A Compact Methodology to Understand, Evaluate, and Predict the Performance of Automatic Target Recognition

PubMed Central

Li, Yanpeng; Li, Xiang; Wang, Hongqiang; Chen, Yiping; Zhuang, Zhaowen; Cheng, Yongqiang; Deng, Bin; Wang, Liandong; Zeng, Yonghu; Gao, Lei

2014-01-01

This paper offers a compacted mechanism to carry out the performance evaluation work for an automatic target recognition (ATR) system: (a) a standard description of the ATR system's output is suggested, a quantity to indicate the operating condition is presented based on the principle of feature extraction in pattern recognition, and a series of indexes to assess the output in different aspects are developed with the application of statistics; (b) performance of the ATR system is interpreted by a quality factor based on knowledge of engineering mathematics; (c) through a novel utility called “context-probability” estimation proposed based on probability, performance prediction for an ATR system is realized. The simulation result shows that the performance of an ATR system can be accounted for and forecasted by the above-mentioned measures. Compared to existing technologies, the novel method can offer more objective performance conclusions for an ATR system. These conclusions may be helpful in knowing the practical capability of the tested ATR system. At the same time, the generalization performance of the proposed method is good. PMID:24967605

ATR applications of minimax entropy models of texture and shape

NASA Astrophysics Data System (ADS)

Zhu, Song-Chun; Yuille, Alan L.; Lanterman, Aaron D.

2001-10-01

Concepts from information theory have recently found favor in both the mainstream computer vision community and the military automatic target recognition community. In the computer vision literature, the principles of minimax entropy learning theory have been used to generate rich probabilitistic models of texture and shape. In addition, the method of types and large deviation theory has permitted the difficulty of various texture and shape recognition tasks to be characterized by 'order parameters' that determine how fundamentally vexing a task is, independent of the particular algorithm used. These information-theoretic techniques have been demonstrated using traditional visual imagery in applications such as simulating cheetah skin textures and such as finding roads in aerial imagery. We discuss their application to problems in the specific application domain of automatic target recognition using infrared imagery. We also review recent theoretical and algorithmic developments which permit learning minimax entropy texture models for infrared textures in reasonable timeframes.

Real Time Intelligent Target Detection and Analysis with Machine Vision

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Padgett, Curtis; Brown, Kenneth

2000-01-01

We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

ATR evaluation through the synthesis of multiple performance measures

NASA Astrophysics Data System (ADS)

Bassham, Christopher B.; Klimack, William K.; Bauer, Kenneth W., Jr.

2002-07-01

This research demonstrates the application of decision analysis (DA) techniques to decisions made within Automatic Target Recognition (ATR) technology development. This work is accomplished to improve the means by which ATR technologies are evaluated. The first step in this research was to create a flexible decision analysis framework that could be applied to several decisions across different ATR programs evaluated by the Comprehensive ATR Scientific Evaluation (COMPASE) Center of the Air Force Research Laboratory (AFRL). For the purposes of this research, a single COMPASE Center representative provided the value, utility, and preference functions for the DA framework. The DA framework employs performance measures collected during ATR classification system (CS) testing to calculate value and utility scores. The authors gathered data from the Moving and Stationary Target Acquisition and Recognition (MSTAR) program to demonstrate how the decision framework could be used to evaluate three different ATR CSs. A decision-maker may use the resultant scores to gain insight into any of the decisions that occur throughout the lifecycle of ATR technologies. Additionally, a means of evaluating ATR CS self-assessment ability is presented. This represents a new criterion that emerged from this study, and no present evaluation metric is known.

Composite Wavelet Filters for Enhanced Automated Target Recognition

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

Optimization of Support Vector Machine (SVM) for Object Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.

2011-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

Grayscale Optical Correlator Workbench

NASA Technical Reports Server (NTRS)

Hanan, Jay; Zhou, Hanying; Chao, Tien-Hsin

2006-01-01

Grayscale Optical Correlator Workbench (GOCWB) is a computer program for use in automatic target recognition (ATR). GOCWB performs ATR with an accurate simulation of a hardware grayscale optical correlator (GOC). This simulation is performed to test filters that are created in GOCWB. Thus, GOCWB can be used as a stand-alone ATR software tool or in combination with GOC hardware for building (target training), testing, and optimization of filters. The software is divided into three main parts, denoted filter, testing, and training. The training part is used for assembling training images as input to a filter. The filter part is used for combining training images into a filter and optimizing that filter. The testing part is used for testing new filters and for general simulation of GOC output. The current version of GOCWB relies on the mathematical software tools from MATLAB binaries for performing matrix operations and fast Fourier transforms. Optimization of filters is based on an algorithm, known as OT-MACH, in which variables specified by the user are parameterized and the best filter is selected on the basis of an average result for correct identification of targets in multiple test images.

Deep learning model-based algorithm for SAR ATR

NASA Astrophysics Data System (ADS)

Friedlander, Robert D.; Levy, Michael; Sudkamp, Elizabeth; Zelnio, Edmund

2018-05-01

Many computer-vision-related problems have successfully applied deep learning to improve the error rates with respect to classifying images. As opposed to optically based images, we have applied deep learning via a Siamese Neural Network (SNN) to classify synthetic aperture radar (SAR) images. This application of Automatic Target Recognition (ATR) utilizes an SNN made up of twin AlexNet-based Convolutional Neural Networks (CNNs). Using the processing power of GPUs, we trained the SNN with combinations of synthetic images on one twin and Moving and Stationary Target Automatic Recognition (MSTAR) measured images on a second twin. We trained the SNN with three target types (T-72, BMP2, and BTR-70) and have used a representative, synthetic model from each target to classify new SAR images. Even with a relatively small quantity of data (with respect to machine learning), we found that the SNN performed comparably to a CNN and had faster convergence. The results of processing showed the T-72s to be the easiest to identify, whereas the network sometimes mixed up the BMP2s and the BTR-70s. In addition we also incorporated two additional targets (M1 and M35) into the validation set. Without as much training (for example, one additional epoch) the SNN did not produce the same results as if all five targets had been trained over all the epochs. Nevertheless, an SNN represents a novel and beneficial approach to SAR ATR.

Analytical performance evaluation of SAR ATR with inaccurate or estimated models

NASA Astrophysics Data System (ADS)

DeVore, Michael D.

2004-09-01

Hypothesis testing algorithms for automatic target recognition (ATR) are often formulated in terms of some assumed distribution family. The parameter values corresponding to a particular target class together with the distribution family constitute a model for the target's signature. In practice such models exhibit inaccuracy because of incorrect assumptions about the distribution family and/or because of errors in the assumed parameter values, which are often determined experimentally. Model inaccuracy can have a significant impact on performance predictions for target recognition systems. Such inaccuracy often causes model-based predictions that ignore the difference between assumed and actual distributions to be overly optimistic. This paper reports on research to quantify the effect of inaccurate models on performance prediction and to estimate the effect using only trained parameters. We demonstrate that for large observation vectors the class-conditional probabilities of error can be expressed as a simple function of the difference between two relative entropies. These relative entropies quantify the discrepancies between the actual and assumed distributions and can be used to express the difference between actual and predicted error rates. Focusing on the problem of ATR from synthetic aperture radar (SAR) imagery, we present estimators of the probabilities of error in both ideal and plug-in tests expressed in terms of the trained model parameters. These estimators are defined in terms of unbiased estimates for the first two moments of the sample statistic. We present an analytical treatment of these results and include demonstrations from simulated radar data.

Adaptive mechanism-based congestion control for networked systems

NASA Astrophysics Data System (ADS)

Liu, Zhi; Zhang, Yun; Chen, C. L. Philip

2013-03-01

In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol-AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.

Applications of independent component analysis in SAR images

NASA Astrophysics Data System (ADS)

Huang, Shiqi; Cai, Xinhua; Hui, Weihua; Xu, Ping

2009-07-01

The detection of faint, small and hidden targets in synthetic aperture radar (SAR) image is still an issue for automatic target recognition (ATR) system. How to effectively separate these targets from the complex background is the aim of this paper. Independent component analysis (ICA) theory can enhance SAR image targets and improve signal clutter ratio (SCR), which benefits to detect and recognize faint targets. Therefore, this paper proposes a new SAR image target detection algorithm based on ICA. In experimental process, the fast ICA (FICA) algorithm is utilized. Finally, some real SAR image data is used to test the method. The experimental results verify that the algorithm is feasible, and it can improve the SCR of SAR image and increase the detection rate for the faint small targets.

Diffusion Maps and Geometric Harmonics for Automatic Target Recognition (ATR). Volume 2. Appendices

DTIC Science & Technology

2007-11-01

of the Perron - Frobenius theorem, it suffices to prove that the chain is irreducible and aperiodic. • The irreducibility is a mere consequence of the...of each atom; this is due to the linear programming constraint that the coefficients be nonnegative 4. Chen et al. [20, 21] describe two algorithms for...projection of x onto the convex cone spanned by Ψ(t) with the origin at the apex; we provide details on computing x̃(t) in Section 4.1.3. Let x̃ (t) H

Recognition, Investigation and Management of Acute Transfusion Reactions

PubMed Central

Al-Riyami, Arwa Z.; Al-Hashmi, Sabria; Al-Arimi, Zainab; Wadsworth, Louis D.; Al-Rawas, Abdulhakim; Al-Khabori, Murtadha; Daar, Shahina

2014-01-01

The recognition and management of transfusion reactions (TRs) are critical to ensure patient safety during and after a blood transfusion. Transfusion reactions are classified into acute transfusion reactions (ATRs) or delayed transfusion reactions, and each category includes different subtypes. Different ATRs share common signs and symptoms which can make categorisation difficult at the beginning of the reaction. Moreover, TRs are often under-recognised and under-reported. To ensure uniform practice and safety, it is necessary to implement a national haemovigilance system and a set of national guidelines establishing policies for blood transfusion and for the detection and management of TRs. In Oman, there are currently no local TR guidelines to guide physicians and hospital blood banks. This paper summarises the available literature and provides consensus guidelines to be used in the recognition, management and reporting of ATRs. PMID:25097764

Investigation of measureable parameters that correlate with automatic target recognition performance in synthetic aperture sonar

NASA Astrophysics Data System (ADS)

Gazagnaire, Julia; Cobb, J. T.; Isaacs, Jason

2015-05-01

There is a desire in the Mine Counter Measure community to develop a systematic method to predict and/or estimate the performance of Automatic Target Recognition (ATR) algorithms that are detecting and classifying mine-like objects within sonar data. Ideally, parameters exist that can be measured directly from the sonar data that correlate with ATR performance. In this effort, two metrics were analyzed for their predictive potential using high frequency synthetic aperture sonar (SAS) images. The first parameter is a measure of contrast. It is essentially the variance in pixel intensity over a fixed partition of relatively small size. An analysis was performed to determine the optimum block size for this contrast calculation. These blocks were then overlapped in the horizontal and vertical direction over the entire image. The second parameter is the one-dimensional K-shape parameter. The K-distribution is commonly used to describe sonar backscatter return from range cells that contain a finite number of scatterers. An Ada-Boosted Decision Tree classifier was used to calculate the probability of classification (Pc) and false alarm rate (FAR) for several types of targets in SAS images from three different data sets. ROC curves as a function of the measured parameters were generated and the correlation between the measured parameters in the vicinity of each of the contacts and the ATR performance was investigated. The contrast and K-shape parameters were considered separately. Additionally, the contrast and K-shape parameter were associated with background texture types using previously labeled high frequency SAS images.

Cascaded automatic target recognition (Cascaded ATR)

NASA Astrophysics Data System (ADS)

Walls, Bradley

2010-04-01

The global war on terror has plunged US and coalition forces into a battle space requiring the continuous adaptation of tactics and technologies to cope with an elusive enemy. As a result, technologies that enhance the intelligence, surveillance, and reconnaissance (ISR) mission making the warfighter more effective are experiencing increased interest. In this paper we show how a new generation of smart cameras built around foveated sensing makes possible a powerful ISR technique termed Cascaded ATR. Foveated sensing is an innovative optical concept in which a single aperture captures two distinct fields of view. In Cascaded ATR, foveated sensing is used to provide a coarse resolution, persistent surveillance, wide field of view (WFOV) detector to accomplish detection level perception. At the same time, within the foveated sensor, these detection locations are passed as a cue to a steerable, high fidelity, narrow field of view (NFOV) detector to perform recognition level perception. Two new ISR mission scenarios, utilizing Cascaded ATR, are proposed.

User acceptance of intelligent avionics: A study of automatic-aided target recognition

NASA Technical Reports Server (NTRS)

Becker, Curtis A.; Hayes, Brian C.; Gorman, Patrick C.

1991-01-01

User acceptance of new support systems typically was evaluated after the systems were specified, designed, and built. The current study attempts to assess user acceptance of an Automatic-Aided Target Recognition (ATR) system using an emulation of such a proposed system. The detection accuracy and false alarm level of the ATR system were varied systematically, and subjects rated the tactical value of systems exhibiting different performance levels. Both detection accuracy and false alarm level affected the subjects' ratings. The data from two experiments suggest a cut-off point in ATR performance below which the subjects saw little tactical value in the system. An ATR system seems to have obvious tactical value only if it functions at a correct detection rate of 0.7 or better with a false alarm level of 0.167 false alarms per square degree or fewer.

Advanced miniature processing handware for ATR applications

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin (Inventor); Daud, Taher (Inventor); Thakoor, Anikumar (Inventor)

2003-01-01

A Hybrid Optoelectronic Neural Object Recognition System (HONORS), is disclosed, comprising two major building blocks: (1) an advanced grayscale optical correlator (OC) and (2) a massively parallel three-dimensional neural-processor. The optical correlator, with its inherent advantages in parallel processing and shift invariance, is used for target of interest (TOI) detection and segmentation. The three-dimensional neural-processor, with its robust neural learning capability, is used for target classification and identification. The hybrid optoelectronic neural object recognition system, with its powerful combination of optical processing and neural networks, enables real-time, large frame, automatic target recognition (ATR).

Unification of automatic target tracking and automatic target recognition

NASA Astrophysics Data System (ADS)

Schachter, Bruce J.

2014-06-01

The subject being addressed is how an automatic target tracker (ATT) and an automatic target recognizer (ATR) can be fused together so tightly and so well that their distinctiveness becomes lost in the merger. This has historically not been the case outside of biology and a few academic papers. The biological model of ATT∪ATR arises from dynamic patterns of activity distributed across many neural circuits and structures (including retina). The information that the brain receives from the eyes is "old news" at the time that it receives it. The eyes and brain forecast a tracked object's future position, rather than relying on received retinal position. Anticipation of the next moment - building up a consistent perception - is accomplished under difficult conditions: motion (eyes, head, body, scene background, target) and processing limitations (neural noise, delays, eye jitter, distractions). Not only does the human vision system surmount these problems, but it has innate mechanisms to exploit motion in support of target detection and classification. Biological vision doesn't normally operate on snapshots. Feature extraction, detection and recognition are spatiotemporal. When vision is viewed as a spatiotemporal process, target detection, recognition, tracking, event detection and activity recognition, do not seem as distinct as they are in current ATT and ATR designs. They appear as similar mechanism taking place at varying time scales. A framework is provided for unifying ATT and ATR.

Detection, recognition, identification, and tracking of military vehicles using biomimetic intelligence

NASA Astrophysics Data System (ADS)

Pace, Paul W.; Sutherland, John

2001-10-01

This project is aimed at analyzing EO/IR images to provide automatic target detection/recognition/identification (ATR/D/I) of militarily relevant land targets. An increase in performance was accomplished using a biomimetic intelligence system functioning on low-cost, commercially available processing chips. Biomimetic intelligence has demonstrated advanced capabilities in the areas of hand- printed character recognition, real-time detection/identification of multiple faces in full 3D perspectives in cluttered environments, advanced capabilities in classification of ground-based military vehicles from SAR, and real-time ATR/D/I of ground-based military vehicles from EO/IR/HRR data in cluttered environments. The investigation applied these tools to real data sets and examined the parameters such as the minimum resolution for target recognition, the effect of target size, rotation, line-of-sight changes, contrast, partial obscuring, background clutter etc. The results demonstrated a real-time ATR/D/I capability against a subset of militarily relevant land targets operating in a realistic scenario. Typical results on the initial EO/IR data indicate probabilities of correct classification of resolved targets to be greater than 95 percent.

Vigilante: Ultrafast Smart Sensor for Target Recognition and Precision Tracking in a Simulated CMD Scenario

NASA Technical Reports Server (NTRS)

Uldomkesmalee, Suraphol; Suddarth, Steven C.

1997-01-01

VIGILANTE is an ultrafast smart sensor testbed for generic Automatic Target Recognition (ATR) applications with a series of capability demonstration focussed on cruise missile defense (CMD). VIGILANTE's sensor/processor architecture is based on next-generation UV/visible/IR sensors and a tera-operations per second sugar-cube processor, as well as supporting airborne vehicle. Excellent results of efficient ATR methodologies that use an eigenvectors/neural network combination and feature-based precision tracking have been demonstrated in the laboratory environment.

Using phase for radar scatterer classification

NASA Astrophysics Data System (ADS)

Moore, Linda J.; Rigling, Brian D.; Penno, Robert P.; Zelnio, Edmund G.

2017-04-01

Traditional synthetic aperture radar (SAR) systems tend to discard phase information of formed complex radar imagery prior to automatic target recognition (ATR). This practice has historically been driven by available hardware storage, processing capabilities, and data link capacity. Recent advances in high performance computing (HPC) have enabled extremely dense storage and processing solutions. Therefore, previous motives for discarding radar phase information in ATR applications have been mitigated. First, we characterize the value of phase in one-dimensional (1-D) radar range profiles with respect to the ability to correctly estimate target features, which are currently employed in ATR algorithms for target discrimination. These features correspond to physical characteristics of targets through radio frequency (RF) scattering phenomenology. Physics-based electromagnetic scattering models developed from the geometrical theory of diffraction are utilized for the information analysis presented here. Information is quantified by the error of target parameter estimates from noisy radar signals when phase is either retained or discarded. Operating conditions (OCs) of signal-tonoise ratio (SNR) and bandwidth are considered. Second, we investigate the value of phase in 1-D radar returns with respect to the ability to correctly classify canonical targets. Classification performance is evaluated via logistic regression for three targets (sphere, plate, tophat). Phase information is demonstrated to improve radar target classification rates, particularly at low SNRs and low bandwidths.

An automatic target recognition system based on SAR image

NASA Astrophysics Data System (ADS)

Li, Qinfu; Wang, Jinquan; Zhao, Bo; Luo, Furen; Xu, Xiaojian

2009-10-01

In this paper, an automatic target recognition (ATR) system based on synthetic aperture radar (SAR) is proposed. This ATR system can play an important role in the simulation of up-to-data battlefield environment and be used in ATR research. To establish an integral and available system, the processing of SAR image was divided into four main stages which are de-noise, detection, cluster-discrimination and segment-recognition, respectively. The first three stages are used for searching region of interest (ROI). Once the ROIs are extracted, the recognition stage will be taken to compute the similarity between the ROIs and the templates in the electromagnetic simulation software National Electromagnetic Scattering Code (NESC). Due to the lack of the SAR raw data, the electromagnetic simulated images are added to the measured SAR background to simulate the battlefield environment8. The purpose of the system is to find the ROIs which can be the artificial military targets such as tanks, armored cars and so on and to categorize the ROIs into the right classes according to the existing templates. From the results we can see that the proposed system achieves a satisfactory result.

Sparse representation based SAR vehicle recognition along with aspect angle.

PubMed

Xing, Xiangwei; Ji, Kefeng; Zou, Huanxin; Sun, Jixiang

2014-01-01

As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC) has attracted much attention in synthetic aperture radar (SAR) automatic target recognition (ATR) recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA), in which the correlation between the vehicle's aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA) feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle's aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

ROBIN: a platform for evaluating automatic target recognition algorithms: I. Overview of the project and presentation of the SAGEM DS competition

NASA Astrophysics Data System (ADS)

Duclos, D.; Lonnoy, J.; Guillerm, Q.; Jurie, F.; Herbin, S.; D'Angelo, E.

2008-04-01

The last five years have seen a renewal of Automatic Target Recognition applications, mainly because of the latest advances in machine learning techniques. In this context, large collections of image datasets are essential for training algorithms as well as for their evaluation. Indeed, the recent proliferation of recognition algorithms, generally applied to slightly different problems, make their comparisons through clean evaluation campaigns necessary. The ROBIN project tries to fulfil these two needs by putting unclassified datasets, ground truths, competitions and metrics for the evaluation of ATR algorithms at the disposition of the scientific community. The scope of this project includes single and multi-class generic target detection and generic target recognition, in military and security contexts. From our knowledge, it is the first time that a database of this importance (several hundred thousands of visible and infrared hand annotated images) has been publicly released. Funded by the French Ministry of Defence (DGA) and by the French Ministry of Research, ROBIN is one of the ten Techno-vision projects. Techno-vision is a large and ambitious government initiative for building evaluation means for computer vision technologies, for various application contexts. ROBIN's consortium includes major companies and research centres involved in Computer Vision R&D in the field of defence: Bertin Technologies, CNES, ECA, DGA, EADS, INRIA, ONERA, MBDA, SAGEM, THALES. This paper, which first gives an overview of the whole project, is focused on one of ROBIN's key competitions, the SAGEM Defence Security database. This dataset contains more than eight hundred ground and aerial infrared images of six different vehicles in cluttered scenes including distracters. Two different sets of data are available for each target. The first set includes different views of each vehicle at close range in a "simple" background, and can be used to train algorithms. The second set contains many views of the same vehicle in different contexts and situations simulating operational scenarios.

Automated target recognition using passive radar and coordinated flight models

NASA Astrophysics Data System (ADS)

Ehrman, Lisa M.; Lanterman, Aaron D.

2003-09-01

Rather than emitting pulses, passive radar systems rely on illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. These systems are particularly attractive since they allow receivers to operate without emitting energy, rendering them covert. Many existing passive radar systems estimate the locations and velocities of targets. This paper focuses on adding an automatic target recognition (ATR) component to such systems. Our approach to ATR compares the Radar Cross Section (RCS) of targets detected by a passive radar system to the simulated RCS of known targets. To make the comparison as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. The estimated positions become inputs for an algorithm that uses a coordinated flight model to compute probable aircraft orientation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of several potential target classes as they execute the estimated maneuvers. The RCS is then scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. The Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern, so that the RCS can be further scaled. The Rician model compares the RCS of the illuminated aircraft with those of the potential targets. This comparison results in target identification.

Towards distributed ATR using subjective logic combination rules with a swarm of UAVs

NASA Astrophysics Data System (ADS)

O'Hara, Stephen; Simon, Michael; Zhu, Qiuming

2007-04-01

In this paper, we present our initial findings demonstrating a cost-effective approach to Aided Target Recognition (ATR) employing a swarm of inexpensive Unmanned Aerial Vehicles (UAVs). We call our approach Distributed ATR (DATR). Our paper describes the utility of DATR for autonomous UAV operations, provides an overview of our methods, and the results of our initial simulation-based implementation and feasibility study. Our technology is aimed towards small and micro UAVs where platform restrictions allow only a modest quality camera and limited on-board computational capabilities. It is understood that an inexpensive sensor coupled with limited processing capability would be challenged in deriving a high probability of detection (P d) while maintaining a low probability of false alarms (P fa). Our hypothesis is that an evidential reasoning approach to fusing the observations of multiple UAVs observing approximately the same scene can raise the P d and lower the P fa sufficiently in order to provide a cost-effective ATR capability. This capability can lead to practical implementations of autonomous, coordinated, multi-UAV operations. In our system, the live video feed from a UAV is processed by a lightweight real-time ATR algorithm. This algorithm provides a set of possible classifications for each detected object over a possibility space defined by a set of exemplars. The classifications for each frame within a short observation interval (a few seconds) are used to generate a belief statement. Our system considers how many frames in the observation interval support each potential classification. A definable function transforms the observational data into a belief value. The belief value, or opinion, represents the UAV's belief that an object of the particular class exists in the area covered during the observation interval. The opinion is submitted as evidence in an evidential reasoning system. Opinions from observations over the same spatial area will have similar index values in the evidence cache. The evidential reasoning system combines observations of similar spatial indexes, discounting older observations based upon a parameterized information aging function. We employ Subjective Logic operations in the discounting and combination of opinions. The result is the consensus opinion from all observations that an object of a given class exists in a given region.

Constraints in distortion-invariant target recognition system simulation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Razzaque, Md A.

2000-11-01

Automatic target recognition (ATR) is a mature but active research area. In an earlier paper, we proposed a novel ATR approach for recognition of targets varying in fine details, rotation, and translation using a Learning Vector Quantization (LVQ) Neural Network (NN). The proposed approach performed segmentation of multiple objects and the identification of the objects using LVQNN. In this current paper, we extend the previous approach for recognition of targets varying in rotation, translation, scale, and combination of all three distortions. We obtain the analytical results of the system level design to show that the approach performs well with some constraints. The first constraint determines the size of the input images and input filters. The second constraint shows the limits on amount of rotation, translation, and scale of input objects. We present the simulation verification of the constraints using DARPA's Moving and Stationary Target Recognition (MSTAR) images with different depression and pose angles. The simulation results using MSTAR images verify the analytical constraints of the system level design.

Deep feature extraction and combination for synthetic aperture radar target classification

NASA Astrophysics Data System (ADS)

Amrani, Moussa; Jiang, Feng

2017-10-01

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

Wurfelspiel-based training data methods for ATR

NASA Astrophysics Data System (ADS)

Peterson, James K.

2004-09-01

A data object is constructed from a P by M Wurfelspiel matrix W by choosing an entry from each column to construct a sequence A0A1"AM-1. Each of the PM possibilities are designed to correspond to the same category according to some chosen measure. This matrix could encode many types of data. (1) Musical fragments, all of which evoke sadness; each column entry is a 4 beat sequence with a chosen A0A1A2 thus 16 beats long (W is P by 3). (2) Paintings, all of which evoke happiness; each column entry is a layer and a given A0A1A2 is a painting constructed using these layers (W is P by 3). (3) abstract feature vectors corresponding to action potentials evoked from a biological cell's exposure to a toxin. The action potential is divided into four relevant regions and each column entry represents the feature vector of a region. A given A0A1A2 is then an abstraction of the excitable cell's output (W is P by 4). (4) abstract feature vectors corresponding to an object such as a face or vehicle. The object is divided into four categories each assigned an abstract feature vector with the resulting concatenation an abstract representation of the object (W is P by 4). All of the examples above correspond to one particular measure (sad music, happy paintings, an introduced toxin, an object to recognize)and hence, when a Wurfelspiel matrix is constructed, relevant training information for recognition is encoded that can be used in many algorithms. The focus of this paper is on the application of these ideas to automatic target recognition (ATR). In addition, we discuss a larger biologically based model of temporal cortex polymodal sensor fusion which can use the feature vectors extracted from the ATR Wurfelspiel data.

Testing Saliency Parameters for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Pandya, Sagar

2012-01-01

A bottom-up visual attention model (the saliency model) is tested to enhance the performance of Automated Target Recognition (ATR). JPL has developed an ATR system that identifies regions of interest (ROI) using a trained OT-MACH filter, and then classifies potential targets as true- or false-positives using machine-learning techniques. In this project, saliency is used as a pre-processing step to reduce the space for performing OT-MACH filtering. Saliency parameters, such as output level and orientation weight, are tuned to detect known target features. Preliminary results are promising and future work entails a rigrous and parameter-based search to gain maximum insight about this method.

Identification of related gene/protein names based on an HMM of name variations.

PubMed

Yeganova, L; Smith, L; Wilbur, W J

2004-04-01

Gene and protein names follow few, if any, true naming conventions and are subject to great variation in different occurrences of the same name. This gives rise to two important problems in natural language processing. First, can one locate the names of genes or proteins in free text, and second, can one determine when two names denote the same gene or protein? The first of these problems is a special case of the problem of named entity recognition, while the second is a special case of the problem of automatic term recognition (ATR). We study the second problem, that of gene or protein name variation. Here we describe a system which, given a query gene or protein name, identifies related gene or protein names in a large list. The system is based on a dynamic programming algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a fully trainable hidden Markov model.

Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition

NASA Astrophysics Data System (ADS)

Yan, Yue

2018-03-01

A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.

Gamma-Ray Simulated Spectrum Deconvolution of a LaBr₃ 1-in. x 1-in. Scintillator for Nondestructive ATR Fuel Burnup On-Site Predictions

DOE PAGES

Navarro, Jorge; Ring, Terry A.; Nigg, David W.

2015-03-01

A deconvolution method for a LaBr₃ 1"x1" detector for nondestructive Advanced Test Reactor (ATR) fuel burnup applications was developed. The method consisted of obtaining the detector response function, applying a deconvolution algorithm to 1”x1” LaBr₃ simulated, data along with evaluating the effects that deconvolution have on nondestructively determining ATR fuel burnup. The simulated response function of the detector was obtained using MCNPX as well with experimental data. The Maximum-Likelihood Expectation Maximization (MLEM) deconvolution algorithm was selected to enhance one-isotope source-simulated and fuel- simulated spectra. The final evaluation of the study consisted of measuring the performance of the fuel burnup calibrationmore » curve for the convoluted and deconvoluted cases. The methodology was developed in order to help design a reliable, high resolution, rugged and robust detection system for the ATR fuel canal capable of collecting high performance data for model validation, along with a system that can calculate burnup and using experimental scintillator detector data.« less

Multi-Stage System for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

2010-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis.

PubMed

Peng, Jiangtao; Peng, Silong; Xie, Qiong; Wei, Jiping

2011-04-01

In order to eliminate the lower order polynomial interferences, a new quantitative calibration algorithm "Baseline Correction Combined Partial Least Squares (BCC-PLS)", which combines baseline correction and conventional PLS, is proposed. By embedding baseline correction constraints into PLS weights selection, the proposed calibration algorithm overcomes the uncertainty in baseline correction and can meet the requirement of on-line attenuated total reflectance Fourier transform infrared (ATR-FTIR) quantitative analysis. The effectiveness of the algorithm is evaluated by the analysis of glucose and marzipan ATR-FTIR spectra. BCC-PLS algorithm shows improved prediction performance over PLS. The root mean square error of cross-validation (RMSECV) on marzipan spectra for the prediction of the moisture is found to be 0.53%, w/w (range 7-19%). The sugar content is predicted with a RMSECV of 2.04%, w/w (range 33-68%). Copyright © 2011 Elsevier B.V. All rights reserved.

Gestational and Lactational Exposure to Atrazine via the Drinking Water Causes Specific Behavioral Deficits and Selectively Alters Monoaminergic Systems in C57BL/6 Mouse Dams, Juvenile and Adult Offspring

PubMed Central

Krishna, Saritha; Ye, Xiaoqin; Filipov, Nikolay M.

2014-01-01

Atrazine (ATR) is one of the most frequently detected pesticides in the U.S. water supply. This study aimed to investigate neurobehavioral and neurochemical effects of ATR in C57BL/6 mouse offspring and dams exposed to a relatively low (3 mg/l, estimated intake 1.4 mg/kg/day) concentration of ATR via the drinking water (DW) from gestational day 6 to postnatal day (PND) 23. Behavioral tests included open field, pole, grip strength, novel object recognition (NOR), forced swim, and marble burying tests. Maternal weight gain and offspring (PND21, 35, and 70) body or brain weights were not affected by ATR. However, ATR-treated dams exhibited decreased NOR performance and a trend toward hyperactivity. Juvenile offspring (PND35) from ATR-exposed dams were hyperactive (both sexes), spent less time swimming (males), and buried more marbles (females). In adult offspring (PND70), the only behavioral change was a sex-specific (females) decreased NOR performance by ATR. Neurochemically, a trend toward increased striatal dopamine (DA) in dams and a significant increase in juvenile offspring (both sexes) was observed. Additionally, ATR exposure decreased perirhinal cortex serotonin in the adult female offspring. These results suggest that perinatal DW exposure to ATR targets the nigrostriatal DA pathway in dams and, especially, juvenile offspring, alters dams’ cognitive performance, induces sex-selective changes involving motor and emotional functions in juvenile offspring, and decreases cognitive ability of adult female offspring, with the latter possibly associated with altered perirhinal cortex serotonin homeostasis. Overall, ATR exposure during gestation and lactation may cause adverse nervous system effects to both offspring and dams. PMID:24913803

ATR performance modeling concepts

NASA Astrophysics Data System (ADS)

Ross, Timothy D.; Baker, Hyatt B.; Nolan, Adam R.; McGinnis, Ryan E.; Paulson, Christopher R.

2016-05-01

Performance models are needed for automatic target recognition (ATR) development and use. ATRs consume sensor data and produce decisions about the scene observed. ATR performance models (APMs) on the other hand consume operating conditions (OCs) and produce probabilities about what the ATR will produce. APMs are needed for many modeling roles of many kinds of ATRs (each with different sensing modality and exploitation functionality combinations); moreover, there are different approaches to constructing the APMs. Therefore, although many APMs have been developed, there is rarely one that fits a particular need. Clarified APM concepts may allow us to recognize new uses of existing APMs and identify new APM technologies and components that better support coverage of the needed APMs. The concepts begin with thinking of ATRs as mapping OCs of the real scene (including the sensor data) to reports. An APM is then a mapping from explicit quantized OCs (represented with less resolution than the real OCs) and latent OC distributions to report distributions. The roles of APMs can be distinguished by the explicit OCs they consume. APMs used in simulations consume the true state that the ATR is attempting to report. APMs used online with the exploitation consume the sensor signal and derivatives, such as match scores. APMs used in sensor management consume neither of those, but estimate performance from other OCs. This paper will summarize the major building blocks for APMs, including knowledge sources, OC models, look-up tables, analytical and learned mappings, and tools for signal synthesis and exploitation.

False alarm reduction by the And-ing of multiple multivariate Gaussian classifiers

NASA Astrophysics Data System (ADS)

Dobeck, Gerald J.; Cobb, J. Tory

2003-09-01

The high-resolution sonar is one of the principal sensors used by the Navy to detect and classify sea mines in minehunting operations. For such sonar systems, substantial effort has been devoted to the development of automated detection and classification (D/C) algorithms. These have been spurred by several factors including (1) aids for operators to reduce work overload, (2) more optimal use of all available data, and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and man-made clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while still maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms have been studied. We refer to this as Algorithm Fusion. The results have been remarkable, including reliable robustness to new environments. This paper describes a method for training several multivariate Gaussian classifiers such that their And-ing dramatically reduces false alarms while maintaining a high probability of classification. This training approach is referred to as the Focused- Training method. This work extends our 2001-2002 work where the Focused-Training method was used with three other types of classifiers: the Attractor-based K-Nearest Neighbor Neural Network (a type of radial-basis, probabilistic neural network), the Optimal Discrimination Filter Classifier (based linear discrimination theory), and the Quadratic Penalty Function Support Vector Machine (QPFSVM). Although our experience has been gained in the area of sea mine detection and classification, the principles described herein are general and can be applied to a wide range of pattern recognition and automatic target recognition (ATR) problems.

PCI bus content-addressable-memory (CAM) implementation on FPGA for pattern recognition/image retrieval in a distributed environment

NASA Astrophysics Data System (ADS)

Megherbi, Dalila B.; Yan, Yin; Tanmay, Parikh; Khoury, Jed; Woods, C. L.

2004-11-01

Recently surveillance and Automatic Target Recognition (ATR) applications are increasing as the cost of computing power needed to process the massive amount of information continues to fall. This computing power has been made possible partly by the latest advances in FPGAs and SOPCs. In particular, to design and implement state-of-the-Art electro-optical imaging systems to provide advanced surveillance capabilities, there is a need to integrate several technologies (e.g. telescope, precise optics, cameras, image/compute vision algorithms, which can be geographically distributed or sharing distributed resources) into a programmable system and DSP systems. Additionally, pattern recognition techniques and fast information retrieval, are often important components of intelligent systems. The aim of this work is using embedded FPGA as a fast, configurable and synthesizable search engine in fast image pattern recognition/retrieval in a distributed hardware/software co-design environment. In particular, we propose and show a low cost Content Addressable Memory (CAM)-based distributed embedded FPGA hardware architecture solution with real time recognition capabilities and computing for pattern look-up, pattern recognition, and image retrieval. We show how the distributed CAM-based architecture offers a performance advantage of an order-of-magnitude over RAM-based architecture (Random Access Memory) search for implementing high speed pattern recognition for image retrieval. The methods of designing, implementing, and analyzing the proposed CAM based embedded architecture are described here. Other SOPC solutions/design issues are covered. Finally, experimental results, hardware verification, and performance evaluations using both the Xilinx Virtex-II and the Altera Apex20k are provided to show the potential and power of the proposed method for low cost reconfigurable fast image pattern recognition/retrieval at the hardware/software co-design level.

ATRX ADD domain links an atypical histone methylation recognition mechanism to human mental-retardation syndrome

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

Iwase, Shigeki; Xiang, Bin; Ghosh, Sharmistha

ATR-X (alpha-thalassemia/mental retardation, X-linked) syndrome is a human congenital disorder that causes severe intellectual disabilities. Mutations in the ATRX gene, which encodes an ATP-dependent chromatin-remodeler, are responsible for the syndrome. Approximately 50% of the missense mutations in affected persons are clustered in a cysteine-rich domain termed ADD (ATRX-DNMT3-DNMT3L, ADD{sub ATRX}), whose function has remained elusive. Here we identify ADD{sub ATRX} as a previously unknown histone H3-binding module, whose binding is promoted by lysine 9 trimethylation (H3K9me3) but inhibited by lysine 4 trimethylation (H3K4me3). The cocrystal structure of ADD{sub ATRX} bound to H3{sub 1-15}K9me3 peptide reveals an atypical composite H3K9me3-binding pocket,more » which is distinct from the conventional trimethyllysine-binding aromatic cage. Notably, H3K9me3-pocket mutants and ATR-X syndrome mutants are defective in both H3K9me3 binding and localization at pericentromeric heterochromatin; thus, we have discovered a unique histone-recognition mechanism underlying the ATR-X etiology.« less

ATRX ADD Domain Links an Atypical Histone Methylation Recognition Mechanism to Human Mental-Retardation Syndrome

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

S Iwase; B Xiang; S Ghosh

ATR-X (alpha-thalassemia/mental retardation, X-linked) syndrome is a human congenital disorder that causes severe intellectual disabilities. Mutations in the ATRX gene, which encodes an ATP-dependent chromatin-remodeler, are responsible for the syndrome. Approximately 50% of the missense mutations in affected persons are clustered in a cysteine-rich domain termed ADD (ATRX-DNMT3-DNMT3L, ADD{sub ATRX}), whose function has remained elusive. Here we identify ADD{sub ATRX} as a previously unknown histone H3-binding module, whose binding is promoted by lysine 9 trimethylation (H3K9me3) but inhibited by lysine 4 trimethylation (H3K4me3). The cocrystal structure of ADD{sub ATRX} bound to H3{sub 1-15}K9me3 peptide reveals an atypical composite H3K9me3-binding pocket,more » which is distinct from the conventional trimethyllysine-binding aromatic cage. Notably, H3K9me3-pocket mutants and ATR-X syndrome mutants are defective in both H3K9me3 binding and localization at pericentromeric heterochromatin; thus, we have discovered a unique histone-recognition mechanism underlying the ATR-X etiology.« less

Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition

NASA Astrophysics Data System (ADS)

Kim, Sungho

2017-06-01

Automatic target recognition (ATR) is a traditionally challenging problem in military applications because of the wide range of infrared (IR) image variations and the limited number of training images. IR variations are caused by various three-dimensional target poses, noncooperative weather conditions (fog and rain), and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches for RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of RGB-CNN to the IR ATR problem fails to work because of the IR database problems (limited database size and IR image variations). An IR variation-reduced deep CNN (IVR-CNN) to cope with the problems is presented. The problem of limited IR database size is solved by a commercial thermal simulator (OKTAL-SE). The second problem of IR variations is mitigated by the proposed shifted ramp function-based intensity transformation. This can suppress the background and enhance the target contrast simultaneously. The experimental results on the synthesized IR images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVR-CNN for military ATR applications.

RecceMan: an interactive recognition assistance for image-based reconnaissance: synergistic effects of human perception and computational methods for object recognition, identification, and infrastructure analysis

NASA Astrophysics Data System (ADS)

El Bekri, Nadia; Angele, Susanne; Ruckhäberle, Martin; Peinsipp-Byma, Elisabeth; Haelke, Bruno

2015-10-01

This paper introduces an interactive recognition assistance system for imaging reconnaissance. This system supports aerial image analysts on missions during two main tasks: Object recognition and infrastructure analysis. Object recognition concentrates on the classification of one single object. Infrastructure analysis deals with the description of the components of an infrastructure and the recognition of the infrastructure type (e.g. military airfield). Based on satellite or aerial images, aerial image analysts are able to extract single object features and thereby recognize different object types. It is one of the most challenging tasks in the imaging reconnaissance. Currently, there are no high potential ATR (automatic target recognition) applications available, as consequence the human observer cannot be replaced entirely. State-of-the-art ATR applications cannot assume in equal measure human perception and interpretation. Why is this still such a critical issue? First, cluttered and noisy images make it difficult to automatically extract, classify and identify object types. Second, due to the changed warfare and the rise of asymmetric threats it is nearly impossible to create an underlying data set containing all features, objects or infrastructure types. Many other reasons like environmental parameters or aspect angles compound the application of ATR supplementary. Due to the lack of suitable ATR procedures, the human factor is still important and so far irreplaceable. In order to use the potential benefits of the human perception and computational methods in a synergistic way, both are unified in an interactive assistance system. RecceMan® (Reconnaissance Manual) offers two different modes for aerial image analysts on missions: the object recognition mode and the infrastructure analysis mode. The aim of the object recognition mode is to recognize a certain object type based on the object features that originated from the image signatures. The infrastructure analysis mode pursues the goal to analyze the function of the infrastructure. The image analyst extracts visually certain target object signatures, assigns them to corresponding object features and is finally able to recognize the object type. The system offers him the possibility to assign the image signatures to features given by sample images. The underlying data set contains a wide range of objects features and object types for different domains like ships or land vehicles. Each domain has its own feature tree developed by aerial image analyst experts. By selecting the corresponding features, the possible solution set of objects is automatically reduced and matches only the objects that contain the selected features. Moreover, we give an outlook of current research in the field of ground target analysis in which we deal with partly automated methods to extract image signatures and assign them to the corresponding features. This research includes methods for automatically determining the orientation of an object and geometric features like width and length of the object. This step enables to reduce automatically the possible object types offered to the image analyst by the interactive recognition assistance system.

Utilization of volume correlation filters for underwater mine identification in LIDAR imagery

NASA Astrophysics Data System (ADS)

Walls, Bradley

2008-04-01

Underwater mine identification persists as a critical technology pursued aggressively by the Navy for fleet protection. As such, new and improved techniques must continue to be developed in order to provide measurable increases in mine identification performance and noticeable reductions in false alarm rates. In this paper we show how recent advances in the Volume Correlation Filter (VCF) developed for ground based LIDAR systems can be adapted to identify targets in underwater LIDAR imagery. Current automated target recognition (ATR) algorithms for underwater mine identification employ spatial based three-dimensional (3D) shape fitting of models to LIDAR data to identify common mine shapes consisting of the box, cylinder, hemisphere, truncated cone, wedge, and annulus. VCFs provide a promising alternative to these spatial techniques by correlating 3D models against the 3D rendered LIDAR data.

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

NASA Technical Reports Server (NTRS)

Knasel, T. Michael

1996-01-01

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Gestational and lactational exposure to atrazine via the drinking water causes specific behavioral deficits and selectively alters monoaminergic systems in C57BL/6 mouse dams, juvenile and adult offspring.

PubMed

Lin, Zhoumeng; Dodd, Celia A; Xiao, Shuo; Krishna, Saritha; Ye, Xiaoqin; Filipov, Nikolay M

2014-09-01

Atrazine (ATR) is one of the most frequently detected pesticides in the U.S. water supply. This study aimed to investigate neurobehavioral and neurochemical effects of ATR in C57BL/6 mouse offspring and dams exposed to a relatively low (3 mg/l, estimated intake 1.4 mg/kg/day) concentration of ATR via the drinking water (DW) from gestational day 6 to postnatal day (PND) 23. Behavioral tests included open field, pole, grip strength, novel object recognition (NOR), forced swim, and marble burying tests. Maternal weight gain and offspring (PND21, 35, and 70) body or brain weights were not affected by ATR. However, ATR-treated dams exhibited decreased NOR performance and a trend toward hyperactivity. Juvenile offspring (PND35) from ATR-exposed dams were hyperactive (both sexes), spent less time swimming (males), and buried more marbles (females). In adult offspring (PND70), the only behavioral change was a sex-specific (females) decreased NOR performance by ATR. Neurochemically, a trend toward increased striatal dopamine (DA) in dams and a significant increase in juvenile offspring (both sexes) was observed. Additionally, ATR exposure decreased perirhinal cortex serotonin in the adult female offspring. These results suggest that perinatal DW exposure to ATR targets the nigrostriatal DA pathway in dams and, especially, juvenile offspring, alters dams' cognitive performance, induces sex-selective changes involving motor and emotional functions in juvenile offspring, and decreases cognitive ability of adult female offspring, with the latter possibly associated with altered perirhinal cortex serotonin homeostasis. Overall, ATR exposure during gestation and lactation may cause adverse nervous system effects to both offspring and dams. © The Author 2014. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Biomorphic networks: approach to invariant feature extraction and segmentation for ATR

NASA Astrophysics Data System (ADS)

Baek, Andrew; Farhat, Nabil H.

1998-10-01

Invariant features in two dimensional binary images are extracted in a single layer network of locally coupled spiking (pulsating) model neurons with prescribed synapto-dendritic response. The feature vector for an image is represented as invariant structure in the aggregate histogram of interspike intervals obtained by computing time intervals between successive spikes produced from each neuron over a given period of time and combining such intervals from all neurons in the network into a histogram. Simulation results show that the feature vectors are more pattern-specific and invariant under translation, rotation, and change in scale or intensity than achieved in earlier work. We also describe an application of such networks to segmentation of line (edge-enhanced or silhouette) images. The biomorphic spiking network's capabilities in segmentation and invariant feature extraction may prove to be, when they are combined, valuable in Automated Target Recognition (ATR) and other automated object recognition systems.

Feature Extraction and Selection Strategies for Automated Target Recognition

NASA Technical Reports Server (NTRS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-01-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Feature extraction and selection strategies for automated target recognition

NASA Astrophysics Data System (ADS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-04-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

The life and death of ATR/sensor fusion and the hope for resurrection

NASA Astrophysics Data System (ADS)

Rogers, Steven K.; Sadowski, Charles; Bauer, Kenneth W.; Oxley, Mark E.; Kabrisky, Matthew; Rogers, Adam; Mott, Stephen D.

2008-04-01

For over half a century, scientists and engineers have worked diligently to advance computational intelligence. One application of interest is how computational intelligence can bring value to our war fighters. Automatic Target Recognition (ATR) and sensor fusion efforts have fallen far short of the desired capabilities. In this article we review the capabilities requested by war fighters. When compared to our current capabilities, it is easy to conclude current Combat Identification (CID) as a Family of Systems (FoS) does a lousy job. The war fighter needed capable, operationalized ATR and sensor fusion systems ten years ago but it did not happen. The article reviews the war fighter needs and the current state of the art. The article then concludes by looking forward to where we are headed to provide the capabilities required.

Integrating visual learning within a model-based ATR system

NASA Astrophysics Data System (ADS)

Carlotto, Mark; Nebrich, Mark

2017-05-01

Automatic target recognition (ATR) systems, like human photo-interpreters, rely on a variety of visual information for detecting, classifying, and identifying manmade objects in aerial imagery. We describe the integration of a visual learning component into the Image Data Conditioner (IDC) for target/clutter and other visual classification tasks. The component is based on an implementation of a model of the visual cortex developed by Serre, Wolf, and Poggio. Visual learning in an ATR context requires the ability to recognize objects independent of location, scale, and rotation. Our method uses IDC to extract, rotate, and scale image chips at candidate target locations. A bootstrap learning method effectively extends the operation of the classifier beyond the training set and provides a measure of confidence. We show how the classifier can be used to learn other features that are difficult to compute from imagery such as target direction, and to assess the performance of the visual learning process itself.

Optimization of a Multi-Stage ATR System for Small Target Identification

NASA Technical Reports Server (NTRS)

Lin, Tsung-Han; Lu, Thomas; Braun, Henry; Edens, Western; Zhang, Yuhan; Chao, Tien- Hsin; Assad, Christopher; Huntsberger, Terrance

2010-01-01

An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.

Epigenetic Telomere Protection by Drosophila DNA Damage Response Pathways

PubMed Central

Oikemus, Sarah R; Queiroz-Machado, Joana; Lai, KuanJu; McGinnis, Nadine; Sunkel, Claudio; Brodsky, Michael H

2006-01-01

Analysis of terminal deletion chromosomes indicates that a sequence-independent mechanism regulates protection of Drosophila telomeres. Mutations in Drosophila DNA damage response genes such as atm/tefu, mre11, or rad50 disrupt telomere protection and localization of the telomere-associated proteins HP1 and HOAP, suggesting that recognition of chromosome ends contributes to telomere protection. However, the partial telomere protection phenotype of these mutations limits the ability to test if they act in the epigenetic telomere protection mechanism. We examined the roles of the Drosophila atm and atr-atrip DNA damage response pathways and the nbs homolog in DNA damage responses and telomere protection. As in other organisms, the atm and atr-atrip pathways act in parallel to promote telomere protection. Cells lacking both pathways exhibit severe defects in telomere protection and fail to localize the protection protein HOAP to telomeres. Drosophila nbs is required for both atm- and atr-dependent DNA damage responses and acts in these pathways during DNA repair. The telomere fusion phenotype of nbs is consistent with defects in each of these activities. Cells defective in both the atm and atr pathways were used to examine if DNA damage response pathways regulate telomere protection without affecting telomere specific sequences. In these cells, chromosome fusion sites retain telomere-specific sequences, demonstrating that loss of these sequences is not responsible for loss of protection. Furthermore, terminally deleted chromosomes also fuse in these cells, directly implicating DNA damage response pathways in the epigenetic protection of telomeres. We propose that recognition of chromosome ends and recruitment of HP1 and HOAP by DNA damage response proteins is essential for the epigenetic protection of Drosophila telomeres. Given the conserved roles of DNA damage response proteins in telomere function, related mechanisms may act at the telomeres of other organisms. PMID:16710445

Epigenetic telomere protection by Drosophila DNA damage response pathways.

PubMed

Oikemus, Sarah R; Queiroz-Machado, Joana; Lai, KuanJu; McGinnis, Nadine; Sunkel, Claudio; Brodsky, Michael H

2006-05-01

Analysis of terminal deletion chromosomes indicates that a sequence-independent mechanism regulates protection of Drosophila telomeres. Mutations in Drosophila DNA damage response genes such as atm/tefu, mre11, or rad50 disrupt telomere protection and localization of the telomere-associated proteins HP1 and HOAP, suggesting that recognition of chromosome ends contributes to telomere protection. However, the partial telomere protection phenotype of these mutations limits the ability to test if they act in the epigenetic telomere protection mechanism. We examined the roles of the Drosophila atm and atr-atrip DNA damage response pathways and the nbs homolog in DNA damage responses and telomere protection. As in other organisms, the atm and atr-atrip pathways act in parallel to promote telomere protection. Cells lacking both pathways exhibit severe defects in telomere protection and fail to localize the protection protein HOAP to telomeres. Drosophila nbs is required for both atm- and atr-dependent DNA damage responses and acts in these pathways during DNA repair. The telomere fusion phenotype of nbs is consistent with defects in each of these activities. Cells defective in both the atm and atr pathways were used to examine if DNA damage response pathways regulate telomere protection without affecting telomere specific sequences. In these cells, chromosome fusion sites retain telomere-specific sequences, demonstrating that loss of these sequences is not responsible for loss of protection. Furthermore, terminally deleted chromosomes also fuse in these cells, directly implicating DNA damage response pathways in the epigenetic protection of telomeres. We propose that recognition of chromosome ends and recruitment of HP1 and HOAP by DNA damage response proteins is essential for the epigenetic protection of Drosophila telomeres. Given the conserved roles of DNA damage response proteins in telomere function, related mechanisms may act at the telomeres of other organisms.

Assessment of COTS IR image simulation tools for ATR development

NASA Astrophysics Data System (ADS)

Seidel, Heiko; Stahl, Christoph; Bjerkeli, Frode; Skaaren-Fystro, Paal

2005-05-01

Following the tendency of increased use of imaging sensors in military aircraft, future fighter pilots will need onboard artificial intelligence e.g. ATR for aiding them in image interpretation and target designation. The European Aeronautic Defence and Space Company (EADS) in Germany has developed an advanced method for automatic target recognition (ATR) which is based on adaptive neural networks. This ATR method can assist the crew of military aircraft like the Eurofighter in sensor image monitoring and thereby reduce the workload in the cockpit and increase the mission efficiency. The EADS ATR approach can be adapted for imagery of visual, infrared and SAR sensors because of the training-based classifiers of the ATR method. For the optimal adaptation of these classifiers they have to be trained with appropriate and sufficient image data. The training images must show the target objects from different aspect angles, ranges, environmental conditions, etc. Incomplete training sets lead to a degradation of classifier performance. Additionally, ground truth information i.e. scenario conditions like class type and position of targets is necessary for the optimal adaptation of the ATR method. In Summer 2003, EADS started a cooperation with Kongsberg Defence & Aerospace (KDA) from Norway. The EADS/KDA approach is to provide additional image data sets for training-based ATR through IR image simulation. The joint study aims to investigate the benefits of enhancing incomplete training sets for classifier adaptation by simulated synthetic imagery. EADS/KDA identified the requirements of a commercial-off-the-shelf IR simulation tool capable of delivering appropriate synthetic imagery for ATR development. A market study of available IR simulation tools and suppliers was performed. After that the most promising tool was benchmarked according to several criteria e.g. thermal emission model, sensor model, targets model, non-radiometric image features etc., resulting in a recommendation. The synthetic image data that are used for the investigation are generated using the recommended tool. Within the scope of this study, ATR performance on IR imagery using classifiers trained on real, synthetic and mixed image sets was evaluated. The performance of the adapted classifiers is assessed using recorded IR imagery with known ground-truth and recommendations are given for the use of COTS IR image simulation tools for ATR development.

A comparison of SAR ATR performance with information theoretic predictions

NASA Astrophysics Data System (ADS)

Blacknell, David

2003-09-01

Performance assessment of automatic target detection and recognition algorithms for SAR systems (or indeed any other sensors) is essential if the military utility of the system / algorithm mix is to be quantified. This is a relatively straightforward task if extensive trials data from an existing system is used. However, a crucial requirement is to assess the potential performance of novel systems as a guide to procurement decisions. This task is no longer straightforward since a hypothetical system cannot provide experimental trials data. QinetiQ has previously developed a theoretical technique for classification algorithm performance assessment based on information theory. The purpose of the study presented here has been to validate this approach. To this end, experimental SAR imagery of targets has been collected using the QinetiQ Enhanced Surveillance Radar to allow algorithm performance assessments as a number of parameters are varied. In particular, performance comparisons can be made for (i) resolutions up to 0.1m, (ii) single channel versus polarimetric (iii) targets in the open versus targets in scrubland and (iv) use versus non-use of camouflage. The change in performance as these parameters are varied has been quantified from the experimental imagery whilst the information theoretic approach has been used to predict the expected variation of performance with parameter value. A comparison of these measured and predicted assessments has revealed the strengths and weaknesses of the theoretical technique as will be discussed in the paper.

Neural Network Target Identification System for False Alarm Reduction

NASA Technical Reports Server (NTRS)

Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

2009-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

Data Intensive Systems (DIS) Benchmark Performance Summary

DTIC Science & Technology

2003-08-01

models assumed by today’s conventional architectures. Such applications include model- based Automatic Target Recognition (ATR), synthetic aperture...radar (SAR) codes, large scale dynamic databases/battlefield integration, dynamic sensor- based processing, high-speed cryptanalysis, high speed...distributed interactive and data intensive simulations, data-oriented problems characterized by pointer- based and other highly irregular data structures

Information theoretic partitioning and confidence based weight assignment for multi-classifier decision level fusion in hyperspectral target recognition applications

NASA Astrophysics Data System (ADS)

Prasad, S.; Bruce, L. M.

