The objective of this study is to demonstrate a sensitive Raman technique for sensing degradation of propellant
stabilizers like MNA and 2-NDPA that are commonly used in some missiles. The functionality of missiles and rockets
are often evaluated by being fired or decomposed at routine time-intervals after prolonged storage. However, these
destructive testing techniques for determining long-term rocket motor aging and shelf-life are extremely costly. If
successful, the Raman technique could be utilized to determine the health of propellant stabilizers without dismantling
the missiles as is commonly done at present. Raman technique is to measure concentrations of propellant stabilizers
between 0.1-2% in glycerin. Two different lasers at 785 nm and 532 nm are used for developing this technique. A
secondary objective is to develop a theoretical model that predicts temperature as a function of time and position inside
the cylindrical storage container of MNA or 2-NDPA stabilizer. This model can help in understanding the thermal
degradation of propellant stabilizers.
ATR in two dimensional images is valuable for precision guidance, battlefield awareness and surveillance
applications. Current ATR methods are largely data-driven and as a result, their recognition accuracy relies
on the quality of training dataset. These methods fail to reliably recognize new target types and targets in
new backgrounds and/or atmospheric conditions. Thus, there is a need for an ATR solution that can
constantly update itself with information from new data samples (samples may belong to existing classes,
background clutter or new target classes). In the paper, this problem is addressed in two steps: 1)
Incremental learning with Fully Adaptive Approximate Nearest Neighbor Classifier (FAAN) - A novel data
structure is designed to allow incremental learning in approximate nearest neighbor classifier. New data
samples are assimilated at reduced complexity and memory without retraining on existing data samples, 2)
Data Categorization using Data Effectiveness Measure (DEM) - DEM of a data sample is a degree to which
each sample belongs to a local cluster of samples. During incremental learning, DEM is used to filter out
redundant samples and outliers, thereby reducing computational complexity and avoiding data imbalance
issues. The performance of FAAN is compared with proprietary Bagging-based Incremental Decision Tree
(ABAFOR) implementation. Tests performed on Army ATR database with over 37,000 samples shows that
while classification accuracy of FAAN is comparable to ABAFOR (both close to 95%), the process of
incremental learning is significantly quicker.
Video compression is critical to many real world applications where the data is being transmitted over data links with limited bandwidth. Previously, information metrics have been used to assess the distortion of target signatures with differing degrees of compression. A recent update to the emerging H.264 standard, called Fidelity Range Extensions (FRExt), permits applications up to 12-bits/sample, and even monochrome only, which are especially relevant for IR applications. This paper tests this new FRExt technology on actual IR sensor data to obtain preliminary results on the compression of 12-bit IR data using state of the art video compression technology.
Video compression is a necessary part of many real world applications where the data is being transmitted over data links with limited bandwidth. Previously, information metrics have been used to assess the distortion of target signatures with differing degrees of compression. This paper makes use of a well-known ATR algorithm to assess the impact of an instantiation of the evolving H.264 compression standard on the ATR detection itself.
Optical correlation systems have been lagging in performance due to the severely limited dynamic range capability which has been somewhat alleviated as newer devices are becoming available. In general, however, it is not clear what dynamic range is required for generally good performance both in the input device and in the filters. Experiments with a digital cross correlator which can accommodate up to 16 bits of input dynamic range was used to establish practical performance limitations for a majority of cross correlators. The experiments were conducted using a reduction of the dynamic range from actual 12 bit data incrementally to smaller levels such that performance measures could be compared. This paper quantifies these results.
Synthetic Discriminant Functions have had several different names over the last ten years of their development from the earliest type called linear combinatorial filters to one of the latest versions called the minimum average correlation energy filter. The ten years of development produced many different variations and efforts toward significant advancement over two dimensional matched filters. Most of this filter development was oriented toward optical implementations however the test results presented here are selected from many filters tested digitally and are considered exemplary of the major types for use in both optical and digital implementations. 2. SDF Development Stages We categorize three major stages in SDF development each of which is considered an improvement over the Standa SDF using N training Images d long previous one. The original or first SDF where d " N. method is a linearly combined reference N set'' using the technique outlined In h . a. . SDF filter Figure 12. liii The limitation of this original T v approach Is that the correlation surface is 1 not guaranteed to be anything specific or N T N defined except at the registered position. In 1 a X1 X V1 a A j123 - - - N other words there is no control over the Ri i1 output correlation surface except at one point. This result is of course in general . much different than matched filter would a R V produce and was not very useful until phase encoding schemes were introduced. 3 The values of v1 t The are the training Images. The phase only encoding shown in ______________________________ Figure 2 and the binary phase encoding Figure 1 produce dramatically improved results. 110 / SPIE Vol 1297 Hybrid Image and Signal Processing /1(1990)
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