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Electrocardiogram (ECG) reflects activity of the central of the blood circulatory system, i.e. the heart. An ECG signal can provide us with a great deal of information on the normal and pathological physiology of heart activity. Thus, ECG is an important non-invasive clinical tool for the diagnosis of heart diseases.          According to the medical definition the most important information in the ECG signal is concentrated in the P wave, QRS complex and T wave. These data include positions and/or magnitudes of the QRS interval, PR interval, QT interval, ST interval, PR segment, and ST segment (see Fig. 1). Based on the above data, doctors can correctly diagnose human heart diseases. Therefore, analyzing the ECG signals of cardiac arrhythmia is very important for doctors to make correct clinical diagnoses. In order to perform ECG signals classification of the cardiac arrhythmia, the first important task is to determine an appropriate set of features. The feature selection method which chooses the best features from original features to have the maximum recognition rate, simplify classified computation and comprehend the causal relation of classified question. Signal Processing   is undoubtedly the best real time implementation of a specific problem. Wavelet Transform is a very powerful technique for feature extraction and can be used along with neural network structures to build computationally efficient models for diagnosis of Biosignals (ECG in this case). This work utilizes the above techniques for diagnosis of an ECG signal by determining its nature as well as exploring the possibility for real-time implementation of the above model. Daubechies wavelet transform and multi-layered perceptron are the computational techniques used for the realization of the above model. The ECG signals were obtained from the MIT-BIH arrhythmia database and are used for the identification of four different types of arrhythmias. The identification was implemented real-time in SIMULINK, to simulate the detection model under test condition and verify its workability.

Electrocardiogram (ECG) reflects activity of the central of the blood circulatory system, i.e. the heart. An ECG signal can provide us with a great deal of information on the normal and pathological physiology of heart activity. Thus, ECG is an important non-invasive clinical tool for the diagnosis of heart diseases.          According to the medical definition the most important information in the ECG signal is concentrated in the P wave, QRS complex and T wave. These data include positions and/or magnitudes of the QRS interval, PR interval, QT interval, ST interval, PR segment, and ST segment (see Fig. 1). Based on the above data, doctors can correctly diagnose human heart diseases. Therefore, analyzing the ECG signals of cardiac arrhythmia is very important for doctors to make correct clinical diagnoses. In order to perform ECG signals classification of the cardiac arrhythmia, the first important task is to determine an appropriate set of features. The feature selection method which chooses the best features from original features to have the maximum recognition rate, simplify classified computation and comprehend the causal relation of classified question. Signal Processing   is undoubtedly the best real time implementation of a specific problem. Wavelet Transform is a very powerful technique for feature extraction and can be used along with neural network structures to build computationally efficient models for diagnosis of Biosignals (ECG in this case). This work utilizes the above techniques for diagnosis of an ECG signal by determining its nature as well as exploring the possibility for real-time implementation of the above model. Daubechies wavelet transform and multi-layered perceptron are the computational techniques used for the realization of the above model. The ECG signals were obtained from the MIT-BIH arrhythmia database and are used for the identification of four different types of arrhythmias. The identification was implemented real-time in SIMULINK, to simulate the detection model under test condition and verify its workability.

Searchers and scientists have been studying neural networks for many years hoping to achieve human-like performance in the fields of speech and pattern recognition and classification. This study will first make an introduction to the field of artificial neural networks, then describe some of the neural nets used in the pattern recognition and classification. A computer simulation program from an algorithmic approach for each one of these networks will be constructed and used to implement the operation of the net. Its ability will be demonstrated in differentiating between different patterns and even correcting a noisy pattern and recognizing it. The Hopfield network, the Hamming network and the Carpenter/Grossberg network will be individually utilized in developing an algorithm for pattern recognition and classification. The maximum- likelihood sequence estimation function will be mapped onto a neural network structure. The application of this structure computations for data detection in digital communications receivers will be described. A computer simulation program will be constructed and used to show that neural networks offer attractive implementation alternatives for MLSE. Keywords: Neural networks; Hopfield net; Hamming net; Carpenter/Grossberg net; Pattern recognition.

Searchers and scientists have been studying neural networks for many years hoping to achieve human-like performance in the fields of speech and pattern recognition and classification. This study will first make an introduction to the field of artificial neural networks, then describe some of the neural nets used in the pattern recognition and classification. A computer simulation program from an algorithmic approach for each one of these networks will be constructed and used to implement the operation of the net. Its ability will be demonstrated in differentiating between different patterns and even correcting a noisy pattern and recognizing it. The Hopfield network, the Hamming network and the Carpenter/Grossberg network will be individually utilized in developing an algorithm for pattern recognition and classification. The maximum- likelihood sequence estimation function will be mapped onto a neural network structure. The application of this structure computations for data detection in digital communications receivers will be described. A computer simulation program will be constructed and used to show that neural networks offer attractive implementation alternatives for MLSE. Keywords: Neural networks; Hopfield net; Hamming net; Carpenter/Grossberg net; Pattern recognition.

