Downloads & Free Reading Options - Results

the prevalence of myopia was around 10%, but today nearly 90% of young adults are afflicted [1]. In the United States and Europe, the prevalence has doubled over the last half-century with half of young adults now having myopia[1]. The consequence of myopia extends beyond the inconvenience of using optical correction. Myopia is associated with increased incidence of diseases such as retinal damage, cataract, and glaucoma [1]. Increased levels of near-work have commonly been associated with the onset of myopia [2]. It is therefore important to gain a better understanding of what is happening from a functional and anatomical standpoint when subjects complete near-work tasks. There are two different types of text, texts that have positive contrast (white text on a darker background) and texts that have negative contrast (black text on a lighter background). Aleman et. al (2018) found that negative contrast texts produced a thinning effect on the choroid (-16.13土4.54 μm), and positive contrast produced a thickening effect on the choroid(9.96土 6.51μm) [3]. Aleman et. al interpreted these results in the context of ON and OFF pathways in the visual system [4]. They suggested negative contrast texts stimulate the OFF pathway, while positive contrast texts stimulate the ON pathway. Choroidal thickening and thinning in both chicks and marmosets has been observed to be a precursor to a decrease or an increase in axial growth respectively [5, 6, 7]. The latter leading to the development of myopia. Trolio et. al documented 20 μm of choroidal thinning in myopia induced marmoset eyes and 12 μm of thickening in eyes induced with hyperopia [5]. It is hypothesized that the choroid either acts as a mechanical buffer or a diffusion barrier for the sclera [5]. Qualitatively, Aleman et al’s data showed the thinning effect was more pronounced in myopic individuals as compared to their emmetropic counterparts [3], but their sample size was insufficient to draw quantitative conclusions. The plasticity of the human choroid is an important link to animal models. For example, Read et. al found that among a group of 39 myopic individuals subfoveal choroidal thickness was 50 μm thinner than nonmyopic age-matched children [8]. Retinal Ganglion Cells are organized in an ON center OFF surround arrangement or vice versa [2]. ON cells become depolarized to an increment in light, while OFF depolarize in response to decrements in light. OFF retinal ganglion cells are twice as numerous and half the size of ON rental ganglion cells in the fovea. Several studies such as Mazade et. al, Lu et. al, and Tyler et. al corroborated the different spatial tuning and cortical connectivity of the ON and OFF pathway [9, 10, 11]. Mazade et al showed maximal cortical responses for small fast stimuli in the OFF pathway and maximal cortical responses for slow slow global stimuli in the ON pathway [10]. Zhong-Lin et. al found negative stimuli (or decrements in light) to be 25% more effective than equal magnitude positive stimuli (or increments in light) across eleven different types of stimuli [11]. Taken together, these studies support a distinct roles for the ON and OFF pathways in the visual system. The goal of our study is threefold. We aim to replicate the changes in choroidal thickness in response to negative and positive contrast with a larger subject group. We aim to measure the sensitivity to positive and negative contrast tasks and compare the ratio of contrast sensitivity to these two contrast types among refractive error/axial length. Finally, we will monitor the changes in sensitivity to positive and negative contrast as the session proceeds as to understand the functional implications choroidal thickness changes may have.

This article is from PLoS ONE , volume 8 . Abstract Interactions between individual members of the B-cell lymphoma 2 (Bcl-2) family of proteins form a regulatory network governing mitochondrial outer membrane permeabilization (MOMP). Bcl-2 family initiated MOMP causes release of the inter-membrane pro-apoptotic proteins to cytosol and creates a cytosolic environment suitable for the executionary phase of apoptosis. We designed the mathematical model of this regulatory network where the synthesis rates of the Bcl-2 family members served as the independent inputs. Using computational simulations, we have then analyzed the response of the model to up-/downregulation of the Bcl-2 proteins. Under several assumptions, and using estimated reaction parameters, a non-linear stimulus-response emerged, whose characteristics are associated with bistability and switch-like behavior. Interestingly, using the principal component analysis (PCA) we have shown that the given model of the Bcl-2 family interactions classifies the random combinations of inputs into two distinct classes, and responds to these by one of the two qualitatively distinct outputs. As we showed, the emergence of this behavior requires specific organization of the interactions between particular Bcl-2 proteins.

