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This paper explores the use of fuzzy difference methods in order to understand the differences between forest classes. The context for this work is provided by REDD+, which seeks to reduce the net emissions of greenhouse gases by rewarding the conservation of forests in developing countries. REDD+ requires that local inventories of forest are undertaken and payments are made on the basis of the amount of forest (and associated carbon storage). At the most basic level this involves classifying land into forest and non-forest. However, the critical issues affecting the uptake, buy-in and ultimately the success of REDD+ are the lack of universally agreed definition of forest to support REDD+ mapping activities, and where such a definition is imposed, the marginalization of local community voices and local landscape conceptualizations. This tension is at the heart of REDD+. This paper addresses these issues by linking methods to quantify changes in fuzzy land cover to the concept of data primitives, which have been previously proposed as a suitable approach to move between land cover classes with different semantics. These are applied to case study that quantifies the difference in areas for two definitions of forest derived from the GLC and FAO definitions of forest. The results show how data primitives allow divergent concepts of forest to be represented and mapped from the same data and how the fuzzy sets approach can be used to quantify the differences and non-intersections of different concepts of forest. Together these methods provide for transparent translations between alternative conceptualizations of forest, allowing for plural notions of forest to be mapped and quantified. In particular, they allow for moving from an object-based notion of forest (and land cover in general) to a field-based one, entirely avoiding the need for forest boundaries.

Robust flutter analysis described in this paper is based on the robust control theory framework. Therefore, a time-domain linear fractional transformation representation of the perturbed aeroelastic system is modeled. Then, the robust stability is analyzed by means of the structured singular value $$\mu$$ μ , which is defined as an alternative measure of robustness. Robust flutter analysis deals with aeroelastic (or aeroservoelastic) stability analysis taking structural dynamics, aerodynamics and/or unmodeled system dynamics uncertainties into account. Flutter is a well-known dynamic aeroelastic instability phenomenon caused by an interaction between structural vibrations and unsteady aerodynamic forces, whereby the level of vibration may trigger large amplitudes, eventually leading to catastrophic failure of the structure. The primary motivation of the robust flutter analysis is that this method allows the computation of the worst-case flutter velocity which can support, for example, the flight test program by a valuable robust flutter boundary. This paper addresses the issue of an approach for aeroelastic robust stability analysis with structural uncertainties with respect to physical symmetric and asymmetric stiffness perturbations on the wing structure by means of tuning beams.

This paper presents an architecture for Automated Valet Parking (AVP) connected to cloud-based IoT services and mobile user interfaces. The goal is to enable AVP services for automatic vehicles. From the user perspective, automatic car drop-off and pick-up are activated via smart phone application, and the user will be able to continuously monitor the vehicle status together with additional services as cleaning or recharge during the parking phase. Further, the IoT platform allows the integration of live services that will interact with automatic driving and parking. As an example, the presented AVP setup includes the operation of service drones to automatically guide a vehicle to the best parking spot. The demonstration in this paper comprises a parking car and a micro aerial vehicle (MAV) connected in real-time through the IoT platform as well as the smart phone application where the car is controlled and supervised.

Summary Main aim of this paper is to compare the classification accuracies of glacier change detection by following classifiers: sub-pixel classification algorithm, indices based supervised classification and object based algorithm using Landsat imageries. It was observed that shadow effect was not removed in sub-pixel based classification which was removed by the indices method. Further the accuracy was improved by object based classification. Objective of the paper is to analyse different classification algorithms and interpret which one gives the best results in mountainous regions. The study showed that object based method was best in mountainous regions as optimum results were obtained in the shadowed covered regions.

This paper takes up the increasingly popular topic of drones — including Unmanned Aerial Vehicles (UAVs), small unmanned aerial Systems (sUAS), Remotely Piloted Aircraft (RPA) and a vast panoply of commercial drones and copters — to argue that our analysis should lie not so much on drones as objects, but as assemblages of the vertical. Drones, I argue, constitute a socio-technical assemblage of the sky and vertical space, which means that our focus should be not (only) on their technological development and capacities (although this is certainly important) but on their wider effects and affects. The latter of these include increasing algorithmic data collection and circulation. In the second part of the paper, I examine what I call "algorithmic governance" inspired by the recent governing algorithms conference at NYU but also drawing from Foucault's work on governmentality. Algorithmic governance constitutes "conduct of conduct" to modulate how "data shadows" or Deleuzian "dividuals" can be created, sold, and passed forward. My particular concern is how this takes place in a geographic register.

International audience; Satellite Image Time Series (SITS) are large datasets containing spatiotemporal information about the surface of the Earth. In order to exploit the potential of such series, SITS analysis techniques have been designed for various applications such as earthquake monitoring, urban expansion assessment or glacier dynamic analysis. In this paper, we present an unsupervised technique for browsing SITS in preliminary explorations, before deciding whether to start deeper and more time consuming analyses. Such methods are lacking in today’s analyst toolbox, especially when it comes to stimulating the reuse of the ever growing list of available SITS. The method presented in this paper builds a summary of a SITS in the form of a set of maps depicting spatiotemporal phenomena. These maps are selected using an entropy-based ranking and a swap randomization technique. The approach is general and can handle either optical or radar SITS. As illustrated on both kinds of SITS, meaningful summaries capturing crustal deformation and environmental phenomena are produced. They can be computed on demand or precomputed once and stored together with the SITS for further usage.

