University of Stuttgart

About: University of Stuttgart is a education organization based out in Stuttgart, Germany. It is known for research contribution in the topics: Laser & Finite element method. The organization has 27715 authors who have published 56370 publications receiving 1363382 citations. The organization is also known as: Universität Stuttgart.

Olfactory receptors in aquatic and terrestrial vertebrates

Abstract: In species representing different levels of vertebrate evolution, olfactory receptor genes have been identified by molecular cloning techniques Comparing the deduced amino-acid sequences revealed that the olfactory receptor gene family of Rana esculenta resembles that of Xenopus laevis, indicating that amphibians in general may comprise two classes of olfactory receptors Whereas teleost fish, including the goldfish Carassius auratus, possess only class I receptors, the `living fossil' Latimeria chalumnae is endowed with both receptor classes; interestingly, most of the class II genes turned out to be pseudogenes Exploring receptor genes in aquatic mammals led to the discovery of a large array of only class II receptor genes in the dolphin Stenella Coeruleoalba; however, all of these genes were found to be non-functional pseudogenes These results support the notion that class I receptors may be specialized for detecting water-soluble odorants and class II receptors for recognizing volatile odorants Comparing the structural features of both receptor classes from various species revealed that they differ mainly in their extracellular loop 3, which may contribute to ligand specificity Comparing the number and diversity of olfactory receptor genes in different species provides insight into the origin and the evolution of this unique gene family

Phase imaging with an untrained neural network.

Abstract: Most of the neural networks proposed so far for computational imaging (CI) in optics employ a supervised training strategy, and thus need a large training set to optimize their weights and biases. Setting aside the requirements of environmental and system stability during many hours of data acquisition, in many practical applications, it is unlikely to be possible to obtain sufficient numbers of ground-truth images for training. Here, we propose to overcome this limitation by incorporating into a conventional deep neural network a complete physical model that represents the process of image formation. The most significant advantage of the resulting physics-enhanced deep neural network (PhysenNet) is that it can be used without training beforehand, thus eliminating the need for tens of thousands of labeled data. We take single-beam phase imaging as an example for demonstration. We experimentally show that one needs only to feed PhysenNet a single diffraction pattern of a phase object, and it can automatically optimize the network and eventually produce the object phase through the interplay between the neural network and the physical model. This opens up a new paradigm of neural network design, in which the concept of incorporating a physical model into a neural network can be generalized to solve many other CI problems.

Predictive Current Control of an Induction Machine Fed by a Matrix Converter With Reactive Power Control

Abstract: A different approach to perform the control of an induction machine fed by a matrix converter (MC) is presented in this paper. The proposed technique is based on predictive control and effectively controls input and output variables to the power converter, as expected from an MC. The method allows the use of all valid switching states, including rotating vectors that are not considered in most control techniques, as space vector modulation or direct torque control for induction machines fed by MCs. Experimental results show the excellent performance of the proposed approach, with low-distortion input currents, adjustable power factor, sinusoidal output currents with smooth frequency transitions, and good speed control in motoring and regeneration conditions, even working under an unbalanced power supply. The implementation and comprehension of the method should be considered simple compared to other control strategies with similar features. The high computational effort required should not be a problem considering recent progresses in digital signal processors-and even less in years to come.

Determination of double-K criterion for crack propagation in quasi-brittle fracture, Part III: Compact tension specimens and wedge splitting specimens

Abstract: This paper shows how the double-K fracture parameters K Ic ini and K Ic un can be determined for concrete using CT-specimens and wedge splitting specimens. The experimental results collected from the fracture tests the very large size CT-specimens and small size wedge splitting specimens carried out by many researchers are utilized to investigate the characters of the obtained double-K fracture parameters K Ic ini and K Ic un . It was found that the double-K fracture parameters K Ic ini and K Ic un determined from fracture tests on the large size CT-specimens are size-independent. And the values of K Ic ini and K Ic un determined from small size wedge splitting specimens with same dimensions are independent of the relative preformed notch length a0/D. However, when the dimensions of small size wedge splitting specimens change from 150×150×150 mm3 to 450×450×450 mm3, the values of K Ic ini and K Ic un slightly depend on the heights of the specimens and do not depend on the thickness of the specimens.