This article is from PLoS ONE , volume 8 . Abstract Interactions between individual members of the B-cell lymphoma 2 (Bcl-2) family of proteins form a regulatory network governing mitochondrial outer membrane permeabilization (MOMP). Bcl-2 family initiated MOMP causes release of the inter-membrane pro-apoptotic proteins to cytosol and creates a cytosolic environment suitable for the executionary phase of apoptosis. We designed the mathematical model of this regulatory network where the synthesis rates of the Bcl-2 family members served as the independent inputs. Using computational simulations, we have then analyzed the response of the model to up-/downregulation of the Bcl-2 proteins. Under several assumptions, and using estimated reaction parameters, a non-linear stimulus-response emerged, whose characteristics are associated with bistability and switch-like behavior. Interestingly, using the principal component analysis (PCA) we have shown that the given model of the Bcl-2 family interactions classifies the random combinations of inputs into two distinct classes, and responds to these by one of the two qualitatively distinct outputs. As we showed, the emergence of this behavior requires specific organization of the interactions between particular Bcl-2 proteins.

This article is from PLoS ONE , volume 8 . Abstract Interactions between individual members of the B-cell lymphoma 2 (Bcl-2) family of proteins form a regulatory network governing mitochondrial outer membrane permeabilization (MOMP). Bcl-2 family initiated MOMP causes release of the inter-membrane pro-apoptotic proteins to cytosol and creates a cytosolic environment suitable for the executionary phase of apoptosis. We designed the mathematical model of this regulatory network where the synthesis rates of the Bcl-2 family members served as the independent inputs. Using computational simulations, we have then analyzed the response of the model to up-/downregulation of the Bcl-2 proteins. Under several assumptions, and using estimated reaction parameters, a non-linear stimulus-response emerged, whose characteristics are associated with bistability and switch-like behavior. Interestingly, using the principal component analysis (PCA) we have shown that the given model of the Bcl-2 family interactions classifies the random combinations of inputs into two distinct classes, and responds to these by one of the two qualitatively distinct outputs. As we showed, the emergence of this behavior requires specific organization of the interactions between particular Bcl-2 proteins.

This article is from PLoS ONE , volume 8 . Abstract Interactions between individual members of the B-cell lymphoma 2 (Bcl-2) family of proteins form a regulatory network governing mitochondrial outer membrane permeabilization (MOMP). Bcl-2 family initiated MOMP causes release of the inter-membrane pro-apoptotic proteins to cytosol and creates a cytosolic environment suitable for the executionary phase of apoptosis. We designed the mathematical model of this regulatory network where the synthesis rates of the Bcl-2 family members served as the independent inputs. Using computational simulations, we have then analyzed the response of the model to up-/downregulation of the Bcl-2 proteins. Under several assumptions, and using estimated reaction parameters, a non-linear stimulus-response emerged, whose characteristics are associated with bistability and switch-like behavior. Interestingly, using the principal component analysis (PCA) we have shown that the given model of the Bcl-2 family interactions classifies the random combinations of inputs into two distinct classes, and responds to these by one of the two qualitatively distinct outputs. As we showed, the emergence of this behavior requires specific organization of the interactions between particular Bcl-2 proteins.

