The design and evaluation of the expected performance of optical systems require sophisticated and reliable information about the surface topography for planned optical elements before they are fabricated. Modern x-ray source facilities are reliant upon the availability of optics with unprecedented quality (surface slope accuracy <0.1 μrad). The problem is especially complex in the case of x-ray optics, particularly for the X-ray Surveyor under development and other missions. The high angular resolution and throughput of future x-ray space observatories requires hundreds of square meters of high-quality optics. The uniqueness of the optics and limited number of proficient vendors makes the fabrication extremely time consuming and expensive, mostly due to the limitations in accuracy and measurement rate of metrology used in fabrication. We discuss improvements in metrology efficacy via comprehensive statistical analysis of a compact volume of metrology data. The data are considered stochastic, and a statistical model called invertible time-invariant linear filter (InTILF) is developed now for two-dimensional (2-D) surface profiles to provide compact description of the 2-D data in addition to one-dimensional data treated so far. The InTILF model captures stochastic patterns in the data and can be used as a quality metric and feedback to polishing processes, avoiding high-resolution metrology measurements over the entire optical surface. The modeling, implemented in our BeatMark™ software, allows simulating metrology data for optics made by the same vendor and technology. The data are vital for reliable specification for optical fabrication, to be exactly adequate for the required system performance.
The design and evaluation of the expected performance of new optical systems requires sophisticated and reliable information about the surface topography for planned optical elements before they are fabricated. The problem is especially complex in the case of x-ray optics, particularly for the X-ray Surveyor under development and other missions. Modern x-ray source facilities are reliant upon the availability of optics with unprecedented quality (surface slope accuracy < 0.1μrad). The high angular resolution and throughput of future x-ray space observatories requires hundreds of square meters of high quality optics. The uniqueness of the optics and limited number of proficient vendors makes the fabrication extremely time consuming and expensive, mostly due to the limitations in accuracy and measurement rate of metrology used in fabrication. We discuss improvements in metrology efficiency via comprehensive statistical analysis of a compact volume of metrology data. The data is considered stochastic and a new statistical model called Invertible Time Invariant Linear Filter (InTILF) is developed now for 2D surface profiles to provide compact description of the 2D data additionally to 1D data treated so far. The model captures faint patterns in the data and serves as a quality metric and feedback to polishing processes, avoiding high resolution metrology measurements over the entire optical surface. The modeling, implemented in our Beatmark software, allows simulating metrology data for optics made by the same vendor and technology. The forecast data is vital for reliable specification for optical fabrication, to be exactly adequate for the required system performance.
Recently, an original method for the statistical modeling of surface topography of state-of-the-art mirrors for usage in xray
optical systems at light source facilities and for astronomical telescopes [Opt. Eng. 51(4), 046501, 2012; ibid. 53(8),
084102 (2014); and ibid. 55(7), 074106 (2016)] has been developed. In modeling, the mirror surface topography is
considered to be a result of a stationary uniform stochastic polishing process and the best fit time-invariant linear filter
(TILF) that optimally parameterizes, with limited number of parameters, the polishing process is determined. The TILF
model allows the surface slope profile of an optic with a newly desired specification to be reliably forecast before
fabrication. With the forecast data, representative numerical evaluations of expected performance of the prospective
mirrors in optical systems under development become possible [Opt. Eng., 54(2), 025108 (2015)]. Here, we suggest and
demonstrate an analytical approach for accounting the imperfections of the used metrology instruments, which are
described by the instrumental point spread function, in the TILF modeling. The efficacy of the approach is demonstrated
with numerical simulations for correction of measurements performed with an autocollimator based surface slope
profiler. Besides solving this major metrological problem, the results of the present work open an avenue for developing
analytical and computational tools for stitching data in the statistical domain, obtained using multiple metrology
instruments measuring significantly different bandwidths of spatial wavelengths.
The design and evaluation of the expected performance of optical systems requires sophisticated and reliable information about the surface topography of planned optical elements before they are fabricated. The problem is especially severe in the case of x-ray optics for modern diffraction-limited-electron-ring and free-electron-laser x-ray facilities, as well as x-ray astrophysics missions, such as the X-ray Surveyor under development. Modern x-ray source facilities are reliant upon the availability of optics of unprecedented quality, with surface slope accuracy <0.1 μrad. The unprecedented high angular resolution and throughput of future x-ray space observatories require high-quality optics of 100 m2 in total area. The uniqueness of the optics and limited number of proficient vendors make the fabrication extremely time-consuming and expensive, mostly due to the limitations in accuracy and measurement rate of metrology used in fabrication. We continue investigating the possibility of improving metrology efficiency via comprehensive statistical treatment of a compact volume of metrology of surface topography, which is considered the result of a stochastic polishing process. We suggest, verify, and discuss an analytical algorithm for identification of an optimal symmetric time-invariant linear filter model with a minimum number of parameters and smallest residual error. If successful, the modeling could provide feedback to deterministic polishing processes, avoiding time-consuming, whole-scale metrology measurements over the entire optical surface with the resolution required to cover the entire desired spatial frequency range. The modeling also allows forecasting of metrology data for optics made by the same vendor and technology. The forecast data are vital for reliable specification for optical fabrication, evaluated from numerical simulation to be exactly adequate for the required system performance, avoiding both over- and underspecification.
