Dr. Kudenov obtained his BS in Electrical Engineering from the University of Alaska Fairbanks in 2005 and his PhD in Optical Sciences from the University of Arizona in 2009. He is currently an Associate Professor in Electrical and Computer Engineering at North Carolina State University. His research interests focus on imaging spectrometer and polarimeter sensors for applications spanning agriculture, plant phenotyping, remote sensing, and quality control. He also serves as the academic advisor for the NC State SPIE student chapter.
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Channeled spectropolarimetry (CSP) is a spectral modulation technique that furnishes snapshot polarimetric measurements. Among the different spectropolarimetric methods, fiber-based techniques are fundamental requirements in specific biomedical procedures, such as endoscopy. However, the optical fiber exhibits high sensitivity to temperature changes, which subsequently incurs errors in the polarimetric reconstructions. We present a calibration technique for fiber-based CSP that leverages phase-shifting interferometry (PSI) to acquire and demodulate the fiber’s retardation phase accurately. This technique provides high robustness against temperature variations and improved polarimetric reconstruction performance. Experimental results demonstrated that the PSI calibrations offer higher stability and accuracy, at different optical fiber temperatures, as compared to reference-beam calibrations.
This course covers imaging polarimeters from an instrumentation-design point of view. Basic polarization elements for the visible, mid-wave infrared, and long-wave infrared are described in terms of Mueller matrices and the Poincaré sphere. Polarization parameters such as the degree of polarization (DOP), the degree of linear polarization (DOLP) and the degree of circular polarization (DOCP) are explained in an imaging context. Emphasis is on imaging systems designed to detect polarized light in a 2-D image format. System concepts are discussed using a Stokes-parameter (s0,s1,s2,s3) image. Imaging-polarimeter systems design, pixel registration, and signal to noise ratios are explored. Temporal artifacts, characterization and calibration techniques are defined.
Use-inspired research and product development, along with machine learning, is becoming well-established in our community. Collecting image and sensor data, within the environments in which a classification or detection software will be deployed, is paramount for training and developing algorithms. This course provides a starting point for deploying embedded optical systems for moderate-scale data collection projects and edge (machine learning/AI) computing applications. Bit depth, image formation, sampling artifacts, camera responsivity, and practical issues related to radiometric transfer are placed into the context of camera selection, computer networking, embedded system compute power, GPIO, cable fabrication, electronic shielding, sensor ruggedization, operating systems, camera software development kits, and programming interfaces. Examples focus on high throughput optical plant phenotyping sensors, deployed in on-line commercial packing operations, to further describe the impact that proper selection of electronics, shielding, and compute methods can have in real-world environments. Key questions answered include “How do I synchronize image collection to external events?” and “How does camera and lens selection impact my data quality?”
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