Infrared spectroscopy is an important technique for measuring airborne chemicals, for pollution monitoring and to warn of toxic compound releases. Infrared spectroscopy provides both detection and identification of airborne components. Computer-assisted classification tools, including pattern recognition and artificial neural network techniques, have been applied to a collection of infrared spectra of organophosphorus compounds, and these have successfully discriminated commercial pesticide compounds from military nerve agents, precursors, and hydrolysis products. Infrared spectra for previous tests came from a commercial infrared library, with permission, from military laboratories, and from defense contractors. In order to further test such classification tools, additional infrared spectra from the NIST gas-phase infrared library were added to the data set. These additional spectra probed the tendency of the trained classifiers to misidentify unrelated spectra into the trained classes. Infrared spectra used in this effort were gathered from a variety of sources. Different instrument operators collected them at a number of locations, in a variety of spectral data collection designs, and they were delivered in a variety of digital formats. The spectra were treated mathematically to remove artifacts from their collection. Preprocessing techniques used included Fisher weighting and principal component analysis. Classifications were made using the k-nearest neighbor classifier, feed forward neural networks, trained with a variety of techniques, and radial basis function networks. The results from these classification techniques will be reported and compared.

A three-step approach has been developed for detecting and classifying the foraging calls of the blue whale, Balaenoptera musculus, and fin whale, Balaenoptera physalus, in passive acoustic recordings. This approach includes a pattern recognition algorithm to reduce the effects of ambient noise and to detect the foraging calls. The detected calls are then classified as blue whale D-calls or fin whale 40-Hz calls using a machine learning technique, a logistic regression classifier. These algorithms have been trained and evaluated using the Detection, Classification, Localization, and Density Estimation (DCLDE) annotated passive acoustic data, which were recorded off the Central and Southern California coast from 2009 to 2013. By using the cross-validation method and DCLDE scoring tool, this research shows high out-of sample performance for these algorithms, namely 96% recall with 92% precision for pattern recognition and 96% accuracy for the logistic regression classifier. The result was published by the Institute of Electrical and Electronics Engineers (2016). The advantages of this automated approach over traditional manual methods are reproducibility, known performance, cost-efficiency, and automation. This approach has the potential to conquer the challenges of detecting and classifying the foraging calls, including the analysis of large acoustic data sets and real-time acoustic data processing.

Thesis (MS)?Naval Postgraduate School, 1964

Searchers and scientists have been studying neural networks for many years hoping to achieve human-like performance in the fields of speech and pattern recognition and classification. In this study, we are first going to make an introduction to the field of artificial neural networks, then we are going to describe some of the neural nets used in the pattern recognition and classification. A computer simulation program from an algorithmic approach for each one of these networks will be constructed and used to implement the operation of the net. Its ability will be demonstrated in differentiating between different patterns and even correcting a noisy pattern and recognizing it. The Hopfield network, the Hamming network and the Carpenter / Grossberg network will be individually utilized in developing an algorithm for pattern recognition and classification. The maximum-likelihood sequence estimation function will be mapped onto a neural network structure. The application of this structure computations for data detection in digital communications receivers will be described. A computer simulation program will be constructed and used to show that neural networks offer attractive implementation alternatives for MLSE.

Searchers and scientists have been studying neural networks for many years hoping to achieve human-like performance in the fields of speech and pattern recognition and classification. In this study, we are first going to make an introduction to the field of artificial neural networks, then we are going to describe some of the neural nets used in the pattern recognition and classification. A computer simulation program from an algorithmic approach for each one of these networks will be constructed and used to implement the operation of the net. Its ability will be demonstrated in differentiating between different patterns and even correcting a noisy pattern and recognizing it. The Hopfield network, the Hamming network and the Carpenter / Grossberg network will be individually utilized in developing an algorithm for pattern recognition and classification. The maximum-likelihood sequence estimation function will be mapped onto a neural network structure. The application of this structure computations for data detection in digital communications receivers will be described. A computer simulation program will be constructed and used to show that neural networks offer attractive implementation alternatives for MLSE.