The exploration and potential future exploitation of solar system bodies requires technologies for precise and safe landings. Current navigation systems for landing probes are relying on a combination of inertial and optical sensor measurements to determine the current flight state with respect to the target body and the desired landing site. With a future transition from single exploration missions to more frequent first exploration and then exploitation missions, the implementation and operation of these missions changes, since it can be expected that a ground infrastructure on the target body is available in the vicinity of the landing site. In a previous paper, the impact of a single ground-based beacon on the navigation performance was investigated depending on the type of radiometric measurements and on the location of the beacon with respect to the landing site. This paper extends this investigation on options for ground-based multiple beacons supporting the on-board navigation system. It analyzes the impact on the achievable navigation accuracy. For that purpose, the paper introduces briefly the existing navigation architecture based on optical navigation and its extension with radiometric measurements. The same scenario of lunar landing as in the previous paper is simulated. The results are analyzed and discussed. They show a single beacon at a large distance along the landing trajectory and multiple beacons close to the landing site can improve the navigation performance. The results show how large the landing area can be increased where a sufficient navigation performance is achieved using the beacons.

In-flight demonstration and verification of a robust flight control design including an advanced anti-windup strategy is presented in this paper. The test aircraft, an Ultrastick 120 unmanned aircraft, is equipped with a pitch- and roll-attitude command flight-control system for remotely controlled augmented flight. The longitudinal aircraft dynamics are enhanced with an H1-based control design. The controller additionally comprises an dvanced anti-windup strategy to avoid integrator windup during actuator saturation. The lateral axis features a linear-parameter varying controller which provides robust performance over the whole velocity range. The controllers as well as the anti-windup algorithm are verified and validated in a non-linear simulation environment and by various flight tests. The paper provides detailed insight in the design process of the controllers, as well as a comprehensive discussion on the simulation and flight test results.

In this paper a particle filter for absolute train localization based on magnetic field measurements is proposed. The filter utilizes distortions of the earth magnetic field introduced by ferromagnetic infrastructure components along the railway track. The distortions are characteristic for a certain part of the track network and therefore are a source of position information. The particle filter introduced in this paper incorporates a prior created map of these distortions to estimate the train position. This only requires low-cost passive magnetometers and a simple movement model that accounts for the limited dynamics of a train. The feasibility of the approach is demonstrated in an evaluation with measurements collected on a train driving in a rural area. Overall a position root mean square error below four meters could be achieved, proving that the magnetic field is a viable source of position information that is independent from other localization systems like GNSS.

Whereas crowdsourcing as a topic has often been addressed in recent literature, web-based crowdworking platforms that manage the interface between crowdsourcers and crowdworkers have not received much attention so far. Furthermore, most of these platforms focus on either the management of external or internal crowds; platforms that handle both groups are rare. This paper investigates such a provider: the German company Across Systems. It uses a hybrid model, offering an individual “mini crowdworking platform” that enables the simultaneous government of external and internal crowds as well as a more traditional marketplace crowdworking platform (crossMarket) where supply and demand meet. Using a single-case study approach, the main contribution of this paper is to shed light on a model that has the potential to change the current crowdworking platform market. We show that managing both external and internal crowds on one platform can increase the acceptance, quality and speed of task completion.

Range accuracy of synthetic aperture radar using modern satellites has been proven in centimeter-range after consideration of geodynamic, atmospheric effects and systematic errors; it is so-called Imaging Geodesy technique. Based on this technique, we have proposed the next-generation of Synthetic Aperture Radar (SAR) products and developed operational software SAR Geodesy Processor in the previous paper. In this paper, we are concentrated on validation of processor and accuracy analysis on a global-scale. Two datasets with different spatial resolutions generated from European Centre for Medium-Range Weather Forecast are used to compensate atmospheric refraction effect in range. At the end, we apply corrections on a Sentinel interferogram by using both datasets.

As bit rates of optical interconnects increase, a large amount of complicated signal conditioning is needed to compensate for the insufficient bandwidth of current modulators. In this paper, we evaluate the reduced equalization requirements of high-bandwidth plasmonic modulators in short-reach transmission experiments. It is shown that transmission of 100 Gbit/s non-return-to-zero (NRZ) and 112 Gbit/s pulse-amplitude modulation-4 over 1 km and 2 km distance is possible without any receiver equalization. At higher bit-rates, such as 120 Gbit/s NRZ, data transmission is demonstrated over 500 m with reduced receiver equalization requirements. Transmission up to 200 Gbit/s over 1 km is also shown with more complex receiver equalization. The reduced complexity of the receiver digital signal processing is attributed to a flat frequency response of at least 108 GHz of the plasmonic modulators. All single wavelength transmissions have been performed at 1540 nm in standard single mode fiber.

International audience; The paper presents a new approach to the human reindetification problem using covariance features. In many cases, a distance operator between signatures, based on generalized eigenvalues, has to be computed efficiently, especially once the real-time response time is expected from the system. This is a challenging problem as many procedures are in fact computationally intensive tasks and must be repeated constantly. To deal with this problem we have successfully designed and then tested a new video surveillance system. To obtain the required high efficiency we took the advantage of highly parallel computing architectures such as FPGA, GPU and CPU units to perform calculations. However, we had to propose a new GPU-based implementation of the distance operator for querying the example database. Thus, in this paper we present experimental evaluation of the proposed solution in the light of the database response time depending on its size.

International audience; Many computer vision problems such as recognition, image retrieval, and tracking require matching two images. Currently, ones try to find as reliable as possible matching techniques with a very little constraint of computational time. In this paper, we are interested in applying image matching technique into robotic problems such as Augmented Reality, Visual Servoing in which the computational time is a critical element. We propose in this paper a real time keypoint based matching method. The novelties of this method include a fast corner detector, a compact corner descriptor based on Principal Component Analysis (PCA) technique and an efficient matching with help of Approximate Nearest Neighbor (ANN) technique. We show that the method gives a very satisfying result on accuracy as well as the computational time. The matching algorithm is applied to command a robot in a visual servoing application. It works at 10-14Hz and is well robust to variations in 3D viewpoint and illumination.

