4,507 research outputs found

    The Challenge of Continuous Mobile Context Sensing

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    National Research Foundation (NRF) Singapore under IDM Futures Funding Initiativ

    On the use of smartphones as novel photogrammetric water gauging instruments: Developing tools for crowdsourcing water levels

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    The term global climate change is omnipresent since the beginning of the last decade. Changes in the global climate are associated with an increase in heavy rainfalls that can cause nearly unpredictable flash floods. Consequently, spatio-temporally high-resolution monitoring of rivers becomes increasingly important. Water gauging stations continuously and precisely measure water levels. However, they are rather expensive in purchase and maintenance and are preferably installed at water bodies relevant for water management. Small-scale catchments remain often ungauged. In order to increase the data density of hydrometric monitoring networks and thus to improve the prediction quality of flood events, new, flexible and cost-effective water level measurement technologies are required. They should be oriented towards the accuracy requirements of conventional measurement systems and facilitate the observation of water levels at virtually any time, even at the smallest rivers. A possible solution is the development of a photogrammetric smartphone application (app) for crowdsourcing water levels, which merely requires voluntary users to take pictures of a river section to determine the water level. Today’s smartphones integrate high-resolution cameras, a variety of sensors, powerful processors, and mass storage. However, they are designed for the mass market and use low-cost hardware that cannot comply with the quality of geodetic measurement technology. In order to investigate the potential for mobile measurement applications, research was conducted on the smartphone as a photogrammetric measurement instrument as part of the doctoral project. The studies deal with the geometric stability of smartphone cameras regarding device-internal temperature changes and with the accuracy potential of rotation parameters measured with smartphone sensors. The results show a high, temperature-related variability of the interior orientation parameters, which is why the calibration of the camera should be carried out during the immediate measurement. The results of the sensor investigations show considerable inaccuracies when measuring rotation parameters, especially the compass angle (errors up to 90° were observed). The same applies to position parameters measured by global navigation satellite system (GNSS) receivers built into smartphones. According to the literature, positional accuracies of about 5 m are possible in best conditions. Otherwise, errors of several 10 m are to be expected. As a result, direct georeferencing of image measurements using current smartphone technology should be discouraged. In consideration of the results, the water gauging app Open Water Levels (OWL) was developed, whose methodological development and implementation constituted the core of the thesis project. OWL enables the flexible measurement of water levels via crowdsourcing without requiring additional equipment or being limited to specific river sections. Data acquisition and processing take place directly in the field, so that the water level information is immediately available. In practice, the user captures a short time-lapse sequence of a river bank with OWL, which is used to calculate a spatio-temporal texture that enables the detection of the water line. In order to translate the image measurement into 3D object space, a synthetic, photo-realistic image of the situation is created from existing 3D data of the river section to be investigated. Necessary approximations of the image orientation parameters are measured by smartphone sensors and GNSS. The assignment of camera image and synthetic image allows for the determination of the interior and exterior orientation parameters by means of space resection and finally the transfer of the image-measured 2D water line into the 3D object space to derive the prevalent water level in the reference system of the 3D data. In comparison with conventionally measured water levels, OWL reveals an accuracy potential of 2 cm on average, provided that synthetic image and camera image exhibit consistent image contents and that the water line can be reliably detected. In the present dissertation, related geometric and radiometric problems are comprehensively discussed. Furthermore, possible solutions, based on advancing developments in smartphone technology and image processing as well as the increasing availability of 3D reference data, are presented in the synthesis of the work. The app Open Water Levels, which is currently available as a beta version and has been tested on selected devices, provides a basis, which, with continuous further development, aims to achieve a final release for crowdsourcing water levels towards the establishment of new and the