Student Research Projects and Master Theses at SCS


Are you studying electrical engineering, informatics, physics or mathematics? Are you inquisitive, communicative and motivated, and never tire of learning? With SCS, you will have the opportunity to realise your student research projects or master thesis. The work is normally shared between two people, or in exceptional cases covered by one person. It should include a research and development component.

If you already have your own idea for your student research project or master thesis, and you have the support of your professor, get in contact with us. Otherwise, let the following ideas give you some inspiration. We will help you transform these ideas into something concrete.

Please contact:

Possible Student Research Projects and Master Theses

  • Detektion von Kabelfehlern in der Aussenanlage SCS - StudienarbeitenEine Herausforderung beim Betrieb von elektrifizierten Bahnstrecken ist die Überwachung von Elementen (z.B. Weichen) und deren Verkabelung in der Aussenanlage. Ein weit verbreitetes Verfahren ist die Überwachung der Adern durch einen Ruhestrom. Speziell bei den heute immer noch weit verbreiteten Lösungen auf Relaisbasis ist die Überwachung des Stromes sehr eingeschränkt. Kurzschlüsse und der Einfluss von Fremdspannungen bleiben daher oft unerkannt.
  • Automatisierte Anamnese von Heizungen und Gebäudeenergieanlagen SCS - StudienarbeitenIn der Schweiz besteht ein grosses Energieeffizienzpotential in Gebäuden. Bestehende Heizungsanlagen in Gebäuden sind heute oft nicht ideal eingestellt, weisen Mängel auf oder sind gar dysfunktional. Zur Detektion dieser Mängel soll ein Prototyp eines automatisierten Anamnese-Systems aufgebaut werden, welches aufgrund von sensorischen Messgrössen (primär Temperaturfühler) die Anlage erstens identifiziert, zweitens auf Fehler analysiert und drittens eine Liste von Handlungsempfehlungen generiert, um das System zu verbessern.
  • Facilitated Maintenance with Augmented Reality SCS - StudienarbeitenOverviewTechnical maintenance on ticket vending machines requires a high level of expertise. Technicians usually need many weeks to get a good understanding of the vending machines, under the tight supervision of their mentor. The goal of the present study is to design and implement an AR (Augmented Reality) system that will facilitate the work of the technicians and reduce their apprenticeship time. In fact, this AR solution will provide in real-time the necessary information with the corresponding documentation of the component that needs maintenance. For instance, a technician will be able to immediately scan the different components in the TVM and determine which are the cables that must be connected between them.
  • Flexible FPGA-based Test Equipment to Model and Characterize the Real-Time Behavior of an Ethernet Ring SCS - StudienarbeitenIn this work, flexible test equipment for Ethernet network hardware and firmware shall be evaluate and commissioned. The goal is to model and characterize an Ethernet network based on BroadR-Reach with ring topology and HSR protocol for its real-time behavior.The test equipment could be based on devices available at Extensions may be developed to further improve functionality.
  • Software-Architecture for SDR SCS - Studienarbeiten
    Alle aktuellen SDR Softwarepakete haben Einschränkungen. Es gibt GNU Radio, welches kompliziert und schwierig zu bedienen ist, und es gibt verschiedene SDR GUIs, welche jedoch nicht einfach erweiterbar sind. Das Ziel ist eine einfach zu bedienende Software zu erstellen, welche es ermöglicht, Radiosignale zu analysieren und Algorithmen auszuprobieren. Fertige Abläufe sollen dann auf Knopfdruck in eine C-Datei exportiert werden können, um eine Integration in Embedded Systeme so leicht wie möglich zu gestalten.
  • Decentralized Ledger eVoting System SCS - StudienarbeiteneVoting is an unsolved problem, mainly because of security concerns connected to centralized IT systems. Blockchain technology enables new ways to solve IT problems in a transparent, tamper-proof and decentralized way. However, scalability of such solutions is still unsatisfactory and there’s still no solution that provides privacy of the single voter and transparency of the overall voting process at the same time.This master thesis aims at implementing a voting solution that scales to Swiss national votes. The technologies to be used could include zero knowledge proofs (zk-SNARKS), homomorphic encryption and smart contracts on a public blockchain (i.e. ethereum). Different layer-two technologies shall be evaluated to provide scalability for this use case.
  • Extend Machine- and Deep Learning into 3D – Enabling volumetric Analysis of Medical Image Data SCS - StudienarbeitenA large part of the medical imaging data available today is available in 3D. This is true for example for magnetic resonance imaging data, computed tomography scan data and many other techniques. This is advantageous as it allows drawing conclusions based not only on single images but taking into account more information. Many medical images have to be further processed by segmenting different objects in the images such as bones or tissue layers. In the past years, the analysis of 2D medical images with Deep Learning methods has become a standard in research. With more and more computing power available, the focus is moving towards 3D analysis to profit from more spatial context.
