Ongoing projects

Automated Open Precision Farming Platform (Utopia) 01/03/2021 - 29/02/2024

Abstract

Precision-farming needs large-scale adoption to increase production at such a level that it significantly contributes to minimizing the gap between actual and required world-production of food. Increasing the measurement and actuation intervals of e.g. monitoring for pests and watering are expected to contribute to e.g. increased yields. Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. In precision agriculture, the goal is to gather and analyze information about the variability of soil/water and plant conditions in order to maximize the efficiency of the farm field. This would also increase the burden on the farmer, as the measurement-time and data-processing time increases significantly. This can be mitigated with Automated (cooperative) Precision Farming with the use of autonomous driving vehicles, vessels, drones and dedicated installations mounted on regular agri-machinery. For the cooperative robotic missions, the data will be tagged with accurate position information and merged with other data in order to create a digital map. To achieve good performance for an intelligent system in autonomous navigation tasks we will also build a 3D world model which will be integrated with a digital twin at plant level in order to improve the local path such that we obtain accurate information. To integrate the data from heterogeneous sensors, a platform will be developed to determine the practicality of the available sensors for the optimization of the spatio-temporal interpolation. This project will focus on a single (standardized) platform where (robotic)paths, monitoring strategies can be set and the drones/USV's/AGV's automatically deployed when certain conditions are met. The measurement data will be available for different stakeholders in the same platform.

Researcher(s)

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Project website

A general line variety model for sensors, allowing stable calibrations that meet the accuracy standards for medical applications. 01/10/2020 - 30/09/2024

Abstract

The popular pinhole model for imaging sensors and the associated calibration procedures appear to be inadequate for some of the new generation sensor technology. Even for classical RGB cameras, this standard model leads to unstable calibrations, with the need for an extra model to remove lens distortion. We propose line varieties as a unifying modelling for a broad set of sensors. As opposed to other previously published attempts in this direction, we identify the sub-varieties that correspond to real sensors. This enables us to extend interpolation techniques and Gaussian processes, to support sensor calibration from small samples of lines. We aim fundamental contributions to the fields of Line Geometry and Probabilistic Numerics. Our goal is to develop the framework for multi-sensor configurations (laser scanners, IR-cameras,…), providing measurement fusion, using the developed line models, and to achieve accuracy levels for sensor-supported Radiotherapy.

Researcher(s)

Research team(s)

D Thermal imaging of people using statistical shape models. 01/10/2020 - 30/09/2022

Abstract

In this project, we will develop an easy to use method to monitor the thermal condition of a person as a function of time, with potential applications entailed in physical treatment or a sports activity. The method employs amongst others thermal imaging. To that end, we create a virtual 3D model of the person of interest. The proposed technique will enable the development of a flexible and mobile measurement system, which can be used in labs, hospitals, rehabilitation centers, sports training facilities, etc.

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Research team(s)

Development of a precision clinical plasma treatment system using environmental sensing and robotic controls. 01/07/2020 - 31/12/2021

Abstract

In the context of clinical treatment of cancers, a major challenge involves the precise delivery of therapeutic agents to the tumor while limiting off-target effects. This is true for multiple treatment modalities including radiotherapy and non-thermal plasma (NTP) therapy. Hence, the main focus of this research project is to introduce the design of a supervisory control structure into a patient-in-the-loop therapeutic application. This system will be developed by integrating 3 components: 1) environmental sensors, 2) a robotic control unit, and 3) a therapeutic device (NTP generator). Since NTP treatment is highly dependent on parameters such as treatment time, application distance, etc. a feedback approach is necessary to compensate for tumor motion induced by the patient during treatment (e.g. respiration). To this end, artificial intelligence tools, including neural networks, will be employed to model the dynamic disturbances of the tumor. The developed self-learning artificial intelligence models will be embedded within model-based controllers to predict and minimize the effect of disturbances. Performance of the control structure will be validated with real-time experiments of plasma delivery in biological systems. The proposed methodology has the potential to improve the precision and accuracy of clinical NTP treatment and consequently minimize damage to healthy tissue.