Koncept pametnog grada često se obrađuje, ali još nije postignuta konzistentna definicija. Ipak, svaki je opis gotovo uvijek usmjeren na njegovu tehnološku komponentu, politike održivog razvoja i omogućavanje visokih kapaciteta za učenje i inovacije. Osim toga, pametni grad ima za cilj povezivanje ljudi, informacija i drugih gradskih elemenata koristeći najsuvremenije tehnologije. Kao rezultat, stvara se održivi, zeleniji grad, potiče konkurentna i inovativna trgovina i povećava opća kvaliteta života.Integriranim prikazom pametnog grada ističe se da ne djeluje izolirano, te stoga, svaki podsustav grada treba razvijati svoju pametnu komponentu. Širok raspon rangiranja koristi se za određivanje pametnosti gradova mapiranjem prednosti i nedostataka svakog analiziranog grada. Kao način integriranja različitih pokazatelja u jednu vrijednost koja će predstavljati rang, najčešće se koristi složeni indeksni pristup.Ipak, složeni indeksi najčešće se formiraju primjenom pristupa jednakih pondera, što se u trenutnoj literaturi žestoko kritizira. U ovom radu pokušavamo pružiti dodanu vrijednost indeksu Smart City primjenom statističkog post-hoc I-distance pristupa. Postupak nam omogućuje osvjetljavanje pitanja osjetljivosti ranga gradova. Primjena post-hoc I-distance definira pokazatelje koji su najvažniji za postupak rangiranja što gradskim donositeljima odluka omogućava da poboljšaju svoje poslovanje, s naglaskom na upravo te pokazatelje., The smart city represents a frequently elaborated concept which however comes short in delivering a consistent definition. Nevertheless, almost every description has always been oriented to its technological component, sustainable development policies, and enabling high capacities for learning and innovation. Moreover, the smart city aims at connecting people, information and other city elements using state-of-the-art technologies. As a result, it creates a sustainable, greener city, pushes forward competitive and innovative commerce, and increases overall life quality. The integrated view of a smart city underlines it does not operate in isolation, which is why every subsystem of a city needs to develop its smart component. A wide range of rankings is used to determine the smartness of cities by mapping out the pros and cons of each analysed city. As the way to integrate various indicators into one value which will represent the rank, a composite index approach is most frequently used. Still, composite indexes are usually formed using the equal weight approach, which is heavily criticised in current literature. In this paper, we try to provide added value to the Smart City Index by implementing the statistical post hoc I-distance approach. The procedure enables us to shed some additional light on the issue of sensitivity of cities’ rank. The application of post hoc I-distance defines indicators which are most significant for the ranking process. It consequently empowers city decision-makers to improve their performance, with a focus on those particular indicators.

With increasing radar activities in the automotive, industrial and private sector, there is a need to test radar sensors in their environment. A radar target simulator can help testing radar systems repeatably. In this paper, the authors present a concept of low-cost hardware for radar target simulation. The theoretical foundations are derived and analyzed. An implementation of a demonstrator operating in the 24 GHz ISM band is shown for which the dynamical range simulation was implemented in a FPGA with fast sampling ADCs and DACs. By using a FIR filtering approach a fine discretization of the range could be reached which will furthermore allow an inherent and automatic Doppler simulation by moving the target.

Modern and energy-optimized buildings often lack an intelligent and advanced control strategy. Instead, conventional rule-based control (RBC) strategies are still mainly used today, which do not exploit the full performance potential of these buildings. Model predictive control (MPC) has proven in simulation studies and pilot cases to be a promising approach to reduce the energy consumption of buildings, while improving occupants’ comfort. However, there is still a lack of implementing MPC in real, large-scale and fully occupied buildings, to further prove this potential in real building operations. This paper describes the implementation and operation of MPC in a large-sized, low-energy office building. The MPC controller was implemented in a section of the building during a three-month test period from February to April 2020, controlling the supply temperature of heating circuits for thermally activated building systems (TABS). Its performance was compared to the default rule-based control which is active in the other building sections. This allows for a detailed evaluation of MPC versus RBC under identical environmental and operational conditions. The MPC controlled building section used 30 % less heating energy than RBC controlled building sections, while the existing high level of thermal comfort could be maintained. Especially in transition periods (i. e. interseasonal periods like late winter/early spring) , the MPC is superior to the conventional heating-curve based control strategy, with heating energy savings of 75 %.

The present study focuses on serially occurring narrations of ‘everyday’ life, more specifically on birthing as narrated by mothers on online forums; the underlying idea being that these narrations happen against the background of cultural narratives.The present paper uses word embedding models to detect typical topics and actors in these narrations. The calculation of word embeddings automatically constructs semantic spaces, where semantic relations (synonymy in particular) can be modeled. This method offers a way to think of synonymy as ‘functional equivalence in discourse’.The present study relies on previous work with n-grams (Bubenhofer, 2018). N-grams are sequences of words that often appear together; their sequential order in different narrations gives insight in narrative patterns. A further step in the analysis is the construction of ‘narrative topoi’, which is achieved through clustering neighboring vectors. The emerging clusters can in turn be grouped into five narrative elements of ‘telling a birth story’: 1) disruption of daily life, 2) personnel, 3) body, 4) fear, 5) joy. While it seems obvious that certain themes ‘belong’ into the narration of a delivery, it is less obvious with what vocabulary these themes are expressed.The presented method of clustering word-embedding-profiles adds tremendously to the modelling of a narrative. Its advantages lie in its potential to show lexical variation, as it also includes rare, non-conformative orthographical variants. Furthermore, it allows for a discourse-specific (and usage-based) view on semantic relations. The same applies to relations between semantic clusters. Seen from a discourse linguistics or cultural analysis perspective, word embeddings renew our understanding of semantics. This shows particularly fruitful if used to analyze (discourse dependent) derivations between semantic spaces.

