Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps

Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications. In this work, we present a methods that uses a deep convolutional neural network (CNN) to infer whether grid cells are covering a moving object or not. Compared to tracking approaches, that use e.g. a particle filter to estimate grid cell velocities and then make a decision for individual grid cells based on this estimate, our approach uses the entire grid map as input image for a CNN that inspects a larger area around each cell and thus takes the structural appearance in the grid map into account to make a decision. Compared to our reference method, our concept yields a performance increase from 83.9% to 97.2%. A runtime optimized version of our approach yields similar improvements with an execution time of just 10 milliseconds.Comment: This is a shorter version of the masters thesis of Florian Piewak and it was accapted at IV 201

Testing the detectability of spatio–temporal climate transitions from paleoclimate networks with the START model

A critical challenge in paleoclimate data analysis is the fact that the proxy data are heterogeneously distributed in space, which affects statistical methods that rely on spatial embedding of data. In the paleoclimate network approach nodes represent paleoclimate proxy time series, and links in the network are given by statistically significant similarities between them. Their location in space, proxy and archive type is coded in the node attributes. <br><br> We develop a semi-empirical model for <b>S</b>patio-<b>T</b>emporally <b>A</b>utoco<b>R</b>related <b>T</b>ime series, inspired by the interplay of different Asian Summer Monsoon (ASM) systems. We use an ensemble of transition runs of this START model to test whether and how spatio–temporal climate transitions could be detectable from (paleo)climate networks. We sample model time series both on a grid and at locations at which paleoclimate data are available to investigate the effect of the spatially heterogeneous availability of data. Node betweenness centrality, averaged over the transition region, does not respond to the transition displayed by the START model, neither in the grid-based nor in the scattered sampling arrangement. The regionally defined measures of regional node degree and cross link ratio, however, are indicative of the changes in both scenarios, although the magnitude of the changes differs according to the sampling. <br><br> We find that the START model is particularly suitable for pseudo-proxy experiments to test the technical reconstruction limits of paleoclimate data based on their location, and we conclude that (paleo)climate networks are suitable for investigating spatio–temporal transitions in the dependence structure of underlying climatic fields

Mammalian Stratum Corneum Contains Physiologic Lipid Thermal Transitions

Using a new high-sensitivity differential scanning calorimeter, capable of very slow scanning rates and large sample volumes, we examined the thermal transitions in neonatal mouse stratum corneum. Both physiological and supraphysiological transitions were found in intact tissue that were displaced on cooling and obliterated by solvent treatment establishing them as lipids. Physiologic peaks were encountered in lipid extracts from the same tissues. With heating and cooling recycling we found a novel effect of thermal “fractionation” of the peaks into discrete subfractions that appeared to correspond roughly the number of bands found on thin-layer chromatography of the lipid extracts

Comparison of correlation analysis techniques for irregularly sampled time series

Geoscientific measurements often provide time series with irregular time sampling, requiring either data reconstruction (interpolation) or sophisticated methods to handle irregular sampling. We compare the linear interpolation technique and different approaches for analyzing the correlation functions and persistence of irregularly sampled time series, as Lomb-Scargle Fourier transformation and kernel-based methods. In a thorough benchmark test we investigate the performance of these techniques. &lt;br&gt;&lt;br&gt; All methods have comparable root mean square errors (RMSEs) for low skewness of the inter-observation time distribution. For high skewness, very irregular data, interpolation bias and RMSE increase strongly. We find a 40 % lower RMSE for the lag-1 autocorrelation function (ACF) for the Gaussian kernel method vs. the linear interpolation scheme,in the analysis of highly irregular time series. For the cross correlation function (CCF) the RMSE is then lower by 60 %. The application of the Lomb-Scargle technique gave results comparable to the kernel methods for the univariate, but poorer results in the bivariate case. Especially the high-frequency components of the signal, where classical methods show a strong bias in ACF and CCF magnitude, are preserved when using the kernel methods. &lt;br&gt;&lt;br&gt; We illustrate the performances of interpolation vs. Gaussian kernel method by applying both to paleo-data from four locations, reflecting late Holocene Asian monsoon variability as derived from speleothem &amp;delta;&lt;sup&gt;18&lt;/sup&gt;O measurements. Cross correlation results are similar for both methods, which we attribute to the long time scales of the common variability. The persistence time (memory) is strongly overestimated when using the standard, interpolation-based, approach. Hence, the Gaussian kernel is a reliable and more robust estimator with significant advantages compared to other techniques and suitable for large scale application to paleo-data

Comparison of correlation analysis techniques for irregularly sampled time series

