Joint diseases: from connexins to gap junctions.

PubMed

Donahue, Henry J; Qu, Roy W; Genetos, Damian C

2017-12-19

Connexons form the basis of hemichannels and gap junctions. They are composed of six tetraspan proteins called connexins. Connexons can function as individual hemichannels, releasing cytosolic factors (such as ATP) into the pericellular environment. Alternatively, two hemichannel connexons from neighbouring cells can come together to form gap junctions, membrane-spanning channels that facilitate cell-cell communication by enabling signalling molecules of approximately 1 kDa to pass from one cell to an adjacent cell. Connexins are expressed in joint tissues including bone, cartilage, skeletal muscle and the synovium. Indicative of their importance as gap junction components, connexins are also known as gap junction proteins, but individual connexin proteins are gaining recognition for their channel-independent roles, which include scaffolding and signalling functions. Considerable evidence indicates that connexons contribute to the function of bone and muscle, but less is known about the function of connexons in other joint tissues. However, the implication that connexins and gap junctional channels might be involved in joint disease, including age-related bone loss, osteoarthritis and rheumatoid arthritis, emphasizes the need for further research into these areas and highlights the therapeutic potential of connexins.

Variations in gap junctional intercellular communication and connexin expression in fibroblasts derived from keloid and hypertrophic scars.

PubMed

Lu, Feng; Gao, JianHua; Ogawa, Rei; Hyakusoku, Hiko

2007-03-01

Expression of connexins and other constituent proteins of gap junctions along with gap junctional intercellular communication are involved in cellular development and differentiation processes. In addition, an increasing number of hereditary skin disorders appear to be linked to connexins. Therefore, in this report, the authors studied in vitro gap junctional intercellular communication function and connexin expression in fibroblasts derived from keloid and hypertrophic scar patients. Fibroblasts harvested from each of six keloid and hypertrophic scar patients were used for this study. Gap junctional intercellular communication function was investigated using the gap fluorescence recovery after photobleaching method, and expression of connexin proteins was studied using quantitative confocal microscopic analyses. Compared with normal skin, a decreased level of gap junctional intercellular communication was seen in fibroblasts derived from hypertrophic scar tissue, whereas an extremely low gap junctional intercellular communication level was detected in fibroblasts derived from keloid tissue. We also detected little connexin 43 (Cx43) protein localized in fibroblasts derived from keloids. Moreover, Cx43 protein levels were much lower in fibroblasts derived from hypertrophic scars than in those derived from normal skin. The authors' data suggest that the loss of gap junctional intercellular communication and connexin expression may affect intercellular recognition and thus break the proliferation and apoptosis balance in fibroblasts derived from keloid and hypertrophic scar tissue.

Exact analytical solution of a classical Josephson tunnel junction problem

NASA Astrophysics Data System (ADS)

Kuplevakhsky, S. V.; Glukhov, A. M.

2010-10-01

We give an exact and complete analytical solution of the classical problem of a Josephson tunnel junction of arbitrary length W ɛ(0,∞) in the presence of external magnetic fields and transport currents. Contrary to a wide-spread belief, the exact analytical solution unambiguously proves that there is no qualitative difference between so-called "small" (W≪1) and "large" junctions (W≫1). Another unexpected physical implication of the exact analytical solution is the existence (in the current-carrying state) of unquantized Josephson vortices carrying fractional flux and located near one of the edges of the junction. We also refine the mathematical definition of critical transport current.

Influence of Child Factors on Health-Care Professionals' Recognition of Common Childhood Mental-Health Problems.

