A scalable dataflow accelerator for real time onboard hyperspectral image classification

© Springer International Publishing Switzerland 2016.Real-time hyperspectral image classification is a necessary primitive in many remotely sensed image analysis applications. Previous work has shown that Support Vector Machines (SVMs) can achieve high classification accuracy, but unfortunately it is very computationally expensive. This paper presents a scalable dataflow accelerator on FPGA for real-time SVM classification of hyperspectral images.To address data dependencies, we adapt multi-class classifier based on Hamming distance. The architecture is scalable to high problem dimensionality and available hardware resources. Implementation results show that the FPGA design achieves speedups of 26x, 1335x, 66x and 14x compared with implementations on ZYNQ, ARM, DSP and Xeon processors. Moreover, one to two orders of magnitude reduction in power consumption is achieved for the AVRIS hyperspectral image datasets

Variation of 52 new Y-STR loci in the Y Chromosome Consortium worldwide panel of 76 diverse individuals

We have established 16 small multiplex reactions of two–four loci to amplify 52 recently described single-copy simple Y-STRs and typed these loci in a worldwide panel of 74 diverse men and two women. Two Y-STRs were found to be commonly multicopy in this sample set and were excluded from the study. Of the remaining 50, four (DYS481, DYS570, DYS576 and DYS643) showed higher diversities than the commonly used loci and can potentially provide increased haplotype discrimination in both forensic and anthropological work. Ten loci showed occasional missing alleles, duplicated peaks or intermediate-sized alleles. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available for this article at http://dx.doi.org/10.1007/s00414-006-0124-8 and is accessible for authorized users

Long term geological record of a global deep subsurface microbial habitat in sand injection complexes

Peer reviewedPublisher PD

Shape-resonant superconductivity in nanofilms: from weak to strong coupling

Ultrathin superconductors of different materials are becoming a powerful platform to find mechanisms for enhancement of superconductivity, exploiting shape resonances in different superconducting properties. Here we evaluate the superconducting gap and its spatial profile, the multiple gap components, and the chemical potential, of generic superconducting nanofilms, considering the pairing attraction and its energy scale as tunable parameters, from weak to strong coupling, at fixed electron density. Superconducting properties are evaluated at mean field level as a function of the thickness of the nanofilm, in order to characterize the shape resonances in the superconducting gap. We find that the most pronounced shape resonances are generated for weakly coupled superconductors, while approaching the strong coupling regime the shape resonances are rounded by a mixing of the subbands due to the large energy gaps extending over large energy scales. Finally, we find that the spatial profile, transverse to the nanofilm, of the superconducting gap acquires a flat behavior in the shape resonance region, indicating that a robust and uniform multigap superconducting state can arise at resonance.Comment: 7 pages, 4 figures. Submitted to the Proceedings of the Superstripes 2016 conferenc