The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

Subject-specific musculoskeletal models of the lower limbs for the prediction of skeletal loads during motion

The determination of skeletal loading conditions in vivo and their relationship to the health of bone tissues, remain an open question. Computational modeling of the musculoskeletal system is the only practicable method providing a valuable approach to muscle and joint loading analyses, although crucial shortcomings limit the translation process of computational methods into the orthopedic and neurological practice. A growing attention focused on subject-specific modeling, particularly when pathological musculoskeletal conditions need to be studied. Nevertheless, subject-specific data cannot be always collected in the research and clinical practice, and there is a lack of efficient methods and frameworks for building models and incorporating them in simulations of motion. The overall aim of the present PhD thesis was to introduce improvements to the state-of-the-art musculoskeletal modeling for the prediction of physiological muscle and joint loads during motion. A threefold goal was articulated as follows: (i) develop state-of-the art subject-specific models and analyze skeletal load predictions; (ii) analyze the sensitivity of model predictions to relevant musculotendon model parameters and kinematic uncertainties; (iii) design an efficient software framework simplifying the effort-intensive phases of subject-specific modeling pre-processing. The first goal underlined the relevance of subject-specific musculoskeletal modeling to determine physiological skeletal loads during gait, corroborating the choice of full subject-specific modeling for the analyses of pathological conditions. The second goal characterized the sensitivity of skeletal load predictions to major musculotendon parameters and kinematic uncertainties, and robust probabilistic methods were applied for methodological and clinical purposes. The last goal created an efficient software framework for subject-specific modeling and simulation, which is practical, user friendly and effort effective. Future research development aims at the implementation of more accurate models describing lower-limb joint mechanics and musculotendon paths, and the assessment of an overall scenario of the crucial model parameters affecting the skeletal load predictions through probabilistic modeling.

Advanced modelling of time domain electromagnetic data with updated hydrogeological interpretations

Several countries have acquired, over the past decades, large amounts of area covering Airborne Electromagnetic data. Contribution of airborne geophysics has dramatically increased for both groundwater resource mapping and management proving how those systems are appropriate for large-scale and efficient groundwater surveying. We start with processing and inversion of two AEM dataset from two different systems collected over the Spiritwood Valley Aquifer area, Manitoba, Canada respectively, the AeroTEM III (commissioned by the Geological Survey of Canada in 2010) and the “Full waveform VTEM” dataset, collected and tested over the same survey area, during the fall 2011. We demonstrate that in the presence of multiple datasets, either AEM and ground data, due processing, inversion, post-processing, data integration and data calibration is the proper approach capable of providing reliable and consistent resistivity models. Our approach can be of interest to many end users, ranging from Geological Surveys, Universities to Private Companies, which are often proprietary of large geophysical databases to be interpreted for geological and\or hydrogeological purposes. In this study we deeply investigate the role of integration of several complimentary types of geophysical data collected over the same survey area. We show that data integration can improve inversions, reduce ambiguity and deliver high resolution results. We further attempt to use the final, most reliable output resistivity models as a solid basis for building a knowledge-driven 3D geological voxel-based model. A voxel approach allows a quantitative understanding of the hydrogeological setting of the area, and it can be further used to estimate the aquifers volumes (i.e. potential amount of groundwater resources) as well as hydrogeological flow model prediction. In addition, we investigated the impact of an AEM dataset towards hydrogeological mapping and 3D hydrogeological modeling, comparing it to having only a ground based TEM dataset and\or to having only boreholes data.

An Integrated Transmission-Media Noise Calibration Software For Deep-Space Radio Science Experiments

The thesis describes the implementation of a calibration, format-translation and data conditioning software for radiometric tracking data of deep-space spacecraft. All of the available propagation-media noise rejection techniques available as features in the code are covered in their mathematical formulations, performance and software implementations. Some techniques are retrieved from literature and current state of the art, while other algorithms have been conceived ex novo. All of the three typical deep-space refractive environments (solar plasma, ionosphere, troposphere) are dealt with by employing specific subroutines. Specific attention has been reserved to the GNSS-based tropospheric path delay calibration subroutine, since it is the most bulky module of the software suite, in terms of both the sheer number of lines of code, and development time. The software is currently in its final stage of development and once completed will serve as a pre-processing stage for orbit determination codes. Calibration of transmission-media noise sources in radiometric observables proved to be an essential operation to be performed of radiometric data in order to meet the more and more demanding error budget requirements of modern deep-space missions. A completely autonomous and all-around propagation-media calibration software is a novelty in orbit determination, although standalone codes are currently employed by ESA and NASA. The described S/W is planned to be compatible with the current standards for tropospheric noise calibration used by both these agencies like the AMC, TSAC and ESA IFMS weather data, and it natively works with the Tracking Data Message file format (TDM) adopted by CCSDS as standard aimed to promote and simplify inter-agency collaboration.

