Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction

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Juan M. Gandarias

2018-02-01

Full Text Available The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM. Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more, with a lower mean of pressure values (up to 72% less than when using a rigid sensor, with a softer grip, which is needed in physical human–robot interaction (pHRI. A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78% with a rigid sensor.

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human-Robot Interaction.

Science.gov (United States)

Gandarias, Juan M; Gómez-de-Gabriel, Jesús M; García-Cerezo, Alfonso J

2018-02-26

The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human-robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.

Supervised and Unsupervised Speaker Adaptation in the NIST 2005 Speaker Recognition Evaluation

National Research Council Canada - National Science Library

Hansen, Eric G; Slyh, Raymond E; Anderson, Timothy R

2006-01-01

Starting in 2004, the annual NIST Speaker Recognition Evaluation (SRE) has added an optional unsupervised speaker adaptation track where test files are processed sequentially and one may update the target model...

An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition

Directory of Open Access Journals (Sweden)

Ming Liu

2017-09-01

Full Text Available Algorithms for locomotion mode recognition (LMR based on surface electromyography and mechanical sensors have recently been developed and could be used for the neural control of powered prosthetic legs. However, the variations in input signals, caused by physical changes at the sensor interface and human physiological changes, may threaten the reliability of these algorithms. This study aimed to investigate the effectiveness of applying adaptive pattern classifiers for LMR. Three adaptive classifiers, i.e., entropy-based adaptation (EBA, LearnIng From Testing data (LIFT, and Transductive Support Vector Machine (TSVM, were compared and offline evaluated using data collected from two able-bodied subjects and one transfemoral amputee. The offline analysis indicated that the adaptive classifier could effectively maintain or restore the performance of the LMR algorithm when gradual signal variations occurred. EBA and LIFT were recommended because of their better performance and higher computational efficiency. Finally, the EBA was implemented for real-time human-in-the-loop prosthesis control. The online evaluation showed that the applied EBA effectively adapted to changes in input signals across sessions and yielded more reliable prosthesis control over time, compared with the LMR without adaptation. The developed novel adaptive strategy may further enhance the reliability of neurally-controlled prosthetic legs.

A Hybrid Acoustic and Pronunciation Model Adaptation Approach for Non-native Speech Recognition

Science.gov (United States)

Oh, Yoo Rhee; Kim, Hong Kook

In this paper, we propose a hybrid model adaptation approach in which pronunciation and acoustic models are adapted by incorporating the pronunciation and acoustic variabilities of non-native speech in order to improve the performance of non-native automatic speech recognition (ASR). Specifically, the proposed hybrid model adaptation can be performed at either the state-tying or triphone-modeling level, depending at which acoustic model adaptation is performed. In both methods, we first analyze the pronunciation variant rules of non-native speakers and then classify each rule as either a pronunciation variant or an acoustic variant. The state-tying level hybrid method then adapts pronunciation models and acoustic models by accommodating the pronunciation variants in the pronunciation dictionary and by clustering the states of triphone acoustic models using the acoustic variants, respectively. On the other hand, the triphone-modeling level hybrid method initially adapts pronunciation models in the same way as in the state-tying level hybrid method; however, for the acoustic model adaptation, the triphone acoustic models are then re-estimated based on the adapted pronunciation models and the states of the re-estimated triphone acoustic models are clustered using the acoustic variants. From the Korean-spoken English speech recognition experiments, it is shown that ASR systems employing the state-tying and triphone-modeling level adaptation methods can relatively reduce the average word error rates (WERs) by 17.1% and 22.1% for non-native speech, respectively, when compared to a baseline ASR system.

Action adaptation during natural unfolding social scenes influences action recognition and inferences made about actor beliefs.

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Keefe, Bruce D; Wincenciak, Joanna; Jellema, Tjeerd; Ward, James W; Barraclough, Nick E

2016-07-01

When observing another individual's actions, we can both recognize their actions and infer their beliefs concerning the physical and social environment. The extent to which visual adaptation influences action recognition and conceptually later stages of processing involved in deriving the belief state of the actor remains unknown. To explore this we used virtual reality (life-size photorealistic actors presented in stereoscopic three dimensions) to see how visual adaptation influences the perception of individuals in naturally unfolding social scenes at increasingly higher levels of action understanding. We presented scenes in which one actor picked up boxes (of varying number and weight), after which a second actor picked up a single box. Adaptation to the first actor's behavior systematically changed perception of the second actor. Aftereffects increased with the duration of the first actor's behavior, declined exponentially over time, and were independent of view direction. Inferences about the second actor's expectation of box weight were also distorted by adaptation to the first actor. Distortions in action recognition and actor expectations did not, however, extend across different actions, indicating that adaptation is not acting at an action-independent abstract level but rather at an action-dependent level. We conclude that although adaptation influences more complex inferences about belief states of individuals, this is likely to be a result of adaptation at an earlier action recognition stage rather than adaptation operating at a higher, more abstract level in mentalizing or simulation systems.

Individual differences in adaptive coding of face identity are linked to individual differences in face recognition ability.

Science.gov (United States)

Rhodes, Gillian; Jeffery, Linda; Taylor, Libby; Hayward, William G; Ewing, Louise

2014-06-01

Despite their similarity as visual patterns, we can discriminate and recognize many thousands of faces. This expertise has been linked to 2 coding mechanisms: holistic integration of information across the face and adaptive coding of face identity using norms tuned by experience. Recently, individual differences in face recognition ability have been discovered and linked to differences in holistic coding. Here we show that they are also linked to individual differences in adaptive coding of face identity, measured using face identity aftereffects. Identity aftereffects correlated significantly with several measures of face-selective recognition ability. They also correlated marginally with own-race face recognition ability, suggesting a role for adaptive coding in the well-known other-race effect. More generally, these results highlight the important functional role of adaptive face-coding mechanisms in face expertise, taking us beyond the traditional focus on holistic coding mechanisms. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Audiovisual cues benefit recognition of accented speech in noise but not perceptual adaptation.

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Banks, Briony; Gowen, Emma; Munro, Kevin J; Adank, Patti

2015-01-01

Perceptual adaptation allows humans to recognize different varieties of accented speech. We investigated whether perceptual adaptation to accented speech is facilitated if listeners can see a speaker's facial and mouth movements. In Study 1, participants listened to sentences in a novel accent and underwent a period of training with audiovisual or audio-only speech cues, presented in quiet or in background noise. A control group also underwent training with visual-only (speech-reading) cues. We observed no significant difference in perceptual adaptation between any of the groups. To address a number of remaining questions, we carried out a second study using a different accent, speaker and experimental design, in which participants listened to sentences in a non-native (Japanese) accent with audiovisual or audio-only cues, without separate training. Participants' eye gaze was recorded to verify that they looked at the speaker's face during audiovisual trials. Recognition accuracy was significantly better for audiovisual than for audio-only stimuli; however, no statistical difference in perceptual adaptation was observed between the two modalities. Furthermore, Bayesian analysis suggested that the data supported the null hypothesis. Our results suggest that although the availability of visual speech cues may be immediately beneficial for recognition of unfamiliar accented speech in noise, it does not improve perceptual adaptation.

Automatic Query Generation and Query Relevance Measurement for Unsupervised Language Model Adaptation of Speech Recognition

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Suzuki Motoyuki

2009-01-01

Full Text Available Abstract We are developing a method of Web-based unsupervised language model adaptation for recognition of spoken documents. The proposed method chooses keywords from the preliminary recognition result and retrieves Web documents using the chosen keywords. A problem is that the selected keywords tend to contain misrecognized words. The proposed method introduces two new ideas for avoiding the effects of keywords derived from misrecognized words. The first idea is to compose multiple queries from selected keyword candidates so that the misrecognized words and correct words do not fall into one query. The second idea is that the number of Web documents downloaded for each query is determined according to the "query relevance." Combining these two ideas, we can alleviate bad effect of misrecognized keywords by decreasing the number of downloaded Web documents from queries that contain misrecognized keywords. Finally, we examine a method of determining the number of iterative adaptations based on the recognition likelihood. Experiments have shown that the proposed stopping criterion can determine almost the optimum number of iterations. In the final experiment, the word accuracy without adaptation (55.29% was improved to 60.38%, which was 1.13 point better than the result of the conventional unsupervised adaptation method (59.25%.

Automatic Query Generation and Query Relevance Measurement for Unsupervised Language Model Adaptation of Speech Recognition

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Akinori Ito

2009-01-01

Full Text Available We are developing a method of Web-based unsupervised language model adaptation for recognition of spoken documents. The proposed method chooses keywords from the preliminary recognition result and retrieves Web documents using the chosen keywords. A problem is that the selected keywords tend to contain misrecognized words. The proposed method introduces two new ideas for avoiding the effects of keywords derived from misrecognized words. The first idea is to compose multiple queries from selected keyword candidates so that the misrecognized words and correct words do not fall into one query. The second idea is that the number of Web documents downloaded for each query is determined according to the “query relevance.” Combining these two ideas, we can alleviate bad effect of misrecognized keywords by decreasing the number of downloaded Web documents from queries that contain misrecognized keywords. Finally, we examine a method of determining the number of iterative adaptations based on the recognition likelihood. Experiments have shown that the proposed stopping criterion can determine almost the optimum number of iterations. In the final experiment, the word accuracy without adaptation (55.29% was improved to 60.38%, which was 1.13 point better than the result of the conventional unsupervised adaptation method (59.25%.

FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

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T. Pasupathi

2014-05-01

Full Text Available Image recognition is a technology which can be used in various applications such as medical image recognition systems, security, defense video tracking, and factory automation. In this paper we present a novel pipelined architecture of an adaptive integrated Artificial Neural Network for image recognition. In our proposed work we have combined the feature of spiking neuron concept with ANN to achieve the efficient architecture for image recognition. The set of training images are trained by ANN and target output has been identified. Real time videos are captured and then converted into frames for testing purpose and the image were recognized. The machine can operate at up to 40 frames/sec using images acquired from the camera. The system has been implemented on XC3S400 SPARTAN-3 Field Programmable Gate Arrays.

HMM Adaptation for Improving a Human Activity Recognition System

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Rubén San-Segundo

2016-09-01

Full Text Available When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs. This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six HMMs (one per activity, and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP approach that adapts the activity HMMs to the user, using some activity examples from this specific user. The main results on a public dataset have reported a significant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides significant improvement in the system’s performance.

The development of adaptive decision making: Recognition-based inference in children and adolescents.

Science.gov (United States)

Horn, Sebastian S; Ruggeri, Azzurra; Pachur, Thorsten

2016-09-01

Judgments about objects in the world are often based on probabilistic information (or cues). A frugal judgment strategy that utilizes memory (i.e., the ability to discriminate between known and unknown objects) as a cue for inference is the recognition heuristic (RH). The usefulness of the RH depends on the structure of the environment, particularly the predictive power (validity) of recognition. Little is known about developmental differences in use of the RH. In this study, the authors examined (a) to what extent children and adolescents recruit the RH when making judgments, and (b) around what age adaptive use of the RH emerges. Primary schoolchildren (M = 9 years), younger adolescents (M = 12 years), and older adolescents (M = 17 years) made comparative judgments in task environments with either high or low recognition validity. Reliance on the RH was measured with a hierarchical multinomial model. Results indicated that primary schoolchildren already made systematic use of the RH. However, only older adolescents adaptively adjusted their strategy use between environments and were better able to discriminate between situations in which the RH led to correct versus incorrect inferences. These findings suggest that the use of simple heuristics does not progress unidirectionally across development but strongly depends on the task environment, in line with the perspective of ecological rationality. Moreover, adaptive heuristic inference seems to require experience and a developed base of domain knowledge. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution

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Shizhen Zhao

2018-06-01

Full Text Available Mobile activity recognition is significant to the development of human-centric pervasive applications including elderly care, personalized recommendations, etc. Nevertheless, the distribution of inertial sensor data can be influenced to a great extent by varying users. This means that the performance of an activity recognition classifier trained by one user’s dataset will degenerate when transferred to others. In this study, we focus on building a personalized classifier to detect four categories of human activities: light intensity activity, moderate intensity activity, vigorous intensity activity, and fall. In order to solve the problem caused by different distributions of inertial sensor signals, a user-adaptive algorithm based on K-Means clustering, local outlier factor (LOF, and multivariate Gaussian distribution (MGD is proposed. To automatically cluster and annotate a specific user’s activity data, an improved K-Means algorithm with a novel initialization method is designed. By quantifying the samples’ informative degree in a labeled individual dataset, the most profitable samples can be selected for activity recognition model adaption. Through experiments, we conclude that our proposed models can adapt to new users with good recognition performance.

Autistic traits are linked to reduced adaptive coding of face identity and selectively poorer face recognition in men but not women.

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Rhodes, Gillian; Jeffery, Linda; Taylor, Libby; Ewing, Louise

2013-11-01

Our ability to discriminate and recognize thousands of faces despite their similarity as visual patterns relies on adaptive, norm-based, coding mechanisms that are continuously updated by experience. Reduced adaptive coding of face identity has been proposed as a neurocognitive endophenotype for autism, because it is found in autism and in relatives of individuals with autism. Autistic traits can also extend continuously into the general population, raising the possibility that reduced adaptive coding of face identity may be more generally associated with autistic traits. In the present study, we investigated whether adaptive coding of face identity decreases as autistic traits increase in an undergraduate population. Adaptive coding was measured using face identity aftereffects, and autistic traits were measured using the Autism-Spectrum Quotient (AQ) and its subscales. We also measured face and car recognition ability to determine whether autistic traits are selectively related to face recognition difficulties. We found that men who scored higher on levels of autistic traits related to social interaction had reduced adaptive coding of face identity. This result is consistent with the idea that atypical adaptive face-coding mechanisms are an endophenotype for autism. Autistic traits were also linked with face-selective recognition difficulties in men. However, there were some unexpected sex differences. In women, autistic traits were linked positively, rather than negatively, with adaptive coding of identity, and were unrelated to face-selective recognition difficulties. These sex differences indicate that autistic traits can have different neurocognitive correlates in men and women and raise the intriguing possibility that endophenotypes of autism can differ in males and females. © 2013 Elsevier Ltd. All rights reserved.

Distance Adaptive Tensor Discriminative Geometry Preserving Projection for Face Recognition

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Ziqiang Wang

2012-09-01

Full Text Available There is a growing interest in dimensionality reduction techniques for face recognition, however, the traditional dimensionality reduction algorithms often transform the input face image data into vectors before embedding. Such vectorization often ignores the underlying data structure and leads to higher computational complexity. To effectively cope with these problems, a novel dimensionality reduction algorithm termed distance adaptive tensor discriminative geometry preserving projection (DATDGPP is proposed in this paper. The key idea of DATDGPP is as follows: first, the face image data are directly encoded in high-order tensor structure so that the relationships among the face image data can be preserved; second, the data-adaptive tensor distance is adopted to model the correlation among different coordinates of tensor data; third, the transformation matrix which can preserve discrimination and local geometry information is obtained by an iteration algorithm. Experimental results on three face databases show that the proposed algorithm outperforms other representative dimensionality reduction algorithms.

Low-resolution expression recognition based on central oblique average CS-LBP with adaptive threshold

Science.gov (United States)

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.

Use of the recognition heuristic depends on the domain's recognition validity, not on the recognition validity of selected sets of objects.

Science.gov (United States)

Pohl, Rüdiger F; Michalkiewicz, Martha; Erdfelder, Edgar; Hilbig, Benjamin E

2017-07-01

According to the recognition-heuristic theory, decision makers solve paired comparisons in which one object is recognized and the other not by recognition alone, inferring that recognized objects have higher criterion values than unrecognized ones. However, success-and thus usefulness-of this heuristic depends on the validity of recognition as a cue, and adaptive decision making, in turn, requires that decision makers are sensitive to it. To this end, decision makers could base their evaluation of the recognition validity either on the selected set of objects (the set's recognition validity), or on the underlying domain from which the objects were drawn (the domain's recognition validity). In two experiments, we manipulated the recognition validity both in the selected set of objects and between domains from which the sets were drawn. The results clearly show that use of the recognition heuristic depends on the domain's recognition validity, not on the set's recognition validity. In other words, participants treat all sets as roughly representative of the underlying domain and adjust their decision strategy adaptively (only) with respect to the more general environment rather than the specific items they are faced with.

ADAPTIVE PARAMETER ESTIMATION OF PERSON RECOGNITION MODEL IN A STOCHASTIC HUMAN TRACKING PROCESS

OpenAIRE

W. Nakanishi; T. Fuse; T. Ishikawa

2015-01-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation ...

Accurate forced-choice recognition without awareness of memory retrieval

OpenAIRE

Voss, Joel L.; Baym, Carol L.; Paller, Ken A.

2008-01-01

Recognition confidence and the explicit awareness of memory retrieval commonly accompany accurate responding in recognition tests. Memory performance in recognition tests is widely assumed to measure explicit memory, but the generality of this assumption is questionable. Indeed, whether recognition in nonhumans is always supported by explicit memory is highly controversial. Here we identified circumstances wherein highly accurate recognition was unaccompanied by hallmark features of explicit ...

Reversal deterioration of renal function accompanied with primary hypothyrodism

OpenAIRE

Dragović Tamara

2012-01-01

Introduction. Hypothyroidism is often accompanied with decline of kidney function, or inability to maintain electrolyte balance. These changes are usually overlooked in everyday practice. Early recognition of this association eliminates unnecessary diagnostic procedures that postpone the adequate treatment. Case report. Two patients with elevated serum creatinine levels due to primary autoimmune hypothyroidism, with complete recovery of creatinine clearance after thyroid hormone substit...

Validation of an uncertainty of illness scale adapted to use with Spanish emergency department patients and their accompanying relatives or friends.

Science.gov (United States)

Ruymán Brito-Brito, Pedro; García-Tesouro, Esther; Fernández-Gutiérrez, Domingo Ángel; García-Hernández, Alfonso Miguel; Fernández-Gutiérrez, Raquel; Burillo-Putze, Guillermo

2018-01-01

To validate a Spanish adaptation of the Mishel Uncertainty of Illness Scale for use with emergency-department (ED) patients and their accompanying relatives or friends (the UIS-ED). We first developed a version of the questionnaire for Spanish ED situations. Next we assessed the content validity index for each of its items, revised it, and reassessed its face validity to produce a second version, which we then piloted in 20 hospital ED patients. A third revised version was then validated in a population of 320 adults (160 patients and 160 accompanying persons) who attended the ED between November 2015 and September 2016. The 12-item UIS-ED (60 points) was administered by 2 nurses while the patients and accompanying persons were in the ED. We gathered sociodemographic and clinical data as well as the subjects' perception about the information they were given. The mean (SD) uncertainty score among patients was 29 (11) points. Accompanying persons had a mean score of 36 (13) points. Factorial analysis confirmed the instrument's construct validity, finding that both dimensions of the original Mishel scale (complexity and ambiguity) were present in 6 items each. Factorial analysis explained 60% of the total variance in the patient version and 67% of the variance in the version for accompanying persons. Reliability statistics were good, with Cronbach's α values ranging from 0.912 to 0.938. Split-half reliability statistics ranged from 0.901 to 0.933. Correlations were significant in the analysis of convergent validity. The UIS-ED questionnaire may prove to be a simple, valid, and reliable way for assessing uncertainty in patients and their accompanying friends or relatives attending Spanish EDs.

Face Recognition by Bunch Graph Method Using a Group Based Adaptive Tolerant Neural Network

OpenAIRE

Aradhana D.; Girish H.; Karibasappa K.; Reddy A. Chennakeshava

2011-01-01

This paper presents a new method for feature extraction from the facial image by using bunch graph method. These extracted geometric features of the face are used subsequently for face recognition by utilizing the group based adaptive neural network. This method is suitable, when the facial images are rotation and translation invariant. Further the technique also free from size invariance of facial image and is capable of identifying the facial images correctly when corrupted w...

Selecting Informative Features of the Helicopter and Aircraft Acoustic Signals in the Adaptive Autonomous Information Systems for Recognition

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V. K. Hohlov

2017-01-01

Full Text Available The article forms the rationale for selecting the informative features of the helicopter and aircraft acoustic signals to solve a problem of their recognition and shows that the most informative ones are the counts of extrema in the energy spectra of the input signals, which represent non-centered random variables. An apparatus of the multiple initial regression coefficients was selected as a mathematical tool of research. The application of digital re-circulators with positive and negative feedbacks, which have the comb-like frequency characteristics, solves the problem of selecting informative features. A distinguishing feature of such an approach is easy agility of the comb frequency characteristics just through the agility of a delay value of digital signal in the feedback circuit. Adding an adaptation block to the selection block of the informative features enables us to ensure the invariance of used informative feature and counts of local extrema of the spectral power density to the airspeed of a helicopter. The paper gives reasons for the principle of adaptation and the structure of the adaptation block. To form the discriminator characteristics are used the cross-correlation statistical characteristics of the quadrature components of acoustic signal realizations, obtained by Hilbert transform. The paper proposes to provide signal recognition using a regression algorithm that allows handling the non-centered informative features and using a priori information about coefficients of initial regression of signal and noise.The simulation in Matlab Simulink has shown that selected informative features of signals in regressive processing of signal realizations of 0.5 s duration have good separability, and based on a set of 100 acoustic signal realizations in each class in full-scale conditions, has proved ensuring a correct recognition probability of 0.975, at least. The considered principles of informative features selection and adaptation can

Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

Science.gov (United States)

Nakanishi, W.; Fuse, T.; Ishikawa, T.

2015-05-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

When the face fits: recognition of celebrities from matching and mismatching faces and voices.

Science.gov (United States)

Stevenage, Sarah V; Neil, Greg J; Hamlin, Iain

2014-01-01

The results of two experiments are presented in which participants engaged in a face-recognition or a voice-recognition task. The stimuli were face-voice pairs in which the face and voice were co-presented and were either "matched" (same person), "related" (two highly associated people), or "mismatched" (two unrelated people). Analysis in both experiments confirmed that accuracy and confidence in face recognition was consistently high regardless of the identity of the accompanying voice. However accuracy of voice recognition was increasingly affected as the relationship between voice and accompanying face declined. Moreover, when considering self-reported confidence in voice recognition, confidence remained high for correct responses despite the proportion of these responses declining across conditions. These results converged with existing evidence indicating the vulnerability of voice recognition as a relatively weak signaller of identity, and results are discussed in the context of a person-recognition framework.

Incremental Learning for Place Recognition in Dynamic Environments

OpenAIRE

Luo, Jie; Pronobis, Andrzej; Caputo, Barbara; Jensfelt, Patric

2007-01-01

This paper proposes a discriminative approach to template-based Vision-based place recognition is a desirable feature for an autonomous mobile system. In order to work in realistic scenarios, visual recognition algorithms should be adaptive, i.e. should be able to learn from experience and adapt continuously to changes in the environment. This paper presents a discriminative incremental learning approach to place recognition. We use a recently introduced version of the incremental SVM, which ...

Adapted all-numerical correlator for face recognition applications

Science.gov (United States)

Elbouz, M.; Bouzidi, F.; Alfalou, A.; Brosseau, C.; Leonard, I.; Benkelfat, B.-E.

2013-03-01

In this study, we suggest and validate an all-numerical implementation of a VanderLugt correlator which is optimized for face recognition applications. The main goal of this implementation is to take advantage of the benefits (detection, localization, and identification of a target object within a scene) of correlation methods and exploit the reconfigurability of numerical approaches. This technique requires a numerical implementation of the optical Fourier transform. We pay special attention to adapt the correlation filter to this numerical implementation. One main goal of this work is to reduce the size of the filter in order to decrease the memory space required for real time applications. To fulfil this requirement, we code the reference images with 8 bits and study the effect of this coding on the performances of several composite filters (phase-only filter, binary phase-only filter). The saturation effect has for effect to decrease the performances of the correlator for making a decision when filters contain up to nine references. Further, an optimization is proposed based for an optimized segmented composite filter. Based on this approach, we present tests with different faces demonstrating that the above mentioned saturation effect is significantly reduced while minimizing the size of the learning data base.

An automatic system for Turkish word recognition using Discrete Wavelet Neural Network based on adaptive entropy

International Nuclear Information System (INIS)

Avci, E.

2007-01-01

In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of two layers: discrete wavelet layer and multi-layer perceptron. The discrete wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of Discrete Wavelet Transform (DWT) and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the used system is evaluated by using noisy Turkish word signals. Test results showing the effectiveness of the proposed automatic system are presented in this paper. The rate of correct recognition is about 92.5% for the sample speech signals. (author)

Compact Acoustic Models for Embedded Speech Recognition

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Lévy Christophe

2009-01-01

Full Text Available Speech recognition applications are known to require a significant amount of resources. However, embedded speech recognition only authorizes few KB of memory, few MIPS, and small amount of training data. In order to fit the resource constraints of embedded applications, an approach based on a semicontinuous HMM system using state-independent acoustic modelling is proposed. A transformation is computed and applied to the global model in order to obtain each HMM state-dependent probability density functions, authorizing to store only the transformation parameters. This approach is evaluated on two tasks: digit and voice-command recognition. A fast adaptation technique of acoustic models is also proposed. In order to significantly reduce computational costs, the adaptation is performed only on the global model (using related speaker recognition adaptation techniques with no need for state-dependent data. The whole approach results in a relative gain of more than 20% compared to a basic HMM-based system fitting the constraints.

Flexible Piezoelectric Sensor-Based Gait Recognition

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Youngsu Cha

2018-02-01

Full Text Available Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %.

Evaluation of Speech Recognition of Cochlear Implant Recipients Using Adaptive, Digital Remote Microphone Technology and a Speech Enhancement Sound Processing Algorithm.

Science.gov (United States)

Wolfe, Jace; Morais, Mila; Schafer, Erin; Agrawal, Smita; Koch, Dawn

2015-05-01

Cochlear implant recipients often experience difficulty with understanding speech in the presence of noise. Cochlear implant manufacturers have developed sound processing algorithms designed to improve speech recognition in noise, and research has shown these technologies to be effective. Remote microphone technology utilizing adaptive, digital wireless radio transmission has also been shown to provide significant improvement in speech recognition in noise. There are no studies examining the potential improvement in speech recognition in noise when these two technologies are used simultaneously. The goal of this study was to evaluate the potential benefits and limitations associated with the simultaneous use of a sound processing algorithm designed to improve performance in noise (Advanced Bionics ClearVoice) and a remote microphone system that incorporates adaptive, digital wireless radio transmission (Phonak Roger). A two-by-two way repeated measures design was used to examine performance differences obtained without these technologies compared to the use of each technology separately as well as the simultaneous use of both technologies. Eleven Advanced Bionics (AB) cochlear implant recipients, ages 11 to 68 yr. AzBio sentence recognition was measured in quiet and in the presence of classroom noise ranging in level from 50 to 80 dBA in 5-dB steps. Performance was evaluated in four conditions: (1) No ClearVoice and no Roger, (2) ClearVoice enabled without the use of Roger, (3) ClearVoice disabled with Roger enabled, and (4) simultaneous use of ClearVoice and Roger. Speech recognition in quiet was better than speech recognition in noise for all conditions. Use of ClearVoice and Roger each provided significant improvement in speech recognition in noise. The best performance in noise was obtained with the simultaneous use of ClearVoice and Roger. ClearVoice and Roger technology each improves speech recognition in noise, particularly when used at the same time

4D Unconstrained Real-time Face Recognition Using a Commodity Depthh Camera

NARCIS (Netherlands)

Schimbinschi, Florin; Wiering, Marco; Mohan, R.E.; Sheba, J.K.

2012-01-01

Robust unconstrained real-time face recognition still remains a challenge today. The recent addition to the market of lightweight commodity depth sensors brings new possibilities for human-machine interaction and therefore face recognition. This article accompanies the reader through a succinct

Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition

Science.gov (United States)

Sultana, Maryam; Bhatti, Naeem; Javed, Sajid; Jung, Soon Ki

2017-09-01

Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.

A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG-Based Emotion Recognition

Directory of Open Access Journals (Sweden)

Xin Chai

2017-05-01

Full Text Available Electroencephalography (EEG-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain adaptation methods offer an effective way to reduce the discrepancy of marginal distribution. However, for EEG sensor signals, both marginal and conditional distributions may be mismatched. In addition, the existing domain adaptation strategies always require a high level of additional computation. To address this problem, a novel strategy named adaptive subspace feature matching (ASFM is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework (without any labeled samples from target subjects. Specifically, we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term. This significantly decreases the time complexity of our domain adaptation procedure. As a result, both marginal and conditional distribution discrepancies between the source domain and unlabeled target domain can be reduced, and logistic regression (LR can be applied to the new source domain in order to train a classifier for use in the target domain, since the aligned source domain follows a distribution which is similar to that of the target domain. We compare our ASFM method with six typical approaches using a public EEG dataset with three affective states: positive, neutral, and negative. Both offline and online evaluations were performed. The subject-to-subject offline experimental results demonstrate that our component achieves a mean accuracy and standard deviation of 80.46% and 6.84%, respectively, as compared with a state-of-the-art method, the subspace alignment auto-encoder (SAAE, which

User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control

Science.gov (United States)

He, Jiayuan; Zhang, Dingguo; Jiang, Ning; Sheng, Xinjun; Farina, Dario; Zhu, Xiangyang

2015-08-01

Objective. Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. Approach. In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. Main results. It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance. Significance. These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.

Pupil dilation during recognition memory: Isolating unexpected recognition from judgment uncertainty.

Science.gov (United States)

Mill, Ravi D; O'Connor, Akira R; Dobbins, Ian G

2016-09-01

Optimally discriminating familiar from novel stimuli demands a decision-making process informed by prior expectations. Here we demonstrate that pupillary dilation (PD) responses during recognition memory decisions are modulated by expectations, and more specifically, that pupil dilation increases for unexpected compared to expected recognition. Furthermore, multi-level modeling demonstrated that the time course of the dilation during each individual trial contains separable early and late dilation components, with the early amplitude capturing unexpected recognition, and the later trailing slope reflecting general judgment uncertainty or effort. This is the first demonstration that the early dilation response during recognition is dependent upon observer expectations and that separate recognition expectation and judgment uncertainty components are present in the dilation time course of every trial. The findings provide novel insights into adaptive memory-linked orienting mechanisms as well as the general cognitive underpinnings of the pupillary index of autonomic nervous system activity. Copyright © 2016 Elsevier B.V. All rights reserved.

A Bayesian classifier for symbol recognition

OpenAIRE

Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick

2007-01-01

URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...

Social capital, conflict, and adaptive collaborative governance: exploring the dialectic

Directory of Open Access Journals (Sweden)

Cynthia McDougall

2015-03-01

Full Text Available Previously lineal and centralized natural resource management and development paradigms have shifted toward the recognition of complexity and dynamism of social-ecological systems, and toward more adaptive, decentralized, and collaborative models. However, certain messy and surprising dynamics remain under-recognized, including the inherent interplay between conflict, social capital, and governance. In this study we consider the dynamic intersections of these three often (seemingly disparate phenomena. In particular, we consider the changes in social capital and conflict that accompanied a transition by local groups toward adaptive collaborative governance. The findings are drawn from multiyear research into community forestry in Nepal using comparative case studies. The study illustrates the complex, surprising, and dialectical relations among these three phenomena. Findings include: a demonstration of the pervasive nature of conflict and "dark side" of social capital; that collaborative efforts changed social capital, rather than simply enhancing it; and that conflict at varying scales ultimately had some constructive influences.

Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods

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Mahesh Jangid

2018-02-01

Full Text Available Handwritten character recognition is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human–robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Devanagari characters using deep convolutional neural networks (DCNN which are one of the recent techniques adopted from the deep learning community. We experimented the ISIDCHAR database provided by (Information Sharing Index ISI, Kolkata and V2DMDCHAR database with six different architectures of DCNN to evaluate the performance and also investigate the use of six recently developed adaptive gradient methods. A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. The results of layer-wise-trained DCNN are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.

A New Adaptive Structural Signature for Symbol Recognition by Using a Galois Lattice as a Classifier.

Science.gov (United States)

Coustaty, M; Bertet, K; Visani, M; Ogier, J

2011-08-01

In this paper, we propose a new approach for symbol recognition using structural signatures and a Galois lattice as a classifier. The structural signatures are based on topological graphs computed from segments which are extracted from the symbol images by using an adapted Hough transform. These structural signatures-that can be seen as dynamic paths which carry high-level information-are robust toward various transformations. They are classified by using a Galois lattice as a classifier. The performance of the proposed approach is evaluated based on the GREC'03 symbol database, and the experimental results we obtain are encouraging.

Different neural processes accompany self-recognition in photographs across the lifespan: an ERP study using dizygotic twins.

Science.gov (United States)

Butler, David L; Mattingley, Jason B; Cunnington, Ross; Suddendorf, Thomas

2013-01-01

Our appearance changes over time, yet we can recognize ourselves in photographs from across the lifespan. Researchers have extensively studied self-recognition in photographs and have proposed that specific neural correlates are involved, but few studies have examined self-recognition using images from different periods of life. Here we compared ERP responses to photographs of participants when they were 5-15, 16-25, and 26-45 years old. We found marked differences between the responses to photographs from these time periods in terms of the neural markers generally assumed to reflect (i) the configural processing of faces (i.e., the N170), (ii) the matching of the currently perceived face to a representation already stored in memory (i.e., the P250), and (iii) the retrieval of information about the person being recognized (i.e., the N400). There was no uniform neural signature of visual self-recognition. To test whether there was anything specific to self-recognition in these brain responses, we also asked participants to identify photographs of their dizygotic twins taken from the same time periods. Critically, this allowed us to minimize the confounding effects of exposure, for it is likely that participants have been similarly exposed to each other's faces over the lifespan. The same pattern of neural response emerged with only one exception: the neural marker reflecting the retrieval of mnemonic information (N400) differed across the lifespan for self but not for twin. These results, as well as our novel approach using twins and photographs from across the lifespan, have wide-ranging consequences for the study of self-recognition and the nature of our personal identity through time.

Different neural processes accompany self-recognition in photographs across the lifespan: an ERP study using dizygotic twins.

Directory of Open Access Journals (Sweden)

David L Butler

Full Text Available Our appearance changes over time, yet we can recognize ourselves in photographs from across the lifespan. Researchers have extensively studied self-recognition in photographs and have proposed that specific neural correlates are involved, but few studies have examined self-recognition using images from different periods of life. Here we compared ERP responses to photographs of participants when they were 5-15, 16-25, and 26-45 years old. We found marked differences between the responses to photographs from these time periods in terms of the neural markers generally assumed to reflect (i the configural processing of faces (i.e., the N170, (ii the matching of the currently perceived face to a representation already stored in memory (i.e., the P250, and (iii the retrieval of information about the person being recognized (i.e., the N400. There was no uniform neural signature of visual self-recognition. To test whether there was anything specific to self-recognition in these brain responses, we also asked participants to identify photographs of their dizygotic twins taken from the same time periods. Critically, this allowed us to minimize the confounding effects of exposure, for it is likely that participants have been similarly exposed to each other's faces over the lifespan. The same pattern of neural response emerged with only one exception: the neural marker reflecting the retrieval of mnemonic information (N400 differed across the lifespan for self but not for twin. These results, as well as our novel approach using twins and photographs from across the lifespan, have wide-ranging consequences for the study of self-recognition and the nature of our personal identity through time.

EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

Directory of Open Access Journals (Sweden)

Suwicha Jirayucharoensak

2014-01-01

Full Text Available Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.

Ventral striatal activity correlates with memory confidence for old- and new-responses in a difficult recognition test.

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Ulrike Schwarze

Full Text Available Activity in the ventral striatum has frequently been associated with retrieval success, i.e., it is higher for hits than correct rejections. Based on the prominent role of the ventral striatum in the reward circuit, its activity has been interpreted to reflect the higher subjective value of hits compared to correct rejections in standard recognition tests. This hypothesis was supported by a recent study showing that ventral striatal activity is higher for correct rejections than hits when the value of rejections is increased by external incentives. These findings imply that the striatal response during recognition is context-sensitive and modulated by the adaptive significance of "oldness" or "newness" to the current goals. The present study is based on the idea that not only external incentives, but also other deviations from standard recognition tests which affect the subjective value of specific response types should modulate striatal activity. Therefore, we explored ventral striatal activity in an unusually difficult recognition test that was characterized by low levels of confidence and accuracy. Based on the human uncertainty aversion, in such a recognition context, the subjective value of all high confident decisions is expected to be higher than usual, i.e., also rejecting items with high certainty is deemed rewarding. In an accompanying behavioural experiment, participants rated the pleasantness of each recognition response. As hypothesized, ventral striatal activity correlated in the current unusually difficult recognition test not only with retrieval success, but also with confidence. Moreover, participants indicated that they were more satisfied by higher confidence in addition to perceived oldness of an item. Taken together, the results are in line with the hypothesis that ventral striatal activity during recognition codes the subjective value of different response types that is modulated by the context of the recognition test.

Adaptive pattern recognition in real-time video-based soccer analysis

DEFF Research Database (Denmark)

Schlipsing, Marc; Salmen, Jan; Tschentscher, Marc

2017-01-01

are taken into account. Our contribution is twofold: (1) the deliberate use of machine learning and pattern recognition techniques allows us to achieve high classification accuracy in varying environments. We systematically evaluate combinations of image features and learning machines in the given online......Computer-aided sports analysis is demanded by coaches and the media. Image processing and machine learning techniques that allow for "live" recognition and tracking of players exist. But these methods are far from collecting and analyzing event data fully autonomously. To generate accurate results......, human interaction is required at different stages including system setup, calibration, supervision of classifier training, and resolution of tracking conflicts. Furthermore, the real-time constraints are challenging: in contrast to other object recognition and tracking applications, we cannot treat data...

Considerations about expected a posteriori estimation in adaptive testing: adaptive a priori, adaptive correction for bias, and adaptive integration interval.

Science.gov (United States)

Raiche, Gilles; Blais, Jean-Guy

2009-01-01

In a computerized adaptive test, we would like to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Unfortunately, decreasing the number of items is accompanied by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. The authors suggest that it is possible to reduced the bias, and even the standard error of the estimate, by applying to each provisional estimation one or a combination of the following strategies: adaptive correction for bias proposed by Bock and Mislevy (1982), adaptive a priori estimate, and adaptive integration interval.

Physics of Automatic Target Recognition

CERN Document Server

Sadjadi, Firooz

2007-01-01

Physics of Automatic Target Recognition addresses the fundamental physical bases of sensing, and information extraction in the state-of-the art automatic target recognition field. It explores both passive and active multispectral sensing, polarimetric diversity, complex signature exploitation, sensor and processing adaptation, transformation of electromagnetic and acoustic waves in their interactions with targets, background clutter, transmission media, and sensing elements. The general inverse scattering, and advanced signal processing techniques and scientific evaluation methodologies being used in this multi disciplinary field will be part of this exposition. The issues of modeling of target signatures in various spectral modalities, LADAR, IR, SAR, high resolution radar, acoustic, seismic, visible, hyperspectral, in diverse geometric aspects will be addressed. The methods for signal processing and classification will cover concepts such as sensor adaptive and artificial neural networks, time reversal filt...

Fractionating the Neural Substrates of Incidental Recognition Memory

Science.gov (United States)

Greene, Ciara M.; Vidaki, Kleio; Soto, David

2015-01-01

Familiar stimuli are typically accompanied by decreases in neural response relative to the presentation of novel items, but these studies often include explicit instructions to discriminate old and new items; this creates difficulties in partialling out the contribution of top-down intentional orientation to the items based on recognition goals.…

Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems

Science.gov (United States)

Abu-Alqumsan, Mohammad; Ebert, Felix; Peer, Angelika

2017-06-01

Objective. This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations. Approach. To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme. Main results. Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface. Significance. Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the

Extended Target Recognition in Cognitive Radar Networks

Directory of Open Access Journals (Sweden)

Xiqin Wang

2010-11-01

Full Text Available We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR based sequential hypothesis testing (SHT framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS. Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.

Accompanied consultations in occupational health.

Science.gov (United States)

Hobson, J; Hobson, H; Sharp, R

2016-04-01

Accompanied consultations are often reported as difficult by occupational physicians but have not been studied in the occupational health setting. To collect information about accompanied consultations and the impact of the companion on the consultation. We collected data on all accompanied consultations by two occupational physicians working in a private sector occupational health service over the course of 16 months. Accompanied consultations were matched to non-accompanied consultations for comparison. We collected data on 108 accompanied consultations. Accompanied consultations were more likely to be connected with ill health retirement (P Occupational health practitioners may benefit from better understanding of accompanied consultations and guidance on their management. © The Author 2015. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Use of digital speech recognition in diagnostics radiology

International Nuclear Information System (INIS)

Arndt, H.; Stockheim, D.; Mutze, S.; Petersein, J.; Gregor, P.; Hamm, B.

1999-01-01

Purpose: Applicability and benefits of digital speech recognition in diagnostic radiology were tested using the speech recognition system SP 6000. Methods: The speech recognition system SP 6000 was integrated into the network of the institute and connected to the existing Radiological Information System (RIS). Three subjects used this system for writing 2305 findings from dictation. After the recognition process the date, length of dictation, time required for checking/correction, kind of examination and error rate were recorded for every dictation. With the same subjects, a correlation was performed with 625 conventionally written finding. Results: After an 1-hour initial training the average error rates were 8.4 to 13.3%. The first adaptation of the speech recognition system (after nine days) decreased the average error rates to 2.4 to 10.7% due to the ability of the program to learn. The 2 nd and 3 rd adaptations resulted only in small changes of the error rate. An individual comparison of the error rate developments in the same kind of investigation showed the relative independence of the error rate on the individual user. Conclusion: The results show that the speech recognition system SP 6000 can be evaluated as an advantageous alternative for quickly recording radiological findings. A comparison between manually writing and dictating the findings verifies the individual differences of the writing speeds and shows the advantage of the application of voice recognition when faced with normal keyboard performance. (orig.) [de

Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS)

Science.gov (United States)

Arozi, Moh; Putri, Farika T.; Ariyanto, Mochammad; Khusnul Ari, M.; Munadi, Setiawan, Joga D.

2017-01-01

People with disabilities are increasing from year to year either due to congenital factors, sickness, accident factors and war. One form of disability is the case of interruptions of hand function. The condition requires and encourages the search for solutions in the form of creating an artificial hand with the ability as a human hand. The development of science in the field of neuroscience currently allows the use of electromyography (EMG) to control the motion of artificial prosthetic hand into the necessary use of EMG as an input signal to control artificial prosthetic hand. This study is the beginning of a significant research planned in the development of artificial prosthetic hand with EMG signal input. This initial research focused on the study of EMG signal recognition. Preliminary results show that the EMG signal recognition using combined discrete wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS) produces accuracy 98.3 % for training and 98.51% for testing. Thus the results can be used as an input signal for Simulink block diagram of a prosthetic hand that will be developed on next study. The research will proceed with the construction of artificial prosthetic hand along with Simulink program controlling and integrating everything into one system.

An HMM-Like Dynamic Time Warping Scheme for Automatic Speech Recognition

Directory of Open Access Journals (Sweden)

Ing-Jr Ding

2014-01-01

Full Text Available In the past, the kernel of automatic speech recognition (ASR is dynamic time warping (DTW, which is feature-based template matching and belongs to the category technique of dynamic programming (DP. Although DTW is an early developed ASR technique, DTW has been popular in lots of applications. DTW is playing an important role for the known Kinect-based gesture recognition application now. This paper proposed an intelligent speech recognition system using an improved DTW approach for multimedia and home automation services. The improved DTW presented in this work, called HMM-like DTW, is essentially a hidden Markov model- (HMM- like method where the concept of the typical HMM statistical model is brought into the design of DTW. The developed HMM-like DTW method, transforming feature-based DTW recognition into model-based DTW recognition, will be able to behave as the HMM recognition technique and therefore proposed HMM-like DTW with the HMM-like recognition model will have the capability to further perform model adaptation (also known as speaker adaptation. A series of experimental results in home automation-based multimedia access service environments demonstrated the superiority and effectiveness of the developed smart speech recognition system by HMM-like DTW.

Adaptive metric learning with deep neural networks for video-based facial expression recognition

Science.gov (United States)

Liu, Xiaofeng; Ge, Yubin; Yang, Chao; Jia, Ping

2018-01-01

Video-based facial expression recognition has become increasingly important for plenty of applications in the real world. Despite that numerous efforts have been made for the single sequence, how to balance the complex distribution of intra- and interclass variations well between sequences has remained a great difficulty in this area. We propose the adaptive (N+M)-tuplet clusters loss function and optimize it with the softmax loss simultaneously in the training phrase. The variations introduced by personal attributes are alleviated using the similarity measurements of multiple samples in the feature space with many fewer comparison times as conventional deep metric learning approaches, which enables the metric calculations for large data applications (e.g., videos). Both the spatial and temporal relations are well explored by a unified framework that consists of an Inception-ResNet network with long short term memory and the two fully connected layer branches structure. Our proposed method has been evaluated with three well-known databases, and the experimental results show that our method outperforms many state-of-the-art approaches.

