Jan Steckel associate research professor

Research Jan Steckel

Research team

Expertise

3D Sonar Acoustic Signalprocessing Embedded systems Electronics Signal Processing Artificial Intelligence

Flanders Make Infrastructure Research Agreement WP 8 Intralogistics Virtual. 09/06/2025 - 08/06/2035

Abstract

In this project, we aim to explore and develop innovative infrastructure to support both simulation and real-world implementation of intra-logistics systems. Our work encompasses various aspects of intra-logistics, including the deployment and utilization of autonomous mobile robots (AMRs) for efficient material handling and transportation within industrial environments. Additionally, we focus on the creation of large-scale simulation environments that allow for rigorous testing and evaluation of advanced learning-based approaches. These simulations enable us to assess the performance, scalability, and robustness of proposed solutions before their real-world deployment. By bridging the gap between simulation and physical implementation, this project seeks to advance the state of the art in intra-logistics, offering scalable, adaptable, and efficient solutions to meet the dynamic demands of modern industries.

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  • Research Project

Flanders Make Infrastructure Research Agreement WP7 Intralogistics Physical. 09/06/2025 - 08/06/2035

Abstract

In this project, we aim to explore and develop innovative infrastructure to support both simulation and real-world implementation of intra-logistics systems. Our work encompasses various aspects of intra-logistics, including the deployment and utilization of autonomous mobile robots (AMRs) for efficient material handling and transportation within industrial environments. Additionally, we focus on the creation of large-scale simulation environments that allow for rigorous testing and evaluation of advanced learning-based approaches. These simulations enable us to assess the performance, scalability, and robustness of proposed solutions before their real-world deployment. By bridging the gap between simulation and physical implementation, this project seeks to advance the state of the art in intra-logistics, offering scalable, adaptable, and efficient solutions to meet the dynamic demands of modern industries.

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  • Research Project

Automatic Sensor Pose Evaluation and Reconfiguration (ASORE-IRVA). 18/06/2024 - 17/06/2029

Abstract

Accurate sensor pose calibration and monitoring are essential for safe and effective autonomous vehicle operation. Current methods, relying on manual recalibration using artificial targets, increase costs and reduce vehicle availability. While state-of-the-art solutions exist, they require application-specific redesigns involving complex mathematical work. ASORE offers an automated approach, eliminating the need for users to handle the mathematical details of calibration. It generates expected sensor observations from a high-level use case description and links these to suitable sensor processing and calibration algorithms, available in the ASORE toolbox. Template models for vehicles, sensors, and landmarks simplify the creation of use cases. Delivered as a user-friendly software toolbox with a GUI, ASORE includes documentation and tutorials to streamline automated sensor calibration. This solution reduces development and maintenance costs, increases automation, and enhances flexibility for diverse applications, benefiting companies by improving the robustness of autonomous vehicle sensing systems.

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  • Research Project

Situational aware navigation and mapping (SITANAV). 01/04/2024 - 31/03/2028

Abstract

SITANAV aims at increasing the reliability and robustness of Autonomous Vehicles (AV) operating in logistic, industrial and agricultural facilities by focusing on the following problems raised by companies in the consortium: 1) the limited flexibility of AVs in complex environments, 2) the high costs when localization requires additional infrastructure, 3) the high deployment and layout reconfiguration costs, and 4) the high memory footprint of discrete metric maps that hinders applications in large environments. SITANAV's key idea to overcome these problems is to increase the situational-awareness in navigation and mapping capabilities of autonomous vehicles. This will provide AVs with higher levels of self-adaptation based on the current situation through explainable decision-making via semantic maps and reasoning. There are three main technological barriers that SITANAV has to overcome: 1) lack of models fordescribing situations, 2) lack of capabilities to reason about objects and maps, and 3) lack of situational awareness in AVs' decision-making for navigation. The approach to remove these barriers is a framework that combines a metric-semantic map with situational models, which describe a set of relations that connect an AV's motion and perception capabilities to a particular situation. For example, when the AV can find the appropriate pieces of information to infer the current situation from the perceived environment, it can select and configure its perception and control behaviors (situational aware decision-making) to achieve the desired robustness and performance for the application at hand (e.g., detecting the situation of a partly blocked pathway and switching to a narrow-space navigation). The proposed method will extend existing graph-based models and tools with new features, reasoning, and query answering mechanisms, to gradually increase AVs' situational assessment capabilities. The improvements will be in small-scale iterations, following a continuous integration approach. This will be accomplished by two running demos (indoor and outdoor) with increasing complexity throughout the project. The SITANAV models and software are designed with forward compatibility in mind, because we can now already foresee many future extensions, such as new types of semantic features, memory and learning capabilities, and the integration of task planning.

