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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2004

Support vector machines

Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

multiangle↗
NASA NTRS2006

Use of Photoelastic Modulators for High-accuracy Spectropolarimetric Imaging of Aerosols

Passive multiangular, multispectral, and polarimetric sensing approaches each have unique strengths for the measurement of tropospheric aerosol column abundances and microphysical properties. Current spaceborne multispectral and multiangular aerosols sensors operate at approximately 1 km resolution. Under NASA's Instrument Incubator Program, we are developing an electro-optic imaging approach that will enable adding high-accuracy polarimetry to such observations. To achieve a degree of linear polarization (DOLP) uncertainty of 0.5%, our approach temporally modulates the linear-polarization component of incoming light at a rapid rate, enabling each detector within a focal-plane array, combined with polarization analyzers, to measure the relative proportions of the linear Stokes components Q or U to the total intensity. Our system uses tandem photoelastic modulators (PEMs) within a high-reflectance, low diattenuation camera design. The two PEMs vibrate at slightly different resonant frequencies, leading to modulation of the polarized light at a heterodyne frequency of ~25 Hz. High-speed (1 kHz) readout of the detector arrays samples the output waveforms from which Q/I and U/I are derived. We report on experimental and theoretical analyses of PEM and optical system performance, along with plans for developing ruggedized PEMs capable of withstanding launch and on-orbit stresses.

photoelastics↗
NASA NTRS2015

Remote Sensing of Aerosols from Satellites: Why Has It Been Do Difficult to Quantify Aerosol-Cloud Interactions for Climate Assessment, and How Can We Make Progress?

The organizers of the National Academy of Sciences Arthur M. Sackler Colloquia Series on Improving Our Fundamental Understanding of the Role of Aerosol-Cloud Interactions in the Climate System would like to post Ralph Kahn's presentation entitled Remote Sensing of Aerosols from Satellites: Why has it been so difficult to quantify aerosol-cloud interactions for climate assessment, and how can we make progress? to their public website.

aerosols↗
NASA NTRS2016

More Than the Sum of the Parts: Satellite Aerosol Remote Sensing, and Its Relationship to Sub-Orbital Measurements and Models

Space-borne instruments are providing increasing amounts of data relating to global aerosol spectral optical depth, horizontal and vertical distribution, and very loose, but spatially and temporally extensive, constraints on particle micro-physical properties. The data sets, and many of the underlying techniques, are evolving rapidly. They represent a vast amount of information, potentially useful to the AAAR community. However, there are also issues, some quite subtle, that scientific users must take into consideration. This tutorial will provide one view of the answers to the following four questions: 1) What satellite-derived aerosol products are available? 2) What are their strengths and limitations? 3) How are they being used now? 4) How might they be used in conjunction with each other, with sub-orbital measurements, and with models to address cutting-edge aerosol questions?

satellite remote sensing↗