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A new approach to structural failure detection and localization is introduced based on acoustic reflections. It is shown that the cross-sectional areas of structural elements can be computed directly in terms of the reflection coefficients of an optimal finite impulse response Wiener filter realized in lattice form.
Computer software, called the Moving Object Detection Program (MODP), is described which detects earth-approaching asteroids in near real time. The software runs on a workstation linked to the output of the drift-scanning CCD camera of the Spacewatch Telescope. MOPD recognizes trailed images, detects motion, and accurately determines angular positions and rates of motion for moving objects in the scan images. The results are obtained a few seconds after the image signals are shifted out of the CCD. During 2 months of trial observations with this system, 304 asteroids were detected down to a limiting apparent magnitude for untrailed images of V = 20.5.
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Detonation nanodiamonds (DNDs) are formed at specific pressures and temperatures during explosions. Different explosives produce varied yields of DNDs within their detonation soot, with Composition B producing the highest yield. Raman spectroscopy (RS) is often used for the characterization of sp 2 - and sp 3 -hybridized carbon allotropes in carbonaceous materials because of distinct disorder and graphitic bands. Bulk diamond also gives a distinct Raman peak at 1332 cm –1 . Furthermore, as bulk diamond decreases in size to nanometer-sized species, the peak red-shifts and broadens, becoming increasingly difficult to detect with RS using visible excitations. Therefore, surface-enhanced Raman spectroscopy (SERS) was used to enhance the diamond peak of DNDs, enabling better detection and faster examination of DNDs within detonation soot. Previous literature of the SERS of DNDs delivered inconsistent results in spectral signatures and SERS substrates. Herein, refining of the methodology for the acquisition of SERS spectra of DNDs was achieved. Before any SERS experiments, the DNDs were first characterized with normal Raman (NR) and scanning electron microscopy. Two routes for SERS enhancement were evaluated: colloidal noble metal nanoparticles and evaporated silver films. Silver films produced the best signal enhancement of DNDs with the best signal-to-noise and peak enhancements observed at 20–30 nm thick silver films at 5% (∼300 μW) laser power. Consistent, reproducible SERS spectra were acquired of small aggregates of DNDs down to ∼500 nm. NR and SERS mapping analysis of DNDs before and after evaporation of silver films revealed the improvements in the detection capabilities of SERS compared with NR.
Unwanted artifacts that often plague astronomical images arise from a number of sources that include imperfect optics, faulty image sensors, cosmic ray hits, and even airplanes and satellites. One major source that is dicult to avoid is known as ghosting, and is caused by the scattering and internal reflections of light o of the telescope’s mechanical and optical components. Detecting ghosting artifacts eciently in the large cosmological surveys that will acquire petabytes of data can be a daunting task. In this paper, we use data from the Dark Energy Survey to develop, train, and validate a machine learning model to detect ghosts based on convolutional neural networks. The model architecture and training procedure is discussed in detail, and the performance on the training and validation set presented. Testing is performed on data and results are compared with those from a ray-tracing algorithm. A proof-of-principle is demonstrated that shows promise for Stage IV surveys and beyond.
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Likelihood ratio analysis for detecting signals in additive non-white noise
Recursive solution to problem of signal detection in presence of additive Gaussian nonstationary nonwhite noise
The problem of detecting the local similarity between templates in a given class and a given image using a hierarchically ordered sequential decision rule is examined. It is proposed that the set of templates be partitioned and a 'representative template' be defined for each of the partitions. Several levels of partitioning are defined. Elimination of mismatching locations and termination of computation can take place at each level of detection. Each level of testing is over a more restrictive subset of the template class than the previous level. Criteria are given for selecting representative templates, the ordering of components of a template vector for error evaluation, and the threshold sequences to be used in deciding about a 'match'. Suboptimal solutions are given satisfying these criteria. Examples showing recognition of linear features in test patterns and photographs obtained by aerial and spaceborne sensors are provided.
Multiseasonal analysis of LANDSAT multispectral images in CCT format permitted the mapping of lithologic facies in the Pedra Branca Granite, using geobotanical associations, which occur in the form of variations in the density of cerrado vegetation, as well as the predominance of certain distinctive vegetation species. Dry season images did not show very good results in lithological differentiation due to anomalous illumination conditions related to the low solar elevation and the homogeneity in the vegetation cover, specially the grasses that become dry during this season. Rainy season image, on the other hand, allowed the separation of the lithological types, a fact that can be attributed to a greater differentiation among the geobotanical associations. As a result of this study, the muscovite-granite facies with greisenization zones, which are lithological indicators of important tin mineralization within the Serra da Pedra Branca Granite, were mapped. This methodology can be sucessfully applied to similar known granite bodies elsewhere in the Tin Province of Goias.
Using concepts of (C,A)-invariant and unobservability (complementary observability) subspaces, a geometric formulation of the failure detection and identification filter problem is stated. Using these geometric concepts, it is shown that it is possible to design a causal linear time-invariant processor that can be used to detect and uniquely identify a component failure in a linear time-invariant system, assuming: (1) The components can fail simultaneously, and (2) The components can fail only one at a time. In addition, a geometric formulation of Beard's failure detection filter problem is stated. This new formulation completely clarifies of output separability and mutual detectability introduced by Beard and also exploits the dual relationship between a restricted version of the failure detection and identification problem and the control decoupling problem. Moreover, the frequency domain interpretation of the results is used to relate the concepts of failure sensitive observers with the generalized parity relations introduced by Chow. This interpretation unifies the various failure detection and identification concepts and design procedures.
A central problem in the detection of long-term trends in the upper stratospheric ozone from orbiting remote sensors involves the separation of instrument drifts from true geophysical changes. Periodic flights of a Solar Backscatter Ultraviolet radiometer (SSBUV) on the Space Shuttle will allow the detection of drifts in optically identical sensors (SBUV/2) carried on operational satellites. A detailed simulation of the SSBUV and SBUV/2 datasets defines the accuracy that can be attained by the in-orbit calibration procedure. The repeatability of the SSBUV calibration from one flight to the next is the most critical variable in the analysis. A repeatability near + or - 1.0 percent is essential for detection and correction of drifts in the SBUV/2 radiance measurements. The simulations show that true geophysical trends in backscattered radiance can be inferred to an accuracy of approximately + or - 1.0 percent per decade when SSBUV flies approximately once per year and provides a precise calibration correction to the SBUV/2 dataset over a full decade.
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Organic chemicals, when present in extraterrestrial samples, afford precious insight into past and modern conditions elsewhere in the Solar System . No single technology identifies all molecular components because naturally occurring molecules have different chemistries (e.g., polar vs. non-polar, low to high molecular weight) and interface with the ambient sample chemistry in a variety of modes (i.e., organics may be bonded, absorbed or trapped by minerals, liquids, gases, or other organics). More than 90% of organic matter in most natural samples on Earth and in meteorites is composed of complex macromolecules (e.g. biopolymers, complex biomolecules, humic substances, kerogen) because the processes that tend to break down organic molecules also tend towards complexation of the more recalcitrant components. Thus, methodologies that tap the molecular information contained within macromolecules may be critical to detecting extraterrestrial organic matter and assessing the sources and processes influencing its nature.