The dynamics of software – and thus also of its development – is an inherent part of the story of how mobilities are made and work. Building on this argument, this ethnographic study explains how navigation software development is caught in a constant culture of acceleration through commercial competition and shifting transport conditions, on the one hand, and a logic of routing and navigation based on creating the fastest route possible for its users, on the other hand. Behind this overall process of technological acceleration lies a multiplicity of forces – sometimes accelerating, but at other times slowing down, stuttering, moving in reverse, or completely coming to a halt in breakdown, shifting the pace of such technological progress. Bringing software development practice into the picture of how mobility systems work (or don’t work) allows us to understand the multiple temporal orders of speeding up and slowing down that push and pull at the fabric of our mobile infrastructures. Doing so will help us counter the popular discourse that our networked, seamless digital technologies are invincible. Based on an ethnography at ‘BerlinTech’, a large commercial navigation software company, this paper provides a multilayered understanding about the temporal forces fuelling our software driven mobility infrastructures.

Crowdfunding is now established as a valid alternative to conventional methods of financing for startups. Unfortunately, to date, research has not investigated how backers can be encouraged to support entrepreneurs beyond funding. The aim of this study is to design and evaluate certain design elements for reward-based crowdfunding platforms that can engage backers in co-creational activities for product development. The study uses a design science research (DSR) approach and the theoretical concept of psychological ownership to inform a new design and then experimentally test that design. The results suggest that the derived artifacts positively influence co-creational activities in crowdfunding and that feelings of psychological ownership play an important mediating role. The contribution of this research is threefold. First, this paper extends crowdfunding’s application potential from merely a method of financing to a method of value creation with customers for product development. Second, the study advances DSR by applying a new DSR approach that shows whether a design performs as hypothesized by theory. Third, this research allows the exploration of backers’ individual behavior as opposed to their collective behavior.

Short range inductive data transfer has become very popular in the last years with the increased interest in technologies like RFID and NFC. Additionally, wireless power transfer has been a field of intensive research and product development. In industrial applications, a combination of both is often needed to replace mechanical contacts, mainly for safety and reliability reasons, especially regarding spark prevention and maintenance reduction. In this paper, we present a compact inductive structure that can be implemented in an existing wireless energy transfer system. The structure is developed by field considerations, leading to a single structure comprising two signal ports that are decoupled from each other, but can be used to transfer a signal to a second structure of that kind with very small interference. An optimisation to achieve comparable channel characteristics is conducted by using 3-D field simulations. Subsequent measurements are conducted to verify the achieved performance.

This paper aims to analyse the intermodal practices of mobility in the bus-metro network of Rennes metropole. Intermodality being strongly linked to the use of urban public transport networks, the Rennes Metropole bus and metro network data provided by Keolis-Rennes provides a very significant part of daily intermodality. To compensate for the lack of information on destinations and correspondances in the ticketing data a reconstruction of trips was made on the basis of hypotheses provided by the literature. The research carried out in the "Mobilletic" project considerably deepened the understanding of the temporal and spatial dimensions of intermodal mobility within Rennes Metropole’s urban public transport network.daily mobility, public transport, data science, data analysis, intermodal transportation

The global Digital Elevation Model (DEM) resulting from the TanDEM-X mission provides information about the world topography with outstanding precision. In fact, performance analysis carried out with the already available data have shown that the global product is well within the requirements of 10 m absolute vertical accuracy and 2 m relative vertical accuracy for flat to moderate terrain. The mission's science phase took place from October 2014 to December 2015. During this phase, bistatic acquisitions with across-track separation between the two satellites up to 3.6 km at the equator were commanded. Since the relative vertical accuracy of InSAR derived elevation models is, in principle, inversely proportional to the system baseline, the TanDEM-X science phase opened the doors for the generation of elevation models with improved quality with respect to the standard product. However, the interferometric processing of the large-baseline data is troublesome due to the increased volume decorrelation and very high frequency of the phase variations. Hence, in order to fully profit from the increased baseline, sophisticated algorithms for the interferometric processing, and, in particular, for the phase unwrapping have to be considered. This paper proposes a novel dual-baseline region-growing framework for the phase unwrapping of the large-baseline interferograms. Results from two experiments with data from the TanDEM-X science phase are discussed, corroborating the expected increased level of detail of the large-baseline DEMs.

Requirements Engineering (RE) has been aiding software-intensive development projects for quite some time now. However, today’s projects growingly demand for agile and human-centered approaches to discover and meet the often fuzzy needs of the various stakeholders involved. In that regard, Design Thinking (DT) has become one of the most promising methods to address complex problems and define innovative solutions. We see potential to combine both, the strongly human-oriented working mode of DT with the more formal, technology-driven world of RE, to develop human-centered solutions more effectively. Yet, little is known how such an integration could be realized and which concrete benefits and challenges to expect. To better understand this endeavor, we conduct a longitudinal case study to identify how DT and RE can work together in an agile development setting from the very beginning of idea conceptualization to market-ready implementation. In this paper, we summarize our research protocol and present first findings how Design Thinking can complement current RE practices through meeting known challenges encountered by the RE community of practitioners. We provide a better understanding of the multi-faceted potential of DT for RE for both, scholars and practitioners, and describe open issues and planned future steps in our study.

