Axel Löffler

CdTe quantum wires achieved by strain-induced lateral confinement

Abstract Nanometer-scale CdTe quantum wires have been fabricated using a two step epitaxial growth process. Modulation of the in-plane lattice constant of a [110] CdTe CdZnTe quantum well grown over a [001] CdTe CdZnTe strained superlattice laterally confines the carriers to one dimension. The photoluminescence of these quantum wires presents a large energy shift relative to the unmodulated [110] quantum well (QW) which is strongly dependent on the excitation density. The results compare very well with a theoretical model assuming no relaxation in the structure. With this calculation, the dependence on excitation density of the wire exciton transition energy can be explained by taking into account the lateral piezoelectric field present in these strained modulated [110] structures.

The Dynamical Nightwatch's Problem Solved by the Autonomous Micro-Robot Khepera

In this paper, we present the implementation, both in a simulator and in a real-robot version, of an efficient solution to the so-called dynamical nightwatchs problem on the micro-robot Khepera. The problem consists mainly in exploring a previously unknown environment while detecting, registering and recognizing light sources which may dynamically be turned on and off. At the end of each round a report is requested from the robot. Therein we made use of an agent-based approach and applied a self-organizing feature map in order to refine some of the behaviour generating control-modules.

A Visualization Tool for the Mini-Robot Khepera: Behavior Analysis and Optimization

The design of behavior generating control structures for real robots acting autonomously in a real and changing environment is a complex task. This is in particular true with respect to the debugging process, the documentation of the encountered behavior, its quantitative analysis and the final evaluation. To successfully implement such a behavior, it is vital to couple the synthesis on a simulator and the experiment on a real robot with a thorough analysis. The available simulator tools in general only allow behavioral snapshots and do not provide the option of online interference. In order to cure these shortcomings, a visualization tool for aposteriori graphical analysis of recorded data sets which gives access to all relevant internal states and parameters of the system is presented. The mini-robot Khepera has been chosen as experimentatory platform.

Excitons in quantum well wires: V-shaped wire, T-shaped wire and strain-induced lateral confinement

SummaryWe calculated the energy and the wave function of the exciton for i) a V-shaped quantum wire, ii) a T-shaped quantum wire and iii) a quantum wire resulting from strain-induced lateral confinement. The full Luttinger and Bir-Pikus Hamiltonian are taken into account in the three calculations. We used different parameters inside the well and at the barrier. The basis states |3/2m> are defined with respect to [110], the direction of the wire in the three kinds of structures, instead of the usual [001] direction. The hole dispersion curve allows one to define an effective mass which is to be introduced in the variational calculation of the exciton binding energy. We show that the hole is confined in a wire only in case i).

A control approach to a biophysical neuron model

In this paper we present a neuron model based on the description of biophysical mechanisms combined with a regulatory mechanism from control theory. The aim of this work is to provide a neuron model that is capable of describing the main features of biological neurons such as maintaining an equilibrium potential using the NaK-ATPase and the generation of action potentials as well as to provide an estimation of the energy consumption of a single cell in a) quiescent mode (or equilibrium state) and b) firing state, when excited by other neurons. The same mechanism has also been used to model the synaptic excitation used in the simulated system.

A Direction Sensitive Network Based on a Biophysical Neurone Model

To our understanding, modelling the dynamics of brain functions on cell level is essential to develop both a deeper understanding and classification of the experimental data as well as a guideline for further research. This paper now presents the implementation and training of a direction sensitive network on the basis of a biophisical neurone model including synaptic excitation, dendritic propagation and action-potential generation. The underlying model not only describes the functional aspects of neural signal processing, but also provides insight into their underlying energy consumption. Moreover, the training data set has been recorded by means of a real robotics system, thus bridging the gap to technical applications.

Implementing neural soft- and hardware on the autonomous mini-robot Khepera

The applicability of neural networks to generate complex behaviour on autonomous systems is demonstrated both at soft- and hardware-level. In particular, the emergence of simple behaviors based on the Braitenberg approach, adaptive sensor calibration by self-organizing maps with a comparison between off- and online learning and a visualisation tool for a posteriori analysis are shown. It is also envisaged to present the working of embedded neural hardware as associative memory and self-organizing maps. In this connection, the mini-robot Khepera serves as an exemplary platform.