OSCAR-QUBE: student made diamond based quantum magnetic field sensor for space applications

Project OSCAR-QUBE (Optical Sensors Based on CARbon materials - QUantum BElgium) is a project from Hasselt University and research institute IMO-IMOMEC that brings together the fields of quantum physics and space exploration. To reach this goal, an interdisciplinary team of physics, electronics engineering and software engineering students created a quantum magnetometer based on nitrogen-vacancy (NV) centers in diamond in the framework of the Orbit-Your-Thesis! programme from ESA Education. In a single year, our team experienced the full lifecycle of a real space experiment from concept and design, to development and testing, to the launch and commissioning onboard the ISS. The resulting sensor is fully functional, with a resolution of < 300 nT/ sqrt(Hz), and has been gathering data in Low Earth Orbit for over six months at this point. From this data, maps of Earth’s magnetic field have been generated and show resemblance to onboard reference data. Currently, both the NV and reference sensor measure a different magnetic field than the one predicted by the International Geomagnetic Reference Field. The reason for this discrepancy is still under investigation. Besides the technological goal of developing a quantum sensor for space magnetometry with a high sensitivity and a wide dynamic range, and the scientific goal of characterizing the magnetic field of the Earth, OSCAR-QUBE also drives student growth. Several of our team members are now (aspiring) ESA Young Graduate Trainees or PhD students in quantum research, and all of us took part in the team competition of the International Astronautical Congress in October 2021, where we won the Hans Von Muldau award. Being an interdisciplinary team, we brought many different skills and viewpoints together, inspiring innovative ideas. However, this could only be done because of our efforts to keep up a good communication and team spirit. We believe that if motivated people work hard to improve the technology, we can change the way magnetometry is done in space

Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling.

Objective and Impact Statement: Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained BC tissue images. Introduction: Accurate assessment of IHC-stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Methods: Our deep learning-based method analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details. Results: This approach addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view, leading to a blind testing classification accuracy of 84.70%, on a dataset of 523 core images from tissue microarrays. Conclusion: This automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might substantially impact cancer treatment planning

Adaptive fuzzy logic with self-adjusting membership functions based tracking control of surface vessels

Tracking control of surface vessels is aimed where the dynamical model includes uncertainties. In an attempt to design a broadly applicable control methodology, a model independent strategy is pursued. The dynamical uncertainties are modeled via a fuzzy logic network. In the design of the controller, proportional derivative feedback was made use of in conjunction with self-adjusting fuzzy logic compensation term obtained by adaptively updating control representative value matrix along with centers and widths of the membership value vector. Stability of the closed loop system is investigated through novel Lyapunov-based arguments and semi-global practical tracking is guaranteed. Numerical simulation studies are performed that support theoretical findings and demonstrate the effectiveness of the proposed method

Robust State/Output-Feedback Control of Robotic Manipulators: An Adaptive Fuzzy-Logic-Based Approach With Self-Organized Membership Functions

This article aims to design a joint space tracking controller for robotic manipulators having uncertainties in their mathematical representations under the additional constraint that joint velocity sensing not being available. A two-part design is followed where in the first part, the modeling uncertainties are dealt with a self-organized adaptive fuzzy-logic (AFL)-based controller where full-state feedback (FSFB) is assumed. The stability analysis yields semiglobally uniformly ultimately bounded tracking results. In the second part, a high-gain joint velocity observer is designed followed by replacing error vectors in the FSFB controller with their saturated versions obtained from the observer design to arrive at a self-organized AFL-based robust output-feedback controller. The stability analysis is performed via a multiple-step Lyapunov-type method where the semiglobal uniform ultimate boundedness of the tracking error is ensured. Comparative experiment results obtained from a planar robotic manipulator are presented to demonstrate the efficacy of the proposed control methodology

Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators

With increasing demand for using robotic manipulators in industrial applications, controllers specific for performing repeatable tasks are required. These controllers must also be robust to model uncertainties. To address this research issue, a repetitive learning control method fused with adaptive fuzzy logic techniques is designed. Specifically, modeling uncertainties are first modeled with a fuzzy logic network and an adaptive fuzzy logic strategy with online tuning is designed. The stability is investigated via Lyapunov type techniques where global uniform ultimate boundedness of closed loop system is guaranteed. Numerical simulation results obtained from a two degree of freedom robot manipulator model and experiments performed on a robot manipulator demonstrate the efficacy of the proposed control methodology. (C) 2021 Elsevier B.V. All rights reserved