Ralf Vandenhouten

Phase transitions in the common brainstem and related systems investigated by nonstationary time series analysis.

Neuronal activities of the reticular formation (RF) of the lower brainstem and the nucleus tractus solitarii (NTS, first relay station of baroreceptor afferents) were recorded together in the anesthized dog with related parameters of EEG, respiration and cardiovascular system. The RF neurons are part of the common brainstem system (CBS) which participates in regulation and coordination of cardiovascular, respiratory, somatomotor systems, and vigilance. Multiple time series of these physiological subsystems yield useful information about internal dynamic coordination of the organism. Essential problems are nonlinearity and instationarity of the signals, due to the dynamic complexity of the systems. Several time-resolving methods are presented to describe nonlinear dynamic couplings in the time course, particularly during phase transitions. The methods are applied to the recorded signals representing the complex couplings of the physiological subsystems. Phase transitions in these systems are detected by recurrence plots of the instationary signals. The pointwise transinformation and the pointwise conditional coupling divergence are measures of the mutual interaction of the subsystems in the state space. If the signals show marked rhythms, instantaneous frequencies and their shiftings are demonstrated by time frequency distributions, and instantaneous phase differences show couplings of oscillating subsystems. Transient signal components are reconstructed by wavelet packet time selective transient reconstruction. These methods are useful means for analyzing coupling characteristics of the complex physiological system, and detailed analyses of internal dynamic coordination of subsystems become possible. During phase transitions of the functional organization (a) the rhythms of the central neuronal activities and the peripheral systems are altered, (b) changes in the coupling between CBS neurons and cardiovascular signals, respiration and the EEG, and (c) between NTS neurons (influenced by baroreceptor afferents) and CBS neurons occur, and (d) the processing of baroreceptor input at the NTS neurons changes. The results of this complex analysis, which could not be done formerly in this manner, confirm and complete former investigations on the dynamic organization of the CBS with its changing relations to peripheral and other central nervous subsystems.

Nonstationary time-series analysis applied to investigation of brainstem system dynamics

Previous investigations of the dynamic organization of the lower brainstem and its relation to peripheral and other central nervous systems were predominantly performed by linear methods. These are based on time-averaging algorithms, which merely can be applied to stationary signal intervals. Thus, the current concept of the common brainstem system (CBS) in the reticular formation (RF) of the lower brainstem and basic types of its functional organization have been developed. Here, the authors present experiments where neuronal activities of the RF and the nucleus tractus solitarii (NTS, first relay station of baroreceptor afferents) were recorded together with related parameters of electroencephalogram (EEG), respiration, and cardiovascular system. The RF neurons are part of the CBS, which participates in regulation and coordination of cardiovascular, respiratory, and motor systems, and vigilance. The physiological time series, thus acquired, yield information about the internal dynamic coordination of the participating regulation processes. The major problem in evaluating these data is the nonlinearity and nonstationarity of the signals. The authors used a set of especially designed time resolving methods to evaluate nonlinear dynamic couplings in the interaction between CBS neurons and cardiovascular signals, respiration and the EEG, and between NTS neurons (influenced by baroreceptor afferents) and CBS neurons.

Phase reconstruction and unwrapping from holographic interferograms of partially absorbent phase objects

A method for automated phase reconstruction from holographic interferograms of nonideal phase objects based on a two-dimensional Fourier transform is described. In particular, the problem of phase unwrapping is solved because earlier techniques are inappropriate for the phase unwrapping from interferograms of partially absorbent objects. A noise-level-dependent criterion for the binary mask that defines the unwrapping path for the flood algorithm is derived. The method shows high noise immunity, and the result is reliable provided that the true phase is free of discontinuities. The phase distribution in the outmasked regions is estimated by a linear least-squares fit to the surrounding unwrapped pixels.

Connecting industrial systems - solutions for high level development of software connectors

The implementation of communication protocols is an important development task that appears frequently in software projects. This article is a vision paper that describes the components of the current available implementation strategies and problems that arise. The article introduces the main existing protocol engineering techniques and puts them into the context of model driven software development. At the end a methodology is introduced for the automatic generation of manager interfaces of Device Agent protocols for the use in a distributed component oriented environment, using ASN.1 and SDL.

Human action recognition with hidden Markov models and neural network derived poses

A human action recognition method is introduced that detects a set of actions in videos by a temporal expansion with hidden Markov models of a pose detection with an artificial neural network. The method was set-up and tested using eleven actions from the MOCAP motion capture database comprising 3,947 frames. A poses alphabet of fourteen relevant poses was defined to be learned by an artificial neural network. It was trained with the skeletons and a manual pose classification of 370 key frame images from the motions resulting in an accuracy of 83.5 %. Three actions prevalent in the used motions were chosen to be recognized with the interplay of one separate hidden Markov model for each action. From the output of the trained artificial neural network for all 1,891 frames of seven of the eleven motions together with a manual pose and action classification the matrices of the hidden Markov models for the four actions were calculated. A tailored maximum likelihood estimation based on the Viterbi algorithm generated an action proposal for each frame. The resulting overall accuracy was 83.2 % precision and 83.7 % recall for recognition of the actions.

