Below are a list of select white papers and research listed by topic.
Imaging
Existing and Potential Standoff Explosives Detection Techniques
Committee on the Review of Existing and Potential Standoff Explosives Detection Techniques, National Research Council
This free PDF was downloaded from: http://www.nap.edu/catalog/10998.html or download here> .
This report addresses the following tasks:
- Describe the characteristics of explosives, bombs, and their components that are or might be used to provide a signature for exploitation in detection technology.
- Consider scientific techniques for exploiting these characteristics to detect explosives and explosive devices. Particular consideration must be given to discriminating possible signals from the background and interferents that can be anticipated in real applications.
- Discuss the potential for integrating such techniques into detection systems that would have sufficient sensitivity without an unacceptable false-positive rate. In proposing possible detection protocols, give consideration to trade-offs between desirable system characteristics, including relative ease of implementation.
- Propose areas for research that might be expected to yield significant advances in practical explosives and bomb detection technology in the near, mid, and long term.
Scientific CMOS Technology
www.fairchildimaging.com/main/documents/sCMOSWhitePaperVersion4-b-LR1.pdf
Synopsis:
Since its inception, CMOS image sensor (CIS) technology has held great potential to become the detector platform of choice for many scientific imaging applications. These demanding fields require a unique combination of sensitivity, speed, dynamic range, resolution, and field of view.
Although CIS technology has steadily improved it has not fully realized its potential, with CCD, and more recently EMCCD, detectors remaining the platforms of choice for the majority of high-end scientific imaging applications.
In this paper we present sCMOS, a breakthrough technology based on next-generation CIS design and fabrication techniques. sCMOS is poised for widespread recognition as a true scientific grade CIS, capable of out-performing most scientific imaging devices on the market today.
Unlike previous generations of CMOS and CCD-based sensors, sCMOS is uniquely capable of simultaneously offering:
• Extremely low noise
• Rapid frame rates
• Wide dynamic range
• High quantum efficiency (QE)
• High resolution
• Large field of view
Imaging Programs
www.imagingprogramsware.com/what-is-hyperspectral-imaging-and-some-applications-for-it-.php
Synopsis:
What Is Hyperspectral Imaging And Some Applications For It
Agriculture
Visible/near-infrared hyperspectral imaging for beef tenderness prediction
Synopsis:
Beef tenderness is an important quality attribute for consumer satisfaction. The current beef quality grading system does not incorporate a direct measure of tenderness because there is currently no accurate, rapid, nondestructive method for predicting tenderness available to the beef industry. The objective of this study was to develop and test a visible/near-infrared hyperspectral imaging system to predict tenderness of 14-day aged, cooked beef from hyperspectral images of fresh ribeye steaks acquired at 14-day post-mortem. A pushbroom hyperspectral imaging system (wavelength range: 400–1000 nm) with a diffuse-flood lighting system was developed and calibrated. Hyperspectral images of beef-steak (n = 111) at 14-day post-mortem were acquired. After imaging, steaks were cooked and slice shear force (SSF) values were collected as a tenderness reference. All images were corrected for reflectance. After reflectance calibration, a region-of-interest (ROI) of 200 × 600 pixels at the center was selected and principal component analysis was carried out on the ROI images to reduce the dimension along the spectral axis. The first five principal components explained over 90% of the variance of all spectral bands in the image. Gray-level textural co-occurrence matrix analysis was conducted to extract second-order statistical textural features from the principal component images. These features were then used in a canonical discriminant model to predict three beef tenderness categories, namely tender (SSF ≤ 205.80 N), intermediate (205.80 N < SSF < 254.80 N), and tough (SSF ≥ 254.80 N). With a leave-one-out cross-validation procedure, the model predicted the three tenderness categories with a 96.4% accuracy. All of the tough samples were correctly identified. Our results indicate that hyperspectral imaging has considerable promise for predicting beef tenderness.
A Low-Cost Remote Sensing Platform for Agriculture
www.bae.uky.edu/precag/PrecisionAg/Development_and_Assessment/phase5/7-6.pdf
Synopsis:
The overall goal of this project is to develop a flexible, low-cost remote sensing system that can be used
for agricultural applications. This goal will be met through the following specific objectives:
1. Develop a lightweight digital imaging system capable of obtaining high-resolution NIR images.
2. Calibrate the digital imaging device using commercially available sensor technologies.
3. Demonstrate the usefulness of the UAV imaging system for nitrogen manage ment.
4. Demonstrate the utility of the UAV system for pre-scouting fields.
Mapping of Individual Oil Palm Trees Using Airborne Hyperspectral Sensing: An Overview
www.ccsenet.org/journal/index.php/apr/article/view/1805/1715
Synopsis
This overview represents a preamble step for developing an approach for mapping individual oil palm trees from airborne hyperspectral imaging. The study generally describes airborne hyperspectral sensors in different fields particularly in agriculture by comparing and analyzing their uniqueness for different applications. The emphasis is on the image processing in identifying and mapping of the individual oil palm trees with the utilization of image histogram to examine the RGB bands. An algorithm is design to discover the involvement of different materials in a single mixed pixel and converting it into a pure pixel. The techniques employ in this connection are Linear Spectral Mixture Analysis (LSMA), Mix to Pure Converter (MPC) and Euclidean Norm.
Multispectral Machine Vision Identification of Lettuce and Weed Seedlings for Automated Weed Control
www.bioone.org/doi/pdf/10.1614/WT-07-104.1
Synopsis
Multispectral images of leaf reflectance in the visible and near infrared region from 384 to 810 nm were used to establish the feasibility of developing a site-specific classifier to distinguish lettuce plants from weeds in California direct-seeded lettuce fields. An average crop vs. weed classification accuracy of 90.3% was obtained in a study of over 7,000 individual spectra representing 150 plants. The classifier utilized reflectance values from a small spatial area (3 mm diameter) of the leaf in order to allow the method to be robust to occlusion and to eliminate the need to identify leaf boundaries for shape-based machine vision recognition. Reflectance spectra were collected in the field using equipment suitable for real-time operation as a weed sensor in an autonomous system for automated weed control
Thermal
1-5 micron wavelength near-mid infrared spectral band InSb Thermal infrared camera system
www.infrared-cameras.org/insb_hyperspectral_near_ir_camera.htm
Synopsis
The IS15™ MWIR Camera is a high performance FLIR thermal infrared device sensitive in the 1 micron to 5 micron spectral range.
Mass Screening of Suspected Febrile Patients with Remote-sensing Infrared Thermography: Alarm Temperature and Optimal Distance
www.ajws.elsevier.com/ajws_archive/20081210712A5694.pdf
Synopsis
Detection of fever has become an essential step in identifying patients who may have severe acute respiratory syndrome (SARS) or avian influenza. This study evaluated infrared thermography (IRT) and compared the influence of different imagers, ambient temperature discrepancy, and the distance between the subject and imager.