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  • Research Project

Conditioned harsh outdoor environment for perception systems of autonomous applications (CAVE). 01/02/2024 - 31/01/2026

Abstract

Autonomous mobile systems might fail for many reasons, but one of them is when the harshness of the environment increases. It is difficult for OEMs, integrators, sensor and hardware components providers to design a robust autonomous mobile system based on traditional testing methods. Especially perception systems are challenged in realistic and relevant harsh conditions (e.g. rain, fog, direct sunlight). Currently, testing of perception systems is done by waiting for these conditions to happen in real-life – which can easily cost weeks of waiting. When an update is done on the hardware of the perception system (e.g. a coating on the lens is added) the exact same test is needed to verify an improvement. However, in real-life this exact same harsh condition cannot be reproduced. So, there is a need for a modular, validated testing facility that allows controllable and measurable conditions, to enable repeatable and controlled harsh conditions. CAVE_INFRA aims to develop a fixed perception test facility which can control and measure rain, snow, fog, illumination, dust and debris conditions, including its digital twin and a real-life validation. We aim to provide the following services: i) Sensing hardware (incl coatings/cleaning systems) and software performance evaluation in harsh conditions, including benchmarking to support sensor selection ii) Harsh condition model and/or sensor model derivation iii) Training or validation of AI models for objects / human detection and pose estimation iv) Degradation tests in harsh conditions v) Generate test data and scenarios that can be used for driving out own research but also for certification purposes and discussions with certification bodies such as TuV. To produce the harsh conditions in realistic scenarios, there are different actuation systems foreseen to respectively actuate the perception system under test, the target objects to be detected, and some of the generated conditions such as diverse illumination systems to create dynamic contrast.

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  • Research Project

ABN HaFreeS MVP Prototype. 01/01/2024 - 31/12/2024

Abstract

In this project we investigated during the first phase (IOF-POC ABN HaFrees Feasibility) whether it was possible to develop a hands-free kit for bicycles. The main goals here were ease of use and call quality at speeds above 10 to 15 km/h. To this end, we mapped the market, from which it became clear that the first focus should be on the functional user (i.e. the professional who wants to use his work-related commute by bike to call colleagues, customers, etc.). The first tests showed that a significant suppression of wind noise is possible using several techniques (selection of good microphones, the choice of an optimal arrangement of each individual microphone combined in an optimal configuration, appropriate shielding i.c.w. the right signal processing algorithms). The techniques on their own do not provide sufficient improvement, but the delta is sufficient so that the combination should allow for a quality conversation at 25km/h. In this second phase of the project, we want to develop a minium viable product (MVP) prototype, which should allow to (1) characterize the product on its main qualities, (2) benchmark the product against competing products, (3) set up tests in view of user feedback and (4) define the further direction of the valorization. An essential hurdle here is the intellectual protection of the technology.

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  • Research Project

Hybrid AI for Predictive Road Maintenance (HAIRoad). 01/10/2023 - 30/09/2025

Abstract

The current approach to monitoring road quality is based on manual inspections and is labor intensive and relatively expensive. Hybrid AI for Predictive Road Maintenance (HAIRoad) aims to use (hybrid) AI to map the condition of the road network and make recommendations for road maintenance. An efficient and robust data pipeline will be developed using MLOps tools, which allow easy switching between model development and implementation/production. Three demonstrators will illustrate the feasibility of the approach: one with the Port of Antwerp Bruges and two at the municipal level. The demonstrators will allow to validate both the more technical aspects and the market potential. HAIRoad will deliver several innovations such as automated detection of the road conditions, new indicators for road management, sensor fusion by combining information from multiple sensors, and the application of hybrid-AI where we will incorporate physical models of road degradation into data-driven machine learning models.

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  • Research Project

Natural objects rendering for economic AI models (NORM.AI). 01/09/2023 - 31/08/2027

Abstract

Natural objects (vegetables, fruits, food, etc.) are omnipresent in different industrial applications: food sorting, vegetable spray treatments, precision & automated farming, etc. Automating these applications to deal with large variabilities of natural objects (object's detection, recognition, pose estimation, etc.), requires innovative technologies that are enabled by Artificial Intelligence (AI) that has the ability to generalize to variabilities. However, training these AI models would require thousands of images / videos with detailed annotations of different items. In the state of the art, one needs >10k images to (re-)train an AI model with an accuracy of >90%, when, in average one minute is needed to annotate one 'real' image, however these can increase drastically depending on the use case at hand and the variability around it. The more variability one wants to cover, the more training images are needed. These findings clearly indicate that in order to be able to deploy AI models in the industrial applications, innovative techniques are highly needed to remove the burdens of data annotations2. These techniques need also to be easily usable by end users to avoid large amount of manual work to update the proposed methodology to new applications. NORM.AI builds further on the successful results from PILS SBO3,4, where rendering techniques were applied to industrial products with CAD (Computer Aided Design) information, to retrieve AI (synthetic) training data from updated CAD with radiance models. While CAD facilitates synthetic data generation in PILS SBO by providing a reference model to start rendering from, the goal of NORM.AI project is to extend this research to Natural objects where no CAD is available. Therefore, defining a reference model to start rendering from, is part of the research in the project. Creating variations from the reference model that takes both spatial & time changes of the natural objects and the natural scenes, as well as finding a sweet spot between real data augmentation techniques & synthetic data generation techniques constitute another research challenge in the project. This research will allow to identify economic scenarios of training data generation, taking into account their effect into AI model's accuracy and robustness. The project focuses into three research applications: 1- Food sorting applications, where 2D images are used to detect & sort fruits & vegetables, as they are coming, for example, in a conveyor system. 2- Crop monitoring applications, where images from 2D cameras, for example, installed in a harvester, are used to detect vine's rows, crop distribution, etc. 3- Weed monitoring applications, where 2D images guide a spraying system to locally sprayweeds in a high precision.

