of offshore wind farms. The company wants to achieve this by creating an unmanned vessel, named the Eagle, which can be analyzed from a remote location.

As students from TU Delft, we have been tasked to create a software framework that allows the Eagle to go to a location by receiving instructions from the user, and to send environmental data back to shore via a long-range connection. In addition, telemetry should be possible using short-range communication.

The software framework for the Eagle has been built with the design goals of modularity and stability in mind. The user is able to configure the Eagle by adjusting a configuration file. This way, the Eagle pursues a modular design; sensor can be attached and detached without changing the software, only an alteration of the configuration file is required. The Eagle is capable of collecting environmental data, and storing it into a local database. The Eagle also contains in-memory data storage for each sensor. Periodically, the Eagle will send data to the cloud using long range communication. And at last, he Eagle is also capable of retrieving instructions from the cloud. Those instructions allow the eagle to perform specific tasks.

West IT Solutions is expanding into a new focus area, ”Smart Planning”. They want to develop new modules for the Enterprise Resource Planning software they resell, Odoo. Odoo allows companies to manage their projects, employees and other resources. The goal of this project was to create a software product that assists users in creating plannings for their projects in Odoo. The product should offer the following features: Visualise a planning for the user’s projects, Facilitate manually adapting the planning and Propose an automatically generated improvement of the planning. During the research phase of the project we met with a client of West, TNO. TNO explained they are performing research for the Dutch navy to create software that assists planners in creating schedules for battleships. With this case problem in mind, we created a list of requirements our software product should adhere to. We discovered that the case problem West provided us with is a subset of the case problem TNO provided. This meant that if we created a model for the case problem of TNO, the model would work for the case problem of West as well. After the research phase was completed, we started development on the software product. The development phase consisted of seven weeks, and in each week new functionalities were added to the product. In addition to adding new functionalities, we also kept refining our models to improve the performance of the automatic planning generation. The final software product consists of an interface that visualises a planning, a model that can be entered into external algorithms to automatically generate plannings and a module that allows users to import existing projects from Odoo. The user can view their planning through a visualisation that allows them to modify it using a drag and drop interaction. These modifications are setting preferences, like starting times for activities or the assignment of certain persons to certain activities. Once the user has set their preferences, they can ask the software to find an optimised planning that keeps their
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

The Dutch electoral voting system is designed in a way that does not fit in the current era of the digital transformation. Almost every procedure during the elections is done manually and change in policy is very difficult as it requires a change in legislation.
This thesis explores a more trustworthy, transparent and less expensive way of verifying the suffrage of a citizen. In order to accomplish this, a literature review is presented about the election process in other countries and new possibilities with state of the art technology are reviewed. Based on these investigations, a solution that would make the current voting pass superfluous is presented. The presented solution in this thesis is built with blockchain and machine readable travel documents. Considering blockchain, this technology is a decentralized ledger of transactions, where every transaction is
immutable and built following predefined rules. These transactions are visible and verifiable for anyone connected to the decentralized network. Due to this decentralized design, the network is resilient against DDoS attacks and has no single point of failure. Machine readable travel documents are used for the authentication. Dutch travel documents (passports, identity cards and driver’s licences) are designed according to the ICAO machine readable travel document standard, which also requires the presence of a biometric chip. This means that every citizen already has access to a PKI (public key infrastructure) key pair, which can be used to sign data. The presented solution combines these technologies and is first of its kind to link a machine readable travel document to a blockchain. These new possibilities can help solving the identity problem for all kinds of
organizations

Being a bank, bunq deals with transaction fraud on a regular basis. All transactions that are handled by bunq are monitored for these cases of fraud by a transaction monitoring system. When this system flags a transaction as being possibly fraudulent, a bunq employee has to manually check this transaction. The problem with the current system is that it proves to be time consuming and labor intensive. This is caused by the fact that there are a lot of transactions which are falsely flagged as possibly fraudulent, these transactions are called false positives. This resulted in a demand for a system which reduced the number of false positives and thereby the time needed to manually check the flagged transactions.

To fulfill this demand, bunq has been working on creating a machine learning model which classifies transactions as fraudulent or legitamte. This machine learning model has shown promising results during test runs on historical data. However, it was not yet production ready, because it was very slow and there existed no connection with the existing bunq back-end.

During the project, a new transaction monitoring system was designed and implemented. The new system uses a combination of a bunq-made machine learning model and a set of pre-defined rules to flag a transaction as possibly fraudulent or not. The final system implementation consists out of 5 different components: (1) an incoming transaction system, responsible for noticing new transactions and segregating those over different workers so that they can be classified in parallel, (2) an information gathering system, which efficiently gathers large sets of needed information for the classification, (3) a machine learning model server, which enables fast communication with altering machine learning models, (4) a set of pre-defined rules, which check transactions for indicators of fraud and (5) a Grafana dashboard which monitors the performance and statistics of the machine learning model, the pre-defined rules and our system.

The system is fully tested by unit and integration tests. Furthermore, new machine learning models and pre-defined rules can be easily adopted. All the above mentioned components are implemented and fully working. The final implementation is focused on integrating the system in the currently existing bunq back-end, because the system will actually be used in production.

ProRail uses multiple custom built software applications to control the train infrastructure in the Netherlands. At this moment these applications experience downtime when they are updated. This interrupts processes that are related to controlling the train services. ProRail asked us to redesign an existing application in such a way that updates can be applied with zero downtime.

To solve this problem we use redundancy with automatic load balancing. We compared multiple solutions and we chose to use Docker’s swarm functionality. We then redesigned the non-critical application Brugkijker, an application to monitor trains approaching a bridge. In the end we could successfully run multiple instances of Brugkijker in Docker and show how the application can be incrementally updated, inducing no downtime for the service as a whole and without losing data coming from upstream. This proposed approach can be used by ProRail to make other noncritical and critical applications highly available.

A large problem that primary schools face is that the ratio of pupils to teachers is too high, the class sizes are too large and this makes it difficult for a single teacher to have a good oversight of how the development of a given child is going. The aim of Interactive Robotics is to tackle this problem by bringing robots into the classroom to aid teachers. They aim to have a single robot in a classroom that has the ability to teach different lessons and subjects; the RekenRobot being specifically for basic arithmetic. During the research phase, ideas were gathered regarding how to create teaching methods that are motivating and stimulating. For instance, personalisation, humanising the robot and adaptability of the teaching material were desired functions. The software for the RekenRobot was built from scratch, using the programming language GOAL, JavaScript, CSS, HTML and JSP. The original target audience of the project were children between the ages of 6 and 8. Later this was changed to cover different school years: 3-4, 5-6 and 7-8, making use of levels with different degrees of difficulty. The robot can work one-on-one with a child, being able to practice addition, subtraction, times tables and telling time, as well as 2 forms of explanations can be given: making use of a bus and a number line. Using no explanation to rather focus on automation is also an option. The idea of the project was to lay the groundwork for the later development of the RekenRobot, as this will be an ongoing project for Interactive Robotics. The application designed in this project will be adapted to become part of the Interactive Robotics system. The first user tests at primary schools yielded a largely positive result. The children were excited and motivated to work with the product. The system is simple enough to require very little explanation. This project was never meant to realise a product that can be deployed tomorrow, but the result is a very solid basis for further improvements.