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Research team(s)

PhairywinD project 01/03/2020 - 28/02/2025

Abstract

In this PhD research we will develop a non-destructive testing technique for the automated and quantitative inspection of bare and coated steel structures during manufacturing and operation, using a hyperspectral camera. Whereas the human eye and color cameras perceive three colors in the visible range, a hyperspectral camera is able to capture several tens of images over a wider wavelength range. This facilitates observation of phenomena that cannot be observed with traditional cameras. Although hyperspectral cameras have already proven their merit in quality control of food products, their use for non-destructive testing is still at its infancy. The main limitation of hyperspectral imaging is the limited spatial resolution. We will develop an image processing technique to artificially increase the spatial resolution of hyperspectral images. We will deploy a technique to continuously scan over the surface of the structure in order to reduce inspection times and we will apply the hyperspectral NDT technique to deblur images.

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Project website

Optimized skin tissue identification by combined thermal and hyperspectral imaging methodology. 01/01/2020 - 31/12/2023

Abstract

The determination of local components in human skin from in-vivo measurements is crucial for medical applications, especially for aiding the diagnostic of skin diseases. In the study of skin cancer and burn wounds and more specifically as a methodology for diagnosis of cancer type and identification of skin penetration depth, it is of great relevance to investigate which cell types are present and how these are distributed at or below the skin surface. Consequently, a number of medical inspection techniques have been developed that can be used for the identification of malignant skin properties and more specifically skin cancer types. However, most of the existing techniques are increasingly contested because they either require destructive sampling (biopsy) or only measure on or under the skin surface (hyperspectral imaging) without identification of the penetration depth or detailed physiology of the maligned skin tissue. As a promising non-contact and non-destructive imaging technology, dynamic infrared thermography (DIRT) inspection will be used in combination with hyperspectral imaging (HSI) and physical modeling for fast and accurate skin property identification but also for assisted medical screening as it is possible to differentiate physiological properties based on a combined thermal-hyperspectral response of the skin. In order to optimize the accuracy and speed of tissue screening, the combined HS+IR measurement methodology will be assisted by numerical modeling.

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Research team(s)

Depth-selective chemical imaging of Cultural Heritage Objects (DICHO). 01/10/2019 - 30/09/2023

Abstract

In spite of its ability to successfully characterize the condition and materials of paintings and other works of art in a non-invasive way, Macro X-Ray Fluorescence imaging (MA-XRF) suffers from a drawback that significantly affects its most valued application: revealing hidden features and overpainted compositions. While the penetrative properties of the primary and secondary X-rays can be used beneficially to reveal subsurface information that is crucial for art historical scholars and conservators, the extent to which a particular layer can be visualized selectively depends on the exclusive presence of an element in that layer. By consequence, layers with a similar elemental signature emerge intermixed in the same distribution image while the exact layer sequence remains unclear. As a result, in many cases, (contested) sample extraction proves mandatory in order to assign the detected elements to a specific layer within the paint stratigraphy. In order to augment chemical imaging with an additional depth-dimension, a dual approach is presented: (1) separating surface signals from deeper signals by expanding the MA-XRF detector angle geometry and exploiting the resulting, potential depth information that lies within the absorption effects on emission line ratios, by adding a level of data-processing to the existing protocol; (2) reconstructing the layer buildup and allocation of the detected signals by including an Infrared thermographic camera (IRT). In order to characterize the number of layers present and their sequence, multi-sine heat excitation will be exploited for the spectral range of 1.5-5μm in combination with dedicated post-processing of the hypercube images in the frequency domain. The proposed multimodal MA-XRF+IRT measurement methodology is developed on paint mockups and validated on historical paintings and wood panels, in collaboration with the Royal Museum of Fine Arts Antwerp.

Researcher(s)

Research team(s)

Automated inspection of infrastructure using drones (AutoDrone) 01/10/2019 - 30/09/2021

Abstract

In this project we will use drones to detect and monitor damage in infrastructure: wind turbines, bridges, buildings, solar panels, pavements, etc. Firstly, an overview of available path planning tools will be given. Secondly, we will develop machine learning tools to automatically detect damage (cracks, potholes, corrosion). The third aim of the project is the development of a methodology to allow a systematic comparison of repeated drone based camera measurements. During the project 9 case studies will be performed. The project is performed by UAntwerpen and WTCB together with a large consortium of companies active in drone based inspections or owners of infrastructure.