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  • Research Project

Strengthening the capacity for excellence of Slovenian and Croatian innovation ecosystems to support the digital and green transitions of maritime regions (INNO2MARE). 01/01/2023 - 31/12/2026

Abstract

The main goal of INNO2MARE is to strengthen the capacity for excellence of Western Slovenian and Adriatic Croatian innovation ecosystems through a set of jointly designed and implemented actions that will support the digital and green transitions of the maritime and connected industries. Based on an in-depth mapping of the ecosystems and needs & gaps analysis, the consortium will formulate a long-term R&I strategy aligned with regional, national and EU strategies, as a visionary framework, and a joint action & investment plan, with concrete steps for building coordinated, resilient, attractive and sustainable maritime innovation ecosystems. To support the joint strategy and provide a model for the future collaborative R&I of the ecosystems' actors, the project will implement three R&I pilot projects that address some of the key challenges related to maritime education and training, security & safety in marine traffic as well as energy conversion and managementsystems' efficiency. These pilots will be the basisfor further development,scale-up and translation of the generated research results into innovative business opportunities through the coordinated mobilisation of public and private funding. The consortium will also implement innovative programmes that will support the engagement of citizens in the innovation processes, knowledge transfer for mutual learning, entrepreneurship & smart skills training and attraction of best talents, involving more than 1.000 participants across the Quadruple Helix. In all the project activities, the two ecosystems will strongly benefit from the sharing of best practices of the Flemish ecosystem, one of the most developed maritime innovation ecosystems globally. The project will contribute to reducing the innovation divide in Europe by systematically connecting the innovation actors within and between the ecosystems and creating synergies in R&I investments' planning and execution, thus developing a true innovation culture

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  • Research Project

Knowledge Based Neural Network Compression: Context-Aware Model Abstractions 01/11/2021 - 31/10/2025

Abstract

In the state-of-the-practice IoT platforms complex decisions based on sensor information are made in a centralized data center. Each sensor sends its information over thereafter a decision is send to actuators. In certain applications the latency imposed by this communication can lead to problems. For this, decisions should be made on the edge devices themselves. This is what the research track on resource and context aware AI is about. We want to develop edge inference systems that dynamically reconfigure to adapt to changing environments and resources constraints. This work is focused on compressing neural networks. In this work we want to extend on the current state-of-the-art on neural network compression by incorporating a knowledge-based pruning method. With knowledge based we mean that we first determine the locations of specific task related knowledge in the network and use this to guide the pruning. This way we can make the networks adjustable to environmental characteristics and hardware constraints. For some tasks in a specific environment, it might be favorable to reduce the accuracy of certain classes in favor of resource gain. For example, the classification of certain types of traffic sign types can be less accurate on highways than in a city center. Based on these requirements we want to selectively prune by locating specific task related concepts. By removing them we expect to achieve higher compression ratios compared to the state-of-the-art.

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  • Research Project

Goldilocks' Fusion: Adaptive and Robust Sensor Fusion in Resource-Constrained Robotic Systems. 01/11/2021 - 31/10/2025

Abstract

In recent years, autonomous robotic systems have gained lots of attention from the academic world and industry. The many applications in industrial fields going from manufacturing, mining and surveillance makes the study on autonomous systems interesting with lots of valorization potential. The cost of these autonomous systems is currently extremely high as expensive computational platforms and sensors suites are used to provide necessary levels of safety and autonomy. Using the measurements from different sensors, an environment representation is created to make navigational decisions. While the environment representation determines the complexity of the behavior that can be achieved, the detail stored in this representation is dependent on the available computational resources and sensor data. The goal of this research project is to enable an autonomous agent to select the optimal heterogenous set of sensors to create an environment representation of the appropriate complexity for the current situation. Resource awareness plays an important role in our research as we aim to reduce computational workloads on the autonomous vehicles, which means less expensive computational platforms can be used. Additionally, increased reliably and accuracy in environment perception will benefit the autonomy of these systems. Less expensive autonomous systems while being efficient in the use of resources will benefit and increase the adoption of autonomous vehicles.

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  • Research Project

Portable Innovation Open Network for Efficiency and Emissions Reduction Solutions (PIONEERS). 01/10/2021 - 30/09/2026

Abstract

PIONEERS brings together four ports with different characteristics, but shared commitments towards meeting the Green Deal goals and Blue Growth socio-economic aims, in order to address the challenge for European ports of reducing GHG emissions while remaining competitive. In order to achieve these ambitions, the Ports of Antwerp, Barcelona, Venlo and Constanta will implement green port innovation demonstrations across four main pillars: clean energy production and supply, sustainable port design, modal shift and flows optimization, and digital transformation. Actions include: renewable energy generation and deployment of electric, hydrogen and methanol vehicles; building and heating networks retrofit for energy efficiency and implementation of circular economy approaches in infrastructure works; together with deployment of digital platforms (utilising AI and 5G technologies) to promote modal shift of passengers and freight, ensure optimised vehicle, vessel and container movements and allocations, and facilitate vehicle automation. These demonstrations form integrated packages aligned with other linked activities of the ports and their neighbouring city communities. Forming an Open Innovation Network for exchange, the ports, technology and support partners will progress through project phases of innovation demonstration, scale-up and cotransferability. Rigorous innovation and transfer processes will address technology evaluation and business case development for exploitation, as well as creating the institutional, regulatory and financial frameworks for green ports to flourish from technical innovation pilots to widespread solutions. These processes will inform and be undertaken in parallel with masterplan development and refinement, providing a Master Plan and roadmap for energy transition at the PIONEERS ports, and handbook to guide green port planning and implementation for different typologies of ports across Europe.

