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Computerized pattern recognition was used to look for characteristics of new nickel-cadmium spacecraft cells which would be predictive of later performance under stressful conditions. It was found that the changes in voltage while a cell was being charged could be used to make a rough estimate of its lifetime. The standard deviation in the predicted lifetime values was somewhat smaller than the standard deviation of the lifetime distribution as a whole, and there are indications that a more extensive data set would yield better results. (Author)

Computerized pattern recognition was used to look for characteristics of new nickel-cadmium spacecraft cells which would be predictive of later performance under stressful conditions. It was found that the changes in voltage while a cell was being charged could be used to make a rough estimate of its lifetime. The standard deviation in the predicted lifetime values was somewhat smaller than the standard deviation of the lifetime distribution as a whole, and there are indications that a more extensive data set would yield better results. (Author)

The primary goal of this research is to study a programmable joint- transform correlator (JTC) using liquid crystal television (LCTV) panels for adaptive real-time pattern recognition applications. The technique can improve the pattern recognition and identification technology that is of interest to the U.S. Army. The technique we studied is a real-time programmable electro-optical architecture. There are several reasons for selecting the optical technique over their digital and electronic counterparts, as follows: Optical technique is capable of handling a large space-bandwidth image; optical technique is capable of performing parallel operations; optics can perform massive interconnections; optical transformation can be operated at high speed, etc. By using the LCTV, the pattern under observation can be correlated with a large number of recallable image memories. In addition, the LCTV technique is rather simple and economical to operate. The LCTV-optical correlator, in principle, can be designed into a compact portable form for insitu application. Brief outlines of the major research findings and publications are provided in this report.

An approach to simultaneous interpretation of objects in complex structures so as to maximize a combined utility function is presented. Results of the application of a computer software system to assign meaning to regions in a segmented image based on the principles described in this paper and on a special interactive sequential classification learning system, which is referenced, are demonstrated.

The primary goal of this work has been to develop non-destructive testing methods as a screening procedure for batteries to predict lifetime and identify probably failure mechanisms. A secondary goal has been to develop criteria for predicting imminent failure from battery performance data. We believe that these goals can be met by the application of computerized pattern recognition to the evaluation of measureable features. In the studies supported by this ONR contract, pattern recognition techniques have been used to analyze data collected previously in established battery testing programs. Because of the ability to evaluate multivariate relationships it was possible to identify previously unobservable multi-dimensional correlations between test data obtained very early in a battery's life-time and its life expectancy and/or failure mechanism. It was the objective of these initial studies to identify the most meaningful types of measurements for lifetime prediction and to establish the feasibility of lifetime prediction. The second function to be provided by pattern recognition methods is to evaluate test data generated subsequently by the new short-term screening tests developed. This will involve collaborative efforts with other laboratories to generate such a data base. Probably several iterations in this procedure will be required before an optimum set of short-term measurements is identified. Such studies are prescribed for future work. (Author)

The primary goal of this work has been to develop non-destructive testing methods as a screening procedure for batteries to predict lifetime and identify probably failure mechanisms. A secondary goal has been to develop criteria for predicting imminent failure from battery performance data. We believe that these goals can be met by the application of computerized pattern recognition to the evaluation of measureable features. In the studies supported by this ONR contract, pattern recognition techniques have been used to analyze data collected previously in established battery testing programs. Because of the ability to evaluate multivariate relationships it was possible to identify previously unobservable multi-dimensional correlations between test data obtained very early in a battery's life-time and its life expectancy and/or failure mechanism. It was the objective of these initial studies to identify the most meaningful types of measurements for lifetime prediction and to establish the feasibility of lifetime prediction. The second function to be provided by pattern recognition methods is to evaluate test data generated subsequently by the new short-term screening tests developed. This will involve collaborative efforts with other laboratories to generate such a data base. Probably several iterations in this procedure will be required before an optimum set of short-term measurements is identified. Such studies are prescribed for future work. (Author)

This article is from International Journal of Molecular Sciences , volume 15 . Abstract Chemometric pattern recognition techniques were employed in order to obtain Structure-Activity Relationship (SAR) models relating the structures of a series of adenosine compounds to the affinity for glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). A training set of 49 compounds was used to build the models and the best ones were obtained with one geometrical and four electronic descriptors. Classification models were externally validated by predictions for a test set of 14 compounds not used in the model building process. Results of good quality were obtained, as verified by the correct classifications achieved. Moreover, the results are in good agreement with previous SAR studies on these molecules, to such an extent that we can suggest that these findings may help in further investigations on ligands of LmGAPDH capable of improving treatment of leishmaniasis.