Researcher(s)

Research team(s)

Project website

Optimized pre-processing using a response surface methodology for improved dynamic active thermographic inspections. 01/01/2019 - 31/12/2022

Abstract

Non-destructive testing using active thermography is still an expanding research area in order to achieve higher accuracy and faster measurements. More and more industrial manufacturers explore the opportunities of active thermography measurements resulting in more complex shapes and materials. Due to these evolutions it becomes nearly impossible to select the most applicable measurement setup in a fast manner. Especially inspections of large parts are a challenge since inspections of the complete part at once is not possible. Dynamic measurements are the solution to inspecting those samples, but consequently this implies new challenges regarding the measurement setup. In order to perform accurate inspections, trial and error is not a suitable solution because this working principle is time-consuming and should be redone every time the test sample changes, the measurement setup alters or when new innovations are discovered. The purpose of this research is to develop and implement an optimisation routine in order to give a suggestion of measurement setup parameters starting from finite element simulations and afterwards updating with knowledge of preliminary measurements. This optimisation routine will be performed using well-known response surface techniques and benchmarked with newly discovered methods. The optimisation routine will be tested on multiple samples in order to inspect the robustness and reliability.

Researcher(s)

Research team(s)

High-Precision Hybrid Laser-based Additive & Subtractive Manufacturing (Hi-PAS). 01/01/2019 - 31/12/2022

Abstract

The consortium aims to achieve two main goals with this SBO project. First, through a rigorous research methodology to better understand how the roughness fatigue life of additive-made metallic components can be significantly improved. We anticipate this by rolling out a multidisciplinary approach, i.e. in terms of surface and shape metrology, non-invasive quantification of the residual stress and mapping of the process parameters that have an influence on the corrosion mechanisms. Moreover, a strong asset in this project is the possibility to also investigate the interrelationships between phenomena. The second main goal is to build up fundamental knowledge on how the laser-based hybrid production process can be substantially improved: In particular to be able to make complex-shaped metallic components with high precision and this without further intensive post-processing (in particular also known as " first-time-right approach).

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Past projects

Visual servoing control in a cluttered environment based on artificial intelligence. 01/04/2019 - 30/03/2020

Abstract

The necessity of designing flexible and versatile systems is one of the most current trends in robotic research. Including visual servoing techniques in an existing robotic system is a very challenging task. In this project a solution for extending the capabilities of a 6 DOF manipulator robot for visual servoing system development, is proposed. In order to achieve this task, different types of visual features (which can be extracted from the image using a visual sensor) are detected and their properties are analyzed. Here, visual features such as point features and image moments are taken into account for designing the controller. An image-based control architecture is designed and a real-time implementation on a manipulator robot is developed. The primary objective of this research project is to converge into an accurate algorithm for object reconstruction in a clutter environment and subsequently helping the robot to perform a visual servoing task. The object reconstruction is done by employing tools from artificial intelligence such as deep Convolutional Neural Network. The image acquisition and image processing together with the computing of the image-based control law will be implemented in Matlab. Thus, a new type of robot driving interface that links the robots' controller with Matlab environment is proposed. Such a user driver interface will allow not only to design and implement real-time controllers but also to perform other tasks such as identification, path planning, etc. Finally, the robustness and stability of the proposed visual feature based control law will be implemented, tested and validated in real-time through multiple experiments.

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Research team(s)

Inspection of road pavements 01/11/2018 - 31/10/2019

Abstract

In this research project we want to take a further step in refining and concretising techniques for an automatic road surface quality inspection for application in the Flemish region, in order to simplify road management and save costs by having the road surface at the right times at the right times to renovate or renew places.

Researcher(s)

Research team(s)

Validation of markerless body tracking for real world gait analysis. 01/07/2018 - 31/12/2019

Abstract

Markerless motion tracking became very popular and common since the introduction of the Microsoft Kinect in 2010 in both the gaming community and industry. To use markerless motion tracking in the field of medical rehabilitation, a higher accuracy and reliability is needed. To achieve this goal, we will combine a 2-D skeleton detection algorithm with the data from multiple 3-D cameras. The developed procedure will be validated with the marker-based Vicon system of the M²OCEAN lab and calibrated 3D body scans of subjects in static position. Afterwards, the technique will be implemented on a treadmill to evaluate the gait of a person. To simulate real world gait information, subjects will wear virtual reality glasses. This virtual environment stimulates the brain and influences the gait of a person, which results in extra information compared to stand alone treadmill walking.