This article is from PLoS ONE , volume 8 . Abstract Interactions between individual members of the B-cell lymphoma 2 (Bcl-2) family of proteins form a regulatory network governing mitochondrial outer membrane permeabilization (MOMP). Bcl-2 family initiated MOMP causes release of the inter-membrane pro-apoptotic proteins to cytosol and creates a cytosolic environment suitable for the executionary phase of apoptosis. We designed the mathematical model of this regulatory network where the synthesis rates of the Bcl-2 family members served as the independent inputs. Using computational simulations, we have then analyzed the response of the model to up-/downregulation of the Bcl-2 proteins. Under several assumptions, and using estimated reaction parameters, a non-linear stimulus-response emerged, whose characteristics are associated with bistability and switch-like behavior. Interestingly, using the principal component analysis (PCA) we have shown that the given model of the Bcl-2 family interactions classifies the random combinations of inputs into two distinct classes, and responds to these by one of the two qualitatively distinct outputs. As we showed, the emergence of this behavior requires specific organization of the interactions between particular Bcl-2 proteins.

Pattern Recognition 1994: Volume 27 , Issue INDEX/CONTENTS. Digitized from IA1646616-06 . Previous issue: sim_pattern-recognition_1993-12_26_12_0 . Next issue: sim_pattern-recognition_1994-01_27_1_0 .

This project quantifies several aspects of a new multiplexed imaging technique proposed by D. S. Davis. The novel approach of this technique involved the use of encoding masks derived from a basin set of two-dimensional Walsh functions. there were two distinct problems addressed by this thesis research. First, a study of computer simulated diffraction patterns of the photon flux through these encoding masks yielded design constraints to be incorporated into a prototype system. These constraints were expressed in a simple mathematical relation in terms of wavelength, diffraction angle, and spatial frequency. A second problem addressed the minimum spatial resolution required for pattern recognition. the conclusion reached was that the minimum number of resolution elements necessary for pattern recognition is 64 in each direction. This determination also fixed the minimum size of the basis set of two-dimensional Walsh functions required for multiplexing, as well as the number of pixels required to display the image.

A pattern Recognition of a probability distribution of amino acids is obtained for selected families of proteins. The mathematical model is derived from a theory of protein families formation which is derived from application of a Pauli's master equation method.

Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This report presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low resolution data. Significance tests on the sort-ordered, sample-size normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications.

Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This report presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low resolution data. Significance tests on the sort-ordered, sample-size normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications.

Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This report presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low resolution data. Significance tests on the sort-ordered, sample-size normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications.

This paper presents a study for two languages, namely Indian and European. The main objective of this study is to pattern recognition of children's stories with special reference to Jack tales, in order to find the distinction among these languages. We consider only the Jack tales, because, they have the same type of ideology among the characters of the stories. In this article, Jack tales are classified into two categories, namely Indian (Assamese and Bengali) and European (English) including thirty seven stories in total. Detailed statistical analyses have been performed by quantifying the texts and presenting them graphically. Non-parametric approaches have been used to test the significant differences among the texts under consideration. It has been shown that there exist significant differences among the writing patterns of these stories written by different authors in both the languages. The run test, confidence bands for empirical distribution functions, Smirnov test and Friedman Test are applied to recognize their writing patterns

This paper presents a study for two languages, namely Indian and European. The main objective of this study is to pattern recognition of children's stories with special reference to Jack tales, in order to find the distinction among these languages. We consider only the Jack tales, because, they have the same type of ideology among the characters of the stories. In this article, Jack tales are classified into two categories, namely Indian (Assamese and Bengali) and European (English) including thirty seven stories in total. Detailed statistical analyses have been performed by quantifying the texts and presenting them graphically. Non-parametric approaches have been used to test the significant differences among the texts under consideration. It has been shown that there exist significant differences among the writing patterns of these stories written by different authors in both the languages. The run test, confidence bands for empirical distribution functions, Smirnov test and Friedman Test are applied to recognize their writing patterns

This volume was digitized and made accessible online due to deterioration of the original print copy.

This volume was digitized and made accessible online due to deterioration of the original print copy.