2007-04-01

There is a growing interest in using multiple sources for automatic target recognition (ATR) applications. One approach is to take multiple, independent observations of a phenomenon and perform a feature level or a decision level fusion for ATR. This paper proposes a method to utilize these types of multi-source fusion techniques to exploit hyperspectral data when only a small number of training pixels are available. Conventional hyperspectral image based ATR techniques project the high dimensional reflectance signature onto a lower dimensional subspace using techniques such as Principal Components Analysis (PCA), Fisher's linear discriminant analysis (LDA), subspace LDA and stepwise LDA. While some of these techniques attempt to solve the curse of dimensionality, or small sample size problem, these are not necessarily optimal projections. In this paper, we present a divide and conquer approach to address the small sample size problem. The hyperspectral space is partitioned into contiguous subspaces such that the discriminative information within each subspace is maximized, and the statistical dependence between subspaces is minimized. We then treat each subspace as a separate source in a multi-source multi-classifier setup and test various decision fusion schemes to determine their efficacy. Unlike previous approaches which use correlation between variables for band grouping, we study the efficacy of higher order statistical information (using average mutual information) for a bottom up band grouping. We also propose a confidence measure based decision fusion technique, where the weights associated with various classifiers are based on their confidence in recognizing the training data. To this end, training accuracies of all classifiers are used for weight assignment in the fusion process of test pixels. The proposed methods are tested using hyperspectral data with known ground truth, such that the efficacy can be quantitatively measured in terms of target recognition accuracies.

Quantification of bovine immunoglobulin G using transmission and attenuated total reflectance infrared spectroscopy.

PubMed

Elsohaby, Ibrahim; McClure, J Trenton; Riley, Christopher B; Shaw, R Anthony; Keefe, Gregory P

2016-01-01

In this study, we evaluated and compared the performance of transmission and attenuated total reflectance (ATR) infrared (IR) spectroscopic methods (in combination with quantification algorithms previously developed using partial least squares regression) for the rapid measurement of bovine serum immunoglobulin G (IgG) concentration, and detection of failure of transfer of passive immunity (FTPI) in dairy calves. Serum samples (n = 200) were collected from Holstein calves 1-11 days of age. Serum IgG concentrations were measured by the reference method of radial immunodiffusion (RID) assay, transmission IR (TIR) and ATR-IR spectroscopy-based assays. The mean IgG concentration measured by RID was 17.22 g/L (SD ±9.60). The mean IgG concentrations predicted by TIR and ATR-IR spectroscopy methods were 15.60 g/L (SD ±8.15) and 15.94 g/L (SD ±8.66), respectively. RID IgG concentrations were positively correlated with IgG levels predicted by TIR (r = 0.94) and ATR-IR (r = 0.92). The correlation between 2 IR spectroscopic methods was 0.94. Using an IgG concentration <10 g/L as the cut-point for FTPI cases, the overall agreement between TIR and ATR-IR methods was 94%, with a corresponding kappa value of 0.84. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for identifying FTPI by TIR were 0.87, 0.97, 0.91, 0.95, and 0.94, respectively. Corresponding values for ATR-IR were 0.87, 0.95, 0.86, 0.95, and 0.93, respectively. Both TIR and ATR-IR spectroscopic approaches can be used for rapid quantification of IgG level in neonatal bovine serum and for diagnosis of FTPI in dairy calves. © 2015 The Author(s).

ATR-FTIR spectroscopy for the determination of Na4EDTA in detergent aqueous solutions.

PubMed

Suárez, Leticia; García, Roberto; Riera, Francisco A; Diez, María A

2013-10-15

Fourier transform infrared spectroscopy in the attenuated total reflectance mode (ATR-FTIR) combined with partial last square (PLS) algorithms was used to design calibration and prediction models for a wide range of tetrasodium ethylenediaminetetraacetate (Na4EDTA) concentrations (0.1 to 28% w/w) in aqueous solutions. The spectra obtained using air and water as a background medium were tested for the best fit. The PLS models designed afforded a sufficient level of precision and accuracy to allow even very small amounts of Na4EDTA to be determined. A root mean square error of nearly 0.37 for the validation set was obtained. Over a concentration range below 5% w/w, the values estimated from a combination of ATR-FTIR spectroscopy and a PLS algorithm model were similar to those obtained from an HPLC analysis of NaFeEDTA complexes and subsequent detection by UV absorbance. However, the lowest detection limit for Na4EDTA concentrations afforded by this spectroscopic/chemometric method was 0.3% w/w. The PLS model was successfully used as a rapid and simple method to quantify Na4EDTA in aqueous solutions of industrial detergents as an alternative to HPLC-UV analysis which involves time-consuming dilution and complexation processes. © 2013 Elsevier B.V. All rights reserved.

Fourier Transform Infrared Spectroscopy (FT-IR) and Simple Algorithm Analysis for Rapid and Non-Destructive Assessment of Developmental Cotton Fibers.

PubMed

Liu, Yongliang; Kim, Hee-Jin

2017-06-22

With cotton fiber growth or maturation, cellulose content in cotton fibers markedly increases. Traditional chemical methods have been developed to determine cellulose content, but it is time-consuming and labor-intensive, mostly owing to the slow hydrolysis process of fiber cellulose components. As one approach, the attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy technique has also been utilized to monitor cotton cellulose formation, by implementing various spectral interpretation strategies of both multivariate principal component analysis (PCA) and 1-, 2- or 3-band/-variable intensity or intensity ratios. The main objective of this study was to compare the correlations between cellulose content determined by chemical analysis and ATR FT-IR spectral indices acquired by the reported procedures, among developmental Texas Marker-1 (TM-1) and immature fiber ( im ) mutant cotton fibers. It was observed that the R value, CI IR , and the integrated intensity of the 895 cm -1 band exhibited strong and linear relationships with cellulose content. The results have demonstrated the suitability and utility of ATR FT-IR spectroscopy, combined with a simple algorithm analysis, in assessing cotton fiber cellulose content, maturity, and crystallinity in a manner which is rapid, routine, and non-destructive.

Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

PubMed

Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

2015-01-01

A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

NASA Technical Reports Server (NTRS)

Tescher, Andrew G. (Editor)

1989-01-01

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

Robust autofocus algorithm for ISAR imaging of moving targets

NASA Astrophysics Data System (ADS)

Li, Jian; Wu, Renbiao; Chen, Victor C.

2000-08-01

A robust autofocus approach, referred to as AUTOCLEAN (AUTOfocus via CLEAN), is proposed for the motion compensation in ISAR (inverse synthetic aperture radar) imaging of moving targets. It is a parametric algorithm based on a very flexible data model which takes into account arbitrary range migration and arbitrary phase errors across the synthetic aperture that may be induced by unwanted radial motion of the target as well as propagation or system instability. AUTOCLEAN can be classified as a multiple scatterer algorithm (MSA), but it differs considerably from other existing MSAs in several aspects: (1) dominant scatterers are selected automatically in the two-dimensional (2-D) image domain; (2) scatterers may not be well-isolated or very dominant; (3) phase and RCS (radar cross section) information from each selected scatterer are combined in an optimal way; (4) the troublesome phase unwrapping step is avoided. AUTOCLEAN is computationally efficient and involves only a sequence of FFTs (fast Fourier Transforms). Another good feature associated with AUTOCLEAN is that its performance can be progressively improved by assuming a larger number of dominant scatterers for the target. Hence it can be easily configured for real-time applications including, for example, ATR (automatic target recognition) of non-cooperative moving targets, and for some other applications where the image quality is of the major concern but not the computational time including, for example, for the development and maintenance of low observable aircrafts. Numerical and experimental results have shown that AUTOCLEAN is a very robust autofocus tool for ISAR imaging.

Convolutional neural network using generated data for SAR ATR with limited samples

NASA Astrophysics Data System (ADS)

Cong, Longjian; Gao, Lei; Zhang, Hui; Sun, Peng

2018-03-01

Being able to adapt all weather at all times, it has been a hot research topic that using Synthetic Aperture Radar(SAR) for remote sensing. Despite all the well-known advantages of SAR, it is hard to extract features because of its unique imaging methodology, and this challenge attracts the research interest of traditional Automatic Target Recognition(ATR) methods. With the development of deep learning technologies, convolutional neural networks(CNNs) give us another way out to detect and recognize targets, when a huge number of samples are available, but this premise is often not hold, when it comes to monitoring a specific type of ships. In this paper, we propose a method to enhance the performance of Faster R-CNN with limited samples to detect and recognize ships in SAR images.

Digital signal processing algorithms for automatic voice recognition

NASA Technical Reports Server (NTRS)

Botros, Nazeih M.

1987-01-01

The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

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.

Risk-Based Aviation Security: Diffusion and Acceptance

DTIC Science & Technology

2012-03-01

Association ATR Automated Target Recognition BDO Behavior Detection Officer BIB Budget-In-Brief CBP Customs and Border Protection CDRH Center...Radiological Health ( CDRH ) (Cerra, 2006), the National Institute for Standards and Technology (NIST) (TSA, n.d. g), and the Johns Hopkins University Applied...safety related to AIT may have come from the Food and Drug Administration’s (FDA) Center for Devices and Radiological Health ( CDRH ), the National

Automated telephone reminder messages can assist electronic diabetes care.

PubMed

Mollon, Brent; Holbrook, Anne M; Keshavjee, Karim; Troyan, Sue; Gaebel, Kathryn; Thabane, Lehana; Perera, Gihan

2008-01-01

Telephone reminder systems have been used to assist in the treatment of many chronic diseases. However, it is unclear if these systems can increase medication and appointment adherence in patients with diabetes without direct patient-provider telephone contact. We tested the feasibility of using an automated telephone reminder system (ATRS) to deliver reminder messages to 253 adults with diabetes enrolled in a randomized controlled trial. Eighty-four percent of the patients were able to register using voice recognition and at least one reminder was delivered to 95% of registered patients over a period of 7.5 months. None of the demographic features studied predicted a patient's ability to enroll or to receive reminder calls. At the end of the study, 63% of patients indicated that they wished to continue to receive ATRS calls. The level of system use as determined by the number of received reminder calls was not associated with a change in the number of physician visits or diabetes-related laboratory tests during follow-up. The clinical benefits and sustainability of ATRS remain unproven, but our results indicate that an automated reminder system can be effective for providing messages to a large group of older patients with diabetes.

Transition from lab to flight demo for model-based FLIR ATR and SAR-FLIR fusion

NASA Astrophysics Data System (ADS)

Childs, Martin B.; Carlson, Karen M.; Pujara, Neeraj

2000-08-01

Model-based automatic target recognition (ATR) using forward- looking infrared (FLIR) imagery, and using FLIR imagery combined with cues from a synthetic aperture radar (SAR) system, has been successfully demonstrated in the laboratory. For the laboratory demonstration, FLIR images, platform location, sensor data, and SAR cues were read in from files stored on computer disk. This ATR system, however, was intended to ultimately be flown in a fighter aircraft. We discuss the transition from laboratory demonstration to flight demonstration for this system. The obvious changes required were in the interfaces: the flight system must get live FLIR imagery from a sensor; it must get platform location, sensor data, and controls from the avionics computer in the aircraft via 1553 bus; and it must get SAR cues from the on-board SAR system, also via 1553 bus. Other changes included the transition to rugged hardware that would withstand the fighter aircraft environment, and the need for the system to be compact and self-contained. Unexpected as well as expected challenges were encountered. We discuss some of these challenges, how they were met, and the performance of the flight-demonstration system.

Stress reaction process-based hierarchical recognition algorithm for continuous intrusion events in optical fiber prewarning system

NASA Astrophysics Data System (ADS)

Qu, Hongquan; Yuan, Shijiao; Wang, Yanping; Yang, Dan

2018-04-01

To improve the recognition performance of optical fiber prewarning system (OFPS), this study proposed a hierarchical recognition algorithm (HRA). Compared with traditional methods, which employ only a complex algorithm that includes multiple extracted features and complex classifiers to increase the recognition rate with a considerable decrease in recognition speed, HRA takes advantage of the continuity of intrusion events, thereby creating a staged recognition flow inspired by stress reaction. HRA is expected to achieve high-level recognition accuracy with less time consumption. First, this work analyzed the continuity of intrusion events and then presented the algorithm based on the mechanism of stress reaction. Finally, it verified the time consumption through theoretical analysis and experiments, and the recognition accuracy was obtained through experiments. Experiment results show that the processing speed of HRA is 3.3 times faster than that of a traditional complicated algorithm and has a similar recognition rate of 98%. The study is of great significance to fast intrusion event recognition in OFPS.

Recognition of strong earthquake-prone areas with a single learning class

NASA Astrophysics Data System (ADS)

Gvishiani, A. D.; Agayan, S. M.; Dzeboev, B. A.; Belov, I. O.

2017-05-01

This article presents a new Barrier recognition algorithm with learning, designed for recognition of earthquake-prone areas. In comparison to the Crust (Kora) algorithm, used by the classical EPA approach, the Barrier algorithm proceeds with learning just on one "pure" high-seismic class. The new algorithm operates in the space of absolute values of the geological-geophysical parameters of the objects. The algorithm is used for recognition of earthquake-prone areas with M ≥ 6.0 in the Caucasus region. Comparative analysis of the Crust and Barrier algorithms justifies their productive coherence.

A Data Analysis Approach of ATR from SAR Images

DTIC Science & Technology

2005-05-01

2 Avenue Gay- Lussac 78851 Elancourt Cedex France 1. ABSTRACT Thales has investigated during these last years a complete target recognition system...VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law , no person shall be subject to a penalty for failing to...Airborne Systems Missile Electronics Business 2 Avenue Gay- Lussac 78851 Elancourt Cedex France 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING

Biomorphic Networks for ATR and Higher-Level Processing.

DTIC Science & Technology

1998-01-10

Publications during this period: 1. N.H. Farhat, "Biomorphic Dynamical Networks for Cognition and Control", Journal of Intelligent and Rototic Systems...34 Neurodynamic networks for recognition of radar targets", Ph.D. dissertation, University of Pennsyl- vania, 1992. 2. J. Wood, "Invariant pattern...167-177,1998. 167 © 1998 Kluwer Academic Publishers. Printed in the Netherlands. Biomorphic Dynamical Networks for Cognition and Control N. H

A Survey on Sentiment Classification in Face Recognition

NASA Astrophysics Data System (ADS)

Qian, Jingyu

2018-01-01

Face recognition has been an important topic for both industry and academia for a long time. K-means clustering, autoencoder, and convolutional neural network, each representing a design idea for face recognition method, are three popular algorithms to deal with face recognition problems. It is worthwhile to summarize and compare these three different algorithms. This paper will focus on one specific face recognition problem-sentiment classification from images. Three different algorithms for sentiment classification problems will be summarized, including k-means clustering, autoencoder, and convolutional neural network. An experiment with the application of these algorithms on a specific dataset of human faces will be conducted to illustrate how these algorithms are applied and their accuracy. Finally, the three algorithms are compared based on the accuracy result.

Handwritten digits recognition based on immune network

NASA Astrophysics Data System (ADS)

Li, Yangyang; Wu, Yunhui; Jiao, Lc; Wu, Jianshe

2011-11-01

With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.

Use of ATR FT-IR spectroscopy in non-destructive and rapid assessment of developmental cotton fibers

USDA-ARS?s Scientific Manuscript database

The knowledge of chemical and compositional components in cotton fibers is of value to cotton breeders and growers for cotton enhancement and to textile processors for quality control. In this work, we applied the previously proposed simple algorithms to analyze the attenuated total reflection Fouri...

Development of an on-site screening system for amphetamine-type stimulant tablets with a portable attenuated total reflection Fourier transform infrared spectrometer.

PubMed

Tsujikawa, Kenji; Kuwayama, Kenji; Miyaguchi, Hajime; Kanamori, Tatsuyuki; Iwata, Yuko T; Yoshida, Takemi; Inoue, Hiroyuki

2008-02-04

We tried to develop a library search system using a portable, attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectrometer for on-site identification of 3,4-methylenedioxymethamphetamine (MDMA) and 3,4-methylenedioxyamphetamine (MDA) tablets. The library consisted of the spectra from mixtures of controlled drugs (e.g. MDMA and ketamine), adulterants (e.g. caffeine), and diluents (e.g. lactose). In the seven library search algorithms, the derivative correlation coefficient showed the best discriminant capability. This was enhanced by segmentation of the search area. The optimized search algorithm was validated by the positive (n=154, e.g. the standard mixtures containing the controlled drug, and the MDMA/MDA tablets confiscated) and negative samples (n=56, e.g. medicinal tablets). All validation samples except for four were judged truly. Final criteria for positive identification were decided on the basis of the results of the validation. In conclusion, a portable ATR-FT-IR spectrometer with our library search system would be a useful tool for on-site identification of amphetamine-type stimulant tablets.

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

NASA Astrophysics Data System (ADS)

Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

Advanced Test Reactor Core Modeling Update Project Annual Report for Fiscal Year 2012

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

David W. Nigg, Principal Investigator; Kevin A. Steuhm, Project Manager

Legacy computational reactor physics software tools and protocols currently used for support of Advanced Test Reactor (ATR) core fuel management and safety assurance, and to some extent, experiment management, are inconsistent with the state of modern nuclear engineering practice, and are difficult, if not impossible, to properly verify and validate (V&V) according to modern standards. Furthermore, the legacy staff knowledge required for application of these tools and protocols from the 1960s and 1970s is rapidly being lost due to staff turnover and retirements. In late 2009, the Idaho National Laboratory (INL) initiated a focused effort, the ATR Core Modeling Updatemore » Project, to address this situation through the introduction of modern high-fidelity computational software and protocols. This aggressive computational and experimental campaign will have a broad strategic impact on the operation of the ATR, both in terms of improved computational efficiency and accuracy for support of ongoing DOE programs as well as in terms of national and international recognition of the ATR National Scientific User Facility (NSUF). The ATR Core Modeling Update Project, targeted for full implementation in phase with the next anticipated ATR Core Internals Changeout (CIC) in the 2014-2015 time frame, began during the last quarter of Fiscal Year 2009, and has just completed its third full year. Key accomplishments so far have encompassed both computational as well as experimental work. A new suite of stochastic and deterministic transport theory based reactor physics codes and their supporting nuclear data libraries (HELIOS, KENO6/SCALE, NEWT/SCALE, ATTILA, and an extended implementation of MCNP5) has been installed at the INL under various licensing arrangements. Corresponding models of the ATR and ATRC are now operational with all five codes, demonstrating the basic feasibility of the new code packages for their intended purpose. Of particular importance, a set of as-run core depletion HELIOS calculations for all ATR cycles since August 2009, Cycle 145A through Cycle 151B, was successfully completed during 2012. This major effort supported a decision late in the year to proceed with the phased incorporation of the HELIOS methodology into the ATR Core Safety Analysis Package (CSAP) preparation process, in parallel with the established PDQ-based methodology, beginning late in Fiscal Year 2012. Acquisition of the advanced SERPENT (VTT-Finland) and MC21 (DOE-NR) Monte Carlo stochastic neutronics simulation codes was also initiated during the year and some initial applications of SERPENT to ATRC experiment analysis were demonstrated. These two new codes will offer significant additional capability, including the possibility of full-3D Monte Carlo fuel management support capabilities for the ATR at some point in the future. Finally, a capability for rigorous sensitivity analysis and uncertainty quantification based on the TSUNAMI system has been implemented and initial computational results have been obtained. This capability will have many applications as a tool for understanding the margins of uncertainty in the new models as well as for validation experiment design and interpretation.« less

Human activity recognition based on feature selection in smart home using back-propagation algorithm.

PubMed

Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

2014-09-01

In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

The Pandora multi-algorithm approach to automated pattern recognition in LAr TPC detectors

NASA Astrophysics Data System (ADS)

Marshall, J. S.; Blake, A. S. T.; Thomson, M. A.; Escudero, L.; de Vries, J.; Weston, J.; MicroBooNE Collaboration

2017-09-01

The development and operation of Liquid Argon Time Projection Chambers (LAr TPCs) for neutrino physics has created a need for new approaches to pattern recognition, in order to fully exploit the superb imaging capabilities offered by this technology. The Pandora Software Development Kit provides functionality to aid the process of designing, implementing and running pattern recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition: individual algorithms each address a specific task in a particular topology; a series of many tens of algorithms then carefully builds-up a picture of the event. The input to the Pandora pattern recognition is a list of 2D Hits. The output from the chain of over 70 algorithms is a hierarchy of reconstructed 3D Particles, each with an identified particle type, vertex and direction.

Partial pathogen protection by tick-bite sensitization and epitope recognition in peptide-immunized HLA DR3 transgenic mice.

PubMed

Shattuck, Wendy M C; Dyer, Megan C; Desrosiers, Joe; Fast, Loren D; Terry, Frances E; Martin, William D; Moise, Leonard; De Groot, Anne S; Mather, Thomas N

2014-01-01

Ticks are notorious vectors of disease for humans, and many species of ticks transmit multiple pathogens, sometimes in the same tick bite. Accordingly, a broad-spectrum vaccine that targets vector ticks and pathogen transmission at the tick/host interface, rather than multiple vaccines against every possible tickborne pathogen, could become an important tool for resolving an emerging public health crisis. The concept for such a tick protective vaccine comes from observations of an acquired tick resistance (ATR) that can develop in non-natural hosts of ticks following sensitization to tick salivary components. Mice are commonly used as models to study immune responses to human pathogens but normal mice are natural hosts for many species of ticks and fail to develop ATR. We evaluated HLA DR3 transgenic (tg) "humanized" mice as a potential model of ATR and assessed the possibility of using this animal model for tick protective vaccine discovery studies. Serial tick infestations with pathogen-free Ixodes scapularis ticks were used to tick-bite sensitize HLA DR3 tg mice. Sensitization resulted in a cytokine skew favoring a Th2 bias as well as partial (57%) protection to infection with Lyme disease spirochetes (Borrelia burgdorferi) following infected tick challenge when compared to tick naïve counterparts. I. scapularis salivary gland homogenate (SGH) and a group of immunoinformatic-predicted T cell epitopes identified from the I. scapularis salivary transcriptome were used separately to vaccinate HLA DR3 tg mice, and these mice also were assessed for both pathogen protection and epitope recognition. Reduced pathogen transmission along with a Th2 skew resulted from SGH vaccination, while no significant protection and a possible T regulatory bias was seen in epitope-vaccinated mice. This study provides the first proof-of-concept for using HLA DR tg "humanized" mice for studying the potential tick protective effects of immunoinformatic- or otherwise-derived tick salivary components as tickborne disease vaccines.

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

NASA Astrophysics Data System (ADS)

Acciarri, R.; Adams, C.; An, R.; Anthony, J.; Asaadi, J.; Auger, M.; Bagby, L.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Camilleri, L.; Caratelli, D.; Carls, B.; Castillo Fernandez, R.; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Cohen, E.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anadón, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero Sanchez, L.; Esquivel, J.; Fadeeva, A. A.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garcia-Gamez, D.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; Hourlier, A.; Huang, E.-C.; James, C.; Jan de Vries, J.; Jen, C.-M.; Jiang, L.; Johnson, R. A.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Martinez Caicedo, D. A.; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Piasetzky, E.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; Rudolf von Rohr, C.; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y.-T.; Tufanli, S.; Usher, T.; Van De Pontseele, W.; Van de Water, R. G.; Viren, B.; Weber, M.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Yates, L.; Zeller, G. P.; Zennamo, J.; Zhang, C.

2018-01-01

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.

Numerical and Experimental Validation of the Optimization Methodologies for a Wing-Tip Structure Equipped with Conventional and Morphing Ailerons =

NASA Astrophysics Data System (ADS)

Koreanschi, Andreea

In order to answer the problem of 'how to reduce the aerospace industry's environment footprint?' new morphing technologies were developed. These technologies were aimed at reducing the aircraft's fuel consumption through reduction of the wing drag. The morphing concept used in the present research consists of replacing the conventional aluminium upper surface of the wing with a flexible composite skin for morphing abilities. For the ATR-42 'Morphing wing' project, the wing models were manufactured entirely from composite materials and the morphing region was optimized for flexibility. In this project two rigid wing models and an active morphing wing model were designed, manufactured and wind tunnel tested. For the CRIAQ MDO 505 project, a full scale wing-tip equipped with two types of ailerons, conventional and morphing, was designed, optimized, manufactured, bench and wind tunnel tested. The morphing concept was applied on a real wing internal structure and incorporated aerodynamic, structural and control constraints specific to a multidisciplinary approach. Numerical optimization, aerodynamic analysis and experimental validation were performed for both the CRIAQ MDO 505 full scale wing-tip demonstrator and the ATR-42 reduced scale wing models. In order to improve the aerodynamic performances of the ATR-42 and CRIAQ MDO 505 wing airfoils, three global optimization algorithms were developed, tested and compared. The three algorithms were: the genetic algorithm, the artificial bee colony and the gradient descent. The algorithms were coupled with the two-dimensional aerodynamic solver XFoil. XFoil is known for its rapid convergence, robustness and use of the semi-empirical e n method for determining the position of the flow transition from laminar to turbulent. Based on the performance comparison between the algorithms, the genetic algorithm was chosen for the optimization of the ATR-42 and CRIAQ MDO 505 wing airfoils. The optimization algorithm was improved during the CRIAQ MDO 505 project for convergence speed by introducing a two-step cross-over function. Structural constraints were introduced in the algorithm at each aero-structural optimization interaction, allowing a better manipulation of the algorithm and giving it more capabilities of morphing combinations. The CRIAQ MDO 505 project envisioned a morphing aileron concept for the morphing upper surface wing. For this morphing aileron concept, two optimization methods were developed. The methods used the already developed genetic algorithm and each method had a different design concept. The first method was based on the morphing upper surface concept, using actuation points to achieve the desired shape. The second method was based on the hinge rotation concept of the conventional aileron but applied at multiple nodes along the aileron camber to achieve the desired shape. Both methods were constrained by manufacturing and aerodynamic requirements. The purpose of the morphing aileron methods was to obtain an aileron shape with a smoother pressure distribution gradient during deflection than the conventional aileron. The aerodynamic optimization results were used for the structural optimization and design of the wing, particularly the flexible composite skin. Due to the structural changes performed on the initial wing-tip structure, an aeroelastic behaviour analysis, more specific on flutter phenomenon, was performed. The analyses were done to ensure the structural integrity of the wing-tip demonstrator during wind tunnel tests. Three wind tunnel tests were performed for the CRIAQ MDO 505 wing-tip demonstrator at the IAR-NRC subsonic wind tunnel facility in Ottawa. The first two tests were performed for the wing-tip equipped with conventional aileron. The purpose of these tests was to validate the control system designed for the morphing upper surface, the numerical optimization and aerodynamic analysis and to evaluate the optimization efficiency on the boundary layer behaviour and the wing drag. The third set of wind tunnel tests was performed on the wing-tip equipped with a morphing aileron. The purpose of this test was to evaluate the performances of the morphing aileron, in conjunction with the active morphing upper surface, and their effect on the lift, drag and boundary layer behaviour. Transition data, obtained from Infrared Thermography, and pressure data, extracted from Kulite and pressure taps recordings, were used to validate the numerical optimization and aerodynamic performances of the wing-tip demonstrator. A set of wind tunnel tests was performed on the ATR-42 rigid wing models at the Price-Paidoussis subsonic wind tunnel at Ecole de technologie Superieure. The results from the pressure taps recordings were used to validate the numerical optimization. A second derivative of the pressure distribution method was applied to evaluate the transition region on the upper surface of the wing models for comparison with the numerical transition values. (Abstract shortened by ProQuest.).

Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils.

PubMed

Jiménez-Sotelo, Paola; Hernández-Martínez, Maylet; Osorio-Revilla, Guillermo; Meza-Márquez, Ofelia Gabriela; García-Ochoa, Felipe; Gallardo-Velázquez, Tzayhrí

2016-07-01

Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R(2) > 0.9961, standard errors of calibration (SEC) in the range of 0.3963-0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.

Variability and robustness of scatterers in HRR/ISAR ground target data and its influence on the ATR performance

NASA Astrophysics Data System (ADS)

Schumacher, R.; Schimpf, H.; Schiller, J.

2011-06-01

The most challenging problem of Automatic Target Recognition (ATR) is the extraction of robust and independent target features which describe the target unambiguously. These features have to be robust and invariant in different senses: in time, between aspect views (azimuth and elevation angle), between target motion (translation and rotation) and between different target variants. Especially for ground moving targets in military applications an irregular target motion is typical, so that a strong variation of the backscattered radar signal with azimuth and elevation angle makes the extraction of stable and robust features most difficult. For ATR based on High Range Resolution (HRR) profiles and / or Inverse Synthetic Aperture Radar (ISAR) images it is crucial that the reference dataset consists of stable and robust features, which, among others, will depend on the target aspect and depression angle amongst others. Here it is important to find an adequate data grid for an efficient data coverage in the reference dataset for ATR. In this paper the variability of the backscattered radar signals of target scattering centers is analyzed for different HRR profiles and ISAR images from measured turntable datasets of ground targets under controlled conditions. Especially the dependency of the features on the elevation angle is analyzed regarding to the ATR of large strip SAR data with a large range of depression angles by using available (I)SAR datasets as reference. In this work the robustness of these scattering centers is analyzed by extracting their amplitude, phase and position. Therefore turntable measurements under controlled conditions were performed targeting an artificial military reference object called STANDCAM. Measures referring to variability, similarity, robustness and separability regarding the scattering centers are defined. The dependency of the scattering behaviour with respect to azimuth and elevation variations is analyzed. Additionally generic types of features (geometrical, statistical), which can be derived especially from (I)SAR images, are applied to the ATR-task. Therefore subsequently the dependence of individual feature values as well as the feature statistics on aspect (i.e. azimuth and elevation) are presented. The Kolmogorov-Smirnov distance will be used to show how the feature statistics is influenced by varying elevation angles. Finally, confusion matrices are computed between the STANDCAM target at all eleven elevation angles. This helps to assess the robustness of ATR performance under the influence of aspect angle deviations between training set and test set.

Feature Extraction Using Attributed Scattering Center Models for Model-Based Automatic Target Recognition (ATR)

DTIC Science & Technology

2005-10-01

section of the coiled arm. Right: measured realized total gain for a square spiral in free space with inductive treatment. . . . . . . . 154 8.5 Initial...appreciable velocities can often be easily separated from clutter returns, slow moving targets of more moderate cross sections can be very difficult to detect...electromagnetic radiation and measuring the energy scattered back. The data obtained as a result of this process is a finite-extent, noisy set of

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain

PubMed Central

Alexopoulou, Dimitra; Wächter, Thomas; Pickersgill, Laura; Eyre, Cecilia; Schroeder, Michael

2008-01-01

Background The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. Results We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. Conclusions Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described. Availability The TFIDF term recognition is available as Web Service, described at PMID:18460175

A GPU-paralleled implementation of an enhanced face recognition algorithm

NASA Astrophysics Data System (ADS)

Chen, Hao; Liu, Xiyang; Shao, Shuai; Zan, Jiguo

2013-03-01

Face recognition algorithm based on compressed sensing and sparse representation is hotly argued in these years. The scheme of this algorithm increases recognition rate as well as anti-noise capability. However, the computational cost is expensive and has become a main restricting factor for real world applications. In this paper, we introduce a GPU-accelerated hybrid variant of face recognition algorithm named parallel face recognition algorithm (pFRA). We describe here how to carry out parallel optimization design to take full advantage of many-core structure of a GPU. The pFRA is tested and compared with several other implementations under different data sample size. Finally, Our pFRA, implemented with NVIDIA GPU and Computer Unified Device Architecture (CUDA) programming model, achieves a significant speedup over the traditional CPU implementations.

A feasibility and optimization study to determine cooling time and burnup of advanced test reactor fuels using a nondestructive technique

NASA Astrophysics Data System (ADS)

Navarro, Jorge

The goal of this study presented is to determine the best available nondestructive technique necessary to collect validation data as well as to determine burnup and cooling time of the fuel elements on-site at the Advanced Test Reactor (ATR) canal. This study makes a recommendation of the viability of implementing a permanent fuel scanning system at the ATR canal and leads to the full design of a permanent fuel scan system. The study consisted at first in determining if it was possible and which equipment was necessary to collect useful spectra from ATR fuel elements at the canal adjacent to the reactor. Once it was establish that useful spectra can be obtained at the ATR canal, the next step was to determine which detector and which configuration was better suited to predict burnup and cooling time of fuel elements nondestructively. Three different detectors of High Purity Germanium (HPGe), Lanthanum Bromide (LaBr3), and High Pressure Xenon (HPXe) in two system configurations of above and below the water pool were used during the study. The data collected and analyzed were used to create burnup and cooling time calibration prediction curves for ATR fuel. The next stage of the study was to determine which of the three detectors tested was better suited for the permanent system. From spectra taken and the calibration curves obtained, it was determined that although the HPGe detector yielded better results, a detector that could better withstand the harsh environment of the ATR canal was needed. The in-situ nature of the measurements required a rugged fuel scanning system, low in maintenance and easy to control system. Based on the ATR canal feasibility measurements and calibration results, it was determined that the LaBr3 detector was the best alternative for canal in-situ measurements; however, in order to enhance the quality of the spectra collected using this scintillator, a deconvolution method was developed. Following the development of the deconvolution method for ATR applications, the technique was tested using one-isotope, multi-isotope, and fuel simulated sources. Burnup calibrations were perfomed using convoluted and deconvoluted data. The calibrations results showed burnup prediction by this method improves using deconvolution. The final stage of the deconvolution method development was to perform an irradiation experiment in order to create a surrogate fuel source to test the deconvolution method using experimental data. A conceptual design of the fuel scan system is path forward using the rugged LaBr 3 detector in an above the water configuration and deconvolution algorithms.

Target recognition of ladar range images using slice image: comparison of four improved algorithms

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Cao, Jingya; Wang, Liang; Zhai, Yu; Cheng, Yang

2017-07-01

Compared with traditional 3-D shape data, ladar range images possess properties of strong noise, shape degeneracy, and sparsity, which make feature extraction and representation difficult. The slice image is an effective feature descriptor to resolve this problem. We propose four improved algorithms on target recognition of ladar range images using slice image. In order to improve resolution invariance of the slice image, mean value detection instead of maximum value detection is applied in these four improved algorithms. In order to improve rotation invariance of the slice image, three new improved feature descriptors-which are feature slice image, slice-Zernike moments, and slice-Fourier moments-are applied to the last three improved algorithms, respectively. Backpropagation neural networks are used as feature classifiers in the last two improved algorithms. The performance of these four improved recognition systems is analyzed comprehensively in the aspects of the three invariances, recognition rate, and execution time. The final experiment results show that the improvements for these four algorithms reach the desired effect, the three invariances of feature descriptors are not directly related to the final recognition performance of recognition systems, and these four improved recognition systems have different performances under different conditions.

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

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

Acciarri, R.; Adams, C.; An, R.

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens ofmore » algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.« less

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

DOE PAGES

Acciarri, R.; Adams, C.; An, R.; ...

2018-01-29

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens ofmore » algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.« less

Score-Level Fusion of Phase-Based and Feature-Based Fingerprint Matching Algorithms

NASA Astrophysics Data System (ADS)

Ito, Koichi; Morita, Ayumi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.

Scale invariant SURF detector and automatic clustering segmentation for infrared small targets detection

NASA Astrophysics Data System (ADS)

Zhang, Haiying; Bai, Jiaojiao; Li, Zhengjie; Liu, Yan; Liu, Kunhong

2017-06-01

The detection and discrimination of infrared small dim targets is a challenge in automatic target recognition (ATR), because there is no salient information of size, shape and texture. Many researchers focus on mining more discriminative information of targets in temporal-spatial. However, such information may not be available with the change of imaging environments, and the targets size and intensity keep changing in different imaging distance. So in this paper, we propose a novel research scheme using density-based clustering and backtracking strategy. In this scheme, the speeded up robust feature (SURF) detector is applied to capture candidate targets in single frame at first. And then, these points are mapped into one frame, so that target traces form a local aggregation pattern. In order to isolate the targets from noises, a newly proposed density-based clustering algorithm, fast search and find of density peak (FSFDP for short), is employed to cluster targets by the spatial intensive distribution. Two important factors of the algorithm, percent and γ , are exploited fully to determine the clustering scale automatically, so as to extract the trace with highest clutter suppression ratio. And at the final step, a backtracking algorithm is designed to detect and discriminate target trace as well as to eliminate clutter. The consistence and continuity of the short-time target trajectory in temporal-spatial is incorporated into the bounding function to speed up the pruning. Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio (SCR). Furthermore, it avoids constructing the candidate target trajectory searching space, so its time complexity is limited to a polynomial level. The extensive experimental results show that it has superior performance in probability of detection (Pd) and false alarm suppressing rate aiming at variety of complex backgrounds.

Model and algorithmic framework for detection and correction of cognitive errors.

PubMed

Feki, Mohamed Ali; Biswas, Jit; Tolstikov, Andrei

2009-01-01

This paper outlines an approach that we are taking for elder-care applications in the smart home, involving cognitive errors and their compensation. Our approach involves high level modeling of daily activities of the elderly by breaking down these activities into smaller units, which can then be automatically recognized at a low level by collections of sensors placed in the homes of the elderly. This separation allows us to employ plan recognition algorithms and systems at a high level, while developing stand-alone activity recognition algorithms and systems at a low level. It also allows the mixing and matching of multi-modality sensors of various kinds that go to support the same high level requirement. Currently our plan recognition algorithms are still at a conceptual stage, whereas a number of low level activity recognition algorithms and systems have been developed. Herein we present our model for plan recognition, providing a brief survey of the background literature. We also present some concrete results that we have achieved for activity recognition, emphasizing how these results are incorporated into the overall plan recognition system.

Physical environment virtualization for human activities recognition

NASA Astrophysics Data System (ADS)

Poshtkar, Azin; Elangovan, Vinayak; Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

2015-05-01

Human activity recognition research relies heavily on extensive datasets to verify and validate performance of activity recognition algorithms. However, obtaining real datasets are expensive and highly time consuming. A physics-based virtual simulation can accelerate the development of context based human activity recognition algorithms and techniques by generating relevant training and testing videos simulating diverse operational scenarios. In this paper, we discuss in detail the requisite capabilities of a virtual environment to aid as a test bed for evaluating and enhancing activity recognition algorithms. To demonstrate the numerous advantages of virtual environment development, a newly developed virtual environment simulation modeling (VESM) environment is presented here to generate calibrated multisource imagery datasets suitable for development and testing of recognition algorithms for context-based human activities. The VESM environment serves as a versatile test bed to generate a vast amount of realistic data for training and testing of sensor processing algorithms. To demonstrate the effectiveness of VESM environment, we present various simulated scenarios and processed results to infer proper semantic annotations from the high fidelity imagery data for human-vehicle activity recognition under different operational contexts.

A rapid ATR-FTIR spectroscopic method for detection of sibutramine adulteration in tea and coffee based on hierarchical cluster and principal component analyses.

PubMed

Cebi, Nur; Yilmaz, Mustafa Tahsin; Sagdic, Osman

2017-08-15

Sibutramine may be illicitly included in herbal slimming foods and supplements marketed as "100% natural" to enhance weight loss. Considering public health and legal regulations, there is an urgent need for effective, rapid and reliable techniques to detect sibutramine in dietetic herbal foods, teas and dietary supplements. This research comprehensively explored, for the first time, detection of sibutramine in green tea, green coffee and mixed herbal tea using ATR-FTIR spectroscopic technique combined with chemometrics. Hierarchical cluster analysis and PCA principle component analysis techniques were employed in spectral range (2746-2656cm -1 ) for classification and discrimination through Euclidian distance and Ward's algorithm. Unadulterated and adulterated samples were classified and discriminated with respect to their sibutramine contents with perfect accuracy without any false prediction. The results suggest that existence of the active substance could be successfully determined at the levels in the range of 0.375-12mg in totally 1.75g of green tea, green coffee and mixed herbal tea by using FTIR-ATR technique combined with chemometrics. Copyright © 2017 Elsevier Ltd. All rights reserved.

A high resolution and high speed 3D imaging system and its application on ATR

NASA Astrophysics Data System (ADS)

Lu, Thomas T.; Chao, Tien-Hsin

2006-04-01

The paper presents an advanced 3D imaging system based on a combination of stereo vision and light projection methods. A single digital camera is used to take only one shot of the object and reconstruct the 3D model of an object. The stereo vision is achieved by employing a prism and mirror setup to split the views and combine them side by side in the camera. The advantage of this setup is its simple system architecture, easy synchronization, fast 3D imaging speed and high accuracy. The 3D imaging algorithms and potential applications are discussed. For ATR applications, it is critically important to extract maximum information for the potential targets and to separate the targets from the background and clutter noise. The added dimension of a 3D model provides additional features of surface profile, range information of the target. It is capable of removing the false shadow from camouflage and reveal the 3D profile of the object. It also provides arbitrary viewing angles and distances for training the filter bank for invariant ATR. The system architecture can be scaled to take large objects and to perform area 3D modeling onboard a UAV.

Vehicle logo recognition using multi-level fusion model

NASA Astrophysics Data System (ADS)

Ming, Wei; Xiao, Jianli

2018-04-01

Vehicle logo recognition plays an important role in manufacturer identification and vehicle recognition. This paper proposes a new vehicle logo recognition algorithm. It has a hierarchical framework, which consists of two fusion levels. At the first level, a feature fusion model is employed to map the original features to a higher dimension feature space. In this space, the vehicle logos become more recognizable. At the second level, a weighted voting strategy is proposed to promote the accuracy and the robustness of the recognition results. To evaluate the performance of the proposed algorithm, extensive experiments are performed, which demonstrate that the proposed algorithm can achieve high recognition accuracy and work robustly.

Bayesian truthing as experimental verification of C4ISR sensors

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Forrester, Thomas; Romanov, Volodymyr; Wang, Wenjian; Nielsen, Thomas; Kostrzewski, Andrew

2015-05-01

In this paper, the general methodology for experimental verification/validation of C4ISR and other sensors' performance, is presented, based on Bayesian inference, in general, and binary sensors, in particular. This methodology, called Bayesian Truthing, defines Performance Metrics for binary sensors in: physics, optics, electronics, medicine, law enforcement, C3ISR, QC, ATR (Automatic Target Recognition), terrorism related events, and many others. For Bayesian Truthing, the sensing medium itself is not what is truly important; it is how the decision process is affected.

Voice Recognition as an Input Modality for the TACCO Preflight Data Insertion Task in the P-3C Aircraft

DTIC Science & Technology

1981-03-01

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Mobile high-performance computing (HPC) for synthetic aperture radar signal processing

NASA Astrophysics Data System (ADS)

Misko, Joshua; Kim, Youngsoo; Qi, Chenchen; Sirkeci, Birsen

2018-04-01

The importance of mobile high-performance computing has emerged in numerous battlespace applications at the tactical edge in hostile environments. Energy efficient computing power is a key enabler for diverse areas ranging from real-time big data analytics and atmospheric science to network science. However, the design of tactical mobile data centers is dominated by power, thermal, and physical constraints. Presently, it is very unlikely to achieve required computing processing power by aggregating emerging heterogeneous many-core processing platforms consisting of CPU, Field Programmable Gate Arrays and Graphic Processor cores constrained by power and performance. To address these challenges, we performed a Synthetic Aperture Radar case study for Automatic Target Recognition (ATR) using Deep Neural Networks (DNNs). However, these DNN models are typically trained using GPUs with gigabytes of external memories and massively used 32-bit floating point operations. As a result, DNNs do not run efficiently on hardware appropriate for low power or mobile applications. To address this limitation, we proposed for compressing DNN models for ATR suited to deployment on resource constrained hardware. This proposed compression framework utilizes promising DNN compression techniques including pruning and weight quantization while also focusing on processor features common to modern low-power devices. Following this methodology as a guideline produced a DNN for ATR tuned to maximize classification throughput, minimize power consumption, and minimize memory footprint on a low-power device.

Collaborative sparse priors for multi-view ATR

NASA Astrophysics Data System (ADS)

Li, Xuelu; Monga, Vishal

2018-04-01

Recent work has seen a surge of sparse representation based classification (SRC) methods applied to automatic target recognition problems. While traditional SRC approaches used l0 or l1 norm to quantify sparsity, spike and slab priors have established themselves as the gold standard for providing general tunable sparse structures on vectors. In this work, we employ collaborative spike and slab priors that can be applied to matrices to encourage sparsity for the problem of multi-view ATR. That is, target images captured from multiple views are expanded in terms of a training dictionary multiplied with a coefficient matrix. Ideally, for a test image set comprising of multiple views of a target, coefficients corresponding to its identifying class are expected to be active, while others should be zero, i.e. the coefficient matrix is naturally sparse. We develop a new approach to solve the optimization problem that estimates the sparse coefficient matrix jointly with the sparsity inducing parameters in the collaborative prior. ATR problems are investigated on the mid-wave infrared (MWIR) database made available by the US Army Night Vision and Electronic Sensors Directorate, which has a rich collection of views. Experimental results show that the proposed joint prior and coefficient estimation method (JPCEM) can: 1.) enable improved accuracy when multiple views vs. a single one are invoked, and 2.) outperform state of the art alternatives particularly when training imagery is limited.

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar

PubMed Central

Shin, Young Hoon; Seo, Jiwon

2016-01-01

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing. PMID:27801867

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar.

PubMed

Shin, Young Hoon; Seo, Jiwon

2016-10-29

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker's vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing.

A unified Hyperglycemia and Diabetic ketoacidosis (DKA) insulin infusion protocol based on an Excel algorithm and implemented via Electronic Medical Record (EMR) in Intensive Care Units.

PubMed

Gupta, Deepashree; Kirn, Meredith; Jamkhana, Zafar A; Lee, Richard; Albert, Stewart G; Rollins, Kimberly M

To assess the efficacy of a unified hyperglycemia and diabetic ketoacidosis (DKA) insulin infusion protocol (IIP), based on an Excel algorithm and implemented as an electronic order set, in achieving glycemic targets and minimizing hypoglycemia. An IIP was instituted in medical and surgical intensive care units for post-cardiac surgery (PCS) and other stress hyperglycemia (SH), diabetes hyperglycemia (DH), and DKA. The IIP initiated therapeutic insulin rates at elevated blood glucose (BG), and decreased insulin when target range was achieved. A convenience sample (n=62) was studied; 20 PCS, 15 with DH, 9 with SH, 8 with diabetes on vasopressors, 7 with diabetes on glucocorticoids and 3 with DKA were assessed. The protocol maintained BG at 144±24.7mg/dL for PCS and 167±36mg/dL for patients with diabetes mellitus. It maintained acceptable target range (ATR) (100mg/dL-180mg/dL) 89% of the time for PCS and 67% of the time for patients with diabetes mellitus. There were no measurements of BG<70mg/dL. The protocol lowered the BG at a similar rate and time period in those with diabetes, DKA and those with or without vasopressors or glucocorticoids. To determine long-term efficacy, a retrospective review of Point of Care (POC) RALS (Remote Automated Data System) BG data 2 years post implementation demonstrated fewer episodes of hypoglycemia<70mg/dL and hyperglycemia>240mg/dL and more BG values within ATR. This IIP maintained ATR without hypoglycemia for patients in the ICU setting without requiring complex nursing calculations. Copyright © 2016 Diabetes India. Published by Elsevier Ltd. All rights reserved.

A genetic algorithm-based framework for wavelength selection on sample categorization.

PubMed

Anzanello, Michel J; Yamashita, Gabrielli; Marcelo, Marcelo; Fogliatto, Flávio S; Ortiz, Rafael S; Mariotti, Kristiane; Ferrão, Marco F

2017-08-01

In forensic and pharmaceutical scenarios, the application of chemometrics and optimization techniques has unveiled common and peculiar features of seized medicine and drug samples, helping investigative forces to track illegal operations. This paper proposes a novel framework aimed at identifying relevant subsets of attenuated total reflectance Fourier transform infrared (ATR-FTIR) wavelengths for classifying samples into two classes, for example authentic or forged categories in case of medicines, or salt or base form in cocaine analysis. In the first step of the framework, the ATR-FTIR spectra were partitioned into equidistant intervals and the k-nearest neighbour (KNN) classification technique was applied to each interval to insert samples into proper classes. In the next step, selected intervals were refined through the genetic algorithm (GA) by identifying a limited number of wavelengths from the intervals previously selected aimed at maximizing classification accuracy. When applied to Cialis®, Viagra®, and cocaine ATR-FTIR datasets, the proposed method substantially decreased the number of wavelengths needed to categorize, and increased the classification accuracy. From a practical perspective, the proposed method provides investigative forces with valuable information towards monitoring illegal production of drugs and medicines. In addition, focusing on a reduced subset of wavelengths allows the development of portable devices capable of testing the authenticity of samples during police checking events, avoiding the need for later laboratorial analyses and reducing equipment expenses. Theoretically, the proposed GA-based approach yields more refined solutions than the current methods relying on interval approaches, which tend to insert irrelevant wavelengths in the retained intervals. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system.

PubMed

Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

2018-04-01

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system

NASA Astrophysics Data System (ADS)

Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

2018-04-01

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.

The implementation of aerial object recognition algorithm based on contour descriptor in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Babayan, Pavel; Smirnov, Sergey; Strotov, Valery

2017-10-01

This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

Atrazine-induced reproductive tract alterations after transplacental and/or lactational exposure in male Long-Evans rats

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

Rayner, Jennifer L.; Reproductive Toxicology Division, Office of Research and Development, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711; Enoch, Rolondo R.

2007-02-01

Studies showed that early postnatal exposure to the herbicide atrazine (ATR) delayed preputial separation (PPS) and increased incidence of prostate inflammation in adult Wistar rats. A cross-fostering paradigm was used in this study to determine if gestational exposure to ATR would also result in altered puberty and reproductive tissue effects in the male rat. Timed-pregnant Long-Evans (LE) rats were dosed by gavage on gestational days (GD) 15-19 with 100 mg ATR/kg body weight (BW) or 1% methylcellulose (controls, C). On postnatal day (PND)1, half litters were cross-fostered, creating 4 treatment groups; C-C, ATR-C, C-ATR, and ATR-ATR (transplacental-milk as source, respectively).more » On PND4, male offspring in the ATR-ATR group weighed significantly less than the C-C males. ATR-ATR male pups had significantly delayed preputial separation (PPS). BWs at PPS for C-ATR and ATR-ATR males were reduced by 6% and 9%, respectively, from that of C-C. On PND120, lateral prostate weights of males in the ATR-ATR group were significantly increased over C-C. Histological examination of lateral and ventral prostates identified an increased distribution of inflammation in the lateral prostates of C-ATR males. By PND220, lateral prostate weights were significantly increased for ATR-C and ATR-ATR, but there were no significant changes in inflammation in either the lateral or ventral prostate. These results suggest that in LE rats, gestational ATR exposure delays PPS when male offspring suckle an ATR dam, but leads to increased lateral prostate weight via transplacental exposure alone. Inflammation present at PND120 does not increase in severity with time.« less

Comparison of Neural Networks and Tabular Nearest Neighbor Encoding for Hyperspectral Signature Classification in Unresolved Object Detection

NASA Astrophysics Data System (ADS)

Schmalz, M.; Ritter, G.; Key, R.

Accurate and computationally efficient spectral signature classification is a crucial step in the nonimaging detection and recognition of spaceborne objects. In classical hyperspectral recognition applications using linear mixing models, signature classification accuracy depends on accurate spectral endmember discrimination [1]. If the endmembers cannot be classified correctly, then the signatures cannot be classified correctly, and object recognition from hyperspectral data will be inaccurate. In practice, the number of endmembers accurately classified often depends linearly on the number of inputs. This can lead to potentially severe classification errors in the presence of noise or densely interleaved signatures. In this paper, we present an comparison of emerging technologies for nonimaging spectral signature classfication based on a highly accurate, efficient search engine called Tabular Nearest Neighbor Encoding (TNE) [3,4] and a neural network technology called Morphological Neural Networks (MNNs) [5]. Based on prior results, TNE can optimize its classifier performance to track input nonergodicities, as well as yield measures of confidence or caution for evaluation of classification results. Unlike neural networks, TNE does not have a hidden intermediate data structure (e.g., the neural net weight matrix). Instead, TNE generates and exploits a user-accessible data structure called the agreement map (AM), which can be manipulated by Boolean logic operations to effect accurate classifier refinement algorithms. The open architecture and programmability of TNE's agreement map processing allows a TNE programmer or user to determine classification accuracy, as well as characterize in detail the signatures for which TNE did not obtain classification matches, and why such mis-matches occurred. In this study, we will compare TNE and MNN based endmember classification, using performance metrics such as probability of correct classification (Pd) and rate of false detections (Rfa). As proof of principle, we analyze classification of multiple closely spaced signatures from a NASA database of space material signatures. Additional analysis pertains to computational complexity and noise sensitivity, which are superior to Bayesian techniques based on classical neural networks. [1] Winter, M.E. "Fast autonomous spectral end-member determination in hyperspectral data," in Proceedings of the 13th International Conference On Applied Geologic Remote Sensing, Vancouver, B.C., Canada, pp. 337-44 (1999). [2] N. Keshava, "A survey of spectral unmixing algorithms," Lincoln Laboratory Journal 14:55-78 (2003). [3] Key, G., M.S. SCHMALZ, F.M. Caimi, and G.X. Ritter. "Performance analysis of tabular nearest neighbor encoding algorithm for joint compression and ATR", in Proceedings SPIE 3814:115-126 (1999). [4] Schmalz, M.S. and G. Key. "Algorithms for hyperspectral signature classification in unresolved object detection using tabular nearest neighbor encoding" in Proceedings of the 2007 AMOS Conference, Maui HI (2007). [5] Ritter, G.X., G. Urcid, and M.S. Schmalz. "Autonomous single-pass endmember approximation using lattice auto-associative memories", Neurocomputing (Elsevier), accepted (June 2008).

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

2018-04-01

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

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.

Multifeature-based high-resolution palmprint recognition.

PubMed

Dai, Jifeng; Zhou, Jie

2011-05-01

Palmprint is a promising biometric feature for use in access control and forensic applications. Previous research on palmprint recognition mainly concentrates on low-resolution (about 100 ppi) palmprints. But for high-security applications (e.g., forensic usage), high-resolution palmprints (500 ppi or higher) are required from which more useful information can be extracted. In this paper, we propose a novel recognition algorithm for high-resolution palmprint. The main contributions of the proposed algorithm include the following: 1) use of multiple features, namely, minutiae, density, orientation, and principal lines, for palmprint recognition to significantly improve the matching performance of the conventional algorithm. 2) Design of a quality-based and adaptive orientation field estimation algorithm which performs better than the existing algorithm in case of regions with a large number of creases. 3) Use of a novel fusion scheme for an identification application which performs better than conventional fusion methods, e.g., weighted sum rule, SVMs, or Neyman-Pearson rule. Besides, we analyze the discriminative power of different feature combinations and find that density is very useful for palmprint recognition. Experimental results on the database containing 14,576 full palmprints show that the proposed algorithm has achieved a good performance. In the case of verification, the recognition system's False Rejection Rate (FRR) is 16 percent, which is 17 percent lower than the best existing algorithm at a False Acceptance Rate (FAR) of 10(-5), while in the identification experiment, the rank-1 live-scan partial palmprint recognition rate is improved from 82.0 to 91.7 percent.

Local ICA for the Most Wanted face recognition

NASA Astrophysics Data System (ADS)

Guan, Xin; Szu, Harold H.; Markowitz, Zvi

2000-04-01

Facial disguises of FBI Most Wanted criminals are inevitable and anticipated in our design of automatic/aided target recognition (ATR) imaging systems. For example, man's facial hairs may hide his mouth and chin but not necessarily the nose and eyes. Sunglasses will cover the eyes but not the nose, mouth, and chins. This fact motivates us to build sets of the independent component analyses bases separately for each facial region of the entire alleged criminal group. Then, given an alleged criminal face, collective votes are obtained from all facial regions in terms of 'yes, no, abstain' and are tallied for a potential alarm. Moreover, and innocent outside shall fall below the alarm threshold and is allowed to pass the checkpoint. Such a PD versus FAR called ROC curve is obtained.

