The role of the harmonic vector average in motion integration

The local speeds of object contours vary systematically with the cosine of the angle between the normal component of the local velocity and the global object motion direction. An array of Gabor elements whose speed changes with local spatial orientation in accordance with this pattern can appear to move as a single surface. The apparent direction of motion of plaids and Gabor arrays has variously been proposed to result from feature tracking, vector addition and vector averaging in addition to the geometrically correct global velocity as indicated by the intersection of constraints (IOC) solution. Here a new combination rule, the harmonic vector average (HVA), is introduced, as well as a new algorithm for computing the IOC solution. The vector sum can be discounted as an integration strategy as it increases with the number of elements. The vector average over local vectors that vary in direction always provides an underestimate of the true global speed. The HVA, however, provides the correct global speed and direction for an unbiased sample of local velocities with respect to the global motion direction, as is the case for a simple closed contour. The HVA over biased samples provides an aggregate velocity estimate that can still be combined through an IOC computation to give an accurate estimate of the global velocity, which is not true of the vector average. Psychophysical results for type II Gabor arrays show perceived direction and speed falls close to the IOC direction for Gabor arrays having a wide range of orientations but the IOC prediction fails as the mean orientation shifts away from the global motion direction and the orientation range narrows. In this case perceived velocity generally defaults to the HVA.

Generalized duality between local vector theories in D=2+1

The existence of an interpolating master action does not guarantee the same spectrum for the interpolated dual theories. In the specific case of a generalized self-dual (GSD) model defined as the addition of the Maxwell term to the self-dual model in D = 2 + 1, previous master actions have furnished a dual gauge theory which is either nonlocal or contains a ghost mode. Here we show that by reducing the Maxwell term to first order by means of an auxiliary field we are able to define a master action which interpolates between the GSD model and a couple of non-interacting Maxwell-Chern-Simons theories of opposite helicities. The presence of an auxiliary field explains the doubling of fields in the dual gauge theory. A generalized duality transformation is defined and both models can be interpreted as self-dual models. Furthermore, it is shown how to obtain the gauge invariant correlators of the non-interacting MCS theories from the correlators of the self-dual field in the GSD model and vice-versa. The derivation of the non-interacting MCS theories from the GSD model, as presented here, works in the opposite direction of the soldering approach.

Weight vector estimation of circular arrays of antennas using Artificial Neural Networks

This paper presents a model for the control of the radiation pattern of a circular array of antennas, shaping it to address the radiation beam in the direction of the user, in order to reduce the transmitted power and to attenuate interference. The control of the array is based on Artificial Neural Networks (ANN) of the type RBF (Radial Basis Functions), trained from samples generated by the Wiener equation. The obtained results suggest that the objective was reached.

Development of a support vector machine-based navigation system for driving a mobile robot through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features. Such features are then submitted to a support vector machine in order to find out the most appropriate route. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route. © 2012 IEEE.

Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science

Exclusive vector meson production in pp collisions at the COMPASS experiment

Produktionsmechanismen für Teilchenproduktion im mittleren Energiebereich wurden in Proton-Proton Kollisionen innerhalb der COMPASS-Kollaboration mit Hilfe des COMPASS-Spektrometers am SPS Beschleuniger am CERN untersucht. Die verschiedenen Produktionsmechanismen werden mittels Produktion der Vektormesonen omega und phi studiert und können die diffraktive Anregung des Strahlteilchens mit anschliessendem Zerfall der Resonanz, zentrale Produktion und den damit verwandten “Shake-off” Mechanismus enthalten. Die für diese Arbeit verwendeten Daten wurden in den Jahren 2008 und 2009 mit 190 GeV/c-Protonen aufgenommen, die auf ein Flüssigwasserstofftarget trafen. Das Target war von einem Rückstoßprotonendetektor umgeben, der ein integraler Bestandteil des neuentwickelten Hadrontriggersystems ist. Für dieses System wurden außerdem einige neue Detektoren gebaut. Die Leistungsfähigkeit des Rückstoßprotonendetektors und des Triggersystems wird untersucht und Effizienzen extrahiert. Außerdem wird sowohl eine Methode zur Rekonstruktion von Rückstoßprotonen als auch eine Methode zur Kalibration des Rückstoßprotonendetektors entwickelt und beschrieben. Die Produktion von omega-Mesonen wurde in der Reaktion pp -> p omega p, omega -> pi+pi-pi0 und die Produktion von phi-Mesonen in der Reaktion pp -> p phi p, phi -> K+K- bei einem Impulsübertrag zwischen 0.1 (GeV/c)^2 und 1 (GeV/c)^2 gemessen. Das Produktionsverhältnis s(pp -> p phi p)/s(pp -> p omega p) wird als Funktion des longitudinalen Impulsanteils xF bestimmt und mit der Vorhersage durch die Zweigregel verglichen. Es ergibt sich eine signifikante Verletzung der Zweigregel, die abhängig von xF ist. Die Verletzung wird in Verbindung zu resonanten Strukturen im pomega-Massenspektrum diskutiert. Die xF-Abhängigkeit verschwindet, wenn man die Region niedriger pomega- und pphi-Masse entfernt, die solche resonanten Strukturen aufweist. Zusätzlich wird die Spinausrichtung bzw. das Spindichtematrixelement rho00 für omega- und phi-Mesonen untersucht. Die Spinausrichtung wird im Helizitätssystemrnanalysiert, welches für eine Abgrenzung von resonanten, diffraktiven Anregungen geeignet ist. Außerdem wird die Spinausrichtung in einem Referenzsystem mit Bezug auf die Richtung des Impulsübertrags untersucht, mit dessen Hilfe zentrale Prozesse wie zentrale Produktion oder “shake-off” abgegrenzt werden. Auch hier wird eine Abhängigkeit von xF und der invarianten Masse des pomega-Systems beobachtet. Diese Abhängigkeit kann wieder auf die resonanten Strukturen in der Produktion von omega-Mesonen zurückgeführt werden. Die Ergebnisse werden abschließend im Hinblick auf die verschiedenen Produktionsmechanismen diskutiert.