We present a new circuit for non-Boolean recognition of binary images. Employing all-spin logic (ASL) devices, we design logic comparators and non-Boolean decision blocks for compact and efficient computation. By manipulation of fan-in number in different stages of the circuit, the structure can be extended for larger training sets or larger images. Operating based on the mainly similarity idea, the system is capable of constructing a mean image and compare it with a separate input image within a short decision time. Taking advantage of the non-volatility of ASL devices, the proposed circuit is capable of hybrid memory/logic operation. Compared with existing CMOS pattern recognition circuits, this work achieves a smaller footprint, lower power consumption, faster decision time and a lower operational voltage. To the best of our knowledge, this is the first fully spin-based complete pattern recognition circuit demonstrated using spintronic devices.

We present a new circuit for non-Boolean recognition of binary images. Employing all-spin logic (ASL) devices, we design logic comparators and non-Boolean decision blocks for compact and efficient computation. By manipulation of fan-in number in different stages of the circuit, the structure can be extended for larger training sets or larger images. Operating based on the mainly similarity idea, the system is capable of constructing a mean image and compare it with a separate input image within a short decision time. Taking advantage of the non-volatility of ASL devices, the proposed circuit is capable of hybrid memory/logic operation. Compared with existing CMOS pattern recognition circuits, this work achieves a smaller footprint, lower power consumption, faster decision time and a lower operational voltage. To the best of our knowledge, this is the first fully spin-based complete pattern recognition circuit demonstrated using spintronic devices.

We show that a ring of phase oscillators coupled with transmission delays can be used as a pattern recognition system. The introduced model encodes patterns as stable periodic orbits. We present a detailed analysis of the underlying dynamics. In particular, we show that the system possesses a multitude of periodic solutions, prove stability results and present a bifurcation analysis. Furthermore, we show successful recognition results using artificial patterns and speech recordings.

The false discovery rate (FDR) and false nondiscovery rate (FNDR) have received considerable attention in the literature on multiple testing. These performance measures are also appropriate for classification, and in this work we develop generalization error analyses for FDR and FNDR when learning a classifier from labeled training data. Unlike more conventional classification performance measures, the empirical FDR and FNDR are not binomial random variables but rather a ratio of binomials, which introduces challenges not addressed in conventional analyses. We develop distribution-free uniform deviation bounds and apply these to obtain finite sample bounds and strong universal consistency.

Results and conclusions are presented on the application of recent developments in pattern recognition to spacecraft star mapping systems. Sensor data for two representative starfields are processed by an adaptive shape-seeking version of the Fc-V algorithm with good results. Cluster validity measures are evaluated, but not found especially useful to this application. Recommendations are given two system configurations worthy of additional study,

Several papers addressing image analysis and pattern recognition techniques for satellite imagery are presented. Texture classification, image rectification and registration, spatial parameter estimation, and surface fitting are discussed.

One potential approach to the quantitative acquisition of discriminatory information that can isolate a single structural state is pattern recognition. The pattern recognition characterizations of micromechanical and morphological materials states via analytical quantiative ultrasonics are outlined. The concepts, terminology, and techniques of statistical pattern recognition are reviewed. Feature extraction and classification and states of the structure can be determined via a program of ultrasonic data generation.

This paper reports the initial results of an effort to develop simple and fast vision algorithms on compact and imbeddable hardware for the guidance and control of an autonomous underwater vehicle. The specific a application involves tracking underwater cables and chains. Feature points are identified in the underwater video images using a technique which combines segmentation by gray level an run length. Hough transformation is then used to find the straight line in the image. The process is performed at a throughput of approximately 1 image per second using a PC-bus video frame grabber and a PC/AT compatible micro-computer.

Technique operates regardless of pattern rotation, translation or magnification and successfully detects out-of-register patterns. It improves accuracy and reduces cost of various optical character recognition devices and page readers and provides data input to computer.