Design method of ARM based embedded iris recognition system

NASA Astrophysics Data System (ADS)

Wang, Yuanbo; He, Yuqing; Hou, Yushi; Liu, Ting

2008-03-01

With the advantages of non-invasiveness, uniqueness, stability and low false recognition rate, iris recognition has been successfully applied in many fields. Up to now, most of the iris recognition systems are based on PC. However, a PC is not portable and it needs more power. In this paper, we proposed an embedded iris recognition system based on ARM. Considering the requirements of iris image acquisition and recognition algorithm, we analyzed the design method of the iris image acquisition module, designed the ARM processing module and its peripherals, studied the Linux platform and the recognition algorithm based on this platform, finally actualized the design method of ARM-based iris imaging and recognition system. Experimental results show that the ARM platform we used is fast enough to run the iris recognition algorithm, and the data stream can flow smoothly between the camera and the ARM chip based on the embedded Linux system. It's an effective method of using ARM to actualize portable embedded iris recognition system.

Hybrid simulated annealing and its application to optimization of hidden Markov models for visual speech recognition.

PubMed

Lee, Jong-Seok; Park, Cheol Hoon

2010-08-01

We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.

A comparative study of mid-infrared diffuse reflection (DR) and attenuated total reflection (ATR) spectroscopy for the detection of fungal infection on RWA2-corn.

PubMed

Kos, Gregor; Krska, Rudolf; Lohninger, Hans; Griffiths, Peter R

2004-01-01

An investigation into the rapid detection of mycotoxin-producing fungi on corn by two mid-infrared spectroscopic techniques was undertaken. Corn samples from a single genotype (RWA2, blanks, and contaminated with Fusarium graminearum) were ground, sieved and, after appropriate sample preparation, subjected to mid-infrared spectroscopy using two different accessories (diffuse reflection and attenuated total reflection). The measured spectra were evaluated with principal component analysis (PCA) and the blank and contaminated samples were classified by cluster analysis. Reference data for fungal metabolites were obtained with conventional methods. After extraction and clean-up, each sample was analyzed for the toxin deoxynivalenol (DON) by gas chromatography with electron capture detection (GC-ECD) and ergosterol (a parameter for the total fungal biomass) by high-performance liquid chromatography with diode array detection (HPLC-DAD). The concentration ranges for contaminated samples were 880-3600 microg/kg for ergosterol and 300-2600 microg/kg for DON. Classification efficiency was 100% for ATR spectra. DR spectra did not show as obvious a clustering of contaminated and blank samples. Results and trends were also observed in single spectra plots. Quantification using a PLS1 regression algorithm showed good correlation with DON reference data, but a rather high standard error of prediction (SEP) with 600 microg/kg (DR) and 490 microg/kg (ATR), respectively, for ergosterol. Comparing measurement procedures and results showed advantages for the ATR technique, mainly owing to its ease of use and the easier interpretation of results that were better with respect to classification and quantification.

A feasibility and optimization study to determine cooling time and burnup of advanced test reactor fuels using a nondestructive technique

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

Navarro, Jorge

2013-12-01

The goal of this study presented is to determine the best available non-destructive technique necessary to collect validation data as well as to determine burn-up and cooling time of the fuel elements onsite at the Advanced Test Reactor (ATR) canal. This study makes a recommendation of the viability of implementing a permanent fuel scanning system at the ATR canal and leads3 to the full design of a permanent fuel scan system. The study consisted at first in determining if it was possible and which equipment was necessary to collect useful spectra from ATR fuel elements at the canal adjacent tomore » the reactor. Once it was establish that useful spectra can be obtained at the ATR canal the next step was to determine which detector and which configuration was better suited to predict burnup and cooling time of fuel elements non-destructively. Three different detectors of High Purity Germanium (HPGe), Lanthanum Bromide (LaBr3), and High Pressure Xenon (HPXe) in two system configurations of above and below the water pool were used during the study. The data collected and analyzed was used to create burnup and cooling time calibration prediction curves for ATR fuel. The next stage of the study was to determine which of the three detectors tested was better suited for the permanent system. From spectra taken and the calibration curves obtained, it was determined that although the HPGe detector yielded better results, a detector that could better withstand the harsh environment of the ATR canal was needed. The in-situ nature of the measurements required a rugged fuel scanning system, low in maintenance and easy to control system. Based on the ATR canal feasibility measurements and calibration results it was determined that the LaBr3 detector was the best alternative for canal in-situ measurements; however in order to enhance the quality of the spectra collected using this scintillator a deconvolution method was developed. Following the development of the deconvolution method for ATR applications the technique was tested using one-isotope, multi-isotope and fuel simulated sources. Burnup calibrations were perfomed using convoluted and deconvoluted data. The calibrations results showed burnup prediction by this method improves using deconvolution. The final stage of the deconvolution method development was to perform an irradiation experiment in order to create a surrogate fuel source to test the deconvolution method using experimental data. A conceptual design of the fuel scan system is path forward using the rugged LaBr3 detector in an above the water configuration and deconvolution algorithms.« less

Research on autonomous identification of airport targets based on Gabor filtering and Radon transform

NASA Astrophysics Data System (ADS)

Yi, Juan; Du, Qingyu; Zhang, Hong jiang; Zhang, Yao lei

2017-11-01

Target recognition is a leading key technology in intelligent image processing and application development at present, with the enhancement of computer processing ability, autonomous target recognition algorithm, gradually improve intelligence, and showed good adaptability. Taking the airport target as the research object, analysis the airport layout characteristics, construction of knowledge model, Gabor filter and Radon transform based on the target recognition algorithm of independent design, image processing and feature extraction of the airport, the algorithm was verified, and achieved better recognition results.

Rapid evaluation and quantitative analysis of thyme, origano and chamomile essential oils by ATR-IR and NIR spectroscopy

NASA Astrophysics Data System (ADS)

Schulz, Hartwig; Quilitzsch, Rolf; Krüger, Hans

2003-12-01

The essential oils obtained from various chemotypes of thyme, origano and chamomile species were studied by ATR/FT-IR as well as NIR spectroscopy. Application of multivariate statistics (PCA, PLS) in conjunction with analytical reference data leads to very good IR and NIR calibration results. For the main essential oil components (e.g. carvacrol, thymol, γ-terpinene, α-bisabolol and β-farnesene) standard errors are in the range of the applied GC reference method. In most cases the multiple coefficients of determination ( R2) are >0.97. Using the IR fingerprint region (900-1400 cm -1) a qualitative discrimination of the individual chemotypes is possible already by visual judgement without to apply any chemometric algorithms.The described rapid and non-destructive methods can be applied in industry to control very easily purifying, blending and redistillation processes of the mentioned essential oils.

Speech recognition for embedded automatic positioner for laparoscope

NASA Astrophysics Data System (ADS)

Chen, Xiaodong; Yin, Qingyun; Wang, Yi; Yu, Daoyin

2014-07-01

In this paper a novel speech recognition methodology based on Hidden Markov Model (HMM) is proposed for embedded Automatic Positioner for Laparoscope (APL), which includes a fixed point ARM processor as the core. The APL system is designed to assist the doctor in laparoscopic surgery, by implementing the specific doctor's vocal control to the laparoscope. Real-time respond to the voice commands asks for more efficient speech recognition algorithm for the APL. In order to reduce computation cost without significant loss in recognition accuracy, both arithmetic and algorithmic optimizations are applied in the method presented. First, depending on arithmetic optimizations most, a fixed point frontend for speech feature analysis is built according to the ARM processor's character. Then the fast likelihood computation algorithm is used to reduce computational complexity of the HMM-based recognition algorithm. The experimental results show that, the method shortens the recognition time within 0.5s, while the accuracy higher than 99%, demonstrating its ability to achieve real-time vocal control to the APL.

75 FR 8476 - Airworthiness Directives; ATR-GIE Avions de Transport Régional Model ATR42 and ATR72 Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-25

... Airworthiness Directives; ATR-GIE Avions de Transport R[eacute]gional Model ATR42 and ATR72 Airplanes AGENCY... FURTHER INFORMATION CONTACT: Tom Rodriguez, Aerospace Engineer, International Branch, ANM-116, Transport... including but not limited to those listed in Table 1 of that AD. Although ATR-GIE Avions de Transport R...

Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Tracking and recognition face in videos with incremental local sparse representation model

NASA Astrophysics Data System (ADS)

Wang, Chao; Wang, Yunhong; Zhang, Zhaoxiang

2013-10-01

This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. First a robust face tracking algorithm is proposed via employing local sparse appearance and covariance pooling method. In the following face recognition stage, with the employment of a novel template update strategy, which combines incremental subspace learning, our recognition algorithm adapts the template to appearance changes and reduces the influence of occlusion and illumination variation. This leads to a robust video-based face tracking and recognition with desirable performance. In the experiments, we test the quality of face recognition in real-world noisy videos on YouTube database, which includes 47 celebrities. Our proposed method produces a high face recognition rate at 95% of all videos. The proposed face tracking and recognition algorithms are also tested on a set of noisy videos under heavy occlusion and illumination variation. The tracking results on challenging benchmark videos demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. In the case of the challenging dataset in which faces undergo occlusion and illumination variation, and tracking and recognition experiments under significant pose variation on the University of California, San Diego (Honda/UCSD) database, our proposed method also consistently demonstrates a high recognition rate.

Face sketch recognition based on edge enhancement via deep learning

NASA Astrophysics Data System (ADS)

Xie, Zhenzhu; Yang, Fumeng; Zhang, Yuming; Wu, Congzhong

2017-11-01

In this paper,we address the face sketch recognition problem. Firstly, we utilize the eigenface algorithm to convert a sketch image into a synthesized sketch face image. Subsequently, considering the low-level vision problem in synthesized face sketch image .Super resolution reconstruction algorithm based on CNN(convolutional neural network) is employed to improve the visual effect. To be specific, we uses a lightweight super-resolution structure to learn a residual mapping instead of directly mapping the feature maps from the low-level space to high-level patch representations, which making the networks are easier to optimize and have lower computational complexity. Finally, we adopt LDA(Linear Discriminant Analysis) algorithm to realize face sketch recognition on synthesized face image before super resolution and after respectively. Extensive experiments on the face sketch database(CUFS) from CUHK demonstrate that the recognition rate of SVM(Support Vector Machine) algorithm improves from 65% to 69% and the recognition rate of LDA(Linear Discriminant Analysis) algorithm improves from 69% to 75%.What'more,the synthesized face image after super resolution can not only better describer image details such as hair ,nose and mouth etc, but also improve the recognition accuracy effectively.

Cognitive object recognition system (CORS)

NASA Astrophysics Data System (ADS)

Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

2010-04-01

We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

Face recognition algorithm using extended vector quantization histogram features.

PubMed

Yan, Yan; Lee, Feifei; Wu, Xueqian; Chen, Qiu

2018-01-01

In this paper, we propose a face recognition algorithm based on a combination of vector quantization (VQ) and Markov stationary features (MSF). The VQ algorithm has been shown to be an effective method for generating features; it extracts a codevector histogram as a facial feature representation for face recognition. Still, the VQ histogram features are unable to convey spatial structural information, which to some extent limits their usefulness in discrimination. To alleviate this limitation of VQ histograms, we utilize Markov stationary features (MSF) to extend the VQ histogram-based features so as to add spatial structural information. We demonstrate the effectiveness of our proposed algorithm by achieving recognition results superior to those of several state-of-the-art methods on publicly available face databases.

A Random Forest-based ensemble method for activity recognition.

PubMed

Feng, Zengtao; Mo, Lingfei; Li, Meng

2015-01-01

This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity recognition. To evaluate the algorithm, PA data collected from the PAMAP (Physical Activity Monitoring for Aging People), which is a standard, publicly available database, was utilized to train and test. The experimental results show that the algorithm is able to correctly recognize 19 PA types with an accuracy of 93.44%, while the training is faster than others. The ensemble classifier system based on the RF (Random Forest) algorithm can achieve high recognition accuracy and fast calculation.

The fast iris image clarity evaluation based on Tenengrad and ROI selection

NASA Astrophysics Data System (ADS)

Gao, Shuqin; Han, Min; Cheng, Xu

2018-04-01

In iris recognition system, the clarity of iris image is an important factor that influences recognition effect. In the process of recognition, the blurred image may possibly be rejected by the automatic iris recognition system, which will lead to the failure of identification. Therefore it is necessary to evaluate the iris image definition before recognition. Considered the existing evaluation methods on iris image definition, we proposed a fast algorithm to evaluate the definition of iris image in this paper. In our algorithm, firstly ROI (Region of Interest) is extracted based on the reference point which is determined by using the feature of the light spots within the pupil, then Tenengrad operator is used to evaluate the iris image's definition. Experiment results show that, the iris image definition algorithm proposed in this paper could accurately distinguish the iris images of different clarity, and the algorithm has the merit of low computational complexity and more effectiveness.

Optimal pattern synthesis for speech recognition based on principal component analysis

NASA Astrophysics Data System (ADS)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis.

PubMed

Nespeca, Maurilio Gustavo; Hatanaka, Rafael Rodrigues; Flumignan, Danilo Luiz; de Oliveira, José Eduardo

2018-01-01

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000-650 cm -1 . The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

PubMed Central

Hatanaka, Rafael Rodrigues; Flumignan, Danilo Luiz; de Oliveira, José Eduardo

2018-01-01

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000–650 cm−1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time. PMID:29629209

Multi-frame knowledge based text enhancement for mobile phone captured videos

NASA Astrophysics Data System (ADS)

Ozarslan, Suleyman; Eren, P. Erhan

2014-02-01

In this study, we explore automated text recognition and enhancement using mobile phone captured videos of store receipts. We propose a method which includes Optical Character Resolution (OCR) enhanced by our proposed Row Based Multiple Frame Integration (RB-MFI), and Knowledge Based Correction (KBC) algorithms. In this method, first, the trained OCR engine is used for recognition; then, the RB-MFI algorithm is applied to the output of the OCR. The RB-MFI algorithm determines and combines the most accurate rows of the text outputs extracted by using OCR from multiple frames of the video. After RB-MFI, KBC algorithm is applied to these rows to correct erroneous characters. Results of the experiments show that the proposed video-based approach which includes the RB-MFI and the KBC algorithm increases the word character recognition rate to 95%, and the character recognition rate to 98%.

[A new peak detection algorithm of Raman spectra].

PubMed

Jiang, Cheng-Zhi; Sun, Qiang; Liu, Ying; Liang, Jing-Qiu; An, Yan; Liu, Bing

2014-01-01

The authors proposed a new Raman peak recognition method named bi-scale correlation algorithm. The algorithm uses the combination of the correlation coefficient and the local signal-to-noise ratio under two scales to achieve Raman peak identification. We compared the performance of the proposed algorithm with that of the traditional continuous wavelet transform method through MATLAB, and then tested the algorithm with real Raman spectra. The results show that the average time for identifying a Raman spectrum is 0.51 s with the algorithm, while it is 0.71 s with the continuous wavelet transform. When the signal-to-noise ratio of Raman peak is greater than or equal to 6 (modern Raman spectrometers feature an excellent signal-to-noise ratio), the recognition accuracy with the algorithm is higher than 99%, while it is less than 84% with the continuous wavelet transform method. The mean and the standard deviations of the peak position identification error of the algorithm are both less than that of the continuous wavelet transform method. Simulation analysis and experimental verification prove that the new algorithm possesses the following advantages: no needs of human intervention, no needs of de-noising and background removal operation, higher recognition speed and higher recognition accuracy. The proposed algorithm is operable in Raman peak identification.

An improved finger-vein recognition algorithm based on template matching

NASA Astrophysics Data System (ADS)

Liu, Yueyue; Di, Si; Jin, Jian; Huang, Daoping

2016-10-01

Finger-vein recognition has became the most popular biometric identify methods. The investigation on the recognition algorithms always is the key point in this field. So far, there are many applicable algorithms have been developed. However, there are still some problems in practice, such as the variance of the finger position which may lead to the image distortion and shifting; during the identification process, some matching parameters determined according to experience may also reduce the adaptability of algorithm. Focus on above mentioned problems, this paper proposes an improved finger-vein recognition algorithm based on template matching. In order to enhance the robustness of the algorithm for the image distortion, the least squares error method is adopted to correct the oblique finger. During the feature extraction, local adaptive threshold method is adopted. As regard as the matching scores, we optimized the translation preferences as well as matching distance between the input images and register images on the basis of Naoto Miura algorithm. Experimental results indicate that the proposed method can improve the robustness effectively under the finger shifting and rotation conditions.

ATR Kinase Inhibition Protects Non-cycling Cells from the Lethal Effects of DNA Damage and Transcription Stress*

PubMed Central

Kemp, Michael G.; Sancar, Aziz

2016-01-01

ATR (ataxia telangiectasia and Rad-3-related) is a protein kinase that maintains genome stability and halts cell cycle phase transitions in response to DNA lesions that block DNA polymerase movement. These DNA replication-associated features of ATR function have led to the emergence of ATR kinase inhibitors as potential adjuvants for DNA-damaging cancer chemotherapeutics. However, whether ATR affects the genotoxic stress response in non-replicating, non-cycling cells is currently unknown. We therefore used chemical inhibition of ATR kinase activity to examine the role of ATR in quiescent human cells. Although ATR inhibition had no obvious effects on the viability of non-cycling cells, inhibition of ATR partially protected non-replicating cells from the lethal effects of UV and UV mimetics. Analyses of various DNA damage response signaling pathways demonstrated that ATR inhibition reduced the activation of apoptotic signaling by these agents in non-cycling cells. The pro-apoptosis/cell death function of ATR is likely due to transcription stress because the lethal effects of compounds that block RNA polymerase movement were reduced in the presence of an ATR inhibitor. These results therefore suggest that whereas DNA polymerase stalling at DNA lesions activates ATR to protect cell viability and prevent apoptosis, the stalling of RNA polymerases instead activates ATR to induce an apoptotic form of cell death in non-cycling cells. These results have important implications regarding the use of ATR inhibitors in cancer chemotherapy regimens. PMID:26940878

A mitosis-specific and R loop-driven ATR pathway promotes faithful chromosome segregation.

PubMed

Kabeche, Lilian; Nguyen, Hai Dang; Buisson, Rémi; Zou, Lee

2018-01-05

The ataxia telangiectasia mutated and Rad3-related (ATR) kinase is crucial for DNA damage and replication stress responses. Here, we describe an unexpected role of ATR in mitosis. Acute inhibition or degradation of ATR in mitosis induces whole-chromosome missegregation. The effect of ATR ablation is not due to altered cyclin-dependent kinase 1 (CDK1) activity, DNA damage responses, or unscheduled DNA synthesis but to loss of an ATR function at centromeres. In mitosis, ATR localizes to centromeres through Aurora A-regulated association with centromere protein F (CENP-F), allowing ATR to engage replication protein A (RPA)-coated centromeric R loops. As ATR is activated at centromeres, it stimulates Aurora B through Chk1, preventing formation of lagging chromosomes. Thus, a mitosis-specific and R loop-driven ATR pathway acts at centromeres to promote faithful chromosome segregation, revealing functions of R loops and ATR in suppressing chromosome instability. Copyright © 2018, American Association for the Advancement of Science.

Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images

PubMed Central

Chen, Yibing; Ogata, Taiki; Ueyama, Tsuyoshi; Takada, Toshiyuki; Ota, Jun

2018-01-01

Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition. PMID:29786665

Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images.

PubMed

Chen, Yibing; Ogata, Taiki; Ueyama, Tsuyoshi; Takada, Toshiyuki; Ota, Jun

2018-05-22

Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition.

Advanced Test Reactor Core Modeling Update Project Annual Report for Fiscal Year 2011

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

David W. Nigg; Devin A. Steuhm

2011-09-01

Legacy computational reactor physics software tools and protocols currently used for support of Advanced Test Reactor (ATR) core fuel management and safety assurance and, to some extent, experiment management are obsolete, inconsistent with the state of modern nuclear engineering practice, and are becoming increasingly difficult to properly verify and validate (V&V). Furthermore, the legacy staff knowledge required for application of these tools and protocols from the 1960s and 1970s is rapidly being lost due to staff turnover and retirements. In 2009 the Idaho National Laboratory (INL) initiated a focused effort to address this situation through the introduction of modern high-fidelitymore » computational software and protocols, with appropriate V&V, within the next 3-4 years via the ATR Core Modeling and Simulation and V&V Update (or 'Core Modeling Update') Project. This aggressive computational and experimental campaign will have a broad strategic impact on the operation of the ATR, both in terms of improved computational efficiency and accuracy for support of ongoing DOE programs as well as in terms of national and international recognition of the ATR National Scientific User Facility (NSUF). The ATR Core Modeling Update Project, targeted for full implementation in phase with the anticipated ATR Core Internals Changeout (CIC) in the 2014 time frame, began during the last quarter of Fiscal Year 2009, and has just completed its first full year. Key accomplishments so far have encompassed both computational as well as experimental work. A new suite of stochastic and deterministic transport theory based reactor physics codes and their supporting nuclear data libraries (SCALE, KENO-6, HELIOS, NEWT, and ATTILA) have been installed at the INL under various permanent sitewide license agreements and corresponding baseline models of the ATR and ATRC are now operational, demonstrating the basic feasibility of these code packages for their intended purpose. Furthermore, a capability for rigorous sensitivity analysis and uncertainty quantification based on the TSUNAMI system is being implemented and initial computational results have been obtained. This capability will have many applications in 2011 and beyond as a tool for understanding the margins of uncertainty in the new models as well as for validation experiment design and interpretation. Finally we note that although full implementation of the new computational models and protocols will extend over a period 3-4 years as noted above, interim applications in the much nearer term have already been demonstrated. In particular, these demonstrations included an analysis that was useful for understanding the cause of some issues in December 2009 that were triggered by a larger than acceptable discrepancy between the measured excess core reactivity and a calculated value that was based on the legacy computational methods. As the Modeling Update project proceeds we anticipate further such interim, informal, applications in parallel with formal qualification of the system under the applicable INL Quality Assurance procedures and standards.« less

[Research on Rapid Discrimination of Edible Oil by ATR Infrared Spectroscopy].

PubMed

Ma, Xiao; Yuan, Hong-fu; Song, Chun-feng; Hu, Ai-qin; Li, Xiao-yu; Zhao, Zhong; Li, Xiu-qin; Guo Zhen; Zhu, Zhi-qiang

2015-07-01

A rapid discrimination method of edible oils, KL-BP model, was proposed by attenuated total reflectance infrared spectroscopy. The model extracts the characteristic of classification from source data by KL and reduces data dimension at the same time. Then the neural network model is constructed by the new data which as the input of the model. 84 edible oil samples which include sesame oil, corn oil, canola oil, blend oil, sunflower oil, peanut oil, olive oil, soybean oil and tea seed oil, were collected and their infrared spectra determined using an ATR FT-IR spectrometer. In order to compare the method performance, principal component analysis (PCA) direct-classification model, KL direct-classification model, PLS-DA model, PCA-BP model and KL-BP model are constructed in this paper. The results show that the recognition rates of PCA, PCA-BP, KL, PLS-DA and KL-BP are 59.1%, 68.2%, 77.3%, 77.3% and 90.9% for discriminating the 9 kinds of edible oils, respectively. KL extracts the eigenvector which make the distance between different class and distance of every class ratio is the largest. So the method can get much more classify information than PCA. BP neural network can effectively enhance the classification ability and accuracy. Taking full of the advantages of KL in extracting more category information in dimension reducing and the features of BP neural network in self-learning, adaptive, nonlinear, the KL-BP method has the best classification ability and recognition accuracy and great importance for rapidly recognizing edible oil in practice.

Locality constrained joint dynamic sparse representation for local matching based face recognition.

PubMed

Wang, Jianzhong; Yi, Yugen; Zhou, Wei; Shi, Yanjiao; Qi, Miao; Zhang, Ming; Zhang, Baoxue; Kong, Jun

2014-01-01

Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.

Identification of a Novel System for Boron Transport: Atr1 Is a Main Boron Exporter in Yeast▿ †

PubMed Central

Kaya, Alaattin; Karakaya, Huseyin C.; Fomenko, Dmitri E.; Gladyshev, Vadim N.; Koc, Ahmet

2009-01-01

Boron is a micronutrient in plants and animals, but its specific roles in cellular processes are not known. To understand boron transport and functions, we screened a yeast genomic DNA library for genes that confer resistance to the element in Saccharomyces cerevisiae. Thirty boron-resistant transformants were isolated, and they all contained the ATR1 (YML116w) gene. Atr1 is a multidrug resistance transport protein belonging to the major facilitator superfamily. C-terminal green fluorescent protein-tagged Atr1 localized to the cell membrane and vacuole, and ATR1 gene expression was upregulated by boron and several stress conditions. We found that atr1Δ mutants were highly sensitive to boron treatment, whereas cells overexpressing ATR1 were boron resistant. In addition, atr1Δ cells accumulated boron, whereas ATR1-overexpressing cells had low intracellular levels of the element. Furthermore, atr1Δ cells showed stronger boron-dependent phenotypes than mutants deficient in genes previously reported to be implicated in boron metabolism. ATR1 is widely distributed in bacteria, archaea, and lower eukaryotes. Our data suggest that Atr1 functions as a boron efflux pump and is required for boron tolerance. PMID:19414602

A Human Activity Recognition System Using Skeleton Data from RGBD Sensors.

PubMed

Cippitelli, Enea; Gasparrini, Samuele; Gambi, Ennio; Spinsante, Susanna

2016-01-01

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images

NASA Astrophysics Data System (ADS)

Yao, Shoukui; Qin, Xiaojuan

2018-02-01

Since the resolution of remote sensing infrared images is low, the features of ship targets become unstable. The issue of how to recognize ships with fuzzy features is an open problem. In this paper, we propose a novel ship target recognition algorithm based on Gaussian mixture models (GMMs). In the proposed algorithm, there are mainly two steps. At the first step, the Hu moments of these ship target images are calculated, and the GMMs are trained on the moment features of ships. At the second step, the moment feature of each ship image is assigned to the trained GMMs for recognition. Because of the scale, rotation, translation invariance property of Hu moments and the power feature-space description ability of GMMs, the GMMs-based ship target recognition algorithm can recognize ship reliably. Experimental results of a large simulating image set show that our approach is effective in distinguishing different ship types, and obtains a satisfactory ship recognition performance.

Word recognition using a lexicon constrained by first/last character decisions

NASA Astrophysics Data System (ADS)

Zhao, Sheila X.; Srihari, Sargur N.

1995-03-01

In lexicon based recognition of machine-printed word images, the size of the lexicon can be quite extensive. The recognition performance is closely related to the size of the lexicon. Recognition performance drops quickly when lexicon size increases. Here, we present an algorithm to improve the word recognition performance by reducing the size of the given lexicon. The algorithm utilizes the information provided by the first and last characters of a word to reduce the size of the given lexicon. Given a word image and a lexicon that contains the word in the image, the first and last characters are segmented and then recognized by a character classifier. The possible candidates based on the results given by the classifier are selected, which give us the sub-lexicon. Then a word shape analysis algorithm is applied to produce the final ranking of the given lexicon. The algorithm was tested on a set of machine- printed gray-scale word images which includes a wide range of print types and qualities.

Vision-based posture recognition using an ensemble classifier and a vote filter

NASA Astrophysics Data System (ADS)

Ji, Peng; Wu, Changcheng; Xu, Xiaonong; Song, Aiguo; Li, Huijun

2016-10-01

Posture recognition is a very important Human-Robot Interaction (HRI) way. To segment effective posture from an image, we propose an improved region grow algorithm which combining with the Single Gauss Color Model. The experiment shows that the improved region grow algorithm can get the complete and accurate posture than traditional Single Gauss Model and region grow algorithm, and it can eliminate the similar region from the background at the same time. In the posture recognition part, and in order to improve the recognition rate, we propose a CNN ensemble classifier, and in order to reduce the misjudgments during a continuous gesture control, a vote filter is proposed and applied to the sequence of recognition results. Comparing with CNN classifier, the CNN ensemble classifier we proposed can yield a 96.27% recognition rate, which is better than that of CNN classifier, and the proposed vote filter can improve the recognition result and reduce the misjudgments during the consecutive gesture switch.

An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System

PubMed Central

Ni, Yepeng; Liu, Jianbo; Liu, Shan; Bai, Yaxin

2016-01-01

With the rapid development of smartphones and wireless networks, indoor location-based services have become more and more prevalent. Due to the sophisticated propagation of radio signals, the Received Signal Strength Indicator (RSSI) shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, we present a novel method to improve the indoor pedestrian positioning accuracy by embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model. The fuzzy pattern recognition algorithm follows the rule that the RSSI fading has a positive correlation to the distance between the measuring point and the AP location even during a dynamic positioning measurement. Through this algorithm, we use the RSSI variation trend to replace the specific RSSI value to achieve a fuzzy positioning. The transition probability of the Hidden Markov Model is trained by the fuzzy pattern recognition algorithm with pedestrian trajectories. Using the Viterbi algorithm with the trained model, we can obtain a set of hidden location states. In our experiments, we demonstrate that, compared with the deterministic pattern matching algorithm, our method can greatly improve the positioning accuracy and shows robust environmental adaptability. PMID:27618053

Optical character recognition of handwritten Arabic using hidden Markov models

NASA Astrophysics Data System (ADS)

Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M.

2011-04-01

The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.

Optical character recognition of handwritten Arabic using hidden Markov models

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

Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.

2011-01-01

The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language ismore » initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.« less

Comparison of NIR chemical imaging with conventional NIR, Raman and ATR-IR spectroscopy for quantification of furosemide crystal polymorphs in ternary powder mixtures.

PubMed

Schönbichler, S A; Bittner, L K H; Weiss, A K H; Griesser, U J; Pallua, J D; Huck, C W

2013-08-01

The aim of this study was to evaluate the ability of near-infrared chemical imaging (NIR-CI), near-infrared (NIR), Raman and attenuated-total-reflectance infrared (ATR-IR) spectroscopy to quantify three polymorphic forms (I, II, III) of furosemide in ternary powder mixtures. For this purpose, partial least-squares (PLS) regression models were developed, and different data preprocessing algorithms such as normalization, standard normal variate (SNV), multiplicative scatter correction (MSC) and 1st to 3rd derivatives were applied to reduce the influence of systematic disturbances. The performance of the methods was evaluated by comparison of the standard error of cross-validation (SECV), R(2), and the ratio performance deviation (RPD). Limits of detection (LOD) and limits of quantification (LOQ) of all methods were determined. For NIR-CI, a SECVcorr-spec and a SECVsingle-pixel corrected were calculated to assess the loss of accuracy by taking advantage of the spatial information. NIR-CI showed a SECVcorr-spec (SECVsingle-pixel corrected) of 2.82% (3.71%), 3.49% (4.65%), and 4.10% (5.06%) for form I, II, III. NIR had a SECV of 2.98%, 3.62%, and 2.75%, and Raman reached 3.25%, 3.08%, and 3.18%. The SECV of the ATR-IR models were 7.46%, 7.18%, and 12.08%. This study proves that NIR-CI, NIR, and Raman are well suited to quantify forms I-III of furosemide in ternary mixtures. Because of the pressure-dependent conversion of form II to form I, ATR-IR was found to be less appropriate for an accurate quantification of the mixtures. In this study, the capability of NIR-CI for the quantification of polymorphic ternary mixtures was compared with conventional spectroscopic techniques for the first time. For this purpose, a new way of spectra selection was chosen, and two kinds of SECVs were calculated to achieve a better comparability of NIR-CI to NIR, Raman, and ATR-IR. Copyright © 2013 Elsevier B.V. All rights reserved.

Hyperspectral target detection analysis of a cluttered scene from a virtual airborne sensor platform using MuSES

NASA Astrophysics Data System (ADS)

Packard, Corey D.; Viola, Timothy S.; Klein, Mark D.

2017-10-01

The ability to predict spectral electro-optical (EO) signatures for various targets against realistic, cluttered backgrounds is paramount for rigorous signature evaluation. Knowledge of background and target signatures, including plumes, is essential for a variety of scientific and defense-related applications including contrast analysis, camouflage development, automatic target recognition (ATR) algorithm development and scene material classification. The capability to simulate any desired mission scenario with forecast or historical weather is a tremendous asset for defense agencies, serving as a complement to (or substitute for) target and background signature measurement campaigns. In this paper, a systematic process for the physical temperature and visible-through-infrared radiance prediction of several diverse targets in a cluttered natural environment scene is presented. The ability of a virtual airborne sensor platform to detect and differentiate targets from a cluttered background, from a variety of sensor perspectives and across numerous wavelengths in differing atmospheric conditions, is considered. The process described utilizes the thermal and radiance simulation software MuSES and provides a repeatable, accurate approach for analyzing wavelength-dependent background and target (including plume) signatures in multiple band-integrated wavebands (multispectral) or hyperspectrally. The engineering workflow required to combine 3D geometric descriptions, thermal material properties, natural weather boundary conditions, all modes of heat transfer and spectral surface properties is summarized. This procedure includes geometric scene creation, material and optical property attribution, and transient physical temperature prediction. Radiance renderings, based on ray-tracing and the Sandford-Robertson BRDF model, are coupled with MODTRAN for the inclusion of atmospheric effects. This virtual hyperspectral/multispectral radiance prediction methodology has been extensively validated and provides a flexible process for signature evaluation and algorithm development.

SAR target recognition and posture estimation using spatial pyramid pooling within CNN

NASA Astrophysics Data System (ADS)

Peng, Lijiang; Liu, Xiaohua; Liu, Ming; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-01-01

Many convolution neural networks(CNN) architectures have been proposed to strengthen the performance on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification on MSTAR database, but few methods concern about the estimation of depression angle and azimuth angle of targets. To get better effect on learning representation of hierarchies of features on both 10-class target classification task and target posture estimation tasks, we propose a new CNN architecture with spatial pyramid pooling(SPP) which can build high hierarchy of features map by dividing the convolved feature maps from finer to coarser levels to aggregate local features of SAR images. Experimental results on MSTAR database show that the proposed architecture can get high recognition accuracy as 99.57% on 10-class target classification task as the most current state-of-art methods, and also get excellent performance on target posture estimation tasks which pays attention to depression angle variety and azimuth angle variety. What's more, the results inspire us the application of deep learning on SAR target posture description.

Hyperspectral face recognition with spatiospectral information fusion and PLS regression.

PubMed

Uzair, Muhammad; Mahmood, Arif; Mian, Ajmal

2015-03-01

Hyperspectral imaging offers new opportunities for face recognition via improved discrimination along the spectral dimension. However, it poses new challenges, including low signal-to-noise ratio, interband misalignment, and high data dimensionality. Due to these challenges, the literature on hyperspectral face recognition is not only sparse but is limited to ad hoc dimensionality reduction techniques and lacks comprehensive evaluation. We propose a hyperspectral face recognition algorithm using a spatiospectral covariance for band fusion and partial least square regression for classification. Moreover, we extend 13 existing face recognition techniques, for the first time, to perform hyperspectral face recognition.We formulate hyperspectral face recognition as an image-set classification problem and evaluate the performance of seven state-of-the-art image-set classification techniques. We also test six state-of-the-art grayscale and RGB (color) face recognition algorithms after applying fusion techniques on hyperspectral images. Comparison with the 13 extended and five existing hyperspectral face recognition techniques on three standard data sets show that the proposed algorithm outperforms all by a significant margin. Finally, we perform band selection experiments to find the most discriminative bands in the visible and near infrared response spectrum.

Changes of air-tissue ratio evaluated by EBCT after cardiopulmonary resuscitation (CPR): validation in swine

NASA Astrophysics Data System (ADS)

Recheis, Wolfgang A.; Schuster, Antonius H.; Kleinsasser, Axel; Loeckinger, Alexander; Hoermann, Christoph; zur Nedden, Dieter

2001-05-01

The purpose was to evaluate changes of the air-tissue ratio (ATR) in previously defined regions of interest after cardiopulmonary resuscitation (CPR) in porcine model. Eight anesthetized and ventilated pigs we scanned in supine position before and 30 minutes after CPR at two different constant PEEP levels (5 cm H2O, 15 cm H2O). Volume scans were obtained using 6 mm slices. The gray values of the lung were divided into steps of 100 HU in order to get access to the changes of ATR. ATR was evaluated in ventral, intermediate and dorsal regions of the lung. CPR for 9 minutes led to an uneven distribution of ventilation. In the ventral region, areas with high ATR increased. Areas with normal ATR decreased. In contrast the dorsal regions with low ATR increased. ATR in the intermediate regions remained almost unchanged. Using the higher PEEP level, areas with normal ATR showed a marked increase accompanied by a decrease of areas with low ATR. After CPR, an uneven distribution of lung aeration was detected. According to the impaired hemodynamics, areas with normal ATR decreased and areas with high and low ATR increased. Using higher PEEP levels improved lung aeration.

Collegial Activity Learning between Heterogeneous Sensors.

PubMed

Feuz, Kyle D; Cook, Diane J

2017-11-01

Activity recognition algorithms have matured and become more ubiquitous in recent years. However, these algorithms are typically customized for a particular sensor platform. In this paper we introduce PECO, a Personalized activity ECOsystem, that transfers learned activity information seamlessly between sensor platforms in real time so that any available sensor can continue to track activities without requiring its own extensive labeled training data. We introduce a multi-view transfer learning algorithm that facilitates this information handoff between sensor platforms and provide theoretical performance bounds for the algorithm. In addition, we empirically evaluate PECO using datasets that utilize heterogeneous sensor platforms to perform activity recognition. These results indicate that not only can activity recognition algorithms transfer important information to new sensor platforms, but any number of platforms can work together as colleagues to boost performance.

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

PubMed Central

Zhu, Zhichuan; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified. PMID:29853983

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO.

PubMed

Li, Yang; Zhu, Zhichuan; Hou, Alin; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

NASA Astrophysics Data System (ADS)

Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y.; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J.; Adatia, Ian

2016-09-01

We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral.

Genome-wide identification of DNA methylation provides insights into the association of gene expression in rice exposed to pesticide atrazine

PubMed Central

Lu, Yi Chen; Feng, Sheng Jun; Zhang, Jing Jing; Luo, Fang; Zhang, Shuang; Yang, Hong

2016-01-01

Atrazine (ATR) is a pesticide widely used for controlling weeds for crop production. Crop contamination with ATR negatively affects crop growth and development. This study presents the first genome-wide single-base-resolution maps of DNA methylation in ATR-exposed rice. Widespread differences were identified in CG and non-CG methylation marks between the ATR-exposed and ATR-free (control) rice. Most of DNA methyltransferases, histone methyltransferases and DNA demethylase were differentially regulated by ATR. We found more genes hypermethylated than those hypomethylated in the regions of upstream, genebody and downstream under ATR exposure. A stringent group of 674 genes (p < 0.05, two-fold change) with a strong preference of differential expression in ATR-exposed rice was identified. Some of the genes were identified in a subset of loss of function mutants defective in DNA methylation/demethylation. Provision of 5-azacytidine (AZA, inhibitor of DNA methylation) promoted the rice growth and reduced ATR content. By UPLC/Q-TOF-MS/MS, 8 degraded products and 9 conjugates of ATR in AZA-treated rice were characterized. Two of them has been newly identified in this study. Our data show that ATR-induced changes in DNA methylation marks are possibly involved in an epigenetic mechanism associated with activation of specific genes responsible for ATR degradation and detoxification. PMID:26739616

78 FR 65183 - Airworthiness Directives; ATR-GIE Avions de Transport Régional Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-31

... Airworthiness Directives; ATR--GIE Avions de Transport R[eacute]gional Airplanes AGENCY: Federal Aviation... airworthiness directive (AD) for certain ATR--GIE Avions de Transport R[eacute]gional Model ATR72-101, -201... service information identified in this AD, contact ATR--GIE Avions de Transport R[eacute]gional, 1, All...

The distribution of atrazine (ATR) and ATR metabolites in the Wistar rat following gestational/lactational exposures

EPA Science Inventory

Gestational/lactational exposure to ATR is reported to alter reproductive/developmental function, yet our understanding of the transfer of ATR and/or its metabolites from the dam to the fetus/offspring is limited. Previously we examined the lactational transfer of CI4-ATR, but sp...

The Conference Proceedings of the 2001 Air Transport Research Society (ATRS) of the WCTR Society. Volume 1

NASA Technical Reports Server (NTRS)

Lee, Yeong-Heok (Editor); Bowen, Brent D. (Editor); Tarry, Scott E. (Editor)

2001-01-01

The ATRS held its Annual conference at Jeju Island, Korea in July 2001. The conference was a success with nearly 140 participants including 70 presenters. This report contains presentations from Volume 1 on the following: Airline and Travel Agent Relationships in Asia;Benchmarking Aviation Safety in the Commercial Airline Industry;Impact of Frequent Flyer Program on the Demand for Air Travel; Application of Genetic Algorithm on Airline Schedule;The Effects of Dual Carrier Designation and Partial Liberalization: The Case of Canada;Defense of Air Carriers and Air Agencies in FAA Enforcement proceedin gs - Damage Control Before the Case Arises; Cost Incentives for Airline Mergers? - An examination on the cost impact of U.S. airline mergers and acquisitions;Airport Regulation, Airline Competition and Canada's Airport System; Airline Competition: The Case of Israel's Domestic Doupoly; Non-Financial Indicators of Airline Distress: A Conceptual Approach;and Airport Privatization: An Empirical Analysis of Financial and Operational Efficiency.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

PubMed

Miaw, Carolina Sheng Whei; Assis, Camila; Silva, Alessandro Rangel Carolino Sales; Cunha, Maria Luísa; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho

2018-07-15

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated. Copyright © 2018 Elsevier Ltd. All rights reserved.

A novel speech processing algorithm based on harmonicity cues in cochlear implant

NASA Astrophysics Data System (ADS)

Wang, Jian; Chen, Yousheng; Zhang, Zongping; Chen, Yan; Zhang, Weifeng

2017-08-01

This paper proposed a novel speech processing algorithm in cochlear implant, which used harmonicity cues to enhance tonal information in Mandarin Chinese speech recognition. The input speech was filtered by a 4-channel band-pass filter bank. The frequency ranges for the four bands were: 300-621, 621-1285, 1285-2657, and 2657-5499 Hz. In each pass band, temporal envelope and periodicity cues (TEPCs) below 400 Hz were extracted by full wave rectification and low-pass filtering. The TEPCs were modulated by a sinusoidal carrier, the frequency of which was fundamental frequency (F0) and its harmonics most close to the center frequency of each band. Signals from each band were combined together to obtain an output speech. Mandarin tone, word, and sentence recognition in quiet listening conditions were tested for the extensively used continuous interleaved sampling (CIS) strategy and the novel F0-harmonic algorithm. Results found that the F0-harmonic algorithm performed consistently better than CIS strategy in Mandarin tone, word, and sentence recognition. In addition, sentence recognition rate was higher than word recognition rate, as a result of contextual information in the sentence. Moreover, tone 3 and 4 performed better than tone 1 and tone 2, due to the easily identified features of the former. In conclusion, the F0-harmonic algorithm could enhance tonal information in cochlear implant speech processing due to the use of harmonicity cues, thereby improving Mandarin tone, word, and sentence recognition. Further study will focus on the test of the F0-harmonic algorithm in noisy listening conditions.

77 FR 67557 - Special Conditions: ATR-GIE Avions de Transport Regional, Models ATR42-500 and ATR72-212A...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-13

... to, or access by, external systems and networks may result in security vulnerabilities to the... configuration may allow the exploitation of network security vulnerabilities resulting in intentional or..., Models ATR42-500 and ATR72-212A Airplanes; Aircraft Electronic System Security Protection From...

Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)

NASA Astrophysics Data System (ADS)

Iqtait, M.; Mohamad, F. S.; Mamat, M.

2018-03-01

Biometric is a pattern recognition system which is used for automatic recognition of persons based on characteristics and features of an individual. Face recognition with high recognition rate is still a challenging task and usually accomplished in three phases consisting of face detection, feature extraction, and expression classification. Precise and strong location of trait point is a complicated and difficult issue in face recognition. Cootes proposed a Multi Resolution Active Shape Models (ASM) algorithm, which could extract specified shape accurately and efficiently. Furthermore, as the improvement of ASM, Active Appearance Models algorithm (AAM) is proposed to extracts both shape and texture of specified object simultaneously. In this paper we give more details about the two algorithms and give the results of experiments, testing their performance on one dataset of faces. We found that the ASM is faster and gains more accurate trait point location than the AAM, but the AAM gains a better match to the texture.

Infrared vehicle recognition using unsupervised feature learning based on K-feature

NASA Astrophysics Data System (ADS)

Lin, Jin; Tan, Yihua; Xia, Haijiao; Tian, Jinwen

2018-02-01

Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. The infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. Then, the unsupervised feature learning based on K-feature, which is generated by Kmeans clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.

Wide-threat detection: recognition of adversarial missions and activity patterns in Empire Challenge 2009

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Shabarekh, Charlotte; Furjanic, Caitlin

2011-06-01

In this paper, we present results of adversarial activity recognition using data collected in the Empire Challenge (EC 09) exercise. The EC09 experiment provided an opportunity to evaluate our probabilistic spatiotemporal mission recognition algorithms using the data from live air-born and ground sensors. Using ambiguous and noisy data about locations of entities and motion events on the ground, the algorithms inferred the types and locations of OPFOR activities, including reconnaissance, cache runs, IED emplacements, logistics, and planning meetings. In this paper, we present detailed summary of the validation study and recognition accuracy results. Our algorithms were able to detect locations and types of over 75% of hostile activities in EC09 while producing 25% false alarms.

An adaptive deep Q-learning strategy for handwritten digit recognition.

PubMed

Qiao, Junfei; Wang, Gongming; Li, Wenjing; Chen, Min

2018-02-22

Handwritten digits recognition is a challenging problem in recent years. Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further improved. In this paper, an adaptive deep Q-learning strategy is proposed to improve accuracy and shorten running time for handwritten digit recognition. The adaptive deep Q-learning strategy combines the feature-extracting capability of deep learning and the decision-making of reinforcement learning to form an adaptive Q-learning deep belief network (Q-ADBN). First, Q-ADBN extracts the features of original images using an adaptive deep auto-encoder (ADAE), and the extracted features are considered as the current states of Q-learning algorithm. Second, Q-ADBN receives Q-function (reward signal) during recognition of the current states, and the final handwritten digits recognition is implemented by maximizing the Q-function using Q-learning algorithm. Finally, experimental results from the well-known MNIST dataset show that the proposed Q-ADBN has a superiority to other similar methods in terms of accuracy and running time. Copyright © 2018 Elsevier Ltd. All rights reserved.

Appearance-based face recognition and light-fields.

PubMed

Gross, Ralph; Matthews, Iain; Baker, Simon

2004-04-01

Arguably the most important decision to be made when developing an object recognition algorithm is selecting the scene measurements or features on which to base the algorithm. In appearance-based object recognition, the features are chosen to be the pixel intensity values in an image of the object. These pixel intensities correspond directly to the radiance of light emitted from the object along certain rays in space. The set of all such radiance values over all possible rays is known as the plenoptic function or light-field. In this paper, we develop a theory of appearance-based object recognition from light-fields. This theory leads directly to an algorithm for face recognition across pose that uses as many images of the face as are available, from one upwards. All of the pixels, whichever image they come from, are treated equally and used to estimate the (eigen) light-field of the object. The eigen light-field is then used as the set of features on which to base recognition, analogously to how the pixel intensities are used in appearance-based face and object recognition.

A Fault Recognition System for Gearboxes of Wind Turbines

NASA Astrophysics Data System (ADS)

Yang, Zhiling; Huang, Haiyue; Yin, Zidong

2017-12-01

Costs of maintenance and loss of power generation caused by the faults of wind turbines gearboxes are the main components of operation costs for a wind farm. Therefore, the technology of condition monitoring and fault recognition for wind turbines gearboxes is becoming a hot topic. A condition monitoring and fault recognition system (CMFRS) is presented for CBM of wind turbines gearboxes in this paper. The vibration signals from acceleration sensors at different locations of gearbox and the data from supervisory control and data acquisition (SCADA) system are collected to CMFRS. Then the feature extraction and optimization algorithm is applied to these operational data. Furthermore, to recognize the fault of gearboxes, the GSO-LSSVR algorithm is proposed, combining the least squares support vector regression machine (LSSVR) with the Glowworm Swarm Optimization (GSO) algorithm. Finally, the results show that the fault recognition system used in this paper has a high rate for identifying three states of wind turbines’ gears; besides, the combination of date features can affect the identifying rate and the selection optimization algorithm presented in this paper can get a pretty good date feature subset for the fault recognition.

Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session testing

PubMed Central

St. Hilaire, Melissa A.; Sullivan, Jason P.; Anderson, Clare; Cohen, Daniel A.; Barger, Laura K.; Lockley, Steven W.; Klerman, Elizabeth B.

2012-01-01

There is currently no “gold standard” marker of cognitive performance impairment resulting from sleep loss. We utilized pattern recognition algorithms to determine which features of data collected under controlled laboratory conditions could most reliably identify cognitive performance impairment in response to sleep loss using data from only one testing session, such as would occur in the “real world” or field conditions. A training set for testing the pattern recognition algorithms was developed using objective Psychomotor Vigilance Task (PVT) and subjective Karolinska Sleepiness Scale (KSS) data collected from laboratory studies during which subjects were sleep deprived for 26 – 52 hours. The algorithm was then tested in data from both laboratory and field experiments. The pattern recognition algorithm was able to identify performance impairment with a single testing session in individuals studied under laboratory conditions using PVT, KSS, length of time awake and time of day information with sensitivity and specificity as high as 82%. When this algorithm was tested on data collected under real-world conditions from individuals whose data were not in the training set, accuracy of predictions for individuals categorized with low performance impairment were as high as 98%. Predictions for medium and severe performance impairment were less accurate. We conclude that pattern recognition algorithms may be a promising method for identifying performance impairment in individuals using only current information about the individual’s behavior. Single testing features (e.g., number of PVT lapses) with high correlation with performance impairment in the laboratory setting may not be the best indicators of performance impairment under real-world conditions. Pattern recognition algorithms should be further tested for their ability to be used in conjunction with other assessments of sleepiness in real-world conditions to quantify performance impairment in response to sleep loss. PMID:22959616

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

PubMed

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

Fast and accurate face recognition based on image compression

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Blasch, Erik

2017-05-01

Image compression is desired for many image-related applications especially for network-based applications with bandwidth and storage constraints. The face recognition community typical reports concentrate on the maximal compression rate that would not decrease the recognition accuracy. In general, the wavelet-based face recognition methods such as EBGM (elastic bunch graph matching) and FPB (face pattern byte) are of high performance but run slowly due to their high computation demands. The PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) algorithms run fast but perform poorly in face recognition. In this paper, we propose a novel face recognition method based on standard image compression algorithm, which is termed as compression-based (CPB) face recognition. First, all gallery images are compressed by the selected compression algorithm. Second, a mixed image is formed with the probe and gallery images and then compressed. Third, a composite compression ratio (CCR) is computed with three compression ratios calculated from: probe, gallery and mixed images. Finally, the CCR values are compared and the largest CCR corresponds to the matched face. The time cost of each face matching is about the time of compressing the mixed face image. We tested the proposed CPB method on the "ASUMSS face database" (visible and thermal images) from 105 subjects. The face recognition accuracy with visible images is 94.76% when using JPEG compression. On the same face dataset, the accuracy of FPB algorithm was reported as 91.43%. The JPEG-compressionbased (JPEG-CPB) face recognition is standard and fast, which may be integrated into a real-time imaging device.

A Palmprint Recognition Algorithm Using Phase-Only Correlation

NASA Astrophysics Data System (ADS)

Ito, Koichi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper presents a palmprint recognition algorithm using Phase-Only Correlation (POC). The use of phase components in 2D (two-dimensional) discrete Fourier transforms of palmprint images makes it possible to achieve highly robust image registration and matching. In the proposed algorithm, POC is used to align scaling, rotation and translation between two palmprint images, and evaluate similarity between them. Experimental evaluation using a palmprint image database clearly demonstrates efficient matching performance of the proposed algorithm.

Simulation and performance of an artificial retina for 40 MHz track reconstruction

DOE PAGES

Abba, A.; Bedeschi, F.; Citterio, M.; ...

2015-03-05

We present the results of a detailed simulation of the artificial retina pattern-recognition algorithm, designed to reconstruct events with hundreds of charged-particle tracks in pixel and silicon detectors at LHCb with LHC crossing frequency of 40 MHz. Performances of the artificial retina algorithm are assessed using the official Monte Carlo samples of the LHCb experiment. We found performances for the retina pattern-recognition algorithm comparable with the full LHCb reconstruction algorithm.

The ATM and Rad3-Related (ATR) Protein Kinase Pathway Is Activated by Herpes Simplex Virus 1 and Required for Efficient Viral Replication.

PubMed

Edwards, Terri G; Bloom, David C; Fisher, Chris

2018-03-15

The ATM and Rad3-related (ATR) protein kinase and its downstream effector Chk1 are key sensors and organizers of the DNA damage response (DDR) to a variety of insults. Previous studies of herpes simplex virus 1 (HSV-1) showed no evidence for activation of the ATR pathway. Here we demonstrate that both Chk1 and ATR were phosphorylated by 3 h postinfection (h.p.i.). Activation of ATR and Chk1 was observed using 4 different HSV-1 strains in multiple cell types, while a specific ATR inhibitor blocked activation. Mechanistic studies point to early viral gene expression as a key trigger for ATR activation. Both pATR and pChk1 localized to the nucleus within viral replication centers, or associated with their periphery, by 3 h.p.i. Significant levels of pATR and pChk1 were also detected in the cytoplasm, where they colocalized with ICP4 and ICP0. Proximity ligation assays confirmed that pATR and pChk1 were closely and specifically associated with ICP4 and ICP0 in both the nucleus and cytoplasm by 3 h.p.i., but not with ICP8 or ICP27, presumably in a multiprotein complex. Chemically distinct ATR and Chk1 inhibitors blocked HSV-1 replication and infectious virion production, while inhibitors of ATM, Chk2, and DNA-dependent protein kinase (DNA-PK) did not. Together our data show that HSV-1 activates the ATR pathway at early stages of infection and that ATR and Chk1 kinase activities play important roles in HSV-1 replication fitness. These findings indicate that the ATR pathway may provide insight for therapeutic approaches. IMPORTANCE Viruses have evolved complex associations with cellular DNA damage response (DDR) pathways, which sense troublesome DNA structures formed during infection. The first evidence for activation of the ATR pathway by HSV-1 is presented. ATR is activated, and its downstream target Chk1 is robustly phosphorylated, during early stages of infection. Both activated proteins are found in the nucleus associated with viral replication compartments and in the cytoplasm associated with viral proteins. We also demonstrate that both ATR and Chk1 kinase activities are important for viral replication. The findings suggest that HSV-1 activates ATR and Chk1 during early stages of infection and utilizes the enzymes to promote its own replication. The observation may be exploitable for antiviral approaches. Copyright © 2018 American Society for Microbiology.

A Shld1-controlled POT1a provides support for repression of ATR signaling at telomeres through RPA exclusion.

PubMed

Gong, Yi; de Lange, Titia

2010-11-12

We previously proposed that POT1 prevents ATR signaling at telomeres by excluding RPA from the single-stranded TTAGGG repeats. Here, we use a Shld1-stabilized degron-POT1a fusion (DD-POT1a) to study the telomeric ATR kinase response. In the absence of Shld1, DD-POT1a degradation resulted in rapid and reversible activation of the ATR pathway in G1 and S/G2. ATR signaling was abrogated by shRNAs to ATR and TopBP1, but shRNAs to the ATM kinase or DNA-PKcs did not affect the telomere damage response. Importantly, ATR signaling in G1 and S/G2 was reduced by shRNAs to RPA. In S/G2, RPA was readily detectable at dysfunctional telomeres, and both POT1a and POT1b were required to exclude RPA and prevent ATR activation. In G1, the accumulation of RPA at dysfunctional telomeres was strikingly less, and POT1a was sufficient to repress ATR signaling. These results support an RPA exclusion model for the repression of ATR signaling at telomeres. Copyright © 2010 Elsevier Inc. All rights reserved.

