3D curves reconstruction from multiple images

In this paper, we propose a new approach for reconstructing 3D curves from a sequence of 2D images taken by uncalibrated cameras. A curve in 3D space is represented by a sequence of 3D points sampled along the curve, and the 3D points are reconstructed by minimizing the distances from their projections to the measured 2D curves on different images (i.e., 2D curve reprojection error). The minimization problem is solved by an iterative algorithm which is guaranteed to converge to a (local) minimum of the 2D reprojection error. Without requiring calibrated cameras or additional point features, our method can reconstruct multiple 3D curves simultaneously from multiple images and it readily handles images with missing and/or partially occluded curves. © 2010 IEEE.published_or_final_versionThe 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia, 1-3 December 2010. In Proceedings of DICTA, 2010, p. 462-46

Local Temperature and Universal Heat Conduction in FPU chains

It is shown numerically that for Fermi Pasta Ulam (FPU) chains with alternating masses and heat baths at slightly different temperatures at the ends, the local temperature (LT) on small scales behaves paradoxically in steady state. This expands the long established problem of equilibration of FPU chains. A well-behaved LT appears to be achieved for equal mass chains; the thermal conductivity is shown to diverge with chain length N as N^(1/3), relevant for the much debated question of the universality of one dimensional heat conduction. The reason why earlier simulations have obtained systematically higher exponents is explained.Comment: 4 pages, 3 figures, revised published versio

Universality of One-Dimensional Heat Conductivity

We show analytically that the heat conductivity of oscillator chains diverges with system size N as N^{1/3}, which is the same as for one-dimensional fluids. For long cylinders, we use the hydrodynamic equations for a crystal in one dimension. This is appropriate for stiff systems such as nanotubes, where the eventual crossover to a fluid only sets in at unrealistically large N. Despite the extra equation compared to a fluid, the scaling of the heat conductivity is unchanged. For strictly one-dimensional chains, we show that the dynamic equations are those of a fluid at all length scales even if the static order extends to very large N. The discrepancy between our results and numerical simulations on Fermi-Pasta-Ulam chains is discussed.Comment: 7 pages, 2 figure

Vitamin D and Its Deficiency in Saudi Arabia

Vitamin D is a hot topic that has attracted attention over the past 10 years, especially since a large proportion of people suffer from this nutrient deficiency. Vitamin D deficiency is estimated to be about 1 billion people all over the world and 50% in Asia and the Middle East. Saudi Arabia has also demonstrated a high prevalence of vitamin D deficiency among healthy Saudi individuals. This chapter provides, in detail, a clear and understandable identification of vitamin D, its function, source, synthesis, metabolism, status, and deficiency. The chapter also focuses on studying vitamin D deficiency in Saudi Arabia based on PubMed’s initial research criteria

Fractal Antennas for Wearable Applications

This chapter focuses on the design and fabrication of different types of flexible and inflexible wearable fractal for modern wireless applications with body-area-networks (BANs). A wearable antenna is intended to be a part of clothing used for modern wireless communication purposes. Fractal technology allowed us to design compact antennas and integrate multiple communication services into one device. The proposed antennas were simulated and measured by CST simulator version 2017 and Agilent N9918A VNA respectively. Furthermore, these antennas were fabricated using folded copper. The measured results agree well with the simulated results

Multi-modal adversarial autoencoders for recommendations of citations and subject labels

We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model