This thesis investigates the feasibility of optically implementing a real-time, pattern recognition system using correlation techniques in a position, scale, and rotation invariant (PSRI) feature space. Input target templates were optically Fourier transformed using an improved high-resolution, high-pass filter positioned in the focal plane of the transforming lens. A logarithmic-polar coordinate transform of the magnitude-squared of the Fourier transform (/FT/2) was performed with an improved method of focusing the magnified /FT/2 onto a computer generated hologram (CGH), which was shown to scale linearly along the horizontal axis and logarithmically along the vertical axis. Optical, matched-filter correlations on the magnitude-squared Fourier transform logarithmic-polar (FLRT) feature space were performed using thermoplastic, phase-relief holography and Vander Lugt filtering. Correlation results prove that scale and rotation changes of the input can be predicted accurately based on linear shifts of the correlation peak. Also, the FLRT feature space is shown to provide excellent discrimination for multiple-input scenes. The need for a cyclic correlation is verified, and digital simulations prove the validity of the

This thesis investigates the feasibility of optically implementing a real-time, pattern recognition system using correlation techniques in a position, scale, and rotation invariant (PSRI) feature space. Input target templates were optically Fourier transformed using an improved high-resolution, high-pass filter positioned in the focal plane of the transforming lens. A logarithmic-polar coordinate transform of the magnitude-squared of the Fourier transform (/FT/2) was performed with an improved method of focusing the magnified /FT/2 onto a computer generated hologram (CGH), which was shown to scale linearly along the horizontal axis and logarithmically along the vertical axis. Optical, matched-filter correlations on the magnitude-squared Fourier transform logarithmic-polar (FLRT) feature space were performed using thermoplastic, phase-relief holography and Vander Lugt filtering. Correlation results prove that scale and rotation changes of the input can be predicted accurately based on linear shifts of the correlation peak. Also, the FLRT feature space is shown to provide excellent discrimination for multiple-input scenes. The need for a cyclic correlation is verified, and digital simulations prove the validity of the

This thesis investigates the feasibility of optically implementing a real-time, pattern recognition system using correlation techniques in a position, scale, and rotation invariant (PSRI) feature space. Input target templates were optically Fourier transformed using an improved high-resolution, high-pass filter positioned in the focal plane of the transforming lens. A logarithmic-polar coordinate transform of the magnitude-squared of the Fourier transform (/FT/2) was performed with an improved method of focusing the magnified /FT/2 onto a computer generated hologram (CGH), which was shown to scale linearly along the horizontal axis and logarithmically along the vertical axis. Optical, matched-filter correlations on the magnitude-squared Fourier transform logarithmic-polar (FLRT) feature space were performed using thermoplastic, phase-relief holography and Vander Lugt filtering. Correlation results prove that scale and rotation changes of the input can be predicted accurately based on linear shifts of the correlation peak. Also, the FLRT feature space is shown to provide excellent discrimination for multiple-input scenes. The need for a cyclic correlation is verified, and digital simulations prove the validity of the

At the time of this report, the project has been authorized to spend $1,997,949.00 for the Phase 1, 2, and 3 tasks plus the one optional task. The project has accumulated expenses of $1,806,631.00. Work on phases 1 and 2 are completed. Work on Phase 3 and the Optional task are in progress.

A previously developed MATLAB-based statistical pattern recognition software package (referred to as STATPACK) has been enhanced. Current enhancements include a one-dimensional feature distribution display, and the addition of a nearest mean vector based classifier.

Various types of pattern deformations are investigated from the syntactic point of view and categorized into two major types: local deformations and structural deformations. Random noise, distortion variations, and substitutions, of pattern primitives belong to the former; syntactic errors due to pattern structural changes, such as primitive deletions and insertions, belong to the latter. Every observed pattern can be regarded as transformed from a pure pattern through these two types of deformations. An error-correcting parsing scheme for local deformations optimum in the Bayes sense is proposed. Two structure-preserved error-correcting parsers, one for string languages, the other tree languages, are also presented. Finally, further researches concerning error-correcting parsings for structural deformations and a complete error-correcting systems for both kinds of deformations are suggested.

Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

The pattern recognition code ARTHUR was tested on a limited cratering data base to examine its utility in studying complex continuous data. The code was found to be a convenient data analysis package but possessed no continuous data capability that is not already available to any researcher. (Author)

Neuron models of associative memory provide a new and prospective technology for reliable date storage and patterns recognition. However, even when the patterns are uncorrelated, the efficiency of most known models of associative memory is low. We developed a new version of associative memory with record characteristics of its storage capacity and noise immunity, which, in addition, is effective when recognizing correlated patterns.

The ALICE High Level Trigger has to process data online, in order to select interesting (sub)events, or to compress data efficiently by modeling techniques.Focusing on the main data source, the Time Projection Chamber (TPC), we present two pattern recognition methods under investigation: a sequential approach "cluster finder" and "track follower") and an iterative approach ("track candidate finder" and "cluster deconvoluter"). We show, that the former is suited for pp and low multiplicity PbPb collisions, whereas the latter might be applicable for high multiplicity PbPb collisions, if it turns out, that more than 8000 charged particles would have to be reconstructed inside the TPC. Based on the developed tracking schemes we show, that using modeling techniques a compression factor of around 10 might be achievable

The ultimate goal is to create an extraterrestrial unmanned system for subsurface mapping and exploration. Neural networks are to be used to recognize anomalies in the profiles that correspond to potentially exploitable subsurface features. The ground penetrating radar (GPR) techniques are likewise identical. Hence, the preliminary research focus on GPR systems will be directly applicable to seismic systems once such systems can be designed for continuous operation. The original GPR profile may be very complex due to electrical behavior of the background, targets, and antennas, much as the seismic record is made complex by multiple reflections, ghosting, and ringing. Because the format of the GPR data is similar to the format of seismic data, seismic processing software may be applied to GPR data to help enhance the data. A neural network may then be trained to more accurately identify anomalies from the processed record than from the original record.

The ultimate goal is to create an extraterrestrial unmanned system for subsurface mapping and exploration. Neural networks are to be used to recognize anomalies in the profiles that correspond to potentially exploitable subsurface features. The ground penetrating radar (GPR) techniques are likewise identical. Hence, the preliminary research focus on GPR systems will be directly applicable to seismic systems once such systems can be designed for continuous operation. The original GPR profile may be very complex due to electrical behavior of the background, targets, and antennas, much as the seismic record is made complex by multiple reflections, ghosting, and ringing. Because the format of the GPR data is similar to the format of seismic data, seismic processing software may be applied to GPR data to help enhance the data. A neural network may then be trained to more accurately identify anomalies from the processed record than from the original record.

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

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 goal of the meeting was to provide a direct interface between the fields of Analytical Spectroscopy (e.g., MS, IR, NMR, UV-VIS) and Chemometrics. The following topics fall within the scope of the program: Spectral Enhancement and Deconvolution, Data Reduction and Compression, Library Storage and Retrieval, Cluster and Classification Analysis, Factor and Discriminant Analysis, Multicomponent (mixture ) Analysis, Automated Spectral Interpretation, Multisource Data Base Integration, and Modeling and Prediction. (Abstracts).

The overall goal of this project has been to further investigate signal processing and pattern recognition techniques as to how they apply in the nondestructive evaluation of materials for classifying ultrasonic pulse echo waveforms. Computer programs were developed to implement algorithms to generate power spectrum, cepstrum, and auto-correlation waveforms from the ultrasonic pulse echo waveforms. These algorithms have a firm statistical foundation and also have properties associated with them that allow the Fast Fourier Transform to be utilized in an efficient manner. Also, statistical features were extracted from the waveforms. The features were then input to pattern recognition techniques in order to classify the data into appropriate material defects. The procedure outlined above was implemented with 49 ultrasonic pulse echo waveforms obtained from flat-bottom holes of eight different diameters. A recognition accuracy of 98% has been attained when the flat-bottom holes are classified into two categories using only one feature from the original ultrasonic pulse echo waveforms reflected from the flat-bottom holes. The same results are achieved when the one feature is either the maximum amplitude, the root-mean-square value, or the variance of the waveform. An unexpected result was also observed when a time series method was applied to the portions of the ultrasonic pulse echo waveforms that were reflected from the backwalls instead of the flat-bottom holes.

