Controlling nonequilibrium phonon populations in single-walled carbon nanotubes

We studied spatially isolated single-walled carbon nanotubes (SWNTs) immobilized in a quasi-planar optical lambda/2-microresonator using confocal microscopy and spectroscopy. The modified photonic mode density within the resonator is used to selectively enhance or inhibit different Raman transitions of SWNTs. Experimental spectra are presented that exhibit single Raman bands only. Calculations of the relative change in the Raman scattering cross sections underline the potential of our microresonator for the optical control of nonequilibrium phonon populations in SWNT.     

Additional modules for versatile and economical PCR-based gene deletion and modification in Saccharomyces cerevisiae

An important recent advance in the functional analysis of Saccharomyces cerevisiae genes is the development of the one-step PCR-mediated technique for deletion and modification of chromosomal genes. This method allows very rapid gene manipulations without requiring plasmid clones of the gene of interest. We describe here a new set of plasmids that serve as templates for the PCR synthesis of fragments that allow a variety of gene modifications. Using as selectable marker the S. cerevisiae TRP1 gene or modules containing the heterologous Schizosaccharomyces pombe his5⁺ or Escherichia coli kan^r gene, these plasmids allow gene deletion, gene overexpression (using the regulatable GAL1 promoter), C- or N-terminal protein tagging [with GFP(S65T), GST, or the 3HA or 13Myc epitope], and partial N- or C-terminal deletions (with or without concomitant protein tagging). Because of the modular nature of the plasmids, they allow efficient and economical use of a small number of PCR primers for a wide variety of gene manipulations. Thus, these plasmids should further facilitate the rapid analysis of gene function in S. cerevisiae. © 1998 John Wiley & Sons, Ltd.     

SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within the framework of wavelet analysis, which reduces speckle in SAR images while preserving the structural features and textural information of the scene. First, we show that the subband decompositions of logarithmically transformed SAR images are accurately modeled by alpha-stable distributions, a family of heavy-tailed densities. Consequently, we exploit this a priori information by designing a maximum a posteriori (MAP) estimator. We use the alpha-stable model to develop a blind speckle-suppression processor that performs a nonlinear operation on the data and we relate this nonlinearity to the degree of non-Gaussianity of the data. Finally, we compare our proposed method to current state-of-the-art soft thresholding techniques applied on real SAR imagery and we quantify the achieved performance improvement.     

Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation

We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading.     

Modelling,validating, and ranking of secure service compositions

In the world of large‐scale applications, software as a service (SaaS) in general and use of microservices, in particular, is bringing service‐oriented architectures to a new level: Systems in general and systems that interact with human users (eg, sociotechnical systems) in particular are built by composing microservices that are developed independently and operated by different parties. At the same time, SaaS applications are used more and more widely by enterprises as well as public services for providing critical services, including those processing security or privacy of relevant data. Therefore, providing secure and reliable service compositions is increasingly needed to ensure the success of SaaS solutions. Building such service compositions securely is still an unsolved problem. In this paper, we present a framework for modelling, validating, and ranking secure service compositions that integrate both automated services as well as services that interact with humans. As a unique feature, our approach for ranking services integrates validated properties (eg, based on the result of formally analysing the source code of a service implementation) as well as contractual properties that are part of the service level agreement and, thus, not necessarily ensured on a technical level.