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Research team(s)

High-efficiency Sensorless Control of a BLDC Motor using Sinusoidal Currents. 01/07/2018 - 31/12/2019

Abstract

A Brushless DC Machine (BLDC) is the optimal motor to use in applications where a more or less constant, controlled, high rotational speed is required. Typical examples include driving: the compressor of a cooling system including refrigerators and air-conditioning, the propellers of a drone, fans and pumps in general, …. The BLDC is responsible for the lion share energy usage of these applications. Moreover, cooling systems consume a lot of energy worldwide because of their ubiquitous presence. On the other hand, for battery fed systems such as drones there is strong desire for increased autonomy. This means there is a strong desire to reduce the energy usage of BLDC driven systems. BLDC motors are typically driven with a square wave current. On the other hand, using sine wave currents could result in an energy efficiency increase of 10%. However, typical BLDC algorithms lack feedback to drive the machine with sine waves. Using an encoder to obtain this position feedback would increase the cost and complexity of the drive system and can be impossible due to limited mounting space. Therefore, so-called sensorless algorithms which estimate feedback signals based on easily measurable voltage and current signals, are of interest. Consequently, the central research question of this STIMPRO is formulated as: Develop and implement a sensorless algorithm to provide feedback for a BLDC drive algorithm using sinusoidal current waveforms and validate its energy saving potential. As a starting point this STIMPRO will consider an estimation algorithm, developed by the promotor, for stepping motors, to use in BLDC drives. This STIMPRO will be used as a kick-start to initiate electrical motor control research at UAntwerp. This project will serve as leverage to move the activities off the promotor in motor control, who started at ZAP at UAntwerp the 1st of September 2018, previously established at UGent to UAntwerp. To do so, the STIMPRO will be used to hire a researcher who will submit an FWO SB proposal. However, if FWO funding is rejected we will not finish this project empty handed. Given the work plan defined in the STIMPRO, and the experience of the promotor the project will certainly result in publications, a test bench, added experience for the hired researcher and the exploration of possible bilateral collaboration with Flemish companies on the subject. The work done in this STIMPRO will be beneficial for the Op3Mech research group as adding research on electrical motors is a vital in the broader robotics research. Moreover, the education on drivelines at the Faculty of Applied Engineering is currently not supported by academic research. Therefore, the research activities initiated in this STIMPRO are vital to continue education on these topics.

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Research team(s)

Fast broadband lock-in thermography for fragile structures using system identification. 01/01/2018 - 31/12/2020

Abstract

In this project a new methodology for product testing and quality control is developed based on infrared lock-in thermography. Infrared thermography permits to visualize the thermal/ warmup response of objects. In particular, lock-in thermography employs a sinusoidal light source to warm up the object being studied. Although pulsed thermography (PT) is commonly used as thermographic inspection technique, this method is not well suited for inspection of fragile structures (art and biological tissue inspection, blood circulation, …) due to the large instant energy emission which involves insufficient controllability and non-uniformity. On the other hand, with traditional lock-in thermography only one defect depth can be inspected at a time. In addition, at least one steady state period of the sine wave excitation is necessary to obtain quantitative results.

Researcher(s)

Research team(s)

Smart integration of numerical models and thermal inspection (SINT) 01/12/2017 - 30/11/2019

Abstract

Combining finite element models with non-destructive testing has enormous potential for valorization. The objective of this project is to develop a reliable damage detection and localization tool by combining NDT thermography data with FE modeling, making use of system identification. As the amount of experimental data is very high and depending on the resolution of the IR camera, the goal is to use virtual modeling in assistance of the NDT tests in order to gain accuracy and time-efficiency.

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Research team(s)

Sensing and simulation for smart assembly and logistics (SENSALO) 01/10/2017 - 30/09/2019

Abstract

In the project we use 3D vision techniques in order to make the assembly process more efficient and safer. This is done by tracking people, products and machines (like cobots) in a manufacturing environment.