ATR localizes to the photoreceptor connecting cilium and deficiency leads to severe photoreceptor degeneration in mice.

PubMed

Valdés-Sánchez, Lourdes; De la Cerda, Berta; Diaz-Corrales, Francisco J; Massalini, Simone; Chakarova, Christina F; Wright, Alan F; Bhattacharya, Shomi S

2013-04-15

Ataxia-telangiectasia and Rad3 (ATR), a sensor of DNA damage, is associated with the regulation and control of cell division. ATR deficit is known to cause Seckel syndrome, characterized by severe proportionate short stature and microcephaly. We used a mouse model for Seckel disease to study the effect of ATR deficit on retinal development and function and we have found a new role for ATR, which is critical for the postnatal development of the photoreceptor (PR) layer in mouse retina. The structural and functional characterization of the ATR(+/s) mouse retinas displayed a specific, severe and early degeneration of rod and cone cells resembling some characteristics of human retinal degenerations. A new localization of ATR in the cilia of PRs and the fact that mutant mice have shorter cilia suggests that the PR degeneration here described results from a ciliary defect.

ATR prohibits replication catastrophe by preventing global exhaustion of RPA.

PubMed

Toledo, Luis Ignacio; Altmeyer, Matthias; Rask, Maj-Britt; Lukas, Claudia; Larsen, Dorthe Helena; Povlsen, Lou Klitgaard; Bekker-Jensen, Simon; Mailand, Niels; Bartek, Jiri; Lukas, Jiri

2013-11-21

ATR, activated by replication stress, protects replication forks locally and suppresses origin firing globally. Here, we show that these functions of ATR are mechanistically coupled. Although initially stable, stalled forks in ATR-deficient cells undergo nucleus-wide breakage after unscheduled origin firing generates an excess of single-stranded DNA that exhausts the nuclear pool of RPA. Partial reduction of RPA accelerated fork breakage, and forced elevation of RPA was sufficient to delay such "replication catastrophe" even in the absence of ATR activity. Conversely, unscheduled origin firing induced breakage of stalled forks even in cells with active ATR. Thus, ATR-mediated suppression of dormant origins shields active forks against irreversible breakage via preventing exhaustion of nuclear RPA. This study elucidates how replicating genomes avoid destabilizing DNA damage. Because cancer cells commonly feature intrinsically high replication stress, this study also provides a molecular rationale for their hypersensitivity to ATR inhibitors. Copyright © 2013 Elsevier Inc. All rights reserved.

DCL System Research Using Advanced Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals

DTIC Science & Technology

2012-09-30

recognition. Algorithm design and statistical analysis and feature analysis. Post -Doctoral Associate, Cornell University, Bioacoustics Research...short. The HPC-ADA was designed based on fielded systems [1-4, 6] that offer a variety of desirable attributes, specifically dynamic resource...The software package was designed to utilize parallel and distributed processing for running recognition and other advanced algorithms. DeLMA

Using an Improved SIFT Algorithm and Fuzzy Closed-Loop Control Strategy for Object Recognition in Cluttered Scenes

PubMed Central

Nie, Haitao; Long, Kehui; Ma, Jun; Yue, Dan; Liu, Jinguo

2015-01-01

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes. PMID:25714094

False match elimination for face recognition based on SIFT algorithm

NASA Astrophysics Data System (ADS)

Gu, Xuyuan; Shi, Ping; Shao, Meide

2011-06-01

The SIFT (Scale Invariant Feature Transform) is a well known algorithm used to detect and describe local features in images. It is invariant to image scale, rotation and robust to the noise and illumination. In this paper, a novel method used for face recognition based on SIFT is proposed, which combines the optimization of SIFT, mutual matching and Progressive Sample Consensus (PROSAC) together and can eliminate the false matches of face recognition effectively. Experiments on ORL face database show that many false matches can be eliminated and better recognition rate is achieved.

An evaluation of talker localization based on direction of arrival estimation and statistical sound source identification

NASA Astrophysics Data System (ADS)

Nishiura, Takanobu; Nakamura, Satoshi

2002-11-01

It is very important to capture distant-talking speech for a hands-free speech interface with high quality. A microphone array is an ideal candidate for this purpose. However, this approach requires localizing the target talker. Conventional talker localization algorithms in multiple sound source environments not only have difficulty localizing the multiple sound sources accurately, but also have difficulty localizing the target talker among known multiple sound source positions. To cope with these problems, we propose a new talker localization algorithm consisting of two algorithms. One is DOA (direction of arrival) estimation algorithm for multiple sound source localization based on CSP (cross-power spectrum phase) coefficient addition method. The other is statistical sound source identification algorithm based on GMM (Gaussian mixture model) for localizing the target talker position among localized multiple sound sources. In this paper, we particularly focus on the talker localization performance based on the combination of these two algorithms with a microphone array. We conducted evaluation experiments in real noisy reverberant environments. As a result, we confirmed that multiple sound signals can be identified accurately between ''speech'' or ''non-speech'' by the proposed algorithm. [Work supported by ATR, and MEXT of Japan.

Implementation and preliminary evaluation of 'C-tone': A novel algorithm to improve lexical tone recognition in Mandarin-speaking cochlear implant users.

PubMed

Ping, Lichuan; Wang, Ningyuan; Tang, Guofang; Lu, Thomas; Yin, Li; Tu, Wenhe; Fu, Qian-Jie

2017-09-01

Because of limited spectral resolution, Mandarin-speaking cochlear implant (CI) users have difficulty perceiving fundamental frequency (F0) cues that are important to lexical tone recognition. To improve Mandarin tone recognition in CI users, we implemented and evaluated a novel real-time algorithm (C-tone) to enhance the amplitude contour, which is strongly correlated with the F0 contour. The C-tone algorithm was implemented in clinical processors and evaluated in eight users of the Nurotron NSP-60 CI system. Subjects were given 2 weeks of experience with C-tone. Recognition of Chinese tones, monosyllables, and disyllables in quiet was measured with and without the C-tone algorithm. Subjective quality ratings were also obtained for C-tone. After 2 weeks of experience with C-tone, there were small but significant improvements in recognition of lexical tones, monosyllables, and disyllables (P < 0.05 in all cases). Among lexical tones, the largest improvements were observed for Tone 3 (falling-rising) and the smallest for Tone 4 (falling). Improvements with C-tone were greater for disyllables than for monosyllables. Subjective quality ratings showed no strong preference for or against C-tone, except for perception of own voice, where C-tone was preferred. The real-time C-tone algorithm provided small but significant improvements for speech performance in quiet with no change in sound quality. Pre-processing algorithms to reduce noise and better real-time F0 extraction would improve the benefits of C-tone in complex listening environments. Chinese CI users' speech recognition in quiet can be significantly improved by modifying the amplitude contour to better resemble the F0 contour.

Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.

PubMed

Osowski, Stanislaw; Siwek, Krzysztof; Siroic, Robert

2011-03-01

This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system. This paper proposes the use of genetic algorithm. It was shown that application of the genetic algorithm is very efficient and allows to reduce significantly the total error of heartbeat recognition. This was confirmed by the numerical experiments performed on the MIT BIH Arrhythmia Database. Copyright © 2011 Elsevier Ltd. All rights reserved.

Real-time polarization imaging algorithm for camera-based polarization navigation sensors.

PubMed

Lu, Hao; Zhao, Kaichun; You, Zheng; Huang, Kaoli

2017-04-10

Biologically inspired polarization navigation is a promising approach due to its autonomous nature, high precision, and robustness. Many researchers have built point source-based and camera-based polarization navigation prototypes in recent years. Camera-based prototypes can benefit from their high spatial resolution but incur a heavy computation load. The pattern recognition algorithm in most polarization imaging algorithms involves several nonlinear calculations that impose a significant computation burden. In this paper, the polarization imaging and pattern recognition algorithms are optimized through reduction to several linear calculations by exploiting the orthogonality of the Stokes parameters without affecting precision according to the features of the solar meridian and the patterns of the polarized skylight. The algorithm contains a pattern recognition algorithm with a Hough transform as well as orientation measurement algorithms. The algorithm was loaded and run on a digital signal processing system to test its computational complexity. The test showed that the running time decreased to several tens of milliseconds from several thousand milliseconds. Through simulations and experiments, it was found that the algorithm can measure orientation without reducing precision. It can hence satisfy the practical demands of low computational load and high precision for use in embedded systems.

Enhanced detoxification and degradation of herbicide atrazine by a group of O-methyltransferases in rice.

PubMed

Lu, Yi Chen; Luo, Fang; Pu, Zhong Ji; Zhang, Shuang; Huang, Meng Tian; Yang, Hong

2016-12-01

Atrazine (ATR) as a toxic herbicide has become one of the seriously environmental contaminants worldwide due to its long-term intensive use in crop production. This study identified novel methyltransferases (MTs) involved in detoxification and degradation of ATR residues in rice plants. From a subset of MTs differentially expressed in ATR-exposed rice, forty-four O-methyltransferase genes were investigated. Total activities were significantly enhanced by ATR in rice tissues. To prove detoxifying capacity of the MTs in rice plants, two rice O-MTs (LOC_Os04g09604 and LOC_Os11g15040) were selected and transformed into yeast cells (Pichia pastoris X-33). The positive transformants accumulated less ATR and showed less toxicity. Using UPLC-TOF-MS/MS, ATR-degraded products in rice and yeast cells were characterized. A novel O-methylated-modified metabolite (atraton) and six other ATR-derivatives were detected. The topological interaction between LOC_Os04g09604 enzyme and its substrate was specially analyzed by homology modeling programs, which was well confirmed by the molecular docking analysis. The significance of the study is to provide a better understanding of mechanisms for the specific detoxification and degradation of ATR residues in rice growing in environmentally relevant ATR-contaminated soils and may hold a potential engineering perspective for generating ATR-resistant rice that helps to minimize ATR residues in crops. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modeling Microalgal Biosediment Formation Based on Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Monitoring.

PubMed

Ogburn, Zachary L; Vogt, Frank

2018-03-01

With increasing amounts of anthropogenic pollutants being released into ecosystems, it becomes ever more important to understand their fate and interactions with living organisms. Microalgae play an important ecological role as they are ubiquitous in marine environments and sequester inorganic pollutants which they transform into organic biomass. Of particular interest in this study is their role as a sink for atmospheric CO 2 , a greenhouse gas, and nitrate, one cause of harmful algal blooms. Novel chemometric hard-modeling methodologies have been developed for interpreting phytoplankton's chemical and physiological adaptations to changes in their growing environment. These methodologies will facilitate investigations of environmental impacts of anthropogenic pollutants on chemical and physiological properties of marine microalgae (here: Nannochloropsis oculata). It has been demonstrated that attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy can gain insights into both and this study only focuses on the latter. From time-series of spectra, the rate of microalgal biomass settling on top of a horizontal ATR element is derived which reflects several of phytoplankton's physiological parameters such as growth rate, cell concentrations, cell size, and buoyancy. In order to assess environmental impacts on such parameters, microalgae cultures were grown under 25 different chemical scenarios covering 200-600 ppm atmospheric CO 2 and 0.35-0.75 mM dissolved NO 3 - . After recording time-series of ATR FT-IR spectra, a multivariate curve resolution-alternating least squares (MCR-ALS) algorithm extracted spectroscopic and time profiles from each data set. From the time profiles, it was found that in the considered concentration ranges only NO 3 - has an impact on the cells' physiological properties. In particular, the cultures' growth rate has been influenced by the ambient chemical conditions. Thus, the presented spectroscopic + chemometric methodology enables investigating the link between chemical conditions in a marine ecosystem and their consequences for phytoplankton living in it.

Approximated mutual information training for speech recognition using myoelectric signals.

PubMed

Guo, Hua J; Chan, A D C

2006-01-01

A new training algorithm called the approximated maximum mutual information (AMMI) is proposed to improve the accuracy of myoelectric speech recognition using hidden Markov models (HMMs). Previous studies have demonstrated that automatic speech recognition can be performed using myoelectric signals from articulatory muscles of the face. Classification of facial myoelectric signals can be performed using HMMs that are trained using the maximum likelihood (ML) algorithm; however, this algorithm maximizes the likelihood of the observations in the training sequence, which is not directly associated with optimal classification accuracy. The AMMI training algorithm attempts to maximize the mutual information, thereby training the HMMs to optimize their parameters for discrimination. Our results show that AMMI training consistently reduces the error rates compared to these by the ML training, increasing the accuracy by approximately 3% on average.

ATR- and ATM-Mediated DNA Damage Response Is Dependent on Excision Repair Assembly during G1 but Not in S Phase of Cell Cycle.

PubMed

Ray, Alo; Blevins, Chessica; Wani, Gulzar; Wani, Altaf A

2016-01-01

Cell cycle checkpoint is mediated by ATR and ATM kinases, as a prompt early response to a variety of DNA insults, and culminates in a highly orchestrated signal transduction cascade. Previously, we defined the regulatory role of nucleotide excision repair (NER) factors, DDB2 and XPC, in checkpoint and ATR/ATM-dependent repair pathway via ATR and ATM phosphorylation and recruitment to ultraviolet radiation (UVR)-induced damage sites. Here, we have dissected the molecular mechanisms of DDB2- and XPC- mediated regulation of ATR and ATM recruitment and activation upon UVR exposures. We show that the ATR and ATM activation and accumulation to UVR-induced damage not only depends on DDB2 and XPC, but also on the NER protein XPA, suggesting that the assembly of an active NER complex is essential for ATR and ATM recruitment. ATR and ATM localization and H2AX phosphorylation at the lesion sites occur as early as ten minutes in asynchronous as well as G1 arrested cells, showing that repair and checkpoint-mediated by ATR and ATM starts early upon UV irradiation. Moreover, our results demonstrated that ATR and ATM recruitment and H2AX phosphorylation are dependent on NER proteins in G1 phase, but not in S phase. We reasoned that in G1 the UVR-induced ssDNA gaps or processed ssDNA, and the bound NER complex promote ATR and ATM recruitment. In S phase, when the UV lesions result in stalled replication forks with long single-stranded DNA, ATR and ATM recruitment to these sites is regulated by different sets of proteins. Taken together, these results provide evidence that UVR-induced ATR and ATM recruitment and activation differ in G1 and S phases due to the existence of distinct types of DNA lesions, which promote assembly of different proteins involved in the process of DNA repair and checkpoint activation.

a Review on State-Of Face Recognition Approaches

NASA Astrophysics Data System (ADS)

Mahmood, Zahid; Muhammad, Nazeer; Bibi, Nargis; Ali, Tauseef

Automatic Face Recognition (FR) presents a challenging task in the field of pattern recognition and despite the huge research in the past several decades; it still remains an open research problem. This is primarily due to the variability in the facial images, such as non-uniform illuminations, low resolution, occlusion, and/or variation in poses. Due to its non-intrusive nature, the FR is an attractive biometric modality and has gained a lot of attention in the biometric research community. Driven by the enormous number of potential application domains, many algorithms have been proposed for the FR. This paper presents an overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases. We highlight the conditions of the image databases with regard to the recognition rate of each approach. This is useful as a quick research overview and for practitioners as well to choose an algorithm for their specified FR application. To provide a comprehensive survey, the paper divides the FR algorithms into three categories: (1) intensity-based, (2) video-based, and (3) 3D based FR algorithms. In each category, the most commonly used algorithms and their performance is reported on standard face databases and a brief critical discussion is carried out.

Toward open set recognition.

PubMed

Scheirer, Walter J; de Rezende Rocha, Anderson; Sapkota, Archana; Boult, Terrance E

2013-07-01

To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of "closed set" recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is "open set" recognition, where incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. This paper explores the nature of open set recognition and formalizes its definition as a constrained minimization problem. The open set recognition problem is not well addressed by existing algorithms because it requires strong generalization. As a step toward a solution, we introduce a novel "1-vs-set machine," which sculpts a decision space from the marginal distances of a 1-class or binary SVM with a linear kernel. This methodology applies to several different applications in computer vision where open set recognition is a challenging problem, including object recognition and face verification. We consider both in this work, with large scale cross-dataset experiments performed over the Caltech 256 and ImageNet sets, as well as face matching experiments performed over the Labeled Faces in the Wild set. The experiments highlight the effectiveness of machines adapted for open set evaluation compared to existing 1-class and binary SVMs for the same tasks.

Object Recognition and Localization: The Role of Tactile Sensors

PubMed Central

Aggarwal, Achint; Kirchner, Frank

2014-01-01

Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments. PMID:24553087

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors

PubMed Central

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-01-01

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms. PMID:26198233

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors.

PubMed

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-07-07

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms.

Requirement of ATR for maintenance of intestinal stem cells in aging Drosophila.

PubMed

Park, Joung-Sun; Na, Hyun-Jin; Pyo, Jung-Hoon; Jeon, Ho-Jun; Kim, Young-Shin; Yoo, Mi-Ae

2015-05-01

The stem cell genomic stability forms the basis for robust tissue homeostasis, particularly in high-turnover tissues. For the genomic stability, DNA damage response (DDR) is essential. This study was focused on the role of two major DDR-related factors, ataxia telangiectasia-mutated (ATM) and ATM- and RAD3-related (ATR) kinases, in the maintenance of intestinal stem cells (ISCs) in the adultDrosophila midgut. We explored the role of ATM and ATR, utilizing immunostaining with an anti-pS/TQ antibody as an indicator of ATM/ATR activation, γ-irradiation as a DNA damage inducer, and the UAS/GAL4 system for cell type-specific knockdown of ATM, ATR, or both during adulthood. The results showed that the pS/TQ signals got stronger with age and after oxidative stress. The pS/TQ signals were found to be more dependent on ATR rather than on ATM in ISCs/enteroblasts (EBs). Furthermore, an ISC/EB-specific knockdown of ATR, ATM, or both decreased the number of ISCs and oxidative stress-induced ISC proliferation. The phenotypic changes that were caused by the ATR knockdown were more pronounced than those caused by the ATM knockdown; however, our data indicate that ATR and ATM are both needed for ISC maintenance and proliferation; ATR seems to play a bigger role than does ATM.

SUMOylation of ATRIP potentiates DNA damage signaling by boosting multiple protein interactions in the ATR pathway

PubMed Central

Wu, Ching-Shyi; Ouyang, Jian; Mori, Eiichiro; Nguyen, Hai Dang; Maréchal, Alexandre; Hallet, Alexander; Chen, David J.; Zou, Lee

2014-01-01

The ATR (ATM [ataxia telangiectasia-mutated]- and Rad3-related) checkpoint is a crucial DNA damage signaling pathway. While the ATR pathway is known to transmit DNA damage signals through the ATR–Chk1 kinase cascade, whether post-translational modifications other than phosphorylation are important for this pathway remains largely unknown. Here, we show that protein SUMOylation plays a key role in the ATR pathway. ATRIP, the regulatory partner of ATR, is modified by SUMO2/3 at K234 and K289. An ATRIP mutant lacking the SUMOylation sites fails to localize to DNA damage and support ATR activation efficiently. Surprisingly, the ATRIP SUMOylation mutant is compromised in the interaction with a protein group, rather than a single protein, in the ATR pathway. Multiple ATRIP-interacting proteins, including ATR, RPA70, TopBP1, and the MRE11–RAD50–NBS1 complex, exhibit reduced binding to the ATRIP SUMOylation mutant in cells and display affinity for SUMO2 chains in vitro, suggesting that they bind not only ATRIP but also SUMO. Fusion of a SUMO2 chain to the ATRIP SUMOylation mutant enhances its interaction with the protein group and partially suppresses its localization and functional defects, revealing that ATRIP SUMOylation promotes ATR activation by providing a unique type of protein glue that boosts multiple protein interactions along the ATR pathway. PMID:24990965

Reconstitution of RPA-covered single-stranded DNA-activated ATR-Chk1 signaling.

PubMed

Choi, Jun-Hyuk; Lindsey-Boltz, Laura A; Kemp, Michael; Mason, Aaron C; Wold, Marc S; Sancar, Aziz

2010-08-03

ATR kinase is a critical upstream regulator of the checkpoint response to various forms of DNA damage. Previous studies have shown that ATR is recruited via its binding partner ATR-interacting protein (ATRIP) to replication protein A (RPA)-covered single-stranded DNA (RPA-ssDNA) generated at sites of DNA damage where ATR is then activated by TopBP1 to phosphorylate downstream targets including the Chk1 signal transducing kinase. However, this critical feature of the human ATR-initiated DNA damage checkpoint signaling has not been demonstrated in a defined system. Here we describe an in vitro checkpoint system in which RPA-ssDNA and TopBP1 are essential for phosphorylation of Chk1 by the purified ATR-ATRIP complex. Checkpoint defective RPA mutants fail to activate ATR kinase in this system, supporting the conclusion that this system is a faithful representation of the in vivo reaction. Interestingly, we find that an alternative form of RPA (aRPA), which does not support DNA replication, can substitute for the checkpoint function of RPA in vitro, thus revealing a potential role for aRPA in the activation of ATR kinase. We also find that TopBP1 is recruited to RPA-ssDNA in a manner dependent on ATRIP and that the N terminus of TopBP1 is required for efficient recruitment and activation of ATR kinase.

An effective approach for iris recognition using phase-based image matching.

PubMed

Miyazawa, Kazuyuki; Ito, Koichi; Aoki, Takafumi; Kobayashi, Koji; Nakajima, Hiroshi

2008-10-01

This paper presents an efficient algorithm for iris recognition using phase-based image matching--an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (versions 1.0 and 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. The 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art Digital Signal Processing (DSP) technology.

Theoretical Aspects of the Patterns Recognition Statistical Theory Used for Developing the Diagnosis Algorithms for Complicated Technical Systems

NASA Astrophysics Data System (ADS)

Obozov, A. A.; Serpik, I. N.; Mihalchenko, G. S.; Fedyaeva, G. A.

2017-01-01

In the article, the problem of application of the pattern recognition (a relatively young area of engineering cybernetics) for analysis of complicated technical systems is examined. It is shown that the application of a statistical approach for hard distinguishable situations could be the most effective. The different recognition algorithms are based on Bayes approach, which estimates posteriori probabilities of a certain event and an assumed error. Application of the statistical approach to pattern recognition is possible for solving the problem of technical diagnosis complicated systems and particularly big powered marine diesel engines.

Iris recognition based on key image feature extraction.

PubMed

Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

2008-01-01

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

Automatic speech recognition research at NASA-Ames Research Center

NASA Technical Reports Server (NTRS)

Coler, Clayton R.; Plummer, Robert P.; Huff, Edward M.; Hitchcock, Myron H.

1977-01-01

A trainable acoustic pattern recognizer manufactured by Scope Electronics is presented. The voice command system VCS encodes speech by sampling 16 bandpass filters with center frequencies in the range from 200 to 5000 Hz. Variations in speaking rate are compensated for by a compression algorithm that subdivides each utterance into eight subintervals in such a way that the amount of spectral change within each subinterval is the same. The recorded filter values within each subinterval are then reduced to a 15-bit representation, giving a 120-bit encoding for each utterance. The VCS incorporates a simple recognition algorithm that utilizes five training samples of each word in a vocabulary of up to 24 words. The recognition rate of approximately 85 percent correct for untrained speakers and 94 percent correct for trained speakers was not considered adequate for flight systems use. Therefore, the built-in recognition algorithm was disabled, and the VCS was modified to transmit 120-bit encodings to an external computer for recognition.

Mandarin Chinese Tone Identification in Cochlear Implants: Predictions from Acoustic Models

PubMed Central

Morton, Kenneth D.; Torrione, Peter A.; Throckmorton, Chandra S.; Collins, Leslie M.

2015-01-01

It has been established that current cochlear implants do not supply adequate spectral information for perception of tonal languages. Comprehension of a tonal language, such as Mandarin Chinese, requires recognition of lexical tones. New strategies of cochlear stimulation such as variable stimulation rate and current steering may provide the means of delivering more spectral information and thus may provide the auditory fine structure required for tone recognition. Several cochlear implant signal processing strategies are examined in this study, the continuous interleaved sampling (CIS) algorithm, the frequency amplitude modulation encoding (FAME) algorithm, and the multiple carrier frequency algorithm (MCFA). These strategies provide different types and amounts of spectral information. Pattern recognition techniques can be applied to data from Mandarin Chinese tone recognition tasks using acoustic models as a means of testing the abilities of these algorithms to transmit the changes in fundamental frequency indicative of the four lexical tones. The ability of processed Mandarin Chinese tones to be correctly classified may predict trends in the effectiveness of different signal processing algorithms in cochlear implants. The proposed techniques can predict trends in performance of the signal processing techniques in quiet conditions but fail to do so in noise. PMID:18706497

HWDA: A coherence recognition and resolution algorithm for hybrid web data aggregation

NASA Astrophysics Data System (ADS)

Guo, Shuhang; Wang, Jian; Wang, Tong

2017-09-01

Aiming at the object confliction recognition and resolution problem for hybrid distributed data stream aggregation, a distributed data stream object coherence solution technology is proposed. Firstly, the framework was defined for the object coherence conflict recognition and resolution, named HWDA. Secondly, an object coherence recognition technology was proposed based on formal language description logic and hierarchical dependency relationship between logic rules. Thirdly, a conflict traversal recognition algorithm was proposed based on the defined dependency graph. Next, the conflict resolution technology was prompted based on resolution pattern matching including the definition of the three types of conflict, conflict resolution matching pattern and arbitration resolution method. At last, the experiment use two kinds of web test data sets to validate the effect of application utilizing the conflict recognition and resolution technology of HWDA.

Online graphic symbol recognition using neural network and ARG matching

NASA Astrophysics Data System (ADS)

Yang, Bing; Li, Changhua; Xie, Weixing

2001-09-01

This paper proposes a novel method for on-line recognition of line-based graphic symbol. The input strokes are usually warped into a cursive form due to the sundry drawing style, and classifying them is very difficult. To deal with this, an ART-2 neural network is used to classify the input strokes. It has the advantages of high recognition rate, less recognition time and forming classes in a self-organized manner. The symbol recognition is achieved by an Attribute Relational Graph (ARG) matching algorithm. The ARG is very efficient for representing complex objects, but computation cost is very high. To over come this, we suggest a fast graph matching algorithm using symbol structure information. The experimental results show that the proposed method is effective for recognition of symbols with hierarchical structure.

Neural network for intelligent query of an FBI forensic database

NASA Astrophysics Data System (ADS)

Uvanni, Lee A.; Rainey, Timothy G.; Balasubramanian, Uma; Brettle, Dean W.; Weingard, Fred; Sibert, Robert W.; Birnbaum, Eric

1997-02-01

Examiner is an automated fired cartridge case identification system utilizing a dual-use neural network pattern recognition technology, called the statistical-multiple object detection and location system (S-MODALS) developed by Booz(DOT)Allen & Hamilton, Inc. in conjunction with Rome Laboratory. S-MODALS was originally designed for automatic target recognition (ATR) of tactical and strategic military targets using multisensor fusion [electro-optical (EO), infrared (IR), and synthetic aperture radar (SAR)] sensors. Since S-MODALS is a learning system readily adaptable to problem domains other than automatic target recognition, the pattern matching problem of microscopic marks for firearms evidence was analyzed using S-MODALS. The physics; phenomenology; discrimination and search strategies; robustness requirements; error level and confidence level propagation that apply to the pattern matching problem of military targets were found to be applicable to the ballistic domain as well. The Examiner system uses S-MODALS to rank a set of queried cartridge case images from the most similar to the least similar image in reference to an investigative fired cartridge case image. The paper presents three independent tests and evaluation studies of the Examiner system utilizing the S-MODALS technology for the Federal Bureau of Investigation.

A New Method of Facial Expression Recognition Based on SPE Plus SVM

NASA Astrophysics Data System (ADS)

Ying, Zilu; Huang, Mingwei; Wang, Zhen; Wang, Zhewei

A novel method of facial expression recognition (FER) is presented, which uses stochastic proximity embedding (SPE) for data dimension reduction, and support vector machine (SVM) for expression classification. The proposed algorithm is applied to Japanese Female Facial Expression (JAFFE) database for FER, better performance is obtained compared with some traditional algorithms, such as PCA and LDA etc.. The result have further proved the effectiveness of the proposed algorithm.

Container-code recognition system based on computer vision and deep neural networks

NASA Astrophysics Data System (ADS)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

Membership-degree preserving discriminant analysis with applications to face recognition.

PubMed

Yang, Zhangjing; Liu, Chuancai; Huang, Pu; Qian, Jianjun

2013-01-01

In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzy k-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

The program complex for vocal recognition

NASA Astrophysics Data System (ADS)

Konev, Anton; Kostyuchenko, Evgeny; Yakimuk, Alexey

2017-01-01

This article discusses the possibility of applying the algorithm of determining the pitch frequency for the note recognition problems. Preliminary study of programs-analogues were carried out for programs with function “recognition of the music”. The software package based on the algorithm for pitch frequency calculation was implemented and tested. It was shown that the algorithm allows recognizing the notes in the vocal performance of the user. A single musical instrument, a set of musical instruments, and a human voice humming a tune can be the sound source. The input file is initially presented in the .wav format or is recorded in this format from a microphone. Processing is performed by sequentially determining the pitch frequency and conversion of its values to the note. According to test results, modification of algorithms used in the complex was planned.

Alterations in ATR in nasal NK/T-cell lymphoma and chronic active Epstein-Barr virus infection.

PubMed

Liu, Angen; Takakuwa, Tetsuya; Luo, Wen-Juan; Fujita, Shigeki; Aozasa, Katsuyuki

2006-07-01

Nasal natural killer (NK)/T-cell lymphoma (NKTCL) and chronic active Epstein-Barr virus infection (CAEBV) are relatively frequent, especially in Asia, and are poor in prognosis. Both diseases are proliferative diseases of NK/T cells that show highly complicated karyotypes, suggesting the involvement of chromosomal instability. ATR is an important gene for DNA damage response and chromosomal stability. To evaluate the role of ATR gene alterations in the pathogenesis of NKTCL and CAEBV, the whole coding region of the ATR gene was examined in cell lines derived from NKTCL and CAEBV, as well as tumor samples from patients. ATR alterations were detected in two of eight NKTCL and in one of three CAEBV lines. Most aberrant transcripts observed were deletions resulting from aberrant splicing. ATR alterations were also detected in four of 10 NKTCL clinical samples. Both NKTCL and CAEBV cell lines with ATR alterations showed a delay or abrogation in repair of ionizing radiation-induced DNA double-strand breaks and ultraviolet-induced DNA single-strand breaks, and both exhibited a defect in p53 accumulation. These findings show that alterations in the ATR gene result in an abnormal response to DNA double-strand break and single-strand break repair, suggesting a role for ATR gene alterations in NKTCL lymphomagenesis.

Research on gait-based human identification

NASA Astrophysics Data System (ADS)

Li, Youguo

Gait recognition refers to automatic identification of individual based on his/her style of walking. This paper proposes a gait recognition method based on Continuous Hidden Markov Model with Mixture of Gaussians(G-CHMM). First, we initialize a Gaussian mix model for training image sequence with K-means algorithm, then train the HMM parameters using a Baum-Welch algorithm. These gait feature sequences can be trained and obtain a Continuous HMM for every person, therefore, the 7 key frames and the obtained HMM can represent each person's gait sequence. Finally, the recognition is achieved by Front algorithm. The experiments made on CASIA gait databases obtain comparatively high correction identification ratio and comparatively strong robustness for variety of bodily angle.

Artificial intelligence tools for pattern recognition

NASA Astrophysics Data System (ADS)

Acevedo, Elena; Acevedo, Antonio; Felipe, Federico; Avilés, Pedro

2017-06-01

In this work, we present a system for pattern recognition that combines the power of genetic algorithms for solving problems and the efficiency of the morphological associative memories. We use a set of 48 tire prints divided into 8 brands of tires. The images have dimensions of 200 x 200 pixels. We applied Hough transform to obtain lines as main features. The number of lines obtained is 449. The genetic algorithm reduces the number of features to ten suitable lines that give thus the 100% of recognition. Morphological associative memories were used as evaluation function. The selection algorithms were Tournament and Roulette wheel. For reproduction, we applied one-point, two-point and uniform crossover.

Atrazine Triggers Mitochondrial Dysfunction and Oxidative Stress in Quail ( Coturnix C. coturnix) Cerebrum via Activating Xenobiotic-Sensing Nuclear Receptors and Modulating Cytochrome P450 Systems.

PubMed

Lin, Jia; Zhao, Hua-Shan; Qin, Lei; Li, Xue-Nan; Zhang, Cong; Xia, Jun; Li, Jin-Long

2018-06-14

The residues from the widely used broad-spectrum environmental herbicide, atrazine (ATR), result in the exposure of nontarget organisms and persist as a global major public health hazard. ATR is neurotoxic and may cause adverse health effects in mammals, birds, and fishes. Nevertheless, the molecular mechanism of ATR induced neurotoxicity remains unclear. To assess the molecular mechanisms of ATR-induced cerebral toxicity through potential oxidative damage, quail were treated with ATR by oral gavage administration at doses of 0, 50, 250, and 500 mg/kg body weight daily for 45 days. Markedly, increases in the amount of swelling of neuronal cells, the percentage of mean damaged mitochondria, mitochondrial malformation, and mitochondrial vacuolar degeneration as well as decreases in the mitochondrial cristae and mitochondrial volume density were observed by light and electron microscopy in the cerebrum of quail. ATR induced toxicities in the expression of mitochondrial function-related genes and promoted oxidative damage, as indicated by effects on oxidative stress indices. These results indicated that ATR exposure can cause neurological disorders and cerebral injury. ATR may initiate apoptosis by activating Bcl-2, Bax, and Caspase3 protein expression but failed to induce autophagy (LC3B has not cleaved to LC3BI/II). Furthermore, ATR induced CYP-related enzymes metabolism disorders by activating the nuclear xenobiotic receptors response (NXRs including AHR, CAR, and PXR) and increased expression of several CYP isoforms (including CYP1B1 and CYP2C18) and thereby producing mitochondrial dysfunction. In this study, we observed ATR exposure resulted in oxidative stress and mitochondrial dysfunction by activating the NXR response and interfering the CYP450s homeostasis in quail cerebrum that supported the molecular mechanism of ATR induced cerebrum toxicity. In conclusion, these results provided new evidence on molecular mechanism of ATR induced neurotoxicity.

Exercise recognition for Kinect-based telerehabilitation.

PubMed

Antón, D; Goñi, A; Illarramendi, A

2015-01-01

An aging population and people's higher survival to diseases and traumas that leave physical consequences are challenging aspects in the context of an efficient health management. This is why telerehabilitation systems are being developed, to allow monitoring and support of physiotherapy sessions at home, which could reduce healthcare costs while also improving the quality of life of the users. Our goal is the development of a Kinect-based algorithm that provides a very accurate real-time monitoring of physical rehabilitation exercises and that also provides a friendly interface oriented both to users and physiotherapists. The two main constituents of our algorithm are the posture classification method and the exercises recognition method. The exercises consist of series of movements. Each movement is composed of an initial posture, a final posture and the angular trajectories of the limbs involved in the movement. The algorithm was designed and tested with datasets of real movements performed by volunteers. We also explain in the paper how we obtained the optimal values for the trade-off values for posture and trajectory recognition. Two relevant aspects of the algorithm were evaluated in our tests, classification accuracy and real-time data processing. We achieved 91.9% accuracy in posture classification and 93.75% accuracy in trajectory recognition. We also checked whether the algorithm was able to process the data in real-time. We found that our algorithm could process more than 20,000 postures per second and all the required trajectory data-series in real-time, which in practice guarantees no perceptible delays. Later on, we carried out two clinical trials with real patients that suffered shoulder disorders. We obtained an exercise monitoring accuracy of 95.16%. We present an exercise recognition algorithm that handles the data provided by Kinect efficiently. The algorithm has been validated in a real scenario where we have verified its suitability. Moreover, we have received a positive feedback from both users and the physiotherapists who took part in the tests.

SU-F-T-20: Novel Catheter Lumen Recognition Algorithm for Rapid Digitization

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

Dise, J; McDonald, D; Ashenafi, M

Purpose: Manual catheter recognition remains a time-consuming aspect of high-dose-rate brachytherapy (HDR) treatment planning. In this work, a novel catheter lumen recognition algorithm was created for accurate and rapid digitization. Methods: MatLab v8.5 was used to create the catheter recognition algorithm. Initially, the algorithm searches the patient CT dataset using an intensity based k-means filter designed to locate catheters. Once the catheters have been located, seed points are manually selected to initialize digitization of each catheter. From each seed point, the algorithm searches locally in order to automatically digitize the remaining catheter. This digitization is accomplished by finding pixels withmore » similar image curvature and divergence parameters compared to the seed pixel. Newly digitized pixels are treated as new seed positions, and hessian image analysis is used to direct the algorithm toward neighboring catheter pixels, and to make the algorithm insensitive to adjacent catheters that are unresolvable on CT, air pockets, and high Z artifacts. The algorithm was tested using 11 HDR treatment plans, including the Syed template, tandem and ovoid applicator, and multi-catheter lung brachytherapy. Digitization error was calculated by comparing manually determined catheter positions to those determined by the algorithm. Results: he digitization error was 0.23 mm ± 0.14 mm axially and 0.62 mm ± 0.13 mm longitudinally at the tip. The time of digitization, following initial seed placement was less than 1 second per catheter. The maximum total time required to digitize all tested applicators was 4 minutes (Syed template with 15 needles). Conclusion: This algorithm successfully digitizes HDR catheters for a variety of applicators with or without CT markers. The minimal axial error demonstrates the accuracy of the algorithm, and its insensitivity to image artifacts and challenging catheter positioning. Future work to automatically place initial seed positions would improve the algorithm speed.« less

The ATR Signaling Pathway Is Disabled during Infection with the Parvovirus Minute Virus of Mice

PubMed Central

Adeyemi, Richard O.

2014-01-01

ABSTRACT The ATR kinase has essential functions in maintenance of genome integrity in response to replication stress. ATR is recruited to RPA-coated single-stranded DNA at DNA damage sites via its interacting partner, ATRIP, which binds to the large subunit of RPA. ATR activation typically leads to activation of the Chk1 kinase among other substrates. We show here that, together with a number of other DNA repair proteins, both ATR and its associated protein, ATRIP, were recruited to viral nuclear replication compartments (autonomous parvovirus-associated replication [APAR] bodies) during replication of the single-stranded parvovirus minute virus of mice (MVM). Chk1, however, was not activated during MVM infection even though viral genomes bearing bound RPA, normally a potent trigger of ATR activation, accumulate in APAR bodies. Failure to activate Chk1 in response to MVM infection was likely due to our observation that Rad9 failed to associate with chromatin at MVM APAR bodies. Additionally, early in infection, prior to the onset of the virus-induced DNA damage response (DDR), stalling of the replication of MVM genomes with hydroxyurea (HU) resulted in Chk1 phosphorylation in a virus dose-dependent manner. However, upon establishment of full viral replication, MVM infection prevented activation of Chk1 in response to HU and various other drug treatments. Finally, ATR phosphorylation became undetectable upon MVM infection, and although virus infection induced RPA32 phosphorylation on serine 33, an ATR-associated phosphorylation site, this phosphorylation event could not be prevented by ATR depletion or inhibition. Together our results suggest that MVM infection disables the ATR signaling pathway. IMPORTANCE Upon infection, the parvovirus MVM activates a cellular DNA damage response that governs virus-induced cell cycle arrest and is required for efficient virus replication. ATM and ATR are major cellular kinases that coordinate the DNA damage response to diverse DNA damage stimuli. Although a significant amount has been discovered about ATM activation during parvovirus infection, involvement of the ATR pathway has been less studied. During MVM infection, Chk1, a major downstream target of ATR, is not detectably phosphorylated even though viral genomes bearing the bound cellular single-strand binding protein RPA, normally a potent trigger of ATR activation, accumulate in viral replication centers. ATR phosphorylation also became undetectable. In addition, upon establishment of full viral replication, MVM infection prevented activation of Chk1 in response to hydroxyurea and various other drug treatments. Our results suggest that MVM infection disables this important cellular signaling pathway. PMID:24965470

The ATR signaling pathway is disabled during infection with the parvovirus minute virus of mice.

PubMed

Adeyemi, Richard O; Pintel, David J

2014-09-01

The ATR kinase has essential functions in maintenance of genome integrity in response to replication stress. ATR is recruited to RPA-coated single-stranded DNA at DNA damage sites via its interacting partner, ATRIP, which binds to the large subunit of RPA. ATR activation typically leads to activation of the Chk1 kinase among other substrates. We show here that, together with a number of other DNA repair proteins, both ATR and its associated protein, ATRIP, were recruited to viral nuclear replication compartments (autonomous parvovirus-associated replication [APAR] bodies) during replication of the single-stranded parvovirus minute virus of mice (MVM). Chk1, however, was not activated during MVM infection even though viral genomes bearing bound RPA, normally a potent trigger of ATR activation, accumulate in APAR bodies. Failure to activate Chk1 in response to MVM infection was likely due to our observation that Rad9 failed to associate with chromatin at MVM APAR bodies. Additionally, early in infection, prior to the onset of the virus-induced DNA damage response (DDR), stalling of the replication of MVM genomes with hydroxyurea (HU) resulted in Chk1 phosphorylation in a virus dose-dependent manner. However, upon establishment of full viral replication, MVM infection prevented activation of Chk1 in response to HU and various other drug treatments. Finally, ATR phosphorylation became undetectable upon MVM infection, and although virus infection induced RPA32 phosphorylation on serine 33, an ATR-associated phosphorylation site, this phosphorylation event could not be prevented by ATR depletion or inhibition. Together our results suggest that MVM infection disables the ATR signaling pathway. Upon infection, the parvovirus MVM activates a cellular DNA damage response that governs virus-induced cell cycle arrest and is required for efficient virus replication. ATM and ATR are major cellular kinases that coordinate the DNA damage response to diverse DNA damage stimuli. Although a significant amount has been discovered about ATM activation during parvovirus infection, involvement of the ATR pathway has been less studied. During MVM infection, Chk1, a major downstream target of ATR, is not detectably phosphorylated even though viral genomes bearing the bound cellular single-strand binding protein RPA, normally a potent trigger of ATR activation, accumulate in viral replication centers. ATR phosphorylation also became undetectable. In addition, upon establishment of full viral replication, MVM infection prevented activation of Chk1 in response to hydroxyurea and various other drug treatments. Our results suggest that MVM infection disables this important cellular signaling pathway. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Computer analysis of ATR-FTIR spectra of paint samples for forensic purposes

NASA Astrophysics Data System (ADS)

Szafarska, Małgorzata; Woźniakiewicz, Michał; Pilch, Mariusz; Zięba-Palus, Janina; Kościelniak, Paweł

2009-04-01

A method of subtraction and normalization of IR spectra (MSN-IR) was developed and successfully applied to extract mathematically the pure paint spectrum from the spectrum of paint coat on different bases, both acquired by the Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) technique. The method consists of several stages encompassing several normalization and subtraction processes. The similarity of the spectrum obtained with the reference spectrum was estimated by means of the normalized Manhattan distance. The utility and performance of the method proposed were tested by examination of five different paints sprayed on plastic (polyester) foil and on fabric materials (cotton). It was found that the numerical algorithm applied is able - in contrast to other mathematical approaches conventionally used for the same aim - to reconstruct a pure paint IR spectrum effectively without a loss of chemical information provided. The approach allows the physical separation of a paint from a base to be avoided, hence a time and work-load of analysis to be considerably reduced. The results obtained prove that the method can be considered as a useful tool which can be applied to forensic purposes.

Facial Emotions Recognition using Gabor Transform and Facial Animation Parameters with Neural Networks

NASA Astrophysics Data System (ADS)

Harit, Aditya; Joshi, J. C., Col; Gupta, K. K.

2018-03-01

The paper proposed an automatic facial emotion recognition algorithm which comprises of two main components: feature extraction and expression recognition. The algorithm uses a Gabor filter bank on fiducial points to find the facial expression features. The resulting magnitudes of Gabor transforms, along with 14 chosen FAPs (Facial Animation Parameters), compose the feature space. There are two stages: the training phase and the recognition phase. Firstly, for the present 6 different emotions, the system classifies all training expressions in 6 different classes (one for each emotion) in the training stage. In the recognition phase, it recognizes the emotion by applying the Gabor bank to a face image, then finds the fiducial points, and then feeds it to the trained neural architecture.

Face recognition algorithm based on Gabor wavelet and locality preserving projections

NASA Astrophysics Data System (ADS)

Liu, Xiaojie; Shen, Lin; Fan, Honghui

2017-07-01

In order to solve the effects of illumination changes and differences of personal features on the face recognition rate, this paper presents a new face recognition algorithm based on Gabor wavelet and Locality Preserving Projections (LPP). The problem of the Gabor filter banks with high dimensions was solved effectively, and also the shortcoming of the LPP on the light illumination changes was overcome. Firstly, the features of global image information were achieved, which used the good spatial locality and orientation selectivity of Gabor wavelet filters. Then the dimensions were reduced by utilizing the LPP, which well-preserved the local information of the image. The experimental results shown that this algorithm can effectively extract the features relating to facial expressions, attitude and other information. Besides, it can reduce influence of the illumination changes and the differences in personal features effectively, which improves the face recognition rate to 99.2%.

Analysis of objects in binary images. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Leonard, Desiree M.

1991-01-01

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

A star recognition method based on the Adaptive Ant Colony algorithm for star sensors.

PubMed

Quan, Wei; Fang, Jiancheng

2010-01-01

A new star recognition method based on the Adaptive Ant Colony (AAC) algorithm has been developed to increase the star recognition speed and success rate for star sensors. This method draws circles, with the center of each one being a bright star point and the radius being a special angular distance, and uses the parallel processing ability of the AAC algorithm to calculate the angular distance of any pair of star points in the circle. The angular distance of two star points in the circle is solved as the path of the AAC algorithm, and the path optimization feature of the AAC is employed to search for the optimal (shortest) path in the circle. This optimal path is used to recognize the stellar map and enhance the recognition success rate and speed. The experimental results show that when the position error is about 50″, the identification success rate of this method is 98% while the Delaunay identification method is only 94%. The identification time of this method is up to 50 ms.

Automatic voice recognition using traditional and artificial neural network approaches

NASA Technical Reports Server (NTRS)

Botros, Nazeih M.

1989-01-01

The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time.

76 FR 47520 - Airworthiness Directives; ATR-GIE Avions de Transport Régional Model ATR42 and ATR72 Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-05

... [left-hand] elevator lower stop assembly was broken at the level of the angles, which may have prevented... Friday, except Federal holidays. For service information identified in this proposed AD, contact ATR-GIE... be available in the AD docket shortly after receipt. FOR FURTHER INFORMATION CONTACT: Tom Rodriguez...

ATR Performance Estimation Seed Program

DTIC Science & Technology

2015-09-28

to produce simulated MCM sonar data and demonstrate the impact of system, environmental, and target scattering effects on ATR detection...settings and achieving better understanding the relative impact of the factors influencing ATR performance. sonar, mine countermeasures, MCM , automatic...simulated MCM sonar data and demonstrate the impact of system, environmental, and target scattering effects on ATR detection/classification performance. The

A fingerprint classification algorithm based on combination of local and global information

NASA Astrophysics Data System (ADS)

Liu, Chongjin; Fu, Xiang; Bian, Junjie; Feng, Jufu

2011-12-01

Fingerprint recognition is one of the most important technologies in biometric identification and has been wildly applied in commercial and forensic areas. Fingerprint classification, as the fundamental procedure in fingerprint recognition, can sharply decrease the quantity for fingerprint matching and improve the efficiency of fingerprint recognition. Most fingerprint classification algorithms are based on the number and position of singular points. Because the singular points detecting method only considers the local information commonly, the classification algorithms are sensitive to noise. In this paper, we propose a novel fingerprint classification algorithm combining the local and global information of fingerprint. Firstly we use local information to detect singular points and measure their quality considering orientation structure and image texture in adjacent areas. Furthermore the global orientation model is adopted to measure the reliability of singular points group. Finally the local quality and global reliability is weighted to classify fingerprint. Experiments demonstrate the accuracy and effectivity of our algorithm especially for the poor quality fingerprint images.

Face recognition using total margin-based adaptive fuzzy support vector machines.

PubMed

Liu, Yi-Hung; Chen, Yen-Ting

2007-01-01

This paper presents a new classifier called total margin-based adaptive fuzzy support vector machines (TAF-SVM) that deals with several problems that may occur in support vector machines (SVMs) when applied to the face recognition. The proposed TAF-SVM not only solves the overfitting problem resulted from the outlier with the approach of fuzzification of the penalty, but also corrects the skew of the optimal separating hyperplane due to the very imbalanced data sets by using different cost algorithm. In addition, by introducing the total margin algorithm to replace the conventional soft margin algorithm, a lower generalization error bound can be obtained. Those three functions are embodied into the traditional SVM so that the TAF-SVM is proposed and reformulated in both linear and nonlinear cases. By using two databases, the Chung Yuan Christian University (CYCU) multiview and the facial recognition technology (FERET) face databases, and using the kernel Fisher's discriminant analysis (KFDA) algorithm to extract discriminating face features, experimental results show that the proposed TAF-SVM is superior to SVM in terms of the face-recognition accuracy. The results also indicate that the proposed TAF-SVM can achieve smaller error variances than SVM over a number of tests such that better recognition stability can be obtained.

Pattern recognition for passive polarimetric data using nonparametric classifiers

NASA Astrophysics Data System (ADS)

Thilak, Vimal; Saini, Jatinder; Voelz, David G.; Creusere, Charles D.

2005-08-01

Passive polarization based imaging is a useful tool in computer vision and pattern recognition. A passive polarization imaging system forms a polarimetric image from the reflection of ambient light that contains useful information for computer vision tasks such as object detection (classification) and recognition. Applications of polarization based pattern recognition include material classification and automatic shape recognition. In this paper, we present two target detection algorithms for images captured by a passive polarimetric imaging system. The proposed detection algorithms are based on Bayesian decision theory. In these approaches, an object can belong to one of any given number classes and classification involves making decisions that minimize the average probability of making incorrect decisions. This minimum is achieved by assigning an object to the class that maximizes the a posteriori probability. Computing a posteriori probabilities requires estimates of class conditional probability density functions (likelihoods) and prior probabilities. A Probabilistic neural network (PNN), which is a nonparametric method that can compute Bayes optimal boundaries, and a -nearest neighbor (KNN) classifier, is used for density estimation and classification. The proposed algorithms are applied to polarimetric image data gathered in the laboratory with a liquid crystal-based system. The experimental results validate the effectiveness of the above algorithms for target detection from polarimetric data.

Multispectral iris recognition based on group selection and game theory

NASA Astrophysics Data System (ADS)

Ahmad, Foysal; Roy, Kaushik

2017-05-01

A commercially available iris recognition system uses only a narrow band of the near infrared spectrum (700-900 nm) while iris images captured in the wide range of 405 nm to 1550 nm offer potential benefits to enhance recognition performance of an iris biometric system. The novelty of this research is that a group selection algorithm based on coalition game theory is explored to select the best patch subsets. In this algorithm, patches are divided into several groups based on their maximum contribution in different groups. Shapley values are used to evaluate the contribution of patches in different groups. Results show that this group selection based iris recognition

Wavelet decomposition based principal component analysis for face recognition using MATLAB

NASA Astrophysics Data System (ADS)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yu, Yongtao; Li, Jonathan; Wen, Chenglu; Guan, Haiyan; Luo, Huan; Wang, Cheng

2016-03-01

This paper presents a novel algorithm for detection and recognition of traffic signs in mobile laser scanning (MLS) data for intelligent transportation-related applications. The traffic sign detection task is accomplished based on 3-D point clouds by using bag-of-visual-phrases representations; whereas the recognition task is achieved based on 2-D images by using a Gaussian-Bernoulli deep Boltzmann machine-based hierarchical classifier. To exploit high-order feature encodings of feature regions, a deep Boltzmann machine-based feature encoder is constructed. For detecting traffic signs in 3-D point clouds, the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.956, 0.946, 0.907, and 0.951, respectively, on the four selected MLS datasets. For on-image traffic sign recognition, a recognition accuracy of 97.54% is achieved by using the proposed hierarchical classifier. Comparative studies with the existing traffic sign detection and recognition methods demonstrate that our algorithm obtains promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.

Indonesian Sign Language Number Recognition using SIFT Algorithm

NASA Astrophysics Data System (ADS)

Mahfudi, Isa; Sarosa, Moechammad; Andrie Asmara, Rosa; Azrino Gustalika, M.

2018-04-01

Indonesian sign language (ISL) is generally used for deaf individuals and poor people communication in communicating. They use sign language as their primary language which consists of 2 types of action: sign and finger spelling. However, not all people understand their sign language so that this becomes a problem for them to communicate with normal people. this problem also becomes a factor they are isolated feel from the social life. It needs a solution that can help them to be able to interacting with normal people. Many research that offers a variety of methods in solving the problem of sign language recognition based on image processing. SIFT (Scale Invariant Feature Transform) algorithm is one of the methods that can be used to identify an object. SIFT is claimed very resistant to scaling, rotation, illumination and noise. Using SIFT algorithm for Indonesian sign language recognition number result rate recognition to 82% with the use of a total of 100 samples image dataset consisting 50 sample for training data and 50 sample images for testing data. Change threshold value get affect the result of the recognition. The best value threshold is 0.45 with rate recognition of 94%.

Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2008-08-01

This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.

ATR architecture for multisensor fusion

NASA Astrophysics Data System (ADS)

Hamilton, Mark K.; Kipp, Teresa A.

1996-06-01

The work of the U.S. Army Research Laboratory (ARL) in the area of algorithms for the identification of static military targets in single-frame electro-optical (EO) imagery has demonstrated great potential in platform-based automatic target identification (ATI). In this case, the term identification is used to mean being able to tell the difference between two military vehicles -- e.g., the M60 from the T72. ARL's work includes not only single-sensor forward-looking infrared (FLIR) ATI algorithms, but also multi-sensor ATI algorithms. We briefly discuss ARL's hybrid model-based/data-learning strategy for ATI, which represents a significant step forward in ATI algorithm design. For example, in the case of single sensor FLIR it allows the human algorithm designer to build directly into the algorithm knowledge that can be adequately modeled at this time, such as the target geometry which directly translates into the target silhouette in the FLIR realm. In addition, it allows structure that is not currently well understood (i.e., adequately modeled) to be incorporated through automated data-learning algorithms, which in a FLIR directly translates into an internal thermal target structure signature. This paper shows the direct applicability of this strategy to both the single-sensor FLIR as well as the multi-sensor FLIR and laser radar.

Probabilistic Open Set Recognition

NASA Astrophysics Data System (ADS)

Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary support vector machines. Building from the success of statistical EVT based recognition methods such as PI-SVM and W-SVM on the open set problem, we present a new general supervised learning algorithm for multi-class classification and multi-class open set recognition called the Extreme Value Local Basis (EVLB). The design of this algorithm is motivated by the observation that extrema from known negative class distributions are the closest negative points to any positive sample during training, and thus should be used to define the parameters of a probabilistic decision model. In the EVLB, the kernel distribution for each positive training sample is estimated via an EVT distribution fit over the distances to the separating hyperplane between positive training sample and closest negative samples, with a subset of the overall positive training data retained to form a probabilistic decision boundary. Using this subset as a frame of reference, the probability of a sample at test time decreases as it moves away from the positive class. Possessing this property, the EVLB is well-suited to open set recognition problems where samples from unknown or novel classes are encountered at test. Our experimental evaluation shows that the EVLB provides a substantial improvement in scalability compared to standard radial basis function kernel machines, as well as P I-SVM and W-SVM, with improved accuracy in many cases. We evaluate our algorithm on open set variations of the standard visual learning benchmarks, as well as with an open subset of classes from Caltech 256 and ImageNet. Our experiments show that PI-SVM, WSVM and EVLB provide significant advances over the previous state-of-the-art solutions for the same tasks.

ATR inhibition broadly sensitizes ovarian cancer cells to chemotherapy independent of BRCA status

PubMed Central

Huntoon, Catherine J.; Flatten, Karen S.; Wahner Hendrickson, Andrea E.; Huehls, Amelia M.; Sutor, Shari L.; Kaufmann, Scott H.; Karnitz, Larry M.