The design of an automatic pattern recognition system involves considerations in three distinct but not totally independent problem areas. The broad selection of pattern characteristics and the design of equipment which will measure these characteristics quickly and efficiently are two aspects of the system which must be resolved. The third is the formulation of a recognition scheme which will establish the significant characteristics and operate upon them to form reliable pattern classifications. This report is concerned primarily with the classification problem and discusses some applications of statistical decision theory and information theory to the design of a recognition scheme. One section of the report presents the results of an experiment and the other problem areas which were considered -- to the extent that characteristics had to be defined and measured before the data could be collected.

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)

Phase 2 Summary: The CNAPS compiler, assembler, application program interface, and Backpropagation Neural Network function library have been ported to the Windows 3.x environment and the CNAPSIPC hardware and integrated with the source-level debugger. The port of the graphical interface for the source-level debugger to Windows 3.x has been completed. These software tools are now in Beta test. Phase 3 Summary: Work has just begun. At the time of this report, the project has been authorized to spend $1,100,000.00 for the Phase 1, 2, and 3 tasks and has accumulated expenses of $707,000.00. Phase 1 is completed. Phase 2 is nearly completed. Work has begun on Phase 3. The contract stipulates that $ 1,299,714.00 are required for the completion of the tasks in Phases 1, 2, and 3. Therefore, an additional $199,714.00 of funding must be authorized to complete all three phases of the project.

This study aims to investigate whether and how putting masks on faces will modulate face recognition abilities and eye movements in ASD adults.

On October 19th, 2013 Mark Passio presented his seminar "Natural Law: The REAL Law of Attraction" at the Yale Omni, organized by Arthur Capozzi & Christopher Capozzi, and produced for distribution by Tragedy and Hope dot com. This is the introductory talk as offered by Arthur Capozzi, which will be followed in this playlist by the 3 parts (morning, afternoon, and evening) of Mark's seminar. Visit: www.WhatOnEarthIsHappening.com for more of Mark Passio's productions Would You Like to Know More? History... So It Doesn't Repeat (Official HD Playlist) http://www.youtube.com/playlist?list=PLmmQ8peduhspYv4j-Cj6zppAO75vDP-_t USEFUL LINKS T&H dot com: http://www.TragedyandHope.com T&H online Research & Development community: https://www.tragedyandhope.com/subscribe/ Follow on Twitter: http://twitter.com/TragedyandHope Watch on YouTube: http://www.youtube.com/TragedyandHopeMag Subscribe to the Peace Revolution podcast produced by T&H: http://www.PeaceRevolution.org Like on Facebook: https://www.facebook.com/TragedyandHopeCommunications Also on Facebook: https://www.facebook.com/theultimatehistorylesson Screen The Ultimate History Lesson: http://www.youtube.com/playlist?list=PL463AA90FD04EC7A2 Link to Richard's brain model: https://www.tragedyandhope.com/the-brain/ ABOUT TRAGEDY AND HOPE: SUMMARY, PURPOSE, FORM, AND FUNCTION Tragedy and Hope provides a portal through which individuals can discover, identify, and integrate useful tools, resources, and activities which stimulate and fortify Cognitive Liberty, providing primary sources, research, and educational methods which facilitate consciousness. Tragedy and Hope's purpose is to enable individuals to research and form groups of independent thinkers to solve humanity's most pressing problems, by identifying the etiology (study of the cause-and-effect origins) and thus understanding our way toward the solutions we seek. Tragedy and Hope's form and function (free to the public) enables hundreds-of-thousands of people around the world, to experience open-source education without upper-limits, and it is therein where the solutions are discovered- by making the problems truly known and understood at a root-cause level. Tragedy and Hope creates, produces, and publishes educational content to help adults understand the world they live in and thereby, develop true self-confidence and serenity of mind; our content focuses on history, philosophy, economics, anthropology, science, communications, and every topic which pertains to learning how to survive and thrive in this world. Tragedy and Hope is funded by those who tune-in to our productions, and the members of the Tragedy and Hope online critical thinking and research community, who subscribe after witnessing the value provided, and voluntarily deciding to contribute in a way to ensure we can continue producing educational media with integrity and consistency. Sacrifice the Tragedy, Preserve the Hope. https://www.tragedyandhope.com/th-origins/ T&H: Re-Contextualizing History one Episode at a Time.