  • Generation of synthetic ground truth for Machine Learning SCS - StudienarbeitenToday’s Machine Learning algorithms rely on large data sets of ground truth. Especially for medical use cases the lack of appropriate data for training can be a limiting factor for successful Deep Learning. For some imaging problems, the creation of synthetic ground truth can effectively overcome the lack of original ground truth and enable successful learning.In this work we aim to explore the potential of synthetic ground truth for tumor segmentation in medical imagery of eyes. The student will work with 3D stacks of optical coherence tomography (OCT) images. The currently implemented Convolutional Neural Networks (CNNs, e.g. U-net) shall be extended to segment tumors using both synthetic ground truth data and state-of-art CNNs.
  • Learning Angles from Ultrasonography Imagery with Machine Learning and Deep Learning SCS - StudienarbeitenPediatricians and general practitioners use ultrasound for diagnostic purposes in their office. Ultrasound is fast, reliable, safe, and is seen as the visual stethoscope of the 21st century. Standardized, high-quality examinations depend on the exact position of the transducer for transmitting and receiving the ultrasound beam. However, the correct plane defining the image cross section obtained from the patient is often difficult to achieve.Advanced technology might support physicians with feedback based on automated analyses of examinations by ultrasound. We will use the example of ultrasound-based diagnosis of developmental dysplasia of the hip (DDH) in newborns to suggest such an approach using Neural Networks.
  • Students Internships AI-based Golf-Coach with Automated Swing Analysis SCS - StudienarbeitenDer Golfschwung gilt als einer der komplexesten Bewegungsabläufe aller Sportarten. Für den Amateurgolfer besteht die Schwierigkeit darin, bei einem Fehlschlag den Fehler in seiner Bewegung zu erkennen und entsprechend zu korrigieren.Ziel 1: Entwicklung einer Physik-Engine mit geeigneten Parametern für die Berechnung der Flugbahn des Golfballs.Ziel 2: Automatisierte Analyse des Bewegungsablaufs des Golfspielers und gezieltes Erkennen fehlerhafter Muster. Dazu soll ein geeignetes Verfahren ausgewählt und umgesetzt werden, wie z.B. Vergleichen mit Bewegungsabläufen von Profis.Der Simulator soll mit Low-Cost COTS Komponenten aufgebaut werden.
  • Self-Calibration for Embedded Stereo Vision System SCS - StudienarbeitenStereo Vision allows accurately measuring scenes in 3D – provided that the cameras are well calibrated (camera pose, intrinsic parameters). Especially in harsh industrial or automotive contexts, the mechanical vibrations, temperature variations, and material fatigue affect opto-mechanical properties of the stereo-rig and its measurement accuracy.A jointly developed intelligent door opener (embedded stereo-system: can detect, discriminate (humans, vehicles), and track 3D objects in real-time. This enables adequate door openings, e.g. suppressing cross-traffic and accounting for the object’s height to minimize energy loss in cooling storage applications. Because calibration drifts can cause e.g. inappropriate openings/closing, periodic or continuous recalibration of the stereo-rig is advantageous.The goal of this thesis is to explore, test and analyze different methods and algorithms for continuous stereo-vision calibration. The solution has to determine the extrinsic and intrinsic camera parameters without any markers or reference objects. Its performance will be measured mainly in overall accuracy, but also calculation time and memory consumption.
  • Towards User-independent 2D / 3D Object Classification of Complex Life Science Images SCS - StudienarbeitenWe develop novel machine vision & mathematical morphology algorithms to analyze complex multi-modal Life Science images.
  • Transfer Learning for Image Segmentation using Convolutional Neural Networks SCS - StudienarbeitenToday’s Machine Learning (ML) success is often limited by a lack of labelled ground truth (GT) data to train the models. This is especially true for applications in medical imaging.Transfer learning (TL) is a state-of-art ML-technique that can be useful to overcome this problem for similar yet distinct tasks. TL tries to apply knowledge or models gained from a task to a second one. In the field of image recognition, TL-methods for deep neural nets re-use parameters trained on source domain data except for the output layer (see image on the right).In medical optical coherence tomography (OCT) of the eye only a limited set of GT-labeled images exists. In addition, there are several device manufacturers and imaging approaches. Thus, transfer learning could 1) enable an easier addition of new device types or imaging techniques, 2) improve the quality of available segmentations even with limited data.
  • Virtual Reality as 3D Ground Truth Generator for AI, Machine Learning and Deep Learning SCS - StudienarbeitenTraining effective Artificial Intelligence (AI) algorithms today often requires large amounts of ground truth data. Typically, this is a laborious, costly and time-consuming process often requiring manual adjustment. These problems can be overcome by combining AI with Virtual Reality (VR): an emerging technology with various applications in medicine, training, business and simulation. VR creates artificial environments that often resemble our real world.This project aims at exploring the potential of VR as 3D ground truth generator for state-of-the-art Machine Learning (ML) and Deep Learning (DL) Algorithms.