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  • Research Project

Knowledge Based Neural Network Compression: Quality-Aware Model Abstractions. 01/10/2021 - 30/09/2025

Abstract

In the state-of-the-practice IoT platforms complex decisions based on sensor information are made in a centralized data center. Each sensor sends its information over thereafter a decision is send to actuators. In certain applications the latency imposed by this communication can lead to problems. In real time applications it is crucial for the decision to be taken immediately. For this complex decisions should be made on the edge devices themselves. This is what the research track on resource and context aware AI is about. In this we want to develop inference edge systems that dynamically reconfigure to adapt to changing environments and resources constraints. This work if focused on compressing AI processing blocks, specifically neural networks. In this work we want to extend on the current state-of-the-art methods on neural network compression by incorporating a knowledge-based pruning method. By knowledge based we mean we want to prune a neural network in a context aware manner. A certain application context will impose requirements of the outputs of the network. For example, on a highway is the detection of pedestrians less important than cars. Based on these requirements we want to selectively prune a network by locating knowledge concepts related to the outputs. By selectively pruning them we expect to achieve higher compression ratios compared to the state-of-the-art for context specific networks.

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  • Research Project

Nexor - Cyber-Physical Systems for the Industry 4.0 era 01/01/2021 - 31/12/2026

Abstract

The fourth industrial revolution (Industry 4.0 as it is commonly referred to) is driven by extreme digitalization, enabled by tremendous computing capacity, smart collaborating machines and wireless computer networks. In the last six years, Nexor — a multi-disciplinary research consortium blending expertise from four Antwerp research labs — has built up a solid track record therein. We are currently strengthening the consortium in order to establish our position in the European eco-system. This project proposal specifies our 2021 - 2026 roadmap, with the explicit aim to empower industrial partners to tackle their industry 4.0 challenges. We follow a demand driven approach, convincing industrial partners to pick up our innovative research ideas, either by means of joint research projects (TRL 5—7) or via technology licenses.

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  • Research Project

Dotation for the structural collaboration with Flanders Make. 01/01/2021 - 31/12/2024

Abstract

Flanders Make's mission is to strengthen the international competitiveness of the Flemish manufacturing industry on the long term through industry-driven, precompetitive, excellent research in the field of mechatronics, product development methods and advanced production technologies and by maximizing valorisation in these areas.

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Embedded and distributed systems. 01/12/2020 - 30/11/2026

Abstract

Distributed embedded systems play a very important role in everyday life, now and even more so in the near future. Many of these embedded systems have one or more sensors for measuring physical quantities like the room's temperature or the position of a person in the building. Due to the increasing functional complexity of the desired applications in combination with the intricate interplay between the components of the system, it can become difficult to optimize the overall performance manually. Furthermore, the current desire for quick time-to-market demands quick design processes focused on adaptability of the design. One way to achieve this quick time-to-market is the (partial) automation of the design process. Distributed embedded systems play a very important role in everyday life, now and even more so in the near future. Many of these embedded systems have one or more sensors for measuring physical quantities like the room's temperature or the position of a person in the building. Due to the increasing functional complexity of the desired applications in combination with the intricate interplay between the components of the system, it can become difficult to optimize the overall performance manually. Furthermore, the current desire for quick time-to-market demands quick design processes focused on adaptability of the design. One way to achieve this quick time-to-market is the (partial) automation of the design process. As the system has to operate in real environments using real sensors, the environment where the system operates in has to be included in the model as well. Simulating physical quantities and realistic environments can become very complex very quickly. The time that has to be invested for achieving accurate simulation results can become too much. Experimental setups can provide the data which is needed to avoid the need for complex simulations. Therefore, a Hardware-in-the-loop and Sensor-in-the-loop approach will be adopted to provide the relevant data at the right time of the modeling process. Strategies for the right spatio-temporal sampling and the right moment to apply HIL/SIL methods are important questions to answer. Once the complete system has been modeled using the realistic models and the platform-specific constraints, hardware generation (VHDL, analog schematics, etc.) and code generation (C-code for embedded processors) from the high-level model can be used to accelerate the design cycle. Large functional changes often translate to small changes in the high-level model, and results often in large changes in the low-level representation. Using the right type of code- and hardware-generation can accelerate the design cycle significantly. Code generation can also be used in the form of prototyping platforms such as large FPGA's to accelerate certain sub-models of the MBD-design. HIL/SIL systems also allow for real-time performance to give rise to sensor flow, which is very important in a wide range of applications.

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  • Research Project

Research Program Artificial Intelligence 01/01/2023 - 31/12/2023

Abstract

The Flanders AI Research Program focuses on demand-driven, leading-edge, generic AI research for numerous applications in the health and care sector and industry, for governments and their citizens. The requirements were indicated by users from these application domains.