Toward fast feature adaptation and localization for real-time face recognition systems

NARCIS (Netherlands)

Zuo, F.; With, de P.H.N.; Ebrahimi, T.; Sikora, T.

2003-01-01

In a home environment, video surveillance employing face detection and recognition is attractive for new applications. Facial feature (e.g. eyes and mouth) localization in the face is an essential task for face recognition because it constitutes an indispensable step for face geometry normalization.

Action recognition is sensitive to the identity of the actor.

Science.gov (United States)

Ferstl, Ylva; Bülthoff, Heinrich; de la Rosa, Stephan

2017-09-01

Recognizing who is carrying out an action is essential for successful human interaction. The cognitive mechanisms underlying this ability are little understood and have been subject of discussions in embodied approaches to action recognition. Here we examine one solution, that visual action recognition processes are at least partly sensitive to the actor's identity. We investigated the dependency between identity information and action related processes by testing the sensitivity of neural action recognition processes to clothing and facial identity information with a behavioral adaptation paradigm. Our results show that action adaptation effects are in fact modulated by both clothing information and the actor's facial identity. The finding demonstrates that neural processes underlying action recognition are sensitive to identity information (including facial identity) and thereby not exclusively tuned to actions. We suggest that such response properties are useful to help humans in knowing who carried out an action. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Face recognition in securing optical telecom network equipment

International Nuclear Information System (INIS)

Ali, N.M.

2015-01-01

In this paper, face recognition is used with a microcontroller based hardware module to secure the telecommunication equipments like ONU (optical network units) or any other telecommunication equipment. The face recognition classifier value optimization adaption is deployed and in this scheme by increasing or decreasing the number of images in the database will automatically generate and adopt the classifier value for recognition of known and unknown persons. On recognizing and unknown persons. On recognizing an unknown person, the hardware module will send an SMS to the concerned security personnel for security preventive measures. (author)

The Godparent Plan: A Pedagogical Strategy for CS1 Accompaniment and CS2 Pedagogical Enhancement

Directory of Open Access Journals (Sweden)

Pedro Guillermo Feijóo-García

2018-02-01

Full Text Available Courses such as CS1 and CS2 can present an interesting pedagogical challenge when it comes to the theory-practice relationship, along with aspects that involve the course's logistics, the programming language used, and the characteristics of the students involved in the process. This study presents an innovative didactic approach, oriented towards the accompaniment of CS1 students by CS2 students at Universidad El Bosque, Colombia, seeking with this Godparent Plan, to provide a personalized accompaniment to first semester students, whereby CS2 students enhance their domain over concepts and skills while accompanying, explaining and teaching younger peers. The results of this study are favorable, outlining a didactic scheme that can be adapted and replicated in other curricular scenarios.

Developmental reversals in recognition memory in children and adults.

Science.gov (United States)

Gross, Julien; Gardiner, Beatrix; Hayne, Harlene

2016-01-01

Older members of a given species typically exhibit superior learning and memory abilities relative to younger members, however, the developmental difference does not always occur in this younger-to-older direction. Developmental reversals are thought to reflect adaptive responses to the unique challenges imposed by the infant's niche. In humans, identification of developmental reversals has largely been precluded because infants, children, and adults are rarely tested using the same experimental procedures. Here, we adapted the visual recognition memory task and tested 3-year-olds and adults using one set of child-oriented stimuli and one set of adult-orientated stimuli. When tested immediately, children and adults exhibited recognition memory for both stimuli. When tested after a 1-week delay, children exhibited recognition memory for the child-oriented stimuli, but not for the adult-oriented stimuli and adults exhibited recognition memory for the adult-oriented stimuli, but not for the child-oriented stimuli. These data have important implications for current theories of memory development. © 2015 Wiley Periodicals, Inc.

Hypergraph-Based Recognition Memory Model for Lifelong Experience

Science.gov (United States)

2014-01-01

Cognitive agents are expected to interact with and adapt to a nonstationary dynamic environment. As an initial process of decision making in a real-world agent interaction, familiarity judgment leads the following processes for intelligence. Familiarity judgment includes knowing previously encoded data as well as completing original patterns from partial information, which are fundamental functions of recognition memory. Although previous computational memory models have attempted to reflect human behavioral properties on the recognition memory, they have been focused on static conditions without considering temporal changes in terms of lifelong learning. To provide temporal adaptability to an agent, in this paper, we suggest a computational model for recognition memory that enables lifelong learning. The proposed model is based on a hypergraph structure, and thus it allows a high-order relationship between contextual nodes and enables incremental learning. Through a simulated experiment, we investigate the optimal conditions of the memory model and validate the consistency of memory performance for lifelong learning. PMID:25371665

Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

Directory of Open Access Journals (Sweden)

Heracleous Panikos

2007-01-01

Full Text Available We present the use of stethoscope and silicon NAM (nonaudible murmur microphones in automatic speech recognition. NAM microphones are special acoustic sensors, which are attached behind the talker's ear and can capture not only normal (audible speech, but also very quietly uttered speech (nonaudible murmur. As a result, NAM microphones can be applied in automatic speech recognition systems when privacy is desired in human-machine communication. Moreover, NAM microphones show robustness against noise and they might be used in special systems (speech recognition, speech transform, etc. for sound-impaired people. Using adaptation techniques and a small amount of training data, we achieved for a 20 k dictation task a word accuracy for nonaudible murmur recognition in a clean environment. In this paper, we also investigate nonaudible murmur recognition in noisy environments and the effect of the Lombard reflex on nonaudible murmur recognition. We also propose three methods to integrate audible speech and nonaudible murmur recognition using a stethoscope NAM microphone with very promising results.

The recognition heuristic: A decade of research

Directory of Open Access Journals (Sweden)

Gerd Gigerenzer

2011-02-01

Full Text Available The recognition heuristic exploits the basic psychological capacity for recognition in order to make inferences about unknown quantities in the world. In this article, we review and clarify issues that emerged from our initial work (Goldstein and Gigerenzer, 1999, 2002, including the distinction between a recognition and an evaluation process. There is now considerable evidence that (i the recognition heuristic predicts the inferences of a substantial proportion of individuals consistently, even in the presence of one or more contradicting cues, (ii people are adaptive decision makers in that accordance increases with larger recognition validity and decreases in situations when the validity is low or wholly indeterminable, and (iii in the presence of contradicting cues, some individuals appear to select different strategies. Little is known about these individual differences, or how to precisely model the alternative strategies. Although some researchers have attributed judgments inconsistent with the use of the recognition heuristic to compensatory processing, little research on such compensatory models has been reported. We discuss extensions of the recognition model, open questions, unanticipated results, and the surprising predictive power of recognition in forecasting.

The Role of Higher Level Adaptive Coding Mechanisms in the Development of Face Recognition

Science.gov (United States)

Pimperton, Hannah; Pellicano, Elizabeth; Jeffery, Linda; Rhodes, Gillian

2009-01-01

DevDevelopmental improvements in face identity recognition ability are widely documented, but the source of children's immaturity in face recognition remains unclear. Differences in the way in which children and adults visually represent faces might underlie immaturities in face recognition. Recent evidence of a face identity aftereffect (FIAE),…

Goal-seeking neural net for recall and recognition

Science.gov (United States)

Omidvar, Omid M.

1990-07-01

Neural networks have been used to mimic cognitive processes which take place in animal brains. The learning capability inherent in neural networks makes them suitable candidates for adaptive tasks such as recall and recognition. The synaptic reinforcements create a proper condition for adaptation, which results in memorization, formation of perception, and higher order information processing activities. In this research a model of a goal seeking neural network is studied and the operation of the network with regard to recall and recognition is analyzed. In these analyses recall is defined as retrieval of stored information where little or no matching is involved. On the other hand recognition is recall with matching; therefore it involves memorizing a piece of information with complete presentation. This research takes the generalized view of reinforcement in which all the signals are potential reinforcers. The neuronal response is considered to be the source of the reinforcement. This local approach to adaptation leads to the goal seeking nature of the neurons as network components. In the proposed model all the synaptic strengths are reinforced in parallel while the reinforcement among the layers is done in a distributed fashion and pipeline mode from the last layer inward. A model of complex neuron with varying threshold is developed to account for inhibitory and excitatory behavior of real neuron. A goal seeking model of a neural network is presented. This network is utilized to perform recall and recognition tasks. The performance of the model with regard to the assigned tasks is presented.

Embedded face recognition using cascaded structures

NARCIS (Netherlands)

Zuo, F.

2006-01-01

During the past few years, there is an increasing demand for smart devices in consumer electronics. These smart devices should be capable of consciously sensing their surroundings and adapting their services according to their environments. Face recognition provides a natural visual interface for

Recognition of extracellular bacteria by NLRs and its role in the development of adaptive immunity

Directory of Open Access Journals (Sweden)

Jonathan eFerrand

2013-10-01

Full Text Available Innate immune recognition of bacteria is the first requirement for mounting an effective immune response able to control infection. Over the previous decade, the general paradigm was that extracellular bacteria were only sensed by cell surface-expressed Toll-like receptors (TLRs, whereas cytoplasmic sensors, including members of the Nod-like receptor (NLR family, were specific to pathogens capable of breaching the host cell membrane. It has become apparent, however, that intracellular innate immune molecules, such as the NLRs, play key roles in the sensing of not only intracellular, but also extracellular bacterial pathogens or their components. In this review, we will discuss the various mechanisms used by bacteria to activate NLR signaling in host cells. These mechanisms include bacterial secretion systems, pore-forming toxins and outer membrane vesicles. We will then focus on the influence of NLR activation on the development of adaptive immune responses in different cell types.

Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

Directory of Open Access Journals (Sweden)

Hiroshi Saruwatari

2007-01-01

Full Text Available We present the use of stethoscope and silicon NAM (nonaudible murmur microphones in automatic speech recognition. NAM microphones are special acoustic sensors, which are attached behind the talker's ear and can capture not only normal (audible speech, but also very quietly uttered speech (nonaudible murmur. As a result, NAM microphones can be applied in automatic speech recognition systems when privacy is desired in human-machine communication. Moreover, NAM microphones show robustness against noise and they might be used in special systems (speech recognition, speech transform, etc. for sound-impaired people. Using adaptation techniques and a small amount of training data, we achieved for a 20 k dictation task a 93.9% word accuracy for nonaudible murmur recognition in a clean environment. In this paper, we also investigate nonaudible murmur recognition in noisy environments and the effect of the Lombard reflex on nonaudible murmur recognition. We also propose three methods to integrate audible speech and nonaudible murmur recognition using a stethoscope NAM microphone with very promising results.

Recent progress in invariant pattern recognition

Science.gov (United States)

Arsenault, Henri H.; Chang, S.; Gagne, Philippe; Gualdron Gonzalez, Oscar

1996-12-01

We present some recent results in invariant pattern recognition, including methods that are invariant under two or more distortions of position, orientation and scale. There are now a few methods that yield good results under changes of both rotation and scale. Some new methods are introduced. These include locally adaptive nonlinear matched filters, scale-adapted wavelet transforms and invariant filters for disjoint noise. Methods using neural networks will also be discussed, including an optical method that allows simultaneous classification of multiple targets.

Cross-sensor iris recognition through kernel learning.

Science.gov (United States)

Pillai, Jaishanker K; Puertas, Maria; Chellappa, Rama

2014-01-01

Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, especially in applications with a large number of enrolled users. However, recent studies show that cross-sensor matching, where the test samples are verified using data enrolled with a different sensor, often lead to reduced performance. In this paper, we propose a machine learning technique to mitigate the cross-sensor performance degradation by adapting the iris samples from one sensor to another. We first present a novel optimization framework for learning transformations on iris biometrics. We then utilize this framework for sensor adaptation, by reducing the distance between samples of the same class, and increasing it between samples of different classes, irrespective of the sensors acquiring them. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to improvement in cross-sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems.

Research on operation and maintenance support system adaptive to human recognition and understanding in human-centered plant

International Nuclear Information System (INIS)

Numano, Masayoshi; Matsuoka, Takeshi; Mitomo, N.

2004-01-01

As a human-centered plant, advanced nuclear power plant needs appropriate role sharing between human and mobile intelligent agents. Human-machine cooperation for plant operation and maintenance activities is also required with an advanced interface. Plant's maintenance is programmed using mobile robots working under the radiation environments instead of human beings. Operation and maintenance support system adaptive to human recognition and understanding should be developed to establish adequate human and machine interface so as to induce human capabilities to the full and enable human to take responsibility for plan's operation. Plant's operation and maintenance can be cooperative activities between human and intelligent automonous agents having surveillance and control functions. Infrastructure of multi-agent simulation system for the support system has been investigated and developed based on work plans derived from the scheduler. (T. Tanaka)

Cough Recognition Based on Mel Frequency Cepstral Coefficients and Dynamic Time Warping

Science.gov (United States)

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.

The recognition heuristic: a review of theory and tests.

Science.gov (United States)

Pachur, Thorsten; Todd, Peter M; Gigerenzer, Gerd; Schooler, Lael J; Goldstein, Daniel G

2011-01-01

The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect - the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference).

The Recognition Heuristic: A Review of Theory and Tests

Directory of Open Access Journals (Sweden)

Thorsten ePachur

2011-07-01

Full Text Available The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a that recognition is often an ecologically valid cue; (b that people often follow recognition when making inferences; (c that recognition supersedes further cue knowledge; (d that its use can produce the less-is-more effect—the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference.

The Recognition Heuristic: A Review of Theory and Tests

Science.gov (United States)

Pachur, Thorsten; Todd, Peter M.; Gigerenzer, Gerd; Schooler, Lael J.; Goldstein, Daniel G.

2011-01-01

The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect – the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference). PMID:21779266

Practical Considerations about Expected A Posteriori Estimation in Adaptive Testing: Adaptive A Priori, Adaptive Correction for Bias, and Adaptive Integration Interval.

Science.gov (United States)

Raiche, Gilles; Blais, Jean-Guy

In a computerized adaptive test (CAT), it would be desirable to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Decreasing the number of items is accompanied, however, by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. G. Raiche (2000) has…

Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition.

Science.gov (United States)

Chai, Xin; Wang, Qisong; Zhao, Yongping; Liu, Xin; Bai, Ou; Li, Yongqiang

2016-12-01

In electroencephalography (EEG)-based emotion recognition systems, the distribution between the training samples and the testing samples may be mismatched if they are sampled from different experimental sessions or subjects because of user fatigue, different electrode placements, varying impedances, etc. Therefore, it is difficult to directly classify the EEG patterns with a conventional classifier. The domain adaptation method, which is aimed at obtaining a common representation across training and test domains, is an effective method for reducing the distribution discrepancy. However, the existing domain adaptation strategies either employ a linear transformation or learn the nonlinearity mapping without a consistency constraint; they are not sufficiently powerful to obtain a similar distribution from highly non-stationary EEG signals. To address this problem, in this paper, a novel component, called the subspace alignment auto-encoder (SAAE), is proposed. Taking advantage of both nonlinear transformation and a consistency constraint, we combine an auto-encoder network and a subspace alignment solution in a unified framework. As a result, the source domain can be aligned with the target domain together with its class label, and any supervised method can be applied to the new source domain to train a classifier for classification in the target domain, as the aligned source domain follows a distribution similar to that of the target domain. We compared our SAAE method with six typical approaches using a public EEG dataset containing three affective states: positive, neutral, and negative. Subject-to-subject and session-to-session evaluations were performed. The subject-to-subject experimental results demonstrate that our component achieves a mean accuracy of 77.88% in comparison with a state-of-the-art method, TCA, which achieves 73.82% on average. In addition, the average classification accuracy of SAAE in the session-to-session evaluation for all the 15 subjects

Applications of pattern recognition theory in diagnostics of nuclear power plants

International Nuclear Information System (INIS)

Cech, J.

1982-01-01

The questions are discussed of the application of the theory of pattern recognition in the diagnostics of nuclear power plants. For the future use of recognition systems in the diagnostics of nuclear power plants it is obvious that like with other complex systems, optimal models will have to be used which will organize the optimal recognition algorithm. The conclusion is presented that for the needs of nuclear power plants special systems will be more suitable for pattern recognition than digital computers which are flexible and adaptible but have a lower decision rate, an insufficient working memory, complicated programs, etc. (Z.M.)

Liquid lens: advances in adaptive optics

Science.gov (United States)

Casey, Shawn Patrick

2010-12-01

'Liquid lens' technologies promise significant advancements in machine vision and optical communications systems. Adaptations for machine vision, human vision correction, and optical communications are used to exemplify the versatile nature of this technology. Utilization of liquid lens elements allows the cost effective implementation of optical velocity measurement. The project consists of a custom image processor, camera, and interface. The images are passed into customized pattern recognition and optical character recognition algorithms. A single camera would be used for both speed detection and object recognition.

Contributions of feature shapes and surface cues to the recognition and neural representation of facial identity.

Science.gov (United States)

Andrews, Timothy J; Baseler, Heidi; Jenkins, Rob; Burton, A Mike; Young, Andrew W

2016-10-01

A full understanding of face recognition will involve identifying the visual information that is used to discriminate different identities and how this is represented in the brain. The aim of this study was to explore the importance of shape and surface properties in the recognition and neural representation of familiar faces. We used image morphing techniques to generate hybrid faces that mixed shape properties (more specifically, second order spatial configural information as defined by feature positions in the 2D-image) from one identity and surface properties from a different identity. Behavioural responses showed that recognition and matching of these hybrid faces was primarily based on their surface properties. These behavioural findings contrasted with neural responses recorded using a block design fMRI adaptation paradigm to test the sensitivity of Haxby et al.'s (2000) core face-selective regions in the human brain to the shape or surface properties of the face. The fusiform face area (FFA) and occipital face area (OFA) showed a lower response (adaptation) to repeated images of the same face (same shape, same surface) compared to different faces (different shapes, different surfaces). From the behavioural data indicating the critical contribution of surface properties to the recognition of identity, we predicted that brain regions responsible for familiar face recognition should continue to adapt to faces that vary in shape but not surface properties, but show a release from adaptation to faces that vary in surface properties but not shape. However, we found that the FFA and OFA showed an equivalent release from adaptation to changes in both shape and surface properties. The dissociation between the neural and perceptual responses suggests that, although they may play a role in the process, these core face regions are not solely responsible for the recognition of facial identity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Misattribution, false recognition and the sins of memory.

Science.gov (United States)

Schacter, D L; Dodson, C S

2001-09-29

Memory is sometimes a troublemaker. Schacter has classified memory's transgressions into seven fundamental 'sins': transience, absent-mindedness, blocking, misattribution, suggestibility, bias and persistence. This paper focuses on one memory sin, misattribution, that is implicated in false or illusory recognition of episodes that never occurred. We present data from cognitive, neuropsychological and neuroimaging studies that illuminate aspects of misattribution and false recognition. We first discuss cognitive research examining possible mechanisms of misattribution associated with false recognition. We also consider ways in which false recognition can be reduced or avoided, focusing in particular on the role of distinctive information. We next turn to neuropsychological research concerning patients with amnesia and Alzheimer's disease that reveals conditions under which such patients are less susceptible to false recognition than are healthy controls, thus providing clues about the brain mechanisms that drive false recognition. We then consider neuroimaging studies concerned with the neural correlates of true and false recognition, examining when the two forms of recognition can and cannot be distinguished on the basis of brain activity. Finally, we argue that even though misattribution and other memory sins are annoying and even dangerous, they can also be viewed as by-products of adaptive features of memory.

Novel Techniques for Dialectal Arabic Speech Recognition

CERN Document Server

Elmahdy, Mohamed; Minker, Wolfgang

2012-01-01

Novel Techniques for Dialectal Arabic Speech describes approaches to improve automatic speech recognition for dialectal Arabic. Since speech resources for dialectal Arabic speech recognition are very sparse, the authors describe how existing Modern Standard Arabic (MSA) speech data can be applied to dialectal Arabic speech recognition, while assuming that MSA is always a second language for all Arabic speakers. In this book, Egyptian Colloquial Arabic (ECA) has been chosen as a typical Arabic dialect. ECA is the first ranked Arabic dialect in terms of number of speakers, and a high quality ECA speech corpus with accurate phonetic transcription has been collected. MSA acoustic models were trained using news broadcast speech. In order to cross-lingually use MSA in dialectal Arabic speech recognition, the authors have normalized the phoneme sets for MSA and ECA. After this normalization, they have applied state-of-the-art acoustic model adaptation techniques like Maximum Likelihood Linear Regression (MLLR) and M...

Reversal deterioration of renal function accompanied with primary hypothyrodism.

Science.gov (United States)

Dragović, Tamara

2012-02-01

Hypothyroidism is often accompanied with decline of kidney function, or inability to maintain electrolyte balance. These changes are usually overlooked in everyday practice. Early recognition of this association eliminates unnecessary diagnostic procedures that postpone the adequate treatment. Two patients with elevated serum creatinine levels due to primary autoimmune hypothyroidism, with complete recovery of creatinine clearance after thyroid hormone substitution therapy are presented. The first patient was a young male whose laboratory tests suggested acute renal failure, and the delicate clinical presentation of reduced thyroid function. The second patient was an elderly woman with a history of a long-term signs and symptoms attributed to ageing, including the deterioration of renal function, with consequently delayed diagnosis of hypothyroidism. Serum thyrotropin and thyroxin levels measurement should be done in all cases of renal failure with undefined renal desease, even if the typical clinical presentation of hypothyroidism is absent. Thyroid hormone assays sholud also be performed in all patients with chronic kidney disease whose kidney function is rapidly worsening.

Accessing Specific Peptide Recognition by Combinatorial Chemistry

DEFF Research Database (Denmark)

Li, Ming

Molecular recognition is at the basis of all processes for life, and plays a central role in many biological processes, such as protein folding, the structural organization of cells and organelles, signal transduction, and the immune response. Hence, my PhD project is entitled “Accessing Specific...... Peptide Recognition by Combinatorial Chemistry”. Molecular recognition is a specific interaction between two or more molecules through noncovalent bonding, such as hydrogen bonding, metal coordination, van der Waals forces, π−π, hydrophobic, or electrostatic interactions. The association involves kinetic....... Combinatorial chemistry was invented in 1980s based on observation of functional aspects of the adaptive immune system. It was employed for drug development and optimization in conjunction with high-throughput synthesis and screening. (chapter 2) Combinatorial chemistry is able to rapidly produce many thousands...

19 CFR 148.4 - Accompanying articles.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 2 2010-04-01 2010-04-01 false Accompanying articles. 148.4 Section 148.4 Customs... (CONTINUED) PERSONAL DECLARATIONS AND EXEMPTIONS General Provisions § 148.4 Accompanying articles. (a) Generally. Articles shall be considered as accompanying a passenger or brought in by him if the articles...

Semantic and visual determinants of face recognition in a prosopagnosic patient.

Science.gov (United States)

Dixon, M J; Bub, D N; Arguin, M

1998-05-01

Prosopagnosia is the neuropathological inability to recognize familiar people by their faces. It can occur in isolation or can coincide with recognition deficits for other nonface objects. Often, patients whose prosopagnosia is accompanied by object recognition difficulties have more trouble identifying certain categories of objects relative to others. In previous research, we demonstrated that objects that shared multiple visual features and were semantically close posed severe recognition difficulties for a patient with temporal lobe damage. We now demonstrate that this patient's face recognition is constrained by these same parameters. The prosopagnosic patient ELM had difficulties pairing faces to names when the faces shared visual features and the names were semantically related (e.g., Tonya Harding, Nancy Kerrigan, and Josee Chouinard -three ice skaters). He made tenfold fewer errors when the exact same faces were associated with semantically unrelated people (e.g., singer Celine Dion, actress Betty Grable, and First Lady Hillary Clinton). We conclude that prosopagnosia and co-occurring category-specific recognition problems both stem from difficulties disambiguating the stored representations of objects that share multiple visual features and refer to semantically close identities or concepts.

The effect of glucose administration on the recollection and familiarity components of recognition memory.

Science.gov (United States)

Sünram-Lea, Sandra I; Dewhurst, Stephen A; Foster, Jonathan K

2008-01-01

Previous research has demonstrated that glucose administration facilitates long-term memory performance. The aim of the present research was to evaluate the effect of glucose administration on different components of long-term recognition memory. Fifty-six healthy young individuals received (a) a drink containing 25 g of glucose or (b) an inert placebo drink. Recollection and familiarity components of recognition memory were measured using the 'remember-know' paradigm. The results revealed that glucose administration led to significantly increased proportion of recognition responses based on recollection, but had no effect on the proportion of recognition responses made through participants' detection of stimulus familiarity. Consequently, the data suggest that glucose administration appears to facilitate recognition memory that is accompanied by recollection of contextual details and episodic richness. The findings also suggest that memory tasks that result in high levels of hippocampal activity may be more likely to be enhanced by glucose administration than tasks that are less reliant on medial temporal lobe structures.

[Analysis of the stability and adaptability of near infrared spectra qualitative analysis model].

Science.gov (United States)

Cao, Wu; Li, Wei-jun; Wang, Ping; Zhang, Li-ping

2014-06-01

The stability and adaptability of model of near infrared spectra qualitative analysis were studied. Method of separate modeling can significantly improve the stability and adaptability of model; but its ability of improving adaptability of model is limited. Method of joint modeling can not only improve the adaptability of the model, but also the stability of model, at the same time, compared to separate modeling, the method can shorten the modeling time, reduce the modeling workload; extend the term of validity of model, and improve the modeling efficiency. The experiment of model adaptability shows that, the correct recognition rate of separate modeling method is relatively low, which can not meet the requirements of application, and joint modeling method can reach the correct recognition rate of 90%, and significantly enhances the recognition effect. The experiment of model stability shows that, the identification results of model by joint modeling are better than the model by separate modeling, and has good application value.

Levels-of-processing effect on frontotemporal function in schizophrenia during word encoding and recognition.

Science.gov (United States)

Ragland, J Daniel; Gur, Ruben C; Valdez, Jeffrey N; Loughead, James; Elliott, Mark; Kohler, Christian; Kanes, Stephen; Siegel, Steven J; Moelter, Stephen T; Gur, Raquel E

2005-10-01

Patients with schizophrenia improve episodic memory accuracy when given organizational strategies through levels-of-processing paradigms. This study tested if improvement is accompanied by normalized frontotemporal function. Event-related blood-oxygen-level-dependent functional magnetic resonance imaging (fMRI) was used to measure activation during shallow (perceptual) and deep (semantic) word encoding and recognition in 14 patients with schizophrenia and 14 healthy comparison subjects. Despite slower and less accurate overall word classification, the patients showed normal levels-of-processing effects, with faster and more accurate recognition of deeply processed words. These effects were accompanied by left ventrolateral prefrontal activation during encoding in both groups, although the thalamus, hippocampus, and lingual gyrus were overactivated in the patients. During word recognition, the patients showed overactivation in the left frontal pole and had a less robust right prefrontal response. Evidence of normal levels-of-processing effects and left prefrontal activation suggests that patients with schizophrenia can form and maintain semantic representations when they are provided with organizational cues and can improve their word encoding and retrieval. Areas of overactivation suggest residual inefficiencies. Nevertheless, the effect of teaching organizational strategies on episodic memory and brain function is a worthwhile topic for future interventional studies.

Online recognition of Chinese characters: the state-of-the-art.

Science.gov (United States)

Liu, Cheng-Lin; Jaeger, Stefan; Nakagawa, Masaki

2004-02-01

Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.

A change in strategy: Static emotion recognition in Malaysian Chinese

Directory of Open Access Journals (Sweden)

Chrystalle B.Y. Tan

2015-12-01

Full Text Available Studies have shown that while East Asians focused on the center of the face to recognize identities, participants adapted their strategy by focusing more on the eyes to identify emotions, suggesting that the eyes may contain salient information pertaining to emotional state in Eastern cultures. However, Western Caucasians employ the same strategy by moving between the eyes and mouth to identify both identities and emotions. Malaysian Chinese have been shown to focus on the eyes and nose more than the mouth during face recognition task, which represents an intermediate between Eastern and Western looking strategies. The current study examined whether Malaysian Chinese continue to employ an intermediate strategy or shift towards an Eastern or Western pattern (by fixating more on the eyes or mouth respectively during an emotion recognition task. Participants focused more on the eyes, followed by the nose then mouth. Directing attention towards the eye region resulted in better recognition of certain own- than other-race emotions. Although the fixation patterns appear similar for both tasks, further analyses showed that fixations on the eyes were reduced whereas fixations on the nose and mouth were increased during emotion recognition, indicating that participants adapt looking strategies based on their aims.

Adaptive techniques for diagnostics of vibrating structures

International Nuclear Information System (INIS)

Skormin, V.A.; Sankar, S.

1983-01-01

An adaptive diagnostic procedure for vibrating structures based on correspondence between current estimates of stiffness matrix and structure status is proposed. Procedure employs adaptive mathematical description of the vibrating structure in frequency domain, statistical techniques for detection and location of changes of structure properties, 'recognition' and prediction of defects. (orig.)

Adaptive weighted local textural features for illumination, expression, and occlusion invariant face recognition

Science.gov (United States)

Cui, Chen; Asari, Vijayan K.

2014-03-01

Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are a affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image

Fast Pedestrian Recognition Based on Multisensor Fusion

Directory of Open Access Journals (Sweden)

Hongyu Hu

2012-01-01

Full Text Available A fast pedestrian recognition algorithm based on multisensor fusion is presented in this paper. Firstly, potential pedestrian locations are estimated by laser radar scanning in the world coordinates, and then their corresponding candidate regions in the image are located by camera calibration and the perspective mapping model. For avoiding time consuming in the training and recognition process caused by large numbers of feature vector dimensions, region of interest-based integral histograms of oriented gradients (ROI-IHOG feature extraction method is proposed later. A support vector machine (SVM classifier is trained by a novel pedestrian sample dataset which adapt to the urban road environment for online recognition. Finally, we test the validity of the proposed approach with several video sequences from realistic urban road scenarios. Reliable and timewise performances are shown based on our multisensor fusing method.

Conformal prediction for reliable machine learning theory, adaptations and applications

CERN Document Server

Balasubramanian, Vineeth; Vovk, Vladimir

2014-01-01

The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detecti

High-performance speech recognition using consistency modeling

Science.gov (United States)

Digalakis, Vassilios; Murveit, Hy; Monaco, Peter; Neumeyer, Leo; Sankar, Ananth

1994-12-01

The goal of SRI's consistency modeling project is to improve the raw acoustic modeling component of SRI's DECIPHER speech recognition system and develop consistency modeling technology. Consistency modeling aims to reduce the number of improper independence assumptions used in traditional speech recognition algorithms so that the resulting speech recognition hypotheses are more self-consistent and, therefore, more accurate. At the initial stages of this effort, SRI focused on developing the appropriate base technologies for consistency modeling. We first developed the Progressive Search technology that allowed us to perform large-vocabulary continuous speech recognition (LVCSR) experiments. Since its conception and development at SRI, this technique has been adopted by most laboratories, including other ARPA contracting sites, doing research on LVSR. Another goal of the consistency modeling project is to attack difficult modeling problems, when there is a mismatch between the training and testing phases. Such mismatches may include outlier speakers, different microphones and additive noise. We were able to either develop new, or transfer and evaluate existing, technologies that adapted our baseline genonic HMM recognizer to such difficult conditions.

Action recognition in depth video from RGB perspective: A knowledge transfer manner

Science.gov (United States)

Chen, Jun; Xiao, Yang; Cao, Zhiguo; Fang, Zhiwen

2018-03-01

Different video modal for human action recognition has becoming a highly promising trend in the video analysis. In this paper, we propose a method for human action recognition from RGB video to Depth video using domain adaptation, where we use learned feature from RGB videos to do action recognition for depth videos. More specifically, we make three steps for solving this problem in this paper. First, different from image, video is more complex as it has both spatial and temporal information, in order to better encode this information, dynamic image method is used to represent each RGB or Depth video to one image, based on this, most methods for extracting feature in image can be used in video. Secondly, as video can be represented as image, so standard CNN model can be used for training and testing for videos, beside, CNN model can be also used for feature extracting as its powerful feature expressing ability. Thirdly, as RGB videos and Depth videos are belong to two different domains, in order to make two different feature domains has more similarity, domain adaptation is firstly used for solving this problem between RGB and Depth video, based on this, the learned feature from RGB video model can be directly used for Depth video classification. We evaluate the proposed method on one complex RGB-D action dataset (NTU RGB-D), and our method can have more than 2% accuracy improvement using domain adaptation from RGB to Depth action recognition.

Facial emotion recognition, socio-occupational functioning and expressed emotions in schizophrenia versus bipolar disorder.

Science.gov (United States)

Thonse, Umesh; Behere, Rishikesh V; Praharaj, Samir Kumar; Sharma, Podila Sathya Venkata Narasimha

2018-06-01

Facial emotion recognition deficits have been consistently demonstrated in patients with severe mental disorders. Expressed emotion is found to be an important predictor of relapse. However, the relationship between facial emotion recognition abilities and expressed emotions and its influence on socio-occupational functioning in schizophrenia versus bipolar disorder has not been studied. In this study we examined 91 patients with schizophrenia and 71 with bipolar disorder for psychopathology, socio occupational functioning and emotion recognition abilities. Primary caregivers of 62 patients with schizophrenia and 49 with bipolar disorder were assessed on Family Attitude Questionnaire to assess their expressed emotions. Patients of schizophrenia and bipolar disorder performed similarly on the emotion recognition task. Patients with schizophrenia group experienced higher critical comments and had a poorer socio-occupational functioning as compared to patients with bipolar disorder. Poorer socio-occupational functioning in patients with schizophrenia was significantly associated with greater dissatisfaction in their caregivers. In patients with bipolar disorder, poorer emotion recognition scores significantly correlated with poorer adaptive living skills and greater hostility and dissatisfaction in their caregivers. The findings of our study suggest that emotion recognition abilities in patients with bipolar disorder are associated with negative expressed emotions leading to problems in adaptive living skills. Copyright © 2018 Elsevier B.V. All rights reserved.

Sensor-Based Activity Recognition with Dynamically Added Context

Directory of Open Access Journals (Sweden)

Jiahui Wen

2015-08-01

Full Text Available An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods.

The Neural Correlates of Everyday Recognition Memory

Science.gov (United States)

Milton, F.; Muhlert, N.; Butler, C. R.; Benattayallah, A.; Zeman, A. Z.

2011-01-01

We used a novel automatic camera, SenseCam, to create a recognition memory test for real-life events. Adapting a "Remember/Know" paradigm, we asked healthy undergraduates, who wore SenseCam for 2 days, in their everyday environments, to classify images as strongly or weakly remembered, strongly or weakly familiar or novel, while brain activation…

Modelling of the temperature field that accompanies friction stir welding

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Nosal Przemysław

2017-01-01

Full Text Available The thermal modelling of the Friction Stir Welding process allows for better recognition and understanding of phenomena occurring during the joining process of different materials. It is of particular importance considering the possibilities of process technology parameters, optimization and the mechanical properties of the joint. This work demonstrates the numerical modelling of temperature distribution accompanying the process of friction stir welding. The axisymmetric problem described by Fourier’s type equation with internal heat source is considered. In order to solve the diffusive initial value problem a fully implicit scheme of the finite difference method is applied. The example under consideration deals with the friction stir welding of a plate (0.7 cm thick made of Al 6082-T6 by use of a tool made of tungsten alloy, whereas the material subjected to welding was TiC powder. Obtained results confirm both quantitatively and qualitatively experimental observations that the superior temperature corresponds to the zone where the pin joints the shoulder.

Superpixel-Based Feature for Aerial Image Scene Recognition

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Hongguang Li

2018-01-01

Full Text Available Image scene recognition is a core technology for many aerial remote sensing applications. Different landforms are inputted as different scenes in aerial imaging, and all landform information is regarded as valuable for aerial image scene recognition. However, the conventional features of the Bag-of-Words model are designed using local points or other related information and thus are unable to fully describe landform areas. This limitation cannot be ignored when the aim is to ensure accurate aerial scene recognition. A novel superpixel-based feature is proposed in this study to characterize aerial image scenes. Then, based on the proposed feature, a scene recognition method of the Bag-of-Words model for aerial imaging is designed. The proposed superpixel-based feature that utilizes landform information establishes top-task superpixel extraction of landforms to bottom-task expression of feature vectors. This characterization technique comprises the following steps: simple linear iterative clustering based superpixel segmentation, adaptive filter bank construction, Lie group-based feature quantification, and visual saliency model-based feature weighting. Experiments of image scene recognition are carried out using real image data captured by an unmanned aerial vehicle (UAV. The recognition accuracy of the proposed superpixel-based feature is 95.1%, which is higher than those of scene recognition algorithms based on other local features.

The cingulo-opercular network provides word-recognition benefit.

Science.gov (United States)

Vaden, Kenneth I; Kuchinsky, Stefanie E; Cute, Stephanie L; Ahlstrom, Jayne B; Dubno, Judy R; Eckert, Mark A

2013-11-27

Recognizing speech in difficult listening conditions requires considerable focus of attention that is often demonstrated by elevated activity in putative attention systems, including the cingulo-opercular network. We tested the prediction that elevated cingulo-opercular activity provides word-recognition benefit on a subsequent trial. Eighteen healthy, normal-hearing adults (10 females; aged 20-38 years) performed word recognition (120 trials) in multi-talker babble at +3 and +10 dB signal-to-noise ratios during a sparse sampling functional magnetic resonance imaging (fMRI) experiment. Blood oxygen level-dependent (BOLD) contrast was elevated in the anterior cingulate cortex, anterior insula, and frontal operculum in response to poorer speech intelligibility and response errors. These brain regions exhibited significantly greater correlated activity during word recognition compared with rest, supporting the premise that word-recognition demands increased the coherence of cingulo-opercular network activity. Consistent with an adaptive control network explanation, general linear mixed model analyses demonstrated that increased magnitude and extent of cingulo-opercular network activity was significantly associated with correct word recognition on subsequent trials. These results indicate that elevated cingulo-opercular network activity is not simply a reflection of poor performance or error but also supports word recognition in difficult listening conditions.

Reversal deterioration of renal function accompanied with primary hypothyrodism

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Dragović Tamara

2012-01-01

Full Text Available Introduction. Hypothyroidism is often accompanied with decline of kidney function, or inability to maintain electrolyte balance. These changes are usually overlooked in everyday practice. Early recognition of this association eliminates unnecessary diagnostic procedures that postpone the adequate treatment. Case report. Two patients with elevated serum creatinine levels due to primary autoimmune hypothyroidism, with complete recovery of creatinine clearance after thyroid hormone substitution therapy are presented. The first patient was a young male whose laboratory tests suggested acute renal failure, and the delicate clinical presentation of reduced thyroid function. The second patient was an elderly woman with a history of a long-term signs and symptoms attributed to ageing, including the deterioration of renal function, with consequently delayed diagnosis of hypothyroidism. Conclusion. Serum thyrotropin and thyroxin levels measurement should be done in all cases of renal failure with undefined renal desease, even if the typical clinical presentation of hypothyroidism is absent. Thyroid hormone assays sholud also be performed in all patients with chronic kidney disease whose kidney function is rapidly worsening.

Adaptive metric kernel regression

DEFF Research Database (Denmark)

Goutte, Cyril; Larsen, Jan

2000-01-01

Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...

Morphological self-organizing feature map neural network with applications to automatic target recognition

Science.gov (United States)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Shared or separate mechanisms for self-face and other-face processing? Evidence from adaptation.

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Brendan eRooney

2012-03-01

Full Text Available Evidence that self-face recognition is dissociable from general face recognition hasimportant implications both for models of social cognition and for our understanding offace recognition. In two studies, we examine how adaptation affects the perception ofpersonally familiar faces, and we use a visual adaptation paradigm to investigatewhether the neural mechanisms underlying the recognition of one’s own and other facesare shared or separate. In Study 1 we show that the representation of personally familiarfaces is rapidly updated by visual experience with unfamiliar faces, so that theperception of one’s own face and a friend’s face is altered by a brief period ofadaptation to distorted unfamiliar faces. In Study 2, participants adapted to images oftheir own and a friend’s face distorted in opposite directions; the contingent aftereffectswe observe are indicative of separate neural populations, but we suggest that thesereflect coding of facial identity rather than of the categories ‘self’ and ‘other’.

Iris double recognition based on modified evolutionary neural network

Science.gov (United States)

Liu, Shuai; Liu, Yuan-Ning; Zhu, Xiao-Dong; Huo, Guang; Liu, Wen-Tao; Feng, Jia-Kai

2017-11-01

Aiming at multicategory iris recognition under illumination and noise interference, this paper proposes a method of iris double recognition based on a modified evolutionary neural network. An equalization histogram and Laplace of Gaussian operator are used to process the iris to suppress illumination and noise interference and Haar wavelet to convert the iris feature to binary feature encoding. Calculate the Hamming distance for the test iris and template iris , and compare with classification threshold, determine the type of iris. If the iris cannot be identified as a different type, there needs to be a secondary recognition. The connection weights in back-propagation (BP) neural network use modified evolutionary neural network to adaptively train. The modified neural network is composed of particle swarm optimization with mutation operator and BP neural network. According to different iris libraries in different circumstances of experimental results, under illumination and noise interference, the correct recognition rate of this algorithm is higher, the ROC curve is closer to the coordinate axis, the training and recognition time is shorter, and the stability and the robustness are better.

Neuroanatomical correlates of impaired decision-making and facial emotion recognition in early Parkinson's disease.

Science.gov (United States)

Ibarretxe-Bilbao, Naroa; Junque, Carme; Tolosa, Eduardo; Marti, Maria-Jose; Valldeoriola, Francesc; Bargallo, Nuria; Zarei, Mojtaba

2009-09-01

Decision-making and recognition of emotions are often impaired in patients with Parkinson's disease (PD). The orbitofrontal cortex (OFC) and the amygdala are critical structures subserving these functions. This study was designed to test whether there are any structural changes in these areas that might explain the impairment of decision-making and recognition of facial emotions in early PD. We used the Iowa Gambling Task (IGT) and the Ekman 60 faces test which are sensitive to the integrity of OFC and amygdala dysfunctions in 24 early PD patients and 24 controls. High-resolution structural magnetic resonance images (MRI) were also obtained. Group analysis using voxel-based morphometry (VBM) showed significant and corrected (P decision-making and recognition of facial emotions occurs at the early stages of PD, (ii) these neuropsychological deficits are accompanied by degeneration of OFC and amygdala, and (iii) bilateral OFC reductions are associated with impaired recognition of emotions, and GM volume loss in left lateral OFC is related to decision-making impairment in PD.

Development of visuo-haptic transfer for object recognition in typical preschool and school-aged children.

Science.gov (United States)

Purpura, Giulia; Cioni, Giovanni; Tinelli, Francesca

2018-07-01

Object recognition is a long and complex adaptive process and its full maturation requires combination of many different sensory experiences as well as cognitive abilities to manipulate previous experiences in order to develop new percepts and subsequently to learn from the environment. It is well recognized that the transfer of visual and haptic information facilitates object recognition in adults, but less is known about development of this ability. In this study, we explored the developmental course of object recognition capacity in children using unimodal visual information, unimodal haptic information, and visuo-haptic information transfer in children from 4 years to 10 years and 11 months of age. Participants were tested through a clinical protocol, involving visual exploration of black-and-white photographs of common objects, haptic exploration of real objects, and visuo-haptic transfer of these two types of information. Results show an age-dependent development of object recognition abilities for visual, haptic, and visuo-haptic modalities. A significant effect of time on development of unimodal and crossmodal recognition skills was found. Moreover, our data suggest that multisensory processes for common object recognition are active at 4 years of age. They facilitate recognition of common objects, and, although not fully mature, are significant in adaptive behavior from the first years of age. The study of typical development of visuo-haptic processes in childhood is a starting point for future studies regarding object recognition in impaired populations.

Adaptive Metric Kernel Regression

DEFF Research Database (Denmark)

Goutte, Cyril; Larsen, Jan

1998-01-01

Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...

Cultural democratization and the politics of recognition: notes on the trajectory of the workers´ party (PT

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Paulo J. Krischke

2005-01-01

Full Text Available These research notes raise the hypothesis that the Worker´s Party (PT actively participates in the cultural democratization of the country - for instance, through its initiatives for the recognition of the citizen´s sociopolitical and cultural diversity. First, there is a need to distinguish between the policies of socioeconomic redistribution and those policies geared to sociopolitical and cultural recognition. Second, it is also necessary to focus on the relations between participatory policies and changes in the political culture. Such relations may show that the increasing support to democracy accompanies the recognition of pluralism and of the right to difference, among the youth, and in the locations administered by the PT during the last decade.

