scholarly journals Raman and Infrared Thermometry for Microsystems

2013 ◽  
Vol 5 (3) ◽  
Author(s):  
Leslie M. Phinney ◽  
Wei-Yang Lu ◽  
Justin R. Serrano
Keyword(s):  
Data Collection ◽  
Spatial Resolution ◽  
Infrared Imaging ◽  
Silicon On Insulator ◽  
Pixel Size ◽  
Full Field ◽  
Infrared Thermometry ◽  
Full Field Measurement ◽  
Typical Data ◽  
Field Temperature

This paper reports and compares Raman and infrared thermometry measurements along the legs and on the shuttle of a SOI (silicon on insulator) bent-beam thermal microactuator. Raman thermometry offers micron spatial resolution and measurement uncertainties of ±10 K. Typical data collection times are a minute per location leading to measurement times on the order of hours for a complete temperature profile. Infrared thermometry obtains a full-field measurement so the data collection time is on the order of a minute. The spatial resolution is determined by the pixel size, 25 μm by 25 μm for the system used, and infrared thermometry also has uncertainties of ±10 K after calibration with a nonpackaged sample. The Raman and infrared measured temperatures agreed both qualitatively and quantitatively. For example, when the thermal microactuator was operated at 7 V, the peak temperature on an interior leg is 437 K ± 10 K and 433 K ± 10 K from Raman and infrared thermometry, respectively. The two techniques are complementary for microsystems characterization when infrared imaging obtains a full-field temperature measurement and Raman thermometry interrogates regions for which higher spatial resolution is required.

scholarly journals Raman and Infrared Thermometry for Microsystems

2011 ◽  
Author(s):  
Leslie M. Phinney ◽  
Wei-Yang Lu ◽  
Justin R. Serrano
Keyword(s):  
Data Collection ◽  
Spatial Resolution ◽  
Silicon On Insulator ◽  
Full Field ◽  
Infrared Thermometry ◽  
Quantitative Measurements ◽  
Full Field Measurement ◽  
Typical Data ◽  
Measurement Uncertainties ◽  
Collection Time

This paper compares measurements made by Raman and infrared thermometry on a SOI (silicon on insulator) bent-beam thermal microactuator. Both techniques are noncontact and used to experimentally measure temperatures along the legs and on the shuttle of the thermal microactuators. Raman thermometry offers micron spatial resolution and measurement uncertainties of ±10 K; however, typical data collection times are a minute per location leading to measurement times on the order of hours for a complete temperature profile. Infrared thermometry obtains a full-field measurement so the data collection time is much shorter; however, the spatial resolution is lower and calibrating the system for quantitative measurements is challenging. By obtaining thermal profiles on the same SOI thermal microactuator, the relative strengths and weaknesses of the two techniques are assessed.

Spectro-mechanical Characterization of Aromaticity and Maturity of Kerogens in Oil Shale at 6 nm Spatial Resolution

2018 ◽  
Author(s):  
Devon Jakob ◽  
Le Wang ◽  
Haomin Wang ◽  
Xiaoji Xu
Keyword(s):  
Spatial Resolution ◽  
Oil Shale ◽  
Infrared Imaging ◽  
Mechanical Characterization ◽  
Optical Diffraction ◽  
Chemical Compositions ◽  
Valuable Insight ◽  
Eagle Ford Shale

<p>In situ measurements of the chemical compositions and mechanical properties of kerogen help understand the formation, transformation, and utilization of organic matter in the oil shale at the nanoscale. However, the optical diffraction limit prevents attainment of nanoscale resolution using conventional spectroscopy and microscopy. Here, we utilize peak force infrared (PFIR) microscopy for multimodal characterization of kerogen in oil shale. The PFIR provides correlative infrared imaging, mechanical mapping, and broadband infrared spectroscopy capability with 6 nm spatial resolution. We observed nanoscale heterogeneity in the chemical composition, aromaticity, and maturity of the kerogens from oil shales from Eagle Ford shale play in Texas. The kerogen aromaticity positively correlates with the local mechanical moduli of the surrounding inorganic matrix, manifesting the Le Chatelier’s principle. In situ spectro-mechanical characterization of oil shale will yield valuable insight for geochemical and geomechanical modeling on the origin and transformation of kerogen in the oil shale.</p>

scholarly journals The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing

2021 ◽  
Vol 13 (10) ◽  
pp. 1958
Author(s):  
Shelly Elbaz ◽  
Efrat Sheffer ◽  
Itamar M. Lensky ◽  
Noam Levin
Keyword(s):  
Spatial Resolution ◽  
Seasonal Changes ◽  
Dominant Role ◽  
Vegetation Indices ◽  
Individual Species ◽  
Viewing Angle ◽  
Pixel Size ◽  
Mediterranean Species ◽  
Near Surface ◽  
Nadir Point

