Pollen profile from sediment core Höllerer See

(expanded by Eberhard Grüger, Göttingen) The site "Höllerer See" is a lake in the northern foreland of the Alps, about 30 km north of the city of Salzburg/Austria, situated in the south-western part of Oberösterreich/Austria. A 2 m long piston core from this locality, consisting entirely of calcareous gyttja, was studied by pollen analysis. The three lowermost samples (1.98, 1.95 and 1.92 m) were deposited during the Preboreal when Pinus and Betula were still the dominating forest trees. High pollen values of thermophilous woody species (mainly Corylus and Quercus, but also Ulmus, Tilia, Fraxinus) prove the Boreal age of the next younger sample (1.91 m). The following two pollen spectra attest that Alnus (1.89 m) and - later (1.88 m) - Fagus had become important members of the local (Alnus) and the regional (Fagus) vegetation. From this level up to the top of the profile these two tree taxa contribute - together with Betula - always 50 to 80 % to the arboreal pollen sum. The upper 1.89 m of sediment of the Höllerer See core evidently date from the Subboreal and the Subatlantic. As Preboreal sediment was stated at the base of the profile it must be concluded that most of the Boreal and the Atlantic is - for whatever reason - not represented by sediment in this core. As no radiocarbon dates are available age estimates of the distinguished pollen zones can be achieved only by correlating major changes of the former vegetation with historical events which probably influenced the then contemporary vegetation. The pollen grains of the Triticum and Hordeum type found in samples of zone 2.1 might indicate the growing of cereals in the region during the Late Bronze Age. The first pollen grains of Secale date from the boundary Hallstatt/Latène Age (zone 2.2). The cereal curves become continuous in Bavarian times (Bajuwarenzeit, Middle Ages, zone 3.3). The Plantago laceolata curve, continuous since 1.7 m depth (zone 2.1), points to animal breeding since the Early Subatlantic (Hallstattzeit). This curve reaches its absolute maximum in Roman time (zone 3.1). Roman time forest clearance caused a drastic decrease of tree pollen curves (start of zone 3.1). Values of anthropogenic indicators as high as in zone 3.1 are found again - after a distinct decrease in zone 3.2 - not till the Bavarians settled in the region (6th century). Maximal Fagus values and the simultaneous total lack of anthropogenic indicators mark the Migration Period (zone 3.2). The Younger Subatlantic (zone 4) is characterized by a decrease of deciduous forests due to medieval forest clearance. At the same time the conifers Pinus and Picea gained in importance. The lake was probably used for retting hemp in Medieval times. The distinction of the pollen grains of Cannabis and Humulus might not be certain in all cases. It is known that hemp as well as hop was cultivated in the study area. Markers were added to the samples at the beginning of pollen preparation (13500 Lycopodium spores, sample volume 0.5 cm**3) and counted together with the pollen grains. Therefore pollen concentrations can be calculated: Concentration = C * F / V (with C = number of grains of a particular pollen type, V = volume of the untreated pollen sample, F = marker added/marker counted). F ranges from 39 to 1688. Factors that large are not suited to produce reliably interpretable pollen concentrations. Consequently no use was made of the pollen concentrations in this thesis, although a concentration diagram is added.

Omnidirectional bearing-only see-and-avoid for small aerial robots:

Motivated by the growing interest in unmanned aerial system's applications in indoor and outdoor settings and the standardisation of visual sensors as vehicle payload. This work presents a collision avoidance approach based on omnidirectional cameras that does not require the estimation of range between two platforms to resolve a collision encounter. It will achieve a minimum separation between the two vehicles involved by maximising the view-angle given by the omnidirectional sensor. Only visual information is used to achieve avoidance under a bearing-only visual servoing approach. We provide theoretical problem formulation, as well as results from real flight using small quadrotors

Cross-entropy optimization for scaling factors of a fuzzy controller: a see-and-avoid approach for unmanned aerial systems

The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.

See-and-avoid quadcopter using fuzzy control optimized by cross-entropy

In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of crossentropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights.