Character animation tools are based on a keyframing metaphor where artists pose characters at selected keyframes and the software automatically interpolates the frames inbetween. Although the quality of the interpolation is critical for achieving a fluid and engaging animation, the tools available to adjust the result of the automatic inbetweening are rudimentary and typically require manual editing of spline parameters. As a result, artists spend a tremendous amount of time posing and setting more keyframes. In this pose-centric workflow, animators use combinations of forward and inverse kinematics. While forward kinematics leads to intuitive interpolations, it does not naturally support positional constraints such as fixed contact points. Inverse kinematics can be used to fix certain points in space at keyframes, but can lead to inferior interpolations, is slow to compute, and does not allow for positional contraints at non-keyframe frames. In this paper, we address these problems by formulating the control of interpolations with positional constraints over time as a space-time optimization problem in the tangent space of the animation curves driving the controls. Our method has the key properties that it (1) allows the manipulation of positions and orientations over time, extending inverse kinematics, (2) does not add new keyframes that might conflict with an artist's preferred keyframe style, and (3) works in the space of artist editable animation curves and hence integrates seamlessly with current pipelines. We demonstrate the utility of the technique in practice via various examples and use cases.

The fusion of complementary information from co-registered multi-modal image data enables a more detailed and more robust understanding of an image scene or specific objects, and is important for several applications in the field of remote sensing. In this paper, the benefits of combining RGB, near infrared (NIR) and thermal infrared (TIR) aerial images for the task of semantic vehicle segmentation through deep neural networks are investigated. Therefore, RGB, NIR and TIR image triplets acquired by the Modular Aerial Camera System (MACS) are precisely co-registered through the application of a virtual camera system and subsequently used for the training of different neural network architectures. Various experiments were conducted to investigate the influence of the different sensor characteristics and an early or late fusion within the network on the quality of the segmentation results.

The automatic matching of corresponding regions in remote sensing imagery acquired by synthetic aperture radar (SAR) and optical sensors is a crucial pre-requesite for many data fusion endeavours such as target recognition, image registration, or 3D-reconstruction by stereogrammetry. Driven by the success of deep learning in conventional optical image matching, we have carried out extensive research with regard to deep matching for SAR-optical multi-sensor image pairs in the recent past. In this paper, we summarize the achieved findings, including different concepts based on (pseudo-)siamese convolutional neural network architectures, hard negative mining, alternative formulations of the underlying loss function, and creation of artificial images by generative adversarial networks. Based on data from state-of-the-art remote sensing missions such as TerraSAR-X, Prism, Worldview-2, and Sentinel-1/2, we show what is already possible today, while highlighting challenges to be tackled by future research endeavors.

Building semantic segmentation is a crucial task for building information modeling (BIM). Current research generally exploits terrestrial image data, which provides only limited view of a building. By contrast, oblique imagery acquired by unmanned aerial vehicle (UAV) can provide richer information of both the building and its surroundings at a larger scale. In this paper, we present a novel pipeline for building semantic segmentation from oblique UAV images using a fully convolutional neural network (FCN). To cope with the lack of UAV image annotations at facade level, we leverage existing ground-view facades databases to simulate various aerial-view images based on estimated homography, yielding abundant synthetic aerial image annotations as training data. The FCN is trained end-to-end and tested on full-tile UAV images. Experiments demonstrate that the incorporation of simulated views can significantly boost the prediction accuracy of the network on UAV images and achieve reasonable segmentation performance.

Collaboration Engineering (CE) is an approach for the design and deployment of repeatable collaborative work practices that can be executed by practitioners themselves without the ongoing support of external collaboration professionals. A key design activity in CE concerns modeling current and future collaborative work practices. CE researchers and practitioners have used the Facilitation Process Model (FPM) technique. However, this modeling technique suffers from a number of shortcomings to model contemporary collaborative work practices. We use a design science approach to identify the main challenges with the original FPM technique, derive requirements and design a revised modeling technique that is based on the current technique enriched by BPMN 2.0 elements. This paper contributes to the CE literature by offering a revised FPM technique that assists CE-designers to capture new forms of collaborative work practices.

The digital age has yielded systems that increasingly reduce the complexity of our everyday lives. As such, smart personal assistants such as Amazon’s Alexa or Apple’s Siri combine the comfort of intuitive natural language interaction with the utility of personalized and situation-dependent information and service provision. However, research on SPAs is becoming increasingly complex and opaque. To reduce complexity, this paper introduces a classification system for SPAs. Based on a systematic literature review, a cluster analysis reveals five SPA archetypes: Adaptive Voice (Vision) Assistants, Chatbot Assistants, Embodied Virtual Assistants, Passive Pervasive Assistants, and Natural Conversation Assistants.