PubMed

Burke, Delia A; Koot, Hans M; de Wilde, Amber; Begeer, Sander

Early recognition of childhood mental-health problems can help minimise long-term negative outcomes. Recognition of mental-health problems, needed for referral and diagnostic evaluation, is largely dependent on health-care professionals' (HCPs) judgement of symptoms presented by the child. This study aimed to establish whether HCPs recognition of mental-health problems varies as a function of three child-related factors (type of problem, number of symptoms, and demographic characteristics). In an online survey, HCPs ( n  = 431) evaluated a series of vignettes describing children with symptoms of mental-health problems. Vignettes varied by problem type (Attention-Deficit/Hyperactivity Disorder (ADHD), Generalised Anxiety Disorder (GAD), Autism Spectrum Disorder (ASD), Conduct Disorder (CD) and Major Depressive Disorder), number of symptoms presented (few and many), and child demographic characteristics (ethnicity, gender, age and socio-economic status (SES)). Results show that recognition of mental-health problems varies by problem type, with ADHD best recognised and GAD worst. Furthermore, recognition varies by the number of symptoms presented. Unexpectedly, a child's gender, ethnicity and family SES did not influence likelihood of problem recognition. These results are the first to reveal differences in HCPs' recognition of various common childhood mental-health problems. HCPs in practice should be advised about poor recognition of GAD, and superior recognition of ADHD, if recognition of all childhood mental-health problems is to be equal.

IJS: An Intelligent Junction Selection Based Routing Protocol for VANET to Support ITS Services.

PubMed

Bhoi, Sourav Kumar; Khilar, Pabitra Mohan

2014-01-01

Selecting junctions intelligently for data transmission provides better intelligent transportation system (ITS) services. The main problem in vehicular communication is high disturbances of link connectivity due to mobility and less density of vehicles. If link conditions are predicted earlier, then there is a less chance of performance degradation. In this paper, an intelligent junction selection based routing protocol (IJS) is proposed to transmit the data in a quickest path, in which the vehicles are mostly connected and have less link connectivity problem. In this protocol, a helping vehicle is set at every junction to control the communication by predicting link failures or network gaps in a route. Helping vehicle at the junction produces a score for every neighboring junction to forward the data to the destination by considering the current traffic information and selects that junction which has minimum score. IJS protocol is implemented and compared with GyTAR, A-STAR, and GSR routing protocols. Simulation results show that IJS performs better in terms of average end-to-end delay, network gap encounter, and number of hops.

IJS: An Intelligent Junction Selection Based Routing Protocol for VANET to Support ITS Services

PubMed Central

Khilar, Pabitra Mohan

2014-01-01

Selecting junctions intelligently for data transmission provides better intelligent transportation system (ITS) services. The main problem in vehicular communication is high disturbances of link connectivity due to mobility and less density of vehicles. If link conditions are predicted earlier, then there is a less chance of performance degradation. In this paper, an intelligent junction selection based routing protocol (IJS) is proposed to transmit the data in a quickest path, in which the vehicles are mostly connected and have less link connectivity problem. In this protocol, a helping vehicle is set at every junction to control the communication by predicting link failures or network gaps in a route. Helping vehicle at the junction produces a score for every neighboring junction to forward the data to the destination by considering the current traffic information and selects that junction which has minimum score. IJS protocol is implemented and compared with GyTAR, A-STAR, and GSR routing protocols. Simulation results show that IJS performs better in terms of average end-to-end delay, network gap encounter, and number of hops. PMID:27433485

Conduct symptoms and emotion recognition in adolescent boys with externalization problems.

PubMed

Aspan, Nikoletta; Vida, Peter; Gadoros, Julia; Halasz, Jozsef

2013-01-01

In adults with antisocial personality disorder, marked alterations in the recognition of facial affect were described. Less consistent data are available on the emotion recognition in adolescents with externalization problems. The aim of the present study was to assess the relation between the recognition of emotions and conduct symptoms in adolescent boys with externalization problems. Adolescent boys with externalization problems referred to Vadaskert Child Psychiatry Hospital participated in the study after informed consent (N = 114, 11-17 years, mean = 13.4). The conduct problems scale of the strengths and difficulties questionnaire (parent and self-report) was used. The performance in a facial emotion recognition test was assessed. Conduct problems score (parent and self-report) was inversely correlated with the overall emotion recognition. In the self-report, conduct problems score was inversely correlated with the recognition of anger, fear, and sadness. Adolescents with high conduct problems scores were significantly worse in the recognition of fear, sadness, and overall recognition than adolescents with low conduct scores, irrespective of age and IQ. Our results suggest that impaired emotion recognition is dimensionally related to conduct problems and might have importance in the development of antisocial behavior.