Radio-adaptive response

International Nuclear Information System (INIS)

Ikushima, Takaji

1991-01-01

An adaptive response to radiation stress was found in cultured Chinese hamster V79 cells, as a suppressed induction of micronuclei (MNs) and sister chromatid exchanges (SCEs) in the cells conditioned by very low doses. The important characteristics of the novel chromosomal response, called radio-adaptive response (RAR), that have newly emerged in this study are: 1) Low doses of beta-rays from tritiated water (HTO) as well as tritiated thymidine can cause the RAR. 2) Thermal neutrons, a high LET radiation, can not act as tritium beta-rays or gamma-rays. 3) The RAR expression is suppressed by an inhibition of protein synthesis. 4) Several proteins are newly synthesized concurrently with the RAR expression after adapting doses, viewed by two-dimensional electrophoresis of cellular proteins. These results suggest that the RAR is an adaptive chromosomal DNA repair induced by very low doses of low LET radiations under restricted conditions, accompanying the inducible specific gene expression. (author)

Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors

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Gaojing Wang

2018-06-01

Full Text Available Human activity recognition (HAR is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applied to HAR. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. However, the window length is generally randomly selected without systematic study. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition. With data collected from smartphone sensors, we tested a range of window lengths on five popular machine-learning methods: decision tree, support vector machine, K-nearest neighbor, Gaussian naïve Bayesian, and adaptive boosting. From the results, we provide recommendations for choosing the appropriate window length. Results corroborate that the influence of window length on the recognition of motion modes is significant but largely limited to pose pattern recognition. For motion mode recognition, a window length between 2.5–3.5 s can provide an optimal tradeoff between recognition performance and speed. Adaptive boosting outperformed the other methods. For pose pattern recognition, 0.5 s was enough to obtain a satisfactory result. In addition, all of the tested methods performed well.

Cancer cell-selective promoter recognition accompanies antitumor effect by glucocorticoid receptor-targeted gold nanoparticle

Science.gov (United States)

Sau, Samaresh; Agarwalla, Pritha; Mukherjee, Sudip; Bag, Indira; Sreedhar, Bojja; Pal-Bhadra, Manika; Patra, Chitta Ranjan; Banerjee, Rajkumar

2014-05-01

Nanoparticles, such as gold nanoparticles (GNP), upon convenient modifications perform multi tasks catering to many biomedical applications. However, GNP or any other type of nanoparticles is yet to achieve the feat of intracellular regulation of endogenous genes of choice such as through manipulation of a gene-promoter in a chromosome. As for gene modulation and delivery, GNP (or other nanoparticles) showed only limited gene therapy potential, which relied on the delivery of `exogenous' genes invoking gene knockdown or replacement. Practically, there are no instances for the nanoparticle-mediated promoter regulation of `endogenous' genes, more so, as a cancer selective phenomenon. In this regard, we report the development of a simple, easily modifiable GNP-formulation, which promoted/up-regulated the expression of a specific category of `endogenous' genes, the glucocorticoid responsive genes. This genetic up-regulation was induced in only cancer cells by modified GNP-mediated transcriptional activation of its cytoplasmic receptor, glucocorticoid receptor (GR). Normal cells and their GR remained primarily unperturbed by this GNP-formulation. The most potent gene up-regulating GNP-formulation down-regulated a cancer-specific proliferative signal, phospho-Akt in cancer cells, which accompanied retardation of tumor growth in the murine melanoma model. We show that GR-targeted GNPs may find potential use in the targeting and modulation of genetic information in cancer towards developing novel anticancer therapeutics.Nanoparticles, such as gold nanoparticles (GNP), upon convenient modifications perform multi tasks catering to many biomedical applications. However, GNP or any other type of nanoparticles is yet to achieve the feat of intracellular regulation of endogenous genes of choice such as through manipulation of a gene-promoter in a chromosome. As for gene modulation and delivery, GNP (or other nanoparticles) showed only limited gene therapy potential, which relied

Automatic anatomy recognition on CT images with pathology

Science.gov (United States)

Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

2016-03-01

Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

Study on municipal road cracking and surface deformation based on image recognition

Science.gov (United States)

Yuan, Haitao; Wang, Shuai; Tan, Jizong

2017-05-01

In recent years, the digital image recognition technology of concrete structure cracks and deformation of binocular vision technology detection of civil engineering structure have made substantial development. As a result, people's understanding of the road engineering structure cracking and surface deformation recognition gives rise to a new situation. For the research on digital image concrete structure cracking and masonry structure surface deformation recognition technology, the key is to break through in the method, and to improve the traditional recognition technology and mode. Only in this way can we continuously improve the security level of the highway, to adapt to the new requirements of the development of new urbanization and modernization. This thesis focuses on and systematically analyzes the digital image road engineering structure cracking and key technologies of surface deformation recognition and its engineering applications. In addition, we change the concrete structure cracking and masonry structure surface deformation recognition pattern, and realize the breakthrough and innovation of the road structure safety testing means and methods.

Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems.

Science.gov (United States)

Fang, Fuming; Shinozaki, Takahiro

2018-01-01

Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data.

Postsurgical Disfigurement Influences Disgust Recognition: A Case-Control Study.

Science.gov (United States)

Lisan, Quentin; George, Nathalie; Hans, Stephane; Laccourreye, Ollivier; Lemogne, Cédric

Little is known about how emotion recognition may be modified in individuals prone to elicit disgust. We sought to determine if subjects with total laryngectomy would present a modified recognition of facial expressions of disgust. A total of 29 patients presenting with a history of advanced-stage laryngeal cancer were recruited, 17 being surgically treated (total laryngectomy) and 12 treated with chemoradiation therapy only. Based on a validated set of images of facial expressions of fear, disgust, surprise, happiness, sadness and anger displayed by 6 actors, we presented participants with expressions of each emotion at 5 levels of increasing intensity and measured their ability to recognize these emotions. Participants with (vs without) laryngectomy showed a higher threshold for the recognition of disgust (3.2. vs 2.7 images needed before emotion recognition, p = 0.03) and a lower success rate of correct recognition (75.5% vs 88.9%, p = 0.03). Subjects presenting with an aesthetic impairment of the head and neck showed poorer performance in disgust recognition when compared with those without disfigurement. These findings might relate either to some perceptual adaptation, habituation phenomenon, or to some higher-level processes related to emotion regulation strategies. Copyright © 2018 Academy of Consultation-Liaison Psychiatry. Published by Elsevier Inc. All rights reserved.

Automatic identification of otological drilling faults: an intelligent recognition algorithm.

Science.gov (United States)

Cao, Tianyang; Li, Xisheng; Gao, Zhiqiang; Feng, Guodong; Shen, Peng

2010-06-01

This article presents an intelligent recognition algorithm that can recognize milling states of the otological drill by fusing multi-sensor information. An otological drill was modified by the addition of sensors. The algorithm was designed according to features of the milling process and is composed of a characteristic curve, an adaptive filter and a rule base. The characteristic curve can weaken the impact of the unstable normal milling process and reserve the features of drilling faults. The adaptive filter is capable of suppressing interference in the characteristic curve by fusing multi-sensor information. The rule base can identify drilling faults through the filtering result data. The experiments were repeated on fresh porcine scapulas, including normal milling and two drilling faults. The algorithm has high rates of identification. This study shows that the intelligent recognition algorithm can identify drilling faults under interference conditions. (c) 2010 John Wiley & Sons, Ltd.

Multistage Data Selection-based Unsupervised Speaker Adaptation for Personalized Speech Emotion Recognition

NARCIS (Netherlands)

Kim, Jaebok; Park, Jeong-Sik

This paper proposes an efficient speech emotion recognition (SER) approach that utilizes personal voice data accumulated on personal devices. A representative weakness of conventional SER systems is the user-dependent performance induced by the speaker independent (SI) acoustic model framework. But,

How does aging affect recognition-based inference? A hierarchical Bayesian modeling approach.

Science.gov (United States)

Horn, Sebastian S; Pachur, Thorsten; Mata, Rui

2015-01-01

The recognition heuristic (RH) is a simple strategy for probabilistic inference according to which recognized objects are judged to score higher on a criterion than unrecognized objects. In this article, a hierarchical Bayesian extension of the multinomial r-model is applied to measure use of the RH on the individual participant level and to re-evaluate differences between younger and older adults' strategy reliance across environments. Further, it is explored how individual r-model parameters relate to alternative measures of the use of recognition and other knowledge, such as adherence rates and indices from signal-detection theory (SDT). Both younger and older adults used the RH substantially more often in an environment with high than low recognition validity, reflecting adaptivity in strategy use across environments. In extension of previous analyses (based on adherence rates), hierarchical modeling revealed that in an environment with low recognition validity, (a) older adults had a stronger tendency than younger adults to rely on the RH and (b) variability in RH use between individuals was larger than in an environment with high recognition validity; variability did not differ between age groups. Further, the r-model parameters correlated moderately with an SDT measure expressing how well people can discriminate cases where the RH leads to a correct vs. incorrect inference; this suggests that the r-model and the SDT measures may offer complementary insights into the use of recognition in decision making. In conclusion, younger and older adults are largely adaptive in their application of the RH, but cognitive aging may be associated with an increased tendency to rely on this strategy. Copyright © 2014 Elsevier B.V. All rights reserved.

Biochip microsystem for bioinformatics recognition and analysis

Science.gov (United States)

Lue, Jaw-Chyng (Inventor); Fang, Wai-Chi (Inventor)

2011-01-01

A system with applications in pattern recognition, or classification, of DNA assay samples. Because DNA reference and sample material in wells of an assay may be caused to fluoresce depending upon dye added to the material, the resulting light may be imaged onto an embodiment comprising an array of photodetectors and an adaptive neural network, with applications to DNA analysis. Other embodiments are described and claimed.

Pathogen recognition in the innate immune response.

Science.gov (United States)

Kumar, Himanshu; Kawai, Taro; Akira, Shizuo

2009-04-28

Immunity against microbial pathogens primarily depends on the recognition of pathogen components by innate receptors expressed on immune and non-immune cells. Innate receptors are evolutionarily conserved germ-line-encoded proteins and include TLRs (Toll-like receptors), RLRs [RIG-I (retinoic acid-inducible gene-I)-like receptors] and NLRs (Nod-like receptors). These receptors recognize pathogens or pathogen-derived products in different cellular compartments, such as the plasma membrane, the endosomes or the cytoplasm, and induce the expression of cytokines, chemokines and co-stimulatory molecules to eliminate pathogens and instruct pathogen-specific adaptive immune responses. In the present review, we will discuss the recent progress in the study of pathogen recognition by TLRs, RLRs and NLRs and their signalling pathways.

Limitations of science and adapting to Nature

International Nuclear Information System (INIS)

Narasimhan, T N

2007-01-01

Historically, science has pursued a premise that Nature can be understood fully, its future predicted precisely, and its behavior controlled at will. However, emerging knowledge indicates that the nature of Earth and biological systems transcends the limits of science, questioning the premise of knowing, prediction, and control. This knowledge has led to the recognition that, for civilized human survival, technological society has to adapt to the constraints of these systems. Simultaneously, spurred by explosive developments in the understanding of materials (non-biological and biological), applied scientific research pursues a contrary goal of controlling the material world, with the promise of spectacular economic growth and human well-being. If adaptation to Nature is so important, why does applied research pursue a contrary course? Adapting to Nature requires a recognition of the limitations of science, and espousal of human values. Although the concept of adapting to Nature is accepted by some, especially conservation ecologists, such an acceptance may not exist in other fields. Also, in a world dominated by democratic ideals of freedom and liberty, the discipline required for adapting to Nature may often be overridden by competition among various segments of society to exercise their respective rights. In extreme cases of catastrophic failure of Earth or biological systems, the imperative for adaptation may fall victim to instinct for survival. In essence, although adequate scientific know-how and technological competence exists to facilitate adaptation to Nature, choosing between that and the pursuit of controlling Nature entails human judgment. What that choice may be when humans have to survive under severe environmental stress cannot be predicted

Limitations of science and adapting to Nature

Energy Technology Data Exchange (ETDEWEB)

Narasimhan, T N [Department of Materials Science and Engineering, Department of Environmental Science, Policy and Management, 210 Hearst Memorial Mining Building, University of California, Berkeley, CA 94720-1760 (United States)

2007-07-15

Historically, science has pursued a premise that Nature can be understood fully, its future predicted precisely, and its behavior controlled at will. However, emerging knowledge indicates that the nature of Earth and biological systems transcends the limits of science, questioning the premise of knowing, prediction, and control. This knowledge has led to the recognition that, for civilized human survival, technological society has to adapt to the constraints of these systems. Simultaneously, spurred by explosive developments in the understanding of materials (non-biological and biological), applied scientific research pursues a contrary goal of controlling the material world, with the promise of spectacular economic growth and human well-being. If adaptation to Nature is so important, why does applied research pursue a contrary course? Adapting to Nature requires a recognition of the limitations of science, and espousal of human values. Although the concept of adapting to Nature is accepted by some, especially conservation ecologists, such an acceptance may not exist in other fields. Also, in a world dominated by democratic ideals of freedom and liberty, the discipline required for adapting to Nature may often be overridden by competition among various segments of society to exercise their respective rights. In extreme cases of catastrophic failure of Earth or biological systems, the imperative for adaptation may fall victim to instinct for survival. In essence, although adequate scientific know-how and technological competence exists to facilitate adaptation to Nature, choosing between that and the pursuit of controlling Nature entails human judgment. What that choice may be when humans have to survive under severe environmental stress cannot be predicted.

Neuro System Structure for Vehicle Recognition and Count in Floating Bridge Specific Conditions

Directory of Open Access Journals (Sweden)

Slobodan Beroš

2012-10-01

Full Text Available The paper presents the research of the sophisticated vehiclerecognition and count system based on the application of theneural network. The basic elements of neural network andadaptive logic network for object recognition are discussed. Theadaptive logic network solution ability based on simple digitalcircuits as crucial in real-time applications is pointed out. Thesimulation based on the use of reduced high level noise pictureand a tree 2. 7. software have shown excellent results. The consideredand simulated adaptive neural network based systemwith its good recognition and convergence is a useful real-timesolution for vehicle recognition and count in the floating bridgesevere conditions.

Applications of evolutionary computation in image processing and pattern recognition

CERN Document Server

Cuevas, Erik; Perez-Cisneros, Marco

2016-01-01

This book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an...

Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

Directory of Open Access Journals (Sweden)

Min Peng

2017-10-01

Full Text Available Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.

Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition.

Science.gov (United States)

Peng, Min; Wang, Chongyang; Chen, Tong; Liu, Guangyuan; Fu, Xiaolan

2017-01-01

Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.

Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

Science.gov (United States)

Peng, Min; Wang, Chongyang; Chen, Tong; Liu, Guangyuan; Fu, Xiaolan

2017-01-01

Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve. PMID:29081753

Surveillance of a nuclear reactor core by use of a pattern recognition method

International Nuclear Information System (INIS)

Invernizzi, Michel.

1982-07-01

A pattern recognition system is described for the surveillance of a PWR reactor. This report contains four chapters. The first one succinctly deals with statistical pattern recognition principles. In the second chapter we show how a surveillance problem may be treated by pattern recognition and we present methods for surveillances (detection of abnormalities), controls (kind of running recognition) and diagnotics (kind of abnormality recognition). The third chapter shows a surveillance method of a nuclear plant. The signals used are the neutron noise observations made by the ionization chambers inserted in the reactor. Abnormality is defined in opposition with the training set witch is supposed to be an exhaustive summary of normality. In the fourth chapter we propose a scheme for an adaptative recognition and a method based on classes modelisations by hyper-spheres. This method has been tested on simulated training sets in two-dimensional feature spaces. It gives solutions to problems of non-linear separability [fr

Stimulus effects and the mediation of recognition memory.

Science.gov (United States)

McAdoo, Ryan M; Key, Kylie N; Gronlund, Scott D

2018-04-19

Two broad approaches characterize the type of evidence that mediates recognition memory: discrete state and continuous. Discrete-state models posit a thresholded memory process that provides accurate information about an item (it is detected) or, failing that, no mnemonic information about the item. Continuous models, in contrast, posit the existence of graded mnemonic information about an item. Evidence favoring 1 approach over the other has been mixed, suggesting the possibility that the mediation of recognition memory may be adaptable and influenced by other factors. We tested this possibility with 2 experiments that varied the semantic similarity of word targets and fillers. Experiment 1, which used semantically similar fillers, displayed evidence of continuous mediation (contrary to Kellen & Klauer, 2015), whereas Experiment 2, which used semantically dissimilar fillers, displayed evidence of discrete mediation. The results have implications for basic theories of recognition memory, as well as for theories of applied domains like eyewitness identification. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Social capital, conflict, and adaptive collaborative governance

NARCIS (Netherlands)

McDougall, C.L.; Ram Banjade, Mani

2015-01-01

Previously lineal and centralized natural resource management and development paradigms have shifted toward the recognition of complexity and dynamism of social-ecological systems, and toward more adaptive, decentralized, and collaborative models. However, certain messy and surprising dynamics

Evaluation of Adaptive Noise Management Technologies for School-Age Children with Hearing Loss.

Science.gov (United States)

Wolfe, Jace; Duke, Mila; Schafer, Erin; Jones, Christine; Rakita, Lori

2017-05-01

Children with hearing loss experience significant difficulty understanding speech in noisy and reverberant situations. Adaptive noise management technologies, such as fully adaptive directional microphones and digital noise reduction, have the potential to improve communication in noise for children with hearing aids. However, there are no published studies evaluating the potential benefits children receive from the use of adaptive noise management technologies in simulated real-world environments as well as in daily situations. The objective of this study was to compare speech recognition, speech intelligibility ratings (SIRs), and sound preferences of children using hearing aids equipped with and without adaptive noise management technologies. A single-group, repeated measures design was used to evaluate performance differences obtained in four simulated environments. In each simulated environment, participants were tested in a basic listening program with minimal noise management features, a manual program designed for that scene, and the hearing instruments' adaptive operating system that steered hearing instrument parameterization based on the characteristics of the environment. Twelve children with mild to moderately severe sensorineural hearing loss. Speech recognition and SIRs were evaluated in three hearing aid programs with and without noise management technologies across two different test sessions and various listening environments. Also, the participants' perceptual hearing performance in daily real-world listening situations with two of the hearing aid programs was evaluated during a four- to six-week field trial that took place between the two laboratory sessions. On average, the use of adaptive noise management technology improved sentence recognition in noise for speech presented in front of the participant but resulted in a decrement in performance for signals arriving from behind when the participant was facing forward. However, the improvement

A Spatial Frequency Account of the Detriment that Local Processing of Navon Letters Has on Face Recognition

Science.gov (United States)

Hills, Peter J.; Lewis, Michael B.

2009-01-01

Five minutes of processing the local features of a Navon letter causes a detriment in subsequent face-recognition performance (Macrae & Lewis, 2002). We hypothesize a perceptual after effect explanation of this effect in which face recognition is less accurate after adapting to high-spatial frequencies at high contrasts. Five experiments were…

The Development of Spatial Frequency Biases in Face Recognition

Science.gov (United States)

Leonard, Hayley C.; Karmiloff-Smith, Annette; Johnson, Mark H.

2010-01-01

Previous research has suggested that a mid-band of spatial frequencies is critical to face recognition in adults, but few studies have explored the development of this bias in children. We present a paradigm adapted from the adult literature to test spatial frequency biases throughout development. Faces were presented on a screen with particular…

Neural Mechanisms and Information Processing in Recognition Systems

Directory of Open Access Journals (Sweden)

Mamiko Ozaki

2014-10-01

Full Text Available Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold.

8 CFR 1292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

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2010-01-01

...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 1292.2...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization...

Threshold models of recognition and the recognition heuristic

Directory of Open Access Journals (Sweden)

Edgar Erdfelder

2011-02-01

Full Text Available According to the recognition heuristic (RH theory, decisions follow the recognition principle: Given a high validity of the recognition cue, people should prefer recognized choice options compared to unrecognized ones. Assuming that the memory strength of choice options is strongly correlated with both the choice criterion and recognition judgments, the RH is a reasonable strategy that approximates optimal decisions with a minimum of cognitive effort (Davis-Stober, Dana, and Budescu, 2010. However, theories of recognition memory are not generally compatible with this assumption. For example, some threshold models of recognition presume that recognition judgments can arise from two types of cognitive states: (1 certainty states in which judgments are almost perfectly correlated with memory strength and (2 uncertainty states in which recognition judgments reflect guessing rather than differences in memory strength. We report an experiment designed to test the prediction that the RH applies to certainty states only. Our results show that memory states rather than recognition judgments affect use of recognition information in binary decisions.

View-invariant gait recognition method by three-dimensional convolutional neural network

Science.gov (United States)

Xing, Weiwei; Li, Ying; Zhang, Shunli

2018-01-01

Gait as an important biometric feature can identify a human at a long distance. View change is one of the most challenging factors for gait recognition. To address the cross view issues in gait recognition, we propose a view-invariant gait recognition method by three-dimensional (3-D) convolutional neural network. First, 3-D convolutional neural network (3DCNN) is introduced to learn view-invariant feature, which can capture the spatial information and temporal information simultaneously on normalized silhouette sequences. Second, a network training method based on cross-domain transfer learning is proposed to solve the problem of the limited gait training samples. We choose the C3D as the basic model, which is pretrained on the Sports-1M and then fine-tune C3D model to adapt gait recognition. In the recognition stage, we use the fine-tuned model to extract gait features and use Euclidean distance to measure the similarity of gait sequences. Sufficient experiments are carried out on the CASIA-B dataset and the experimental results demonstrate that our method outperforms many other methods.

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

Science.gov (United States)

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.

Adaptive Moving Object Tracking Integrating Neural Networks And Intelligent Processing

Science.gov (United States)

Lee, James S. J.; Nguyen, Dziem D.; Lin, C.

1989-03-01

A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.

49 CFR 591.6 - Documents accompanying declarations.

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2010-10-01

... SAFETY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) IMPORTATION OF VEHICLES AND EQUIPMENT SUBJECT TO FEDERAL SAFETY, BUMPER AND THEFT PREVENTION STANDARDS § 591.6 Documents accompanying... be accompanied by a statement substantiating that the vehicle was not manufactured for use on the...

Towards Adaptive Spoken Dialog Systems

CERN Document Server

Schmitt, Alexander

2013-01-01

In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recognition of negative dialog patterns in Spoken Dialog Systems (SDS). The presented stochastic methods allow a flexible, portable and  accurate use.  Beginning with the foundations of machine learning and pattern recognition, this monograph examines how frequently users show negative emotions in spoken dialog systems and develop novel approaches to speech-based emotion recognition using hybrid approach to model emotions. The authors make use of statistical methods based on acoustic, linguistic and contextual features to examine the relationship between the interaction flow and the occurrence of emotions using non-acted  recordings several thousand real users from commercial and non-commercial SDS. Additionally, the authors present novel statistical methods that spot problems within a dialog based on interaction patterns. The approaches enable future SDS to offer m...

Foreign Language Analysis and Recognition (FLARe) Initial Progress

Science.gov (United States)

2012-11-29

speaker diarization code was optimized to execute faster and yield a lower Diarization Error Rate (DER). Minimizing the file read and write operations...PLP features were calculated using the same procedure described in Section 2.1.1 A second set of models was estimated that include Speaker Adaptive...non-SAT HMMs. Constrained Maximum Likelihood Linear Regression (CMLLR) transforms were estimated for each speaker , and recognition lattices were

Semisupervised kernel marginal Fisher analysis for face recognition.

Science.gov (United States)

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.

Reactor noise analysis by statistical pattern recognition methods

International Nuclear Information System (INIS)

Howington, L.C.; Gonzalez, R.C.

1976-01-01

A multivariate statistical pattern recognition system for reactor noise analysis is presented. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, updating, and data compacting capabilities. System design emphasizes control of the false-alarm rate. Its abilities to learn normal patterns, to recognize deviations from these patterns, and to reduce the dimensionality of data with minimum error were evaluated by experiments at the Oak Ridge National Laboratory (ORNL) High-Flux Isotope Reactor. Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the pattern recognition system

Shape adaptive, robust iris feature extraction from noisy iris images.

Science.gov (United States)

Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

2013-10-01

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate.

9 CFR 93.409 - Articles accompanying ruminants.

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2010-01-01

... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Articles accompanying ruminants. 93.409 Section 93.409 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE, DEPARTMENT...; REQUIREMENTS FOR MEANS OF CONVEYANCE AND SHIPPING CONTAINERS Ruminants § 93.409 Articles accompanying ruminants...

Stages of processing in associative recognition: evidence from behavior, EEG, and classification.

Science.gov (United States)

Borst, Jelmer P; Schneider, Darryl W; Walsh, Matthew M; Anderson, John R

2013-12-01

In this study, we investigated the stages of information processing in associative recognition. We recorded EEG data while participants performed an associative recognition task that involved manipulations of word length, associative fan, and probe type, which were hypothesized to affect the perceptual encoding, retrieval, and decision stages of the recognition task, respectively. Analyses of the behavioral and EEG data, supplemented with classification of the EEG data using machine-learning techniques, provided evidence that generally supported the sequence of stages assumed by a computational model developed in the Adaptive Control of Thought-Rational cognitive architecture. However, the results suggested a more complex relationship between memory retrieval and decision-making than assumed by the model. Implications of the results for modeling associative recognition are discussed. The study illustrates how a classifier approach, in combination with focused manipulations, can be used to investigate the timing of processing stages.

A neuroanatomical predictor of mirror self-recognition in chimpanzees.

Science.gov (United States)

Hecht, E E; Mahovetz, L M; Preuss, T M; Hopkins, W D

2017-01-01

The ability to recognize one's own reflection is shared by humans and only a few other species, including chimpanzees. However, this ability is highly variable across individual chimpanzees. In humans, self-recognition involves a distributed, right-lateralized network including frontal and parietal regions involved in the production and perception of action. The superior longitudinal fasciculus (SLF) is a system of white matter tracts linking these frontal and parietal regions. The current study measured mirror self-recognition (MSR) and SLF anatomy in 60 chimpanzees using diffusion tensor imaging. Successful self-recognition was associated with greater rightward asymmetry in the white matter of SLFII and SLFIII, and in SLFIII's gray matter terminations in Broca's area. We observed a visible progression of SLFIII's prefrontal extension in apes that show negative, ambiguous, and compelling evidence of MSR. Notably, SLFIII's terminations in Broca's area are not right-lateralized or particularly pronounced at the population level in chimpanzees, as they are in humans. Thus, chimpanzees with more human-like behavior show more human-like SLFIII connectivity. These results suggest that self-recognition may have co-emerged with adaptations to frontoparietal circuitry. © The Author (2016). Published by Oxford University Press.

Gait Recognition Based on Outermost Contour

Directory of Open Access Journals (Sweden)

Lili Liu

2011-10-01

Full Text Available Gait recognition aims to identify people by the way they walk. In this paper, a simple but e ective gait recognition method based on Outermost Contour is proposed. For each gait image sequence, an adaptive silhouette extraction algorithm is firstly used to segment the frames of the sequence and a series of postprocessing is applied to obtain the normalized silhouette images with less noise. Then a novel feature extraction method based on Outermost Contour is performed. Principal Component Analysis (PCA is adopted to reduce the dimensionality of the distance signals derived from the Outermost Contours of silhouette images. Then Multiple Discriminant Analysis (MDA is used to optimize the separability of gait features belonging to di erent classes. Nearest Neighbor (NN classifier and Nearest Neighbor classifier with respect to class Exemplars (ENN are used to classify the final feature vectors produced by MDA. In order to verify the e ectiveness and robustness of our feature extraction algorithm, we also use two other classifiers: Backpropagation Neural Network (BPNN and Support Vector Machine (SVM for recognition. Experimental results on a gait database of 100 people show that the accuracy of using MDA, BPNN and SVM can achieve 97.67%, 94.33% and 94.67%, respectively.

16 CFR 1500.125 - Labeling requirements for accompanying literature.

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2010-01-01

... 16 Commercial Practices 2 2010-01-01 2010-01-01 false Labeling requirements for accompanying literature. 1500.125 Section 1500.125 Commercial Practices CONSUMER PRODUCT SAFETY COMMISSION FEDERAL... REGULATIONS § 1500.125 Labeling requirements for accompanying literature. When any accompanying literature...

9 CFR 93.508 - Articles accompanying swine.

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2010-01-01

... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Articles accompanying swine. 93.508 Section 93.508 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE, DEPARTMENT OF... FOR MEANS OF CONVEYANCE AND SHIPPING CONTAINERS Swine § 93.508 Articles accompanying swine. No litter...

Pattern recognition

CERN Document Server

Theodoridis, Sergios

2003-01-01

Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to ""learn"" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10

CRISPR-Cas: Adapting to change.

Science.gov (United States)

Jackson, Simon A; McKenzie, Rebecca E; Fagerlund, Robert D; Kieper, Sebastian N; Fineran, Peter C; Brouns, Stan J J

2017-04-07

Bacteria and archaea are engaged in a constant arms race to defend against the ever-present threats of viruses and invasion by mobile genetic elements. The most flexible weapons in the prokaryotic defense arsenal are the CRISPR-Cas adaptive immune systems. These systems are capable of selective identification and neutralization of foreign DNA and/or RNA. CRISPR-Cas systems rely on stored genetic memories to facilitate target recognition. Thus, to keep pace with a changing pool of hostile invaders, the CRISPR memory banks must be regularly updated with new information through a process termed CRISPR adaptation. In this Review, we outline the recent advances in our understanding of the molecular mechanisms governing CRISPR adaptation. Specifically, the conserved protein machinery Cas1-Cas2 is the cornerstone of adaptive immunity in a range of diverse CRISPR-Cas systems. Copyright © 2017, American Association for the Advancement of Science.

The many shades of prion strain adaptation.

Science.gov (United States)

Baskakov, Ilia V

2014-01-01

In several recent studies transmissible prion disease was induced in animals by inoculation with recombinant prion protein amyloid fibrils produced in vitro. Serial transmission of amyloid fibrils gave rise to a new class of prion strains of synthetic origin. Gradual transformation of disease phenotypes and PrP(Sc) properties was observed during serial transmission of synthetic prions, a process that resembled the phenomenon of prion strain adaptation. The current article discusses the remarkable parallels between phenomena of prion strain adaptation that accompanies cross-species transmission and the evolution of synthetic prions occurring within the same host. Two alternative mechanisms underlying prion strain adaptation and synthetic strain evolution are discussed. The current article highlights the complexity of the prion transmission barrier and strain adaptation and proposes that the phenomenon of prion adaptation is more common than previously thought.

9 CFR 93.307 - Articles accompanying horses.

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2010-01-01

... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Articles accompanying horses. 93.307... FOR MEANS OF CONVEYANCE AND SHIPPING CONTAINERS Horses § 93.307 Articles accompanying horses. No..., blankets, or other things used for or about horses governed by the regulations this part, shall be landed...

A Dynamic Time Warping Approach to Real-Time Activity Recognition for Food Preparation

Science.gov (United States)

Pham, Cuong; Plötz, Thomas; Olivier, Patrick

We present a dynamic time warping based activity recognition system for the analysis of low-level food preparation activities. Accelerometers embedded into kitchen utensils provide continuous sensor data streams while people are using them for cooking. The recognition framework analyzes frames of contiguous sensor readings in real-time with low latency. It thereby adapts to the idiosyncrasies of utensil use by automatically maintaining a template database. We demonstrate the effectiveness of the classification approach by a number of real-world practical experiments on a publically available dataset. The adaptive system shows superior performance compared to a static recognizer. Furthermore, we demonstrate the generalization capabilities of the system by gradually reducing the amount of training samples. The system achieves excellent classification results even if only a small number of training samples is available, which is especially relevant for real-world scenarios.

Authentication: From Passwords to Biometrics: An implementation of a speaker recognition system on Android

OpenAIRE

Heimark, Erlend

2012-01-01

We implement a biometric authentication system on the Android platform, which is based on text-dependent speaker recognition. The Android version used in the application is Android 4.0. The application makes use of the Modular Audio Recognition Framework, from which many of the algorithms are adapted in the processes of preprocessing and feature extraction. In addition, we employ the Dynamic Time Warping (DTW) algorithm for the comparison of different voice features. A training procedure is i...

State recognition of the viscoelastic sandwich structure based on the adaptive redundant second generation wavelet packet transform, permutation entropy and the wavelet support vector machine

International Nuclear Information System (INIS)

Qu, Jinxiu; Zhang, Zhousuo; Guo, Ting; Luo, Xue; Sun, Chuang; Li, Bing; Wen, Jinpeng

2014-01-01

The viscoelastic sandwich structure is widely used in mechanical equipment, yet the structure always suffers from damage during long-term service. Therefore, state recognition of the viscoelastic sandwich structure is very necessary for monitoring structural health states and keeping the equipment running with high reliability. Through the analysis of vibration response signals, this paper presents a novel method for this task based on the adaptive redundant second generation wavelet packet transform (ARSGWPT), permutation entropy (PE) and the wavelet support vector machine (WSVM). In order to tackle the non-linearity existing in the structure vibration response, the PE is introduced to reveal the state changes of the structure. In the case of complex non-stationary vibration response signals, in order to obtain more effective information regarding the structural health states, the ARSGWPT, which can adaptively match the characteristics of a given signal, is proposed to process the vibration response signals, and then multiple PE features are extracted from the resultant wavelet packet coefficients. The WSVM, which can benefit from the conventional SVM as well as wavelet theory, is applied to classify the various structural states automatically. In this study, to achieve accurate and automated state recognition, the ARSGWPT, PE and WSVM are combined for signal processing, feature extraction and state classification, respectively. To demonstrate the effectiveness of the proposed method, a typical viscoelastic sandwich structure is designed, and the different degrees of preload on the structure are used to characterize the various looseness states. The test results show that the proposed method can reliably recognize the different looseness states of the viscoelastic sandwich structure, and the WSVM can achieve a better classification performance than the conventional SVM. Moreover, the superiority of the proposed ARSGWPT in processing the complex vibration response

EEG signatures accompanying auditory figure-ground segregation.

Science.gov (United States)

Tóth, Brigitta; Kocsis, Zsuzsanna; Háden, Gábor P; Szerafin, Ágnes; Shinn-Cunningham, Barbara G; Winkler, István

2016-11-01

In everyday acoustic scenes, figure-ground segregation typically requires one to group together sound elements over both time and frequency. Electroencephalogram was recorded while listeners detected repeating tonal complexes composed of a random set of pure tones within stimuli consisting of randomly varying tonal elements. The repeating pattern was perceived as a figure over the randomly changing background. It was found that detection performance improved both as the number of pure tones making up each repeated complex (figure coherence) increased, and as the number of repeated complexes (duration) increased - i.e., detection was easier when either the spectral or temporal structure of the figure was enhanced. Figure detection was accompanied by the elicitation of the object related negativity (ORN) and the P400 event-related potentials (ERPs), which have been previously shown to be evoked by the presence of two concurrent sounds. Both ERP components had generators within and outside of auditory cortex. The amplitudes of the ORN and the P400 increased with both figure coherence and figure duration. However, only the P400 amplitude correlated with detection performance. These results suggest that 1) the ORN and P400 reflect processes involved in detecting the emergence of a new auditory object in the presence of other concurrent auditory objects; 2) the ORN corresponds to the likelihood of the presence of two or more concurrent sound objects, whereas the P400 reflects the perceptual recognition of the presence of multiple auditory objects and/or preparation for reporting the detection of a target object. Copyright © 2016. Published by Elsevier Inc.

EEG signatures accompanying auditory figure-ground segregation

Science.gov (United States)

Tóth, Brigitta; Kocsis, Zsuzsanna; Háden, Gábor P.; Szerafin, Ágnes; Shinn-Cunningham, Barbara; Winkler, István

2017-01-01

In everyday acoustic scenes, figure-ground segregation typically requires one to group together sound elements over both time and frequency. Electroencephalogram was recorded while listeners detected repeating tonal complexes composed of a random set of pure tones within stimuli consisting of randomly varying tonal elements. The repeating pattern was perceived as a figure over the randomly changing background. It was found that detection performance improved both as the number of pure tones making up each repeated complex (figure coherence) increased, and as the number of repeated complexes (duration) increased – i.e., detection was easier when either the spectral or temporal structure of the figure was enhanced. Figure detection was accompanied by the elicitation of the object related negativity (ORN) and the P400 event-related potentials (ERPs), which have been previously shown to be evoked by the presence of two concurrent sounds. Both ERP components had generators within and outside of auditory cortex. The amplitudes of the ORN and the P400 increased with both figure coherence and figure duration. However, only the P400 amplitude correlated with detection performance. These results suggest that 1) the ORN and P400 reflect processes involved in detecting the emergence of a new auditory object in the presence of other concurrent auditory objects; 2) the ORN corresponds to the likelihood of the presence of two or more concurrent sound objects, whereas the P400 reflects the perceptual recognition of the presence of multiple auditory objects and/or preparation for reporting the detection of a target object. PMID:27421185

Information adaptation the interplay between Shannon information and semantic information in cognition

CERN Document Server

Haken, Hermann

2015-01-01

This monograph demonstrates the interplay between Shannon information and semantic information in cognition. It shows that Shannon’s information acts as driving force for the formation of semantic information; and vice versa, namely, that semantic information participates in the formation of Shannonian information. The authors show that in cognition, Shannonian and semantic information are interrelated as two aspects of a cognitive process termed as information adaptation. In the latter the mind/brain adapts to the environment by the deflating and/or inflating of the information conveyed by the environment. In the process of information adaptation, quantitative variations in Shannon’s information entail different meanings while different meanings affect the quantity of information. The book illustrates the above conceptually and mathematically by reference to three cognitive processes: pattern recognition, face learning and the recognition of a moving object.

8 CFR 292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

Science.gov (United States)

2010-01-01

...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 292.2...; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization established in the United...

Adaptive Matrices and Filters for Color Texture Classification

NARCIS (Netherlands)

Giotis, Ioannis; Bunte, Kerstin; Petkov, Nicolai; Biehl, Michael

In this paper we introduce an integrative approach towards color texture classification and recognition using a supervised learning framework. Our approach is based on Generalized Learning Vector Quantization (GLVQ), extended by an adaptive distance measure, which is defined in the Fourier domain,

Blind Recognition of Binary BCH Codes for Cognitive Radios

Directory of Open Access Journals (Sweden)

Jing Zhou

2016-01-01

Full Text Available A novel algorithm of blind recognition of Bose-Chaudhuri-Hocquenghem (BCH codes is proposed to solve the problem of Adaptive Coding and Modulation (ACM in cognitive radio systems. The recognition algorithm is based on soft decision situations. The code length is firstly estimated by comparing the Log-Likelihood Ratios (LLRs of the syndromes, which are obtained according to the minimum binary parity check matrixes of different primitive polynomials. After that, by comparing the LLRs of different minimum polynomials, the code roots and generator polynomial are reconstructed. When comparing with some previous approaches, our algorithm yields better performance even on very low Signal-Noise-Ratios (SNRs with lower calculation complexity. Simulation results show the efficiency of the proposed algorithm.

Using self-organizing maps adaptive resonance theory (CARTMAP) for manufacturing feature recognition

Science.gov (United States)

Yu, Jason S.; Dagli, Cihan H.

1993-10-01

The invariant image preprocessing of moment invariants generates an invariant representation of object features which are insensitive to position, orientation, size, illusion, and contrast change. In this study ARTMAP is used for 3-D object recognition of manufacturing parts through these invariant characteristics. The analog of moment invariants created through the image preprocessing is interpreted by a binary code which is used to predict the manufacturing part through ARTMAP.

Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods.

Science.gov (United States)

Arcos-García, Álvaro; Álvarez-García, Juan A; Soria-Morillo, Luis M

2018-03-01

This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements. Copyright © 2018 Elsevier Ltd. All rights reserved.

Structural pattern recognition methods based on string comparison for fusion databases

International Nuclear Information System (INIS)

Dormido-Canto, S.; Farias, G.; Dormido, R.; Vega, J.; Sanchez, J.; Duro, N.; Vargas, H.; Ratta, G.; Pereira, A.; Portas, A.

2008-01-01

Databases for fusion experiments are designed to store several million waveforms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques allow the identification of similar plasma behaviours. This article is focused on the comparison of structural pattern recognition methods. A pattern can be composed of simpler sub-patterns, where the most elementary sub-patterns are known as primitives. Selection of primitives is an essential issue in structural pattern recognition methods, because they determine what types of structural components can be constructed. However, it should be noted that there is not a general solution to extract structural features (primitives) from data. So, four different ways to compute the primitives of plasma waveforms are compared: (1) constant length primitives, (2) adaptive length primitives, (3) concavity method and (4) concavity method for noisy signals. Each method defines a code alphabet and, in this way, the pattern recognition problem is carried out via string comparisons. Results of the four methods with the TJ-II stellarator databases will be discussed

Structural pattern recognition methods based on string comparison for fusion databases

Energy Technology Data Exchange (ETDEWEB)

Dormido-Canto, S. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain)], E-mail: sebas@dia.uned.es; Farias, G.; Dormido, R. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, 28040, Madrid (Spain); Sanchez, J.; Duro, N.; Vargas, H. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain); Ratta, G.; Pereira, A.; Portas, A. [Asociacion EURATOM/CIEMAT para Fusion, 28040, Madrid (Spain)

2008-04-15

Databases for fusion experiments are designed to store several million waveforms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques allow the identification of similar plasma behaviours. This article is focused on the comparison of structural pattern recognition methods. A pattern can be composed of simpler sub-patterns, where the most elementary sub-patterns are known as primitives. Selection of primitives is an essential issue in structural pattern recognition methods, because they determine what types of structural components can be constructed. However, it should be noted that there is not a general solution to extract structural features (primitives) from data. So, four different ways to compute the primitives of plasma waveforms are compared: (1) constant length primitives, (2) adaptive length primitives, (3) concavity method and (4) concavity method for noisy signals. Each method defines a code alphabet and, in this way, the pattern recognition problem is carried out via string comparisons. Results of the four methods with the TJ-II stellarator databases will be discussed.

Radio-adaptive response

International Nuclear Information System (INIS)

Ikushima, T.

1992-01-01

An adaptive response to radiation stress was found as a suppressed induction of chromosomal damage including micronuclei and sister chromatid exchanges in cultured Chinese hamster V79 cells pre-exposed to very low doses of ionizing radiations. The mechanism underlying this novel chromosomal response, called 'radio-adaptive response (RAR)' has been studied progressively. The following results were obtained in recent experiments. 1. Low doses of β-rays from tritiated water (HTO) as well as tritium-thymidine can cause RAR. 2. Thermal neutrons, a high LET radiation, can not act as tritium β-rays or γ-rays. 3. The RAR expression is suppressed not only by the treatment with an inhibitor of protein synthesis but also by RNA synthesis inhibition. 4. Several proteins are newly synthesized concurrently with the RAR expression after the adapting doses, viewed by two-dimensional electrophoresis of cellular proteins. These results suggests that the RAR might be a cellular stress response to a signal produced preferentially by very low doses of low LET radiation under restricted conditions, accompany the inducible specific gene expression. (author)

Speech recognition: impact on workflow and report availability

International Nuclear Information System (INIS)

Glaser, C.; Trumm, C.; Nissen-Meyer, S.; Francke, M.; Kuettner, B.; Reiser, M.

2005-01-01

With ongoing technical refinements speech recognition systems (SRS) are becoming an increasingly attractive alternative to traditional methods of preparing and transcribing medical reports. The two main components of any SRS are the acoustic model and the language model. Features of modern SRS with continuous speech recognition are macros with individually definable texts and report templates as well as the option to navigate in a text or to control SRS or RIS functions by speech recognition. The best benefit from SRS can be obtained if it is integrated into a RIS/RIS-PACS installation. Report availability and time efficiency of the reporting process (related to recognition rate, time expenditure for editing and correcting a report) are the principal determinants of the clinical performance of any SRS. For practical purposes the recognition rate is estimated by the error rate (unit ''word''). Error rates range from 4 to 28%. Roughly 20% of them are errors in the vocabulary which may result in clinically relevant misinterpretation. It is thus mandatory to thoroughly correct any transcribed text as well as to continuously train and adapt the SRS vocabulary. The implementation of SRS dramatically improves report availability. This is most pronounced for CT and CR. However, the individual time expenditure for (SRS-based) reporting increased by 20-25% (CR) and according to literature data there is an increase by 30% for CT and MRI. The extent to which the transcription staff profits from SRS depends largely on its qualification. Online dictation implies a workload shift from the transcription staff to the reporting radiologist. (orig.) [de

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

Science.gov (United States)

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.

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

Jing Chen

2015-12-01

Full Text Available 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.

Entropy in molecular recognition by proteins.