Low complexity decoding algorithms are necessary to meet data rate requirements in excess of 1 Tbps. In this paper, we study one and two bit message passing algorithms for belief propagation decoding of low-density parity-check (LDPC) codes and analyze them by density evolution. The variable nodes (VNs) exploit soft information from the channel output. To decrease the data flow, the messages exchanged between check nodes (CNs) and VNs are represented by one or two bits. The newly proposed quaternary message passing (QMP) algorithm is compared asymptotically and in finite length simulations to binary message passing (BMP) and ternary message passing (TMP) for spectrally efficient communication with higher-order modulation and probabilistic amplitude shaping (PAS). To showcase the potential for high throughput forward error correction, spatially coupled LDPC codes and a target spectral efficiency (SE) of 3 bits/QAM symbol are considered. Gains of about 0.7 dB and 0.1 dB are observed compared to BMP and TMP, respectively. The gap to unquantized belief propagation (BP) decoding is reduced to about 0.75 dB. For smaller code rates, the gain of QMP compared to TMP is more pronounced and amounts to 0.24 dB in the considered example., Accepted for IEEE/OSA Journal on Lightwave Technology

This paper presents a system calibration method for a trifocal sensor, which is sensitive to different spectral bands. The trifocal camera system consists of a stereo camera, operating in the visual (VIS) spectrum and a thermal imaging camera, operating in the Long-Wave- Infrared (LWIR) spectrum. Intrinsic parameters and spatial alignment are determined simultaneously. As calibration target a passive aluminium chessboard is used. Corner detection and subsequent bundle adjustment is done on all synchronized image triplets. The remaining reprojection errors are in the sub-pixel range and enable the system to generate metric point clouds, colored with thermal intensities in real-time.

In this paper, we introduce the problem of cross-modal visuo-tactile object recognition with robotic active exploration. With this term, we mean that the robot observes a set of objects with visual perception, and later on, it is able to recognize such objects only with tactile exploration, without having touched any object before. Using a machine learning terminology, in our application, we have a visual training set and a tactile test set, or vice versa. To tackle this problem, we propose an approach constituted by four steps: finding a visuo-tactile common representation, defining a suitable set of features, transferring the features across the domains, and classifying the objects. We show the results of our approach using a set of 15 objects, collecting 40 visual examples and five tactile examples for each object. The proposed approach achieves an accuracy of 94.7%, which is comparable with the accuracy of the monomodal case, i.e., when using visual data both as training set and test set. Moreover, it performs well compared to the human ability, which we have roughly estimated carrying out an experiment with ten participants.

Most travel demand models simplify the representation of trips from/to external zones. Despite the higher frequency of internal daily travel, long-distance travel cannot be neglected due to its high contribution to the travelled distance. In this paper we integrate two different models that represent those two travel segments. MITO is an agent-based model for travel demand implemented in the metropolitan area of Munich. DEMO is a macroscopic model that generates travel demand in Germany. DEMO is used to represent the external, long-distance travel from, to or through the MITO study area. DEMO demand is disaggregated and is jointly assigned with MITO trips using the transport model MATSim. The integration significantly improves the validation of the model against traffic counts and facilitates the simulation capabilities when the scenarios affect major roads.

With air transportation growing and current civil aeronautical communication systems reaching their capacity limit in high density areas, the need for new aeronautical communication technologies becomes apparent. The biggest challenge in recent years is the transition from analogue voice to digital data communication and the related trend towards an increased autonomous data processing. A promising candidate for the digital future communication infrastructure in continental areas is the terrestrial long-range L-band Digital Aeronautical Communications System (LDACS), which is currently in the process of being standardized by the International Civil Aviation Organization (ICAO). As safety and security are strongly intertwined in civil aviation, every installation of LDACS requires protection against cyber-attacks. This paper introduces a cybersecurity architecture for LDACS and proposes suitable security algorithm, which can achieve the security objectives on top of the architecture. Therefore we integrate new security functions within the existing protocol stack of LDACS. We provide an architecture for user data encryption, data integrity, authenticated key agreement, entity authentication, broadcast channel protection, and key and access management.

Summary Breast cancer is significant health problem diagnosed mostly in women worldwide. Therefore, early detection of breast cancer is performed with the help of digital mammography, which can reduce mortality rate. This paper presents wrapper based feature selection approach for wavelet co-occurrence feature (WCF) using Genetic Fuzzy System (GFS) in mammogram classification problem. The performance of GFS algorithm is explained using mini-MIAS database. WCF features are obtained from detail wavelet coefficients at each level of decomposition of mammogram image. At first level of decomposition, 18 features are applied to GFS algorithm, which selects 5 features with an average classification success rate of 39.64%. Subsequently, at second level it selects 9 features from 36 features and the classification success rate is improved to 56.75%. For third level, 16 features are selected from 54 features and average success rate is improved to 64.98%. Lastly, at fourth level 72 features are applied to GFS, which selects 16 features and thereby increasing average success rate to 89.47%. Hence, GFS algorithm is the effective way of obtaining optimal set of feature in breast cancer diagnosis.

Summary In this paper, we proposed superscalar pipelined inner product computation unit for signed-unsigned number operating at 16 GHz. This is designed using five stage pipelined operation with four 8 × 8 multipliers operating in parallel. Superscalar pipelined is designed to compute four 8 × 8 products in parallel in three clock cycles. In the fourth clock cycle of the pipeline operation, two inner products are computed using two adders in parallel. Fifth stage of the pipeline is designed to compute the final product by adding two inner partial products. Upon the pipeline is filled up, every clock cycle the new product of 16 × 16-bit signed unsigned number is obtained. The worst delay measured among the pipeline stage is 0.062 ns, and this delay is considered as the clock cycle period. With the delay of 0.062 ns clock cycle period, the pipeline stage can be operated with 16 GHz synchronous clock signal. Each superscalar pipeline stage is implemented using 45 nm CMOS process technology, and the comparison of results shows that the delay is decreased by 38%, area is reduced by 45% and power dissipation is saved by 32%.