Quadcopter see and avoid using a fuzzy controller

Unmanned Aerial Vehicles (UAVs) industry is a fast growing sector. Nowadays, the market offers numerous possibilities for off-the-shelf UAVs such as quadrotors or fixed-wings. Until UAVs demonstrate advance capabilities such as autonomous collision avoidance they will be segregated and restricted to flight in controlled environments. This work presents a visual fuzzy servoing system for obstacle avoidance using UAVs. To accomplish this task we used the visual information from the front camera. Images are processed off-board and the result send to the Fuzzy Logic controller which then send commands to modify the orientation of the aircraft. Results from flight test are presented with a commercial off-the-shelf platform.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand- avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions.

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand-avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

Using the Cross-Entropy Method for Control Optimization: A Case Study of See-and-Avoid on Unmanned Aerial Vehicles

This paper presents an adaptation of the Cross-Entropy (CE) method to optimize fuzzy logic controllers. The CE is a recently developed optimization method based on a general Monte-Carlo approach to combinatorial and continuous multi-extremal optimization and importance sampling. This work shows the application of this optimization method to optimize the inputs gains, the location and size of the different membership functions' sets of each variable, as well as the weight of each rule from the rule's base of a fuzzy logic controller (FLC). The control system approach presented in this work was designed to command the orientation of an unmanned aerial vehicle (UAV) to modify its trajectory for avoiding collisions. An onboard looking forward camera was used to sense the environment of the UAV. The information extracted by the image processing algorithm is the only input of the fuzzy control approach to avoid the collision with a predefined object. Real tests with a quadrotor have been done to corroborate the improved behavior of the optimized controllers at different stages of the optimization process.

gamma/delta and other unconventional T lymphocytes: what do they see and what do they do?

T lymphocytes recognize specific ligands by clonally distributed T-cell receptors (TCR). In humans and most animals, the vast majority of T cells express a TCR composed of an alpha chain and a beta chain, whereas a minor T-cell population is characterized by the TCR gamma/delta. Almost all of our knowledge about T cells stems from alpha/beta T cells and only now are we beginning to understand gamma/delta T cells. In contrast to conventional alpha/beta T cells, which are specific for antigenic peptides presented by gene products of the major histocompatibility complex, gamma/delta T cells directly recognize proteins and even nonproteinacious phospholigands. These findings reveal that gamma/delta T cells and alpha/beta T cells recognize antigen in a fundamentally different way and hence mitigate the dogma of exclusive peptide-major histocompatibility complex recognition by T cells. A role for gamma/delta T cells in antimicrobial immunity has been firmly established. Although some gamma/delta T cells perform effector functions, regulation of the professional and the nonprofessional immune system seems to be of at least equal importance. The prominent residence of gamma/delta T cells in epithelial tissues and the rapid mobilization of gamma/delta T cells in response to infection are consistent with such regulatory activities under physiological and pathologic conditions. Thus, although gamma/delta T cells are a minor fraction of all T cells, they are not just uninfluential kin of alpha/beta T cells but have their unique raison d'être.

Lack of bipolar see-saw in response to Southern Ocean wind reduction

A cessation of the Atlantic meridional overturning circulation (AMOC) significantly reduces northward oceanic heat transport. In response to anomalous freshwater flux, this leads to the classic 'bipolar see-saw' pattern of northern cooling and southern warming in surface air and ocean temperatures. By contrast, as shown here in a coupled climate model, both northern and southern cooling are observed for an AMOC reduction in response to reduced wind stress in the Southern Ocean (SO). For very weak SO wind stress, not only the overturning circulation collapses, but sea ice export from the SO is strongly reduced. Consequently, sea ice extent and albedo increase in this region. The resulting cooling overcompensates the warming by the reduced northward heat transport. The effect depends continuously on changes in wind stress and is reversed for increased winds. It may have consequences for abrupt climate change, the last deglaciation and climate sensitivity to increasing atmospheric CO_2 concentration.