Software zur automatischen Tumordiagnose in der funktionalen MRT der weiblichen Brust

In den westlichen Industrieländern ist das Mammakarzinom der häufigste bösartige Tumor der Frau. Sein weltweiter Anteil an allen Krebserkrankungen der Frau beläuft sich auf etwa 21 %. Inzwischen ist jede neunte Frau bedroht, während ihres Lebens an Brustkrebs zu erkranken. Die alterstandardisierte Mortalitätrate liegt derzeit bei knapp 27 %.rnrnDas Mammakarzinom hat eine relative geringe Wachstumsrate. Die Existenz eines diagnostischen Verfahrens, mit dem alle Mammakarzinome unter 10 mm Durchmesser erkannt und entfernt werden, würden den Tod durch Brustkrebs praktisch beseitigen. Denn die 20-Jahres-Überlebungsrate bei Erkrankung durch initiale Karzinome der Größe 5 bis 10 mm liegt mit über 95 % sehr hoch.rnrnMit der Kontrastmittel gestützten Bildgebung durch die MRT steht eine relativ junge Untersuchungsmethode zur Verfügung, die sensitiv genug zur Erkennung von Karzinomen ab einer Größe von 3 mm Durchmesser ist. Die diagnostische Methodik ist jedoch komplex, fehleranfällig, erfordert eine lange Einarbeitungszeit und somit viel Erfahrung des Radiologen.rnrnEine Computer unterstützte Diagnosesoftware kann die Qualität einer solch komplexen Diagnose erhöhen oder zumindest den Prozess beschleunigen. Das Ziel dieser Arbeit ist die Entwicklung einer vollautomatischen Diagnose Software, die als Zweitmeinungssystem eingesetzt werden kann. Meines Wissens existiert eine solche komplette Software bis heute nicht.rnrnDie Software führt eine Kette von verschiedenen Bildverarbeitungsschritten aus, die dem Vorgehen des Radiologen nachgeahmt wurden. Als Ergebnis wird eine selbstständige Diagnose für jede gefundene Läsion erstellt: Zuerst eleminiert eine 3d Bildregistrierung Bewegungsartefakte als Vorverarbeitungsschritt, um die Bildqualität der nachfolgenden Verarbeitungsschritte zu verbessern. Jedes kontrastanreichernde Objekt wird durch eine regelbasierte Segmentierung mit adaptiven Schwellwerten detektiert. Durch die Berechnung kinetischer und morphologischer Merkmale werden die Eigenschaften der Kontrastmittelaufnahme, Form-, Rand- und Textureeigenschaften für jedes Objekt beschrieben. Abschließend werden basierend auf den erhobenen Featurevektor durch zwei trainierte neuronale Netze jedes Objekt in zusätzliche Funde oder in gut- oder bösartige Läsionen klassifiziert.rnrnDie Leistungsfähigkeit der Software wurde auf Bilddaten von 101 weiblichen Patientinnen getested, die 141 histologisch gesicherte Läsionen enthielten. Die Vorhersage der Gesundheit dieser Läsionen ergab eine Sensitivität von 88 % bei einer Spezifität von 72 %. Diese Werte sind den in der Literatur bekannten Vorhersagen von Expertenradiologen ähnlich. Die Vorhersagen enthielten durchschnittlich 2,5 zusätzliche bösartige Funde pro Patientin, die sich als falsch klassifizierte Artefakte herausstellten.rn

Ansätze zur Verbesserung von Qualität und Wirtschaftlichkeit bei generativen Verfahren durch Optimierung des Pre-Processes