Summary This paper presents a technique that aimed to accomplish an efficient balance between video compression using H.265 protocol and retention of 8K resolution. The study implements multi-level of optimization in the encoding process using H.265 where JPEG2000 standards play a crucial role. The study also applies a novel concept of orthogonal projection that manages pixels metadata required in every frame transition followed by motion compensation. By using multiple file formats of 30 video datasets, the outcome of the study is found to be accomplishing approximately 49% of enhancement in data quality and around 59% of improvement in video compression in comparison to the existing techniques of HEVC-based video compression.

Summary In this paper, we present a software tool capable of predicting the final quality of triple play services by using the most common assessment metrics. The quality of speech and video in network environment is a growing concern of all the internet service providers to carry the multimedia traffic without the excessive delays and losses, which degrade the quality of multimedia as it is perceived by the end users. Prediction mathematical model is based on results obtained from many performed testing scenarios simulating real behavior in the network. Based on the proposed model, speech or video quality is calculated with regard to policies applied for packet processing by routers and to the level of total network utilization. The application cannot only predict QoS parameters but also generate the source code of particular QoS policy setting according to the user interaction and apply the policy to the routers in the network. Contribution of the work consists of a new software tool enables network administrators and designers to improve and optimize network traffic efficiently.

Summary A cellular neural network (CNN) is a massively parallel analog array processor capable of solving various complex processing problems by using specific templates that characterize the synaptic connections. The hardware implementation and applications of CNN have attracted a great deal of attention. Recently, memristors with nanometer-scale and variable gradual conductance have been exploited to make compact and programmable electric synapses. This paper proposes and studies a novel memristive CNN (Mt-CNN) with time-variant templates realized by memristor crossbar synaptic circuits. The template parameters are estimated analytically. The Mt-CNN provides a promising solution to hardware realization of real-time template updating processes, which can be used to effectively deal with various complicated problems of cascaded processing. Its effectiveness and advantages are demonstrated by practical examples of edge detection on noisy images.

Innovative products are interacting with the users based on smart sensors and algorithms. Logistics as one example is changing when automated guided vehicles are integrated in the process, supporting or even replacing workers. The way these products are interacting with humans and how they react to certain situations, will determine usability and user experience and therefore the success of use. Developing such products is based on innovative concepts that need to be evaluated and refined at early project stages. Using virtual-reality based user scenarios is one adequate option to do so. This paper describes technical as well as study-based approaches on how potential concepts are realized as virtual prototypes and evaluated by real users. It concludes with the evaluation results of a pilot study but also with general limitations and benefits as best practice advice for this kind of virtual prototyping techniques., aw&I Conference, Bd. 3 (2018): innteract2018

In this work, we propose a novel approach to prioritize the depth map computation of multi-view stereo (MVS) to obtain compact 3D point clouds of high quality and completeness at low computational cost. Our prioritization approach operates before the MVS algorithm is executed and consists of two steps. In the first step, we aim to find a good set of matching partners for each view. In the second step, we rank the resulting view clusters (i.e. key views with matching partners) according to their impact on the fulfillment of desired quality parameters such as completeness, ground resolution and accuracy. Additional to geometric analysis, we use a novel machine learning technique for training a confidence predictor. The purpose of this confidence predictor is to estimate the chances of a successful depth reconstruction for each pixel in each image for one specific MVS algorithm based on the RGB images and the image constellation. The underlying machine learning technique does not require any ground truth or manually labeled data for training, but instead adapts ideas from depth map fusion for providing a supervision signal. The trained confidence predictor allows us to evaluate the quality of image constellations and their potential impact to the resulting 3D reconstruction and thus builds a solid foundation for our prioritization approach. In our experiments, we are thus able to reach more than 70% of the maximal reachable quality fulfillment using only 5% of the available images as key views. For evaluating our approach within and across different domains, we use two completely different scenarios, i.e. cultural heritage preservation and reconstruction of single family houses., Comment: This paper was accepted to ISPRS Journal of Photogrammetry and Remote Sensing (https://www.journals.elsevier.com/isprs-journal-of-photogrammetry-and-remote-sensing) on March 21, 2018. The official version will be made available on ScienceDirect (https://www.sciencedirect.com)

Modern spaceborne synthetic aperture radar (SAR) sensors, such as TerraSAR-X/TanDEM-X and COSMO-SkyMed, can deliver very high resolution (VHR) data beyond the inherent spatial scales (on the order of 1m) of buildings, constituting invaluable data source for large-scale urban mapping. Processing this VHR data with advanced interferometric techniques, such as SAR tomography (TomoSAR), enables the generation of 3-D (or even 4-D) TomoSAR point clouds from space. In this paper, we present a novel and generic workflow that exploits these TomoSAR point clouds in a way that is capable to automatically produce benchmark annotated (buildings/non-buildings) SAR datasets. These annotated datasets (building masks) have been utilized to construct and train the state-of-the-art deep Fully Convolution Neural Networks with an additional Conditional Random Field represented as a Recurrent Neural Network to detect building regions in a single VHR SAR image. The results of building detection are illustrated and validated over TerraSAR-X VHR spotlight SAR image covering approximately 39 km2 - almost the whole city of Berlin - with mean pixel accuracies of around 93.84%.

Significantly outperforming traditional machine learning methods, deep convolutional neural networks have gained increasing popularity in the application of image classification and segmentation. Nevertheless, deep learning-based methods usually require a large amount of training data, which is quite labor-intensive and time-demanding. To deal with the problem in generating training data, we propose in this paper a novel approach to generate image annotations by transferring labels from aerial images to UAV images and refine the annotations using a densely connected CRF model with an embedded naive Bayes classifier. The generated annotations not only present correct semantic labels, but also preserve accurate class boundaries. To validate the utility of these automatic annotations, we deploy them as training data for pixel-wise image segmentation and compare the results with the segmentation using manual annotations. Experiment results demonstrate that the automatic annotations can achieve comparable segmentation accuracy as the manual annotations.