2013-01-01

Replication stress and DNA damage activate the ATR-CHK1 checkpoint signaling pathway that licenses repair and cell survival processes. In this study, we examined the respective roles of the ATR and CHK1 kinases in ovarian cancer cells using genetic and pharmacological inhibitors of in combination with cisplatin, topotecan, gemcitabine and the poly(ADP-ribose)-polymerase (PARP) inhibitor veliparib (ABT-888), four agents with clinical activity in ovarian cancer. RNAi-mediated depletion or inhibition of ATR sensitized ovarian cancer cells to all four agents. In contrast, while cisplatin, topotecan and gemcitabine each activated CHK1, RNAi-mediated depletion or inhibition of this kinase in cells sensitized them only to gemcitabine. Unexpectedly, we found that neither the ATR kinase inhibitor VE-821 or the CHK1 inhibitor MK-8776 blocked ATR-mediated CHK1 phosphorylation or autophosphorylation, two commonly used readouts for inhibition of the ATR-CHK1 pathway. Instead, their ability to sensitize cells correlated with enhanced CDC25A levels. Additionally, we also found that VE-821 could further sensitize BRCA1-depleted cells to cisplatin, topotecan and veliparib beyond the potent sensitization already caused by their deficiency in homologous recombination. Taken together, our results established that ATR and CHK1 inhibitors differentially sensitize ovarian cancer cells to commonly used chemotherapy agents, and that CHK1 phosphorylation status may not offer a reliable marker for inhibition of the ATR-CHK1 pathway. A key implication of our work is the clinical rationale it provides to evaluate ATR inhibitors in combination with PARP inhibitors in BRCA1/2-deficient cells. PMID:23548269

ATR-X mutations cause impaired nuclear location and altered DNA binding properties of the XNP/ATR-X protein.

PubMed

Cardoso, C; Lutz, Y; Mignon, C; Compe, E; Depetris, D; Mattei, M G; Fontes, M; Colleaux, L

2000-10-01

Mutations in the XNP/ATR-X gene, located in Xq13.3, are associated with several X linked mental retardation syndromes, the best known being alpha thalassaemia with mental retardation (ATR-X). The XNP/ATR-X protein belongs to the family of SWI/SNF DNA helicases and contains three C2-C2 type zinc fingers of unknown function. Previous studies have shown that 65% of mutations of XNP have been found within the zinc finger domain (encoded by exons 7, 8, and the beginning of exon 9) while 35% of the mutations have been found in the helicase domain extending over 3 kb at the C-terminus of the protein. Although different types of mutations have been identified, no specific genotype-phenotype correlation has been found, suggesting that gene alteration leads to a loss of function irrespective of mutation type. Our aims were to understand the function of the XNP/ATR-X protein better, with specific attention to the functional consequences of mutations to the zinc finger domain. We used monoclonal antibodies directed against the XNP/ATR-X protein and performed immunocytochemical and western blot analyses, which showed altered or absent XNP/ATR-X expression in cells of affected patients. In addition, we used in vitro experiments to show that the zinc finger domain can mediate double stranded DNA binding and found that the DNA binding capacity of mutant forms in ATR-X patients is severely reduced. These data provide insights into the understanding of the functional significance of XNP/ATR-X mutations.

ATM and ATR play complementary roles in the behavior of excitatory and inhibitory vesicle populations.

PubMed

Cheng, Aifang; Zhao, Teng; Tse, Kai-Hei; Chow, Hei-Man; Cui, Yong; Jiang, Liwen; Du, Shengwang; Loy, Michael M T; Herrup, Karl

2018-01-09

ATM (ataxia-telangiectasia mutated) and ATR (ATM and Rad3-related) are large PI3 kinases whose human mutations result in complex syndromes that include a compromised DNA damage response (DDR) and prominent nervous system phenotypes. Both proteins are nuclear-localized in keeping with their DDR functions, yet both are also found in cytoplasm, including on neuronal synaptic vesicles. In ATM- or ATR-deficient neurons, spontaneous vesicle release is reduced, but a drop in ATM or ATR level also slows FM4-64 dye uptake. In keeping with this, both proteins bind to AP-2 complex components as well as to clathrin, suggesting roles in endocytosis and vesicle recycling. The two proteins play complementary roles in the DDR; ATM is engaged in the repair of double-strand breaks, while ATR deals mainly with single-strand damage. Unexpectedly, this complementarity extends to these proteins' synaptic function as well. Superresolution microscopy and coimmunoprecipitation reveal that ATM associates exclusively with excitatory (VGLUT1 + ) vesicles, while ATR associates only with inhibitory (VGAT + ) vesicles. The levels of ATM and ATR respond to each other; when ATM is deficient, ATR levels rise, and vice versa. Finally, blocking NMDA, but not GABA, receptors causes ATM levels to rise while ATR levels respond to GABA, but not NMDA, receptor blockade. Taken together, our data suggest that ATM and ATR are part of the cellular "infrastructure" that maintains the excitatory/inhibitory balance of the nervous system. This idea has important implications for the human diseases resulting from their genetic deficiency.

A synthetic lethal screen identifies ATR-inhibition as a novel therapeutic approach for POLD1-deficient cancers

PubMed Central

Hocke, Sandra; Guo, Yang; Job, Albert; Orth, Michael; Ziesch, Andreas; Lauber, Kirsten; De Toni, Enrico N; Gress, Thomas M.; Herbst, Andreas; Göke, Burkhard; Gallmeier, Eike

2016-01-01

The phosphoinositide 3-kinase-related kinase ATR represents a central checkpoint regulator and mediator of DNA-repair. Its inhibition selectively eliminates certain subsets of cancer cells in various tumor types, but the underlying genetic determinants remain enigmatic. Here, we applied a synthetic lethal screen directed against 288 DNA-repair genes using the well-defined ATR knock-in model of DLD1 colorectal cancer cells to identify potential DNA-repair defects mediating these effects. We identified a set of DNA-repair proteins, whose knockdown selectively killed ATR-deficient cancer cells. From this set, we further investigated the profound synthetic lethal interaction between ATR and POLD1. ATR-dependent POLD1 knockdown-induced cell killing was reproducible pharmacologically in POLD1-depleted DLD1 cells and a panel of other colorectal cancer cell lines by using chemical inhibitors of ATR or its major effector kinase CHK1. Mechanistically, POLD1 depletion in ATR-deficient cells caused caspase-dependent apoptosis without preceding cell cycle arrest and increased DNA-damage along with impaired DNA-repair. Our data could have clinical implications regarding tumor genotype-based cancer therapy, as inactivating POLD1 mutations have recently been identified in small subsets of colorectal and endometrial cancers. POLD1 deficiency might thus represent a predictive marker for treatment response towards ATR- or CHK1-inhibitors that are currently tested in clinical trials. PMID:26755646

Mutation of the Inducible ARABIDOPSIS THALIANA CYTOCHROME P450 REDUCTASE2 Alters Lignin Composition and Improves Saccharification1[W][OPEN

PubMed Central

Sundin, Lisa; Vanholme, Ruben; Geerinck, Jan; Goeminne, Geert; Höfer, René; Kim, Hoon; Ralph, John; Boerjan, Wout

2014-01-01

ARABIDOPSIS THALIANA CYTOCHROME P450 REDUCTASE1 (ATR1) and ATR2 provide electrons from NADPH to a large number of CYTOCHROME P450 (CYP450) enzymes in Arabidopsis (Arabidopsis thaliana). Whereas ATR1 is constitutively expressed, the expression of ATR2 appears to be induced during lignin biosynthesis and upon stresses. Therefore, ATR2 was hypothesized to be preferentially involved in providing electrons to the three CYP450s involved in lignin biosynthesis: CINNAMATE 4-HYDROXYLASE (C4H), p-COUMARATE 3-HYDROXYLASE1 (C3H1), and FERULATE 5-HYDROXYLASE1 (F5H1). Here, we show that the atr2 mutation resulted in a 6% reduction in total lignin amount in the main inflorescence stem and a compositional shift of the remaining lignin to a 10-fold higher fraction of p-hydroxyphenyl units at the expense of syringyl units. Phenolic profiling revealed shifts in lignin-related phenolic metabolites, in particular with the substrates of C4H, C3H1 and F5H1 accumulating in atr2 mutants. Glucosinolate and flavonol glycoside biosynthesis, both of which also rely on CYP450 activities, appeared less affected. The cellulose in the atr2 inflorescence stems was more susceptible to enzymatic hydrolysis after alkaline pretreatment, making ATR2 a potential target for engineering plant cell walls for biofuel production. PMID:25315601

A Synthetic Lethal Screen Identifies DNA Repair Pathways that Sensitize Cancer Cells to Combined ATR Inhibition and Cisplatin Treatments

PubMed Central

Mohni, Kareem N.; Thompson, Petria S.; Luzwick, Jessica W.; Glick, Gloria G.; Pendleton, Christopher S.; Lehmann, Brian D.; Pietenpol, Jennifer A.; Cortez, David

2015-01-01

The DNA damage response kinase ATR may be a useful cancer therapeutic target. ATR inhibition synergizes with loss of ERCC1, ATM, XRCC1 and DNA damaging chemotherapy agents. Clinical trials have begun using ATR inhibitors in combination with cisplatin. Here we report the first synthetic lethality screen with a combination treatment of an ATR inhibitor (ATRi) and cisplatin. Combination treatment with ATRi/cisplatin is synthetically lethal with loss of the TLS polymerase ζ and 53BP1. Other DNA repair pathways including homologous recombination and mismatch repair do not exhibit synthetic lethal interactions with ATRi/cisplatin, even though loss of some of these repair pathways sensitizes cells to cisplatin as a single-agent. We also report that ATRi strongly synergizes with PARP inhibition, even in homologous recombination-proficient backgrounds. Lastly, ATR inhibitors were able to resensitize cisplatin-resistant cell lines to cisplatin. These data provide a comprehensive analysis of DNA repair pathways that exhibit synthetic lethality with ATR inhibitors when combined with cisplatin chemotherapy, and will help guide patient selection strategies as ATR inhibitors progress into the cancer clinic. PMID:25965342

Automated target recognition and tracking using an optical pattern recognition neural network

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin

1991-01-01

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

Recognizing Age-Separated Face Images: Humans and Machines

PubMed Central

Yadav, Daksha; Singh, Richa; Vatsa, Mayank; Noore, Afzel

2014-01-01

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components - facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario. PMID:25474200

Recognizing age-separated face images: humans and machines.

PubMed

Yadav, Daksha; Singh, Richa; Vatsa, Mayank; Noore, Afzel

2014-01-01

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components--facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario.

A 2D range Hausdorff approach to 3D facial recognition.

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

Koch, Mark William; Russ, Trina Denise; Little, Charles Quentin

2004-11-01

This paper presents a 3D facial recognition algorithm based on the Hausdorff distance metric. The standard 3D formulation of the Hausdorff matching algorithm has been modified to operate on a 2D range image, enabling a reduction in computation from O(N2) to O(N) without large storage requirements. The Hausdorff distance is known for its robustness to data outliers and inconsistent data between two data sets, making it a suitable choice for dealing with the inherent problems in many 3D datasets due to sensor noise and object self-occlusion. For optimal performance, the algorithm assumes a good initial alignment between probe and templatemore » datasets. However, to minimize the error between two faces, the alignment can be iteratively refined. Results from the algorithm are presented using 3D face images from the Face Recognition Grand Challenge database version 1.0.« less

Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

NASA Astrophysics Data System (ADS)

Yao, Ruigen; Pakzad, Shamim N.

2012-08-01

Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms.

Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population.

PubMed

Bokov, Plamen; Mahut, Bruno; Flaud, Patrice; Delclaux, Christophe

2016-03-01

Respiratory diseases in children are a common reason for physician visits. A diagnostic difficulty arises when parents hear wheezing that is no longer present during the medical consultation. Thus, an outpatient objective tool for recognition of wheezing is of clinical value. We developed a wheezing recognition algorithm from recorded respiratory sounds with a Smartphone placed near the mouth. A total of 186 recordings were obtained in a pediatric emergency department, mostly in toddlers (mean age 20 months). After exclusion of recordings with artefacts and those with a single clinical operator auscultation, 95 recordings with the agreement of two operators on auscultation diagnosis (27 with wheezing and 68 without) were subjected to a two phase algorithm (signal analysis and pattern classifier using machine learning algorithms) to classify records. The best performance (71.4% sensitivity and 88.9% specificity) was observed with a Support Vector Machine-based algorithm. We further tested the algorithm over a set of 39 recordings having a single operator and found a fair agreement (kappa=0.28, CI95% [0.12, 0.45]) between the algorithm and the operator. The main advantage of such an algorithm is its use in contact-free sound recording, thus valuable in the pediatric population. Copyright © 2016 Elsevier Ltd. All rights reserved.

Posttraumatic play in young children exposed to terrorism: An empirical study.

PubMed

Cohen, Esther; Chazan, Saralea; Lerner, Moran; Maimon, Efrat

2010-03-01

The phenomenon of "posttraumatic play" (PTP) has received much clinical recognition and little empirical support. The objective of this study was to examine various aspects of PTP in young children exposed to terror events and their relation to posttraumatic stress disorder (PTSD). Individual play sessions, conducted with 29 young Israeli children directly exposed to terrorism (M age = 5.47, SD = 1.34) and 25 matched unexposed children (M age = 5.62, SD = 0.87), were coded using the Children's Play Therapy Instrument-Adaptation for Terror Research (CPTI-ATR; S.E. Chazan & E. Cohen, 2003). Analyses using these ratings showed (a) significant differences between the two groups, (b) significant associations with the caregiver's reports on child's exposure, and (c) significant associations with the caregiver's reports on the child's PTSD symptoms. Play activity ratings of predominant negative affects, frequent acting-out/morbid themes, lowered developmental level, and reduced awareness of the child of him- or herself as a player significantly predicted more PTSD symptoms. PTP which included more coping strategies classified as "overwhelmed reexperiencing" and less "reenactment with soothing" was associated with a higher level of PTSD. Play analysis with the CPTI-ATR may be helpful in identifying PTSD in children and also guide the selection of therapeutic techniques. Copyright © 2010 Michigan Association for Infant Mental Health.

Spatially Invariant Vector Quantization: A pattern matching algorithm for multiple classes of image subject matter including pathology.

PubMed

Hipp, Jason D; Cheng, Jerome Y; Toner, Mehmet; Tompkins, Ronald G; Balis, Ulysses J

2011-02-26

HISTORICALLY, EFFECTIVE CLINICAL UTILIZATION OF IMAGE ANALYSIS AND PATTERN RECOGNITION ALGORITHMS IN PATHOLOGY HAS BEEN HAMPERED BY TWO CRITICAL LIMITATIONS: 1) the availability of digital whole slide imagery data sets and 2) a relative domain knowledge deficit in terms of application of such algorithms, on the part of practicing pathologists. With the advent of the recent and rapid adoption of whole slide imaging solutions, the former limitation has been largely resolved. However, with the expectation that it is unlikely for the general cohort of contemporary pathologists to gain advanced image analysis skills in the short term, the latter problem remains, thus underscoring the need for a class of algorithm that has the concurrent properties of image domain (or organ system) independence and extreme ease of use, without the need for specialized training or expertise. In this report, we present a novel, general case pattern recognition algorithm, Spatially Invariant Vector Quantization (SIVQ), that overcomes the aforementioned knowledge deficit. Fundamentally based on conventional Vector Quantization (VQ) pattern recognition approaches, SIVQ gains its superior performance and essentially zero-training workflow model from its use of ring vectors, which exhibit continuous symmetry, as opposed to square or rectangular vectors, which do not. By use of the stochastic matching properties inherent in continuous symmetry, a single ring vector can exhibit as much as a millionfold improvement in matching possibilities, as opposed to conventional VQ vectors. SIVQ was utilized to demonstrate rapid and highly precise pattern recognition capability in a broad range of gross and microscopic use-case settings. With the performance of SIVQ observed thus far, we find evidence that indeed there exist classes of image analysis/pattern recognition algorithms suitable for deployment in settings where pathologists alone can effectively incorporate their use into clinical workflow, as a turnkey solution. We anticipate that SIVQ, and other related class-independent pattern recognition algorithms, will become part of the overall armamentarium of digital image analysis approaches that are immediately available to practicing pathologists, without the need for the immediate availability of an image analysis expert.

Aided target recognition processing of MUDSS sonar data

NASA Astrophysics Data System (ADS)

Lau, Brian; Chao, Tien-Hsin

1998-09-01

The Mobile Underwater Debris Survey System (MUDSS) is a collaborative effort by the Navy and the Jet Propulsion Lab to demonstrate multi-sensor, real-time, survey of underwater sites for ordnance and explosive waste (OEW). We describe the sonar processing algorithm, a novel target recognition algorithm incorporating wavelets, morphological image processing, expansion by Hermite polynomials, and neural networks. This algorithm has found all planted targets in MUDSS tests and has achieved spectacular success upon another Coastal Systems Station (CSS) sonar image database.

Classifier dependent feature preprocessing methods

NASA Astrophysics Data System (ADS)

Rodriguez, Benjamin M., II; Peterson, Gilbert L.

2008-04-01

In mobile applications, computational complexity is an issue that limits sophisticated algorithms from being implemented on these devices. This paper provides an initial solution to applying pattern recognition systems on mobile devices by combining existing preprocessing algorithms for recognition. In pattern recognition systems, it is essential to properly apply feature preprocessing tools prior to training classification models in an attempt to reduce computational complexity and improve the overall classification accuracy. The feature preprocessing tools extended for the mobile environment are feature ranking, feature extraction, data preparation and outlier removal. Most desktop systems today are capable of processing a majority of the available classification algorithms without concern of processing while the same is not true on mobile platforms. As an application of pattern recognition for mobile devices, the recognition system targets the problem of steganalysis, determining if an image contains hidden information. The measure of performance shows that feature preprocessing increases the overall steganalysis classification accuracy by an average of 22%. The methods in this paper are tested on a workstation and a Nokia 6620 (Symbian operating system) camera phone with similar results.

Recognition of plant parts with problem-specific algorithms

NASA Astrophysics Data System (ADS)

Schwanke, Joerg; Brendel, Thorsten; Jensch, Peter F.; Megnet, Roland

1994-06-01

Automatic micropropagation is necessary to produce cost-effective high amounts of biomass. Juvenile plants are dissected in clean- room environment on particular points on the stem or the leaves. A vision-system detects possible cutting points and controls a specialized robot. This contribution is directed to the pattern- recognition algorithms to detect structural parts of the plant.

Enhanced facial texture illumination normalization for face recognition.

PubMed

Luo, Yong; Guan, Ye-Peng

2015-08-01

An uncontrolled lighting condition is one of the most critical challenges for practical face recognition applications. An enhanced facial texture illumination normalization method is put forward to resolve this challenge. An adaptive relighting algorithm is developed to improve the brightness uniformity of face images. Facial texture is extracted by using an illumination estimation difference algorithm. An anisotropic histogram-stretching algorithm is proposed to minimize the intraclass distance of facial skin and maximize the dynamic range of facial texture distribution. Compared with the existing methods, the proposed method can more effectively eliminate the redundant information of facial skin and illumination. Extensive experiments show that the proposed method has superior performance in normalizing illumination variation and enhancing facial texture features for illumination-insensitive face recognition.

Computer Recognition of Facial Profiles

DTIC Science & Technology

1974-08-01

facial recognition 20. ABSTRACT (Continue on reverse side It necessary and Identify by block number) A system for the recognition of human faces from...21 2.6 Classification Algorithms ........... ... 32 III FACIAL RECOGNITION AND AUTOMATIC TRAINING . . . 37 3.1 Facial Profile Recognition...provide a fair test of the classification system. The work of Goldstein, Harmon, and Lesk [81 indicates, however, that for facial recognition , a ten class

Research on Palmprint Identification Method Based on Quantum Algorithms

PubMed Central

Zhang, Zhanzhan

2014-01-01

Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT) is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%. PMID:25105165

Effect of irbesartan on development of atrial fibrosis and atrial fibrillation in a canine atrial tachycardia model with left ventricular dysfunction, association with p53.

PubMed

Kataoka, Naoya; Nishida, Kunihiro; Kinoshita, Koshi; Sakamoto, Tamotsu; Nakatani, Yosuke; Tsujino, Yasushi; Mizumaki, Koichi; Inoue, Hiroshi; Kinugawa, Koichiro

2016-12-01

Effects of an angiotensin II receptor blocker, irbesartan (IRB), on the development of atrial fibrosis and atrial fibrillation (AF) were assessed in a canine model of atrial tachycardia remodeling (ATR) with left ventricular dysfunction, together with its possible association with involvement of p53. Atrial tachypacing (400 bpm for 4 weeks) was used to induce ATR in beagles treated with placebo (ATR-dogs, n = 6) or irbesartan (IRB-dogs, n = 5). Non-paced sham dogs served as control (Control-dogs, n = 4). ATR- and IRB-dogs developed tachycardia-induced left ventricular dysfunction. Atrial effective refractory period (AERP) shortened (83 ± 5 ms, p < 0.05), inter-atrial conduction time prolonged (72 ± 2 ms, p < 0.05), and AF duration increased (29 ± 5 s, p < 0.05 vs. baseline) after 4 weeks in ATR-dogs. ATR-dogs also had a larger area of atrial fibrous tissue (5.2 ± 0.5 %, p < 0.05 vs. Control). All these changes, except for AERP, were attenuated in IRB-dogs (92 ± 3 ms, 56 ± 3 ms, 9 ± 5 s, and 2.5 ± 0.7 %, respectively; p < 0.05 vs. ATR for each). In ATR-dogs, p53 expression in the left atrium decreased by 42 % compared with Control-dogs (p < 0.05); however, it was highly expressed in IRB-dogs (+89 % vs. ATR). Transforming growth factor (TGF)-β1 expression was enhanced in ATR-dogs (p < 0.05 vs. Control) but reduced in IRB-dogs (p < 0.05 vs. ATR). Irbesartan suppresses atrial fibrosis and AF development in a canine ATR model with left ventricular dysfunction in association with p53.

Face Recognition Using Local Quantized Patterns and Gabor Filters

NASA Astrophysics Data System (ADS)

Khryashchev, V.; Priorov, A.; Stepanova, O.; Nikitin, A.

2015-05-01

The problem of face recognition in a natural or artificial environment has received a great deal of researchers' attention over the last few years. A lot of methods for accurate face recognition have been proposed. Nevertheless, these methods often fail to accurately recognize the person in difficult scenarios, e.g. low resolution, low contrast, pose variations, etc. We therefore propose an approach for accurate and robust face recognition by using local quantized patterns and Gabor filters. The estimation of the eye centers is used as a preprocessing stage. The evaluation of our algorithm on different samples from a standardized FERET database shows that our method is invariant to the general variations of lighting, expression, occlusion and aging. The proposed approach allows about 20% correct recognition accuracy increase compared with the known face recognition algorithms from the OpenCV library. The additional use of Gabor filters can significantly improve the robustness to changes in lighting conditions.

A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors

PubMed Central

Han, Manhyung; Bang, Jae Hun; Nugent, Chris; McClean, Sally; Lee, Sungyoung

2014-01-01

Activity recognition for the purposes of recognizing a user's intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user's activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%. PMID:25184486

Atrazine-xenobiotic nuclear receptor interactions induce cardiac inflammation and endoplasmic reticulum stress in quail (Coturnix coturnix coturnix).

PubMed

Li, Xue-Nan; Zuo, Yu-Zhu; Qin, Lei; Liu, Wei; Li, Yan-Hua; Li, Jin-Long

2018-05-09

Atrazine (ATR) is one of the most extensively used herbicide that eventually leaches into groundwater and surface water from agricultural areas. Exposure to ATR does harm to the health of human and animals, especially the heart. However, ATR exposure caused cardiotoxicity in bird remains unclear. To evaluate ATR-exerted potential cardiotoxicity in heart, quail were exposed with 0, 50, 250, and 500 mg/kg BW/day ATR by gavage treatment for 45 days. Cardiac histopathological alternation was observed in ATR-induced quail. ATR exposure increased the Cytochrome P450s and Cytochrome b5 contents, Cytochrome P450 (CYP) enzyme system (APND, ERND, AH, and NCR) activities and the expression of CYP isoforms (CYP1B1, CYP2C18, CYP2D6, CYP3A4, CYP3A7, and CYP4B1) in quail heart. The expression of nuclear xenobiotic receptors (NXRs) was also influenced in the heart by ATR exposure. ATR exposure significantly caused the up-regulation of pro-inflammatory cytokines (TNF-α, IL-6, NF-κB, and IL-8), down-regulation of anti-inflammatory cytokines (IL-10) expression levels and increased NO content and iNOS activity. The present research provides new insights into the mechanism that ATR-induced cardiotoxicity through up-regulating the expression levels of GRP78 and XBP-1s, triggering ER stress, activating the expression of IRE1α/TRAF2/NF-κB signaling pathway related factors (IRE1α, TRAF2, IKK, and NF-κB) and inducing an inflammatory response in quail hearts. In conclusion, ATR exposure could induce cardiac inflammatory injury via activating NXRs responses, disrupting CYP homeostasis and CYP isoforms transcription, altering NO metabolism and triggering ER stress and inflammatory response by activating IRE1α/TRAF2/NF-κB signaling pathway. Copyright © 2018 Elsevier Ltd. All rights reserved.

Atrazine is primarily responsible for the toxicity of long-term exposure to a combination of atrazine and inorganic arsenic in the nigrostriatal system of the albino rat.

PubMed

Bardullas, Ulises; Giordano, Magda; Rodríguez, Verónica Mireya

2013-01-01

Chronic and simultaneous exposure to a variety of chemicals present in the environment is an unavoidable fact. However, given the complexity of studying chemical mixtures, most toxicological studies have focused on the effects of short-term exposure to single substances. The aim of this study was to evaluate the effects on the nigrostriatal system of the chronic, simultaneous exposure to two widely distributed substances that have been identified as potential dopaminergic system toxicants, inorganic arsenic (iAs) and atrazine (ATR). Six groups of rats were treated daily for one year with atrazine (10mg ATR/kg), inorganic arsenic (0.5 or 50mgiAs/L of drinking water), or a combination of ATR+0.5mgiAs/L or ATR+50mgiAs/L. The 50mgiAs/L group showed locomotor hypoactivity, while all treatments decreased motor coordination in contrast no effects of treatment were found on the place and response learning tasks. Regarding markers for liver and muscle damage, there were no differences between groups in creatine kinase (CK) or aspartate transaminase (AST) activities, while decreases in lactate dehydrogenase (LDH) levels were found in some exposed groups. The striatal DA content was significantly reduced in ATR, 0.5mgiAs/L, ATR+0.5mgiAs/L, and ATR+50mgiAs/L groups, in comparison to the control group. The number of mesencephalic tyrosine hydroxylase positive cells decreased in the ATR and ATR+0.5mgiAs/L groups compared to the control. In contrast, immunoreactivity to cytochrome oxidase was reduced compared to the control in all treated groups, except for the group treated with 0.5iAsmg alone. Our results indicate that ATR has deleterious effects on dopaminergic neurons and that the combination of ATR and iAs does not exacerbate these effects. © 2013 Elsevier Inc. All rights reserved.

Wellness engineering for better quality of life of aging baby boomer

NASA Astrophysics Data System (ADS)

Szu, Harold

2007-04-01

Current health care system serving 78M aging baby-boomers is no longer sustainable, as the cost about 1/5 GDP will reach 1/4 GDT when all is retired in decades, unless the system is changed. We design a high-tech safe net to enhance the timeliness of early correct treatment execution (otherwise, causing 1/4 mortality associated with an escalating legal fee waste). We follow the common sense that "a stitch in time saves nine," and adopt the military surveillance know-how in designing early warning health management system, comprising of smart sensor pairs for point-care surveillance. However, the grand plan of affordable smart sensors hardware for households requires an ODM & OEM teaming to conduct parallel designing and sequential marketing strategy. The military software strategy combating a treacherous adversary enemy match well with point cares surveillance overcoming real world microorganism variability. Moreover, such smart military software provides self-reference change detection, not by traditional cohort ensemble average, but by individual own higher order statistics (HOS) independent component analysis (ICA), which take the advantage of known initial condition for each individual and desirable over-sampling daily dynamics. The triggering of warning follows the military algorithms comprising of Receiver Operation Characteristics (ROC) and Automatic Target Recognition (ATR). To further reduce the unwanted false negative rate, a benchmarked is made against the traditional cohort-ensemble baseline average & the upper & lower bounds of variance as adopted by the gatekeepers - Medical Doctors (MD) and Nurses.

A crucial role for ATR in the regulation of deoxycytidine kinase activity.

PubMed

Beyaert, Maxime; Starczewska, Eliza; Van Den Neste, Eric; Bontemps, Françoise

2016-01-15

Deoxycytidine kinase (dCK) (EC 2.7.1.74) is a key enzyme for salvage of deoxynucleosides and activation of numerous anticancer and antiviral nucleoside analogs. dCK activity is enhanced in response to several genotoxic treatments, which has been correlated with an increase of dCK phosphorylation at Ser-74. ATM was recently identified as the kinase responsible for Ser-74 phosphorylation and dCK activation after ionizing radiation (IR). Here, we investigated the role of ATM and the related kinase ATR in dCK activation induced by other types of DNA damage. Using ATM-deficient cells or the ATM inhibitor KU-60019, we found that ATM was not required for dCK activation caused by UV light, aphidicolin, cladribine, and unexpectedly also IR. On the other hand, the selective ATR inhibitor VE-821 significantly reduced up-regulation of dCK activity induced by these genotoxic agents, though not IR, and also down-regulated basal dCK activity. A role for ATR in the control of dCK activity was confirmed by using ATR siRNA and ATR-Seckel cells. ATR was also found to directly phosphorylate dCK at Ser-74 in vitro. Further studies revealed that ATR, which is also activated in response to IR, although later than ATM, was responsible for IR-induced dCK activation in ATM-deficient cells or in the presence of KU-60019. Overall, our results demonstrate that ATR controls basal dCK activity and dCK activation in response to replication stress and indicate that ATR can activate dCK after IR if ATM is lacking or inhibited. Copyright © 2015 Elsevier Inc. All rights reserved.

Evolutionary force of AT-rich repeats to trap genomic and episomal DNAs into the rice genome: lessons from endogenous pararetrovirus.

PubMed

Liu, Ruifang; Koyanagi, Kanako O; Chen, Sunlu; Kishima, Yuji

2012-12-01

In plant genomes, the incorporation of DNA segments is not a common method of artificial gene transfer. Nevertheless, various segments of pararetroviruses have been found in plant genomes in recent decades. The rice genome contains a number of segments of endogenous rice tungro bacilliform virus-like sequences (ERTBVs), many of which are present between AT dinucleotide repeats (ATrs). Comparison of genomic sequences between two closely related rice subspecies, japonica and indica, allowed us to verify the preferential insertion of ERTBVs into ATrs. In addition to ERTBVs, the comparative analyses showed that ATrs occasionally incorporate repeat sequences including transposable elements, and a wide range of other sequences. Besides the known genomic sequences, the insertion sequences also represented DNAs of unclear origins together with ERTBVs, suggesting that ATrs have integrated episomal DNAs that would have been suspended in the nucleus. Such insertion DNAs might be trapped by ATrs in the genome in a host-dependent manner. Conversely, other simple mono- and dinucleotide sequence repeats (SSR) were less frequently involved in insertion events relative to ATrs. Therefore, ATrs could be regarded as hot spots of double-strand breaks that induce non-homologous end joining. The insertions within ATrs occasionally generated new gene-related sequences or involved structural modifications of existing genes. Likewise, in a comparison between Arabidopsis thaliana and Arabidopsis lyrata, the insertions preferred ATrs to other SSRs. Therefore ATrs in plant genomes could be considered as genomic dumping sites that have trapped various DNA molecules and may have exerted a powerful evolutionary force. © 2012 The Authors. The Plant Journal © 2012 Blackwell Publishing Ltd.

Approach to recognition of flexible form for credit card expiration date recognition as example

NASA Astrophysics Data System (ADS)

Sheshkus, Alexander; Nikolaev, Dmitry P.; Ingacheva, Anastasia; Skoryukina, Natalya

2015-12-01

In this paper we consider a task of finding information fields within document with flexible form for credit card expiration date field as example. We discuss main difficulties and suggest possible solutions. In our case this task is to be solved on mobile devices therefore computational complexity has to be as low as possible. In this paper we provide results of the analysis of suggested algorithm. Error distribution of the recognition system shows that suggested algorithm solves the task with required accuracy.

Comparison of crisp and fuzzy character networks in handwritten word recognition

NASA Technical Reports Server (NTRS)

Gader, Paul; Mohamed, Magdi; Chiang, Jung-Hsien

1992-01-01

Experiments involving handwritten word recognition on words taken from images of handwritten address blocks from the United States Postal Service mailstream are described. The word recognition algorithm relies on the use of neural networks at the character level. The neural networks are trained using crisp and fuzzy desired outputs. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level.

Cross-cultural adaptation and validation of Persian Achilles tendon Total Rupture Score.

PubMed

Ansari, Noureddin Nakhostin; Naghdi, Soofia; Hasanvand, Sahar; Fakhari, Zahra; Kordi, Ramin; Nilsson-Helander, Katarina

2016-04-01

To cross-culturally adapt the Achilles tendon Total Rupture Score (ATRS) to Persian language and to preliminary evaluate the reliability and validity of a Persian ATRS. A cross-sectional and prospective cohort study was conducted to translate and cross-culturally adapt the ATRS to Persian language (ATRS-Persian) following steps described in guidelines. Thirty patients with total Achilles tendon rupture and 30 healthy subjects participated in this study. Psychometric properties of floor/ceiling effects (responsiveness), internal consistency reliability, test-retest reliability, standard error of measurement (SEM), smallest detectable change (SDC), construct validity, and discriminant validity were tested. Factor analysis was performed to determine the ATRS-Persian structure. There were no floor or ceiling effects that indicate the content and responsiveness of ATRS-Persian. Internal consistency was high (Cronbach's α 0.95). Item-total correlations exceeded acceptable standard of 0.3 for the all items (0.58-0.95). The test-retest reliability was excellent [(ICC)agreement 0.98]. SEM and SDC were 3.57 and 9.9, respectively. Construct validity was supported by a significant correlation between the ATRS-Persian total score and the Persian Foot and Ankle Outcome Score (PFAOS) total score and PFAOS subscales (r = 0.55-0.83). The ATRS-Persian significantly discriminated between patients and healthy subjects. Explanatory factor analysis revealed 1 component. The ATRS was cross-culturally adapted to Persian and demonstrated to be a reliable and valid instrument to measure functional outcomes in Persian patients with Achilles tendon rupture. II.

Chitosan based atorvastatin nanocrystals: effect of cationic charge on particle size, formulation stability, and in-vivo efficacy

PubMed Central

Kurakula, Mallesh; El-Helw, AM; Sobahi, Tariq R; Abdelaal, Magdy Y

2015-01-01

Cationic charged chitosan as stabilizer was evaluated in preparation of nanocrystals using probe sonication method. The influence of cationic charge densities of chitosan (low CSL, medium CSM, high CSH molecular weights) and Labrasol® in solubility enhancement and modifying the release was investigated, using atorvastatin (ATR) as poorly soluble model drug. Compared to CSM and CSH; low cationic charge of CSL acted as both electrostatic and steric stabilizer by significant size reduction to 394 nm with charge of 21.5 meV. Solubility of ATR-CSL increased to 60-fold relative to pure ATR and ATR-L. Nanocrystals were characterized for physiochemical properties. Scanning electron microscopy revealed scaffold-like structures with high surface area. X-ray powder diffractometry and differential scanning calorimetry revealed crystalline to slight amorphous state changes after cationic charge size reduction. Fourier transform-infrared spectra indicated no potent drug-excipient interactions. The enhanced dissolution profile of ATR-CSL indicates that sustained release was achieved compared with ATR-L and Lipitor®. Anti-hyperlipidemic performance was pH dependent where ATR-CSL exhibited 2.5-fold higher efficacy at pH 5 compared to pH 6 and Lipitor®. Stability studies indicated marked changes in size and charge for ATR-L compared to ATR-CSL exemplifying importance of the stabilizer. Therefore, nanocrystals developed with CSL as a stabilizer is a promising choice to enhance dissolution, stability, and in-vivo efficacy of major Biopharmaceutical Classification System II/IV drugs. PMID:25609947

Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems.

PubMed

Hermosilla, Gabriel; Gallardo, Francisco; Farias, Gonzalo; San Martin, Cesar

2015-07-23

The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other.

Study on recognition algorithm for paper currency numbers based on neural network

NASA Astrophysics Data System (ADS)

Li, Xiuyan; Liu, Tiegen; Li, Yuanyao; Zhang, Zhongchuan; Deng, Shichao

2008-12-01

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems

PubMed Central

Hermosilla, Gabriel; Gallardo, Francisco; Farias, Gonzalo; San Martin, Cesar

2015-01-01

The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other. PMID:26213932

Basics of identification measurement technology

NASA Astrophysics Data System (ADS)

Klikushin, Yu N.; Kobenko, V. Yu; Stepanov, P. P.

2018-01-01

All available algorithms and suitable for pattern recognition do not give 100% guarantee, therefore there is a field of scientific night activity in this direction, studies are relevant. It is proposed to develop existing technologies for pattern recognition in the form of application of identification measurements. The purpose of the study is to identify the possibility of recognizing images using identification measurement technologies. In solving problems of pattern recognition, neural networks and hidden Markov models are mainly used. A fundamentally new approach to the solution of problems of pattern recognition based on the technology of identification signal measurements (IIS) is proposed. The essence of IIS technology is the quantitative evaluation of the shape of images using special tools and algorithms.

A study of speech emotion recognition based on hybrid algorithm

NASA Astrophysics Data System (ADS)

Zhu, Ju-xia; Zhang, Chao; Lv, Zhao; Rao, Yao-quan; Wu, Xiao-pei

2011-10-01

To effectively improve the recognition accuracy of the speech emotion recognition system, a hybrid algorithm which combines Continuous Hidden Markov Model (CHMM), All-Class-in-One Neural Network (ACON) and Support Vector Machine (SVM) is proposed. In SVM and ACON methods, some global statistics are used as emotional features, while in CHMM method, instantaneous features are employed. The recognition rate by the proposed method is 92.25%, with the rejection rate to be 0.78%. Furthermore, it obtains the relative increasing of 8.53%, 4.69% and 0.78% compared with ACON, CHMM and SVM methods respectively. The experiment result confirms the efficiency of distinguishing anger, happiness, neutral and sadness emotional states.

Analysis and Recognition of Curve Type as The Basis of Object Recognition in Image

NASA Astrophysics Data System (ADS)

Nugraha, Nurma; Madenda, Sarifuddin; Indarti, Dina; Dewi Agushinta, R.; Ernastuti

2016-06-01

An object in an image when analyzed further will show the characteristics that distinguish one object with another object in an image. Characteristics that are used in object recognition in an image can be a color, shape, pattern, texture and spatial information that can be used to represent objects in the digital image. The method has recently been developed for image feature extraction on objects that share characteristics curve analysis (simple curve) and use the search feature of chain code object. This study will develop an algorithm analysis and the recognition of the type of curve as the basis for object recognition in images, with proposing addition of complex curve characteristics with maximum four branches that will be used for the process of object recognition in images. Definition of complex curve is the curve that has a point of intersection. By using some of the image of the edge detection, the algorithm was able to do the analysis and recognition of complex curve shape well.

Automatic detection and recognition of traffic signs in stereo images based on features and probabilistic neural networks

NASA Astrophysics Data System (ADS)

Sheng, Yehua; Zhang, Ka; Ye, Chun; Liang, Cheng; Li, Jian

2008-04-01

Considering the problem of automatic traffic sign detection and recognition in stereo images captured under motion conditions, a new algorithm for traffic sign detection and recognition based on features and probabilistic neural networks (PNN) is proposed in this paper. Firstly, global statistical color features of left image are computed based on statistics theory. Then for red, yellow and blue traffic signs, left image is segmented to three binary images by self-adaptive color segmentation method. Secondly, gray-value projection and shape analysis are used to confirm traffic sign regions in left image. Then stereo image matching is used to locate the homonymy traffic signs in right image. Thirdly, self-adaptive image segmentation is used to extract binary inner core shapes of detected traffic signs. One-dimensional feature vectors of inner core shapes are computed by central projection transformation. Fourthly, these vectors are input to the trained probabilistic neural networks for traffic sign recognition. Lastly, recognition results in left image are compared with recognition results in right image. If results in stereo images are identical, these results are confirmed as final recognition results. The new algorithm is applied to 220 real images of natural scenes taken by the vehicle-borne mobile photogrammetry system in Nanjing at different time. Experimental results show a detection and recognition rate of over 92%. So the algorithm is not only simple, but also reliable and high-speed on real traffic sign detection and recognition. Furthermore, it can obtain geometrical information of traffic signs at the same time of recognizing their types.

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

PubMed Central

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. PMID:27977767

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

PubMed

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

Optimization of bio-ethanol autothermal reforming and carbon monoxide removal processes

NASA Astrophysics Data System (ADS)

Markova, D.; Bazbauers, G.; Valters, K.; Alhucema Arias, R.; Weuffen, C.; Rochlitz, L.

Experimental investigation of bio-ethanol autothermal reforming (ATR) and water-gas shift (WGS) processes for hydrogen production and regression analysis of the data is performed in the study. The main goal was to obtain regression relations between the most critical dependent variables such as hydrogen, carbon monoxide and methane content in the reformate gas and independent factors such as air-to-fuel ratio (λ), steam-to-carbon ratio (S/C), inlet temperature of reactants into reforming process (T ATRin), pressure (p) and temperature (T ATR) in the ATR reactor from the experimental data. Purpose of the regression models is to provide optimum values of the process factors that give the maximum amount of hydrogen. The experimental ATR system consisted of an evaporator, an ATR reactor and a one-stage WGS reactor. Empirical relations between hydrogen, carbon monoxide, methane content and the controlling parameters downstream of the ATR reactor are shown in the work. The optimization results show that within the considered range of the process factors the maximum hydrogen concentration of 42 dry vol. % and yield of 3.8 mol mol -1 of ethanol downstream of the ATR reactor can be achieved at S/C = 2.5, λ = 0.20-0.23, p = 0.4 bar, T ATRin = 230 °C, T ATR = 640 °C.

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

PubMed

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

Comments on "Including the effects of temperature-dependent opacities in the implicit Monte Carlo algorithm" by N.A. Gentile [J. Comput. Phys. 230 (2011) 5100-5114

NASA Astrophysics Data System (ADS)

Ghosh, Karabi

2017-02-01

We briefly comment on a paper by N.A. Gentile [J. Comput. Phys. 230 (2011) 5100-5114] in which the Fleck factor has been modified to include the effects of temperature-dependent opacities in the implicit Monte Carlo algorithm developed by Fleck and Cummings [1,2]. Instead of the Fleck factor, f = 1 / (1 + βcΔtσP), the author derived the modified Fleck factor g = 1 / (1 + βcΔtσP - min [σP‧ (aTr4 - aT4)cΔt/ρCV, 0 ]) to be used in the Implicit Monte Carlo (IMC) algorithm in order to obtain more accurate solutions with much larger time steps. Here β = 4 aT3 / ρCV, σP is the Planck opacity and the derivative of Planck opacity w.r.t. the material temperature is σP‧ = dσP / dT.

Improving a HMM-based off-line handwriting recognition system using MME-PSO optimization

NASA Astrophysics Data System (ADS)

Hamdani, Mahdi; El Abed, Haikal; Hamdani, Tarek M.; Märgner, Volker; Alimi, Adel M.

2011-01-01

One of the trivial steps in the development of a classifier is the design of its architecture. This paper presents a new algorithm, Multi Models Evolvement (MME) using Particle Swarm Optimization (PSO). This algorithm is a modified version of the basic PSO, which is used to the unsupervised design of Hidden Markov Model (HMM) based architectures. For instance, the proposed algorithm is applied to an Arabic handwriting recognizer based on discrete probability HMMs. After the optimization of their architectures, HMMs are trained with the Baum- Welch algorithm. The validation of the system is based on the IfN/ENIT database. The performance of the developed approach is compared to the participating systems at the 2005 competition organized on Arabic handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The final system is a combination between an optimized HMM with 6 other HMMs obtained by a simple variation of the number of states. An absolute improvement of 6% of word recognition rate with about 81% is presented. This improvement is achieved comparing to the basic system (ARAB-IfN). The proposed recognizer outperforms also most of the known state-of-the-art systems.

A fast 3-D object recognition algorithm for the vision system of a special-purpose dexterous manipulator

NASA Technical Reports Server (NTRS)

Hung, Stephen H. Y.

1989-01-01

A fast 3-D object recognition algorithm that can be used as a quick-look subsystem to the vision system for the Special-Purpose Dexterous Manipulator (SPDM) is described. Global features that can be easily computed from range data are used to characterize the images of a viewer-centered model of an object. This algorithm will speed up the processing by eliminating the low level processing whenever possible. It may identify the object, reject a set of bad data in the early stage, or create a better environment for a more powerful algorithm to carry the work further.

What does voice-processing technology support today?

PubMed Central

Nakatsu, R; Suzuki, Y

1995-01-01

This paper describes the state of the art in applications of voice-processing technologies. In the first part, technologies concerning the implementation of speech recognition and synthesis algorithms are described. Hardware technologies such as microprocessors and DSPs (digital signal processors) are discussed. Software development environment, which is a key technology in developing applications software, ranging from DSP software to support software also is described. In the second part, the state of the art of algorithms from the standpoint of applications is discussed. Several issues concerning evaluation of speech recognition/synthesis algorithms are covered, as well as issues concerning the robustness of algorithms in adverse conditions. Images Fig. 3 PMID:7479720

ATR-dependent phosphorylation of FANCA on serine 1449 after DNA damage is important for FA pathway function

PubMed Central

Collins, Natalie B.; Wilson, James B.; Bush, Thomas; Thomashevski, Andrei; Roberts, Kate J.; Jones, Nigel J.

2009-01-01

Previous work has shown several proteins defective in Fanconi anemia (FA) are phosphorylated in a functionally critical manner. FANCA is phosphorylated after DNA damage and localized to chromatin, but the site and significance of this phosphorylation are unknown. Mass spectrometry of FANCA revealed one phosphopeptide, phosphorylated on serine 1449. Serine 1449 phosphorylation was induced after DNA damage but not during S phase, in contrast to other posttranslational modifications of FA proteins. Furthermore, the S1449A mutant failed to completely correct a variety of FA-associated phenotypes. The DNA damage response is coordinated by phosphorylation events initiated by apical kinases ATM (ataxia telangectasia mutated) and ATR (ATM and Rad3-related), and ATR is essential for proper FA pathway function. Serine 1449 is in a consensus ATM/ATR site, phosphorylation in vivo is dependent on ATR, and ATR phosphorylated FANCA on serine 1449 in vitro. Phosphorylation of FANCA on serine 1449 is a DNA damage–specific event that is downstream of ATR and is functionally important in the FA pathway. PMID:19109555

The Advanced Test Reactor National Scientific User Facility Advancing Nuclear Technology

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

T. R. Allen; J. B. Benson; J. A. Foster

2009-05-01

To help ensure the long-term viability of nuclear energy through a robust and sustained research and development effort, the U.S. Department of Energy (DOE) designated the Advanced Test Reactor and associated post-irradiation examination facilities a National Scientific User Facility (ATR NSUF), allowing broader access to nuclear energy researchers. The mission of the ATR NSUF is to provide access to world-class nuclear research facilities, thereby facilitating the advancement of nuclear science and technology. The ATR NSUF seeks to create an engaged academic and industrial user community that routinely conducts reactor-based research. Cost free access to the ATR and PIE facilities ismore » granted based on technical merit to U.S. university-led experiment teams conducting non-proprietary research. Proposals are selected via independent technical peer review and relevance to DOE mission. Extensive publication of research results is expected as a condition for access. During FY 2008, the first full year of ATR NSUF operation, five university-led experiments were awarded access to the ATR and associated post-irradiation examination facilities. The ATR NSUF has awarded four new experiments in early FY 2009, and anticipates awarding additional experiments in the fall of 2009 as the results of the second 2009 proposal call. As the ATR NSUF program mature over the next two years, the capability to perform irradiation research of increasing complexity will become available. These capabilities include instrumented irradiation experiments and post-irradiation examinations on materials previously irradiated in U.S. reactor material test programs. The ATR critical facility will also be made available to researchers. An important component of the ATR NSUF an education program focused on the reactor-based tools available for resolving nuclear science and technology issues. The ATR NSUF provides education programs including a summer short course, internships, faculty-student team projects and faculty/staff exchanges. In June of 2008, the first week-long ATR NSUF Summer Session was attended by 68 students, university faculty and industry representatives. The Summer Session featured presentations by 19 technical experts from across the country and covered topics including irradiation damage mechanisms, degradation of reactor materials, LWR and gas reactor fuels, and non-destructive evaluation. High impact research results from leveraging the entire research infrastructure, including universities, industry, small business, and the national laboratories. To increase overall research capability, ATR NSUF seeks to form strategic partnerships with university facilities that add significant nuclear research capability to the ATR NSUF and are accessible to all ATR NSUF users. Current partner facilities include the MIT Reactor, the University of Michigan Irradiated Materials Testing Laboratory, the University of Wisconsin Characterization Laboratory, and the University of Nevada, Las Vegas transmission Electron Microscope User Facility. Needs for irradiation of material specimens at tightly controlled temperatures are being met by dedication of a large in-pile pressurized water loop facility for use by ATR NSUF users. Several environmental mechanical testing systems are under construction to determine crack growth rates and fracture toughness on irradiated test systems.« less

The effect of atrazine on puberty in male wistar rats: an evaluation in the protocol for the assessment of pubertal development and thyroid function.

PubMed

Stoker, T E; Laws, S C; Guidici, D L; Cooper, R L

2000-11-01

Since atrazine (ATR), a chlorotriazine herbicide, has been shown previously to alter the secretion of luteinizing hormone (LH) and prolactin (PRL) through a direct effect on the central nervous system (CNS), we hypothesized that exposure to ATR in the EDSTAC male pubertal protocol (juvenile to peripubertal) would alter the development of the male rat reproductive system. We dosed intact male Wistar rats from postnatal day (PND) 23 to 53 and examined several reproductive endpoints. ATR (0, 12.5, 25, 50, 100, 150, or 200 mg/kg) was administered by gavage and an additional pair-fed group was added to compare the effects of any decreased food consumption in the high dose group. Preputial separation (PPS) was significantly delayed in the 12.5, 50, 100, 150, and 200 mg/kg ATR dose groups. PPS was also delayed in the pair-fed group, although significantly less than in the high dose-ATR group. The males were killed on PND 53 or 54, and pituitary, thyroid, testes, epididymides, seminal vesicles, and ventral and lateral prostates were removed. ATR (50 to 200 mg/kg) treatment resulted in a significant reduction in ventral prostate weights, as did the reduced food consumption of the pair-fed group. Testes weights were unaffected by atrazine treatment. Seminal vesicle and epididymal weights were decreased in the high dose-ATR group and the control pair-fed group. However, the difference in epididymal weights was no longer significantly different when body weight was entered as a covariable. Intratesticular testosterone was significantly decreased in the high dose-ATR group on PND 45, but apparent decreases in serum testosterone were not statistically significantly on PND 53. There was a trend for a decrease in luteinizing hormone (LH) as the dose of ATR increased; however, dose group mean LH was not different from controls. Due to the variability of serum prolactin concentrations on PND 53, no significant difference was identified. Although prolactin is involved in the maintenance of LH receptors prior to puberty, we observed no difference in LH receptor number at PND 45 or 53. Serum estrone and estradiol showed dose-related increases that were significant only in the 200 mg/kg-ATR group. No differences were observed in thyroid stimulating hormone (TSH) and thyroxine (T4) between the ATR groups and the control; however triiodothyronine (T3) was elevated in the high dose-ATR group. No differences in hormone levels were observed in the pair-fed animals. These results indicate that ATR delays puberty in the male rat and its mode of action appears to be altering the secretion of steroids and having subsequent effects on the development of the reproductive tract, which appear to be due to ATR's effects on the CNS. Thus, ATR tested positive in the pubertal male screen that the Endocrine-Disrupter Screening and Testing Advisory Committee (EDSTAC) is considering as an optional screen for endocrine disrupters.

A multifaceted independent performance analysis of facial subspace recognition algorithms.

PubMed

Bajwa, Usama Ijaz; Taj, Imtiaz Ahmad; Anwar, Muhammad Waqas; Wang, Xuan

2013-01-01

Face recognition has emerged as the fastest growing biometric technology and has expanded a lot in the last few years. Many new algorithms and commercial systems have been proposed and developed. Most of them use Principal Component Analysis (PCA) as a base for their techniques. Different and even conflicting results have been reported by researchers comparing these algorithms. The purpose of this study is to have an independent comparative analysis considering both performance and computational complexity of six appearance based face recognition algorithms namely PCA, 2DPCA, A2DPCA, (2D)(2)PCA, LPP and 2DLPP under equal working conditions. This study was motivated due to the lack of unbiased comprehensive comparative analysis of some recent subspace methods with diverse distance metric combinations. For comparison with other studies, FERET, ORL and YALE databases have been used with evaluation criteria as of FERET evaluations which closely simulate real life scenarios. A comparison of results with previous studies is performed and anomalies are reported. An important contribution of this study is that it presents the suitable performance conditions for each of the algorithms under consideration.

Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

PubMed Central

Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

2016-01-01

This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems. PMID:27347978

A novel rotational invariants target recognition method for rotating motion blurred images

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Gong, Meiling; Dong, Mingwei; Zeng, Yiliang; Zhang, Yuzhen

2017-11-01

The imaging of the image sensor is blurred due to the rotational motion of the carrier and reducing the target recognition rate greatly. Although the traditional mode that restores the image first and then identifies the target can improve the recognition rate, it takes a long time to recognize. In order to solve this problem, a rotating fuzzy invariants extracted model was constructed that recognizes target directly. The model includes three metric layers. The object description capability of metric algorithms that contain gray value statistical algorithm, improved round projection transformation algorithm and rotation-convolution moment invariants in the three metric layers ranges from low to high, and the metric layer with the lowest description ability among them is as the input which can eliminate non pixel points of target region from degenerate image gradually. Experimental results show that the proposed model can improve the correct target recognition rate of blurred image and optimum allocation between the computational complexity and function of region.

An evolution based biosensor receptor DNA sequence generation algorithm.

PubMed

Kim, Eungyeong; Lee, Malrey; Gatton, Thomas M; Lee, Jaewan; Zang, Yupeng

2010-01-01

A biosensor is composed of a bioreceptor, an associated recognition molecule, and a signal transducer that can selectively detect target substances for analysis. DNA based biosensors utilize receptor molecules that allow hybridization with the target analyte. However, most DNA biosensor research uses oligonucleotides as the target analytes and does not address the potential problems of real samples. The identification of recognition molecules suitable for real target analyte samples is an important step towards further development of DNA biosensors. This study examines the characteristics of DNA used as bioreceptors and proposes a hybrid evolution-based DNA sequence generating algorithm, based on DNA computing, to identify suitable DNA bioreceptor recognition molecules for stable hybridization with real target substances. The Traveling Salesman Problem (TSP) approach is applied in the proposed algorithm to evaluate the safety and fitness of the generated DNA sequences. This approach improves efficiency and stability for enhanced and variable-length DNA sequence generation and allows extension to generation of variable-length DNA sequences with diverse receptor recognition requirements.

Gabor filter based fingerprint image enhancement

NASA Astrophysics Data System (ADS)

Wang, Jin-Xiang

2013-03-01

Fingerprint recognition technology has become the most reliable biometric technology due to its uniqueness and invariance, which has been most convenient and most reliable technique for personal authentication. The development of Automated Fingerprint Identification System is an urgent need for modern information security. Meanwhile, fingerprint preprocessing algorithm of fingerprint recognition technology has played an important part in Automatic Fingerprint Identification System. This article introduces the general steps in the fingerprint recognition technology, namely the image input, preprocessing, feature recognition, and fingerprint image enhancement. As the key to fingerprint identification technology, fingerprint image enhancement affects the accuracy of the system. It focuses on the characteristics of the fingerprint image, Gabor filters algorithm for fingerprint image enhancement, the theoretical basis of Gabor filters, and demonstration of the filter. The enhancement algorithm for fingerprint image is in the windows XP platform with matlab.65 as a development tool for the demonstration. The result shows that the Gabor filter is effective in fingerprint image enhancement technology.