Coenzyme Recognition and Gene Regulation by a Flavin Mononucleotide Riboswitch

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

Serganov, A.; Huang, L; Patel, D

2009-01-01

The biosynthesis of several protein cofactors is subject to feedback regulation by riboswitches. Flavin mononucleotide (FMN)-specific riboswitches also known as RFN elements, direct expression of bacterial genes involved in the biosynthesis and transport of riboflavin (vitamin B2) and related compounds. Here we present the crystal structures of the Fusobacterium nucleatum riboswitch bound to FMN, riboflavin and antibiotic roseoflavin. The FMN riboswitch structure, centred on an FMN-bound six-stem junction, does not fold by collinear stacking of adjacent helices, typical for folding of large RNAs. Rather, it adopts a butterfly-like scaffold, stapled together by opposingly directed but nearly identically folded peripheral domains.more » FMN is positioned asymmetrically within the junctional site and is specifically bound to RNA through interactions with the isoalloxazine ring chromophore and direct and Mg{sup 2+}-mediated contacts with the phosphate moiety. Our structural data, complemented by binding and footprinting experiments, imply a largely pre-folded tertiary RNA architecture and FMN recognition mediated by conformational transitions within the junctional binding pocket. The inherent plasticity of the FMN-binding pocket and the availability of large openings make the riboswitch an attractive target for structure-based design of FMN-like antimicrobial compounds. Our studies also explain the effects of spontaneous and antibiotic-induced deregulatory mutations and provided molecular insights into FMN-based control of gene expression in normal and riboflavin-overproducing bacterial strains.« less

Meclofenamic acid blocks the gap junction communication between the retinal pigment epithelial cells.

PubMed

Ning, N; Wen, Y; Li, Y; Li, J

2013-11-01

Nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly used to manage the pain and inflammation. NSAIDs can cause serious side effects, including vision problems. However, the underlying mechanisms are still unclear. Therefore, we aimed to investigate the effect of meclofenamic acid (MFA) on retinal pigment epithelium (RPE). In our study, we applied image analysis and whole-cell patch clamp recording to directly measure the effect of MFA on the gap junctional coupling between RPE cells. Analysis of Lucifer yellow (LY) transfer revealed that the gap junction communication existed between RPE cells. Functional experiments using the whole-cell configuration of the patch clamp technique showed that a gap junction conductance also existed between this kind of cells. Importantly, MFA largely inhibited the gap junction conductance and induced the uncoupling of RPE cells. Other NSAIDs, like aspirin and flufenamic acid (FFA), had the same effect. The gap junction functionally existed in RPE cells, which can be blocked by MFA. These findings may explain, at least partially, the vision problems with certain clinically used NSAIDs.

The Fanconi anemia associated protein FAAP24 uses two substrate specific binding surfaces for DNA recognition

PubMed Central

Wienk, Hans; Slootweg, Jack C.; Speerstra, Sietske; Kaptein, Robert; Boelens, Rolf; Folkers, Gert E.

