Sound propagation in choked ducts

The linearized equations describing the propagation of sound in variable area ducts containing flow are shown to be singular when the duct mean flow is sonic. The singularity is removed when previously ignored nonlinear terms are retained. The results of a numerical study, for the case of plane waves propagating in a one-dimensional converging-diverging duct, show that the sound field is adequately described by the linearized equations only when the axial mean flow Mach number at the duct throat M sub th 0.6. For M sub th 0.6, the numerical results showed that acoustic energy flux was not conserved. An attempt was made to extend the study to include the nonlinear behavior of the sound field. Meaningful results were not obtained due, primarily, to numerical difficulties

On-Site Wireless Power Generation

Conventional wireless power transfer systems consist of a microwave power generator and a microwave power receiver separated by some distance. To realize efficient power transfer, the system is typically brought to resonance, and the coupled-antenna mode is optimized to reduce radiation into the surrounding space. In this scheme, any modification of the receiver position or of its electromagnetic properties results in the necessity of dynamically tuning the whole system to restore the resonant matching condition. It implies poor robustness to the receiver location and load impedance, as well as additional energy consumption in the control network. In this study, we introduce a new paradigm for wireless power delivery based on which the whole system, including transmitter and receiver and the space in between, forms a unified microwave power generator. In our proposed scenario the load itself becomes part of the generator. Microwave oscillations are created directly at the receiver location, eliminating the need for dynamical tuning of the system within the range of the self-oscillation regime. The proposed concept has relevant connections with the recent interest in parity-time symmetric systems, in which balanced loss and gain distributions enable unusual electromagnetic responses.Comment: 10 pages, 13 figure

The classification of traveling wave solutions and superposition of multi-solutions to Camassa-Holm equation with dispersion

Under the traveling wave transformation, Camassa-Holm equation with dispersion is reduced to an integrable ODE whose general solution can be obtained using the trick of one-parameter group. Furthermore combining complete discrimination system for polynomial, the classifications of all single traveling wave solutions to the Camassa-Holm equation with dispersion is obtained. In particular, an affine subspace structure in the set of the solutions of the reduced ODE is obtained. More general, an implicit linear structure in Camassa-Holm equation with dispersion is found. According to the linear structure, we obtain the superposition of multi-solutions to Camassa-Holm equation with dispersion

Scalar-Kinetic Branes

This work tries to find out thick brane solutions in braneworld scenarios described by a real scalar field in the presence of a scalar-kinetic term $F(X,\phi)=X\phi^m$ with a single extra dimension, where $X=\frac12\nabla_M\phi\nabla^M\phi$ stands for the standard kinetic term and $m=0,1,2\cdots$. We mainly consider bent branes, namely de Sitter and Anti-de Sitter four-dimensional slices. The solutions of a flat brane are obtained when taking the four-dimensional cosmological constant $\Lambda_4\rightarrow 0$. When the parameter $m=0$, these solutions turn to those of the standard scenario. The localization and spectrum of graviton on these branes are also analyzed.Comment: 10 pages, no figures, accepted by EP

Part Detector Discovery in Deep Convolutional Neural Networks

Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called "part detector discovery" (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at http://www.inf-cv.uni-jena.de/part_discovery and https://github.com/cvjena/PartDetectorDisovery.Comment: Accepted for publication on Asian Conference on Computer Vision (ACCV) 201