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Siemens AG is a German multinational conglomerate company headquartered in Berlin and Munich. It is the largest engineering company in Europe. The principal divisions of the company are Industry, Energy, Healthcare, and Infrastructure & Cities, which represent the main activities of the company. The company is a prominent maker of medical diagnostics equipment and its medical health-care division, which generates about 12 percent of the company's total sales, is its second-most profitable unit, after the industrial automation division.Siemens and its subsidiaries employ approximately 343,000 people worldwide and reported global revenue of around €71.9 billion in 2014 according to their annual report. Wikipedia.


Hartmann D.,Siemens AG
New Journal of Physics | Year: 2010

Here, we report on a new approach for adaptive path finding in microscopic simulations of pedestrian dynamics. The approach extends a widely used concept based on scalar navigation fields-the so-called floor field method. Adopting a continuum perspective, navigation fields used in our approach correspond to the shortest distances to the pedestrian's targets with respect to arbitrary metrics, e.g. metrics depending on the local terrain. If the metric correlates inversely with the expected speed, these distances could be interpreted as expected travel times. Following this approach, it is guaranteed that virtual pedestrians navigate along the steepest descent of the navigation field and thus follow geodesies. Using the Eikonal equation, i.e. a continuum model, navigation fields can be determined with respect to arbitrary metrics in an efficient manner. The fast marching method used in this work offers a fast method to solve the Eikonal equation (complexity N log N, where N is degree of freedom). Increasing computational efforts with respect to classical approaches only mildly, the consideration of locally varying metrics allows a realistic adaptive movement behavior like the avoidance of certain terrains. The method is outlined using a simple cellular automaton approach. Extensions to other microscopic models, e.g. cellular automata approaches or social force models, are possible. © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft.


Jian B.,Siemens AG | Vemuri B.C.,University of Florida
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2011

In this paper, we present a unified framework for the rigid and nonrigid point set registration problem in the presence of significant amounts of noise and outliers. The key idea of this registration framework is to represent the input point sets using Gaussian mixture models. Then, the problem of point set registration is reformulated as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized. We show that the popular iterative closest point (ICP) method [1] and several existing point set registration methods [2], [3], [4], [5], [6], [7] in the field are closely related and can be reinterpreted meaningfully in our general framework. Our instantiation of this general framework is based on the the L2 distance between two Gaussian mixtures, which has the closed-form expression and in turn leads to a computationally efficient registration algorithm. The resulting registration algorithm exhibits inherent statistical robustness, has an intuitive interpretation, and is simple to implement. We also provide theoretical and experimental comparisons with other robust methods for point set registration. © 2011 IEEE.


Conti M.,Siemens AG
European Journal of Nuclear Medicine and Molecular Imaging | Year: 2011

TOF PET is characterized by a better trade-off between contrast and noise in the image. This property is enhanced in more challenging operating conditions, allowing for example shorter examinations or low counts, successful scanning of larger patients, low uptake, visualization of smaller lesions, and incomplete data sampling. In this paper, the correlation between the time resolution of a TOF PET scanner and the improvement in signal-to-noise in the image is introduced and discussed. A set of performance advantages is presented which include better image quality, shorter scan times, lower dose, higher spatial resolution, lower sensitivity to inconsistent data, and the opportunity for new architectures with missing angles. The recent scientific literature that reports the first experimental evidence of such advantages in oncology clinical data is reviewed. Finally, the directions for possible improvement of the time resolution of the present generation of TOF PET scanners are discussed. © 2011 Springer-Verlag.


Wehrl H.F.,Siemens AG
Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine | Year: 2011

The combination of positron emission tomography and MR in one system is currently emerging and opens up new domains in the functional examinations of living systems. This article reports on relevant influences of a positron emission tomography insert on MR imaging. The basic conditions of main magnetic field and RF field homogeneity were measured as well as image quality and signal-to-noise ratio when applying the usual MR sequence types including echo-planar techniques. Moreover, the influence of the positron emission tomography insert on the RF noise level and on RF interferences was measured by comparing results achieved with and without the positron emission tomography insert. The temporal stability of EPI imaging with and without the positron emission tomography insert was assessed. Small but significant decreases in the signal-to-noise ratio were revealed when the positron emission tomography insert was present, whereas B(0) and B(1) homogeneity as well as RF noise level were not adversely affected. A higher signal intensity drift was found for EPI imaging studies; however, this can be compensated by post processing. In summary, this study shows that positron emission tomography inserts can be designed for and used within an MR system practically, without substantially affecting the MR image quality. © 2010 Wiley-Liss, Inc.


Liao L.,Siemens AG
IEEE Transactions on Industrial Electronics | Year: 2014

In prognostics approaches, features (e.g., vibration level, root mean square or outputs from signal processing techniques) extracted from the measurement (e.g., vibration, current, and pressure, etc.) are often used or modeled as an indicator to the equipment's health condition. When faults are detected or when increasing/decreasing trends are shown in the health indicator, prediction algorithms are applied to extrapolate the future behavior and predict remaining useful life (RUL). However, it is difficult to make an accurate prediction if the trend of the health indicator is not obvious through the entire life cycle or if the trend is only shown right before a failure occurs. The challenge lies in whether an advanced feature (e.g., a mathematical combination of a group of the extracted features) can be found to clearly present/correlate with the fault progression. A genetic programming method is proposed to address the challenge of automatically discovering advanced feature(s), which can well capture the fault progression, from the measurement or extracted features in the purpose of RUL prediction. © 2013 IEEE.

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