An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications.

PubMed

Park, Seong-Wook; Park, Junyoung; Bong, Kyeongryeol; Shin, Dongjoo; Lee, Jinmook; Choi, Sungpill; Yoo, Hoi-Jun

2015-12-01

Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm × 4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a battery-powered platform with real-time deep inference and deep learning operation. It consumes 185 mW average power, and 213.1 mW peak power at 200 MHz operating frequency and 1.2 V supply voltage. It achieves 411.3 GOPS peak performance and 1.93 TOPS/W energy efficiency, which is 2.07× higher than the state-of-the-art.

Products recognition on shop-racks from local scale-invariant features

NASA Astrophysics Data System (ADS)

Zawistowski, Jacek; Kurzejamski, Grzegorz; Garbat, Piotr; Naruniec, Jacek

2016-04-01

This paper presents a system designed for the multi-object detection purposes and adjusted for the application of product search on the market shelves. System uses well known binary keypoint detection algorithms for finding characteristic points in the image. One of the main idea is object recognition based on Implicit Shape Model method. Authors of the article proposed many improvements of the algorithm. Originally fiducial points are matched with a very simple function. This leads to the limitations in the number of objects parts being success- fully separated, while various methods of classification may be validated in order to achieve higher performance. Such an extension implies research on training procedure able to deal with many objects categories. Proposed solution opens a new possibilities for many algorithms demanding fast and robust multi-object recognition.

Efficacy of ATR inhibitors as single agents in Ewing sarcoma

PubMed Central

Lecona, Emilio; Murga, Matilde; Callen, Elsa; Azorin, Daniel; Alonso, Javier; Lopez, Andres J.; Nussenzweig, Andre; Fernandez-Capetillo, Oscar

2016-01-01

Ewing sarcomas (ES) are pediatric bone tumors that arise from a driver translocation, most frequently EWS/FLI1. Current ES treatment involves DNA damaging agents, yet the basis for the sensitivity to these therapies remains unknown. Oncogene-induced replication stress (RS) is a known source of endogenous DNA damage in cancer, which is suppressed by ATR and CHK1 kinases. We here show that ES suffer from high endogenous levels of RS, rendering them particularly dependent on the ATR pathway. Accordingly, two independent ATR inhibitors show in vitro toxicity in ES cell lines as well as in vivo efficacy in ES xenografts as single agents. Expression of EWS/FLI1 or EWS/ERG oncogenic translocations sensitizes non-ES cells to ATR inhibitors. Our data shed light onto the sensitivity of ES to genotoxic agents, and identify ATR inhibitors as a potential therapy for Ewing Sarcomas. PMID:27577084

Quest Hierarchy for Hyperspectral Face Recognition

DTIC Science & Technology

2011-03-01

numerous face recognition algorithms available, several very good literature surveys are available that include Abate [29], Samal [110], Kong [18], Zou...Perception, Japan (January 1994). [110] Samal , Ashok and P. Iyengar, Automatic Recognition and Analysis of Human Faces and Facial Expressions: A Survey

Comparison of photo-matching algorithms commonly used for photographic capture-recapture studies.

PubMed

Matthé, Maximilian; Sannolo, Marco; Winiarski, Kristopher; Spitzen-van der Sluijs, Annemarieke; Goedbloed, Daniel; Steinfartz, Sebastian; Stachow, Ulrich

2017-08-01

Photographic capture-recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost-effectiveness. Recently, several computer-aided photo-matching algorithms have been developed to more efficiently match images of unique individuals in databases with thousands of images. However, the identification accuracy of these algorithms can severely bias estimates of vital rates and population size. Therefore, it is important to understand the performance and limitations of state-of-the-art photo-matching algorithms prior to implementation in capture-recapture studies involving possibly thousands of images. Here, we compared the performance of four photo-matching algorithms; Wild-ID, I3S Pattern+, APHIS, and AmphIdent using multiple amphibian databases of varying image quality. We measured the performance of each algorithm and evaluated the performance in relation to database size and the number of matching images in the database. We found that algorithm performance differed greatly by algorithm and image database, with recognition rates ranging from 100% to 22.6% when limiting the review to the 10 highest ranking images. We found that recognition rate degraded marginally with increased database size and could be improved considerably with a higher number of matching images in the database. In our study, the pixel-based algorithm of AmphIdent exhibited superior recognition rates compared to the other approaches. We recommend carefully evaluating algorithm performance prior to using it to match a complete database. By choosing a suitable matching algorithm, databases of sizes that are unfeasible to match "by eye" can be easily translated to accurate individual capture histories necessary for robust demographic estimates.

Identification of ATR-Chk1 pathway inhibitors that selectively target p53-deficient cells without directly suppressing ATR catalytic activity

PubMed Central

Kawasumi, Masaoki; Bradner, James E.; Tolliday, Nicola; Thibodeau, Renee; Sloan, Heather; Brummond, Kay M.; Nghiem, Paul

2014-01-01

Resistance to DNA-damaging chemotherapy is a barrier to effective treatment that appears to be augmented by p53 functional deficiency in many cancers. In p53-deficient cells where the G1/S checkpoint is compromised, cell viability after DNA damage relies upon intact intra-S and G2/M checkpoints mediated by the ATR and Chk1 kinases. Thus, a logical rationale to sensitize p53-deficient cancers to DNA-damaging chemotherapy is through the use of ATP-competitive inhibitors of ATR or Chk1. To discover small molecules that may act on uncharacterized components of the ATR pathway, we performed a phenotype-based screen of 9,195 compounds for their ability to inhibit hydroxyurea-induced phosphorylation of Ser345 on Chk1, known to be a critical ATR substrate. This effort led to the identification of four small-molecule compounds, three of which were derived from known bioactive library (anthothecol, dihydrocelastryl, and erysolin) and one of which was a novel synthetic compound termed MARPIN. These compounds all inhibited ATR-selective phosphorylation and sensitized p53-deficient cancer cells to DNA-damaging agents in vitro and in vivo. Notably, these compounds did not inhibit ATR catalytic activity in vitro, unlike typical ATP-competitive inhibitors, but acted in a mechanistically distinct manner to disable ATR-Chk1 function. Our results highlight a set of novel molecular probes to further elucidate druggable mechanisms to improve cancer therapeutic responses produced by DNA-damaging drugs. PMID:25336189

Identification of ATR-Chk1 pathway inhibitors that selectively target p53-deficient cells without directly suppressing ATR catalytic activity.

PubMed

Kawasumi, Masaoki; Bradner, James E; Tolliday, Nicola; Thibodeau, Renee; Sloan, Heather; Brummond, Kay M; Nghiem, Paul

2014-12-15

Resistance to DNA-damaging chemotherapy is a barrier to effective treatment that appears to be augmented by p53 functional deficiency in many cancers. In p53-deficient cells in which the G1-S checkpoint is compromised, cell viability after DNA damage relies upon intact intra-S and G2-M checkpoints mediated by the ATR (ataxia telangiectasia and Rad3 related) and Chk1 kinases. Thus, a logical rationale to sensitize p53-deficient cancers to DNA-damaging chemotherapy is through the use of ATP-competitive inhibitors of ATR or Chk1. To discover small molecules that may act on uncharacterized components of the ATR pathway, we performed a phenotype-based screen of 9,195 compounds for their ability to inhibit hydroxyurea-induced phosphorylation of Ser345 on Chk1, known to be a critical ATR substrate. This effort led to the identification of four small-molecule compounds, three of which were derived from known bioactive library (anthothecol, dihydrocelastryl, and erysolin) and one of which was a novel synthetic compound termed MARPIN. These compounds all inhibited ATR-selective phosphorylation and sensitized p53-deficient cancer cells to DNA-damaging agents in vitro and in vivo. Notably, these compounds did not inhibit ATR catalytic activity in vitro, unlike typical ATP-competitive inhibitors, but acted in a mechanistically distinct manner to disable ATR-Chk1 function. Our results highlight a set of novel molecular probes to further elucidate druggable mechanisms to improve cancer therapeutic responses produced by DNA-damaging drugs. ©2014 American Association for Cancer Research.

Rice (Oryza sativa) Laccases Involved in Modification and Detoxification of Herbicides Atrazine and Isoproturon Residues in Plants.

PubMed

Huang, Meng Tian; Lu, Yi Chen; Zhang, Shuang; Luo, Fang; Yang, Hong

2016-08-24

Atrazine (ATR) and isoproturon (IPU) as herbicides have become serious environmental contaminants due to their overuse in crop production. Although ATR and IPU in soils are easily absorbed by many crops, the mechanisms for their degradation or detoxification in plants are poorly understood. This study identified a group of novel genes encoding laccases (EC 1.10.3.2) that are possibly involved in catabolism or detoxification of ATR and IPU residues in rice. Transcriptome profiling shows at least 22 differentially expressed laccase genes in ATR/IPU-exposed rice. Some of the laccase genes were validated by RT-PCR analysis. The biochemical properties of the laccases were analyzed, and their activities in rice were induced under ATR/IPU exposure. To investigate the roles of laccases in degrading or detoxifying ATR/IPU in rice, transgenic yeast cells (Pichia pastoris X-33) expressing two rice laccase genes (LOC_Os01g63180 and LOC_Os12g15680) were generated. Both transformants were found to accumulate less ATR/IPU compared to the control. The ATR/IPU-degraded products in the transformed yeast cells using UPLC-TOF-MS/MS were further characterized. Two metabolites, hydroxy-dehydrogenated atrazine (HDHA) and 2-OH-isopropyl-IPU, catalyzed by laccases were detected in the eukaryotic cells. These results indicate that the laccase-coding genes identified here could confer degradation or detoxification of the herbicides and suggest that the laccases could be one of the important enzymatic pathways responsible for ATR/IPU degradation/detoxification in rice.

30 CFR 75.209 - Automated Temporary Roof Support (ATRS) systems.

Code of Federal Regulations, 2011 CFR

2011-07-01

... paragraph shall be met according to the following schedule: (1) All new machines ordered after March 28... the left, right or beyond the ATRS system, shall not exceed 5 feet. (e) Each ATRS system shall meet...

30 CFR 75.209 - Automated Temporary Roof Support (ATRS) systems.

Code of Federal Regulations, 2010 CFR

2010-07-01

... paragraph shall be met according to the following schedule: (1) All new machines ordered after March 28... the left, right or beyond the ATRS system, shall not exceed 5 feet. (e) Each ATRS system shall meet...

A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

ERIC Educational Resources Information Center

Chahine, Firas Safwan

2012-01-01

Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality

PubMed Central

Mehta, Dhwani; Siddiqui, Mohammad Faridul Haque

2018-01-01

Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human–Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam. PMID:29389845

Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality.

PubMed

Mehta, Dhwani; Siddiqui, Mohammad Faridul Haque; Javaid, Ahmad Y

2018-02-01

Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human-Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam.

The orally active and bioavailable ATR kinase inhibitor AZD6738 potentiates the anti-tumor effects of cisplatin to resolve ATM-deficient non-small cell lung cancer in vivo.

PubMed

Vendetti, Frank P; Lau, Alan; Schamus, Sandra; Conrads, Thomas P; O'Connor, Mark J; Bakkenist, Christopher J

2015-12-29

ATR and ATM are DNA damage signaling kinases that phosphorylate several thousand substrates. ATR kinase activity is increased at damaged replication forks and resected DNA double-strand breaks (DSBs). ATM kinase activity is increased at DSBs. ATM has been widely studied since ataxia telangiectasia individuals who express no ATM protein are the most radiosensitive patients identified. Since ATM is not an essential protein, it is widely believed that ATM kinase inhibitors will be well-tolerated in the clinic. ATR has been widely studied, but advances have been complicated by the finding that ATR is an essential protein and it is widely believed that ATR kinase inhibitors will be toxic in the clinic. We describe AZD6738, an orally active and bioavailable ATR kinase inhibitor. AZD6738 induces cell death and senescence in non-small cell lung cancer (NSCLC) cell lines. AZD6738 potentiates the cytotoxicity of cisplatin and gemcitabine in NSCLC cell lines with intact ATM kinase signaling, and potently synergizes with cisplatin in ATM-deficient NSCLC cells. In contrast to expectations, daily administration of AZD6738 and ATR kinase inhibition for 14 consecutive days is tolerated in mice and enhances the therapeutic efficacy of cisplatin in xenograft models. Remarkably, the combination of cisplatin and AZD6738 resolves ATM-deficient lung cancer xenografts.

Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

PubMed Central

Mala, S.; Latha, K.

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185

Feature selection in classification of eye movements using electrooculography for activity recognition.

PubMed

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

Object recognition and localization from 3D point clouds by maximum-likelihood estimation

NASA Astrophysics Data System (ADS)

Dantanarayana, Harshana G.; Huntley, Jonathan M.

2017-08-01

We present an algorithm based on maximum-likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike `interest point'-based algorithms which normally discard such data. Compared to the 6D Hough transform, it has negligible memory requirements, and is computationally efficient compared to iterative closest point algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degrees of freedom (d.f.) example is given, followed by a full 6 d.f. analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an RMS alignment error as low as 0.3 mm.

Influence of time and length size feature selections for human activity sequences recognition.

PubMed

Fang, Hongqing; Chen, Long; Srinivasan, Raghavendiran

2014-01-01

In this paper, Viterbi algorithm based on a hidden Markov model is applied to recognize activity sequences from observed sensors events. Alternative features selections of time feature values of sensors events and activity length size feature values are tested, respectively, and then the results of activity sequences recognition performances of Viterbi algorithm are evaluated. The results show that the selection of larger time feature values of sensor events and/or smaller activity length size feature values will generate relatively better results on the activity sequences recognition performances. © 2013 ISA Published by ISA All rights reserved.

Stereo vision with distance and gradient recognition

NASA Astrophysics Data System (ADS)

Kim, Soo-Hyun; Kang, Suk-Bum; Yang, Tae-Kyu

2007-12-01

Robot vision technology is needed for the stable walking, object recognition and the movement to the target spot. By some sensors which use infrared rays and ultrasonic, robot can overcome the urgent state or dangerous time. But stereo vision of three dimensional space would make robot have powerful artificial intelligence. In this paper we consider about the stereo vision for stable and correct movement of a biped robot. When a robot confront with an inclination plane or steps, particular algorithms are needed to go on without failure. This study developed the recognition algorithm of distance and gradient of environment by stereo matching process.

Near-infrared face recognition utilizing open CV software

NASA Astrophysics Data System (ADS)

Sellami, Louiza; Ngo, Hau; Fowler, Chris J.; Kearney, Liam M.

2014-06-01

Commercially available hardware, freely available algorithms, and authors' developed software are synergized successfully to detect and recognize subjects in an environment without visible light. This project integrates three major components: an illumination device operating in near infrared (NIR) spectrum, a NIR capable camera and a software algorithm capable of performing image manipulation, facial detection and recognition. Focusing our efforts in the near infrared spectrum allows the low budget system to operate covertly while still allowing for accurate face recognition. In doing so a valuable function has been developed which presents potential benefits in future civilian and military security and surveillance operations.

Fusing face-verification algorithms and humans.

PubMed

O'Toole, Alice J; Abdi, Hervé; Jiang, Fang; Phillips, P Jonathon

2007-10-01

It has been demonstrated recently that state-of-the-art face-recognition algorithms can surpass human accuracy at matching faces over changes in illumination. The ranking of algorithms and humans by accuracy, however, does not provide information about whether algorithms and humans perform the task comparably or whether algorithms and humans can be fused to improve performance. In this paper, we fused humans and algorithms using partial least square regression (PLSR). In the first experiment, we applied PLSR to face-pair similarity scores generated by seven algorithms participating in the Face Recognition Grand Challenge. The PLSR produced an optimal weighting of the similarity scores, which we tested for generality with a jackknife procedure. Fusing the algorithms' similarity scores using the optimal weights produced a twofold reduction of error rate over the most accurate algorithm. Next, human-subject-generated similarity scores were added to the PLSR analysis. Fusing humans and algorithms increased the performance to near-perfect classification accuracy. These results are discussed in terms of maximizing face-verification accuracy with hybrid systems consisting of multiple algorithms and humans.

The MITLL NIST LRE 2015 Language Recognition System

DTIC Science & Technology

2016-05-06

The MITLL NIST LRE 2015 Language Recognition System Pedro Torres-Carrasquillo, Najim Dehak*, Elizabeth Godoy, Douglas Reynolds, Fred Richardson...most recent MIT Lincoln Laboratory language recognition system developed for the NIST 2015 Language Recognition Evaluation (LRE). The submission...Task The National Institute of Science and Technology ( NIST ) has conducted formal evaluations of language detection algorithms since 1994. In

The MITLL NIST LRE 2015 Language Recognition system

DTIC Science & Technology

2016-02-05

The MITLL NIST LRE 2015 Language Recognition System Pedro Torres-Carrasquillo, Najim Dehak*, Elizabeth Godoy, Douglas Reynolds, Fred Richardson...recent MIT Lincoln Laboratory language recognition system developed for the NIST 2015 Language Recognition Evaluation (LRE). The submission features a...National Institute of Science and Technology ( NIST ) has conducted formal evaluations of language detection algorithms since 1994. In previous

Effectiveness of feature and classifier algorithms in character recognition systems

NASA Astrophysics Data System (ADS)

Wilson, Charles L.

1993-04-01

At the first Census Optical Character Recognition Systems Conference, NIST generated accuracy data for more than character recognition systems. Most systems were tested on the recognition of isolated digits and upper and lower case alphabetic characters. The recognition experiments were performed on sample sizes of 58,000 digits, and 12,000 upper and lower case alphabetic characters. The algorithms used by the 26 conference participants included rule-based methods, image-based methods, statistical methods, and neural networks. The neural network methods included Multi-Layer Perceptron's, Learned Vector Quantitization, Neocognitrons, and cascaded neural networks. In this paper 11 different systems are compared using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that used different algorithms for feature extraction and recognition performed with very high levels of correlation. This is true for neural network systems, hybrid systems, and statistically based systems, and leads to the conclusion that neural networks have not yet demonstrated a clear superiority to more conventional statistical methods. Comparison of these results with the models of Vapnick (for estimation problems), MacKay (for Bayesian statistical models), Moody (for effective parameterization), and Boltzmann models (for information content) demonstrate that as the limits of training data variance are approached, all classifier systems have similar statistical properties. The limiting condition can only be approached for sufficiently rich feature sets because the accuracy limit is controlled by the available information content of the training set, which must pass through the feature extraction process prior to classification.

Methods and Descriptive Epidemiology of Services Provided by Athletic Trainers in High Schools: The National Athletic Treatment, Injury and Outcomes Network Study.

PubMed

Kerr, Zachary Y; Dompier, Thomas P; Dalton, Sara L; Miller, Sayers John; Hayden, Ross; Marshall, Stephen W

2015-12-01

Research is limited on the extent and nature of the care provided by athletic trainers (ATs) to student-athletes in the high school setting. To describe the methods of the National Athletic Treatment, Injury and Outcomes Network (NATION) project and provide the descriptive epidemiology of AT services for injury care in 27 high school sports. Descriptive epidemiology study. Athletic training room (ATR) visits and AT services data collected in 147 high schools from 26 states. High school student-athletes participating in 13 boys' sports and 14 girls' sports during the 2011-2012 through 2013-2014 academic years. The number of ATR visits and individual AT services, as well as the mean number of ATR visits (per injury) and AT services (per injury and ATR visit) were calculated by sport and for time-loss (TL) and non-time-loss (NTL) injuries. Over the 3-year period, 210 773 ATR visits and 557 381 AT services were reported for 50 604 injuries. Most ATR visits (70%) were for NTL injuries. Common AT services were therapeutic activities or exercise (45.4%), modalities (18.6%), and AT evaluation and reevaluation (15.9%), with an average of 4.17 ± 6.52 ATR visits and 11.01 ± 22.86 AT services per injury. Compared with NTL injuries, patients with TL injuries accrued more ATR visits (7.76 versus 3.47; P < .001) and AT services (18.60 versus 9.56; P < .001) per injury. An average of 2.24 ± 1.33 AT services were reported per ATR visit. Compared with TL injuries, NTL injuries had a larger average number of AT services per ATR visit (2.28 versus 2.05; P < .001). These findings highlight the broad spectrum of care provided by ATs to high school student-athletes and demonstrate that patients with NTL injuries require substantial amounts of AT services.

Cross-modal face recognition using multi-matcher face scores

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Blasch, Erik

2015-05-01

The performance of face recognition can be improved using information fusion of multimodal images and/or multiple algorithms. When multimodal face images are available, cross-modal recognition is meaningful for security and surveillance applications. For example, a probe face is a thermal image (especially at nighttime), while only visible face images are available in the gallery database. Matching a thermal probe face onto the visible gallery faces requires crossmodal matching approaches. A few such studies were implemented in facial feature space with medium recognition performance. In this paper, we propose a cross-modal recognition approach, where multimodal faces are cross-matched in feature space and the recognition performance is enhanced with stereo fusion at image, feature and/or score level. In the proposed scenario, there are two cameras for stereo imaging, two face imagers (visible and thermal images) in each camera, and three recognition algorithms (circular Gaussian filter, face pattern byte, linear discriminant analysis). A score vector is formed with three cross-matched face scores from the aforementioned three algorithms. A classifier (e.g., k-nearest neighbor, support vector machine, binomial logical regression [BLR]) is trained then tested with the score vectors by using 10-fold cross validations. The proposed approach was validated with a multispectral stereo face dataset from 105 subjects. Our experiments show very promising results: ACR (accuracy rate) = 97.84%, FAR (false accept rate) = 0.84% when cross-matching the fused thermal faces onto the fused visible faces by using three face scores and the BLR classifier.

[Algorithm for the automated processing of rheosignals].

PubMed

Odinets, G S

1988-01-01

Algorithm for rheosignals recognition for a microprocessing device with a representation apparatus and with automated and manual cursor control was examined. The algorithm permits to automate rheosignals registrating and processing taking into account their changeability.

Total sulfur determination in residues of crude oil distillation using FT-IR/ATR and variable selection methods

NASA Astrophysics Data System (ADS)

Müller, Aline Lima Hermes; Picoloto, Rochele Sogari; Mello, Paola de Azevedo; Ferrão, Marco Flores; dos Santos, Maria de Fátima Pereira; Guimarães, Regina Célia Lourenço; Müller, Edson Irineu; Flores, Erico Marlon Moraes

2012-04-01

Total sulfur concentration was determined in atmospheric residue (AR) and vacuum residue (VR) samples obtained from petroleum distillation process by Fourier transform infrared spectroscopy with attenuated total reflectance (FT-IR/ATR) in association with chemometric methods. Calibration and prediction set consisted of 40 and 20 samples, respectively. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). Different treatments and pre-processing steps were also evaluated for the development of models. The pre-treatment based on multiplicative scatter correction (MSC) and the mean centered data were selected for models construction. The use of siPLS as variable selection method provided a model with root mean square error of prediction (RMSEP) values significantly better than those obtained by PLS model using all variables. The best model was obtained using siPLS algorithm with spectra divided in 20 intervals and combinations of 3 intervals (911-824, 823-736 and 737-650 cm-1). This model produced a RMSECV of 400 mg kg-1 S and RMSEP of 420 mg kg-1 S, showing a correlation coefficient of 0.990.

A dynamical pattern recognition model of gamma activity in auditory cortex

PubMed Central

Zavaglia, M.; Canolty, R.T.; Schofield, T.M.; Leff, A.P.; Ursino, M.; Knight, R.T.; Penny, W.D.

2012-01-01

This paper describes a dynamical process which serves both as a model of temporal pattern recognition in the brain and as a forward model of neuroimaging data. This process is considered at two separate levels of analysis: the algorithmic and implementation levels. At an algorithmic level, recognition is based on the use of Occurrence Time features. Using a speech digit database we show that for noisy recognition environments, these features rival standard cepstral coefficient features. At an implementation level, the model is defined using a Weakly Coupled Oscillator (WCO) framework and uses a transient synchronization mechanism to signal a recognition event. In a second set of experiments, we use the strength of the synchronization event to predict the high gamma (75–150 Hz) activity produced by the brain in response to word versus non-word stimuli. Quantitative model fits allow us to make inferences about parameters governing pattern recognition dynamics in the brain. PMID:22327049

Structural model constructing for optical handwritten character recognition

NASA Astrophysics Data System (ADS)

Khaustov, P. A.; Spitsyn, V. G.; Maksimova, E. I.

2017-02-01

The article is devoted to the development of the algorithms for optical handwritten character recognition based on the structural models constructing. The main advantage of these algorithms is the low requirement regarding the number of reference images. The one-pass approach to a thinning of the binary character representation has been proposed. This approach is based on the joint use of Zhang-Suen and Wu-Tsai algorithms. The effectiveness of the proposed approach is confirmed by the results of the experiments. The article includes the detailed description of the structural model constructing algorithm’s steps. The proposed algorithm has been implemented in character processing application and has been approved on MNIST handwriting characters database. Algorithms that could be used in case of limited reference images number were used for the comparison.

A physiologically based pharmacokinetic model for atrazine and its main metabolites in the adult male C57BL/6 mouse

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

Lin Zhoumeng; Interdisciplinary Toxicology Program, University of Georgia, Athens, GA 30602; Fisher, Jeffrey W.

Atrazine (ATR) is a chlorotriazine herbicide that is widely used and relatively persistent in the environment. In laboratory rodents, excessive exposure to ATR is detrimental to the reproductive, immune, and nervous systems. To better understand the toxicokinetics of ATR and to fill the need for a mouse model, a physiologically based pharmacokinetic (PBPK) model for ATR and its main chlorotriazine metabolites (Cl-TRIs) desethyl atrazine (DE), desisopropyl atrazine (DIP), and didealkyl atrazine (DACT) was developed for the adult male C57BL/6 mouse. Taking advantage of all relevant and recently made available mouse-specific data, a flow-limited PBPK model was constructed. The ATR andmore » DACT sub-models included blood, brain, liver, kidney, richly and slowly perfused tissue compartments, as well as plasma protein binding and red blood cell binding, whereas the DE and DIP sub-models were constructed as simple five-compartment models. The model adequately simulated plasma levels of ATR and Cl-TRIs and urinary dosimetry of Cl-TRIs at four single oral dose levels (250, 125, 25, and 5 mg/kg). Additionally, the model adequately described the dose dependency of brain and liver ATR and DACT concentrations. Cumulative urinary DACT amounts were accurately predicted across a wide dose range, suggesting the model's potential use for extrapolation to human exposures by performing reverse dosimetry. The model was validated using previously reported data for plasma ATR and DACT in mice and rats. Overall, besides being the first mouse PBPK model for ATR and its Cl-TRIs, this model, by analogy, provides insights into tissue dosimetry for rats. The model could be used in tissue dosimetry prediction and as an aid in the exposure assessment to this widely used herbicide.« less

Replaced platelet concentrates containing a new additive solution, M-sol: safety and efficacy for pediatric patients.

PubMed

Yanagisawa, Ryu; Shimodaira, Shigetaka; Kojima, Shunsuke; Nakasone, Nobuhiko; Ishikawa, Shinsuke; Momose, Kayo; Honda, Takayuki; Yoshikawa, Kentaro; Saito, Shoji; Tanaka, Miyuki; Nakazawa, Yozo; Sakashita, Kazuo; Shiohara, Masaaki; Akino, Mitsuaki; Hirayama, Junichi; Azuma, Hiroshi; Koike, Kenichi

2013-09-01

Allergic transfusion reactions (ATRs), particularly those caused by plasma-rich platelet concentrates (P-PCs), are an important concern in transfusion medicine. Replacing P-PCs with PCs containing M-sol (M-sol-R-PCs) is expected to prevent ATRs. However, this has not yet been verified by sufficient clinical evidence. A retrospective cohort study was performed between 2008 and 2011. Pediatric patients with hematologic disorders, solid tumors, primary immunodeficiency disorders, or inherited metabolic disorders were transfused with M-sol-R-PCs between 2010 and 2011; the transfusions of P-PCs administered between 2008 and 2011 were compared in terms of frequency and severity of ATRs, corrected count increment (CCI), and occurrence of bleeding. Data were collected for 6 consecutive months on a per-patient basis. Data obtained during 2008 to 2011 showed that of the 78 patients receiving 515 P-PC transfusions, 14 (17.9%) had 17 ATRs (3.3%); 14 and three ATRs were of Grades 1 and 2, respectively. In 2010 to 2011, 49 patients received 620 transfusions of M-sol-R-PCs, and two patients (4.1%) had Grade 1 ATRs (0.3%). Thus, the frequency of ATRs per bag and per patient differed significantly between the two transfusions. No steroid agents were used for the prevention or treatment of ATRs in the M-sol-R-PC group. The CCI (24 hr) for M-sol-R-PCs did not differ from that for P-PCs. M-sol-R-PCs were found to be effective in preventing ATRs without loss of transfusion efficiency in children; however, its efficacy should be further evaluated in prospective clinical trials. © 2012 American Association of Blood Banks.

Atrazine enhances progesterone production through activation of multiple signaling pathways in FSH-stimulated rat granulosa cells: evidence for premature luteinization.

PubMed

Pogrmic-Majkic, Kristina; Samardzija, Dragana; Fa, Svetlana; Hrubik, Jelena; Glisic, Branka; Kaisarevic, Sonja; Andric, Nebojsa

2014-11-01

Premature luteinization is a possible cause of infertility in women. It is currently unknown whether environmental chemicals can induce changes associated with premature luteinization. Using rat granulosa cells (GC) in vitro, we demonstrated that exposure to atrazine (ATR), a widely used herbicide, causes GC phenotype that resembles that of human premature luteinization. At the end of the 48-h stimulation with FSH, ATR-exposed GC showed (1) higher levels of progesterone, (2) overexpression of luteal markers (Star and Cyp11a1), and (3) an increase in progesterone:estradiol ratio above 1. Mechanistic experiments were conducted to understand the signaling events engaged by ATR that lead to this phenotype. Western blot analysis revealed prolonged phosphorylation of protein kinase B (AKT) and cAMP response element-binding protein (CREB) in ATR- and FSH-exposed GC. An increased level of ERK1/2-dependent transcriptional factor CCATT/enhancer-binding protein beta (CEBPB) was observed after 4 h of ATR exposure. Inhibitors of PI3K (wortmannin) and MEK (U0126) prevented ATR-induced rise in progesterone level and expression of luteal markers in FSH-stimulated GC. Atrazine intensified AKT and CEBPB signaling and caused Star overexpression in forskolin-stimulated GC but not in epidermal growth factor (EGF)-stimulated GC. In the presence of rolipram, a specific inhibitor of phosphodiesterase 4 (PDE4), ATR was not able to further elevate AKT phosphorylation, CEBPB protein level, and Star mRNA in FSH-stimulated GC, suggesting that ATR inhibits PDE4. Overall, this study showed that ATR acts as a FSH sensitizer leading to enhanced cAMP, AKT, and CEBPB signaling and progesterone biosynthesis, which promotes premature luteinization phenotype in GC. © 2014 by the Society for the Study of Reproduction, Inc.

Unsupervised learning in persistent sensing for target recognition by wireless ad hoc networks of ground-based sensors

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

In previous work by the author, effective persistent and pervasive sensing for recognition and tracking of battlefield targets were seen to be achieved, using intelligent algorithms implemented by distributed mobile agents over a composite system of unmanned aerial vehicles (UAVs) for persistence and a wireless network of unattended ground sensors for pervasive coverage of the mission environment. While simulated performance results for the supervised algorithms of the composite system are shown to provide satisfactory target recognition over relatively brief periods of system operation, this performance can degrade by as much as 50% as target dynamics in the environment evolve beyond the period of system operation in which the training data are representative. To overcome this limitation, this paper applies the distributed approach using mobile agents to the network of ground-based wireless sensors alone, without the UAV subsystem, to provide persistent as well as pervasive sensing for target recognition and tracking. The supervised algorithms used in the earlier work are supplanted by unsupervised routines, including competitive-learning neural networks (CLNNs) and new versions of support vector machines (SVMs) for characterization of an unknown target environment. To capture the same physical phenomena from battlefield targets as the composite system, the suite of ground-based sensors can be expanded to include imaging and video capabilities. The spatial density of deployed sensor nodes is increased to allow more precise ground-based location and tracking of detected targets by active nodes. The "swarm" mobile agents enabling WSN intelligence are organized in a three processing stages: detection, recognition and sustained tracking of ground targets. Features formed from the compressed sensor data are down-selected according to an information-theoretic algorithm that reduces redundancy within the feature set, reducing the dimension of samples used in the target recognition and tracking routines. Target tracking is based on simplified versions of Kalman filtration. Accuracy of recognition and tracking of implemented versions of the proposed suite of unsupervised algorithms is somewhat degraded from the ideal. Target recognition and tracking by supervised routines and by unsupervised SVM and CLNN routines in the ground-based WSN is evaluated in simulations using published system values and sensor data from vehicular targets in ground-surveillance scenarios. Results are compared with previously published performance for the system of the ground-based sensor network (GSN) and UAV swarm.

Subject independent facial expression recognition with robust face detection using a convolutional neural network.

PubMed

Matsugu, Masakazu; Mori, Katsuhiko; Mitari, Yusuke; Kaneda, Yuji

2003-01-01

Reliable detection of ordinary facial expressions (e.g. smile) despite the variability among individuals as well as face appearance is an important step toward the realization of perceptual user interface with autonomous perception of persons. We describe a rule-based algorithm for robust facial expression recognition combined with robust face detection using a convolutional neural network. In this study, we address the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. The result shows reliable detection of smiles with recognition rate of 97.6% for 5600 still images of more than 10 subjects. The proposed algorithm demonstrated the ability to discriminate smiling from talking based on the saliency score obtained from voting visual cues. To the best of our knowledge, it is the first facial expression recognition model with the property of subject independence combined with robustness to variability in facial appearance.

Anticipatory Traumatic Reaction: Outcomes Arising From Secondary Exposure to Disasters and Large-Scale Threats.

PubMed

Hopwood, Tanya L; Schutte, Nicola S; Loi, Natasha M

2017-09-01

Two studies, with a total of 707 participants, developed and examined the reliability and validity of a measure for anticipatory traumatic reaction (ATR), a novel construct describing a form of distress that may occur in response to threat-related media reports and discussions. Exploratory and confirmatory factor analysis resulted in a scale comprising three subscales: feelings related to future threat; preparatory thoughts and actions; and disruption to daily activities. Internal consistency was .93 for the overall ATR scale. The ATR scale demonstrated convergent validity through associations with negative affect, depression, anxiety, stress, neuroticism, and repetitive negative thinking. The scale showed discriminant validity in relationships to Big Five characteristics. The ATR scale had some overlap with a measure of posttraumatic stress disorder, but also showed substantial separate variance. This research provides preliminary evidence for the novel construct of ATR as well as a measure of the construct. The ATR scale will allow researchers to further investigate anticipatory traumatic reaction in the fields of trauma, clinical practice, and social psychology.

In Vitro Analysis of the Role of Replication Protein A (RPA) and RPA Phosphorylation in ATR-mediated Checkpoint Signaling*

PubMed Central

Lindsey-Boltz, Laura A.; Reardon, Joyce T.; Wold, Marc S.; Sancar, Aziz

2012-01-01

Replication protein A (RPA) plays essential roles in DNA metabolism, including replication, checkpoint, and repair. Recently, we described an in vitro system in which the phosphorylation of human Chk1 kinase by ATR (ataxia telangiectasia mutated and Rad3-related) is dependent on RPA bound to single-stranded DNA. Here, we report that phosphorylation of other ATR targets, p53 and Rad17, has the same requirements and that RPA is also phosphorylated in this system. At high p53 or Rad17 concentrations, RPA phosphorylation is inhibited and, in this system, RPA with phosphomimetic mutations cannot support ATR kinase function, whereas a non-phosphorylatable RPA mutant exhibits full activity. Phosphorylation of these ATR substrates depends on the recruitment of ATR and the substrates by RPA to the RPA-ssDNA complex. Finally, mutant RPAs lacking checkpoint function exhibit essentially normal activity in nucleotide excision repair, revealing RPA separation of function for checkpoint and excision repair. PMID:22948311

In vitro analysis of the role of replication protein A (RPA) and RPA phosphorylation in ATR-mediated checkpoint signaling.

PubMed

Lindsey-Boltz, Laura A; Reardon, Joyce T; Wold, Marc S; Sancar, Aziz

2012-10-19

Replication protein A (RPA) plays essential roles in DNA metabolism, including replication, checkpoint, and repair. Recently, we described an in vitro system in which the phosphorylation of human Chk1 kinase by ATR (ataxia telangiectasia mutated and Rad3-related) is dependent on RPA bound to single-stranded DNA. Here, we report that phosphorylation of other ATR targets, p53 and Rad17, has the same requirements and that RPA is also phosphorylated in this system. At high p53 or Rad17 concentrations, RPA phosphorylation is inhibited and, in this system, RPA with phosphomimetic mutations cannot support ATR kinase function, whereas a non-phosphorylatable RPA mutant exhibits full activity. Phosphorylation of these ATR substrates depends on the recruitment of ATR and the substrates by RPA to the RPA-ssDNA complex. Finally, mutant RPAs lacking checkpoint function exhibit essentially normal activity in nucleotide excision repair, revealing RPA separation of function for checkpoint and excision repair.

Bayesian paradox in homeland security and homeland defense

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Forrester, Thomas; Wang, Wenjian

2011-06-01

In this paper we discuss a rather surprising result of Bayesian inference analysis: performance of a broad variety of sensors depends not only on a sensor system itself, but also on CONOPS parameters in such a way that even an excellent sensor system can perform poorly if absolute probabilities of a threat (target) are lower than a false alarm probability. This result, which we call Bayesian paradox, holds not only for binary sensors as discussed in the lead author's previous papers, but also for a more general class of multi-target sensors, discussed also in this paper. Examples include: ATR (automatic target recognition), luggage X-ray inspection for explosives, medical diagnostics, car engine diagnostics, judicial decisions, and many other issues.

The Maximum Likelihood Estimation of Signature Transformation /MLEST/ algorithm. [for affine transformation of crop inventory data

NASA Technical Reports Server (NTRS)

Thadani, S. G.

1977-01-01

The Maximum Likelihood Estimation of Signature Transformation (MLEST) algorithm is used to obtain maximum likelihood estimates (MLE) of affine transformation. The algorithm has been evaluated for three sets of data: simulated (training and recognition segment pairs), consecutive-day (data gathered from Landsat images), and geographical-extension (large-area crop inventory experiment) data sets. For each set, MLEST signature extension runs were made to determine MLE values and the affine-transformed training segment signatures were used to classify the recognition segments. The classification results were used to estimate wheat proportions at 0 and 1% threshold values.

Tracking Small Artists

NASA Astrophysics Data System (ADS)

Russell, James C.; Klette, Reinhard; Chen, Chia-Yen

Tracks of small animals are important in environmental surveillance, where pattern recognition algorithms allow species identification of the individuals creating tracks. These individuals can also be seen as artists, presented in their natural environments with a canvas upon which they can make prints. We present tracks of small mammals and reptiles which have been collected for identification purposes, and re-interpret them from an esthetic point of view. We re-classify these tracks not by their geometric qualities as pattern recognition algorithms would, but through interpreting the 'artist', their brush strokes and intensity. We describe the algorithms used to enhance and present the work of the 'artists'.

Effects of the Hydration State on the Mid-Infrared Spectra of Urea and Creatinine in Relation to Urine Analyses

PubMed Central

Oliver, Katherine V.; Maréchal, Amandine

2016-01-01

When analyzing solutes by Fourier transform infrared (FT-IR) spectroscopy in attenuated total reflection (ATR) mode, drying of samples onto the ATR crystal surface can greatly increase solute band intensities and, therefore, aid detection of minor components. However, analysis of such spectra is complicated by the existence of alternative partial hydration states of some substances that can significantly alter their infrared signatures. This is illustrated here with urea, which is a dominant component of urine. The effects of hydration state on its infrared spectrum were investigated both by incubation in atmospheres of fixed relative humidities and by recording serial spectra during the drying process. Significant changes of absorption band positions and shapes were observed. Decomposition of the CN antisymmetric stretching (νas) band in all states was possible with four components whose relative intensities varied with hydration state. These correspond to the solution (1468 cm–1) and dry (1464 cm–1) states and two intermediate (1454 cm–1 and 1443 cm–1) forms that arise from specific urea–water and/or urea–urea interactions. Such intermediate forms of other compounds can also be formed, as demonstrated here with creatinine. Recognition of these states and their accommodation in analyses of materials such as dried urine allows more precise decomposition of spectra so that weaker bands of diagnostic interest can be more accurately defined. PMID:27170705

Gondola-shaped tetra-rhenium metallacycles modified evanescent wave infrared chemical sensors for selective determination of volatile organic compounds.

PubMed

Huang, Genin Gary; Lee, Chung-Jay; Tsai, Bo-Chan; Yang, Jyisy; Sathiyendiran, Malaichamy; Lu, Kuang-Lieh

2011-07-15

Water-stable and cavity-contained rhenium metallacycles were synthesized, and their ability to selectively interact with volatile organic compounds (VOCs) systematically studied using attenuated total reflection infrared (ATR-IR) spectroscopy. Integrating the unique properties of rhenium metallacycles into optical sensing technologies significantly improves selectivity in detecting aromatic compounds. To explore the interaction of rhenium metallacycles with VOCs, the surface of ATR sensing elements was modified with the synthesized rhenium metallacycles and used to detect VOCs. The results indicate that rhenium metallacycles have crown ether-like recognition sites, which can selectively interact with aromatic compounds, especially those bearing polar functional groups. The IR absorption bands of rhenium metallacycles shift significantly upon adsorption of aromatic VOCs, revealing a strong interaction between the tetra-rhenium metallacycles and guest aromatic compounds. Optimizing the thickness of the metallacycles coated on the surface of the sensing element led to rapid response in detection. The dynamic range of response was generally up to 30 mg/L with detection limits ca. 30 μg/L. Further studies of the effect of interferences indicate that recovery can be higher than 95% for most of the compounds tested. The results on the flow-cell device indicated that the performances were similar to a static detection system but the detection of VOCs can be largely simplified. Copyright © 2011 Elsevier B.V. All rights reserved.

Three-dimensional fingerprint recognition by using convolution neural network

NASA Astrophysics Data System (ADS)

Tian, Qianyu; Gao, Nan; Zhang, Zonghua

2018-01-01

With the development of science and technology and the improvement of social information, fingerprint recognition technology has become a hot research direction and been widely applied in many actual fields because of its feasibility and reliability. The traditional two-dimensional (2D) fingerprint recognition method relies on matching feature points. This method is not only time-consuming, but also lost three-dimensional (3D) information of fingerprint, with the fingerprint rotation, scaling, damage and other issues, a serious decline in robustness. To solve these problems, 3D fingerprint has been used to recognize human being. Because it is a new research field, there are still lots of challenging problems in 3D fingerprint recognition. This paper presents a new 3D fingerprint recognition method by using a convolution neural network (CNN). By combining 2D fingerprint and fingerprint depth map into CNN, and then through another CNN feature fusion, the characteristics of the fusion complete 3D fingerprint recognition after classification. This method not only can preserve 3D information of fingerprints, but also solves the problem of CNN input. Moreover, the recognition process is simpler than traditional feature point matching algorithm. 3D fingerprint recognition rate by using CNN is compared with other fingerprint recognition algorithms. The experimental results show that the proposed 3D fingerprint recognition method has good recognition rate and robustness.

Atmospheric turbulence and sensor system effects on biometric algorithm performance

NASA Astrophysics Data System (ADS)

Espinola, Richard L.; Leonard, Kevin R.; Byrd, Kenneth A.; Potvin, Guy

2015-05-01

Biometric technologies composed of electro-optical/infrared (EO/IR) sensor systems and advanced matching algorithms are being used in various force protection/security and tactical surveillance applications. To date, most of these sensor systems have been widely used in controlled conditions with varying success (e.g., short range, uniform illumination, cooperative subjects). However the limiting conditions of such systems have yet to be fully studied for long range applications and degraded imaging environments. Biometric technologies used for long range applications will invariably suffer from the effects of atmospheric turbulence degradation. Atmospheric turbulence causes blur, distortion and intensity fluctuations that can severely degrade image quality of electro-optic and thermal imaging systems and, for the case of biometrics technology, translate to poor matching algorithm performance. In this paper, we evaluate the effects of atmospheric turbulence and sensor resolution on biometric matching algorithm performance. We use a subset of the Facial Recognition Technology (FERET) database and a commercial algorithm to analyze facial recognition performance on turbulence degraded facial images. The goal of this work is to understand the feasibility of long-range facial recognition in degraded imaging conditions, and the utility of camera parameter trade studies to enable the design of the next generation biometrics sensor systems.

Real-time skeleton tracking for embedded systems

NASA Astrophysics Data System (ADS)

Coleca, Foti; Klement, Sascha; Martinetz, Thomas; Barth, Erhardt

2013-03-01

Touch-free gesture technology is beginning to become more popular with consumers and may have a significant future impact on interfaces for digital photography. However, almost every commercial software framework for gesture and pose detection is aimed at either desktop PCs or high-powered GPUs, making mobile implementations for gesture recognition an attractive area for research and development. In this paper we present an algorithm for hand skeleton tracking and gesture recognition that runs on an ARM-based platform (Pandaboard ES, OMAP 4460 architecture). The algorithm uses self-organizing maps to fit a given topology (skeleton) into a 3D point cloud. This is a novel way of approaching the problem of pose recognition as it does not employ complex optimization techniques or data-based learning. After an initial background segmentation step, the algorithm is ran in parallel with heuristics, which detect and correct artifacts arising from insufficient or erroneous input data. We then optimize the algorithm for the ARM platform using fixed-point computation and the NEON SIMD architecture the OMAP4460 provides. We tested the algorithm with two different depth-sensing devices (Microsoft Kinect, PMD Camboard). For both input devices we were able to accurately track the skeleton at the native framerate of the cameras.

Sensor-Aware Recognition and Tracking for Wide-Area Augmented Reality on Mobile Phones

PubMed Central

Chen, Jing; Cao, Ruochen; Wang, Yongtian

2015-01-01

Wide-area registration in outdoor environments on mobile phones is a challenging task in mobile augmented reality fields. We present a sensor-aware large-scale outdoor augmented reality system for recognition and tracking on mobile phones. GPS and gravity information is used to improve the VLAD performance for recognition. A kind of sensor-aware VLAD algorithm, which is self-adaptive to different scale scenes, is utilized to recognize complex scenes. Considering vision-based registration algorithms are too fragile and tend to drift, data coming from inertial sensors and vision are fused together by an extended Kalman filter (EKF) to achieve considerable improvements in tracking stability and robustness. Experimental results show that our method greatly enhances the recognition rate and eliminates the tracking jitters. PMID:26690439

Sensor-Aware Recognition and Tracking for Wide-Area Augmented Reality on Mobile Phones.

PubMed

Chen, Jing; Cao, Ruochen; Wang, Yongtian

2015-12-10

Wide-area registration in outdoor environments on mobile phones is a challenging task in mobile augmented reality fields. We present a sensor-aware large-scale outdoor augmented reality system for recognition and tracking on mobile phones. GPS and gravity information is used to improve the VLAD performance for recognition. A kind of sensor-aware VLAD algorithm, which is self-adaptive to different scale scenes, is utilized to recognize complex scenes. Considering vision-based registration algorithms are too fragile and tend to drift, data coming from inertial sensors and vision are fused together by an extended Kalman filter (EKF) to achieve considerable improvements in tracking stability and robustness. Experimental results show that our method greatly enhances the recognition rate and eliminates the tracking jitters.

Selection of Norway spruce somatic embryos by computer vision

NASA Astrophysics Data System (ADS)

Hamalainen, Jari J.; Jokinen, Kari J.

1993-05-01

A computer vision system was developed for the classification of plant somatic embryos. The embryos are in a Petri dish that is transferred with constant speed and they are recognized as they pass a line scan camera. A classification algorithm needs to be installed for every plant species. This paper describes an algorithm for the recognition of Norway spruce (Picea abies) embryos. A short review of conifer micropropagation by somatic embryogenesis is also given. The recognition algorithm is based on features calculated from the boundary of the object. Only part of the boundary corresponding to the developing cotyledons (2 - 15) and the straight sides of the embryo are used for recognition. An index of the length of the cotyledons describes the developmental stage of the embryo. The testing set for classifier performance consisted of 118 embryos and 478 nonembryos. With the classification tolerances chosen 69% of the objects classified as embryos by a human classifier were selected and 31$% rejected. Less than 1% of the nonembryos were classified as embryos. The basic features developed can probably be easily adapted for the recognition of other conifer somatic embryos.

ATRAZINE DISPOSITION IN PREGNANT AND LACTATING LONG-EVANS RATS

EPA Science Inventory

Atrazine (ATR) is a widely used herbicide shown to delay early mammary development in female offspring of gestationally exposed rats. The effects of ATR can be induced by in utero exposure and/or suckling from a dam exposed during late pregnancy, but ATR is reported to have a hal...

XNP-1/ATR-X acts with RB, HP1 and the NuRD complex during larval development in C. elegans.

PubMed

Cardoso, Carlos; Couillault, Carole; Mignon-Ravix, Cecile; Millet, Anne; Ewbank, Jonathan J; Fontés, Michel; Pujol, Nathalie

2005-02-01

Mutations in the XNP/ATR-X gene cause several X-linked mental retardation syndromes in humans. The XNP/ATR-X gene encodes a DNA-helicase belonging to the SNF2 family. It has been proposed that XNP/ATR-X might be involved in chromatin remodelling. The lack of a mouse model for the ATR-X syndrome has, however, hampered functional studies of XNP/ATR-X. C. elegans possesses one homolog of the XNP/ATR-X gene, named xnp-1. By analysing a deletion mutant, we show that xnp-1 is required for the development of the embryo and the somatic gonad. Moreover, we show that abrogation of xnp-1 function in combination with inactivation of genes of the NuRD complex, as well as lin-35/Rb and hpl-2/HP1 leads to a stereotyped block of larval development with a cessation of growth but not of cell division. We also demonstrate a specific function for xnp-1 together with lin-35 or hpl-2 in the control of transgene expression, a process known to be dependent on chromatin remodelling. This study thus demonstrates that in vivo XNP-1 acts in association with RB, HP1 and the NuRD complex during development.

Effects of timing of signal indicating jump directions on knee biomechanics in jump-landing-jump tasks.

PubMed

Stephenson, Mitchell L; Hinshaw, Taylour J; Wadley, Haley A; Zhu, Qin; Wilson, Margaret A; Byra, Mark; Dai, Boyi

2018-03-01

A variety of the available time to react (ATR) has been utilised to study knee biomechanics during reactive jump-landing tasks. The purpose was to quantify knee kinematics and kinetics during a jump-land-jump task of three possible directions as the ATR was reduced. Thirty-four recreational athletes performed 45 trials of a jump-land-jump task, during which the direction of the second jump (lateral, medial or vertical) was indicated before they initiated the first jump, the instant they initiated the first jump, 300 ms before landing, 150 ms before landing or at the instant of landing. Knee joint angles and moments close to the instant of landing were significantly different when the ATR was equal to or more than 300 ms before landing, but became similar when the ATR was 150 ms or 0 ms before landing. As the ATR was decreased, knee moments decreased for the medial jump direction, but increased for the lateral jump direction. When the ATR is shorter than an individual's reaction time, the movement pattern cannot be pre-planned before landing. Knee biomechanics are dependent on the timing of the signal and the subsequent jump direction. Precise control of timing and screening athletes with low ATR are suggested.

Asarone from Acori Tatarinowii Rhizoma Potentiates the Nerve Growth Factor-Induced Neuronal Differentiation in Cultured PC12 Cells: A Signaling Mediated by Protein Kinase A

PubMed Central

Lam, Kelly Y. C.; Chen, Jianping; Lam, Candy T. W.; Wu, Qiyun; Yao, Ping; Dong, Tina T. X.; Lin, Huangquan; Tsim, Karl W. K.

2016-01-01

Acori Tatarinowii Rhizoma (ATR), the rhizome of Acorus tatarinowii Schott, is being used clinically to treat neurological disorders. The volatile oil of ATR is being considered as an active ingredient. Here, α-asarone and β-asarone, accounting about 95% of ATR oil, were evaluated for its function in stimulating neurogenesis. In cultured PC12 cells, application of ATR volatile oil, α-asarone or β-asarone, stimulated the expression of neurofilaments, a bio-marker for neurite outgrowth, in a concentration-dependent manner. The co-treatment of ATR volatile oil, α-asarone or β-asarone, with low concentration of nerve growth factor (NGF) potentiated the NGF-induced neuronal differentiation in cultured PC12 cells. In addition, application of protein kinase A inhibitors, H89 and KT5720, in cultures blocked the ATR-induced neurofilament expression, as well as the phosphorylation of cAMP-responsive element binding protein (CREB). In the potentiation of NGF-induced signaling in cultured PC12 cells, α-asarone and β-asarone showed synergistic effects. These results proposed the neurite-promoting asarone, or ATR volatile oil, could be useful in finding potential drugs for treating various neurodegenerative diseases, in which neurotrophin deficiency is normally involved. PMID:27685847

A fusion approach for coarse-to-fine target recognition

NASA Astrophysics Data System (ADS)

Folkesson, Martin; Grönwall, Christina; Jungert, Erland

2006-04-01

A fusion approach in a query based information system is presented. The system is designed for querying multimedia data bases, and here applied to target recognition using heterogeneous data sources. The recognition process is coarse-to-fine, with an initial attribute estimation step and a following matching step. Several sensor types and algorithms are involved in each of these two steps. An independence of the matching results, on the origin of the estimation results, is observed. It allows for distribution of data between algorithms in an intermediate fusion step, without risk of data incest. This increases the overall chance of recognising the target. An implementation of the system is described.

Document Form and Character Recognition using SVM

NASA Astrophysics Data System (ADS)

Park, Sang-Sung; Shin, Young-Geun; Jung, Won-Kyo; Ahn, Dong-Kyu; Jang, Dong-Sik

2009-08-01

Because of development of computer and information communication, EDI (Electronic Data Interchange) has been developing. There is OCR (Optical Character Recognition) of Pattern recognition technology for EDI. OCR contributed to changing many manual in the past into automation. But for the more perfect database of document, much manual is needed for excluding unnecessary recognition. To resolve this problem, we propose document form based character recognition method in this study. Proposed method is divided into document form recognition part and character recognition part. Especially, in character recognition, change character into binarization by using SVM algorithm and extract more correct feature value.

Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

NASA Astrophysics Data System (ADS)

Lee, Chien-Cheng; Huang, Shin-Sheng; Shih, Cheng-Yuan

2010-12-01

This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

NASA Astrophysics Data System (ADS)

Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.

2018-04-01

This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

[State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral Regression Discriminant Analysis].

PubMed

Yu, Shuang; Liu, Guo-hai; Xia, Rong-sheng; Jiang, Hui

2016-01-01

In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct recognition rate of the Adaboost-SRDA-NN model achieved 100% in the validation set. The overall results demonstrate that SRDA algorithm can effectively achieve the spectral feature information extraction to the spectral dimension reduction in model calibration process of qualitative analysis of NIR spectroscopy. In addition, the Adaboost lifting algorithm can improve the classification accuracy of the final model. The results obtained in this work can provide research foundation for developing online monitoring instruments for the monitoring of SSF process.

An improved silhouette for human pose estimation

NASA Astrophysics Data System (ADS)

Hawes, Anthony H.; Iftekharuddin, Khan M.

2017-08-01

We propose a novel method for analyzing images that exploits the natural lines of a human poses to find areas where self-occlusion could be present. Errors caused by self-occlusion cause several modern human pose estimation methods to mis-identify body parts, which reduces the performance of most action recognition algorithms. Our method is motivated by the observation that, in several cases, occlusion can be reasoned using only boundary lines of limbs. An intelligent edge detection algorithm based on the above principle could be used to augment the silhouette with information useful for pose estimation algorithms and push forward progress on occlusion handling for human action recognition. The algorithm described is applicable to computer vision scenarios involving 2D images and (appropriated flattened) 3D images.