Science.gov (United States)

Caro, José A; Harpole, Kyle W; Kasinath, Vignesh; Lim, Jackwee; Granja, Jeffrey; Valentine, Kathleen G; Sharp, Kim A; Wand, A Joshua

2017-06-20

Molecular recognition by proteins is fundamental to molecular biology. Dissection of the thermodynamic energy terms governing protein-ligand interactions has proven difficult, with determination of entropic contributions being particularly elusive. NMR relaxation measurements have suggested that changes in protein conformational entropy can be quantitatively obtained through a dynamical proxy, but the generality of this relationship has not been shown. Twenty-eight protein-ligand complexes are used to show a quantitative relationship between measures of fast side-chain motion and the underlying conformational entropy. We find that the contribution of conformational entropy can range from favorable to unfavorable, which demonstrates the potential of this thermodynamic variable to modulate protein-ligand interactions. For about one-quarter of these complexes, the absence of conformational entropy would render the resulting affinity biologically meaningless. The dynamical proxy for conformational entropy or "entropy meter" also allows for refinement of the contributions of solvent entropy and the loss in rotational-translational entropy accompanying formation of high-affinity complexes. Furthermore, structure-based application of the approach can also provide insight into long-lived specific water-protein interactions that escape the generic treatments of solvent entropy based simply on changes in accessible surface area. These results provide a comprehensive and unified view of the general role of entropy in high-affinity molecular recognition by proteins.

Unaware person recognition from the body when face identification fails.

Science.gov (United States)

Rice, Allyson; Phillips, P Jonathon; Natu, Vaidehi; An, Xiaobo; O'Toole, Alice J

2013-11-01

How does one recognize a person when face identification fails? Here, we show that people rely on the body but are unaware of doing so. State-of-the-art face-recognition algorithms were used to select images of people with almost no useful identity information in the face. Recognition of the face alone in these cases was near chance level, but recognition of the person was accurate. Accuracy in identifying the person without the face was identical to that in identifying the whole person. Paradoxically, people reported relying heavily on facial features over noninternal face and body features in making their identity decisions. Eye movements indicated otherwise, with gaze duration and fixations shifting adaptively toward the body and away from the face when the body was a better indicator of identity than the face. This shift occurred with no cost to accuracy or response time. Human identity processing may be partially inaccessible to conscious awareness.

Adapted Physical Education and Therapeutic Recreation in Schools

Science.gov (United States)

Etzel-Wise, D; Mears, B

2004-01-01

Adapted physical education is a mandated service, whereas therapeutic recreation and traditional recreation are considered related services under the Individuals with Disabilities Education Act. In this article, the authors describe the distinctions between the services, recognition of need for referral, methods of assessment, sample…

Sensor agnostic object recognition using a map seeking circuit

Science.gov (United States)

Overman, Timothy L.; Hart, Michael

2012-05-01

Automatic object recognition capabilities are traditionally tuned to exploit the specific sensing modality they were designed to. Their successes (and shortcomings) are tied to object segmentation from the background, they typically require highly skilled personnel to train them, and they become cumbersome with the introduction of new objects. In this paper we describe a sensor independent algorithm based on the biologically inspired technology of map seeking circuits (MSC) which overcomes many of these obstacles. In particular, the MSC concept offers transparency in object recognition from a common interface to all sensor types, analogous to a USB device. It also provides a common core framework that is independent of the sensor and expandable to support high dimensionality decision spaces. Ease in training is assured by using commercially available 3D models from the video game community. The search time remains linear no matter how many objects are introduced, ensuring rapid object recognition. Here, we report results of an MSC algorithm applied to object recognition and pose estimation from high range resolution radar (1D), electrooptical imagery (2D), and LIDAR point clouds (3D) separately. By abstracting the sensor phenomenology from the underlying a prior knowledge base, MSC shows promise as an easily adaptable tool for incorporating additional sensor inputs.

Ignoring Memory Hints: The Stubborn Influence of Environmental Cues on Recognition Memory

Science.gov (United States)

Selmeczy, Diana; Dobbins, Ian G.

2017-01-01

Recognition judgments can benefit from the use of environmental cues that signal the general likelihood of encountering familiar versus unfamiliar stimuli. While incorporating such cues is often adaptive, there are circumstances (e.g., eyewitness testimony) in which observers should fully ignore environmental cues in order to preserve memory…

A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

Science.gov (United States)

Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748

Leadership as an Emergent Phenomenon: A Framework for Complexity and Adaptability

National Research Council Canada - National Science Library

Martinez, Sandra M

2008-01-01

The recognition by some military leaders about the need for a different paradigm of leadership that responds to requirements for adaptability in complex environments has not necessarily translated into action...

The Development of Adaptive Decision Making: Recognition-Based Inference in Children and Adolescents

Science.gov (United States)

Horn, Sebastian S.; Ruggeri, Azzurra; Pachur, Thorsten

2016-01-01

Judgments about objects in the world are often based on probabilistic information (or cues). A frugal judgment strategy that utilizes memory (i.e., the ability to discriminate between known and unknown objects) as a cue for inference is the recognition heuristic (RH). The usefulness of the RH depends on the structure of the environment,…

Spoken language identification system adaptation in under-resourced environments

CSIR Research Space (South Africa)

Kleynhans, N

2013-12-01

Full Text Available Speech Recognition (ASR) systems in the developing world is severely inhibited. Given that few task-specific corpora exist and speech technology systems perform poorly when deployed in a new environment, we investigate the use of acoustic model adaptation...

Character Recognition Using Genetically Trained Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

1998-10-01

Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

Action recognition using mined hierarchical compound features.

Science.gov (United States)

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical

Adapting Speech Recognition in Augmented Reality for Mobile Devices in Outdoor Environments

OpenAIRE

Pascoal, Rui; Ribeiro, Ricardo; Batista, Fernando; de Almeida, Ana

2017-01-01

This paper describes the process of integrating automatic speech recognition (ASR) into a mobile application and explores the benefits and challenges of integrating speech with augmented reality (AR) in outdoor environments. The augmented reality allows end-users to interact with the information displayed and perform tasks, while increasing the user’s perception about the real world by adding virtual information to it. Speech is the most natural way of communication: it allows hands-free inte...

Language Model Combination and Adaptation Using Weighted Finite State Transducers

Science.gov (United States)

Liu, X.; Gales, M. J. F.; Hieronymus, J. L.; Woodland, P. C.

2010-01-01

In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaption may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences

The accompanying adult: authority to give consent in the UK.

Science.gov (United States)

Lal, Seema Madhur Lata; Parekh, Susan; Mason, Carol; Roberts, Graham

2007-05-01

Children may be accompanied by various people when attending for dental treatment. Before treatment is started, there is a legal requirement that the operator obtain informed consent for the proposed procedure. In the case of minors, the person authorized to give consent (parental responsibility) is usually a parent. To ascertain if accompanying persons of children attending the Department of Paediatric Dentistry at the Eastman Dental Hospital, London were empowered to give consent for the child's dental treatment. A total of 250 accompanying persons of children attending were selected, over a 6-month period. A questionnaire was used to establish whether the accompanying person(s) were authorized to give consent. The study showed that 12% of accompanying persons had no legal authority to give consent for the child's dental treatment. Clinicians need to be aware of the status of persons accompanying children to ensure valid consent is obtained.

Motor cortical processing is causally involved in object recognition.

Science.gov (United States)

Decloe, Rebecca; Obhi, Sukhvinder S

2013-12-14

Motor activity during vicarious experience of actions is a widely reported and studied phenomenon, and motor system activity also accompanies observation of graspable objects in the absence of any actions. Such motor activity is thought to reflect simulation of the observed action, or preparation to interact with the object, respectively. Here, in an initial exploratory study, we ask whether motor activity during observation of object directed actions is involved in processes related to recognition of the object after initial exposure. Single pulse Transcranial Magnetic Stimulation (TMS) was applied over the thumb representation of the motor cortex, or over the vertex, during observation of a model thumb typing on a cell-phone, and performance on a phone recognition task at the end of the trial was assessed. Disrupting motor processing over the thumb representation 100 ms after the onset of the typing video impaired the ability to recognize the phone in the recognition test, whereas there was no such effect for TMS applied over the vertex and no TMS trials. Furthermore, this effect only manifested for videos observed from the first person perspective. In an additional control condition, there was no evidence for any effects of TMS to the thumb representation or vertex when observing and recognizing non-action related shape stimuli. Overall, these data provide evidence that motor cortical processing during observation of object-directed actions from a first person perspective is causally linked to the formation of enduring representations of objects-of-action.

Applying perceptual and adaptive learning techniques for teaching introductory histopathology

Directory of Open Access Journals (Sweden)

Sally Krasne

2013-01-01

Full Text Available Background: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive science have identified new approaches to address this problem. Methods: We adapted a new approach for enhancing pattern recognition of basic pathologic processes in skin histopathology images that utilizes perceptual learning techniques, allowing learners to see relevant structure in novel cases along with adaptive learning algorithms that space and sequence different categories (e.g. diagnoses that appear during a learning session based on each learner′s accuracy and response time (RT. We developed a perceptual and adaptive learning module (PALM that utilized 261 unique images of cell injury, inflammation, neoplasia, or normal histology at low and high magnification. Accuracy and RT were tracked and integrated into a "Score" that reflected students rapid recognition of the pathologies and pre- and post-tests were given to assess the effectiveness. Results: Accuracy, RT and Scores significantly improved from the pre- to post-test with Scores showing much greater improvement than accuracy alone. Delayed post-tests with previously unseen cases, given after 6-7 weeks, showed a decline in accuracy relative to the post-test for 1 st -year students, but not significantly so for 2 nd -year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1 st and 2 nd year students suggesting good retention of pattern recognition. Student evaluations were very favorable. Conclusion: A web-based learning module based on the principles of cognitive science showed an evidence for improved recognition of histopathology patterns by medical students.

Relative preservation of the recognition of positive facial expression "happiness" in Alzheimer disease.

Science.gov (United States)

Maki, Yohko; Yoshida, Hiroshi; Yamaguchi, Tomoharu; Yamaguchi, Haruyasu

2013-01-01

Positivity recognition bias has been reported for facial expression as well as memory and visual stimuli in aged individuals, whereas emotional facial recognition in Alzheimer disease (AD) patients is controversial, with possible involvement of confounding factors such as deficits in spatial processing of non-emotional facial features and in verbal processing to express emotions. Thus, we examined whether recognition of positive facial expressions was preserved in AD patients, by adapting a new method that eliminated the influences of these confounding factors. Sensitivity of six basic facial expressions (happiness, sadness, surprise, anger, disgust, and fear) was evaluated in 12 outpatients with mild AD, 17 aged normal controls (ANC), and 25 young normal controls (YNC). To eliminate the factors related to non-emotional facial features, averaged faces were prepared as stimuli. To eliminate the factors related to verbal processing, the participants were required to match the images of stimulus and answer, avoiding the use of verbal labels. In recognition of happiness, there was no difference in sensitivity between YNC and ANC, and between ANC and AD patients. AD patients were less sensitive than ANC in recognition of sadness, surprise, and anger. ANC were less sensitive than YNC in recognition of surprise, anger, and disgust. Within the AD patient group, sensitivity of happiness was significantly higher than those of the other five expressions. In AD patient, recognition of happiness was relatively preserved; recognition of happiness was most sensitive and was preserved against the influences of age and disease.

Conflict adaptation in patients diagnosed with schizophrenia.

Science.gov (United States)

Abrahamse, Elger; Ruitenberg, Marit; Boddewyn, Sarah; Oreel, Edith; de Schryver, Maarten; Morrens, Manuel; van Dijck, Jean-Philippe

2017-11-01

Cognitive control impairments may contribute strongly to the overall cognitive deficits observed in patients diagnosed with schizophrenia. In the current study we explore a specific cognitive control function referred to as conflict adaptation. Previous studies on conflict adaptation in schizophrenia showed equivocal results, and, moreover, were plagued by confounded research designs. Here we assessed for the first time conflict adaptation in schizophrenia with a design that avoided the major confounds of feature integration and stimulus-response contingency learning. Sixteen patients diagnosed with schizophrenia and sixteen healthy, matched controls performed a vocal Stroop task to determine the congruency sequence effect - a marker of conflict adaptation. A reliable congruency sequence effect was observed for both healthy controls and patients diagnosed with schizophrenia. These findings indicate that schizophrenia is not necessarily accompanied by impaired conflict adaptation. As schizophrenia has been related to abnormal functioning in core conflict adaptation areas such as anterior cingulate and dorsolateral prefrontal cortex, further research is required to better understand the precise impact of such abnormal brain functioning at the behavioral level. Copyright © 2017 Elsevier B.V. All rights reserved.

Delivering organisational adaptation through legislative mechanisms: Evidence from the Adaptation Reporting Power (Climate Change Act 2008).

Science.gov (United States)

Jude, S R; Drew, G H; Pollard, S J T; Rocks, S A; Jenkinson, K; Lamb, R

2017-01-01

There is increasing recognition that organisations, particularly in key infrastructure sectors, are potentially vulnerable to climate change and extreme weather events, and require organisational responses to ensure they are resilient and adaptive. However, detailed evidence of how adaptation is facilitated, implemented and reported, particularly through legislative mechanisms is lacking. The United Kingdom Climate Change Act (2008), introduced the Adaptation Reporting Power, enabling the Government to direct so-called reporting authorities to report their climate change risks and adaptation plans. We describe the authors' unique role and experience supporting the Department for Environment, Food and Rural Affairs (Defra) during the Adaptation Reporting Power's first round. An evaluation framework, used to review the adaptation reports, is presented alongside evidence on how the process provides new insights into adaptation activities and triggered organisational change in 78% of reporting authorities, including the embedding of climate risk and adaptation issues. The role of legislative mechanisms and risk-based approaches in driving and delivering adaptation is discussed alongside future research needs, including the development of organisational maturity models to determine resilient and well adapting organisations. The Adaptation Reporting Power process provides a basis for similar initiatives in other countries, although a clear engagement strategy to ensure buy-in to the process and research on its long-term legacy, including the potential merits of voluntary approaches, is required. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Channel Compensation for Speaker Recognition using MAP Adapted PLDA and Denoising DNNs

Science.gov (United States)

2016-06-21

05 Jabra Cellphone Earwrap Mic 06 Motorola Cellphone Earbud 07 Olympus Pearlcorder 08 Radio Shack Computer Desktop Mic Table 1: Mixer 1 and 2...EER and min DCF vs λ for 2cov map adapt PLDA the MAP adapted PLDA model using a λ of 0.5. The remain- ing rows demonstrate the impact of the feature...degrading perfor- mance on conversational telephone speech. To assess the per- formance impact of the denoising DNN on telephony data we evaluated the

Optical Pattern Recognition

Science.gov (United States)

Yu, Francis T. S.; Jutamulia, Suganda

2008-10-01

Contributors; Preface; 1. Pattern recognition with optics Francis T. S. Yu and Don A. Gregory; 2. Hybrid neural networks for nonlinear pattern recognition Taiwei Lu; 3. Wavelets, optics, and pattern recognition Yao Li and Yunglong Sheng; 4. Applications of the fractional Fourier transform to optical pattern recognition David Mendlovic, Zeev Zalesky and Haldum M. Oxaktas; 5. Optical implementation of mathematical morphology Tien-Hsin Chao; 6. Nonlinear optical correlators with improved discrimination capability for object location and recognition Leonid P. Yaroslavsky; 7. Distortion-invariant quadratic filters Gregory Gheen; 8. Composite filter synthesis as applied to pattern recognition Shizhou Yin and Guowen Lu; 9. Iterative procedures in electro-optical pattern recognition Joseph Shamir; 10. Optoelectronic hybrid system for three-dimensional object pattern recognition Guoguang Mu, Mingzhe Lu and Ying Sun; 11. Applications of photrefractive devices in optical pattern recognition Ziangyang Yang; 12. Optical pattern recognition with microlasers Eung-Gi Paek; 13. Optical properties and applications of bacteriorhodopsin Q. Wang Song and Yu-He Zhang; 14. Liquid-crystal spatial light modulators Aris Tanone and Suganda Jutamulia; 15. Representations of fully complex functions on real-time spatial light modulators Robert W. Cohn and Laurence G. Hassbrook; Index.

Recognition memory is improved by a structured temporal framework during encoding

Directory of Open Access Journals (Sweden)

Sathesan eThavabalasingam

2016-01-01

Full Text Available In order to function optimally within our environment, we continuously extract temporal patterns from our experiences and formulate expectations that facilitate adaptive behavior. Given that our memories are embedded within spatiotemporal contexts, an intriguing possibility is that mnemonic processes are sensitive to the temporal structure of events. To test this hypothesis, in a series of behavioral experiments we manipulated the regularity of interval durations at encoding to create temporally structured and unstructured frameworks. Our findings revealed enhanced recognition memory (d’ for stimuli that were explicitly encoded within a temporally structured versus unstructured framework. Encoding information within a temporally structured framework was also associated with a reduction in the negative effects of proactive interference and was linked to greater recollective recognition memory. Furthermore, rhythmic temporal structure was found to enhance recognition memory for incidentally encoded information. Collectively, these results support the possibility that we possess a greater capacity to learn and subsequently remember temporally structured information.

Climate change adaptation and social sciences

International Nuclear Information System (INIS)

Charles, L.

2013-01-01

Climate change subjects societies to a large range of uncertainties concerning the future and their development orientation. It came up as a scientific global problem, extended to political concerns first at a global and then national scales. Though it has long been the object of economic approaches which have notably contributed to its recognition, particularly the Stern Report, social sciences have hardly been mobilized as part of policies to counteract it. Social sciences strongly question the notion of climate change being built as a global scale transcendent phenomenon, analyzed by several authors. With the rise of adaptation policies, the question becomes even more important. Adaptation first comes up as a spontaneous behaviour, independent of policy, in close relationship to social dimensions as a basic way through which climate change is grasped collectively. Thus adaptation policies' social aspects need to be carefully worked in relation with more general goals for adaptation policies to be implemented efficiently, on the basis of wide interactions between local and global scales. (author)

A new selective developmental deficit: Impaired object recognition with normal face recognition.

Science.gov (United States)

Germine, Laura; Cashdollar, Nathan; Düzel, Emrah; Duchaine, Bradley

2011-05-01

Studies of developmental deficits in face recognition, or developmental prosopagnosia, have shown that individuals who have not suffered brain damage can show face recognition impairments coupled with normal object recognition (Duchaine and Nakayama, 2005; Duchaine et al., 2006; Nunn et al., 2001). However, no developmental cases with the opposite dissociation - normal face recognition with impaired object recognition - have been reported. The existence of a case of non-face developmental visual agnosia would indicate that the development of normal face recognition mechanisms does not rely on the development of normal object recognition mechanisms. To see whether a developmental variant of non-face visual object agnosia exists, we conducted a series of web-based object and face recognition tests to screen for individuals showing object recognition memory impairments but not face recognition impairments. Through this screening process, we identified AW, an otherwise normal 19-year-old female, who was then tested in the lab on face and object recognition tests. AW's performance was impaired in within-class visual recognition memory across six different visual categories (guns, horses, scenes, tools, doors, and cars). In contrast, she scored normally on seven tests of face recognition, tests of memory for two other object categories (houses and glasses), and tests of recall memory for visual shapes. Testing confirmed that her impairment was not related to a general deficit in lower-level perception, object perception, basic-level recognition, or memory. AW's results provide the first neuropsychological evidence that recognition memory for non-face visual object categories can be selectively impaired in individuals without brain damage or other memory impairment. These results indicate that the development of recognition memory for faces does not depend on intact object recognition memory and provide further evidence for category-specific dissociations in visual

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

Directory of Open Access Journals (Sweden)

Yuanshen Zhao

2016-01-01

Full Text Available Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion.

Science.gov (United States)

Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

2016-01-29

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.

Deep Belief Networks Based Toponym Recognition for Chinese Text

Directory of Open Access Journals (Sweden)

Shu Wang

2018-06-01

Full Text Available In Geographical Information Systems, geo-coding is used for the task of mapping from implicitly geo-referenced data to explicitly geo-referenced coordinates. At present, an enormous amount of implicitly geo-referenced information is hidden in unstructured text, e.g., Wikipedia, social data and news. Toponym recognition is the foundation of mining this useful geo-referenced information by identifying words as toponyms in text. In this paper, we propose an adapted toponym recognition approach based on deep belief network (DBN by exploring two key issues: word representation and model interpretation. A Skip-Gram model is used in the word representation process to represent words with contextual information that are ignored by current word representation models. We then determine the core hyper-parameters of the DBN model by illustrating the relationship between the performance and the hyper-parameters, e.g., vector dimensionality, DBN structures and probability thresholds. The experiments evaluate the performance of the Skip-Gram model implemented by the Word2Vec open-source tool, determine stable hyper-parameters and compare our approach with a conditional random field (CRF based approach. The experimental results show that the DBN model outperforms the CRF model with smaller corpus. When the corpus size is large enough, their statistical metrics become approaching. However, their recognition results express differences and complementarity on different kinds of toponyms. More importantly, combining their results can directly improve the performance of toponym recognition relative to their individual performances. It seems that the scale of the corpus has an obvious effect on the performance of toponym recognition. Generally, there is no adequate tagged corpus on specific toponym recognition tasks, especially in the era of Big Data. In conclusion, we believe that the DBN-based approach is a promising and powerful method to extract geo

Increased deficits in emotion recognition and regulation in children and adolescents with exogenous obesity.

Science.gov (United States)

Percinel, Ipek; Ozbaran, Burcu; Kose, Sezen; Simsek, Damla Goksen; Darcan, Sukran

2018-03-01

In this study we aimed to evaluate emotion recognition and emotion regulation skills of children with exogenous obesity between the ages of 11 and 18 years and compare them with healthy controls. The Schedule for Affective Disorders and Schizophrenia for School Aged Children was used for psychiatric evaluations. Emotion recognition skills were evaluated using Faces Test and Reading the Mind in the Eyes Test. The Difficulties in Emotions Regulation Scale was used for evaluating skills of emotion regulation. Children with obesity had lower scores on Faces Test and Reading the Mind in the Eyes Test, and experienced greater difficulty in emotional regulation skills. Improved understanding of emotional recognition and emotion regulation in young people with obesity may improve their social adaptation and help in the treatment of their disorder. To the best of our knowledge, this is the first study to evaluate both emotional recognition and emotion regulation functions in obese children and obese adolescents between 11 and 18 years of age.

The pattern recognition molecule ficolin-1 exhibits differential binding to lymphocyte subsets, providing a novel link between innate and adaptive immunity

DEFF Research Database (Denmark)

Genster, Ninette; Ma, Ying Jie; Munthe-Fog, Lea

2014-01-01

is unknown. Recognition of healthy host cells by a pattern recognition molecule constitutes a potential hazard to self cells and tissues, emphasizing the importance of further elucidating the reported self-recognition. In the current study we investigated the potential recognition of lymphocytes by ficolin-1...... and demonstrated that CD56(dim) NK-cells and both CD4(+) and CD8(+) subsets of activated T-cells were recognized by ficolin-1. In contrast we did not detect binding of ficolin-1 to CD56(bright) NK-cells, NKT-cells, resting T-cells or B-cells. Furthermore, we showed that the protein-lymphocyte interaction occurred...

Reflowing-driven paragraph recognition for electronic books in PDF

Science.gov (United States)

Fang, Jing; Tang, Zhi; Gao, Liangcai

2011-01-01

When reading electronic books on handheld devices, content sometimes should be reflowed and recomposed to adapt for small-screen mobile devices. According to people's reading practice, it is reasonable to reflow the text content based on paragraphs. Hence, this paper addresses the requirement and proposes a set of novel methods on paragraph recognition for electronic books in PDF. The proposed methods consist of three steps, namely, physical structure analysis, paragraph segmentation, and reading order detection. We make use of locally ordered property of PDF documents and layout style of books to improve traditional page recognition results. In addition, we employ the optimal matching of Bipartite Graph technology to detect paragraphs' reading order. Experiments show that our methods achieve high accuracy. It is noteworthy that, the research has been applied in a commercial software package for Chinese E-book production.

Manduca sexta recognition and resistance among allopolyploid Nicotiana host plants

OpenAIRE

Lou, Yonggen; Baldwin, Ian T.

2003-01-01

Allopolyploid speciation occurs instantly when the genomes of different species combine to produce self-fertile offspring and has played a central role in the evolution of higher plants, but its consequences for adaptive responses are unknown. We compare herbivore-recognition and -resistance responses of the diploid species and putative ancestral parent Nicotiana attenuata with those of the two derived allopolyploid species Nicotiana clevelandii and Nicotiana bigelovii. Manduca sexta larvae a...

Semantic Network Adaptation Based on QoS Pattern Recognition for Multimedia Streams

Science.gov (United States)

Exposito, Ernesto; Gineste, Mathieu; Lamolle, Myriam; Gomez, Jorge

This article proposes an ontology based pattern recognition methodology to compute and represent common QoS properties of the Application Data Units (ADU) of multimedia streams. The use of this ontology by mechanisms located at different layers of the communication architecture will allow implementing fine per-packet self-optimization of communication services regarding the actual application requirements. A case study showing how this methodology is used by error control mechanisms in the context of wireless networks is presented in order to demonstrate the feasibility and advantages of this approach.

Contribution to automatic speech recognition. Analysis of the direct acoustical signal. Recognition of isolated words and phoneme identification

International Nuclear Information System (INIS)

Dupeyrat, Benoit

1981-01-01

This report deals with the acoustical-phonetic step of the automatic recognition of the speech. The parameters used are the extrema of the acoustical signal (coded in amplitude and duration). This coding method, the properties of which are described, is simple and well adapted to a digital processing. The quality and the intelligibility of the coded signal after reconstruction are particularly satisfactory. An experiment for the automatic recognition of isolated words has been carried using this coding system. We have designed a filtering algorithm operating on the parameters of the coding. Thus the characteristics of the formants can be derived under certain conditions which are discussed. Using these characteristics the identification of a large part of the phonemes for a given speaker was achieved. Carrying on the studies has required the development of a particular methodology of real time processing which allowed immediate evaluation of the improvement of the programs. Such processing on temporal coding of the acoustical signal is extremely powerful and could represent, used in connection with other methods an efficient tool for the automatic processing of the speech.(author) [fr

Building social-ecological resilience through adaptive comanagement in the Cache River Watershed of southern Illinois

Science.gov (United States)

Kofi. Akamani

2014-01-01

There is growing recognition that the sustainable governance of water resources requires building social-ecological resilience against future surprises. Adaptive comanagement, a distinct institutional mechanism that combines the learning focus of adaptive management with the multilevel linkages of comanagement, has recently emerged as a promising mechanism for building...

Deep learning architecture for recognition of abnormal activities

Science.gov (United States)

Khatrouch, Marwa; Gnouma, Mariem; Ejbali, Ridha; Zaied, Mourad

2018-04-01

The video surveillance is one of the key areas in computer vision researches. The scientific challenge in this field involves the implementation of automatic systems to obtain detailed information about individuals and groups behaviors. In particular, the detection of abnormal movements of groups or individuals requires a fine analysis of frames in the video stream. In this article, we propose a new method to detect anomalies in crowded scenes. We try to categorize the video in a supervised mode accompanied by unsupervised learning using the principle of the autoencoder. In order to construct an informative concept for the recognition of these behaviors, we use a technique of representation based on the superposition of human silhouettes. The evaluation of the UMN dataset demonstrates the effectiveness of the proposed approach.

Speech Recognition for the iCub Platform

Directory of Open Access Journals (Sweden)

Bertrand Higy

2018-02-01

Full Text Available This paper describes open source software (available at https://github.com/robotology/natural-speech to build automatic speech recognition (ASR systems and run them within the YARP platform. The toolkit is designed (i to allow non-ASR experts to easily create their own ASR system and run it on iCub and (ii to build deep learning-based models specifically addressing the main challenges an ASR system faces in the context of verbal human–iCub interactions. The toolkit mostly consists of Python, C++ code and shell scripts integrated in YARP. As additional contribution, a second codebase (written in Matlab is provided for more expert ASR users who want to experiment with bio-inspired and developmental learning-inspired ASR systems. Specifically, we provide code for two distinct kinds of speech recognition: “articulatory” and “unsupervised” speech recognition. The first is largely inspired by influential neurobiological theories of speech perception which assume speech perception to be mediated by brain motor cortex activities. Our articulatory systems have been shown to outperform strong deep learning-based baselines. The second type of recognition systems, the “unsupervised” systems, do not use any supervised information (contrary to most ASR systems, including our articulatory systems. To some extent, they mimic an infant who has to discover the basic speech units of a language by herself. In addition, we provide resources consisting of pre-trained deep learning models for ASR, and a 2.5-h speech dataset of spoken commands, the VoCub dataset, which can be used to adapt an ASR system to the typical acoustic environments in which iCub operates.

One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM.

Science.gov (United States)

Rodriguez, Mario; Orrite, Carlos; Medrano, Carlos; Makris, Dimitrios

2016-05-10

This paper presents a novel activity class representation using a single sequence for training. The contribution of this representation lays on the ability to train an one-shot learning recognition system, useful in new scenarios where capturing and labeling sequences is expensive or impractical. The method uses a universal background model of local descriptors obtained from source databases available on-line and adapts it to a new sequence in the target scenario through a maximum a posteriori adaptation. Each activity sample is encoded in a sequence of normalized bag of features and modeled by a new hidden Markov model formulation, where the expectation-maximization algorithm for training is modified to deal with observations consisting in vectors in a unit simplex. Extensive experiments in recognition have been performed using one-shot learning over the public datasets Weizmann, KTH, and IXMAS. These experiments demonstrate the discriminative properties of the representation and the validity of application in recognition systems, achieving state-of-the-art results.

Fault condition recognition of rolling bearing in bridge crane based on PSO–KPCA

Directory of Open Access Journals (Sweden)

He Yan

2017-01-01

Full Text Available When the rolling bearing in bridge crane gets out of order and often accompanies with occurrence of nonlinear behaviours, its fault information is weak and it is difficult to extract fault features and to distinguish diverse failure modes. Kernel principal component analysis (KPCA may realize nonlinear mapping to solve nonlinear problems. In the paper the particle swarm optimization （PSO）is applied to optimization of kernel function parameter to reduce its bind set-up. The optimal mathematical model of kernel parameters is constructed by means of thought of fisher discriminate functions .And then it is used to bridge crane rolling bearing simulated faults recognition. The simulation results show that KPCA optimized by PSO can effectively classify fault conditions of rolling bearing. It can be concluded that non-linear mapping capability of KPCA after its function parameter by PSO is greatly improved and the KPCA-PSO is very suit for slight and incipient mechanical fault condition recognition.

Speech Recognition

Directory of Open Access Journals (Sweden)

Adrian Morariu

2009-01-01

Full Text Available This paper presents a method of speech recognition by pattern recognition techniques. Learning consists in determining the unique characteristics of a word (cepstral coefficients by eliminating those characteristics that are different from one word to another. For learning and recognition, the system will build a dictionary of words by determining the characteristics of each word to be used in the recognition. Determining the characteristics of an audio signal consists in the following steps: noise removal, sampling it, applying Hamming window, switching to frequency domain through Fourier transform, calculating the magnitude spectrum, filtering data, determining cepstral coefficients.

Recognition of speaker-dependent continuous speech with KEAL

Science.gov (United States)

Mercier, G.; Bigorgne, D.; Miclet, L.; Le Guennec, L.; Querre, M.

1989-04-01

A description of the speaker-dependent continuous speech recognition system KEAL is given. An unknown utterance, is recognized by means of the followng procedures: acoustic analysis, phonetic segmentation and identification, word and sentence analysis. The combination of feature-based, speaker-independent coarse phonetic segmentation with speaker-dependent statistical classification techniques is one of the main design features of the acoustic-phonetic decoder. The lexical access component is essentially based on a statistical dynamic programming technique which aims at matching a phonemic lexical entry containing various phonological forms, against a phonetic lattice. Sentence recognition is achieved by use of a context-free grammar and a parsing algorithm derived from Earley's parser. A speaker adaptation module allows some of the system parameters to be adjusted by matching known utterances with their acoustical representation. The task to be performed, described by its vocabulary and its grammar, is given as a parameter of the system. Continuously spoken sentences extracted from a 'pseudo-Logo' language are analyzed and results are presented.

Motor cortical processing is causally involved in object recognition

Science.gov (United States)

2013-01-01

Background Motor activity during vicarious experience of actions is a widely reported and studied phenomenon, and motor system activity also accompanies observation of graspable objects in the absence of any actions. Such motor activity is thought to reflect simulation of the observed action, or preparation to interact with the object, respectively. Results Here, in an initial exploratory study, we ask whether motor activity during observation of object directed actions is involved in processes related to recognition of the object after initial exposure. Single pulse Transcranial Magnetic Stimulation (TMS) was applied over the thumb representation of the motor cortex, or over the vertex, during observation of a model thumb typing on a cell-phone, and performance on a phone recognition task at the end of the trial was assessed. Disrupting motor processing over the thumb representation 100 ms after the onset of the typing video impaired the ability to recognize the phone in the recognition test, whereas there was no such effect for TMS applied over the vertex and no TMS trials. Furthermore, this effect only manifested for videos observed from the first person perspective. In an additional control condition, there was no evidence for any effects of TMS to the thumb representation or vertex when observing and recognizing non-action related shape stimuli. Conclusion Overall, these data provide evidence that motor cortical processing during observation of object-directed actions from a first person perspective is causally linked to the formation of enduring representations of objects-of-action. PMID:24330638

Music, memory, and Alzheimer's disease: is music recognition spared in dementia, and how can it be assessed?

Science.gov (United States)

Cuddy, Lola L; Duffin, Jacalyn

2005-01-01

Despite intriguing and suggestive clinical observations, no formal research has assessed the possible sparing of musical recognition and memory in Alzheimer's dementia (AD). A case study is presented of an 84-year old woman with severe cognitive impairment implicating AD, but for whom music recognition and memory, according to her caregivers, appeared to be spared. The hypotheses addressed were, first, that memory for familiar music may be spared in dementia, and second, that musical recognition and memory may be reliably assessed with existing tests if behavioral observation is employed to overcome the problem of verbal or written communication. Our hypotheses were stimulated by the patient EN, for whom diagnosis of AD became probable in 2000. With severe problems in memory, language, and cognition, she now has a mini-mental status score of 8 (out of 30) and is unable to understand or recall standard instructions. In order to assess her music recognition abilities, three tests from the previous literature were adapted for behavioral observation. Two tests involved the discrimination of familiar melodies from unfamiliar melodies. The third involved the detection of distortions ("wrong" notes) in familiar melodies and discrimination of distorted melodies from melodies correctly reproduced. Test melodies were presented to EN on a CD player and her responses were observed by two test administrators. EN responded to familiar melodies by singing along, usually with the words, and often continuing to sing after the stimulus had stopped. She never responded to the unfamiliar melodies. She responded to distorted melodies with facial expressions - surprise, laughter, a frown, or an exclamation, "Oh, dear!"; she never responded in this way to the undistorted melodies. Allowing these responses as indicators of detection, the results for EN were in the normal or near normal range of scores for elderly controls. As well, lyrics to familiar melodies, spoken in a conversational

Recognition and Toleration

DEFF Research Database (Denmark)

Lægaard, Sune

2010-01-01

Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms...... or interpretations of recognition and toleration are considered, confusing and problematic uses of the terms are noted, and the compatibility of toleration and recognition is discussed. The article argues that there is a range of legitimate and importantly different conceptions of both toleration and recognition...

Superior voice recognition in a patient with acquired prosopagnosia and object agnosia.

Science.gov (United States)

Hoover, Adria E N; Démonet, Jean-François; Steeves, Jennifer K E

2010-11-01

Anecdotally, it has been reported that individuals with acquired prosopagnosia compensate for their inability to recognize faces by using other person identity cues such as hair, gait or the voice. Are they therefore superior at the use of non-face cues, specifically voices, to person identity? Here, we empirically measure person and object identity recognition in a patient with acquired prosopagnosia and object agnosia. We quantify person identity (face and voice) and object identity (car and horn) recognition for visual, auditory, and bimodal (visual and auditory) stimuli. The patient is unable to recognize faces or cars, consistent with his prosopagnosia and object agnosia, respectively. He is perfectly able to recognize people's voices and car horns and bimodal stimuli. These data show a reverse shift in the typical weighting of visual over auditory information for audiovisual stimuli in a compromised visual recognition system. Moreover, the patient shows selectively superior voice recognition compared to the controls revealing that two different stimulus domains, persons and objects, may not be equally affected by sensory adaptation effects. This also implies that person and object identity recognition are processed in separate pathways. These data demonstrate that an individual with acquired prosopagnosia and object agnosia can compensate for the visual impairment and become quite skilled at using spared aspects of sensory processing. In the case of acquired prosopagnosia it is advantageous to develop a superior use of voices for person identity recognition in everyday life. Copyright © 2010 Elsevier Ltd. All rights reserved.

Secondary atomic effects accompanying nuclear transitions

International Nuclear Information System (INIS)

Walen, R.J.; Briancon, C.

1975-01-01

Some consequences of the production of inner-shell vacancies in γ-ray internal conversion are considered and internal ionization or shakeoff accompanying β decay and nuclear electron capture are discussed

Design and test of a hybrid foot force sensing and GPS system for richer user mobility activity recognition.

Science.gov (United States)

Zhang, Zelun; Poslad, Stefan

2013-11-01

Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals.

Pigmentary glaucoma accompanied by Usher syndrome.

Science.gov (United States)

Koucheki, Behrooz; Jalali, Kamran Hodjat

2012-08-01

To report a case of pigmentary glaucoma (PG) accompanied by Usher syndrome. Case report. The results were presented after standard ocular examination, visual field test, anterior segment and fundus photography, electroretinography, and otolaryngology consultation were conducted. Typical retinitis pigmentosa, flat electroretinography, congenital sensorineural hearing loss, high intraocular pressure, Krukenberg spindle, iris concavity, radial iris transillumination defect, severe pigment deposition on the trabecular meshwork, and glaucomatous optic nerve damage were indicative of PG accompanied by Usher syndrome. In some rare cases, PG may coexist with Usher syndrome. Common findings of Usher syndrome, including night blindness, impaired vision, visual field defects, and retinal changes may distract the clinician from considering the diagnosis of glaucoma. Such association should be borne in mind to make a timely diagnosis and treatment possible.

On the Time Course of Vocal Emotion Recognition

Science.gov (United States)

Pell, Marc D.; Kotz, Sonja A.

2011-01-01

How quickly do listeners recognize emotions from a speaker's voice, and does the time course for recognition vary by emotion type? To address these questions, we adapted the auditory gating paradigm to estimate how much vocal information is needed for listeners to categorize five basic emotions (anger, disgust, fear, sadness, happiness) and neutral utterances produced by male and female speakers of English. Semantically-anomalous pseudo-utterances (e.g., The rivix jolled the silling) conveying each emotion were divided into seven gate intervals according to the number of syllables that listeners heard from sentence onset. Participants (n = 48) judged the emotional meaning of stimuli presented at each gate duration interval, in a successive, blocked presentation format. Analyses looked at how recognition of each emotion evolves as an utterance unfolds and estimated the “identification point” for each emotion. Results showed that anger, sadness, fear, and neutral expressions are recognized more accurately at short gate intervals than happiness, and particularly disgust; however, as speech unfolds, recognition of happiness improves significantly towards the end of the utterance (and fear is recognized more accurately than other emotions). When the gate associated with the emotion identification point of each stimulus was calculated, data indicated that fear (M = 517 ms), sadness (M = 576 ms), and neutral (M = 510 ms) expressions were identified from shorter acoustic events than the other emotions. These data reveal differences in the underlying time course for conscious recognition of basic emotions from vocal expressions, which should be accounted for in studies of emotional speech processing. PMID:22087275

Camera-laser fusion sensor system and environmental recognition for humanoids in disaster scenarios

International Nuclear Information System (INIS)

Lee, Inho; Oh, Jaesung; Oh, Jun-Ho; Kim, Inhyeok

2017-01-01

This research aims to develop a vision sensor system and a recognition algorithm to enable a humanoid to operate autonomously in a disaster environment. In disaster response scenarios, humanoid robots that perform manipulation and locomotion tasks must identify the objects in the environment from those challenged by the call by the United States’ Defense Advanced Research Projects Agency, e.g., doors, valves, drills, debris, uneven terrains, and stairs, among others. In order for a humanoid to undertake a number of tasks, we con- struct a camera–laser fusion system and develop an environmental recognition algorithm. Laser distance sensor and motor are used to obtain 3D cloud data. We project the 3D cloud data onto a 2D image according to the intrinsic parameters of the camera and the distortion model of the lens. In this manner, our fusion sensor system performs functions such as those performed by the RGB-D sensor gener- ally used in segmentation research. Our recognition algorithm is based on super-pixel segmentation and random sampling. The proposed approach clusters the unorganized cloud data according to geometric characteristics, namely, proximity and co-planarity. To assess the feasibility of our system and algorithm, we utilize the humanoid robot, DRC-HUBO, and the results are demonstrated in the accompanying video.

Camera-laser fusion sensor system and environmental recognition for humanoids in disaster scenarios

Energy Technology Data Exchange (ETDEWEB)

Lee, Inho [Institute for Human and Machine Cognition (IHMC), Florida (United States); Oh, Jaesung; Oh, Jun-Ho [Korea Advanced Institute of Science and Technology (KAIST), Daejeon (Korea, Republic of); Kim, Inhyeok [NAVER Green Factory, Seongnam (Korea, Republic of)

2017-06-15

This research aims to develop a vision sensor system and a recognition algorithm to enable a humanoid to operate autonomously in a disaster environment. In disaster response scenarios, humanoid robots that perform manipulation and locomotion tasks must identify the objects in the environment from those challenged by the call by the United States’ Defense Advanced Research Projects Agency, e.g., doors, valves, drills, debris, uneven terrains, and stairs, among others. In order for a humanoid to undertake a number of tasks, we con- struct a camera–laser fusion system and develop an environmental recognition algorithm. Laser distance sensor and motor are used to obtain 3D cloud data. We project the 3D cloud data onto a 2D image according to the intrinsic parameters of the camera and the distortion model of the lens. In this manner, our fusion sensor system performs functions such as those performed by the RGB-D sensor gener- ally used in segmentation research. Our recognition algorithm is based on super-pixel segmentation and random sampling. The proposed approach clusters the unorganized cloud data according to geometric characteristics, namely, proximity and co-planarity. To assess the feasibility of our system and algorithm, we utilize the humanoid robot, DRC-HUBO, and the results are demonstrated in the accompanying video.

Knowledge fusion: An approach to time series model selection followed by pattern recognition

International Nuclear Information System (INIS)

Bleasdale, S.A.; Burr, T.L.; Scovel, J.C.; Strittmatter, R.B.

1996-03-01

This report describes work done during FY 95 that was sponsored by the Department of Energy, Office of Nonproliferation and National Security, Knowledge Fusion Project. The project team selected satellite sensor data to use as the one main example for the application of its analysis algorithms. The specific sensor-fusion problem has many generic features, which make it a worthwhile problem to attempt to solve in a general way. The generic problem is to recognize events of interest from multiple time series that define a possibly noisy background. By implementing a suite of time series modeling and forecasting methods and using well-chosen alarm criteria, we reduce the number of false alarms. We then further reduce the number of false alarms by analyzing all suspicious sections of data, as judged by the alarm criteria, with pattern recognition methods. An accompanying report (Ref 1) describes the implementation and application of this 2-step process for separating events from unusual background and applies a suite of forecasting methods followed by a suite of pattern recognition methods. This report goes into more detail about one of the forecasting methods and one of the pattern recognition methods and is applied to the same kind of satellite-sensor data that is described in Ref. 1

The real thing? Adaptations, transformations and burlesques of Shakespeare, historic and post-modern The real thing? Adaptations, transformations and burlesques of Shakespeare, historic and post-modern

Directory of Open Access Journals (Sweden)

Manfred Draudt

2008-04-01

Full Text Available The practice of adapting great authors to fit current requirements is not just a recent phenomenon. The first great wave of adaptations of Shakespeare came after the period of the closing of theatres in 1642, with the advent of the Restoration in 1660. Political change was accompanied by a radical change in tastes, ideals and conditions: theatres were roofed in and artificially illuminated (like the earlier private theatres; there was also elaborate changeable scenery; and for the first time female roles were taken by professional actresses. Most importantly, French neo-classicism was adopted as the fashionable theory that shaped both the form and the language of plays. The practice of adapting great authors to fit current requirements is not just a recent phenomenon. The first great wave of adaptations of Shakespeare came after the period of the closing of theatres in 1642, with the advent of the Restoration in 1660. Political change was accompanied by a radical change in tastes, ideals and conditions: theatres were roofed in and artificially illuminated (like the earlier private theatres; there was also elaborate changeable scenery; and for the first time female roles were taken by professional actresses. Most importantly, French neo-classicism was adopted as the fashionable theory that shaped both the form and the language of plays.