Modelling wind direction from satellite scatterometer data

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we apply two novel techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Bend sensors with direction recognition based on long-period gratings written in D-shaped fibre

The curvature- or bend-sensing response of long-period gratings (LPGs) UV inscribed in D-shaped fiber has been investigated experimentally. Strong fiber-orientation dependence of the spectral response when such LPGs are subjected to bending at different directions has been observed and is shown to form the basis for a new class of single-device sensor with vector-sensing capability. Potential applications utilizing the linear response and unique bend-orientation characteristics of the devices are discussed.

Vector bending sensors based on fibre Bragg gratings inscribed by infrared femtosecond laser

A novel, direction-sensitive bending sensor based on an asymmetric fiber Bragg grating (FBG) inscribed by an infrared femtosecond laser was demonstrated. The technique is based on tight transverse confinement of the femto-inscribed structures and can be directly applied in conventional, untreated singlemode fibers. The FBG structure was inscribed by an amplified, titanium sapphire laser system. The grating cross-section was elongated along the direction of the laser beam with the transverse dimensions of approximately 1 by 2 μm. It was suggested that the sensitivity of the device can be improved by inscribing smaller spatial features and by implementing more complex grating designs aimed at maximizing the effect of strain.

Vector bending sensors based on fiber bragg gratings inscribed by an infrared femtosecond laser

A direction-sensitive bend sensor in standard single-mode fiber is demonstrated for the first time based on an axially-offset fiber Bragg grating, directly written by an infrared femtosecond laser.

Bend sensors with direction recognition based on long-period gratings written in D-shaped fiber

The curvature- or bend-sensing response of long-period gratings (LPGs) UV inscribed in D-shaped fiber has been investigated experimentally. Strong fiber-orientation dependence of the spectral response when such LPGs are subjected to bending at different directions has been observed and is shown to form the basis for a new class of single-device sensor with vector-sensing capability. Potential applications utilizing the linear response and unique bend-orientation characteristics of the devices are discussed.

Monitoring static shape memory polymers using a fiber Bragg grating as a vector-bending sensor

We propose and demonstrate a technique for monitoring the recovery deformation of the shape-memory polymers (SMP) using a surface-attached fiber Bragg grating (FBG) as a vector-bending sensor. The proposed sensing scheme could monitor the pure bending deformation for the SMP sample. When the SMP sample undergoes concave or convex bending, the resonance wavelength of the FBG will have red-shift or blue-shift according to the tensile or compressive stress gradient along the FBG. As the results show, the bending sensitivity is around 4.07  nm/cm−1. The experimental results clearly indicate that the deformation of such an SMP sample can be effectively monitored by the attached FBG not just for the bending curvature but also the bending direction.

A single theoretical framework for circular features processing in humans: orientation and direction of motion compared

Common computational principles underlie processing of various visual features in the cortex. They are considered to create similar patterns of contextual modulations in behavioral studies for different features as orientation and direction of motion. Here, I studied the possibility that a single theoretical framework, implemented in different visual areas, of circular feature coding and processing could explain these similarities in observations. Stimuli were created that allowed direct comparison of the contextual effects on orientation and motion direction with two different psychophysical probes: changes in weak and strong signal perception. One unique simplified theoretical model of circular feature coding including only inhibitory interactions, and decoding through standard vector average, successfully predicted the similarities in the two domains, while different feature population characteristics explained well the differences in modulation on both experimental probes. These results demonstrate how a single computational principle underlies processing of various features across the cortices.