Geoscientific measurements often provide time series with irregular time sampling, requiring either data reconstruction (interpolation) or sophisticated methods to handle irregular sampling. We compare the linear interpolation technique and different approaches for analyzing the correlation functions and persistence of irregularly sampled time series, as Lomb-Scargle Fourier transformation and kernel-based methods. In a thorough benchmark test we investigate the performance of these techniques. All methods have comparable root mean square errors (RMSEs) for low skewness of the inter-observation time distribution. For high skewness, very irregular data, interpolation bias and RMSE increase strongly. We find a 40 % lower RMSE for the lag-1 autocorrelation function (ACF) for the Gaussian kernel method vs. the linear interpolation scheme,in the analysis of highly irregular time series. For the cross correlation function (CCF) the RMSE is then lower by 60 %. The application of the Lomb-Scargle technique gave results comparable to the kernel methods for the univariate, but poorer results in the bivariate case. Especially the high-frequency components of the signal, where classical methods show a strong bias in ACF and CCF magnitude, are preserved when using the kernel methods. We illustrate the performances of interpolation vs. Gaussian kernel method by applying both to paleo-data from four locations, reflecting late Holocene Asian monsoon variability as derived from speleothem δ18O measurements. Cross correlation results are similar for both methods, which we attribute to the long time scales of the common variability. The persistence time (memory) is strongly overestimated when using the standard, interpolation-based, approach. Hence, the Gaussian kernel is a reliable and more robust estimator with significant advantages compared to other techniques and suitable for large scale application to paleo-data

Networks from Flows - From Dynamics to Topology

Complex network approaches have recently been applied to continuous spatial dynamical systems, like climate, successfully uncovering the system's interaction structure. However the relationship between the underlying atmospheric or oceanic flow's dynamics and the estimated network measures have remained largely unclear. We bridge this crucial gap in a bottom-up approach and define a continuous analytical analogue of Pearson correlation networks for advection-diffusion dynamics on a background flow. Analysing complex networks of prototypical flows and from time series data of the equatorial Pacific, we find that our analytical model reproduces the most salient features of these networks and thus provides a general foundation of climate networks. The relationships we obtain between velocity field and network measures show that line-like structures of high betweenness mark transition zones in the flow rather than, as previously thought, the propagation of dynamical information

'Word from the street' : when non-electoral representative claims meet electoral representation in the United Kingdom

Taking the specific case of street protests in the UK – the ‘word from the street’– this article examines recent (re)conceptualizations of political representation, most particularly Saward’s notion of ‘representative claim’. The specific example of nonelectoral claims articulated by protestors and demonstrators in the UK is used to illustrate: the processes of making, constituting, evaluating and accepting claims for and by constituencies and audiences; and the continuing distinctiveness of claims based upon electoral representation. Two basic questions structure the analysis: first, why would the political representative claims of elected representatives trump the nonelectoral claims of mass demonstrators and, second, in what ways does the ‘perceived legitimacy’ of the former differ from the latter

On the influence of spatial sampling on climate networks

Peer reviewedPublisher PD

Synthetic and Enhanced Vision Systems for NextGen (SEVS) Simulation and Flight Test Performance Evaluation

The Synthetic and Enhanced Vision Systems for NextGen (SEVS) simulation and flight tests are jointly sponsored by NASA's Aviation Safety Program, Vehicle Systems Safety Technology project and the Federal Aviation Administration (FAA). The flight tests were conducted by a team of Honeywell, Gulfstream Aerospace Corporation and NASA personnel with the goal of obtaining pilot-in-the-loop test data for flight validation, verification, and demonstration of selected SEVS operational and system-level performance capabilities. Nine test flights (38 flight hours) were conducted over the summer and fall of 2011. The evaluations were flown in Gulfstream.s G450 flight test aircraft outfitted with the SEVS technology under very low visibility instrument meteorological conditions. Evaluation pilots flew 108 approaches in low visibility weather conditions (600 ft to 2400 ft visibility) into various airports from Louisiana to Maine. In-situ flight performance and subjective workload and acceptability data were collected in collaboration with ground simulation studies at LaRC.s Research Flight Deck simulator

Motion-Base Simulator Evaluation of an Aircraft Using an External Vision System

Twelve air transport-rated pilots participated as subjects in a motion-base simulation experiment to evaluate the use of eXternal Vision Systems (XVS) as enabling technologies for future supersonic aircraft without forward facing windows. Three head-up flight display concepts were evaluated -a monochromatic, collimated Head-up Display (HUD) and a color, non-collimated XVS display with a field-of-view (FOV) equal to and also, one significantly larger than the collimated HUD. Approach, landing, departure, and surface operations were conducted. Additionally, the apparent angle-of-attack (AOA) was varied (high/low) to investigate the vertical field-of-view display requirements and peripheral, side window visibility was experimentally varied. The data showed that lateral approach tracking performance and lateral landing position were excellent regardless of AOA, display FOV, display collimation or whether peripheral cues were present. However, the data showed glide slope approach tracking appears to be affected by display size (i.e., FOV) and collimation. The monochrome, collimated HUD and color, uncollimated XVS with Full FOV display had (statistically equivalent) glide path performance improvements over the XVS with HUD FOV display. Approach path performance results indicated that collimation may not be a requirement for an XVS display if the XVS display is large enough and employs color. Subjective assessments of mental workload and situation awareness also indicated that an uncollimated XVS display may be feasible. Motion cueing appears to have improved localizer tracking and touchdown sink rate across all displays