Cognitive penetrability and emotion recognition in human facial expressions

Directory of Open Access Journals (Sweden)

Francesco eMarchi

2015-06-01

Full Text Available Do our background beliefs, desires, and mental images influence our perceptual experience of the emotions of others? In this paper, we will address the possibility of cognitive penetration of perceptual experience in the domain of social cognition. In particular, we focus on emotion recognition based on the visual experience of facial expressions. After introducing the current debate on cognitive penetration, we review examples of perceptual adaptation for facial expressions of emotion. This evidence supports the idea that facial expressions are perceptually processed as wholes. That is, the perceptual system integrates lower-level facial features, such as eyebrow orientation, mouth angle etc., into facial compounds. We then present additional experimental evidence showing that in some cases, emotion recognition on the basis of facial expression is sensitive to and modified by the background knowledge of the subject. We argue that such sensitivity is best explained as a difference in the visual experience of the facial expression, not just as a modification of the judgment based on this experience. The difference in experience is characterized as the result of the interference of background knowledge with the perceptual integration process for faces. Thus, according to the best explanation, we have to accept cognitive penetration in some cases of emotion recognition. Finally, we highlight a recent model of social vision in order to propose a mechanism for cognitive penetration used in the face-based recognition of emotion.

The role of native-language phonology in the auditory word identification and visual word recognition of Russian-English bilinguals.

Science.gov (United States)

Shafiro, Valeriy; Kharkhurin, Anatoliy V

2009-03-01

Does native language phonology influence visual word processing in a second language? This question was investigated in two experiments with two groups of Russian-English bilinguals, differing in their English experience, and a monolingual English control group. Experiment 1 tested visual word recognition following semantic categorization of words containing four phonological vowel contrasts (/i/-/u/,/I/-/A/,/i/-/I/,/epsilon/-/ae/). Experiment 2 assessed auditory identification accuracy of words containing these four contrasts. Both bilingual groups demonstrated reduced accuracy in auditory identification of two English vowel contrasts absent in their native phonology (/i/-/I/,epsilon/-/ae/). For late- bilinguals, auditory identification difficulty was accompanied by poor visual word recognition for one difficult contrast (/i/-/I/). Bilinguals' visual word recognition moderately correlated with their auditory identification of difficult contrasts. These results indicate that native language phonology can play a role in visual processing of second language words. However, this effect may be considerably constrained by orthographic systems of specific languages.

LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition.

Science.gov (United States)

Katić, Darko; Julliard, Chantal; Wekerle, Anna-Laura; Kenngott, Hannes; Müller-Stich, Beat Peter; Dillmann, Rüdiger; Speidel, Stefanie; Jannin, Pierre; Gibaud, Bernard

2015-09-01

The rise of intraoperative information threatens to outpace our abilities to process it. Context-aware systems, filtering information to automatically adapt to the current needs of the surgeon, are necessary to fully profit from computerized surgery. To attain context awareness, representation of medical knowledge is crucial. However, most existing systems do not represent knowledge in a reusable way, hindering also reuse of data. Our purpose is therefore to make our computational models of medical knowledge sharable, extensible and interoperational with established knowledge representations in the form of the LapOntoSPM ontology. To show its usefulness, we apply it to situation interpretation, i.e., the recognition of surgical phases based on surgical activities. Considering best practices in ontology engineering and building on our ontology for laparoscopy, we formalized the workflow of laparoscopic adrenalectomies, cholecystectomies and pancreatic resections in the framework of OntoSPM, a new standard for surgical process models. Furthermore, we provide a rule-based situation interpretation algorithm based on SQWRL to recognize surgical phases using the ontology. The system was evaluated on ground-truth data from 19 manually annotated surgeries. The aim was to show that the phase recognition capabilities are equal to a specialized solution. The recognition rates of the new system were equal to the specialized one. However, the time needed to interpret a situation rose from 0.5 to 1.8 s on average which is still viable for practical application. We successfully integrated medical knowledge for laparoscopic surgeries into OntoSPM, facilitating knowledge and data sharing. This is especially important for reproducibility of results and unbiased comparison of recognition algorithms. The associated recognition algorithm was adapted to the new representation without any loss of classification power. The work is an important step to standardized knowledge and data

Complex adaptive systems ecology

DEFF Research Database (Denmark)

Sommerlund, Julie

2003-01-01

In the following, I will analyze two articles called Complex Adaptive Systems EcologyI & II (Molin & Molin, 1997 & 2000). The CASE-articles are some of the more quirkyarticles that have come out of the Molecular Microbial Ecology Group - a groupwhere I am currently making observational studies....... They are the result of acooperation between Søren Molin, professor in the group, and his brother, JanMolin, professor at Department of Organization and Industrial Sociology atCopenhagen Business School. The cooperation arises from the recognition that bothmicrobial ecology and sociology/organization theory works...

Critical thinking of student nurses during clinical accompaniment.

Science.gov (United States)

Uys, B Y; Meyer, S M

2005-08-01

The purpose of this study was to investigate the methods of clinical accompaniment used by clinical facilitators in practice. The findings of the study also reflected facilitators' perceptions regarding critical thinking and the facilitation thereof. A quantitative research design was used. A literature study was conducted to identify the methods of accompaniment that facilitate critical thinking. Data was collected by means of a questionnaire developed for that purpose. Making a content-related validity judgment, and involving seven clinical facilitators in an academic institution, ensured the validity of the questionnaire. The results of the study indicated that various clinical methods of accompaniment were used. To a large extent, these methods correlated with those discussed in the literature review. The researcher further concluded that the concepts 'critical thinking' and 'facilitation' were not interpreted correctly by the respondents, and would therefore not be implemented in a proper manner in nursing practice. Furthermore, it seemed evident that tutor-driven learning realised more often than student-driven learning. In this regard, the requirement of outcomes-based education was not satisfied. The researcher is therefore of the opinion that a practical programme for the development of critical thinking skills during clinical accompaniment must be developed within the framework of outcomes-based education.

Critical thinking of student nurses during clinical accompaniment

Directory of Open Access Journals (Sweden)

BY Uys

2005-09-01

Full Text Available The purpose of this study was to investigate the methods of clinical accompaniment used by clinical facilitators in practice. The findings of the study also reflected facilitators’ perceptions regarding critical thinking and the facilitation thereof. A quantitative research design was used. A literature study was conducted to identify the methods of accompaniment that facilitate critical thinking. Data was collected by means of a questionnaire developed for that purpose. Making a content-related validity judgment, and involving seven clinical facilitators in an academic institution, ensured the validity of the questionnaire. The results of the study indicated that various clinical methods of accompaniment were used. To a large extent, these methods correlated with those discussed in the literature review. The researcher further concluded that the concepts ‘critical thinking’ and ‘facilitation’ were not interpreted correctly by the respondents, and would therefore not be implemented in a proper manner in nursing practice. Furthermore, it seemed evident that tutor-driven learning realised more often than student-driven learning. In this regard, the requirement of outcomes-based education was not satisfied. The researcher is therefore of the opinion that a practical programme for the development of critical thinking skills during clinical accompaniment must be developed within the framework of outcomes-based education.

Re-thinking employee recognition: understanding employee experiences of recognition

OpenAIRE

Smith, Charlotte

2013-01-01

Despite widespread acceptance of the importance of employee recognition for both individuals and organisations and evidence of its increasing use in organisations, employee recognition has received relatively little focused attention from academic researchers. Particularly lacking is research exploring the lived experience of employee recognition and the interpretations and meanings which individuals give to these experiences. Drawing on qualitative interviews conducted as part of my PhD rese...

Voice reinstatement modulates neural indices of continuous word recognition.

Science.gov (United States)

Campeanu, Sandra; Craik, Fergus I M; Backer, Kristina C; Alain, Claude

2014-09-01

The present study was designed to examine listeners' ability to use voice information incidentally during spoken word recognition. We recorded event-related brain potentials (ERPs) during a continuous recognition paradigm in which participants indicated on each trial whether the spoken word was "new" or "old." Old items were presented at 2, 8 or 16 words following the first presentation. Context congruency was manipulated by having the same word repeated by either the same speaker or a different speaker. The different speaker could share the gender, accent or neither feature with the word presented the first time. Participants' accuracy was greatest when the old word was spoken by the same speaker than by a different speaker. In addition, accuracy decreased with increasing lag. The correct identification of old words was accompanied by an enhanced late positivity over parietal sites, with no difference found between voice congruency conditions. In contrast, an earlier voice reinstatement effect was observed over frontal sites, an index of priming that preceded recollection in this task. Our results provide further evidence that acoustic and semantic information are integrated into a unified trace and that acoustic information facilitates spoken word recollection. Copyright © 2014 Elsevier Ltd. All rights reserved.

Recognition of flow in everyday life using sensor agent robot with laser range finder

Science.gov (United States)

Goshima, Misa; Mita, Akira

2011-04-01

In the present paper, we suggest an algorithm for a sensor agent robot with a laser range finder to recognize the flows of residents in the living spaces in order to achieve flow recognition in the living spaces, recognition of the number of people in spaces, and the classification of the flows. House reform is or will be demanded to prolong the lifetime of the home. Adaption for the individuals is needed for our aging society which is growing at a rapid pace. Home autonomous mobile robots will become popular in the future for aged people to assist them in various situations. Therefore we have to collect various type of information of human and living spaces. However, a penetration in personal privacy must be avoided. It is essential to recognize flows in everyday life in order to assist house reforms and aging societies in terms of adaption for the individuals. With background subtraction, extra noise removal, and the clustering based k-means method, we got an average accuracy of more than 90% from the behavior from 1 to 3 persons, and also confirmed the reliability of our system no matter the position of the sensor. Our system can take advantages from autonomous mobile robots and protect the personal privacy. It hints at a generalization of flow recognition methods in the living spaces.

Critical thinking of student nurses during clinical accompaniment

OpenAIRE

BY Uys; SM Meyer

2005-01-01

The purpose of this study was to investigate the methods of clinical accompaniment used by clinical facilitators in practice. The findings of the study also reflected facilitators’ perceptions regarding critical thinking and the facilitation thereof. A quantitative research design was used. A literature study was conducted to identify the methods of accompaniment that facilitate critical thinking. Data was collected by means of a questionnaire developed for that purpose. Making a content-rela...

Genome and transcriptome adaptation accompanying emergence of the definitive type 2 host-restricted Salmonella enterica serovar Typhimurium pathovar.

Science.gov (United States)

Kingsley, Robert A; Kay, Sally; Connor, Thomas; Barquist, Lars; Sait, Leanne; Holt, Kathryn E; Sivaraman, Karthi; Wileman, Thomas; Goulding, David; Clare, Simon; Hale, Christine; Seshasayee, Aswin; Harris, Simon; Thomson, Nicholas R; Gardner, Paul; Rabsch, Wolfgang; Wigley, Paul; Humphrey, Tom; Parkhill, Julian; Dougan, Gordon

2013-08-27

Salmonella enterica serovar Typhimurium definitive type 2 (DT2) is host restricted to Columba livia (rock or feral pigeon) but is also closely related to S. Typhimurium isolates that circulate in livestock and cause a zoonosis characterized by gastroenteritis in humans. DT2 isolates formed a distinct phylogenetic cluster within S. Typhimurium based on whole-genome-sequence polymorphisms. Comparative genome analysis of DT2 94-213 and S. Typhimurium SL1344, DT104, and D23580 identified few differences in gene content with the exception of variations within prophages. However, DT2 94-213 harbored 22 pseudogenes that were intact in other closely related S. Typhimurium strains. We report a novel in silico approach to identify single amino acid substitutions in proteins that have a high probability of a functional impact. One polymorphism identified using this method, a single-residue deletion in the Tar protein, abrogated chemotaxis to aspartate in vitro. DT2 94-213 also exhibited an altered transcriptional profile in response to culture at 42°C compared to that of SL1344. Such differentially regulated genes included a number involved in flagellum biosynthesis and motility. IMPORTANCE Whereas Salmonella enterica serovar Typhimurium can infect a wide range of animal species, some variants within this serovar exhibit a more limited host range and altered disease potential. Phylogenetic analysis based on whole-genome sequences can identify lineages associated with specific virulence traits, including host adaptation. This study represents one of the first to link pathogen-specific genetic signatures, including coding capacity, genome degradation, and transcriptional responses to host adaptation within a Salmonella serovar. We performed comparative genome analysis of reference and pigeon-adapted definitive type 2 (DT2) S. Typhimurium isolates alongside phenotypic and transcriptome analyses, to identify genetic signatures linked to host adaptation within the DT2 lineage.

Emotion recognition from speech: tools and challenges

Science.gov (United States)

Al-Talabani, Abdulbasit; Sellahewa, Harin; Jassim, Sabah A.

2015-05-01

Human emotion recognition from speech is studied frequently for its importance in many applications, e.g. human-computer interaction. There is a wide diversity and non-agreement about the basic emotion or emotion-related states on one hand and about where the emotion related information lies in the speech signal on the other side. These diversities motivate our investigations into extracting Meta-features using the PCA approach, or using a non-adaptive random projection RP, which significantly reduce the large dimensional speech feature vectors that may contain a wide range of emotion related information. Subsets of Meta-features are fused to increase the performance of the recognition model that adopts the score-based LDC classifier. We shall demonstrate that our scheme outperform the state of the art results when tested on non-prompted databases or acted databases (i.e. when subjects act specific emotions while uttering a sentence). However, the huge gap between accuracy rates achieved on the different types of datasets of speech raises questions about the way emotions modulate the speech. In particular we shall argue that emotion recognition from speech should not be dealt with as a classification problem. We shall demonstrate the presence of a spectrum of different emotions in the same speech portion especially in the non-prompted data sets, which tends to be more "natural" than the acted datasets where the subjects attempt to suppress all but one emotion.

Graphical symbol recognition

OpenAIRE

K.C. , Santosh; Wendling , Laurent

2015-01-01

International audience; The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic s...

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

Science.gov (United States)

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Mohammed Hasan Abdulameer

2014-01-01

Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

Modeling the Process of Color Image Recognition Using ART2 Neural Network

Directory of Open Access Journals (Sweden)

Todor Petkov

2015-09-01

Full Text Available This paper thoroughly describes the use of unsupervised adaptive resonance theory ART2 neural network for the purposes of image color recognition of x-ray images and images taken by nuclear magnetic resonance. In order to train the network, the pixel values of RGB colors are regarded as learning vectors with three values, one for red, one for green and one for blue were used. At the end the trained network was tested by the values of pictures and determines the design, or how to visualize the converted picture. As a result we had the same pictures with colors according to the network. Here we use the generalized net to prepare a model that describes the process of the color image recognition.

Training-induced adaptation of oxidative phosphorylation in skeletal muscles.

Science.gov (United States)

Korzeniewski, Bernard; Zoladz, Jerzy A

2003-08-15

Muscle training/conditioning improves the adaptation of oxidative phosphorylation in skeletal muscles to physical exercise. However, the mechanisms underlying this adaptation are still not understood fully. By quantitative analysis of the existing experimental results, we show that training-induced acceleration of oxygen-uptake kinetics at the onset of exercise and improvement of ATP/ADP stability due to physical training are mainly caused by an increase in the amount of mitochondrial proteins and by an intensification of the parallel activation of ATP usage and ATP supply (increase in direct stimulation of oxidative phosphorylation complexes accompanying stimulation of ATP consumption) during exercise.

Learning and recognition of on-premise signs from weakly labeled street view images.

Science.gov (United States)

Tsai, Tsung-Hung; Cheng, Wen-Huang; You, Chuang-Wen; Hu, Min-Chun; Tsui, Arvin Wen; Chi, Heng-Yu

2014-03-01

Camera-enabled mobile devices are commonly used as interaction platforms for linking the user's virtual and physical worlds in numerous research and commercial applications, such as serving an augmented reality interface for mobile information retrieval. The various application scenarios give rise to a key technique of daily life visual object recognition. On-premise signs (OPSs), a popular form of commercial advertising, are widely used in our living life. The OPSs often exhibit great visual diversity (e.g., appearing in arbitrary size), accompanied with complex environmental conditions (e.g., foreground and background clutter). Observing that such real-world characteristics are lacking in most of the existing image data sets, in this paper, we first proposed an OPS data set, namely OPS-62, in which totally 4649 OPS images of 62 different businesses are collected from Google's Street View. Further, for addressing the problem of real-world OPS learning and recognition, we developed a probabilistic framework based on the distributional clustering, in which we proposed to exploit the distributional information of each visual feature (the distribution of its associated OPS labels) as a reliable selection criterion for building discriminative OPS models. Experiments on the OPS-62 data set demonstrated the outperformance of our approach over the state-of-the-art probabilistic latent semantic analysis models for more accurate recognitions and less false alarms, with a significant 151.28% relative improvement in the average recognition rate. Meanwhile, our approach is simple, linear, and can be executed in a parallel fashion, making it practical and scalable for large-scale multimedia applications.

Enhanced iris recognition method based on multi-unit iris images

Science.gov (United States)

Shin, Kwang Yong; Kim, Yeong Gon; Park, Kang Ryoung

2013-04-01

For the purpose of biometric person identification, iris recognition uses the unique characteristics of the patterns of the iris; that is, the eye region between the pupil and the sclera. When obtaining an iris image, the iris's image is frequently rotated because of the user's head roll toward the left or right shoulder. As the rotation of the iris image leads to circular shifting of the iris features, the accuracy of iris recognition is degraded. To solve this problem, conventional iris recognition methods use shifting of the iris feature codes to perform the matching. However, this increases the computational complexity and level of false acceptance error. To solve these problems, we propose a novel iris recognition method based on multi-unit iris images. Our method is novel in the following five ways compared with previous methods. First, to detect both eyes, we use Adaboost and a rapid eye detector (RED) based on the iris shape feature and integral imaging. Both eyes are detected using RED in the approximate candidate region that consists of the binocular region, which is determined by the Adaboost detector. Second, we classify the detected eyes into the left and right eyes, because the iris patterns in the left and right eyes in the same person are different, and they are therefore considered as different classes. We can improve the accuracy of iris recognition using this pre-classification of the left and right eyes. Third, by measuring the angle of head roll using the two center positions of the left and right pupils, detected by two circular edge detectors, we obtain the information of the iris rotation angle. Fourth, in order to reduce the error and processing time of iris recognition, adaptive bit-shifting based on the measured iris rotation angle is used in feature matching. Fifth, the recognition accuracy is enhanced by the score fusion of the left and right irises. Experimental results on the iris open database of low-resolution images showed that the

Component Pin Recognition Using Algorithms Based on Machine Learning

Science.gov (United States)

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.

On the Susceptibility of Adaptive Memory to False Memory Illusions

Science.gov (United States)

Howe, Mark L.; Derbish, Mary H.

2010-01-01

Previous research has shown that survival-related processing of word lists enhances retention for that material. However, the claim that survival-related memories are more accurate has only been examined when true recall and recognition of neutral material has been measured. In the current experiments, we examined the adaptive memory superiority…

Statistical Pattern Recognition

CERN Document Server

Webb, Andrew R

2011-01-01

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.  It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields,

The impact of foreign postings on accompanying military spouses: an ethnographic study

Directory of Open Access Journals (Sweden)

Gillian Blakely

2014-08-01

Full Text Available As part of an ethnographic study, the impact of foreign postings on spouses who accompany military personnel was explored. Individual interviews and focus groups with 34 British military spouses based in one location in southern Europe were conducted. Key findings suggested that reaction to a foreign posting was a reflection of personal attitudes, prior experiences, support, ability to adjust to change and strength of relationship with the serving spouse and community. For many the experience was positive due to the increased opportunity for family time, for others this helped to compensate for the difficulties experienced. Some military spouses experienced significant distress on the posting, particularly if the family was not well-supported. The potential implications of military spouses not adapting to foreign postings have significant implications for healthcare practice. Provision of more appropriate support resources before and during the posting would facilitate the transition for the military spouse and their family.

DAXX envelops a histone H3.3-H4 dimer for H3.3-specific recognition

Energy Technology Data Exchange (ETDEWEB)

Elsässer, Simon J; Huang, Hongda; Lewis, Peter W; Chin, Jason W; Allis, C David; Patel, Dinshaw J [MSKCC; (Rockefeller); (MRC)

2013-01-24

Histone chaperones represent a structurally and functionally diverse family of histone-binding proteins that prevent promiscuous interactions of histones before their assembly into chromatin. DAXX is a metazoan histone chaperone specific to the evolutionarily conserved histone variant H3.3. Here we report the crystal structures of the DAXX histone-binding domain with a histone H3.3–H4 dimer, including mutants within DAXX and H3.3, together with in vitro and in vivo functional studies that elucidate the principles underlying H3.3 recognition specificity. Occupying 40% of the histone surface-accessible area, DAXX wraps around the H3.3–H4 dimer, with complex formation accompanied by structural transitions in the H3.3–H4 histone fold. DAXX uses an extended α-helical conformation to compete with major inter-histone, DNA and ASF1 interaction sites. Our structural studies identify recognition elements that read out H3.3-specific residues, and functional studies address the contributions of Gly90 in H3.3 and Glu225 in DAXX to chaperone-mediated H3.3 variant recognition specificity.

Adaptability of mitosporic stage in Sphaerodes mycoparasitica towards its mycoparasitic-polyphagous lifestyle.

Science.gov (United States)

Vujanovic, Vladimir; Kim, Seon Hwa

2017-01-01

Sphaerodes mycoparasitica Vuj. is a Fusarium-specific mycoparasite. Some recent discoveries recognize its biotrophic polyphagous lifestyle as an interesting biocontrol property against a broad spectrum of mycotoxigenic Fusarium hosts. Secondary metabolites such as mycotoxins produced by Fusarium spp. may play an important role in the signaling process, allowing an early mycoparasite-host recognition. A multiple-paper-disc assay has been conducted to test S. mycoparasitica hyphal adaptability to filtrates of 12 Fusarium spp. This study shows that shifts of adapted and nonadapted hyphal migration towards different Fusarium-host filtrates may partly explain S. mycoparasitica polyphagous lifestyle, and its adaptability depending on host preference or compatibility. In terms of host compatibility, the current findings suggest that S. mycoparasitica tends to prefer native Fusarium hosts more related to its origin and propose that the mycoparasite could possess diphasic interactions such as biotrophic-attraction and antagonistic-inhibition relationships based on relative radial growth. This implies that the mycoparasite may use a group of mycotoxins produced by specific Fusarium spp. as an adaptive selective mechanism that facilitates a parasite-host recognition and further successful mycoparasitism. In particular, relative polarity or hydrophilicity/hydrophobicity of mycotoxins may be related to solubility and absorption properties in hyphae of the mycoparasite. Taken together, the studies of host compatibility and adaptability depending on host filtrates will aid in understanding complex mechanisms of S. mycoparasitica, as a promising model organism for a specific biotrophic mycoparasite to enhance and improve biocontrol efficacy against Fusaria.

Rotation, scale, and translation invariant pattern recognition using feature extraction

Science.gov (United States)

Prevost, Donald; Doucet, Michel; Bergeron, Alain; Veilleux, Luc; Chevrette, Paul C.; Gingras, Denis J.

1997-03-01

A rotation, scale and translation invariant pattern recognition technique is proposed.It is based on Fourier- Mellin Descriptors (FMD). Each FMD is taken as an independent feature of the object, and a set of those features forms a signature. FMDs are naturally rotation invariant. Translation invariance is achieved through pre- processing. A proper normalization of the FMDs gives the scale invariance property. This approach offers the double advantage of providing invariant signatures of the objects, and a dramatic reduction of the amount of data to process. The compressed invariant feature signature is next presented to a multi-layered perceptron neural network. This final step provides some robustness to the classification of the signatures, enabling good recognition behavior under anamorphically scaled distortion. We also present an original feature extraction technique, adapted to optical calculation of the FMDs. A prototype optical set-up was built, and experimental results are presented.

PSYCHOLOGICAL PECULIARITIES OF ADAPTIVITY IN AFRICAN STUDENTS

Directory of Open Access Journals (Sweden)

L I Badalova

2015-12-01

Full Text Available The article analyzes the results of the empirical research of adaptability as a basic characteristic of a personality; it was conducted on a sample of the representatives of different nationalities of African students. The research was made with the use of the capsule version of the blank methods, elaborated by A.I Krupnov, as well as the qualitative analysis of the adaptability components manifestation and the connections between them with the use of the correlation analysis. The qualitative analysis of the manifestation degree of each adaptability component shows that the adaptation index has the highest ranking. This proves that African students can adjust well to the new social and cultural environment while taking classes. However the respondents showed high levels of nostalgia and detachment. These feelings are accompanied by negative emotions (emotional stress and prevent a normal integration into the new society. The development of the self-confidence and addressing the affective disposition can be regarded as the major resources for compensation and correction of the negative influence of adaptability weaknesses. At the same time there is a connection between nostalgia and adaptation parameters. This phenomenon is the indicative of a positive role of experiencing a painful homesickness in the process of adaptation of the African students. This refers to the two-directional functional ability of nostalgia and its value for the successful integration into a new society.

Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

Science.gov (United States)

Luo, Guangchun; Qin, Ke; Wang, Nan; Niu, Weina

2018-01-01

Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy. PMID:29494543

Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Zhiyuan Ma

2018-03-01

Full Text Available Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.

Rotation-invariant neural pattern recognition system with application to coin recognition.

Science.gov (United States)

Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

1992-01-01

In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

Metastatic Basal Cell Carcinoma Accompanying Gorlin Syndrome

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Yeliz Bilir

2014-01-01

Full Text Available Gorlin-Goltz syndrome or basal cell nevus syndrome is an autosomal dominant syndrome characterized by skeletal anomalies, numerous cysts observed in the jaw, and multiple basal cell carcinoma of the skin, which may be accompanied by falx cerebri calcification. Basal cell carcinoma is the most commonly skin tumor with slow clinical course and low metastatic potential. Its concomitance with Gorlin syndrome, resulting from a mutation in a tumor suppressor gene, may substantially change morbidity and mortality. A 66-year-old male patient with a history of recurrent basal cell carcinoma was presented with exophthalmus in the left eye and the lesions localized in the left lateral orbita and left zygomatic area. His physical examination revealed hearing loss, gapped teeth, highly arched palate, and frontal prominence. Left orbital mass, cystic masses at frontal and ethmoidal sinuses, and multiple pulmonary nodules were detected at CT scans. Basal cell carcinoma was diagnosed from biopsy of ethmoid sinus. Based on the clinical and typical radiological characteristics (falx cerebri calcification, bifid costa, and odontogenic cysts, the patient was diagnosed with metastatic skin basal cell carcinoma accompanied by Gorlin syndrome. Our case is a basal cell carcinoma with aggressive course accompanying a rarely seen syndrome.

Explicit and implicit processes in behavioural adaptation to road width.

Science.gov (United States)

Lewis-Evans, Ben; Charlton, Samuel G

2006-05-01

The finding that drivers may react to safety interventions in a way that is contrary to what was intended is the phenomenon of behavioural adaptation. This phenomenon has been demonstrated across various safety interventions and has serious implications for road safety programs the world over. The present research used a driving simulator to assess behavioural adaptation in drivers' speed and lateral displacement in response to manipulations of road width. Of interest was whether behavioural adaptation would occur and whether we could determine whether it was the result of explicit, conscious decisions or implicit perceptual processes. The results supported an implicit, zero perceived risk model of behavioural adaptation with reduced speeds on a narrowed road accompanied by increased ratings of risk and a marked inability of the participants to identify that any change in road width had occurred.

A Real-time Face/Hand Tracking Method for Chinese Sign Language Recognition

Institute of Scientific and Technical Information of China (English)

无

2002-01-01

This paper introduces a new Chinese Sign Language recognition (CSLR) system and a method of real-time tracking face and hand applied in the system. In the method, an improved agent algorithm is used to extract the region of face and hand and track them. Kalman filter is introduced to forecast the position and rectangle of search, and self-adapting of target color is designed to counteract the effect of illumination.

Adapting to Changing Memory Retrieval Demands: Evidence from Event-Related Potentials

Science.gov (United States)

Benoit, Roland G.; Werkle-Bergner, Markus; Mecklinger, Axel; Kray, Jutta

2009-01-01

This study investigated preparatory processes involved in adapting to changing episodic memory retrieval demands. Event-related potentials (ERPs) were recorded while participants performed a general old/new recognition task and a specific task that also required retrieval of perceptual details. The relevant task remained either constant or changed…

Affect-Based Adaptation of an Applied Video Game for Educational Purposes

Science.gov (United States)

Bontchev, Boyan; Vassileva, Dessislava

2017-01-01

Purpose: This paper aims to clarify how affect-based adaptation can improve implicit recognition of playing style of individuals during game sessions. This study presents the "Rush for Gold" game using dynamic difficulty adjustment of tasks based on both player performance and affectation inferred through electrodermal activity and…

7 CFR 319.37-12 - Prohibited articles accompanying restricted articles.

Science.gov (United States)

2010-01-01

... 7 Agriculture 5 2010-01-01 2010-01-01 false Prohibited articles accompanying restricted articles... Stock, Plants, Roots, Bulbs, Seeds, and Other Plant Products 1,2 § 319.37-12 Prohibited articles accompanying restricted articles. A restricted article for importation into the United States shall not be...

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

Science.gov (United States)

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.

Novice Collaboration in Solo and Accompaniment Improvisation

DEFF Research Database (Denmark)

Hansen, Anne-Marie; Andersen, Hans Jørgen

2012-01-01

-accompaniment relationship. Results of interaction data and video analysis show that 1) teams related to each other through their experience with verbal conversation, 2) users searched for harmonic relations and 3) were able to establish rhythmical grounding. The paper concludes with some design guidelines for future solo...... in order to understand how future shared electronic music instruments can be de-signed to encourage non-musicians to engage in social action through music improvisation. A combination of quantitative and qualitative analysis was used to find characteristics in co-expression found in a solo......-accompaniment shared improvisation interfaces: How real time analysis of co-expression can be mapped to ad-ditional sound feedback that supports, strengthens and evolves co-expression in improvisation....

Information-Dispersion-Entropy-Based Blind Recognition of Binary BCH Codes in Soft Decision Situations

Directory of Open Access Journals (Sweden)

Yimeng Zhang

2013-05-01

Full Text Available A method of blind recognition of the coding parameters for binary Bose-Chaudhuri-Hocquenghem (BCH codes is proposed in this paper. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary BCH codes, while the coding parameters are unknown. The problem can be addressed on the context of the non-cooperative communications or adaptive coding and modulations (ACM for cognitive radio networks. The recognition processing includes two major procedures: code length estimation and generator polynomial reconstruction. A hard decision method has been proposed in a previous literature. In this paper we propose the recognition approach in soft decision situations with Binary-Phase-Shift-Key modulations and Additive-White-Gaussian-Noise (AWGN channels. The code length is estimated by maximizing the root information dispersion entropy function. And then we search for the code roots to reconstruct the primitive and generator polynomials. By utilizing the soft output of the channel, the recognition performance is improved and the simulations show the efficiency of the proposed algorithm.

Partial costs of global climate change adaptation for the supply of raw industrial and municipal water: a methodology and application

NARCIS (Netherlands)

Ward, P.J.; Strzepek, K.; Pauw, W.P.; Brander, L.M.; Hughes, G.; Aerts, J.C.J.M.

2010-01-01

Despite growing recognition of the importance of climate change adaptation, few global estimates of the costs involved are available for the water supply sector. We present a methodology for estimating partial global and regional adaptation costs for raw industrial and domestic water supply, for a

22 CFR 40.102 - Guardian required to accompany excluded alien.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Guardian required to accompany excluded alien. 40.102 Section 40.102 Foreign Relations DEPARTMENT OF STATE VISAS REGULATIONS PERTAINING TO BOTH... Guardian required to accompany excluded alien. INA 212(a)(9)(B) is not applicable at the time of visa...

Knowing beans: Human mirror mechanisms revealed through motor adaptation

Directory of Open Access Journals (Sweden)

Arthur M Glenberg

2010-11-01

Full Text Available Human mirror mechanisms (MMs respond during both performed and observed action and appear to underlie action goal recognition. We introduce a behavioral procedure for discovering and clarifying functional MM properties: Blindfolded participants repeatedly move beans either toward or away from themselves to induce motor adaptation. Then, the bias for perceiving direction of ambiguous visual movement in depth is measured. Bias is affected by a number of beans moved, b movement direction, and c similarity of the visual stimulus to the hand used to move beans. This cross-modal adaptation pattern supports both the validity of human MMs and functionality of our testing instrument. We also discuss related work that extends the motor adaptation paradigm to investigate contributions of MMs to speech perception and language comprehension.

On the Design of Smart Homes: A Framework for Activity Recognition in Home Environment.

Science.gov (United States)

Cicirelli, Franco; Fortino, Giancarlo; Giordano, Andrea; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea

2016-09-01

A smart home is a home environment enriched with sensing, actuation, communication and computation capabilities which permits to adapt it to inhabitants preferences and requirements. Establishing a proper strategy of actuation on the home environment can require complex computational tasks on the sensed data. This is the case of activity recognition, which consists in retrieving high-level knowledge about what occurs in the home environment and about the behaviour of the inhabitants. The inherent complexity of this application domain asks for tools able to properly support the design and implementation phases. This paper proposes a framework for the design and implementation of smart home applications focused on activity recognition in home environments. The framework mainly relies on the Cloud-assisted Agent-based Smart home Environment (CASE) architecture offering basic abstraction entities which easily allow to design and implement Smart Home applications. CASE is a three layered architecture which exploits the distributed multi-agent paradigm and the cloud technology for offering analytics services. Details about how to implement activity recognition onto the CASE architecture are supplied focusing on the low-level technological issues as well as the algorithms and the methodologies useful for the activity recognition. The effectiveness of the framework is shown through a case study consisting of a daily activity recognition of a person in a home environment.

Hand Gesture Recognition Using Ultrasonic Waves

KAUST Repository

AlSharif, Mohammed Hussain

2016-04-01

Gesturing is a natural way of communication between people and is used in our everyday conversations. Hand gesture recognition systems are used in many applications in a wide variety of fields, such as mobile phone applications, smart TVs, video gaming, etc. With the advances in human-computer interaction technology, gesture recognition is becoming an active research area. There are two types of devices to detect gestures; contact based devices and contactless devices. Using ultrasonic waves for determining gestures is one of the ways that is employed in contactless devices. Hand gesture recognition utilizing ultrasonic waves will be the focus of this thesis work. This thesis presents a new method for detecting and classifying a predefined set of hand gestures using a single ultrasonic transmitter and a single ultrasonic receiver. This method uses a linear frequency modulated ultrasonic signal. The ultrasonic signal is designed to meet the project requirements such as the update rate, the range of detection, etc. Also, it needs to overcome hardware limitations such as the limited output power, transmitter, and receiver bandwidth, etc. The method can be adapted to other hardware setups. Gestures are identified based on two main features; range estimation of the moving hand and received signal strength (RSS). These two factors are estimated using two simple methods; channel impulse response (CIR) and cross correlation (CC) of the reflected ultrasonic signal from the gesturing hand. A customized simple hardware setup was used to classify a set of hand gestures with high accuracy. The detection and classification were done using methods of low computational cost. This makes the proposed method to have a great potential for the implementation in many devices including laptops and mobile phones. The predefined set of gestures can be used for many control applications.

Engineering responsive polymer building blocks with host-guest molecular recognition for functional applications.

Science.gov (United States)

Hu, Jinming; Liu, Shiyong

2014-07-15

CONSPECTUS: All living organisms and soft matter are intrinsically responsive and adaptive to external stimuli. Inspired by this fact, tremendous effort aiming to emulate subtle responsive features exhibited by nature has spurred the invention of a diverse range of responsive polymeric materials. Conventional stimuli-responsive polymers are constructed via covalent bonds and can undergo reversible or irreversible changes in chemical structures, physicochemical properties, or both in response to a variety of external stimuli. They have been imparted with a variety of emerging applications including drug and gene delivery, optical sensing and imaging, diagnostics and therapies, smart coatings and textiles, and tissue engineering. On the other hand, in comparison with molecular chemistry held by covalent bonds, supramolecular chemistry built on weak and reversible noncovalent interactions has emerged as a powerful and versatile strategy for materials fabrication due to its facile accessibility, extraordinary reversibility and adaptivity, and potent applications in diverse fields. Typically involving more than one type of noncovalent interactions (e.g., hydrogen bonding, metal coordination, hydrophobic association, electrostatic interactions, van der Waals forces, and π-π stacking), host-guest recognition refers to the formation of supramolecular inclusion complexes between two or more entities connected together in a highly controlled and cooperative manner. The inherently reversible and adaptive nature of host-guest molecular recognition chemistry, stemming from multiple noncovalent interactions, has opened up a new platform to construct novel types of stimuli-responsive materials. The introduction of host-guest chemistry not only enriches the realm of responsive materials but also confers them with promising new applications. Most intriguingly, the integration of responsive polymer building blocks with host-guest recognition motifs will endow the former with

ARG1 Functions in the Physiological Adaptation of Undifferentiated Plant Cells to Spaceflight

Science.gov (United States)

Zupanska, Agata K.; Schultz, Eric R.; Yao, JiQiang; Sng, Natasha J.; Zhou, Mingqi; Callaham, Jordan B.; Ferl, Robert J.; Paul, Anna-Lisa

2017-11-01

Scientific access to spaceflight and especially the International Space Station has revealed that physiological adaptation to spaceflight is accompanied or enabled by changes in gene expression that significantly alter the transcriptome of cells in spaceflight. A wide range of experiments have shown that plant physiological adaptation to spaceflight involves gene expression changes that alter cell wall and other metabolisms. However, while transcriptome profiling aptly illuminates changes in gene expression that accompany spaceflight adaptation, mutation analysis is required to illuminate key elements required for that adaptation. Here we report how transcriptome profiling was used to gain insight into the spaceflight adaptation role of Altered response to gravity 1 (Arg1), a gene known to affect gravity responses in plants on Earth. The study compared expression profiles of cultured lines of Arabidopsis thaliana derived from wild-type (WT) cultivar Col-0 to profiles from a knock-out line deficient in the gene encoding ARG1 (ARG1 KO), both on the ground and in space. The cell lines were launched on SpaceX CRS-2 as part of the Cellular Expression Logic (CEL) experiment of the BRIC-17 spaceflight mission. The cultured cell lines were grown within 60 mm Petri plates in Petri Dish Fixation Units (PDFUs) that were housed within the Biological Research In Canisters (BRIC) hardware. Spaceflight samples were fixed on orbit. Differentially expressed genes were identified between the two environments (spaceflight and comparable ground controls) and the two genotypes (WT and ARG1 KO). Each genotype engaged unique genes during physiological adaptation to the spaceflight environment, with little overlap. Most of the genes altered in expression in spaceflight in WT cells were found to be Arg1-dependent, suggesting a major role for that gene in the physiological adaptation of undifferentiated cells to spaceflight.

An LRR/malectin receptor-like kinase mediates resistance to non-adapted and adapted powdery mildew fungi in barley and wheat

Directory of Open Access Journals (Sweden)

Jeyaraman Rajaraman

2016-12-01

Full Text Available Pattern recognition receptors (PRRs belonging to the multigene family of receptor-like kinases (RLKs are the sensing devices of plants for microbe- or pathogen-associated molecular patterns released from microbial organisms. Here we describe Rnr8 (for required for nonhost resistance 8 encoding HvLEMK1, a LRR-malectin domain-containing transmembrane RLK that mediates nonhost resistance of barley to the non-adapted wheat powdery mildew fungus Blumeria graminis f.sp. tritici. Transgenic barley lines with silenced HvLEMK1 allow entry and colony growth of the non-adapted pathogen, although sporulation was reduced and final colony size did not reach that of the adapted barley powdery mildew fungus Blumeria graminis f.sp. hordei. Transient expression of the barley or wheat LEMK1 genes enhanced resistance in wheat to the adapted wheat powdery mildew fungus while expression of the same genes did not protect barley from attack by the barley powdery mildew fungus. The results suggest that HvLEMK1 is a factor mediating nonhost resistance in barley and quantitative host resistance in wheat to the wheat powdery mildew fungus.

An LRR/Malectin Receptor-Like Kinase Mediates Resistance to Non-adapted and Adapted Powdery Mildew Fungi in Barley and Wheat.

Science.gov (United States)

Rajaraman, Jeyaraman; Douchkov, Dimitar; Hensel, Götz; Stefanato, Francesca L; Gordon, Anna; Ereful, Nelzo; Caldararu, Octav F; Petrescu, Andrei-Jose; Kumlehn, Jochen; Boyd, Lesley A; Schweizer, Patrick

2016-01-01

Pattern recognition receptors (PRRs) belonging to the multigene family of receptor-like kinases (RLKs) are the sensing devices of plants for microbe- or pathogen-associated molecular patterns released from microbial organisms. Here we describe Rnr8 (for Required for non-host resistance 8 ) encoding HvLEMK1, a LRR-malectin domain-containing transmembrane RLK that mediates non-host resistance of barley to the non-adapted wheat powdery mildew fungus Blumeria graminis f.sp. tritici . Transgenic barley lines with silenced HvLEMK1 allow entry and colony growth of the non-adapted pathogen, although sporulation was reduced and final colony size did not reach that of the adapted barley powdery mildew fungus B. graminis f.sp. hordei . Transient expression of the barley or wheat LEMK1 genes enhanced resistance in wheat to the adapted wheat powdery mildew fungus while expression of the same genes did not protect barley from attack by the barley powdery mildew fungus. The results suggest that HvLEMK1 is a factor mediating non-host resistance in barley and quantitative host resistance in wheat to the wheat powdery mildew fungus.

A Generalized Pyramid Matching Kernel for Human Action Recognition in Realistic Videos

Directory of Open Access Journals (Sweden)

Wenjun Zhang

2013-10-01

Full Text Available Human action recognition is an increasingly important research topic in the fields of video sensing, analysis and understanding. Caused by unconstrained sensing conditions, there exist large intra-class variations and inter-class ambiguities in realistic videos, which hinder the improvement of recognition performance for recent vision-based action recognition systems. In this paper, we propose a generalized pyramid matching kernel (GPMK for recognizing human actions in realistic videos, based on a multi-channel “bag of words” representation constructed from local spatial-temporal features of video clips. As an extension to the spatial-temporal pyramid matching (STPM kernel, the GPMK leverages heterogeneous visual cues in multiple feature descriptor types and spatial-temporal grid granularity levels, to build a valid similarity metric between two video clips for kernel-based classification. Instead of the predefined and fixed weights used in STPM, we present a simple, yet effective, method to compute adaptive channel weights of GPMK based on the kernel target alignment from training data. It incorporates prior knowledge and the data-driven information of different channels in a principled way. The experimental results on three challenging video datasets (i.e., Hollywood2, Youtube and HMDB51 validate the superiority of our GPMK w.r.t. the traditional STPM kernel for realistic human action recognition and outperform the state-of-the-art results in the literature.

[Prosopagnosia and facial expression recognition].

Science.gov (United States)

Koyama, Shinichi

2014-04-01

This paper reviews clinical neuropsychological studies that have indicated that the recognition of a person's identity and the recognition of facial expressions are processed by different cortical and subcortical areas of the brain. The fusiform gyrus, especially the right fusiform gyrus, plays an important role in the recognition of identity. The superior temporal sulcus, amygdala, and medial frontal cortex play important roles in facial-expression recognition. Both facial recognition and facial-expression recognition are highly intellectual processes that involve several regions of the brain.

Problems with radiation protection concerning volunteers accompanying radiological patients

International Nuclear Information System (INIS)

Adrian Daoud

2008-01-01

Full text: The purpose of this work is to point out, within the framework of the Radiation Protection guidelines, the irregular situation of the 'volunteer' or 'accompanying person' who accompanies anyone requiring medical treatment with ionising radiation, as well as to suggest a possible justification for such role. It should be noted that most of these persons are subject to ionising radiation without knowing anything about the effects that it could cause on them, so that their condition could be hardly considered as 'voluntary'. There are several circumstances under which the presence of accompanying persons is required, being different among them. Several examples could be mentioned such as: those who are accompanying a direct relative (family bonds), those who are acting in service during their normal work (social workers, policemen) and even those who are forced by unusual under an accidental situation. The qualitative classification that radiological protection established in society concerning radiation risks for people in general enables to set mechanisms of justification, optimisation and dose limitation for each category, being perfectly identified which of them each person belongs to. But the figure of 'accompanying person' has been excluded from such characterisation. They are subject to radiation exposure without knowing it, or without having any information concerning the potential risks. For them, no balance between the net benefit of an adequate medical treatment versus potential health detriment may be applied as for the case of a patient. Thus, their exposure could be not justified. It is not the purpose of this work to question radiological medicine or its practices, but to clarify certain aspects involving members of the public in general, patients and members of the radiological community, as well as to propose lines of action concerning this subject. We conclude that it is not the volunteer who should decide about medical actions, a role

Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks

Science.gov (United States)

Chen, Bo; Zang, Chuanzhi

2009-03-01

This paper presents an artificial immune pattern recognition (AIPR) approach for the damage detection and classification in structures. An AIPR-based Structure Damage Classifier (AIPR-SDC) has been developed by mimicking immune recognition and learning mechanisms. The structure damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to generate recognition feature vectors that are able to match the training data. In addition, the immune learning algorithm can learn and remember various data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control - American Society of Civil Engineers) Structural Health Monitoring Task Group. The validation results show a better classification success rate comparing to some of other classification algorithms.

