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Die Lokalisierung in Innenräumen, die sich nicht auf Satellitennavigation wie GPS verlassen kann, ist nach wie vor eine Herausforderung. Viele Methoden zur Lokalisierung in Innenräumen beruhen auf Funksignalen oder anderen Sendern (z.B. Infrarot), die eine kostenintensive Infrastruktur erfordern, die in einem Gebäude installiert werden muss. Außerdem sind diese Techniken ungenau, wenn Reflexionen der Signale in einem Gebäude auftreten. Die Forschungsgruppe Telematik der TH Wildau hat für diese Problemstellung ein optisches Verfahren zur Innenraumlokalisierung entwickelt, das die Kamera eines mobilen Geräts verwendet. Dieser Ansatz erfordert nur die Verfügbarkeit von mehreren identifizierbaren Landmarken, die kostengünstig in einem Gebäude installiert werden können. In diesem Beitrag soll eine Möglichkeit vorgestellt werden, dieses landmarkenbasierte Verfahren mit der Augmented Reality-Bibliothek ARCore von Google zu erweitern, um eine kontinuierliche Positionsermittlung zu ermöglichen, auch wenn vorübergehend keine Landmarken durch die Gerätekamera erfasst werden können.
Das Erkennen von Personen auf Fotos und in Videos geschieht in aktuellen Klassifizierungsverfahren durch die Zuordnung eines Köperteils zu jedem Pixel. Anschließend werden die gefundenen Körperteile zu Skeletten zusammengefasst. Im Falle mehrerer abgebildeter Personen ergibt sich das Problem der Zuordnung der Körperteile zu den verschiedenen Skeletten. Es wurde in dieser Arbeit ein auf dem Suurballe-Algorithmus basierendes graphentheoretisches Verfahren entwickelt, das diese Aufgabe löst. Aufbauend auf im Wesentlichen abstandsabhängigen Kantengewichten wird eine k-kürzeste-disjunkte-Wege-Suche durchgeführt. Unter Einbezug von möglicherweise fehlenden Körperteilen durch Unsichtbar-Knoten und automatisches Aussortieren zu viel gefundener Körperteile werden so die k-optimalen Skelette gefunden. Die Methode wurde an idealisierten computergenerierten Bildern mit einer Trefferquote von 100 % gefundener Personen getestet. Ein Test mit realen Bilddaten lieferte eine Trefferquote von 71,7 %.
This paper presents a solution for detecting movement and spreading profiles by analyzing sensor data in the facility management. First of all, a scenario is defined and described. On the basis of this scenario an approach is made using integration of events over time and location. For making the integration possible the required information, which the data objects have to provide, is identified. At the end a solution is presented using Complex Event Processing to implement the integration of events.
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.
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.
Selected examples from experiments in humans and dogs with time series of reticular neurons, respiration, arterial blood pressure and cutaneous forehead blood content fluctuations were analysed using multiscaled time-frequency distribution, post-event-scan and pointwise transinformation. We found in both experiments a “0.15-Hz rhythm” exhibiting periods of spindle waves (increasing and decreasing amplitudes), phase synchronized with respiration at 1:2 and 1:1 integer number ratios. At times of wave-epochs and n:m phase synchronization, the 0.15-Hz rhythm appeared in heart rate and arterial blood pressure. As phase synchronization of the 0.15-Hz rhythm with respiration was established at a 1:1 integer number ratio, all cardiovascular-respiratory oscillations were synchronized at 0.15 Hz. Analysis of a canine experiment supplied evidence that the emergence of the 0.15-Hz rhythm and n:m phase synchronization appears to result from a decline in the level of the general activity of the organism associated with a decline in the level of activity of reticular neurons in the lower brainstem network. These findings corroborate the notion of the 0.15-Hz rhythm as a marker of the “trophotropic mode of operation” first introduced by W.R. Hess.