Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing

We have investigated the use of DualEELS in elementally sensitive tilt series tomography in the scanning transmission electron microscope. A procedure is implemented using deconvolution to remove the effects of multiple scattering, followed by normalisation by the zero loss peak intensity. This is performed to produce a signal that is linearly dependent on the projected density of the element in each pixel. This method is compared with one that does not include deconvolution (although normalisation by the zero loss peak intensity is still performed). Additionaly, we compare the 3D reconstruction using a new compressed sensing algorithm, DLET, with the well-established SIRT algorithm. VC precipitates, which are extracted from a steel on a carbon replica, are used in this study. It is found that the use of this linear signal results in a very even density throughout the precipitates. However, when deconvolution is omitted, a slight density reduction is observed in the cores of the precipitates (a so-called cupping artefact). Additionally, it is clearly demonstrated that the 3D morphology is much better reproduced using the DLET algorithm, with very little elongation in the missing wedge direction. It is therefore concluded that reliable elementally sensitive tilt tomography using EELS requires the appropriate use of DualEELS together with a suitable reconstruction algorithm, such as the compressed sensing based reconstruction algorithm used here, to make the best use of the limited data volume and signal to noise inherent in core-loss EELS

GENFIRE: A generalized Fourier iterative reconstruction algorithm for high-resolution 3D imaging

Tomography has made a radical impact on diverse fields ranging from the study of 3D atomic arrangements in matter to the study of human health in medicine. Despite its very diverse applications, the core of tomography remains the same, that is, a mathematical method must be implemented to reconstruct the 3D structure of an object from a number of 2D projections. In many scientific applications, however, the number of projections that can be measured is limited due to geometric constraints, tolerable radiation dose and/or acquisition speed. Thus it becomes an important problem to obtain the best-possible reconstruction from a limited number of projections. Here, we present the mathematical implementation of a tomographic algorithm, termed GENeralized Fourier Iterative REconstruction (GENFIRE). By iterating between real and reciprocal space, GENFIRE searches for a global solution that is concurrently consistent with the measured data and general physical constraints. The algorithm requires minimal human intervention and also incorporates angular refinement to reduce the tilt angle error. We demonstrate that GENFIRE can produce superior results relative to several other popular tomographic reconstruction techniques by numerical simulations, and by experimentally by reconstructing the 3D structure of a porous material and a frozen-hydrated marine cyanobacterium. Equipped with a graphical user interface, GENFIRE is freely available from our website and is expected to find broad applications across different disciplines.Comment: 18 pages, 6 figure

Coronagraphic Low Order Wavefront Sensor: Principle and Application to a Phase-Induced Amplitude Coronagraph

High contrast coronagraphic imaging of the immediate surrounding of stars requires exquisite control of low-order wavefront aberrations, such as tip-tilt (pointing) and focus. We propose an accurate, efficient and easy to implement technique to measure such aberrations in coronagraphs which use a focal plane mask to block starlight. The Coronagraphic Low Order Wavefront Sensor (CLOWFS) produces a defocused image of a reflective focal plane ring to measure low order aberrations. Even for small levels of wavefront aberration, the proposed scheme produces large intensity signals which can be easily measured, and therefore does not require highly accurate calibration of either the detector or optical elements. The CLOWFS achieves nearly optimal sensitivity and is immune from non-common path errors. This technique is especially well suited for high performance low inner working angle (IWA) coronagraphs. On phase-induced amplitude apodization (PIAA) type coronagraphs, it can unambiguously recover aberrations which originate from either side of the beam shaping introduced by the PIAA optics. We show that the proposed CLOWFS can measure sub-milliarcsecond telescope pointing errors several orders of magnitude faster than would be possible in the coronagraphic science focal plane alone, and can also accurately calibrate residual coronagraphic leaks due to residual low order aberrations. We have demonstrated 1e-3 lambda/D pointing stability in a laboratory demonstration of the CLOWFS on a PIAA type coronagraph

S-DIMM+ height characterization of day-time seeing using solar granulation

To evaluate site quality and to develop multi-conjugative adaptive optics systems for future large solar telescopes, characterization of contributions to seeing from heights up to at least 12 km above the telescope is needed. We describe a method for evaluating contributions to seeing from different layers along the line-of-sight to the Sun. The method is based on Shack Hartmann wavefront sensor data recorded over a large field-of-view with solar granulation and uses only measurements of differential image displacements from individual exposures, such that the measurements are not degraded by residual tip-tilt errors. We conclude that the proposed method allows good measurements when Fried's parameter r_0 is larger than about 7.5 cm for the ground layer and that these measurements should provide valuable information for site selection and multi-conjugate development for the future European Solar Telescope. A major limitation is the large field of view presently used for wavefront sensing, leading to uncomfortably large uncertainties in r_0 at 30 km distance.Comment: Accepted by AA 22/01/2010 (12 pages, 11 figures

Control limitations from distributed sensing: theory and Extremely Large Telescope application

We investigate performance bounds for feedback control of distributed plants where the controller can be centralized (i.e. it has access to measurements from the whole plant), but sensors only measure differences between neighboring subsystem outputs. Such "distributed sensing" can be a technological necessity in applications where system size exceeds accuracy requirements by many orders of magnitude. We formulate how distributed sensing generally limits feedback performance robust to measurement noise and to model uncertainty, without assuming any controller restrictions (among others, no "distributed control" restriction). A major practical consequence is the necessity to cut down integral action on some modes. We particularize the results to spatially invariant systems and finally illustrate implications of our developments for stabilizing the segmented primary mirror of the European Extremely Large Telescope.Comment: submitted to Automatic

Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0