Track and vertex reconstruction: From classical to adaptive methods

International Nuclear Information System (INIS)

Strandlie, Are; Fruehwirth, Rudolf

2010-01-01

This paper reviews classical and adaptive methods of track and vertex reconstruction in particle physics experiments. Adaptive methods have been developed to meet the experimental challenges at high-energy colliders, in particular, the CERN Large Hadron Collider. They can be characterized by the obliteration of the traditional boundaries between pattern recognition and statistical estimation, by the competition between different hypotheses about what constitutes a track or a vertex, and by a high level of flexibility and robustness achieved with a minimum of assumptions about the data. The theoretical background of some of the adaptive methods is described, and it is shown that there is a close connection between the two main branches of adaptive methods: neural networks and deformable templates, on the one hand, and robust stochastic filters with annealing, on the other hand. As both classical and adaptive methods of track and vertex reconstruction presuppose precise knowledge of the positions of the sensitive detector elements, the paper includes an overview of detector alignment methods and a survey of the alignment strategies employed by past and current experiments.

S3-2: Colorfulness Perception Adapting to Natural Scenes

Directory of Open Access Journals (Sweden)

Yoko Mizokami

2012-10-01

Full Text Available Our visual system has the ability to adapt to the color characteristics of environment and maintain stable color appearance. Many researches on chromatic adaptation and color constancy suggested that the different levels of visual processes involve the adaptation mechanism. In the case of colorfulness perception, it has been shown that the perception changes with adaptation to chromatic contrast modulation and to surrounding chromatic variance. However, it is still not clear how the perception changes in natural scenes and what levels of visual mechanisms contribute to the perception. Here, I will mainly present our recent work on colorfulness-adaptation in natural images. In the experiment, we examined whether the colorfulness perception of an image was influenced by the adaptation to natural images with different degrees of saturation. Natural and unnatural (shuffled or phase-scrambled images are used for adapting and test images, and all combinations of adapting and test images were tested (e.g., the combination of natural adapting images and a shuffled test image. The results show that colorfulness perception was influenced by adaptation to the saturation of images. A test image appeared less colorful after adaptation to saturated images, and vice versa. The effect of colorfulness adaptation was the strongest for the combination of natural adapting and natural test images. The fact that the naturalness of the spatial structure in an image affects the strength of the adaptation effect implies that the recognition of natural scene would play an important role in the adaptation mechanism.

Nonlinear filtering for character recognition in low quality document images

Science.gov (United States)

Diaz-Escobar, Julia; Kober, Vitaly

2014-09-01

Optical character recognition in scanned printed documents is a well-studied task, where the captured conditions like sheet position, illumination, contrast and resolution are controlled. Nowadays, it is more practical to use mobile devices for document capture than a scanner. So as a consequence, the quality of document images is often poor owing to presence of geometric distortions, nonhomogeneous illumination, low resolution, etc. In this work we propose to use multiple adaptive nonlinear composite filters for detection and classification of characters. Computer simulation results obtained with the proposed system are presented and discussed.

Facial and Bodily Expressions for Control and Adaptation of Games (ECAG 2008)

NARCIS (Netherlands)

Nijholt, Antinus; Poppe, Ronald Walter; Unknown, [Unknown

2008-01-01

In this workshop of the 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2008), the emphasis is on research on facial and bodily expressions for the control and adaptation of games. We distinguish between two forms of expressions, depending on whether the user has the

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition

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Vito Janko

2017-12-01

Full Text Available The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition.

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Janko, Vito; Luštrek, Mitja

2017-12-29

The recognition of the user's context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system's energy expenditure and the system's accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

Kernel learning algorithms for face recognition

CERN Document Server

Li, Jun-Bao; Pan, Jeng-Shyang

2013-01-01

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new

Accompanying therapy with melatonin at radiation therapy for uterine body cancer

International Nuclear Information System (INIS)

Prokhach, N.E.; Sorochan, P.P.; Gromakova, Yi.A.; Krugova, M.; Sukhyin, V.S.

2011-01-01

The results of treatment for uterine body cancer using post-operative radiation therapy (RT) accompanied by melatonin administration are analyzed. Accompanying therapy with melatonin limited negative RT influence on hematological and immune indices and prevented aggravation of quality of life.

Dopamine D1 receptor activation leads to object recognition memory in a coral reef fish.

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Hamilton, Trevor J; Tresguerres, Martin; Kline, David I

2017-07-01

Object recognition memory is the ability to identify previously seen objects and is an adaptive mechanism that increases survival for many species throughout the animal kingdom. Previously believed to be possessed by only the highest order mammals, it is now becoming clear that fish are also capable of this type of memory formation. Similar to the mammalian hippocampus, the dorsolateral pallium regulates distinct memory processes and is modulated by neurotransmitters such as dopamine. Caribbean bicolour damselfish ( Stegastes partitus ) live in complex environments dominated by coral reef structures and thus likely possess many types of complex memory abilities including object recognition. This study used a novel object recognition test in which fish were first presented two identical objects, then after a retention interval of 10 min with no objects, the fish were presented with a novel object and one of the objects they had previously encountered in the first trial. We demonstrate that the dopamine D 1 -receptor agonist (SKF 38393) induces the formation of object recognition memories in these fish. Thus, our results suggest that dopamine-receptor mediated enhancement of spatial memory formation in fish represents an evolutionarily conserved mechanism in vertebrates. © 2017 The Author(s).

Discriminant WSRC for Large-Scale Plant Species Recognition

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Shanwen Zhang

2017-01-01

Full Text Available In sparse representation based classification (SRC and weighted SRC (WSRC, it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted.

Emotion recognition in early Parkinson's disease patients undergoing deep brain stimulation or dopaminergic therapy: a comparison to healthy participants

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Lindsey G. McIntosh

2015-01-01

Full Text Available Parkinson’s disease (PD is traditionally regarded as a neurodegenerative movement disorder, however, nigrostriatal dopaminergic degeneration is also thought to disrupt non-motor loops connecting basal ganglia to areas in frontal cortex involved in cognition and emotion processing. PD patients are impaired on tests of emotion recognition, but it is difficult to disentangle this deficit from the more general cognitive dysfunction that frequently accompanies disease progression. Testing for emotion recognition deficits early in the disease course, prior to cognitive decline, better assesses the sensitivity of these non-motor corticobasal ganglia-thalamocortical loops involved in emotion processing to early degenerative change in basal ganglia circuits. In addition, contrasting this with a group of healthy aging individuals demonstrates changes in emotion processing specific to the degeneration of basal ganglia circuitry in PD. Early PD patients (EPD were recruited from a randomized clinical trial testing the safety and tolerability of deep brain stimulation of the subthalamic nucleus (STN-DBS in early-staged PD. EPD patients were previously randomized to receive optimal drug therapy only (ODT, or drug therapy plus STN-DBS (ODT+DBS. Matched healthy elderly controls (HEC and young controls (HYC also participated in this study. Participants completed two control tasks and three emotion recognition tests that varied in stimulus domain. EPD patients were impaired on all emotion recognition tasks compared to HEC. Neither therapy type (ODT or ODT+DBS nor therapy state (ON/OFF altered emotion recognition performance in this study. Finally, HEC were impaired on vocal emotion recognition relative to HYC, suggesting a decline related to healthy aging. This study supports the existence of impaired emotion recognition early in the PD course, implicating an early disruption of fronto-striatal loops mediating emotional function.

Pattern Recognition

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Aleš Procházka

2018-05-01

Full Text Available Multimodal signal analysis based on sophisticated sensors, efficient communicationsystems and fast parallel processing methods has a rapidly increasing range of multidisciplinaryapplications. The present paper is devoted to pattern recognition, machine learning, and the analysisof sleep stages in the detection of sleep disorders using polysomnography (PSG data, includingelectroencephalography (EEG, breathing (Flow, and electro-oculogram (EOG signals. The proposedmethod is based on the classification of selected features by a neural network system with sigmoidaland softmax transfer functions using Bayesian methods for the evaluation of the probabilities of theseparate classes. The application is devoted to the analysis of the sleep stages of 184 individualswith different diagnoses, using EEG and further PSG signals. Data analysis points to an averageincrease of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of theRapid Eye Movement (REM stages by 0.8% per 10 years. The mean classification accuracy for givensets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1 and89.6% (STD:1.9, respectively. The proposed methods enable the use of adaptive learning processesfor the detection and classification of health disorders based on prior specialist experience andman–machine interaction.

Arthropods Associate with their Red Wood ant Host without Matching Nestmate Recognition Cues.

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Parmentier, Thomas; Dekoninck, Wouter; Wenseleers, Tom

2017-07-01

Social insect colonies provide a valuable resource that attracts and offers shelter to a large community of arthropods. Previous research has suggested that many specialist parasites of social insects chemically mimic their host in order to evade aggression. In the present study, we carry out a systematic study to test how common such chemical deception is across a group of 22 arthropods that are associated with red wood ants (Formica rufa group). In contrast to the examples of chemical mimicry documented in some highly specialized parasites in previous studies, we find that most of the rather unspecialized red wood ant associates surveyed did not use mimicry of the cuticular hydrocarbon recognition cues to evade host detection. Instead, we found that myrmecophiles with lower cuticular hydrocarbon concentrations provoked less host aggression. Therefore, some myrmecophiles with low hydrocarbon concentrations appear to evade host detection via a strategy known as chemical insignificance. Others showed no chemical disguise at all and, instead, relied on behavioral adaptations such as particular defense or evasion tactics, in order to evade host aggression. Overall, this study indicates that unspecialized myrmecophiles do not require the matching of host recognition cues and advanced strategies of chemical mimicry, but can integrate in a hostile ant nest via either chemical insignificance or specific behavioral adaptations.

Sudden Event Recognition: A Survey

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Mohd Asyraf Zulkifley

2013-08-01

Full Text Available Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1 the importance of a sudden event over a general anomalous event; (2 frameworks used in sudden event recognition; (3 the requirements and comparative studies of a sudden event recognition system and (4 various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.

Methods of Psychological and Pedagogical Accompaniment of First-Year Students in Process of Adapting to Learning at University

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Maralova, Tatyana P.; Filipenkova, Olesya G.; Galitskikh, Elena O.; Shulga, Tatiana I.; Sidyacheva, Natalya V.; Ovsyanik, Olga A.

2016-01-01

The relevance of the study is conditioned by the complexity of students' adaptation to learning at University due to the change of social environment, an alarming feelings about the precise self-determination, lack of knowledge in opportunities for self-expression in art, science, sport and public life. The purpose of the paper is to identify…

Distinct motor strategies underlying split-belt adaptation in human walking and running.

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Ogawa, Tetsuya; Kawashima, Noritaka; Obata, Hiroki; Kanosue, Kazuyuki; Nakazawa, Kimitaka

2015-01-01

The aim of the present study was to elucidate the adaptive and de-adaptive nature of human running on a split-belt treadmill. The degree of adaptation and de-adaptation was compared with those in walking by calculating the antero-posterior component of the ground reaction force (GRF). Adaptation to walking and running on a split-belt resulted in a prominent asymmetry in the movement pattern upon return to the normal belt condition, while the two components of the GRF showed different behaviors depending on the gaits. The anterior braking component showed prominent adaptive and de-adaptive behaviors in both gaits. The posterior propulsive component, on the other hand, exhibited such behavior only in running, while that in walking showed only short-term aftereffect (lasting less than 10 seconds) accompanied by largely reactive responses. These results demonstrate a possible difference in motor strategies (that is, the use of reactive feedback and adaptive feedforward control) by the central nervous system (CNS) for split-belt locomotor adaptation between walking and running. The present results provide basic knowledge on neural control of human walking and running as well as possible strategies for gait training in athletic and rehabilitation scenes.

Distinct motor strategies underlying split-belt adaptation in human walking and running.

Directory of Open Access Journals (Sweden)

Tetsuya Ogawa

Full Text Available The aim of the present study was to elucidate the adaptive and de-adaptive nature of human running on a split-belt treadmill. The degree of adaptation and de-adaptation was compared with those in walking by calculating the antero-posterior component of the ground reaction force (GRF. Adaptation to walking and running on a split-belt resulted in a prominent asymmetry in the movement pattern upon return to the normal belt condition, while the two components of the GRF showed different behaviors depending on the gaits. The anterior braking component showed prominent adaptive and de-adaptive behaviors in both gaits. The posterior propulsive component, on the other hand, exhibited such behavior only in running, while that in walking showed only short-term aftereffect (lasting less than 10 seconds accompanied by largely reactive responses. These results demonstrate a possible difference in motor strategies (that is, the use of reactive feedback and adaptive feedforward control by the central nervous system (CNS for split-belt locomotor adaptation between walking and running. The present results provide basic knowledge on neural control of human walking and running as well as possible strategies for gait training in athletic and rehabilitation scenes.

A LITERATURE SURVEY ON VARIOUS ILLUMINATION NORMALIZATION TECHNIQUES FOR FACE RECOGNITION WITH FUZZY K NEAREST NEIGHBOUR CLASSIFIER

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A. Thamizharasi

2015-05-01

Full Text Available The face recognition is popular in video surveillance, social networks and criminal identifications nowadays. The performance of face recognition would be affected by variations in illumination, pose, aging and partial occlusion of face by Wearing Hats, scarves and glasses etc. The illumination variations are still the challenging problem in face recognition. The aim is to compare the various illumination normalization techniques. The illumination normalization techniques include: Log transformations, Power Law transformations, Histogram equalization, Adaptive histogram equalization, Contrast stretching, Retinex, Multi scale Retinex, Difference of Gaussian, DCT, DCT Normalization, DWT, Gradient face, Self Quotient, Multi scale Self Quotient and Homomorphic filter. The proposed work consists of three steps. First step is to preprocess the face image with the above illumination normalization techniques; second step is to create the train and test database from the preprocessed face images and third step is to recognize the face images using Fuzzy K nearest neighbor classifier. The face recognition accuracy of all preprocessing techniques is compared using the AR face database of color images.

A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier

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Haryati Jaafar

2015-01-01

Full Text Available Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN, was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

Application of the pattern recognition technique to fast reactor acoustic monitoring

International Nuclear Information System (INIS)

Brunet, M.; Val, M.

1981-10-01

The early detection of operating anomalies is an aim involving the safety of fast reactors. The most likely accident is the complete or partial blocking up of an assembly, one of the signs of which is the boiling of the sodium contained in it. This boiling is accompanied by the production of acoustic waves, the detection of which by appropriate sensors appears to be an efficient way of monitoring the core. For a number of years the CEA has been conducting an experimental programme for studying the detection of boiling by acoustic means. The text presents the various different experiments undertaken and then draws a parallel between the results obtained by conventional processing procedures and those obtained by applying the shape recognition method to the same basic data [fr

Perceived Task-Difficulty Recognition from Log-File Information for the Use in Adaptive Intelligent Tutoring Systems

Science.gov (United States)

Janning, Ruth; Schatten, Carlotta; Schmidt-Thieme, Lars

2016-01-01

Recognising students' emotion, affect or cognition is a relatively young field and still a challenging task in the area of intelligent tutoring systems. There are several ways to use the output of these recognition tasks within the system. The approach most often mentioned in the literature is using it for giving feedback to the students. The…

Early life stress impairs social recognition due to a blunted response of vasopressin release within the septum of adult male rats.

Science.gov (United States)

Lukas, Michael; Bredewold, Remco; Landgraf, Rainer; Neumann, Inga D; Veenema, Alexa H

2011-07-01

Early life stress poses a risk for the development of psychopathologies characterized by disturbed emotional, social, and cognitive performance. We used maternal separation (MS, 3h daily, postnatal days 1-14) to test whether early life stress impairs social recognition performance in juvenile (5-week-old) and adult (16-week-old) male Wistar rats. Social recognition was tested in the social discrimination test and defined by increased investigation by the experimental rat towards a novel rat compared with a previously encountered rat. Juvenile control and MS rats demonstrated successful social recognition at inter-exposure intervals of 30 and 60 min. However, unlike adult control rats, adult MS rats failed to discriminate between a previously encountered and a novel rat after 60 min. The social recognition impairment of adult MS rats was accompanied by a lack of a rise in arginine vasopressin (AVP) release within the lateral septum seen during social memory acquisition in adult control rats. This blunted response of septal AVP release was social stimulus-specific because forced swimming induced a rise in septal AVP release in both control and MS rats. Retrodialysis of AVP (1 μg/ml, 3.3 μl/min, 30 min) into the lateral septum during social memory acquisition restored social recognition in adult MS rats at the 60-min interval. These studies demonstrate that MS impairs social recognition performance in adult rats, which is likely caused by blunted septal AVP activation. Impaired social recognition may be linked to MS-induced changes in other social behaviors like aggression as shown previously. Copyright © 2010 Elsevier Ltd. All rights reserved.

An Accurate and Efficient User Authentication Mechanism on Smart Glasses Based on Iris Recognition

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Yung-Hui Li

2017-01-01

Full Text Available In modern society, mobile devices (such as smart phones and wearable devices have become indispensable to almost everyone, and people store personal data in devices. Therefore, how to implement user authentication mechanism for private data protection on mobile devices is a very important issue. In this paper, an intelligent iris recognition mechanism is designed to solve the problem of user authentication in wearable smart glasses. Our contributions include hardware and software. On the hardware side, we design a set of internal infrared camera modules, including well-designed infrared light source and lens module, which is able to take clear iris images within 2~5 cm. On the software side, we propose an innovative iris segmentation algorithm which is both efficient and accurate to be used on smart glasses device. Another improvement to the traditional iris recognition is that we propose an intelligent Hamming distance (HD threshold adaptation method which dynamically fine-tunes the HD threshold used for verification according to empirical data collected. Our final system can perform iris recognition with 66 frames per second on a smart glasses platform with 100% accuracy. As far as we know, this system is the world’s first application of iris recognition on smart glasses.

Innate immune recognition and activation during HIV infection

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Larsen Carsten S

2010-06-01

Full Text Available Abstract The pathogenesis of HIV infection, and in particular the development of immunodeficiency, remains incompletely understood. Whichever intricate molecular mechanisms are at play between HIV and the host, it is evident that the organism is incapable of restricting and eradicating the invading pathogen. Both innate and adaptive immune responses are raised, but they appear to be insufficient or too late to eliminate the virus. Moreover, the picture is complicated by the fact that the very same cells and responses aimed at eliminating the virus seem to play deleterious roles by driving ongoing immune activation and progressive immunodeficiency. Whereas much knowledge exists on the role of adaptive immunity during HIV infection, it has only recently been appreciated that the innate immune response also plays an important part in HIV pathogenesis. In this review, we present current knowledge on innate immune recognition and activation during HIV infection based on studies in cell culture, non-human primates, and HIV-infected individuals, and discuss the implications for the understanding of HIV immunopathogenesis.

The Improvement of Behavior Recognition Accuracy of Micro Inertial Accelerometer by Secondary Recognition Algorithm

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Yu Liu

2014-05-01

Full Text Available Behaviors of “still”, “walking”, “running”, “jumping”, “upstairs” and “downstairs” can be recognized by micro inertial accelerometer of low cost. By using the features as inputs to the well-trained BP artificial neural network which is selected as classifier, those behaviors can be recognized. But the experimental results show that the recognition accuracy is not satisfactory. This paper presents secondary recognition algorithm and combine it with BP artificial neural network to improving the recognition accuracy. The Algorithm is verified by the Android mobile platform, and the recognition accuracy can be improved more than 8 %. Through extensive testing statistic analysis, the recognition accuracy can reach 95 % through BP artificial neural network and the secondary recognition, which is a reasonable good result from practical point of view.

Adaptation to antifaces and the perception of correct famous identity in an average face

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Anthony eLittle

2012-02-01

Full Text Available Previous experiments have examined exposure to anti-identities (faces that possess traits opposite to an identity through a population average, finding that exposure to antifaces enhances recognition of the plus-identity images. Here we examine adaptation to antifaces using famous female celebrities. We demonstrate: that exposure to a color and shape transformed antiface of a celebrity increases the likelihood of perceiving the identity from which the antiface was manufactured in a composite face and that the effect shows size invariance (Experiment 1, equivalent effects are seen in internet and laboratory based studies (Experiment 2, adaptation to shape-only antifaces has stronger effects on identity recognition than adaptation to color-only antifaces (Experiment 3, and exposure to male versions of the antifaces does not influence the perception of female’s faces (Experiment 4. Across these studies we found an effect of order where aftereffects were more pronounced in early than later trials. Overall, our studies delineate several aspects of identity aftereffects and support the proposal that identity is coded relative to other faces with special reference to a relatively sex-specific mean face representation.

Development of Adaptive AE Signal Pattern Recognition Program and Application to Classification of Defects in Metal Contact Regions of Rotating Component

International Nuclear Information System (INIS)

Lee, K. Y.; Lee, C. M.; Kim, J. S.

1996-01-01

In this study, the artificial defects in rotary compressor are classified using pattern recognition of acoustic emission signal. For this purpose the computer program is developed. The neural network classifier is compared with the statistical classifier such as the linear discriminant function classifier and empirical Bayesian classifier. It is concluded that the former is better. It is possible to acquire the recognition rate of above 99% by neural network classifier

Is otolithic vertigo accompanied by hearing loss caused by sacculocochlear endolymphatic hydrops?

Science.gov (United States)

Murofushi, Toshihisa; Komiyama, Sakurako; Hayashi, Yushi; Yoshimura, Eriko

2016-01-01

Otolithic vertigo is sometimes accompanied by hearing loss. Otolithic vertigo accompanied by hearing loss seems to be caused by sacculocochlear endolymphatic hydrops. To clarify the lesion site and pathophysiology of otolithic vertigo (OV) accompanied by hearing loss. The clinical records of four patients (two men and two women) that had been diagnosed with OV accompanied by hearing loss according to pre-determined diagnostic criteria were reviewed. The patients' main symptoms involved a sensation of movement in the pitch plane. All of the patients had low frequency-dominant hearing loss and either exhibited decreased cervical vestibular evoked myogenic potentials (cVEMP) or did not produce cVEMP. Two patients produced normal ocular VEMP (oVEMP). Caloric tests obtained normal results in all patients.

Ignorance- versus evidence-based decision making: a decision time analysis of the recognition heuristic.

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Hilbig, Benjamin E; Pohl, Rüdiger F

2009-09-01

According to part of the adaptive toolbox notion of decision making known as the recognition heuristic (RH), the decision process in comparative judgments-and its duration-is determined by whether recognition discriminates between objects. By contrast, some recently proposed alternative models predict that choices largely depend on the amount of evidence speaking for each of the objects and that decision times thus depend on the evidential difference between objects, or the degree of conflict between options. This article presents 3 experiments that tested predictions derived from the RH against those from alternative models. All experiments used naturally recognized objects without teaching participants any information and thus provided optimal conditions for application of the RH. However, results supported the alternative, evidence-based models and often conflicted with the RH. Recognition was not the key determinant of decision times, whereas differences between objects with respect to (both positive and negative) evidence predicted effects well. In sum, alternative models that allow for the integration of different pieces of information may well provide a better account of comparative judgments. (c) 2009 APA, all rights reserved.

Adaptation to walking with an exoskeleton that assists ankle extension.

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Galle, S; Malcolm, P; Derave, W; De Clercq, D

2013-07-01

The goal of this study was to investigate adaptation to walking with bilateral ankle-foot exoskeletons with kinematic control that assisted ankle extension during push-off. We hypothesized that subjects would show a neuromotor and metabolic adaptation during a 24min walking trial with a powered exoskeleton. Nine female subjects walked on a treadmill at 1.36±0.04ms(-1) during 24min with a powered exoskeleton and 4min with an unpowered exoskeleton. Subjects showed a metabolic adaptation after 18.5±5.0min, followed by an adapted period. Metabolic cost, electromyography and kinematics were compared between the unpowered condition, the beginning of the adaptation and the adapted period. In the beginning of the adaptation (4min), a reduction in metabolic cost of 9% was found compared to the unpowered condition. This reduction was accompanied by reduced muscular activity in the plantarflexor muscles, as the powered exoskeleton delivered part of the necessary ankle extension moment. During the adaptation this metabolic reduction further increased to 16%, notwithstanding a constant exoskeleton assistance. This increased reduction is the result of a neuromotor adaptation in which subjects adapt to walking with the exoskeleton, thereby reducing muscular activity in all leg muscles. Because of the fast adaptation and the significant reductions in metabolic cost we want to highlight the potential of an ankle-foot exoskeleton with kinematic control that assists ankle extension during push-off. Copyright © 2013 Elsevier B.V. All rights reserved.

The geography of sex-specific selection, local adaptation, and sexual dimorphism.

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Connallon, Tim

2015-09-01

Local adaptation and sexual dimorphism are iconic evolutionary scenarios of intraspecific adaptive differentiation in the face of gene flow. Although theory has traditionally considered local adaptation and sexual dimorphism as conceptually distinct processes, emerging data suggest that they often act concurrently during evolutionary diversification. Here, I merge theories of local adaptation in space and sex-specific adaptation over time, and show that their confluence yields several new predictions about the roles of context-specific selection, migration, and genetic correlations, in adaptive diversification. I specifically revisit two influential predictions from classical studies of clinal adaptation and sexual dimorphism: (1) that local adaptation should decrease with distance from the species' range center and (2) that opposing directional selection between the sexes (sexual antagonism) should inevitably accompany the evolution of sexual dimorphism. I show that both predictions can break down under clinally varying selection. First, the geography of local adaptation can be sexually dimorphic, with locations of relatively high local adaptation differing profoundly between the sexes. Second, the intensity of sexual antagonism varies across the species' range, with subpopulations near the range center representing hotspots for antagonistic selection. The results highlight the context-dependent roles of migration versus sexual conflict as primary constraints to adaptive diversification. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

9 CFR 93.208 - Articles accompanying poultry.

Science.gov (United States)

2010-01-01

... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Articles accompanying poultry. 93.208... AGRICULTURE EXPORTATION AND IMPORTATION OF ANIMALS (INCLUDING POULTRY) AND ANIMAL PRODUCTS IMPORTATION OF CERTAIN ANIMALS, BIRDS, FISH, AND POULTRY, AND CERTAIN ANIMAL, BIRD, AND POULTRY PRODUCTS; REQUIREMENTS...

Audio-Visual Speech Recognition Using Lip Information Extracted from Side-Face Images

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Koji Iwano

2007-03-01

Full Text Available This paper proposes an audio-visual speech recognition method using lip information extracted from side-face images as an attempt to increase noise robustness in mobile environments. Our proposed method assumes that lip images can be captured using a small camera installed in a handset. Two different kinds of lip features, lip-contour geometric features and lip-motion velocity features, are used individually or jointly, in combination with audio features. Phoneme HMMs modeling the audio and visual features are built based on the multistream HMM technique. Experiments conducted using Japanese connected digit speech contaminated with white noise in various SNR conditions show effectiveness of the proposed method. Recognition accuracy is improved by using the visual information in all SNR conditions. These visual features were confirmed to be effective even when the audio HMM was adapted to noise by the MLLR method.

Features Selection for Skin Micro-Image Symptomatic Recognition

Institute of Scientific and Technical Information of China (English)

HUYue-li; CAOJia-lin; ZHAOQian; FENGXu

2004-01-01

Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92% using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

Features Selection for Skin Micro-Image Symptomatic Recognition

Institute of Scientific and Technical Information of China (English)

HU Yue-li; CAO Jia-lin; ZHAO Qian; FENG Xu

2004-01-01

Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92 % using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

Manduca sexta recognition and resistance among allopolyploid Nicotiana host plants

Science.gov (United States)

Lou, Yonggen; Baldwin, Ian T.

2003-01-01

Allopolyploid speciation occurs instantly when the genomes of different species combine to produce self-fertile offspring and has played a central role in the evolution of higher plants, but its consequences for adaptive responses are unknown. We compare herbivore-recognition and -resistance responses of the diploid species and putative ancestral parent Nicotiana attenuata with those of the two derived allopolyploid species Nicotiana clevelandii and Nicotiana bigelovii. Manduca sexta larvae attack all three species, and in N. attenuata attack is recognized when larval oral secretions are introduced to wounds during feeding, resulting in a jasmonate burst, a systemic amplification of trypsin inhibitor accumulation, and a release of volatile organic compounds, which function as a coordinated defense response that slows caterpillar growth and increases the probability of their being attacked. Most aspects of this recognition response are retained with modifications in one allotetraploid (N. bigelovii) but lost in the other (N. clevelandii). Differences between diploid and tetraploid species were apparent in delays (maximum 1 and 0.5 h, respectively) in the jasmonate burst, the elicitation of trypsin inhibitors and release of volatile organic compounds, and the constitutive levels of nicotine, trypsin inhibitors, diterpene glycosides, rutin, and caffeoylputrescine in the leaves. Resistance to M. sexta larvae attack was most strongly associated with diterpene glycosides, which were higher in the diploid than in the two allotetraploid species. Because M. sexta elicitors differentially regulate a large proportion of the N. attenuata transcriptome, we propose that these species are suited for the study of the evolution of adaptive responses requiring trans-activation mechanisms. PMID:14530394

Genetic determinants of mate recognition in Brachionus manjavacas (Rotifera).

Science.gov (United States)

Snell, Terry W; Shearer, Tonya L; Smith, Hilary A; Kubanek, Julia; Gribble, Kristin E; Welch, David B Mark

2009-09-09

repeats are kept nearly identical through a process of concerted evolution. Information-rich molecules like surface glycoproteins are well adapted for chemical communication and aquatic animals may have evolved signaling systems based on these compounds, whereas insects use cuticular hydrocarbons. Owing to its critical role in mating, the mate recognition pheromone gene will be a useful molecular marker for exploring the mechanisms and rates of selection and the evolution of reproductive isolation and speciation using rotifers as a model system. The phylogenetic variation in the mate recognition pheromone gene can now be studied in conjunction with the large amount of ecological and population genetic data being gathered for the Brachionus plicatilis species complex to understand better the evolutionary drivers of cryptic speciation.

The Legal Recognition of Sign Languages

Science.gov (United States)

De Meulder, Maartje

2015-01-01

This article provides an analytical overview of the different types of explicit legal recognition of sign languages. Five categories are distinguished: constitutional recognition, recognition by means of general language legislation, recognition by means of a sign language law or act, recognition by means of a sign language law or act including…

Comparison of the neural correlates of retrieval success in tests of cued recall and recognition memory.

Science.gov (United States)

Okada, Kayoko; Vilberg, Kaia L; Rugg, Michael D

2012-03-01

The neural correlates of successful retrieval on tests of word stem recall and recognition memory were compared. In the recall test, subjects viewed word stems, half of which were associated with studied items and half with unstudied items, and for each stem attempted to recall a corresponding study word. In the recognition test, old/new judgments were made on old and new words. The neural correlates of successful retrieval were identified by contrasting activity elicited by correctly endorsed test items. Old > new effects common to the two tasks were found in medial and lateral parietal and right entorhinal cortex. Common new > old effects were identified in medial and left frontal cortex, and left anterior intra-parietal sulcus. Greater old > new effects were evident for cued recall in inferior parietal regions abutting those demonstrating common effects, whereas larger new > old effects were found for recall in left frontal cortex and the anterior cingulate. New > old effects were also found for the recall task in right lateral anterior prefrontal cortex, where they were accompanied by old > new effects during recognition. It is concluded that successful recall and recognition are associated with enhanced activity in a common set of recollection-sensitive parietal regions, and that the greater activation in these regions during recall reflects the greater dependence of that task on recollection. Larger new > old effects during recall are interpreted as reflections of the greater opportunity for iterative retrieval attempts when retrieval cues are partial rather than copy cues. Copyright © 2011 Wiley Periodicals, Inc.

Value-sensitive clinical accompaniment in community nursing science

Directory of Open Access Journals (Sweden)

Sonya Beukes

2010-11-01

The goal of this study was to explore and describe the experiences of students with regard to value-sensitive clinical accompaniment in the community nursing environment. An exploratory, descriptive and contextual design was used. Interactions between community nurses and students during clinical accompaniment were explored for value sensitivity by means of video recordings,participant observation and focus group interviews. Data were collected by means of video recordings, participant observation and focus group interviews. The data were analysed and coded by the researcher and the external coder, using an inductive descriptive method to identify important segments of the regularity of behaviour. The focus group interviews were transcribed, analysed and coded by the researcher and the external coder, using Tesch’s steps of analysis (Creswell 1994:155–156.Lincoln and Guba’s criteria (1985:290 for trustworthiness were applied to the study. The general findings indicate that clinical accompaniment in community nursing is not value sensitive and, as a result, guidelines for value-sensitive clinical accompaniment need to be developed for undergraduate students in the community nursing environment. The following values (values for which guidelines need to be developed were identified: respect during clinical accompaniment,value-sensitive communication and sensitivity to the quality of clinical accompaniment. Opsomming Kliniese gemeenskapsgesondheidsfasiliteite waar voorgraadse studente geplaas word vir gemeenskapsverpleegkundepraktika is dinamies en het groot veranderinge oor die laaste paar jare ondergaan. In die kliniese veld verteenwoordig gemeenskapsverpleegkundiges en voorgraadse studente verskillende rasse en taal- en etniese groepe in die Suid-Afrikaanse bevolking, elkeen met verskillende waardes. Albei partye – studente en gemeenskapsverpleegkundiges – het gerapporteer dat waardekonflik weens verskillende kulture en waardes tydens kliniese begeleiding

Sibling recognition and the development of identity: intersubjective consequences of sibling differentiation in the sister relationship.

Science.gov (United States)

Vivona, Jeanine M

2013-01-01

Identity is, among other things, a means to adapt to the others around whom one must fit. Psychoanalytic theory has highlighted ways in which the child fits in by emulating important others, especially through identification. Alternately, the child may fit into the family and around important others through differentiation, an unconscious process that involves developing or accentuating qualities and desires in oneself that are expressly different from the perceived qualities of another person and simultaneously suppressing qualities and desires that are perceived as similar. With two clinical vignettes centered on the sister relationship, the author demonstrates that recognition of identity differences that result from sibling differentiation carries special significance in the sibling relationship and simultaneously poses particular intersubjective challenges. To the extent that the spotlight of sibling recognition delimits the lateral space one may occupy, repeatedly frustrated desires for sibling recognition may have enduring consequences for one's sense of self-worth and expectations of relationships with peers and partners.

Human activity recognition and prediction

CERN Document Server

2016-01-01

This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques. .

Adaptive false memory: Imagining future scenarios increases false memories in the DRM paradigm.

Science.gov (United States)

Dewhurst, Stephen A; Anderson, Rachel J; Grace, Lydia; van Esch, Lotte

2016-10-01

Previous research has shown that rating words for their relevance to a future scenario enhances memory for those words. The current study investigated the effect of future thinking on false memory using the Deese/Roediger-McDermott (DRM) procedure. In Experiment 1, participants rated words from 6 DRM lists for relevance to a past or future event (with or without planning) or in terms of pleasantness. In a surprise recall test, levels of correct recall did not vary between the rating tasks, but the future rating conditions led to significantly higher levels of false recall than the past and pleasantness conditions did. Experiment 2 found that future rating led to higher levels of false recognition than did past and pleasantness ratings but did not affect correct recognition. The effect in false recognition was, however, eliminated when DRM items were presented in random order. Participants in Experiment 3 were presented with both DRM lists and lists of unrelated words. Future rating increased levels of false recognition for DRM lures but did not affect correct recognition for DRM or unrelated lists. The findings are discussed in terms of the view that false memories can be associated with adaptive memory functions.

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition †

Science.gov (United States)

Janko, Vito

2017-01-01

The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy. PMID:29286301

Adaptive and perceptual learning technologies in medical education and training.

Science.gov (United States)

Kellman, Philip J

2013-10-01

Recent advances in the learning sciences offer remarkable potential to improve medical education and maximize the benefits of emerging medical technologies. This article describes 2 major innovation areas in the learning sciences that apply to simulation and other aspects of medical learning: Perceptual learning (PL) and adaptive learning technologies. PL technology offers, for the first time, systematic, computer-based methods for teaching pattern recognition, structural intuition, transfer, and fluency. Synergistic with PL are new adaptive learning technologies that optimize learning for each individual, embed objective assessment, and implement mastery criteria. The author describes the Adaptive Response-Time-based Sequencing (ARTS) system, which uses each learner's accuracy and speed in interactive learning to guide spacing, sequencing, and mastery. In recent efforts, these new technologies have been applied in medical learning contexts, including adaptive learning modules for initial medical diagnosis and perceptual/adaptive learning modules (PALMs) in dermatology, histology, and radiology. Results of all these efforts indicate the remarkable potential of perceptual and adaptive learning technologies, individually and in combination, to improve learning in a variety of medical domains. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

Specific recognition of reproductive parasite workers by nest-entrance guards in the bumble bee Bombus terrestris.

Science.gov (United States)

Blacher, Pierre; Boreggio, Laurie; Leroy, Chloé; Devienne, Paul; Châline, Nicolas; Chameron, Stéphane

2013-12-10

The impact of social parasites on their hosts' fitness is a strong selective pressure that can lead to the evolution of adapted defence strategies. Guarding the nest to prevent the intrusion of parasites is a widespread response of host species. If absolute rejection of strangers provides the best protection against parasites, more fine-tuned strategies can prove more adaptive. Guarding is indeed costly and not all strangers constitute a real threat. That is particularly true for worker reproductive parasitism in social insects since only a fraction of non-nestmate visitors, the fertile ones, can readily engage in parasitic reproduction. Guards should thus be more restrictive towards fertile than sterile non-nestmate workers. We here tested this hypothesis by examining the reaction of nest-entrance guards towards nestmate and non-nestmate workers with varying fertility levels in the bumble bee Bombus terrestris. Because social recognition in social insects mainly relies on cuticular lipids (CLs), chemical analysis was also conducted to examine whether workers' CLs could convey the relevant information upon which guards could base their decision. We thus aimed to determine whether an adapted defensive strategy to worker reproductive parasitism has evolved in B. terrestris colonies. Chemical analysis revealed that the cuticular chemical profiles of workers encode information about both their colony membership and their current fertility, therefore providing potential recognition cues for a suitable adjustment of the guards' defensive decisions. We found that guards were similarly tolerant towards sterile non-nestmate workers than towards nestmate workers. However, as predicted, guards responded more aggressively towards fertile non-nestmates. Our results show that B. terrestris guards discriminate non-nestmates that differ in their reproductive potential and respond more strongly to the individuals that are a greatest threat for the colony. Cuticular hydrocarbons are

User Modeling for Activity Recognition and Support in Ambient Assisted Living

DEFF Research Database (Denmark)

Hossain, Shabbir; Valente, Pedro Ricardo da Nova; Hallenborg, Kasper

Current research work shows that progress on AI and wireless sensor networks, made it possible to improve the quality of life of the people with disabilities using recent technologies [1]. Ambient Assisted Living (AAL) is one of the well-known research areas that has a goal to use ambient...... on user modeling to be more efficient to adapt the changes of user capabilities and preferences which is strongly correlated with the prime challenges of AAL. In this paper, a user model has been proposed that tends to be used in the autonomous and reliable recognition....

Radar automatic target recognition (ATR) and non-cooperative target recognition (NCTR)

CERN Document Server

Blacknell, David

2013-01-01

The ability to detect and locate targets by day or night, over wide areas, regardless of weather conditions has long made radar a key sensor in many military and civil applications. However, the ability to automatically and reliably distinguish different targets represents a difficult challenge. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) captures material presented in the NATO SET-172 lecture series to provide an overview of the state-of-the-art and continuing challenges of radar target recognition. Topics covered include the problem as applied to th

Recognition and Toleration

DEFF Research Database (Denmark)

Lægaard, Sune

2010-01-01

Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms or inter...

39 CFR 320.7 - Suspension for advertisements accompanying parcels or periodicals.

Science.gov (United States)

2010-07-01

... 39 Postal Service 1 2010-07-01 2010-07-01 false Suspension for advertisements accompanying parcels or periodicals. 320.7 Section 320.7 Postal Service UNITED STATES POSTAL SERVICE RESTRICTIONS ON PRIVATE CARRIAGE OF LETTERS SUSPENSION OF THE PRIVATE EXPRESS STATUTES § 320.7 Suspension for advertisements accompanying parcels or periodicals. (a) Th...

Super Normal Vector for Human Activity Recognition with Depth Cameras.

Science.gov (United States)

Yang, Xiaodong; Tian, YingLi

2017-05-01

The advent of cost-effectiveness and easy-operation depth cameras has facilitated a variety of visual recognition tasks including human activity recognition. This paper presents a novel framework for recognizing human activities from video sequences captured by depth cameras. We extend the surface normal to polynormal by assembling local neighboring hypersurface normals from a depth sequence to jointly characterize local motion and shape information. We then propose a general scheme of super normal vector (SNV) to aggregate the low-level polynormals into a discriminative representation, which can be viewed as a simplified version of the Fisher kernel representation. In order to globally capture the spatial layout and temporal order, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time cells. In the extensive experiments, the proposed approach achieves superior performance to the state-of-the-art methods on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D.

Neural-network classifiers for automatic real-world aerial image recognition

Science.gov (United States)

Greenberg, Shlomo; Guterman, Hugo

1996-08-01

We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

Artificial neural network for bubbles pattern recognition on the images

International Nuclear Information System (INIS)

Poletaev, I E; Pervunin, K S; Tokarev, M P

2016-01-01

Two-phase bubble flows have been used in many technological and energy processes as processing oil, chemical and nuclear reactors. This explains large interest to experimental and numerical studies of such flows last several decades. Exploiting of optical diagnostics for analysis of the bubble flows allows researchers obtaining of instantaneous velocity fields and gaseous phase distribution with the high spatial resolution non-intrusively. Behavior of light rays exhibits an intricate manner when they cross interphase boundaries of gaseous bubbles hence the identification of the bubbles images is a complicated problem. This work presents a method of bubbles images identification based on a modern technology of deep learning called convolutional neural networks (CNN). Neural networks are able to determine overlapping, blurred, and non-spherical bubble images. They can increase accuracy of the bubble image recognition, reduce the number of outliers, lower data processing time, and significantly decrease the number of settings for the identification in comparison with standard recognition methods developed before. In addition, usage of GPUs speeds up the learning process of CNN owning to the modern adaptive subgradient optimization techniques. (paper)

Development of equally intelligible Telugu sentence-lists to test speech recognition in noise.

Science.gov (United States)

Tanniru, Kishore; Narne, Vijaya Kumar; Jain, Chandni; Konadath, Sreeraj; Singh, Niraj Kumar; Sreenivas, K J Ramadevi; K, Anusha

2017-09-01

To develop sentence lists in the Telugu language for the assessment of speech recognition threshold (SRT) in the presence of background noise through identification of the mean signal-to-noise ratio required to attain a 50% sentence recognition score (SRTn). This study was conducted in three phases. The first phase involved the selection and recording of Telugu sentences. In the second phase, 20 lists, each consisting of 10 sentences with equal intelligibility, were formulated using a numerical optimisation procedure. In the third phase, the SRTn of the developed lists was estimated using adaptive procedures on individuals with normal hearing. A total of 68 native Telugu speakers with normal hearing participated in the study. Of these, 18 (including the speakers) performed on various subjective measures in first phase, 20 performed on sentence/word recognition in noise for second phase and 30 participated in the list equivalency procedures in third phase. In all, 15 lists of comparable difficulty were formulated as test material. The mean SRTn across these lists corresponded to -2.74 (SD = 0.21). The developed sentence lists provided a valid and reliable tool to measure SRTn in Telugu native speakers.

Cardiovascular adaptations to marathon running : the marathoner's heart.

Science.gov (United States)

Thompson, Paul D

2007-01-01

Endurance exercise training produces a series of cardiac adaptations including resting bradycardia, first and second degree atrioventricular block, increased intolerance to orthostatic stress, and enlargement of the left ventricular walls and of all cardiac chambers. Cardiac dimensions may be increased beyond the upper limits of normal and some endurance athletes demonstrate mild reductions in estimated left ventricular ejection fraction. Among athletes, such adaptations occur primarily in well trained endurance athletes. Clinicians should be aware of the cardiac changes accompanying endurance training to avoid unnecessary evaluation of physiological changes. On the other hand, the presence of conduction abnormalities or cardiac enlargement in low level or recreational athletes should prompt a search for pathological causes. Many of these findings were presented in the 1977 report on the marathon and have simply been better defined with subsequent studies.

Super-recognition in development: A case study of an adolescent with extraordinary face recognition skills.