This research is directed toward optical DNA sequence analysis, which is a specific application of high resolution, optical pattern recognition. The response times and contrast ratios of four different liquid crystal televisions were measured. The use of liquid crystal television in both a joint transform correlator and a VanderLugt correlator was investigated experimentally and is compared with the use of photographic film to search for a character within a data set of characters.

This research is directed toward optical DNA sequence analysis, which is a specific application of high resolution, optical pattern recognition. The response times and contrast ratios of four different liquid crystal televisions were measured. The use of liquid crystal television in both a joint transform correlator and a VanderLugt correlator was investigated experimentally and is compared with the use of photographic film to search for a character within a data set of characters.

The particular problem with which the research was concerned was the development of a technique to discriminate between coughs and other audible phenomena which originate in a hospital environment. Pattern recognition provided such a technique. Experimental data was available in the form of audio tape recordings. Implementation of a Bayes categorization decision requires knowledge of the underlying conditional joint probability density functions of the measures which typify the patterns to be recognized. An adaptive pattern classifier model was presented which circumvented the difficulty of estimating these functions. The model is generally applicable to the two-class case in which the patterns to be classified consist of sequential segments of data known to have originated from the same class. The model took the form of a layered machine. The first stage was a minimum distance classifier with respect to point sets while the second stage utilized the first stage binary valued outputs to implement a Bayes decision. The feature extraction and measure selection problems were examined experimentally. Feature calculation algorithms were developed which are generally applicable to time varying signals. The effectiveness of an algorithm for selection of a set of candidate measures was verified. Experimental results indicated that, for the particular data with which the research was concerned, an assumption of Markoff-1 dependency between sequential first stage decisions of the pattern classifier was warranted. A pattern classifier which was based on this assumption classified 97.9% of the patterns presented to its input correctly.

The automation of wear debris characterization by SEM/EDX analysis using mathematical pattern recognition techniques offers a reliable identification of particle alloy composition. It is less time consuming than an individual check for the best fitting material. Another advantage is the full automatic generated Word report, which can be easily distributed by LAN or email.

This report presents a summary of the basic principles of pattern recognition and statistical decision theory and applies them to the problem of classifying radar returns. While pattern recognition techniques have been applied to radar signal detection problems, they have rarely been used in testing hypothesis for classifying radar returns. Two techniques, the parametric Bayes and the non-parametric K-Nearest Neighbor algorithms, were compared using simulated radar backscatter data. The error rate of these algorithms was the chief criterion used for the evaluation of performance. The results showed that the Nearest Neighbor technique gives a smaller error rate than the Bayes technique for the limited data sets tested. (Author)

The primary goal of this research is to study a programmable joint- transform correlator (JTC) using liquid crystal television (LCTV) panels for adaptive realtime pattern recognition applications. The technique can improve the pattern recognition and identification technology that is of interest to the U.S. Army. The technique we studied is a real-time programmable electro-optical architecture. There are several reasons for selecting the optical technique over their digital and electronic counterparts, as follows: Optical technique is capable of handling a large space-bandwidth image; optical technique is capable of performing parallel operations; optics can perform massive interconnections; optical transformation can be operated at high speed, etc. By using the LCTV, the pattern under observation can be correlated with a large number of recallable image memories. In addition, the LCTV technique is rather simple and economical to operate. The LCTV-optical correlator, in principle, can be designed into a compact portable form for insitu application. Brief outlines of the major research findings and publications are provided in this report.... Joint transform correlator, Liquid crystal television, Correlator, Optical pattern recognition.

The ability of a set of simple predicates to capture characteristic patterns in a parametric representation of vowels in continuous speech was investigated with the aid of an efficient conjunctive pattern recognition and classification system. The results compare favorably with those produced by a cluster-based minimal Euclidean distance technique, run over the identical training and test samples. The predicates used are similar to auditory receptive fields.