Pattern recognition is a statistical technique that allows one to find or predict a property of chemicals that is not directly measurable, but is known to depend upon certain features or properties of the chemicals via some totally unknown relationship. This technique has been applied to a multitude of scientific problems. The same technique was used to classify a chemical according to its relative hazard in bulk water-transportation based on chemical structure and macro-scale properties such as density, vapor pressure, structure-fragments, solubilities, etc. Using the Linear-Learning Machine, the overall prediction of the 47 compounds in training set was 68% correct. The predicted classifications of the 240 compounds in the test set are approximately 68% correct. There are many difficulties associated with properly classifying compounds on the basis of variable derived from structural fragments that must be solved before great reliance can be placed on the results of a Linear-Learning Machine classification. (Author)

Significant progress has been made in the classification of surface conditions (land uses) with computer-implemented techniques based on the use of ERTS digital data and pattern recognition software. The supervised technique presently used at the NASA Earth Resources Laboratory is based on maximum likelihood ratioing with a digital table look-up approach to classification. After classification, colors are assigned to the various surface conditions (land uses) classified, and the color-coded classification is film recorded on either positive or negative 9 1/2 in. film at the scale desired. Prints of the film strips are then mosaicked and photographed to produce a land use map in the format desired. Computer extraction of statistical information is performed to show the extent of each surface condition (land use) within any given land unit that can be identified in the image. Evaluations of the product indicate that classification accuracy is well within the limits for use by land resource managers and administrators. Classifications performed with digital data acquired during different seasons indicate that the combination of two or more classifications offer even better accuracy.

Significant progress has been made in the classification of surface conditions (land uses) with computer-implemented techniques based on the use of ERTS digital data and pattern recognition software. The supervised technique presently used at the NASA Earth Resources Laboratory is based on maximum likelihood ratioing with a digital table look-up approach to classification. After classification, colors are assigned to the various surface conditions (land uses) classified, and the color-coded classification is film recorded on either positive or negative 9 1/2 in. film at the scale desired. Prints of the film strips are then mosaicked and photographed to produce a land use map in the format desired. Computer extraction of statistical information is performed to show the extent of each surface condition (land use) within any given land unit that can be identified in the image. Evaluations of the product indicate that classification accuracy is well within the limits for use by land resource managers and administrators. Classifications performed with digital data acquired during different seasons indicate that the combination of two or more classifications offer even better accuracy.

A three-step approach has been developed for detecting and classifying the foraging calls of the blue whale, Balaenoptera musculus, and fin whale, Balaenoptera physalus, in passive acoustic recordings. This approach includes a pattern recognition algorithm to reduce the effects of ambient noise and to detect the foraging calls. The detected calls are then classified as blue whale D-calls or fin whale 40-Hz calls using a machine learning technique, a logistic regression classifier. These algorithms have been trained and evaluated using the Detection, Classification, Localization, and Density Estimation (DCLDE) annotated passive acoustic data, which were recorded off the Central and Southern California coast from 2009 to 2013. By using the cross-validation method and DCLDE scoring tool, this research shows high out-of-sample performance for these algorithms, namely 96% recall with 92% precision for pattern recognition and 96% accuracy for the logistic regression classifier. The result was published by the Institute of Electrical and Electronics Engineers (2016). The advantages of this automated approach over traditional manual methods are reproducibility, known performance, cost-efficiency, and automation. This approach has the potential to conquer the challenges of detecting and classifying the foraging calls, including the analysis of large acoustic data sets and real-time acoustic data processing.

A three-step approach has been developed for detecting and classifying the foraging calls of the blue whale, Balaenoptera musculus, and fin whale, Balaenoptera physalus, in passive acoustic recordings. This approach includes a pattern recognition algorithm to reduce the effects of ambient noise and to detect the foraging calls. The detected calls are then classified as blue whale D-calls or fin whale 40-Hz calls using a machine learning technique, a logistic regression classifier. These algorithms have been trained and evaluated using the Detection, Classification, Localization, and Density Estimation (DCLDE) annotated passive acoustic data, which were recorded off the Central and Southern California coast from 2009 to 2013. By using the cross-validation method and DCLDE scoring tool, this research shows high out-of-sample performance for these algorithms, namely 96% recall with 92% precision for pattern recognition and 96% accuracy for the logistic regression classifier. The result was published by the Institute of Electrical and Electronics Engineers (2016). The advantages of this automated approach over traditional manual methods are reproducibility, known performance, cost-efficiency, and automation. This approach has the potential to conquer the challenges of detecting and classifying the foraging calls, including the analysis of large acoustic data sets and real-time acoustic data processing.

A three-step approach has been developed for detecting and classifying the foraging calls of the blue whale, Balaenoptera musculus, and fin whale, Balaenoptera physalus, in passive acoustic recordings. This approach includes a pattern recognition algorithm to reduce the effects of ambient noise and to detect the foraging calls. The detected calls are then classified as blue whale D-calls or fin whale 40-Hz calls using a machine learning technique, a logistic regression classifier. These algorithms have been trained and evaluated using the Detection, Classification, Localization, and Density Estimation (DCLDE) annotated passive acoustic data, which were recorded off the Central and Southern California coast from 2009 to 2013. By using the cross-validation method and DCLDE scoring tool, this research shows high out-of-sample performance for these algorithms, namely 96% recall with 92% precision for pattern recognition and 96% accuracy for the logistic regression classifier. The result was published by the Institute of Electrical and Electronics Engineers (2016). The advantages of this automated approach over traditional manual methods are reproducibility, known performance, cost-efficiency, and automation. This approach has the potential to conquer the challenges of detecting and classifying the foraging calls, including the analysis of large acoustic data sets and real-time acoustic data processing.

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories. Results: We develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be “chained” in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience. Conclusions: BIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation. We also demonstrate the effectiveness of 3D anisotropic wavelet in classifying both 3D image sets and ROIs.

Thesis advisor, Tri T. Ha

The various studies conducted for classification of handwritten signatures of people have shown that the task is difficult because there is intra personal differences among the signatures of the same person. The signatures of the same person vary with time, age of the person and also because of the emotional state of a person.

It has been widely known that data fusion, neural network and statistical pattern recognition technologies can be applied to target identification and classification problems. The main objective of this paper is to find out which of these techniques would be easy to use and provide acceptable results. We had selected the Multi-sensor Correlation Model 1 from the field of data fusion technology. The concept of this model is based on the coefficient of similarity. For target identification problem, one have to estimate the coefficient of similarity between a known target (X) and the target (Y) to be identified. If the coefficient is closed to one , then it implied that target (Y) is the same as target (X), otherwise if the coefficient is close to zero, then it implied that target (Y) is not the same as target (X). It is mathematical simple and easy to implement. The Bayesian Model 2 was selected from the field of statistical pattern recognition technology, This is a conditional probability model. For target identification problem, one have to calculate the posterior probability of a known target (X) given the target (Y) to one to be identified. If the conditional probability is close to one , then it implied that target (x) and target (Y) is the same, otherwise if it is close to zero, then it implied that target(X) and target(Y) is not the same. This model required multivariate normal assumption, probability density function, and apriori probability of the targets. It is not easy to apply. The Backpropagation Model 3 was selected from the field of neural network technology, It is a three layered network; input, hidden and output layers. For target identification problem, one has to train the network with the known target (X), then apply the unknown target(Y) to the trained network as an input layer, if the output layer has a higher energy value , thentecWe use two published 4 numerical dat

Journal of Research of the National Bureau of Standards

Pattern recognition and classification systems have been under development for several years. This paper examines one of these systems, which has been called an adaptive linear neuron, to determine how the desired classification is achieved and how this system might be used in the practical field of character recognition. Specifically, the following ideas are discussed in this paper: (1) The basic concepts of linear separability and iterative adaption by an adaptive linear neuron (Adaline), as applied to the pattern recognition and classification problem; (2) Four possible iterative adaption schemes which may be used to train as Adaline; (3) Use of Multiple Adalines (Madaline) and two logic layers to increase system capability; and (4) Use of Adaline in the practical fields of Speech Recognition, Weather Forecasting and Adaptive Control Systems and the possible use of Madaline in the Character Recognition field.

Currently, the United States Navy performs routine intrusive maintenance on CH-46 helicopter gearboxes in order to diagnose and correct possible fault condition. (incipient fault) which could eventually lead to gearbox failure. This type of preventative maintenance is costly and it decreases mission readiness by temporarily grounding usable helicopter. Non-invasive detection of these fault conditions would save tine and prove cost-effective in both manpower and materials. This research deals with the development of a non-invasive fault detector through a combination of digital signal processing and artificial neural network (ANN) technology. The detector will classify incipient faults based on real-tine vibration data taken from the gearbox itself. Neural networks are systems of interconnected units that are trained to compute a specific output as a non-linear function of their inputs. For sons tine the United States Navy has been interested in the use of artificial neural networks in monitoring the health of helicopter gearboxes. In order to determine the detection sensitivity of this method in comparison with traditional invasive methods, the USN funded Westland Helicopters Ltd to conduct a series of CH-46 gearbox rig tests. In these tests, the gearbox was seeded with nine different fault conditions. This seeded fault testing provided the vibration data necessary to develop and test the feasibility of en artificial neural network for fault classification. This research deals with the formation of the pattern vectors to be used in the neural network classifier, the construction of the classification network, and an analysis of results.

This paper introduces a probabilistic model that is capable of diagnosing and classifying cognitive errors in a general problem-solving domain. The model is different from the usual deterministic strategies common in the area of artificial intelligence because the item response theory is utilized for handling the variability of response errors. As for illustrating the model, the dataset obtained form a 38-item fraction addition test is used, and the students' responses are classified into 34 groups of misconceptions. These groups are predetermined by the result of an error analysis previously done, and validated with the error diagnostic program written by a typical formal logic approach. Keywords: cognitive errors, item response theory, bugs, fractions, pattern classification, caution index.

Statistical methods for pattern recognition and classification applications