2013-01-01

To maintain the integrity of the genome, multiple DNA repair systems exist to repair damaged DNA. Recognition of altered DNA, including bulky adducts, pyrimidine dimers and interstrand crosslinks (ICL), partially depends on proteins containing helix-hairpin-helix (HhH) domains. To understand how ICL is specifically recognized by the Fanconi anemia proteins FANCM and FAAP24, we determined the structure of the HhH domain of FAAP24. Although it resembles other HhH domains, the FAAP24 domain contains a canonical hairpin motif followed by distorted motif. The HhH domain can bind various DNA substrates; using nuclear magnetic resonance titration experiments, we demonstrate that the canonical HhH motif is required for double-stranded DNA (dsDNA) binding, whereas the unstructured N-terminus can interact with single-stranded DNA. Both DNA binding surfaces are used for binding to ICL-like single/double-strand junction-containing DNA substrates. A structural model for FAAP24 bound to dsDNA has been made based on homology with the translesion polymerase iota. Site-directed mutagenesis, sequence conservation and charge distribution support the dsDNA-binding model. Analogous to other HhH domain-containing proteins, we suggest that multiple FAAP24 regions together contribute to binding to single/double-strand junction, which could contribute to specificity in ICL DNA recognition. PMID:23661679

Influences to ADHD Problem Recognition: Mixed-Method Investigation and Recommendations to Reduce Disparities for Latino Youth.

PubMed

Haack, Lauren M; Meza, Jocelyn; Jiang, Yuanyuan; Araujo, Eva Jimenez; Pfiffner, Linda

2018-05-16

ADHD problem recognition serves as the first step of help seeking for ethnic minority families, such as Latinos, who underutilize ADHD services. The current mixed-method study explores underlying factors influencing recognition of ADHD problems in a sample of 159 school-aged youth. Parent-teacher informant discrepancy results suggest that parent ethnicity, problem domain, and child age influence ADHD problem recognition. Emerging themes from semi-structured qualitative interviews/focus groups conducted with eighteen Spanish-speaking Latino parents receiving school-based services for attention and behavior concerns support a range of recognized ADHD problems, beliefs about causes, and reactions to ADHD identification. Findings provide recommendations for reducing disparities in ADHD problem recognition and subsequent help seeking.

Discontinuous solutions of Hamilton-Jacobi equations on networks

NASA Astrophysics Data System (ADS)

Graber, P. J.; Hermosilla, C.; Zidani, H.

2017-12-01

This paper studies optimal control problems on networks without controllability assumptions at the junctions. The Value Function associated with the control problem is characterized as the solution to a system of Hamilton-Jacobi equations with appropriate junction conditions. The novel feature of the result lies in that the controllability conditions are not needed and the characterization remains valid even when the Value Function is not continuous.

Recognition vs Reverse Engineering in Boolean Concepts Learning

ERIC Educational Resources Information Center

Shafat, Gabriel; Levin, Ilya

2012-01-01

This paper deals with two types of logical problems--recognition problems and reverse engineering problems, and with the interrelations between these types of problems. The recognition problems are modeled in the form of a visual representation of various objects in a common pattern, with a composition of represented objects in the pattern.…

Molecular optoelectronics: the interaction of molecular conduction junctions with light.

PubMed

Galperin, Michael; Nitzan, Abraham

2012-07-14

The interaction of light with molecular conduction junctions is attracting growing interest as a challenging experimental and theoretical problem on one hand, and because of its potential application as a characterization and control tool on the other. It stands at the interface between two important fields, molecular electronics and molecular plasmonics and has attracted attention as a challenging scientific problem with potentially important technological consequences. Here we review the present state of the art of this field, focusing on several key phenomena and applications: using light as a switching device, using light to control junction transport in the adiabatic and non-adiabatic regimes, light generation in biased junctions and Raman scattering from such systems. This field has seen remarkable progress in the past decade, and the growing availability of scanning tip configurations that can combine optical and electrical probes suggests that further progress towards the goal of realizing molecular optoelectronics on the nanoscale is imminent.