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  • Research Project

Echo-acoustic signalling of aposematic and cryptic insects – A bat inspired modelling approach (EchoBug). 01/09/2022 - 31/08/2024

Abstract

In the arms race between prey and predators, diverse anti-predator defence mechanisms evolved. To avoid predation, many insects developed camouflage (crypsis) or chemicals that render them distasteful or toxic. To warn of their unpalatability, many insects evolved striking warning colours or patterns (aposematism). Insects comprise most of the diet of bats. Some of these nocturnal predators glean resting, silent, motionless diurnal insects from the vegetation. Instead of using vision during foraging, they produce ultrasonic calls and detect their prey through echolocation. Here, I want to research whether visually cryptic or aposematic insects also have cryptic or aposematic acoustic reflection properties, to hide from or signal their unpalatability to echolocating bats. I will use bio-inspired sensor systems to acquire echo-acoustic sonar recordings of selected insect species and conduct behavioural prey-detection and -capture experiments using live bats to explore the prevailing acoustic predator-prey interactions. Based on these experiments, I will apply neural network algorithms for classifying and analysing the distinguishing features in different insect echoes. This approach will allow an in-depth investigation of the underlying acoustic mechanisms of the interaction between prey and predators and will inform and inspire biomimetic applications for detecting and identifying objects by sonar. Further, the project will lead to synergism between the research fields of biology and engineering in the study of animal interactions and bio-inspired robotics.

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  • Research Project

ABN HaFreeS Feasibility. 01/09/2022 - 31/12/2023

Abstract

In this project, we take the first steps in the development a novel hands-free communication set for use on bicycles. The main advantages of our solution in comparison to current solutions are call quality and convenience. Call quality is our main selling point: wind noise, traffic noise and contact noise impede comfortable calling at a speed above 10-15 km/h with the current available technology; we aim to overcome these shortcomings using technology building blocks available in Cosys-lab that have matured in other application domains. We mainly focus on showing technology feasibility, initiating a market study and perform initial user outreach activities. We will also start preparing the design of an MVP prototype. These activities are an essential first step to determine if it is worthwhile to pursue the end goal of commercializing the solution in a spin-off. If the technology is shown to work and the valorisation potential lives up to our expectations, we will undertake further steps in later projects to develop a deep market insight, a convincing MVP prototype and a solid value chain. These elements are necessary to reach the end goal of starting a VC-funding-free spin-off, bootstrapping with funding gathered in a crowdfunding campaign.

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Flanders AI 01/01/2022 - 31/12/2022

Abstract

The Flemish AI research program aims to stimulate strategic basic research focusing on AI at the different Flemish universities and knowledge institutes. This research must be applicable and relevant for the Flemish industry. Concretely, 4 grand challenges 1. Help to make complex decisions: focusses on the complex decision-making despite the potential presence of wrongful or missing information in the datasets. 2. Extract and process information at the edge: focusses on the use of AI systems at the edge instead of in the cloud through the integration of software and hardware and the development of algorithms that require less power and other resources. 3. Interact autonomously with other decision-making entities: focusses on the collaboration between different autonomous AI systems. 4. Communicate and collaborate seamlessly with humans: focusses on the natural interaction between humans and AI systems and the development of AI systems that can understand complex environments and can apply human-like reasoning.

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Mixed absolute and relative localization (MARLOC). 01/04/2021 - 31/03/2023

Abstract

In this project, we will combine fixed infrastructure localization (markers, UWB) with relative localization perception (SLAM, odometry) to arrive at a highly accurate and robust location estimate for mobile robots. We will work on simulation models and performance prediction based on deep learning techniques to optimize the localization setup.

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  • Research Project

ModAU - Modernized Auscultations for Remote Monitoring. 01/01/2021 - 31/12/2022

Abstract

In hospitals where there is a significant workload, performing auscultations can be a timeconsuming process, which also exposes the medical personnel to potentially contagious diseases. Current systems that allow remote auscultations are often not fit for use with large amounts of patients, long-term use, or are limited in terms of functionality. The major drawback in current remote auscultation systems is the relatively bulky acoustic coupler which makes part of the stethoscope assembly. This physical dimension reduces the applicability for long-term monitoring, because of the discomfort for the patient and the inherent risk of decubitus wounds. In this project, we will investigate the construction of thinner stethoscopes, increasing patient comfort

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  • Research Project

V-CALSA – Visual Computer-Aided Lung Sound Analysis. 01/10/2020 - 30/09/2024

Abstract

Lung auscultation, which is the process of listening to breath sounds, is one of the most commonly used examinations to evaluate respiratory health. Over the last decades computational methods have been developed for the analysis of recorded lung sounds. Computer Aided Lung Sound Analysis (CALSA) aims to overcome limitations associated with standard lung auscultation by removing the subjective component of the process and allowing quantification of lung sound characteristics. To date, no accepted standard for data acquisition and analysis has been set and none of the proposed approaches have been successfully implemented in clinical practice. During this project we will develop a simple but robust visual representation for CALSA, which can be easily interpreted by health care professionals. Several clinical studies described in this project aim to validate this analysis and to study the ability of CALSA to measure the severity of RSV-bronchiolitis and the effects of respiratory therapy. Digital auscultation has the potential to be a sensitive, objective and non-invasive tool by providing regional information associated with local changes in the airways.