expansion of existing monitoring networks.Der Begriff des globalen Klimawandels ist seit Beginn des letzten Jahrzehnts allgegenwĂ€rtig. Die VerĂ€nderung des Weltklimas ist mit einer Zunahme von Starkregenereignissen verbunden, die nahezu unvorhersehbare Sturzfluten verursachen können. Folglich gewinnt die raumzeitlich hochaufgelöste Überwachung von FließgewĂ€ssern zunehmend an Bedeutung. Pegelmessstationen erfassen kontinuierlich und prĂ€zise WasserstĂ€nde, sind jedoch in Anschaffung und Wartung sehr teuer und werden vorzugsweise an wasserwirtschaftlich-relevanten GewĂ€ssern installiert. Kleinere GewĂ€sser bleiben hĂ€ufig unbeobachtet. Um die Datendichte hydrometrischer Messnetze zu erhöhen und somit die VorhersagequalitĂ€t von Hochwasserereignissen zu verbessern, sind neue, kostengĂŒnstige und flexibel einsetzbare Wasserstandsmesstechnologien erforderlich. Diese sollten sich an den Genauigkeitsanforderungen konventioneller Messsysteme orientieren und die Beobachtung von WasserstĂ€nden zu praktisch jedem Zeitpunkt, selbst an den kleinsten FlĂŒssen, ermöglichen. Ein Lösungsvorschlag ist die Entwicklung einer photogrammetrischen Smartphone-Anwendung (App) zum Crowdsourcing von WasserstĂ€nden mit welcher freiwillige Nutzer lediglich Bilder eines Flussabschnitts aufnehmen mĂŒssen, um daraus den Wasserstand zu bestimmen. Heutige Smartphones integrieren hochauflösende Kameras, eine Vielzahl von Sensoren, leistungsfĂ€hige Prozessoren und Massenspeicher. Sie sind jedoch fĂŒr den Massenmarkt konzipiert und verwenden kostengĂŒnstige Hardware, die nicht der QualitĂ€t geodĂ€tischer Messtechnik entsprechen kann. Um das Einsatzpotential in mobilen Messanwendungen zu eruieren, sind Untersuchungen zum Smartphone als photogrammetrisches Messinstrument im Rahmen des Promotionsprojekts durchgefĂŒhrt worden. Die Studien befassen sich mit der geometrischen StabilitĂ€t von Smartphone-Kameras bezĂŒglich gerĂ€teinterner TemperaturĂ€nderungen und mit dem Genauigkeitspotential von mit Smartphone-Sensoren gemessenen Rotationsparametern. Die Ergebnisse zeigen eine starke, temperaturbedingte VariabilitĂ€t der inneren Orientierungsparameter, weshalb die Kalibrierung der Kamera zum unmittelbaren Messzeitpunkt erfolgen sollte. Die Ergebnisse der Sensoruntersuchungen zeigen große Ungenauigkeiten bei der Messung der Rotationsparameter, insbesondere des Kompasswinkels (Fehler von bis zu 90° festgestellt). Selbiges gilt auch fĂŒr Positionsparameter, gemessen durch in Smartphones eingebaute EmpfĂ€nger fĂŒr Signale globaler Navigationssatellitensysteme (GNSS). Wie aus der Literatur zu entnehmen ist, lassen sich unter besten Bedingungen Lagegenauigkeiten von etwa 5 m erreichen. Abseits davon sind Fehler von mehreren 10 m zu erwarten. Infolgedessen ist von einer direkten Georeferenzierung von Bildmessungen mittels aktueller Smartphone-Technologie abzusehen. Unter BerĂŒcksichtigung der gewonnenen Erkenntnisse wurde die Pegel-App Open Water Levels (OWL) entwickelt, deren methodische Entwicklung und Implementierung den Kern der Arbeit bildete. OWL ermöglicht die flexible Messung von WasserstĂ€nden via Crowdsourcing, ohne dabei zusĂ€tzliche AusrĂŒstung zu verlangen oder auf spezifische Flussabschnitte beschrĂ€nkt zu sein. Datenaufnahme und Verarbeitung erfolgen direkt im Feld, so dass die Pegelinformationen sofort verfĂŒgbar sind. Praktisch nimmt der Anwender mit OWL eine kurze Zeitraffersequenz eines Flussufers auf, die zur Berechnung einer Raum-Zeit-Textur dient und die Erkennung der Wasserlinie ermöglicht. Zur Übersetzung der Bildmessung in den 3D-Objektraum wird aus vorhandenen 3D-Daten des zu untersuchenden Flussabschnittes ein synthetisches, photorealistisches Abbild der Aufnahmesituation erstellt. Erforderliche NĂ€herungen der Bildorientierungsparameter werden von Smartphone-Sensoren und GNSS gemessen. Die Zuordnung von Kamerabild und synthetischem Bild erlaubt die Bestimmung der inneren und Ă€ußeren Orientierungsparameter mittels rĂ€umlichen RĂŒckwĂ€rtsschnitt. Nach Rekonstruktion der Aufnahmesituation lĂ€sst sich die im Bild gemessene 2D-Wasserlinie in den 3D-Objektraum projizieren und der vorherrschende Wasserstand im Referenzsystem der 3D-Daten ableiten. Im Soll-Ist-Vergleich mit konventionell gemessenen Pegeldaten zeigt OWL ein erreichbares Genauigkeitspotential von durchschnittlich 2 cm, insofern synthetisches und reales Kamerabild einen möglichst konsistenten Bildinhalt aufweisen und die Wasserlinie zuverlĂ€ssig detektiert werden kann. In der vorliegenden Dissertation werden damit verbundene geometrische und radiometrische Probleme ausfĂŒhrlich diskutiert sowie LösungsansĂ€tze, auf der Basis fortschreitender Entwicklungen von Smartphone-Technologie und Bildverarbeitung sowie der zunehmenden VerfĂŒgbarkeit von 3D-Referenzdaten, in der Synthese der Arbeit vorgestellt. Mit der gegenwĂ€rtig als Betaversion vorliegenden und auf ausgewĂ€hlten GerĂ€ten getesteten App Open Water Levels wurde eine Basis geschaffen, die mit kontinuierlicher Weiterentwicklung eine finale Freigabe fĂŒr das Crowdsourcing von WasserstĂ€nden und damit den Aufbau neuer und die Erweiterung bestehender Monitoring-Netzwerke anstrebt