Mittels generativer Fertigung ist es heute möglich die, Entwicklungszeit und Ferti-gungsdauer von Prototypen, Produkten und Werkzeugen zu verkürzen. Neben dieser Zeitersparnis sind die im Vergleich zu konventionellen Fertigungsverfahren unwe-sentlichen Geometriebeschränkungen für den Anwender von besonderem Interesse. Dieses Alleinstellungsmerkmal der generativen Fertigung macht es möglich auch komplexe Geometrie wirtschaftlich herzustellen. Voraussetzung für eine wirtschaftli-che und fehlerminimierte Fertigung ist hierbei eine möglichst optimale Prozessvorbe-reitung (Pre-Processing). Dabei sind insbesondere die Schritte der Bauteilorientie-rung, der Stützkonstruktionserzeugung, der Schichtzerlegung sowie der Bauraum-ausnutzung von Interesse. Auch wenn diese Punkte wesentlich zur Qualität und Wirtschaftlichkeit beitragen, sind die Erkenntnisse für den unerfahrenen Anwender nur unzureichend dokumentiert, wodurch eine möglichst effiziente Fertigung zu-nächst ausgeschlossen werden kann. Anhand unterschiedlicher Beispiele sollen dem Anwender hier die Möglichkeiten zur Optimierung dieser Pre-Processing Schritte er-läutert werden. In diesem Rahmen werden die aktuellen Forschungsergebnisse des Lehrstuhls Rechnereinsatz in der Konstruktion, Institut für Produkt Engineering der Universität Duisburg-Essen in Bezug auf die Optimierung der Bauteilorientierung, der variablen Schichtzerlegung und der Optimierung der Bauraumausnutzung vorgestellt.

A Skull-Stripping Filter for ITK

Skull-stripping (or brain extraction) is an important pre-processing step in neuroimage analysis. This document describes a skull-stripping filter implemented using the Insight Toolkit ITK, which we named itk::StripTsImageFilter. It is a composite filter based on existing ITK classes. The filter has been implemented with usability, robustness, speed and versatility in mind, rather than accuracy. This makes it useful for many pre-processing tasks in neuroimage analysis. This paper is accompanied by the source code, input data and a testing environment.

Approximate decoding approaches for network coded correlated data

This paper considers a framework where data from correlated sources are transmitted with the help of network coding in ad hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth variations. We show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples showing how the proposed algorithm can be deployed in sensor networks and distributed imaging applications.

EARLINET: potential operationality of a research network

In the framework of ACTRIS (Aerosols, Clouds, and Trace Gases Research Infrastructure Network) summer 2012 measurement campaign (8 June–17 July 2012), EARLINET organized and performed a controlled exercise of feasibility to demonstrate its potential to perform operational, coordinated measurements and deliver products in near-real time. Eleven lidar stations participated in the exercise which started on 9 July 2012 at 06:00 UT and ended 72 h later on 12 July at 06:00 UT. For the first time, the single calculus chain (SCC) – the common calculus chain developed within EARLINET for the automatic evaluation of lidar data from raw signals up to the final products – was used. All stations sent in real-time measurements of a 1 h duration to the SCC server in a predefined netcdf file format. The pre-processing of the data was performed in real time by the SCC, while the optical processing was performed in near-real time after the exercise ended. 98 and 79 % of the files sent to SCC were successfully pre-processed and processed, respectively. Those percentages are quite large taking into account that no cloud screening was performed on the lidar data. The paper draws present and future SCC users' attention to the most critical parameters of the SCC product configuration and their possible optimal value but also to the limitations inherent to the raw data. The continuous use of SCC direct and derived products in heterogeneous conditions is used to demonstrate two potential applications of EARLINET infrastructure: the monitoring of a Saharan dust intrusion event and the evaluation of two dust transport models. The efforts made to define the measurements protocol and to configure properly the SCC pave the way for applying this protocol for specific applications such as the monitoring of special events, atmospheric modeling, climate research and calibration/validation activities of spaceborne observations.

Evaluation of 3-dimensional superimposition techniques on various skeletal structures of the head using surface models.

OBJECTIVES To test the applicability, accuracy, precision, and reproducibility of various 3D superimposition techniques for radiographic data, transformed to triangulated surface data. METHODS Five superimposition techniques (3P: three-point registration; AC: anterior cranial base; AC + F: anterior cranial base + foramen magnum; BZ: both zygomatic arches; 1Z: one zygomatic arch) were tested using eight pairs of pre-existing CT data (pre- and post-treatment). These were obtained from non-growing orthodontic patients treated with rapid maxillary expansion. All datasets were superimposed by three operators independently, who repeated the whole procedure one month later. Accuracy was assessed by the distance (D) between superimposed datasets on three form-stable anatomical areas, located on the anterior cranial base and the foramen magnum. Precision and reproducibility were assessed using the distances between models at four specific landmarks. Non parametric multivariate models and Bland-Altman difference plots were used for analyses. RESULTS There was no difference among operators or between time points on the accuracy of each superimposition technique (p>0.05). The AC + F technique was the most accurate (D<0.17 mm), as expected, followed by AC and BZ superimpositions that presented similar level of accuracy (D<0.5 mm). 3P and 1Z were the least accurate superimpositions (0.790.05), the detected structural changes differed significantly between different techniques (p<0.05). Bland-Altman difference plots showed that BZ superimposition was comparable to AC, though it presented slightly higher random error. CONCLUSIONS Superimposition of 3D datasets using surface models created from voxel data can provide accurate, precise, and reproducible results, offering also high efficiency and increased post-processing capabilities. In the present study population, the BZ superimposition was comparable to AC, with the added advantage of being applicable to scans with a smaller field of view.