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  • Research Project

Echo-acoustic signalling of aposematic and cryptic insects – A bat inspired modelling approach (EchoBug). 01/05/2020 - 30/04/2021

Abstract

In this project we investigate acoustic aposematic signalling in insects. We combine acoustic measurements with computational bat behaviour modelling to gain insights into the effects of aposematic signalling on the bat's perception mechanisms.

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AutoRIO. 01/04/2020 - 30/06/2022

Abstract

In this project we develop robust navigation strategies for AGVs which need to operate in both indoor and outdoor conditions. We evaluate various sensor subsystems which can support the navigation applications

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SmartFlush. 01/02/2020 - 31/07/2021

Abstract

In this project we develop smart flushing solutions together with our industrial partner, IPEE nv. We use advanced techniques to improve the processing of their proprietary sensor data. We also operationalize a deployement setup.

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3D sonar sensing for inland shipping applications.. 01/02/2020 - 31/01/2021

Abstract

In this project, we will evaluate the applicability of the eRTIS 3D sonar sensor in autonomous indoor shipping applications. We will collaborate with a supplier of indoor autonomous shipping solutions to provide an experimental platform which can be used to evaluate the sensing capabilities of the sensor setup. Furthermore, we will work on water-proofing of our technology, which is an important asset for the overall sensor performance.

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Integration, deployment and operationalization of an experimental fluttering insect measurement sonar. 01/01/2020 - 31/12/2021

Abstract

In this project we operationalize an ensonification setup for fluttering insects. Through the implementation of a 32 channel phased microphone array in combination with a high-speed video camera we develop a multimodal setup which can record and localize echoes originating from fluttering insects. These echoes can be overlayed with high-speed video data.

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Scientific chair 'Industrial Acoustic Condition Monitoring'. 01/10/2019 - 30/09/2022

Abstract

In this research chair we will investigate the efficacy of array signal processing for industrial condition monitoring. Through the combination of novel embedded systems technologies as well as advanced signal processing paradigms we will create an experimental setup with which advanced condition monitoring and predictive maintenance scenarios can be investigatedN

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A Hybrid SLAM approach for autonomous mobile systems (HySLAM_SBO). 01/07/2019 - 31/03/2024

Abstract

In HySLAM,we will investigate the introduction of semantics in SLAM. We will introduce new probabilistic models which are based on scene understanding to increase the conditioning of the SLAM problem. Taking into account the underlying dynamics of the objects, and their effect on the perceptual scene, can help to increase the robustness of the SLAM algorithms. We will demonstrate the efficacy of the algorithm in a 2D and 3D test case.

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Rsearch Programm Artificial Intelligence. 01/07/2019 - 31/12/2021

Abstract

The Flemish AI research program aims to stimulate strategic basic research focusing on AI at the different Flemish universities and knowledge institutes. This research must be applicable and relevant for the Flemish industry. Concretely, 4 grand challenges 1. Help to make complex decisions: focusses on the complex decision-making despite the potential presence of wrongful or missing information in the datasets. 2. Extract and process information at the edge: focusses on the use of AI systems at the edge instead of in the cloud through the integration of software and hardware and the development of algorithms that require less power and other resources. 3. Interact autonomously with other decision-making entities: focusses on the collaboration between different autonomous AI systems. 4. Communicate and collaborate seamlessly with humans: focusses on the natural interaction between humans and AI systems and the development of AI systems that can understand complex environments and can apply human-like reasoning.

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HR-RTIS: High-Resolution Real Time Imaging Sonar Sensor. 01/07/2019 - 31/12/2020

Abstract

For autonomous vehicles, sonar sensors can pose a real alternative to optical sensing techniques such as laser scanners and 3D cameras in situations where these optical techniques fail. The failure of these optical systems can be caused by medium distortions such as dust or fog, or sensor contaminations such as mud splashes. In the CoSys research group we develop advanced 3D sonar sensors for industrial applications, which are currently being validated in various industrial application niches. During this proposed STIMPRO project we propose to expose the uncover the dynamic range in the strengths of echoes created in relevant industrial environments and their spatial distribution in that environment. To this extend, we propose a high-resolution microphone array consisting of 1000 microphones, which will allow the creation of high-resolution and high dynamic range 3D sonar images. The sensor will provide us with essential insights into the reflective properties of relevant environments and will allow us to improve the low-cost sensors which we are famous for worldwide.

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3D audio personalization for virtual reality applications. 01/07/2018 - 30/06/2019

Abstract

Our previous research resulted in a low-cost and user-friendly do-it-yourself method that allows a user to measure their Head Related Transfer Function (HRTF) at home. While it is recognized that personalized 3D audio can add significant value to VR applications in a Business to Business environment, e.g. VR safety training, it appears that in addition to an efficient way of personalizing 3D audio, i.e. an HRTF measurement, two more elements are missing. First, the user having to assemble him/herself the measurement system from a number of commercially available components is perceived as a major obstacle. Second, the absence of standard software allowing effective use of personalized 3D audio acts as a significant impediment to its exploitation in applications. In this project we propose to remove these two obstacles to the use of personalized 3D audio in such Business to Business applications. The first obstacle will be addressed by developing a hardware module capable of capturing and transmitting both head movements and binaural microphone signals to a smartphone/laptop. In addition, we will extend Unity, a development platform widely used in the VR and game world, with a 3D audio module. This software module will allow application-developers to include personalized 3D audio in a standardized way in their products. Users of 2 these products can then upload their measured HRTF and experience the advantages of personalized 3D audio.

