Form und Filter - Algorithmen der Bilderverarbeitung und Stilanalyse

Die Grundbegriffe Wölfflins lassen sich mit Hilfe digitaler Algorithmen nachmodellieren. Welches Wissen wird damit gewonnen? Auch Wölfflin hat auf mediale Veränderungen reagiert, indem er die Doppel-Projektion von Dias in den binären Differenzen der Grundbegriffe nachbildete. Sie lesen der Projektion zweier Bilder eine historische Differenz aus, die für die Disziplin der Kunstgeschichte grundlegend ist. Ein digitale Nachbildung dieser Differenz wäre tautologisch: sie würde ein gewusstes Wissen wiederholen. Fruchtbar wird der Einsatz digitaler Algorithmen dann, wenn sie nicht nur etwas bekanntes abbilden, sondern wenn man fragt, zu welcher "methodischen Grenzerweiterung" sie beitragen könnten.

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.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

An Ensemble Kalman Filter multi-tracer assimilation: Determining uncertain ocean model parameters for improved climate-carbon cycle projections

An Ensemble Kalman Filter is applied to assimilate observed tracer fields in various combinations in the Bern3D ocean model. Each tracer combination yields a set of optimal transport parameter values that are used in projections with prescribed CO2 stabilization pathways. The assimilation of temperature and salinity fields yields a too vigorous ventilation of the thermocline and the deep ocean, whereas the inclusion of CFC-11 and radiocarbon improves the representation of physical and biogeochemical tracers and of ventilation time scales. Projected peak uptake rates and cumulative uptake of CO2 by the ocean are around 20% lower for the parameters determined with CFC-11 and radiocarbon as additional target compared to those with salinity and temperature only. Higher surface temperature changes are simulated in the Greenland–Norwegian–Iceland Sea and in the Southern Ocean when CFC-11 is included in the Ensemble Kalman model tuning. These findings highlights the importance of ocean transport calibration for the design of near-term and long-term CO2 emission mitigation strategies and for climate projections.

Optimizing Models of the North Atlantic Spring Bloom Using Physical, Chemical and Bio-Optical Observations from a Lagrangian Float

The North Atlantic spring bloom is one of the main events that lead to carbon export to the deep ocean and drive oceanic uptake of CO(2) from the atmosphere. Here we use a suite of physical, bio-optical and chemical measurements made during the 2008 spring bloom to optimize and compare three different models of biological carbon export. The observations are from a Lagrangian float that operated south of Iceland from early April to late June, and were calibrated with ship-based measurements. The simplest model is representative of typical NPZD models used for the North Atlantic, while the most complex model explicitly includes diatoms and the formation of fast sinking diatom aggregates and cysts under silicate limitation. We carried out a variational optimization and error analysis for the biological parameters of all three models, and compared their ability to replicate the observations. The observations were sufficient to constrain most phytoplankton-related model parameters to accuracies of better than 15 %. However, the lack of zooplankton observations leads to large uncertainties in model parameters for grazing. The simulated vertical carbon flux at 100 m depth is similar between models and agrees well with available observations, but at 600 m the simulated flux is larger by a factor of 2.5 to 4.5 for the model with diatom aggregation. While none of the models can be formally rejected based on their misfit with the available observations, the model that includes export by diatom aggregation has a statistically significant better fit to the observations and more accurately represents the mechanisms and timing of carbon export based on observations not included in the optimization. Thus models that accurately simulate the upper 100 m do not necessarily accurately simulate export to deeper depths.

Survival effects of inferior vena cava filter in patients with acute symptomatic venous thromboembolism and a significant bleeding risk.

OBJECTIVES The purpose of this study was to investigate the survival effects of inferior vena cava filters in patients with venous thromboembolism (VTE) who had a significant bleeding risk. BACKGROUND The effectiveness of inferior vena cava filter use among patients with acute symptomatic VTE and known significant bleeding risk remains unclear. METHODS In this prospective cohort study of patients with acute VTE identified from the RIETE (Computerized Registry of Patients With Venous Thromboembolism), we assessed the association between inferior vena cava filter insertion for known significant bleeding risk and the outcomes of all-cause mortality, pulmonary embolism (PE)-related mortality, and VTE rates through 30 days after the initiation of VTE treatment. Propensity score matching was used to adjust for the likelihood of receiving a filter. RESULTS Of the 40,142 eligible patients who had acute symptomatic VTE, 371 underwent filter placement because of known significant bleeding risk. A total of 344 patients treated with a filter were matched with 344 patients treated without a filter. Propensity score-matched pairs showed a nonsignificant trend toward lower risk of all-cause death for filter insertion compared with no insertion (6.6% vs. 10.2%; p = 0.12). The risk-adjusted PE-related mortality rate was lower for filter insertion than no insertion (1.7% vs. 4.9%; p = 0.03). Risk-adjusted recurrent VTE rates were higher for filter insertion than for no insertion (6.1% vs. 0.6%; p < 0.001). CONCLUSIONS In patients presenting with VTE and with a significant bleeding risk, inferior vena cava filter insertion compared with anticoagulant therapy was associated with a lower risk of PE-related death and a higher risk of recurrent VTE. However, study design limitations do not imply a causal relationship between filter insertion and outcome.

Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.

Passively Track WiFi Users with an Enhanced Particle Filter using Power-based Ranging

Passive positioning systems produce user location information for third-party providers of positioning services. Since the tracked wireless devices do not participate in the positioning process, passive positioning can only rely on simple, measurable radio signal parameters, such as timing or power information. In this work, we provide a passive tracking system for WiFi signals with an enhanced particle filter using fine-grained power-based ranging. Our proposed particle filter provides an improved likelihood function on observation parameters and is equipped with a modified coordinated turn model to address the challenges in a passive positioning system. The anchor nodes for WiFi signal sniffing and target positioning use software defined radio techniques to extract channel state information to mitigate multipath effects. By combining the enhanced particle filter and a set of enhanced ranging methods, our system can track mobile targets with an accuracy of 1.5m for 50% and 2.3m for 90% in a complex indoor environment. Our proposed particle filter significantly outperforms the typical bootstrap particle filter, extended Kalman filter and trilateration algorithms.

Source apportionment and dynamic changes of carbonaceous aerosols during the haze bloom-decay process in China based on radiocarbon and organic molecular tracers

Fine carbonaceous aerosols (CAs) is the key factor influencing the currently filthy air in megacities in China, yet few studies simultaneously focus on the origins of different CAs species using specific and powerful source tracers. Here, we present a detailed source apportionment for various CAs fractions, including organic carbon (OC), water-soluble OC (WSOC), water-insoluble OC (WIOC), elemental carbon (EC) and secondary OC (SOC) in the largest cities of North (Beijing, BJ) and South China (Guangzhou, GZ), using the measurements of radiocarbon and anhydrosugars. Results show that non-fossil fuel sources such as biomass burning and biogenic emission make a significant contribution to the total CAs in Chinese megacities: 56±4 in BJ and 46±5% in GZ, respectively. The relative contributions of primary fossil carbon from coal and liquid petroleum combustions, primary non-fossil carbon and secondary organic carbon (SOC) to total carbon are 19, 28 and 54% in BJ, and 40, 15 and 46% in GZ, respectively. Non-fossil fuel sources account for 52 in BJ and 71% in GZ of SOC, respectively. These results suggest that biomass burning has a greater influence on regional particulate air pollution in North China than in South China. We observed an unabridged haze bloom-decay process in South China, which illustrates that both primary and secondary matter from fossil sources played a key role in the blooming phase of the pollution episode, while haze phase is predominantly driven by fossil-derived secondary organic matter and nitrate.

Interannual Variability in Timing of Bloom Initiation in the California Current System

In the California Current System the spring transition from poleward to equatorward alongshore wind stress heralds the beginning of upwelling-favorable conditions. The phytoplankton response to this transition is investigated using 8 years ( 1998-2005) of daily, 4-km resolution, Sea-viewing Wide Field of view Sensor ( SeaWiFS) chlorophyll a concentration data. Cluster analysis of the chlorophyll a time series at each location is used to separate the inshore upwelling region from offshore and oligotrophic areas. An objective method for estimating the timing of bloom initiation is used to construct a map of the mean bloom start date. Interannual variability in bloom timing and magnitude is investigated in four regions: 45 degrees N - 50 degrees N, 40 degrees N - 45 degrees N, 35 degrees N - 40 degrees N and 20 degrees N - 35 degrees N. Daily satellite derived wind data ( QuikSCAT) allow the timing of the first episode of persistently upwelling favorable winds to be estimated. Bloom initiation generally coincides with the onset of upwelling winds ( +/- 15 days). South of similar to 35 degrees N, where winds are southward year-round, the timing of increased chlorophyll concentration corresponds closely to timing of the seasonal increase in upwelling intensity. A 1-D model and satellite derived photosynthetically available radiation data are used to estimate time series of depth- averaged irradiance. In the far north of the region (> 46 degrees N) light is shown to limit phytoplankton growth in early spring. In 2005 the spring bloom in the northern regions (> 35 degrees N) had a "false start''. A sharp increase in chl a in February quickly receded, and a sustained increase in biomass was delayed until July. We hypothesize that this resulted in a mismatch in timing of food availability to higher trophic levels.