Researcher(s)

Research team(s)

Project website

Evaluation and simulation of the contact pressure in biological intercalary reconstruction surgery after bone sarcoma resection. 01/10/2017 - 30/09/2019

Abstract

Bone cancer affects children and young adults and requires wide removal of bone, leaving large defects. In order to save the limb and to restore its function in a lasting way, dead bone from bone banks or sterilised removed bone (graft) is used to fill the defect and is fixed by plates and screws. Still, in some patients a gap between the dead graft and the remaining living bone is seen, causing a delayed healing which leads to prolonged non-weight bearing periods (>1 year) and reoperations. We aim to reduce the healing time by introducing a predefined compression force to a graft, comparable to methods used in fracture fixation and megaprosthesis ingrowth. However, no literature is available evaluating the compression force and its effect on graft healing. Moreover, as bone cancer is extremely rare, this small patient group is often ignored for research funding to improve the current knowledge. We need to reproduce this compression force in a reliable way in different patients and different bone parts. Therefore we need to develop a standardised surgical procedure and determine the relation between the compression force and the surgical variables, eg screw positioning. Data from in vitro cyclic loading experiments and the patient's characteristics will be used for virtual simulation of compression force during level walking. These data will be essential for the future introduction and development of innovative techniques such as patient-specific instruments and implants.

Researcher(s)

Research team(s)

Mechanical pathways in the onset and progression of cartilage lesions of the hip joint. 01/10/2016 - 30/09/2018

Abstract

The hip functions as a ball and socket joint, with cartilage layers that cover the joint surfaces on both sides protecting it from impacts and permitting smooth movements. When the cartilage is impaired by mechanical, infectious or inflammatory causes, the joint might eventually wear down - a disabling condition known as osteoarthritis. Recent literature indicates that up to 80% of all hip osteoarthritis cases might be related to subtle variations in the joint geometry.: These variations have been suggested to give rise to peak joint stresses and altered load distributions on the cartilage. Although the mechanism is getting increasingly recognized in the literature, profound understanding of its true impact is lacking. Further, the prevalence of these morphological variations is reported to be much higher than the actual number of patients presenting for treatment. The aim of this thesis is to explore the impact of variation in hip joint anatomy on load distribution during daily living activities. I intend to clarify the role of mechanical drivers in the onset and progression of cartilage lesions of the hip joint by means of advanced multidimensional statistics and personalized load and stress predictions. The final step of this thesis will be to gradually transfer these findings into clinical practice and at the operating theatre by providing virtual pre-surgical planning, accurately implemented during surgery, using state of the art navigation technology.

Researcher(s)

Research team(s)

Toward a pinhole-free model for a Time-of-Flight camera, furnishing featureless procedures for calibration and navigation. 01/10/2016 - 11/05/2018

Abstract

A new generation of digital cameras makes use of emitted light pulses, more precisely the time between the emission and the reception of the reflected pulse, for computing the depth of the viewed object. This "Time-of-Flight" principle is replacing other 3D-scan strategies such as stereovision and structured light. Though the concept and possibilities of a ToF-camera essentially differs from these that are offered by "classical" optical cameras, the computer vision community still falls back on proven methods for calibration and structure-from-motion issues. We propose new techniques, fully exploiting the Time-of-Flight power, avoiding detection and recognition of features in the image. In a further step, we intend to design a new camera model, more general than the familiar pinhole model, providing a uniform framework for both lateral as depth calibration of ToF-cameras. The theory will be validated by simulations and real experiments (executed by a computer driven robot manipulator). Finally, real life applications will be considered, in cooperation with some of our industrial partners.

Researcher(s)

Research team(s)

Thermal hyperspectral material characterization for Art Conservation based on hypercubes. 01/07/2016 - 31/12/2017

Abstract

In the study of historical paintings and more specifically as a preparation for restoration activities of such artefacts, it is of great relevance to investigate which materials and degradation products are present and how these are distributed at or below the painting surface. Commonly used non-destructive in situ methods such as X-ray fluorescence (XRF) and X-ray diffraction (XRD), are only used for spot analyses and require several minutes to record a spectrum from a single sample position, resulting in long scanning times required to record the data hypercubes. As an alternative, thermography inspection, as a non-contact and non-destructive technique is used for material parameter identification but also for art inspection as it is possible to differentiate chemical compounds. Therefore the goal of this research proposal is to improve non-invasive macroscopic material characterization of flat objects, both from an industrial and cultural heritage context, by augmenting existing elemental imaging technology with more species specific imaging of organic and inorganic compounds and this by combining the established X-ray based approaches with IR thermography and hyperspectral (HS) images. A combined X-ray, IR thermography and HS technique eliminates the disadvantages of these techniques and results in a faster measurement and material identification technique with respect to measurement time but also accuracy of the material parameter identification.