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Screen printing facilities and high resolution Raman imaging of (printed) surfaces and materials. 01/05/2018 - 30/04/2021

Abstract

This Hercules proposal concerns screen printing facilities. Screen printing facilities enable UAntwerp to pioneer in the field of electronics, sensors and photocatalysis by (1) developing unique (photo)sensors/detectors (e.g. electrochemical sensors, photovoltaics, photocatalysis) by printing (semi)conducting materials on substrates, (2) designing parts of Internet of Things modules with more flexibility and more dynamically, meanwhile creating a unique valorization potential and IP position.

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    AirLeakSLAM: On-line detection of pressured-air leaks in industrial environments using passive and active ultrasonic sensing. 01/05/2018 - 30/06/2019

    Abstract

    A large amount of energy is lost annually due to leaks in compressed air networks. The combination of SLAM and 3D-ultrasonic measurement techniques enables to automate the measurement and registration of these leaks without requiring manpower. Therefore, measurements can be conducted in a continuous (on line) instead of an incidentally manner. The goal of the project is to demonstrate the power and the opportunities of the system for the user of the compressed air system, and to further quantify the value creation opportunity.

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    Avoidance of collisions and obstacles in narrow lanes (AVCON_ICON). 01/02/2018 - 31/01/2020

    Abstract

    In this project, we will investigate various methods for implementing obstacle avoidance in narrow corridors. We design a suite of sensors which provide the control algorithms with the required information.

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    Biologically-inspired 3D radar sensor supporting intelligent robotic behavior in complex and cluttered environments. 01/01/2017 - 25/01/2021

    Abstract

    The goal of this research is to produce a compact, light-weight sensor which will enable an unmanned aerial vehicle to autonomously traverse trajectories through cluttered environments, such as a forest, while sensing and avoiding objects. The sensor is inspired by biological echolocation as performed by bats, which involves emitting an ultrasonic signal and closely listening to its reflections. By analyzing how each received echo differs from the emitted signal and how they mutually vary between its two ears, the bat can determine where the object which reflected the signal is located. Additionally, using sequences of these echoes makes is possible to determine the movement through the environment. We will mimic these features in our system to achieve the same results. For our application we use radio waves (radar) instead of sound (sonar), because these travel at a much greater speeds, while allowing the sensor to operate under circumstances where optical cameras would fail, such as at night, in rain, fog, smoke, etc. Furthermore, we propose a control scheme inspired by cognition, such as insect intelligence, to steer the robot. The idea is to implement a layered system of behavioral units, each with its own goal. Examples of these units include, stopping to avoid a collision, dodging an obstacle, and following a corridor. The system will then execute the behavior with the highest priority which is active at each given moment, creating an overall emergent intelligence.

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    Advanced array signal processing for industrial in-air sonar applications. 01/01/2017 - 31/12/2020

    Abstract

    This basic research project seeks to advance our knowledge of in-air sonar sensing towards new industrial applications where traditional sensing techniques (optical, radar) suffer from physical limitations such as the environment (dust, mist) or limited object reflectivity (RF penetration). The knowledge gaps, identified by previous industrial collaborations, are to be answered by a mix of algebraic analysis, numerical computations and experimental prototype engineering. The focus will be on the application of advanced array signal processing techniques and real-time embedded systems. The outcome of this project will be a strengthened knowledge of in-air sonar sensing and additional background IP for future projects concerning economic exploitation of our technology.

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    Localization system for accurate tracking and navigation for autonomous operation (LOCATOR_ICON) 01/11/2016 - 31/10/2018

    Abstract

    In order to choose the right combination and placement of sensors to perform sensor-fusion based indoor localization in industrial environments, a framework for designing systems for global and relative localization can facilitate the development. To quantify the performance of various sensors in this operational context, models of these sensors need to be developed. These models will be probabilistic in nature in order to be used with the aforementioned sensor fusion techniques and to calculate confidence intervals where safety is an issue. The sensor models will be parametrized and will be able to incorporate in-situ experimental measurements to make the simulations more accurate.

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    Scale-free passive acoustic localization using a wireless synchronized sensor network. 01/10/2016 - 30/09/2020

    Abstract

    During this project we will develop a framework which allows passive localization of acoustic sources using a synchronized wireless sensor network. Synchronization of the wireless microphone array will be performed using a distributed synchronization scheme absent of a master time representation. The framework will support automatic calibration of the microphone array with minimal human intervention. The location estimate of the acoustic sources will be performed using a probabilistic localization algorithm in combination with known statistics about the behavior of the acoustic source. The framework will be virtually scale-free, which means that the sensor network can be used for tracking a wide variety of acoustic sources in a wide variety of application domains.