Effects of Surface Forcing on Interannual Variability of the Fall Phytoplankton Bloom in the Gulf of Maine Revealed Using a Process-Oriented Model

SeaWiFS (Sea-viewing Wide Field-of-view Sensor) chlorophyll data revealed strong interannual variability in fall phytoplankton dynamics in the Gulf of Maine, with 3 general features in any one year: (1) rapid chlorophyll increases in response to storm events in fall; (2) gradual chlorophyll increases in response to seasonal wind-and cooling-induced mixing that gradually deepens the mixed layer; and (3) the absence of any observable fall bloom. We applied a mixed-layer box model and a 1-dimensional physical-biological numerical model to examine the influence of physical forcing (surface wind, heat flux, and freshening) on the mixed-layer dynamics and its impact on the entrainment of deep-water nutrients and thus on the appearance of fall bloom. The model results suggest that during early fall, the surface mixed-layer depth is controlled by both wind-and cooling-induced mixing. Strong interannual variability in mixed-layer depth has a direct impact on short-and long-term vertical nutrient fluxes and thus the fall bloom. Phytoplankton concentrations over time are sensitive to initial pre-bloom profiles of nutrients. The strength of the initial stratification can affect the modeled phytoplankton concentration, while the timing of intermittent freshening events is related to the significant interannual variability of fall blooms.

Ein Tropfapparat verbunden mit Filter für Seewasseraquarien

Von H. Barfod

EVALUATION OF THE EFFECTIVENESS OF ANISOTROPIC ANALYTICAL ALGORITHM IN FLATTENED AND FLATTENING-FILTER-FREE BEAMS FOR HIGH ENERGY LUNG DOSE DELIVERY USING THE RADIOLOGICAL PHYSICS CENTER LUNG PHANTOM

The effectiveness of the Anisotropic Analytical Algorithm (AAA) implemented in the Eclipse treatment planning system (TPS) was evaluated using theRadiologicalPhysicsCenteranthropomorphic lung phantom using both flattened and flattening-filter-free high energy beams. Radiation treatment plans were developed following the Radiation Therapy Oncology Group and theRadiologicalPhysicsCenterguidelines for lung treatment using Stereotactic Radiation Body Therapy. The tumor was covered such that at least 95% of Planning Target Volume (PTV) received 100% of the prescribed dose while ensuring that normal tissue constraints were followed as well. Calculated doses were exported from the Eclipse TPS and compared with the experimental data as measured using thermoluminescence detectors (TLD) and radiochromic films that were placed inside the phantom. The results demonstrate that the AAA superposition-convolution algorithm is able to calculate SBRT treatment plans with all clinically used photon beams in the range from 6 MV to 18 MV. The measured dose distribution showed a good agreement with the calculated distribution using clinically acceptable criteria of ±5% dose or 3mm distance to agreement. These results show that in a heterogeneous environment a 3D pencil beam superposition-convolution algorithms with Monte Carlo pre-calculated scatter kernels, such as AAA, are able to reliably calculate dose, accounting for increased lateral scattering due to the loss of electronic equilibrium in low density medium. The data for high energy plans (15 MV and 18 MV) showed very good tumor coverage in contrast to findings by other investigators for less sophisticated dose calculation algorithms, which demonstrated less than expected tumor doses and generally worse tumor coverage for high energy plans compared to 6MV plans. This demonstrates that the modern superposition-convolution AAA algorithm is a significant improvement over previous algorithms and is able to calculate doses accurately for SBRT treatment plans in the highly heterogeneous environment of the thorax for both lower (≤12 MV) and higher (greater than 12 MV) beam energies.

Thinning Scab-resistant Apples with Liquid Lime Sulfur Sprays during Bloom

Growing scab-resistant apple cultivars on fully dwarfing rootstocks increases the feasibility for producing organically grown apples in the Midwest. However, in an organic orchard, fruit thinning to optimize crop load must be done by hand at a very high labor expense. The alternative is biennial bearing and inconsistent supply to meet consumer demands. Recently, sprays containing organicapproved materials such as liquid lime sulfur, fish, and various vegetable oils, salts, and kaolin have been tried alone or in combination for thinning apples with some degree of success. Lime sulfur alone or in combination with spray oil was recently labeled for use in Washington state orchards. This study was undertaken to evaluate the effectiveness of liquid lime sulfur alone and in combination with spray oil applied at various times during bloom on thinning three scab-resistant apple cultivars under Iowa conditions.