Researcher(s)

Research team(s)

Non-linear and time-varying data-based modeling of rotating machinery. 01/07/2016 - 31/12/2017

Abstract

Rotating machines appear in many application fields ranging from large scale applications (e.g. wind turbines) to smaller ones (e.g. medical fluid pumps). The availability of a mathematical model for the dynamical behavior is of crucial importance for the design, prediction and control of these rotating systems. In the scientific domain of "system identification", the mathematical model of the system under test is retrieved through experimental input-output data. Since the dynamical characterization of rotating machines is non-linear as well as time-varying, it cannot be modeled adequately using classical existing estimation or identification methods. The aim of this project is then to develop a theoretical framework to model the time-varying and non-linear dynamical behavior of rotating machinery from experimental data. The proposed methodology consists of modeling the non-linear and time-varying dynamical character of rotating devices through a collection of linear periodically time-varying models. In this project, we will focus on the identification and validation of the non-linear and time-varying dynamics of a mechanical rotor suspended on hydrodynamic plain bearings. The novel approach consists of four main steps: (i) Construction of a non-linear and time-varying virtual model of "fluid-driven" bearing—rotor systems starting from the laws of physics; (ii) Development of a parametric identification technique; (iii) realization and adjustments of the controllable rotor—bearing setup; (iv) validation of the theoretical framework on the real-life rotor—bearing setup.

Researcher(s)

Research team(s)

Planning of optimal trajectories for optical 3D sensors by means of tensor voting 01/01/2016 - 31/12/2019

Abstract

Camera positioning in vision applications is challenging, but of crucial importance. This is definitely the case when the goal is to make a 3D scan. This is because camera positions determine which parts of an object are visible and which measuring accuracy will be achieved. Our ambition is to automatically determine the scan path or positions of the camera during a 3D scan for a known object. To solve this engineering problem we will use mathematical techniques as 'tensor voting' and 'surface fitting'. The final algorithm provides the industry with the following advantages: 1. Faster and more efficient 3D scans 2. More complicated objects can be scanned 3. Automatic scan planning for every type of 3D sensor/camera in one model. 4. Automatic scan planning for specific measurement setups currently used in industry. This reduces the need for expensive experts.

Researcher(s)

Research team(s)

3D imaging assisted vibration measurements for product testing and quality control. 01/10/2015 - 30/09/2019

Abstract

In this project we will develop a technique that combines information from 3D time-of-flight camera's and computer-aided-design drawings of a product in order to facilitate product testing and quality control. The proposed procedure firstly allows the test engineer to automatically determine the sensor positions on a product. Secondly, we develop a methodology to perform vibration measurements on moving components (wind turbines, wind screen wipers, etc. or products on a conveyor belt).

Researcher(s)

Research team(s)

Frequency domain identification of quasi time-periodic systems with applications in the mechanical and biomedical engineering. 01/10/2015 - 30/04/2017

Abstract

Quasi time-periodic phenomena show up in many engineering fields. One could think of wind turbines or helicopters with rotational speed fluctuations, the vibrations and acoustic noise generated in combustion engines, the electrical impedance of a living heart with heart rate variability for cardio-vascular monitoring, respiratory systems with breathing rate variability, to name a few. Those systems in engineering have the special property that their dynamic behavior changes quasi-periodically over time. The irregularity of the periodicity in the above-mentioned applications can be faithfully modeled by virtue of a periodically time-varying model with varying periodicity. This way of modeling bridges the gap between the well established identification framework for linear time-invariant systems and the more complex approaches for non-linear time-variant systems. The extraction of experimental quasi time-periodic models in the frequency domain meant for physical interpretation, analysis, prediction or control can be a useful step for the practicing engineer. Hence, the main focus of this project will be the development of a generalized identification framework for quasi time-periodic systems with applications in the mechanical and bio-medical engineering.