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    Embedded and distributed systems. 01/12/2015 - 30/11/2020

    Abstract

    Distributed embedded systems play a very important role in everyday life, now and even more so in the near future. Many of these embedded systems have one or more sensors for measuring physical quantities like the room's temperature or the position of a person in the building. Due to the increasing functional complexity of the desired applications in combination with the intricate interplay between the components of the system, it can become difficult to optimize the overall performance manually. Furthermore, the current desire for quick time-to-market demands quick design processes focused on adaptability of the design. One way to achieve this quick time-to-market is the (partial) automation of the design process. Distributed embedded systems play a very important role in everyday life, now and even more so in the near future. Many of these embedded systems have one or more sensors for measuring physical quantities like the room's temperature or the position of a person in the building. Due to the increasing functional complexity of the desired applications in combination with the intricate interplay between the components of the system, it can become difficult to optimize the overall performance manually. Furthermore, the current desire for quick time-to-market demands quick design processes focused on adaptability of the design. One way to achieve this quick time-to-market is the (partial) automation of the design process. As the system has to operate in real environments using real sensors, the environment where the system operates in has to be included in the model as well. Simulating physical quantities and realistic environments can become very complex very quickly. The time that has to be invested for achieving accurate simulation results can become too much. Experimental setups can provide the data which is needed to avoid the need for complex simulations. Therefore, a Hardware-in-the-loop and Sensor-in-the-loop approach will be adopted to provide the relevant data at the right time of the modeling process. Strategies for the right spatio-temporal sampling and the right moment to apply HIL/SIL methods are important questions to answer. Once the complete system has been modeled using the realistic models and the platform-specific constraints, hardware generation (VHDL, analog schematics, etc.) and code generation (C-code for embedded processors) from the high-level model can be used to accelerate the design cycle. Large functional changes often translate to small changes in the high-level model, and results often in large changes in the low-level representation. Using the right type of code- and hardware-generation can accelerate the design cycle significantly. Code generation can also be used in the form of prototyping platforms such as large FPGA's to accelerate certain sub-models of the MBD-design. HIL/SIL systems also allow for real-time performance to give rise to sensor flow, which is very important in a wide range of applications.

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    Cost-effective vibroacoustic monitoring (vibmon_icon). 01/10/2015 - 31/12/2017

    Abstract

    The Cost effective vibroacoustic monitoring project will attempt to prove the technical and economic feasibility of cost effective vibroacoustic monitoring systems for continuous online condition and process monitoring of rotating machine elements in quasi stationary conditions. The project will make use of new opportunities enabled by the advent of cost effective sensors, like MEMS accelerometers, microphones, and microphone arrays, and cost effective embedded platforms that in combination can provide an efficient solution for continuous monitoring. The generic part of the project will assess the technical limitations of cost effective sensors compared with high-end ones and will overcome this limitations by develop novel digital signal processing algorithms for: • Automatic pre-processing and data cleaning of raw data recorded by cost-effective sensors in order to eliminate non-physical features present in the signals generated by certain cost effective sensors; • Feature extraction for fault detection and identification that can provide reliable diagnostic information and can deal the technical limitations of cost-effective sensors like limited bandwidth, high noise density, and lower sensitivity; • Online tachometer-less estimation of rotational speed in order to reduce the cost of the total solution by eliminating high precision speed sensors; • Reducing of the amount of data generated by the monitoring system while maximizing the amount of information to diminish the communication and data stream handling costs; The project will develop a technology validation platform for a cost effective vibroacoustic monitoring system including sensors, acquisition hardware, embedded processing unit and local digital signal processing software.

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    Mobile Robotic Platform for Embodied Sensor Development 01/02/2015 - 31/12/2015

    Abstract

    This project aims at implementing a mobile robotic platform for supporting our research effort concentrated at the development of intelligent sensors for healthcare applications. The robotic platform will support our research effort by enabling the collection of large amounts of experimental data for extracting sensor models, calibration algorithms and in the development of sensors aimed at the application in autonomous robotic systems.

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    Next generation of heterogeneous sensor networks (NEXOR). 01/01/2015 - 31/12/2020

    Abstract

    This project represents a research contract awarded by the University of Antwerp. The supervisor provides the Antwerp University research mentioned in the title of the project under the conditions stipulated by the university.

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    Deriving sampling and memory strategies for 3D sonar systems to support electric wheelchair users 01/12/2014 - 30/11/2015

    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.

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    E'CHO 3D Sonar sensors applied to Automated Guided Vehicles. 01/01/2013 - 31/12/2014

    Abstract

    The goal of the E'CHO project is to evaluate a 3D sonar sensor, which has been developed and patented at the Univeristy of Antwerp, in realistic industrial environments. More specific, we will investigate the methods for applying the sonar sensor in two specific tasks on Automated Guided Vehicles (AGVs) which are being developed by Egemin Automation Handling

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    3D sonar for electric wheelchairs - proof of concept. 01/01/2013 - 31/12/2014

    Abstract

    This project, supported by the Swedish Promobilia Foundation, tests the feasibility of a 3D sonar system on electric wheelchairs. The goal of the project is to implement different algorithms which support the user of the wheelchair using environmental information generated by the sonar sensor. This could be used to implement for example obstacle avoidance behaviour.

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