An experiment for two-color photoionization using high intensity extreme-UNT free electron and near-IR laser pulses

We describe an experimental system designed for single-shot photoelectron spectroscopy on free atoms and molecules at the Free Electron Laser in Hamburg (FLASH at DESY). The combination of the extreme ultra-violet (EUV) Free Electron Laser and a temporally synchronized optical fs laser (Ti:Sapphire) enables a variety of two-color pump-probe experiments. The spectral, temporal and spatial characteristics of both the EUV FEL and the optical laser pulses, the experimental procedure to control their overlap as well as the performance of an electron spectrometer used to obtain single-shot photoelectron spectra are discussed. As an illustration of the capabilities of this set-up, some results on two-photon two-color ionization of rare gases are presented. (c) 2007 Elsevier B.V. All rights reserved.

Object tracking using SIFT features and mean shift

A scale invariant feature transform (SIFT) based mean shift algorithm is presented for object tracking in real scenarios. SIFT features are used to correspond the region of interests across frames. Meanwhile, mean shift is applied to conduct similarity search via color histograms. The probability distributions from these two measurements are evaluated in an expectation–maximization scheme so as to achieve maximum likelihood estimation of similar regions. This mutual support mechanism can lead to consistent tracking performance if one of the two measurements becomes unstable. Experimental work demonstrates that the proposed mean shift/SIFT strategy improves the tracking performance of the classical mean shift and SIFT tracking algorithms in complicated real scenarios.

Verteporfin Photodynamic Therapy Cohort Study Report 2: Clinical Measures of Vision and Health-Related Quality of Life

Purpose: To quantify decreases in health-related quality of life (HRQoL) for given deterioration in clinical measures of vision; to describe the shape of these relationships; and to test whether the gradients of these relationships change with duration of visual loss.

Wildlife tracking data management: a new vision

To date, the processing of wildlife location data has relied on a diversity of software and file formats. Data management and the following spatial and statistical analyses were undertaken in multiple steps, involving many time-consuming importing/exporting phases. Recent technological advancements in tracking systems have made large, continuous, high-frequency datasets of wildlife behavioral data available, such as those derived from the global positioning system (GPS) and other animal-attached sensor devices. These data can be further complemented by a wide range of other information about the animals’ environment. Management of these large and diverse datasets for modelling animal behaviour and ecology can prove challenging, slowing down analysis and increasing the probability of mistakes in data handling. We address these issues by critically evaluating the requirements for good management of GPS data for wildlife biology. We highlight that dedicated data management tools and expertise are needed. We explore current research in wildlife data management. We suggest a general direction of development, based on a modular software architecture with a spatial database at its core, where interoperability, data model design and integration with remote-sensing data sources play an important role in successful GPS data handling.