The design and evaluation of the expected performance of new optical systems requires sophisticated and reliable information about the surface topography for planned optical elements before they are fabricated. The problem is especially severe in the case of x-ray optics for modern diffraction-limited-electron-ring and free-electron-laser x-ray facilities, as well as x-ray astrophysics missions, such as the X-ray Surveyor under development. Modern x-ray source facilities are reliant upon the availability of optics of unprecedented quality, with surface slope accuracy < 0.1μrad. The unprecedented high angular resolution and throughput of future x-ray space observatories require high quality optics of hundreds square meters in total area. The uniqueness of the optics and limited number of proficient vendors makes the fabrication extremely time consuming and expensive, mostly due to the limitations in accuracy and measurement rate of metrology used in fabrication. In this work we continue investigating the possibility to improve metrology efficiency via comprehensive statistical treatment of a compact volume of metrology data, considered to be a result of a stochastic polishing process. If successful, the modeling could provide a feedback to deterministic polishing processes, avoiding time-consuming, whole scale metrology measurements over the entire optical surface with the resolution required to cover the entire desired spatial frequency range. The modeling also allows forecasting metrology data for optics made by the same vendor and technology. The forecast data is vital for reliable specification for optical fabrication, evaluated from numerical simulation to be exactly adequate for the required system performance, avoiding both over- and underspecification.
We investigate the time-invariant linear filter (TILF) approach to optimally parameterize the surface metrology of high-quality x-ray optics considered as a result of a stationary uniform random process. The approach is a generalization of autoregressive moving average (ARMA) modeling of one-dimensional slope measurements with x-ray mirrors considered. We show that the suggested TILF approximation has all the advantages of one-sided autoregressive and ARMA modeling, allowing a high degree of confidence when fitting the metrology data with a limited number of parameters. Compared to ARMA modeling, the TILF approximation gains in terms of better fitting accuracy and the absence of the causality limitation. Moreover, the TILF approach can be directly generalized to two-dimensional random fields. With the determined model parameters, the surface topography of prospective beamline optics can be reliably forecast before they are fabricated. These forecast metrology data, containing essential and reliable statistical information about the existing optics which are fabricated by the same vendor and technology, but generally, have different sizes, and slope and height root-mean-square variations, are vitally needed for numerical simulations of the performance of new x-ray beamlines and those under upgrade.
Numerical simulations of the performance of new x-ray beamlines and those under upgrade require sophisticated and
reliable information about the expected surface slope and height distributions of prospective beamline optics before they
are fabricated. Ideally, such information is based on metrology data obtained with existing optics, which are fabricated
by the same vendor and technology, but generally, have different sizes, and slope and height rms variations. In a recent
work [Opt. Eng. 51(4), 046501, 2012], it has been demonstrated that autoregressive moving average (ARMA) modeling
of one-dimensional (1D) slope measurements with x-ray mirrors allows a high degree of confidence when fitting the
metrology data with a limited number of parameters. With the parameters of the ARMA model, the surface slope profile
of an optic with the desired specification can reliably be forecast. Here, we investigate the time-invariant linear filter
(TILF) approach to optimally parameterize surface metrology of high quality x-ray optics thought of as a result of a
stationary uniform random process. We show that the TILF approximation has all advantages of one-sided AR and
ARMA modeling, but it additionally gains in terms of better fitting accuracy and absence of the causality limitation.
Moreover, the suggested TILF approach can be directly generalized to 2D random fields. This work is supported by the
U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
Seismic Unattended Ground Sensors (UGS) are low cost and covert, making them a suitable candidate for border patrol.
Current seismic UGS systems use cadence-based intrusion detection algorithms and are easily confused between humans
and animals. The poor discrimination ability between humans and animals results in missed detections as well as higher
false (nuisance) alarm rates. In order for seismic UGS systems to be deployed successfully, new signal processing
algorithms with better discrimination ability between humans and animals are needed. We have characterized the
seismic signals using frequency domain and time-frequency domain statistics, which improve the discrimination
between humans, animals and vehicles.
With recent changes in threats and methods of warfighting and the use of unmanned aircrafts, ISR (Intelligence,
Surveillance and Reconnaissance) activities have become critical to the military's efforts to maintain situational
awareness and neutralize the enemy's activities. The identification and tracking of dismounts from surveillance
video is an important step in this direction. Our approach combines advanced ultra fast registration techniques to
identify moving objects with a classification algorithm based on both static and kinematic features of the objects.
Our objective was to push the acceptable resolution beyond the capability of industry standard feature extraction
methods such as SIFT (Scale Invariant Feature Transform) based features and inspired by it, SURF (Speeded-Up
Robust Feature). Both of these methods utilize single frame images. We exploited the temporal component of the
video signal to develop kinematic features. Of particular interest were the easily distinguishable frequencies
characteristic of bipedal human versus quadrupedal animal motion. We examine limits of performance, frame rates
and resolution required for human, animal and vehicles discrimination. A few seconds of video signal with the
acceptable frame rate allow us to lower resolution requirements for individual frames as much as by a factor of
five, which translates into the corresponding increase of the acceptable standoff distance between the sensor and
the object of interest.
In this paper, we address the problem of robust detection of dismounts from low-resolution video data sequences. We
outline a methodology based on SSCI's ultra-fast image alignment algorithm, and a combination of static and kinematic
features for dismount detection. We perform the dismount detection classification using a learning classifier algorithm.
Our results are promising and very valuable for low-resolution imagery where previous techniques for dismount
detection such as SURF and SIFT features do not perform very well.
This paper presents the result of a new algorithm designed to improve the success rate, precision and accuracy of the measurements for low contrast targets produced by STI. The paper will also review the algorithm and discuss the result of target design optimization. Results will be provided from multiple lots with multiple wafer analysis demonstrating the effectiveness of the algorithm. Measurement yields improve from the 35 percent-50 percent success rate using current algorithms to 99 percent-100 percent success rate using the new algorithm. Precision was improved from 10nm to 3nm, and as low as 1.2 nanometers 3(sigma) . The true success of the algorithm is not just the improved measurement success, precision and accuracy; but it is in the verification that the edges are detected and measured accurately. Many current algorithms are giving estimates.
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