Science.gov (United States)

Bennetts, Rachel J; Mole, Joseph; Bate, Sarah

2017-09-01

Face recognition abilities vary widely. While face recognition deficits have been reported in children, it is unclear whether superior face recognition skills can be encountered during development. This paper presents O.B., a 14-year-old female with extraordinary face recognition skills: a "super-recognizer" (SR). O.B. demonstrated exceptional face-processing skills across multiple tasks, with a level of performance that is comparable to adult SRs. Her superior abilities appear to be specific to face identity: She showed an exaggerated face inversion effect and her superior abilities did not extend to object processing or non-identity aspects of face recognition. Finally, an eye-movement task demonstrated that O.B. spent more time than controls examining the nose - a pattern previously reported in adult SRs. O.B. is therefore particularly skilled at extracting and using identity-specific facial cues, indicating that face and object recognition are dissociable during development, and that super recognition can be detected in adolescence.

Fast Segmentation of Colour Apple Image under All-Weather Natural Conditions for Vision Recognition of Picking Robots

Directory of Open Access Journals (Sweden)

Wei Ji

2016-02-01

Full Text Available In order to resolve the poor real-time performance problem of the normalized cut (Ncut method in apple vision recognition of picking robots, a fast segmentation method of colour apple images based on the adaptive mean-shift and Ncut methods is proposed in this paper. Firstly, the traditional Ncut method based on pixels is changed into the Ncut method based on regions by the adaptive mean-shift initial segmenting. In this way, the number of peaks and edges in the image is dramatically reduced and the computation speed is improved. Secondly, the image is divided into regional maps by extracting the R-B colour feature, which not only reduces the quantity of regions, but also to some extent overcomes the effect on illumination. On this basis, every region map is expressed by a region point, so the undirected graph of the R-B colour grey-level feature is attained. Finally, regarding the undirected graph as the input of Ncut, we construct the weight matrix W by region points and determine the number of clusters based on the decision-theoretic rough set. The adaptive clustering segmentation can be implemented by an Ncut algorithm. Experimental results show that the maximum segmentation error is 3% and the average recognition time is less than 0.7s, which can meet the requirements of a real-time picking robot.

Ultra-fast ipsilateral DPOAE adaptation not modulated by attention?

Science.gov (United States)

Dalhoff, Ernst; Zelle, Dennis; Gummer, Anthony W.

2018-05-01

Efferent stimulation of outer hair cells is supposed to attenuate cochlear amplification of sound waves and is accompanied by reduced DPOAE amplitudes. Recently, a method using two subsequent f2 pulses during presentation of a longer f1 pulse was introduced to measure fast ipsilateral adaptation effects on separated DPOAE components. Compensating primary-tone onsets for their latencies at the f2-tonotopic place, the average adaptation measured in four normal-hearing subjects was 5.0 dB with a time constant below 5 ms. In the present study, two experiments were performed to determine the origin of this ultra-fast ipsilateral adaptation effect. The first experiment measured ultra-fast ipsilateral adaptation using a two-pulse paradigm at three frequencies in the four subjects, while controlling for visual attention of the subjects. The other experiment also controlled for visual attention, but utilized a sequence of f2 short pulses in the presence of a continuous f1 tone to sample ipsilateral adaptation effects with longer time constants in eight subjects. In the first experiment, no significant change in the ultra-fast adaptation between non-directed attention and visual attention could be detected. In contrast, the second experiment revealed significant changes in the magnitude of the slower ipsilateral adaptation in the visual-attention condition. In conclusion, the lack of an attentional influence indicates that the ultra-fast ipsilateral DPOAE adaptation is not solely mediated by the medial olivocochlear reflex.

A motivational determinant of facial emotion recognition: regulatory focus affects recognition of emotions in faces.

Science.gov (United States)

Sassenrath, Claudia; Sassenberg, Kai; Ray, Devin G; Scheiter, Katharina; Jarodzka, Halszka

2014-01-01

Two studies examined an unexplored motivational determinant of facial emotion recognition: observer regulatory focus. It was predicted that a promotion focus would enhance facial emotion recognition relative to a prevention focus because the attentional strategies associated with promotion focus enhance performance on well-learned or innate tasks - such as facial emotion recognition. In Study 1, a promotion or a prevention focus was experimentally induced and better facial emotion recognition was observed in a promotion focus compared to a prevention focus. In Study 2, individual differences in chronic regulatory focus were assessed and attention allocation was measured using eye tracking during the facial emotion recognition task. Results indicated that the positive relation between a promotion focus and facial emotion recognition is mediated by shorter fixation duration on the face which reflects a pattern of attention allocation matched to the eager strategy in a promotion focus (i.e., striving to make hits). A prevention focus did not have an impact neither on perceptual processing nor on facial emotion recognition. Taken together, these findings demonstrate important mechanisms and consequences of observer motivational orientation for facial emotion recognition.

Prediction Reweighting for Domain Adaptation.

Science.gov (United States)

Shuang Li; Shiji Song; Gao Huang

2017-07-01

There are plenty of classification methods that perform well when training and testing data are drawn from the same distribution. However, in real applications, this condition may be violated, which causes degradation of classification accuracy. Domain adaptation is an effective approach to address this problem. In this paper, we propose a general domain adaptation framework from the perspective of prediction reweighting, from which a novel approach is derived. Different from the major domain adaptation methods, our idea is to reweight predictions of the training classifier on testing data according to their signed distance to the domain separator, which is a classifier that distinguishes training data (from source domain) and testing data (from target domain). We then propagate the labels of target instances with larger weights to ones with smaller weights by introducing a manifold regularization method. It can be proved that our reweighting scheme effectively brings the source and target domains closer to each other in an appropriate sense, such that classification in target domain becomes easier. The proposed method can be implemented efficiently by a simple two-stage algorithm, and the target classifier has a closed-form solution. The effectiveness of our approach is verified by the experiments on artificial datasets and two standard benchmarks, a visual object recognition task and a cross-domain sentiment analysis of text. Experimental results demonstrate that our method is competitive with the state-of-the-art domain adaptation algorithms.

Pattern recognition & machine learning

CERN Document Server

Anzai, Y

1992-01-01

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

A REVIEW: OPTICAL CHARACTER RECOGNITION

OpenAIRE

Swati Tomar*1 & Amit Kishore2

2018-01-01

This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determine that have been proposed to realize the center of character recognition in an optical character recognition system. Even though, sufficient studies and papers are describes the techniques for converting textual content from a paper document into machine readable form. Optical character recognition is a process where the computer understands automatically the image of handwritten ...

Darwin revisited: The vagus nerve is a causal element in controlling recognition of other's emotions.

Science.gov (United States)

Colzato, Lorenza S; Sellaro, Roberta; Beste, Christian

2017-07-01

Charles Darwin proposed that via the vagus nerve, the tenth cranial nerve, emotional facial expressions are evolved, adaptive and serve a crucial communicative function. In line with this idea, the later-developed polyvagal theory assumes that the vagus nerve is the key phylogenetic substrate that regulates emotional and social behavior. The polyvagal theory assumes that optimal social interaction, which includes the recognition of emotion in faces, is modulated by the vagus nerve. So far, in humans, it has not yet been demonstrated that the vagus plays a causal role in emotion recognition. To investigate this we employed transcutaneous vagus nerve stimulation (tVNS), a novel non-invasive brain stimulation technique that modulates brain activity via bottom-up mechanisms. A sham/placebo-controlled, randomized cross-over within-subjects design was used to infer a causal relation between the stimulated vagus nerve and the related ability to recognize emotions as indexed by the Reading the Mind in the Eyes Test in 38 healthy young volunteers. Active tVNS, compared to sham stimulation, enhanced emotion recognition for easy items, suggesting that it promoted the ability to decode salient social cues. Our results confirm that the vagus nerve is causally involved in emotion recognition, supporting Darwin's argumentation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Compensation for the signal processing characteristics of ultrasound B-mode scanners in adaptive speckle reduction.

Science.gov (United States)

Crawford, D C; Bell, D S; Bamber, J C

1993-01-01

A systematic method to compensate for nonlinear amplification of individual ultrasound B-scanners has been investigated in order to optimise performance of an adaptive speckle reduction (ASR) filter for a wide range of clinical ultrasonic imaging equipment. Three potential methods have been investigated: (1) a method involving an appropriate selection of the speckle recognition feature was successful when the scanner signal processing executes simple logarithmic compressions; (2) an inverse transform (decompression) of the B-mode image was effective in correcting for the measured characteristics of image data compression when the algorithm was implemented in full floating point arithmetic; (3) characterising the behaviour of the statistical speckle recognition feature under conditions of speckle noise was found to be the method of choice for implementation of the adaptive speckle reduction algorithm in limited precision integer arithmetic. In this example, the statistical features of variance and mean were investigated. The third method may be implemented on commercially available fast image processing hardware and is also better suited for transfer into dedicated hardware to facilitate real-time adaptive speckle reduction. A systematic method is described for obtaining ASR calibration data from B-mode images of a speckle producing phantom.

Non-native Listeners’ Recognition of High-Variability Speech Using PRESTO

Science.gov (United States)

Tamati, Terrin N.; Pisoni, David B.

2015-01-01

Background Natural variability in speech is a significant challenge to robust successful spoken word recognition. In everyday listening environments, listeners must quickly adapt and adjust to multiple sources of variability in both the signal and listening environments. High-variability speech may be particularly difficult to understand for non-native listeners, who have less experience with the second language (L2) phonological system and less detailed knowledge of sociolinguistic variation of the L2. Purpose The purpose of this study was to investigate the effects of high-variability sentences on non-native speech recognition and to explore the underlying sources of individual differences in speech recognition abilities of non-native listeners. Research Design Participants completed two sentence recognition tasks involving high-variability and low-variability sentences. They also completed a battery of behavioral tasks and self-report questionnaires designed to assess their indexical processing skills, vocabulary knowledge, and several core neurocognitive abilities. Study Sample Native speakers of Mandarin (n = 25) living in the United States recruited from the Indiana University community participated in the current study. A native comparison group consisted of scores obtained from native speakers of English (n = 21) in the Indiana University community taken from an earlier study. Data Collection and Analysis Speech recognition in high-variability listening conditions was assessed with a sentence recognition task using sentences from PRESTO (Perceptually Robust English Sentence Test Open-Set) mixed in 6-talker multitalker babble. Speech recognition in low-variability listening conditions was assessed using sentences from HINT (Hearing In Noise Test) mixed in 6-talker multitalker babble. Indexical processing skills were measured using a talker discrimination task, a gender discrimination task, and a forced-choice regional dialect categorization task. Vocabulary

A new time-adaptive discrete bionic wavelet transform for enhancing speech from adverse noise environment

Science.gov (United States)

Palaniswamy, Sumithra; Duraisamy, Prakash; Alam, Mohammad Showkat; Yuan, Xiaohui

2012-04-01

Automatic speech processing systems are widely used in everyday life such as mobile communication, speech and speaker recognition, and for assisting the hearing impaired. In speech communication systems, the quality and intelligibility of speech is of utmost importance for ease and accuracy of information exchange. To obtain an intelligible speech signal and one that is more pleasant to listen, noise reduction is essential. In this paper a new Time Adaptive Discrete Bionic Wavelet Thresholding (TADBWT) scheme is proposed. The proposed technique uses Daubechies mother wavelet to achieve better enhancement of speech from additive non- stationary noises which occur in real life such as street noise and factory noise. Due to the integration of human auditory system model into the wavelet transform, bionic wavelet transform (BWT) has great potential for speech enhancement which may lead to a new path in speech processing. In the proposed technique, at first, discrete BWT is applied to noisy speech to derive TADBWT coefficients. Then the adaptive nature of the BWT is captured by introducing a time varying linear factor which updates the coefficients at each scale over time. This approach has shown better performance than the existing algorithms at lower input SNR due to modified soft level dependent thresholding on time adaptive coefficients. The objective and subjective test results confirmed the competency of the TADBWT technique. The effectiveness of the proposed technique is also evaluated for speaker recognition task under noisy environment. The recognition results show that the TADWT technique yields better performance when compared to alternate methods specifically at lower input SNR.

Vehicle recognition and tracking using a generic multi-sensor and multi-algorithm fusion approach

OpenAIRE

Nashashibi , Fawzi; Khammari , Ayoub; Laurgeau , Claude

2008-01-01

International audience; This paper tackles the problem of improving the robustness of vehicle detection for Adaptive Cruise Control (ACC) applications. Our approach is based on a multisensor and a multialgorithms data fusion for vehicle detection and recognition. Our architecture combines two sensors: a frontal camera and a laser scanner. The improvement of the robustness stems from two aspects. First, we addressed the vision-based detection by developing an original approach based on fine gr...

Long-Term Social Recognition Memory in Zebrafish.

Science.gov (United States)

Madeira, Natália; Oliveira, Rui F

2017-08-01

In species in which individuals live in stable social groups, individual recognition is expected to evolve to allow individuals to remember past interactions with different individuals and adjust future behavior toward them accordingly. Thus, social memory is expected to be a ubiquitous component of social cognition of social species. However, few studies have investigated the occurrence of social memory in non-mammals. Here we evaluated the ability of zebrafish (Danio rerio) to recognize different conspecifics and to retain this information in long lasting (i.e. 24 h) memories. We used a social discrimination paradigm, adapted from mouse studies, in which the focal individual meets two pairs of conspecifics in two consecutive days: one conspecific is the same in both days and the other is different between days 1 and 2. If animals have the ability to discriminate between different conspecifics, it is predicted that they will spend more time exploring the novel than the familiar (i.e. already seen in day 1) conspecific. In this study, zebrafish with access to both olfactory and visual conspecific cues exhibited consistent recognition of a previously encountered (familiar) conspecific after a 24 h delay. This result supports the hypothesis that long-term social memory, previously described in mammals, is also present in zebrafish, hence extending the evidence for the presence of this type of memory to teleost fish.

Challenging ocular image recognition

Science.gov (United States)

Pauca, V. Paúl; Forkin, Michael; Xu, Xiao; Plemmons, Robert; Ross, Arun A.

2011-06-01

Ocular recognition is a new area of biometric investigation targeted at overcoming the limitations of iris recognition performance in the presence of non-ideal data. There are several advantages for increasing the area beyond the iris, yet there are also key issues that must be addressed such as size of the ocular region, factors affecting performance, and appropriate corpora to study these factors in isolation. In this paper, we explore and identify some of these issues with the goal of better defining parameters for ocular recognition. An empirical study is performed where iris recognition methods are contrasted with texture and point operators on existing iris and face datasets. The experimental results show a dramatic recognition performance gain when additional features are considered in the presence of poor quality iris data, offering strong evidence for extending interest beyond the iris. The experiments also highlight the need for the direct collection of additional ocular imagery.

Recognition of microbial glycolipids by Natural Killer T cells

Directory of Open Access Journals (Sweden)

Dirk Michael Zajonc

2015-08-01

Full Text Available T cells can recognize microbial antigens when presented by dedicated antigen-presenting molecules. While peptides are presented by classical members of the Major Histocompatibility (MHC family (MHC I and II, lipids, glycolipids and lipopeptides can be presented by the non-classical MHC member CD1. The best studied subset of lipid-reactive T cells are Type I Natural killer T (iNKT cells that recognize a variety of different antigens when presented by the non-classical MHCI homolog CD1d. iNKT cells have been shown to be important for the protection against various microbial pathogens, including B. burgdorferi the causative agents of Lyme disease and S. pneumoniae, which causes pneumococcal meningitis and community-acquired pneumonia. Both pathogens carry microbial glycolipids that can trigger the T cell antigen receptor (TCR, leading to iNKT cell activation. iNKT cells have an evolutionary conserved TCR alpha chain, yet retain the ability to recognize structurally diverse glycolipids. They do so using a conserved recognition mode, in which the TCR enforces a conserved binding orientation on CD1d. TCR binding is accompanied by structural changes within the TCR binding site of CD1d, as well as the glycolipid antigen itself. In addition to direct recognition of microbial antigens, iNKT cells can also be activated by a combination of cytokines (IL-12/IL-18 and TCR stimulation. Many microbes carry TLR antigens and microbial infections can lead to TLR activation. The subsequent cytokine response in turn lower the threshold of TCR mediated iNKT cell activation, especially when weak microbial or even self-antigens are presented during the cause of the infection. In summary, iNKT cells can be directly activated through TCR triggering of strong antigens, while cytokines produced by the innate immune response may be necessary for TCR triggering and iNKT cell activation in the presence of weak antigens. Here we will review the molecular basis of iNKT cell

Noise adaptation in integrate-and fire neurons.

Science.gov (United States)

Rudd, M E; Brown, L G

1997-07-01

The statistical spiking response of an ensemble of identically prepared stochastic integrate-and-fire neurons to a rectangular input current plus gaussian white noise is analyzed. It is shown that, on average, integrate-and-fire neurons adapt to the root-mean-square noise level of their input. This phenomenon is referred to as noise adaptation. Noise adaptation is characterized by a decrease in the average neural firing rate and an accompanying decrease in the average value of the generator potential, both of which can be attributed to noise-induced resets of the generator potential mediated by the integrate-and-fire mechanism. A quantitative theory of noise adaptation in stochastic integrate-and-fire neurons is developed. It is shown that integrate-and-fire neurons, on average, produce transient spiking activity whenever there is an increase in the level of their input noise. This transient noise response is either reduced or eliminated over time, depending on the parameters of the model neuron. Analytical methods are used to prove that nonleaky integrate-and-fire neurons totally adapt to any constant input noise level, in the sense that their asymptotic spiking rates are independent of the magnitude of their input noise. For leaky integrate-and-fire neurons, the long-run noise adaptation is not total, but the response to noise is partially eliminated. Expressions for the probability density function of the generator potential and the first two moments of the potential distribution are derived for the particular case of a nonleaky neuron driven by gaussian white noise of mean zero and constant variance. The functional significance of noise adaptation for the performance of networks comprising integrate-and-fire neurons is discussed.

Improving Face Verification in Photo Albums by Combining Facial Recognition and Metadata With Cross-Matching

Science.gov (United States)

2017-12-01

with our method. The third chapter presents the description and implementation of our approach. We provide a definition of the dataset, the...a means of classification using the shape and the texture. 16 Figure 7. 3D Alignment Pipeline. Adapted from [20]. In 2014, Facebook announced...Stating that face recognition consists of four main stages, detect ⟹ align ⟹ represent ⟹ classify, the Facebook team’s intent is to revisit the

Real-time implementation of an interactive jazz accompaniment system

Science.gov (United States)

Deshpande, Nikhil

Modern computational algorithms and digital signal processing (DSP) are able to combine with human performers without forced or predetermined structure in order to create dynamic and real-time accompaniment systems. With modern computing power and intelligent algorithm layout and design, it is possible to achieve more detailed auditory analysis of live music. Using this information, computer code can follow and predict how a human's musical performance evolves, and use this to react in a musical manner. This project builds a real-time accompaniment system to perform together with live musicians, with a focus on live jazz performance and improvisation. The system utilizes a new polyphonic pitch detector and embeds it in an Ableton Live system - combined with Max for Live - to perform elements of audio analysis, generation, and triggering. The system also relies on tension curves and information rate calculations from the Creative Artificially Intuitive and Reasoning Agent (CAIRA) system to help understand and predict human improvisation. These metrics are vital to the core system and allow for extrapolated audio analysis. The system is able to react dynamically to a human performer, and can successfully accompany the human as an entire rhythm section.

Unrealistic optimism and 'nosognosia': illness recognition in the healthy brain.

Science.gov (United States)

McKay, Ryan; Buchmann, Andreas; Germann, Nicole; Yu, Shancong; Brugger, Peter

2014-12-01

At the centenary of research on anosognosia, the time seems ripe to supplement work in anosognosic patients with empirical studies on nosognosia in healthy participants. To this end, we adopted a signal detection framework to investigate the lateralized recognition of illness words--an operational measure of nosognosia--in healthy participants. As positively biased reports about one's current health status (anosognosia) and future health status (unrealistic optimism) have both been associated with deficient right hemispheric functioning, and conversely with undisturbed left hemispheric functioning, we hypothesised that more optimistic participants would adopt a more conservative response criterion, and/or display less sensitivity, when identifying illnesses in our nosognosia task; especially harmful illnesses presented to the left hemisphere via the right visual field. Thirty-two healthy right-handed men estimated their own relative risk of contracting a series of illnesses in the future, and then completed a novel computer task assessing their recognition of illness names presented to the left or right visual field. To check that effects were specific to the recognition of illness (rather than reflecting recognition of lexical items per se), we also administered a standard lateralized lexical decision task. Highly optimistic participants tended to be more conservative in detecting illnesses, especially harmful illnesses presented to the right visual field. Contrary to expectation, they were also more sensitive to illness names in this half-field. We suggest that, in evolutionary terms, unrealistic optimism may be an adaptive trait that combines a high perceptual sensitivity to threat with a high threshold for acknowledging its presence. The signal detection approach to nosognosia developed here may open up new avenues for the understanding of anosognosia in neurological patients. Copyright © 2014 Elsevier Ltd. All rights reserved.

Genetic specificity of face recognition.

Science.gov (United States)

Shakeshaft, Nicholas G; Plomin, Robert

2015-10-13

Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities.

Face Detection and Recognition

National Research Council Canada - National Science Library

Jain, Anil K

2004-01-01

This report describes research efforts towards developing algorithms for a robust face recognition system to overcome many of the limitations found in existing two-dimensional facial recognition systems...

Cases to accompany contemporary strategy analysis

OpenAIRE

R. Grant

2007-01-01

This volume represents an ongoing committment to examining the concepts and techniques of business strategy analysis in the context of real business situations. The cases have been written to accompany Contemporary Strategy Analysis textbook. Each case presents issues that illuminate the ideas, concepts, and analytical techniques contained in one or more chapters of the textbook. Most important, the cases promote deep learning by students of strategic management by requiring the application o...

Aberrant neural networks for the recognition memory of socially relevant information in patients with schizophrenia.

Science.gov (United States)

Oh, Jooyoung; Chun, Ji-Won; Kim, Eunseong; Park, Hae-Jeong; Lee, Boreom; Kim, Jae-Jin

2017-01-01

Patients with schizophrenia exhibit several cognitive deficits, including memory impairment. Problems with recognition memory can hinder socially adaptive behavior. Previous investigations have suggested that altered activation of the frontotemporal area plays an important role in recognition memory impairment. However, the cerebral networks related to these deficits are not known. The aim of this study was to elucidate the brain networks required for recognizing socially relevant information in patients with schizophrenia performing an old-new recognition task. Sixteen patients with schizophrenia and 16 controls participated in this study. First, the subjects performed the theme-identification task during functional magnetic resonance imaging. In this task, pictures depicting social situations were presented with three words, and the subjects were asked to select the best theme word for each picture. The subjects then performed an old-new recognition task in which they were asked to discriminate whether the presented words were old or new. Task performance and neural responses in the old-new recognition task were compared between the subject groups. An independent component analysis of the functional connectivity was performed. The patients with schizophrenia exhibited decreased discriminability and increased activation of the right superior temporal gyrus compared with the controls during correct responses. Furthermore, aberrant network activities were found in the frontopolar and language comprehension networks in the patients. The functional connectivity analysis showed aberrant connectivity in the frontopolar and language comprehension networks in the patients with schizophrenia, and these aberrations possibly contribute to their low recognition performance and social dysfunction. These results suggest that the frontopolar and language comprehension networks are potential therapeutic targets in patients with schizophrenia.

Forensic Face Recognition: A Survey

NARCIS (Netherlands)

Ali, Tauseef; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; Quaglia, Adamo; Epifano, Calogera M.

2012-01-01

The improvements of automatic face recognition during the last 2 decades have disclosed new applications like border control and camera surveillance. A new application field is forensic face recognition. Traditionally, face recognition by human experts has been used in forensics, but now there is a

Voice Recognition in Face-Blind Patients

Science.gov (United States)

Liu, Ran R.; Pancaroglu, Raika; Hills, Charlotte S.; Duchaine, Brad; Barton, Jason J. S.

2016-01-01

Right or bilateral anterior temporal damage can impair face recognition, but whether this is an associative variant of prosopagnosia or part of a multimodal disorder of person recognition is an unsettled question, with implications for cognitive and neuroanatomic models of person recognition. We assessed voice perception and short-term recognition of recently heard voices in 10 subjects with impaired face recognition acquired after cerebral lesions. All 4 subjects with apperceptive prosopagnosia due to lesions limited to fusiform cortex had intact voice discrimination and recognition. One subject with bilateral fusiform and anterior temporal lesions had a combined apperceptive prosopagnosia and apperceptive phonagnosia, the first such described case. Deficits indicating a multimodal syndrome of person recognition were found only in 2 subjects with bilateral anterior temporal lesions. All 3 subjects with right anterior temporal lesions had normal voice perception and recognition, 2 of whom performed normally on perceptual discrimination of faces. This confirms that such lesions can cause a modality-specific associative prosopagnosia. PMID:25349193

Extramedullary plasmacytoma involving perirenal space accompanied by extramedullary hematopoiesis and amyloid deposition.

Science.gov (United States)

Mimura, Rie; Kamishima, Tamotsu; Kubota, Kanako C; Nakano, Fumihito; Yabe, Ichiro; Sasaki, Hidenao; Maruyama, Satoru; Shinohara, Nobuo; Harris, Ardene A; Haga, Hironori; Shirato, Hiroki; Terae, Satoshi

2010-05-01

A 62-year-old man was referred to us after unsuccessful treatment of bilateral weakness in his upper and lower extremities with paresthesia in both lower extremities. Computed tomography (CT) revealed soft tissue masses in the left kidney along the capsule and paraaortic region that were of relatively low attenuation with accompanying granular calcifications. Pathological diagnosis of the biopsy specimen was extramedullary plasmacytoma accompanied by extramedullary hematopoiesis and amyloid deposition. Although the CT findings correlated well with the pathological results, the case was extremely atypical for extramedullary plasmacytoma in respect to location and the accompaniment with extramedullary hematopoiesis.

Invariant Face recognition Using Infrared Images

International Nuclear Information System (INIS)

Zahran, E.G.

2012-01-01

Over the past few decades, face recognition has become a rapidly growing research topic due to the increasing demands in many applications of our daily life such as airport surveillance, personal identification in law enforcement, surveillance systems, information safety, securing financial transactions, and computer security. The objective of this thesis is to develop a face recognition system capable of recognizing persons with a high recognition capability, low processing time, and under different illumination conditions, and different facial expressions. The thesis presents a study for the performance of the face recognition system using two techniques; the Principal Component Analysis (PCA), and the Zernike Moments (ZM). The performance of the recognition system is evaluated according to several aspects including the recognition rate, and the processing time. Face recognition systems that use visual images are sensitive to variations in the lighting conditions and facial expressions. The performance of these systems may be degraded under poor illumination conditions or for subjects of various skin colors. Several solutions have been proposed to overcome these limitations. One of these solutions is to work in the Infrared (IR) spectrum. IR images have been suggested as an alternative source of information for detection and recognition of faces, when there is little or no control over lighting conditions. This arises from the fact that these images are formed due to thermal emissions from skin, which is an intrinsic property because these emissions depend on the distribution of blood vessels under the skin. On the other hand IR face recognition systems still have limitations with temperature variations and recognition of persons wearing eye glasses. In this thesis we will fuse IR images with visible images to enhance the performance of face recognition systems. Images are fused using the wavelet transform. Simulation results show that the fusion of visible and

Neural mechanisms of individual and sexual recognition in Syrian hamsters (Mesocricetus auratus).

Science.gov (United States)

Petrulis, Aras

2009-06-25

Recognizing the individual and sexual identities of conspecifics is critical for adaptive social behavior and, in most mammals this information is communicated primarily by chemosensory cues. Due to its heavy reliance on odor cues, we have used the Syrian hamster as our model species for investigating the neural regulation of social recognition. Using lesion, electrophysiological and immunocytochemical techniques, separate neural pathways underlying recognition of individual odors and guidance of sex-typical responses to opposite-sex odors have been identified in both male and female hamsters. Specifically, we have found that recognition of individual odor identity requires olfactory bulb connections to entorhinal cortex (ENT) rather than other chemoreceptive brain regions. This kind of social memory does not appear to require the hippocampus and may, instead, depend on ENT connections with piriform cortex. In contrast, sexual recognition, through either differential investigation or scent marking toward opposite-sex odors, depends on both olfactory and vomeronasal system input to the corticomedial amygdala. Preference for investigating opposite-sex odors requires primarily olfactory input to the medial amygdala (ME) whereas appropriately targeted scent marking responses require vomeronasal input to ME as well as to other structures. Within the ME, the anterior section (MEa) appears important for evaluating or classifying social odors whereas the posterodorsal region (MEpd) may be more involved in generating approach to social odors. Evidence is presented that analysis of social odors may initially be done in MEa and then communicated to MEpd, perhaps through micro-circuits that separately process male and female odors.

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

Science.gov (United States)

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.

End-Stop Exemplar Based Recognition

DEFF Research Database (Denmark)

Olsen, Søren I.

2003-01-01

An approach to exemplar based recognition of visual shapes is presented. The shape information is described by attributed interest points (keys) detected by an end-stop operator. The attributes describe the statistics of lines and edges local to the interest point, the position of neighboring int...... interest points, and (in the training phase) a list of recognition names. Recognition is made by a simple voting procedure. Preliminary experiments indicate that the recognition is robust to noise, small deformations, background clutter and partial occlusion....

Speech Recognition on Mobile Devices

DEFF Research Database (Denmark)

Tan, Zheng-Hua; Lindberg, Børge

2010-01-01

in the mobile context covering motivations, challenges, fundamental techniques and applications. Three ASR architectures are introduced: embedded speech recognition, distributed speech recognition and network speech recognition. Their pros and cons and implementation issues are discussed. Applications within......The enthusiasm of deploying automatic speech recognition (ASR) on mobile devices is driven both by remarkable advances in ASR technology and by the demand for efficient user interfaces on such devices as mobile phones and personal digital assistants (PDAs). This chapter presents an overview of ASR...

Markov Models for Handwriting Recognition

CERN Document Server

Plotz, Thomas

2011-01-01

Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden

Examining ERP correlates of recognition memory: Evidence of accurate source recognition without recollection

Science.gov (United States)

Addante, Richard, J.; Ranganath, Charan; Yonelinas, Andrew, P.

2012-01-01

Recollection is typically associated with high recognition confidence and accurate source memory. However, subjects sometimes make accurate source memory judgments even for items that are not confidently recognized, and it is not known whether these responses are based on recollection or some other memory process. In the current study, we measured event related potentials (ERPs) while subjects made item and source memory confidence judgments in order to determine whether recollection supported accurate source recognition responses for items that were not confidently recognized. In line with previous studies, we found that recognition memory was associated with two ERP effects: an early on-setting FN400 effect, and a later parietal old-new effect [Late Positive Component (LPC)], which have been associated with familiarity and recollection, respectively. The FN400 increased gradually with item recognition confidence, whereas the LPC was only observed for highly confident recognition responses. The LPC was also related to source accuracy, but only for items that had received a high confidence item recognition response; accurate source judgments to items that were less confidently recognized did not exhibit the typical ERP correlate of recollection or familiarity, but rather showed a late, broadly distributed negative ERP difference. The results indicate that accurate source judgments of episodic context can occur even when recollection fails. PMID:22548808

Evaluating music emotion recognition:Lessons from music genre recognition?

OpenAIRE

Sturm, Bob L.

2013-01-01

A fundamental problem with nearly all work in music genre recognition (MGR)is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER syste...

Word Recognition in Auditory Cortex

Science.gov (United States)

DeWitt, Iain D. J.

2013-01-01

Although spoken word recognition is more fundamental to human communication than text recognition, knowledge of word-processing in auditory cortex is comparatively impoverished. This dissertation synthesizes current models of auditory cortex, models of cortical pattern recognition, models of single-word reading, results in phonetics and results in…

[Comparative studies of face recognition].

Science.gov (United States)

Kawai, Nobuyuki

2012-07-01

Every human being is proficient in face recognition. However, the reason for and the manner in which humans have attained such an ability remain unknown. These questions can be best answered-through comparative studies of face recognition in non-human animals. Studies in both primates and non-primates show that not only primates, but also non-primates possess the ability to extract information from their conspecifics and from human experimenters. Neural specialization for face recognition is shared with mammals in distant taxa, suggesting that face recognition evolved earlier than the emergence of mammals. A recent study indicated that a social insect, the golden paper wasp, can distinguish their conspecific faces, whereas a closely related species, which has a less complex social lifestyle with just one queen ruling a nest of underlings, did not show strong face recognition for their conspecifics. Social complexity and the need to differentiate between one another likely led humans to evolve their face recognition abilities.

Peroneal tendon displacement accompanying intra-articular calcaneal fractures.

Science.gov (United States)

Toussaint, Rull James; Lin, Darius; Ehrlichman, Lauren K; Ellington, J Kent; Strasser, Nicholas; Kwon, John Y

2014-02-19

Peroneal tendon displacement (subluxation or dislocation) accompanying an intra-articular calcaneal fracture is often undetected and under-treated. The goals of this study were to determine (1) the prevalence of peroneal tendon displacement accompanying intra-articular calcaneal fractures, (2) the association of tendon displacement with fracture classifications, (3) the association of tendon displacement with heel width, and (4) the rate of missed diagnosis of the tendon displacement on radiographs and computed tomography (CT) scans and the resulting treatment rate. A retrospective radiographic review of all calcaneal fractures presenting at three institutions from June 30, 2006, to June 30, 2011, was performed. CT imaging of 421 intra-articular calcaneal fractures involving the posterior facet was available for review. The prevalence of peroneal tendon displacement was noted and its associations with fracture classification and heel width were evaluated. Peroneal tendon displacement was identified in 118 (28.0%) of the 421 calcaneal fracture cases. The presence of tendon displacement was significantly associated with joint-depression fractures compared with tongue-type fractures (p displacement had been identified in the radiology reports. Although sixty-five (55.1%) of the fractures with tendon displacement had been treated with internal fixation, the tendon displacement was treated surgically in only seven (10.8%) of these cases. Analysis of CT images showed a 28% prevalence of peroneal tendon displacement accompanying intra-articular calcaneal fractures. Surgeons and radiologists are encouraged to consider this association.

Surgical management of complete penile duplication accompanied by multiple anomalies.

Science.gov (United States)

Karaca, Irfan; Turk, Erdal; Ucan, A Basak; Yayla, Derya; Itirli, Gulcin; Ercal, Derya

2014-09-01

Diphallus (penile duplication) is very rare and seen once every 5.5 million births. It can be isolated, but is usually accompanied by other congenital anomalies. Previous studies have reported many concurrent anomalies, such as bladder extrophy, cloacal extrophy, duplicated bladder, scrotal abnormalities, hypospadias, separated symphysis pubis, intestinal anomalies and imperforate anus; no penile duplication case accompanied by omphalocele has been reported. We present the surgical management of a patient with multiple anomalies, including complete penile duplication, hypo-gastric omphalocele and extrophic rectal duplication.

A biologically inspired neural network model to transformation invariant object recognition

Science.gov (United States)

Iftekharuddin, Khan M.; Li, Yaqin; Siddiqui, Faraz

2007-09-01

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to

Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

Science.gov (United States)

Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

2017-12-01

The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genetic determinants of mate recognition in Brachionus manjavacas (Rotifera

Directory of Open Access Journals (Sweden)

Kubanek Julia

2009-09-01

, even at synonymous positions, suggests that the repeats are kept nearly identical through a process of concerted evolution. Information-rich molecules like surface glycoproteins are well adapted for chemical communication and aquatic animals may have evolved signaling systems based on these compounds, whereas insects use cuticular hydrocarbons. Conclusion Owing to its critical role in mating, the mate recognition pheromone gene will be a useful molecular marker for exploring the mechanisms and rates of selection and the evolution of reproductive isolation and speciation using rotifers as a model system. The phylogenetic variation in the mate recognition pheromone gene can now be studied in conjunction with the large amount of ecological and population genetic data being gathered for the Brachionus plicatilis species complex to understand better the evolutionary drivers of cryptic speciation.

Multivariate statistical pattern recognition system for reactor noise analysis

International Nuclear Information System (INIS)

Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

1976-01-01

A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system

Multivariate statistical pattern recognition system for reactor noise analysis

International Nuclear Information System (INIS)

Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

1975-01-01

A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system. 19 references

Predicting consonant recognition and confusions in normal-hearing listeners

DEFF Research Database (Denmark)

Zaar, Johannes; Dau, Torsten

2017-01-01

, Kollmeier, and Kohlrausch [(1997). J. Acoust. Soc. Am. 102, 2892–2905]. The model was evaluated based on the extensive consonant perception data set provided by Zaar and Dau [(2015). J. Acoust. Soc. Am. 138, 1253–1267], which was obtained with normal-hearing listeners using 15 consonant-vowel combinations...... confusion groups. The large predictive power of the proposed model suggests that adaptive processes in the auditory preprocessing in combination with a cross-correlation based template-matching back end can account for some of the processes underlying consonant perception in normal-hearing listeners....... The proposed model may provide a valuable framework, e.g., for investigating the effects of hearing impairment and hearing-aid signal processing on phoneme recognition....

Visual Recognition Memory across Contexts

Science.gov (United States)

Jones, Emily J. H.; Pascalis, Olivier; Eacott, Madeline J.; Herbert, Jane S.

2011-01-01

In two experiments, we investigated the development of representational flexibility in visual recognition memory during infancy using the Visual Paired Comparison (VPC) task. In Experiment 1, 6- and 9-month-old infants exhibited recognition when familiarization and test occurred in the same room, but showed no evidence of recognition when…

Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

Directory of Open Access Journals (Sweden)

I. Topalova

2005-03-01

Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

Directory of Open Access Journals (Sweden)

I. Topalova

2008-11-01

Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors

Directory of Open Access Journals (Sweden)

Minglin Wu

2016-10-01

Full Text Available In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction. Based on the feature extraction guided by the built physical model, various classifiers are accomplished to recognize different driving behaviors. Leveraging the system, normal driving behaviors (such as accelerating, braking, lane changing and turning with caution and aggressive driving behaviors (such as accelerating, braking, lane changing and turning with a sudden can be classified with a high accuracy of 93.25%. Compared with traditional driving behavior recognition methods using machine learning only, the proposed system possesses a solid theoretical basis, performs better and has good prospects.

Sign Language Recognition System using Neural Network for Digital Hardware Implementation

International Nuclear Information System (INIS)

Vargas, Lorena P; Barba, Leiner; Torres, C O; Mattos, L

2011-01-01

This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

Stimulus-independent semantic bias misdirects word recognition in older adults.

Science.gov (United States)

Rogers, Chad S; Wingfield, Arthur

2015-07-01

Older adults' normally adaptive use of semantic context to aid in word recognition can have a negative consequence of causing misrecognitions, especially when the word actually spoken sounds similar to a word that more closely fits the context. Word-pairs were presented to young and older adults, with the second word of the pair masked by multi-talker babble varying in signal-to-noise ratio. Results confirmed older adults' greater tendency to misidentify words based on their semantic context compared to the young adults, and to do so with a higher level of confidence. This age difference was unaffected by differences in the relative level of acoustic masking.

Listening to a non-native speaker: Adaptation and generalization

Science.gov (United States)

Clarke, Constance M.

2004-05-01

Non-native speech can cause perceptual difficulty for the native listener, but experience can moderate this difficulty. This study explored the perceptual benefit of a brief (approximately 1 min) exposure to foreign-accented speech using a cross-modal word matching paradigm. Processing speed was tracked by recording reaction times (RTs) to visual probe words following English sentences produced by a Spanish-accented speaker. In experiment 1, RTs decreased significantly over 16 accented utterances and by the end were equal to RTs to a native voice. In experiment 2, adaptation to one Spanish-accented voice improved perceptual efficiency for a new Spanish-accented voice, indicating that abstract properties of accented speech are learned during adaptation. The control group in Experiment 2 also adapted to the accented voice during the test block, suggesting adaptation can occur within two to four sentences. The results emphasize the flexibility of the human speech processing system and the need for a mechanism to explain this adaptation in models of spoken word recognition. [Research supported by an NSF Graduate Research Fellowship and the University of Arizona Cognitive Science Program.] a)Currently at SUNY at Buffalo, Dept. of Psych., Park Hall, Buffalo, NY 14260, cclarke2@buffalo.edu

On the susceptibility of adaptive memory to false memory illusions

OpenAIRE

Howe, M. L.; Derbish, M. H.

2010-01-01

Previous research has shown that survival-related processing of word lists enhances retention for that material. However, the claim that survival-related memories are more accurate has only been examined when true recall and recognition of neutral material has been measured. In the current experiments, we examined the adaptive memory superiority effect for different types of processing and material, measuring accuracy more directly by comparing true and false recollection rates. Survival-rela...

Non Audio-Video gesture recognition system

DEFF Research Database (Denmark)

Craciunescu, Razvan; Mihovska, Albena Dimitrova; Kyriazakos, Sofoklis

2016-01-01

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current research focus includes on the emotion...... recognition from the face and hand gesture recognition. Gesture recognition enables humans to communicate with the machine and interact naturally without any mechanical devices. This paper investigates the possibility to use non-audio/video sensors in order to design a low-cost gesture recognition device...

Improving emotion recognition systems by embedding cardiorespiratory coupling

International Nuclear Information System (INIS)

Valenza, Gaetano; Lanatá, Antonio; Scilingo, Enzo Pasquale

2013-01-01

This work aims at showing improved performances of an emotion recognition system embedding information gathered from cardiorespiratory (CR) coupling. Here, we propose a novel methodology able to robustly identify up to 25 regions of a two-dimensional space model, namely the well-known circumplex model of affect (CMA). The novelty of embedding CR coupling information in an autonomic nervous system-based feature space better reveals the sympathetic activations upon emotional stimuli. A CR synchrogram analysis was used to quantify such a coupling in terms of number of heartbeats per respiratory period. Physiological data were gathered from 35 healthy subjects emotionally elicited by means of affective pictures of the international affective picture system database. In this study, we finely detected five levels of arousal and five levels of valence as well as the neutral state, whose combinations were used for identifying 25 different affective states in the CMA plane. We show that the inclusion of the bivariate CR measures in a previously developed system based only on monovariate measures of heart rate variability, respiration dynamics and electrodermal response dramatically increases the recognition accuracy of a quadratic discriminant classifier, obtaining more than 90% of correct classification per class. Finally, we propose a comprehensive description of the CR coupling during sympathetic elicitation adapting an existing theoretical nonlinear model with external driving. The theoretical idea behind this model is that the CR system is comprised of weakly coupled self-sustained oscillators that, when exposed to an external perturbation (i.e. sympathetic activity), becomes synchronized and less sensible to input variations. Given the demonstrated role of the CR coupling, this model can constitute a general tool which is easily embedded in other model-based emotion recognition systems. (paper)

1Interaction between serum BDNF and aerobic fitness predicts recognition memory in healthy young adults

Science.gov (United States)

Whiteman, Andrew; Young, Daniel E.; He, Xuemei; Chen, Tai C.; Wagenaar, Robert C.; Stern, Chantal; Schon, Karin

2013-01-01

Convergent evidence from human and non-human animal studies suggests aerobic exercise and increased aerobic capacity may be beneficial for brain health and cognition. It is thought growth factors may mediate this putative relationship, particularly by augmenting plasticity mechanisms in the hippocampus, a brain region critical for learning and memory. Among these factors, glucocorticoids, brain derived neurotrophic factor (BDNF), insulin-like growth factor-1 (IGF-1), and vascular endothelial growth factor (VEGF), hormones that have considerable and diverse physiological importance, are thought to effect normal and exercise-induced hippocampal plasticity. Despite these predictions, relatively few published human studies have tested hypotheses that relate exercise and fitness to the hippocampus, and none have considered the potential links to all of these hormonal components. Here we present cross-sectional data from a study of recognition memory; serum BDNF, cortisol, IGF-1, and VEGF levels; and aerobic capacity in healthy young adults. We measured circulating levels of these hormones together with performance on a recognition memory task, and a standard graded treadmill test of aerobic fitness. Regression analyses demonstrated BDNF and aerobic fitness predict recognition memory in an interactive manner. In addition, IGF-1 was positively associated with aerobic fitness, but not with recognition memory. Our results may suggest an exercise adaptation-related change in the BDNF dose-response curve that relates to hippocampal memory. PMID:24269495

Pathology and failure in the design and implementation of adaptive management

Science.gov (United States)

Allen, Craig R.; Gunderson, Lance H.

