On initial-value and self-similar solutions of the compressible Euler equations

We examine numerically the issue of convergence for initial-value solutions and similarity solutions of the compressible Euler equations in two dimensions in the presence of vortex sheets (slip lines). We consider the problem of a normal shock wave impacting an inclined density discontinuity in the presence of a solid boundary. Two solution techniques are examined: the first solves the Euler equations by a Godunov method as an initial-value problem and the second as a boundary value problem, after invoking self-similarity. Our results indicate nonconvergence of the initial-value calculation at fixed time, with increasing spatial-temporal resolution. The similarity solution appears to converge to the weak 'zero-temperature' solution of the Euler equations in the presence of the slip line. Some speculations on the geometric character of solutions of the initial-value problem are presented

CMB observations using the SKA

We examine the prospects for observations of CMB anisotropy with the SKA; we discuss the advantages of interferometric SKA imaging, observing strategies, calibration issues and the achievable sensitivity. Although the SKA will probably operate at cm wavelengths, where discrete source confusion dominates the CMB anisotropy, its extreme sensitivity to point sources will make it possible to subtract the source contamination at these wavelengths and thereby image the low surface brightness CMB anisotropies on small angular scales. The SKA, operating at 10-20 GHz, may usefully make high-l observations of the CMB anisotropy spectrum and survey the sky for Sunyaev-Zeldovich decrements.Comment: 4 pages. invited talk presented at the XXVIIth General Assembly of the URSI, 17-24 Aug 2002, Maastricht, The Netherland

Real-time food intake classification and energy expenditure estimation on a mobile device

© 2015 IEEE.Assessment of food intake has a wide range of applications in public health and life-style related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation suitable for realtime mobile device execution. A Fischer Vector representation together with a set of linear classifiers are used to categorize food intake. Daily energy expenditure estimation is achieved by using the built-in inertial motion sensors of the mobile device. The performance of the vision-based food recognition algorithm is compared to the current state-of-the-art, showing improved accuracy and high computational efficiency suitable for realtime feedback. Detailed user studies have also been performed to demonstrate the practical value of the software environment