Researcher(s)

Research team(s)

Toward a pinhole-free model for a Time-of-Flight camera, furnishing featureless procedures for calibration and navigation 01/10/2015 - 31/12/2015

Abstract

A new generation of digital cameras makes use of emitted light pulses, more precisely the time between the emission and the reception of the reflected pulse, for computing the depth of the viewed object. This "Time-of-Flight" principle is replacing other 3D-scan strategies such as stereovision and structured light. Though the concept and possibilities of a ToF-camera essentially differs from these that are offered by "classical" optical cameras, the computer vision community still falls back on proven methods for calibration and structure-from-motion issues. We propose new techniques, fully exploiting the Time-of-Flight power, avoiding detection and recognition of features in the image. In a further step, we intend to design a new camera model, more general than the familiar pinhole model, providing a uniform framework for both lateral as depth calibration of ToF-cameras. The theory will be validated by simulations and real experiments (executed by a computer driven robot manipulator). Finally, real life applications will be considered, in cooperation with some of our industrial partners.

Researcher(s)

  • Promotor: Penne Rudi
  • Co-promotor: Mertens Luc
  • Fellow: Bogaerts Boris

Research team(s)

Development of a guideline for the objective comparison of Time-of-Flight cameras 01/07/2015 - 31/12/2016

Abstract

Due to a lack of standardization, it is impossible for Time-of-Flight (ToF) camera users to come to an objective evaluation during a benchmarking. This project aims to create an objective method for the comparison of Time-of-Flight (ToF) cameras. This can be done by performing controlled measurements using cameras mounted on a robot arm to study the influence of the integration time, reflection coefficients and the incident angle.

Researcher(s)

  • Promotor: Van Barel Gregory

Research team(s)

Project website

Automated visual classification of empty bottles for a soda manufacturer company Ordal. 01/06/2015 - 30/09/2015

Abstract

The overall goal is to develop an automated process that is able to increase the intake rate of empty-bottles. At this moment an operator is full occupied by this task and maybe this could be changed in a system in which the operator is nearby but become able to do other tasks meanwhile. The correct classification of Ordal bottles with respect to all foreign bottles forms a challenge and would end in an increase in production. Suspicious containers are handled through a bypass.

Researcher(s)

  • Promotor: Mertens Luc

Research team(s)

Smart Data Clouds. 01/12/2014 - 30/11/2016

Abstract

This project represents a research agreement between the UA and on the onther hand IWT. UA provides IWT research results mentioned in the title of the project under the conditions as stipulated in this contract.

Researcher(s)

Research team(s)

Mechanical pathways in the onset and progression of cartilage lesions of the hip joint. 01/10/2014 - 30/09/2016

Abstract

The hip functions as a ball and socket joint, with cartilage layers that cover the joint surfaces on both sides protecting it from impacts and permitting smooth movements. When the cartilage is impaired by mechanical, infectious or inflammatory causes, the joint might eventually wear down - a disabling condition known as osteoarthritis. Recent literature indicates that up to 80% of all hip osteoarthritis cases might be related to subtle variations in the joint geometry. These variations have been suggested to give rise to peak joint stresses and altered load distributions on the cartilage. Although the mechanism is getting increasingly recognized in the literature, profound understanding of its true impact is lacking. Further, the prevalence of these morphological variations is reported to be much higher than the actual number of patients presenting for treatment. The aim of this thesis is to explore the impact of variation in hip joint anatomy on load distribution during daily living activities. I intend to clarify the role of mechanical drivers in the onset and progression of cartilage lesions of the hip joint by means of advanced multidimensional statistics and personalized load and stress predictions. The final step of this thesis will be to gradually transfer these findings into clinical practice and at the operating theatre by providing virtual pre-surgical planning, accurately implemented during surgery, using state of the art navigation technology.

Researcher(s)

Research team(s)

Robust procedures for elliptic or ellipsoidal point clouds with noisy boundaries 01/07/2014 - 31/12/2015

Abstract

We focus on 2D point sets with an elliptic shape and 3D point sets with an ellipsoidal shape, e.g. in camera images or a data fusion setting. Noise on these data points forces us to look for robust procedures that derive the quantities we need. Motivating case study: Suppose that the image is taken by a calibrated camera from a ball with known radius, what is the position of this ball relative to the camera?

Researcher(s)

Research team(s)

Project website