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Full-Text Articles in Engineering
High Wind Alerts: A System Created With Observations From The X-Band Teaching And Research Radar, Lauren Warner
High Wind Alerts: A System Created With Observations From The X-Band Teaching And Research Radar, Lauren Warner
The Journal of Purdue Undergraduate Research
Following the August 13, 2011, Indiana State Fair stage collapse tragedy, caused by a wind gust from an approaching thunderstorm, Purdue University enforced a wind speed restriction of 30 mph (13 m s-1) for tents at outdoor events. During these events, volunteers stand outside with handheld anemometers, measuring and reporting when the wind speeds exceed this limit. In this study, we report testing of a new system to automate high-wind alerts based on observations from a Doppler radar, the X-band Teaching and Research Radar (XTRRA), near Purdue’s campus. XTRRA scans over campus at low elevations approximately every 5 minutes. Using …
Remote Sensing Using I-Band And S-Band Signals Of Opportunity, Kadir Efecik, Benjamin R. Nold, James L. Garrison
Remote Sensing Using I-Band And S-Band Signals Of Opportunity, Kadir Efecik, Benjamin R. Nold, James L. Garrison
The Summer Undergraduate Research Fellowship (SURF) Symposium
Measurement of soil moisture, especially the root zone soil moisture, is important in agriculture, meteorology, and hydrology. Root zone soil moisture is concerned with the first meter down the soil. Active and passive remote sensing methods used today utilizing L-band(1-2GHz) are physically limited to a sensing depth of about 5 cm or less. To remotely sense the soil moisture in the deeper parts of the soil, the frequency should be lowered. Lower frequencies cannot be used in active spaceborne instruments because of their need for larger antennas, radio frequency interference (RFI), and frequency spectrum allocations. Ground-based passive remote sensing using …
Experimental Testing And Validation Of P-Band Bi-Static Remote Sensing Of Soil Moisture In 137-138mhz Range, Xiangyu Qu, Yao-Cheng Lin, James L. Garrison
Experimental Testing And Validation Of P-Band Bi-Static Remote Sensing Of Soil Moisture In 137-138mhz Range, Xiangyu Qu, Yao-Cheng Lin, James L. Garrison
The Summer Undergraduate Research Fellowship (SURF) Symposium
Remote sensing using readily available communication signal transmitted by ORBCOMM satellites at very high frequency (VHF) range (137-138MHz) is a promising method for detecting the root zone soil moisture content. The radio wave reflectivity of soil is strongly correlated to soil moisture content. Therefore, if we were able to measure the reflectivity, we might be able to estimate the soil moisture content. In this preliminary study, we analyze direct signal data from the satellites to investigate and verify communication channels in frequency range of interest and their characteristics (bandwidth, pattern, etc.). The analysis of direct signal data is also used …
P-Band Satellite Remote Sensing Antenna, Nishtha Sinha, James L. Garrison, Lin Yao-Cheng
P-Band Satellite Remote Sensing Antenna, Nishtha Sinha, James L. Garrison, Lin Yao-Cheng
The Summer Undergraduate Research Fellowship (SURF) Symposium
Today, there are a huge number of satellites out there in the space orbiting the earth, and there are specific frequency bands allocated for data transmission from these satellites. Signals from these satellites can be accessed at different places on earth, and used for remote sensing. Lower frequency bands are being used in this project, which have not been used earlier for remote sensing. The main idea of this study is to use the properties of two P-band communication satellites to assess their utility for ‘reflectometry’. This remote sensing method is based upon the comparison of the direct and reflected …
Manifold Learning Based Spectral Unmixing Of Hyperspectral Remote Sensing Data, Jun-Hwa Chi
Manifold Learning Based Spectral Unmixing Of Hyperspectral Remote Sensing Data, Jun-Hwa Chi
Open Access Dissertations
Nonlinear mixing effects inherent in hyperspectral data are not properly represented in linear spectral unmixing models. Although direct nonlinear unmixing models provide capability to capture nonlinear phenomena, they are difficult to formulate and the results are not always generalizable. Manifold learning based spectral unmixing accommodates nonlinearity in the data in the feature extraction stage followed by linear mixing, thereby incorporating some characteristics of nonlinearity while retaining advantages of linear unmixing approaches. Since endmember selection is critical to successful spectral unmixing, it is important to select proper endmembers from the manifold space. However, excessive computational burden hinders development of manifolds for …
Support Vector Selection And Adaptation For Classification Of Remote Sensing Images, Gulsen Taskin Kaya, Okan Ersoy
Support Vector Selection And Adaptation For Classification Of Remote Sensing Images, Gulsen Taskin Kaya, Okan Ersoy
Department of Electrical and Computer Engineering Technical Reports
Classification of nonlinearly separable data by nonlinear support vector machines is often a difficult task especially due to the necessity of a choosing a convenient kernel type. In this study, we propose a new classification method called support vector selection and adaptation (SVSA) that is applicable to both linearly and nonlinearly separable data in terms of some reference vectors generated by processing of support vectors obtained from the linear SVM. The method consists of two steps called selection and adaptation. In these two steps, once the support vectors are obtained by a linear SVM, some of them are rejected and …