Optimization of internet content filtering-Combined with KNN and OCAT algorithms

NASA Astrophysics Data System (ADS)

Guo, Tianze; Wu, Lingjing; Liu, Jiaming

2018-04-01

The face of the status quo that rampant illegal content in the Internet, the result of traditional way to filter information, keyword recognition and manual screening, is getting worse. Based on this, this paper uses OCAT algorithm nested by KNN classification algorithm to construct a corpus training library that can dynamically learn and update, which can be improved on the filter corpus for constantly updated illegal content of the network, including text and pictures, and thus can better filter and investigate illegal content and its source. After that, the research direction will focus on the simplified updating of recognition and comparison algorithms and the optimization of the corpus learning ability in order to improve the efficiency of filtering, save time and resources.

A Fuzzy Aproach For Facial Emotion Recognition

NASA Astrophysics Data System (ADS)

Gîlcă, Gheorghe; Bîzdoacă, Nicu-George

2015-09-01

This article deals with an emotion recognition system based on the fuzzy sets. Human faces are detected in images with the Viola - Jones algorithm and for its tracking in video sequences we used the Camshift algorithm. The detected human faces are transferred to the decisional fuzzy system, which is based on the variable fuzzyfication measurements of the face: eyebrow, eyelid and mouth. The system can easily determine the emotional state of a person.

An automatic iris occlusion estimation method based on high-dimensional density estimation.

PubMed

Li, Yung-Hui; Savvides, Marios

2013-04-01

Iris masks play an important role in iris recognition. They indicate which part of the iris texture map is useful and which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. The accuracy of the iris mask is extremely important. The performance of the iris recognition system will decrease dramatically when the iris mask is inaccurate, even when the best recognition algorithm is used. Traditionally, people used the rule-based algorithms to estimate iris masks from iris images. However, the accuracy of the iris masks generated this way is questionable. In this work, we propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Experimental results show that the masks generated by the proposed algorithm increase the iris recognition rate on both ICE2 and UBIRIS dataset, verifying the effectiveness and importance of our proposed method for iris occlusion estimation.

Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine

NASA Astrophysics Data System (ADS)

Graham, James; Ternovskiy, Igor V.

2013-06-01

We applied a two stage unsupervised hierarchical learning system to model complex dynamic surveillance and cyber space monitoring systems using a non-commercial version of the NeoAxis visualization software. The hierarchical scene learning and recognition approach is based on hierarchical expectation maximization, and was linked to a 3D graphics engine for validation of learning and classification results and understanding the human - autonomous system relationship. Scene recognition is performed by taking synthetically generated data and feeding it to a dynamic logic algorithm. The algorithm performs hierarchical recognition of the scene by first examining the features of the objects to determine which objects are present, and then determines the scene based on the objects present. This paper presents a framework within which low level data linked to higher-level visualization can provide support to a human operator and be evaluated in a detailed and systematic way.

Automated thematic mapping and change detection of ERTS-A images

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1975-01-01

The author has identified the following significant results. In the first part of the investigation, spatial and spectral features were developed which were employed to automatically recognize terrain features through a clustering algorithm. In this part of the investigation, the size of the cell which is the number of digital picture elements used for computing the spatial and spectral features was varied. It was determined that the accuracy of terrain recognition decreases slowly as the cell size is reduced and coincides with increased cluster diffuseness. It was also proven that a cell size of 17 x 17 pixels when used with the clustering algorithm results in high recognition rates for major terrain classes. ERTS-1 data from five diverse geographic regions of the United States were processed through the clustering algorithm with 17 x 17 pixel cells. Simple land use maps were produced and the average terrain recognition accuracy was 82 percent.

Component Pin Recognition Using Algorithms Based on Machine Learning

NASA Astrophysics Data System (ADS)

Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang

2018-04-01

The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.

Validation of High-Fidelity Reactor Physics Models for Support of the KJRR Experimental Campaign in the Advanced Test Reactor

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

Nigg, David W.; Nielsen, Joseph W.; Norman, Daren R.

The Korea Atomic Energy Research Institute is currently in the process of qualifying a Low-Enriched Uranium fuel element design for the new Ki-Jang Research Reactor (KJRR). As part of this effort, a prototype KJRR fuel element was irradiated for several operating cycles in the Northeast Flux Trap of the Advanced Test Reactor (ATR) at the Idaho National Laboratory. The KJRR fuel element contained a very large quantity of fissile material (618g 235U) in comparison with historical ATR experiment standards (<1g 235U), and its presence in the ATR flux trap was expected to create a neutronic configuration that would be wellmore » outside of the approved validation envelope for the reactor physics analysis methods used to support ATR operations. Accordingly it was necessary, prior to high-power irradiation of the KJRR fuel element in the ATR, to conduct an extensive set of new low-power physics measurements with the KJRR fuel element installed in the ATR Critical Facility (ATRC), a companion facility to the ATR that is located in an immediately adjacent building, sharing the same fuel handling and storage canal. The new measurements had the objective of expanding the validation envelope for the computational reactor physics tools used to support ATR operations and safety analysis to include the planned KJRR irradiation in the ATR and similar experiments that are anticipated in the future. The computational and experimental results demonstrated that the neutronic behavior of the KJRR fuel element in the ATRC is well-understood, both in terms of its general effects on core excess reactivity and fission power distributions, its effects on the calibration of the core lobe power measurement system, as well as in terms of its own internal fission rate distribution and total fission power per unit ATRC core power. Taken as a whole, these results have significantly extended the ATR physics validation envelope, thereby enabling an entire new class of irradiation experiments.« less

Methods and Descriptive Epidemiology of Services Provided by Athletic Trainers in High Schools: The National Athletic Treatment, Injury and Outcomes Network Study

PubMed Central

Kerr, Zachary Y.; Dompier, Thomas P.; Dalton, Sara L.; Miller, Sayers John; Hayden, Ross; Marshall, Stephen W.

2015-01-01

Context Research is limited on the extent and nature of the care provided by athletic trainers (ATs) to student-athletes in the high school setting. Objective To describe the methods of the National Athletic Treatment, Injury and Outcomes Network (NATION) project and provide the descriptive epidemiology of AT services for injury care in 27 high school sports. Design Descriptive epidemiology study. Setting Athletic training room (ATR) visits and AT services data collected in 147 high schools from 26 states. Patients or Other Participants High school student-athletes participating in 13 boys' sports and 14 girls' sports during the 2011−2012 through 2013−2014 academic years. Main Outcome Measure(s) The number of ATR visits and individual AT services, as well as the mean number of ATR visits (per injury) and AT services (per injury and ATR visit) were calculated by sport and for time-loss (TL) and non–time-loss (NTL) injuries. Results Over the 3-year period, 210 773 ATR visits and 557 381 AT services were reported for 50 604 injuries. Most ATR visits (70%) were for NTL injuries. Common AT services were therapeutic activities or exercise (45.4%), modalities (18.6%), and AT evaluation and reevaluation (15.9%), with an average of 4.17 ± 6.52 ATR visits and 11.01 ± 22.86 AT services per injury. Compared with NTL injuries, patients with TL injuries accrued more ATR visits (7.76 versus 3.47; P < .001) and AT services (18.60 versus 9.56; P < .001) per injury. An average of 2.24 ± 1.33 AT services were reported per ATR visit. Compared with TL injuries, NTL injuries had a larger average number of AT services per ATR visit (2.28 versus 2.05; P < .001). Conclusions These findings highlight the broad spectrum of care provided by ATs to high school student-athletes and demonstrate that patients with NTL injuries require substantial amounts of AT services. PMID:26678290

A new optimized GA-RBF neural network algorithm.

PubMed

Jia, Weikuan; Zhao, Dean; Shen, Tian; Su, Chunyang; Hu, Chanli; Zhao, Yuyan

2014-01-01

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.

A Rabbit Anti-pT1989 ATR Monoclonal Antibody for Use in Immunoassays | NCI Technology Transfer Center | TTC

Cancer.gov

Researchers at the National Cancer Institute (NCI) have developed a monoclonal antibody against ataxia telangiectasia-mutated and Rad3-related (ATR) kinase phosphorylated at threonine 1989. The antibody can be used for pharmacodynamic assays to quantify drug action on the ATR target.

ATR-FTIR Spectroscopy in the Undergraduate Chemistry Laboratory: Part I--Fundamentals and Examples

ERIC Educational Resources Information Center

Schuttlefield, Jennifer D.; Grassian, Vicki H.

2008-01-01

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is a useful technique for measuring the infrared spectra of solids and liquids as well as probing adsorption on particle surfaces. Several examples of the use of FTIR-ATR spectroscopy in different undergraduate chemistry laboratory courses are presented here. These…

PUBERTAL DEVELOPMENT IN FEMALE WISTAR RATS FOLLOWING EXPOSURE TO PROPAZINE AND ATRAZINE BIOTRANSFORMATION BY-PRODUCTS, DIAMINO-S-CHLOROTRIAZINE AND HYDROXYATRAZINE

EPA Science Inventory

We have shown previously that the chlorotriazine herbicide, atrazine (ATR), delays the onset of pubertal development in female rats. ATR and its by-products of microbial degradation are present in soil and groundwater. Since current maximum contaminant levels are set only for ATR...

Distinct but Concerted Roles of ATR, DNA-PK, and Chk1 in Countering Replication Stress during S Phase

PubMed Central

Buisson, Rémi; Boisvert, Jessica L.; Benes, Cyril H.; Zou, Lee

2015-01-01

The ATR-Chk1 pathway is critical for DNA damage responses and cell cycle progression. Chk1 inhibition is more deleterious to cycling cells than ATR inhibition, raising questions about ATR and Chk1 functions in the absence of extrinsic replication stress. Here, we show that a key role of ATR in S phase is to coordinate RRM2 accumulation and origin firing. ATR inhibitor (ATRi) induces massive ssDNA accumulation and replication catastrophe in a fraction of early S-phase cells. In other S-phase cells, however, ATRi induces moderate ssDNA and triggers a DNA-PK and Chk1-mediated backup pathway to suppress origin firing. The backup pathway creates a threshold such that ATRi selectively kills cells under high replication stress, whereas Chk1 inhibitor induces cell death at a lower threshold. The levels of ATRi-induced ssDNA correlate with ATRi sensitivity in a panel of cell lines, suggesting that ATRi-induced ssDNA could be predictive of ATRi sensitivity in cancer cells. PMID:26365377

Direct Binding to Replication Protein A (RPA)-coated Single-stranded DNA Allows Recruitment of the ATR Activator TopBP1 to Sites of DNA Damage*

PubMed Central

Acevedo, Julyana; Yan, Shan; Michael, W. Matthew

2016-01-01

A critical event for the ability of cells to tolerate DNA damage and replication stress is activation of the ATR kinase. ATR activation is dependent on the BRCT (BRCA1 C terminus) repeat-containing protein TopBP1. Previous work has shown that recruitment of TopBP1 to sites of DNA damage and stalled replication forks is necessary for downstream events in ATR activation; however, the mechanism for this recruitment was not known. Here, we use protein binding assays and functional studies in Xenopus egg extracts to show that TopBP1 makes a direct interaction, via its BRCT2 domain, with RPA-coated single-stranded DNA. We identify a point mutant that abrogates this interaction and show that this mutant fails to accumulate at sites of DNA damage and that the mutant cannot activate ATR. These data thus supply a mechanism for how the critical ATR activator, TopBP1, senses DNA damage and stalled replication forks to initiate assembly of checkpoint signaling complexes. PMID:27129245

Image preprocessing study on KPCA-based face recognition

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Dehua

2015-12-01

Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

PubMed

Bourobou, Serge Thomas Mickala; Yoo, Younghwan

2015-05-21

This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

The software peculiarities of pattern recognition in track detectors

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

Starkov, N.

The different kinds of nuclear track recognition algorithms are represented. Several complicated samples of use them in physical experiments are considered. The some processing methods of complicated images are described.

The chinese version of achilles tendon total rupture score: cross-cultural adaptation, reliability and validity.

PubMed

Cui, Jin; Jia, Zhenyu; Zhi, Xin; Li, Xiaoqun; Zhai, Xiao; Cao, Liehu; Weng, Weizong; Zhang, Jun; Wang, Lin; Chen, Xiao; Su, Jiacan

2017-01-05

The Achilles tendon Total Rupture Score (ATRS), which is originally developed in 2007 in Swedish, is the only patient-reported outcome measure (PROM) for specific outcome assessment of an Achilles tendon rupture.Purpose of this study is to translate and cross-culturally adapt Achilles tendon Total Rupture Score (ATRS) into simplified Chinese, and primarily evaluate the responsiveness, reliability and validity. International recognized guideline which was designed by Beaton was followed to make the translation of ATRS from English into simplified Chinese version (CH-ATRS). A prospective cohort study was carried out for the cross-cultural adaptation. There were 112 participants included into the study. Psychometric properties including floor and ceiling effects, Cronbach's alpha, intraclass correlation coefficient, effect size, standard response mean, and construct validity were tested. The mean scores of CH-ATRS are 57.42 ± 13.70. No sign of floor or ceiling effect was found of CH-ATRS. High level of internal consistency was supported by the value of Cronbach's alpha (0.893). ICC (0.979, 95%CI: 0.984-0.993) was high to indicate the high test-retest reliability. Great responsive ness was proved with the high absolute value of ES and SRM (0.84 and 8.98, respectively). The total CH-ATRS score had very good correlation with physical function and body pain subscales of SF-36 (r = -0.758 and r = -0.694, respectively, p < 0.001), while poor correlation with vitality and role physical subscales of SF-36 (r = -0.033 and r = -0.025, respectively, p ≥ 0.05), which supported construct validity of CH-ATRS. This Chinese version of Achilles tendon Total Rupture Score (CH-ATRS) can be used as a reliable and valid instrument for Achilles tendon rupture assessing in Chinese-speaking population. Level of evidence II.

Disposition of the herbicide 2-chloro-4-(ethylamino)-6-(isopropylamino)-s-triazine (Atrazine) and its major metabolites in mice: a liquid chromatography/mass spectrometry analysis of urine, plasma, and tissue levels.

PubMed

Ross, Matthew K; Jones, Toni L; Filipov, Nikolay M

2009-04-01

2-Chloro-4-(ethylamino)-6-(isopropylamino)-s-triazine (atrazine, ATR) is a toxicologically important and widely used herbicide. Recent studies have shown that it can elicit neurological, immunological, developmental, and biochemical alterations in several model organisms, including in mice. Because disposition data in mice are lacking, we evaluated ATR's metabolism and tissue dosimetry after single oral exposures (5-250 mg/kg) in C57BL/6 mice using liquid chromatography/mass spectrometry (Ross and Filipov, 2006). ATR was metabolized and cleared rapidly; didealkyl ATR (DACT) was the major metabolite detected in urine, plasma, and tissues. Plasma ATR peaked at 1 h postdosing and rapidly declined, whereas DACT peaked at 2 h and slowly declined. Most ATR and metabolite residues were excreted within the first 24 h. However, substantial amounts of DACT were still present in 25- to 48-h and 49- to 72-h urine. ATR reached maximal brain levels (0.06-1.5 microM) at 4 h (5-125 mg/kg) and 1 h (250 mg/kg) after dosing, but levels quickly declined to <0.1 microM by 12 h in all the groups. In contrast, strikingly high concentrations of DACT (1.5-50 microM), which are comparable with liver DACT levels, were detectable in brain at 2 h. Brain DACT levels slowly declined, paralleling the kinetics of plasma DACT. Our findings suggest that in mice ATR is widely distributed and extensively metabolized and that DACT is a major metabolite detected in the brain at high levels and is ultimately excreted in urine. Our study provides a starting point for the establishment of models that link target tissue dose to biological effects caused by ATR and its in vivo metabolites.

Implementation theory of distortion-invariant pattern recognition for optical and digital signal processing systems

NASA Astrophysics Data System (ADS)

Lhamon, Michael Earl

A pattern recognition system which uses complex correlation filter banks requires proportionally more computational effort than single-real valued filters. This introduces increased computation burden but also introduces a higher level of parallelism, that common computing platforms fail to identify. As a result, we consider algorithm mapping to both optical and digital processors. For digital implementation, we develop computationally efficient pattern recognition algorithms, referred to as, vector inner product operators that require less computational effort than traditional fast Fourier methods. These algorithms do not need correlation and they map readily onto parallel digital architectures, which imply new architectures for optical processors. These filters exploit circulant-symmetric matrix structures of the training set data representing a variety of distortions. By using the same mathematical basis as with the vector inner product operations, we are able to extend the capabilities of more traditional correlation filtering to what we refer to as "Super Images". These "Super Images" are used to morphologically transform a complicated input scene into a predetermined dot pattern. The orientation of the dot pattern is related to the rotational distortion of the object of interest. The optical implementation of "Super Images" yields feature reduction necessary for using other techniques, such as artificial neural networks. We propose a parallel digital signal processor architecture based on specific pattern recognition algorithms but general enough to be applicable to other similar problems. Such an architecture is classified as a data flow architecture. Instead of mapping an algorithm to an architecture, we propose mapping the DSP architecture to a class of pattern recognition algorithms. Today's optical processing systems have difficulties implementing full complex filter structures. Typically, optical systems (like the 4f correlators) are limited to phase-only implementation with lower detection performance than full complex electronic systems. Our study includes pseudo-random pixel encoding techniques for approximating full complex filtering. Optical filter bank implementation is possible and they have the advantage of time averaging the entire filter bank at real time rates. Time-averaged optical filtering is computational comparable to billions of digital operations-per-second. For this reason, we believe future trends in high speed pattern recognition will involve hybrid architectures of both optical and DSP elements.

Application of an auditory model to speech recognition.

PubMed

Cohen, J R

1989-06-01

Some aspects of auditory processing are incorporated in a front end for the IBM speech-recognition system [F. Jelinek, "Continuous speech recognition by statistical methods," Proc. IEEE 64 (4), 532-556 (1976)]. This new process includes adaptation, loudness scaling, and mel warping. Tests show that the design is an improvement over previous algorithms.

Analysis of contour images using optics of spiral beams

NASA Astrophysics Data System (ADS)

Volostnikov, V. G.; Kishkin, S. A.; Kotova, S. P.

2018-03-01

An approach is outlined to the recognition of contour images using computer technology based on coherent optics principles. A mathematical description of the recognition process algorithm and the results of numerical modelling are presented. The developed approach to the recognition of contour images using optics of spiral beams is described and justified.

Gate-Level Commercial Microelectronics Verification with Standard Cell Recognition

DTIC Science & Technology

2015-03-26

21 2.2.1.4 Algorithm Insufficiencies as Applied to DARPA’s Cir- cuit Verification Efforts . . . . . . . . . . . . . . . . . . 22 vi Page...58 4.2 Discussion of SCR Algorithm and Code . . . . . . . . . . . . . . . . . . . 91 4.2.1 Explication of SCR Algorithm ...93 4.2.2 Algorithm Attributes . . . . . . . . . . . . . . . . . . . . . . . . . 118 4.3 Advantages of Transistor-level Verification with SCR

ECG Sensor Card with Evolving RBP Algorithms for Human Verification.

PubMed

Tseng, Kuo-Kun; Huang, Huang-Nan; Zeng, Fufu; Tu, Shu-Yi

2015-08-21

It is known that cardiac and respiratory rhythms in electrocardiograms (ECGs) are highly nonlinear and non-stationary. As a result, most traditional time-domain algorithms are inadequate for characterizing the complex dynamics of the ECG. This paper proposes a new ECG sensor card and a statistical-based ECG algorithm, with the aid of a reduced binary pattern (RBP), with the aim of achieving faster ECG human identity recognition with high accuracy. The proposed algorithm has one advantage that previous ECG algorithms lack-the waveform complex information and de-noising preprocessing can be bypassed; therefore, it is more suitable for non-stationary ECG signals. Experimental results tested on two public ECG databases (MIT-BIH) from MIT University confirm that the proposed scheme is feasible with excellent accuracy, low complexity, and speedy processing. To be more specific, the advanced RBP algorithm achieves high accuracy in human identity recognition and is executed at least nine times faster than previous algorithms. Moreover, based on the test results from a long-term ECG database, the evolving RBP algorithm also demonstrates superior capability in handling long-term and non-stationary ECG signals.

Muscle-tendon glucose uptake in Achilles tendon rupture and tendinopathy before and after eccentric rehabilitation: Comparative case reports.

PubMed

Masood, Tahir; Kalliokoski, Kari; Bojsen-Møller, Jens; Finni, Taija

2016-09-01

Achilles tendon rupture (ATR) is the most common tendon rupture injury. The consequences of ATR on metabolic activity of the Achilles tendon and ankle plantarflexors are unknown. Furthermore, the effects of eccentric rehabilitation on metabolic activity patterns of Achilles tendon and ankle plantarflexors in ATR patients have not been reported thus far. We present a case study demonstrating glucose uptake (GU) in the Achilles tendon, the triceps surae, and the flexor hallucis longus of a post-surgical ATR patient before and after a 5-month eccentric rehabilitation. At baseline, three months post-surgery, all muscles and Achilles tendon displayed much higher GU in the ATR patient compared to a healthy individual despite lower plantarflexion force. After the rehabilitation, plantarflexion force increased in the operated leg while muscle GU was considerably reduced. The triceps surae muscles showed similar values to the healthy control. When compared to the healthy or a matched patient with Achilles tendon pain after 12 weeks of rehabilitation, Achilles tendon GU levels of ATR patient remained greater after the rehabilitation. Past studies have shown a shift in the metabolic fuel utilization towards glycolysis due to immobilization. Further research, combined with immuno-histological investigation, is needed to fully understand the mechanism behind excessive glucose uptake in ATR cases. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dynamic response of active twist rotor blades

NASA Astrophysics Data System (ADS)

Cesnik, Carlos E. S.; Shin, Sang Joon; Wilbur, Matthew L.

2001-02-01

Dynamic characteristics of active twist rotor (ATR) blades are investigated analytically and experimentally in this paper. The ATR system is intended for vibration and potentially for noise reductions in helicopters through individual blade control. An aeroelastic model is developed to identify frequency response characteristics of the ATR blade with integral, generally anisotropic, strain actuators embedded in its composite construction. An ATR prototype blade was designed and manufactured to experimentally study the vibration reduction capabilities of such systems. Several bench and hover tests were conducted and those results are presented and discussed here. Selected results on sensitivity of the ATR system to collective setting (i.e. blade loading), blade rpm (i.e. centrifugal force and blade station velocity), and media density (i.e. altitude) are presented. They indicated that the twist actuation authority of the ATR blade is independent of the collective setting up to approximately 10P, and dependent on rotational speed and altitude near the torsional resonance frequency due to its dependency on the aerodynamic damping. The proposed model captures very well the physics and sensitivities to selected test parameters of the ATR system. The numerical result of the blade torsional loads show an average error of 20% in magnitude and virtually no difference in phase for the blade frequency response. Overall, the active blade model is in very good agreement with the experiments and can be used to analyze and design future active helicopter blade systems.

Achilles Tendon Reflex (ATR) in response to short exposures of microgravity and hypergravity

NASA Technical Reports Server (NTRS)

Fujii, M.; Jaweed, M.

1992-01-01

Previous studies indicate that latency and amplitude of the Achilles tendon reflex (ATR) are reduced after exposure to microgravity for 28 days. The objective of this study was to quantitatively measure the latency of ATR during brief (20 sec) exposure to microgravity in KC-135 parabolic flights. Methods: The ATR was elicited in ten men during parabolic flight with the ankle held neutrally, planarflexed, and dorsiflexed. During flight, the ATR was elicited during the zero G and 1.8 G phases. Postflight testing was performed flying back to the airfield. Latencies to onset of the ATR were calculated and analyses of variance were performed to determine the effect of gravity and ankle position on latency. Result: The mean latencies for zero-G, 1.8-G and postflight with the ankle in the neutral position were 32.7 plus or minus 0.5 ms, and 33.1 plus or minus 0.7 ms respectively, which were not significantly different. There was a trend toward prolongation of latencies postflight. The mean latency for those who were motion sick was 32.1 plus or minus 0.1 ms compared to 34.0 plus or minus 0.3 ms for those who were not sick. Conclusions: These studies indicate that neither the level of gravity nor ankle position significantly affected the latency of the ATR.

Dual inhibition of ATR and ATM potentiates the activity of trabectedin and lurbinectedin by perturbing the DNA damage response and homologous recombination repair.

PubMed

Lima, Michelle; Bouzid, Hana; Soares, Daniele G; Selle, Frédéric; Morel, Claire; Galmarini, Carlos M; Henriques, João A P; Larsen, Annette K; Escargueil, Alexandre E

2016-05-03

Trabectedin (Yondelis®, ecteinascidin-743, ET-743) is a marine-derived natural product approved for treatment of advanced soft tissue sarcoma and relapsed platinum-sensitive ovarian cancer. Lurbinectedin is a novel anticancer agent structurally related to trabectedin. Both ecteinascidins generate DNA double-strand breaks that are processed through homologous recombination repair (HRR), thereby rendering HRR-deficient cells particularly sensitive. We here characterize the DNA damage response (DDR) to trabectedin and lurbinectedin in HeLa cells. Our results show that both compounds activate the ATM/Chk2 (ataxia-telangiectasia mutated/checkpoint kinase 2) and ATR/Chk1 (ATM and RAD3-related/checkpoint kinase 1) pathways. Interestingly, pharmacological inhibition of Chk1/2, ATR or ATM is not accompanied by any significant improvement of the cytotoxic activity of the ecteinascidins while dual inhibition of ATM and ATR strongly potentiates it. Accordingly, concomitant inhibition of both ATR and ATM is an absolute requirement to efficiently block the formation of γ-H2AX, MDC1, BRCA1 and Rad51 foci following exposure to the ecteinascidins. These results are not restricted to HeLa cells, but are shared by cisplatin-sensitive and -resistant ovarian carcinoma cells. Together, our data identify ATR and ATM as central coordinators of the DDR to ecteinascidins and provide a mechanistic rationale for combining these compounds with ATR and ATM inhibitors.

Development and testing of incident detection algorithms. Vol. 2, research methodology and detailed results.

DOT National Transportation Integrated Search

1976-04-01

The development and testing of incident detection algorithms was based on Los Angeles and Minneapolis freeway surveillance data. Algorithms considered were based on times series and pattern recognition techniques. Attention was given to the effects o...

Ambulance Clinical Triage for Acute Stroke Treatment: Paramedic Triage Algorithm for Large Vessel Occlusion.

PubMed

Zhao, Henry; Pesavento, Lauren; Coote, Skye; Rodrigues, Edrich; Salvaris, Patrick; Smith, Karen; Bernard, Stephen; Stephenson, Michael; Churilov, Leonid; Yassi, Nawaf; Davis, Stephen M; Campbell, Bruce C V

2018-04-01

Clinical triage scales for prehospital recognition of large vessel occlusion (LVO) are limited by low specificity when applied by paramedics. We created the 3-step ambulance clinical triage for acute stroke treatment (ACT-FAST) as the first algorithmic LVO identification tool, designed to improve specificity by recognizing only severe clinical syndromes and optimizing paramedic usability and reliability. The ACT-FAST algorithm consists of (1) unilateral arm drift to stretcher <10 seconds, (2) severe language deficit (if right arm is weak) or gaze deviation/hemineglect assessed by simple shoulder tap test (if left arm is weak), and (3) eligibility and stroke mimic screen. ACT-FAST examination steps were retrospectively validated, and then prospectively validated by paramedics transporting culturally and linguistically diverse patients with suspected stroke in the emergency department, for the identification of internal carotid or proximal middle cerebral artery occlusion. The diagnostic performance of the full ACT-FAST algorithm was then validated for patients accepted for thrombectomy. In retrospective (n=565) and prospective paramedic (n=104) validation, ACT-FAST displayed higher overall accuracy and specificity, when compared with existing LVO triage scales. Agreement of ACT-FAST between paramedics and doctors was excellent (κ=0.91; 95% confidence interval, 0.79-1.0). The full ACT-FAST algorithm (n=60) assessed by paramedics showed high overall accuracy (91.7%), sensitivity (85.7%), specificity (93.5%), and positive predictive value (80%) for recognition of endovascular-eligible LVO. The 3-step ACT-FAST algorithm shows higher specificity and reliability than existing scales for clinical LVO recognition, despite requiring just 2 examination steps. The inclusion of an eligibility step allowed recognition of endovascular-eligible patients with high accuracy. Using a sequential algorithmic approach eliminates scoring confusion and reduces assessment time. Future studies will test whether field application of ACT-FAST by paramedics to bypass suspected patients with LVO directly to endovascular-capable centers can reduce delays to endovascular thrombectomy. © 2018 American Heart Association, Inc.

EFFECTS OF ATRAZINE (ATR), DEISOPROPYLATRAZINE (DIA), AND DIAMINOCHLOROTRIAZINE (DACT) ON THE HYPOTHALAMIC-PITUITARY-ADRENAL (HPA) AXIS IN FEMALE RATS

EPA Science Inventory

Previously we reported that a single dose of ATR herbicide stimulated HPA axis activation in the male rat while its primary metabolite, DACT, did so to a lesser extent. In this study, we evaluated the effects of ATR, DACT, and an intermediate metabolite, DIA, on adrenocorticotrop...

78 FR 42898 - Airworthiness Directives; ATR-GIE Avions de Transport Régional Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-18

... identified in this proposed AD, contact ATR-GIE Avions de Transport R[eacute]gional, 1, All[eacute]e Pierre... Transport R[eacute]gional Airplanes AGENCY: Federal Aviation Administration (FAA), DOT. ACTION: Notice of...-GIE Avions de Transport R[eacute]gional Model ATR72-101, - 201, -102, -202, -211, -212, and -212A...

Enhancing speech recognition using improved particle swarm optimization based hidden Markov model.

PubMed

Selvaraj, Lokesh; Ganesan, Balakrishnan

2014-01-01

Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

Illumination-invariant hand gesture recognition

NASA Astrophysics Data System (ADS)

Mendoza-Morales, América I.; Miramontes-Jaramillo, Daniel; Kober, Vitaly

2015-09-01

In recent years, human-computer interaction (HCI) has received a lot of interest in industry and science because it provides new ways to interact with modern devices through voice, body, and facial/hand gestures. The application range of the HCI is from easy control of home appliances to entertainment. Hand gesture recognition is a particularly interesting problem because the shape and movement of hands usually are complex and flexible to be able to codify many different signs. In this work we propose a three step algorithm: first, detection of hands in the current frame is carried out; second, hand tracking across the video sequence is performed; finally, robust recognition of gestures across subsequent frames is made. Recognition rate highly depends on non-uniform illumination of the scene and occlusion of hands. In order to overcome these issues we use two Microsoft Kinect devices utilizing combined information from RGB and infrared sensors. The algorithm performance is tested in terms of recognition rate and processing time.

[Research progress of multi-model medical image fusion and recognition].

PubMed

Zhou, Tao; Lu, Huiling; Chen, Zhiqiang; Ma, Jingxian

2013-10-01

Medical image fusion and recognition has a wide range of applications, such as focal location, cancer staging and treatment effect assessment. Multi-model medical image fusion and recognition are analyzed and summarized in this paper. Firstly, the question of multi-model medical image fusion and recognition is discussed, and its advantage and key steps are discussed. Secondly, three fusion strategies are reviewed from the point of algorithm, and four fusion recognition structures are discussed. Thirdly, difficulties, challenges and possible future research direction are discussed.

Image based book cover recognition and retrieval

NASA Astrophysics Data System (ADS)

Sukhadan, Kalyani; Vijayarajan, V.; Krishnamoorthi, A.; Bessie Amali, D. Geraldine

2017-11-01

In this we are developing a graphical user interface using MATLAB for the users to check the information related to books in real time. We are taking the photos of the book cover using GUI, then by using MSER algorithm it will automatically detect all the features from the input image, after this it will filter bifurcate non-text features which will be based on morphological difference between text and non-text regions. We implemented a text character alignment algorithm which will improve the accuracy of the original text detection. We will also have a look upon the built in MATLAB OCR recognition algorithm and an open source OCR which is commonly used to perform better detection results, post detection algorithm is implemented and natural language processing to perform word correction and false detection inhibition. Finally, the detection result will be linked to internet to perform online matching. More than 86% accuracy can be obtained by this algorithm.

An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments.

PubMed

Yang, Yifei; Tan, Minjia; Dai, Yuewei

2017-01-01

A ship power equipments' fault monitoring signal usually provides few samples and the data's feature is non-linear in practical situation. This paper adopts the method of the least squares support vector machine (LSSVM) to deal with the problem of fault pattern identification in the case of small sample data. Meanwhile, in order to avoid involving a local extremum and poor convergence precision which are induced by optimizing the kernel function parameter and penalty factor of LSSVM, an improved Cuckoo Search (CS) algorithm is proposed for the purpose of parameter optimization. Based on the dynamic adaptive strategy, the newly proposed algorithm improves the recognition probability and the searching step length, which can effectively solve the problems of slow searching speed and low calculation accuracy of the CS algorithm. A benchmark example demonstrates that the CS-LSSVM algorithm can accurately and effectively identify the fault pattern types of ship power equipments.

Comparing an FPGA to a Cell for an Image Processing Application

NASA Astrophysics Data System (ADS)

Rakvic, Ryan N.; Ngo, Hau; Broussard, Randy P.; Ives, Robert W.

2010-12-01

Modern advancements in configurable hardware, most notably Field-Programmable Gate Arrays (FPGAs), have provided an exciting opportunity to discover the parallel nature of modern image processing algorithms. On the other hand, PlayStation3 (PS3) game consoles contain a multicore heterogeneous processor known as the Cell, which is designed to perform complex image processing algorithms at a high performance. In this research project, our aim is to study the differences in performance of a modern image processing algorithm on these two hardware platforms. In particular, Iris Recognition Systems have recently become an attractive identification method because of their extremely high accuracy. Iris matching, a repeatedly executed portion of a modern iris recognition algorithm, is parallelized on an FPGA system and a Cell processor. We demonstrate a 2.5 times speedup of the parallelized algorithm on the FPGA system when compared to a Cell processor-based version.

Mutation in the 5' alternatively spliced region of the XNP/ATR-X gene causes Chudley-Lowry syndrome.

PubMed

Abidi, Fatima E; Cardoso, Carlos; Lossi, Anne-Marie; Lowry, Robert Brian; Depetris, Danielle; Mattéi, Marie-Geneviève; Lubs, Herbert A; Stevenson, Roger E; Fontes, Michel; Chudley, Albert E; Schwartz, Charles E

2005-02-01

The Chudley-Lowry syndrome (ChLS, MIM 309490) is an X-linked recessive condition characterized by moderate to severe mental retardation, short stature, mild obesity, hypogonadism, and distinctive facial features characterized by depressed nasal bridge, anteverted nares, inverted-V-shaped upper lip, and macrostomia. The original Chudley-Lowry family consists of three affected males in two generations. Linkage analysis had localized the gene to a large interval, Xp21-Xq26 and an obligate carrier was demonstrated to have highly skewed X inactivation. The combination of the clinical phenotype, consistent with that of the patients with ATR-X syndrome, the skewed X-inactivation pattern in a carrier female, as well as the mapping interval including band Xq13.3, prompted us to consider the XNP/ATR-X gene being involved in this syndrome. Using RT-PCR analysis, we screened the entire XNP/ATR-X gene and found a mutation in exon 2 (c.109C > T) giving rise to a stop codon at position 37 (p.R37X). Western blot and immunocytochemical analyses using a specific monoclonal antibody directed against XNP/ATR-X showed the protein to be present in lymphoblastoid cells from one affected male, despite the premature stop codon. To explain these discordant results, we further analyzed the 5' region of the XNP/ATR-X gene and found three alternative transcripts, which differ in the presence or absence of exon 2, and the length of exon 1. Our data suggest that ChLS is allelic to the ATR-X syndrome with its less severe phenotype being due to the presence of some XNP/ATR-X protein.

Atrazine activates multiple signaling pathways enhancing the rapid hCG-induced androgenesis in rat Leydig cells.

PubMed

Pogrmic-Majkic, Kristina; Fa, Svetlana; Samardzija, Dragana; Hrubik, Jelena; Kaisarevic, Sonja; Andric, Nebojsa

2016-08-10

Atrazine (ATR) is an endocrine disruptor that affects steroidogenic process, resulting in disruption of reproductive function of the male and female gonads. In this study, we used the primary culture of peripubertal Leydig cells to investigate the effect of ATR on the rapid androgen production stimulated by human chorionic gonadotropin (hCG). We demonstrated that ATR activated multiple signaling pathways enhancing the rapid hCG-stimulated androgen biosynthesis in Leydig cells. Low hCG concentration (0.25ng/mL) caused cAMP-independent, but ERK1/2-dependent increase in androgen production after 60min of incubation. Co-treatment with ATR for 60min enhanced the cAMP production in hCG-stimulated cells. Accumulation of androgens was prevented by addition of U0126, N-acetyl-l-cysteine and AG1478. Co-treatment with hCG and ATR for 60min did not alter steroidogenic acute regulatory protein (Star) mRNA level in Leydig cells. After 120min, hCG further increased androgenesis in Leydig cells that was sensitive to inhibition of the cAMP/PKA, ERK1/2 and ROS signaling pathways. Co-treatment with ATR for 120min further enhanced the hCG-induced androgen production, which was prevented by inhibition of the calcium, PKC and EGFR signaling cascades. After 120min, ATR enhanced the expression of Star mRNA in hCG-stimulated Leydig cells through activation of the PKA and PKC pathway. Collectively, these data suggest that exposure to ATR caused perturbations in multiple signaling pathways, thus enhancing the rapid hCG-dependent androgen biosynthesis in peripubertal Leydig cells. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Custom fractional factorial designs to develop atorvastatin self-nanoemulsifying and nanosuspension delivery systems--enhancement of oral bioavailability.

PubMed

Hashem, Fahima M; Al-Sawahli, Majid M; Nasr, Mohamed; Ahmed, Osama A A

2015-01-01

Poor water solubility of a drug is a major challenge in drug delivery research and a main cause for limited bioavailability and pharmacokinetic parameters. This work aims to utilize custom fractional factorial design to assess the development of self-nanoemulsifying drug delivery systems (SNEDDS) and solid nanosuspensions (NS) in order to enhance the oral delivery of atorvastatin (ATR). According to the design, 14 experimental runs of ATR SNEDDS were formulated utilizing the highly ATR solubilizing SNEDDS components: oleic acid, Tween 80, and propylene glycol. In addition, 12 runs of NS were formulated by the antisolvent precipitation-ultrasonication method. Optimized formulations of SNEDDS and solid NS, deduced from the design, were characterized. Optimized SNEDDS formula exhibited mean globule size of 73.5 nm, zeta potential magnitude of -24.1 mV, and 13.5 μs/cm of electrical conductivity. Optimized solid NS formula exhibited mean particle size of 260.3 nm, 7.4 mV of zeta potential, and 93.2% of yield percentage. Transmission electron microscopy showed SNEDDS droplets formula as discrete spheres. The solid NS morphology showed flaky nanoparticles with irregular shapes using scanning electron microscopy. The release behavior of the optimized SNEDDS formula showed 56.78% of cumulative ATR release after 10 minutes. Solid NS formula showed lower rate of release in the first 30 minutes. Bioavailability estimation in Wistar albino rats revealed an augmentation in ATR bioavailability, relative to ATR suspension and the commercial tablets, from optimized ATR SNEDDS and NS formulations by 193.81% and 155.31%, respectively. The findings of this work showed that the optimized nanocarriers enhance the oral delivery and pharmacokinetic profile of ATR.

RPA-Binding Protein ETAA1 Is an ATR Activator Involved in DNA Replication Stress Response.

PubMed

Lee, Yuan-Cho; Zhou, Qing; Chen, Junjie; Yuan, Jingsong

2016-12-19

ETAA1 (Ewing tumor-associated antigen 1), also known as ETAA16, was identified as a tumor-specific antigen in the Ewing family of tumors. However, the biological function of this protein remains unknown. Here, we report the identification of ETAA1 as a DNA replication stress response protein. ETAA1 specifically interacts with RPA (Replication protein A) via two conserved RPA-binding domains and is therefore recruited to stalled replication forks. Interestingly, further analysis of ETAA1 function revealed that ETAA1 participates in the activation of ATR signaling pathway via a conserved ATR-activating domain (AAD) located near its N terminus. Importantly, we demonstrate that both RPA binding and ATR activation are required for ETAA1 function at stalled replication forks to maintain genome stability. Therefore, our data suggest that ETAA1 is a new ATR activator involved in replication checkpoint control. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Methodology for Loading the Advanced Test Reactor Driver Core for Experiment Analysis

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

Cowherd, Wilson M.; Nielsen, Joseph W.; Choe, Dong O.

In support of experiments in the ATR, a new methodology was devised for loading the ATR Driver Core. This methodology will replace the existing methodology used by the INL Neutronic Analysis group to analyze experiments. Studied in this paper was the as-run analysis for ATR Cycle 152B, specifically comparing measured lobe powers and eigenvalue calculations.

ATR-FTIR Spectroscopy in the Undergraduate Chemistry Laboratory: Part II--A Physical Chemistry Laboratory Experiment on Surface Adsorption

ERIC Educational Resources Information Center

Schuttlefield, Jennifer D.; Larsen, Sarah C.; Grassian, Vicki H.

2008-01-01

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is a useful technique for measuring the infrared spectra of solids and liquids as well as probing adsorption on particle surfaces. The use of FTIR-ATR spectroscopy in organic and inorganic chemistry laboratory courses as well as in undergraduate research was presented…

Effects of atrazine (ATR), deisopropylatrazine (DIA), Diaminochlorotriazine (DACT) on the hypothalamic-pituitary-adrenal (HPA) axis in female rats

EPA Science Inventory

We previously reported that a single dose of the herbicide ATR stimulated the HPA axis in the male rat while equimolar doses of its primary metabolite, DACT, had a minimal effect. In this study, we evaluated the effects of one or four daily doses of ATR, DACT, and an intermediat...

PLS-LS-SVM based modeling of ATR-IR as a robust method in detection and qualification of alprazolam

NASA Astrophysics Data System (ADS)

Parhizkar, Elahehnaz; Ghazali, Mohammad; Ahmadi, Fatemeh; Sakhteman, Amirhossein

2017-02-01

According to the United States pharmacopeia (USP), Gold standard technique for Alprazolam determination in dosage forms is HPLC, an expensive and time-consuming method that is not easy to approach. In this study chemometrics assisted ATR-IR was introduced as an alternative method that produce similar results in fewer time and energy consumed manner. Fifty-eight samples containing different concentrations of commercial alprazolam were evaluated by HPLC and ATR-IR method. A preprocessing approach was applied to convert raw data obtained from ATR-IR spectra to normal matrix. Finally, a relationship between alprazolam concentrations achieved by HPLC and ATR-IR data was established using PLS-LS-SVM (partial least squares least squares support vector machines). Consequently, validity of the method was verified to yield a model with low error values (root mean square error of cross validation equal to 0.98). The model was able to predict about 99% of the samples according to R2 of prediction set. Response permutation test was also applied to affirm that the model was not assessed by chance correlations. At conclusion, ATR-IR can be a reliable method in manufacturing process in detection and qualification of alprazolam content.

ON-LINE MONITORING OF I&C TRANSMITTERS AND SENSORS FOR CALIBRATION VERIFICATION AND RESPONSE TIME TESTING WAS SUCCESSFULLY IMPLEMENTED AT ATR

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

Erickson, Phillip A.; O'Hagan, Ryan; Shumaker, Brent

The Advanced Test Reactor (ATR) has always had a comprehensive procedure to verify the performance of its critical transmitters and sensors, including RTDs, and pressure, level, and flow transmitters. These transmitters and sensors have been periodically tested for response time and calibration verification to ensure accuracy. With implementation of online monitoring techniques at ATR, the calibration verification and response time testing of these transmitters and sensors are verified remotely, automatically, hands off, include more portions of the system, and can be performed at almost any time during process operations. The work was done under a DOE funded SBIR project carriedmore » out by AMS. As a result, ATR is now able to save the manpower that has been spent over the years on manual calibration verification and response time testing of its temperature and pressure sensors and refocus those resources towards more equipment reliability needs. More importantly, implementation of OLM will help enhance the overall availability, safety, and efficiency. Together with equipment reliability programs of ATR, the integration of OLM will also help with I&C aging management goals of the Department of Energy and long-time operation of ATR.« less

Training Spiking Neural Models Using Artificial Bee Colony

PubMed Central

Vazquez, Roberto A.; Garro, Beatriz A.

2015-01-01

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, S.; Bez, H. E.

2007-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm's robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

Improving on hidden Markov models: An articulatorily constrained, maximum likelihood approach to speech recognition and speech coding

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

Hogden, J.

The goal of the proposed research is to test a statistical model of speech recognition that incorporates the knowledge that speech is produced by relatively slow motions of the tongue, lips, and other speech articulators. This model is called Maximum Likelihood Continuity Mapping (Malcom). Many speech researchers believe that by using constraints imposed by articulator motions, we can improve or replace the current hidden Markov model based speech recognition algorithms. Unfortunately, previous efforts to incorporate information about articulation into speech recognition algorithms have suffered because (1) slight inaccuracies in our knowledge or the formulation of our knowledge about articulation maymore » decrease recognition performance, (2) small changes in the assumptions underlying models of speech production can lead to large changes in the speech derived from the models, and (3) collecting measurements of human articulator positions in sufficient quantity for training a speech recognition algorithm is still impractical. The most interesting (and in fact, unique) quality of Malcom is that, even though Malcom makes use of a mapping between acoustics and articulation, Malcom can be trained to recognize speech using only acoustic data. By learning the mapping between acoustics and articulation using only acoustic data, Malcom avoids the difficulties involved in collecting articulator position measurements and does not require an articulatory synthesizer model to estimate the mapping between vocal tract shapes and speech acoustics. Preliminary experiments that demonstrate that Malcom can learn the mapping between acoustics and articulation are discussed. Potential applications of Malcom aside from speech recognition are also discussed. Finally, specific deliverables resulting from the proposed research are described.« less

Tera-Ops Processing for ATR

NASA Technical Reports Server (NTRS)

Udomkesmalee, Suraphol; Padgett, Curtis; Zhu, David; Lung, Gerald; Howard, Ayanna

2000-01-01

A three-dimensional microelectronic device (3DANN-R) capable of performing general image convolution at the speed of 1012 operations/second (ops) in a volume of less than 1.5 cubic centimeter has been successfully built under the BMDO/JPL VIGILANTE program. 3DANN-R was developed in partnership with Irvine Sensors Corp., Costa Mesa, California. 3DANN-R is a sugar-cube-sized, low power image convolution engine that in its core computation circuitry is capable of performing 64 image convolutions with large (64x64) windows at video frame rates. This paper explores potential applications of 3DANN-R such as target recognition, SAR and hyperspectral data processing, and general machine vision using real data and discuss technical challenges for providing deployable systems for BMDO surveillance and interceptor programs.

Plastic surgery and the biometric e-passport: implications for facial recognition.

PubMed

Ologunde, Rele

2015-04-01

This correspondence comments on the challenges of plastic reconstructive and aesthetic surgery on the facial recognition algorithms employed by biometric passports. The limitations of facial recognition technology in patients who have undergone facial plastic surgery are also discussed. Finally, the advice of the UK HM passport office to people who undergo facial surgery is reported.

Pattern recognition: A basis for remote sensing data analysis

NASA Technical Reports Server (NTRS)

Swain, P. H.

1973-01-01

The theoretical basis for the pattern-recognition-oriented algorithms used in the multispectral data analysis software system is discussed. A model of a general pattern recognition system is presented. The receptor or sensor is usually a multispectral scanner. For each ground resolution element the receptor produces n numbers or measurements corresponding to the n channels of the scanner.

[A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

PubMed

Zhang, Y; Liu, A; Yu, K

1999-06-01

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

Stairs and Doors Recognition as Natural Landmarks Based on Clouds of 3D Edge-Points from RGB-D Sensors for Mobile Robot Localization.

PubMed

Souto, Leonardo A V; Castro, André; Gonçalves, Luiz Marcos Garcia; Nascimento, Tiago P

2017-08-08

Natural landmarks are the main features in the next step of the research in localization of mobile robot platforms. The identification and recognition of these landmarks are crucial to better localize a robot. To help solving this problem, this work proposes an approach for the identification and recognition of natural marks included in the environment using images from RGB-D (Red, Green, Blue, Depth) sensors. In the identification step, a structural analysis of the natural landmarks that are present in the environment is performed. The extraction of edge points of these landmarks is done using the 3D point cloud obtained from the RGB-D sensor. These edge points are smoothed through the S l 0 algorithm, which minimizes the standard deviation of the normals at each point. Then, the second step of the proposed algorithm begins, which is the proper recognition of the natural landmarks. This recognition step is done as a real-time algorithm that extracts the points referring to the filtered edges and determines to which structure they belong to in the current scenario: stairs or doors. Finally, the geometrical characteristics that are intrinsic to the doors and stairs are identified. The approach proposed here has been validated with real robot experiments. The performed tests verify the efficacy of our proposed approach.

Stairs and Doors Recognition as Natural Landmarks Based on Clouds of 3D Edge-Points from RGB-D Sensors for Mobile Robot Localization†

PubMed Central

Castro, André; Nascimento, Tiago P.

2017-01-01

Natural landmarks are the main features in the next step of the research in localization of mobile robot platforms. The identification and recognition of these landmarks are crucial to better localize a robot. To help solving this problem, this work proposes an approach for the identification and recognition of natural marks included in the environment using images from RGB-D (Red, Green, Blue, Depth) sensors. In the identification step, a structural analysis of the natural landmarks that are present in the environment is performed. The extraction of edge points of these landmarks is done using the 3D point cloud obtained from the RGB-D sensor. These edge points are smoothed through the Sl0 algorithm, which minimizes the standard deviation of the normals at each point. Then, the second step of the proposed algorithm begins, which is the proper recognition of the natural landmarks. This recognition step is done as a real-time algorithm that extracts the points referring to the filtered edges and determines to which structure they belong to in the current scenario: stairs or doors. Finally, the geometrical characteristics that are intrinsic to the doors and stairs are identified. The approach proposed here has been validated with real robot experiments. The performed tests verify the efficacy of our proposed approach. PMID:28786925

Gene/protein name recognition based on support vector machine using dictionary as features.

PubMed

Mitsumori, Tomohiro; Fation, Sevrani; Murata, Masaki; Doi, Kouichi; Doi, Hirohumi

2005-01-01

Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

Low-resolution expression recognition based on central oblique average CS-LBP with adaptive threshold

NASA Astrophysics Data System (ADS)

Han, Sheng; Xi, Shi-qiong; Geng, Wei-dong

2017-11-01

In order to solve the problem of low recognition rate of traditional feature extraction operators under low-resolution images, a novel algorithm of expression recognition is proposed, named central oblique average center-symmetric local binary pattern (CS-LBP) with adaptive threshold (ATCS-LBP). Firstly, the features of face images can be extracted by the proposed operator after pretreatment. Secondly, the obtained feature image is divided into blocks. Thirdly, the histogram of each block is computed independently and all histograms can be connected serially to create a final feature vector. Finally, expression classification is achieved by using support vector machine (SVM) classifier. Experimental results on Japanese female facial expression (JAFFE) database show that the proposed algorithm can achieve a recognition rate of 81.9% when the resolution is as low as 16×16, which is much better than that of the traditional feature extraction operators.

An Interactive Image Segmentation Method in Hand Gesture Recognition

PubMed Central

Chen, Disi; Li, Gongfa; Sun, Ying; Kong, Jianyi; Jiang, Guozhang; Tang, Heng; Ju, Zhaojie; Yu, Hui; Liu, Honghai

2017-01-01

In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy. PMID:28134818

A Taxonomy of 3D Occluded Objects Recognition Techniques

NASA Astrophysics Data System (ADS)

Soleimanizadeh, Shiva; Mohamad, Dzulkifli; Saba, Tanzila; Al-ghamdi, Jarallah Saleh

2016-03-01

The overall performances of object recognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring object recognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

Fuzzy support vector machines for adaptive Morse code recognition.

PubMed

Yang, Cheng-Hong; Jin, Li-Cheng; Chuang, Li-Yeh

2006-11-01

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature.

Intra-pulse modulation recognition using short-time ramanujan Fourier transform spectrogram

NASA Astrophysics Data System (ADS)

Ma, Xiurong; Liu, Dan; Shan, Yunlong

2017-12-01

Intra-pulse modulation recognition under negative signal-to-noise ratio (SNR) environment is a research challenge. This article presents a robust algorithm for the recognition of 5 types of radar signals with large variation range in the signal parameters in low SNR using the combination of the Short-time Ramanujan Fourier transform (ST-RFT) and pseudo-Zernike moments invariant features. The ST-RFT provides the time-frequency distribution features for 5 modulations. The pseudo-Zernike moments provide invariance properties that are able to recognize different modulation schemes on different parameter variation conditions from the ST-RFT spectrograms. Simulation results demonstrate that the proposed algorithm achieves the probability of successful recognition (PSR) of over 90% when SNR is above -5 dB with large variation range in the signal parameters: carrier frequency (CF) for all considered signals, hop size (HS) for frequency shift keying (FSK) signals, and the time-bandwidth product for Linear Frequency Modulation (LFM) signals.

Comparative study on ATR-FTIR calibration models for monitoring solution concentration in cooling crystallization

NASA Astrophysics Data System (ADS)

Zhang, Fangkun; Liu, Tao; Wang, Xue Z.; Liu, Jingxiang; Jiang, Xiaobin

2017-02-01

In this paper calibration model building based on using an ATR-FTIR spectroscopy is investigated for in-situ measurement of the solution concentration during a cooling crystallization process. The cooling crystallization of L-glutamic Acid (LGA) as a case is studied here. It was found that using the metastable zone (MSZ) data for model calibration can guarantee the prediction accuracy for monitoring the operating window of cooling crystallization, compared to the usage of undersaturated zone (USZ) spectra for model building as traditionally practiced. Calibration experiments were made for LGA solution under different concentrations. Four candidate calibration models were established using different zone data for comparison, by using a multivariate partial least-squares (PLS) regression algorithm for the collected spectra together with the corresponding temperature values. Experiments under different process conditions including the changes of solution concentration and operating temperature were conducted. The results indicate that using the MSZ spectra for model calibration can give more accurate prediction of the solution concentration during the crystallization process, while maintaining accuracy in changing the operating temperature. The primary reason of prediction error was clarified as spectral nonlinearity for in-situ measurement between USZ and MSZ. In addition, an LGA cooling crystallization experiment was performed to verify the sensitivity of these calibration models for monitoring the crystal growth process.

Total sulfur determination in residues of crude oil distillation using FT-IR/ATR and variable selection methods.

PubMed

Müller, Aline Lima Hermes; Picoloto, Rochele Sogari; de Azevedo Mello, Paola; Ferrão, Marco Flores; de Fátima Pereira dos Santos, Maria; Guimarães, Regina Célia Lourenço; Müller, Edson Irineu; Flores, Erico Marlon Moraes

2012-04-01

Total sulfur concentration was determined in atmospheric residue (AR) and vacuum residue (VR) samples obtained from petroleum distillation process by Fourier transform infrared spectroscopy with attenuated total reflectance (FT-IR/ATR) in association with chemometric methods. Calibration and prediction set consisted of 40 and 20 samples, respectively. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). Different treatments and pre-processing steps were also evaluated for the development of models. The pre-treatment based on multiplicative scatter correction (MSC) and the mean centered data were selected for models construction. The use of siPLS as variable selection method provided a model with root mean square error of prediction (RMSEP) values significantly better than those obtained by PLS model using all variables. The best model was obtained using siPLS algorithm with spectra divided in 20 intervals and combinations of 3 intervals (911-824, 823-736 and 737-650 cm(-1)). This model produced a RMSECV of 400 mg kg(-1) S and RMSEP of 420 mg kg(-1) S, showing a correlation coefficient of 0.990. Copyright © 2011 Elsevier B.V. All rights reserved.

Elimination of interference from water in KBr disk FT-IR spectra of solid biomaterials by chemometrics solved with kinetic modeling.