In this paper we present an approach to combine error state estimation with total state monocular simultaneous localization and mapping (SLAM) in a single Unscented Kalman Filter (UKF). The map features use the inverse depth parametrization for undelayed initialization and for the ability to use low-parallax features with unknown depth information. Furthermore, a new map feature initialization method is presented using the Unscented transform (UT). This method allows to capture all correlations between the map features and the error state variables without the necessity to calculate any Jacobian matrices.

Exploratory visual analysis is often required to assist human operator to understand and interpret Earth Observation (EO) images. Optimal image representation offers cognitive support in discovering relevant facts about the scene with respect to a particular application. This is of crucial importance for training data sets selection in all Machine Learning tasks, particularly in the design of active learning tools for multispectral (MS) EO data. This paper proposes a deep neural network (DNN) based method to compress, learn and reveal the most significant information included in the spectral bands of EO data in support of relevant visualization for image content analysis. The advanced method uses a DNN to discover the most suggestive pseudo-color representation able to highlight the entire MS image content better than the particular 3 bands selection (R, G, B). We propose the use of information theory and the concept of mutual information to rank the spectral bands based on the amount of information contained, by applying the minimum-redundancy-maximum-relevance (mRMR) criterion on a the image so that we obtain the ranked bands. A DNN stacked autoencoder based paradigm is developed in order to extract and compress in three bands the overall information from the MS EO data. The developed method is demonstrated and validated for Sentinel 2 dataset.

Ultra-wideband (UWB) is a promising technology for positioning systems that has undergone massive research development in recent years. Most UWB positioning systems assume prior knowledge on the positions of the UWB anchors. Without knowing the UWB anchor positions, an accurate position estimate of an UWB tag is difficult. Hence, this paper presents a novel cooperative pedestrian simultaneous localization and mapping (SLAM) algorithm using an UWB positioning system without the necessity to have the prior information on the UWB anchor positions. To obtain accurate estimates of the UWB anchor positions, the estimations of the UWB anchor positions are shared between different mobile units. An increased accuracy of the estimated UWB anchor position relates directly to a more accurate position estimate of the mobile unit position. We evaluate the proposed algorithm based on measurements with multiple mobile units and fixed anchors with unknown positions. The evaluations show that an accurate position estimation of both the mobile units and the anchors is possible without any prior knowledge on the UWB anchor positions.

Today’s voice-based air-ground communica-tion system for aircraft guidance is suffering from increasing saturation of the VHF band in high den-sity areas. Therefore the European Union strives for a transition from analog voice communication to more spectrum efficient digital communication. This transition shall be realized, among others, through the development and implementation of the L-band Digital Aeronautical Communications System (LDACS). In order to verify the suitability of LDACS for both communications and navigation, a flight trial campaign will be performed within the nationally funded German project MICONAV. The objective of this paper is to present the planned LDACS measurement campaign. LDACS will be validated under realistic conditions: Four ground stations transmit signals; one airborne station onboard an aircraft will receive the communication messages and additionally utilize the signals from the ground stations for navigation.

Earth observation (EO) has become a valuable source of comprehensive, reliable, and persistent information for a wide number of applications. However, dealing with the complexity of land cover is sometimes difficult, as the variety of EO sensors reflects in the multitude of details recorded in several types of image data. Their properties dictate the category and nature of the perceptible land structures. The data heterogeneity hampers proper understanding, preventing the definition of universal procedures for content exploitation. The main shortcomings are due to the different human and sensor perception on objects, as well as to the lack of coincidence between visual elements and similarities obtained by computation. In order to bridge these sensory and semantic gaps, the paper presents a compound framework for EO image information extraction. The proposed approach acts like a common ground between the user's understanding, who is visually shortsighted to the visible domain, and the machines numerical interpretation of a much wider information. A hierarchical data representation is considered. At first, basic elements are automatically computed. Then, users can enforce their judgement on the data processing results until semantic structures are revealed. This procedure completes a user-machine knowledge transfer. The interaction is formalized as a dialogue, where communication is determined by a set of parameters guiding the computational process at each level of representation. The purpose is to maintain the data-driven observable connected to the level of semantics and to human awareness. The proposed concept offers flexibility and interoperability to users, allowing them to generate those results that best fit their application scenario. The experiments performed on different satellite images demonstrate the ability to increase the performances in case of semantic annotation by adjusting a set of parameters to the particularities of the analyzed data.

Purpose Agile software development helps software producing organizations to respond to manifold challenges. While prior research focused on agility as a project or process phenomenon, the authors suggest that agility is an emergent phenomenon on the team level. The paper aims to discuss this issue. Design/methodology/approach Using the theory of complex adaptive systems (CASs), the study captures the multiple influencing levels of software development teams (SDTs) and their interplay with self-organization and emergence. The authors investigate three agile SDTs in different contextual environments that participate with four or more different roles each. Findings The results suggest self-organization as a central process when understanding team agility. While contextual factors often provide restriction on self-organization, they can help the team to enhance its autonomy. Research limitations/implications The theoretical contributions result from the development and test of theory grounded propositions and the investigation of mature agile development teams. Practical implications The findings help practitioners to improve the cost-effectiveness ratio of their team’s operations. Originality/value The study provides empirical evidence for the emergence of team agility in agile SDTs. Using the lens of CAS, the study suggests the importance of the team’s autonomy.