    Towards a Framework for Smart Manufacturing adoption in Small and Medium-sized Enterprises

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    Smart Manufacturing (SM) paradigm adoption can scale production with demand without compromising on the time for order fulfillment. A smart manufacturing system (SMS) is vertically and horizontally connected, and thus it can minimize the chances of miscommunication. Employees in an SME are aware of the operational requirements and their responsibilities. The machine schedules are prepared based on the tasks a machine must perform. Predictive maintenance reduces the downtime of machines. Design software optimizes the product design. Production feasibility is checked with the help of simulation. The concepts of product life cycle management are considered for waste reduction. Employee safety, and ergonomics, identifying new business opportunities and markets, focus on employee education and skill enhancement are some of the other advantages of SM paradigm adoption. This dissertation develops an SM paradigm adoption framework for manufacturing SMEs by employing the instrumental research approach. The first step in the framework identified the technical aspects of SM, and this step was followed by identifying the research gaps in the suggested methods (in literature) and managerial aspects for adopting SM paradigm. The technical and the managerial aspects were integrated into a toolkit for manufacturing SMEs. This toolkit contains seven modular toolboxes that can be installed in five levels, depending on an SME’s readiness towards SM. The framework proposed in this dissertation focuses on how an SME’s readiness can be assessed and based on its present readiness what tools and practices the SMEs need to have to realize their tailored vision of SM. The framework was validated with the help of two SMEs cases that have recently adopted SM practices

    Sensor fusion of IMU and BLE using a well-condition triangle approach for BLE positioning

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    Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesGPS has been a de-facto standard for outdoor positioning. For indoor positioning different systems exist. But there is no general solution to fit all situations. A popular choice among service provider is BLE-based IPS. BLE-has low cost, low power consumption, and tit is are compatible with newer smartphones. These factors make it suitable for mass market applications with an estimated market of 10 billion USD by 2020. Although, BLEbased IPS have advantages over its counterparts, it has not solved the position accuracy problem yet. More research is needed to meet the position accuracy required for indoor LBS. In this thesis, two ways for accuracy improvement were tested i) a new algorithm for BLE-based IPS was proposed and ii) fusion of BLE position estimates with IMU position estimates was implemented. The first way exploits a concept from control survey called well-conditioned triangle. Theoretically, a well-conditioned triangle is an equilateral triangle but for in practice, triangles whose angles are greater than 30° and less than 120° are considered well-conditioned. Triangles which do not satisfy well-condition are illconditioned. An estimated position has the least error if the geometry from which it is estimated satisfy well-condition. Ill-conditioned triangle should not be used for position estimation. The proposed algorithm checked for well-condition among the closest detected beacons and output estimates only when the beacons geometry satisfied well-condition. The proposed algorithm was compared with weighted centroid (WC) algorithm. Proposed algorithm did not improve on the accuracy but the variance in error was highly reduced. The second way tested was fusion of BLE and IMU using Kålmån filter. Fusion generally gives better results but a noteworthy result from fusion was that the position estimates during turns were accurate. When used separately, both BLE and IMU estimates showed errors in turns. Fusion with IMU improved the accuracy. More research is required to improve accuracy of BLE-based IPS. Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 2, 1, 2 (input data, prepossessing, methods, computational environment, results)

    Building the knowledge base for environmental action and sustainability

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    An aesthetics of touch: investigating the language of design relating to form

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    How well can designers communicate qualities of touch? This paper presents evidence that they have some capability to do so, much of which appears to have been learned, but at present make limited use of such language. Interviews with graduate designer-makers suggest that they are aware of and value the importance of touch and materiality in their work, but lack a vocabulary to fully relate to their detailed explanations of other aspects such as their intent or selection of materials. We believe that more attention should be paid to the verbal dialogue that happens in the design process, particularly as other researchers show that even making-based learning also has a strong verbal element to it. However, verbal language alone does not appear to be adequate for a comprehensive language of touch. Graduate designers-makers’ descriptive practices combined non-verbal manipulation within verbal accounts. We thus argue that haptic vocabularies do not simply describe material qualities, but rather are situated competences that physically demonstrate the presence of haptic qualities. Such competencies are more important than groups of verbal vocabularies in isolation. Design support for developing and extending haptic competences must take this wide range of considerations into account to comprehensively improve designers’ capabilities
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