MSX design driven by targets and backgrounds

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76427/1/AIAA-1997-308-727.pd

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Depth estimation of inner wall defects by means of infrared thermography

There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

Modeling and Simulation of a Long-Wave Infrared Polarimetric Sensor for Space Object Detection and Characterization

Long-Wave Infrared (LWIR, wavelength \u3e 8 um) polarimetric measurements can be used to characterize space objects. A simulation of a sensor for collection of LWIR polarimetric signatures of space objects has been assembled using two software packages: MATLAB, and FRED. A statistical approach developed for unresolved visible light polarimetric observations of GEO satellites has been adapted for unresolved LWIR polarimetric observations of LEO satellites, showing both that well-known objects can be recognized and anomalies--for example, a major change in shape due to the presence in the scene of another object--can be detected. Though the satellites are effectively point sources, the aggregate polarization values across many measurements can be used to differentiate objects of different shape and material composition

In Situ Thermal Inspection of Automated Fiber Placement Operations for Tow and Ply Defect Detection

The advent of Automated Fiber Placement (AFP) systems have aided the rapid manufacturing of composite aerospace structures. One of the challenges that AFP systems pose is the uniformity of the deposited prepreg tape layers, which complicates detection of laps, gaps, overlaps and twists. The current detection method used in industry involves halting fabrication and performing a time consuming, visual inspection of each tape layer. Typical AFP systems use a quartz lamp to heat the base layer to make the surface tacky as it deposits another tape layer. The innovation proposed in this paper is to use the preheated base layer as a through-transmission heat source for inspecting the newly added tape layer in situ using a thermographic camera mounted on to the AFP hardware. Such a system would not only increase manufacturing throughput by reducing inspection times, but it would also aid in process development for new structural designs or material systems by providing data on as-built parts. To this end, a small thermal camera was mounted onto an AFP robotic research platform at NASA, and thermal data was collected during typical and experimental layup operations. The data was post processed to reveal defects such as tow overlap/gap, wrinkling, and peel-up. Defects that would have been impossible to detect visually were also discovered in the data, such as poor/loss of adhesion between plies and the effects of vacuum debulking. This paper will cover the results of our experiments, and the plans for future versions of this inspection system

Preliminary Results from NEOWISE: An Enhancement to the Wide-field Infrared Survey Explorer for Solar System Science

The Wide-field Infrared Survey Explorer (WISE) has surveyed the entire sky at four infrared wavelengths with greatly improved sensitivity and spatial resolution compared to its predecessors, the Infrared Astronomical Satellite and the Cosmic Background Explorer. NASA's Planetary Science Division has funded an enhancement to the WISE data processing system called "NEOWISE" that allows detection and archiving of moving objects found in the WISE data. NEOWISE has mined the WISE images for a wide array of small bodies in our solar system, including near-Earth objects (NEOs), Main Belt asteroids, comets, Trojans, and Centaurs. By the end of survey operations in 2011 February, NEOWISE identified over 157,000 asteroids, including more than 500 NEOs and ~120 comets. The NEOWISE data set will enable a panoply of new scientific investigations

Astrometry with the Wide-Field InfraRed Space Telescope

The Wide-Field InfraRed Space Telescope (WFIRST) will be capable of delivering precise astrometry for faint sources over the enormous field of view of its main camera, the Wide-Field Imager (WFI). This unprecedented combination will be transformative for the many scientific questions that require precise positions, distances, and velocities of stars. We describe the expectations for the astrometric precision of the WFIRST WFI in different scenarios, illustrate how a broad range of science cases will see significant advances with such data, and identify aspects of WFIRST's design where small adjustments could greatly improve its power as an astrometric instrument.Comment: version accepted to JATI

Applicability of multispectral Sentinel data for mineral exploration by use of remote sensing and geospatial technologies:A case study in Northern Chile

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe objective of this MSc thesis is to prove that the Sentinel-2 satellite has the same capabilities for mineral exploration than another satellite considered the “reference technology” by the minerals industry. Since there have been an extensive use and applications of the Landsat-8 satellite for mineral exploration, this satellite is considered in this case the “reference technology”. To prove the capability of the Sentinel-2, a sequence of key applications applied on the Landsat-8 satellite for mineral exploration have been carried out using the Sentinel-2 on a specific mine site. Mine site for this investigation is the Escondida mine in northern Chile. Through several remote sensing applications such as band combinations, band ratios, PCA analyses, and pixel´s classification both satellites, Landsat-8 and Sentinel-2 have been tested on Escondida and results have been obtained and discussed. As a conclusion of this analysis, the capability of Sentinel-2 for mineral exploration has been proved, potential improvements have been identified and limitations in its prospective use have been indicated