Design and quality metrics of point patterns for coded structured light illumination with diffractive optical elements in optical 3D sensors

Structured light has become a widespread technique for the development of camera-based 3D sensors. The structured illumination provides texture to homogeneous objects and thus allows for the reliable determination of the disparity of each object point in a stereo-camera setting. Even a monocular 3D sensor is possible if the light projector has a fixed relative position to the camera and if the structured light is coded, i.e. the position within the whole light pattern can be reconstructed uniquely from a small local window of the pattern, the uniqueness window. Coded patterns with such a uniqueness property are called Perfect SubMaps (PSM). In our paper we focus on the design and evaluation of the subset of symmetric isolated binary toroidal PSMs (SIBTPSM) for structured light patterns, because of their beneficial properties with respect to the signal-to-noise ratio and the use with laser light sources and DOEs. We define several figures of merit that are relevant for the practical use of PSMs in a 3D sensor: the PSM size, the size of the uniqueness window, the Hamming distance, the density, and the homogeneity. We have created SIBTPSMs using our own dedicated algorithms and have designed and fabricated DOEs that produce these patterns with large fan angles of 61° × 47° when used with near-infrared diode lasers (λ = 830nm). We analyze the influence of these characteristics on the 3D measurement process by theory, simulations, and experiments. The patterns of publicly available DOEs based on SIBTPSMs are used for comparison and reference. Our results show that the PSM width, the uniqueness window size, the minimum and average Hamming distances, and the uniformity have strong impact on either speed or quality of the 3D reconstruction, whereas the point density and the PSM height are of minor importance.

Multiple human skeleton recognition in RGB and depth images with graph theory, anatomic refinement of point clouds and machine learning

Computer visual recognition of multiple human poses infers technological benefit in a variety of systems, including security surveillance, medical therapeutics, sports analytics and many more. For this goal the set of detected body parts on color or depth images must be aligned to reconstruct the skeletons of the humans. Here, an algorithm is introduced that models the body part point clouds using principal component analysis to obtain anatomically correct positions of joints, and that assembles the redundant and/or incomplete number of candidate joints with graph theoretical methods using Suurballes k-shortest disjoint paths algorithm to build the skeletons. The computations were applied to MOCAP database motions rendered in Blender to produce idealized classified point clouds, and to real human depth images classified with decision forests similar to Shotton et al. For MOCAP data, in 68 images showing 3 persons all 204 skeletons were correctly aligned using 4.285 of 4.682 joints with no false assignment. For 33 real human images each showing 3 people, 71 skeletons were correctly detected with 1 false detection and 17 misses, which is promising with respect to non-perfect body part classification in real world.

Using distributed feature detection for an assistive work system

The real-time detection of context information in marker-less Augmented Reality (AR) scenarios with a large amount of key-point based descriptors still represents a scientific challenge. In this demo paper we present a mobile assistive work system that uses key-point based descriptors in marker-less AR to automatically detect the current working context of an employee and display the required information for the user. Our system is used in workshops for the maintenance of train vehicles. In this use case the mobile application has to detect a large amount of different tools and train components (i.e. objects). We use marker-less detection algorithms, which require the existence of referential images, whose amount grows linear to the number of objects that have to be detected. However, a large amount of referential images makes it impossible to execute the whole detection process on the mobile device. Therefore we implemented a distributed computation of the marker-less detection, which allows to find many different objects. In this paper we describe the components of the system, the image processing technologies used, and current limitations that we have identified.

Neue Ansätze der Bürgerbeteiligung bei der Lösung regionaler Problemlagen: Einsatzszenario eines sozio-technischen Assistenzsystems

Burgerbeteiligung ermoglicht den Burgerinnen und Burgern die Teilhabe an politischen Entscheidungsprozessen und Entscheidungen in unterschiedlichen Formen und auf verschiedenen politischen Ebenen. Burgerbeteiligungsverfahren haben sich in den letzten Jahren stetig weiterentwickelt. Neben klassischen Burgerbeteiligungsverfahren, die unter anderem auch in Verwaltungsverfahrensgesetzen oder dem Baurecht gesetzlich verankert und obligatorisch sind, konnen auch eine Vielzahl von modernen fakultativen partizipativen Verfahren eingesetzt werden (dazu ausfuhrlich Nanz/Fritsche, 2012).

An advanced data processing environment based on data flow diagrams with a flexible triggering and execution model

This paper introduces procedures which facilitate the development of and the linking between modular data processing operations into different and versatile system solutions. Based on the concept of data flow diagrams, processes and data flows are considered. With the ability of a process to harness any amount of in-and outputs with different sources and destinations, it is possible to model any conceivable data processing problem. By aggregating a diagram as a new process, it is possible to outsource partial process chains to different abstraction levels. As a result, even very complex solutions can be modelled and explained in a clear way. The introduced uniform execution flow while traversing the graph creates a good readability which facilitates the detection of weaknesses. Moreover, the solution is comprehensive and advances the cooperation in teams and development.