In the Netherlands an innovative programme for early detection of chronic obstructive pulmonary disease (COPD) in primary care among patients aged 40–70 years has been evaluated in both an effect study and a pilot implementation study. Health-care providers identified four obstacles for successful implementation of a COPD early detection programme. This Brief Communication describes the most important results of a qualitative study using in-depth interviews.
We present a novel anomaly-based detection approach capable of detecting botnet Command and Control traffic in an enterprise network by estimating the trustworthiness of the traffic destinations. A traffic flow is classified as anomalous if its destination identifier does not origin from: human input, prior traffic from a trusted destination, or a defined set of legitimate applications. This allows for real-time detection of diverse types of Command and Control traffic. The detection approach and its accuracy are evaluated by experiments in a controlled environment.
Mondkapjes, of mondmaskers, zijn door de SARS-COV-2 pandemie niet meer uit het straatbeeld weg te denken. De kwaliteit en comfort van de pasvorm van medische en niet-medische mondmaskers wordt bepaald door hoe goed het mondmasker overeenkomt met de afmetingen van het gezicht van de drager. Echter is er geen goed overzicht van de antropometrie van het gelaat van de Nederlandse bevolking waardoor de pasvorm van mondmaskers nu vaak niet optimaal is. Er is dus vraag naar een laagdrempelige en veilige manier om gezichtskenmerken in kaart te brengen en betere ontwerprichtlijnen voor mondkapjes. Driedimensionaal (3D) scannen doormiddel van Light Detection and Ranging (LiDaR) technologie in combinatie met slimme algoritmes lijkt wellicht een manier om gezichtskenmerken snel en laagdrempelig vast te leggen bij grote groepen mensen. Daarnaast geeft het 3D scannen van gezichten de mogelijkheid om niet enkel de afmetingen van gezichten te meten, maar ook 3D pasvisualisaties uit te voeren. Hoewel 3D scannen geen nieuwe technologie is, is de LiDaR technologie pas sinds 2020 geïntegreerd in de Ipad en Iphone waardoor het toegankelijk gemaakt is voor consumenten. Doormiddel van een research through design benadering zal onderzocht worden of deze technologie gebruikt kan worden om betrouwbare en valide opnames te maken van gezichten en of er op basis hiervan ontwerprichtlijnen ontwikkeld kunnen worden. In dit KIEM GoCi-project zal daarnaast ingezet worden om een kennisbasis en netwerk op te bouwen voor een vervolg aanvraag over de inzet van 3D technologieën in de mode-industrie.
The utilization of drones in various industries, such as agriculture, infrastructure inspection, and surveillance, has significantly increased in recent years. However, navigating low-altitude environments poses a challenge due to potential collisions with “unseen” obstacles like power lines and poles, leading to safety concerns and equipment damage. Traditional obstacle avoidance systems often struggle with detecting thin and transparent obstacles, making them ill-suited for scenarios involving power lines, which are essential yet difficult to perceive visually. Together with partners that are active in logistics and safety and security domains, this project proposal aims at conducting feasibility study on advanced obstacle detection and avoidance system for low-flying drones. To that end, the main research question is, “How can AI-enabled, robust and module invisible obstacle avoidance technology can be developed for low-flying drones? During this feasibility study, cutting-edge sensor technologies, such as LiDAR, radar, camera and advanced machine learning algorithms will be investigated to what extent they can be used be to accurately detect “Not easily seen” obstacles in real-time. The successful conclusion of this project will lead to a bigger project that aims to contribute to the advancement of drone safety and operational capabilities in low-altitude environments, opening new possibilities for applications in industries where low-flying drones and obstacle avoidance are critical.
Agricultural/horticultural products account for 9% of Dutch gross domestic product. Yearly expansion of production involves major challenges concerning labour costs and plant health control. For growers, one of the most urgent problems is pest detection, as pests cause up to 10% harvest loss, while the use of chemicals is increasingly prohibited. For consumers, food safety is increasingly important. A potential solution for both challenges is frequent and automated pest monitoring. Although technological developments such as propeller-based drones and robotic arms are in full swing, these are not suitable for vertical horticulture (e.g. tomatoes, cucumbers). A better solution for less labour intensive pest detection in vertical crop horticulture, is a bio-inspired FW-MAV: Flapping Wings Micro Aerial Vehicle. Within this project we will develop tiny FW-MAVs inspired by insect agility, with high manoeuvrability for close plant inspection, even through leaves without damage. This project focusses on technical design, testing and prototyping of FW-MAV and on autonomous flight through vertically growing crops in greenhouses. The three biggest technical challenges for FW-MAV development are: 1) size, lower flight speed and hovering; 2) Flight time; and 3) Energy efficiency. The greenhouse environment and pest detection functionality pose additional challenges such as autonomous flight, high manoeuvrability, vertical take-off/landing, payload of sensors and other equipment. All of this is a multidisciplinary challenge requiring cross-domain collaboration between several partners, such as growers, biologists, entomologists and engineers with expertise in robotics, mechanics, aerodynamics, electronics, etc. In this project a co-creation based collaboration is established with all stakeholders involved, integrating technical and biological aspects.