2011-01-01

The conceptual underpinnings for adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex ecological systems as a result non-linear interactions among components and emergence, yet management decisions must still be made. The strength of adaptive management is in the recognition and confrontation of such uncertainty. Rather than ignore uncertainty, or use it to preclude management actions, adaptive management can foster resilience and flexibility to cope with an uncertain future, and develop safe to fail management approaches that acknowledge inevitable changes and surprises. Since its initial introduction, adaptive management has been hailed as a solution to endless trial and error approaches to complex natural resource management challenges. However, its implementation has failed more often than not. It does not produce easy answers, and it is appropriate in only a subset of natural resource management problems. Clearly adaptive management has great potential when applied appropriately. Just as clearly adaptive management has seemingly failed to live up to its high expectations. Why? We outline nine pathologies and challenges that can lead to failure in adaptive management programs. We focus on general sources of failures in adaptive management, so that others can avoid these pitfalls in the future. Adaptive management can be a powerful and beneficial tool when applied correctly to appropriate management problems; the challenge is to keep the concept of adaptive management from being hijacked for inappropriate use.

Comparing Face Detection and Recognition Techniques

OpenAIRE

Korra, Jyothi

2016-01-01

This paper implements and compares different techniques for face detection and recognition. One is find where the face is located in the images that is face detection and second is face recognition that is identifying the person. We study three techniques in this paper: Face detection using self organizing map (SOM), Face recognition by projection and nearest neighbor and Face recognition using SVM.

Exemplar Based Recognition of Visual Shapes

DEFF Research Database (Denmark)

Olsen, Søren I.

2005-01-01

This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoint....... The matching is insensitive to rotations, limited scalings and small deformations. The recognition is robust to noise, background clutter and partial occlusion. Recognition is possible from few training images and improve with the number of training images.......This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoints...

Facilitating change and adaptation: the experiences of current and bereaved carers of patients with severe chronic obstructive pulmonary disease.

Science.gov (United States)

Philip, Jennifer; Gold, Michelle; Brand, Caroline; Miller, Belinda; Douglass, Jo; Sundararajan, Vijaya

2014-04-01

Patients with severe chronic obstructive pulmonary disease (COPD) experience substantial symptom burden, psychological and social morbidity. The experience of this illness has an impact beyond the patient. This study seeks to understand the experiences and needs of family carers of people with severe COPD. Semistructured interviews were held with current and bereaved carers of people with severe COPD. Several areas of content were targeted in the interviews, including the experience of caring for someone with COPD, views of treatment and prognosis, information and communication needs, and the understanding of palliative care. Data were analyzed thematically. The carers' and bereaved carers' experiences and needs around COPD are best understood as a dynamic of change, recognition, and adaptation. Carers faced many changes as the patients' general condition deteriorated. These were changes in the nature of caring tasks, in their relationships, and their own expectations. Carers usually recognized change had happened and sought to adapt through new approaches, new equipment, a new stance of thinking, and in most cases, continued caring. Within this theme of change, recognition, and adaptation were a series of subthemes: (1) the impact of caring, (2) recognizing the role of the carer, and (3) the needs of the carer including their needs from palliative care services. The impact of caring borne by family carers is substantial and life changing. Health professionals may assist carers in their role through acknowledgement, facilitating recognition of the changes that have occurred (and their implications), and enabling creative adaptive responses for carers. Such assistance is likely to enhance the ability of carers to continue in this demanding role.

Superficial Priming in Episodic Recognition

Science.gov (United States)

Dopkins, Stephen; Sargent, Jesse; Ngo, Catherine T.

2010-01-01

We explored the effect of superficial priming in episodic recognition and found it to be different from the effect of semantic priming in episodic recognition. Participants made recognition judgments to pairs of items, with each pair consisting of a prime item and a test item. Correct positive responses to the test item were impeded if the prime…

Specification for projects of radiogeologic recognition

International Nuclear Information System (INIS)

1979-01-01

This instruction is a guidance to achievement of radiogeologic recognition projects. The radiogeologic recognition is a prospecting method that join the classic geologic recognition with measures of rock radioactivity. (C.M.)

Why recognition is rational

Directory of Open Access Journals (Sweden)

Clintin P. Davis-Stober

2010-07-01

Full Text Available The Recognition Heuristic (Gigerenzer and Goldstein, 1996; Goldstein and Gigerenzer, 2002 makes the counter-intuitive prediction that a decision maker utilizing less information may do as well as, or outperform, an idealized decision maker utilizing more information. We lay a theoretical foundation for the use of single-variable heuristics such as the Recognition Heuristic as an optimal decision strategy within a linear modeling framework. We identify conditions under which over-weighting a single predictor is a mini-max strategy among a class of a priori chosen weights based on decision heuristics with respect to a measure of statistical lack of fit we call ``risk''. These strategies, in turn, outperform standard multiple regression as long as the amount of data available is limited. We also show that, under related conditions, weighting only one variable and ignoring all others produces the same risk as ignoring the single variable and weighting all others. This approach has the advantage of generalizing beyond the original environment of the Recognition Heuristic to situations with more than two choice options, binary or continuous representations of recognition, and to other single variable heuristics. We analyze the structure of data used in some prior recognition tasks and find that it matches the sufficient conditions for optimality in our results. Rather than being a poor or adequate substitute for a compensatory model, the Recognition Heuristic closely approximates an optimal strategy when a decision maker has finite data about the world.

Infant visual attention and object recognition.

Science.gov (United States)

Reynolds, Greg D

2015-05-15

This paper explores the role visual attention plays in the recognition of objects in infancy. Research and theory on the development of infant attention and recognition memory are reviewed in three major sections. The first section reviews some of the major findings and theory emerging from a rich tradition of behavioral research utilizing preferential looking tasks to examine visual attention and recognition memory in infancy. The second section examines research utilizing neural measures of attention and object recognition in infancy as well as research on brain-behavior relations in the early development of attention and recognition memory. The third section addresses potential areas of the brain involved in infant object recognition and visual attention. An integrated synthesis of some of the existing models of the development of visual attention is presented which may account for the observed changes in behavioral and neural measures of visual attention and object recognition that occur across infancy. Copyright © 2015 Elsevier B.V. All rights reserved.

Research of Ant Colony Optimized Adaptive Control Strategy for Hybrid Electric Vehicle

Directory of Open Access Journals (Sweden)

Linhui Li

2014-01-01

Full Text Available Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy’s energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters “charge and discharge equivalent factors” for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.

Limitations to Thermoregulation and Acclimatization Challenge Human Adaptation to Global Warming

Directory of Open Access Journals (Sweden)

Elizabeth G. Hanna

2015-07-01

Full Text Available Human thermoregulation and acclimatization are core components of the human coping mechanism for withstanding variations in environmental heat exposure. Amidst growing recognition that curtailing global warming to less than two degrees is becoming increasing improbable, human survival will require increasing reliance on these mechanisms. The projected several fold increase in extreme heat events suggests we need to recalibrate health protection policies and ratchet up adaptation efforts. Climate researchers, epidemiologists, and policy makers engaged in climate change adaptation and health protection are not commonly drawn from heat physiology backgrounds. Injecting a scholarly consideration of physiological limitations to human heat tolerance into the adaptation and policy literature allows for a broader understanding of heat health risks to support effective human adaptation and adaptation planning. This paper details the physiological and external environmental factors that determine human thermoregulation and acclimatization. We present a model to illustrate the interrelationship between elements that modulate the physiological process of thermoregulation. Limitations inherent in these processes, and the constraints imposed by differing exposure levels, and thermal comfort seeking on achieving acclimatization, are then described. Combined, these limitations will restrict the likely contribution that acclimatization can play in future human adaptation to global warming. We postulate that behavioral and technological adaptations will need to become the dominant means for human individual and societal adaptations as global warming progresses.

Limitations to Thermoregulation and Acclimatization Challenge Human Adaptation to Global Warming.

Science.gov (United States)

Hanna, Elizabeth G; Tait, Peter W

2015-07-15

Human thermoregulation and acclimatization are core components of the human coping mechanism for withstanding variations in environmental heat exposure. Amidst growing recognition that curtailing global warming to less than two degrees is becoming increasing improbable, human survival will require increasing reliance on these mechanisms. The projected several fold increase in extreme heat events suggests we need to recalibrate health protection policies and ratchet up adaptation efforts. Climate researchers, epidemiologists, and policy makers engaged in climate change adaptation and health protection are not commonly drawn from heat physiology backgrounds. Injecting a scholarly consideration of physiological limitations to human heat tolerance into the adaptation and policy literature allows for a broader understanding of heat health risks to support effective human adaptation and adaptation planning. This paper details the physiological and external environmental factors that determine human thermoregulation and acclimatization. We present a model to illustrate the interrelationship between elements that modulate the physiological process of thermoregulation. Limitations inherent in these processes, and the constraints imposed by differing exposure levels, and thermal comfort seeking on achieving acclimatization, are then described. Combined, these limitations will restrict the likely contribution that acclimatization can play in future human adaptation to global warming. We postulate that behavioral and technological adaptations will need to become the dominant means for human individual and societal adaptations as global warming progresses.

A Rare Entity: Bilateral First Rib Fractures Accompanying Bilateral Scapular Fractures

OpenAIRE

Gulbahar, Gultekin; Kaplan, Tevfik; Turker, Hasan Bozkurt; Gundogdu, Ahmet Gokhan; Han, Serdar

2015-01-01

First rib fractures are scarce due to their well-protected anatomic locations. Bilateral first rib fractures accompanying bilateral scapular fractures are very rare, although they may be together with scapular and clavicular fractures. According to our knowledge, no case of bilateral first rib fractures accompanying bilateral scapular fractures has been reported, so we herein discussed the diagnosis, treatment, and complications of bone fractures due to thoracic trauma in bias of this rare en...

A Rare Entity: Bilateral First Rib Fractures Accompanying Bilateral Scapular Fractures.

Science.gov (United States)

Gulbahar, Gultekin; Kaplan, Tevfik; Turker, Hasan Bozkurt; Gundogdu, Ahmet Gokhan; Han, Serdar

2015-01-01

First rib fractures are scarce due to their well-protected anatomic locations. Bilateral first rib fractures accompanying bilateral scapular fractures are very rare, although they may be together with scapular and clavicular fractures. According to our knowledge, no case of bilateral first rib fractures accompanying bilateral scapular fractures has been reported, so we herein discussed the diagnosis, treatment, and complications of bone fractures due to thoracic trauma in bias of this rare entity.

Image Quality Enhancement Using the Direction and Thickness of Vein Lines for Finger-Vein Recognition

Directory of Open Access Journals (Sweden)

Young Ho Park

2012-10-01

Full Text Available On the basis of the increased emphasis placed on the protection of privacy, biometric recognition systems using physical or behavioural characteristics such as fingerprints, facial characteristics, iris and finger-vein patterns or the voice have been introduced in applications including door access control, personal certification, Internet banking and ATM machines. Among these, finger-vein recognition is advantageous in that it involves the use of inexpensive and small devices that are difficult to counterfeit. In general, finger-vein recognition systems capture images by using near infrared (NIR illumination in conjunction with a camera. However, such systems can face operational difficulties, since the scattering of light from the skin can make capturing a clear image difficult. To solve this problem, we proposed new image quality enhancement method that measures the direction and thickness of vein lines. This effort represents novel research in four respects. First, since vein lines are detected in input images based on eight directional profiles of a grey image instead of binarized images, the detection error owing to the non-uniform illumination of the finger area can be reduced. Second, our method adaptively determines a Gabor filter for the optimal direction and width on the basis of the estimated direction and thickness of a detected vein line. Third, by applying this optimized Gabor filter, a clear vein image can be obtained. Finally, the further processing of the morphological operation is applied in the Gabor filtered image and the resulting image is combined with the original one, through which finger-vein image of a higher quality is obtained. Experimental results from application of our proposed image enhancement method show that the equal error rate (EER of finger-vein recognition decreases to approximately 0.4% in the case of a local binary pattern-based recognition and to approximately 0.3% in the case of a wavelet transform

Climate change adaptation and forests in South Asia: Policy for multiple stakeholders

Energy Technology Data Exchange (ETDEWEB)

Deshingkar, P.

1997-12-31

Moving from a general understanding of the potential dangers of climate change to real policy and investment requires changes in priorities at the level of government as well as the individual. Information should be disseminated through regional technical co-operation as well as improved communication between relevant institutions within countries. Besides fulfilling scientific and economic criteria for sustainability, forest adaption strategies in South asian countries should aim to be participatory and low cost. In the short term, maximizing the utility of existing institutions and skills may be more practical. The removal of market distortions will also enhance adaptive capacity. Continued research and technological innovation must accompany efforts to change management practices. The immediate priority for donor assistance in this area is to conduct vulnerability assessments, evaluate the constraints and develop a menu of adaption options based on multi criteria analysis of different objectives

Two distinct sensing pathways allow recognition of Klebsiella pneumoniae by Dictyostelium amoebae.

Science.gov (United States)

Lima, Wanessa C; Balestrino, Damien; Forestier, Christiane; Cosson, Pierre

2014-03-01

Recognition of bacteria by metazoans is mediated by receptors that recognize different types of microorganisms and elicit specific cellular responses. The soil amoebae Dictyostelium discoideum feeds upon a variable mixture of environmental bacteria, and it is expected to recognize and adapt to various food sources. To date, however, no bacteria-sensing mechanisms have been described. In this study, we isolated a Dictyostelium mutant (fspA KO) unable to grow in the presence of non-capsulated Klebsiella pneumoniae bacteria, but growing as efficiently as wild-type cells in the presence of other bacteria, such as Bacillus subtilis. fspA KO cells were also unable to respond to K. pneumoniae and more specifically to bacterially secreted folate in a chemokinetic assay, while they responded readily to B. subtilis. Remarkably, both WT and fspA KO cells were able to grow in the presence of capsulated LM21 K. pneumoniae, and responded to purified capsule, indicating that capsule recognition may represent an alternative, FspA-independent mechanism for K. pneumoniae sensing. When LM21 capsule synthesis genes were deleted, growth and chemokinetic response were lost for fspA KO cells, but not for WT cells. Altogether, these results indicate that Dictyostelium amoebae use specific recognition mechanisms to respond to different K. pneumoniae elements. © 2013 John Wiley & Sons Ltd.

Recognition of human face images by the free flying wasp Vespula vulgaris

Directory of Open Access Journals (Sweden)

Aurore Avarguès-Weber

2017-08-01

Full Text Available The capacity to recognize perceptually similar complex visual stimuli such as human faces has classically been thought to require a large primate, and/or mammalian brain with neurobiological adaptations. However, recent work suggests that the relatively small brain of a paper wasp, Polistes fuscatus, possesses specialized face processing capabilities. In parallel, the honeybee, Apis mellifera, has been shown to be able to rely on configural learning for extensive visual learning, thus converging with primate visual processing. Therefore, the honeybee may be able to recognize human faces, and show sophisticated learning performance due to its foraging lifestyle involving visiting and memorizing many flowers. We investigated the visual capacities of the widespread invasive wasp Vespula vulgaris, which is unlikely to have any specialization for face processing. Freely flying individual wasps were trained in an appetitive-aversive differential conditioning procedure to discriminate between perceptually similar human face images from a standard face recognition test. The wasps could then recognize the target face from novel dissimilar or similar human faces, but showed a significant drop in performance when the stimuli were rotated by 180°, thus paralleling results acquired on a similar protocol with honeybees. This result confirms that a general visual system can likely solve complex recognition tasks, the first stage to evolve a visual expertise system to face recognition, even in the absence of neurobiological or behavioral specialization.

Automatic anatomy recognition in whole-body PET/CT images

Energy Technology Data Exchange (ETDEWEB)

Wang, Huiqian [College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China and Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Udupa, Jayaram K., E-mail: jay@mail.med.upenn.edu; Odhner, Dewey; Tong, Yubing; Torigian, Drew A. [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Zhao, Liming [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 and Research Center of Intelligent System and Robotics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China)

2016-01-15

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process

Automatic anatomy recognition in whole-body PET/CT images

International Nuclear Information System (INIS)

Wang, Huiqian; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.; Zhao, Liming

2016-01-01

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process

Probabilistic Open Set Recognition

Science.gov (United States)

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

Hemispheric lateralization of linguistic prosody recognition in comparison to speech and speaker recognition.

Science.gov (United States)

Kreitewolf, Jens; Friederici, Angela D; von Kriegstein, Katharina

2014-11-15

Hemispheric specialization for linguistic prosody is a controversial issue. While it is commonly assumed that linguistic prosody and emotional prosody are preferentially processed in the right hemisphere, neuropsychological work directly comparing processes of linguistic prosody and emotional prosody suggests a predominant role of the left hemisphere for linguistic prosody processing. Here, we used two functional magnetic resonance imaging (fMRI) experiments to clarify the role of left and right hemispheres in the neural processing of linguistic prosody. In the first experiment, we sought to confirm previous findings showing that linguistic prosody processing compared to other speech-related processes predominantly involves the right hemisphere. Unlike previous studies, we controlled for stimulus influences by employing a prosody and speech task using the same speech material. The second experiment was designed to investigate whether a left-hemispheric involvement in linguistic prosody processing is specific to contrasts between linguistic prosody and emotional prosody or whether it also occurs when linguistic prosody is contrasted against other non-linguistic processes (i.e., speaker recognition). Prosody and speaker tasks were performed on the same stimulus material. In both experiments, linguistic prosody processing was associated with activity in temporal, frontal, parietal and cerebellar regions. Activation in temporo-frontal regions showed differential lateralization depending on whether the control task required recognition of speech or speaker: recognition of linguistic prosody predominantly involved right temporo-frontal areas when it was contrasted against speech recognition; when contrasted against speaker recognition, recognition of linguistic prosody predominantly involved left temporo-frontal areas. The results show that linguistic prosody processing involves functions of both hemispheres and suggest that recognition of linguistic prosody is based on

NOBLE - Flexible concept recognition for large-scale biomedical natural language processing.

Science.gov (United States)

Tseytlin, Eugene; Mitchell, Kevin; Legowski, Elizabeth; Corrigan, Julia; Chavan, Girish; Jacobson, Rebecca S

2016-01-14

Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus. NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system's matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator. We describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE's performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems. NOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.

Using workflows to explore and optimise named entity recognition for chemistry.

Directory of Open Access Journals (Sweden)

Balakrishna Kolluru

Full Text Available Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain, OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM and pattern recognition based classifiers. Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR.

Haloarcula hispanica CRISPR authenticates PAM of a target sequence to prime discriminative adaptation.

Science.gov (United States)

Li, Ming; Wang, Rui; Xiang, Hua

2014-06-01

The prokaryotic immune system CRISPR/Cas (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated genes) adapts to foreign invaders by acquiring their short deoxyribonucleic acid (DNA) fragments as spacers, which guide subsequent interference to foreign nucleic acids based on sequence matching. The adaptation mechanism avoiding acquiring 'self' DNA fragments is poorly understood. In Haloarcula hispanica, we previously showed that CRISPR adaptation requires being primed by a pre-existing spacer partially matching the invader DNA. Here, we further demonstrate that flanking a fully-matched target sequence, a functional PAM (protospacer adjacent motif) is still required to prime adaptation. Interestingly, interference utilizes only four PAM sequences, whereas adaptation-priming tolerates as many as 23 PAM sequences. This relaxed PAM selectivity explains how adaptation-priming maximizes its tolerance of PAM mutations (that escape interference) while avoiding mis-targeting the spacer DNA within CRISPR locus. We propose that the primed adaptation, which hitches and cooperates with the interference pathway, distinguishes target from non-target by CRISPR ribonucleic acid guidance and PAM recognition. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

A Rare Entity: Bilateral First Rib Fractures Accompanying Bilateral Scapular Fractures

Directory of Open Access Journals (Sweden)

Gultekin Gulbahar

2015-01-01

Full Text Available First rib fractures are scarce due to their well-protected anatomic locations. Bilateral first rib fractures accompanying bilateral scapular fractures are very rare, although they may be together with scapular and clavicular fractures. According to our knowledge, no case of bilateral first rib fractures accompanying bilateral scapular fractures has been reported, so we herein discussed the diagnosis, treatment, and complications of bone fractures due to thoracic trauma in bias of this rare entity.

Viewpoint Manifolds for Action Recognition

Directory of Open Access Journals (Sweden)

Souvenir Richard

2009-01-01

Full Text Available Abstract Action recognition from video is a problem that has many important applications to human motion analysis. In real-world settings, the viewpoint of the camera cannot always be fixed relative to the subject, so view-invariant action recognition methods are needed. Previous view-invariant methods use multiple cameras in both the training and testing phases of action recognition or require storing many examples of a single action from multiple viewpoints. In this paper, we present a framework for learning a compact representation of primitive actions (e.g., walk, punch, kick, sit that can be used for video obtained from a single camera for simultaneous action recognition and viewpoint estimation. Using our method, which models the low-dimensional structure of these actions relative to viewpoint, we show recognition rates on a publicly available dataset previously only achieved using multiple simultaneous views.

Physiological arousal in processing recognition information

Directory of Open Access Journals (Sweden)

Guy Hochman

2010-07-01

Full Text Available The recognition heuristic (RH; Goldstein and Gigerenzer, 2002 suggests that, when applicable, probabilistic inferences are based on a noncompensatory examination of whether an object is recognized or not. The overall findings on the processes that underlie this fast and frugal heuristic are somewhat mixed, and many studies have expressed the need for considering a more compensatory integration of recognition information. Regardless of the mechanism involved, it is clear that recognition has a strong influence on choices, and this finding might be explained by the fact that recognition cues arouse affect and thus receive more attention than cognitive cues. To test this assumption, we investigated whether recognition results in a direct affective signal by measuring physiological arousal (i.e., peripheral arterial tone in the established city-size task. We found that recognition of cities does not directly result in increased physiological arousal. Moreover, the results show that physiological arousal increased with increasing inconsistency between recognition information and additional cue information. These findings support predictions derived by a compensatory Parallel Constraint Satisfaction model rather than predictions of noncompensatory models. Additional results concerning confidence ratings, response times, and choice proportions further demonstrated that recognition information and other cognitive cues are integrated in a compensatory manner.

Page Recognition: Quantum Leap In Recognition Technology

Science.gov (United States)

Miller, Larry

1989-07-01

No milestone has proven as elusive as the always-approaching "year of the LAN," but the "year of the scanner" might claim the silver medal. Desktop scanners have been around almost as long as personal computers. And everyone thinks they are used for obvious desktop-publishing and business tasks like scanning business documents, magazine articles and other pages, and translating those words into files your computer understands. But, until now, the reality fell far short of the promise. Because it's true that scanners deliver an accurate image of the page to your computer, but the software to recognize this text has been woefully disappointing. Old optical-character recognition (OCR) software recognized such a limited range of pages as to be virtually useless to real users. (For example, one OCR vendor specified 12-point Courier font from an IBM Selectric typewriter: the same font in 10-point, or from a Diablo printer, was unrecognizable!) Computer dealers have told me the chasm between OCR expectations and reality is so broad and deep that nine out of ten prospects leave their stores in disgust when they learn the limitations. And this is a very important, very unfortunate gap. Because the promise of recognition -- what people want it to do -- carries with it tremendous improvements in our productivity and ability to get tons of written documents into our computers where we can do real work with it. The good news is that a revolutionary new development effort has led to the new technology of "page recognition," which actually does deliver the promise we've always wanted from OCR. I'm sure every reader appreciates the breakthrough represented by the laser printer and page-makeup software, a combination so powerful it created new reasons for buying a computer. A similar breakthrough is happening right now in page recognition: the Macintosh (and, I must admit, other personal computers) equipped with a moderately priced scanner and OmniPage software (from Caere

Robust Visual Knowledge Transfer via Extreme Learning Machine Based Domain Adaptation.

Science.gov (United States)

Zhang, Lei; Zhang, David

2016-08-10

We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine based cross-domain network learning framework, that is called Extreme Learning Machine (ELM) based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the -norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as base classifiers. The network output weights cannot only be analytically determined, but also transferrable. Additionally, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semi-supervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition, demonstrate that our EDA methods outperform existing cross-domain learning methods.

Severe Hypoglycemia Accompanied with Thyroid Crisis

Directory of Open Access Journals (Sweden)

Yuki Nakatani

2012-01-01

Full Text Available We report a 32-year-old Japanese women with severe hypoglycemia accompanied with thyroid crisis. She complained of dyspnea, general fatigue, and leg edema. She was diagnosed with hyperthyroidism with congestive heart failure and liver dysfunction. Soon after admission, sudden cardiopulmonary arrest occurred. She was then transferred to the intensive care unit. Her serum glucose level was 7 mg/dl. Intravenous glucose, hydrocortisone, diuretics, and continuous hemodiafiltration (CHDF saved her. We considered that hypoglycemia occurred due to heart failure and liver dysfunction due to thyroid crisis.

Effects of Age and Working Memory Capacity on Speech Recognition Performance in Noise Among Listeners With Normal Hearing.

Science.gov (United States)

Gordon-Salant, Sandra; Cole, Stacey Samuels

2016-01-01

This study aimed to determine if younger and older listeners with normal hearing who differ on working memory span perform differently on speech recognition tests in noise. Older adults typically exhibit poorer speech recognition scores in noise than younger adults, which is attributed primarily to poorer hearing sensitivity and more limited working memory capacity in older than younger adults. Previous studies typically tested older listeners with poorer hearing sensitivity and shorter working memory spans than younger listeners, making it difficult to discern the importance of working memory capacity on speech recognition. This investigation controlled for hearing sensitivity and compared speech recognition performance in noise by younger and older listeners who were subdivided into high and low working memory groups. Performance patterns were compared for different speech materials to assess whether or not the effect of working memory capacity varies with the demands of the specific speech test. The authors hypothesized that (1) normal-hearing listeners with low working memory span would exhibit poorer speech recognition performance in noise than those with high working memory span; (2) older listeners with normal hearing would show poorer speech recognition scores than younger listeners with normal hearing, when the two age groups were matched for working memory span; and (3) an interaction between age and working memory would be observed for speech materials that provide contextual cues. Twenty-eight older (61 to 75 years) and 25 younger (18 to 25 years) normal-hearing listeners were assigned to groups based on age and working memory status. Northwestern University Auditory Test No. 6 words and Institute of Electrical and Electronics Engineers sentences were presented in noise using an adaptive procedure to measure the signal-to-noise ratio corresponding to 50% correct performance. Cognitive ability was evaluated with two tests of working memory (Listening

Online handwritten mathematical expression recognition

Science.gov (United States)

Büyükbayrak, Hakan; Yanikoglu, Berrin; Erçil, Aytül

2007-01-01

We describe a system for recognizing online, handwritten mathematical expressions. The system is designed with a user-interface for writing scientific articles, supporting the recognition of basic mathematical expressions as well as integrals, summations, matrices etc. A feed-forward neural network recognizes symbols which are assumed to be single-stroke and a recursive algorithm parses the expression by combining neural network output and the structure of the expression. Preliminary results show that writer-dependent recognition rates are very high (99.8%) while writer-independent symbol recognition rates are lower (75%). The interface associated with the proposed system integrates the built-in recognition capabilities of the Microsoft's Tablet PC API for recognizing textual input and supports conversion of hand-drawn figures into PNG format. This enables the user to enter text, mathematics and draw figures in a single interface. After recognition, all output is combined into one LATEX code and compiled into a PDF file.

Side-View Face Recognition

NARCIS (Netherlands)

Santemiz, P.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; van den Biggelaar, Olivier

As a widely used biometrics, face recognition has many advantages such as being non-intrusive, natural and passive. On the other hand, in real-life scenarios with uncontrolled environment, pose variation up to side-view positions makes face recognition a challenging work. In this paper we discuss

[Neurological disease and facial recognition].

Science.gov (United States)

Kawamura, Mitsuru; Sugimoto, Azusa; Kobayakawa, Mutsutaka; Tsuruya, Natsuko

2012-07-01

To discuss the neurological basis of facial recognition, we present our case reports of impaired recognition and a review of previous literature. First, we present a case of infarction and discuss prosopagnosia, which has had a large impact on face recognition research. From a study of patient symptoms, we assume that prosopagnosia may be caused by unilateral right occipitotemporal lesion and right cerebral dominance of facial recognition. Further, circumscribed lesion and degenerative disease may also cause progressive prosopagnosia. Apperceptive prosopagnosia is observed in patients with posterior cortical atrophy (PCA), pathologically considered as Alzheimer's disease, and associative prosopagnosia in frontotemporal lobar degeneration (FTLD). Second, we discuss face recognition as part of communication. Patients with Parkinson disease show social cognitive impairments, such as difficulty in facial expression recognition and deficits in theory of mind as detected by the reading the mind in the eyes test. Pathological and functional imaging studies indicate that social cognitive impairment in Parkinson disease is possibly related to damages in the amygdalae and surrounding limbic system. The social cognitive deficits can be observed in the early stages of Parkinson disease, and even in the prodromal stage, for example, patients with rapid eye movement (REM) sleep behavior disorder (RBD) show impairment in facial expression recognition. Further, patients with myotonic dystrophy type 1 (DM 1), which is a multisystem disease that mainly affects the muscles, show social cognitive impairment similar to that of Parkinson disease. Our previous study showed that facial expression recognition impairment of DM 1 patients is associated with lesion in the amygdalae and insulae. Our study results indicate that behaviors and personality traits in DM 1 patients, which are revealed by social cognitive impairment, are attributable to dysfunction of the limbic system.

Temporal and spatial adaptations during the acquisition of a reversal movement.

Science.gov (United States)

van Loon, E M; Buekers, M J; Helsen, W; Magill, R A

1998-03-01

Adjustments of the biphasic movement in a coincidence anticipation task were studied using an erroneous knowledge of results (KR) paradigm. Forty participants received either no KR, correct KR, erroneous (+100 ms) KR, or 100 trials of correct KR followed by 50 trials of erroneous KR. Kinematic analyses revealed that for this 100-50 KR group the extension part of the movement was temporally adjusted under the influence of erroneous KR. Although accompanied by a decrease in movement amplitude, this did not account for the temporal shift in movement outcome, because all groups showed a reduction in amplitude. It is argued that changing external time constraints mainly results in temporal adaptations. However, spatial adaptations do play a role in kinematic changes during acquisition.

Recognition of an Independent Self-Consciousness

DEFF Research Database (Denmark)

Bjerre, Henrik Jøker

2009-01-01

Hegel's concept in the Phenomenology of the Spirit of the "recognition of an independent self-consciousness" is investigated as a point of separation for contemporary philosophy of recognition. I claim that multiculturalism and the theories of recognition (such as Axel Honneth's) based on empiric...... psychology neglect or deny crucial metaphysical aspects of the Hegelian legacy. Instead, I seek to point at an additional, "spiritual", level of recognition, based on the concept of the subject in Lacanian psychoanalysis....

Case-Based Policy and Goal Recognition

Science.gov (United States)

2015-09-30

Policy and Goal Recognizer (PaGR), a case- based system for multiagent keyhole recognition. PaGR is a knowledge recognition component within a decision...However, unlike our agent in the BVR domain, these recognition agents have access to perfect information. Single-agent keyhole plan recognition can be...listed below: 1. Facing Target 2. Closing on Target 3. Target Range 4. Within a Target’s Weapon Range 5. Has Target within Weapon Range 6. Is in Danger

Harmonization versus Mutual Recognition

DEFF Research Database (Denmark)

Jørgensen, Jan Guldager; Schröder, Philipp

The present paper examines trade liberalization driven by the coordination of product standards. For oligopolistic firms situated in separate markets that are initially sheltered by national standards, mutual recognition of standards implies entry and reduced profits at home paired with the oppor......The present paper examines trade liberalization driven by the coordination of product standards. For oligopolistic firms situated in separate markets that are initially sheltered by national standards, mutual recognition of standards implies entry and reduced profits at home paired...... countries and three firms, where firms first lobby for the policy coordination regime (harmonization versus mutual recognition), and subsequently, in case of harmonization, the global standard is auctioned among the firms. We discuss welfare effects and conclude with policy implications. In particular......, harmonized standards may fail to harvest the full pro-competitive effects from trade liberalization compared to mutual recognition; moreover, the issue is most pronounced in markets featuring price competition....

Clinical accompaniment: the critical care nursing students’ experiences in a private hospital

Directory of Open Access Journals (Sweden)

N. Tsele

2000-09-01

Full Text Available The quality of clinical accompaniment of the student enrolled for the post-basic diploma in Medical and Surgical Nursing Science: Critical Care Nursing (General is an important dimension of the educational/learning programme. The clinical accompanist/mentor is responsible for ensuring the student’s compliance with the clinical outcomes of the programme in accordance with the requirements laid down by the Nursing Education Institution and the South African Nursing Council. The purpose of this study was to explore and describe the experiences of the students enrolled for a post-basic diploma in Medical and Surgical Nursing Science: Critical Care Nursing (General, in relation to the clinical accompaniment in a private hospital in Gauteng. An exploratory, descriptive and phenomenological research design was utilised and individual interviews were conducted with the ten students in the research hospital. A content analysis was conducted and the results revealed both positive and negative experiences by the students in the internal and external worlds. The recommendations include the formulation of standards for clinical accompaniment of students. the evaluation of the quality of clinical accompaniment of students and empowerment of the organisation, clinical accompanists/mentors and clinicians.

Developing the Sufficiency Perception Scale in the Area of Accompaniment with Piano for Candidate Music Teachers

OpenAIRE

PİJİ, Duygu

2013-01-01

The music teacher’s accompanying the songs by piano in music education at schoolappears to be important regarding the development of melody, rhythm and harmony feelingsof the students. The music teacher candidates receive their knowledge and skill in the areaof piano accompaniment through the Accompaniment lesson within the Music TeachingLicense Program. In this research, with the purpose of defining the perception of sufficiencyregarding the accompaniment levels of music teacher candidates a...

Automotive Mechanics: Handbook to Accompany VESL Vocabulary Cards.

Science.gov (United States)

Shay, Gail; Lenhart, Debra

This manual is one of four self-contained components of a larger handbook designed to assist secondary and postsecondary instructors and support staff in meeting the needs of limited-English-proficiency (LEP) students in vocational training programs. Together with an accompanying set of vocational English as a second language (VESL) vocabulary…

State Toleration, Religious Recognition and Equality

DEFF Research Database (Denmark)

Lægaard, Sune

2013-01-01

In debates about multiculturalism, it is widely claimed that ‘toleration is not enough’ and that we need to go ‘beyond toleration’ to some form of politics of recognition in order to satisfactorily address contemporary forms of cultural diversity (e.g. the presence in Europe of Muslim minorities...... a conceptual question of whether the relation between states and minorities can be categoriseized in terms of recognition or toleration, but about a normative question of whether and how toleration and recognition secures equality. When toleration is inadequate, this is often because it institutionaliseizes...... and upholds specific inequalities. But politics of recognition may equally well institute inequalities, and in such cases unequal recognition may not be preferable to toleration....

Implementing Culture Change in Nursing Homes: An Adaptive Leadership Framework.

Science.gov (United States)

Corazzini, Kirsten; Twersky, Jack; White, Heidi K; Buhr, Gwendolen T; McConnell, Eleanor S; Weiner, Madeline; Colón-Emeric, Cathleen S

2015-08-01

To describe key adaptive challenges and leadership behaviors to implement culture change for person-directed care. The study design was a qualitative, observational study of nursing home staff perceptions of the implementation of culture change in each of 3 nursing homes. We conducted 7 focus groups of licensed and unlicensed nursing staff, medical care providers, and administrators. Questions explored perceptions of facilitators and barriers to culture change. Using a template organizing style of analysis with immersion/crystallization, themes of barriers and facilitators were coded for adaptive challenges and leadership. Six key themes emerged, including relationships, standards and expectations, motivation and vision, workload, respect of personhood, and physical environment. Within each theme, participants identified barriers that were adaptive challenges and facilitators that were examples of adaptive leadership. Commonly identified challenges were how to provide person-directed care in the context of extant rules or policies or how to develop staff motivated to provide person-directed care. Implementing culture change requires the recognition of adaptive challenges for which there are no technical solutions, but which require reframing of norms and expectations, and the development of novel and flexible solutions. Managers and administrators seeking to implement person-directed care will need to consider the role of adaptive leadership to address these adaptive challenges. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Vision-Based Navigation and Recognition

National Research Council Canada - National Science Library

Rosenfeld, Azriel

1998-01-01

.... (4) Invariants: both geometric and other types. (5) Human faces: Analysis of images of human faces, including feature extraction, face recognition, compression, and recognition of facial expressions...

Vision-Based Navigation and Recognition

National Research Council Canada - National Science Library

Rosenfeld, Azriel

1996-01-01

.... (4) Invariants -- both geometric and other types. (5) Human faces: Analysis of images of human faces, including feature extraction, face recognition, compression, and recognition of facial expressions...

An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control.

Science.gov (United States)

Adewuyi, Adenike A; Hargrove, Levi J; Kuiken, Todd A

2016-04-01

Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for application to partial-hand prosthetic control.

Voice congruency facilitates word recognition.

Science.gov (United States)

Campeanu, Sandra; Craik, Fergus I M; Alain, Claude

2013-01-01

Behavioral studies of spoken word memory have shown that context congruency facilitates both word and source recognition, though the level at which context exerts its influence remains equivocal. We measured event-related potentials (ERPs) while participants performed both types of recognition task with words spoken in four voices. Two voice parameters (i.e., gender and accent) varied between speakers, with the possibility that none, one or two of these parameters was congruent between study and test. Results indicated that reinstating the study voice at test facilitated both word and source recognition, compared to similar or no context congruency at test. Behavioral effects were paralleled by two ERP modulations. First, in the word recognition test, the left parietal old/new effect showed a positive deflection reflective of context congruency between study and test words. Namely, the same speaker condition provided the most positive deflection of all correctly identified old words. In the source recognition test, a right frontal positivity was found for the same speaker condition compared to the different speaker conditions, regardless of response success. Taken together, the results of this study suggest that the benefit of context congruency is reflected behaviorally and in ERP modulations traditionally associated with recognition memory.

Voice congruency facilitates word recognition.

Directory of Open Access Journals (Sweden)

Sandra Campeanu

Full Text Available Behavioral studies of spoken word memory have shown that context congruency facilitates both word and source recognition, though the level at which context exerts its influence remains equivocal. We measured event-related potentials (ERPs while participants performed both types of recognition task with words spoken in four voices. Two voice parameters (i.e., gender and accent varied between speakers, with the possibility that none, one or two of these parameters was congruent between study and test. Results indicated that reinstating the study voice at test facilitated both word and source recognition, compared to similar or no context congruency at test. Behavioral effects were paralleled by two ERP modulations. First, in the word recognition test, the left parietal old/new effect showed a positive deflection reflective of context congruency between study and test words. Namely, the same speaker condition provided the most positive deflection of all correctly identified old words. In the source recognition test, a right frontal positivity was found for the same speaker condition compared to the different speaker conditions, regardless of response success. Taken together, the results of this study suggest that the benefit of context congruency is reflected behaviorally and in ERP modulations traditionally associated with recognition memory.

Side-View Face Recognition

NARCIS (Netherlands)

Santemiz, P.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

2010-01-01

Side-view face recognition is a challenging problem with many applications. Especially in real-life scenarios where the environment is uncontrolled, coping with pose variations up to side-view positions is an important task for face recognition. In this paper we discuss the use of side view face

Emotional Intelligence and Adaptive Success of Nurses Caring for People with Mental Retardation and Severe Behavior Problems

Science.gov (United States)

Gerits, Linda; Derksen, Jan J. L.; Verbruggen, Antoine B.

2004-01-01

The emotional intelligence profiles, gender differences, and adaptive success of 380 Dutch nurses caring for people with mental retardation and accompanying severe behavior problems are reported. Data were collected with the Bar-On Emotional Quotient Inventory, Utrecht-Coping List, Utrecht-Burnout Scale, MMPI-2, and GAMA. Absence due to illness…

Document recognition serving people with disabilities

Science.gov (United States)

Fruchterman, James R.

2007-01-01

Document recognition advances have improved the lives of people with print disabilities, by providing accessible documents. This invited paper provides perspectives on the author's career progression from document recognition professional to social entrepreneur applying this technology to help people with disabilities. Starting with initial thoughts about optical character recognition in college, it continues with the creation of accurate omnifont character recognition that did not require training. It was difficult to make a reading machine for the blind in a commercial setting, which led to the creation of a nonprofit social enterprise to deliver these devices around the world. This network of people with disabilities scanning books drove the creation of Bookshare.org, an online library of scanned books. Looking forward, the needs for improved document recognition technology to further lower the barriers to reading are discussed. Document recognition professionals should be proud of the positive impact their work has had on some of society's most disadvantaged communities.

Infant Visual Recognition Memory

Science.gov (United States)

Rose, Susan A.; Feldman, Judith F.; Jankowski, Jeffery J.

2004-01-01

Visual recognition memory is a robust form of memory that is evident from early infancy, shows pronounced developmental change, and is influenced by many of the same factors that affect adult memory; it is surprisingly resistant to decay and interference. Infant visual recognition memory shows (a) modest reliability, (b) good discriminant…

Adaptation in the innate immune system and heterologous innate immunity.

Science.gov (United States)

Martin, Stefan F

2014-11-01

The innate immune system recognizes deviation from homeostasis caused by infectious or non-infectious assaults. The threshold for its activation seems to be established by a calibration process that includes sensing of microbial molecular patterns from commensal bacteria and of endogenous signals. It is becoming increasingly clear that adaptive features, a hallmark of the adaptive immune system, can also be identified in the innate immune system. Such adaptations can result in the manifestation of a primed state of immune and tissue cells with a decreased activation threshold. This keeps the system poised to react quickly. Moreover, the fact that the innate immune system recognizes a wide variety of danger signals via pattern recognition receptors that often activate the same signaling pathways allows for heterologous innate immune stimulation. This implies that, for example, the innate immune response to an infection can be modified by co-infections or other innate stimuli. This "design feature" of the innate immune system has many implications for our understanding of individual susceptibility to diseases or responsiveness to therapies and vaccinations. In this article, adaptive features of the innate immune system as well as heterologous innate immunity and their implications are discussed.

Detection of network attacks based on adaptive resonance theory

Science.gov (United States)

Bukhanov, D. G.; Polyakov, V. M.

2018-05-01

The paper considers an approach to intrusion detection systems using a neural network of adaptive resonant theory. It suggests the structure of an intrusion detection system consisting of two types of program modules. The first module manages connections of user applications by preventing the undesirable ones. The second analyzes the incoming network traffic parameters to check potential network attacks. After attack detection, it notifies the required stations using a secure transmission channel. The paper describes the experiment on the detection and recognition of network attacks using the test selection. It also compares the obtained results with similar experiments carried out by other authors. It gives findings and conclusions on the sufficiency of the proposed approach. The obtained information confirms the sufficiency of applying the neural networks of adaptive resonant theory to analyze network traffic within the intrusion detection system.

Similarity measures for face recognition

CERN Document Server

Vezzetti, Enrico

2015-01-01

Face recognition has several applications, including security, such as (authentication and identification of device users and criminal suspects), and in medicine (corrective surgery and diagnosis). Facial recognition programs rely on algorithms that can compare and compute the similarity between two sets of images. This eBook explains some of the similarity measures used in facial recognition systems in a single volume. Readers will learn about various measures including Minkowski distances, Mahalanobis distances, Hansdorff distances, cosine-based distances, among other methods. The book also summarizes errors that may occur in face recognition methods. Computer scientists "facing face" and looking to select and test different methods of computing similarities will benefit from this book. The book is also useful tool for students undertaking computer vision courses.

Bidirectional Modulation of Recognition Memory.

Science.gov (United States)

Ho, Jonathan W; Poeta, Devon L; Jacobson, Tara K; Zolnik, Timothy A; Neske, Garrett T; Connors, Barry W; Burwell, Rebecca D

2015-09-30

Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects. For example, animals and humans with perirhinal damage are unable to distinguish familiar from novel objects in recognition memory tasks. In the normal brain, perirhinal neurons respond to novelty and familiarity by increasing or decreasing firing rates. Recent work also implicates oscillatory activity in the low-beta and low-gamma frequency bands in sensory detection, perception, and recognition. Using optogenetic methods in a spontaneous object exploration (SOR) task, we altered recognition memory performance in rats. In the SOR task, normal rats preferentially explore novel images over familiar ones. We modulated exploratory behavior in this task by optically stimulating channelrhodopsin-expressing perirhinal neurons at various frequencies while rats looked at novel or familiar 2D images. Stimulation at 30-40 Hz during looking caused rats to treat a familiar image as if it were novel by increasing time looking at the image. Stimulation at 30-40 Hz was not effective in increasing exploration of novel images. Stimulation at 10-15 Hz caused animals to treat a novel image as familiar by decreasing time looking at the image, but did not affect looking times for images that were already familiar. We conclude that optical stimulation of PER at different frequencies can alter visual recognition memory bidirectionally. Significance statement: Recognition of novelty and familiarity are important for learning, memory, and decision making. Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects, but how novelty and familiarity are encoded and transmitted in the brain is not known. Perirhinal neurons respond to novelty and familiarity by changing firing rates, but recent work suggests that brain oscillations may also be important for recognition. In this study, we showed that stimulation of

A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs

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