Discriminating between woody plant species using a single image is not straightforward due to similarity in their spectral signatures, and limitations in the spatial resolution of many sensors. Seasonal changes in vegetation indices can potentially improve vegetation mapping; however, for mapping at the individual species level, very high spatial resolution is needed. In this study we examined the ability of the Israel/French satellite of VENμS and other sensors with higher spatial resolutions, for identifying woody Mediterranean species, based on the seasonal patterns of vegetation indices (VIs). For the study area, we chose a site with natural and highly heterogeneous vegetation in the Judean Mountains (Israel), which well represents the Mediterranean maquis vegetation of the region. We used three sensors from which the indices were derived: a consumer-grade ground-based camera (weekly images at VIS-NIR; six VIs; 547 individual plants), UAV imagery (11 images, five bands, seven VIs) resampled to 14, 30, 125, and 500 cm to simulate the spatial resolutions available from some satellites, and VENμS Level 1 product (with a nominal spatial resolution of 5.3 m at nadir; seven VIs; 1551 individual plants). The various sensors described seasonal changes in the species’ VIs at different levels of success. Strong correlations between the near-surface sensors for a given VI and species mostly persisted for all spatial resolutions ≤125 cm. The UAV ExG index presented high correlations with the ground camera data in most species (pixel size ≤125 cm; 9 of 12 species with R ≥ 0.85; p < 0.001), and high classification accuracies (pixel size ≤30 cm; 8 species with >70%), demonstrating the possibility for detailed species mapping from space. The seasonal dynamics of the species obtained from VENμS demonstrated the dominant role of ephemeral herbaceous vegetation on the signal recorded by the sensor. The low variance between the species as observed from VENμS may be explained by its coarse spatial resolution (effective ground spatial resolution of 7.5) and its non-nadir viewing angle (29.7°) over the study area. However, considering the challenging characteristics of the research site, it may be that using a VENμS type sensor (with a spatial resolution of ~1 m) from a nadir point of view and in more homogeneous and dense areas would allow for detailed mapping of Mediterranean species based on their seasonality.

scholarly journals A Blended Sea Ice Concentration Product from AMSR2 and VIIRS

2021 ◽  
Vol 13 (15) ◽  
pp. 2982
Author(s):  
Richard Dworak ◽  
Yinghui Liu ◽  
Jeffrey Key ◽  
Walter N. Meier
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Infrared Imaging ◽  
High Spatial Resolution ◽  
Sea Ice Concentration ◽  
Clear Sky ◽  
Ice Concentration ◽  
Best Linear Unbiased ◽  
The Individual ◽  
Sky Conditions

An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE) method is used to combine the AMSR2 and VIIRS SIC for a blended product at 1 km resolution under clear-sky conditions. Under cloudy-sky conditions the AMSR2 SIC with bias correction is used. For validation, high spatial resolution Landsat data are collocated with VIIRS and AMSR2 from 1 February 2017 to 31 October 2019. Bias, standard deviation, and root mean squared errors are calculated for the SICs of VIIRS, AMSR2, and the blended field. The blended SIC outperforms the individual VIIRS and AMSR2 SICs. The higher spatial resolution VIIRS data provide beneficial information to improve upon AMSR2 SIC under clear-sky conditions, especially during the summer melt season, as the AMSR2 SIC has a consistent negative bias near and above the melting point.

Effect of multi-level and multi-scale spectral data source on vineyard state assessment via spectral vegetation indices

2021 ◽  
Author(s):  
Antonello Bonfante ◽  
Arturo Erbaggio ◽  
Eugenia Monaco ◽  
Rossella Albrizio ◽  
Pasquale Giorio ◽  
...  
Keyword(s):  
Climate Change ◽  
Data Collection ◽  
State Estimation ◽  
Spectral Data ◽  
Spatial Resolution ◽  
Spectral Region ◽  
Vegetation Indices ◽  
Plant Management ◽  
Spectral Vegetation Indices ◽  
Data Source

&lt;p&gt;Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality, under climate change conditions. Climate change is one of the major challenges for high incomes crops, as the vineyards for high-quality wines, since it is expected to drastically modify plant growth, with possible negative effects especially in arid and semi-arid regions of Europe. In this context, the reduction of negative environmental impacts of intensive agriculture (e.g. soil degradation), can be realized by means of high spatial and temporal resolution of field crop monitoring, aiming to manage the local spatial variability.&lt;/p&gt;&lt;p&gt;The monitoring of spatial behaviour of plants during the growing season represents an opportunity to improve the plant management, the farmer incomes and to preserve the environmental health, but it represents an additional cost for the farmer.&lt;/p&gt;&lt;p&gt;The UAS-based imagery might provide detailed and accurate information across visible and near infrared spectral regions to support monitoring (crucial for precision agriculture) with limitation in bands and then on spectral vegetation indices (Vis) provided. VIs are a well-known and widely used method for crop state estimation. The ability to monitor crop state by such indices is an important tool for agricultural management. While differences in imagery and point-based spectroscopy are obvious, their impact on crop state estimation by VIs is not well-studied. The aim of this study was to assess the performance level of the selected VIs calculated from reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500nm with spatial resolution of &lt;2m) through Convolutional Neural Network (CNN) approach (Brook et al., 2020), UAS-based multispectral (5 bands across 450-800nm spectral region with spatial resolution of 5cm) imagery and point-based field spectroscopy (collecting 600 wavelength across&amp;#160; 400-1000nm spectral region with a surface footprint of 1-2cm) in application to crop state estimation.&lt;/p&gt;&lt;p&gt;The test site is a portion of vineyard placed in southern Italy cultivated on Greco cultivar, in which the soil-plant and atmosphere system has been monitored during the 2020 vintage also through ecophysiological analyses. The data analysis will follow the methodology presented in a recently published paper (Polinova et al., 2018).&lt;/p&gt;&lt;p&gt;The study will connect the method and scale of spectral data collection with in vivo plant monitoring and prove that it has a significant impact on the vegetation state estimation results. It should be noted that each spectral data source has its advantages and drawbacks. The plant parameter of interest should determine not only the VIs type suitable for analysis but also the method of data collection.&lt;/p&gt;&lt;p&gt;The contribution has been realized within the CNR BIO-ECO project.&lt;/p&gt;

The Effect of Uniaxial Cyclic Deformation on the Evolution of Phase Transformation Fronts in Pseudoelastic NiTi Wire

2001 ◽  
Author(s):  
Mark A. Iadicola ◽  
John A. Shaw
Keyword(s):  
Mechanical Response ◽  
Infrared Imaging ◽  
Partial Transformation ◽  
Niti Wire ◽  
Full Field ◽  
Temperature Experiment ◽  
Background Temperature ◽  
Higher Temperature ◽  
Lower Temperature ◽  
Niti Wires

Abstract Experiments are presented of the response of pseudoelastic NiTi wires subjected to displacement controlled cycles. A custom built thermo-mechanical testing apparatus is used to control the background temperature field of the wire specimen while allowing the evolution of transformation fronts to be tracked by full field infrared imaging. Two experiments under similar end-displacement histories, but at temperatures ≈8°C apart, are shown to give remarkably different cyclic responses. The mechanical response for the lower temperature experiment continued to soften but retained its shape through 43 partial transformation cycles, and the pattern of transformation fronts seemed to reach a steady state. The response for the higher temperature experiment showed a change in shape of the mechanical response and distinct changes in transformation front patterns over 31 partial transformation cycles.

High spatial resolution full-field microscopy using a desktop-size soft x-ray laser

2007 ◽  
Author(s):  
Courtney A. Brewer ◽  
Fernando Brizuela ◽  
Dale Martz ◽  
Georgiy Vaschenko ◽  
Mario C. Marconi ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Full Field ◽  
X Ray

scholarly journals Bioimaging Using Full Field and Contact EUV and SXR Microscopes with Nanometer Spatial Resolution

2017 ◽  
Vol 7 (6) ◽  
pp. 548 ◽  
Author(s):  
Przemysław Wachulak ◽  
Alfio Torrisi ◽  
Mesfin Ayele ◽  
Joanna Czwartos ◽  
Andrzej Bartnik ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Full Field
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