On October 19th, 2013 Mark Passio presented his seminar "Natural Law: The REAL Law of Attraction" at the Yale Omni, organized by Arthur Capozzi & Christopher Capozzi, and produced for distribution by Tragedy and Hope dot com. This is the introductory talk as offered by Arthur Capozzi, which will be followed in this playlist by the 3 parts (morning, afternoon, and evening) of Mark's seminar. Visit: www.WhatOnEarthIsHappening.com for more of Mark Passio's productions Would You Like to Know More? History... So It Doesn't Repeat (Official HD Playlist) http://www.youtube.com/playlist?list=PLmmQ8peduhspYv4j-Cj6zppAO75vDP-_t USEFUL LINKS T&H dot com: http://www.TragedyandHope.com T&H online Research & Development community: https://www.tragedyandhope.com/subscribe/ Follow on Twitter: http://twitter.com/TragedyandHope Watch on YouTube: http://www.youtube.com/TragedyandHopeMag Subscribe to the Peace Revolution podcast produced by T&H: http://www.PeaceRevolution.org Like on Facebook: https://www.facebook.com/TragedyandHopeCommunications Also on Facebook: https://www.facebook.com/theultimatehistorylesson Screen The Ultimate History Lesson: http://www.youtube.com/playlist?list=PL463AA90FD04EC7A2 Link to Richard's brain model: https://www.tragedyandhope.com/the-brain/ ABOUT TRAGEDY AND HOPE: SUMMARY, PURPOSE, FORM, AND FUNCTION Tragedy and Hope provides a portal through which individuals can discover, identify, and integrate useful tools, resources, and activities which stimulate and fortify Cognitive Liberty, providing primary sources, research, and educational methods which facilitate consciousness. Tragedy and Hope's purpose is to enable individuals to research and form groups of independent thinkers to solve humanity's most pressing problems, by identifying the etiology (study of the cause-and-effect origins) and thus understanding our way toward the solutions we seek. Tragedy and Hope's form and function (free to the public) enables hundreds-of-thousands of people around the world, to experience open-source education without upper-limits, and it is therein where the solutions are discovered- by making the problems truly known and understood at a root-cause level. Tragedy and Hope creates, produces, and publishes educational content to help adults understand the world they live in and thereby, develop true self-confidence and serenity of mind; our content focuses on history, philosophy, economics, anthropology, science, communications, and every topic which pertains to learning how to survive and thrive in this world. Tragedy and Hope is funded by those who tune-in to our productions, and the members of the Tragedy and Hope online critical thinking and research community, who subscribe after witnessing the value provided, and voluntarily deciding to contribute in a way to ensure we can continue producing educational media with integrity and consistency. Sacrifice the Tragedy, Preserve the Hope. https://www.tragedyandhope.com/th-origins/ T&H: Re-Contextualizing History one Episode at a Time.

Computer program in Ruby language which is recognizing complex emotions in text like articles or poem. Version without copyrighted items (poem to check and database of keywords).

In this paper, we compare different existing approaches employed in data mining of big proof libraries in automated and interactive theorem proving.

Series on color-contrast modularity and pattern recognition after a series of templates made in the 1950s by Russian scientist M. Bongard.