The ‘dirt diary’ is a do-book that was used to interact with residents to gain a clear view on the true waste journey in households. The do-book is contains several assignments for residents around different types of waste, for example plastics, organic waste, paper and textile. Assignments include drawing a map of waste solutions in the kitchen, photographing waste generated when preparing a meal and describing how they dispose of the waste in the kitchen and at communal containers. The do-books completed by the residents were analysed by the researchers for each waste type, studying behaviour exhibited and underlying motives for that behaviour. The do-books proved to be a valuable tool to gain understanding of people's behaviour around disposing waste, the opportunities for waste separation they have in and around their homes and their motivations for separating waste or not. This should lead to touch points to create interventions on automatic behaviour so that a sustainable change in this behaviour can take place.This do-book was exhibited as boundary object at the Collaboration for Impact exhibition, eccompanying the publication Collaboartion for Impact,
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Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5% is achieved for distinguishing two important potato-plant diseases.
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In this paper we propose a head detection method using range data from a stereo camera. The method is based on a technique that has been introduced in the domain of voxel data. For application in stereo cameras, the technique is extended (1) to be applicable to stereo data, and (2) to be robust with regard to noise and variation in environmental settings. The method consists of foreground selection, head detection, and blob separation, and, to improve results in case of misdetections, incorporates a means for people tracking. It is tested in experiments with actual stereo data, gathered from three distinct real-life scenarios. Experimental results show that the proposed method performs well in terms of both precision and recall. In addition, the method was shown to perform well in highly crowded situations. From our results, we may conclude that the proposed method provides a strong basis for head detection in applications that utilise stereo cameras.
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In dit project wordt de haalbaarheid bestudeerd voor het maken van nanoporeuze membranen met behulp van gangbare processen in de halfgeleiderindustrie. Nanoporeuze membranen bieden onder meer de mogelijkheid om op energie-efficiënte en milieuvriendelijke manier water te ontzouten of het scheiden van vluchtige componenten als alternatief voor destillatie. Recent zijn veel nieuwe nanoporeuze materialen gerapporteerd. Succesvolle toepassingen op het gebied van katalyse, sensoren en scheidingen, waaronder ook eerste voorbeelden van kleinschalige nanofiltratie, geven de potentie van dergelijke materialen aan voor een toepassing op het gebied van nanofiltratie op grotere schaal. Echter, het ontbreekt momenteel aan goede, eenvoudige methoden om deze opschaling voor ultradunne (sub-micron), nanoporeuze membranen te realiseren. In dit project zal wordt een methode bestudeerd en geïmplementeerd waarmee dit wel mogelijk is.