PubMed

Gordon, Sherald H; Harry-O'kuru, Rogers E; Mohamed, Abdellatif A

2017-11-01

Infrared analysis of proteins and polysaccharides by the well known KBr disk technique is notoriously frustrated and defeated by absorbed water interference in the important amide and hydroxyl regions of spectra. This interference has too often been overlooked or ignored even when the resulting distortion is critical or even fatal, as in quantitative analyses of protein secondary structure, because the water has been impossible to measure or eliminate. Therefore, a new chemometric method was devised that corrects spectra of materials in KBr disks by mathematically eliminating the water interference. A new concept termed the Beer-Lambert law absorbance ratio (R-matrix) model was augmented with water concentration ratios computed via an exponential decay kinetic model of the water absorption process in KBr, which rendered the otherwise indeterminate system of linear equations determinate and thus possible to solve in a formal analytic manner. Consequently, the heretofore baffling KBr water elimination problem is now solved once and for all. Using the new formal solution, efforts to eliminate water interference from KBr disks in research will be defeated no longer. Resulting spectra of protein were much more accurate than attenuated total reflection (ATR) spectra corrected using the well-accepted Advanced ATR Correction Algorithm. Published by Elsevier B.V.

The Mucciardi-Gose Clustering Algorithm and Its Applications in Automatic Pattern Recognition.

DTIC Science & Technology

A procedure known as the Mucciardi- Gose clustering algorithm, CLUSTR, for determining the geometrical or statistical relationships among groups of N...discussion of clustering algorithms is given; the particular advantages of the Mucciardi- Gose procedure are described. The mathematical basis for, and the

Cough Recognition Based on Mel Frequency Cepstral Coefficients and Dynamic Time Warping

NASA Astrophysics Data System (ADS)

Zhu, Chunmei; Liu, Baojun; Li, Ping

Cough recognition provides important clinical information for the treatment of many respiratory diseases, but the assessment of cough frequency over a long period of time remains unsatisfied for either clinical or research purpose. In this paper, according to the advantage of dynamic time warping (DTW) and the characteristic of cough recognition, an attempt is made to adapt DTW as the recognition algorithm for cough recognition. The process of cough recognition based on mel frequency cepstral coefficients (MFCC) and DTW is introduced. Experiment results of testing samples from 3 subjects show that acceptable performances of cough recognition are obtained by DTW with a small training set.

Application of laboratory and portable attenuated total reflectance infrared spectroscopic approaches for rapid quantification of alpaca serum immunoglobulin G

PubMed Central

Burns, Jennifer B.; Riley, Christopher B.; Shaw, R. Anthony; McClure, J. Trenton

2017-01-01

The objective of this study was to develop and compare the performance of laboratory grade and portable attenuated total reflectance infrared (ATR-IR) spectroscopic approaches in combination with partial least squares regression (PLSR) for the rapid quantification of alpaca serum IgG concentration, and the identification of low IgG (<1000 mg/dL), which is consistent with the diagnosis of failure of transfer of passive immunity (FTPI) in neonates. Serum samples (n = 175) collected from privately owned, healthy alpacas were tested by the reference method of radial immunodiffusion (RID) assay, and laboratory grade and portable ATR-IR spectrometers. Various pre-processing strategies were applied to the ATR-IR spectra that were linked to corresponding RID-IgG concentrations, and then randomly split into two sets: calibration (training) and test sets. PLSR was applied to the calibration set and calibration models were developed, and the test set was used to assess the accuracy of the analytical method. For the test set, the Pearson correlation coefficients between the IgG measured by RID and predicted by both laboratory grade and portable ATR-IR spectrometers was 0.91. The average differences between reference serum IgG concentrations and the two IR-based methods were 120.5 mg/dL and 71 mg/dL for the laboratory and portable ATR-IR-based assays, respectively. Adopting an IgG concentration <1000 mg/dL as the cut-point for FTPI cases, the sensitivity, specificity, and accuracy for identifying serum samples below this cut point by laboratory ATR-IR assay were 86, 100 and 98%, respectively (within the entire data set). Corresponding values for the portable ATR-IR assay were 95, 99 and 99%, respectively. These results suggest that the two different ATR-IR assays performed similarly for rapid qualitative evaluation of alpaca serum IgG and for diagnosis of IgG <1000 mg/dL, the portable ATR-IR spectrometer performed slightly better, and provides more flexibility for potential application in the field. PMID:28651006

Advanced Test Reactor Safety Basis Upgrade Lessons Learned Relative to Design Basis Verification and Safety Basis Management

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

G. L. Sharp; R. T. McCracken

The Advanced Test Reactor (ATR) is a pressurized light-water reactor with a design thermal power of 250 MW. The principal function of the ATR is to provide a high neutron flux for testing reactor fuels and other materials. The reactor also provides other irradiation services such as radioisotope production. The ATR and its support facilities are located at the Test Reactor Area of the Idaho National Engineering and Environmental Laboratory (INEEL). An audit conducted by the Department of Energy's Office of Independent Oversight and Performance Assurance (DOE OA) raised concerns that design conditions at the ATR were not adequately analyzedmore » in the safety analysis and that legacy design basis management practices had the potential to further impact safe operation of the facility.1 The concerns identified by the audit team, and issues raised during additional reviews performed by ATR safety analysts, were evaluated through the unreviewed safety question process resulting in shutdown of the ATR for more than three months while these concerns were resolved. Past management of the ATR safety basis, relative to facility design basis management and change control, led to concerns that discrepancies in the safety basis may have developed. Although not required by DOE orders or regulations, not performing design basis verification in conjunction with development of the 10 CFR 830 Subpart B upgraded safety basis allowed these potential weaknesses to be carried forward. Configuration management and a clear definition of the existing facility design basis have a direct relation to developing and maintaining a high quality safety basis which properly identifies and mitigates all hazards and postulated accident conditions. These relations and the impact of past safety basis management practices have been reviewed in order to identify lessons learned from the safety basis upgrade process and appropriate actions to resolve possible concerns with respect to the current ATR safety basis. The need for a design basis reconstitution program for the ATR has been identified along with the use of sound configuration management principles in order to support safe and efficient facility operation.« less

In Vivo and In Vitro Effects of ATM/ATR Signaling Pathway on Proliferation, Apoptosis, and Radiosensitivity of Nasopharyngeal Carcinoma Cells.

PubMed

Wang, Ming; Liu, Gang; Shan, Guo-Ping; Wang, Bing-Bing

2017-08-01

The study investigated the ability of ataxia-telangiectasia mutated (ATM)/Rad3-related (ATR) signaling pathway to influence the proliferation, apoptosis, and radiosensitivity of nasopharyngeal carcinoma (NPC) cells. NPC tissues and corresponding adjacent normal tissues were collected from 143 NPC patients. The NPC CNE2 cells were assigned into a control group, X-ray group, CGK-733 group, and X-ray+CGK-733 group. The mRNA levels of ATM and ATR were evaluated using quantitative real-time polymerase chain reaction (qRT-PCR) and the protein levels of ATM and ATR using western blotting. The positive expression of ATM and ATR in tissues and nude mouse tumor tissues was determined by immunohistochemistry. Cell proliferation, migration, invasion, and apoptosis rates were analyzed by the 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di- phenytetrazoliumromide (MTT) assay, scratch test, transwell assay, and flow cytometry, respectively. A nude mouse model of NPC was established to observe tumor volume and growth. The mRNA levels of ATR and ATM and the expression of ATR and ATM protein in NPC tissues were significantly higher than those in adjacent normal tissues. The colony formation assay showed that the colony-forming rate decreased, showing radiation dose-dependent and CGK-733 concentration-dependent manners. Expression of ATM, ATR, Chk1, and Chk2 was evidently increased in the X-ray, CGK-733, and X-ray+CGK-733groups compared with the control group, and the aforementioned expression was highest in the X-ray+CGK-733 group among the four groups. The cell proliferation, invasion, and migration were decreased, tumor volume decreased and cell apoptosis increased in the X-ray, CGK-733, and X-ray+CGK-733 groups compared with the control group; the X-ray+CGK-733 group exhibited lowest cell proliferation, invasion and migration, smallest tumor volume, and highest cell apoptosis among the four groups. Inhibition of ATM/ATR signaling pathway reduces proliferation and enhances apoptosis and radiosensitivity of NPC cells.

Application of laboratory and portable attenuated total reflectance infrared spectroscopic approaches for rapid quantification of alpaca serum immunoglobulin G.

PubMed

Elsohaby, Ibrahim; Burns, Jennifer B; Riley, Christopher B; Shaw, R Anthony; McClure, J Trenton

2017-01-01

The objective of this study was to develop and compare the performance of laboratory grade and portable attenuated total reflectance infrared (ATR-IR) spectroscopic approaches in combination with partial least squares regression (PLSR) for the rapid quantification of alpaca serum IgG concentration, and the identification of low IgG (<1000 mg/dL), which is consistent with the diagnosis of failure of transfer of passive immunity (FTPI) in neonates. Serum samples (n = 175) collected from privately owned, healthy alpacas were tested by the reference method of radial immunodiffusion (RID) assay, and laboratory grade and portable ATR-IR spectrometers. Various pre-processing strategies were applied to the ATR-IR spectra that were linked to corresponding RID-IgG concentrations, and then randomly split into two sets: calibration (training) and test sets. PLSR was applied to the calibration set and calibration models were developed, and the test set was used to assess the accuracy of the analytical method. For the test set, the Pearson correlation coefficients between the IgG measured by RID and predicted by both laboratory grade and portable ATR-IR spectrometers was 0.91. The average differences between reference serum IgG concentrations and the two IR-based methods were 120.5 mg/dL and 71 mg/dL for the laboratory and portable ATR-IR-based assays, respectively. Adopting an IgG concentration <1000 mg/dL as the cut-point for FTPI cases, the sensitivity, specificity, and accuracy for identifying serum samples below this cut point by laboratory ATR-IR assay were 86, 100 and 98%, respectively (within the entire data set). Corresponding values for the portable ATR-IR assay were 95, 99 and 99%, respectively. These results suggest that the two different ATR-IR assays performed similarly for rapid qualitative evaluation of alpaca serum IgG and for diagnosis of IgG <1000 mg/dL, the portable ATR-IR spectrometer performed slightly better, and provides more flexibility for potential application in the field.

SVD compression for magnetic resonance fingerprinting in the time domain.

PubMed

McGivney, Debra F; Pierre, Eric; Ma, Dan; Jiang, Yun; Saybasili, Haris; Gulani, Vikas; Griswold, Mark A

2014-12-01

Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.

Statistical process control using optimized neural networks: a case study.

PubMed

Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

2014-09-01

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Influence of quality of images recorded in far infrared on pattern recognition based on neural networks and Eigenfaces algorithm

NASA Astrophysics Data System (ADS)

Jelen, Lukasz; Kobel, Joanna; Podbielska, Halina

2003-11-01

This paper discusses the possibility of exploiting of the tennovision registration and artificial neural networks for facial recognition systems. A biometric system that is able to identify people from thermograms is presented. To identify a person we used the Eigenfaces algorithm. For the face detection in the picture the backpropagation neural network was designed. For this purpose thermograms of 10 people in various external conditions were studies. The Eigenfaces algorithm calculated an average face and then the set of characteristic features for each studied person was produced. The neural network has to detect the face in the image before it actually can be identified. We used five hidden layers for that purpose. It was shown that the errors in recognition depend on the feature extraction, for low quality pictures the error was so high as 30%. However, for pictures with a good feature extraction the results of proper identification higher then 90%, were obtained.

SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain

PubMed Central

McGivney, Debra F.; Pierre, Eric; Ma, Dan; Jiang, Yun; Saybasili, Haris; Gulani, Vikas; Griswold, Mark A.

2016-01-01

Magnetic resonance fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition (SVD), which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously. PMID:25029380

Marginal Fisher analysis and its variants for human gait recognition and content- based image retrieval.

PubMed

Xu, Dong; Yan, Shuicheng; Tao, Dacheng; Lin, Stephen; Zhang, Hong-Jiang

2007-11-01

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based image retrieval (CBIR). In this paper, we present extensions of our recently proposed marginal Fisher analysis (MFA) to address these problems. For human gait recognition, we first present a direct application of MFA, then inspired by recent advances in matrix and tensor-based dimensionality reduction algorithms, we present matrix-based MFA for directly handling 2-D input in the form of gray-level averaged images. For CBIR, we deal with the relevance feedback problem by extending MFA to marginal biased analysis, in which within-class compactness is characterized only by the distances between each positive sample and its neighboring positive samples. In addition, we present a new technique to acquire a direct optimal solution for MFA without resorting to objective function modification as done in many previous algorithms. We conduct comprehensive experiments on the USF HumanID gait database and the Corel image retrieval database. Experimental results demonstrate that MFA and its extensions outperform related algorithms in both applications.

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

PubMed Central

Sánchez, Daniela; Melin, Patricia

2017-01-01

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition. PMID:28894461

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition.

PubMed

Sánchez, Daniela; Melin, Patricia; Castillo, Oscar

2017-01-01

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition.

User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm

PubMed Central

Bourobou, Serge Thomas Mickala; Yoo, Younghwan

2015-01-01

This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home. PMID:26007738

Pattern-Recognition Algorithm for Locking Laser Frequency

NASA Technical Reports Server (NTRS)

Karayan, Vahag; Klipstein, William; Enzer, Daphna; Yates, Philip; Thompson, Robert; Wells, George

2006-01-01

A computer program serves as part of a feedback control system that locks the frequency of a laser to one of the spectral peaks of cesium atoms in an optical absorption cell. The system analyzes a saturation absorption spectrum to find a target peak and commands a laser-frequency-control circuit to minimize an error signal representing the difference between the laser frequency and the target peak. The program implements an algorithm consisting of the following steps: Acquire a saturation absorption signal while scanning the laser through the frequency range of interest. Condition the signal by use of convolution filtering. Detect peaks. Match the peaks in the signal to a pattern of known spectral peaks by use of a pattern-recognition algorithm. Add missing peaks. Tune the laser to the desired peak and thereafter lock onto this peak. Finding and locking onto the desired peak is a challenging problem, given that the saturation absorption signal includes noise and other spurious signal components; the problem is further complicated by nonlinearity and shifting of the voltage-to-frequency correspondence. The pattern-recognition algorithm, which is based on Hausdorff distance, is what enables the program to meet these challenges.

Pattern Recognition for a Flight Dynamics Monte Carlo Simulation

NASA Technical Reports Server (NTRS)

Restrepo, Carolina; Hurtado, John E.

2011-01-01

The design, analysis, and verification and validation of a spacecraft relies heavily on Monte Carlo simulations. Modern computational techniques are able to generate large amounts of Monte Carlo data but flight dynamics engineers lack the time and resources to analyze it all. The growing amounts of data combined with the diminished available time of engineers motivates the need to automate the analysis process. Pattern recognition algorithms are an innovative way of analyzing flight dynamics data efficiently. They can search large data sets for specific patterns and highlight critical variables so analysts can focus their analysis efforts. This work combines a few tractable pattern recognition algorithms with basic flight dynamics concepts to build a practical analysis tool for Monte Carlo simulations. Current results show that this tool can quickly and automatically identify individual design parameters, and most importantly, specific combinations of parameters that should be avoided in order to prevent specific system failures. The current version uses a kernel density estimation algorithm and a sequential feature selection algorithm combined with a k-nearest neighbor classifier to find and rank important design parameters. This provides an increased level of confidence in the analysis and saves a significant amount of time.

SAR image classification based on CNN in real and simulation datasets

NASA Astrophysics Data System (ADS)

Peng, Lijiang; Liu, Ming; Liu, Xiaohua; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-04-01

Convolution neural network (CNN) has made great success in image classification tasks. Even in the field of synthetic aperture radar automatic target recognition (SAR-ATR), state-of-art results has been obtained by learning deep representation of features on the MSTAR benchmark. However, the raw data of MSTAR have shortcomings in training a SAR-ATR model because of high similarity in background among the SAR images of each kind. This indicates that the CNN would learn the hierarchies of features of backgrounds as well as the targets. To validate the influence of the background, some other SAR images datasets have been made which contains the simulation SAR images of 10 manufactured targets such as tank and fighter aircraft, and the backgrounds of simulation SAR images are sampled from the whole original MSTAR data. The simulation datasets contain the dataset that the backgrounds of each kind images correspond to the one kind of backgrounds of MSTAR targets or clutters and the dataset that each image shares the random background of whole MSTAR targets or clutters. In addition, mixed datasets of MSTAR and simulation datasets had been made to use in the experiments. The CNN architecture proposed in this paper are trained on all datasets mentioned above. The experimental results shows that the architecture can get high performances on all datasets even the backgrounds of the images are miscellaneous, which indicates the architecture can learn a good representation of the targets even though the drastic changes on background.

Effects of the Hydration State on the Mid-Infrared Spectra of Urea and Creatinine in Relation to Urine Analyses.

PubMed

Oliver, Katherine V; Maréchal, Amandine; Rich, Peter R

2016-06-01

When analyzing solutes by Fourier transform infrared (FT-IR) spectroscopy in attenuated total reflection (ATR) mode, drying of samples onto the ATR crystal surface can greatly increase solute band intensities and, therefore, aid detection of minor components. However, analysis of such spectra is complicated by the existence of alternative partial hydration states of some substances that can significantly alter their infrared signatures. This is illustrated here with urea, which is a dominant component of urine. The effects of hydration state on its infrared spectrum were investigated both by incubation in atmospheres of fixed relative humidities and by recording serial spectra during the drying process. Significant changes of absorption band positions and shapes were observed. Decomposition of the CN antisymmetric stretching (νas) band in all states was possible with four components whose relative intensities varied with hydration state. These correspond to the solution (1468 cm(-1)) and dry (1464 cm(-1)) states and two intermediate (1454 cm(-1) and 1443 cm(-1)) forms that arise from specific urea-water and/or urea-urea interactions. Such intermediate forms of other compounds can also be formed, as demonstrated here with creatinine. Recognition of these states and their accommodation in analyses of materials such as dried urine allows more precise decomposition of spectra so that weaker bands of diagnostic interest can be more accurately defined. © The Author(s) 2016.

Demonstration of a 3D vision algorithm for space applications

NASA Technical Reports Server (NTRS)

Defigueiredo, Rui J. P. (Editor)

1987-01-01

This paper reports an extension of the MIAG algorithm for recognition and motion parameter determination of general 3-D polyhedral objects based on model matching techniques and using movement invariants as features of object representation. Results of tests conducted on the algorithm under conditions simulating space conditions are presented.

Target recognitions in multiple-camera closed-circuit television using color constancy

NASA Astrophysics Data System (ADS)

Soori, Umair; Yuen, Peter; Han, Ji Wen; Ibrahim, Izzati; Chen, Wentao; Hong, Kan; Merfort, Christian; James, David; Richardson, Mark

2013-04-01

People tracking in crowded scenes from closed-circuit television (CCTV) footage has been a popular and challenging task in computer vision. Due to the limited spatial resolution in the CCTV footage, the color of people's dress may offer an alternative feature for their recognition and tracking. However, there are many factors, such as variable illumination conditions, viewing angles, and camera calibration, that may induce illusive modification of intrinsic color signatures of the target. Our objective is to recognize and track targets in multiple camera views using color as the detection feature, and to understand if a color constancy (CC) approach may help to reduce these color illusions due to illumination and camera artifacts and thereby improve target recognition performance. We have tested a number of CC algorithms using various color descriptors to assess the efficiency of target recognition from a real multicamera Imagery Library for Intelligent Detection Systems (i-LIDS) data set. Various classifiers have been used for target detection, and the figure of merit to assess the efficiency of target recognition is achieved through the area under the receiver operating characteristics (AUROC). We have proposed two modifications of luminance-based CC algorithms: one with a color transfer mechanism and the other using a pixel-wise sigmoid function for an adaptive dynamic range compression, a method termed enhanced luminance reflectance CC (ELRCC). We found that both algorithms improve the efficiency of target recognitions substantially better than that of the raw data without CC treatment, and in some cases the ELRCC improves target tracking by over 100% within the AUROC assessment metric. The performance of the ELRCC has been assessed over 10 selected targets from three different camera views of the i-LIDS footage, and the averaged target recognition efficiency over all these targets is found to be improved by about 54% in AUROC after the data are processed by the proposed ELRCC algorithm. This amount of improvement represents a reduction of probability of false alarm by about a factor of 5 at the probability of detection of 0.5. Our study concerns mainly the detection of colored targets; and issues for the recognition of white or gray targets will be addressed in a forthcoming study.

The impact of privacy protection filters on gender recognition

NASA Astrophysics Data System (ADS)

Ruchaud, Natacha; Antipov, Grigory; Korshunov, Pavel; Dugelay, Jean-Luc; Ebrahimi, Touradj; Berrani, Sid-Ahmed

2015-09-01

Deep learning-based algorithms have become increasingly efficient in recognition and detection tasks, especially when they are trained on large-scale datasets. Such recent success has led to a speculation that deep learning methods are comparable to or even outperform human visual system in its ability to detect and recognize objects and their features. In this paper, we focus on the specific task of gender recognition in images when they have been processed by privacy protection filters (e.g., blurring, masking, and pixelization) applied at different strengths. Assuming a privacy protection scenario, we compare the performance of state of the art deep learning algorithms with a subjective evaluation obtained via crowdsourcing to understand how privacy protection filters affect both machine and human vision.

Semisupervised kernel marginal Fisher analysis for face recognition.

PubMed

Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

2013-01-01

Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.

Measurement Marker Recognition In A Time Sequence Of Infrared Images For Biomedical Applications

NASA Astrophysics Data System (ADS)

Fiorini, A. R.; Fumero, R.; Marchesi, R.

1986-03-01

In thermographic measurements, quantitative surface temperature evaluation is often uncertain. The main reason is in the lack of available reference points in transient conditions. Reflective markers were used for automatic marker recognition and pixel coordinate computations. An algorithm selects marker icons to match marker references where particular luminance conditions are satisfied. Automatic marker recognition allows luminance compensation and temperature calibration of recorded infrared images. A biomedical application is presented: the dynamic behaviour of the surface temperature distributions is investigated in order to study the performance of two different pumping systems for extracorporeal circulation. Sequences of images are compared and results are discussed. Finally, the algorithm allows to monitor the experimental environment and to alert for the presence of unusual experimental conditions.

Behavioral features recognition and oestrus detection based on fast approximate clustering algorithm in dairy cows

NASA Astrophysics Data System (ADS)

Tian, Fuyang; Cao, Dong; Dong, Xiaoning; Zhao, Xinqiang; Li, Fade; Wang, Zhonghua

2017-06-01

Behavioral features recognition was an important effect to detect oestrus and sickness in dairy herds and there is a need for heat detection aid. The detection method was based on the measure of the individual behavioural activity, standing time, and temperature of dairy using vibrational sensor and temperature sensor in this paper. The data of behavioural activity index, standing time, lying time and walking time were sent to computer by lower power consumption wireless communication system. The fast approximate K-means algorithm (FAKM) was proposed to deal the data of the sensor for behavioral features recognition. As a result of technical progress in monitoring cows using computers, automatic oestrus detection has become possible.

Terrain type recognition using ERTS-1 MSS images

NASA Technical Reports Server (NTRS)

Gramenopoulos, N.

1973-01-01

For the automatic recognition of earth resources from ERTS-1 digital tapes, both multispectral and spatial pattern recognition techniques are important. Recognition of terrain types is based on spatial signatures that become evident by processing small portions of an image through selected algorithms. An investigation of spatial signatures that are applicable to ERTS-1 MSS images is described. Artifacts in the spatial signatures seem to be related to the multispectral scanner. A method for suppressing such artifacts is presented. Finally, results of terrain type recognition for one ERTS-1 image are presented.

Multi-modal low cost mobile indoor surveillance system on the Robust Artificial Intelligence-based Defense Electro Robot (RAIDER)

NASA Astrophysics Data System (ADS)

Nair, Binu M.; Diskin, Yakov; Asari, Vijayan K.

2012-10-01

We present an autonomous system capable of performing security check routines. The surveillance machine, the Clearpath Husky robotic platform, is equipped with three IP cameras with different orientations for the surveillance tasks of face recognition, human activity recognition, autonomous navigation and 3D reconstruction of its environment. Combining the computer vision algorithms onto a robotic machine has given birth to the Robust Artificial Intelligencebased Defense Electro-Robot (RAIDER). The end purpose of the RAIDER is to conduct a patrolling routine on a single floor of a building several times a day. As the RAIDER travels down the corridors off-line algorithms use two of the RAIDER's side mounted cameras to perform a 3D reconstruction from monocular vision technique that updates a 3D model to the most current state of the indoor environment. Using frames from the front mounted camera, positioned at the human eye level, the system performs face recognition with real time training of unknown subjects. Human activity recognition algorithm will also be implemented in which each detected person is assigned to a set of action classes picked to classify ordinary and harmful student activities in a hallway setting.The system is designed to detect changes and irregularities within an environment as well as familiarize with regular faces and actions to distinguish potentially dangerous behavior. In this paper, we present the various algorithms and their modifications which when implemented on the RAIDER serves the purpose of indoor surveillance.

Human Activity Recognition from Body Sensor Data using Deep Learning.

PubMed

Hassan, Mohammad Mehedi; Huda, Shamsul; Uddin, Md Zia; Almogren, Ahmad; Alrubaian, Majed

2018-04-16

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

Operational Philosophy for the Advanced Test Reactor National Scientific User Facility

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

J. Benson; J. Cole; J. Jackson

2013-02-01

In 2007, the Department of Energy (DOE) designated the Advanced Test Reactor (ATR) as a National Scientific User Facility (NSUF). At its core, the ATR NSUF Program combines access to a portion of the available ATR radiation capability, the associated required examination and analysis facilities at the Idaho National Laboratory (INL), and INL staff expertise with novel ideas provided by external contributors (universities, laboratories, and industry). These collaborations define the cutting edge of nuclear technology research in high-temperature and radiation environments, contribute to improved industry performance of current and future light-water reactors (LWRs), and stimulate cooperative research between user groupsmore » conducting basic and applied research. To make possible the broadest access to key national capability, the ATR NSUF formed a partnership program that also makes available access to critical facilities outside of the INL. Finally, the ATR NSUF has established a sample library that allows access to pre-irradiated samples as needed by national research teams.« less

Oxidative Stress Response Tips the Balance in Aspergillus terreus Amphotericin B Resistance

PubMed Central

Blatzer, Michael; Posch, Wilfried; Steger, Marion; Binder, Ulrike; Lass-Flörl, Cornelia

2017-01-01

ABSTRACT In this study, we characterize the impact of antioxidative enzymes in amphotericin B (AmB)-resistant (ATR) and rare AmB-susceptible (ATS) clinical Aspergillus terreus isolates. We elucidate expression profiles of superoxide dismutase (SOD)- and catalase (CAT)-encoding genes, enzymatic activities of SODs, and superoxide anion production and signaling pathways involved in the oxidative stress response (OSR) in ATS and ATR strains under AmB treatment conditions. We show that ATR strains possess almost doubled basal SOD activity compared to that of ATS strains and that ATR strains exhibit an enhanced OSR, with significantly higher sod2 mRNA levels and significantly increased cat transcripts in ATR strains upon AmB treatment. In particular, inhibition of SOD and CAT proteins renders resistant isolates considerably susceptible to the drug in vitro. In conclusion, this study shows that SODs and CATs are crucial for AmB resistance in A. terreus and that targeting the OSR might offer new treatment perspectives for resistant species. PMID:28739793

Access to Recovery and Recidivism among Former Prison Inmates

PubMed Central

Ray, Bradley; Grommon, Eric; Buchanan, Victoria; Brown, Brittany

2015-01-01

Access to Recovery (ATR) is a SAMHSA-funded initiative that offers a mix of clinical and supportive services for substance abuse. ATR clients choose which services will help to overcome barriers in their road to recovery, and a recovery consultant provides vouchers and helps link the client to these community resources. One of ATR's goals was to provide services to those involved in the criminal justice system in the hopes that addressing substance abuse issues could reduce subsequent criminal behaviors. This study examines this goal by looking at recidivism among a sample of clients in one state's ATR program who returned to the community after incarceration. Results suggest there were few differential effects of service selections on subsequent recidivism. However, there are significant differences in recidivism rates among the agencies that provided ATR services. Agencies with more resources and a focus on prisoner reentry had better recidivism outcomes than those that focus only on substance abuse services. PMID:26385191

LIM Protein Ajuba associates with the RPA complex through direct cell cycle-dependent interaction with the RPA70 subunit.

PubMed

Fowler, Sandy; Maguin, Pascal; Kalan, Sampada; Loayza, Diego

2018-06-22

DNA damage response pathways are essential for genome stability and cell survival. Specifically, the ATR kinase is activated by DNA replication stress. An early event in this activation is the recruitment and phosphorylation of RPA, a single stranded DNA binding complex composed of three subunits, RPA70, RPA32 and RPA14. We have previously shown that the LIM protein Ajuba associates with RPA, and that depletion of Ajuba leads to potent activation of ATR. In this study, we provide evidence that the Ajuba-RPA interaction occurs through direct protein contact with RPA70, and that their association is cell cycle-regulated and is reduced upon DNA replication stress. We propose a model in which Ajuba negatively regulates the ATR pathway by directly interacting with RPA70, thereby preventing inappropriate ATR activation. Our results provide a framework to further our understanding of the mechanism of ATR regulation in human cells in the context of cellular transformation.

In-core flux sensor evaluations at the ATR critical facility

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

Troy Unruh; Benjamin Chase; Joy Rempe

2014-09-01

Flux detector evaluations were completed as part of a joint Idaho State University (ISU) / Idaho National Laboratory (INL) / French Atomic Energy commission (CEA) ATR National Scientific User Facility (ATR NSUF) project to compare the accuracy, response time, and long duration performance of several flux detectors. Special fixturing developed by INL allows real-time flux detectors to be inserted into various ATRC core positions and perform lobe power measurements, axial flux profile measurements, and detector cross-calibrations. Detectors initially evaluated in this program include the French Atomic Energy Commission (CEA)-developed miniature fission chambers; specialized self-powered neutron detectors (SPNDs) developed by themore » Argentinean National Energy Commission (CNEA); specially developed commercial SPNDs from Argonne National Laboratory. As shown in this article, data obtained from this program provides important insights related to flux detector accuracy and resolution for subsequent ATR and CEA experiments and flux data required for bench-marking models in the ATR V&V Upgrade Initiative.« less

Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

PubMed

Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

2012-06-01

This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

Tip-enhanced near-field optical microscope with side-on and ATR-mode sample excitation for super-resolution Raman imaging of surfaces

NASA Astrophysics Data System (ADS)

Heilman, A. L.; Gordon, M. J.

2016-06-01

A tip-enhanced near-field optical microscope with side-on and attenuated total reflectance (ATR) excitation and collection is described and used to demonstrate sub-diffraction-limited (super-resolution) optical and chemical characterization of surfaces. ATR illumination is combined with an Au optical antenna tip to show that (i) the tip can quantitatively transduce the optical near-field (evanescent waves) above the surface by scattering photons into the far-field, (ii) the ATR geometry enables excitation and characterization of surface plasmon polaritons (SPPs), whose associated optical fields are shown to enhance Raman scattering from a thin layer of copper phthalocyanine (CuPc), and (iii) SPPs can be used to plasmonically excite the tip for super-resolution chemical imaging of patterned CuPc via tip-enhanced Raman spectroscopy (TERS). ATR-illumination TERS is also quantitatively compared with the more conventional side-on illumination scheme. In both cases, spatial resolution was better than 40 nm and tip on/tip off Raman enhancement factors were >6500. Furthermore, ATR illumination was shown to provide similar Raman signal levels at lower "effective" pump powers due to additional optical energy delivered by SPPs to the active region in the tip-surface gap.

Extract of Allium tuberosum Rottler ex Spreng Promoted the Hair Growth through Regulating the Expression of IGF-1

PubMed Central

Park, Ki Moon; Kim, Dong Woo; Lee, Seung Ho

2015-01-01

Allium tuberosum Rottler ex Spreng (ATRES) has been used as a traditional medicine for the treatment of abdominal pain, diarrhea, and asthma. In this study, we investigated the hair growth promoting activities of ATRES on telogenic C57BL6/N mice. Hair growth was significantly increased in the dorsal skin of ethanol extract of ATRES treated mouse group compared with the control mouse group. To enrich the hair promoting activity, an ethanol-insoluble fraction was further extracted in sequence with n-hexane, dichloromethane, ethyl acetate, n-butanol, and distilled water. Interestingly, we found that extraction with n-butanol is most efficient in producing the hair promoting activity. In addition, the soluble fraction of the n-butanol extract was further separated by silica gel chromatography and thin layer chromatography (TLC) resulting in isolating four single fractions which have hair growth regeneration potential. Furthermore, administration of ATRES extracts to dorsal skin area increased the number of hair follicles compared with control mouse group. Interestingly, administration of ATRES extract stimulated the expression of insulin-like growth factor-1 (IGF-1) but not of keratin growth factor (KGF) or vascular endothelial growth factor (VEGF). Taken together, these results suggest that ATRES possesses strong hair growth promoting potential which controls the expression of IGF-1. PMID:26078771

10 CFR 830 Major Modification Determination for the Advanced Test Reactor Remote Monitoring and Management Capability

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

Bohachek, Randolph Charles

2015-09-01

The Advanced Test Reactor (ATR; TRA-670), which is located in the ATR Complex at Idaho National Laboratory, was constructed in the 1960s for the purpose of irradiating reactor fuels and materials. Other irradiation services, such as radioisotope production, are also performed at ATR. While ATR is safely fulfilling current mission requirements, assessments are continuing. These assessments intend to identify areas to provide defense–in-depth and improve safety for ATR. One of the assessments performed by an independent group of nuclear industry experts recommended that a remote accident management capability be provided. The report stated that: “contemporary practice in commercial power reactorsmore » is to provide a remote shutdown station or stations to allow shutdown of the reactor and management of long-term cooling of the reactor (i.e., management of reactivity, inventory, and cooling) should the main control room be disabled (e.g., due to a fire in the control room or affecting the control room).” This project will install remote reactor monitoring and management capabilities for ATR. Remote capabilities will allow for post scram reactor management and monitoring in the event the main Reactor Control Room (RCR) must be evacuated.« less

Tip-enhanced near-field optical microscope with side-on and ATR-mode sample excitation for super-resolution Raman imaging of surfaces

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

Heilman, A. L.; Gordon, M. J.

A tip-enhanced near-field optical microscope with side-on and attenuated total reflectance (ATR) excitation and collection is described and used to demonstrate sub-diffraction-limited (super-resolution) optical and chemical characterization of surfaces. ATR illumination is combined with an Au optical antenna tip to show that (i) the tip can quantitatively transduce the optical near-field (evanescent waves) above the surface by scattering photons into the far-field, (ii) the ATR geometry enables excitation and characterization of surface plasmon polaritons (SPPs), whose associated optical fields are shown to enhance Raman scattering from a thin layer of copper phthalocyanine (CuPc), and (iii) SPPs can be used tomore » plasmonically excite the tip for super-resolution chemical imaging of patterned CuPc via tip-enhanced Raman spectroscopy (TERS). ATR-illumination TERS is also quantitatively compared with the more conventional side-on illumination scheme. In both cases, spatial resolution was better than 40 nm and tip on/tip off Raman enhancement factors were >6500. Furthermore, ATR illumination was shown to provide similar Raman signal levels at lower “effective” pump powers due to additional optical energy delivered by SPPs to the active region in the tip-surface gap.« less

Towards ultrasound enhanced mid-IR spectroscopy for sensing bacteria in aqueous solutions

NASA Astrophysics Data System (ADS)

Freitag, Stephan; Schwaighofer, Andreas; Radel, Stefan; Lendl, Bernhard

2018-02-01

We employ attenuated total reflection (ATR) mid-IR technology for sensing of bacteria present in aqueous solution. In ATR spectroscopy, the penetration depth of the evanescent field extends to approx. 1-2 micrometers into the aqueous solution depending on the refractive index of the employed materials (Si, ZnS, Ge) used as attenuated total reflection (ATR) element and the geometry of the optical set-up. Due to the flow profile in the microfluidic cell, an additional force is required to bring particles into the evanescent field for measurement. For that purpose, we employ standing ultrasound waves produced between a sound source vibrating at approx. 2 MHz and the ATR crystal acting as a reflector. This ultrasonic trap is integrated into the microfluidic channel. As aqueous solution is passing through that acoustofluidic cell, particles are concentrated in the nodal plane of the standing ultrasound wave, forming particle conglomerates. By selecting appropriate experimental conditions, it is then possible to press bacteria against the crystal surface for interaction with the evanescent wave (as well as to keep them away from the ATR element). Our current work aims at establishing a custommade US-ATR-IR setup for signal enhancement of bacteria (e.g. E. coli, P. aeruginosa as well as Salmonella) in drinking water.

ATR suppresses endogenous DNA damage and allows completion of homologous recombination repair.

PubMed

Brown, Adam D; Sager, Brian W; Gorthi, Aparna; Tonapi, Sonal S; Brown, Eric J; Bishop, Alexander J R

2014-01-01

DNA replication fork stalling or collapse that arises from endogenous damage poses a serious threat to genome stability, but cells invoke an intricate signaling cascade referred to as the DNA damage response (DDR) to prevent such damage. The gene product ataxia telangiectasia and Rad3-related (ATR) responds primarily to replication stress by regulating cell cycle checkpoint control, yet it's role in DNA repair, particularly homologous recombination (HR), remains unclear. This is of particular interest since HR is one way in which replication restart can occur in the presence of a stalled or collapsed fork. Hypomorphic mutations in human ATR cause the rare autosomal-recessive disease Seckel syndrome, and complete loss of Atr in mice leads to embryonic lethality. We recently adapted the in vivo murine pink-eyed unstable (pun) assay for measuring HR frequency to be able to investigate the role of essential genes on HR using a conditional Cre/loxP system. Our system allows for the unique opportunity to test the effect of ATR loss on HR in somatic cells under physiological conditions. Using this system, we provide evidence that retinal pigment epithelium (RPE) cells lacking ATR have decreased density with abnormal morphology, a decreased frequency of HR and an increased level of chromosomal damage.

ATR and transmission analysis of pigments by means of far infrared spectroscopy.

PubMed

Kendix, Elsebeth L; Prati, Silvia; Joseph, Edith; Sciutto, Giorgia; Mazzeo, Rocco

2009-06-01

In the field of FTIR spectroscopy, the far infrared (FIR) spectral region has been so far less investigated than the mid-infrared (MIR), even though it presents great advantages in the characterization of those inorganic compounds, which are inactive in the MIR, such as some art pigments, corrosion products, etc. Furthermore, FIR spectroscopy is complementary to Raman spectroscopy if the fluorescence effects caused by the latter analytical technique are considered. In this paper, ATR in the FIR region is proposed as an alternative method to transmission for the analyses of pigments. This methodology was selected in order to reduce the sample amount needed for analysis, which is a must when examining cultural heritage materials. A selection of pigments have been analyzed in both ATR and transmission mode, and the resulting spectra were compared with each other. To better perform this comparison, an evaluation of the possible effect induced by the thermal treatment needed for the preparation of the polyethylene pellets on the transmission spectra of the samples has been carried out. Therefore, pigments have been analyzed in ATR mode before and after heating them at the same temperature employed for the polyethylene pellet preparation. The results showed that while the heating treatment causes only small changes in the intensity of some bands, the ATR spectra were characterized by differences in both intensity and band shifts towards lower frequencies if compared with those recorded in transmission mode. All pigments' transmission and ATR spectra are presented and discussed, and the ATR method was validated on a real case study.

Quantitative determination of band distortions in diamond attenuated total reflectance infrared spectra.

PubMed

Boulet-Audet, Maxime; Buffeteau, Thierry; Boudreault, Simon; Daugey, Nicolas; Pézolet, Michel

2010-06-24

Due to its unmatched hardness and chemical inertia, diamond offers many advantages over other materials for extreme conditions and routine analysis by attenuated total reflection (ATR) infrared spectroscopy. Its low refractive index can offer up to a 6-fold absorbance increase compared to germanium. Unfortunately, it also results for strong bands in spectral distortions compared to transmission experiments. The aim of this paper is to present a methodological approach to determine quantitatively the degree of the spectral distortions in ATR spectra. This approach requires the determination of the optical constants (refractive index and extinction coefficient) of the investigated sample. As a typical example, the optical constants of the fibroin protein of the silk worm Bombyx mori have been determined from the polarized ATR spectra obtained using both diamond and germanium internal reflection elements. The positions found for the amide I band by germanium and diamond ATR are respectively 6 and 17 cm(-1) lower than the true value dtermined from the k(nu) spectrum, which is calculated to be 1659 cm(-1). To determine quantitatively the effect of relevant parameters such as the film thickness and the protein concentration, various spectral simulations have also been performed. The use of a thinner film probed by light polarized in the plane of incidence and diluting the protein sample can help in obtaining ATR spectra that are closer to their transmittance counterparts. To extend this study to any system, the ATR distortion amplitude has been evaluated using spectral simulations performed for bands of various intensities and widths. From these simulations, a simple empirical relationship has been found to estimate the band shift from the experimental band height and width that could be of practical use for ATR users. This paper shows that the determination of optical constants provides an efficient way to recover the true spectrum shape and band frequencies of distorted ATR spectra.

Contribution of enhanced engagement of antigen presentation machinery to the clinical immunogenicity of a human interleukin (IL)-21 receptor-blocking therapeutic antibody.

PubMed

Xue, L; Hickling, T; Song, R; Nowak, J; Rup, B

2016-01-01

Reliable risk assessment for biotherapeutics requires accurate evaluation of risk factors associated with immunogenicity. Immunogenicity risk assessment tools were developed and applied to investigate the immunogenicity of a fully human therapeutic monoclonal antibody, ATR-107 [anti-interleukin (IL)-21 receptor] that elicited anti-drug antibodies (ADA) in 76% of healthy subjects in a Phase 1 study. Because the ATR-107 target is expressed on dendritic cells (DCs), the immunogenicity risk related to engagement with DC and antigen presentation pathways was studied. Despite the presence of IL-21R on DCs, ATR-107 did not bind to the DCs more extensively than the control therapeutic antibody (PF-1) that had elicited low clinical ADA incidence. However, ATR-107, but not the control therapeutic antibody, was translocated to the DC late endosomes, co-localized with intracellular antigen-D related (HLA-DR) molecules and presented a dominant T cell epitope overlapping the complementarity determining region 2 (CDR2) of the light chain. ATR-107 induced increased DC activation exemplified by up-regulation of DC surface expression of CD86, CD274 (PD-L1) and CD40, increased expansion of activated DC populations expressing CD86(hi), CD40(hi), CD83(hi), programmed death ligand 1 (PD-L1)(hi), HLA-DR(hi) or CCR7(hi), as well as elevated secretion of tumour necrosis factor (TNF)-α by DCs. DCs exposed to ATR-107 stimulated an autologous T cell proliferative response in human donor cells, in concert with the detection of immunoglobulin (Ig)G-type anti-ATR-107 antibody response in clinical samples. Collectively, the enhanced engagement of antigen presentation machinery by ATR-107 was suggested. The approaches and findings described in this study may be relevant to identifying lower immunogenicity risk targets and therapeutic molecules. © 2015 British Society for Immunology.

ATR-101, a selective ACAT1 inhibitor, decreases ACTH-stimulated cortisol concentrations in dogs with naturally occurring Cushing's syndrome.

PubMed

Langlois, Daniel K; Fritz, Michele C; Schall, William D; Bari Olivier, N; Smedley, Rebecca C; Pearson, Paul G; Bailie, Marc B; Hunt, Stephen W

2018-05-02

Cushing's syndrome in humans shares many similarities with its counterpart in dogs in terms of etiology (pituitary versus adrenal causes), clinical signs, and pathophysiologic sequelae. In both species, treatment of pituitary- and adrenal-dependent disease is met with limitations. ATR-101, a selective inhibitor of ACAT1 (acyl coenzyme A:cholesterol acyltransferase 1), is a novel small molecule therapeutic currently in clinical development for the treatment of adrenocortical carcinoma, congenital adrenal hyperplasia, and Cushing's syndrome in humans. Previous studies in healthy dogs have shown that ATR-101 treatment led to rapid, dose-dependent decreases in adrenocorticotropic hormone (ACTH) stimulated cortisol levels. The purpose of this clinical study was to investigate the effects of ATR-101 in dogs with Cushing's syndrome. ATR-101 pharmacokinetics and activity were assessed in 10 dogs with naturally-occurring Cushing's syndrome, including 7 dogs with pituitary-dependent disease and 3 dogs with adrenal-dependent disease. ATR-101 was administered at 3 mg/kg PO once daily for one week, followed by 30 mg/kg PO once daily for one (n = 4) or three (n = 6) weeks. Clinical, biochemical, adrenal hormonal, and pharmacokinetic data were obtained weekly for study duration. ATR-101 exposure increased with increasing dose. ACTH-stimulated cortisol concentrations, the primary endpoint for the study, were significantly decreased with responders (9 of 10 dogs) experiencing a mean ± standard deviation reduction in cortisol levels of 50 ± 17% at study completion. Decreases in pre-ACTH-stimulated cortisol concentrations were observed in some dogs although overall changes in pre-ACTH cortisol concentrations were not significant. The compound was well-tolerated and no serious drug-related adverse effects were reported. This study highlights the potential utility of naturally occurring canine Cushing's syndrome as a model for human disease and provides proof of concept for ATR-101 as a novel agent for the treatment of endocrine disorders like Cushing's syndrome in humans.

Online adaptive neural control of a robotic lower limb prosthesis

NASA Astrophysics Data System (ADS)

Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.

2018-02-01

Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.

Non-Cooperative Target Recognition by Means of Singular Value Decomposition Applied to Radar High Resolution Range Profiles †

PubMed Central

López-Rodríguez, Patricia; Escot-Bocanegra, David; Fernández-Recio, Raúl; Bravo, Ignacio

2015-01-01

Radar high resolution range profiles are widely used among the target recognition community for the detection and identification of flying targets. In this paper, singular value decomposition is applied to extract the relevant information and to model each aircraft as a subspace. The identification algorithm is based on angle between subspaces and takes place in a transformed domain. In order to have a wide database of radar signatures and evaluate the performance, simulated range profiles are used as the recognition database while the test samples comprise data of actual range profiles collected in a measurement campaign. Thanks to the modeling of aircraft as subspaces only the valuable information of each target is used in the recognition process. Thus, one of the main advantages of using singular value decomposition, is that it helps to overcome the notable dissimilarities found in the shape and signal-to-noise ratio between actual and simulated profiles due to their difference in nature. Despite these differences, the recognition rates obtained with the algorithm are quite promising. PMID:25551484

Classification of motor activities through derivative dynamic time warping applied on accelerometer data.

PubMed

Muscillo, Rossana; Conforto, Silvia; Schmid, Maurizio; Caselli, Paolo; D'Alessio, Tommaso

2007-01-01

In the context of tele-monitoring, great interest is presently devoted to physical activity, mainly of elderly or people with disabilities. In this context, many researchers studied the recognition of activities of daily living by using accelerometers. The present work proposes a novel algorithm for activity recognition that considers the variability in movement speed, by using dynamic programming. This objective is realized by means of a matching and recognition technique that determines the distance between the signal input and a set of previously defined templates. Two different approaches are here presented, one based on Dynamic Time Warping (DTW) and the other based on the Derivative Dynamic Time Warping (DDTW). The algorithm was applied to the recognition of gait, climbing and descending stairs, using a biaxial accelerometer placed on the shin. The results on DDTW, obtained by using only one sensor channel on the shin showed an average recognition score of 95%, higher than the values obtained with DTW (around 85%). Both DTW and DDTW consistently show higher classification rate than classical Linear Time Warping (LTW).

AGR-3/4 Final Data Qualification Report for ATR Cycles 151A through 155B-1

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

Pham, Binh T.

2015-03-01

This report provides the qualification status of experimental data for the entire Advanced Gas Reactor 3/4 (AGR 3/4) fuel irradiation. AGR-3/4 is the third in a series of planned irradiation experiments conducted in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) for the AGR Fuel Development and Qualification Program, which supports development of the advanced reactor technology under the INL ART Technology Development Office (TDO). The main objective of AGR-3/4 irradiation is to provide a known source of fission products for subsequent transport through compact matrix and structural graphite materials due to the presence of designed-to-fail fuel particles.more » Full power irradiation of the AGR 3/4 test began on December 14, 2011 (ATR Cycle 151A), and was completed on April 12, 2014 (end of ATR Cycle 155B) after 369.1 effective full power days of irradiation. The AGR-3/4 test was in the reactor core for eight of the ten ATR cycles between 151A and 155B. During the unplanned outage cycle, 153A, the experiment was removed from the ATR northeast flux trap (NEFT) location and stored in the ATR canal. This was to prevent overheating of fuel compacts due to higher than normal ATR power during the subsequent Powered Axial Locator Mechanism cycle, 153B. The AGR 3/4 test was inserted back into the ATR NEFT location during the outage of ATR Cycle 154A on April 26, 2013. Therefore, the AGR-3/4 irradiation data received during these 2 cycles (153A and 153B) are irrelevant and their qualification status isnot included in this report. Additionally, during ATR Cycle 152A the ATR core ran at low power for a short enough duration that the irradiation data are not used for physics and thermal calculations. However, the qualification status of irradiation data for this cycle is still covered in this report. As a result, this report includes data from 8 ATR Cycles: 151A, 151B, 152A, 152B, 154A, 154B, 155A, and 155B, as recorded in the Nuclear Data Management and Analysis System (NDMAS). The AGR 3/4 data streams addressed in this report include thermocouple (TC) temperatures, sweep gas data (flow rates, pressure, and moisture content), and Fission Product Monitoring System (FPMS) data (release rates, release to birth rate ratios [R/Bs], and particle failure counts) for each of the twelve capsules in the AGR 3/4 experiment. During Outage Cycle 155A, fourteen flow meters were installed downstream from fourteen FPMS monitors to measure flows from the monitors; qualification status of these data are also included in the report. The final data qualification status for these data streams is determined by a Data Review Committee (DRC) composed of AGR technical leads, Sitewide Quality Assurance (QA), and NDMAS analysts. For ATR Cycles 151A through 154B, the DRC convened on February 12, 2014, reviewed the data acquisition process, and considered whether the data met the requirements for data collection as specified in QA approved INL ART TDO data collection plans. The DRC also examined the results of NDMAS data testing and statistical analyses, and confirmed the qualification status of the data as given in this report. The qualification status of AGR-3/4 irradiation data during the first six cycles were previously reported in INL/EXT-14-31186 document. This report presents data qualification status for the entire AGR-3/4 irradiation.« less

Label consistent K-SVD: learning a discriminative dictionary for recognition.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2013-11-01

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

Image registration under translation and rotation in two-dimensional planes using Fourier slice theorem.

PubMed

Pohit, M; Sharma, J

2015-05-10

Image recognition in the presence of both rotation and translation is a longstanding problem in correlation pattern recognition. Use of log polar transform gives a solution to this problem, but at a cost of losing the vital phase information from the image. The main objective of this paper is to develop an algorithm based on Fourier slice theorem for measuring the simultaneous rotation and translation of an object in a 2D plane. The algorithm is applicable for any arbitrary object shift for full 180° rotation.

Novel search algorithms for a mid-infrared spectral library of cotton contaminants.

PubMed

Loudermilk, J Brian; Himmelsbach, David S; Barton, Franklin E; de Haseth, James A

2008-06-01

During harvest, a variety of plant based contaminants are collected along with cotton lint. The USDA previously created a mid-infrared, attenuated total reflection (ATR), Fourier transform infrared (FT-IR) spectral library of cotton contaminants for contaminant identification as the contaminants have negative impacts on yarn quality. This library has shown impressive identification rates for extremely similar cellulose based contaminants in cases where the library was representative of the samples searched. When spectra of contaminant samples from crops grown in different geographic locations, seasons, and conditions and measured with a different spectrometer and accessories were searched, identification rates for standard search algorithms decreased significantly. Six standard algorithms were examined: dot product, correlation, sum of absolute values of differences, sum of the square root of the absolute values of differences, sum of absolute values of differences of derivatives, and sum of squared differences of derivatives. Four categories of contaminants derived from cotton plants were considered: leaf, stem, seed coat, and hull. Experiments revealed that the performance of the standard search algorithms depended upon the category of sample being searched and that different algorithms provided complementary information about sample identity. These results indicated that choosing a single standard algorithm to search the library was not possible. Three voting scheme algorithms based on result frequency, result rank, category frequency, or a combination of these factors for the results returned by the standard algorithms were developed and tested for their capability to overcome the unpredictability of the standard algorithms' performances. The group voting scheme search was based on the number of spectra from each category of samples represented in the library returned in the top ten results of the standard algorithms. This group algorithm was able to identify correctly as many test spectra as the best standard algorithm without relying on human choice to select a standard algorithm to perform the searches.

Character recognition using a neural network model with fuzzy representation

NASA Technical Reports Server (NTRS)

Tavakoli, Nassrin; Seniw, David

1992-01-01

The degree to which digital images are recognized correctly by computerized algorithms is highly dependent upon the representation and the classification processes. Fuzzy techniques play an important role in both processes. In this paper, the role of fuzzy representation and classification on the recognition of digital characters is investigated. An experimental Neural Network model with application to character recognition was developed. Through a set of experiments, the effect of fuzzy representation on the recognition accuracy of this model is presented.

Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition

NASA Astrophysics Data System (ADS)

Popko, E. A.; Weinstein, I. A.

2016-08-01

Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.

Identity Recognition Algorithm Using Improved Gabor Feature Selection of Gait Energy Image

NASA Astrophysics Data System (ADS)

Chao, LIANG; Ling-yao, JIA; Dong-cheng, SHI

2017-01-01

This paper describes an effective gait recognition approach based on Gabor features of gait energy image. In this paper, the kernel Fisher analysis combined with kernel matrix is proposed to select dominant features. The nearest neighbor classifier based on whitened cosine distance is used to discriminate different gait patterns. The approach proposed is tested on the CASIA and USF gait databases. The results show that our approach outperforms other state of gait recognition approaches in terms of recognition accuracy and robustness.

The development of line-scan image recognition algorithms for the detection of frass on mature tomatoes

USDA-ARS?s Scientific Manuscript database

In this research, a multispectral algorithm derived from hyperspectral line-scan fluorescence imaging under violet LED excitation was developed for the detection of frass contamination on mature tomatoes. The algorithm utilized the fluorescence intensities at two wavebands, 664 nm and 690 nm, for co...

ATR National Scientific User Facility 2009 Annual Report

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

Todd R. Allen; Mitchell K. Meyer; Frances Marshall

2010-11-01

This report describes activities of the ATR NSUF from FY-2008 through FY-2009 and includes information on partner facilities, calls for proposals, users week and education programs. The report also contains project information on university research projects that were awarded by ATR NSUF in the fiscal years 2008 & 2009. This research is university-proposed researcher under a user facility agreement. All intellectual property from these experiments belongs to the university per the user agreement.

Research and implementation of finger-vein recognition algorithm

NASA Astrophysics Data System (ADS)

Pang, Zengyao; Yang, Jie; Chen, Yilei; Liu, Yin

2017-06-01

In finger vein image preprocessing, finger angle correction and ROI extraction are important parts of the system. In this paper, we propose an angle correction algorithm based on the centroid of the vein image, and extract the ROI region according to the bidirectional gray projection method. Inspired by the fact that features in those vein areas have similar appearance as valleys, a novel method was proposed to extract center and width of palm vein based on multi-directional gradients, which is easy-computing, quick and stable. On this basis, an encoding method was designed to determine the gray value distribution of texture image. This algorithm could effectively overcome the edge of the texture extraction error. Finally, the system was equipped with higher robustness and recognition accuracy by utilizing fuzzy threshold determination and global gray value matching algorithm. Experimental results on pairs of matched palm images show that, the proposed method has a EER with 3.21% extracts features at the speed of 27ms per image. It can be concluded that the proposed algorithm has obvious advantages in grain extraction efficiency, matching accuracy and algorithm efficiency.

Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors

NASA Astrophysics Data System (ADS)

Chen, Xiang; Li, Jingchao; Han, Hui; Ying, Yulong

2018-05-01

Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of the signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without the Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors formed the multi-dimensional eigenvectors as signal subtle features, which were used for radiation source signal recognition by the grey relation algorithm. The experimental results show that, compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance.