An evaluation of open set recognition for FLIR images

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2015-05-01

Typical supervised classification algorithms label inputs according to what was learned in a training phase. Thus, test inputs that were not seen in training are always given incorrect labels. Open set recognition algorithms address this issue by accounting for inputs that are not present in training and providing the classifier with an option to reject" unknown samples. A number of such techniques have been developed in the literature, many of which are based on support vector machines (SVMs). One approach, the 1-vs-set machine, constructs a slab" in feature space using the SVM hyperplane. Inputs falling on one side of the slab or within the slab belong to a training class, while inputs falling on the far side of the slab are rejected. We note that rejection of unknown inputs can be achieved by thresholding class posterior probabilities. Another recently developed approach, the Probabilistic Open Set SVM (POS-SVM), empirically determines good probability thresholds. We apply the 1-vs-set machine, POS-SVM, and closed set SVMs to FLIR images taken from the Comanche SIG dataset. Vehicles in the dataset are divided into three general classes: wheeled, armored personnel carrier (APC), and tank. For each class, a coarse pose estimate (front, rear, left, right) is taken. In a closed set sense, we analyze these algorithms for prediction of vehicle class and pose. To test open set performance, one or more vehicle classes are held out from training. By considering closed and open set performance separately, we may closely analyze both inter-class discrimination and threshold effectiveness.

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

PubMed

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. Copyright © 2015 Elsevier Ltd. All rights reserved.

Contribution of intrinsic properties and synaptic inputs to motoneuron discharge patterns: a simulation study

PubMed Central

ElBasiouny, Sherif M.; Rymer, W. Zev; Heckman, C. J.

2012-01-01

Motoneuron discharge patterns reflect the interaction of synaptic inputs with intrinsic conductances. Recent work has focused on the contribution of conductances mediating persistent inward currents (PICs), which amplify and prolong the effects of synaptic inputs on motoneuron discharge. Certain features of human motor unit discharge are thought to reflect a relatively stereotyped activation of PICs by excitatory synaptic inputs; these features include rate saturation and de-recruitment at a lower level of net excitation than that required for recruitment. However, PIC activation is also influenced by the pattern and spatial distribution of inhibitory inputs that are activated concurrently with excitatory inputs. To estimate the potential contributions of PIC activation and synaptic input patterns to motor unit discharge patterns, we examined the responses of a set of cable motoneuron models to different patterns of excitatory and inhibitory inputs. The models were first tuned to approximate the current- and voltage-clamp responses of low- and medium-threshold spinal motoneurons studied in decerebrate cats and then driven with different patterns of excitatory and inhibitory inputs. The responses of the models to excitatory inputs reproduced a number of features of human motor unit discharge. However, the pattern of rate modulation was strongly influenced by the temporal and spatial pattern of concurrent inhibitory inputs. Thus, even though PIC activation is likely to exert a strong influence on firing rate modulation, PIC activation in combination with different patterns of excitatory and inhibitory synaptic inputs can produce a wide variety of motor unit discharge patterns. PMID:22031773

Density-Dependent Quantized Least Squares Support Vector Machine for Large Data Sets.

PubMed

Nan, Shengyu; Sun, Lei; Chen, Badong; Lin, Zhiping; Toh, Kar-Ann

2017-01-01

Based on the knowledge that input data distribution is important for learning, a data density-dependent quantization scheme (DQS) is proposed for sparse input data representation. The usefulness of the representation scheme is demonstrated by using it as a data preprocessing unit attached to the well-known least squares support vector machine (LS-SVM) for application on big data sets. Essentially, the proposed DQS adopts a single shrinkage threshold to obtain a simple quantization scheme, which adapts its outputs to input data density. With this quantization scheme, a large data set is quantized to a small subset where considerable sample size reduction is generally obtained. In particular, the sample size reduction can save significant computational cost when using the quantized subset for feature approximation via the Nyström method. Based on the quantized subset, the approximated features are incorporated into LS-SVM to develop a data density-dependent quantized LS-SVM (DQLS-SVM), where an analytic solution is obtained in the primal solution space. The developed DQLS-SVM is evaluated on synthetic and benchmark data with particular emphasis on large data sets. Extensive experimental results show that the learning machine incorporating DQS attains not only high computational efficiency but also good generalization performance.

Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation.

PubMed

Oliveira, Roberta B; Pereira, Aledir S; Tavares, João Manuel R S

2017-10-01

The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results. Copyright © 2017 Elsevier B.V. All rights reserved.

System and Method for Modeling the Flow Performance Features of an Object

NASA Technical Reports Server (NTRS)

Jorgensen, Charles (Inventor); Ross, James (Inventor)

1997-01-01

The method and apparatus includes a neural network for generating a model of an object in a wind tunnel from performance data on the object. The network is trained from test input signals (e.g., leading edge flap position, trailing edge flap position, angle of attack, and other geometric configurations, and power settings) and test output signals (e.g., lift, drag, pitching moment, or other performance features). In one embodiment, the neural network training method employs a modified Levenberg-Marquardt optimization technique. The model can be generated 'real time' as wind tunnel testing proceeds. Once trained, the model is used to estimate performance features associated with the aircraft given geometric configuration and/or power setting input. The invention can also be applied in other similar static flow modeling applications in aerodynamics, hydrodynamics, fluid dynamics, and other such disciplines. For example, the static testing of cars, sails, and foils, propellers, keels, rudders, turbines, fins, and the like, in a wind tunnel, water trough, or other flowing medium.

Inverse Proportional Relationship Between Switching-Time Length and Fractal-Like Structure for Continuous Tracking Movement

NASA Astrophysics Data System (ADS)

Hirakawa, Takehito; Suzuki, Hiroo; Gohara, Kazutoshi; Yamamoto, Yuji

We investigate the relationship between the switching-time length T and the fractal-like feature that characterizes the behavior of dissipative dynamical systems excited by external temporal inputs for tracking movement. Seven healthy right-handed male participants were asked to continuously track light-emitting diodes that were located on the right and left sides in front of them. These movements were performed under two conditions: when the same input pattern was repeated (the periodic-input condition) and when two different input patterns were switched stochastically (the switching-input condition). The repeated time lengths of input patterns during these conditions were 2.00, 1.00, 0.75, 0.50, 0.35, and 0.25s. The movements of a lever held between a participant’s thumb and index finger were measured by a motion-capture system and were analyzed with respect to position and velocity. The condition in which the same input was repeated revealed that two different stable trajectories existed in a cylindrical state space, while the condition in which the inputs were switched induced transitions between these two trajectories. These two different trajectories were considered as excited attractors. The transitions between the two excited attractors produced eight trajectories; they were then characterized by a fractal-like feature as a third-order sequence effect. Moreover, correlation dimensions, which are typically used to evaluate fractal-like features, calculated from the set on the Poincaré section increased as the switching-time length T decreased. These results suggest that an inverse proportional relationship exists between the switching-time length T and the fractal-like feature of human movement.

Communications and control for electric power systems: Power flow classification for static security assessment

NASA Technical Reports Server (NTRS)

Niebur, D.; Germond, A.

1993-01-01

This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

NASA Astrophysics Data System (ADS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-04-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

Evaluation of Spectral and Prosodic Features of Speech Affected by Orthodontic Appliances Using the Gmm Classifier

NASA Astrophysics Data System (ADS)

Přibil, Jiří; Přibilová, Anna; Ďuračkoá, Daniela

2014-01-01

The paper describes our experiment with using the Gaussian mixture models (GMM) for classification of speech uttered by a person wearing orthodontic appliances. For the GMM classification, the input feature vectors comprise the basic and the complementary spectral properties as well as the supra-segmental parameters. Dependence of classification correctness on the number of the parameters in the input feature vector and on the computation complexity is also evaluated. In addition, an influence of the initial setting of the parameters for GMM training process was analyzed. Obtained recognition results are compared visually in the form of graphs as well as numerically in the form of tables and confusion matrices for tested sentences uttered using three configurations of orthodontic appliances.

Feature selection for elderly faller classification based on wearable sensors.

PubMed

Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-05-30

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data. A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling. The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier. Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Modeling Spoken Word Recognition Performance by Pediatric Cochlear Implant Users using Feature Identification

PubMed Central

Frisch, Stefan A.; Pisoni, David B.

2012-01-01

Objective Computational simulations were carried out to evaluate the appropriateness of several psycholinguistic theories of spoken word recognition for children who use cochlear implants. These models also investigate the interrelations of commonly used measures of closed-set and open-set tests of speech perception. Design A software simulation of phoneme recognition performance was developed that uses feature identification scores as input. Two simulations of lexical access were developed. In one, early phoneme decisions are used in a lexical search to find the best matching candidate. In the second, phoneme decisions are made only when lexical access occurs. Simulated phoneme and word identification performance was then applied to behavioral data from the Phonetically Balanced Kindergarten test and Lexical Neighborhood Test of open-set word recognition. Simulations of performance were evaluated for children with prelingual sensorineural hearing loss who use cochlear implants with the MPEAK or SPEAK coding strategies. Results Open-set word recognition performance can be successfully predicted using feature identification scores. In addition, we observed no qualitative differences in performance between children using MPEAK and SPEAK, suggesting that both groups of children process spoken words similarly despite differences in input. Word recognition ability was best predicted in the model in which phoneme decisions were delayed until lexical access. Conclusions Closed-set feature identification and open-set word recognition focus on different, but related, levels of language processing. Additional insight for clinical intervention may be achieved by collecting both types of data. The most successful model of performance is consistent with current psycholinguistic theories of spoken word recognition. Thus it appears that the cognitive process of spoken word recognition is fundamentally the same for pediatric cochlear implant users and children and adults with normal hearing. PMID:11132784

Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using Time-Domain Features

NASA Astrophysics Data System (ADS)

Samanta, B.; Al-Balushi, K. R.

2003-03-01

A procedure is presented for fault diagnosis of rolling element bearings through artificial neural network (ANN). The characteristic features of time-domain vibration signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN consisting of input, hidden and output layers. The features are obtained from direct processing of the signal segments using very simple preprocessing. The input layer consists of five nodes, one each for root mean square, variance, skewness, kurtosis and normalised sixth central moment of the time-domain vibration signals. The inputs are normalised in the range of 0.0 and 1.0 except for the skewness which is normalised between -1.0 and 1.0. The output layer consists of two binary nodes indicating the status of the machine—normal or defective bearings. Two hidden layers with different number of neurons have been used. The ANN is trained using backpropagation algorithm with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The effects of some preprocessing techniques like high-pass, band-pass filtration, envelope detection (demodulation) and wavelet transform of the vibration signals, prior to feature extraction, are also studied. The results show the effectiveness of the ANN in diagnosis of the machine condition. The proposed procedure requires only a few features extracted from the measured vibration data either directly or with simple preprocessing. The reduced number of inputs leads to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines.

Supervised neural network classification of pre-sliced cooked pork ham images using quaternionic singular values.

PubMed

Valous, Nektarios A; Mendoza, Fernando; Sun, Da-Wen; Allen, Paul

2010-03-01

The quaternionic singular value decomposition is a technique to decompose a quaternion matrix (representation of a colour image) into quaternion singular vector and singular value component matrices exposing useful properties. The objective of this study was to use a small portion of uncorrelated singular values, as robust features for the classification of sliced pork ham images, using a supervised artificial neural network classifier. Images were acquired from four qualities of sliced cooked pork ham typically consumed in Ireland (90 slices per quality), having similar appearances. Mahalanobis distances and Pearson product moment correlations were used for feature selection. Six highly discriminating features were used as input to train the neural network. An adaptive feedforward multilayer perceptron classifier was employed to obtain a suitable mapping from the input dataset. The overall correct classification performance for the training, validation and test set were 90.3%, 94.4%, and 86.1%, respectively. The results confirm that the classification performance was satisfactory. Extracting the most informative features led to the recognition of a set of different but visually quite similar textural patterns based on quaternionic singular values. Copyright 2009 Elsevier Ltd. All rights reserved.

Dimensionality Reduction Through Classifier Ensembles

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)

1999-01-01

In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.

Deep SOMs for automated feature extraction and classification from big data streaming

NASA Astrophysics Data System (ADS)

Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

2017-03-01

In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

Features selection and classification to estimate elbow movements

NASA Astrophysics Data System (ADS)

Rubiano, A.; Ramírez, J. L.; El Korso, M. N.; Jouandeau, N.; Gallimard, L.; Polit, O.

2015-11-01

In this paper, we propose a novel method to estimate the elbow motion, through the features extracted from electromyography (EMG) signals. The features values are normalized and then compared to identify potential relationships between the EMG signal and the kinematic information as angle and angular velocity. We propose and implement a method to select the best set of features, maximizing the distance between the features that correspond to flexion and extension movements. Finally, we test the selected features as inputs to a non-linear support vector machine in the presence of non-idealistic conditions, obtaining an accuracy of 99.79% in the motion estimation results.

Sentiment analysis of feature ranking methods for classification accuracy

NASA Astrophysics Data System (ADS)

Joseph, Shashank; Mugauri, Calvin; Sumathy, S.

2017-11-01

Text pre-processing and feature selection are important and critical steps in text mining. Text pre-processing of large volumes of datasets is a difficult task as unstructured raw data is converted into structured format. Traditional methods of processing and weighing took much time and were less accurate. To overcome this challenge, feature ranking techniques have been devised. A feature set from text preprocessing is fed as input for feature selection. Feature selection helps improve text classification accuracy. Of the three feature selection categories available, the filter category will be the focus. Five feature ranking methods namely: document frequency, standard deviation information gain, CHI-SQUARE, and weighted-log likelihood -ratio is analyzed.

Rotation invariant features for wear particle classification

NASA Astrophysics Data System (ADS)

Arof, Hamzah; Deravi, Farzin

1997-09-01

This paper investigates the ability of a set of rotation invariant features to classify images of wear particles found in used lubricating oil of machinery. The rotation invariant attribute of the features is derived from the property of the magnitudes of Fourier transform coefficients that do not change with spatial shift of the input elements. By analyzing individual circular neighborhoods centered at every pixel in an image, local and global texture characteristics of an image can be described. A number of input sequences are formed by the intensities of pixels on concentric rings of various radii measured from the center of each neighborhood. Fourier transforming the sequences would generate coefficients whose magnitudes are invariant to rotation. Rotation invariant features extracted from these coefficients were utilized to classify wear particle images that were obtained from a number of different particles captured at different orientations. In an experiment involving images of 6 classes, the circular neighborhood features obtained a 91% recognition rate which compares favorably to a 76% rate achieved by features of a 6 by 6 co-occurrence matrix.

Application of preprocessing filtering on Decision Tree C4.5 and rough set theory

NASA Astrophysics Data System (ADS)

Chan, Joseph C. C.; Lin, Tsau Y.

2001-03-01

This paper compares two artificial intelligence methods: the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the feature (attribute) transformations, which allows users to input formulas and create new attributes. Also, the application produces three varieties of data set with delaying, averaging, and summation. The results prove the improvement of pre-processing by applying feature (attribute) transformations on Decision Tree C4.5. Moreover, the comparison between Decision Tree C4.5 and Rough Set Theory is based on the clarity, automation, accuracy, dimensionality, raw data, and speed, which is supported by the rules sets generated by both algorithms on three different sets of data.

A data mining framework for time series estimation.

PubMed

Hu, Xiao; Xu, Peng; Wu, Shaozhi; Asgari, Shadnaz; Bergsneider, Marvin

2010-04-01

Time series estimation techniques are usually employed in biomedical research to derive variables less accessible from a set of related and more accessible variables. These techniques are traditionally built from systems modeling approaches including simulation, blind decovolution, and state estimation. In this work, we define target time series (TTS) and its related time series (RTS) as the output and input of a time series estimation process, respectively. We then propose a novel data mining framework for time series estimation when TTS and RTS represent different sets of observed variables from the same dynamic system. This is made possible by mining a database of instances of TTS, its simultaneously recorded RTS, and the input/output dynamic models between them. The key mining strategy is to formulate a mapping function for each TTS-RTS pair in the database that translates a feature vector extracted from RTS to the dissimilarity between true TTS and its estimate from the dynamic model associated with the same TTS-RTS pair. At run time, a feature vector is extracted from an inquiry RTS and supplied to the mapping function associated with each TTS-RTS pair to calculate a dissimilarity measure. An optimal TTS-RTS pair is then selected by analyzing these dissimilarity measures. The associated input/output model of the selected TTS-RTS pair is then used to simulate the TTS given the inquiry RTS as an input. An exemplary implementation was built to address a biomedical problem of noninvasive intracranial pressure assessment. The performance of the proposed method was superior to that of a simple training-free approach of finding the optimal TTS-RTS pair by a conventional similarity-based search on RTS features. 2009 Elsevier Inc. All rights reserved.

Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

PubMed Central

Boonjing, Veera; Intakosum, Sarun

2016-01-01

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span. PMID:27974883

Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend.

PubMed

Inthachot, Montri; Boonjing, Veera; Intakosum, Sarun

2016-01-01

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

BagReg: Protein inference through machine learning.

PubMed

Zhao, Can; Liu, Dao; Teng, Ben; He, Zengyou

2015-08-01

Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.

Three-Class Mammogram Classification Based on Descriptive CNN Features

PubMed Central

Zhang, Qianni; Jadoon, Adeel

2017-01-01

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques. PMID:28191461

Three-Class Mammogram Classification Based on Descriptive CNN Features.

PubMed

Jadoon, M Mohsin; Zhang, Qianni; Haq, Ihsan Ul; Butt, Sharjeel; Jadoon, Adeel

2017-01-01

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

Computer vision-based method for classification of wheat grains using artificial neural network.

PubMed

Sabanci, Kadir; Kayabasi, Ahmet; Toktas, Abdurrahim

2017-06-01

A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains. Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10 -6 by the simplified ANN model. This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Learning representative features for facial images based on a modified principal component analysis

NASA Astrophysics Data System (ADS)

Averkin, Anton; Potapov, Alexey

2013-05-01

The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

Computational Intelligence Modeling of the Macromolecules Release from PLGA Microspheres-Focus on Feature Selection.

PubMed

Zawbaa, Hossam M; Szlȩk, Jakub; Grosan, Crina; Jachowicz, Renata; Mendyk, Aleksander

2016-01-01

Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP), multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR). The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE) of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven.

Computational Intelligence Modeling of the Macromolecules Release from PLGA Microspheres—Focus on Feature Selection

PubMed Central

Zawbaa, Hossam M.; Szlȩk, Jakub; Grosan, Crina; Jachowicz, Renata; Mendyk, Aleksander

2016-01-01

Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP), multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR). The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE) of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven. PMID:27315205

Mathematics Programming on the Apple II and IBM PC.

ERIC Educational Resources Information Center

Myers, Roy E.; Schneider, David I.

1987-01-01

Details the features of BASIC used in mathematics programming and provides the information needed to translate between the Apple II and IBM PC computers. Discusses inputing a user-defined function, setting scroll windows, displaying subscripts and exponents, variable names, mathematical characters and special symbols. (TW)

Are the Traditional Curricula Dispensable? A Feature Pattern to Compare Different Types of Curriculum and a Critical View of Educational Standards and Essential Curricula in Germany

ERIC Educational Resources Information Center

Scholl, Daniel

2012-01-01

The results of international school achievement studies had major educational implications in many European countries, especially for the control concepts of education. This becomes exemplarily apparent in Germany, in which a large-scale educational reform was set in motion. Thereby, the education system was set from an input- to output-oriented…

Set covering algorithm, a subprogram of the scheduling algorithm for mission planning and logistic evaluation

NASA Technical Reports Server (NTRS)

Chang, H.

1976-01-01

A computer program using Lemke, Salkin and Spielberg's Set Covering Algorithm (SCA) to optimize a traffic model problem in the Scheduling Algorithm for Mission Planning and Logistics Evaluation (SAMPLE) was documented. SCA forms a submodule of SAMPLE and provides for input and output, subroutines, and an interactive feature for performing the optimization and arranging the results in a readily understandable form for output.

Learning relevant features of data with multi-scale tensor networks

NASA Astrophysics Data System (ADS)

Miles Stoudenmire, E.

2018-07-01

Inspired by coarse-graining approaches used in physics, we show how similar algorithms can be adapted for data. The resulting algorithms are based on layered tree tensor networks and scale linearly with both the dimension of the input and the training set size. Computing most of the layers with an unsupervised algorithm, then optimizing just the top layer for supervised classification of the MNIST and fashion MNIST data sets gives very good results. We also discuss mixing a prior guess for supervised weights together with an unsupervised representation of the data, yielding a smaller number of features nevertheless able to give good performance.

Ways to suppress click and pop for class D amplifiers

NASA Astrophysics Data System (ADS)

Haishi, Wang; Bo, Zhang; Jiang, Sun

2012-08-01

Undesirable audio click and pop may be generated in a speaker or headphone. Compared to linear (class A/B/AB) amplifiers, class D amplifiers that comprise of an input stage and a modulation stage are more prone to producing click and pop. This article analyzes sources that generate click and pop in class D amplifiers, and corresponding ways to suppress them. For a class D amplifier with a single-ended input, click and pop is likely to be due to two factors. One is from a voltage difference (VDIF) between the voltage of an input capacitance (VCIN) and a reference voltage (VREF) of the input stage, and the other one is from the non-linear switching during the setting up of the bias and feedback voltages/currents (BFVC) of the modulation stage. In this article, a fast charging loop is introduced into the input stage to charge VCIN to roughly near VREF. Then a correction loop further charges or discharges VCIN, substantially equalizing it with VREF. Dummy switches are introduced into the modulation stage to provide switching signals for setting up BFVC, and the power switches are disabled until the BFVC are set up successfully. A two channel single-ended class D amplifier with the above features is fabricated with 0.5 μm Bi-CMOS process. Road test and fast Fourier transform analysis indicate that there is no noticeable click and pop.

Invariant object recognition based on the generalized discrete radon transform

NASA Astrophysics Data System (ADS)

Easley, Glenn R.; Colonna, Flavia

2004-04-01

We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.

Semantic classification of business images

NASA Astrophysics Data System (ADS)

Erol, Berna; Hull, Jonathan J.

2006-01-01

Digital cameras are becoming increasingly common for capturing information in business settings. In this paper, we describe a novel method for classifying images into the following semantic classes: document, whiteboard, business card, slide, and regular images. Our method is based on combining low-level image features, such as text color, layout, and handwriting features with high-level OCR output analysis. Several Support Vector Machine Classifiers are combined for multi-class classification of input images. The system yields 95% accuracy in classification.

Neural network analysis for geological interpretation of tomographic images beneath the Japan Islands

NASA Astrophysics Data System (ADS)

Kuwatani, T.; Toriumi, M.

2009-12-01

Recent advances in methodologies of geophysical observations, such as seismic tomography, seismic reflection method and geomagnetic method, provide us a large amount and a wide variety of data for physical properties of a crust and upper mantle (e.g. Matsubara et al. (2008)). However, it has still been difficult to specify a rock type and its physical conditions, mainly because (1) available data usually have a lot of error and uncertainty, and (2) physical properties of rocks are greatly affected by fluid and microstructures. The objective interpretation and quantitative evaluation for lithology and fluid-related structure require the statistical analyses of integrated geophysical and geological data. Self-Organizing Maps (SOMs) are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data (Kohonen 2001). SOMs are powerful neural network techniques to classify and interpret multiattribute data sets. Results of SOM classifications can be represented as 2D images, called feature maps which illustrate the complexity and interrelationships among input data sets. Recently, some works have used SOM in order to interpret multidimensional, non-linear, and highly noised geophysical data for purposes of geological prediction (e.g. Klose 2006; Tselentis et al. 2007; Bauer et al. 2008). This paper describes the application of SOM to the 3D velocity structure beneath the whole Japan islands (e.g. Matsubara et al. 2008). From the obtained feature maps, we can specify the lithology and qualitatively evaluate the effect of fluid-related structures. Moreover, re-projection of feature maps onto the 3D velocity structures resulted in detailed images of the structures within the plates. The Pacific plate and the Philippine Sea plate subducting beneath the Eurasian plate can be imaged more clearly than the original P- and S-wave velocity structures. In order to understand more precise prediction of lithology and its structure, we will use the additional input data sets, such as tomographic images of random velocity fluctuation (Takahashi et al. 2009) and b-value mapping data. Additionally, different kinds of data sets, including the experimental and petrological results (e.g. Christensen 1991; Hacker et al. 2003) can be applied to our analyses.

The N400 as a snapshot of interactive processing: evidence from regression analyses of orthographic neighbor and lexical associate effects

PubMed Central

Laszlo, Sarah; Federmeier, Kara D.

2010-01-01

Linking print with meaning tends to be divided into subprocesses, such as recognition of an input's lexical entry and subsequent access of semantics. However, recent results suggest that the set of semantic features activated by an input is broader than implied by a view wherein access serially follows recognition. EEG was collected from participants who viewed items varying in number and frequency of both orthographic neighbors and lexical associates. Regression analysis of single item ERPs replicated past findings, showing that N400 amplitudes are greater for items with more neighbors, and further revealed that N400 amplitudes increase for items with more lexical associates and with higher frequency neighbors or associates. Together, the data suggest that in the N400 time window semantic features of items broadly related to inputs are active, consistent with models in which semantic access takes place in parallel with stimulus recognition. PMID:20624252

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers.

PubMed

Mougiakakou, Stavroula G; Valavanis, Ioannis K; Nikita, Alexandra; Nikita, Konstantina S

2007-09-01

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. Number of regions of interests (ROIs) corresponding to C1-C4 have been defined by experienced radiologists in non-enhanced liver CT images. For each ROI, five distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. Two different ECs were constructed and compared. The first one consists of five multilayer perceptron neural networks (NNs), each using as input one of the computed texture feature sets or its reduced version after genetic algorithm-based feature selection. The second EC comprised five different primary classifiers, namely one multilayer perceptron NN, one probabilistic NN, and three k-nearest neighbor classifiers, each fed with the combination of the five texture feature sets or their reduced versions. The final decision of each EC was extracted by using appropriate voting schemes, while bootstrap re-sampling was utilized in order to estimate the generalization ability of the CAD architectures based on the available relatively small-sized data set. The best mean classification accuracy (84.96%) is achieved by the second EC using a fused feature set, and the weighted voting scheme. The fused feature set was obtained after appropriate feature selection applied to specific subsets of the original feature set. The comparative assessment of the various CAD architectures shows that combining three types of classifiers with a voting scheme, fed with identical feature sets obtained after appropriate feature selection and fusion, may result in an accurate system able to assist differential diagnosis of focal liver lesions from non-enhanced CT images.

Prototype Focal-Plane-Array Optoelectronic Image Processor

NASA Technical Reports Server (NTRS)

Fang, Wai-Chi; Shaw, Timothy; Yu, Jeffrey

1995-01-01

Prototype very-large-scale integrated (VLSI) planar array of optoelectronic processing elements combines speed of optical input and output with flexibility of reconfiguration (programmability) of electronic processing medium. Basic concept of processor described in "Optical-Input, Optical-Output Morphological Processor" (NPO-18174). Performs binary operations on binary (black and white) images. Each processing element corresponds to one picture element of image and located at that picture element. Includes input-plane photodetector in form of parasitic phototransistor part of processing circuit. Output of each processing circuit used to modulate one picture element in output-plane liquid-crystal display device. Intended to implement morphological processing algorithms that transform image into set of features suitable for high-level processing; e.g., recognition.

Scaling of global input-output networks

NASA Astrophysics Data System (ADS)

Liang, Sai; Qi, Zhengling; Qu, Shen; Zhu, Ji; Chiu, Anthony S. F.; Jia, Xiaoping; Xu, Ming

2016-06-01

Examining scaling patterns of networks can help understand how structural features relate to the behavior of the networks. Input-output networks consist of industries as nodes and inter-industrial exchanges of products as links. Previous studies consider limited measures for node strengths and link weights, and also ignore the impact of dataset choice. We consider a comprehensive set of indicators in this study that are important in economic analysis, and also examine the impact of dataset choice, by studying input-output networks in individual countries and the entire world. Results show that Burr, Log-Logistic, Log-normal, and Weibull distributions can better describe scaling patterns of global input-output networks. We also find that dataset choice has limited impacts on the observed scaling patterns. Our findings can help examine the quality of economic statistics, estimate missing data in economic statistics, and identify key nodes and links in input-output networks to support economic policymaking.

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

PubMed

Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

2018-04-02

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

RADC Fault Tolerant System Reliability Evaluation Facility

DTIC Science & Technology

1989-10-01

Adiagnostic fault handling circuitry for limited confi gurati ons 1epalrable 5ystes No TeS TSTsLmtda # H this point Por odic m ai ntenance qFo No *!i0 Teles...for using the "group" feature of MIREM. Groups must be inputted directly into an architectural file. Such a feature is needed for modeling internal...Sample System To Illustrate REST This system contains five sets which may be individual components or redundant groups of components, There are four

Methods for automatically analyzing humpback song units.

PubMed

Rickwood, Peter; Taylor, Andrew

2008-03-01

This paper presents mathematical techniques for automatically extracting and analyzing bioacoustic signals. Automatic techniques are described for isolation of target signals from background noise, extraction of features from target signals and unsupervised classification (clustering) of the target signals based on these features. The only user-provided inputs, other than raw sound, is an initial set of signal processing and control parameters. Of particular note is that the number of signal categories is determined automatically. The techniques, applied to hydrophone recordings of humpback whales (Megaptera novaeangliae), produce promising initial results, suggesting that they may be of use in automated analysis of not only humpbacks, but possibly also in other bioacoustic settings where automated analysis is desirable.

Getting the Gist of Events: Recognition of Two-Participant Actions from Brief Displays

PubMed Central

Hafri, Alon; Papafragou, Anna; Trueswell, John C.

2013-01-01

Unlike rapid scene and object recognition from brief displays, little is known about recognition of event categories and event roles from minimal visual information. In three experiments, we displayed naturalistic photographs of a wide range of two-participant event scenes for 37 ms and 73 ms followed by a mask, and found that event categories (the event gist, e.g., ‘kicking’, ‘pushing’, etc.) and event roles (i.e., Agent and Patient) can be recognized rapidly, even with various actor pairs and backgrounds. Norming ratings from a subsequent experiment revealed that certain physical features (e.g., outstretched extremities) that correlate with Agent-hood could have contributed to rapid role recognition. In a final experiment, using identical twin actors, we then varied these features in two sets of stimuli, in which Patients had Agent-like features or not. Subjects recognized the roles of event participants less accurately when Patients possessed Agent-like features, with this difference being eliminated with two-second durations. Thus, given minimal visual input, typical Agent-like physical features are used in role recognition but, with sufficient input from multiple fixations, people categorically determine the relationship between event participants. PMID:22984951

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuo, J; Su, K; Department of Radiology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, Ohio

Purpose: Accurate and robust photon attenuation derived from MR is essential for PET/MR and MR-based radiation treatment planning applications. Although the fuzzy C-means (FCM) algorithm has been applied for pseudo-CT generation, the input feature combination and the number of clusters have not been optimized. This study aims to optimize both for clinically practical pseudo-CT generation. Methods: Nine volunteers were recruited. A 190-second, single-acquisition UTE-mDixon with 25% (angular) sampling and 3D radial readout was performed to acquire three primitive MR features at TEs of 0.1, 1.5, and 2.8 ms: the free-induction-decay (FID), the first and the second echo images. Three derivedmore » images, Dixon-fat and Dixon-water generated by two-point Dixon water/fat separation, and R2* (1/T2*) map, were also created. To identify informative inputs for generating a pseudo-CT image volume, all 63 combinations, choosing one to six of the feature images, were used as inputs to FCM for pseudo-CT generation. Further, the number of clusters was varied from four to seven to find the optimal approach. Mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R) of different combinations were compared for feature selection. Results: Among the 63 feature combinations, the four that resulted in the best MAPD and R were further compared along with the set containing all six features. The results suggested that R2* and Dixon-water are the most informative features. Further, including FID also improved the performance of pseudo-CT generation. Consequently, the set containing FID, Dixon-water, and R2* resulted in the most accurate, robust pseudo-CT when the number of cluster equals to five (5C). The clusters were interpreted as air, fat, bone, brain, and fluid. The six-cluster Result additionally included bone marrow. Conclusion: The results suggested that FID, Dixon-water, R2* are the most important features. The findings can be used to facilitate pseudo-CT generation for unsupervised clustering. Please note that the project was completed with partial funding from the Ohio Department of Development grant TECH 11-063 and a sponsored research agreement with Philips Healthcare that is managed by Case Western Reserve University. As noted in the affiliations, some of the authors are Philips employees.« less

Tasks and premises in quantum state determination

NASA Astrophysics Data System (ADS)

Carmeli, Claudio; Heinosaari, Teiko; Schultz, Jussi; Toigo, Alessandro

2014-02-01

The purpose of quantum tomography is to determine an unknown quantum state from measurement outcome statistics. There are two obvious ways to generalize this setting. First, our task need not be the determination of any possible input state but only some input states, for instance pure states. Second, we may have some prior information, or premise, which guarantees that the input state belongs to some subset of states, for instance the set of states with rank less than half of the dimension of the Hilbert space. We investigate state determination under these two supplemental features, concentrating on the cases where the task and the premise are statements about the rank of the unknown state. We characterize the structure of quantum observables (positive operator valued measures) that are capable of fulfilling these type of determination tasks. After the general treatment we focus on the class of covariant phase space observables, thus providing physically relevant examples of observables both capable and incapable of performing these tasks. In this context, the effect of noise is discussed.

Uncertainty Analysis for a Jet Flap Airfoil

NASA Technical Reports Server (NTRS)

Green, Lawrence L.; Cruz, Josue

2006-01-01

An analysis of variance (ANOVA) study was performed to quantify the potential uncertainties of lift and pitching moment coefficient calculations from a computational fluid dynamics code, relative to an experiment, for a jet flap airfoil configuration. Uncertainties due to a number of factors including grid density, angle of attack and jet flap blowing coefficient were examined. The ANOVA software produced a numerical model of the input coefficient data, as functions of the selected factors, to a user-specified order (linear, 2-factor interference, quadratic, or cubic). Residuals between the model and actual data were also produced at each of the input conditions, and uncertainty confidence intervals (in the form of Least Significant Differences or LSD) for experimental, computational, and combined experimental / computational data sets were computed. The LSD bars indicate the smallest resolvable differences in the functional values (lift or pitching moment coefficient) attributable solely to changes in independent variable, given just the input data points from selected data sets. The software also provided a collection of diagnostics which evaluate the suitability of the input data set for use within the ANOVA process, and which examine the behavior of the resultant data, possibly suggesting transformations which should be applied to the data to reduce the LSD. The results illustrate some of the key features of, and results from, the uncertainty analysis studies, including the use of both numerical (continuous) and categorical (discrete) factors, the effects of the number and range of the input data points, and the effects of the number of factors considered simultaneously.

Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sahiner, B.; Chan, H.P.; Petrick, N.

1996-10-01

The authors investigated the classification of regions of interest (ROI`s) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a back-propagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained form the ROI`s using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequentlymore » used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROI`s containing biopsy-proven masses and 504 ROI`s containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms.« less

Achromatical Optical Correlator

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Liu, Hua-Kuang

1989-01-01

Signal-to-noise ratio exceeds that of monochromatic correlator. Achromatical optical correlator uses multiple-pinhole diffraction of dispersed white light to form superposed multiple correlations of input and reference images in output plane. Set of matched spatial filters made by multiple-exposure holographic process, each exposure using suitably-scaled input image and suitable angle of reference beam. Recording-aperture mask translated to appropriate horizontal position for each exposure. Noncoherent illumination suitable for applications involving recognition of color and determination of scale. When fully developed achromatical correlators will be useful for recognition of patterns; for example, in industrial inspection and search for selected features in aerial photographs.

A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets.

PubMed

Li, Der-Chiang; Liu, Chiao-Wen; Hu, Susan C

2011-05-01

Medical data sets are usually small and have very high dimensionality. Too many attributes will make the analysis less efficient and will not necessarily increase accuracy, while too few data will decrease the modeling stability. Consequently, the main objective of this study is to extract the optimal subset of features to increase analytical performance when the data set is small. This paper proposes a fuzzy-based non-linear transformation method to extend classification related information from the original data attribute values for a small data set. Based on the new transformed data set, this study applies principal component analysis (PCA) to extract the optimal subset of features. Finally, we use the transformed data with these optimal features as the input data for a learning tool, a support vector machine (SVM). Six medical data sets: Pima Indians' diabetes, Wisconsin diagnostic breast cancer, Parkinson disease, echocardiogram, BUPA liver disorders dataset, and bladder cancer cases in Taiwan, are employed to illustrate the approach presented in this paper. This research uses the t-test to evaluate the classification accuracy for a single data set; and uses the Friedman test to show the proposed method is better than other methods over the multiple data sets. The experiment results indicate that the proposed method has better classification performance than either PCA or kernel principal component analysis (KPCA) when the data set is small, and suggest creating new purpose-related information to improve the analysis performance. This paper has shown that feature extraction is important as a function of feature selection for efficient data analysis. When the data set is small, using the fuzzy-based transformation method presented in this work to increase the information available produces better results than the PCA and KPCA approaches. Copyright © 2011 Elsevier B.V. All rights reserved.

Patterns of Adult-Child Linguistic Interaction in Integrated Day Care Groups.

PubMed

Girolametto, Luigi; Hoaken, Lisa; Weitzman, Elaine; Lieshout, Riet van

2000-04-01

This study investigated the language input of eight childcare providers to children with developmental disabilities, including language delay, who were integrated into community day care centers. Structural and discourse features of the adults' language input was compared across two groups (integrated, typical) and two naturalistic day care contexts (book reading, play dough activity). The eight children with developmental disabilities and language delay were between 33-50 months of age; 32 normally developing peers ranged in age from 32-53 months of age. Adult-child interactions were transcribed and coded to yield estimates of structural indices (number of utterances, rate, mean length of utterances, ratio of different words to total words used (TTR) and discourse features (directive, interactive, language-modelling) of their language input. The language input addressed to the children with developmental disabilities was directive and not finely tuned to their expressive language levels. In turn, these children interacted infrequently with the adult or with the other children. Contextual comparisons indicated that the play dough activity promoted adult-child interaction that was less directive and more interaction-promoting than book reading, and that children interacted more frequently in the play-dough activity. Implications for speech-language pathologists include the need for collaborative consultation in integrated settings, modification of adult-child play contexts to promote interaction, and training childcare providers to use language input that promotes communication development.

Classification

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.

2011-01-01

A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. In supervised learning, a set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. This chapter discusses methods to perform machine learning, with examples involving astronomy.

A general prediction model for the detection of ADHD and Autism using structural and functional MRI.

PubMed

Sen, Bhaskar; Borle, Neil C; Greiner, Russell; Brown, Matthew R G

2018-01-01

This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder. These filters are then convolved with the original MRI image using an unsupervised convolutional network. The resulting features are used as input to a linear support vector machine (SVM) classifier. (2) The LeFMF learner produces a diagnostic model by first computing spatial non-stationary independent components of the fMRI scans, which it uses to decompose each subject's fMRI scan into the time courses of these common spatial components. These features can then be used with a learner by themselves or in combination with other features to produce the model. Regardless of which approach is used, the final set of features are input to a linear support vector machine (SVM) classifier. (3) Finally, the overall LeFMSF learner uses the combined features obtained from the two feature extraction processes in (1) and (2) above as input to an SVM classifier, achieving an accuracy of 0.673 on the ADHD-200 holdout data and 0.643 on the ABIDE holdout data. Both of these results, obtained with the same LeFMSF framework, are the best known, over all hold-out accuracies on these datasets when only using imaging data-exceeding previously-published results by 0.012 for ADHD and 0.042 for Autism. Our results show that combining multi-modal features can yield good classification accuracy for diagnosis of ADHD and Autism, which is an important step towards computer-aided diagnosis of these psychiatric diseases and perhaps others as well.

Search time critically depends on irrelevant subset size in visual search.

PubMed

Benjamins, Jeroen S; Hooge, Ignace T C; van Elst, Jacco C; Wertheim, Alexander H; Verstraten, Frans A J

2009-02-01

In order for our visual system to deal with the massive amount of sensory input, some of this input is discarded, while other parts are processed [Wolfe, J. M. (1994). Guided search 2.0: a revised model of visual search. Psychonomic Bulletin and Review, 1, 202-238]. From the visual search literature it is unclear how well one set of items can be selected that differs in only one feature from target (a 1F set), while another set of items can be ignored that differs in two features from target (a 2F set). We systematically varied the percentage of 2F non-targets to determine the contribution of these non-targets to search behaviour. Increasing the percentage 2F non-targets, that have to be ignored, was expected to result in increasingly faster search, since it decreases the size of 1F set that has to be searched. Observers searched large displays for a target in the 1F set with a variable percentage of 2F non-targets. Interestingly, when the search displays contained 5% 2F non-targets, the search time was longer compared to the search time in other conditions. This effect of 2F non-targets on performance was independent of set size. An inspection of the saccades revealed that saccade target selection did not contribute to the longer search times in displays with 5% 2F non-targets. Occurrence of longer search times in displays containing 5% 2F non-targets might be attributed to covert processes related to visual analysis of the fixated part of the display. Apparently, visual search performance critically depends on the percentage of irrelevant 2F non-targets.

Self-organizing neural networks--an alternative way of cluster analysis in clinical chemistry.

PubMed

Reibnegger, G; Wachter, H

1996-04-15

Supervised learning schemes have been employed by several workers for training neural networks designed to solve clinical problems. We demonstrate that unsupervised techniques can also produce interesting and meaningful results. Using a data set on the chemical composition of milk from 22 different mammals, we demonstrate that self-organizing feature maps (Kohonen networks) as well as a modified version of error backpropagation technique yield results mimicking conventional cluster analysis. Both techniques are able to project a potentially multi-dimensional input vector onto a two-dimensional space whereby neighborhood relationships remain conserved. Thus, these techniques can be used for reducing dimensionality of complicated data sets and for enhancing comprehensibility of features hidden in the data matrix.

Seismic signal auto-detecing from different features by using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

2017-12-01

We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.

Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

PubMed Central

Alvarez-Meza, Andres M.; Orozco-Gutierrez, Alvaro; Castellanos-Dominguez, German

2017-01-01

We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand. PMID:29056897

Automated Detection of Stereotypical Motor Movements in Autism Spectrum Disorder Using Recurrence Quantification Analysis

PubMed Central

Großekathöfer, Ulf; Manyakov, Nikolay V.; Mihajlović, Vojkan; Pandina, Gahan; Skalkin, Andrew; Ness, Seth; Bangerter, Abigail; Goodwin, Matthew S.

2017-01-01

A number of recent studies using accelerometer features as input to machine learning classifiers show promising results for automatically detecting stereotypical motor movements (SMM) in individuals with Autism Spectrum Disorder (ASD). However, replicating these results across different types of accelerometers and their position on the body still remains a challenge. We introduce a new set of features in this domain based on recurrence plot and quantification analyses that are orientation invariant and able to capture non-linear dynamics of SMM. Applying these features to an existing published data set containing acceleration data, we achieve up to 9% average increase in accuracy compared to current state-of-the-art published results. Furthermore, we provide evidence that a single torso sensor can automatically detect multiple types of SMM in ASD, and that our approach allows recognition of SMM with high accuracy in individuals when using a person-independent classifier. PMID:28261082

Automated Detection of Stereotypical Motor Movements in Autism Spectrum Disorder Using Recurrence Quantification Analysis.

PubMed

Großekathöfer, Ulf; Manyakov, Nikolay V; Mihajlović, Vojkan; Pandina, Gahan; Skalkin, Andrew; Ness, Seth; Bangerter, Abigail; Goodwin, Matthew S

2017-01-01

A number of recent studies using accelerometer features as input to machine learning classifiers show promising results for automatically detecting stereotypical motor movements (SMM) in individuals with Autism Spectrum Disorder (ASD). However, replicating these results across different types of accelerometers and their position on the body still remains a challenge. We introduce a new set of features in this domain based on recurrence plot and quantification analyses that are orientation invariant and able to capture non-linear dynamics of SMM. Applying these features to an existing published data set containing acceleration data, we achieve up to 9% average increase in accuracy compared to current state-of-the-art published results. Furthermore, we provide evidence that a single torso sensor can automatically detect multiple types of SMM in ASD, and that our approach allows recognition of SMM with high accuracy in individuals when using a person-independent classifier.

Type 2 Diabetes Screening Test by Means of a Pulse Oximeter.

PubMed

Moreno, Enrique Monte; Lujan, Maria Jose Anyo; Rusinol, Montse Torrres; Fernandez, Paqui Juarez; Manrique, Pilar Nunez; Trivino, Cristina Aragon; Miquel, Magda Pedrosa; Rodriguez, Marife Alvarez; Burguillos, M Jose Gonzalez

2017-02-01

In this paper, we propose a method for screening for the presence of type 2 diabetes by means of the signal obtained from a pulse oximeter. The screening system consists of two parts: the first analyzes the signal obtained from the pulse oximeter, and the second consists of a machine-learning module. The system consists of a front end that extracts a set of features form the pulse oximeter signal. These features are based on physiological considerations. The set of features were the input of a machine-learning algorithm that determined the class of the input sample, i.e., whether the subject had diabetes or not. The machine-learning algorithms were random forests, gradient boosting, and linear discriminant analysis as benchmark. The system was tested on a database of [Formula: see text] subjects (two samples per subject) collected from five community health centers. The mean receiver operating characteristic area found was [Formula: see text]% (median value [Formula: see text]% and range [Formula: see text]%), with a specificity =  [Formula: see text]% for a threshold that gave a sensitivity = [Formula: see text]%. We present a screening method for detecting diabetes that has a performance comparable to the glycated haemoglobin (haemoglobin A1c HbA1c) test, does not require blood extraction, and yields results in less than 5 min.

Three-dimensional object recognition using similar triangles and decision trees

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

1993-01-01

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

DOE-2 sample run book: Version 2.1E

DOE Office of Scientific and Technical Information (OSTI.GOV)

Winkelmann, F.C.; Birdsall, B.E.; Buhl, W.F.

1993-11-01

The DOE-2 Sample Run Book shows inputs and outputs for a variety of building and system types. The samples start with a simple structure and continue to a high-rise office building, a medical building, three small office buildings, a bar/lounge, a single-family residence, a small office building with daylighting, a single family residence with an attached sunspace, a ``parameterized`` building using input macros, and a metric input/output example. All of the samples use Chicago TRY weather. The main purpose of the Sample Run Book is instructional. It shows the relationship of LOADS-SYSTEMS-PLANT-ECONOMICS inputs, displays various input styles, and illustrates manymore » of the basic and advanced features of the program. Many of the sample runs are preceded by a sketch of the building showing its general appearance and the zoning used in the input. In some cases we also show a 3-D rendering of the building as produced by the program DrawBDL. Descriptive material has been added as comments in the input itself. We find that a number of users have loaded these samples onto their editing systems and use them as ``templates`` for creating new inputs. Another way of using them would be to store various portions as files that can be read into the input using the {number_sign}{number_sign} include command, which is part of the Input Macro feature introduced in version DOE-2.lD. Note that the energy rate structures here are the same as in the DOE-2.lD samples, but have been rewritten using the new DOE-2.lE commands and keywords for ECONOMICS. The samples contained in this report are the same as those found on the DOE-2 release files. However, the output numbers that appear here may differ slightly from those obtained from the release files. The output on the release files can be used as a check set to compare results on your computer.« less

Artificial neural networks for acoustic target recognition

NASA Astrophysics Data System (ADS)

Robertson, James A.; Mossing, John C.; Weber, Bruce A.

1995-04-01

Acoustic sensors can be used to detect, track and identify non-line-of-sight targets passively. Attempts to alter acoustic emissions often result in an undesirable performance degradation. This research project investigates the use of neural networks for differentiating between features extracted from the acoustic signatures of sources. Acoustic data were filtered and digitized using a commercially available analog-digital convertor. The digital data was transformed to the frequency domain for additional processing using the FFT. Narrowband peak detection algorithms were incorporated to select peaks above a user defined SNR. These peaks were then used to generate a set of robust features which relate specifically to target components in varying background conditions. The features were then used as input into a backpropagation neural network. A K-means unsupervised clustering algorithm was used to determine the natural clustering of the observations. Comparisons between a feature set consisting of the normalized amplitudes of the first 250 frequency bins of the power spectrum and a set of 11 harmonically related features were made. Initial results indicate that even though some different target types had a tendency to group in the same clusters, the neural network was able to differentiate the targets. Successful identification of acoustic sources under varying operational conditions with high confidence levels was achieved.

Visual Odometry Based on Structural Matching of Local Invariant Features Using Stereo Camera Sensor

PubMed Central

Núñez, Pedro; Vázquez-Martín, Ricardo; Bandera, Antonio

2011-01-01

This paper describes a novel sensor system to estimate the motion of a stereo camera. Local invariant image features are matched between pairs of frames and linked into image trajectories at video rate, providing the so-called visual odometry, i.e., motion estimates from visual input alone. Our proposal conducts two matching sessions: the first one between sets of features associated to the images of the stereo pairs and the second one between sets of features associated to consecutive frames. With respect to previously proposed approaches, the main novelty of this proposal is that both matching algorithms are conducted by means of a fast matching algorithm which combines absolute and relative feature constraints. Finding the largest-valued set of mutually consistent matches is equivalent to finding the maximum-weighted clique on a graph. The stereo matching allows to represent the scene view as a graph which emerge from the features of the accepted clique. On the other hand, the frame-to-frame matching defines a graph whose vertices are features in 3D space. The efficiency of the approach is increased by minimizing the geometric and algebraic errors to estimate the final displacement of the stereo camera between consecutive acquired frames. The proposed approach has been tested for mobile robotics navigation purposes in real environments and using different features. Experimental results demonstrate the performance of the proposal, which could be applied in both industrial and service robot fields. PMID:22164016

An intelligent system based on fuzzy probabilities for medical diagnosis– a study in aphasia diagnosis*

PubMed Central

Moshtagh-Khorasani, Majid; Akbarzadeh-T, Mohammad-R; Jahangiri, Nader; Khoobdel, Mehdi

2009-01-01

BACKGROUND: Aphasia diagnosis is particularly challenging due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. METHODS: Fuzzy probability is proposed here as the basic framework for handling the uncertainties in medical diagnosis and particularly aphasia diagnosis. To efficiently construct this fuzzy probabilistic mapping, statistical analysis is performed that constructs input membership functions as well as determines an effective set of input features. RESULTS: Considering the high sensitivity of performance measures to different distribution of testing/training sets, a statistical t-test of significance is applied to compare fuzzy approach results with NN results as well as author's earlier work using fuzzy logic. The proposed fuzzy probability estimator approach clearly provides better diagnosis for both classes of data sets. Specifically, for the first and second type of fuzzy probability classifiers, i.e. spontaneous speech and comprehensive model, P-values are 2.24E-08 and 0.0059, respectively, strongly rejecting the null hypothesis. CONCLUSIONS: The technique is applied and compared on both comprehensive and spontaneous speech test data for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. Statistical analysis confirms that the proposed approach can significantly improve accuracy using fewer Aphasia features. PMID:21772867

The prediction of airborne and structure-borne noise potential for a tire

NASA Astrophysics Data System (ADS)

Sakamoto, Nicholas Y.

Tire/pavement interaction noise is a major component of both exterior pass-by noise and vehicle interior noise. The current testing methods for ranking tires from loud to quiet require expensive equipment, multiple tires, and/or long experimental set-up and run times. If a laboratory based off-vehicle test could be used to identify the airborne and structure-borne potential of a tire from its dynamic characteristics, a relative ranking of a large group of tires could be performed at relatively modest expense. This would provide a smaller sample set of tires for follow-up testing and thus save expense for automobile OEMs. The focus of this research was identifying key noise features from a tire/pavement experiment. These results were compared against a stationary tire test in which the natural response of the tire to a forced input was measured. Since speed was identified as having some effect on the noise, an input function was also developed to allow the tires to be ranked at an appropriate speed. A relative noise model was used on a second sample set of tires to verify if the ranking could be used against interior vehicle measurements. While overall level analysis of the specified spectrum had mixed success, important noise generating features were identified, and the methods used could be improved to develop a standard off-vehicle test to predict a tire's noise potential.

Inverse dynamics of underactuated mechanical systems: A simple case study and experimental verification

NASA Astrophysics Data System (ADS)

Blajer, W.; Dziewiecki, K.; Kołodziejczyk, K.; Mazur, Z.

2011-05-01

Underactuated systems are featured by fewer control inputs than the degrees-of-freedom, m < n. The determination of an input control strategy that forces such a system to complete a set of m specified motion tasks is a challenging task, and the explicit solution existence is conditioned to differential flatness of the problem. The flatness-based solution denotes that all the 2 n states and m control inputs can be algebraically expressed in terms of the m specified outputs and their time derivatives up to a certain order, which is in practice attainable only for simple systems. In this contribution the problem is posed in a more practical way as a set of index-three differential-algebraic equations, and the solution is obtained numerically. The formulation is then illustrated by a two-degree-of-freedom underactuated system composed of two rotating discs connected by a torsional spring, in which the pre-specified motion of one of the discs is actuated by the torque applied to the other disc, n = 2 and m = 1. Experimental verification of the inverse simulation control methodology is reported.

Retinal image quality assessment based on image clarity and content

NASA Astrophysics Data System (ADS)

Abdel-Hamid, Lamiaa; El-Rafei, Ahmed; El-Ramly, Salwa; Michelson, Georg; Hornegger, Joachim

2016-09-01

Retinal image quality assessment (RIQA) is an essential step in automated screening systems to avoid misdiagnosis caused by processing poor quality retinal images. A no-reference transform-based RIQA algorithm is introduced that assesses images based on five clarity and content quality issues: sharpness, illumination, homogeneity, field definition, and content. Transform-based RIQA algorithms have the advantage of considering retinal structures while being computationally inexpensive. Wavelet-based features are proposed to evaluate the sharpness and overall illumination of the images. A retinal saturation channel is designed and used along with wavelet-based features for homogeneity assessment. The presented sharpness and illumination features are utilized to assure adequate field definition, whereas color information is used to exclude nonretinal images. Several publicly available datasets of varying quality grades are utilized to evaluate the feature sets resulting in area under the receiver operating characteristic curve above 0.99 for each of the individual feature sets. The overall quality is assessed by a classifier that uses the collective features as an input vector. The classification results show superior performance of the algorithm in comparison to other methods from literature. Moreover, the algorithm addresses efficiently and comprehensively various quality issues and is suitable for automatic screening systems.

FuzzObserver

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Bayard, David

2006-01-01

Fuzzy Feature Observation Planner for Small Body Proximity Observations (FuzzObserver) is a developmental computer program, to be used along with other software, for autonomous planning of maneuvers of a spacecraft near an asteroid, comet, or other small astronomical body. Selection of terrain features and estimation of the position of the spacecraft relative to these features is an essential part of such planning. FuzzObserver contributes to the selection and estimation by generating recommendations for spacecraft trajectory adjustments to maintain the spacecraft's ability to observe sufficient terrain features for estimating position. The input to FuzzObserver consists of data from terrain images, including sets of data on features acquired during descent toward, or traversal of, a body of interest. The name of this program reflects its use of fuzzy logic to reason about the terrain features represented by the data and extract corresponding trajectory-adjustment rules. Linguistic fuzzy sets and conditional statements enable fuzzy systems to make decisions based on heuristic rule-based knowledge derived by engineering experts. A major advantage of using fuzzy logic is that it involves simple arithmetic calculations that can be performed rapidly enough to be useful for planning within the short times typically available for spacecraft maneuvers.

Oversampling the Minority Class in the Feature Space.

PubMed

Perez-Ortiz, Maria; Gutierrez, Pedro Antonio; Tino, Peter; Hervas-Martinez, Cesar

2016-09-01

The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. One common approach oversamples the minority class through convex combination of its patterns. We explore the general idea of synthetic oversampling in the feature space induced by a kernel function (as opposed to input space). If the kernel function matches the underlying problem, the classes will be linearly separable and synthetically generated patterns will lie on the minority class region. Since the feature space is not directly accessible, we use the empirical feature space (EFS) (a Euclidean space isomorphic to the feature space) for oversampling purposes. The proposed method is framed in the context of support vector machines, where the imbalanced data sets can pose a serious hindrance. The idea is investigated in three scenarios: 1) oversampling in the full and reduced-rank EFSs; 2) a kernel learning technique maximizing the data class separation to study the influence of the feature space structure (implicitly defined by the kernel function); and 3) a unified framework for preferential oversampling that spans some of the previous approaches in the literature. We support our investigation with extensive experiments over 50 imbalanced data sets.

A meta-structural understanding of measurement

NASA Astrophysics Data System (ADS)

Mari, Luca; Maul, Andrew; Torres Irribarra, David; Wilson, Mark

2016-11-01

It is not always clear to what extent the logic and vocabulary of measurement as used in different scientific disciplines are mutually coherent, nor how measurement can be demarcated from, say, opinion. In recent decades there have been a number of attempts to provide necessary and/or sufficient sets of conditions for when measurement is achieved, usually in terms either of inputs (e.g., whether an evaluated property is a quantity), or outputs (e.g., whether a procedure assigns numbers according to a rule). We argue instead that the public trust attributed to measurement is best justified in terms of the structural features of the process rather than of its inputs or outputs.

New features in the design code Tlie

NASA Astrophysics Data System (ADS)

van Zeijts, Johannes

1993-12-01

We present features recently installed in the arbitrary-order accelerator design code Tlie. The code uses the MAD input language, and implements programmable extensions modeled after the C language that make it a powerful tool in a wide range of applications: from basic beamline design to high precision-high order design and even control room applications. The basic quantities important in accelerator design are easily accessible from inside the control language. Entities like parameters in elements (strength, current), transfer maps (either in Taylor series or in Lie algebraic form), lines, and beams (either as sets of particles or as distributions) are among the type of variables available. These variables can be set, used as arguments in subroutines, or just typed out. The code is easily extensible with new datatypes.

The Effects of Audiovisual Inputs on Solving the Cocktail Party Problem in the Human Brain: An fMRI Study.

PubMed

Li, Yuanqing; Wang, Fangyi; Chen, Yongbin; Cichocki, Andrzej; Sejnowski, Terrence

2017-09-25

At cocktail parties, our brains often simultaneously receive visual and auditory information. Although the cocktail party problem has been widely investigated under auditory-only settings, the effects of audiovisual inputs have not. This study explored the effects of audiovisual inputs in a simulated cocktail party. In our fMRI experiment, each congruent audiovisual stimulus was a synthesis of 2 facial movie clips, each of which could be classified into 1 of 2 emotion categories (crying and laughing). Visual-only (faces) and auditory-only stimuli (voices) were created by extracting the visual and auditory contents from the synthesized audiovisual stimuli. Subjects were instructed to selectively attend to 1 of the 2 objects contained in each stimulus and to judge its emotion category in the visual-only, auditory-only, and audiovisual conditions. The neural representations of the emotion features were assessed by calculating decoding accuracy and brain pattern-related reproducibility index based on the fMRI data. We compared the audiovisual condition with the visual-only and auditory-only conditions and found that audiovisual inputs enhanced the neural representations of emotion features of the attended objects instead of the unattended objects. This enhancement might partially explain the benefits of audiovisual inputs for the brain to solve the cocktail party problem. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

YADCLAN: yet another digitally-controlled linear artificial neuron.

PubMed

Frenger, Paul

2003-01-01

This paper updates the author's 1999 RMBS presentation on digitally controlled linear artificial neuron design. Each neuron is based on a standard operational amplifier having excitatory and inhibitory inputs, variable gain, an amplified linear analog output and an adjustable threshold comparator for digital output. This design employs a 1-wire serial network of digitally controlled potentiometers and resistors whose resistance values are set and read back under microprocessor supervision. This system embodies several unique and useful features, including: enhanced neuronal stability, dynamic reconfigurability and network extensibility. This artificial neuronal is being employed for feature extraction and pattern recognition in an advanced robotic application.

SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS.

PubMed

Merelli, Ivan; Calabria, Andrea; Cozzi, Paolo; Viti, Federica; Mosca, Ettore; Milanesi, Luciano

2013-01-01

The capability of correlating specific genotypes with human diseases is a complex issue in spite of all advantages arisen from high-throughput technologies, such as Genome Wide Association Studies (GWAS). New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects. We propose a new bioinformatics approach to support biological data mining in the analysis and interpretation of SNPs associated to pathologies. This system can be employed to design custom genotyping chips for disease-oriented studies and to re-score GWAS results. The proposed method relies (1) on the data integration of public resources using a gene-centric database design, (2) on the evaluation of a set of static biomolecular annotations, defined as features, and (3) on the SNP scoring function, which computes SNP scores using parameters and weights set by users. We employed a machine learning classifier to set default feature weights and an ontological annotation layer to enable the enrichment of the input gene set. We implemented our method as a web tool called SNPranker 2.0 (http://www.itb.cnr.it/snpranker), improving our first published release of this system. A user-friendly interface allows the input of a list of genes, SNPs or a biological process, and to customize the features set with relative weights. As result, SNPranker 2.0 returns a list of SNPs, localized within input and ontologically enriched genes, combined with their prioritization scores. Different databases and resources are already available for SNPs annotation, but they do not prioritize or re-score SNPs relying on a-priori biomolecular knowledge. SNPranker 2.0 attempts to fill this gap through a user-friendly integrated web resource. End users, such as researchers in medical genetics and epidemiology, may find in SNPranker 2.0 a new tool for data mining and interpretation able to support SNPs analysis. Possible scenarios are GWAS data re-scoring, SNPs selection for custom genotyping arrays and SNPs/diseases association studies.

Using input feature information to improve ultraviolet retrieval in neural networks

NASA Astrophysics Data System (ADS)

Sun, Zhibin; Chang, Ni-Bin; Gao, Wei; Chen, Maosi; Zempila, Melina

2017-09-01

In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.

Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine

PubMed Central

2011-01-01

Background Cardiotocography (CTG) is the most widely used tool for fetal surveillance. The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved. Several approaches have been proposed for the effective interpretation of FHR. In this paper, a new approach for FHR feature extraction based on empirical mode decomposition (EMD) is proposed, which was used along with support vector machine (SVM) for the classification of FHR recordings as 'normal' or 'at risk'. Methods The FHR were recorded from 15 subjects at a sampling rate of 4 Hz and a dataset consisting of 90 randomly selected records of 20 minutes duration was formed from these. All records were labelled as 'normal' or 'at risk' by two experienced obstetricians. A training set was formed by 60 records, the remaining 30 left as the testing set. The standard deviations of the EMD components are input as features to a support vector machine (SVM) to classify FHR samples. Results For the training set, a five-fold cross validation test resulted in an accuracy of 86% whereas the overall geometric mean of sensitivity and specificity was 94.8%. The Kappa value for the training set was .923. Application of the proposed method to the testing set (30 records) resulted in a geometric mean of 81.5%. The Kappa value for the testing set was .684. Conclusions Based on the overall performance of the system it can be stated that the proposed methodology is a promising new approach for the feature extraction and classification of FHR signals. PMID:21244712

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

PubMed

Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2017-01-01

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

Electrometer Amplifier With Overload Protection

NASA Technical Reports Server (NTRS)

Woeller, F. H.; Alexander, R.

1986-01-01

Circuit features low noise, input offset, and high linearity. Input preamplifier includes input-overload protection and nulling circuit to subtract dc offset from output. Prototype dc amplifier designed for use with ion detector has features desirable in general laboratory and field instrumentation.

Context-based automated defect classification system using multiple morphological masks

DOEpatents

Gleason, Shaun S.; Hunt, Martin A.; Sari-Sarraf, Hamed

2002-01-01

Automatic detection of defects during the fabrication of semiconductor wafers is largely automated, but the classification of those defects is still performed manually by technicians. This invention includes novel digital image analysis techniques that generate unique feature vector descriptions of semiconductor defects as well as classifiers that use these descriptions to automatically categorize the defects into one of a set of pre-defined classes. Feature extraction techniques based on multiple-focus images, multiple-defect mask images, and segmented semiconductor wafer images are used to create unique feature-based descriptions of the semiconductor defects. These feature-based defect descriptions are subsequently classified by a defect classifier into categories that depend on defect characteristics and defect contextual information, that is, the semiconductor process layer(s) with which the defect comes in contact. At the heart of the system is a knowledge database that stores and distributes historical semiconductor wafer and defect data to guide the feature extraction and classification processes. In summary, this invention takes as its input a set of images containing semiconductor defect information, and generates as its output a classification for the defect that describes not only the defect itself, but also the location of that defect with respect to the semiconductor process layers.

Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Jiang, Yuan Yuan; Kim, Sung Min

2015-01-01

This paper focuses on the improvement of the diagnostic accuracy of focal liver lesions by quantifying the key features of cysts, hemangiomas, and malignant lesions on ultrasound images. The focal liver lesions were divided into 29 cysts, 37 hemangiomas, and 33 malignancies. A total of 42 hybrid textural features that composed of 5 first order statistics, 18 gray level co-occurrence matrices, 18 Law's, and echogenicity were extracted. A total of 29 key features that were selected by principal component analysis were used as a set of inputs for a feed-forward neural network. For each lesion, the performance of the diagnosis was evaluated by using the positive predictive value, negative predictive value, sensitivity, specificity, and accuracy. The results of the experiment indicate that the proposed method exhibits great performance, a high diagnosis accuracy of over 96% among all focal liver lesion groups (cyst vs. hemangioma, cyst vs. malignant, and hemangioma vs. malignant) on ultrasound images. The accuracy was slightly increased when echogenicity was included in the optimal feature set. These results indicate that it is possible for the proposed method to be applied clinically.

Knowledge-based approach to video content classification

NASA Astrophysics Data System (ADS)

Chen, Yu; Wong, Edward K.

2001-01-01

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.

Knowledge-based approach to video content classification

NASA Astrophysics Data System (ADS)

Chen, Yu; Wong, Edward K.

2000-12-01

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.

Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases

PubMed Central

Nixon, Mark S.; Komogortsev, Oleg V.

2017-01-01

We introduce the intraclass correlation coefficient (ICC) to the biometric community as an index of the temporal persistence, or stability, of a single biometric feature. It requires, as input, a feature on an interval or ratio scale, and which is reasonably normally distributed, and it can only be calculated if each subject is tested on 2 or more occasions. For a biometric system, with multiple features available for selection, the ICC can be used to measure the relative stability of each feature. We show, for 14 distinct data sets (1 synthetic, 8 eye-movement-related, 2 gait-related, and 2 face-recognition-related, and one brain-structure-related), that selecting the most stable features, based on the ICC, resulted in the best biometric performance generally. Analyses based on using only the most stable features produced superior Rank-1-Identification Rate (Rank-1-IR) performance in 12 of 14 databases (p = 0.0065, one-tailed), when compared to other sets of features, including the set of all features. For Equal Error Rate (EER), using a subset of only high-ICC features also produced superior performance in 12 of 14 databases (p = 0. 0065, one-tailed). In general, then, for our databases, prescreening potential biometric features, and choosing only highly reliable features yields better performance than choosing lower ICC features or than choosing all features combined. We also determined that, as the ICC of a group of features increases, the median of the genuine similarity score distribution increases and the spread of this distribution decreases. There was no statistically significant similar relationships for the impostor distributions. We believe that the ICC will find many uses in biometric research. In case of the eye movement-driven biometrics, the use of reliable features, as measured by ICC, allowed to us achieve the authentication performance with EER = 2.01%, which was not possible before. PMID:28575030

Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases.

PubMed

Friedman, Lee; Nixon, Mark S; Komogortsev, Oleg V

2017-01-01

We introduce the intraclass correlation coefficient (ICC) to the biometric community as an index of the temporal persistence, or stability, of a single biometric feature. It requires, as input, a feature on an interval or ratio scale, and which is reasonably normally distributed, and it can only be calculated if each subject is tested on 2 or more occasions. For a biometric system, with multiple features available for selection, the ICC can be used to measure the relative stability of each feature. We show, for 14 distinct data sets (1 synthetic, 8 eye-movement-related, 2 gait-related, and 2 face-recognition-related, and one brain-structure-related), that selecting the most stable features, based on the ICC, resulted in the best biometric performance generally. Analyses based on using only the most stable features produced superior Rank-1-Identification Rate (Rank-1-IR) performance in 12 of 14 databases (p = 0.0065, one-tailed), when compared to other sets of features, including the set of all features. For Equal Error Rate (EER), using a subset of only high-ICC features also produced superior performance in 12 of 14 databases (p = 0. 0065, one-tailed). In general, then, for our databases, prescreening potential biometric features, and choosing only highly reliable features yields better performance than choosing lower ICC features or than choosing all features combined. We also determined that, as the ICC of a group of features increases, the median of the genuine similarity score distribution increases and the spread of this distribution decreases. There was no statistically significant similar relationships for the impostor distributions. We believe that the ICC will find many uses in biometric research. In case of the eye movement-driven biometrics, the use of reliable features, as measured by ICC, allowed to us achieve the authentication performance with EER = 2.01%, which was not possible before.

Image quality assessment using deep convolutional networks

NASA Astrophysics Data System (ADS)

Li, Yezhou; Ye, Xiang; Li, Yong

2017-12-01

This paper proposes a method of accurately assessing image quality without a reference image by using a deep convolutional neural network. Existing training based methods usually utilize a compact set of linear filters for learning features of images captured by different sensors to assess their quality. These methods may not be able to learn the semantic features that are intimately related with the features used in human subject assessment. Observing this drawback, this work proposes training a deep convolutional neural network (CNN) with labelled images for image quality assessment. The ReLU in the CNN allows non-linear transformations for extracting high-level image features, providing a more reliable assessment of image quality than linear filters. To enable the neural network to take images of any arbitrary size as input, the spatial pyramid pooling (SPP) is introduced connecting the top convolutional layer and the fully-connected layer. In addition, the SPP makes the CNN robust to object deformations to a certain extent. The proposed method taking an image as input carries out an end-to-end learning process, and outputs the quality of the image. It is tested on public datasets. Experimental results show that it outperforms existing methods by a large margin and can accurately assess the image quality on images taken by different sensors of varying sizes.

Forecasting air quality time series using deep learning.

PubMed

Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

2018-04-13

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.

Adaptive training of cortical feature maps for a robot sensorimotor controller.

PubMed

Adams, Samantha V; Wennekers, Thomas; Denham, Sue; Culverhouse, Phil F

2013-08-01

This work investigates self-organising cortical feature maps (SOFMs) based upon the Kohonen Self-Organising Map (SOM) but implemented with spiking neural networks. In future work, the feature maps are intended as the basis for a sensorimotor controller for an autonomous humanoid robot. Traditional SOM methods require some modifications to be useful for autonomous robotic applications. Ideally the map training process should be self-regulating and not require predefined training files or the usual SOM parameter reduction schedules. It would also be desirable if the organised map had some flexibility to accommodate new information whilst preserving previous learnt patterns. Here methods are described which have been used to develop a cortical motor map training system which goes some way towards addressing these issues. The work is presented under the general term 'Adaptive Plasticity' and the main contribution is the development of a 'plasticity resource' (PR) which is modelled as a global parameter which expresses the rate of map development and is related directly to learning on the afferent (input) connections. The PR is used to control map training in place of a traditional learning rate parameter. In conjunction with the PR, random generation of inputs from a set of exemplar patterns is used rather than predefined datasets and enables maps to be trained without deciding in advance how much data is required. An added benefit of the PR is that, unlike a traditional learning rate, it can increase as well as decrease in response to the demands of the input and so allows the map to accommodate new information when the inputs are changed during training. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Use of Artificial Neural Networks for Forecasting the Electric Demand of Stand-Alone Consumers

NASA Astrophysics Data System (ADS)

Ivanin, O. A.; Direktor, L. B.

2018-05-01

The problem of short-term forecasting of electric power demand of stand-alone consumers (small inhabited localities) situated outside centralized power supply areas is considered. The basic approaches to modeling the electric power demand depending on the forecasting time frame and the problems set, as well as the specific features of such modeling, are described. The advantages and disadvantages of the methods used for the short-term forecast of the electric demand are indicated, and difficulties involved in the solution of the problem are outlined. The basic principles of arranging artificial neural networks are set forth; it is also shown that the proposed method is preferable when the input information necessary for prediction is lacking or incomplete. The selection of the parameters that should be included into the list of the input data for modeling the electric power demand of residential areas using artificial neural networks is validated. The structure of a neural network is proposed for solving the problem of modeling the electric power demand of residential areas. The specific features of generation of the training dataset are outlined. The results of test modeling of daily electric demand curves for some settlements of Kamchatka and Yakutia based on known actual electric demand curves are provided. The reliability of the test modeling has been validated. A high value of the deviation of the modeled curve from the reference curve obtained in one of the four reference calculations is explained. The input data and the predicted power demand curves for the rural settlement of Kuokuiskii Nasleg are provided. The power demand curves were modeled for four characteristic days of the year, and they can be used in the future for designing a power supply system for the settlement. To enhance the accuracy of the method, a series of measures based on specific features of a neural network's functioning are proposed.

Hierarchical Time-Lagged Independent Component Analysis: Computing Slow Modes and Reaction Coordinates for Large Molecular Systems.

PubMed

Pérez-Hernández, Guillermo; Noé, Frank

2016-12-13

Analysis of molecular dynamics, for example using Markov models, often requires the identification of order parameters that are good indicators of the rare events, i.e. good reaction coordinates. Recently, it has been shown that the time-lagged independent component analysis (TICA) finds the linear combinations of input coordinates that optimally represent the slow kinetic modes and may serve in order to define reaction coordinates between the metastable states of the molecular system. A limitation of the method is that both computing time and memory requirements scale with the square of the number of input features. For large protein systems, this exacerbates the use of extensive feature sets such as the distances between all pairs of residues or even heavy atoms. Here we derive a hierarchical TICA (hTICA) method that approximates the full TICA solution by a hierarchical, divide-and-conquer calculation. By using hTICA on distances between heavy atoms we identify previously unknown relaxation processes in the bovine pancreatic trypsin inhibitor.

Activity Scratchpad Prototype: Simplifying the Rover Activity Planning Cycle

NASA Technical Reports Server (NTRS)

Abramyan, Lucy

2005-01-01

The Mars Exploration Rover mission depends on the Science Activity Planner as its primary interface to the Spirit and Opportunity Rovers. Scientists alternate between a series of mouse clicks and keyboard inputs to create a set of instructions for the rovers. To accelerate planning by minimizing mouse usage, a rover planning editor should receive the majority of inputted commands from the keyboard. Thorough investigation of the Eclipse platform's Java editor has provided the understanding of the base model for the Activity Scratchpad. Desirable Eclipse features can be mapped to specific rover planning commands, such as auto-completion for activity titles and content assist for target names. A custom editor imitating the Java editor's features was created with an XML parser for experimenting purposes. The prototype editor minimized effort for redundant tasks and significantly improved the visual representation of XML syntax by highlighting keywords, coloring rules, folding projections, and providing hover assist, templates and an outline view of the code.

MATE: Machine Learning for Adaptive Calibration Template Detection

PubMed Central

Donné, Simon; De Vylder, Jonas; Goossens, Bart; Philips, Wilfried

2016-01-01

The problem of camera calibration is two-fold. On the one hand, the parameters are estimated from known correspondences between the captured image and the real world. On the other, these correspondences themselves—typically in the form of chessboard corners—need to be found. Many distinct approaches for this feature template extraction are available, often of large computational and/or implementational complexity. We exploit the generalized nature of deep learning networks to detect checkerboard corners: our proposed method is a convolutional neural network (CNN) trained on a large set of example chessboard images, which generalizes several existing solutions. The network is trained explicitly against noisy inputs, as well as inputs with large degrees of lens distortion. The trained network that we evaluate is as accurate as existing techniques while offering improved execution time and increased adaptability to specific situations with little effort. The proposed method is not only robust against the types of degradation present in the training set (lens distortions, and large amounts of sensor noise), but also to perspective deformations, e.g., resulting from multi-camera set-ups. PMID:27827920

Automated placement of interfaces in conformational kinetics calculations using machine learning

NASA Astrophysics Data System (ADS)

Grazioli, Gianmarc; Butts, Carter T.; Andricioaei, Ioan

2017-10-01

Several recent implementations of algorithms for sampling reaction pathways employ a strategy for placing interfaces or milestones across the reaction coordinate manifold. Interfaces can be introduced such that the full feature space describing the dynamics of a macromolecule is divided into Voronoi (or other) cells, and the global kinetics of the molecular motions can be calculated from the set of fluxes through the interfaces between the cells. Although some methods of this type are exact for an arbitrary set of cells, in practice, the calculations will converge fastest when the interfaces are placed in regions where they can best capture transitions between configurations corresponding to local minima. The aim of this paper is to introduce a fully automated machine-learning algorithm for defining a set of cells for use in kinetic sampling methodologies based on subdividing the dynamical feature space; the algorithm requires no intuition about the system or input from the user and scales to high-dimensional systems.

Automated placement of interfaces in conformational kinetics calculations using machine learning.

PubMed

Grazioli, Gianmarc; Butts, Carter T; Andricioaei, Ioan

2017-10-21

Several recent implementations of algorithms for sampling reaction pathways employ a strategy for placing interfaces or milestones across the reaction coordinate manifold. Interfaces can be introduced such that the full feature space describing the dynamics of a macromolecule is divided into Voronoi (or other) cells, and the global kinetics of the molecular motions can be calculated from the set of fluxes through the interfaces between the cells. Although some methods of this type are exact for an arbitrary set of cells, in practice, the calculations will converge fastest when the interfaces are placed in regions where they can best capture transitions between configurations corresponding to local minima. The aim of this paper is to introduce a fully automated machine-learning algorithm for defining a set of cells for use in kinetic sampling methodologies based on subdividing the dynamical feature space; the algorithm requires no intuition about the system or input from the user and scales to high-dimensional systems.

Quantitative analysis and feature recognition in 3-D microstructural data sets

NASA Astrophysics Data System (ADS)

Lewis, A. C.; Suh, C.; Stukowski, M.; Geltmacher, A. B.; Spanos, G.; Rajan, K.

2006-12-01

A three-dimensional (3-D) reconstruction of an austenitic stainless-steel microstructure was used as input for an image-based finite-element model to simulate the anisotropic elastic mechanical response of the microstructure. The quantitative data-mining and data-warehousing techniques used to correlate regions of high stress with critical microstructural features are discussed. Initial analysis of elastic stresses near grain boundaries due to mechanical loading revealed low overall correlation with their location in the microstructure. However, the use of data-mining and feature-tracking techniques to identify high-stress outliers revealed that many of these high-stress points are generated near grain boundaries and grain edges (triple junctions). These techniques also allowed for the differentiation between high stresses due to boundary conditions of the finite volume reconstructed, and those due to 3-D microstructural features.

Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells

PubMed Central

Dähne, Sven; Wilbert, Niko; Wiskott, Laurenz

2014-01-01

The developing visual system of many mammalian species is partially structured and organized even before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal structuring processes. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in primary visual cortex (V1). Here we present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA), to a biologically plausible model of retinal waves. Previously, SFA has been successfully applied to model parts of the visual system, most notably in reproducing a rich set of complex-cell features by training SFA with quasi-natural image sequences. In the present work, we obtain SFA units that share a number of properties with cortical complex-cells by training on simulated retinal waves. The emergence of two distinct properties of the SFA units (phase invariance and orientation tuning) is thoroughly investigated via control experiments and mathematical analysis of the input-output functions found by SFA. The results support the idea that retinal waves share relevant temporal and spatial properties with natural visual input. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system such that it is best prepared for coding input from the natural world. PMID:24810948

Classification of Normal and Apoptotic Cells from Fluorescence Microscopy Images Using Generalized Polynomial Chaos and Level Set Function.

PubMed

Du, Yuncheng; Budman, Hector M; Duever, Thomas A

2016-06-01

Accurate automated quantitative analysis of living cells based on fluorescence microscopy images can be very useful for fast evaluation of experimental outcomes and cell culture protocols. In this work, an algorithm is developed for fast differentiation of normal and apoptotic viable Chinese hamster ovary (CHO) cells. For effective segmentation of cell images, a stochastic segmentation algorithm is developed by combining a generalized polynomial chaos expansion with a level set function-based segmentation algorithm. This approach provides a probabilistic description of the segmented cellular regions along the boundary, from which it is possible to calculate morphological changes related to apoptosis, i.e., the curvature and length of a cell's boundary. These features are then used as inputs to a support vector machine (SVM) classifier that is trained to distinguish between normal and apoptotic viable states of CHO cell images. The use of morphological features obtained from the stochastic level set segmentation of cell images in combination with the trained SVM classifier is more efficient in terms of differentiation accuracy as compared with the original deterministic level set method.

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

NASA Astrophysics Data System (ADS)

Pakyuz-Charrier, Evren; Lindsay, Mark; Ogarko, Vitaliy; Giraud, Jeremie; Jessell, Mark

2018-04-01

Three-dimensional (3-D) geological structural modeling aims to determine geological information in a 3-D space using structural data (foliations and interfaces) and topological rules as inputs. This is necessary in any project in which the properties of the subsurface matters; they express our understanding of geometries in depth. For that reason, 3-D geological models have a wide range of practical applications including but not restricted to civil engineering, the oil and gas industry, the mining industry, and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator's parameterization) and the inherent lack of knowledge in areas where there are no observations combined with input uncertainty (observational, conceptual and technical errors). Because 3-D geological models are often used for impactful decision-making it is critical that all 3-D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data measurement uncertainty propagation in implicit 3-D geological modeling. This aim is achieved using Monte Carlo simulation for uncertainty estimation (MCUE), a stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3-D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of incorrect disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e., scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminish the occurrence of artifacts.

Image processing tool for automatic feature recognition and quantification

DOEpatents

Chen, Xing; Stoddard, Ryan J.

2017-05-02

A system for defining structures within an image is described. The system includes reading of an input file, preprocessing the input file while preserving metadata such as scale information and then detecting features of the input file. In one version the detection first uses an edge detector followed by identification of features using a Hough transform. The output of the process is identified elements within the image.

Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

NASA Astrophysics Data System (ADS)

Vasheghani Farahani, Jamileh; Zare, Mehdi; Lucas, Caro

2012-04-01

Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

Diverse Region-Based CNN for Hyperspectral Image Classification.

PubMed

Zhang, Mengmeng; Li, Wei; Du, Qian

2018-06-01

Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

Correlation pattern recognition: optimal parameters for quality standards control of chocolate marshmallow candy

NASA Astrophysics Data System (ADS)

Flores, Jorge L.; García-Torales, G.; Ponce Ávila, Cristina

2006-08-01

This paper describes an in situ image recognition system designed to inspect the quality standards of the chocolate pops during their production. The essence of the recognition system is the localization of the events (i.e., defects) in the input images that affect the quality standards of pops. To this end, processing modules, based on correlation filter, and segmentation of images are employed with the objective of measuring the quality standards. Therefore, we designed the correlation filter and defined a set of features from the correlation plane. The desired values for these parameters are obtained by exploiting information about objects to be rejected in order to find the optimal discrimination capability of the system. Regarding this set of features, the pop can be correctly classified. The efficacy of the system has been tested thoroughly under laboratory conditions using at least 50 images, containing 3 different types of possible defects.

Simulation of lake ice and its effect on the late-Pleistocene evaporation rate of Lake Lahontan

USGS Publications Warehouse

Hostetler, S.W.

1991-01-01

A model of lake ice was coupled with a model of lake temperature and evaporation to assess the possible effect of ice cover on the late-Pleistocene evaporation rate of Lake Lahontan. The simulations were done using a data set based on proxy temperature indicators and features of the simulated late-Pleistocene atmospheric circulation over western North America. When a data set based on a mean-annual air temperature of 3?? C (7?? C colder than present) and reduced solar radiation from jet-stream induced cloud cover was used as input to the model, ice cover lasting ??? 4 months was simulated. Simulated evaporation rates (490-527 mm a-1) were ??? 60% lower than the present-day evaporation rate (1300 mm a-1) of Pyramid Lake. With this reduced rate of evaporation, water inputs similar to the 1983 historical maxima that occurred in the Lahontan basin would have been sufficient to maintain the 13.5 ka BP high stand of Lake Lahontan. ?? 1991 Springer-Verlag.

Design, development and validation of software for modelling dietary exposure to food chemicals and nutrients.

PubMed

McNamara, C; Naddy, B; Rohan, D; Sexton, J

2003-10-01

The Monte Carlo computational system for stochastic modelling of dietary exposure to food chemicals and nutrients is presented. This system was developed through a European Commission-funded research project. It is accessible as a Web-based application service. The system allows and supports very significant complexity in the data sets used as the model input, but provides a simple, general purpose, linear kernel for model evaluation. Specific features of the system include the ability to enter (arbitrarily) complex mathematical or probabilistic expressions at each and every input data field, automatic bootstrapping on subjects and on subject food intake diaries, and custom kernels to apply brand information such as market share and loyalty to the calculation of food and chemical intake.

A parametric multiclass Bayes error estimator for the multispectral scanner spatial model performance evaluation

NASA Technical Reports Server (NTRS)

Mobasseri, B. G.; Mcgillem, C. D.; Anuta, P. E. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The probability of correct classification of various populations in data was defined as the primary performance index. The multispectral data being of multiclass nature as well, required a Bayes error estimation procedure that was dependent on a set of class statistics alone. The classification error was expressed in terms of an N dimensional integral, where N was the dimensionality of the feature space. The multispectral scanner spatial model was represented by a linear shift, invariant multiple, port system where the N spectral bands comprised the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial, and hence, spectral correlation matrices through the systems, was developed.

Automatic archaeological feature extraction from satellite VHR images

NASA Astrophysics Data System (ADS)

Jahjah, Munzer; Ulivieri, Carlo

2010-05-01

Archaeological applications need a methodological approach on a variable scale able to satisfy the intra-site (excavation) and the inter-site (survey, environmental research). The increased availability of high resolution and micro-scale data has substantially favoured archaeological applications and the consequent use of GIS platforms for reconstruction of archaeological landscapes based on remotely sensed data. Feature extraction of multispectral remotely sensing image is an important task before any further processing. High resolution remote sensing data, especially panchromatic, is an important input for the analysis of various types of image characteristics; it plays an important role in the visual systems for recognition and interpretation of given data. The methods proposed rely on an object-oriented approach based on a theory for the analysis of spatial structures called mathematical morphology. The term "morphology" stems from the fact that it aims at analysing object shapes and forms. It is mathematical in the sense that the analysis is based on the set theory, integral geometry, and lattice algebra. Mathematical morphology has proven to be a powerful image analysis technique; two-dimensional grey tone images are seen as three-dimensional sets by associating each image pixel with an elevation proportional to its intensity level. An object of known shape and size, called the structuring element, is then used to investigate the morphology of the input set. This is achieved by positioning the origin of the structuring element to every possible position of the space and testing, for each position, whether the structuring element either is included or has a nonempty intersection with the studied set. The shape and size of the structuring element must be selected according to the morphology of the searched image structures. Other two feature extraction techniques were used, eCognition and ENVI module SW, in order to compare the results. These techniques were applied to different archaeological sites in Turkmenistan (Nisa) and in Iraq (Babylon); a further change detection analysis was applied to the Babylon site using two HR images as a pre-post second gulf war. We had different results or outputs, taking into consideration the fact that the operative scale of sensed data determines the final result of the elaboration and the output of the information quality, because each of them was sensitive to specific shapes in each input image, we had mapped linear and nonlinear objects, updating archaeological cartography, automatic change detection analysis for the Babylon site. The discussion of these techniques has the objective to provide the archaeological team with new instruments for the orientation and the planning of a remote sensing application.

Annual land cover change mapping using MODIS time series to improve emissions inventories.

NASA Astrophysics Data System (ADS)

López Saldaña, G.; Quaife, T. L.; Clifford, D.

2014-12-01

Understanding and quantifying land surface changes is necessary for estimating greenhouse gas and ammonia emissions, and for meeting air quality limits and targets. More sophisticated inventories methodologies for at least key emission source are needed due to policy-driven air quality directives. Quantifying land cover changes on an annual basis requires greater spatial and temporal disaggregation of input data. The main aim of this study is to develop a methodology for using Earth Observations (EO) to identify annual land surface changes that will improve emissions inventories from agriculture and land use/land use change and forestry (LULUCF) in the UK. First goal is to find the best sets of input features that describe accurately the surface dynamics. In order to identify annual and inter-annual land surface changes, a times series of surface reflectance was used to capture seasonal variability. Daily surface reflectance images from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500m resolution were used to invert a Bidirectional Reflectance Distribution Function (BRDF) model to create the seamless time series. Given the limited number of cloud-free observations, a BRDF climatology was used to constrain the model inversion and where no high-scientific quality observations were available at all, as a gap filler. The Land Cover Map 2007 (LC2007) produced by the Centre for Ecology & Hydrology (CEH) was used for training and testing purposes. A prototype land cover product was created for 2006 to 2008. Several machine learning classifiers were tested as well as different sets of input features going from the BRDF parameters to spectral Albedo. We will present the results of the time series development and the first exercises when creating the prototype land cover product.

A recurrent neural model for proto-object based contour integration and figure-ground segregation.

PubMed

Hu, Brian; Niebur, Ernst

2017-12-01

Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects ("proto-objects") based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al. Journal of Neuroscience, 20(17), 6594-6611 2000; Qiu et al. Nature Neuroscience, 10(11), 1492-1499 2007; Chen et al. Neuron, 82(3), 682-694 2014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.

Evolutionary optimization of radial basis function classifiers for data mining applications.

PubMed

Buchtala, Oliver; Klimek, Manuel; Sick, Bernhard

2005-10-01

In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

Diagnostic support for glaucoma using retinal images: a hybrid image analysis and data mining approach.

PubMed

Yu, Jin; Abidi, Syed Sibte Raza; Artes, Paul; McIntyre, Andy; Heywood, Malcolm

2005-01-01

The availability of modern imaging techniques such as Confocal Scanning Laser Tomography (CSLT) for capturing high-quality optic nerve images offer the potential for developing automatic and objective methods for diagnosing glaucoma. We present a hybrid approach that features the analysis of CSLT images using moment methods to derive abstract image defining features. The features are then used to train classifers for automatically distinguishing CSLT images of normal and glaucoma patient. As a first, in this paper, we present investigations in feature subset selction methods for reducing the relatively large input space produced by the moment methods. We use neural networks and support vector machines to determine a sub-set of moments that offer high classification accuracy. We demonstratee the efficacy of our methods to discriminate between healthy and glaucomatous optic disks based on shape information automatically derived from optic disk topography and reflectance images.

Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.

PubMed

Kallenberg, Michiel; Petersen, Kersten; Nielsen, Mads; Ng, Andrew Y; Pengfei Diao; Igel, Christian; Vachon, Celine M; Holland, Katharina; Winkel, Rikke Rass; Karssemeijer, Nico; Lillholm, Martin

2016-05-01

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.

Dissuasive exit signage for building fire evacuation.

PubMed

Olander, Joakim; Ronchi, Enrico; Lovreglio, Ruggiero; Nilsson, Daniel

2017-03-01

This work presents the result of a questionnaire study which investigates the design of dissuasive emergency signage, i.e. signage conveying a message of not utilizing a specific exit door. The work analyses and tests a set of key features of dissuasive emergency signage using the Theory of Affordances. The variables having the largest impact on observer preference, interpretation and noticeability of the signage have been identified. Results show that features which clearly negate the exit-message of the original positive exit signage are most effective, for instance a red X-marking placed across the entirety of the exit signage conveys a clear dissuasive message. Other features of note are red flashing lights and alternation of colour. The sense of urgency conveyed by the sign is largely affected by sensory inputs such as red flashing lights or other features which cause the signs to break the tendencies of normalcy. Copyright © 2016 Elsevier Ltd. All rights reserved.

Real-time seam tracking control system based on line laser visions

NASA Astrophysics Data System (ADS)

Zou, Yanbiao; Wang, Yanbo; Zhou, Weilin; Chen, Xiangzhi

2018-07-01

A set of six-degree-of-freedom robotic welding automatic tracking platform was designed in this study to realize the real-time tracking of weld seams. Moreover, the feature point tracking method and the adaptive fuzzy control algorithm in the welding process were studied and analyzed. A laser vision sensor and its measuring principle were designed and studied, respectively. Before welding, the initial coordinate values of the feature points were obtained using morphological methods. After welding, the target tracking method based on Gaussian kernel was used to extract the real-time feature points of the weld. An adaptive fuzzy controller was designed to input the deviation value of the feature points and the change rate of the deviation into the controller. The quantization factors, scale factor, and weight function were adjusted in real time. The input and output domains, fuzzy rules, and membership functions were constantly updated to generate a series of smooth bias robot voltage. Three groups of experiments were conducted on different types of curve welds in a strong arc and splash noise environment using the welding current of 120 A short-circuit Metal Active Gas (MAG) Arc Welding. The tracking error was less than 0.32 mm and the sensor's metrical frequency can be up to 20 Hz. The end of the torch run smooth during welding. Weld trajectory can be tracked accurately, thereby satisfying the requirements of welding applications.

Identification of differences in health impact modelling of salt reduction

PubMed Central

Geleijnse, Johanna M.; van Raaij, Joop M. A.; Cappuccio, Francesco P.; Cobiac, Linda C.; Scarborough, Peter; Nusselder, Wilma J.; Jaccard, Abbygail; Boshuizen, Hendriek C.

2017-01-01

We examined whether specific input data and assumptions explain outcome differences in otherwise comparable health impact assessment models. Seven population health models estimating the impact of salt reduction on morbidity and mortality in western populations were compared on four sets of key features, their underlying assumptions and input data. Next, assumptions and input data were varied one by one in a default approach (the DYNAMO-HIA model) to examine how it influences the estimated health impact. Major differences in outcome were related to the size and shape of the dose-response relation between salt and blood pressure and blood pressure and disease. Modifying the effect sizes in the salt to health association resulted in the largest change in health impact estimates (33% lower), whereas other changes had less influence. Differences in health impact assessment model structure and input data may affect the health impact estimate. Therefore, clearly defined assumptions and transparent reporting for different models is crucial. However, the estimated impact of salt reduction was substantial in all of the models used, emphasizing the need for public health actions. PMID:29182636

Off-line data reduction

NASA Astrophysics Data System (ADS)

Gutowski, Marek W.

1992-12-01

Presented is a novel, heuristic algorithm, based on fuzzy set theory, allowing for significant off-line data reduction. Given the equidistant data, the algorithm discards some points while retaining others with their original values. The fraction of original data points retained is typically {1}/{6} of the initial value. The reduced data set preserves all the essential features of the input curve. It is possible to reconstruct the original information to high degree of precision by means of natural cubic splines, rational cubic splines or even linear interpolation. Main fields of application should be non-linear data fitting (substantial savings in CPU time) and graphics (storage space savings).

Requirements for a multifunctional code architecture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tiihonen, O.; Juslin, K.

1997-07-01

The present paper studies a set of requirements for a multifunctional simulation software architecture in the light of experiences gained in developing and using the APROS simulation environment. The huge steps taken in the development of computer hardware and software during the last ten years are changing the status of the traditional nuclear safety analysis software. The affordable computing power on the safety analysts table by far exceeds the possibilities offered to him/her ten years ago. At the same time the features of everyday office software tend to set standards to the way the input data and calculational results aremore » managed.« less

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

NASA Astrophysics Data System (ADS)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

PubMed Central

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

MO-AB-BRA-10: Cancer Therapy Outcome Prediction Based On Dempster-Shafer Theory and PET Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lian, C; University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen; Li, H

2015-06-15

Purpose: In cancer therapy, utilizing FDG-18 PET image-based features for accurate outcome prediction is challenging because of 1) limited discriminative information within a small number of PET image sets, and 2) fluctuant feature characteristics caused by the inferior spatial resolution and system noise of PET imaging. In this study, we proposed a new Dempster-Shafer theory (DST) based approach, evidential low-dimensional transformation with feature selection (ELT-FS), to accurately predict cancer therapy outcome with both PET imaging features and clinical characteristics. Methods: First, a specific loss function with sparse penalty was developed to learn an adaptive low-rank distance metric for representing themore » dissimilarity between different patients’ feature vectors. By minimizing this loss function, a linear low-dimensional transformation of input features was achieved. Also, imprecise features were excluded simultaneously by applying a l2,1-norm regularization of the learnt dissimilarity metric in the loss function. Finally, the learnt dissimilarity metric was applied in an evidential K-nearest-neighbor (EK- NN) classifier to predict treatment outcome. Results: Twenty-five patients with stage II–III non-small-cell lung cancer and thirty-six patients with esophageal squamous cell carcinomas treated with chemo-radiotherapy were collected. For the two groups of patients, 52 and 29 features, respectively, were utilized. The leave-one-out cross-validation (LOOCV) protocol was used for evaluation. Compared to three existing linear transformation methods (PCA, LDA, NCA), the proposed ELT-FS leads to higher prediction accuracy for the training and testing sets both for lung-cancer patients (100+/−0.0, 88.0+/−33.17) and for esophageal-cancer patients (97.46+/−1.64, 83.33+/−37.8). The ELT-FS also provides superior class separation in both test data sets. Conclusion: A novel DST- based approach has been proposed to predict cancer treatment outcome using PET image features and clinical characteristics. A specific loss function has been designed for robust accommodation of feature set incertitude and imprecision, facilitating adaptive learning of the dissimilarity metric for the EK-NN classifier.« less

Manchester visual query language

NASA Astrophysics Data System (ADS)

Oakley, John P.; Davis, Darryl N.; Shann, Richard T.

1993-04-01

We report a database language for visual retrieval which allows queries on image feature information which has been computed and stored along with images. The language is novel in that it provides facilities for dealing with feature data which has actually been obtained from image analysis. Each line in the Manchester Visual Query Language (MVQL) takes a set of objects as input and produces another, usually smaller, set as output. The MVQL constructs are mainly based on proven operators from the field of digital image analysis. An example is the Hough-group operator which takes as input a specification for the objects to be grouped, a specification for the relevant Hough space, and a definition of the voting rule. The output is a ranked list of high scoring bins. The query could be directed towards one particular image or an entire image database, in the latter case the bins in the output list would in general be associated with different images. We have implemented MVQL in two layers. The command interpreter is a Lisp program which maps each MVQL line to a sequence of commands which are used to control a specialized database engine. The latter is a hybrid graph/relational system which provides low-level support for inheritance and schema evolution. In the paper we outline the language and provide examples of useful queries. We also describe our solution to the engineering problems associated with the implementation of MVQL.

Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches.

PubMed

Kilic, Niyazi; Hosgormez, Erkan

2016-03-01

Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were investigated. Six feature set models were constructed including different physical parameters and they fed into the ensemble classifiers as input features. As ensemble learning techniques, bagging, gradient boosting and random subspace (RSM) were used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients were classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure were also used to evaluate diagnostic performance of the proposed ensemble classification system. The classification accuracy has reached to 98.85 % by the combination of model 6 (five BMD + five T-score values) using RSM-RF classifier. The findings of this paper suggest that the patients will be able to be warned before a bone fracture occurred, by just examining some physical parameters that can easily be measured without invasive operations.

Estimation of relative effectiveness of phylogenetic programs by machine learning.

PubMed

Krivozubov, Mikhail; Goebels, Florian; Spirin, Sergei

2014-04-01

Reconstruction of phylogeny of a protein family from a sequence alignment can produce results of different quality. Our goal is to predict the quality of phylogeny reconstruction basing on features that can be extracted from the input alignment. We used Fitch-Margoliash (FM) method of phylogeny reconstruction and random forest as a predictor. For training and testing the predictor, alignments of orthologous series (OS) were used, for which the result of phylogeny reconstruction can be evaluated by comparison with trees of corresponding organisms. Our results show that the quality of phylogeny reconstruction can be predicted with more than 80% precision. Also, we tried to predict which phylogeny reconstruction method, FM or UPGMA, is better for a particular alignment. With the used set of features, among alignments for which the obtained predictor predicts a better performance of UPGMA, 56% really give a better result with UPGMA. Taking into account that in our testing set only for 34% alignments UPGMA performs better, this result shows a principal possibility to predict the better phylogeny reconstruction method basing on features of a sequence alignment.

Veterinary software application for comparison of thermograms for pathology evaluation

NASA Astrophysics Data System (ADS)

Pant, Gita; Umbaugh, Scott E.; Dahal, Rohini; Lama, Norsang; Marino, Dominic J.; Sackman, Joseph

2017-09-01

The bilateral symmetry property in mammals allows for the detection of pathology by comparison of opposing sides. For any pathological disorder, thermal patterns differ compared to the normal body part. A software application for veterinary clinics has been under development to input two thermograms of body parts on both sides, one normal and the other unknown, and the application compares them based on extracted features and appropriate similarity and difference measures and outputs the likelihood of pathology. Here thermographic image data from 19° C to 40° C was linearly remapped to create images with 256 gray level values. Features were extracted from these images, including histogram, texture and spectral features. The comparison metrics used are the vector inner product, Tanimoto, Euclidean, city block, Minkowski and maximum value metric. Previous research with the anterior cruciate ligament (ACL) pathology in dogs suggested any thermogram variation below a threshold of 40% of Euclidean distance is normal and above 40% is abnormal. Here the 40% threshold was applied to a new ACL image set and achieved a sensitivity of 75%, an improvement from the 55% sensitivity of the previous work. With the new data set it was determined that using a threshold of 20% provided a much improved 92% sensitivity metric. However, this will require further research to determine the corresponding specificity success rate. Additionally, it was found that the anterior view provided better results than the lateral view. It was also determined that better results were obtained with all three feature sets than with just the histogram and texture sets. Further experiments are ongoing with larger image datasets, and pathologies, new features and comparison metric evaluation for determination of more accurate threshold values to separate normal and abnormal images.

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

PubMed

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

Classification

NASA Astrophysics Data System (ADS)

Oza, Nikunj

2012-03-01

A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. A set of training examples— examples with known output values—is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate’s measurements. The generalization performance of a learned model (how closely the target outputs and the model’s predicted outputs agree for patterns that have not been presented to the learning algorithm) would provide an indication of how well the model has learned the desired mapping. More formally, a classification learning algorithm L takes a training set T as its input. The training set consists of |T| examples or instances. It is assumed that there is a probability distribution D from which all training examples are drawn independently—that is, all the training examples are independently and identically distributed (i.i.d.). The ith training example is of the form (x_i, y_i), where x_i is a vector of values of several features and y_i represents the class to be predicted.* In the sunspot classification example given above, each training example would represent one sunspot’s classification (y_i) and the corresponding set of measurements (x_i). The output of a supervised learning algorithm is a model h that approximates the unknown mapping from the inputs to the outputs. In our example, h would map from the sunspot measurements to the type of sunspot. We may have a test set S—a set of examples not used in training that we use to test how well the model h predicts the outputs on new examples. Just as with the examples in T, the examples in S are assumed to be independent and identically distributed (i.i.d.) draws from the distribution D. We measure the error of h on the test set as the proportion of test cases that h misclassifies: 1/|S| Sigma(x,y union S)[I(h(x)!= y)] where I(v) is the indicator function—it returns 1 if v is true and 0 otherwise. In our sunspot classification example, we would identify additional examples of sunspots that were not used in generating the model, and use these to determine how accurate the model is—the fraction of the test samples that the model classifies correctly. An example of a classification model is the decision tree shown in Figure 23.1. We will discuss the decision tree learning algorithm in more detail later—for now, we assume that, given a training set with examples of sunspots, this decision tree is derived. This can be used to classify previously unseen examples of sunpots. For example, if a new sunspot’s inputs indicate that its "Group Length" is in the range 10-15, then the decision tree would classify the sunspot as being of type “E,” whereas if the "Group Length" is "NULL," the "Magnetic Type" is "bipolar," and the "Penumbra" is "rudimentary," then it would be classified as type "C." In this chapter, we will add to the above description of classification problems. We will discuss decision trees and several other classification models. In particular, we will discuss the learning algorithms that generate these classification models, how to use them to classify new examples, and the strengths and weaknesses of these models. We will end with pointers to further reading on classification methods applied to astronomy data.

Process-based interpretation of conceptual hydrological model performance using a multinational catchment set

NASA Astrophysics Data System (ADS)

Poncelet, Carine; Merz, Ralf; Merz, Bruno; Parajka, Juraj; Oudin, Ludovic; Andréassian, Vazken; Perrin, Charles

2017-08-01

Most of previous assessments of hydrologic model performance are fragmented, based on small number of catchments, different methods or time periods and do not link the results to landscape or climate characteristics. This study uses large-sample hydrology to identify major catchment controls on daily runoff simulations. It is based on a conceptual lumped hydrological model (GR6J), a collection of 29 catchment characteristics, a multinational set of 1103 catchments located in Austria, France, and Germany and four runoff model efficiency criteria. Two analyses are conducted to assess how features and criteria are linked: (i) a one-dimensional analysis based on the Kruskal-Wallis test and (ii) a multidimensional analysis based on regression trees and investigating the interplay between features. The catchment features most affecting model performance are the flashiness of precipitation and streamflow (computed as the ratio of absolute day-to-day fluctuations by the total amount in a year), the seasonality of evaporation, the catchment area, and the catchment aridity. Nonflashy, nonseasonal, large, and nonarid catchments show the best performance for all the tested criteria. We argue that this higher performance is due to fewer nonlinear responses (higher correlation between precipitation and streamflow) and lower input and output variability for such catchments. Finally, we show that, compared to national sets, multinational sets increase results transferability because they explore a wider range of hydroclimatic conditions.

Segmentation and classification of brain images using firefly and hybrid kernel-based support vector machine

NASA Astrophysics Data System (ADS)

Selva Bhuvaneswari, K.; Geetha, P.

2017-05-01

Magnetic resonance imaging segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumour detection techniques are presented in the literature. The entire segmentation process of our proposed work comprises three phases: threshold generation with dynamic modified region growing phase, texture feature generation phase and region merging phase. by dynamically changing two thresholds in the modified region growing approach, the first phase of the given input image can be performed as dynamic modified region growing process, in which the optimisation algorithm, firefly algorithm help to optimise the two thresholds in modified region growing. After obtaining the region growth segmented image using modified region growing, the edges can be detected with edge detection algorithm. In the second phase, the texture feature can be extracted using entropy-based operation from the input image. In region merging phase, the results obtained from the texture feature-generation phase are combined with the results of dynamic modified region growing phase and similar regions are merged using a distance comparison between regions. After identifying the abnormal tissues, the classification can be done by hybrid kernel-based SVM (Support Vector Machine). The performance analysis of the proposed method will be carried by K-cross fold validation method. The proposed method will be implemented in MATLAB with various images.

Classification of brain tumours using short echo time 1H MR spectra

NASA Astrophysics Data System (ADS)

Devos, A.; Lukas, L.; Suykens, J. A. K.; Vanhamme, L.; Tate, A. R.; Howe, F. A.; Majós, C.; Moreno-Torres, A.; van der Graaf, M.; Arús, C.; Van Huffel, S.

2004-09-01

The purpose was to objectively compare the application of several techniques and the use of several input features for brain tumour classification using Magnetic Resonance Spectroscopy (MRS). Short echo time 1H MRS signals from patients with glioblastomas ( n = 87), meningiomas ( n = 57), metastases ( n = 39), and astrocytomas grade II ( n = 22) were provided by six centres in the European Union funded INTERPRET project. Linear discriminant analysis, least squares support vector machines (LS-SVM) with a linear kernel and LS-SVM with radial basis function kernel were applied and evaluated over 100 stratified random splittings of the dataset into training and test sets. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of binary classifiers, while the percentage of correct classifications was used to evaluate the multiclass classifiers. The influence of several factors on the classification performance has been tested: L2- vs. water normalization, magnitude vs. real spectra and baseline correction. The effect of input feature reduction was also investigated by using only the selected frequency regions containing the most discriminatory information, and peak integrated values. Using L2-normalized complete spectra the automated binary classifiers reached a mean test AUC of more than 0.95, except for glioblastomas vs. metastases. Similar results were obtained for all classification techniques and input features except for water normalized spectra, where classification performance was lower. This indicates that data acquisition and processing can be simplified for classification purposes, excluding the need for separate water signal acquisition, baseline correction or phasing.

Computing all hybridization networks for multiple binary phylogenetic input trees.

PubMed

Albrecht, Benjamin

2015-07-30

The computation of phylogenetic trees on the same set of species that are based on different orthologous genes can lead to incongruent trees. One possible explanation for this behavior are interspecific hybridization events recombining genes of different species. An important approach to analyze such events is the computation of hybridization networks. This work presents the first algorithm computing the hybridization number as well as a set of representative hybridization networks for multiple binary phylogenetic input trees on the same set of taxa. To improve its practical runtime, we show how this algorithm can be parallelized. Moreover, we demonstrate the efficiency of the software Hybroscale, containing an implementation of our algorithm, by comparing it to PIRNv2.0, which is so far the best available software computing the exact hybridization number for multiple binary phylogenetic trees on the same set of taxa. The algorithm is part of the software Hybroscale, which was developed specifically for the investigation of hybridization networks including their computation and visualization. Hybroscale is freely available(1) and runs on all three major operating systems. Our simulation study indicates that our approach is on average 100 times faster than PIRNv2.0. Moreover, we show how Hybroscale improves the interpretation of the reported hybridization networks by adding certain features to its graphical representation.

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.

PubMed

Enns, Eva A; Cipriano, Lauren E; Simons, Cyrena T; Kong, Chung Yin

2015-02-01

To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting. For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results. Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. © The Author(s) 2014.

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach

PubMed Central

Enns, Eva A.; Cipriano, Lauren E.; Simons, Cyrena T.; Kong, Chung Yin

2014-01-01

Background To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single “goodness-of-fit” (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. Methods We demonstrate the Pareto frontier approach in the calibration of two models: a simple, illustrative Markov model and a previously-published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to two possible weighted-sum GOF scoring systems, and compare the health economic conclusions arising from these different definitions of best-fitting. Results For the simple model, outcomes evaluated over the best-fitting input sets according to the two weighted-sum GOF schemes were virtually non-overlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95%CI: 72,500 – 87,600] vs. $139,700 [95%CI: 79,900 - 182,800] per QALY gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95%CI: 64,900 – 156,200] per QALY gained). The TAVR model yielded similar results. Conclusions Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. PMID:24799456

Setting and changing feature priorities in visual short-term memory.

PubMed

Kalogeropoulou, Zampeta; Jagadeesh, Akshay V; Ohl, Sven; Rolfs, Martin

2017-04-01

Many everyday tasks require prioritizing some visual features over competing ones, both during the selection from the rich sensory input and while maintaining information in visual short-term memory (VSTM). Here, we show that observers can change priorities in VSTM when, initially, they attended to a different feature. Observers reported from memory the orientation of one of two spatially interspersed groups of black and white gratings. Using colored pre-cues (presented before stimulus onset) and retro-cues (presented after stimulus offset) predicting the to-be-reported group, we manipulated observers' feature priorities independently during stimulus encoding and maintenance, respectively. Valid pre-cues reliably increased observers' performance (reduced guessing, increased report precision) as compared to neutral ones; invalid pre-cues had the opposite effect. Valid retro-cues also consistently improved performance (by reducing random guesses), even if the unexpected group suddenly became relevant (invalid-valid condition). Thus, feature-based attention can reshape priorities in VSTM protecting information that would otherwise be forgotten.

Development of a User Interface for a Regression Analysis Software Tool

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.

A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data.

PubMed

Sharma, Ram C; Hara, Keitarou; Hirayama, Hidetake

2017-01-01

This paper presents the performance and evaluation of a number of machine learning classifiers for the discrimination between the vegetation physiognomic classes using the satellite based time-series of the surface reflectance data. Discrimination of six vegetation physiognomic classes, Evergreen Coniferous Forest, Evergreen Broadleaf Forest, Deciduous Coniferous Forest, Deciduous Broadleaf Forest, Shrubs, and Herbs, was dealt with in the research. Rich-feature data were prepared from time-series of the satellite data for the discrimination and cross-validation of the vegetation physiognomic types using machine learning approach. A set of machine learning experiments comprised of a number of supervised classifiers with different model parameters was conducted to assess how the discrimination of vegetation physiognomic classes varies with classifiers, input features, and ground truth data size. The performance of each experiment was evaluated by using the 10-fold cross-validation method. Experiment using the Random Forests classifier provided highest overall accuracy (0.81) and kappa coefficient (0.78). However, accuracy metrics did not vary much with experiments. Accuracy metrics were found to be very sensitive to input features and size of ground truth data. The results obtained in the research are expected to be useful for improving the vegetation physiognomic mapping in Japan.

Comparative performance analysis of cervix ROI extraction and specular reflection removal algorithms for uterine cervix image analysis

NASA Astrophysics Data System (ADS)

Xue, Zhiyun; Antani, Sameer; Long, L. Rodney; Jeronimo, Jose; Thoma, George R.

2007-03-01

Cervicography is a technique for visual screening of uterine cervix images for cervical cancer. One of our research goals is the automated detection in these images of acetowhite (AW) lesions, which are sometimes correlated with cervical cancer. These lesions are characterized by the whitening of regions along the squamocolumnar junction on the cervix when treated with 5% acetic acid. Image preprocessing is required prior to invoking AW detection algorithms on cervicographic images for two reasons: (1) to remove Specular Reflections (SR) caused by camera flash, and (2) to isolate the cervix region-of-interest (ROI) from image regions that are irrelevant to the analysis. These image regions may contain medical instruments, film markup, or other non-cervix anatomy or regions, such as vaginal walls. We have qualitatively and quantitatively evaluated the performance of alternative preprocessing algorithms on a test set of 120 images. For cervix ROI detection, all approaches use a common feature set, but with varying combinations of feature weights, normalization, and clustering methods. For SR detection, while one approach uses a Gaussian Mixture Model on an intensity/saturation feature set, a second approach uses Otsu thresholding on a top-hat transformed input image. Empirical results are analyzed to derive conclusions on the performance of each approach.

Unconstrained handwritten numeral recognition based on radial basis competitive and cooperative networks with spatio-temporal feature representation.

PubMed

Lee, S; Pan, J J

1996-01-01

This paper presents a new approach to representation and recognition of handwritten numerals. The approach first transforms a two-dimensional (2-D) spatial representation of a numeral into a three-dimensional (3-D) spatio-temporal representation by identifying the tracing sequence based on a set of heuristic rules acting as transformation operators. A multiresolution critical-point segmentation method is then proposed to extract local feature points, at varying degrees of scale and coarseness. A new neural network architecture, referred to as radial-basis competitive and cooperative network (RCCN), is presented especially for handwritten numeral recognition. RCCN is a globally competitive and locally cooperative network with the capability of self-organizing hidden units to progressively achieve desired network performance, and functions as a universal approximator of arbitrary input-output mappings. Three types of RCCNs are explored: input-space RCCN (IRCCN), output-space RCCN (ORCCN), and bidirectional RCCN (BRCCN). Experiments against handwritten zip code numerals acquired by the U.S. Postal Service indicated that the proposed method is robust in terms of variations, deformations, transformations, and corruption, achieving about 97% recognition rate.

Application of Classification Methods for Forecasting Mid-Term Power Load Patterns

NASA Astrophysics Data System (ADS)

Piao, Minghao; Lee, Heon Gyu; Park, Jin Hyoung; Ryu, Keun Ho

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed approach in this paper consists of three stages: (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

PubMed Central

Ramirez-Villegas, Juan F.; Lam-Espinosa, Eric; Ramirez-Moreno, David F.; Calvo-Echeverry, Paulo C.; Agredo-Rodriguez, Wilfredo

2011-01-01

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis. PMID:21386966

Polynomic nonlinear dynamical systems - A residual sensitivity method for model reduction

NASA Technical Reports Server (NTRS)

Yurkovich, S.; Bugajski, D.; Sain, M.

1985-01-01

The motivation for using polynomic combinations of system states and inputs to model nonlinear dynamics systems is founded upon the classical theories of analysis and function representation. A feature of such representations is the need to make available all possible monomials in these variables, up to the degree specified, so as to provide for the description of widely varying functions within a broad class. For a particular application, however, certain monomials may be quite superfluous. This paper examines the possibility of removing monomials from the model in accordance with the level of sensitivity displayed by the residuals to their absence. Critical in these studies is the effect of system input excitation, and the effect of discarding monomial terms, upon the model parameter set. Therefore, model reduction is approached iteratively, with inputs redesigned at each iteration to ensure sufficient excitation of remaining monomials for parameter approximation. Examples are reported to illustrate the performance of such model reduction approaches.

CLASSIFYING MEDICAL IMAGES USING MORPHOLOGICAL APPEARANCE MANIFOLDS.

PubMed

Varol, Erdem; Gaonkar, Bilwaj; Davatzikos, Christos

2013-12-31

Input features for medical image classification algorithms are extracted from raw images using a series of pre processing steps. One common preprocessing step in computational neuroanatomy and functional brain mapping is the nonlinear registration of raw images to a common template space. Typically, the registration methods used are parametric and their output varies greatly with changes in parameters. Most results reported previously perform registration using a fixed parameter setting and use the results as input to the subsequent classification step. The variation in registration results due to choice of parameters thus translates to variation of performance of the classifiers that depend on the registration step for input. Analogous issues have been investigated in the computer vision literature, where image appearance varies with pose and illumination, thereby making classification vulnerable to these confounding parameters. The proposed methodology addresses this issue by sampling image appearances as registration parameters vary, and shows that better classification accuracies can be obtained this way, compared to the conventional approach.

Heliocentric interplanetary low thrust trajectory optimization program, supplement 1, part 2

NASA Technical Reports Server (NTRS)

Mann, F. I.; Horsewood, J. L.

1978-01-01

The improvements made to the HILTOP electric propulsion trajectory computer program are described. A more realistic propulsion system model was implemented in which various thrust subsystem efficiencies and specific impulse are modeled as variable functions of power available to the propulsion system. The number of operating thrusters are staged, and the beam voltage is selected from a set of five (or less) constant voltages, based upon the application of variational calculus. The constant beam voltages may be optimized individually or collectively. The propulsion system logic is activated by a single program input key in such a manner as to preserve the HILTOP logic. An analysis describing these features, a complete description of program input quantities, and sample cases of computer output illustrating the program capabilities are presented.

AUTOGEN

DOE Office of Scientific and Technical Information (OSTI.GOV)

2003-05-29

AUTOGEN computes collision-free sequences of robot motion instructions to permit traversal of three-dimensional space curves. Order and direction of curve traversal and orientation of end effector are constraided by a set of manufacturing rules. Input can be provided as a collection of solid models or in terms of wireframe objects and structural cross-section definitions. Entity juxtaposition can be inferred, with appropriate structural features automatically provided. Process control is asserted as a function of position and orientation along each space curve, and is currently implemented for welding processes.

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.

Surprise! Infants consider possible bases of generalization for a single input example.

PubMed

Gerken, LouAnn; Dawson, Colin; Chatila, Razanne; Tenenbaum, Josh

2015-01-01

Infants have been shown to generalize from a small number of input examples. However, existing studies allow two possible means of generalization. One is via a process of noting similarities shared by several examples. Alternatively, generalization may reflect an implicit desire to explain the input. The latter view suggests that generalization might occur when even a single input example is surprising, given the learner's current model of the domain. To test the possibility that infants are able to generalize based on a single example, we familiarized 9-month-olds with a single three-syllable input example that contained either one surprising feature (syllable repetition, Experiment 1) or two features (repetition and a rare syllable, Experiment 2). In both experiments, infants generalized only to new strings that maintained all of the surprising features from familiarization. This research suggests that surprise can promote very rapid generalization. © 2014 John Wiley & Sons Ltd.

A novel content-based medical image retrieval method based on query topic dependent image features (QTDIF)

NASA Astrophysics Data System (ADS)

Xiong, Wei; Qiu, Bo; Tian, Qi; Mueller, Henning; Xu, Changsheng

2005-04-01

Medical image retrieval is still mainly a research domain with a large variety of applications and techniques. With the ImageCLEF 2004 benchmark, an evaluation framework has been created that includes a database, query topics and ground truth data. Eleven systems (with a total of more than 50 runs) compared their performance in various configurations. The results show that there is not any one feature that performs well on all query tasks. Key to successful retrieval is rather the selection of features and feature weights based on a specific set of input features, thus on the query task. In this paper we propose a novel method based on query topic dependent image features (QTDIF) for content-based medical image retrieval. These feature sets are designed to capture both inter-category and intra-category statistical variations to achieve good retrieval performance in terms of recall and precision. We have used Gaussian Mixture Models (GMM) and blob representation to model medical images and construct the proposed novel QTDIF for CBIR. Finally, trained multi-class support vector machines (SVM) are used for image similarity ranking. The proposed methods have been tested over the Casimage database with around 9000 images, for the given 26 image topics, used for imageCLEF 2004. The retrieval performance has been compared with the medGIFT system, which is based on the GNU Image Finding Tool (GIFT). The experimental results show that the proposed QTDIF-based CBIR can provide significantly better performance than systems based general features only.

Adaptive Water Sampling based on Unsupervised Clustering

NASA Astrophysics Data System (ADS)

Py, F.; Ryan, J.; Rajan, K.; Sherman, A.; Bird, L.; Fox, M.; Long, D.

2007-12-01

Autonomous Underwater Vehicles (AUVs) are widely used for oceanographic surveys, during which data is collected from a number of on-board sensors. Engineers and scientists at MBARI have extended this approach by developing a water sampler specialy for the AUV, which can sample a specific patch of water at a specific time. The sampler, named the Gulper, captures 2 liters of seawater in less than 2 seconds on a 21" MBARI Odyssey AUV. Each sample chamber of the Gulper is filled with seawater through a one-way valve, which protrudes through the fairing of the AUV. This new kind of device raises a new problem: when to trigger the gulper autonomously? For example, scientists interested in studying the mobilization and transport of shelf sediments would like to detect intermediate nepheloïd layers (INLs). To be able to detect this phenomenon we need to extract a model based on AUV sensors that can detect this feature in-situ. The formation of such a model is not obvious as identification of this feature is generally based on data from multiple sensors. We have developed an unsupervised data clustering technique to extract the different features which will then be used for on-board classification and triggering of the Gulper. We use a three phase approach: 1) use data from past missions to learn the different classes of data from sensor inputs. The clustering algorithm will then extract the set of features that can be distinguished within this large data set. 2) Scientists on shore then identify these features and point out which correspond to those of interest (e.g. nepheloïd layer, upwelling material etc) 3) Embed the corresponding classifier into the AUV control system to indicate the most probable feature of the water depending on sensory input. The triggering algorithm looks to this result and triggers the Gulper if the classifier indicates that we are within the feature of interest with a predetermined threshold of confidence. We have deployed this method of online classification and sampling based on AUV depth and HOBI Labs Hydroscat-2 sensor data. Using approximately 20,000 data samples the clustering algorithm generated 14 clusters with one identified as corresponding to a nepheloïd layer. We demonstrate that such a technique can be used to reliably and efficiently sample water based on multiple sources of data in real-time.

Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals.

PubMed

Hu, Jianfeng

2017-01-01

Purpose: Driving fatigue has become one of the important causes of road accidents, there are many researches to analyze driver fatigue. EEG is becoming increasingly useful in the measuring fatigue state. Manual interpretation of EEG signals is impossible, so an effective method for automatic detection of EEG signals is crucial needed. Method: In order to evaluate the complex, unstable, and non-linear characteristics of EEG signals, four feature sets were computed from EEG signals, in which fuzzy entropy (FE), sample entropy (SE), approximate Entropy (AE), spectral entropy (PE), and combined entropies (FE + SE + AE + PE) were included. All these feature sets were used as the input vectors of AdaBoost classifier, a boosting method which is fast and highly accurate. To assess our method, several experiments including parameter setting and classifier comparison were conducted on 28 subjects. For comparison, Decision Trees (DT), Support Vector Machine (SVM) and Naive Bayes (NB) classifiers are used. Results: The proposed method (combination of FE and AdaBoost) yields superior performance than other schemes. Using FE feature extractor, AdaBoost achieves improved area (AUC) under the receiver operating curve of 0.994, error rate (ERR) of 0.024, Precision of 0.969, Recall of 0.984, F1 score of 0.976, and Matthews correlation coefficient (MCC) of 0.952, compared to SVM (ERR at 0.035, Precision of 0.957, Recall of 0.974, F1 score of 0.966, and MCC of 0.930 with AUC of 0.990), DT (ERR at 0.142, Precision of 0.857, Recall of 0.859, F1 score of 0.966, and MCC of 0.716 with AUC of 0.916) and NB (ERR at 0.405, Precision of 0.646, Recall of 0.434, F1 score of 0.519, and MCC of 0.203 with AUC of 0.606). It shows that the FE feature set and combined feature set outperform other feature sets. AdaBoost seems to have better robustness against changes of ratio of test samples for all samples and number of subjects, which might therefore aid in the real-time detection of driver fatigue through the classification of EEG signals. Conclusion: By using combination of FE features and AdaBoost classifier to detect EEG-based driver fatigue, this paper ensured confidence in exploring the inherent physiological mechanisms and wearable application.

Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals

PubMed Central

Hu, Jianfeng

2017-01-01

Purpose: Driving fatigue has become one of the important causes of road accidents, there are many researches to analyze driver fatigue. EEG is becoming increasingly useful in the measuring fatigue state. Manual interpretation of EEG signals is impossible, so an effective method for automatic detection of EEG signals is crucial needed. Method: In order to evaluate the complex, unstable, and non-linear characteristics of EEG signals, four feature sets were computed from EEG signals, in which fuzzy entropy (FE), sample entropy (SE), approximate Entropy (AE), spectral entropy (PE), and combined entropies (FE + SE + AE + PE) were included. All these feature sets were used as the input vectors of AdaBoost classifier, a boosting method which is fast and highly accurate. To assess our method, several experiments including parameter setting and classifier comparison were conducted on 28 subjects. For comparison, Decision Trees (DT), Support Vector Machine (SVM) and Naive Bayes (NB) classifiers are used. Results: The proposed method (combination of FE and AdaBoost) yields superior performance than other schemes. Using FE feature extractor, AdaBoost achieves improved area (AUC) under the receiver operating curve of 0.994, error rate (ERR) of 0.024, Precision of 0.969, Recall of 0.984, F1 score of 0.976, and Matthews correlation coefficient (MCC) of 0.952, compared to SVM (ERR at 0.035, Precision of 0.957, Recall of 0.974, F1 score of 0.966, and MCC of 0.930 with AUC of 0.990), DT (ERR at 0.142, Precision of 0.857, Recall of 0.859, F1 score of 0.966, and MCC of 0.716 with AUC of 0.916) and NB (ERR at 0.405, Precision of 0.646, Recall of 0.434, F1 score of 0.519, and MCC of 0.203 with AUC of 0.606). It shows that the FE feature set and combined feature set outperform other feature sets. AdaBoost seems to have better robustness against changes of ratio of test samples for all samples and number of subjects, which might therefore aid in the real-time detection of driver fatigue through the classification of EEG signals. Conclusion: By using combination of FE features and AdaBoost classifier to detect EEG-based driver fatigue, this paper ensured confidence in exploring the inherent physiological mechanisms and wearable application. PMID:28824409

Automated embolic signal detection using Deep Convolutional Neural Network.

PubMed

Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

2017-07-01

This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure

PubMed Central

Barros, Allan Kardec; Ohnishi, Noboru

2016-01-01

Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and k-nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods. PMID:27891509

Analysis of wheezes using wavelet higher order spectral features.

PubMed

Taplidou, Styliani A; Hadjileontiadis, Leontios J

2010-07-01

Wheezes are musical breath sounds, which usually imply an existing pulmonary obstruction, such as asthma and chronic obstructive pulmonary disease (COPD). Although many studies have addressed the problem of wheeze detection, a limited number of scientific works has focused in the analysis of wheeze characteristics, and in particular, their time-varying nonlinear characteristics. In this study, an effort is made to reveal and statistically analyze the nonlinear characteristics of wheezes and their evolution over time, as they are reflected in the quadratic phase coupling of their harmonics. To this end, the continuous wavelet transform (CWT) is used in combination with third-order spectra to define the analysis domain, where the nonlinear interactions of the harmonics of wheezes and their time variations are revealed by incorporating instantaneous wavelet bispectrum and bicoherence, which provide with the instantaneous biamplitude and biphase curves. Based on this nonlinear information pool, a set of 23 features is proposed for the nonlinear analysis of wheezes. Two complementary perspectives, i.e., general and detailed, related to average performance and to localities, respectively, were used in the construction of the feature set, in order to embed trends and local behaviors, respectively, seen in the nonlinear interaction of the harmonic elements of wheezes over time. The proposed feature set was evaluated on a dataset of wheezes, acquired from adult patients with diagnosed asthma and COPD from a lung sound database. The statistical evaluation of the feature set revealed discrimination ability between the two pathologies for all data subgroupings. In particular, when the total breathing cycle was examined, all 23 features, but one, showed statistically significant difference between the COPD and asthma pathologies, whereas for the subgroupings of inspiratory and expiratory phases, 18 out of 23 and 22 out of 23 features exhibited discrimination power, respectively. This paves the way for the use of the wavelet higher order spectral features as an input vector to an efficient classifier. Apparently, this would integrate the intrinsic characteristics of wheezes within computerized diagnostic tools toward their more efficient evaluation.

Residual Shuffling Convolutional Neural Networks for Deep Semantic Image Segmentation Using Multi-Modal Data

NASA Astrophysics Data System (ADS)

Chen, K.; Weinmann, M.; Gao, X.; Yan, M.; Hinz, S.; Jutzi, B.; Weinmann, M.

2018-05-01

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation.

Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System

NASA Technical Reports Server (NTRS)

Zhang, Yuhan; Lu, Dr. Thomas

2010-01-01

The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.

Constraint programming based biomarker optimization.

PubMed

Zhou, Manli; Luo, Youxi; Sun, Guoquan; Mai, Guoqin; Zhou, Fengfeng

2015-01-01

Efficient and intuitive characterization of biological big data is becoming a major challenge for modern bio-OMIC based scientists. Interactive visualization and exploration of big data is proven to be one of the successful solutions. Most of the existing feature selection algorithms do not allow the interactive inputs from users in the optimizing process of feature selection. This study investigates this question as fixing a few user-input features in the finally selected feature subset and formulates these user-input features as constraints for a programming model. The proposed algorithm, fsCoP (feature selection based on constrained programming), performs well similar to or much better than the existing feature selection algorithms, even with the constraints from both literature and the existing algorithms. An fsCoP biomarker may be intriguing for further wet lab validation, since it satisfies both the classification optimization function and the biomedical knowledge. fsCoP may also be used for the interactive exploration of bio-OMIC big data by interactively adding user-defined constraints for modeling.

Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings.

PubMed

Tigges, P; Kathmann, N; Engel, R R

1997-07-01

Though artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low, the identification of decision relevant features for ANN input data is still a crucial issue. The experience of the ANN designer and the existing knowledge and understanding of the problem seem to be the only links for a specific construction. In the present study a backpropagation ANN based on modified raw data inputs showed encouraging results. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a new sparse and efficient input data presentation. This data coding obtained by a semiautomatic procedure combining existing expert knowledge and the internal representation structures of the raw data based ANN yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% compared with the raw data ANN. An overall correct classification of 92% of so far unknown data was realized. The proposed method of extracting internal ANN knowledge for the production of a better input data representation is not restricted to EOG recordings, and could be used in various fields of signal analysis.

Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments.

PubMed

Zheng, Ce; Kurgan, Lukasz

2008-10-10

beta-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of beta-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based beta-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. We show that (1) all four predicted secondary structures are useful; (2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential beta-turns, while the remaining four amino acids are useful to predict non-beta-turns. Empirical evaluation using three nonredundant datasets shows favorable Q total, Q predicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Q total barrier and achieves Q total = 80.9%, MCC = 0.47, and Q predicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Experiments show that the proposed method constitutes an improvement over the competing prediction methods. The proposed prediction model can better discriminate between beta-turns and non-beta-turns due to obtaining lower numbers of false positive predictions. The prediction model and datasets are freely available at http://biomine.ece.ualberta.ca/BTNpred/BTNpred.html.

Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

PubMed Central

Zheng, Ce; Kurgan, Lukasz

2008-01-01

Background β-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of β-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based β-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. Results We show that (1) all four predicted secondary structures are useful; (2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential β-turns, while the remaining four amino acids are useful to predict non-β-turns. Empirical evaluation using three nonredundant datasets shows favorable Qtotal, Qpredicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Qtotal barrier and achieves Qtotal = 80.9%, MCC = 0.47, and Qpredicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Conclusion Experiments show that the proposed method constitutes an improvement over the competing prediction methods. The proposed prediction model can better discriminate between β-turns and non-β-turns due to obtaining lower numbers of false positive predictions. The prediction model and datasets are freely available at . PMID:18847492

Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach.

PubMed

Liang, Muxuan; Li, Zhizhong; Chen, Ting; Zeng, Jianyang

2015-01-01

Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.

Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images.

PubMed

Badawi, A M; Derbala, A S; Youssef, A M

1999-08-01

Computerized ultrasound tissue characterization has become an objective means for diagnosis of liver diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases are rather confusing and highly dependent upon the sonographer's experience. This often causes a bias effects in the diagnostic procedure and limits its objectivity and reproducibility. Computerized tissue characterization to assist quantitatively the sonographer for the accurate differentiation and to minimize the degree of risk is thus justified. Fuzzy logic has emerged as one of the most active area in classification. In this paper, we present an approach that employs Fuzzy reasoning techniques to automatically differentiate diffuse liver diseases using numerical quantitative features measured from the ultrasound images. Fuzzy rules were generated from over 140 cases consisting of normal, fatty, and cirrhotic livers. The input to the fuzzy system is an eight dimensional vector of feature values: the mean gray level (MGL), the percentile 10%, the contrast (CON), the angular second moment (ASM), the entropy (ENT), the correlation (COR), the attenuation (ATTEN) and the speckle separation. The output of the fuzzy system is one of the three categories: cirrhosis, fatty or normal. The steps done for differentiating the pathologies are data acquisition and feature extraction, dividing the input spaces of the measured quantitative data into fuzzy sets. Based on the expert knowledge, the fuzzy rules are generated and applied using the fuzzy inference procedures to determine the pathology. Different membership functions are developed for the input spaces. This approach has resulted in very good sensitivities and specificity for classifying diffused liver pathologies. This classification technique can be used in the diagnostic process, together with the history information, laboratory, clinical and pathological examinations.

Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

PubMed Central

Cha, Kenny H.; Hadjiiski, Lubomir; Samala, Ravi K.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.

2016-01-01

Purpose: The authors are developing a computerized system for bladder segmentation in CT urography (CTU) as a critical component for computer-aided detection of bladder cancer. Methods: A deep-learning convolutional neural network (DL-CNN) was trained to distinguish between the inside and the outside of the bladder using 160 000 regions of interest (ROI) from CTU images. The trained DL-CNN was used to estimate the likelihood of an ROI being inside the bladder for ROIs centered at each voxel in a CTU case, resulting in a likelihood map. Thresholding and hole-filling were applied to the map to generate the initial contour for the bladder, which was then refined by 3D and 2D level sets. The segmentation performance was evaluated using 173 cases: 81 cases in the training set (42 lesions, 21 wall thickenings, and 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, and 13 normal bladders). The computerized segmentation accuracy using the DL likelihood map was compared to that using a likelihood map generated by Haar features and a random forest classifier, and that using our previous conjoint level set analysis and segmentation system (CLASS) without using a likelihood map. All methods were evaluated relative to the 3D hand-segmented reference contours. Results: With DL-CNN-based likelihood map and level sets, the average volume intersection ratio, average percent volume error, average absolute volume error, average minimum distance, and the Jaccard index for the test set were 81.9% ± 12.1%, 10.2% ± 16.2%, 14.0% ± 13.0%, 3.6 ± 2.0 mm, and 76.2% ± 11.8%, respectively. With the Haar-feature-based likelihood map and level sets, the corresponding values were 74.3% ± 12.7%, 13.0% ± 22.3%, 20.5% ± 15.7%, 5.7 ± 2.6 mm, and 66.7% ± 12.6%, respectively. With our previous CLASS with local contour refinement (LCR) method, the corresponding values were 78.0% ± 14.7%, 16.5% ± 16.8%, 18.2% ± 15.0%, 3.8 ± 2.3 mm, and 73.9% ± 13.5%, respectively. Conclusions: The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Compared to our previous CLASS with LCR method, which required two user inputs to initialize the segmentation, DL-CNN with level sets achieved better segmentation performance while using a single user input. Compared to the Haar-feature-based likelihood map, the DL-CNN-based likelihood map could guide the level sets to achieve better segmentation. The results demonstrate the feasibility of our new approach of using DL-CNN in combination with level sets for segmentation of the bladder. PMID:27036584

Defining Geodetic Reference Frame using Matlab®: PlatEMotion 2.0

NASA Astrophysics Data System (ADS)

Cannavò, Flavio; Palano, Mimmo

2016-03-01

We describe the main features of the developed software tool, namely PlatE-Motion 2.0 (PEM2), which allows inferring the Euler pole parameters by inverting the observed velocities at a set of sites located on a rigid block (inverse problem). PEM2 allows also calculating the expected velocity value for any point located on the Earth providing an Euler pole (direct problem). PEM2 is the updated version of a previous software tool initially developed for easy-to-use file exchange with the GAMIT/GLOBK software package. The software tool is developed in Matlab® framework and, as the previous version, includes a set of MATLAB functions (m-files), GUIs (fig-files), map data files (mat-files) and user's manual as well as some example input files. New changes in PEM2 include (1) some bugs fixed, (2) improvements in the code, (3) improvements in statistical analysis, (4) new input/output file formats. In addition, PEM2 can be now run under the majority of operating systems. The tool is open source and freely available for the scientific community.

Gender-specific automatic valence recognition of affective olfactory stimulation through the analysis of the electrodermal activity.

PubMed

Greco, Alberto; Lanata, Antonio; Valenza, Gaetano; Di Francesco, Fabio; Scilingo, Enzo Pasquale

2016-08-01

This study reports on the development of a gender-specific classification system able to discern between two valence levels of smell, through information gathered from electrodermal activity (EDA) dynamics. Specifically, two odorants were administered to 32 healthy volunteers (16 males) while monitoring EDA. CvxEDA model was used to process the EDA signal and extract features from both tonic and phasic components. The feature set was used as input to a K-NN classifier implementing a leave-one-subject-out procedure. Results show strong differences in the accuracy of valence recognition between men (62.5%) and women (78%). We can conclude that affective olfactory stimulation significantly affect EDA dynamics with a highly specific gender dependency.

Framework GRASP: routine library for optimize processing of aerosol remote sensing observation

NASA Astrophysics Data System (ADS)

Fuertes, David; Torres, Benjamin; Dubovik, Oleg; Litvinov, Pavel; Lapyonok, Tatyana; Ducos, Fabrice; Aspetsberger, Michael; Federspiel, Christian

The present the development of a Framework for the Generalized Retrieval of Aerosol and Surface Properties (GRASP) developed by Dubovik et al., (2011). The framework is a source code project that attempts to strengthen the value of the GRASP inversion algorithm by transforming it into a library that will be used later for a group of customized application modules. The functions of the independent modules include the managing of the configuration of the code execution, as well as preparation of the input and output. The framework provides a number of advantages in utilization of the code. First, it implements loading data to the core of the scientific code directly from memory without passing through intermediary files on disk. Second, the framework allows consecutive use of the inversion code without the re-initiation of the core routine when new input is received. These features are essential for optimizing performance of the data production in processing of large observation sets, such as satellite images by the GRASP. Furthermore, the framework is a very convenient tool for further development, because this open-source platform is easily extended for implementing new features. For example, it could accommodate loading of raw data directly onto the inversion code from a specific instrument not included in default settings of the software. Finally, it will be demonstrated that from the user point of view, the framework provides a flexible, powerful and informative configuration system.

Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials.

PubMed

Bailly, Clément; Bodet-Milin, Caroline; Couespel, Solène; Necib, Hatem; Kraeber-Bodéré, Françoise; Ansquer, Catherine; Carlier, Thomas

2016-01-01

This study aimed to investigate the variability of textural features (TF) as a function of acquisition and reconstruction parameters within the context of multi-centric trials. The robustness of 15 selected TFs were studied as a function of the number of iterations, the post-filtering level, input data noise, the reconstruction algorithm and the matrix size. A combination of several reconstruction and acquisition settings was devised to mimic multi-centric conditions. We retrospectively studied data from 26 patients enrolled in a diagnostic study that aimed to evaluate the performance of PET/CT 68Ga-DOTANOC in gastro-entero-pancreatic neuroendocrine tumors. Forty-one tumors were extracted and served as the database. The coefficient of variation (COV) or the absolute deviation (for the noise study) was derived and compared statistically with SUVmax and SUVmean results. The majority of investigated TFs can be used in a multi-centric context when each parameter is considered individually. The impact of voxel size and noise in the input data were predominant as only 4 TFs presented a high/intermediate robustness against SUV-based metrics (Entropy, Homogeneity, RP and ZP). When combining several reconstruction settings to mimic multi-centric conditions, most of the investigated TFs were robust enough against SUVmax except Correlation, Contrast, LGRE, LGZE and LZLGE. Considering previously published results on either reproducibility or sensitivity against delineation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimilarity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials.

An Extended Normalization Model of Attention Accounts for Feature-Based Attentional Enhancement of Both Response and Coherence Gain

PubMed Central

Krishna, B. Suresh; Treue, Stefan

2016-01-01

Paying attention to a sensory feature improves its perception and impairs that of others. Recent work has shown that a Normalization Model of Attention (NMoA) can account for a wide range of physiological findings and the influence of different attentional manipulations on visual performance. A key prediction of the NMoA is that attention to a visual feature like an orientation or a motion direction will increase the response of neurons preferring the attended feature (response gain) rather than increase the sensory input strength of the attended stimulus (input gain). This effect of feature-based attention on neuronal responses should translate to similar patterns of improvement in behavioral performance, with psychometric functions showing response gain rather than input gain when attention is directed to the task-relevant feature. In contrast, we report here that when human subjects are cued to attend to one of two motion directions in a transparent motion display, attentional effects manifest as a combination of input and response gain. Further, the impact on input gain is greater when attention is directed towards a narrow range of motion directions than when it is directed towards a broad range. These results are captured by an extended NMoA, which either includes a stimulus-independent attentional contribution to normalization or utilizes direction-tuned normalization. The proposed extensions are consistent with the feature-similarity gain model of attention and the attentional modulation in extrastriate area MT, where neuronal responses are enhanced and suppressed by attention to preferred and non-preferred motion directions respectively. PMID:27977679

Effective Fingerprint Quality Estimation for Diverse Capture Sensors

PubMed Central

Xie, Shan Juan; Yoon, Sook; Shin, Jinwook; Park, Dong Sun

2010-01-01

Recognizing the quality of fingerprints in advance can be beneficial for improving the performance of fingerprint recognition systems. The representative features to assess the quality of fingerprint images from different types of capture sensors are known to vary. In this paper, an effective quality estimation system that can be adapted for different types of capture sensors is designed by modifying and combining a set of features including orientation certainty, local orientation quality and consistency. The proposed system extracts basic features, and generates next level features which are applicable for various types of capture sensors. The system then uses the Support Vector Machine (SVM) classifier to determine whether or not an image should be accepted as input to the recognition system. The experimental results show that the proposed method can perform better than previous methods in terms of accuracy. In the meanwhile, the proposed method has an ability to eliminate residue images from the optical and capacitive sensors, and the coarse images from thermal sensors. PMID:22163632

An Italian battery for the assessment of semantic memory disorders.

PubMed

Catricalà, Eleonora; Della Rosa, Pasquale A; Ginex, Valeria; Mussetti, Zoe; Plebani, Valentina; Cappa, Stefano F

2013-06-01

We report the construction and standardization of a new comprehensive battery of tests for the assessment of semantic memory disorders. The battery is constructed on a common set of 48 stimuli, belonging to both living and non-living categories, rigidly controlled for several confounding variables, and is based on an empirically derived corpus of semantic features. It includes six tasks, in order to assess semantic memory through different modalities of input and output: two naming tasks, one with colored pictures and the other in response to an oral description, a word-picture matching task, a picture sorting task, a free generation of features task and a sentence verification task. Normative data on 106 Italian subjects pooled across homogenous subgroups for age, sex and education are reported. The new battery allows an in-depth investigation of category-specific disorders and of progressive semantic memory deficits at features level, overcoming some of the limitations of existing tests.

Process service quality evaluation based on Dempster-Shafer theory and support vector machine.

PubMed

Pei, Feng-Que; Li, Dong-Bo; Tong, Yi-Fei; He, Fei

2017-01-01

Human involvement influences traditional service quality evaluations, which triggers an evaluation's low accuracy, poor reliability and less impressive predictability. This paper proposes a method by employing a support vector machine (SVM) and Dempster-Shafer evidence theory to evaluate the service quality of a production process by handling a high number of input features with a low sampling data set, which is called SVMs-DS. Features that can affect production quality are extracted by a large number of sensors. Preprocessing steps such as feature simplification and normalization are reduced. Based on three individual SVM models, the basic probability assignments (BPAs) are constructed, which can help the evaluation in a qualitative and quantitative way. The process service quality evaluation results are validated by the Dempster rules; the decision threshold to resolve conflicting results is generated from three SVM models. A case study is presented to demonstrate the effectiveness of the SVMs-DS method.

Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface.

PubMed

Siuly; Li, Yan; Paul Wen, Peng

2014-03-01

Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography.

PubMed

Vomweg, T W; Buscema, M; Kauczor, H U; Teifke, A; Intraligi, M; Terzi, S; Heussel, C P; Achenbach, T; Rieker, O; Mayer, D; Thelen, M

2003-09-01

The aim of this study was to evaluate the capability of improved artificial neural networks (ANN) and additional novel training methods in distinguishing between benign and malignant breast lesions in contrast-enhanced magnetic resonance-mammography (MRM). A total of 604 histologically proven cases of contrast-enhanced lesions of the female breast at MRI were analyzed. Morphological, dynamic and clinical parameters were collected and stored in a database. The data set was divided into several groups using random or experimental methods [Training & Testing (T&T) algorithm] to train and test different ANNs. An additional novel computer program for input variable selection was applied. Sensitivity and specificity were calculated and compared with a statistical method and an expert radiologist. After optimization of the distribution of cases among the training and testing sets by the T & T algorithm and the reduction of input variables by the Input Selection procedure a highly sophisticated ANN achieved a sensitivity of 93.6% and a specificity of 91.9% in predicting malignancy of lesions within an independent prediction sample set. The best statistical method reached a sensitivity of 90.5% and a specificity of 68.9%. An expert radiologist performed better than the statistical method but worse than the ANN (sensitivity 92.1%, specificity 85.6%). Features extracted out of dynamic contrast-enhanced MRM and additional clinical data can be successfully analyzed by advanced ANNs. The quality of the resulting network strongly depends on the training methods, which are improved by the use of novel training tools. The best results of an improved ANN outperform expert radiologists.

Impaired visual search in rats reveals cholinergic contributions to feature binding in visuospatial attention.

PubMed

Botly, Leigh C P; De Rosa, Eve

2012-10-01

The visual search task established the feature integration theory of attention in humans and measures visuospatial attentional contributions to feature binding. We recently demonstrated that the neuromodulator acetylcholine (ACh), from the nucleus basalis magnocellularis (NBM), supports the attentional processes required for feature binding using a rat digging-based task. Additional research has demonstrated cholinergic contributions from the NBM to visuospatial attention in rats. Here, we combined these lines of evidence and employed visual search in rats to examine whether cortical cholinergic input supports visuospatial attention specifically for feature binding. We trained 18 male Long-Evans rats to perform visual search using touch screen-equipped operant chambers. Sessions comprised Feature Search (no feature binding required) and Conjunctive Search (feature binding required) trials using multiple stimulus set sizes. Following acquisition of visual search, 8 rats received bilateral NBM lesions using 192 IgG-saporin to selectively reduce cholinergic afferentation of the neocortex, which we hypothesized would selectively disrupt the visuospatial attentional processes needed for efficient conjunctive visual search. As expected, relative to sham-lesioned rats, ACh-NBM-lesioned rats took significantly longer to locate the target stimulus on Conjunctive Search, but not Feature Search trials, thus demonstrating that cholinergic contributions to visuospatial attention are important for feature binding in rats.

FaceTOON: a unified platform for feature-based cartoon expression generation

NASA Astrophysics Data System (ADS)

Zaharia, Titus; Marre, Olivier; Prêteux, Françoise; Monjaux, Perrine

2008-02-01

This paper presents the FaceTOON system, a semi-automatic platform dedicated to the creation of verbal and emotional facial expressions, within the applicative framework of 2D cartoon production. The proposed FaceTOON platform makes it possible to rapidly create 3D facial animations with a minimum amount of user interaction. In contrast with existing commercial 3D modeling softwares, which usually require from the users advanced 3D graphics skills and competences, the FaceTOON system is based exclusively on 2D interaction mechanisms, the 3D modeling stage being completely transparent for the user. The system takes as input a neutral 3D face model, free of any facial feature, and a set of 2D drawings, representing the desired facial features. A 2D/3D virtual mapping procedure makes it possible to obtain a ready-for-animation model which can be directly manipulated and deformed for generating expressions. The platform includes a complete set of dedicated tools for 2D/3D interactive deformation, pose management, key-frame interpolation and MPEG-4 compliant animation and rendering. The proposed FaceTOON system is currently considered for industrial evaluation and commercialization by the Quadraxis company.

Analytical solutions for one-, two-, and three-dimensional solute transport in ground-water systems with uniform flow

USGS Publications Warehouse

Wexler, Eliezer J.

1992-01-01

Analytical solutions to the advective-dispersive solute-transport equation are useful in predicting the fate of solutes in ground water. Analytical solutions compiled from available literature or derived by the author are presented for a variety of boundary condition types and solute-source configurations in one-, two-, and three-dimensional systems having uniform ground-water flow. A set of user-oriented computer programs was created to evaluate these solutions and to display the results in tabular and computer-graphics format. These programs incorporate many features that enhance their accuracy, ease of use, and versatility. Documentation for the programs describes their operation and required input data, and presents the results of sample problems. Derivations of selected solutions, source codes for the computer programs, and samples of program input and output also are included.

Parameters Selection for Bivariate Multiscale Entropy Analysis of Postural Fluctuations in Fallers and Non-Fallers Older Adults.

PubMed

Ramdani, Sofiane; Bonnet, Vincent; Tallon, Guillaume; Lagarde, Julien; Bernard, Pierre Louis; Blain, Hubert

2016-08-01

Entropy measures are often used to quantify the regularity of postural sway time series. Recent methodological developments provided both multivariate and multiscale approaches allowing the extraction of complexity features from physiological signals; see "Dynamical complexity of human responses: A multivariate data-adaptive framework," in Bulletin of Polish Academy of Science and Technology, vol. 60, p. 433, 2012. The resulting entropy measures are good candidates for the analysis of bivariate postural sway signals exhibiting nonstationarity and multiscale properties. These methods are dependant on several input parameters such as embedding parameters. Using two data sets collected from institutionalized frail older adults, we numerically investigate the behavior of a recent multivariate and multiscale entropy estimator; see "Multivariate multiscale entropy: A tool for complexity analysis of multichannel data," Physics Review E, vol. 84, p. 061918, 2011. We propose criteria for the selection of the input parameters. Using these optimal parameters, we statistically compare the multivariate and multiscale entropy values of postural sway data of non-faller subjects to those of fallers. These two groups are discriminated by the resulting measures over multiple time scales. We also demonstrate that the typical parameter settings proposed in the literature lead to entropy measures that do not distinguish the two groups. This last result confirms the importance of the selection of appropriate input parameters.

Exploring convolutional neural networks for drug–drug interaction extraction

PubMed Central

Segura-Bedmar, Isabel; Martínez, Paloma

2017-01-01

Abstract Drug–drug interaction (DDI), which is a specific type of adverse drug reaction, occurs when a drug influences the level or activity of another drug. Natural language processing techniques can provide health-care professionals with a novel way of reducing the time spent reviewing the literature for potential DDIs. The current state-of-the-art for the extraction of DDIs is based on feature-engineering algorithms (such as support vector machines), which usually require considerable time and effort. One possible alternative to these approaches includes deep learning. This technique aims to automatically learn the best feature representation from the input data for a given task. The purpose of this paper is to examine whether a convolutional neural network (CNN), which only uses word embeddings as input features, can be applied successfully to classify DDIs from biomedical texts. Proposed herein, is a CNN architecture with only one hidden layer, thus making the model more computationally efficient, and we perform detailed experiments in order to determine the best settings of the model. The goal is to determine the best parameter of this basic CNN that should be considered for future research. The experimental results show that the proposed approach is promising because it attained the second position in the 2013 rankings of the DDI extraction challenge. However, it obtained worse results than previous works using neural networks with more complex architectures. PMID:28605776

A New 3D Object Pose Detection Method Using LIDAR Shape Set

PubMed Central

Kim, Jung-Un

2018-01-01

In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape set that reconstructs the shape surrounding the object more clearly by using the LIDAR point information projected on the object. The LIDAR shape set restores object shape edges from a bird’s eye view by filtering LIDAR points projected on a 2D pixel-based front view. In this study, we use this shape set for two purposes. The first is to supplement the shape set with a LIDAR Feature map, and the second is to divide the entire shape set according to the gradient of the depth and density to create a 2D and 3D bounding box proposal for each object. We present a multimodal fusion framework that classifies objects and restores the 3D pose of each object using enhanced feature maps and shape-based proposals. The network structure consists of a VGG -based object classifier that receives multiple inputs and a LIDAR-based Region Proposal Networks (RPN) that identifies object poses. It works in a very intuitive and efficient manner and can be extended to other classes other than vehicles. Our research has outperformed object classification accuracy (Average Precision, AP) and 3D pose restoration accuracy (3D bounding box recall rate) based on the latest studies conducted with KITTI data sets. PMID:29547551

A New 3D Object Pose Detection Method Using LIDAR Shape Set.

PubMed

Kim, Jung-Un; Kang, Hang-Bong

2018-03-16

In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape set that reconstructs the shape surrounding the object more clearly by using the LIDAR point information projected on the object. The LIDAR shape set restores object shape edges from a bird's eye view by filtering LIDAR points projected on a 2D pixel-based front view. In this study, we use this shape set for two purposes. The first is to supplement the shape set with a LIDAR Feature map, and the second is to divide the entire shape set according to the gradient of the depth and density to create a 2D and 3D bounding box proposal for each object. We present a multimodal fusion framework that classifies objects and restores the 3D pose of each object using enhanced feature maps and shape-based proposals. The network structure consists of a VGG -based object classifier that receives multiple inputs and a LIDAR-based Region Proposal Networks (RPN) that identifies object poses. It works in a very intuitive and efficient manner and can be extended to other classes other than vehicles. Our research has outperformed object classification accuracy (Average Precision, AP) and 3D pose restoration accuracy (3D bounding box recall rate) based on the latest studies conducted with KITTI data sets.

Robotics control using isolated word recognition of voice input

NASA Technical Reports Server (NTRS)

Weiner, J. M.

1977-01-01

A speech input/output system is presented that can be used to communicate with a task oriented system. Human speech commands and synthesized voice output extend conventional information exchange capabilities between man and machine by utilizing audio input and output channels. The speech input facility is comprised of a hardware feature extractor and a microprocessor implemented isolated word or phrase recognition system. The recognizer offers a medium sized (100 commands), syntactically constrained vocabulary, and exhibits close to real time performance. The major portion of the recognition processing required is accomplished through software, minimizing the complexity of the hardware feature extractor.

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

PubMed

Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification

PubMed Central

Feng, Yang; Jiang, Jiancheng; Tong, Xin

2015-01-01

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing. PMID:27185970

Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering.

PubMed

Rodríguez-Sotelo, J L; Peluffo-Ordoñez, D; Cuesta-Frau, D; Castellanos-Domínguez, G

2012-10-01

The computer-assisted analysis of biomedical records has become an essential tool in clinical settings. However, current devices provide a growing amount of data that often exceeds the processing capacity of normal computers. As this amount of information rises, new demands for more efficient data extracting methods appear. This paper addresses the task of data mining in physiological records using a feature selection scheme. An unsupervised method based on relevance analysis is described. This scheme uses a least-squares optimization of the input feature matrix in a single iteration. The output of the algorithm is a feature weighting vector. The performance of the method was assessed using a heartbeat clustering test on real ECG records. The quantitative cluster validity measures yielded a correctly classified heartbeat rate of 98.69% (specificity), 85.88% (sensitivity) and 95.04% (general clustering performance), which is even higher than the performance achieved by other similar ECG clustering studies. The number of features was reduced on average from 100 to 18, and the temporal cost was a 43% lower than in previous ECG clustering schemes. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Linear and Non-Linear Visual Feature Learning in Rat and Humans

PubMed Central

Bossens, Christophe; Op de Beeck, Hans P.

2016-01-01

The visual system processes visual input in a hierarchical manner in order to extract relevant features that can be used in tasks such as invariant object recognition. Although typically investigated in primates, recent work has shown that rats can be trained in a variety of visual object and shape recognition tasks. These studies did not pinpoint the complexity of the features used by these animals. Many tasks might be solved by using a combination of relatively simple features which tend to be correlated. Alternatively, rats might extract complex features or feature combinations which are nonlinear with respect to those simple features. In the present study, we address this question by starting from a small stimulus set for which one stimulus-response mapping involves a simple linear feature to solve the task while another mapping needs a well-defined nonlinear combination of simpler features related to shape symmetry. We verified computationally that the nonlinear task cannot be trivially solved by a simple V1-model. We show how rats are able to solve the linear feature task but are unable to acquire the nonlinear feature. In contrast, humans are able to use the nonlinear feature and are even faster in uncovering this solution as compared to the linear feature. The implications for the computational capabilities of the rat visual system are discussed. PMID:28066201

Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores.

PubMed

Pham, Thuy T; Moore, Steven T; Lewis, Simon John Geoffrey; Nguyen, Diep N; Dutkiewicz, Eryk; Fuglevand, Andrew J; McEwan, Alistair L; Leong, Philip H W

2017-11-01

Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).

Slow feature analysis: unsupervised learning of invariances.

PubMed

Wiskott, Laurenz; Sejnowski, Terrence J

2002-04-01

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images.

PubMed

Hajimani, Elmira; Ruano, M G; Ruano, A E

2017-07-01

This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%). Copyright © 2017. Published by Elsevier B.V.

Electron microprobe analysis program for biological specimens: BIOMAP

NASA Technical Reports Server (NTRS)

Edwards, B. F.

1972-01-01

BIOMAP is a Univac 1108 compatible program which facilitates the electron probe microanalysis of biological specimens. Input data are X-ray intensity data from biological samples, the X-ray intensity and composition data from a standard sample and the electron probe operating parameters. Outputs are estimates of the weight percentages of the analyzed elements, the distribution of these estimates for sets of red blood cells and the probabilities for correlation between elemental concentrations. An optional feature statistically estimates the X-ray intensity and residual background of a principal standard relative to a series of standards.

Fault-Tree Compiler

NASA Technical Reports Server (NTRS)

Butler, Ricky W.; Boerschlein, David P.

1993-01-01

Fault-Tree Compiler (FTC) program, is software tool used to calculate probability of top event in fault tree. Gates of five different types allowed in fault tree: AND, OR, EXCLUSIVE OR, INVERT, and M OF N. High-level input language easy to understand and use. In addition, program supports hierarchical fault-tree definition feature, which simplifies tree-description process and reduces execution time. Set of programs created forming basis for reliability-analysis workstation: SURE, ASSIST, PAWS/STEM, and FTC fault-tree tool (LAR-14586). Written in PASCAL, ANSI-compliant C language, and FORTRAN 77. Other versions available upon request.

Knowledge-based low-level image analysis for computer vision systems

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.; Baxi, Himanshu; Ranganath, M. V.

1988-01-01

Two algorithms for entry-level image analysis and preliminary segmentation are proposed which are flexible enough to incorporate local properties of the image. The first algorithm involves pyramid-based multiresolution processing and a strategy to define and use interlevel and intralevel link strengths. The second algorithm, which is designed for selected window processing, extracts regions adaptively using local histograms. The preliminary segmentation and a set of features are employed as the input to an efficient rule-based low-level analysis system, resulting in suboptimal meaningful segmentation.

Single neuron computation: from dynamical system to feature detector.

PubMed

Hong, Sungho; Agüera y Arcas, Blaise; Fairhall, Adrienne L

2007-12-01

White noise methods are a powerful tool for characterizing the computation performed by neural systems. These methods allow one to identify the feature or features that a neural system extracts from a complex input and to determine how these features are combined to drive the system's spiking response. These methods have also been applied to characterize the input-output relations of single neurons driven by synaptic inputs, simulated by direct current injection. To interpret the results of white noise analysis of single neurons, we would like to understand how the obtained feature space of a single neuron maps onto the biophysical properties of the membrane, in particular, the dynamics of ion channels. Here, through analysis of a simple dynamical model neuron, we draw explicit connections between the output of a white noise analysis and the underlying dynamical system. We find that under certain assumptions, the form of the relevant features is well defined by the parameters of the dynamical system. Further, we show that under some conditions, the feature space is spanned by the spike-triggered average and its successive order time derivatives.

An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States

USGS Publications Warehouse

Wendel, Jochen; Buttenfield, Barbara P.; Stanislawski, Larry V.

2016-01-01

Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.

LiPISC: A Lightweight and Flexible Method for Privacy-Aware Intersection Set Computation

PubMed Central

Huang, Shiyong; Ren, Yi; Choo, Kim-Kwang Raymond

2016-01-01

Privacy-aware intersection set computation (PISC) can be modeled as secure multi-party computation. The basic idea is to compute the intersection of input sets without leaking privacy. Furthermore, PISC should be sufficiently flexible to recommend approximate intersection items. In this paper, we reveal two previously unpublished attacks against PISC, which can be used to reveal and link one input set to another input set, resulting in privacy leakage. We coin these as Set Linkage Attack and Set Reveal Attack. We then present a lightweight and flexible PISC scheme (LiPISC) and prove its security (including against Set Linkage Attack and Set Reveal Attack). PMID:27326763

LiPISC: A Lightweight and Flexible Method for Privacy-Aware Intersection Set Computation.

PubMed

Ren, Wei; Huang, Shiyong; Ren, Yi; Choo, Kim-Kwang Raymond

2016-01-01

Privacy-aware intersection set computation (PISC) can be modeled as secure multi-party computation. The basic idea is to compute the intersection of input sets without leaking privacy. Furthermore, PISC should be sufficiently flexible to recommend approximate intersection items. In this paper, we reveal two previously unpublished attacks against PISC, which can be used to reveal and link one input set to another input set, resulting in privacy leakage. We coin these as Set Linkage Attack and Set Reveal Attack. We then present a lightweight and flexible PISC scheme (LiPISC) and prove its security (including against Set Linkage Attack and Set Reveal Attack).

Modeling Tools for Propulsion Analysis and Computational Fluid Dynamics on the Internet

NASA Technical Reports Server (NTRS)

Muss, J. A.; Johnson, C. W.; Gotchy, M. B.

2000-01-01

The existing RocketWeb(TradeMark) Internet Analysis System (httr)://www.iohnsonrockets.com/rocketweb) provides an integrated set of advanced analysis tools that can be securely accessed over the Internet. Since these tools consist of both batch and interactive analysis codes, the system includes convenient methods for creating input files and evaluating the resulting data. The RocketWeb(TradeMark) system also contains many features that permit data sharing which, when further developed, will facilitate real-time, geographically diverse, collaborative engineering within a designated work group. Adding work group management functionality while simultaneously extending and integrating the system's set of design and analysis tools will create a system providing rigorous, controlled design development, reducing design cycle time and cost.

Evaluation and recommendation of sensitivity analysis methods for application to Stochastic Human Exposure and Dose Simulation models.

PubMed

Mokhtari, Amirhossein; Christopher Frey, H; Zheng, Junyu

2006-11-01

Sensitivity analyses of exposure or risk models can help identify the most significant factors to aid in risk management or to prioritize additional research to reduce uncertainty in the estimates. However, sensitivity analysis is challenged by non-linearity, interactions between inputs, and multiple days or time scales. Selected sensitivity analysis methods are evaluated with respect to their applicability to human exposure models with such features using a testbed. The testbed is a simplified version of a US Environmental Protection Agency's Stochastic Human Exposure and Dose Simulation (SHEDS) model. The methods evaluated include the Pearson and Spearman correlation, sample and rank regression, analysis of variance, Fourier amplitude sensitivity test (FAST), and Sobol's method. The first five methods are known as "sampling-based" techniques, wheras the latter two methods are known as "variance-based" techniques. The main objective of the test cases was to identify the main and total contributions of individual inputs to the output variance. Sobol's method and FAST directly quantified these measures of sensitivity. Results show that sensitivity of an input typically changed when evaluated under different time scales (e.g., daily versus monthly). All methods provided similar insights regarding less important inputs; however, Sobol's method and FAST provided more robust insights with respect to sensitivity of important inputs compared to the sampling-based techniques. Thus, the sampling-based methods can be used in a screening step to identify unimportant inputs, followed by application of more computationally intensive refined methods to a smaller set of inputs. The implications of time variation in sensitivity results for risk management are briefly discussed.

Reconstruction of nonlinear wave propagation

DOEpatents

Fleischer, Jason W; Barsi, Christopher; Wan, Wenjie

2013-04-23

Disclosed are systems and methods for characterizing a nonlinear propagation environment by numerically propagating a measured output waveform resulting from a known input waveform. The numerical propagation reconstructs the input waveform, and in the process, the nonlinear environment is characterized. In certain embodiments, knowledge of the characterized nonlinear environment facilitates determination of an unknown input based on a measured output. Similarly, knowledge of the characterized nonlinear environment also facilitates formation of a desired output based on a configurable input. In both situations, the input thus characterized and the output thus obtained include features that would normally be lost in linear propagations. Such features can include evanescent waves and peripheral waves, such that an image thus obtained are inherently wide-angle, farfield form of microscopy.

Self-organizing neural integration of pose-motion features for human action recognition

PubMed Central

Parisi, German I.; Weber, Cornelius; Wermter, Stefan

2015-01-01

The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented toward human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR) networks that obtain progressively generalized representations of sensory inputs and learn inherent spatio-temporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best results for a public benchmark of domestic daily actions. PMID:26106323

A digital prediction algorithm for a single-phase boost PFC

NASA Astrophysics Data System (ADS)

Qing, Wang; Ning, Chen; Weifeng, Sun; Shengli, Lu; Longxing, Shi

2012-12-01

A novel digital control algorithm for digital control power factor correction is presented, which is called the prediction algorithm and has a feature of a higher PF (power factor) with lower total harmonic distortion, and a faster dynamic response with the change of the input voltage or load current. For a certain system, based on the current system state parameters, the prediction algorithm can estimate the track of the output voltage and the inductor current at the next switching cycle and get a set of optimized control sequences to perfectly track the trajectory of input voltage. The proposed prediction algorithm is verified at different conditions, and computer simulation and experimental results under multi-situations confirm the effectiveness of the prediction algorithm. Under the circumstances that the input voltage is in the range of 90-265 V and the load current in the range of 20%-100%, the PF value is larger than 0.998. The startup and the recovery times respectively are about 0.1 s and 0.02 s without overshoot. The experimental results also verify the validity of the proposed method.

Single-image super-resolution based on Markov random field and contourlet transform

NASA Astrophysics Data System (ADS)

Wu, Wei; Liu, Zheng; Gueaieb, Wail; He, Xiaohai

2011-04-01

Learning-based methods are well adopted in image super-resolution. In this paper, we propose a new learning-based approach using contourlet transform and Markov random field. The proposed algorithm employs contourlet transform rather than the conventional wavelet to represent image features and takes into account the correlation between adjacent pixels or image patches through the Markov random field (MRF) model. The input low-resolution (LR) image is decomposed with the contourlet transform and fed to the MRF model together with the contourlet transform coefficients from the low- and high-resolution image pairs in the training set. The unknown high-frequency components/coefficients for the input low-resolution image are inferred by a belief propagation algorithm. Finally, the inverse contourlet transform converts the LR input and the inferred high-frequency coefficients into the super-resolved image. The effectiveness of the proposed method is demonstrated with the experiments on facial, vehicle plate, and real scene images. A better visual quality is achieved in terms of peak signal to noise ratio and the image structural similarity measurement.

ProQ3D: improved model quality assessments using deep learning.

PubMed

Uziela, Karolis; Menéndez Hurtado, David; Shu, Nanjiang; Wallner, Björn; Elofsson, Arne

2017-05-15

Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features). ProQ3D is freely available both as a webserver and a stand-alone program at http://proq3.bioinfo.se/. arne@bioinfo.se. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Incorporation of local structure into kriging models for the prediction of atomistic properties in the water decamer.

PubMed

Davie, Stuart J; Di Pasquale, Nicodemo; Popelier, Paul L A

2016-10-15

Machine learning algorithms have been demonstrated to predict atomistic properties approaching the accuracy of quantum chemical calculations at significantly less computational cost. Difficulties arise, however, when attempting to apply these techniques to large systems, or systems possessing excessive conformational freedom. In this article, the machine learning method kriging is applied to predict both the intra-atomic and interatomic energies, as well as the electrostatic multipole moments, of the atoms of a water molecule at the center of a 10 water molecule (decamer) cluster. Unlike previous work, where the properties of small water clusters were predicted using a molecular local frame, and where training set inputs (features) were based on atomic index, a variety of feature definitions and coordinate frames are considered here to increase prediction accuracy. It is shown that, for a water molecule at the center of a decamer, no single method of defining features or coordinate schemes is optimal for every property. However, explicitly accounting for the structure of the first solvation shell in the definition of the features of the kriging training set, and centring the coordinate frame on the atom-of-interest will, in general, return better predictions than models that apply the standard methods of feature definition, or a molecular coordinate frame. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Solar electric propulsion thrust subsystem development

NASA Technical Reports Server (NTRS)

Masek, T. D.

1973-01-01

The Solar Electric Propulsion System developed under this program was designed to demonstrate all the thrust subsystem functions needed on an unmanned planetary vehicle. The demonstration included operation of the basic elements, power matching input and output voltage regulation, three-axis thrust vector control, subsystem automatic control including failure detection and correction capability (using a PDP-11 computer), operation of critical elements in thermal-vacuum-, zero-gravity-type propellant storage, and data outputs from all subsystem elements. The subsystem elements, functions, unique features, and test setup are described. General features and capabilities of the test-support data system are also presented. The test program culminated in a 1500-h computer-controlled, system-functional demonstration. This included simultaneous operation of two thruster/power conditioner sets. The results of this testing phase satisfied all the program goals.

Wrapping Python around MODFLOW/MT3DMS based groundwater models

NASA Astrophysics Data System (ADS)

Post, V.

2008-12-01

Numerical models that simulate groundwater flow and solute transport require a great amount of input data that is often organized into different files. A large proportion of the input data consists of spatially-distributed model parameters. The model output consists of a variety data such as heads, fluxes and concentrations. Typically all files have different formats. Consequently, preparing input and managing output is a complex and error-prone task. Proprietary software tools are available that facilitate the preparation of input files and analysis of model outcomes. The use of such software may be limited if it does not support all the features of the groundwater model or when the costs of such tools are prohibitive. Therefore a Python library was developed that contains routines to generate input files and process output files of MODFLOW/MT3DMS based models. The library is freely available and has an open structure so that the routines can be customized and linked into other scripts and libraries. The current set of functions supports the generation of input files for MODFLOW and MT3DMS, including the capability to read spatially-distributed input parameters (e.g. hydraulic conductivity) from PNG files. Both ASCII and binary output files can be read efficiently allowing for visualization of, for example, solute concentration patterns in contour plots with superimposed flow vectors using matplotlib. Series of contour plots are then easily saved as an animation. The subroutines can also be used within scripts to calculate derived quantities such as the mass of a solute within a particular region of the model domain. Using Python as a wrapper around groundwater models provides an efficient and flexible way of processing input and output data, which is not constrained by limitations of third-party products.

Directed Design of Experiments for Validating Probability of Detection Capability of a Testing System

NASA Technical Reports Server (NTRS)

Generazio, Edward R. (Inventor)

2012-01-01

A method of validating a probability of detection (POD) testing system using directed design of experiments (DOE) includes recording an input data set of observed hit and miss or analog data for sample components as a function of size of a flaw in the components. The method also includes processing the input data set to generate an output data set having an optimal class width, assigning a case number to the output data set, and generating validation instructions based on the assigned case number. An apparatus includes a host machine for receiving the input data set from the testing system and an algorithm for executing DOE to validate the test system. The algorithm applies DOE to the input data set to determine a data set having an optimal class width, assigns a case number to that data set, and generates validation instructions based on the case number.

Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover

NASA Astrophysics Data System (ADS)

de Oliveira Silveira, Eduarda Martiniano; de Menezes, Michele Duarte; Acerbi Júnior, Fausto Weimar; Castro Nunes Santos Terra, Marcela; de Mello, José Márcio

2017-07-01

Accurate mapping and monitoring of savanna and semiarid woodland biomes are needed to support the selection of areas of conservation, to provide sustainable land use, and to improve the understanding of vegetation. The potential of geostatistical features, derived from medium spatial resolution satellite imagery, to characterize contrasted landscape vegetation cover and improve object-based image classification is studied. The study site in Brazil includes cerrado sensu stricto, deciduous forest, and palm swamp vegetation cover. Sentinel 2 and Landsat 8 images were acquired and divided into objects, for each of which a semivariogram was calculated using near-infrared (NIR) and normalized difference vegetation index (NDVI) to extract the set of geostatistical features. The features selected by principal component analysis were used as input data to train a random forest algorithm. Tests were conducted, combining spectral and geostatistical features. Change detection evaluation was performed using a confusion matrix and its accuracies. The semivariogram curves were efficient to characterize spatial heterogeneity, with similar results using NIR and NDVI from Sentinel 2 and Landsat 8. Accuracy was significantly greater when combining geostatistical features with spectral data, suggesting that this method can improve image classification results.

Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

PubMed

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

NASA Astrophysics Data System (ADS)

Lee, Jongpil; Nam, Juhan

2017-08-01

Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

Discriminative Dictionary Learning With Two-Level Low Rank and Group Sparse Decomposition for Image Classification.

PubMed

Wen, Zaidao; Hou, Zaidao; Jiao, Licheng

2017-11-01

Discriminative dictionary learning (DDL) framework has been widely used in image classification which aims to learn some class-specific feature vectors as well as a representative dictionary according to a set of labeled training samples. However, interclass similarities and intraclass variances among input samples and learned features will generally weaken the representability of dictionary and the discrimination of feature vectors so as to degrade the classification performance. Therefore, how to explicitly represent them becomes an important issue. In this paper, we present a novel DDL framework with two-level low rank and group sparse decomposition model. In the first level, we learn a class-shared and several class-specific dictionaries, where a low rank and a group sparse regularization are, respectively, imposed on the corresponding feature matrices. In the second level, the class-specific feature matrix will be further decomposed into a low rank and a sparse matrix so that intraclass variances can be separated to concentrate the corresponding feature vectors. Extensive experimental results demonstrate the effectiveness of our model. Compared with the other state-of-the-arts on several popular image databases, our model can achieve a competitive or better performance in terms of the classification accuracy.

A no-reference bitstream-based perceptual model for video quality estimation of videos affected by coding artifacts and packet losses

NASA Astrophysics Data System (ADS)

Pandremmenou, K.; Shahid, M.; Kondi, L. P.; Lövström, B.

2015-03-01

In this work, we propose a No-Reference (NR) bitstream-based model for predicting the quality of H.264/AVC video sequences, affected by both compression artifacts and transmission impairments. The proposed model is based on a feature extraction procedure, where a large number of features are calculated from the packet-loss impaired bitstream. Many of the features are firstly proposed in this work, and the specific set of the features as a whole is applied for the first time for making NR video quality predictions. All feature observations are taken as input to the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. LASSO indicates the most important features, and using only them, it is possible to estimate the Mean Opinion Score (MOS) with high accuracy. Indicatively, we point out that only 13 features are able to produce a Pearson Correlation Coefficient of 0.92 with the MOS. Interestingly, the performance statistics we computed in order to assess our method for predicting the Structural Similarity Index and the Video Quality Metric are equally good. Thus, the obtained experimental results verified the suitability of the features selected by LASSO as well as the ability of LASSO in making accurate predictions through sparse modeling.

An efficient indexing scheme for binary feature based biometric database

NASA Astrophysics Data System (ADS)

Gupta, P.; Sana, A.; Mehrotra, H.; Hwang, C. Jinshong

2007-04-01

The paper proposes an efficient indexing scheme for binary feature template using B+ tree. In this scheme the input image is decomposed into approximation, vertical, horizontal and diagonal coefficients using the discrete wavelet transform. The binarized approximation coefficient at second level is divided into four quadrants of equal size and Hamming distance (HD) for each quadrant with respect to sample template of all ones is measured. This HD value of each quadrant is used to generate upper and lower range values which are inserted into B+ tree. The nodes of tree at first level contain the lower and upper range values generated from HD of first quadrant. Similarly, lower and upper range values for the three quadrants are stored in the second, third and fourth level respectively. Finally leaf node contains the set of identifiers. At the time of identification, the test image is used to generate HD for four quadrants. Then the B+ tree is traversed based on the value of HD at every node and terminates to leaf nodes with set of identifiers. The feature vector for each identifier is retrieved from the particular bin of secondary memory and matched with test feature template to get top matches. The proposed scheme is implemented on ear biometric database collected at IIT Kanpur. The system is giving an overall accuracy of 95.8% at penetration rate of 34%.

Research on oral test modeling based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

A computer program (MACPUMP) for interactive aquifer-test analysis

USGS Publications Warehouse

Day-Lewis, F. D.; Person, M.A.; Konikow, Leonard F.

1995-01-01

This report introduces MACPUMP (Version 1.0), an aquifer-test-analysis package for use with Macintosh4 computers. The report outlines the input- data format, describes the solutions encoded in the program, explains the menu-items, and offers a tutorial illustrating the use of the program. The package reads list-directed aquifer-test data from a file, plots the data to the screen, generates and plots type curves for several different test conditions, and allows mouse-controlled curve matching. MACPUMP features pull-down menus, a simple text viewer for displaying data-files, and optional on-line help windows. This version includes the analytical solutions for nonleaky and leaky confined aquifers, using both type curves and straight-line methods, and for the analysis of single-well slug tests using type curves. An executable version of the code and sample input data sets are included on an accompanying floppy disk.

Analytical solutions for one-, two-, and three-dimensional solute transport in ground-water systems with uniform flow

USGS Publications Warehouse

Wexler, Eliezer J.

1989-01-01

Analytical solutions to the advective-dispersive solute-transport equation are useful in predicting the fate of solutes in ground water. Analytical solutions compiled from available literature or derived by the author are presented in this report for a variety of boundary condition types and solute-source configurations in one-, two-, and three-dimensional systems with uniform ground-water flow. A set of user-oriented computer programs was created to evaluate these solutions and to display the results in tabular and computer-graphics format. These programs incorporate many features that enhance their accuracy, ease of use, and versatility. Documentation for the programs describes their operation and required input data, and presents the results of sample problems. Derivations of select solutions, source codes for the computer programs, and samples of program input and output also are included.

GrouseFlocks: steerable exploration of graph hierarchy space.

PubMed

Archambault, Daniel; Munzner, Tamara; Auber, David

2008-01-01

Several previous systems allow users to interactively explore a large input graph through cuts of a superimposed hierarchy. This hierarchy is often created using clustering algorithms or topological features present in the graph. However, many graphs have domain-specific attributes associated with the nodes and edges, which could be used to create many possible hierarchies providing unique views of the input graph. GrouseFlocks is a system for the exploration of this graph hierarchy space. By allowing users to see several different possible hierarchies on the same graph, the system helps users investigate graph hierarchy space instead of a single fixed hierarchy. GrouseFlocks provides a simple set of operations so that users can create and modify their graph hierarchies based on selections. These selections can be made manually or based on patterns in the attribute data provided with the graph. It provides feedback to the user within seconds, allowing interactive exploration of this space.

[Development of performance evaluation and management system on advanced schistosomiasis medical treatment].

PubMed

Zhou, Xiao-Rong; Huang, Shui-Sheng; Gong, Xin-Guo; Cen, Li-Ping; Zhang, Cong; Zhu, Hong; Yang, Jun-Jing; Chen, Li

2012-04-01

To construct a performance evaluation and management system on advanced schistosomiasis medical treatment, and analyze and evaluate the work of the advanced schistosomiasis medical treatment over the years. By applying the database management technique and C++ programming technique, we inputted the information of the advanced schistosomiasis cases into the system, and comprehensively evaluated the work of the advanced schistosomiasis medical treatment through the cost-effect analysis, cost-effectiveness analysis, and cost-benefit analysis. We made a set of software formula about cost-effect analysis, cost-effectiveness analysis, and cost-benefit analysis. This system had many features such as clear building, easy to operate, friendly surface, convenient information input and information search. It could benefit the performance evaluation of the province's advanced schistosomiasis medical treatment work. This system can satisfy the current needs of advanced schistosomiasis medical treatment work and can be easy to be widely used.

Computational methods in the development of a knowledge-based system for the prediction of solid catalyst performance.

PubMed

Procelewska, Joanna; Galilea, Javier Llamas; Clerc, Frederic; Farrusseng, David; Schüth, Ferdi

2007-01-01

The objective of this work is the construction of a correlation between characteristics of heterogeneous catalysts, encoded in a descriptor vector, and their experimentally measured performances in the propene oxidation reaction. In this paper the key issue in the modeling process, namely the selection of adequate input variables, is explored. Several data-driven feature selection strategies were applied in order to obtain an estimate of the differences in variance and information content of various attributes, furthermore to compare their relative importance. Quantitative property activity relationship techniques using probabilistic neural networks have been used for the creation of various semi-empirical models. Finally, a robust classification model, assigning selected attributes of solid compounds as input to an appropriate performance class in the model reaction was obtained. It has been evident that the mathematical support for the primary attributes set proposed by chemists can be highly desirable.

Probing Reliability of Transport Phenomena Based Heat Transfer and Fluid Flow Analysis in Autogeneous Fusion Welding Process

NASA Astrophysics Data System (ADS)

Bag, S.; de, A.

2010-09-01

The transport phenomena based heat transfer and fluid flow calculations in weld pool require a number of input parameters. Arc efficiency, effective thermal conductivity, and viscosity in weld pool are some of these parameters, values of which are rarely known and difficult to assign a priori based on the scientific principles alone. The present work reports a bi-directional three-dimensional (3-D) heat transfer and fluid flow model, which is integrated with a real number based genetic algorithm. The bi-directional feature of the integrated model allows the identification of the values of a required set of uncertain model input parameters and, next, the design of process parameters to achieve a target weld pool dimension. The computed values are validated with measured results in linear gas-tungsten-arc (GTA) weld samples. Furthermore, a novel methodology to estimate the overall reliability of the computed solutions is also presented.

Fuzzy Neuron: Method and Hardware Realization

NASA Technical Reports Server (NTRS)

Krasowski, Michael J.; Prokop, Norman F.

2014-01-01

This innovation represents a method by which single-to-multi-input, single-to-many-output system transfer functions can be estimated from input/output data sets. This innovation can be run in the background while a system is operating under other means (e.g., through human operator effort), or may be utilized offline using data sets created from observations of the estimated system. It utilizes a set of fuzzy membership functions spanning the input space for each input variable. Linear combiners associated with combinations of input membership functions are used to create the output(s) of the estimator. Coefficients are adjusted online through the use of learning algorithms.

Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Shin, Eun Seok; Kim, Sung Min

2018-01-01

The purpose of this study was to propose a hybrid ensemble classifier to characterize coronary plaque regions in intravascular ultrasound (IVUS) images. Pixels were allocated to one of four tissues (fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), and dense calcium (DC)) through processes of border segmentation, feature extraction, feature selection, and classification. Grayscale IVUS images and their corresponding virtual histology images were acquired from 11 patients with known or suspected coronary artery disease using 20 MHz catheter. A total of 102 hybrid textural features including first order statistics (FOS), gray level co-occurrence matrix (GLCM), extended gray level run-length matrix (GLRLM), Laws, local binary pattern (LBP), intensity, and discrete wavelet features (DWF) were extracted from IVUS images. To select optimal feature sets, genetic algorithm was implemented. A hybrid ensemble classifier based on histogram and texture information was then used for plaque characterization in this study. The optimal feature set was used as input of this ensemble classifier. After tissue characterization, parameters including sensitivity, specificity, and accuracy were calculated to validate the proposed approach. A ten-fold cross validation approach was used to determine the statistical significance of the proposed method. Our experimental results showed that the proposed method had reliable performance for tissue characterization in IVUS images. The hybrid ensemble classification method outperformed other existing methods by achieving characterization accuracy of 81% for FFT and 75% for NC. In addition, this study showed that Laws features (SSV and SAV) were key indicators for coronary tissue characterization. The proposed method had high clinical applicability for image-based tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Chemical function based pharmacophore generation of endothelin-A selective receptor antagonists.

PubMed

Funk, Oliver F; Kettmann, Viktor; Drimal, Jan; Langer, Thierry

2004-05-20

Both quantitative and qualitative chemical function based pharmacophore models of endothelin-A (ET(A)) selective receptor antagonists were generated by using the two algorithms HypoGen and HipHop, respectively, which are implemented in the Catalyst molecular modeling software. The input for HypoGen is a training set of 18 ET(A) antagonists exhibiting IC(50) values ranging between 0.19 nM and 67 microM. The best output hypothesis consists of five features: two hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI) function. The highest scoring Hip Hop model consists of six features: three hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI). It is the result of an input of three highly active, selective, and structurally diverse ET(A) antagonists. The predictive power of the quantitative model could be approved by using a test set of 30 compounds, whose activity values spread over 6 orders of magnitude. The two pharmacophores were tested according to their ability to extract known endothelin antagonists from the 3D molecular structure database of Derwent's World Drug Index. Thereby the main part of selective ET(A) antagonistic entries was detected by the two hypotheses. Furthermore, the pharmacophores were used to screen the Maybridge database. Six compounds were chosen from the output hit lists for in vitro testing of their ability to displace endothelin-1 from its receptor. Two of these are new potential lead compounds because they are structurally novel and exhibit satisfactory activity in the binding assay.

A trainable decisions-in decision-out (DEI-DEO) fusion system

NASA Astrophysics Data System (ADS)

Dasarathy, Belur V.

1998-03-01

Most of the decision fusion systems proposed hitherto in the literature for multiple data source (sensor) environments operate on the basis of pre-defined fusion logic, be they crisp (deterministic), probabilistic, or fuzzy in nature, with no specific learning phase. The fusion systems that are trainable, i.e., ones that have a learning phase, mostly operate in the features-in-decision-out mode, which essentially reduces the fusion process functionally to a pattern classification task in the joint feature space. In this study, a trainable decisions-in-decision-out fusion system is described which estimates a fuzzy membership distribution spread across the different decision choices based on the performance of the different decision processors (sensors) corresponding to each training sample (object) which is associated with a specific ground truth (true decision). Based on a multi-decision space histogram analysis of the performance of the different processors over the entire training data set, a look-up table associating each cell of the histogram with a specific true decision is generated which forms the basis for the operational phase. In the operational phase, for each set of decision inputs, a pointer to the look-up table learnt previously is generated from which a fused decision is derived. This methodology, although primarily designed for fusing crisp decisions from the multiple decision sources, can be adapted for fusion of fuzzy decisions as well if such are the inputs from these sources. Examples, which illustrate the benefits and limitations of the crisp and fuzzy versions of the trainable fusion systems, are also included.

Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials

PubMed Central

Bailly, Clément; Bodet-Milin, Caroline; Couespel, Solène; Necib, Hatem; Kraeber-Bodéré, Françoise; Ansquer, Catherine; Carlier, Thomas

2016-01-01

Purpose This study aimed to investigate the variability of textural features (TF) as a function of acquisition and reconstruction parameters within the context of multi-centric trials. Methods The robustness of 15 selected TFs were studied as a function of the number of iterations, the post-filtering level, input data noise, the reconstruction algorithm and the matrix size. A combination of several reconstruction and acquisition settings was devised to mimic multi-centric conditions. We retrospectively studied data from 26 patients enrolled in a diagnostic study that aimed to evaluate the performance of PET/CT 68Ga-DOTANOC in gastro-entero-pancreatic neuroendocrine tumors. Forty-one tumors were extracted and served as the database. The coefficient of variation (COV) or the absolute deviation (for the noise study) was derived and compared statistically with SUVmax and SUVmean results. Results The majority of investigated TFs can be used in a multi-centric context when each parameter is considered individually. The impact of voxel size and noise in the input data were predominant as only 4 TFs presented a high/intermediate robustness against SUV-based metrics (Entropy, Homogeneity, RP and ZP). When combining several reconstruction settings to mimic multi-centric conditions, most of the investigated TFs were robust enough against SUVmax except Correlation, Contrast, LGRE, LGZE and LZLGE. Conclusion Considering previously published results on either reproducibility or sensitivity against delineation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimilarity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials. PMID:27467882

Expected energy-based restricted Boltzmann machine for classification.

PubMed

Elfwing, S; Uchibe, E; Doya, K

2015-04-01

In classification tasks, restricted Boltzmann machines (RBMs) have predominantly been used in the first stage, either as feature extractors or to provide initialization of neural networks. In this study, we propose a discriminative learning approach to provide a self-contained RBM method for classification, inspired by free-energy based function approximation (FE-RBM), originally proposed for reinforcement learning. For classification, the FE-RBM method computes the output for an input vector and a class vector by the negative free energy of an RBM. Learning is achieved by stochastic gradient-descent using a mean-squared error training objective. In an earlier study, we demonstrated that the performance and the robustness of FE-RBM function approximation can be improved by scaling the free energy by a constant that is related to the size of network. In this study, we propose that the learning performance of RBM function approximation can be further improved by computing the output by the negative expected energy (EE-RBM), instead of the negative free energy. To create a deep learning architecture, we stack several RBMs on top of each other. We also connect the class nodes to all hidden layers to try to improve the performance even further. We validate the classification performance of EE-RBM using the MNIST data set and the NORB data set, achieving competitive performance compared with other classifiers such as standard neural networks, deep belief networks, classification RBMs, and support vector machines. The purpose of using the NORB data set is to demonstrate that EE-RBM with binary input nodes can achieve high performance in the continuous input domain. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

PubMed

Li, Songfeng; Wei, Jun; Chan, Heang-Ping; Helvie, Mark A; Roubidoux, Marilyn A; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir M; Samala, Ravi K

2018-01-09

Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input 'for processing' DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79  ±  0.13 and Pearson's correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning

NASA Astrophysics Data System (ADS)

Li, Songfeng; Wei, Jun; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir M.; Samala, Ravi K.

2018-01-01

Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input ‘for processing’ DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice’s coefficient (DC) of 0.79  ±  0.13 and Pearson’s correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

NASA Technical Reports Server (NTRS)

Harrivel, Angela R.; Stephens, Chad L.; Milletich, Robert J.; Heinich, Christina M.; Last, Mary Carolyn; Napoli, Nicholas J.; Abraham, Nijo A.; Prinzel, Lawrence J.; Motter, Mark A.; Pope, Alan T.

2017-01-01

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

MSClique: Multiple Structure Discovery through the Maximum Weighted Clique Problem.

PubMed

Sanroma, Gerard; Penate-Sanchez, Adrian; Alquézar, René; Serratosa, Francesc; Moreno-Noguer, Francesc; Andrade-Cetto, Juan; González Ballester, Miguel Ángel

2016-01-01

We present a novel approach for feature correspondence and multiple structure discovery in computer vision. In contrast to existing methods, we exploit the fact that point-sets on the same structure usually lie close to each other, thus forming clusters in the image. Given a pair of input images, we initially extract points of interest and extract hierarchical representations by agglomerative clustering. We use the maximum weighted clique problem to find the set of corresponding clusters with maximum number of inliers representing the multiple structures at the correct scales. Our method is parameter-free and only needs two sets of points along with their tentative correspondences, thus being extremely easy to use. We demonstrate the effectiveness of our method in multiple-structure fitting experiments in both publicly available and in-house datasets. As shown in the experiments, our approach finds a higher number of structures containing fewer outliers compared to state-of-the-art methods.

Eye-gaze and intent: Application in 3D interface control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schryver, J.C.; Goldberg, J.H.

1993-06-01

Computer interface control is typically accomplished with an input ``device`` such as keyboard, mouse, trackball, etc. An input device translates a users input actions, such as mouse clicks and key presses, into appropriate computer commands. To control the interface, the user must first convert intent into the syntax of the input device. A more natural means of computer control is possible when the computer can directly infer user intent, without need of intervening input devices. We describe an application of eye-gaze-contingent control of an interactive three-dimensional (3D) user interface. A salient feature of the user interface is natural input, withmore » a heightened impression of controlling the computer directly by the mind. With this interface, input of rotation and translation are intuitive, whereas other abstract features, such as zoom, are more problematic to match with user intent. This paper describes successes with implementation to date, and ongoing efforts to develop a more sophisticated intent inferencing methodology.« less

Eye-gaze and intent: Application in 3D interface control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schryver, J.C.; Goldberg, J.H.

1993-01-01

Computer interface control is typically accomplished with an input device'' such as keyboard, mouse, trackball, etc. An input device translates a users input actions, such as mouse clicks and key presses, into appropriate computer commands. To control the interface, the user must first convert intent into the syntax of the input device. A more natural means of computer control is possible when the computer can directly infer user intent, without need of intervening input devices. We describe an application of eye-gaze-contingent control of an interactive three-dimensional (3D) user interface. A salient feature of the user interface is natural input, withmore » a heightened impression of controlling the computer directly by the mind. With this interface, input of rotation and translation are intuitive, whereas other abstract features, such as zoom, are more problematic to match with user intent. This paper describes successes with implementation to date, and ongoing efforts to develop a more sophisticated intent inferencing methodology.« less

The ART of representation: Memory reduction and noise tolerance in a neural network vision system

NASA Astrophysics Data System (ADS)

Langley, Christopher S.

The Feature Cerebellar Model Arithmetic Computer (FCMAC) is a multiple-input-single-output neural network that can provide three-degree-of-freedom (3-DOF) pose estimation for a robotic vision system. The FCMAC provides sufficient accuracy to enable a manipulator to grasp an object from an arbitrary pose within its workspace. The network learns an appearance-based representation of an object by storing coarsely quantized feature patterns. As all unique patterns are encoded, the network size grows uncontrollably. A new architecture is introduced herein, which combines the FCMAC with an Adaptive Resonance Theory (ART) network. The ART module categorizes patterns observed during training into a set of prototypes that are used to build the FCMAC. As a result, the network no longer grows without bound, but constrains itself to a user-specified size. Pose estimates remain accurate since the ART layer tends to discard the least relevant information first. The smaller network performs recall faster, and in some cases is better for generalization, resulting in a reduction of error at recall time. The ART-Under-Constraint (ART-C) algorithm is extended to include initial filling with randomly selected patterns (referred to as ART-F). In experiments using a real-world data set, the new network performed equally well using less than one tenth the number of coarse patterns as a regular FCMAC. The FCMAC is also extended to include real-valued input activations. As a result, the network can be tuned to reject a variety of types of noise in the image feature detection. A quantitative analysis of noise tolerance was performed using four synthetic noise algorithms, and a qualitative investigation was made using noisy real-world image data. In validation experiments, the FCMAC system outperformed Radial Basis Function (RBF) networks for the 3-DOF problem, and had accuracy comparable to that of Principal Component Analysis (PCA) and superior to that of Shape Context Matching (SCM), both of which estimate orientation only.

Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

NASA Astrophysics Data System (ADS)

Jiang, Hongkai; Li, Xingqiu; Shao, Haidong; Zhao, Ke

2018-06-01

Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.

New bandwidth selection criterion for Kernel PCA: approach to dimensionality reduction and classification problems.

PubMed

Thomas, Minta; De Brabanter, Kris; De Moor, Bart

2014-05-10

DNA microarrays are potentially powerful technology for improving diagnostic classification, treatment selection, and prognostic assessment. The use of this technology to predict cancer outcome has a history of almost a decade. Disease class predictors can be designed for known disease cases and provide diagnostic confirmation or clarify abnormal cases. The main input to this class predictors are high dimensional data with many variables and few observations. Dimensionality reduction of these features set significantly speeds up the prediction task. Feature selection and feature transformation methods are well known preprocessing steps in the field of bioinformatics. Several prediction tools are available based on these techniques. Studies show that a well tuned Kernel PCA (KPCA) is an efficient preprocessing step for dimensionality reduction, but the available bandwidth selection method for KPCA was computationally expensive. In this paper, we propose a new data-driven bandwidth selection criterion for KPCA, which is related to least squares cross-validation for kernel density estimation. We propose a new prediction model with a well tuned KPCA and Least Squares Support Vector Machine (LS-SVM). We estimate the accuracy of the newly proposed model based on 9 case studies. Then, we compare its performances (in terms of test set Area Under the ROC Curve (AUC) and computational time) with other well known techniques such as whole data set + LS-SVM, PCA + LS-SVM, t-test + LS-SVM, Prediction Analysis of Microarrays (PAM) and Least Absolute Shrinkage and Selection Operator (Lasso). Finally, we assess the performance of the proposed strategy with an existing KPCA parameter tuning algorithm by means of two additional case studies. We propose, evaluate, and compare several mathematical/statistical techniques, which apply feature transformation/selection for subsequent classification, and consider its application in medical diagnostics. Both feature selection and feature transformation perform well on classification tasks. Due to the dynamic selection property of feature selection, it is hard to define significant features for the classifier, which predicts classes of future samples. Moreover, the proposed strategy enjoys a distinctive advantage with its relatively lesser time complexity.

Pedestrian detection in infrared image using HOG and Autoencoder

NASA Astrophysics Data System (ADS)

Chen, Tianbiao; Zhang, Hao; Shi, Wenjie; Zhang, Yu

2017-11-01

In order to guarantee the safety of driving at night, vehicle-mounted night vision system was used to detect pedestrian in front of cars and send alarm to prevent the potential dangerous. To decrease the false positive rate (FPR) and increase the true positive rate (TPR), a pedestrian detection method based on HOG and Autoencoder (HOG+Autoencoder) was presented. Firstly, the HOG features of input images were computed and encoded by Autoencoder. Then the encoded features were classified by Softmax. In the process of training, Autoencoder was trained unsupervised. Softmax was trained with supervision. Autoencoder and Softmax were stacked into a model and fine-tuned by labeled images. Experiment was conducted to compare the detection performance between HOG and HOG+Autoencoder, using images collected by vehicle-mounted infrared camera. There were 80000 images for training set and 20000 for the testing set, with a rate of 1:3 between positive and negative images. The result shows that when TPR is 95%, FPR of HOG+Autoencoder is 0.4%, while the FPR of HOG is 5% with the same TPR.

PTBS segmentation scheme for synthetic aperture radar

NASA Astrophysics Data System (ADS)

Friedland, Noah S.; Rothwell, Brian J.

1995-07-01

The Image Understanding Group at Martin Marietta Technologies in Denver, Colorado has developed a model-based synthetic aperture radar (SAR) automatic target recognition (ATR) system using an integrated resource architecture (IRA). IRA, an adaptive Markov random field (MRF) environment, utilizes information from image, model, and neighborhood resources to create a discrete, 2D feature-based world description (FBWD). The IRA FBWD features are peak, target, background and shadow (PTBS). These features have been shown to be very useful for target discrimination. The FBWD is used to accrue evidence over a model hypothesis set. This paper presents the PTBS segmentation process utilizing two IRA resources. The image resource (IR) provides generic (the physics of image formation) and specific (the given image input) information. The neighborhood resource (NR) provides domain knowledge of localized FBWD site behaviors. A simulated annealing optimization algorithm is used to construct a `most likely' PTBS state. Results on simulated imagery illustrate the power of this technique to correctly segment PTBS features, even when vehicle signatures are immersed in heavy background clutter. These segmentations also suppress sidelobe effects and delineate shadows.

Fold assessment for comparative protein structure modeling.

PubMed

Melo, Francisco; Sali, Andrej

2007-11-01

Accurate and automated assessment of both geometrical errors and incompleteness of comparative protein structure models is necessary for an adequate use of the models. Here, we describe a composite score for discriminating between models with the correct and incorrect fold. To find an accurate composite score, we designed and applied a genetic algorithm method that searched for a most informative subset of 21 input model features as well as their optimized nonlinear transformation into the composite score. The 21 input features included various statistical potential scores, stereochemistry quality descriptors, sequence alignment scores, geometrical descriptors, and measures of protein packing. The optimized composite score was found to depend on (1) a statistical potential z-score for residue accessibilities and distances, (2) model compactness, and (3) percentage sequence identity of the alignment used to build the model. The accuracy of the composite score was compared with the accuracy of assessment by single and combined features as well as by other commonly used assessment methods. The testing set was representative of models produced by automated comparative modeling on a genomic scale. The composite score performed better than any other tested score in terms of the maximum correct classification rate (i.e., 3.3% false positives and 2.5% false negatives) as well as the sensitivity and specificity across the whole range of thresholds. The composite score was implemented in our program MODELLER-8 and was used to assess models in the MODBASE database that contains comparative models for domains in approximately 1.3 million protein sequences.

Walsh-Hadamard transform kernel-based feature vector for shot boundary detection.

PubMed

Lakshmi, Priya G G; Domnic, S

2014-12-01

Video shot boundary detection (SBD) is the first step of video analysis, summarization, indexing, and retrieval. In SBD process, videos are segmented into basic units called shots. In this paper, a new SBD method is proposed using color, edge, texture, and motion strength as vector of features (feature vector). Features are extracted by projecting the frames on selected basis vectors of Walsh-Hadamard transform (WHT) kernel and WHT matrix. After extracting the features, based on the significance of the features, weights are calculated. The weighted features are combined to form a single continuity signal, used as input for Procedure Based shot transition Identification process (PBI). Using the procedure, shot transitions are classified into abrupt and gradual transitions. Experimental results are examined using large-scale test sets provided by the TRECVID 2007, which has evaluated hard cut and gradual transition detection. To evaluate the robustness of the proposed method, the system evaluation is performed. The proposed method yields F1-Score of 97.4% for cut, 78% for gradual, and 96.1% for overall transitions. We have also evaluated the proposed feature vector with support vector machine classifier. The results show that WHT-based features can perform well than the other existing methods. In addition to this, few more video sequences are taken from the Openvideo project and the performance of the proposed method is compared with the recent existing SBD method.

Optimal inverse functions created via population-based optimization.

PubMed

Jennings, Alan L; Ordóñez, Raúl

2014-06-01

Finding optimal inputs for a multiple-input, single-output system is taxing for a system operator. Population-based optimization is used to create sets of functions that produce a locally optimal input based on a desired output. An operator or higher level planner could use one of the functions in real time. For the optimization, each agent in the population uses the cost and output gradients to take steps lowering the cost while maintaining their current output. When an agent reaches an optimal input for its current output, additional agents are generated in the output gradient directions. The new agents then settle to the local optima for the new output values. The set of associated optimal points forms an inverse function, via spline interpolation, from a desired output to an optimal input. In this manner, multiple locally optimal functions can be created. These functions are naturally clustered in input and output spaces allowing for a continuous inverse function. The operator selects the best cluster over the anticipated range of desired outputs and adjusts the set point (desired output) while maintaining optimality. This reduces the demand from controlling multiple inputs, to controlling a single set point with no loss in performance. Results are demonstrated on a sample set of functions and on a robot control problem.

A fuzzy neural network for intelligent data processing

NASA Astrophysics Data System (ADS)

Xie, Wei; Chu, Feng; Wang, Lipo; Lim, Eng Thiam

2005-03-01

In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial membership functions, or initial weights. Fuzzy IF-THEN rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy; irrelevant inputs are detected and deleted, and membership functions and network weights are trained with a gradient descent algorithm, i.e., error backpropagation. Experimental studies on synthesized data sets demonstrate that the proposed Fuzzy Neural Network is able to achieve accuracy comparable to or higher than both a feedforward crisp neural network, i.e., NeuroRule, and a decision tree, i.e., C4.5, with more compact rule bases for most of the data sets used in our experiments. The FNN has achieved outstanding results for cancer classification based on microarray data. The excellent classification result for Small Round Blue Cell Tumors (SRBCTs) data set is shown. Compared with other published methods, we have used a much fewer number of genes for perfect classification, which will help researchers directly focus their attention on some specific genes and may lead to discovery of deep reasons of the development of cancers and discovery of drugs.

Confidence-Based Feature Acquisition

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; desJardins, Marie; MacGlashan, James

2010-01-01

Confidence-based Feature Acquisition (CFA) is a novel, supervised learning method for acquiring missing feature values when there is missing data at both training (learning) and test (deployment) time. To train a machine learning classifier, data is encoded with a series of input features describing each item. In some applications, the training data may have missing values for some of the features, which can be acquired at a given cost. A relevant JPL example is that of the Mars rover exploration in which the features are obtained from a variety of different instruments, with different power consumption and integration time costs. The challenge is to decide which features will lead to increased classification performance and are therefore worth acquiring (paying the cost). To solve this problem, CFA, which is made up of two algorithms (CFA-train and CFA-predict), has been designed to greedily minimize total acquisition cost (during training and testing) while aiming for a specific accuracy level (specified as a confidence threshold). With this method, it is assumed that there is a nonempty subset of features that are free; that is, every instance in the data set includes these features initially for zero cost. It is also assumed that the feature acquisition (FA) cost associated with each feature is known in advance, and that the FA cost for a given feature is the same for all instances. Finally, CFA requires that the base-level classifiers produce not only a classification, but also a confidence (or posterior probability).

Feature generation using genetic programming with application to fault classification.

PubMed

Guo, Hong; Jack, Lindsay B; Nandi, Asoke K

2005-02-01

One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. In this paper, a GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover autimatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results--using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM--have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionaly, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.

Low-cost TDRSS communications for NASA's long duration balloon project

NASA Technical Reports Server (NTRS)

Israel, David J.

1993-01-01

A new transponder and RF ground support equipment for the NASA Tracking and Data Relay Satellite System (TDRSS) intended to support long duration scientific balloon flights in Antarctica are described. The new balloon class transponder features a highly integrated spread spectrum receiver design based on programmable charge coupled device (CCD) correlators and digital signal processing chips. The correlator chip is a Lincoln Labs 4ABC with four CCD channels. The balloon transponder is capable of reporting an estimate of its input bit error rate using digital signal processing. The TDRSS user RF test set is based on a set of RF ground support equipment capable of providing both the RF communications and direct control and monitoring necessary for transponder testing and a two-way RF link for preflight testing.

Application Research of QRCode Barcode in Validation of Express Delivery

NASA Astrophysics Data System (ADS)

Liu, Zhihai; Zeng, Qingliang; Wang, Chenglong; Lu, Qing

The barcode technology has become an important way in the field of information input and identify automatically. With the outstanding features of big storage capacity, secure, rich encoding character set and fast decoding, the two-dimensional(2D) QRcode(Quick Response Barcode) has become an important choice of commerce barcode. The development of wireless communications technology and the popularization and application of mobile device has set the foundation of 2D barcode used in business. In this paper, the characteristics and the compositions of 2D QRcode are described, the secure validation workflows and contents of QRcode in goods express delivery are discussed, the encoding process of QRcode is showed, and the system framework is analyzed and established. At last, the system compositions and functions of each part are discussed.

Effects of image compression and degradation on an automatic diabetic retinopathy screening algorithm

NASA Astrophysics Data System (ADS)

Agurto, C.; Barriga, S.; Murray, V.; Pattichis, M.; Soliz, P.

2010-03-01

Diabetic retinopathy (DR) is one of the leading causes of blindness among adult Americans. Automatic methods for detection of the disease have been developed in recent years, most of them addressing the segmentation of bright and red lesions. In this paper we present an automatic DR screening system that does approach the problem through the segmentation of features. The algorithm determines non-diseased retinal images from those with pathology based on textural features obtained using multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions. The decomposition is represented as features that are the inputs to a classifier. The algorithm achieves 0.88 area under the ROC curve (AROC) for a set of 280 images from the MESSIDOR database. The algorithm is then used to analyze the effects of image compression and degradation, which will be present in most actual clinical or screening environments. Results show that the algorithm is insensitive to illumination variations, but high rates of compression and large blurring effects degrade its performance.

Dispersing artifacts in FT-STS: a comparison of set point effects across acquisition modes

NASA Astrophysics Data System (ADS)

Macdonald, A. J.; Tremblay-Johnston, Y.-S.; Grothe, S.; Chi, S.; Dosanjh, P.; Johnston, S.; Burke, S. A.

2016-10-01

Fourier-transform scanning tunnelling spectroscopy (FT-STS), or quasiparticle interference, has become an influential tool for the study of a wide range of important materials in condensed matter physics. However, FT-STS in complex materials is often challenging to interpret, requiring significant theoretical input in many cases, making it crucial to understand potential artifacts of the measurement. Here, we compare the most common modes of acquiring FT-STS data and show through both experiment and simulations that artifact features can arise that depend on how the tip height is stabilized throughout the course of the measurement. The most dramatic effect occurs when a series of dI/dV maps at different energies are acquired with simultaneous constant current feedback; here a feature that disperses in energy appears that is not observed in other measurement modes. Such artifact features are similar to those arising from real physical processes in the sample and are susceptible to misinterpretation.

A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents

PubMed Central

Colomer Granero, Adrián; Fuentes-Hurtado, Félix; Naranjo Ornedo, Valery; Guixeres Provinciale, Jaime; Ausín, Jose M.; Alcañiz Raya, Mariano

2016-01-01

This work focuses on finding the most discriminatory or representative features that allow to classify commercials according to negative, neutral and positive effectiveness based on the Ace Score index. For this purpose, an experiment involving forty-seven participants was carried out. In this experiment electroencephalography (EEG), electrocardiography (ECG), Galvanic Skin Response (GSR) and respiration data were acquired while subjects were watching a 30-min audiovisual content. This content was composed by a submarine documentary and nine commercials (one of them the ad under evaluation). After the signal pre-processing, four sets of features were extracted from the physiological signals using different state-of-the-art metrics. These features computed in time and frequency domains are the inputs to several basic and advanced classifiers. An average of 89.76% of the instances was correctly classified according to the Ace Score index. The best results were obtained by a classifier consisting of a combination between AdaBoost and Random Forest with automatic selection of features. The selected features were those extracted from GSR and HRV signals. These results are promising in the audiovisual content evaluation field by means of physiological signal processing. PMID:27471462

Predictive analysis effectiveness in determining the epidemic disease infected area

NASA Astrophysics Data System (ADS)

Ibrahim, Najihah; Akhir, Nur Shazwani Md.; Hassan, Fadratul Hafinaz

2017-10-01

Epidemic disease outbreak had caused nowadays community to raise their great concern over the infectious disease controlling, preventing and handling methods to diminish the disease dissemination percentage and infected area. Backpropagation method was used for the counter measure and prediction analysis of the epidemic disease. The predictive analysis based on the backpropagation method can be determine via machine learning process that promotes the artificial intelligent in pattern recognition, statistics and features selection. This computational learning process will be integrated with data mining by measuring the score output as the classifier to the given set of input features through classification technique. The classification technique is the features selection of the disease dissemination factors that likely have strong interconnection between each other in causing infectious disease outbreaks. The predictive analysis of epidemic disease in determining the infected area was introduced in this preliminary study by using the backpropagation method in observation of other's findings. This study will classify the epidemic disease dissemination factors as the features for weight adjustment on the prediction of epidemic disease outbreaks. Through this preliminary study, the predictive analysis is proven to be effective method in determining the epidemic disease infected area by minimizing the error value through the features classification.

Automation of lidar-based hydrologic feature extraction workflows using GIS

NASA Astrophysics Data System (ADS)

Borlongan, Noel Jerome B.; de la Cruz, Roel M.; Olfindo, Nestor T.; Perez, Anjillyn Mae C.

2016-10-01

With the advent of LiDAR technology, higher resolution datasets become available for use in different remote sensing and GIS applications. One significant application of LiDAR datasets in the Philippines is in resource features extraction. Feature extraction using LiDAR datasets require complex and repetitive workflows which can take a lot of time for researchers through manual execution and supervision. The Development of the Philippine Hydrologic Dataset for Watersheds from LiDAR Surveys (PHD), a project under the Nationwide Detailed Resources Assessment Using LiDAR (Phil-LiDAR 2) program, created a set of scripts, the PHD Toolkit, to automate its processes and workflows necessary for hydrologic features extraction specifically Streams and Drainages, Irrigation Network, and Inland Wetlands, using LiDAR Datasets. These scripts are created in Python and can be added in the ArcGIS® environment as a toolbox. The toolkit is currently being used as an aid for the researchers in hydrologic feature extraction by simplifying the workflows, eliminating human errors when providing the inputs, and providing quick and easy-to-use tools for repetitive tasks. This paper discusses the actual implementation of different workflows developed by Phil-LiDAR 2 Project 4 in Streams, Irrigation Network and Inland Wetlands extraction.

A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents.

PubMed

Colomer Granero, Adrián; Fuentes-Hurtado, Félix; Naranjo Ornedo, Valery; Guixeres Provinciale, Jaime; Ausín, Jose M; Alcañiz Raya, Mariano

2016-01-01

This work focuses on finding the most discriminatory or representative features that allow to classify commercials according to negative, neutral and positive effectiveness based on the Ace Score index. For this purpose, an experiment involving forty-seven participants was carried out. In this experiment electroencephalography (EEG), electrocardiography (ECG), Galvanic Skin Response (GSR) and respiration data were acquired while subjects were watching a 30-min audiovisual content. This content was composed by a submarine documentary and nine commercials (one of them the ad under evaluation). After the signal pre-processing, four sets of features were extracted from the physiological signals using different state-of-the-art metrics. These features computed in time and frequency domains are the inputs to several basic and advanced classifiers. An average of 89.76% of the instances was correctly classified according to the Ace Score index. The best results were obtained by a classifier consisting of a combination between AdaBoost and Random Forest with automatic selection of features. The selected features were those extracted from GSR and HRV signals. These results are promising in the audiovisual content evaluation field by means of physiological signal processing.

A fuzzy measure approach to motion frame analysis for scene detection. M.S. Thesis - Houston Univ.

NASA Technical Reports Server (NTRS)

Leigh, Albert B.; Pal, Sankar K.

1992-01-01

This paper addresses a solution to the problem of scene estimation of motion video data in the fuzzy set theoretic framework. Using fuzzy image feature extractors, a new algorithm is developed to compute the change of information in each of two successive frames to classify scenes. This classification process of raw input visual data can be used to establish structure for correlation. The algorithm attempts to fulfill the need for nonlinear, frame-accurate access to video data for applications such as video editing and visual document archival/retrieval systems in multimedia environments.

A high-fidelity airbus benchmark for system fault detection and isolation and flight control law clearance

NASA Astrophysics Data System (ADS)

Goupil, Ph.; Puyou, G.

2013-12-01

This paper presents a high-fidelity generic twin engine civil aircraft model developed by Airbus for advanced flight control system research. The main features of this benchmark are described to make the reader aware of the model complexity and representativeness. It is a complete representation including the nonlinear rigid-body aircraft model with a full set of control surfaces, actuator models, sensor models, flight control laws (FCL), and pilot inputs. Two applications of this benchmark in the framework of European projects are presented: FCL clearance using optimization and advanced fault detection and diagnosis (FDD).

Space shuttle main engine fault detection using neural networks

NASA Technical Reports Server (NTRS)

Bishop, Thomas; Greenwood, Dan; Shew, Kenneth; Stevenson, Fareed

1991-01-01

A method for on-line Space Shuttle Main Engine (SSME) anomaly detection and fault typing using a feedback neural network is described. The method involves the computation of features representing time-variance of SSME sensor parameters, using historical test case data. The network is trained, using backpropagation, to recognize a set of fault cases. The network is then able to diagnose new fault cases correctly. An essential element of the training technique is the inclusion of randomly generated data along with the real data, in order to span the entire input space of potential non-nominal data.

Innovations: clinical computing: an audio computer-assisted self-interviewing system for research and screening in public mental health settings.

PubMed

Bertollo, David N; Alexander, Mary Jane; Shinn, Marybeth; Aybar, Jalila B

2007-06-01

This column describes the nonproprietary software Talker, used to adapt screening instruments to audio computer-assisted self-interviewing (ACASI) systems for low-literacy populations and other populations. Talker supports ease of programming, multiple languages, on-site scoring, and the ability to update a central research database. Key features include highly readable text display, audio presentation of questions and audio prompting of answers, and optional touch screen input. The scripting language for adapting instruments is briefly described as well as two studies in which respondents provided positive feedback on its use.

Memcomputing with membrane memcapacitive systems

NASA Astrophysics Data System (ADS)

Pershin, Y. V.; Traversa, F. L.; Di Ventra, M.

2015-06-01

We show theoretically that networks of membrane memcapacitive systems—capacitors with memory made out of membrane materials—can be used to perform a complete set of logic gates in a massively parallel way by simply changing the external input amplitudes, but not the topology of the network. This polymorphism is an important characteristic of memcomputing (computing with memories) that closely reproduces one of the main features of the brain. A practical realization of these membrane memcapacitive systems, using, e.g., graphene or other 2D materials, would be a step forward towards a solid-state realization of memcomputing with passive devices.

Phonological Feature Re-Assembly and the Importance of Phonetic Cues

ERIC Educational Resources Information Center

Archibald, John

2009-01-01

It is argued that new phonological features can be acquired in second languages, but that both feature acquisition and feature re-assembly are affected by the robustness of phonetic cues in the input.

Shift and rotation invariant photorefractive crystal-based associative memory

NASA Astrophysics Data System (ADS)

Uang, Chii-Maw; Lin, Wei-Feng; Lu, Ming-Huei; Lu, Guowen; Lu, Mingzhe

1995-08-01

A shift and rotation invariant photorefractive (PR) crystal based associative memory is addressed. The proposed associative memory has three layers: the feature extraction, inner- product, and output mapping layers. The feature extraction is performed by expanding an input object into a set of circular harmonic expansions (CHE) in the Fourier domain to acquire both the shift and rotation invariant properties. The inner product operation is performed by taking the advantage of Bragg diffraction of the bulky PR-crystal. The output mapping is achieved by using the massive storage capacity of the PR-crystal. In the training process, memories are stored in another PR-crystal by using the wavelength multiplexing technique. During the recall process, the output from the winner-take-all processor decides which wavelength should be used to read out the memory from the PR-crystal.

Evidence for Working Memory Storage Operations in Perceptual Cortex

PubMed Central

Sreenivasan, Kartik K.; Gratton, Caterina; Vytlacil, Jason; D’Esposito, Mark

2014-01-01

Isolating the short-term storage component of working memory (WM) from the myriad of associated executive processes has been an enduring challenge. Recent efforts have identified patterns of activity in visual regions that contain information about items being held in WM. However, it remains unclear (i) whether these representations withstand intervening sensory input and (ii) how communication between multimodal association cortex and unimodal perceptual regions supporting WM representations is involved in WM storage. We present evidence that the features of a face held in WM are stored within face processing regions, that these representations persist across subsequent sensory input, and that information about the match between sensory input and memory representation is relayed forward from perceptual to prefrontal regions. Participants were presented with a series of probe faces and indicated whether each probe matched a Target face held in WM. We parametrically varied the feature similarity between probe and Target faces. Activity within face processing regions scaled linearly with the degree of feature similarity between the probe face and the features of the Target face, suggesting that the features of the Target face were stored in these regions. Furthermore, directed connectivity measures revealed that the direction of information flow that was optimal for performance was from sensory regions that stored the features of the Target face to dorsal prefrontal regions, supporting the notion that sensory input is compared to representations stored within perceptual regions and relayed forward. Together, these findings indicate that WM storage operations are carried out within perceptual cortex. PMID:24436009

A global/local affinity graph for image segmentation.

PubMed

Xiaofang Wang; Yuxing Tang; Masnou, Simon; Liming Chen

2015-04-01

Construction of a reliable graph capturing perceptual grouping cues of an image is fundamental for graph-cut based image segmentation methods. In this paper, we propose a novel sparse global/local affinity graph over superpixels of an input image to capture both short- and long-range grouping cues, and thereby enabling perceptual grouping laws, including proximity, similarity, continuity, and to enter in action through a suitable graph-cut algorithm. Moreover, we also evaluate three major visual features, namely, color, texture, and shape, for their effectiveness in perceptual segmentation and propose a simple graph fusion scheme to implement some recent findings from psychophysics, which suggest combining these visual features with different emphases for perceptual grouping. In particular, an input image is first oversegmented into superpixels at different scales. We postulate a gravitation law based on empirical observations and divide superpixels adaptively into small-, medium-, and large-sized sets. Global grouping is achieved using medium-sized superpixels through a sparse representation of superpixels' features by solving a ℓ0-minimization problem, and thereby enabling continuity or propagation of local smoothness over long-range connections. Small- and large-sized superpixels are then used to achieve local smoothness through an adjacent graph in a given feature space, and thus implementing perceptual laws, for example, similarity and proximity. Finally, a bipartite graph is also introduced to enable propagation of grouping cues between superpixels of different scales. Extensive experiments are carried out on the Berkeley segmentation database in comparison with several state-of-the-art graph constructions. The results show the effectiveness of the proposed approach, which outperforms state-of-the-art graphs using four different objective criteria, namely, the probabilistic rand index, the variation of information, the global consistency error, and the boundary displacement error.

Local linear discriminant analysis framework using sample neighbors.

PubMed

Fan, Zizhu; Xu, Yong; Zhang, David

2011-07-01

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach. The algorithms of LDA usually perform well under the following two assumptions. The first assumption is that the global data structure is consistent with the local data structure. The second assumption is that the input data classes are Gaussian distributions. However, in real-world applications, these assumptions are not always satisfied. In this paper, we propose an improved LDA framework, the local LDA (LLDA), which can perform well without needing to satisfy the above two assumptions. Our LLDA framework can effectively capture the local structure of samples. According to different types of local data structure, our LLDA framework incorporates several different forms of linear feature extraction approaches, such as the classical LDA and principal component analysis. The proposed framework includes two LLDA algorithms: a vector-based LLDA algorithm and a matrix-based LLDA (MLLDA) algorithm. MLLDA is directly applicable to image recognition, such as face recognition. Our algorithms need to train only a small portion of the whole training set before testing a sample. They are suitable for learning large-scale databases especially when the input data dimensions are very high and can achieve high classification accuracy. Extensive experiments show that the proposed algorithms can obtain good classification results.

Margined winner-take-all: New learning rule for pattern recognition.

PubMed

Fukushima, Kunihiko

2018-01-01

The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector) is used for this purpose. This paper proposes a new learning rule called margined Winner-Take-All (mWTA) for training the deepest layer. Every time when a training pattern is presented during the learning, if the result of recognition by WTA (Winner-Take-All) is an error, a new cell is generated in the deepest layer. Here we put a certain amount of margin to the WTA. In other words, only during the learning, a certain amount of handicap is given to cells of classes other than that of the training vector, and the winner is chosen under this handicap. By introducing the margin to the WTA, we can generate a compact set of cells, with which a high recognition rate can be obtained with a small computational cost. The ability of this mWTA is demonstrated by computer simulation. Copyright © 2017 Elsevier Ltd. All rights reserved.

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

PubMed

Fang, Chao; Shang, Yi; Xu, Dong

2018-05-01

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

Processing Device for High-Speed Execution of an Xrisc Computer Program

NASA Technical Reports Server (NTRS)

Ng, Tak-Kwong (Inventor); Mills, Carl S. (Inventor)

2016-01-01

A processing device for high-speed execution of a computer program is provided. A memory module may store one or more computer programs. A sequencer may select one of the computer programs and controls execution of the selected program. A register module may store intermediate values associated with a current calculation set, a set of output values associated with a previous calculation set, and a set of input values associated with a subsequent calculation set. An external interface may receive the set of input values from a computing device and provides the set of output values to the computing device. A computation interface may provide a set of operands for computation during processing of the current calculation set. The set of input values are loaded into the register and the set of output values are unloaded from the register in parallel with processing of the current calculation set.

Interdicting an Adversary’s Economy Viewed As a Trade Sanction Inoperability Input Output Model

DTIC Science & Technology

2017-03-01

set of sectors. The design of an economic sanction, in the context of this thesis, is the selection of the sector or set of sectors to sanction...We propose two optimization models. The first, the Trade Sanction Inoperability Input-output Model (TS-IIM), selects the sector or set of sectors that...Interdependency analysis: Extensions to demand reduction inoperability input-output modeling and portfolio selection . Unpublished doctoral dissertation

Nonlinear features for product inspection

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1999-03-01

Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work, the MRDF is applied to standard features (rather than iconic data). The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC (receiver operating characteristic) data.

Automatic classification of tissue malignancy for breast carcinoma diagnosis.

PubMed

Fondón, Irene; Sarmiento, Auxiliadora; García, Ana Isabel; Silvestre, María; Eloy, Catarina; Polónia, António; Aguiar, Paulo

2018-05-01

Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images. We provide four malignancy levels as the output of the system: normal, benign, in situ and invasive. The method is based on the calculation of three sets of features related to nuclei, colour regions and textures considering local characteristics and global image properties. By taking advantage of well-established image processing techniques, we build a feature vector for each image that serves as an input to an SVM (Support Vector Machine) classifier with a quadratic kernel. The method has been rigorously evaluated, first with a 5-fold cross-validation within an initial set of 120 images, second with an external set of 30 different images and third with images with artefacts included. Accuracy levels range from 75.8% when the 5-fold cross-validation was performed to 75% with the external set of new images and 61.11% when the extremely difficult images were added to the classification experiment. The experimental results indicate that the proposed method is capable of distinguishing between four malignancy levels with high accuracy. Our results are close to those obtained with recent deep learning-based methods. Moreover, it performs better than other state-of-the-art methods based on feature extraction, and it can help improve the CAD of breast cancer. Copyright © 2018 Elsevier Ltd. All rights reserved.

Inefficient conjunction search made efficient by concurrent spoken delivery of target identity.

PubMed

Reali, Florencia; Spivey, Michael J; Tyler, Melinda J; Terranova, Joseph

2006-08-01

Visual search based on a conjunction of two features typically elicits reaction times that increase linearly as a function of the number of distractors, whereas search based on a single feature is essentially unaffected by set size. These and related findings have often been interpreted as evidence of a serial search stage that follows a parallel search stage. However, a wide range of studies has been showing a form of blending of these two processes. For example, when a spoken instruction identifies the conjunction target concurrently with the visual display, the effect of set size is significantly reduced, suggesting that incremental linguistic processing of the first feature adjective and then the second feature adjective may facilitate something approximating a parallel extraction of objects during search for the target. Here, we extend these results to a variety of experimental designs. First, we replicate the result with a mixed-trials design (ruling out potential strategies associated with the blocked design of the original study). Second, in a mixed-trials experiment, the order of adjective types in the spoken query varies randomly across conditions. In a third experiment, we extend the effect to a triple-conjunction search task. A fourth (control) experiment demonstrates that these effects are not due to an efficient odd-one-out search that ignores the linguistic input. This series of experiments, along with attractor-network simulations of the phenomena, provide further evidence toward understanding linguistically mediated influences in real-time visual search processing.

Classification of burn wounds using support vector machines

NASA Astrophysics Data System (ADS)

Acha, Begona; Serrano, Carmen; Palencia, Sergio; Murillo, Juan Jose

2004-05-01

The purpose of this work is to improve a previous method developed by the authors for the classification of burn wounds into their depths. The inputs of the system are color and texture information, as these are the characteristics observed by physicians in order to give a diagnosis. Our previous work consisted in segmenting the burn wound from the rest of the image and classifying the burn into its depth. In this paper we focus on the classification problem only. We already proposed to use a Fuzzy-ARTMAP neural network (NN). However, we may take advantage of new powerful classification tools such as Support Vector Machines (SVM). We apply the five-folded cross validation scheme to divide the database into training and validating sets. Then, we apply a feature selection method for each classifier, which will give us the set of features that yields the smallest classification error for each classifier. Features used to classify are first-order statistical parameters extracted from the L*, u* and v* color components of the image. The feature selection algorithms used are the Sequential Forward Selection (SFS) and the Sequential Backward Selection (SBS) methods. As data of the problem faced here are not linearly separable, the SVM was trained using some different kernels. The validating process shows that the SVM method, when using a Gaussian kernel of variance 1, outperforms classification results obtained with the rest of the classifiers, yielding an error classification rate of 0.7% whereas the Fuzzy-ARTMAP NN attained 1.6 %.

Quasi-Global Precipitation as Depicted in the GPCPV2.2 and TMPA V7

NASA Technical Reports Server (NTRS)

Huffman, George J.; Bolvin, David T.; Nelkin, Eric J.; Adler, Robert F.

2012-01-01

After a lengthy incubation period, the year 2012 saw the release of the Global Precipitation Climatology Project (GPCP) Version 2.2 monthly dataset and the TRMM Multi-satellite Precipitation Analysis (TMPA) Version 7. One primary feature of the new data sets is that DMSP SSMIS data are now used, which entailed a great deal of development work to overcome calibration issues. In addition, the GPCP V2.2 included a slight upgrade to the gauge analysis input datasets, particularly over China, while the TMPA V7 saw more-substantial upgrades: 1) The gauge analysis record in Version 6 used the (older) GPCP monitoring product through April 2005 and the CAMS analysis thereafter, which introduced an inhomogeneity. Version 7 uses the Version 6 GPCC Full analysis, switching to the Version 4 Monitoring analysis thereafter. 2) The inhomogeneously processed AMSU record in Version 6 is uniformly processed in Version 7. 3) The TMI and SSMI input data have been upgraded to the GPROF2010 algorithm. The global-change, water cycle, and other user communities are acutely interested in how these data sets compare, as consistency between differently processed, long-term, quasi-global data sets provides some assurance that the statistics computed from them provide a good representation of the atmosphere's behavior. Within resolution differences, the two data sets agree well over land as the gauge data (which tend to dominate the land results) are the same in both. Over ocean the results differ more because the satellite products used for calibration are based on very different algorithms and the dominant input data sets are different. The time series of tropical (30 N-S) ocean average precipitation shows that the TMPA V7 follows the TMI-PR Combined Product calibrator, although running approximately 5% higher on average. The GPCP and TMPA time series are fairly consistent, although the GPCP runs approximately 10% lower than the TMPA, and has a somewhat larger interannual variation. As well, the GPCP and TMPA interannual variations have an apparent phase shift, with GPCP running a few months later. Additional diagnostics will include mean maps and selected scatter plots.

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

PubMed Central

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate. PMID:22736979

Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection.

PubMed

Kumar Myakalwar, Ashwin; Spegazzini, Nicolas; Zhang, Chi; Kumar Anubham, Siva; Dasari, Ramachandra R; Barman, Ishan; Kumar Gundawar, Manoj

2015-08-19

Despite its intrinsic advantages, translation of laser induced breakdown spectroscopy for material identification has been often impeded by the lack of robustness of developed classification models, often due to the presence of spurious correlations. While a number of classifiers exhibiting high discriminatory power have been reported, efforts in establishing the subset of relevant spectral features that enable a fundamental interpretation of the segmentation capability and avoid the 'curse of dimensionality' have been lacking. Using LIBS data acquired from a set of secondary explosives, we investigate judicious feature selection approaches and architect two different chemometrics classifiers -based on feature selection through prerequisite knowledge of the sample composition and genetic algorithm, respectively. While the full spectral input results in classification rate of ca.92%, selection of only carbon to hydrogen spectral window results in near identical performance. Importantly, the genetic algorithm-derived classifier shows a statistically significant improvement to ca. 94% accuracy for prospective classification, even though the number of features used is an order of magnitude smaller. Our findings demonstrate the impact of rigorous feature selection in LIBS and also hint at the feasibility of using a discrete filter based detector thereby enabling a cheaper and compact system more amenable to field operations.

Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection

PubMed Central

Kumar Myakalwar, Ashwin; Spegazzini, Nicolas; Zhang, Chi; Kumar Anubham, Siva; Dasari, Ramachandra R.; Barman, Ishan; Kumar Gundawar, Manoj

2015-01-01

Despite its intrinsic advantages, translation of laser induced breakdown spectroscopy for material identification has been often impeded by the lack of robustness of developed classification models, often due to the presence of spurious correlations. While a number of classifiers exhibiting high discriminatory power have been reported, efforts in establishing the subset of relevant spectral features that enable a fundamental interpretation of the segmentation capability and avoid the ‘curse of dimensionality’ have been lacking. Using LIBS data acquired from a set of secondary explosives, we investigate judicious feature selection approaches and architect two different chemometrics classifiers –based on feature selection through prerequisite knowledge of the sample composition and genetic algorithm, respectively. While the full spectral input results in classification rate of ca.92%, selection of only carbon to hydrogen spectral window results in near identical performance. Importantly, the genetic algorithm-derived classifier shows a statistically significant improvement to ca. 94% accuracy for prospective classification, even though the number of features used is an order of magnitude smaller. Our findings demonstrate the impact of rigorous feature selection in LIBS and also hint at the feasibility of using a discrete filter based detector thereby enabling a cheaper and compact system more amenable to field operations. PMID:26286630

Self-supervised ARTMAP.

PubMed

Amis, Gregory P; Carpenter, Gail A

2010-03-01

Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semi-supervised learning). In each case input patterns have a fixed number of features throughout training and testing. Human and machine learning contexts present additional opportunities for expanding incomplete knowledge from formal training, via self-directed learning that incorporates features not previously experienced. This article defines a new self-supervised learning paradigm to address these richer learning contexts, introducing a neural network called self-supervised ARTMAP. Self-supervised learning integrates knowledge from a teacher (labeled patterns with some features), knowledge from the environment (unlabeled patterns with more features), and knowledge from internal model activation (self-labeled patterns). Self-supervised ARTMAP learns about novel features from unlabeled patterns without destroying partial knowledge previously acquired from labeled patterns. A category selection function bases system predictions on known features, and distributed network activation scales unlabeled learning to prediction confidence. Slow distributed learning on unlabeled patterns focuses on novel features and confident predictions, defining classification boundaries that were ambiguous in the labeled patterns. Self-supervised ARTMAP improves test accuracy on illustrative low-dimensional problems and on high-dimensional benchmarks. Model code and benchmark data are available from: http://techlab.eu.edu/SSART/. Copyright 2009 Elsevier Ltd. All rights reserved.

STARS: A general-purpose finite element computer program for analysis of engineering structures

NASA Technical Reports Server (NTRS)

Gupta, K. K.

1984-01-01

STARS (Structural Analysis Routines) is primarily an interactive, graphics-oriented, finite-element computer program for analyzing the static, stability, free vibration, and dynamic responses of damped and undamped structures, including rotating systems. The element library consists of one-dimensional (1-D) line elements, two-dimensional (2-D) triangular and quadrilateral shell elements, and three-dimensional (3-D) tetrahedral and hexahedral solid elements. These elements enable the solution of structural problems that include truss, beam, space frame, plane, plate, shell, and solid structures, or any combination thereof. Zero, finite, and interdependent deflection boundary conditions can be implemented by the program. The associated dynamic response analysis capability provides for initial deformation and velocity inputs, whereas the transient excitation may be either forces or accelerations. An effective in-core or out-of-core solution strategy is automatically employed by the program, depending on the size of the problem. Data input may be at random within a data set, and the program offers certain automatic data-generation features. Input data are formatted as an optimal combination of free and fixed formats. Interactive graphics capabilities enable convenient display of nodal deformations, mode shapes, and element stresses.

Optic nerve signals in a neuromorphic chip I: Outer and inner retina models.

PubMed

Zaghloul, Kareem A; Boahen, Kwabena

2004-04-01

We present a novel model for the mammalian retina and analyze its behavior. Our outer retina model performs bandpass spatiotemporal filtering. It is comprised of two reciprocally connected resistive grids that model the cone and horizontal cell syncytia. We show analytically that its sensitivity is proportional to the space-constant-ratio of the two grids while its half-max response is set by the local average intensity. Thus, this outer retina model realizes luminance adaptation. Our inner retina model performs high-pass temporal filtering. It features slow negative feedback whose strength is modulated by a locally computed measure of temporal contrast, modeling two kinds of amacrine cells, one narrow-field, the other wide-field. We show analytically that, when the input is spectrally pure, the corner-frequency tracks the input frequency. But when the input is broadband, the corner frequency is proportional to contrast. Thus, this inner retina model realizes temporal frequency adaptation as well as contrast gain control. We present CMOS circuit designs for our retina model in this paper as well. Experimental measurements from the fabricated chip, and validation of our analytical results, are presented in the companion paper [Zaghloul and Boahen (2004)].

Bone marrow cavity segmentation using graph-cuts with wavelet-based texture feature.

PubMed

Shigeta, Hironori; Mashita, Tomohiro; Kikuta, Junichi; Seno, Shigeto; Takemura, Haruo; Ishii, Masaru; Matsuda, Hideo

2017-10-01

Emerging bioimaging technologies enable us to capture various dynamic cellular activities [Formula: see text]. As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone remodeling, osteoporosis, and so on. To reduce manual inputs to segment BMC, we classified the texture pattern using wavelet transformation and support vector machine. We also integrated the result of texture pattern classification into the graph-cuts-based image segmentation method because texture analysis does not consider spatial continuity. Our method is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The proposed method with graph-cuts and texture pattern classification performs well without manual inputs by a user.

Sensitivity Analysis for Probabilistic Neural Network Structure Reduction.

PubMed

Kowalski, Piotr A; Kusy, Maciej

2018-05-01

In this paper, we propose the use of local sensitivity analysis (LSA) for the structure simplification of the probabilistic neural network (PNN). Three algorithms are introduced. The first algorithm applies LSA to the PNN input layer reduction by selecting significant features of input patterns. The second algorithm utilizes LSA to remove redundant pattern neurons of the network. The third algorithm combines the proposed two and constitutes the solution of how they can work together. PNN with a product kernel estimator is used, where each multiplicand computes a one-dimensional Cauchy function. Therefore, the smoothing parameter is separately calculated for each dimension by means of the plug-in method. The classification qualities of the reduced and full structure PNN are compared. Furthermore, we evaluate the performance of PNN, for which global sensitivity analysis (GSA) and the common reduction methods are applied, both in the input layer and the pattern layer. The models are tested on the classification problems of eight repository data sets. A 10-fold cross validation procedure is used to determine the prediction ability of the networks. Based on the obtained results, it is shown that the LSA can be used as an alternative PNN reduction approach.

Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model.

PubMed

Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

2016-01-01

In computer-aided detection of microcalcifications (MCs), the detection accuracy is often compromised by frequent occurrence of false positives (FPs), which can be attributed to a number of factors, including imaging noise, inhomogeneity in tissue background, linear structures, and artifacts in mammograms. In this study, the authors investigated a unified classification approach for combating the adverse effects of these heterogeneous factors for accurate MC detection. To accommodate FPs caused by different factors in a mammogram image, the authors developed a classification model to which the input features were adapted according to the image context at a detection location. For this purpose, the input features were defined in two groups, of which one group was derived from the image intensity pattern in a local neighborhood of a detection location, and the other group was used to characterize how a MC is different from its structural background. Owing to the distinctive effect of linear structures in the detector response, the authors introduced a dummy variable into the unified classifier model, which allowed the input features to be adapted according to the image context at a detection location (i.e., presence or absence of linear structures). To suppress the effect of inhomogeneity in tissue background, the input features were extracted from different domains aimed for enhancing MCs in a mammogram image. To demonstrate the flexibility of the proposed approach, the authors implemented the unified classifier model by two widely used machine learning algorithms, namely, a support vector machine (SVM) classifier and an Adaboost classifier. In the experiment, the proposed approach was tested for two representative MC detectors in the literature [difference-of-Gaussians (DoG) detector and SVM detector]. The detection performance was assessed using free-response receiver operating characteristic (FROC) analysis on a set of 141 screen-film mammogram (SFM) images (66 cases) and a set of 188 full-field digital mammogram (FFDM) images (95 cases). The FROC analysis results show that the proposed unified classification approach can significantly improve the detection accuracy of two MC detectors on both SFM and FFDM images. Despite the difference in performance between the two detectors, the unified classifiers can reduce their FP rate to a similar level in the output of the two detectors. In particular, with true-positive rate at 85%, the FP rate on SFM images for the DoG detector was reduced from 1.16 to 0.33 clusters/image (unified SVM) and 0.36 clusters/image (unified Adaboost), respectively; similarly, for the SVM detector, the FP rate was reduced from 0.45 clusters/image to 0.30 clusters/image (unified SVM) and 0.25 clusters/image (unified Adaboost), respectively. Similar FP reduction results were also achieved on FFDM images for the two MC detectors. The proposed unified classification approach can be effective for discriminating MCs from FPs caused by different factors (such as MC-like noise patterns and linear structures) in MC detection. The framework is general and can be applicable for further improving the detection accuracy of existing MC detectors.

Three-input gate logic circuits on chemically assembled single-electron transistors with organic and inorganic hybrid passivation layers

PubMed Central

Majima, Yutaka; Hackenberger, Guillaume; Azuma, Yasuo; Kano, Shinya; Matsuzaki, Kosuke; Susaki, Tomofumi; Sakamoto, Masanori; Teranishi, Toshiharu

2017-01-01

Abstract Single-electron transistors (SETs) are sub-10-nm scale electronic devices based on conductive Coulomb islands sandwiched between double-barrier tunneling barriers. Chemically assembled SETs with alkanethiol-protected Au nanoparticles show highly stable Coulomb diamonds and two-input logic operations. The combination of bottom-up and top-down processes used to form the passivation layer is vital for realizing multi-gate chemically assembled SET circuits, as this combination enables us to connect conventional complementary metal oxide semiconductor (CMOS) technologies via planar processes. Here, three-input gate exclusive-OR (XOR) logic operations are demonstrated in passivated chemically assembled SETs. The passivation layer is a hybrid bilayer of self-assembled monolayers (SAMs) and pulsed laser deposited (PLD) aluminum oxide (AlOx), and top-gate electrodes were prepared on the hybrid passivation layers. Top and two-side-gated SETs showed clear Coulomb oscillation and diamonds for each of the three available gates, and three-input gate XOR logic operation was clearly demonstrated. These results show the potential of chemically assembled SETs to work as logic devices with multi-gate inputs using organic and inorganic hybrid passivation layers. PMID:28634499

Three-input gate logic circuits on chemically assembled single-electron transistors with organic and inorganic hybrid passivation layers.

PubMed

Majima, Yutaka; Hackenberger, Guillaume; Azuma, Yasuo; Kano, Shinya; Matsuzaki, Kosuke; Susaki, Tomofumi; Sakamoto, Masanori; Teranishi, Toshiharu

2017-01-01

Single-electron transistors (SETs) are sub-10-nm scale electronic devices based on conductive Coulomb islands sandwiched between double-barrier tunneling barriers. Chemically assembled SETs with alkanethiol-protected Au nanoparticles show highly stable Coulomb diamonds and two-input logic operations. The combination of bottom-up and top-down processes used to form the passivation layer is vital for realizing multi-gate chemically assembled SET circuits, as this combination enables us to connect conventional complementary metal oxide semiconductor (CMOS) technologies via planar processes. Here, three-input gate exclusive-OR (XOR) logic operations are demonstrated in passivated chemically assembled SETs. The passivation layer is a hybrid bilayer of self-assembled monolayers (SAMs) and pulsed laser deposited (PLD) aluminum oxide (AlO[Formula: see text]), and top-gate electrodes were prepared on the hybrid passivation layers. Top and two-side-gated SETs showed clear Coulomb oscillation and diamonds for each of the three available gates, and three-input gate XOR logic operation was clearly demonstrated. These results show the potential of chemically assembled SETs to work as logic devices with multi-gate inputs using organic and inorganic hybrid passivation layers.

Automatic detection of anomalies in screening mammograms

PubMed Central

2013-01-01

Background Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. Methods In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. Results The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature for the unaided human expert. Conclusions Classifiers based on wavelet-derived features proved to be highly sensitive to a range of pathologies, as a result Type II errors were nearly eliminated. Pre-sorting the images changed the prior probability in the sorted database from 37% to 74%. PMID:24330643

Effects of band selection on endmember extraction for forestry applications

NASA Astrophysics Data System (ADS)

Karathanassi, Vassilia; Andreou, Charoula; Andronis, Vassilis; Kolokoussis, Polychronis

2014-10-01

In spectral unmixing theory, data reduction techniques play an important role as hyperspectral imagery contains an immense amount of data, posing many challenging problems such as data storage, computational efficiency, and the so called "curse of dimensionality". Feature extraction and feature selection are the two main approaches for dimensionality reduction. Feature extraction techniques are used for reducing the dimensionality of the hyperspectral data by applying transforms on hyperspectral data. Feature selection techniques retain the physical meaning of the data by selecting a set of bands from the input hyperspectral dataset, which mainly contain the information needed for spectral unmixing. Although feature selection techniques are well-known for their dimensionality reduction potentials they are rarely used in the unmixing process. The majority of the existing state-of-the-art dimensionality reduction methods set criteria to the spectral information, which is derived by the whole wavelength, in order to define the optimum spectral subspace. These criteria are not associated with any particular application but with the data statistics, such as correlation and entropy values. However, each application is associated with specific land c over materials, whose spectral characteristics present variations in specific wavelengths. In forestry for example, many applications focus on tree leaves, in which specific pigments such as chlorophyll, xanthophyll, etc. determine the wavelengths where tree species, diseases, etc., can be detected. For such applications, when the unmixing process is applied, the tree species, diseases, etc., are considered as the endmembers of interest. This paper focuses on investigating the effects of band selection on the endmember extraction by exploiting the information of the vegetation absorbance spectral zones. More precisely, it is explored whether endmember extraction can be optimized when specific sets of initial bands related to leaf spectral characteristics are selected. Experiments comprise application of well-known signal subspace estimation and endmember extraction methods on a hyperspectral imagery that presents a forest area. Evaluation of the extracted endmembers showed that more forest species can be extracted as endmembers using selected bands.

Online learning from input versus offline memory evolution in adult word learning: effects of neighborhood density and phonologically related practice.

PubMed

Storkel, Holly L; Bontempo, Daniel E; Pak, Natalie S

2014-10-01

In this study, the authors investigated adult word learning to determine how neighborhood density and practice across phonologically related training sets influence online learning from input during training versus offline memory evolution during no-training gaps. Sixty-one adults were randomly assigned to learn low- or high-density nonwords. Within each density condition, participants were trained on one set of words and then were trained on a second set of words, consisting of phonological neighbors of the first set. Learning was measured in a picture-naming test. Data were analyzed using multilevel modeling and spline regression. Steep learning during input was observed, with new words from dense neighborhoods and new words that were neighbors of recently learned words (i.e., second-set words) being learned better than other words. In terms of memory evolution, large and significant forgetting was observed during 1-week gaps in training. Effects of density and practice during memory evolution were opposite of those during input. Specifically, forgetting was greater for high-density and second-set words than for low-density and first-set words. High phonological similarity, regardless of source (i.e., known words or recent training), appears to facilitate online learning from input but seems to impede offline memory evolution.

Computational intelligence models to predict porosity of tablets using minimum features

PubMed Central

Khalid, Mohammad Hassan; Kazemi, Pezhman; Perez-Gandarillas, Lucia; Michrafy, Abderrahim; Szlęk, Jakub; Jachowicz, Renata; Mendyk, Aleksander

2017-01-01

The effects of different formulations and manufacturing process conditions on the physical properties of a solid dosage form are of importance to the pharmaceutical industry. It is vital to have in-depth understanding of the material properties and governing parameters of its processes in response to different formulations. Understanding the mentioned aspects will allow tighter control of the process, leading to implementation of quality-by-design (QbD) practices. Computational intelligence (CI) offers an opportunity to create empirical models that can be used to describe the system and predict future outcomes in silico. CI models can help explore the behavior of input parameters, unlocking deeper understanding of the system. This research endeavor presents CI models to predict the porosity of tablets created by roll-compacted binary mixtures, which were milled and compacted under systematically varying conditions. CI models were created using tree-based methods, artificial neural networks (ANNs), and symbolic regression trained on an experimental data set and screened using root-mean-square error (RMSE) scores. The experimental data were composed of proportion of microcrystalline cellulose (MCC) (in percentage), granule size fraction (in micrometers), and die compaction force (in kilonewtons) as inputs and porosity as an output. The resulting models show impressive generalization ability, with ANNs (normalized root-mean-square error [NRMSE] =1%) and symbolic regression (NRMSE =4%) as the best-performing methods, also exhibiting reliable predictive behavior when presented with a challenging external validation data set (best achieved symbolic regression: NRMSE =3%). Symbolic regression demonstrates the transition from the black box modeling paradigm to more transparent predictive models. Predictive performance and feature selection behavior of CI models hints at the most important variables within this factor space. PMID:28138223

Computational intelligence models to predict porosity of tablets using minimum features.

PubMed

Khalid, Mohammad Hassan; Kazemi, Pezhman; Perez-Gandarillas, Lucia; Michrafy, Abderrahim; Szlęk, Jakub; Jachowicz, Renata; Mendyk, Aleksander

2017-01-01

The effects of different formulations and manufacturing process conditions on the physical properties of a solid dosage form are of importance to the pharmaceutical industry. It is vital to have in-depth understanding of the material properties and governing parameters of its processes in response to different formulations. Understanding the mentioned aspects will allow tighter control of the process, leading to implementation of quality-by-design (QbD) practices. Computational intelligence (CI) offers an opportunity to create empirical models that can be used to describe the system and predict future outcomes in silico. CI models can help explore the behavior of input parameters, unlocking deeper understanding of the system. This research endeavor presents CI models to predict the porosity of tablets created by roll-compacted binary mixtures, which were milled and compacted under systematically varying conditions. CI models were created using tree-based methods, artificial neural networks (ANNs), and symbolic regression trained on an experimental data set and screened using root-mean-square error (RMSE) scores. The experimental data were composed of proportion of microcrystalline cellulose (MCC) (in percentage), granule size fraction (in micrometers), and die compaction force (in kilonewtons) as inputs and porosity as an output. The resulting models show impressive generalization ability, with ANNs (normalized root-mean-square error [NRMSE] =1%) and symbolic regression (NRMSE =4%) as the best-performing methods, also exhibiting reliable predictive behavior when presented with a challenging external validation data set (best achieved symbolic regression: NRMSE =3%). Symbolic regression demonstrates the transition from the black box modeling paradigm to more transparent predictive models. Predictive performance and feature selection behavior of CI models hints at the most important variables within this factor space.

[Severity classification of chronic obstructive pulmonary disease based on deep learning].

PubMed

Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

2017-12-01

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

Relevant, irredundant feature selection and noisy example elimination.

PubMed

Lashkia, George V; Anthony, Laurence

2004-04-01

In many real-world situations, the method for computing the desired output from a set of inputs is unknown. One strategy for solving these types of problems is to learn the input-output functionality from examples in a training set. However, in many situations it is difficult to know what information is relevant to the task at hand. Subsequently, researchers have investigated ways to deal with the so-called problem of consistency of attributes, i.e., attributes that can distinguish examples from different classes. In this paper, we first prove that the notion of relevance of attributes is directly related to the consistency of attributes, and show how relevant, irredundant attributes can be selected. We then compare different relevant attribute selection algorithms, and show the superiority of algorithms that select irredundant attributes over those that select relevant attributes. We also show that searching for an "optimal" subset of attributes, which is considered to be the main purpose of attribute selection, is not the best way to improve the accuracy of classifiers. Employing sets of relevant, irredundant attributes improves classification accuracy in many more cases. Finally, we propose a new method for selecting relevant examples, which is based on filtering the so-called pattern frequency domain. By identifying examples that are nontypical in the determination of relevant, irredundant attributes, irrelevant examples can be eliminated prior to the learning process. Empirical results using artificial and real databases show the effectiveness of the proposed method in selecting relevant examples leading to improved performance even on greatly reduced training sets.

Utilizing Hierarchical Clustering to improve Efficiency of Self-Organizing Feature Map to Identify Hydrological Homogeneous Regions

NASA Astrophysics Data System (ADS)

Farsadnia, Farhad; Ghahreman, Bijan

2016-04-01

Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self-Organizing feature Map (SOM) method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces. At first by principal component analysis, we reduced SOM input matrix dimension, then the SOM was used to form a two-dimensional features map. To determine homogeneous regions for flood frequency analysis, SOM output nodes were used as input into the Ward method. Generally, the regions identified by the clustering algorithms are not statistically homogeneous. Consequently, they have to be adjusted to improve their homogeneity. After adjustment of the homogeneity regions by L-moment tests, five hydrologic homogeneous regions were identified. Finally, adjusted regions were created by a two-level SOM and then the best regional distribution function and associated parameters were selected by the L-moment approach. The results showed that the combination of self-organizing maps and Ward hierarchical clustering by principal components as input is more effective than the hierarchical method, by principal components or standardized inputs to achieve hydrologic homogeneous regions.

Evolution of Bow-Tie Architectures in Biology

PubMed Central

Friedlander, Tamar; Mayo, Avraham E.; Tlusty, Tsvi; Alon, Uri

2015-01-01

Bow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network—that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved. PMID:25798588

New nonlinear features for inspection, robotics, and face recognition

NASA Astrophysics Data System (ADS)

Casasent, David P.; Talukder, Ashit

1999-10-01

Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work, the MRDF is applied to standard features (rather than iconic data). The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC (receiver operating characteristic) data. Other applications of these new feature spaces in robotics and face recognition are also noted.

New feature extraction method for classification of agricultural products from x-ray images

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

1999-01-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work the MRDF is applied to standard features. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

PredPPCrys: accurate prediction of sequence cloning, protein production, purification and crystallization propensity from protein sequences using multi-step heterogeneous feature fusion and selection.

PubMed

Wang, Huilin; Wang, Mingjun; Tan, Hao; Li, Yuan; Zhang, Ziding; Song, Jiangning

2014-01-01

X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed 'PredPPCrys' using the support vector machine (SVM). Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I). Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II), which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization targets of currently non-crystallizable proteins were provided as compendium data, which are anticipated to facilitate target selection and design for the worldwide structural genomics consortium. PredPPCrys is freely available at http://www.structbioinfor.org/PredPPCrys.

An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

PubMed

Lin, Chin-Teng; Pal, Nikhil R; Wu, Shang-Lin; Liu, Yu-Ting; Lin, Yang-Yin

2015-07-01

We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

Hand-Based Biometric Analysis

NASA Technical Reports Server (NTRS)

Bebis, George

2013-01-01

Hand-based biometric analysis systems and techniques provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an input image. Additionally, the analysis uses re-use of commonly seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation.

Novel face-detection method under various environments

NASA Astrophysics Data System (ADS)

Jing, Min-Quan; Chen, Ling-Hwei

2009-06-01

We propose a method to detect a face with different poses under various environments. On the basis of skin color information, skin regions are first extracted from an input image. Next, the shoulder part is cut out by using shape information and the head part is then identified as a face candidate. For a face candidate, a set of geometric features is applied to determine if it is a profile face. If not, then a set of eyelike rectangles extracted from the face candidate and the lighting distribution are used to determine if the face candidate is a nonprofile face. Experimental results show that the proposed method is robust under a wide range of lighting conditions, different poses, and races. The detection rate for the HHI face database is 93.68%. For the Champion face database, the detection rate is 95.15%.

The Peculiarities in O-Type Galaxy Clusters

NASA Astrophysics Data System (ADS)

Panko, E. A.; Emelyanov, S. I.

We present the results of analysis of 2D distribution of galaxies in galaxy cluster fields. The Catalogue of Galaxy Clusters and Groups PF (Panko & Flin) was used as input observational data set. We selected open rich PF galaxy clusters, containing 100 and more galaxies for our study. According to Panko classification scheme open galaxy clusters (O-type) have no concentration to the cluster center. The data set contains both pure O-type clusters and O-type clusters with overdence belts, namely OL and OF types. According to Rood & Sastry and Struble & Rood ideas, the open galaxy clusters are the beginning stage of cluster evolution. We found in the O-type clusters some types of statistically significant regular peculiarities, such as two crossed belts or curved strip. We suppose founded features connected with galaxy clusters evolution and the distribution of DM inside the clusters.

Enhancing Critical Infrastructure and Key Resources (CIKR) Level-0 Physical Process Security Using Field Device Distinct Native Attribute Features

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lopez, Juan; Liefer, Nathan C.; Busho, Colin R.

Here, the need for improved Critical Infrastructure and Key Resource (CIKR) security is unquestioned and there has been minimal emphasis on Level-0 (PHY Process) improvements. Wired Signal Distinct Native Attribute (WS-DNA) Fingerprinting is investigated here as a non-intrusive PHY-based security augmentation to support an envisioned layered security strategy. Results are based on experimental response collections from Highway Addressable Remote Transducer (HART) Differential Pressure Transmitter (DPT) devices from three manufacturers (Yokogawa, Honeywell, Endress+Hauer) installed in an automated process control system. Device discrimination is assessed using Time Domain (TD) and Slope-Based FSK (SB-FSK) fingerprints input to Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML)more » and Random Forest (RndF) classifiers. For 12 different classes (two devices per manufacturer at two distinct set points), both classifiers performed reliably and achieved an arbitrary performance benchmark of average cross-class percent correct of %C > 90%. The least challenging cross-manufacturer results included near-perfect %C ≈ 100%, while the more challenging like-model (serial number) discrimination results included 90%< %C < 100%, with TD Fingerprinting marginally outperforming SB-FSK Fingerprinting; SB-FSK benefits from having less stringent response alignment and registration requirements. The RndF classifier was most beneficial and enabled reliable selection of dimensionally reduced fingerprint subsets that minimize data storage and computational requirements. The RndF selected feature sets contained 15% of the full-dimensional feature sets and only suffered a worst case %CΔ = 3% to 4% performance degradation.« less

Deep Learning Based Binaural Speech Separation in Reverberant Environments.

PubMed

Zhang, Xueliang; Wang, DeLiang

2017-05-01

Speech signal is usually degraded by room reverberation and additive noises in real environments. This paper focuses on separating target speech signal in reverberant conditions from binaural inputs. Binaural separation is formulated as a supervised learning problem, and we employ deep learning to map from both spatial and spectral features to a training target. With binaural inputs, we first apply a fixed beamformer and then extract several spectral features. A new spatial feature is proposed and extracted to complement the spectral features. The training target is the recently suggested ideal ratio mask. Systematic evaluations and comparisons show that the proposed system achieves very good separation performance and substantially outperforms related algorithms under challenging multi-source and reverberant environments.

ModelMuse - A Graphical User Interface for MODFLOW-2005 and PHAST

USGS Publications Warehouse

Winston, Richard B.

2009-01-01

ModelMuse is a graphical user interface (GUI) for the U.S. Geological Survey (USGS) models MODFLOW-2005 and PHAST. This software package provides a GUI for creating the flow and transport input file for PHAST and the input files for MODFLOW-2005. In ModelMuse, the spatial data for the model is independent of the grid, and the temporal data is independent of the stress periods. Being able to input these data independently allows the user to redefine the spatial and temporal discretization at will. This report describes the basic concepts required to work with ModelMuse. These basic concepts include the model grid, data sets, formulas, objects, the method used to assign values to data sets, and model features. The ModelMuse main window has a top, front, and side view of the model that can be used for editing the model, and a 3-D view of the model that can be used to display properties of the model. ModelMuse has tools to generate and edit the model grid. It also has a variety of interpolation methods and geographic functions that can be used to help define the spatial variability of the model. ModelMuse can be used to execute both MODFLOW-2005 and PHAST and can also display the results of MODFLOW-2005 models. An example of using ModelMuse with MODFLOW-2005 is included in this report. Several additional examples are described in the help system for ModelMuse, which can be accessed from the Help menu.

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

NASA Astrophysics Data System (ADS)

Bouboulis, Pantelis; Chouvardas, Symeon; Theodoridis, Sergios

2018-04-01

We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources, in a distributed setting. In contrast, the proposed method approximates the solution as a fixed-size vector (of larger dimension than the input space) using Random Fourier Features. This paves the way to use standard linear combine-then-adapt techniques. To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented. Conditions for asymptotic convergence and boundness of the networkwise regret are also provided. The simulated tests illustrate the performance of the proposed scheme.

Achieving reutilization of scheduling software through abstraction and generalization

NASA Technical Reports Server (NTRS)

Wilkinson, George J.; Monteleone, Richard A.; Weinstein, Stuart M.; Mohler, Michael G.; Zoch, David R.; Tong, G. Michael

1995-01-01

Reutilization of software is a difficult goal to achieve particularly in complex environments that require advanced software systems. The Request-Oriented Scheduling Engine (ROSE) was developed to create a reusable scheduling system for the diverse scheduling needs of the National Aeronautics and Space Administration (NASA). ROSE is a data-driven scheduler that accepts inputs such as user activities, available resources, timing contraints, and user-defined events, and then produces a conflict-free schedule. To support reutilization, ROSE is designed to be flexible, extensible, and portable. With these design features, applying ROSE to a new scheduling application does not require changing the core scheduling engine, even if the new application requires significantly larger or smaller data sets, customized scheduling algorithms, or software portability. This paper includes a ROSE scheduling system description emphasizing its general-purpose features, reutilization techniques, and tasks for which ROSE reuse provided a low-risk solution with significant cost savings and reduced software development time.

Bio++: a set of C++ libraries for sequence analysis, phylogenetics, molecular evolution and population genetics.

PubMed

Dutheil, Julien; Gaillard, Sylvain; Bazin, Eric; Glémin, Sylvain; Ranwez, Vincent; Galtier, Nicolas; Belkhir, Khalid

2006-04-04

A large number of bioinformatics applications in the fields of bio-sequence analysis, molecular evolution and population genetics typically share input/output methods, data storage requirements and data analysis algorithms. Such common features may be conveniently bundled into re-usable libraries, which enable the rapid development of new methods and robust applications. We present Bio++, a set of Object Oriented libraries written in C++. Available components include classes for data storage and handling (nucleotide/amino-acid/codon sequences, trees, distance matrices, population genetics datasets), various input/output formats, basic sequence manipulation (concatenation, transcription, translation, etc.), phylogenetic analysis (maximum parsimony, markov models, distance methods, likelihood computation and maximization), population genetics/genomics (diversity statistics, neutrality tests, various multi-locus analyses) and various algorithms for numerical calculus. Implementation of methods aims at being both efficient and user-friendly. A special concern was given to the library design to enable easy extension and new methods development. We defined a general hierarchy of classes that allow the developer to implement its own algorithms while remaining compatible with the rest of the libraries. Bio++ source code is distributed free of charge under the CeCILL general public licence from its website http://kimura.univ-montp2.fr/BioPP.

Classification of posture maintenance data with fuzzy clustering algorithms

NASA Technical Reports Server (NTRS)

Bezdek, James C.

1991-01-01

Sensory inputs from the visual, vestibular, and proprioreceptive systems are integrated by the central nervous system to maintain postural equilibrium. Sustained exposure to microgravity causes neurosensory adaptation during spaceflight, which results in decreased postural stability until readaptation occurs upon return to the terrestrial environment. Data which simulate sensory inputs under various conditions were collected in conjunction with JSC postural control studies using a Tilt-Translation Device (TTD). The University of West Florida proposed applying the Fuzzy C-Means Clustering (FCM) Algorithms to this data with a view towards identifying various states and stages. Data supplied by NASA/JSC were submitted to the FCM algorithms in an attempt to identify and characterize cluster substructure in a mixed ensemble of pre- and post-adaptational TTD data. Following several unsuccessful trials with FCM using a full 11 dimensional data set, a set of two channels (features) were found to enable FCM to separate pre- from post-adaptational TTD data. The main conclusions are that: (1) FCM seems able to separate pre- from post-TTD subject no. 2 on the one trial that was used, but only in certain subintervals of time; and (2) Channels 2 (right rear transducer force) and 8 (hip sway bar) contain better discrimination information than other supersets and combinations of the data that were tried so far.

enoLOGOS: a versatile web tool for energy normalized sequence logos

PubMed Central

Workman, Christopher T.; Yin, Yutong; Corcoran, David L.; Ideker, Trey; Stormo, Gary D.; Benos, Panayiotis V.

2005-01-01

enoLOGOS is a web-based tool that generates sequence logos from various input sources. Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position. Currently, the available web servers are able to create logo images from a set of aligned sequences, but none of them generates weighted sequence logos directly from energy measurements or other sources. With the advent of high-throughput technologies for estimating the contact energy of different DNA sequences, tools that can create logos directly from binding affinity data are useful to researchers. enoLOGOS generates sequence logos from a variety of input data, including energy measurements, probability matrices, alignment matrices, count matrices and aligned sequences. Furthermore, enoLOGOS can represent the mutual information of different positions of the consensus sequence, a unique feature of this tool. Another web interface for our software, C2H2-enoLOGOS, generates logos for the DNA-binding preferences of the C2H2 zinc-finger transcription factor family members. enoLOGOS and C2H2-enoLOGOS are accessible over the web at . PMID:15980495

Classification of complex information: inference of co-occurring affective states from their expressions in speech.

PubMed

Sobol-Shikler, Tal; Robinson, Peter

2010-07-01

We present a classification algorithm for inferring affective states (emotions, mental states, attitudes, and the like) from their nonverbal expressions in speech. It is based on the observations that affective states can occur simultaneously and different sets of vocal features, such as intonation and speech rate, distinguish between nonverbal expressions of different affective states. The input to the inference system was a large set of vocal features and metrics that were extracted from each utterance. The classification algorithm conducted independent pairwise comparisons between nine affective-state groups. The classifier used various subsets of metrics of the vocal features and various classification algorithms for different pairs of affective-state groups. Average classification accuracy of the 36 pairwise machines was 75 percent, using 10-fold cross validation. The comparison results were consolidated into a single ranked list of the nine affective-state groups. This list was the output of the system and represented the inferred combination of co-occurring affective states for the analyzed utterance. The inference accuracy of the combined machine was 83 percent. The system automatically characterized over 500 affective state concepts from the Mind Reading database. The inference of co-occurring affective states was validated by comparing the inferred combinations to the lexical definitions of the labels of the analyzed sentences. The distinguishing capabilities of the system were comparable to human performance.

Explaining neural signals in human visual cortex with an associative learning model.

PubMed

Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

Non-intrusive uncertainty quantification of computational fluid dynamics simulations: notes on the accuracy and efficiency

NASA Astrophysics Data System (ADS)

Zimoń, Małgorzata; Sawko, Robert; Emerson, David; Thompson, Christopher

2017-11-01

Uncertainty quantification (UQ) is increasingly becoming an indispensable tool for assessing the reliability of computational modelling. Efficient handling of stochastic inputs, such as boundary conditions, physical properties or geometry, increases the utility of model results significantly. We discuss the application of non-intrusive generalised polynomial chaos techniques in the context of fluid engineering simulations. Deterministic and Monte Carlo integration rules are applied to a set of problems, including ordinary differential equations and the computation of aerodynamic parameters subject to random perturbations. In particular, we analyse acoustic wave propagation in a heterogeneous medium to study the effects of mesh resolution, transients, number and variability of stochastic inputs. We consider variants of multi-level Monte Carlo and perform a novel comparison of the methods with respect to numerical and parametric errors, as well as computational cost. The results provide a comprehensive view of the necessary steps in UQ analysis and demonstrate some key features of stochastic fluid flow systems.

Logic gates based all-optical binary half adder using triple core photonic crystal fiber

NASA Astrophysics Data System (ADS)

Uthayakumar, T.; Vasantha Jayakantha Raja, R.

2018-06-01

This study presents the implementation of an all-optical binary logic half adder by employing a triple core photonic crystal fiber (TPCF). The noteworthy feature of the present investigation is that an identical set of TPCF schemes, which demonstrated all-optical logic functions in our previous report, has revealed the ability to demonstrate the successful half adder operation. The control signal (CS) power defining the extinction ratios of the output ports for the considered symmetric planar and triangular TPCFs is evaluated through a numerical algorithm. Through suitable CS power and input combinations, the logic outputs are generated from extinction ratios to demonstrate the half adder operation. The results obtained display the significant influence of the input conditions on the delivery of half adder operation for different TPCF schemes considered. Furthermore, chloroform filled TPCF structures demonstrated the efficient low power half adder operation with a significant figure of merit, compared to that of the silica counterpart.

A general method for generating bathymetric data for hydrodynamic computer models

USGS Publications Warehouse

Burau, J.R.; Cheng, R.T.

1989-01-01

To generate water depth data from randomly distributed bathymetric data for numerical hydrodymamic models, raw input data from field surveys, water depth data digitized from nautical charts, or a combination of the two are sorted to given an ordered data set on which a search algorithm is used to isolate data for interpolation. Water depths at locations required by hydrodynamic models are interpolated from the bathymetric data base using linear or cubic shape functions used in the finite-element method. The bathymetric database organization and preprocessing, the search algorithm used in finding the bounding points for interpolation, the mathematics of the interpolation formulae, and the features of the automatic generation of water depths at hydrodynamic model grid points are included in the analysis. This report includes documentation of two computer programs which are used to: (1) organize the input bathymetric data; and (2) to interpolate depths for hydrodynamic models. An example of computer program operation is drawn from a realistic application to the San Francisco Bay estuarine system. (Author 's abstract)

Anisotropic tubular filtering for automatic detection of acid-fast bacilli in Ziehl-Neelsen stained sputum smear samples

NASA Astrophysics Data System (ADS)

Raza, Shan-e.-Ahmed; Marjan, M. Q.; Arif, Muhammad; Butt, Farhana; Sultan, Faisal; Rajpoot, Nasir M.

2015-03-01

One of the main factors for high workload in pulmonary pathology in developing countries is the relatively large proportion of tuberculosis (TB) cases which can be detected with high throughput using automated approaches. TB is caused by Mycobacterium tuberculosis, which appears as thin, rod-shaped acid-fast bacillus (AFB) in Ziehl-Neelsen (ZN) stained sputum smear samples. In this paper, we present an algorithm for automatic detection of AFB in digitized images of ZN stained sputum smear samples under a light microscope. A key component of the proposed algorithm is the enhancement of raw input image using a novel anisotropic tubular filter (ATF) which suppresses the background noise while simultaneously enhancing strong anisotropic features of AFBs present in the image. The resulting image is then segmented using color features and candidate AFBs are identified. Finally, a support vector machine classifier using morphological features from candidate AFBs decides whether a given image is AFB positive or not. We demonstrate the effectiveness of the proposed ATF method with two different feature sets by showing that the proposed image analysis pipeline results in higher accuracy and F1-score than the same pipeline with standard median filtering for image enhancement.

A feature based comparison of pen and swipe based signature characteristics.

PubMed

Robertson, Joshua; Guest, Richard

2015-10-01

Dynamic Signature Verification (DSV) is a biometric modality that identifies anatomical and behavioral characteristics when an individual signs their name. Conventionally signature data has been captured using pen/tablet apparatus. However, the use of other devices such as the touch-screen tablets has expanded in recent years affording the possibility of assessing biometric interaction on this new technology. To explore the potential of employing DSV techniques when a user signs or swipes with their finger, we report a study to correlate pen and finger generated features. Investigating the stability and correlation between a set of characteristic features recorded in participant's signatures and touch-based swipe gestures, a statistical analysis was conducted to assess consistency between capture scenarios. The results indicate that there is a range of static and dynamic features such as the rate of jerk, size, duration and the distance the pen traveled that can lead to interoperability between these two systems for input methods for use within a potential biometric context. It can be concluded that this data indicates that a general principle is that the same underlying constructional mechanisms are evident. Copyright © 2015 Elsevier B.V. All rights reserved.

An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks.

PubMed

Sareen, Sanjay; Sood, Sandeep K; Gupta, Sunil Kumar

2016-11-01

Epilepsy is one of the most common neurological disorders which is characterized by the spontaneous and unforeseeable occurrence of seizures. An automatic prediction of seizure can protect the patients from accidents and save their life. In this article, we proposed a mobile-based framework that automatically predict seizures using the information contained in electroencephalography (EEG) signals. The wireless sensor technology is used to capture the EEG signals of patients. The cloud-based services are used to collect and analyze the EEG data from the patient's mobile phone. The features from the EEG signal are extracted using the fast Walsh-Hadamard transform (FWHT). The Higher Order Spectral Analysis (HOSA) is applied to FWHT coefficients in order to select the features set relevant to normal, preictal and ictal states of seizure. We subsequently exploit the selected features as input to a k-means classifier to detect epileptic seizure states in a reasonable time. The performance of the proposed model is tested on Amazon EC2 cloud and compared in terms of execution time and accuracy. The findings show that with selected HOS based features, we were able to achieve a classification accuracy of 94.6 %.

Evolution of late stage differentiates in the Palisades Sill, New York and New Jersey

NASA Astrophysics Data System (ADS)

Block, Karin A.; Steiner, Jeffrey C.; Puffer, John H.; Jones, Kevin M.; Goldstein, Steven L.

2015-08-01

The Palisades Sill at Upper Nyack, NY contains evolved rocks that crystallized as ferrodiabase and ferrogranophyre and occupy 50% to 60% of the local thickness. 143Nd/144Nd isotope values for rocks representing Palisades diversity range between 0.512320 and 0.512331, and indicate a homogeneous source for the Palisades and little or no contamination from shallow crustal sediments. Petrographic analysis of ferrodiabase suggests that strong iron enrichment was the result of prolonged quiescence in cycles of magmatic input. Ferrogranophyres in the updip northern Palisades at Upper Nyack are members of a suite of cogenetic rocks with similar composition to 'sandwich horizon' rocks of the southern Palisades at Fort Lee, NJ, but display distinct mineralogical and textural features. Differences in textural and mineralogical features are attributed to a) updip (lateral) migration of residual liquid as the sill propagated closer to the surface; b) deformation caused by tectonic shifts; and c) crystallization in the presence of deuteric hydrothermal fluids resulting in varying degrees of alteration. A model connecting multiple magmatic pulses, compaction and mobilization of residual liquid by compositional convection, closed-system differentiation, synchronous with tapping of the sill for extrusion of coeval basaltic subaerial flows is presented. The persistence of a low-temperature mushy layer, represented by ferrogranophyres, supports the possibility of a long-lived conduit subject to reopening after periods of quiescence in magmatic input, leading to the extrusion of the multiple flows of the Orange Mountain Basalt and perhaps even subsequent Preakness Basalt flows, depending on solidification conditions. A sub-Newark Basin network of sills subjected to similar protracted input of pulses as hypothesized for the Palisades was likely responsible for 600 ka of magmatic activity required to emplace a third set of Watchung flood basalts, the Hook Mountain Basalt.

Going beyond Input Quantity: "Wh"-Questions Matter for Toddlers' Language and Cognitive Development

ERIC Educational Resources Information Center

Rowe, Meredith L.; Leech, Kathryn A.; Cabrera, Natasha

2017-01-01

There are clear associations between the overall quantity of input children are exposed to and their vocabulary acquisition. However, by uncovering specific features of the input that matter, we can better understand the mechanisms involved in vocabulary learning. We examine whether exposure to "wh"-questions, a challenging quality of…

Effects of Textual Enhancement and Input Enrichment on L2 Development

ERIC Educational Resources Information Center

Rassaei, Ehsan

2015-01-01

Research on second language (L2) acquisition has recently sought to include formal instruction into second and foreign language classrooms in a more unobtrusive and implicit manner. Textual enhancement and input enrichment are two techniques which are aimed at drawing learners' attention to specific linguistic features in input and at the same…

Protein structure based prediction of catalytic residues.

PubMed

Fajardo, J Eduardo; Fiser, Andras

2013-02-22

Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation. We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods. We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases.

Adaptive fusion of infrared and visible images in dynamic scene

NASA Astrophysics Data System (ADS)

Yang, Guang; Yin, Yafeng; Man, Hong; Desai, Sachi

2011-11-01

Multiple modalities sensor fusion has been widely employed in various surveillance and military applications. A variety of image fusion techniques including PCA, wavelet, curvelet and HSV has been proposed in recent years to improve human visual perception for object detection. One of the main challenges for visible and infrared image fusion is to automatically determine an optimal fusion strategy for different input scenes along with an acceptable computational cost. This paper, we propose a fast and adaptive feature selection based image fusion method to obtain high a contrast image from visible and infrared sensors for targets detection. At first, fuzzy c-means clustering is applied on the infrared image to highlight possible hotspot regions, which will be considered as potential targets' locations. After that, the region surrounding the target area is segmented as the background regions. Then image fusion is locally applied on the selected target and background regions by computing different linear combination of color components from registered visible and infrared images. After obtaining different fused images, histogram distributions are computed on these local fusion images as the fusion feature set. The variance ratio which is based on Linear Discriminative Analysis (LDA) measure is employed to sort the feature set and the most discriminative one is selected for the whole image fusion. As the feature selection is performed over time, the process will dynamically determine the most suitable feature for the image fusion in different scenes. Experiment is conducted on the OSU Color-Thermal database, and TNO Human Factor dataset. The fusion results indicate that our proposed method achieved a competitive performance compared with other fusion algorithms at a relatively low computational cost.

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

PubMed Central

Lechevalier, D.; Ak, R.; Ferguson, M.; Law, K. H.; Lee, Y.-T. T.; Rachuri, S.

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain. PMID:29202125

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML).

PubMed

Park, J; Lechevalier, D; Ak, R; Ferguson, M; Law, K H; Lee, Y-T T; Rachuri, S

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.

Synaptic State Matching: A Dynamical Architecture for Predictive Internal Representation and Feature Detection

PubMed Central

Tavazoie, Saeed

2013-01-01

Here we explore the possibility that a core function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single unifying computational framework. PMID:23991161

Emulating RRTMG Radiation with Deep Neural Networks for the Accelerated Model for Climate and Energy

NASA Astrophysics Data System (ADS)

Pal, A.; Norman, M. R.

2017-12-01

The RRTMG radiation scheme in the Accelerated Model for Climate and Energy Multi-scale Model Framework (ACME-MMF), is a bottleneck and consumes approximately 50% of the computational time. To simulate a case using RRTMG radiation scheme in ACME-MMF with high throughput and high resolution will therefore require a speed-up of this calculation while retaining physical fidelity. In this study, RRTMG radiation is emulated with Deep Neural Networks (DNNs). The first step towards this goal is to run a case with ACME-MMF and generate input data sets for the DNNs. A principal component analysis of these input data sets are carried out. Artificial data sets are created using the previous data sets to cover a wider space. These artificial data sets are used in a standalone RRTMG radiation scheme to generate outputs in a cost effective manner. These input-output pairs are used to train multiple architectures DNNs(1). Another DNN(2) is trained using the inputs to predict the error. A reverse emulation is trained to map the output to input. An error controlled code is developed with the two DNNs (1 and 2) and will determine when/if the original parameterization needs to be used.

Recursive Feature Extraction in Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-08-14

ReFeX extracts recursive topological features from graph data. The input is a graph as a csv file and the output is a csv file containing feature values for each node in the graph. The features are based on topological counts in the neighborhoods of each nodes, as well as recursive summaries of neighbors' features.

An interactive framework for acquiring vision models of 3-D objects from 2-D images.

PubMed

Motai, Yuichi; Kak, Avinash

2004-02-01

This paper presents a human-computer interaction (HCI) framework for building vision models of three-dimensional (3-D) objects from their two-dimensional (2-D) images. Our framework is based on two guiding principles of HCI: 1) provide the human with as much visual assistance as possible to help the human make a correct input; and 2) verify each input provided by the human for its consistency with the inputs previously provided. For example, when stereo correspondence information is elicited from a human, his/her job is facilitated by superimposing epipolar lines on the images. Although that reduces the possibility of error in the human marked correspondences, such errors are not entirely eliminated because there can be multiple candidate points close together for complex objects. For another example, when pose-to-pose correspondence is sought from a human, his/her job is made easier by allowing the human to rotate the partial model constructed in the previous pose in relation to the partial model for the current pose. While this facility reduces the incidence of human-supplied pose-to-pose correspondence errors, such errors cannot be eliminated entirely because of confusion created when multiple candidate features exist close together. Each input provided by the human is therefore checked against the previous inputs by invoking situation-specific constraints. Different types of constraints (and different human-computer interaction protocols) are needed for the extraction of polygonal features and for the extraction of curved features. We will show results on both polygonal objects and object containing curved features.

Set Theory Applied to Uniquely Define the Inputs to Territorial Systems in Emergy Analyses

EPA Science Inventory

The language of set theory can be utilized to represent the emergy involved in all processes. In this paper we use set theory in an emergy evaluation to ensure an accurate representation of the inputs to territorial systems. We consider a generic territorial system and we describ...

A neural network detection model of spilled oil based on the texture analysis of SAR image

NASA Astrophysics Data System (ADS)

An, Jubai; Zhu, Lisong

2006-01-01

A Radial Basis Function Neural Network (RBFNN) Model is investigated for the detection of spilled oil based on the texture analysis of SAR imagery. In this paper, to take the advantage of the abundant texture information of SAR imagery, the texture features are extracted by both wavelet transform and the Gray Level Co-occurrence matrix. The RBFNN Model is fed with a vector of these texture features. The RBFNN Model is trained and tested by the sample data set of the feature vectors. Finally, a SAR image is classified by this model. The classification results of a spilled oil SAR image show that the classification accuracy for oil spill is 86.2 by the RBFNN Model using both wavelet texture and gray texture, while the classification accuracy for oil spill is 78.0 by same RBFNN Model using only wavelet texture as the input of this RBFNN model. The model using both wavelet transform and the Gray Level Co-occurrence matrix is more effective than that only using wavelet texture. Furthermore, it keeps the complicated proximity and has a good performance of classification.

Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

PubMed Central

Yang, Xinyi

2016-01-01

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. PMID:27610128

Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.

PubMed

Pang, Shan; Yang, Xinyi

2016-01-01

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

Quantitative and descriptive comparison of four acoustic analysis systems: vowel measurements.

PubMed

Burris, Carlyn; Vorperian, Houri K; Fourakis, Marios; Kent, Ray D; Bolt, Daniel M

2014-02-01

This study examines accuracy and comparability of 4 trademarked acoustic analysis software packages (AASPs): Praat, WaveSurfer, TF32, and CSL by using synthesized and natural vowels. Features of AASPs are also described. Synthesized and natural vowels were analyzed using each of the AASP's default settings to secure 9 acoustic measures: fundamental frequency (F0), formant frequencies (F1-F4), and formant bandwidths (B1-B4). The discrepancy between the software measured values and the input values (synthesized, previously reported, and manual measurements) was used to assess comparability and accuracy. Basic AASP features are described. Results indicate that Praat, WaveSurfer, and TF32 generate accurate and comparable F0 and F1-F4 data for synthesized vowels and adult male natural vowels. Results varied by vowel for women and children, with some serious errors. Bandwidth measurements by AASPs were highly inaccurate as compared with manual measurements and published data on formant bandwidths. Values of F0 and F1-F4 are generally consistent and fairly accurate for adult vowels and for some child vowels using the default settings in Praat, WaveSurfer, and TF32. Manipulation of default settings yields improved output values in TF32 and CSL. Caution is recommended especially before accepting F1-F4 results for children and B1-B4 results for all speakers.

Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data.

PubMed

Lepre, Jorge; Rice, J Jeremy; Tu, Yuhai; Stolovitzky, Gustavo

2004-05-01

Despite the growing literature devoted to finding differentially expressed genes in assays probing different tissues types, little attention has been paid to the combinatorial nature of feature selection inherent to large, high-dimensional gene expression datasets. New flexible data analysis approaches capable of searching relevant subgroups of genes and experiments are needed to understand multivariate associations of gene expression patterns with observed phenotypes. We present in detail a deterministic algorithm to discover patterns of multivariate gene associations in gene expression data. The patterns discovered are differential with respect to a control dataset. The algorithm is exhaustive and efficient, reporting all existent patterns that fit a given input parameter set while avoiding enumeration of the entire pattern space. The value of the pattern discovery approach is demonstrated by finding a set of genes that differentiate between two types of lymphoma. Moreover, these genes are found to behave consistently in an independent dataset produced in a different laboratory using different arrays, thus validating the genes selected using our algorithm. We show that the genes deemed significant in terms of their multivariate statistics will be missed using other methods. Our set of pattern discovery algorithms including a user interface is distributed as a package called Genes@Work. This package is freely available to non-commercial users and can be downloaded from our website (http://www.research.ibm.com/FunGen).

Spectrotemporal dynamics of auditory cortical synaptic receptive field plasticity.

PubMed

Froemke, Robert C; Martins, Ana Raquel O

2011-09-01

The nervous system must dynamically represent sensory information in order for animals to perceive and operate within a complex, changing environment. Receptive field plasticity in the auditory cortex allows cortical networks to organize around salient features of the sensory environment during postnatal development, and then subsequently refine these representations depending on behavioral context later in life. Here we review the major features of auditory cortical receptive field plasticity in young and adult animals, focusing on modifications to frequency tuning of synaptic inputs. Alteration in the patterns of acoustic input, including sensory deprivation and tonal exposure, leads to rapid adjustments of excitatory and inhibitory strengths that collectively determine the suprathreshold tuning curves of cortical neurons. Long-term cortical plasticity also requires co-activation of subcortical neuromodulatory control nuclei such as the cholinergic nucleus basalis, particularly in adults. Regardless of developmental stage, regulation of inhibition seems to be a general mechanism by which changes in sensory experience and neuromodulatory state can remodel cortical receptive fields. We discuss recent findings suggesting that the microdynamics of synaptic receptive field plasticity unfold as a multi-phase set of distinct phenomena, initiated by disrupting the balance between excitation and inhibition, and eventually leading to wide-scale changes to many synapses throughout the cortex. These changes are coordinated to enhance the representations of newly-significant stimuli, possibly for improved signal processing and language learning in humans. Copyright © 2011 Elsevier B.V. All rights reserved.

Spectrotemporal Dynamics of Auditory Cortical Synaptic Receptive Field Plasticity

PubMed Central

Froemke, Robert C.; Martins, Ana Raquel O.

2011-01-01

The nervous system must dynamically represent sensory information in order for animals to perceive and operate within a complex, changing environment. Receptive field plasticity in the auditory cortex allows cortical networks to organize around salient features of the sensory environment during postnatal development, and then subsequently refine these representations depending on behavioral context later in life. Here we review the major features of auditory cortical receptive field plasticity in young and adult animals, focusing on modifications to frequency tuning of synaptic inputs. Alteration in the patterns of acoustic input, including sensory deprivation and tonal exposure, leads to rapid adjustments of excitatory and inhibitory strengths that collectively determine the suprathreshold tuning curves of cortical neurons. Long-term cortical plasticity also requires co-activation of subcortical neuromodulatory control nuclei such as the cholinergic nucleus basalis, particularly in adults. Regardless of developmental stage, regulation of inhibition seems to be a general mechanism by which changes in sensory experience and neuromodulatory state can remodel cortical receptive fields. We discuss recent findings suggesting that the microdynamics of synaptic receptive field plasticity unfold as a multi-phase set of distinct phenomena, initiated by disrupting the balance between excitation and inhibition, and eventually leading to wide-scale changes to many synapses throughout the cortex. These changes are coordinated to enhance the representations of newly-significant stimuli, possibly for improved signal processing and language learning in humans. PMID:21426927

Analysis and selection of optimal function implementations in massively parallel computer

DOEpatents

Archer, Charles Jens [Rochester, MN; Peters, Amanda [Rochester, MN; Ratterman, Joseph D [Rochester, MN

2011-05-31

An apparatus, program product and method optimize the operation of a parallel computer system by, in part, collecting performance data for a set of implementations of a function capable of being executed on the parallel computer system based upon the execution of the set of implementations under varying input parameters in a plurality of input dimensions. The collected performance data may be used to generate selection program code that is configured to call selected implementations of the function in response to a call to the function under varying input parameters. The collected performance data may be used to perform more detailed analysis to ascertain the comparative performance of the set of implementations of the function under the varying input parameters.

New control concepts for uncertain water resources systems: 1. Theory

NASA Astrophysics Data System (ADS)

Georgakakos, Aris P.; Yao, Huaming

1993-06-01

A major complicating factor in water resources systems management is handling unknown inputs. Stochastic optimization provides a sound mathematical framework but requires that enough data exist to develop statistical input representations. In cases where data records are insufficient (e.g., extreme events) or atypical of future input realizations, stochastic methods are inadequate. This article presents a control approach where input variables are only expected to belong in certain sets. The objective is to determine sets of admissible control actions guaranteeing that the system will remain within desirable bounds. The solution is based on dynamic programming and derived for the case where all sets are convex polyhedra. A companion paper (Yao and Georgakakos, this issue) addresses specific applications and problems in relation to reservoir system management.

Chemical sensors are hybrid-input memristors

NASA Astrophysics Data System (ADS)

Sysoev, V. I.; Arkhipov, V. E.; Okotrub, A. V.; Pershin, Y. V.

2018-04-01

Memristors are two-terminal electronic devices whose resistance depends on the history of input signal (voltage or current). Here we demonstrate that the chemical gas sensors can be considered as memristors with a generalized (hybrid) input, namely, with the input consisting of the voltage, analyte concentrations and applied temperature. The concept of hybrid-input memristors is demonstrated experimentally using a single-walled carbon nanotubes chemical sensor. It is shown that with respect to the hybrid input, the sensor exhibits some features common with memristors such as the hysteretic input-output characteristics. This different perspective on chemical gas sensors may open new possibilities for smart sensor applications.

Low-Rank Discriminant Embedding for Multiview Learning.

PubMed

Li, Jingjing; Wu, Yue; Zhao, Jidong; Lu, Ke

2017-11-01

This paper focuses on the specific problem of multiview learning where samples have the same feature set but different probability distributions, e.g., different viewpoints or different modalities. Since samples lying in different distributions cannot be compared directly, this paper aims to learn a latent subspace shared by multiple views assuming that the input views are generated from this latent subspace. Previous approaches usually learn the common subspace by either maximizing the empirical likelihood, or preserving the geometric structure. However, considering the complementarity between the two objectives, this paper proposes a novel approach, named low-rank discriminant embedding (LRDE), for multiview learning by taking full advantage of both sides. By further considering the duality between data points and features of multiview scene, i.e., data points can be grouped based on their distribution on features, while features can be grouped based on their distribution on the data points, LRDE not only deploys low-rank constraints on both sample level and feature level to dig out the shared factors across different views, but also preserves geometric information in both the ambient sample space and the embedding feature space by designing a novel graph structure under the framework of graph embedding. Finally, LRDE jointly optimizes low-rank representation and graph embedding in a unified framework. Comprehensive experiments in both multiview manner and pairwise manner demonstrate that LRDE performs much better than previous approaches proposed in recent literatures.

Method of generating features optimal to a dataset and classifier

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bruillard, Paul J.; Gosink, Luke J.; Jarman, Kenneth D.

A method of generating features optimal to a particular dataset and classifier is disclosed. A dataset of messages is inputted and a classifier is selected. An algebra of features is encoded. Computable features that are capable of describing the dataset from the algebra of features are selected. Irredundant features that are optimal for the classifier and the dataset are selected.

Reconfigurable Fault Tolerance for FPGAs

NASA Technical Reports Server (NTRS)

Shuler, Robert, Jr.

2010-01-01

The invention allows a field-programmable gate array (FPGA) or similar device to be efficiently reconfigured in whole or in part to provide higher capacity, non-redundant operation. The redundant device consists of functional units such as adders or multipliers, configuration memory for the functional units, a programmable routing method, configuration memory for the routing method, and various other features such as block RAM, I/O (random access memory, input/output) capability, dedicated carry logic, etc. The redundant device has three identical sets of functional units and routing resources and majority voters that correct errors. The configuration memory may or may not be redundant, depending on need. For example, SRAM-based FPGAs will need some type of radiation-tolerant configuration memory, or they will need triple-redundant configuration memory. Flash or anti-fuse devices will generally not need redundant configuration memory. Some means of loading and verifying the configuration memory is also required. These are all components of the pre-existing redundant FPGA. This innovation modifies the voter to accept a MODE input, which specifies whether ordinary voting is to occur, or if redundancy is to be split. Generally, additional routing resources will also be required to pass data between sections of the device created by splitting the redundancy. In redundancy mode, the voters produce an output corresponding to the two inputs that agree, in the usual fashion. In the split mode, the voters select just one input and convey this to the output, ignoring the other inputs. In a dual-redundant system (as opposed to triple-redundant), instead of a voter, there is some means to latch or gate a state update only when both inputs agree. In this case, the invention would require modification of the latch or gate so that it would operate normally in redundant mode, and would separately latch or gate the inputs in non-redundant mode.

Stereoscopic Feature Tracking System for Retrieving Velocity of Surface Waters

NASA Astrophysics Data System (ADS)

Zuniga Zamalloa, C. C.; Landry, B. J.

2017-12-01

The present work is concerned with the surface velocity retrieval of flows using a stereoscopic setup and finding the correspondence in the images via feature tracking (FT). The feature tracking provides a key benefit of substantially reducing the level of user input. In contrast to other commonly used methods (e.g., normalized cross-correlation), FT does not require the user to prescribe interrogation window sizes and removes the need for masking when specularities are present. The results of the current FT methodology are comparable to those obtained via Large Scale Particle Image Velocimetry while requiring little to no user input which allowed for rapid, automated processing of imagery.

Evolvable Neural Software System

NASA Technical Reports Server (NTRS)

Curtis, Steven A.

2009-01-01

The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

LiveWire interactive boundary extraction algorithm based on Haar wavelet transform and control point set direction search

NASA Astrophysics Data System (ADS)

Cheng, Jun; Zhang, Jun; Tian, Jinwen

2015-12-01

Based on deep analysis of the LiveWire interactive boundary extraction algorithm, a new algorithm focusing on improving the speed of LiveWire algorithm is proposed in this paper. Firstly, the Haar wavelet transform is carried on the input image, and the boundary is extracted on the low resolution image obtained by the wavelet transform of the input image. Secondly, calculating LiveWire shortest path is based on the control point set direction search by utilizing the spatial relationship between the two control points users provide in real time. Thirdly, the search order of the adjacent points of the starting node is set in advance. An ordinary queue instead of a priority queue is taken as the storage pool of the points when optimizing their shortest path value, thus reducing the complexity of the algorithm from O[n2] to O[n]. Finally, A region iterative backward projection method based on neighborhood pixel polling has been used to convert dual-pixel boundary of the reconstructed image to single-pixel boundary after Haar wavelet inverse transform. The algorithm proposed in this paper combines the advantage of the Haar wavelet transform and the advantage of the optimal path searching method based on control point set direction search. The former has fast speed of image decomposition and reconstruction and is more consistent with the texture features of the image and the latter can reduce the time complexity of the original algorithm. So that the algorithm can improve the speed in interactive boundary extraction as well as reflect the boundary information of the image more comprehensively. All methods mentioned above have a big role in improving the execution efficiency and the robustness of the algorithm.

Role Discovery in Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-08-14

RolX takes the features from Re-FeX or any other feature matrix as input and outputs role assignments (clusters). The output of RolX is a csv file containing the node-role memberships and a csv file containing the role-feature definitions.

RET selection on state-of-the-art NAND flash

NASA Astrophysics Data System (ADS)

Lafferty, Neal V.; He, Yuan; Pei, Jinhua; Shao, Feng; Liu, QingWei; Shi, Xuelong

2015-03-01

We present results generated using a new gauge-based Resolution Enhancement Technique (RET) Selection flow during the technology set up phase of a 3x-node NAND Flash product. As a testcase, we consider a challenging critical level for this ash product. The RET solutions include inverse lithography technology (ILT) optimized masks with sub-resolution assist features (SRAF) and companion illumination sources developed using a new pixel based Source Mask Optimization (SMO) tool that uses measurement gauges as a primary input. The flow includes verification objectives which allow tolerancing of particular measurement gauges based on lithographic criteria. Relative importance for particular gauges may also be set, to aid in down-selection from several candidate sources. The end result is a sensitive, objective score of RET performance. Using these custom-defined importance metrics, decisions on the final RET style can be made in an objective way.

Bayesian learning of visual chunks by human observers

PubMed Central

Orbán, Gergő; Fiser, József; Aslin, Richard N.; Lengyel, Máté

2008-01-01

Efficient and versatile processing of any hierarchically structured information requires a learning mechanism that combines lower-level features into higher-level chunks. We investigated this chunking mechanism in humans with a visual pattern-learning paradigm. We developed an ideal learner based on Bayesian model comparison that extracts and stores only those chunks of information that are minimally sufficient to encode a set of visual scenes. Our ideal Bayesian chunk learner not only reproduced the results of a large set of previous empirical findings in the domain of human pattern learning but also made a key prediction that we confirmed experimentally. In accordance with Bayesian learning but contrary to associative learning, human performance was well above chance when pair-wise statistics in the exemplars contained no relevant information. Thus, humans extract chunks from complex visual patterns by generating accurate yet economical representations and not by encoding the full correlational structure of the input. PMID:18268353

ConfocalGN: A minimalistic confocal image generator

NASA Astrophysics Data System (ADS)

Dmitrieff, Serge; Nédélec, François

Validating image analysis pipelines and training machine-learning segmentation algorithms require images with known features. Synthetic images can be used for this purpose, with the advantage that large reference sets can be produced easily. It is however essential to obtain images that are as realistic as possible in terms of noise and resolution, which is challenging in the field of microscopy. We describe ConfocalGN, a user-friendly software that can generate synthetic microscopy stacks from a ground truth (i.e. the observed object) specified as a 3D bitmap or a list of fluorophore coordinates. This software can analyze a real microscope image stack to set the noise parameters and directly generate new images of the object with noise characteristics similar to that of the sample image. With a minimal input from the user and a modular architecture, ConfocalGN is easily integrated with existing image analysis solutions.

Face recognition via Gabor and convolutional neural network

NASA Astrophysics Data System (ADS)

Lu, Tongwei; Wu, Menglu; Lu, Tao

2018-04-01

In recent years, the powerful feature learning and classification ability of convolutional neural network have attracted widely attention. Compared with the deep learning, the traditional machine learning algorithm has a good explanatory which deep learning does not have. Thus, In this paper, we propose a method to extract the feature of the traditional algorithm as the input of convolution neural network. In order to reduce the complexity of the network, the kernel function of Gabor wavelet is used to extract the feature from different position, frequency and direction of target image. It is sensitive to edge of image which can provide good direction and scale selection. The extraction of the image from eight directions on a scale are as the input of network that we proposed. The network have the advantage of weight sharing and local connection and texture feature of the input image can reduce the influence of facial expression, gesture and illumination. At the same time, we introduced a layer which combined the results of the pooling and convolution can extract deeper features. The training network used the open source caffe framework which is beneficial to feature extraction. The experiment results of the proposed method proved that the network structure effectively overcame the barrier of illumination and had a good robustness as well as more accurate and rapid than the traditional algorithm.

COSP for Windows: Strategies for Rapid Analyses of Cyclic Oxidation Behavior

NASA Technical Reports Server (NTRS)

Smialek, James L.; Auping, Judith V.

2002-01-01

COSP is a publicly available computer program that models the cyclic oxidation weight gain and spallation process. Inputs to the model include the selection of an oxidation growth law and a spalling geometry, plus oxide phase, growth rate, spall constant, and cycle duration parameters. Output includes weight change, the amounts of retained and spalled oxide, the total oxygen and metal consumed, and the terminal rates of weight loss and metal consumption. The present version is Windows based and can accordingly be operated conveniently while other applications remain open for importing experimental weight change data, storing model output data, or plotting model curves. Point-and-click operating features include multiple drop-down menus for input parameters, data importing, and quick, on-screen plots showing one selection of the six output parameters for up to 10 models. A run summary text lists various characteristic parameters that are helpful in describing cyclic behavior, such as the maximum weight change, the number of cycles to reach the maximum weight gain or zero weight change, the ratio of these, and the final rate of weight loss. The program includes save and print options as well as a help file. Families of model curves readily show the sensitivity to various input parameters. The cyclic behaviors of nickel aluminide (NiAl) and a complex superalloy are shown to be properly fitted by model curves. However, caution is always advised regarding the uniqueness claimed for any specific set of input parameters,

An algorithm for calculating minimum Euclidean distance between two geographic features

NASA Astrophysics Data System (ADS)

Peuquet, Donna J.

1992-09-01

An efficient algorithm is presented for determining the shortest Euclidean distance between two features of arbitrary shape that are represented in quadtree form. These features may be disjoint point sets, lines, or polygons. It is assumed that the features do not overlap. Features also may be intertwined and polygons may be complex (i.e. have holes). Utilizing a spatial divide-and-conquer approach inherent in the quadtree data model, the basic rationale is to narrow-in on portions of each feature quickly that are on a facing edge relative to the other feature, and to minimize the number of point-to-point Euclidean distance calculations that must be performed. Besides offering an efficient, grid-based alternative solution, another unique and useful aspect of the current algorithm is that is can be used for rapidly calculating distance approximations at coarser levels of resolution. The overall process can be viewed as a top-down parallel search. Using one list of leafcode addresses for each of the two features as input, the algorithm is implemented by successively dividing these lists into four sublists for each descendant quadrant. The algorithm consists of two primary phases. The first determines facing adjacent quadrant pairs where part or all of the two features are separated between the two quadrants, respectively. The second phase then determines the closest pixel-level subquadrant pairs within each facing quadrant pair at the lowest level. The key element of the second phase is a quick estimate distance heuristic for further elimination of locations that are not as near as neighboring locations.

Reconfigurable data path processor

NASA Technical Reports Server (NTRS)

Donohoe, Gregory (Inventor)

2005-01-01

A reconfigurable data path processor comprises a plurality of independent processing elements. Each of the processing elements advantageously comprising an identical architecture. Each processing element comprises a plurality of data processing means for generating a potential output. Each processor is also capable of through-putting an input as a potential output with little or no processing. Each processing element comprises a conditional multiplexer having a first conditional multiplexer input, a second conditional multiplexer input and a conditional multiplexer output. A first potential output value is transmitted to the first conditional multiplexer input, and a second potential output value is transmitted to the second conditional multiplexer output. The conditional multiplexer couples either the first conditional multiplexer input or the second conditional multiplexer input to the conditional multiplexer output, according to an output control command. The output control command is generated by processing a set of arithmetic status-bits through a logical mask. The conditional multiplexer output is coupled to a first processing element output. A first set of arithmetic bits are generated according to the processing of the first processable value. A second set of arithmetic bits may be generated from a second processing operation. The selection of the arithmetic status-bits is performed by an arithmetic-status bit multiplexer selects the desired set of arithmetic status bits from among the first and second set of arithmetic status bits. The conditional multiplexer evaluates the select arithmetic status bits according to logical mask defining an algorithm for evaluating the arithmetic status bits.

Automated method for measuring the extent of selective logging damage with airborne LiDAR data

NASA Astrophysics Data System (ADS)

Melendy, L.; Hagen, S. C.; Sullivan, F. B.; Pearson, T. R. H.; Walker, S. M.; Ellis, P.; Kustiyo; Sambodo, Ari Katmoko; Roswintiarti, O.; Hanson, M. A.; Klassen, A. W.; Palace, M. W.; Braswell, B. H.; Delgado, G. M.

2018-05-01

Selective logging has an impact on the global carbon cycle, as well as on the forest micro-climate, and longer-term changes in erosion, soil and nutrient cycling, and fire susceptibility. Our ability to quantify these impacts is dependent on methods and tools that accurately identify the extent and features of logging activity. LiDAR-based measurements of these features offers significant promise. Here, we present a set of algorithms for automated detection and mapping of critical features associated with logging - roads/decks, skid trails, and gaps - using commercial airborne LiDAR data as input. The automated algorithm was applied to commercial LiDAR data collected over two logging concessions in Kalimantan, Indonesia in 2014. The algorithm results were compared to measurements of the logging features collected in the field soon after logging was complete. The automated algorithm-mapped road/deck and skid trail features match closely with features measured in the field, with agreement levels ranging from 69% to 99% when adjusting for GPS location error. The algorithm performed most poorly with gaps, which, by their nature, are variable due to the unpredictable impact of tree fall versus the linear and regular features directly created by mechanical means. Overall, the automated algorithm performs well and offers significant promise as a generalizable tool useful to efficiently and accurately capture the effects of selective logging, including the potential to distinguish reduced impact logging from conventional logging.

Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures

PubMed Central

Bryson, David M.; Ofria, Charles

2013-01-01

We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architecture to seven different computational environments designed to present a range of evolutionary challenges. Our goal was to engineer a general purpose architecture that would be effective under a broad range of evolutionary conditions. We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow. We also tested a variety of methods to regulate expression: (4) explicit labels that allow programs to dynamically refer to specific genome positions, (5) position-relative search instructions, and (6) multiple new flow control instructions, including conditionals and jumps. Each of these features also adds complication to the instruction set and risks slowing evolution due to epistatic interactions. Two features (multiple argument specification and separated I/O) demonstrated substantial improvements in the majority of test environments, along with versions of each of the remaining architecture modifications that show significant improvements in multiple environments. However, some tested modifications were detrimental, though most exhibit no systematic effects on evolutionary potential, highlighting the robustness of digital evolution. Combined, these observations enhance our understanding of how instruction architecture impacts evolutionary potential, enabling the creation of architectures that support more rapid evolution of complex solutions to a broad range of challenges. PMID:24376669

Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods.

PubMed

Uyar, Asli; Bener, Ayse; Ciray, H Nadir

2015-08-01

Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies. To predict implantation outcome of individual embryos in an IVF cycle with the aim of providing decision support on the number of embryos transferred. Retrospective cohort study. Electronic health records of one of the largest IVF clinics in Turkey. The study data set included 2453 embryos transferred at day 2 or day 3 after intracytoplasmic sperm injection (ICSI). Each embryo was represented with 18 clinical features and a class label, +1 or -1, indicating positive and negative implantation outcomes, respectively. For each classifier tested, a model was developed using two-thirds of the data set, and prediction performance was evaluated on the remaining one-third of the samples using receiver operating characteristic (ROC) analysis. The training-testing procedure was repeated 10 times on randomly split (two-thirds to one-third) data. The relative predictive values of clinical input characteristics were assessed using information gain feature weighting and forward feature selection methods. The naïve Bayes model provided 80.4% accuracy, 63.7% sensitivity, and 17.6% false alarm rate in embryo-based implantation prediction. Multiple embryo implantations were predicted at a 63.8% sensitivity level. Predictions using the proposed model resulted in higher accuracy compared with expert judgment alone (on average, 75.7% and 60.1%, respectively). A machine learning-based decision support system would be useful in improving the success rates of IVF treatment. © The Author(s) 2014.

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases.

PubMed

Creasy, John M; Midya, Abhishek; Chakraborty, Jayasree; Adams, Lauryn B; Gomes, Camilla; Gonen, Mithat; Seastedt, Kenneth P; Sutton, Elizabeth J; Cercek, Andrea; Kemeny, Nancy E; Shia, Jinru; Balachandran, Vinod P; Kingham, T Peter; Allen, Peter J; DeMatteo, Ronald P; Jarnagin, William R; D'Angelica, Michael I; Do, Richard K G; Simpson, Amber L

2018-06-19

This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM). Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R 2 . Clinicopatholologic factors were assessed for correlation with response. 157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R 2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05). Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies. • Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be measured with quantitative imaging. • Prediction model constructed that predicts volumetric response with 20% error suggesting that quantitative imaging holds promise to better select patients for specific treatments.

A review of instruments to measure interprofessional team-based primary care.

PubMed

Shoemaker, Sarah J; Parchman, Michael L; Fuda, Kathleen Kerwin; Schaefer, Judith; Levin, Jessica; Hunt, Meaghan; Ricciardi, Richard

2016-07-01

Interprofessional team-based care is increasingly regarded as an important feature of delivery systems redesigned to provide more efficient and higher quality care, including primary care. Measurement of the functioning of such teams might enable improvement of team effectiveness and could facilitate research on team-based primary care. Our aims were to develop a conceptual framework of high-functioning primary care teams to identify and review instruments that measure the constructs identified in the framework, and to create a searchable, web-based atlas of such instruments (available at: http://primarycaremeasures.ahrq.gov/team-based-care/ ). Our conceptual framework was developed from existing frameworks, the teamwork literature, and expert input. The framework is based on an Input-Mediator-Output model and includes 12 constructs to which we mapped both instruments as a whole, and individual instrument items. Instruments were also reviewed for relevance to measuring team-based care, and characterized. Instruments were identified from peer-reviewed and grey literature, measure databases, and expert input. From nearly 200 instruments initially identified, we found 48 to be relevant to measuring team-based primary care. The majority of instruments were surveys (n = 44), and the remainder (n = 4) were observational checklists. Most instruments had been developed/tested in healthcare settings (n = 30) and addressed multiple constructs, most commonly communication (n = 42), heedful interrelating (n = 42), respectful interactions (n = 40), and shared explicit goals (n = 37). The majority of instruments had some reliability testing (n = 39) and over half included validity testing (n = 29). Currently available instruments offer promise to researchers and practitioners to assess teams' performance, but additional work is needed to adapt these instruments for primary care settings.

AQUATOX Features and Tools

EPA Pesticide Factsheets

Numerous features have been included to facilitate the modeling process, from model setup and data input, presentation and analysis of results, to easy export of results to spreadsheet programs for additional analysis.

A soft computing based approach using modified selection strategy for feature reduction of medical systems.

PubMed

Zuhtuogullari, Kursat; Allahverdi, Novruz; Arikan, Nihat

2013-01-01

The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes) with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data.

A Soft Computing Based Approach Using Modified Selection Strategy for Feature Reduction of Medical Systems

PubMed Central

Zuhtuogullari, Kursat; Allahverdi, Novruz; Arikan, Nihat

2013-01-01

The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes) with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data. PMID:23573172

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

PubMed Central

Ma, Suliang; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan

2018-01-01

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods. PMID:29659548

Tele-Autonomous control involving contact. Final Report Thesis; [object localization

NASA Technical Reports Server (NTRS)

Shao, Lejun; Volz, Richard A.; Conway, Lynn; Walker, Michael W.

1990-01-01

Object localization and its application in tele-autonomous systems are studied. Two object localization algorithms are presented together with the methods of extracting several important types of object features. The first algorithm is based on line-segment to line-segment matching. Line range sensors are used to extract line-segment features from an object. The extracted features are matched to corresponding model features to compute the location of the object. The inputs of the second algorithm are not limited only to the line features. Featured points (point to point matching) and featured unit direction vectors (vector to vector matching) can also be used as the inputs of the algorithm, and there is no upper limit on the number of the features inputed. The algorithm will allow the use of redundant features to find a better solution. The algorithm uses dual number quaternions to represent the position and orientation of an object and uses the least squares optimization method to find an optimal solution for the object's location. The advantage of using this representation is that the method solves for the location estimation by minimizing a single cost function associated with the sum of the orientation and position errors and thus has a better performance on the estimation, both in accuracy and speed, than that of other similar algorithms. The difficulties when the operator is controlling a remote robot to perform manipulation tasks are also discussed. The main problems facing the operator are time delays on the signal transmission and the uncertainties of the remote environment. How object localization techniques can be used together with other techniques such as predictor display and time desynchronization to help to overcome these difficulties are then discussed.

High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features.

PubMed

Jones, David T; Kandathil, Shaun M

2018-04-26

In addition to substitution frequency data from protein sequence alignments, many state-of-the-art methods for contact prediction rely on additional sources of information, or features, of protein sequences in order to predict residue-residue contacts, such as solvent accessibility, predicted secondary structure, and scores from other contact prediction methods. It is unclear how much of this information is needed to achieve state-of-the-art results. Here, we show that using deep neural network models, simple alignment statistics contain sufficient information to achieve state-of-the-art precision. Our prediction method, DeepCov, uses fully convolutional neural networks operating on amino-acid pair frequency or covariance data derived directly from sequence alignments, without using global statistical methods such as sparse inverse covariance or pseudolikelihood estimation. Comparisons against CCMpred and MetaPSICOV2 show that using pairwise covariance data calculated from raw alignments as input allows us to match or exceed the performance of both of these methods. Almost all of the achieved precision is obtained when considering relatively local windows (around 15 residues) around any member of a given residue pairing; larger window sizes have comparable performance. Assessment on a set of shallow sequence alignments (fewer than 160 effective sequences) indicates that the new method is substantially more precise than CCMpred and MetaPSICOV2 in this regime, suggesting that improved precision is attainable on smaller sequence families. Overall, the performance of DeepCov is competitive with the state of the art, and our results demonstrate that global models, which employ features from all parts of the input alignment when predicting individual contacts, are not strictly needed in order to attain precise contact predictions. DeepCov is freely available at https://github.com/psipred/DeepCov. d.t.jones@ucl.ac.uk.

Inverse analysis of turbidites by machine learning

NASA Astrophysics Data System (ADS)

Naruse, H.; Nakao, K.

2017-12-01

This study aims to propose a method to estimate paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine-learning technique. In this method, numerical simulation was repeated under various initial conditions, which produces a data set of characteristic features of turbidites. Then, this data set of turbidites is used for supervised training of a deep-learning neural network (NN). Quantities of characteristic features of turbidites in the training data set are given to input nodes of NN, and output nodes are expected to provide the estimates of initial condition of the turbidity current. The optimization of weight coefficients of NN is then conducted to reduce root-mean-square of the difference between the true conditions and the output values of NN. The empirical relationship with numerical results and the initial conditions is explored in this method, and the discovered relationship is used for inversion of turbidity currents. This machine learning can potentially produce NN that estimates paleo-hydraulic conditions from data of ancient turbidites. We produced a preliminary implementation of this methodology. A forward model based on 1D shallow-water equations with a correction of density-stratification effect was employed. This model calculates a behavior of a surge-like turbidity current transporting mixed-size sediment, and outputs spatial distribution of volume per unit area of each grain-size class on the uniform slope. Grain-size distribution was discretized 3 classes. Numerical simulation was repeated 1000 times, and thus 1000 beds of turbidites were used as the training data for NN that has 21000 input nodes and 5 output nodes with two hidden-layers. After the machine learning finished, independent simulations were conducted 200 times in order to evaluate the performance of NN. As a result of this test, the initial conditions of validation data were successfully reconstructed by NN. The estimated values show very small deviation from the true parameters. Comparing to previous inverse modeling of turbidity currents, our methodology is superior especially in the efficiency of computation. Also, our methodology has advantage in extensibility and applicability to various sediment transport processes such as pyroclastic flows or debris flows.

A Supervised Learning Process to Validate Online Disease Reports for Use in Predictive Models.

PubMed

Patching, Helena M M; Hudson, Laurence M; Cooke, Warrick; Garcia, Andres J; Hay, Simon I; Roberts, Mark; Moyes, Catherine L

2015-12-01

Pathogen distribution models that predict spatial variation in disease occurrence require data from a large number of geographic locations to generate disease risk maps. Traditionally, this process has used data from public health reporting systems; however, using online reports of new infections could speed up the process dramatically. Data from both public health systems and online sources must be validated before they can be used, but no mechanisms exist to validate data from online media reports. We have developed a supervised learning process to validate geolocated disease outbreak data in a timely manner. The process uses three input features, the data source and two metrics derived from the location of each disease occurrence. The location of disease occurrence provides information on the probability of disease occurrence at that location based on environmental and socioeconomic factors and the distance within or outside the current known disease extent. The process also uses validation scores, generated by disease experts who review a subset of the data, to build a training data set. The aim of the supervised learning process is to generate validation scores that can be used as weights going into the pathogen distribution model. After analyzing the three input features and testing the performance of alternative processes, we selected a cascade of ensembles comprising logistic regressors. Parameter values for the training data subset size, number of predictors, and number of layers in the cascade were tested before the process was deployed. The final configuration was tested using data for two contrasting diseases (dengue and cholera), and 66%-79% of data points were assigned a validation score. The remaining data points are scored by the experts, and the results inform the training data set for the next set of predictors, as well as going to the pathogen distribution model. The new supervised learning process has been implemented within our live site and is being used to validate the data that our system uses to produce updated predictive disease maps on a weekly basis.

Design of experiments and data analysis challenges in calibration for forensics applications

DOE PAGES

Anderson-Cook, Christine M.; Burr, Thomas L.; Hamada, Michael S.; ...

2015-07-15

Forensic science aims to infer characteristics of source terms using measured observables. Our focus is on statistical design of experiments and data analysis challenges arising in nuclear forensics. More specifically, we focus on inferring aspects of experimental conditions (of a process to produce product Pu oxide powder), such as temperature, nitric acid concentration, and Pu concentration, using measured features of the product Pu oxide powder. The measured features, Y, include trace chemical concentrations and particle morphology such as particle size and shape of the produced Pu oxide power particles. Making inferences about the nature of inputs X that were usedmore » to create nuclear materials having particular characteristics, Y, is an inverse problem. Therefore, statistical analysis can be used to identify the best set (or sets) of Xs for a new set of observed responses Y. One can fit a model (or models) such as Υ = f(Χ) + error, for each of the responses, based on a calibration experiment and then “invert” to solve for the best set of Xs for a new set of Ys. This perspectives paper uses archived experimental data to consider aspects of data collection and experiment design for the calibration data to maximize the quality of the predicted Ys in the forward models; that is, we assume that well-estimated forward models are effective in the inverse problem. In addition, we consider how to identify a best solution for the inferred X, and evaluate the quality of the result and its robustness to a variety of initial assumptions, and different correlation structures between the responses. In addition, we also briefly review recent advances in metrology issues related to characterizing particle morphology measurements used in the response vector, Y.« less

Design of experiments and data analysis challenges in calibration for forensics applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson-Cook, Christine M.; Burr, Thomas L.; Hamada, Michael S.

Forensic science aims to infer characteristics of source terms using measured observables. Our focus is on statistical design of experiments and data analysis challenges arising in nuclear forensics. More specifically, we focus on inferring aspects of experimental conditions (of a process to produce product Pu oxide powder), such as temperature, nitric acid concentration, and Pu concentration, using measured features of the product Pu oxide powder. The measured features, Y, include trace chemical concentrations and particle morphology such as particle size and shape of the produced Pu oxide power particles. Making inferences about the nature of inputs X that were usedmore » to create nuclear materials having particular characteristics, Y, is an inverse problem. Therefore, statistical analysis can be used to identify the best set (or sets) of Xs for a new set of observed responses Y. One can fit a model (or models) such as Υ = f(Χ) + error, for each of the responses, based on a calibration experiment and then “invert” to solve for the best set of Xs for a new set of Ys. This perspectives paper uses archived experimental data to consider aspects of data collection and experiment design for the calibration data to maximize the quality of the predicted Ys in the forward models; that is, we assume that well-estimated forward models are effective in the inverse problem. In addition, we consider how to identify a best solution for the inferred X, and evaluate the quality of the result and its robustness to a variety of initial assumptions, and different correlation structures between the responses. In addition, we also briefly review recent advances in metrology issues related to characterizing particle morphology measurements used in the response vector, Y.« less

A genetic fuzzy system for unstable angina risk assessment.

PubMed

Dong, Wei; Huang, Zhengxing; Ji, Lei; Duan, Huilong

2014-02-18

Unstable Angina (UA) is widely accepted as a critical phase of coronary heart disease with patients exhibiting widely varying risks. Early risk assessment of UA is at the center of the management program, which allows physicians to categorize patients according to the clinical characteristics and stratification of risk and different prognosis. Although many prognostic models have been widely used for UA risk assessment in clinical practice, a number of studies have highlighted possible shortcomings. One serious drawback is that existing models lack the ability to deal with the intrinsic uncertainty about the variables utilized. In order to help physicians refine knowledge for the stratification of UA risk with respect to vagueness in information, this paper develops an intelligent system combining genetic algorithm and fuzzy association rule mining. In detail, it models the input information's vagueness through fuzzy sets, and then applies a genetic fuzzy system on the acquired fuzzy sets to extract the fuzzy rule set for the problem of UA risk assessment. The proposed system is evaluated using a real data-set collected from the cardiology department of a Chinese hospital, which consists of 54 patient cases. 9 numerical patient features and 17 categorical patient features that appear in the data-set are selected in the experiments. The proposed system made the same decisions as the physician in 46 (out of a total of 54) tested cases (85.2%). By comparing the results that are obtained through the proposed system with those resulting from the physician's decision, it has been found that the developed model is highly reflective of reality. The proposed system could be used for educational purposes, and with further improvements, could assist and guide young physicians in their daily work.

Distinct findings from the steady-state analysis of a microbial model with time-invariant or seasonal driving forces

NASA Astrophysics Data System (ADS)

Wang, G.; Mayes, M. A.

2017-12-01

Microbially-explicit soil organic matter (SOM) decomposition models are thought to be more biologically realistic than conventional models. Current testing or evaluation of microbial models majorly uses steady-state analysis with time-invariant forces (i.e., soil temperature, moisture and litter input). The findings from such simplified analyses are assumed to be capable of representing the model responses in field soil conditions with seasonal driving forces. Here we show that the steady-state modeling results with seasonal forces may result in distinct findings from the simulations with time-invariant forcing data. We evaluate the response of soil organic C (SOC) to litter addition (L+) in a subtropical pine forest using the calibrated Microbial-ENzyme Decomposition (MEND) model. We implemented two sets of modeling analyses, with each set including two scenarios, i.e., control (CR) vs. litter-addition (L+). The first set (Set1) uses fixed soil temperature and moisture, and constant litter input under Scenario CR vs. increased constant litter input under Scenario L+. The second set (Set2) employs hourly soil temperature and moisture and monthly litter input under Scenario CR. Under Scenario L+ of Set2, A logistic function with an upper plateau represents the increasing trend of litter input to SOM. We conduct long-term simulations to ensure that the models reach steady-states for Set1 or dynamic equilibrium for Set2. Litter addition of Set2 causes an increase of SOC by 29%. However, the steady-state SOC pool sizes of Set1 would not respond to L+ as long as the chemical composition of litter remained the same. Our results indicate the necessity to implement dynamic model simulations with seasonal forcing data, which could lead to modeling results qualitatively different from the steady-state analysis with time-invariant forcing data.

ChIP-chip versus ChIP-seq: Lessons for experimental design and data analysis

PubMed Central

2011-01-01

Background Chromatin immunoprecipitation (ChIP) followed by microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) allows genome-wide discovery of protein-DNA interactions such as transcription factor bindings and histone modifications. Previous reports only compared a small number of profiles, and little has been done to compare histone modification profiles generated by the two technologies or to assess the impact of input DNA libraries in ChIP-seq analysis. Here, we performed a systematic analysis of a modENCODE dataset consisting of 31 pairs of ChIP-chip/ChIP-seq profiles of the coactivator CBP, RNA polymerase II (RNA PolII), and six histone modifications across four developmental stages of Drosophila melanogaster. Results Both technologies produce highly reproducible profiles within each platform, ChIP-seq generally produces profiles with a better signal-to-noise ratio, and allows detection of more peaks and narrower peaks. The set of peaks identified by the two technologies can be significantly different, but the extent to which they differ varies depending on the factor and the analysis algorithm. Importantly, we found that there is a significant variation among multiple sequencing profiles of input DNA libraries and that this variation most likely arises from both differences in experimental condition and sequencing depth. We further show that using an inappropriate input DNA profile can impact the average signal profiles around genomic features and peak calling results, highlighting the importance of having high quality input DNA data for normalization in ChIP-seq analysis. Conclusions Our findings highlight the biases present in each of the platforms, show the variability that can arise from both technology and analysis methods, and emphasize the importance of obtaining high quality and deeply sequenced input DNA libraries for ChIP-seq analysis. PMID:21356108

Adding dynamic rules to self-organizing fuzzy systems

NASA Technical Reports Server (NTRS)

Buhusi, Catalin V.

1992-01-01

This paper develops a Dynamic Self-Organizing Fuzzy System (DSOFS) capable of adding, removing, and/or adapting the fuzzy rules and the fuzzy reference sets. The DSOFS background consists of a self-organizing neural structure with neuron relocation features which will develop a map of the input-output behavior. The relocation algorithm extends the topological ordering concept. Fuzzy rules (neurons) are dynamically added or released while the neural structure learns the pattern. The DSOFS advantages are the automatic synthesis and the possibility of parallel implementation. A high adaptation speed and a reduced number of neurons is needed in order to keep errors under some limits. The computer simulation results are presented in a nonlinear systems modelling application.

Neutron total cross-section of hydrogenous and deuterated 1- and 2-propanol and n-butanol measured using the VESUVIO spectrometer

NASA Astrophysics Data System (ADS)

Rodríguez Palomino, L. A.; Dawidowski, J.; Márquez Damián, J. I.; Cuello, G. J.; Romanelli, G.; Krzystyniak, M.

2017-10-01

This work presents the total cross sections of a set of normal and deuterated alcohols (hydrogenous 1- and 2-propanol and n-butanol, 1-propanol(OD) and fully deuterated 2-propanol and n-butanol), measured at spectrometer VESUVIO (ISIS spallation neutron source, United Kingdom). Granada's Synthetic Model was applied to describe those systems and a satisfactory agreement with the measured total cross section was achieved in the range of energies from 10-3 to 100 eV. The input parameters of the model were determined from the essential features of the vibrational spectra of the atoms that compose the systems, which were studied using Molecular Dynamics.

STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python

PubMed Central

Wils, Stefan; Schutter, Erik De

2008-01-01

We describe how the use of the Python language improved the user interface of the program STEPS. STEPS is a simulation platform for modeling and stochastic simulation of coupled reaction-diffusion systems with complex 3-dimensional boundary conditions. Setting up such models is a complicated process that consists of many phases. Initial versions of STEPS relied on a static input format that did not cleanly separate these phases, limiting modelers in how they could control the simulation and becoming increasingly complex as new features and new simulation algorithms were added. We solved all of these problems by tightly integrating STEPS with Python, using SWIG to expose our existing simulation code. PMID:19623245

Digital Beamforming Synthetic Aperture Radar Developments at NASA Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Rincon, Rafael; Fatoyinbo, Temilola; Osmanoglu, Batuhan; Lee, Seung Kuk; Du Toit, Cornelis F.; Perrine, Martin; Ranson, K. Jon; Sun, Guoqing; Deshpande, Manohar; Beck, Jaclyn;

Learned filters for object detection in multi-object visual tracking

NASA Astrophysics Data System (ADS)

Stamatescu, Victor; Wong, Sebastien; McDonnell, Mark D.; Kearney, David

2016-05-01

We investigate the application of learned convolutional filters in multi-object visual tracking. The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks. This work follows recent results in the field of machine learning that demonstrate the use learned filters for enhanced object detection and classification. Here we employ a track-before-detect approach to multi-object tracking, where tracking guides the detection process. The object detection provides a probabilistic input image calculated by selecting from features obtained using banks of generative or discriminative learned filters. We present a systematic evaluation of these convolutional filters using a real-world data set that examines their performance as generic object detectors.

QX MAN: Q and X file manipulation

NASA Technical Reports Server (NTRS)

Krein, Mark A.

1992-01-01

QX MAN is a grid and solution file manipulation program written primarily for the PARC code and the GRIDGEN family of grid generation codes. QX MAN combines many of the features frequently encountered in grid generation, grid refinement, the setting-up of initial conditions, and post processing. QX MAN allows the user to manipulate single block and multi-block grids (and their accompanying solution files) by splitting, concatenating, rotating, translating, re-scaling, and stripping or adding points. In addition, QX MAN can be used to generate an initial solution file for the PARC code. The code was written to provide several formats for input and output in order for it to be useful in a broad spectrum of applications.

Large-scale urban point cloud labeling and reconstruction

NASA Astrophysics Data System (ADS)

Zhang, Liqiang; Li, Zhuqiang; Li, Anjian; Liu, Fangyu

2018-04-01

The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. In this paper, a novel framework is proposed for classification and reconstruction of airborne laser scanning point cloud data. To label point clouds, we present a rectified linear units neural network named ReLu-NN where the rectified linear units (ReLu) instead of the traditional sigmoid are taken as the activation function in order to speed up the convergence. Since the features of the point cloud are sparse, we reduce the number of neurons by the dropout to avoid over-fitting of the training process. The set of feature descriptors for each 3D point is encoded through self-taught learning, and forms a discriminative feature representation which is taken as the input of the ReLu-NN. The segmented building points are consolidated through an edge-aware point set resampling algorithm, and then they are reconstructed into 3D lightweight models using the 2.5D contouring method (Zhou and Neumann, 2010). Compared with deep learning approaches, the ReLu-NN introduced can easily classify unorganized point clouds without rasterizing the data, and it does not need a large number of training samples. Most of the parameters in the network are learned, and thus the intensive parameter tuning cost is significantly reduced. Experimental results on various datasets demonstrate that the proposed framework achieves better performance than other related algorithms in terms of classification accuracy and reconstruction quality.

Software-hardware complex for the input of telemetric information obtained from rocket studies of the radiation of the earth's upper atmosphere

NASA Astrophysics Data System (ADS)

Bazdrov, I. I.; Bortkevich, V. S.; Khokhlov, V. N.

2004-10-01

This paper describes a software-hardware complex for the input into a personal computer of telemetric information obtained by means of telemetry stations TRAL KR28, RTS-8, and TRAL K2N. Structural and functional diagrams are given of the input device and the hardware complex. Results that characterize the features of the input process and selective data of optical measurements of atmospheric radiation are given. © 2004

Automated Classification of ROSAT Sources Using Heterogeneous Multiwavelength Source Catalogs

NASA Technical Reports Server (NTRS)

McGlynn, Thomas; Suchkov, A. A.; Winter, E. L.; Hanisch, R. J.; White, R. L.; Ochsenbein, F.; Derriere, S.; Voges, W.; Corcoran, M. F.

2004-01-01

We describe an on-line system for automated classification of X-ray sources, ClassX, and present preliminary results of classification of the three major catalogs of ROSAT sources, RASS BSC, RASS FSC, and WGACAT, into six class categories: stars, white dwarfs, X-ray binaries, galaxies, AGNs, and clusters of galaxies. ClassX is based on a machine learning technology. It represents a system of classifiers, each classifier consisting of a considerable number of oblique decision trees. These trees are built as the classifier is 'trained' to recognize various classes of objects using a training sample of sources of known object types. Each source is characterized by a preselected set of parameters, or attributes; the same set is then used as the classifier conducts classification of sources of unknown identity. The ClassX pipeline features an automatic search for X-ray source counterparts among heterogeneous data sets in on-line data archives using Virtual Observatory protocols; it retrieves from those archives all the attributes required by the selected classifier and inputs them to the classifier. The user input to ClassX is typically a file with target coordinates, optionally complemented with target IDs. The output contains the class name, attributes, and class probabilities for all classified targets. We discuss ways to characterize and assess the classifier quality and performance and present the respective validation procedures. Based on both internal and external validation, we conclude that the ClassX classifiers yield reasonable and reliable classifications for ROSAT sources and have the potential to broaden class representation significantly for rare object types.

A comparison of various algorithms to extract Magic Formula tyre model coefficients for vehicle dynamics simulations

NASA Astrophysics Data System (ADS)

Vijay Alagappan, A.; Narasimha Rao, K. V.; Krishna Kumar, R.

2015-02-01

Tyre models are a prerequisite for any vehicle dynamics simulation. Tyre models range from the simplest mathematical models that consider only the cornering stiffness to a complex set of formulae. Among all the steady-state tyre models that are in use today, the Magic Formula tyre model is unique and most popular. Though the Magic Formula tyre model is widely used, obtaining the model coefficients from either the experimental or the simulation data is not straightforward due to its nonlinear nature and the presence of a large number of coefficients. A common procedure used for this extraction is the least-squares minimisation that requires considerable experience for initial guesses. Various researchers have tried different algorithms, namely, gradient and Newton-based methods, differential evolution, artificial neural networks, etc. The issues involved in all these algorithms are setting bounds or constraints, sensitivity of the parameters, the features of the input data such as the number of points, noisy data, experimental procedure used such as slip angle sweep or tyre measurement (TIME) procedure, etc. The extracted Magic Formula coefficients are affected by these variants. This paper highlights the issues that are commonly encountered in obtaining these coefficients with different algorithms, namely, least-squares minimisation using trust region algorithms, Nelder-Mead simplex, pattern search, differential evolution, particle swarm optimisation, cuckoo search, etc. A key observation is that not all the algorithms give the same Magic Formula coefficients for a given data. The nature of the input data and the type of the algorithm decide the set of the Magic Formula tyre model coefficients.

Two-view information fusion for improvement of computer-aided detection (CAD) of breast masses on mammograms

NASA Astrophysics Data System (ADS)

Wei, Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Zhou, Chuan; Ge, Jun; Zhang, Yiheng

2006-03-01

We are developing a two-view information fusion method to improve the performance of our CAD system for mass detection. Mass candidates on each mammogram were first detected with our single-view CAD system. Potential object pairs on the two-view mammograms were then identified by using the distance between the object and the nipple. Morphological features, Hessian feature, correlation coefficients between the two paired objects and texture features were used as input to train a similarity classifier that estimated a similarity scores for each pair. Finally, a linear discriminant analysis (LDA) classifier was used to fuse the score from the single-view CAD system and the similarity score. A data set of 475 patients containing 972 mammograms with 475 biopsy-proven masses was used to train and test the CAD system. All cases contained the CC view and the MLO or LM view. We randomly divided the data set into two independent sets of 243 cases and 232 cases. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained from averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 85% and 80% on the test set, the single-view CAD system achieved an FP rate of 2.0, 1.5, and 1.2 FPs/image, respectively. With the two-view fusion system, the FP rates were reduced to 1.7, 1.3, and 1.0 FPs/image, respectively, at the corresponding sensitivities. The improvement was found to be statistically significant (p<0.05) by the AFROC method. Our results indicate that the two-view fusion scheme can improve the performance of mass detection on mammograms.

OpenCL based machine learning labeling of biomedical datasets

NASA Astrophysics Data System (ADS)

Amoros, Oscar; Escalera, Sergio; Puig, Anna

2011-03-01

In this paper, we propose a two-stage labeling method of large biomedical datasets through a parallel approach in a single GPU. Diagnostic methods, structures volume measurements, and visualization systems are of major importance for surgery planning, intra-operative imaging and image-guided surgery. In all cases, to provide an automatic and interactive method to label or to tag different structures contained into input data becomes imperative. Several approaches to label or segment biomedical datasets has been proposed to discriminate different anatomical structures in an output tagged dataset. Among existing methods, supervised learning methods for segmentation have been devised to easily analyze biomedical datasets by a non-expert user. However, they still have some problems concerning practical application, such as slow learning and testing speeds. In addition, recent technological developments have led to widespread availability of multi-core CPUs and GPUs, as well as new software languages, such as NVIDIA's CUDA and OpenCL, allowing to apply parallel programming paradigms in conventional personal computers. Adaboost classifier is one of the most widely applied methods for labeling in the Machine Learning community. In a first stage, Adaboost trains a binary classifier from a set of pre-labeled samples described by a set of features. This binary classifier is defined as a weighted combination of weak classifiers. Each weak classifier is a simple decision function estimated on a single feature value. Then, at the testing stage, each weak classifier is independently applied on the features of a set of unlabeled samples. In this work, we propose an alternative representation of the Adaboost binary classifier. We use this proposed representation to define a new GPU-based parallelized Adaboost testing stage using OpenCL. We provide numerical experiments based on large available data sets and we compare our results to CPU-based strategies in terms of time and labeling speeds.

Continuous-Time Bilinear System Identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan

2003-01-01

The objective of this paper is to describe a new method for identification of a continuous-time multi-input and multi-output bilinear system. The approach is to make judicious use of the linear-model properties of the bilinear system when subjected to a constant input. Two steps are required in the identification process. The first step is to use a set of pulse responses resulting from a constant input of one sample period to identify the state matrix, the output matrix, and the direct transmission matrix. The second step is to use another set of pulse responses with the same constant input over multiple sample periods to identify the input matrix and the coefficient matrices associated with the coupling terms between the state and the inputs. Numerical examples are given to illustrate the concept and the computational algorithm for the identification method.

Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

PubMed

Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

2017-10-12

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

R & D GTDS SST: Code Flowcharts and Input

DTIC Science & Technology

1995-01-01

trajectory from a given set of initial conditions Typical output is in the form of a printer le of Cartesian coordinates and Keplerian orbital ... orbiting the Earth The input data specied for an EPHEM run are i Initial elements and epoch ii Orbit generator selection iii Conversion of osculating...discussed ELEMENT sets coordinate system reference central body and rst components of initial state ELEMENT sets the second

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images.

PubMed

Wang, Hongkai; Zhou, Zongwei; Li, Yingci; Chen, Zhonghua; Lu, Peiou; Wang, Wenzhi; Liu, Wanyu; Yu, Lijuan

2017-12-01

This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18 F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.

The impact of feature selection on one and two-class classification performance for plant microRNAs.

PubMed

Khalifa, Waleed; Yousef, Malik; Saçar Demirci, Müşerref Duygu; Allmer, Jens

2016-01-01

MicroRNAs (miRNAs) are short nucleotide sequences that form a typical hairpin structure which is recognized by a complex enzyme machinery. It ultimately leads to the incorporation of 18-24 nt long mature miRNAs into RISC where they act as recognition keys to aid in regulation of target mRNAs. It is involved to determine miRNAs experimentally and, therefore, machine learning is used to complement such endeavors. The success of machine learning mostly depends on proper input data and appropriate features for parameterization of the data. Although, in general, two-class classification (TCC) is used in the field; because negative examples are hard to come by, one-class classification (OCC) has been tried for pre-miRNA detection. Since both positive and negative examples are currently somewhat limited, feature selection can prove to be vital for furthering the field of pre-miRNA detection. In this study, we compare the performance of OCC and TCC using eight feature selection methods and seven different plant species providing positive pre-miRNA examples. Feature selection was very successful for OCC where the best feature selection method achieved an average accuracy of 95.6%, thereby being ∼29% better than the worst method which achieved 66.9% accuracy. While the performance is comparable to TCC, which performs up to 3% better than OCC, TCC is much less affected by feature selection and its largest performance gap is ∼13% which only occurs for two of the feature selection methodologies. We conclude that feature selection is crucially important for OCC and that it can perform on par with TCC given the proper set of features.

Identification and Description of Alternative Means of Accomplishing IMS Operational Features.

ERIC Educational Resources Information Center

Dave, Ashok

The operational features of feasible alternative configurations for a computer-based instructional management system are identified. Potential alternative means and components of accomplishing these features are briefly described. Included are aspects of data collection, data input, data transmission, data reception, scanning and processing,…

Mathematical morphology-based shape feature analysis for Chinese character recognition systems

NASA Astrophysics Data System (ADS)

Pai, Tun-Wen; Shyu, Keh-Hwa; Chen, Ling-Fan; Tai, Gwo-Chin

1995-04-01

This paper proposes an efficient technique of shape feature extraction based on the application of mathematical morphology theory. A new shape complexity index for preclassification of machine printed Chinese Character Recognition (CCR) is also proposed. For characters represented in different fonts/sizes or in a low resolution environment, a more stable local feature such as shape structure is preferred for character recognition. Morphological valley extraction filters are applied to extract the protrusive strokes from four sides of an input Chinese character. The number of extracted local strokes reflects the shape complexity of each side. These shape features of characters are encoded as corresponding shape complexity indices. Based on the shape complexity index, data base is able to be classified into 16 groups prior to recognition procedures. The performance of associating with shape feature analysis reclaims several characters from misrecognized character sets and results in an average of 3.3% improvement of recognition rate from an existing recognition system. In addition to enhance the recognition performance, the extracted stroke information can be further analyzed and classified its own stroke type. Therefore, the combination of extracted strokes from each side provides a means for data base clustering based on radical or subword components. It is one of the best solutions for recognizing high complexity characters such as Chinese characters which are divided into more than 200 different categories and consist more than 13,000 characters.

Robust skin color-based moving object detection for video surveillance

NASA Astrophysics Data System (ADS)

Kaliraj, Kalirajan; Manimaran, Sudha

2016-07-01

Robust skin color-based moving object detection for video surveillance is proposed. The objective of the proposed algorithm is to detect and track the target under complex situations. The proposed framework comprises four stages, which include preprocessing, skin color-based feature detection, feature classification, and target localization and tracking. In the preprocessing stage, the input image frame is smoothed using averaging filter and transformed into YCrCb color space. In skin color detection, skin color regions are detected using Otsu's method of global thresholding. In the feature classification, histograms of both skin and nonskin regions are constructed and the features are classified into foregrounds and backgrounds based on Bayesian skin color classifier. The foreground skin regions are localized by a connected component labeling process. Finally, the localized foreground skin regions are confirmed as a target by verifying the region properties, and nontarget regions are rejected using the Euler method. At last, the target is tracked by enclosing the bounding box around the target region in all video frames. The experiment was conducted on various publicly available data sets and the performance was evaluated with baseline methods. It evidently shows that the proposed algorithm works well against slowly varying illumination, target rotations, scaling, fast, and abrupt motion changes.

Online dimensionality reduction using competitive learning and Radial Basis Function network.

PubMed

Tomenko, Vladimir

2011-06-01

The general purpose dimensionality reduction method should preserve data interrelations at all scales. Additional desired features include online projection of new data, processing nonlinearly embedded manifolds and large amounts of data. The proposed method, called RBF-NDR, combines these features. RBF-NDR is comprised of two modules. The first module learns manifolds by utilizing modified topology representing networks and geodesic distance in data space and approximates sampled or streaming data with a finite set of reference patterns, thus achieving scalability. Using input from the first module, the dimensionality reduction module constructs mappings between observation and target spaces. Introduction of specific loss function and synthesis of the training algorithm for Radial Basis Function network results in global preservation of data structures and online processing of new patterns. The RBF-NDR was applied for feature extraction and visualization and compared with Principal Component Analysis (PCA), neural network for Sammon's projection (SAMANN) and Isomap. With respect to feature extraction, the method outperformed PCA and yielded increased performance of the model describing wastewater treatment process. As for visualization, RBF-NDR produced superior results compared to PCA and SAMANN and matched Isomap. For the Topic Detection and Tracking corpus, the method successfully separated semantically different topics. Copyright © 2011 Elsevier Ltd. All rights reserved.

Computer program for analysis of high speed, single row, angular contact, spherical roller bearing, SASHBEAN. Volume 1: User's guide

NASA Technical Reports Server (NTRS)

Aggarwal, Arun K.

1993-01-01

The computer program SASHBEAN (Sikorsky Aircraft Spherical Roller High Speed Bearing Analysis) analyzes and predicts the operating characteristics of a Single Row, Angular Contact, Spherical Roller Bearing (SRACSRB). The program runs on an IBM or IBM compatible personal computer, and for a given set of input data analyzes the bearing design for it's ring deflections (axial and radial), roller deflections, contact areas and stresses, induced axial thrust, rolling element and cage rotation speeds, lubrication parameters, fatigue lives, and amount of heat generated in the bearing. The dynamic loading of rollers due to centrifugal forces and gyroscopic moments, which becomes quite significant at high speeds, is fully considered in this analysis. For a known application and it's parameters, the program is also capable of performing steady-state and time-transient thermal analyses of the bearing system. The steady-state analysis capability allows the user to estimate the expected steady-state temperature map in and around the bearing under normal operating conditions. On the other hand, the transient analysis feature provides the user a means to simulate the 'lost lubricant' condition and predict a time-temperature history of various critical points in the system. The bearing's 'time-to-failure' estimate may also be made from this (transient) analysis by considering the bearing as failed when a certain temperature limit is reached in the bearing components. The program is fully interactive and allows the user to get started and access most of its features with a minimal of training. For the most part, the program is menu driven, and adequate help messages were provided to guide a new user through various menu options and data input screens. All input data, both for mechanical and thermal analyses, are read through graphical input screens, thereby eliminating any need of a separate text editor/word processor to edit/create data files. Provision is also available to select and view the contents of output files on the monitor screen if no paper printouts are required. A separate volume (Volume-2) of this documentation describes, in detail, the underlying mathematical formulations, assumptions, and solution algorithms of this program.

Quantity and Quality of Caregivers' Linguistic Input to 18-Month and 3-Year-Old Children Who Are Hard of Hearing.

PubMed

Ambrose, Sophie E; Walker, Elizabeth A; Unflat-Berry, Lauren M; Oleson, Jacob J; Moeller, Mary Pat

2015-01-01

The primary objective of this study was to examine the quantity and quality of caregiver talk directed to children who are hard of hearing (CHH) compared with children with normal hearing (CNH). For the CHH only, the study explored how caregiver input changed as a function of child age (18 months versus 3 years), which child and family factors contributed to variance in caregiver linguistic input at 18 months and 3 years, and how caregiver talk at 18 months related to child language outcomes at 3 years. Participants were 59 CNH and 156 children with bilateral, mild-to-severe hearing loss. When children were approximately 18 months and/or 3 years of age, caregivers and children participated in a 5-min semistructured, conversational interaction. Interactions were transcribed and coded for two features of caregiver input representing quantity (number of total utterances and number of total words) and four features representing quality (number of different words, mean length of utterance in morphemes, proportion of utterances that were high level, and proportion of utterances that were directing). In addition, at the 18-month visit, parents completed a standardized questionnaire regarding their child's communication development. At the 3-year visit, a clinician administered a standardized language measure. At the 18-month visit, the CHH were exposed to a greater proportion of directing utterances than the CNH. At the 3-year visit, there were significant differences between the CNH and CHH for number of total words and all four of the quality variables, with the CHH being exposed to fewer words and lower quality input. Caregivers generally provided higher quality input to CHH at the 3-year visit compared with the 18-month visit. At the 18-month visit, quantity variables, but not quality variables, were related to several child and family factors. At the 3-year visit, the variable most strongly related to caregiver input was child language. Longitudinal analyses indicated that quality, but not quantity, of caregiver linguistic input at 18 months was related to child language abilities at 3 years, with directing utterances accounting for significant unique variance in child language outcomes. Although caregivers of CHH increased their use of quality features of linguistic input over time, the differences when compared with CNH suggest that some caregivers may need additional support to provide their children with optimal language learning environments. This is particularly important given the relationships that were identified between quality features of caregivers' linguistic input and children's language abilities. Family supports should include a focus on developing a style that is conversational eliciting as opposed to directive.

The GIS weasel - An interface for the development of spatial information in modeling

USGS Publications Warehouse

Viger, R.J.; Markstrom, S.M.; Leavesley, G.H.; ,

2005-01-01

The GIS Weasel is a map and Graphical User Interface (GUI) driven tool that has been developed as an aid to modelers in the delineation, characterization of geographic features, and their parameterization for use in distributed or lumped parameter physical process models. The interface does not require user expertise in geographic information systems (GIS). The user does need knowledge of how the model will use the output from the GIS Weasel. The GIS Weasel uses Workstation ArcInfo and its the Grid extension. The GIS Weasel will run on all platforms that Workstation ArcInfo runs (i.e. numerous flavors of Unix and Microsoft Windows).The GIS Weasel requires an input ArcInfo grid of some topographical description of the Area of Interest (AOI). This is normally a digital elevation model, but can be the surface of a ground water table or any other data that flow direction can be resolved from. The user may define the AOI as a custom drainage area based on an interactively specified watershed outlet point, or use a previously created map. The user is then able to use any combination of the GIS Weasel's tool set to create one or more maps for depicting different kinds of geographic features. Once the spatial feature maps have been prepared, then the GIS Weasel s many parameterization routines can be used to create descriptions of each element in each of the user s created maps. Over 200 parameterization routines currently exist, generating information about shape, area, and topological association with other features of the same or different maps, as well many types of information based on ancillary data layers such as soil and vegetation properties. These tools easily integrate other similarly formatted data sets.

Dose-volume histogram prediction using density estimation.

PubMed

Skarpman Munter, Johanna; Sjölund, Jens

2015-09-07

Knowledge of what dose-volume histograms can be expected for a previously unseen patient could increase consistency and quality in radiotherapy treatment planning. We propose a machine learning method that uses previous treatment plans to predict such dose-volume histograms. The key to the approach is the framing of dose-volume histograms in a probabilistic setting.The training consists of estimating, from the patients in the training set, the joint probability distribution of some predictive features and the dose. The joint distribution immediately provides an estimate of the conditional probability of the dose given the values of the predictive features. The prediction consists of estimating, from the new patient, the distribution of the predictive features and marginalizing the conditional probability from the training over this. Integrating the resulting probability distribution for the dose yields an estimate of the dose-volume histogram.To illustrate how the proposed method relates to previously proposed methods, we use the signed distance to the target boundary as a single predictive feature. As a proof-of-concept, we predicted dose-volume histograms for the brainstems of 22 acoustic schwannoma patients treated with stereotactic radiosurgery, and for the lungs of 9 lung cancer patients treated with stereotactic body radiation therapy. Comparing with two previous attempts at dose-volume histogram prediction we find that, given the same input data, the predictions are similar.In summary, we propose a method for dose-volume histogram prediction that exploits the intrinsic probabilistic properties of dose-volume histograms. We argue that the proposed method makes up for some deficiencies in previously proposed methods, thereby potentially increasing ease of use, flexibility and ability to perform well with small amounts of training data.

Feature extraction using molecular planes for fuzzy relational clustering of a flexible dopamine reuptake inhibitor.

PubMed

Banerjee, Amit; Misra, Milind; Pai, Deepa; Shih, Liang-Yu; Woodley, Rohan; Lu, Xiang-Jun; Srinivasan, A R; Olson, Wilma K; Davé, Rajesh N; Venanzi, Carol A

2007-01-01

Six rigid-body parameters (Shift, Slide, Rise, Tilt, Roll, Twist) are commonly used to describe the relative displacement and orientation of successive base pairs in a nucleic acid structure. The present work adapts this approach to describe the relative displacement and orientation of any two planes in an arbitrary molecule-specifically, planes which contain important pharmacophore elements. Relevant code from the 3DNA software package (Nucleic Acids Res. 2003, 31, 5108-5121) was generalized to treat molecular fragments other than DNA bases as input for the calculation of the corresponding rigid-body (or "planes") parameters. These parameters were used to construct feature vectors for a fuzzy relational clustering study of over 700 conformations of a flexible analogue of the dopamine reuptake inhibitor, GBR 12909. Several cluster validity measures were used to determine the optimal number of clusters. Translational (Shift, Slide, Rise) rather than rotational (Tilt, Roll, Twist) features dominate clustering based on planes that are relatively far apart, whereas both types of features are important to clustering when the pair of planes are close by. This approach was able to classify the data set of molecular conformations into groups and to identify representative conformers for use as template conformers in future Comparative Molecular Field Analysis studies of GBR 12909 analogues. The advantage of using the planes parameters, rather than the combination of atomic coordinates and angles between molecular planes used in our previous fuzzy relational clustering of the same data set (J. Chem. Inf. Model. 2005, 45, 610-623), is that the present clustering results are independent of molecular superposition and the technique is able to identify clusters in the molecule considered as a whole. This approach is easily generalizable to any two planes in any molecule.

PredPPCrys: Accurate Prediction of Sequence Cloning, Protein Production, Purification and Crystallization Propensity from Protein Sequences Using Multi-Step Heterogeneous Feature Fusion and Selection

PubMed Central

Wang, Huilin; Wang, Mingjun; Tan, Hao; Li, Yuan; Zhang, Ziding; Song, Jiangning

2014-01-01

X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed ‘PredPPCrys’ using the support vector machine (SVM). Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I). Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II), which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization targets of currently non-crystallizable proteins were provided as compendium data, which are anticipated to facilitate target selection and design for the worldwide structural genomics consortium. PredPPCrys is freely available at http://www.structbioinfor.org/PredPPCrys. PMID:25148528

RatLab: an easy to use tool for place code simulations

PubMed Central

Schönfeld, Fabian; Wiskott, Laurenz

2013-01-01

In this paper we present the RatLab toolkit, a software framework designed to set up and simulate a wide range of studies targeting the encoding of space in rats. It provides open access to our modeling approach to establish place and head direction cells within unknown environments and it offers a set of parameters to allow for the easy construction of a variety of enclosures for a virtual rat as well as controlling its movement pattern over the course of experiments. Once a spatial code is formed RatLab can be used to modify aspects of the enclosure or movement pattern and plot the effect of such modifications on the spatial representation, i.e., place and head direction cell activity. The simulation is based on a hierarchical Slow Feature Analysis (SFA) network that has been shown before to establish a spatial encoding of new environments using visual input data only. RatLab encapsulates such a network, generates the visual training data, and performs all sampling automatically—with each of these stages being further configurable by the user. RatLab was written with the intention to make our SFA model more accessible to the community and to that end features a range of elements to allow for experimentation with the model without the need for specific programming skills. PMID:23908627

A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine.

PubMed

Bahrami, Sheyda; Shamsi, Mousa

2017-01-01

Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.

Mathematical models of the simplest fuzzy PI/PD controllers with skewed input and output fuzzy sets.

PubMed

Mohan, B M; Sinha, Arpita

2008-07-01

This paper unveils mathematical models for fuzzy PI/PD controllers which employ two skewed fuzzy sets for each of the two-input variables and three skewed fuzzy sets for the output variable. The basic constituents of these models are Gamma-type and L-type membership functions for each input, trapezoidal/triangular membership functions for output, intersection/algebraic product triangular norm, maximum/drastic sum triangular conorm, Mamdani minimum/Larsen product/drastic product inference method, and center of sums defuzzification method. The existing simplest fuzzy PI/PD controller structures derived via symmetrical fuzzy sets become special cases of the mathematical models revealed in this paper. Finally, a numerical example along with its simulation results are included to demonstrate the effectiveness of the simplest fuzzy PI controllers.

Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets

NASA Astrophysics Data System (ADS)

Ge, Xuming

2017-08-01

The coarse registration of point clouds from urban building scenes has become a key topic in applications of terrestrial laser scanning technology. Sampling-based algorithms in the random sample consensus (RANSAC) model have emerged as mainstream solutions to address coarse registration problems. In this paper, we propose a novel combined solution to automatically align two markerless point clouds from building scenes. Firstly, the method segments non-ground points from ground points. Secondly, the proposed method detects feature points from each cross section and then obtains semantic keypoints by connecting feature points with specific rules. Finally, the detected semantic keypoints from two point clouds act as inputs to a modified 4PCS algorithm. Examples are presented and the results compared with those of K-4PCS to demonstrate the main contributions of the proposed method, which are the extension of the original 4PCS to handle heavy datasets and the use of semantic keypoints to improve K-4PCS in relation to registration accuracy and computational efficiency.

Neural network classification of myoelectric signal for prosthesis control.

PubMed

Kelly, M F; Parker, P A; Scott, R N

1991-12-01

An alternate approach to deriving control for multidegree of freedom prosthetic arms is considered. By analyzing a single-channel myoelectric signal (MES), we can extract information that can be used to identify different contraction patterns in the upper arm. These contraction patterns are generated by subjects without previous training and are naturally associated with specific functions. Using a set of normalized MES spectral features, we can identify contraction patterns for four arm functions, specifically extension and flexion of the elbow and pronation and supination of the forearm. Performing identification independent of signal power is advantageous because this can then be used as a means for deriving proportional rate control for a prosthesis. An artificial neural network implementation is applied in the classification task. By using three single-layer perceptron networks, the MES is classified, with the spectral representations as input features. Trials performed on five subjects with normal limbs resulted in an average classification performance level of 85% for the four functions. Copyright © 1991. Published by Elsevier Ltd.

The neural correlates of semantic richness: evidence from an fMRI study of word learning.

PubMed

Ferreira, Roberto A; Göbel, Silke M; Hymers, Mark; Ellis, Andrew W

2015-04-01

We investigated the neural correlates of concrete nouns with either many or few semantic features. A group of 21 participants underwent two days of training and were then asked to categorize 40 newly learned words and a set of matched familiar words as living or nonliving in an MRI scanner. Our results showed that the most reliable effects of semantic richness were located in the left angular gyrus (AG) and middle temporal gyrus (MTG), where activation was higher for semantically rich than poor words. Other areas showing the same pattern included bilateral precuneus and posterior cingulate gyrus. Our findings support the view that AG and anterior MTG, as part of the multimodal network, play a significant role in representing and integrating semantic features from different input modalities. We propose that activation in bilateral precuneus and posterior cingulate gyrus reflects interplay between AG and episodic memory systems during semantic retrieval. Copyright © 2015 Elsevier Inc. All rights reserved.

Automatic classification of sleep stages based on the time-frequency image of EEG signals.

PubMed

Bajaj, Varun; Pachori, Ram Bilas

2013-12-01

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Standardization of a Hierarchical Transactive Control System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hammerstrom, Donald J.; Oliver, Terry V.; Melton, Ronald B.

2010-12-03

The authors describe work they have conducted toward the generalization and standardization of the transactive control approach that was first demonstrated in the Olympic Peninsula Project for the management of a transmission constraint. The newly generalized approach addresses several potential shortfalls of the prior approach: First, the authors have formalized a hierarchical node structure which defines the nodes and the functional signal pathways between these nodes. Second, by fully generalizing the inputs, outputs, and functional responsibilities of each node, the authors make the approach available to a much wider set of responsive assets and operational objectives. Third, the new, generalizedmore » approach defines transactive signals that include the predicted day-ahead future. This predictive feature allows the market-like bids and offers to become resolved iteratively over time, thus allowing the behaviors of responsive assets to be called upon both for the present and as future dispatch decisions are being made. The hierarchical transactive control approach is a key feature of a proposed Pacific Northwest smart grid demonstration.« less

3D fluid-structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS

NASA Astrophysics Data System (ADS)

Saeed, R. A.; Galybin, A. N.; Popov, V.

2013-01-01

This paper discusses condition monitoring and fault diagnosis in Francis turbine based on integration of numerical modelling with several different artificial intelligence (AI) techniques. In this study, a numerical approach for fluid-structure (turbine runner) analysis is presented. The results of numerical analysis provide frequency response functions (FRFs) data sets along x-, y- and z-directions under different operating load and different position and size of faults in the structure. To extract features and reduce the dimensionality of the obtained FRF data, the principal component analysis (PCA) has been applied. Subsequently, the extracted features are formulated and fed into multiple artificial neural networks (ANN) and multiple adaptive neuro-fuzzy inference systems (ANFIS) in order to identify the size and position of the damage in the runner and estimate the turbine operating conditions. The results demonstrated the effectiveness of this approach and provide satisfactory accuracy even when the input data are corrupted with certain level of noise.

A trans-cultural comparison of the organisation of care at headache centres world-wide.

PubMed

Bhola, Ria; Goadsby, Peter J

2011-02-01

The need to provide better outcomes for patients with headache, and to minimise the costs involved in doing so, has prompted the search for new modes of service delivery by exploring the service organisation and nursing role from various cultural, economic and global perspectives. This study was based on comparisons with the UK headache service up to 2007, the point at which this study was set up. This UK service was based at the National Hospital for Neurology and Neurosurgery (NHNN, UCLH Trust). Data were obtained from US headache centres in 2008 and from centres in Copenhagen, Bangkok, Sydney and Porto Alegre in 2009. A comparison shows the key components of services at all centres showing the team structure and size of service. Prominent features at the centres included: team-working, regular meetings, educational input, good access and communication among team members, headache-trained neurologists, specialist nursing at most centres, and the input of psychological and physical therapists at some centres. The problems of tertiary headache care are very similar throughout the world and seem to transcend ethnic, cultural and economic considerations.

An incremental approach to genetic-algorithms-based classification.

PubMed

Guan, Sheng-Uei; Zhu, Fangming

2005-04-01

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multiagent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an "integration" operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed.

Multi-pass amplifier architecture for high power laser systems

DOEpatents

Manes, Kenneth R; Spaeth, Mary L; Erlandson, Alvin C

2014-04-01

A main amplifier system includes a first reflector operable to receive input light through a first aperture and direct the input light along an optical path. The input light is characterized by a first polarization. The main amplifier system also includes a first polarizer operable to reflect light characterized by the first polarization state. The main amplifier system further includes a first and second set of amplifier modules. Each of the first and second set of amplifier modules includes an entrance window, a quarter wave plate, a plurality of amplifier slablets arrayed substantially parallel to each other, and an exit window. The main amplifier system additionally includes a set of mirrors operable to reflect light exiting the first set of amplifier modules to enter the second set of amplifier modules and a second polarizer operable to reflect light characterized by a second polarization state.

ORFer--retrieval of protein sequences and open reading frames from GenBank and storage into relational databases or text files.

PubMed

Büssow, Konrad; Hoffmann, Steve; Sievert, Volker

2002-12-19

Functional genomics involves the parallel experimentation with large sets of proteins. This requires management of large sets of open reading frames as a prerequisite of the cloning and recombinant expression of these proteins. A Java program was developed for retrieval of protein and nucleic acid sequences and annotations from NCBI GenBank, using the XML sequence format. Annotations retrieved by ORFer include sequence name, organism and also the completeness of the sequence. The program has a graphical user interface, although it can be used in a non-interactive mode. For protein sequences, the program also extracts the open reading frame sequence, if available, and checks its correct translation. ORFer accepts user input in the form of single or lists of GenBank GI identifiers or accession numbers. It can be used to extract complete sets of open reading frames and protein sequences from any kind of GenBank sequence entry, including complete genomes or chromosomes. Sequences are either stored with their features in a relational database or can be exported as text files in Fasta or tabulator delimited format. The ORFer program is freely available at http://www.proteinstrukturfabrik.de/orfer. The ORFer program allows for fast retrieval of DNA sequences, protein sequences and their open reading frames and sequence annotations from GenBank. Furthermore, storage of sequences and features in a relational database is supported. Such a database can supplement a laboratory information system (LIMS) with appropriate sequence information.

Use of software tools in the development of real time software systems

NASA Technical Reports Server (NTRS)

Garvey, R. C.

1981-01-01

The transformation of a preexisting software system into a larger and more versatile system with different mission requirements is discussed. The history of this transformation is used to illustrate the use of structured real time programming techniques and tools to produce maintainable and somewhat transportable systems. The predecessor system is a single ground diagnostic system; its purpose is to exercise a computer controlled hardware set prior to its deployment in its functional environment, as well as test the equipment set by supplying certain well known stimulas. The successor system (FTE) is required to perform certain testing and control functions while this hardware set is in its functional environment. Both systems must deal with heavy user input/output loads and a new I/O requirement is included in the design of the FTF system. Human factors are enhanced by adding an improved console interface and special function keyboard handler. The additional features require the inclusion of much new software to the original set from which FTF was developed. As a result, it is necessary to split the system into a duel programming configuration with high rates of interground communications. A generalized information routing mechanism is used to support this configuration.

JAva GUi for Applied Research (JAGUAR) v 3.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

JAGUAR is a Java software tool for automatically rendering a graphical user interface (GUI) from a structured input specification. It is designed as a plug-in to the Eclipse workbench to enable users to create, edit, and externally execute analysis application input decks and then view the results. JAGUAR serves as a GUI for Sandia's DAKOTA software toolkit for optimization and uncertainty quantification. It will include problem (input deck)set-up, option specification, analysis execution, and results visualization. Through the use of wizards, templates, and views, JAGUAR helps uses navigate the complexity of DAKOTA's complete input specification. JAGUAR is implemented in Java, leveragingmore » Eclipse extension points and Eclipse user interface. JAGUAR parses a DAKOTA NIDR input specification and presents the user with linked graphical and plain text representations of problem set-up and option specification for DAKOTA studies. After the data has been input by the user, JAGUAR generates one or more input files for DAKOTA, executes DAKOTA, and captures and interprets the results« less

A Low Cost Simulation System to Demonstrate Pilot Induced Oscillation Phenomenon

NASA Technical Reports Server (NTRS)

Ali, Syed Firasat

1997-01-01

A flight simulation system with graphics and software on Silicon Graphics computer workstations has been installed in the Flight Vehicle Design Laboratory at Tuskegee University. The system has F-15E flight simulation software from NASA Dryden which uses the graphics of SGI flight simulation demos. On the system, thus installed, a study of pilot induced oscillations is planned for future work. Preliminary research is conducted by obtaining two sets of straight level flights with pilot in the loop. In one set of flights no additional delay is used between the stick input and the appearance of airplane response on the computer monitor. In another set of flights, a 500 ms additional delay is used. The flight data is analyzed to find cross correlations between deflections of control surfaces and response of the airplane. The pilot dynamics features depicted from cross correlations of straight level flights are discussed in this report. The correlations presented here will serve as reference material for the corresponding correlations in a future study of pitch attitude tracking tasks involving pilot induced oscillations.

Looking for underlying features in automatic and reviewed seismic bulletins through a neural network

NASA Astrophysics Data System (ADS)

Carluccio, R.; Console, R.; Chiappini, M.; Chiappini, S.

2009-12-01

SEL1 bulletins are, among all IDC products, a fundamental tool for NDCs in their task of national assessment of compliance with the CTBT. This is because SEL1s are expected to be disseminated within 2 hours from the occurrence of any detected waveform event, and the National Authorities are supposed to take a political decision in nearly real time, especially in the case when the event could triggers the request for an on site inspection. In this context not only the rapidity, but also the reliability of the SEL1 is a fundamental requirement. Our last years experience gained in the comparison between SEL1 and Italian Seismic Bulletin events has shown that SEL1s usually contain a big fraction of bogus events (sometimes close to 50%). This is due to many factors, all related to the availability of processing data and to the fast automatic algorithms involved. On the other hand, REBs are much more reliable as proved by our experience. Therefore, in spite of their relevant time delay by which they are distributed, which prevents their real-time use, REBs can be still useful in a retrospective way as reference information for comparison with SEL1s. This study tries to set up a sort of logical filter on the SEL1s that, while maintaining the rapidity requirements, improves their reliability. Our idea is based on the assumption that the SEL1s are produced by systematic algorithm of phase association and therefore some patterns among the input and output data could exist and be recognized. Our approach was initially based on a set of rules suggested by human experts on their personal experience, and its application on large datasets on a global scale. Other approaches not involving human interaction (data mining techniques) do exist. This study refers specifically to a semi-automatic approach: fitting of multi-parametric relationships hidden in the data set, through the application of neural networks by an algorithm of supervised learning. Full SEL1 and REB bulletins from Jan 2005 to Oct 2008 have been inserted in a database, together with IMS stations availability information. Part of these data have been used to create two sets of independent data (learning and verifying) used to train a "feed-forward" supervised neural network. A network supervised training algorithm using "confirmation flag" values has been used. In order to optimize network training input a significant, not redundant subset of input parameters has been looked for with the help of a genetic algorithm search tool. A suitable 12 input subset has been found and a network architecture of 12-20-1 has thus been chosen and trained on a 15094 records data set. Different runs of training sequences have been conducted, all showing CCR (Correct Classification Rate) values of the order of 75% - 80%. The trained network behavior is shown in term of ROC curve and input-out success-error matrices. The results of the analysis on our testing and validating data groups appear promising.

Detection of illegal transfer of videos over the Internet

NASA Astrophysics Data System (ADS)

Chaisorn, Lekha; Sainui, Janya; Manders, Corey

2010-07-01

In this paper, a method for detecting infringements or modifications of a video in real-time is proposed. The method first segments a video stream into shots, after which it extracts some reference frames as keyframes. This process is performed employing a Singular Value Decomposition (SVD) technique developed in this work. Next, for each input video (represented by its keyframes), ordinal-based signature and SIFT (Scale Invariant Feature Transform) descriptors are generated. The ordinal-based method employs a two-level bitmap indexing scheme to construct the index for each video signature. The first level clusters all input keyframes into k clusters while the second level converts the ordinal-based signatures into bitmap vectors. On the other hand, the SIFT-based method directly uses the descriptors as the index. Given a suspect video (being streamed or transferred on the Internet), we generate the signature (ordinal and SIFT descriptors) then we compute similarity between its signature and those signatures in the database based on ordinal signature and SIFT descriptors separately. For similarity measure, besides the Euclidean distance, Boolean operators are also utilized during the matching process. We have tested our system by performing several experiments on 50 videos (each about 1/2 hour in duration) obtained from the TRECVID 2006 data set. For experiments set up, we refer to the conditions provided by TRECVID 2009 on "Content-based copy detection" task. In addition, we also refer to the requirements issued in the call for proposals by MPEG standard on the similar task. Initial result shows that our framework is effective and robust. As compared to our previous work, on top of the achievement we obtained by reducing the storage space and time taken in the ordinal based method, by introducing the SIFT features, we could achieve an overall accuracy in F1 measure of about 96% (improved about 8%).

Earth observing system. Output data products and input requirements, version 2.0. Volume 3: Algorithm summary tables and non-EOS data products

NASA Technical Reports Server (NTRS)

Lu, Yun-Chi; Chang, Hyo Duck; Krupp, Brian; Kumar, Ravindar; Swaroop, Anand

1992-01-01

Volume 3 assists Earth Observing System (EOS) investigators in locating required non-EOS data products by identifying their non-EOS input requirements and providing the information on data sets available at various Distributed Active Archive Centers (DAAC's), including those from Pathfinder Activities and Earth Probes. Volume 3 is intended to complement, not to duplicate, the the EOSDIS Science Data Plan (SDP) by providing detailed data set information which was not presented in the SDP. Section 9 of this volume discusses the algorithm summary tables containing information on retrieval algorithms, expected outputs and required input data. Section 10 describes the non-EOS input requirements of instrument teams and IDS investigators. Also described are the current and future data holdings of the original seven DAACS and data products planned from the future missions and projects including Earth Probes and Pathfinder Activities. Section 11 describes source of information used in compiling data set information presented in this volume. A list of data set attributes used to describe various data sets is presented in section 12 along with their descriptions. Finally, Section 13 presents the SPSO's future plan to improve this report .

Complexity and non-commutativity of learning operations on graphs.

PubMed

Atmanspacher, Harald; Filk, Thomas

2006-07-01

We present results from numerical studies of supervised learning operations in small recurrent networks considered as graphs, leading from a given set of input conditions to predetermined outputs. Graphs that have optimized their output for particular inputs with respect to predetermined outputs are asymptotically stable and can be characterized by attractors, which form a representation space for an associative multiplicative structure of input operations. As the mapping from a series of inputs onto a series of such attractors generally depends on the sequence of inputs, this structure is generally non-commutative. Moreover, the size of the set of attractors, indicating the complexity of learning, is found to behave non-monotonically as learning proceeds. A tentative relation between this complexity and the notion of pragmatic information is indicated.

Timing discriminator using leading-edge extrapolation

DOEpatents

Gottschalk, Bernard

1983-01-01

A discriminator circuit to recover timing information from slow-rising pulses by means of an output trailing edge, a fixed time after the starting corner of the input pulse, which is nearly independent of risetime and threshold setting. This apparatus comprises means for comparing pulses with a threshold voltage; a capacitor to be charged at a certain rate when the input signal is one-third threshold voltage, and at a lower rate when the input signal is two-thirds threshold voltage; current-generating means for charging the capacitor; means for comparing voltage capacitor with a bias voltage; a flip-flop to be set when the input pulse reaches threshold voltage and reset when capacitor voltage reaches the bias voltage; and a clamping means for discharging the capacitor when the input signal returns below one-third threshold voltage.

Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

NASA Astrophysics Data System (ADS)

Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

2017-10-01

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

Assessing efficiency of spatial sampling using combined coverage analysis in geographical and feature spaces

NASA Astrophysics Data System (ADS)

Hengl, Tomislav

2015-04-01

Efficiency of spatial sampling largely determines success of model building. This is especially important for geostatistical mapping where an initial sampling plan should provide a good representation or coverage of both geographical (defined by the study area mask map) and feature space (defined by the multi-dimensional covariates). Otherwise the model will need to extrapolate and, hence, the overall uncertainty of the predictions will be high. In many cases, geostatisticians use point data sets which are produced using unknown or inconsistent sampling algorithms. Many point data sets in environmental sciences suffer from spatial clustering and systematic omission of feature space. But how to quantify these 'representation' problems and how to incorporate this knowledge into model building? The author has developed a generic function called 'spsample.prob' (Global Soil Information Facilities package for R) and which simultaneously determines (effective) inclusion probabilities as an average between the kernel density estimation (geographical spreading of points; analysed using the spatstat package in R) and MaxEnt analysis (feature space spreading of points; analysed using the MaxEnt software used primarily for species distribution modelling). The output 'iprob' map indicates whether the sampling plan has systematically missed some important locations and/or features, and can also be used as an input for geostatistical modelling e.g. as a weight map for geostatistical model fitting. The spsample.prob function can also be used in combination with the accessibility analysis (cost of field survey are usually function of distance from the road network, slope and land cover) to allow for simultaneous maximization of average inclusion probabilities and minimization of total survey costs. The author postulates that, by estimating effective inclusion probabilities using combined geographical and feature space analysis, and by comparing survey costs to representation efficiency, an optimal initial sampling plan can be produced which satisfies both criteria: (a) good representation (i.e. within a tolerance threshold), and (b) minimized survey costs. This sampling analysis framework could become especially interesting for generating sampling plans in new areas e.g. for which no previous spatial prediction model exists. The presentation includes data processing demos with standard soil sampling data sets Ebergotzen (Germany) and Edgeroi (Australia), also available via the GSIF package.

Can responses to basic non-numerical visual features explain neural numerosity responses?

PubMed

Harvey, Ben M; Dumoulin, Serge O

2017-04-01

Humans and many animals can distinguish between stimuli that differ in numerosity, the number of objects in a set. Human and macaque parietal lobes contain neurons that respond to changes in stimulus numerosity. However, basic non-numerical visual features can affect neural responses to and perception of numerosity, and visual features often co-vary with numerosity. Therefore, it is debated whether numerosity or co-varying low-level visual features underlie neural and behavioral responses to numerosity. To test the hypothesis that non-numerical visual features underlie neural numerosity responses in a human parietal numerosity map, we analyze responses to a group of numerosity stimulus configurations that have the same numerosity progression but vary considerably in their non-numerical visual features. Using ultra-high-field (7T) fMRI, we measure responses to these stimulus configurations in an area of posterior parietal cortex whose responses are believed to reflect numerosity-selective activity. We describe an fMRI analysis method to distinguish between alternative models of neural response functions, following a population receptive field (pRF) modeling approach. For each stimulus configuration, we first quantify the relationships between numerosity and several non-numerical visual features that have been proposed to underlie performance in numerosity discrimination tasks. We then determine how well responses to these non-numerical visual features predict the observed fMRI responses, and compare this to the predictions of responses to numerosity. We demonstrate that a numerosity response model predicts observed responses more accurately than models of responses to simple non-numerical visual features. As such, neural responses in cognitive processing need not reflect simpler properties of early sensory inputs. Copyright © 2017 Elsevier Inc. All rights reserved.

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Perception of Sentence Stress in Speech Correlates With the Temporal Unpredictability of Prosodic Features.

PubMed

Kakouros, Sofoklis; Räsänen, Okko

2016-09-01

Numerous studies have examined the acoustic correlates of sentential stress and its underlying linguistic functionality. However, the mechanism that connects stress cues to the listener's attentional processing has remained unclear. Also, the learnability versus innateness of stress perception has not been widely discussed. In this work, we introduce a novel perspective to the study of sentential stress and put forward the hypothesis that perceived sentence stress in speech is related to the unpredictability of prosodic features, thereby capturing the attention of the listener. As predictability is based on the statistical structure of the speech input, the hypothesis also suggests that stress perception is a result of general statistical learning mechanisms. To study this idea, computational simulations are performed where temporal prosodic trajectories are modeled with an n-gram model. Probabilities of the feature trajectories are subsequently evaluated on a set of novel utterances and compared to human perception of stress. The results show that the low-probability regions of F0 and energy trajectories are strongly correlated with stress perception, giving support to the idea that attention and unpredictability of sensory stimulus are mutually connected. Copyright © 2015 Cognitive Science Society, Inc.

A novel comparator featured with input data characteristic

NASA Astrophysics Data System (ADS)

Jiang, Xiaobo; Ye, Desheng; Xu, Xiangmin; Zheng, Shuai

2016-03-01

Two types of low-power asynchronous comparators featured with input data statistical characteristic are proposed in this article. The asynchronous ripple comparator stops comparing at the first unequal bit but delivers the result to the least significant bit. The pre-stop asynchronous comparator can completely stop comparing and obtain results immediately. The proposed and contrastive comparators were implemented in SMIC 0.18 μm process with different bit widths. Simulation shows that the proposed pre-stop asynchronous comparator features the lowest power consumption, shortest average propagation delay and highest area efficiency among the comparators. Data path of low-density parity check decoder using the proposed pre-stop asynchronous comparators are most power efficient compared with other data paths with synthesised, clock gating and bitwise competition logic comparators.

Full-order optimal compensators for flow control: the multiple inputs case

NASA Astrophysics Data System (ADS)

Semeraro, Onofrio; Pralits, Jan O.

2018-03-01

Flow control has been the subject of numerous experimental and theoretical works. We analyze full-order, optimal controllers for large dynamical systems in the presence of multiple actuators and sensors. The full-order controllers do not require any preliminary model reduction or low-order approximation: this feature allows us to assess the optimal performance of an actuated flow without relying on any estimation process or further hypothesis on the disturbances. We start from the original technique proposed by Bewley et al. (Meccanica 51(12):2997-3014, 2016. https://doi.org/10.1007/s11012-016-0547-3), the adjoint of the direct-adjoint (ADA) algorithm. The algorithm is iterative and allows bypassing the solution of the algebraic Riccati equation associated with the optimal control problem, typically infeasible for large systems. In this numerical work, we extend the ADA iteration into a more general framework that includes the design of controllers with multiple, coupled inputs and robust controllers (H_{∞} methods). First, we demonstrate our results by showing the analytical equivalence between the full Riccati solutions and the ADA approximations in the multiple inputs case. In the second part of the article, we analyze the performance of the algorithm in terms of convergence of the solution, by comparing it with analogous techniques. We find an excellent scalability with the number of inputs (actuators), making the method a viable way for full-order control design in complex settings. Finally, the applicability of the algorithm to fluid mechanics problems is shown using the linearized Kuramoto-Sivashinsky equation and the Kármán vortex street past a two-dimensional cylinder.

Temporally diverse firing patterns in olfactory receptor neurons underlie spatiotemporal neural codes for odors

PubMed Central

Raman, Baranidharan; Joseph, Joby; Tang, Jeff; Stopfer, Mark

2010-01-01

Odorants are represented as spatiotemporal patterns of spikes in neurons of the antennal lobe (AL, insects) and olfactory bulb (OB, vertebrates). These response patterns have been thought to arise primarily from interactions within the AL/OB, an idea supported, in part, by the assumption that olfactory receptor neurons (ORNs) respond to odorants with simple firing patterns. However, activating the AL directly with simple pulses of current evoked responses in AL neurons that were much less diverse, complex, and enduring than responses elicited by odorants. Similarly, models of the AL driven by simplistic inputs generated relatively simple output. How then are dynamic neural codes for odors generated? Consistent with recent results from several other species, our recordings from locust ORNs showed a great diversity of temporal structure. Further, we found that, viewed as a population, many response features of ORNs were remarkably similar to those observed within the AL. Using a set of computational models constrained by our electrophysiological recordings, we found that the temporal heterogeneity of responses of ORNs critically underlies the generation of spatiotemporal odor codes in the AL. A test then performed in vivo confirmed that, given temporally homogeneous input, the AL cannot create diverse spatiotemporal patterns on its own; however, given temporally heterogeneous input, the AL generated realistic firing patterns. Finally, given the temporally structured input provided by ORNs, we clarified several separate, additional contributions of the AL to olfactory information processing. Thus, our results demonstrate the origin and subsequent reformatting of spatiotemporal neural codes for odors. PMID:20147528

A novel approach for fire recognition using hybrid features and manifold learning-based classifier

NASA Astrophysics Data System (ADS)

Zhu, Rong; Hu, Xueying; Tang, Jiajun; Hu, Sheng

2018-03-01

Although image/video based fire recognition has received growing attention, an efficient and robust fire detection strategy is rarely explored. In this paper, we propose a novel approach to automatically identify the flame or smoke regions in an image. It is composed to three stages: (1) a block processing is applied to divide an image into several nonoverlapping image blocks, and these image blocks are identified as suspicious fire regions or not by using two color models and a color histogram-based similarity matching method in the HSV color space, (2) considering that compared to other information, the flame and smoke regions have significant visual characteristics, so that two kinds of image features are extracted for fire recognition, where local features are obtained based on the Scale Invariant Feature Transform (SIFT) descriptor and the Bags of Keypoints (BOK) technique, and texture features are extracted based on the Gray Level Co-occurrence Matrices (GLCM) and the Wavelet-based Analysis (WA) methods, and (3) a manifold learning-based classifier is constructed based on two image manifolds, which is designed via an improve Globular Neighborhood Locally Linear Embedding (GNLLE) algorithm, and the extracted hybrid features are used as input feature vectors to train the classifier, which is used to make decision for fire images or non fire images. Experiments and comparative analyses with four approaches are conducted on the collected image sets. The results show that the proposed approach is superior to the other ones in detecting fire and achieving a high recognition accuracy and a low error rate.

Fusion of shallow and deep features for classification of high-resolution remote sensing images

NASA Astrophysics Data System (ADS)

Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

2018-02-01

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

Scene segmentation of natural images using texture measures and back-propagation

NASA Technical Reports Server (NTRS)

Sridhar, Banavar; Phatak, Anil; Chatterji, Gano

1993-01-01

Knowledge of the three-dimensional world is essential for many guidance and navigation applications. A sequence of images from an electro-optical sensor can be processed using optical flow algorithms to provide a sparse set of ranges as a function of azimuth and elevation. A natural way to enhance the range map is by interpolation. However, this should be undertaken with care since interpolation assumes continuity of range. The range is continuous in certain parts of the image and can jump at object boundaries. In such situations, the ability to detect homogeneous object regions by scene segmentation can be used to determine regions in the range map that can be enhanced by interpolation. The use of scalar features derived from the spatial gray-level dependence matrix for texture segmentation is explored. Thresholding of histograms of scalar texture features is done for several images to select scalar features which result in a meaningful segmentation of the images. Next, the selected scalar features are used with a neural net to automate the segmentation procedure. Back-propagation is used to train the feed forward neural network. The generalization of the network approach to subsequent images in the sequence is examined. It is shown that the use of multiple scalar features as input to the neural network result in a superior segmentation when compared with a single scalar feature. It is also shown that the scalar features, which are not useful individually, result in a good segmentation when used together. The methodology is applied to both indoor and outdoor images.

Parametric analysis of parameters for electrical-load forecasting using artificial neural networks

NASA Astrophysics Data System (ADS)

Gerber, William J.; Gonzalez, Avelino J.; Georgiopoulos, Michael

1997-04-01

Accurate total system electrical load forecasting is a necessary part of resource management for power generation companies. The better the hourly load forecast, the more closely the power generation assets of the company can be configured to minimize the cost. Automating this process is a profitable goal and neural networks should provide an excellent means of doing the automation. However, prior to developing such a system, the optimal set of input parameters must be determined. The approach of this research was to determine what those inputs should be through a parametric study of potentially good inputs. Input parameters tested were ambient temperature, total electrical load, the day of the week, humidity, dew point temperature, daylight savings time, length of daylight, season, forecast light index and forecast wind velocity. For testing, a limited number of temperatures and total electrical loads were used as a basic reference input parameter set. Most parameters showed some forecasting improvement when added individually to the basic parameter set. Significantly, major improvements were exhibited with the day of the week, dew point temperatures, additional temperatures and loads, forecast light index and forecast wind velocity.

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes.

PubMed

Kim, Sungjin; Jinich, Adrián; Aspuru-Guzik, Alán

2017-04-24

We propose a multiple descriptor multiple kernel (MultiDK) method for efficient molecular discovery using machine learning. We show that the MultiDK method improves both the speed and accuracy of molecular property prediction. We apply the method to the discovery of electrolyte molecules for aqueous redox flow batteries. Using multiple-type-as opposed to single-type-descriptors, we obtain more relevant features for machine learning. Following the principle of "wisdom of the crowds", the combination of multiple-type descriptors significantly boosts prediction performance. Moreover, by employing multiple kernels-more than one kernel function for a set of the input descriptors-MultiDK exploits nonlinear relations between molecular structure and properties better than a linear regression approach. The multiple kernels consist of a Tanimoto similarity kernel and a linear kernel for a set of binary descriptors and a set of nonbinary descriptors, respectively. Using MultiDK, we achieve an average performance of r 2 = 0.92 with a test set of molecules for solubility prediction. We also extend MultiDK to predict pH-dependent solubility and apply it to a set of quinone molecules with different ionizable functional groups to assess their performance as flow battery electrolytes.

A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers

NASA Astrophysics Data System (ADS)

Dzung Nguyen, Sy; Choi, Seung-Bok

2012-08-01

This paper proposes a new algorithm, named establishing neuro-fuzzy system (ENFS), to identify dynamic characteristics of smart dampers such as magnetorheological (MR) and electrorheological (ER) dampers. In the ENFS, data clustering is performed based on the proposed algorithm named partitioning data space (PDS). Firstly, the PDS builds data clusters in joint input-output data space with appropriate constraints. The role of these constraints is to create reasonable data distribution in clusters. The ENFS then uses these clusters to perform the following tasks. Firstly, the fuzzy sets expressing characteristics of data clusters are established. The structure of the fuzzy sets is adjusted to be suitable for features of the data set. Secondly, an appropriate structure of neuro-fuzzy (NF) expressed by an optimal number of labeled data clusters and the fuzzy-set groups is determined. After the ENFS is introduced, its effectiveness is evaluated by a prediction-error-comparative work between the proposed method and some other methods in identifying numerical data sets such as ‘daily data of stock A’, or in identifying a function. The ENFS is then applied to identify damping force characteristics of the smart dampers. In order to evaluate the effectiveness of the ENFS in identifying the damping forces of the smart dampers, the prediction errors are presented by comparing with experimental results.

Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

NASA Astrophysics Data System (ADS)

Sanal Kumar, K. P.; Bhavani, R., Dr.

2017-08-01

Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

Analytical modeling of operating characteristics of premixing-prevaporizing fuel-air mixing passages. Volume 2: User's manual

NASA Technical Reports Server (NTRS)

Anderson, O. L.; Chiappetta, L. M.; Edwards, D. E.; Mcvey, J. B.

1982-01-01

A user's manual describing the operation of three computer codes (ADD code, PTRAK code, and VAPDIF code) is presented. The general features of the computer codes, the input/output formats, run streams, and sample input cases are described.

An Investigation of the Application of Artificial Neural Networks to Adaptive Optics Imaging Systems

DTIC Science & Technology

1991-12-01

neural network and the feedforward neural network studied is the single layer perceptron artificial neural network . The recurrent artificial neural network input...features are the wavefront sensor slope outputs and neighboring actuator feedback commands. The feedforward artificial neural network input

Enhancing Access to Drought Information Using the CUAHSI Hydrologic Information System

NASA Astrophysics Data System (ADS)

Schreuders, K. A.; Tarboton, D. G.; Horsburgh, J. S.; Sen Gupta, A.; Reeder, S.

2011-12-01

The National Drought Information System (NIDIS) Upper Colorado River Basin pilot study is investigating and establishing capabilities for better dissemination of drought information for early warning and management. As part of this study we are using and extending functionality from the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) to provide better access to drought-related data in the Upper Colorado River Basin. The CUAHSI HIS is a federated system for sharing hydrologic data. It is comprised of multiple data servers, referred to as HydroServers, that publish data in a standard XML format called Water Markup Language (WaterML), using web services referred to as WaterOneFlow web services. HydroServers can also publish geospatial data using Open Geospatial Consortium (OGC) web map, feature and coverage services and are capable of hosting web and map applications that combine geospatial datasets with observational data served via web services. HIS also includes a centralized metadata catalog that indexes data from registered HydroServers and a data access client referred to as HydroDesktop. For NIDIS, we have established a HydroServer to publish drought index values as well as the input data used in drought index calculations. Primary input data required for drought index calculation include streamflow, precipitation, reservoir storages, snow water equivalent, and soil moisture. We have developed procedures to redistribute the input data to the time and space scales chosen for drought index calculation, namely half monthly time intervals for HUC 10 subwatersheds. The spatial redistribution approaches used for each input parameter are dependent on the spatial linkages for that parameter, i.e., the redistribution procedure for streamflow is dependent on the upstream/downstream connectivity of the stream network, and the precipitation redistribution procedure is dependent on elevation to account for orographic effects. A set of drought indices are then calculated from the redistributed data. We have created automated data and metadata harvesters that periodically scan and harvest new data from each of the input databases, and calculates extensions to the resulting derived data sets, ensuring that the data available on the drought server is kept up to date. This paper will describe this system, showing how it facilitates the integration of data from multiple sources to inform the planning and management of water resources during drought. The system may be accessed at http://drought.usu.edu.

Testing an Instructional Model in a University Educational Setting from the Student's Perspective

ERIC Educational Resources Information Center

Betoret, Fernando Domenech

2006-01-01

We tested a theoretical model that hypothesized relationships between several variables from input, process and product in an educational setting, from the university student's perspective, using structural equation modeling. In order to carry out the analysis, we measured in sequential order the input (referring to students' personal…

Integrating Public Input into Healthcare Priority-Setting Decisions

ERIC Educational Resources Information Center

Mitton, Craig; Smith, Neale; Peacock, Stuart; Evoy, Brian; Abelson, Julia

2011-01-01

Decision makers are pressed to involve the public in priority setting. However, public input is only one form of evidence. So, how can information from the public be combined with other knowledge? The authors qualitatively analysed articles that explicitly address this question. We identified the other forms of information that tend to be used in…

Comparing fixed and variable-width Gaussian networks.

PubMed

Kůrková, Věra; Kainen, Paul C

2014-09-01

The role of width of Gaussians in two types of computational models is investigated: Gaussian radial-basis-functions (RBFs) where both widths and centers vary and Gaussian kernel networks which have fixed widths but varying centers. The effect of width on functional equivalence, universal approximation property, and form of norms in reproducing kernel Hilbert spaces (RKHS) is explored. It is proven that if two Gaussian RBF networks have the same input-output functions, then they must have the same numbers of units with the same centers and widths. Further, it is shown that while sets of input-output functions of Gaussian kernel networks with two different widths are disjoint, each such set is large enough to be a universal approximator. Embedding of RKHSs induced by "flatter" Gaussians into RKHSs induced by "sharper" Gaussians is described and growth of the ratios of norms on these spaces with increasing input dimension is estimated. Finally, large sets of argminima of error functionals in sets of input-output functions of Gaussian RBFs are described. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Power Conditioning Stage Based on Analog-Circuit MPPT Control and a Superbuck Converter for Thermoelectric Generators in Spacecraft Power Systems

NASA Astrophysics Data System (ADS)

Sun, Kai; Wu, Hongfei; Cai, Yan; Xing, Yan

2014-06-01

A thermoelectric generator (TEG) is a very important kind of power supply for spacecraft, especially for deep-space missions, due to its long lifetime and high reliability. To develop a practical TEG power supply for spacecraft, a power conditioning stage is indispensable, being employed to convert the varying output voltage of the TEG modules to a definite voltage for feeding batteries or loads. To enhance the system reliability, a power conditioning stage based on analog-circuit maximum-power-point tracking (MPPT) control and a superbuck converter is proposed in this paper. The input of this power conditioning stage is connected to the output of the TEG modules, and the output of this stage is connected to the battery and loads. The superbuck converter is employed as the main circuit, featuring low input current ripples and high conversion efficiency. Since for spacecraft power systems reliable operation is the key target for control circuits, a reset-set flip-flop-based analog circuit is used as the basic control circuit to implement MPPT, being much simpler than digital control circuits and offering higher reliability. Experiments have verified the feasibility and effectiveness of the proposed power conditioning stage. The results show the advantages of the proposed stage, such as maximum utilization of TEG power, small input ripples, and good stability.

Estimating floodwater depths from flood inundation maps and topography

USGS Publications Warehouse

Cohen, Sagy; Brakenridge, G. Robert; Kettner, Albert; Bates, Bradford; Nelson, Jonathan M.; McDonald, Richard R.; Huang, Yu-Fen; Munasinghe, Dinuke; Zhang, Jiaqi

2018-01-01

Information on flood inundation extent is important for understanding societal exposure, water storage volumes, flood wave attenuation, future flood hazard, and other variables. A number of organizations now provide flood inundation maps based on satellite remote sensing. These data products can efficiently and accurately provide the areal extent of a flood event, but do not provide floodwater depth, an important attribute for first responders and damage assessment. Here we present a new methodology and a GIS-based tool, the Floodwater Depth Estimation Tool (FwDET), for estimating floodwater depth based solely on an inundation map and a digital elevation model (DEM). We compare the FwDET results against water depth maps derived from hydraulic simulation of two flood events, a large-scale event for which we use medium resolution input layer (10 m) and a small-scale event for which we use a high-resolution (LiDAR; 1 m) input. Further testing is performed for two inundation maps with a number of challenging features that include a narrow valley, a large reservoir, and an urban setting. The results show FwDET can accurately calculate floodwater depth for diverse flooding scenarios but also leads to considerable bias in locations where the inundation extent does not align well with the DEM. In these locations, manual adjustment or higher spatial resolution input is required.

Large-Scale Low-Cost NGS Library Preparation Using a Robust Tn5 Purification and Tagmentation Protocol

PubMed Central

Hennig, Bianca P.; Velten, Lars; Racke, Ines; Tu, Chelsea Szu; Thoms, Matthias; Rybin, Vladimir; Besir, Hüseyin; Remans, Kim; Steinmetz, Lars M.

2017-01-01

Efficient preparation of high-quality sequencing libraries that well represent the biological sample is a key step for using next-generation sequencing in research. Tn5 enables fast, robust, and highly efficient processing of limited input material while scaling to the parallel processing of hundreds of samples. Here, we present a robust Tn5 transposase purification strategy based on an N-terminal His6-Sumo3 tag. We demonstrate that libraries prepared with our in-house Tn5 are of the same quality as those processed with a commercially available kit (Nextera XT), while they dramatically reduce the cost of large-scale experiments. We introduce improved purification strategies for two versions of the Tn5 enzyme. The first version carries the previously reported point mutations E54K and L372P, and stably produces libraries of constant fragment size distribution, even if the Tn5-to-input molecule ratio varies. The second Tn5 construct carries an additional point mutation (R27S) in the DNA-binding domain. This construct allows for adjustment of the fragment size distribution based on enzyme concentration during tagmentation, a feature that opens new opportunities for use of Tn5 in customized experimental designs. We demonstrate the versatility of our Tn5 enzymes in different experimental settings, including a novel single-cell polyadenylation site mapping protocol as well as ultralow input DNA sequencing. PMID:29118030

Spatial-area selective retrieval of multiple object-place associations in a hierarchical cognitive map formed by theta phase coding.

PubMed

Sato, Naoyuki; Yamaguchi, Yoko

2009-06-01

The human cognitive map is known to be hierarchically organized consisting of a set of perceptually clustered landmarks. Patient studies have demonstrated that these cognitive maps are maintained by the hippocampus, while the neural dynamics are still poorly understood. The authors have shown that the neural dynamic "theta phase precession" observed in the rodent hippocampus may be capable of forming hierarchical cognitive maps in humans. In the model, a visual input sequence consisting of object and scene features in the central and peripheral visual fields, respectively, results in the formation of a hierarchical cognitive map for object-place associations. Surprisingly, it is possible for such a complex memory structure to be formed in a few seconds. In this paper, we evaluate the memory retrieval of object-place associations in the hierarchical network formed by theta phase precession. The results show that multiple object-place associations can be retrieved with the initial cue of a scene input. Importantly, according to the wide-to-narrow unidirectional connections among scene units, the spatial area for object-place retrieval can be controlled by the spatial area of the initial cue input. These results indicate that the hierarchical cognitive maps have computational advantages on a spatial-area selective retrieval of multiple object-place associations. Theta phase precession dynamics is suggested as a fundamental neural mechanism of the human cognitive map.

Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia

2014-01-01

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. PMID:25133210

Feature extraction from high resolution satellite imagery as an input to the development and rapid update of a METRANS geographic information system (GIS).

DOT National Transportation Integrated Search

2011-06-01

This report describes an accuracy assessment of extracted features derived from three : subsets of Quickbird pan-sharpened high resolution satellite image for the area of the : Port of Los Angeles, CA. Visual Learning Systems Feature Analyst and D...

Current Source Logic Gate

NASA Technical Reports Server (NTRS)

Krasowski, Michael J. (Inventor); Prokop, Norman F. (Inventor)

2017-01-01

A current source logic gate with depletion mode field effect transistor ("FET") transistors and resistors may include a current source, a current steering switch input stage, and a resistor divider level shifting output stage. The current source may include a transistor and a current source resistor. The current steering switch input stage may include a transistor to steer current to set an output stage bias point depending on an input logic signal state. The resistor divider level shifting output stage may include a first resistor and a second resistor to set the output stage point and produce valid output logic signal states. The transistor of the current steering switch input stage may function as a switch to provide at least two operating points.

Cardiac arrhythmia beat classification using DOST and PSO tuned SVM.

PubMed

Raj, Sandeep; Ray, Kailash Chandra; Shankar, Om

2016-11-01

The increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals. Hence, this study aims to improve the classification accuracy rate and provide an automated diagnostic solution for the detection of cardiac arrhythmias. The proposed methodology consists of four stages, i.e. filtering, R-peak detection, feature extraction and classification stages. In this study, Wavelet based approach is used to filter the raw ECG signal, whereas Pan-Tompkins algorithm is used for detecting the R-peak inside the ECG signal. In the feature extraction stage, discrete orthogonal Stockwell transform (DOST) approach is presented for an efficient time-frequency representation (i.e. morphological descriptors) of a time domain signal and retains the absolute phase information to distinguish the various non-stationary behavior ECG signals. Moreover, these morphological descriptors are further reduced in lower dimensional space by using principal component analysis and combined with the dynamic features (i.e based on RR-interval of the ECG signals) of the input signal. This combination of two different kinds of descriptors represents each feature set of an input signal that is utilized for classification into subsequent categories by employing PSO tuned support vector machines (SVM). The proposed methodology is validated on the baseline MIT-BIH arrhythmia database and evaluated under two assessment schemes, yielding an improved overall accuracy of 99.18% for sixteen classes in the category-based and 89.10% for five classes (mapped according to AAMI standard) in the patient-based assessment scheme respectively to the state-of-art diagnosis. The results reported are further compared to the existing methodologies in literature. The proposed feature representation of cardiac signals based on symmetrical features along with PSO based optimization technique for the SVM classifier reported an improved classification accuracy in both the assessment schemes evaluated on the benchmark MIT-BIH arrhythmia database and hence can be utilized for automated computer-aided diagnosis of cardiac arrhythmia beats. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Protein structure based prediction of catalytic residues

PubMed Central

2013-01-01

Background Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation. Results We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods. Conclusions We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases. PMID:23433045

40 CFR 96.76 - Additional requirements to provide heat input data for allocations purposes.

Code of Federal Regulations, 2011 CFR

2011-07-01

... heat input data for allocations purposes. 96.76 Section 96.76 Protection of Environment ENVIRONMENTAL... to provide heat input data for allocations purposes. (a) The owner or operator of a unit that elects... also monitor and report heat input at the unit level using the procedures set forth in part 75 of this...

40 CFR 96.76 - Additional requirements to provide heat input data for allocations purposes.

Code of Federal Regulations, 2013 CFR

2013-07-01

... heat input data for allocations purposes. 96.76 Section 96.76 Protection of Environment ENVIRONMENTAL... to provide heat input data for allocations purposes. (a) The owner or operator of a unit that elects... also monitor and report heat input at the unit level using the procedures set forth in part 75 of this...

40 CFR 96.76 - Additional requirements to provide heat input data for allocations purposes.

Code of Federal Regulations, 2012 CFR

2012-07-01

... heat input data for allocations purposes. 96.76 Section 96.76 Protection of Environment ENVIRONMENTAL... to provide heat input data for allocations purposes. (a) The owner or operator of a unit that elects... also monitor and report heat input at the unit level using the procedures set forth in part 75 of this...

40 CFR 96.76 - Additional requirements to provide heat input data for allocations purposes.

Code of Federal Regulations, 2014 CFR

2014-07-01

... heat input data for allocations purposes. 96.76 Section 96.76 Protection of Environment ENVIRONMENTAL... to provide heat input data for allocations purposes. (a) The owner or operator of a unit that elects... also monitor and report heat input at the unit level using the procedures set forth in part 75 of this...

A new method of building footprints detection using airborne laser scanning data and multispectral image

NASA Astrophysics Data System (ADS)

Luo, Yiping; Jiang, Ting; Gao, Shengli; Wang, Xin

2010-10-01

It presents a new approach for detecting building footprints in a combination of registered aerial image with multispectral bands and airborne laser scanning data synchronously obtained by Leica-Geosystems ALS40 and Applanix DACS-301 on the same platform. A two-step method for building detection was presented consisting of selecting 'building' candidate points and then classifying candidate points. A digital surface model(DSM) derived from last pulse laser scanning data was first filtered and the laser points were classified into classes 'ground' and 'building or tree' based on mathematic morphological filter. Then, 'ground' points were resample into digital elevation model(DEM), and a Normalized DSM(nDSM) was generated from DEM and DSM. The candidate points were selected from 'building or tree' points by height value and area threshold in nDSM. The candidate points were further classified into building points and tree points by using the support vector machines(SVM) classification method. Two classification tests were carried out using features only from laser scanning data and associated features from two input data sources. The features included height, height finite difference, RGB bands value, and so on. The RGB value of points was acquired by matching laser scanning data and image using collinear equation. The features of training points were presented as input data for SVM classification method, and cross validation was used to select best classification parameters. The determinant function could be constructed by the classification parameters and the class of candidate points was determined by determinant function. The result showed that associated features from two input data sources were superior to features only from laser scanning data. The accuracy of more than 90% was achieved for buildings in first kind of features.

SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu

Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT,more » status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.« less

The Comparison of Visual Working Memory Representations with Perceptual Inputs

PubMed Central

Hyun, Joo-seok; Woodman, Geoffrey F.; Vogel, Edward K.; Hollingworth, Andrew

2008-01-01

The human visual system can notice differences between memories of previous visual inputs and perceptions of new visual inputs, but the comparison process that detects these differences has not been well characterized. This study tests the hypothesis that differences between the memory of a stimulus array and the perception of a new array are detected in a manner that is analogous to the detection of simple features in visual search tasks. That is, just as the presence of a task-relevant feature in visual search can be detected in parallel, triggering a rapid shift of attention to the object containing the feature, the presence of a memory-percept difference along a task-relevant dimension can be detected in parallel, triggering a rapid shift of attention to the changed object. Supporting evidence was obtained in a series of experiments that examined manual reaction times, saccadic reaction times, and event-related potential latencies. However, these experiments also demonstrated that a slow, limited-capacity process must occur before the observer can make a manual change-detection response. PMID:19653755

Contribution of sublinear and supralinear dendritic integration to neuronal computations

PubMed Central

Tran-Van-Minh, Alexandra; Cazé, Romain D.; Abrahamsson, Therése; Cathala, Laurence; Gutkin, Boris S.; DiGregorio, David A.

2015-01-01

Nonlinear dendritic integration is thought to increase the computational ability of neurons. Most studies focus on how supralinear summation of excitatory synaptic responses arising from clustered inputs within single dendrites result in the enhancement of neuronal firing, enabling simple computations such as feature detection. Recent reports have shown that sublinear summation is also a prominent dendritic operation, extending the range of subthreshold input-output (sI/O) transformations conferred by dendrites. Like supralinear operations, sublinear dendritic operations also increase the repertoire of neuronal computations, but feature extraction requires different synaptic connectivity strategies for each of these operations. In this article we will review the experimental and theoretical findings describing the biophysical determinants of the three primary classes of dendritic operations: linear, sublinear, and supralinear. We then review a Boolean algebra-based analysis of simplified neuron models, which provides insight into how dendritic operations influence neuronal computations. We highlight how neuronal computations are critically dependent on the interplay of dendritic properties (morphology and voltage-gated channel expression), spiking threshold and distribution of synaptic inputs carrying particular sensory features. Finally, we describe how global (scattered) and local (clustered) integration strategies permit the implementation of similar classes of computations, one example being the object feature binding problem. PMID:25852470

Neural net target-tracking system using structured laser patterns

NASA Astrophysics Data System (ADS)

Cho, Jae-Wan; Lee, Yong-Bum; Lee, Nam-Ho; Park, Soon-Yong; Lee, Jongmin; Choi, Gapchu; Baek, Sunghyun; Park, Dong-Sun

1996-06-01

In this paper, we describe a robot endeffector tracking system using sensory information from recently-announced structured pattern laser diodes, which can generate images with several different types of structured pattern. The neural network approach is employed to recognize the robot endeffector covering the situation of three types of motion: translation, scaling and rotation. Features for the neural network to detect the position of the endeffector are extracted from the preprocessed images. Artificial neural networks are used to store models and to match with unknown input features recognizing the position of the robot endeffector. Since a minimal number of samples are used for different directions of the robot endeffector in the system, an artificial neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network trained with the back propagation learning is used to detect the position of the robot endeffector. Another feedforward neural network module is used to estimate the motion from a sequence of images and to control movements of the robot endeffector. COmbining the tow neural networks for recognizing the robot endeffector and estimating the motion with the preprocessing stage, the whole system keeps tracking of the robot endeffector effectively.

Timing discriminator using leading-edge extrapolation

DOEpatents

Gottschalk, B.

1981-07-30

A discriminator circuit to recover timing information from slow-rising pulses by means of an output trailing edge, a fixed time after the starting corner of the input pulse, which is nearly independent of risetime and threshold setting is described. This apparatus comprises means for comparing pulses with a threshold voltage; a capacitor to be charged at a certain rate when the input signal is one-third threshold voltage, and at a lower rate when the input signal is two-thirds threshold voltage; current-generating means for charging the capacitor; means for comparing voltage capacitor with a bias voltage; a flip-flop to be set when the input pulse reaches threshold voltage and reset when capacitor voltage reaches the bias voltage; and a clamping means for discharging the capacitor when the input signal returns below one-third threshold voltage.

Event-related potentials reveal the relations between feature representations at different levels of abstraction.

PubMed

Hannah, Samuel D; Shedden, Judith M; Brooks, Lee R; Grundy, John G

2016-11-01

In this paper, we use behavioural methods and event-related potentials (ERPs) to explore the relations between informational and instantiated features, as well as the relation between feature abstraction and rule type. Participants are trained to categorize two species of fictitious animals and then identify perceptually novel exemplars. Critically, two groups are given a perfectly predictive counting rule that, according to Hannah and Brooks (2009. Featuring familiarity: How a familiar feature instantiation influences categorization. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 63, 263-275. Retrieved from http://doi.org/10.1037/a0017919), should orient them to using abstract informational features when categorizing the novel transfer items. A third group is taught a feature list rule, which should orient them to using detailed instantiated features. One counting-rule group were taught their rule before any exposure to the actual stimuli, and the other immediately after training, having learned the instantiations first. The feature-list group were also taught their rule after training. The ERP results suggest that at test, the two counting-rule groups processed items differently, despite their identical rule. This not only supports the distinction that informational and instantiated features are qualitatively different feature representations, but also implies that rules can readily operate over concrete inputs, in contradiction to traditional approaches that assume that rules necessarily act on abstract inputs.

Analyses of requirements for computer control and data processing experiment subsystems. Volume 2: ATM experiment S-056 image data processing system software development

NASA Technical Reports Server (NTRS)

1972-01-01

The IDAPS (Image Data Processing System) is a user-oriented, computer-based, language and control system, which provides a framework or standard for implementing image data processing applications, simplifies set-up of image processing runs so that the system may be used without a working knowledge of computer programming or operation, streamlines operation of the image processing facility, and allows multiple applications to be run in sequence without operator interaction. The control system loads the operators, interprets the input, constructs the necessary parameters for each application, and cells the application. The overlay feature of the IBSYS loader (IBLDR) provides the means of running multiple operators which would otherwise overflow core storage.

Music acquisition: effects of enculturation and formal training on development.

PubMed

Hannon, Erin E; Trainor, Laurel J

2007-11-01

Musical structure is complex, consisting of a small set of elements that combine to form hierarchical levels of pitch and temporal structure according to grammatical rules. As with language, different systems use different elements and rules for combination. Drawing on recent findings, we propose that music acquisition begins with basic features, such as peripheral frequency-coding mechanisms and multisensory timing connections, and proceeds through enculturation, whereby everyday exposure to a particular music system creates, in a systematic order of acquisition, culture-specific brain structures and representations. Finally, we propose that formal musical training invokes domain-specific processes that affect salience of musical input and the amount of cortical tissue devoted to its processing, as well as domain-general processes of attention and executive functioning.

Preferences in Data Production Planning

NASA Technical Reports Server (NTRS)

Golden, Keith; Brafman, Ronen; Pang, Wanlin

2005-01-01

This paper discusses the data production problem, which consists of transforming a set of (initial) input data into a set of (goal) output data. There are typically many choices among input data and processing algorithms, each leading to significantly different end products. To discriminate among these choices, the planner supports an input language that provides a number of constructs for specifying user preferences over data (and plan) properties. We discuss these preference constructs, how we handle them to guide search, and additional challenges in the area of preference management that this important application domain offers.

Regularizing Unpredictable Variation: Evidence from a Natural Language Setting

ERIC Educational Resources Information Center

Hendricks, Alison Eisel; Miller, Karen; Jackson, Carrie N.

2018-01-01

While previous sociolinguistic research has demonstrated that children faithfully acquire probabilistic input constrained by sociolinguistic and linguistic factors (e.g., gender and socioeconomic status), research suggests children regularize inconsistent input-probabilistic input that is not sociolinguistically constrained (e.g., Hudson Kam &…

Fuzzy Arden Syntax: A fuzzy programming language for medicine.

PubMed

Vetterlein, Thomas; Mandl, Harald; Adlassnig, Klaus-Peter

2010-05-01

The programming language Arden Syntax has been optimised for use in clinical decision support systems. We describe an extension of this language named Fuzzy Arden Syntax, whose original version was introduced in S. Tiffe's dissertation on "Fuzzy Arden Syntax: Representation and Interpretation of Vague Medical Knowledge by Fuzzified Arden Syntax" (Vienna University of Technology, 2003). The primary aim is to provide an easy means of processing vague or uncertain data, which frequently appears in medicine. For both propositional and number data types, fuzzy equivalents have been added to Arden Syntax. The Boolean data type was generalised to represent any truth degree between the two extremes 0 (falsity) and 1 (truth); fuzzy data types were introduced to represent fuzzy sets. The operations on truth values and real numbers were generalised accordingly. As the conditions to decide whether a certain programme unit is executed or not may be indeterminate, a Fuzzy Arden Syntax programme may split. The data in the different branches may be optionally aggregated subsequently. Fuzzy Arden Syntax offers the possibility to formulate conveniently Medical Logic Modules (MLMs) based on the principle of a continuously graded applicability of statements. Furthermore, ad hoc decisions about sharp value boundaries can be avoided. As an illustrative example shows, an MLM making use of the features of Fuzzy Arden Syntax is not significantly more complex than its Arden Syntax equivalent; in the ideal case, a programme handling crisp data remains practically unchanged when compared to its fuzzified version. In the latter case, the output data, which can be a set of weighted alternatives, typically depends continuously from the input data. In typical applications an Arden Syntax MLM can produce a different output after only slight changes of the input; discontinuities are in fact unavoidable when the input varies continuously but the output is taken from a discrete set of possibilities. This inconvenience can, however, be attenuated by means of certain mechanisms on which the programme flow under Fuzzy Arden Syntax is based. To write a programme making use of these possibilities is not significantly more difficult than to write a programme according to the usual practice. 2010 Elsevier B.V. All rights reserved.

Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification.

PubMed

Wang, Lei; Pedersen, Peder C; Agu, Emmanuel; Strong, Diane M; Tulu, Bengisu

2017-09-01

The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: in the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.

Portable data collection device

DOEpatents

French, P.D.

1996-06-11

The present invention provides a portable data collection device that has a variety of sensors that are interchangeable with a variety of input ports in the device. The various sensors include a data identification feature that provides information to the device regarding the type of physical data produced by each sensor and therefore the type of sensor itself. The data identification feature enables the device to locate the input port where the sensor is connected and self adjust when a sensor is removed or replaced. The device is able to collect physical data, whether or not a function of a time. 7 figs.

Portable data collection device

DOEpatents

French, Patrick D.

1996-01-01

The present invention provides a portable data collection device that has a variety of sensors that are interchangeable with a variety of input ports in the device. The various sensors include a data identification feature that provides information to the device regarding the type of physical data produced by each sensor and therefore the type of sensor itself. The data identification feature enables the device to locate the input port where the sensor is connected and self adjust when a sensor is removed or replaced. The device is able to collect physical data, whether or not a function of a time.

Imer-product array processor for retrieval of stored images represented by bipolar binary (+1,-1) pixels using partial input trinary pixels represented by (+1,-1)

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Awwal, Abdul A. S. (Inventor); Karim, Mohammad A. (Inventor)

1993-01-01

An inner-product array processor is provided with thresholding of the inner product during each iteration to make more significant the inner product employed in estimating a vector to be used as the input vector for the next iteration. While stored vectors and estimated vectors are represented in bipolar binary (1,-1), only those elements of an initial partial input vector that are believed to be common with those of a stored vector are represented in bipolar binary; the remaining elements of a partial input vector are set to 0. This mode of representation, in which the known elements of a partial input vector are in bipolar binary form and the remaining elements are set equal to 0, is referred to as trinary representation. The initial inner products corresponding to the partial input vector will then be equal to the number of known elements. Inner-product thresholding is applied to accelerate convergence and to avoid convergence to a negative input product.

Definition of a Robust Supervisory Control Scheme for Sodium-Cooled Fast Reactors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ponciroli, R.; Passerini, S.; Vilim, R. B.

In this work, an innovative control approach for metal-fueled Sodium-cooled Fast Reactors is proposed. With respect to the classical approach adopted for base-load Nuclear Power Plants, an alternative control strategy for operating the reactor at different power levels by respecting the system physical constraints is presented. In order to achieve a higher operational flexibility along with ensuring that the implemented control loops do not influence the system inherent passive safety features, a dedicated supervisory control scheme for the dynamic definition of the corresponding set-points to be supplied to the PID controllers is designed. In particular, the traditional approach based onmore » the adoption of tabulated lookup tables for the set-point definition is found not to be robust enough when failures of the implemented SISO (Single Input Single Output) actuators occur. Therefore, a feedback algorithm based on the Reference Governor approach, which allows for the optimization of reference signals according to the system operating conditions, is proposed.« less

New activity pattern in human interactive dynamics

NASA Astrophysics Data System (ADS)

Formentin, Marco; Lovison, Alberto; Maritan, Amos; Zanzotto, Giovanni

2015-09-01

We investigate the response function of human agents as demonstrated by written correspondence, uncovering a new pattern for how the reactive dynamics of individuals is distributed across the set of each agent’s contacts. In long-term empirical data on email, we find that the set of response times considered separately for the messages to each different correspondent of a given writer, generate a family of heavy-tailed distributions, which have largely the same features for all agents, and whose characteristic times grow exponentially with the rank of each correspondent. We furthermore show that this new behavioral pattern emerges robustly by considering weighted moving averages of the priority-conditioned response-time probabilities generated by a basic prioritization model. Our findings clarify how the range of priorities in the inputs from one’s environment underpin and shape the dynamics of agents embedded in a net of reactive relations. These newly revealed activity patterns might be universal, being present in other general interactive environments, and constrain future models of communication and interaction networks, affecting their architecture and evolution.

Characteristic features of determining the labor input and estimated cost of the development and manufacture of equipment

NASA Technical Reports Server (NTRS)

Kurmanaliyev, T. I.; Breslavets, A. V.

1974-01-01

The difficulties in obtaining exact calculation data for the labor input and estimated cost are noted. The method of calculating the labor cost of the design work using the provisional normative indexes with respect to individual forms of operations is proposed. Values of certain coefficients recommended for use in the practical calculations of the labor input for the development of new scientific equipment for space research are presented.

Integration of RAM-SCB into the Space Weather Modeling Framework

DOE PAGES

Welling, Daniel; Toth, Gabor; Jordanova, Vania Koleva; ...

2018-02-07

We present that numerical simulations of the ring current are a challenging endeavor. They require a large set of inputs, including electric and magnetic fields and plasma sheet fluxes. Because the ring current broadly affects the magnetosphere-ionosphere system, the input set is dependent on the ring current region itself. This makes obtaining a set of inputs that are self-consistent with the ring current difficult. To overcome this challenge, researchers have begun coupling ring current models to global models of the magnetosphere-ionosphere system. This paper describes the coupling between the Ring current Atmosphere interaction Model with Self-Consistent Magnetic field (RAM-SCB) tomore » the models within the Space Weather Modeling Framework. Full details on both previously introduced and new coupling mechanisms are defined. Finally, the impact of self-consistently including the ring current on the magnetosphere-ionosphere system is illustrated via a set of example simulations.« less

Integration of RAM-SCB into the Space Weather Modeling Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Welling, Daniel; Toth, Gabor; Jordanova, Vania Koleva

We present that numerical simulations of the ring current are a challenging endeavor. They require a large set of inputs, including electric and magnetic fields and plasma sheet fluxes. Because the ring current broadly affects the magnetosphere-ionosphere system, the input set is dependent on the ring current region itself. This makes obtaining a set of inputs that are self-consistent with the ring current difficult. To overcome this challenge, researchers have begun coupling ring current models to global models of the magnetosphere-ionosphere system. This paper describes the coupling between the Ring current Atmosphere interaction Model with Self-Consistent Magnetic field (RAM-SCB) tomore » the models within the Space Weather Modeling Framework. Full details on both previously introduced and new coupling mechanisms are defined. Finally, the impact of self-consistently including the ring current on the magnetosphere-ionosphere system is illustrated via a set of example simulations.« less

`Dem DEMs: Comparing Methods of Digital Elevation Model Creation

NASA Astrophysics Data System (ADS)

Rezza, C.; Phillips, C. B.; Cable, M. L.

2017-12-01

Topographic details of Europa's surface yield implications for large-scale processes that occur on the moon, including surface strength, modification, composition, and formation mechanisms for geologic features. In addition, small scale details presented from this data are imperative for future exploration of Europa's surface, such as by a potential Europa Lander mission. A comparison of different methods of Digital Elevation Model (DEM) creation and variations between them can help us quantify the relative accuracy of each model and improve our understanding of Europa's surface. In this work, we used data provided by Phillips et al. (2013, AGU Fall meeting, abs. P34A-1846) and Schenk and Nimmo (2017, in prep.) to compare DEMs that were created using Ames Stereo Pipeline (ASP), SOCET SET, and Paul Schenk's own method. We began by locating areas of the surface with multiple overlapping DEMs, and our initial comparisons were performed near the craters Manannan, Pwyll, and Cilix. For each region, we used ArcGIS to draw profile lines across matching features to determine elevation. Some of the DEMs had vertical or skewed offsets, and thus had to be corrected. The vertical corrections were applied by adding or subtracting the global minimum of the data set to create a common zero-point. The skewed data sets were corrected by rotating the plot so that it had a global slope of zero and then subtracting for a zero-point vertical offset. Once corrections were made, we plotted the three methods on one graph for each profile of each region. Upon analysis, we found relatively good feature correlation between the three methods. The smoothness of a DEM depends on both the input set of images and the stereo processing methods used. In our comparison, the DEMs produced by SOCET SET were less smoothed than those from ASP or Schenk. Height comparisons show that ASP and Schenk's model appear similar, alternating in maximum height. SOCET SET has more topographic variability due to its decreased smoothing, which is borne out by preliminary offset calculations. In the future, we plan to expand upon this preliminary work with more regions of Europa, continue quantifying the height differences and relative accuracy of each method, and generate more DEMs to expand our available comparison regions.

Recognizing Action Units for Facial Expression Analysis

PubMed Central

Tian, Ying-li; Kanade, Takeo; Cohn, Jeffrey F.

2010-01-01

Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions, such as happiness, anger, surprise, and fear. Such prototypic expressions, however, occur rather infrequently. Human emotions and intentions are more often communicated by changes in one or a few discrete facial features. In this paper, we develop an Automatic Face Analysis (AFA) system to analyze facial expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal-view face image sequence. The AFA system recognizes fine-grained changes in facial expression into action units (AUs) of the Facial Action Coding System (FACS), instead of a few prototypic expressions. Multistate face and facial component models are proposed for tracking and modeling the various facial features, including lips, eyes, brows, cheeks, and furrows. During tracking, detailed parametric descriptions of the facial features are extracted. With these parameters as the inputs, a group of action units (neutral expression, six upper face AUs and 10 lower face AUs) are recognized whether they occur alone or in combinations. The system has achieved average recognition rates of 96.4 percent (95.4 percent if neutral expressions are excluded) for upper face AUs and 96.7 percent (95.6 percent with neutral expressions excluded) for lower face AUs. The generalizability of the system has been tested by using independent image databases collected and FACS-coded for ground-truth by different research teams. PMID:25210210

Evaluation of the ACEC Benchmark Suite for Real-Time Applications

DTIC Science & Technology

1990-07-23

1.0 benchmark suite waSanalyzed with respect to its measuring of Ada real-time features such as tasking, memory management, input/output, scheduling...and delay statement, Chapter 13 features , pragmas, interrupt handling, subprogram overhead, numeric computations etc. For most of the features that...meant for programming real-time systems. The ACEC benchmarks have been analyzed extensively with respect to their measuring of Ada real-time features

Influential input classification in probabilistic multimedia models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maddalena, Randy L.; McKone, Thomas E.; Hsieh, Dennis P.H.

1999-05-01

Monte Carlo analysis is a statistical simulation method that is often used to assess and quantify the outcome variance in complex environmental fate and effects models. Total outcome variance of these models is a function of (1) the uncertainty and/or variability associated with each model input and (2) the sensitivity of the model outcome to changes in the inputs. To propagate variance through a model using Monte Carlo techniques, each variable must be assigned a probability distribution. The validity of these distributions directly influences the accuracy and reliability of the model outcome. To efficiently allocate resources for constructing distributions onemore » should first identify the most influential set of variables in the model. Although existing sensitivity and uncertainty analysis methods can provide a relative ranking of the importance of model inputs, they fail to identify the minimum set of stochastic inputs necessary to sufficiently characterize the outcome variance. In this paper, we describe and demonstrate a novel sensitivity/uncertainty analysis method for assessing the importance of each variable in a multimedia environmental fate model. Our analyses show that for a given scenario, a relatively small number of input variables influence the central tendency of the model and an even smaller set determines the shape of the outcome distribution. For each input, the level of influence depends on the scenario under consideration. This information is useful for developing site specific models and improving our understanding of the processes that have the greatest influence on the variance in outcomes from multimedia models.« less

Puerto Rico water resources planning model program description

USGS Publications Warehouse

Moody, D.W.; Maddock, Thomas; Karlinger, M.R.; Lloyd, J.J.

1973-01-01

Because the use of the Mathematical Programming System -Extended (MPSX) to solve large linear and mixed integer programs requires the preparation of many input data cards, a matrix generator program to produce the MPSX input data from a much more limited set of data may expedite the use of the mixed integer programming optimization technique. The Model Definition and Control Program (MODCQP) is intended to assist a planner in preparing MPSX input data for the Puerto Rico Water Resources Planning Model. The model utilizes a mixed-integer mathematical program to identify a minimum present cost set of water resources projects (diversions, reservoirs, ground-water fields, desalinization plants, water treatment plants, and inter-basin transfers of water) which will meet a set of future water demands and to determine their sequence of construction. While MODCOP was specifically written to generate MPSX input data for the planning model described in this report, the program can be easily modified to reflect changes in the model's mathematical structure.

Generalization of some hidden subgroup algorithms for input sets of arbitrary size

NASA Astrophysics Data System (ADS)

Poslu, Damla; Say, A. C. Cem

2006-05-01

We consider the problem of generalizing some quantum algorithms so that they will work on input domains whose cardinalities are not necessarily powers of two. When analyzing the algorithms we assume that generating superpositions of arbitrary subsets of basis states whose cardinalities are not necessarily powers of two perfectly is possible. We have taken Ballhysa's model as a template and have extended it to Chi, Kim and Lee's generalizations of the Deutsch-Jozsa algorithm and to Simon's algorithm. With perfectly equal superpositions of input sets of arbitrary size, Chi, Kim and Lee's generalized Deutsch-Jozsa algorithms, both for evenly-distributed and evenly-balanced functions, worked with one-sided error property. For Simon's algorithm the success probability of the generalized algorithm is the same as that of the original for input sets of arbitrary cardinalities with equiprobable superpositions, since the property that the measured strings are all those which have dot product zero with the string we search, for the case where the function is 2-to-1, is not lost.

Building accurate historic and future climate MEPDG input files for Louisiana DOTD : tech summary.

DOT National Transportation Integrated Search

2017-02-01

The new pavement design process (originally MEPDG, then DARWin-ME, and now Pavement ME Design) requires two types : of inputs to infl uence the prediction of pavement distress for a selected set of pavement materials and structure. One input is : tra...

Wide field imaging - I. Applications of neural networks to object detection and star/galaxy classification

NASA Astrophysics Data System (ADS)

Andreon, S.; Gargiulo, G.; Longo, G.; Tagliaferri, R.; Capuano, N.

2000-12-01

Astronomical wide-field imaging performed with new large-format CCD detectors poses data reduction problems of unprecedented scale, which are difficult to deal with using traditional interactive tools. We present here NExt (Neural Extractor), a new neural network (NN) based package capable of detecting objects and performing both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first distinguished from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold; they are then classified as stars or as galaxies through diagnostic diagrams having variables chosen according to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of `what an object is' (i.e. it keeps all structures composed of more than one pixel) and performs the detection via an unsupervised NN, approaching detection as a clustering problem that has been thoroughly studied in the artificial intelligence literature. The first part of the NExt procedure consists of an optimal compression of the redundant information contained in the pixels via a mapping from pixel intensities to a subspace individualized through principal component analysis. At magnitudes fainter than the completeness limit, stars are usually almost indistinguishable from galaxies, and therefore the parameters characterizing the two classes do not lie in disconnected subspaces, thus preventing the use of unsupervised methods. We therefore adopted a supervised NN (i.e. a NN that first finds the rules to classify objects from examples and then applies them to the whole data set). In practice, each object is classified depending on its membership of the regions mapping the input feature space in the training set. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features we use a NN to select the most significant features among the large number of measured ones, and then we use these selected features to perform the classification task. In order to optimize the performance of the system, we implemented and tested several different models of NN. The comparison of the NExt performance with that of the best detection and classification package known to the authors (SExtractor) shows that NExt is at least as effective as the best traditional packages.

Adaptive non-predictor control of lower triangular uncertain nonlinear systems with an unknown time-varying delay in the input

NASA Astrophysics Data System (ADS)

Koo, Min-Sung; Choi, Ho-Lim

2018-01-01

In this paper, we consider a control problem for a class of uncertain nonlinear systems in which there exists an unknown time-varying delay in the input and lower triangular nonlinearities. Usually, in the existing results, input delays have been coupled with feedforward (or upper triangular) nonlinearities; in other words, the combination of lower triangular nonlinearities and input delay has been rare. Motivated by the existing controller for input-delayed chain of integrators with nonlinearity, we show that the control of input-delayed nonlinear systems with two particular types of lower triangular nonlinearities can be done. As a control solution, we propose a newly designed feedback controller whose main features are its dynamic gain and non-predictor approach. Three examples are given for illustration.

Case Assignment in Typically Developing English-Speaking Children: A Paired Priming Study

ERIC Educational Resources Information Center

Wisman Weil, Lisa Marie

2013-01-01

This study utilized a paired priming paradigm to examine the influence of input features on case assignment in typically developing English-speaking children. The Input Ambiguity Hypothesis (Pelham, 2011) was experimentally tested to help explain why children produce subject pronoun case errors. Analyses of third singular "-s" marking on…

Input-Based Grammar Pedagogy: A Comparison of Two Possibilities

ERIC Educational Resources Information Center

Marsden, Emma

2005-01-01

This article presents arguments for using listening and reading activities as an option for techniques in grammar pedagogy. It describes two possible approaches: Processing Instruction (PI) and Enriched Input (EI), and examples of their key features are included in the appendices. The article goes on to report on a classroom based quasi-experiment…

Electrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli.

PubMed

Aumentado-Armstrong, Tristan; Metzen, Michael G; Sproule, Michael K J; Chacron, Maurice J

2015-10-01

Neurons that respond selectively but in an invariant manner to a given feature of natural stimuli have been observed across species and systems. Such responses emerge in higher brain areas, thereby suggesting that they occur by integrating afferent input. However, the mechanisms by which such integration occurs are poorly understood. Here we show that midbrain electrosensory neurons can respond selectively and in an invariant manner to heterogeneity in behaviorally relevant stimulus waveforms. Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input. To test this hypothesis, we built a model based on the Hodgkin-Huxley formalism that received realistic afferent input. We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally. Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli. We discuss the implications of our findings for the electrosensory and other systems.