IBAMar DATABASE: 4 decades of oceanographic sampling on the Western Mediterranean Sea

IBAMar (http://www.ba.ieo.es/ibamar) is a regional database that puts together all physical and biochemical data obtained by multiparametric probes (CTDs equipped with different sensors), during the cruises managed by the Balearic Center of the Spanish Institute of Oceanography (COB-IEO). It has been recently extended to include data obtained with classical hydro casts using oceanographic Niskin or Nansen bottles. The result is a database that includes a main core of hydrographic data: temperature (T), salinity (S), dissolved oxygen (DO), fluorescence and turbidity; complemented by bio-chemical data: dissolved inorganic nutrients (phosphate, nitrate, nitrite and silicate) and chlorophyll-a. In IBAMar Database, different technologies and methodologies were used by different teams along the four decades of data sampling in the COB-IEO. Despite of this fact, data have been reprocessed using the same protocols, and a standard QC has been applied to each variable. Therefore it provides a regional database of homogeneous, good quality data. Data acquisition and quality control (QC): 94% of the data are CTDs Sbe911 and Sbe25. S and DO were calibrated on board using water samples, whenever a Rossetta was available (70% of the cases). All CTD data from Seabird CTDs were reviewed and post processed with the software provided by Sea-Bird Electronics. Data were averaged to get 1 dbar vertical resolution. General sampling methodology and pre processing are described in https://ibamardatabase.wordpress.com/home/). Manual QC include visual checks of metadata, duplicate data and outliers. Automatic QC include range check of variables by area (north of Balearic Islands, south of BI and Alboran Sea) and depth (27 standard levels), check for spikes and check for density inversions. Nutrients QC includes a preliminary control and a range check on the observed level of the data to detect outliers around objectively analyzed data fields. A quality flag is assigned as an integer number, depending on the result of the QC check.

Multi-temporal mapping of seagrass cover, species and biomass of the Eastern Banks, Moreton Bay, Australia, with links to shapefiles

The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100 m**2) scales. However, landscape scale (> 100 km**2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142 km**2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.

Reflectances transect from coast to open sea, Baltic Sea

In this study, retrievals of the medium resolution imaging spectrometer (MERIS) reflectances and water quality products using 4 different coastal processing algorithms freely available are assessed by comparison against sea-truthing data. The study is based on a pair-wise comparison using processor-dependent quality flags for the retrieval of valid common macro-pixels. This assessment is required in order to ensure the reliability of monitoring systems based on MERIS data, such as the Swedish coastal and lake monitoring system (http.vattenkvalitet.se). The results show that the pre-processing with the Improved Contrast between Ocean and Land (ICOL) processor, correcting for adjacency effects, improve the retrieval of spectral reflectance for all processors, Therefore, it is recommended that the ICOL processor should be applied when Baltic coastal waters are investigated. Chlorophyll was retrieved best using the FUB (Free University of Berlin) processing algorithm, although overestimations in the range 18-26.5%, dependent on the compared pairs, were obtained. At low chlorophyll concentrations (< 2.5 mg/m**3), random errors dominated in the retrievals with the MEGS (MERIS ground segment processor) processor. The lowest bias and random errors were obtained with MEGS for suspended particulate matter, for which overestimations in te range of 8-16% were found. Only the FUB retrieved CDOM (Coloured Dissolved Organic Matter) correlate with in situ values. However, a large systematic underestimation appears in the estimates that nevertheless may be corrected for by using a~local correction factor. The MEGS has the potential to be used as an operational processing algorithm for the Himmerfjärden bay and adjacent areas, but it requires further improvement of the atmospheric correction for the blue bands and better definition at relatively low chlorophyll concentrations in presence of high CDOM attenuation.