A charge-based model of Junction Barrier Schottky rectifiers

NASA Astrophysics Data System (ADS)

Latorre-Rey, Alvaro D.; Mudholkar, Mihir; Quddus, Mohammed T.; Salih, Ali

2018-06-01

A new charge-based model of the electric field distribution for Junction Barrier Schottky (JBS) diodes is presented, based on the description of the charge-sharing effect between the vertical Schottky junction and the lateral pn-junctions that constitute the active cell of the device. In our model, the inherently 2-D problem is transformed into a simple but accurate 1-D problem which has a closed analytical solution that captures the reshaping and reduction of the electric field profile responsible for the improved electrical performance of these devices, while preserving physically meaningful expressions that depend on relevant device parameters. The validation of the model is performed by comparing calculated electric field profiles with drift-diffusion simulations of a JBS device showing good agreement. Even though other fully 2-D models already available provide higher accuracy, they lack physical insight making the proposed model an useful tool for device design.

Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons

NASA Astrophysics Data System (ADS)

Sengupta, Abhronil; Panda, Priyadarshini; Wijesinghe, Parami; Kim, Yusung; Roy, Kaushik

2016-07-01

Brain-inspired computing architectures attempt to mimic the computations performed in the neurons and the synapses in the human brain in order to achieve its efficiency in learning and cognitive tasks. In this work, we demonstrate the mapping of the probabilistic spiking nature of pyramidal neurons in the cortex to the stochastic switching behavior of a Magnetic Tunnel Junction in presence of thermal noise. We present results to illustrate the efficiency of neuromorphic systems based on such probabilistic neurons for pattern recognition tasks in presence of lateral inhibition and homeostasis. Such stochastic MTJ neurons can also potentially provide a direct mapping to the probabilistic computing elements in Belief Networks for performing regenerative tasks.

A comment on the dependence of LED’s efficiency on the junction ideality factor

NASA Astrophysics Data System (ADS)

Sethi, Anubhav; Gupta, Yashika; Arun, P.

2018-05-01

P–n junctions form the basic building blocks for any semiconductor device. Therefore, the complete understanding of the junction characteristics is very important. Although being a widely discussed topic in electronics, there are still some gaps such as finding the value and significance of the junction ideality factor, that needs to be addressed. In this article we have discussed the problems faced while extracting the ideality factor from the I–V characteristics of a p–n LED and its significance in device performance.

Grand Junction, Colorado: how a community drew on its values to shape a superior health system.

PubMed

Thorson, Marsha; Brock, Jane; Mitchell, Jason; Lynn, Joanne

2010-09-01

For the past decade, the high-quality, relatively low-cost health care delivered in Grand Junction, Colorado, has led that community to outperform most others in the United States. Medicare patients in Grand Junction have fewer hospitalizations, shorter hospitalizations, and lower mortality rates after hospitalization than do Medicare patients in comparison hospitals. Effective, efficient care is delivered in Grand Junction through separate, self-governing organizations that perceive health care as a community resource. This article describes how the various stakeholders in Grand Junction have addressed problems and set standards for the system. The lessons could apply to broader health reform efforts in communities around the country.

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions

PubMed Central

Box, Simon

2014-01-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human ‘player’ to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable. PMID:26064570

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions.

PubMed

Box, Simon

2014-12-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human 'player' to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable.

Tight junctions negatively regulate mechanical forces applied to adherens junctions in vertebrate epithelial tissue.

PubMed

Hatte, Guillaume; Prigent, Claude; Tassan, Jean-Pierre

2018-02-05

Epithelia are layers of polarised cells tightly bound to each other by adhesive contacts. Epithelia act as barriers between an organism and its external environment. Understanding how epithelia maintain their essential integrity while remaining sufficiently plastic to allow events such as cytokinesis to take place is a key biological problem. In vertebrates, the remodelling and reinforcement of adherens junctions maintains epithelial integrity during cytokinesis. The involvement of tight junctions in cell division, however, has remained unexplored. Here, we examine the role of tight junctions during cytokinesis in the epithelium of the Xenopus laevis embryo. Depletion of the tight junction-associated proteins ZO-1 and GEF-H1 leads to altered cytokinesis duration and contractile ring geometry. Using a tension biosensor, we show that cytokinesis defects originate from misregulation of tensile forces applied to adherens junctions. Our results reveal that tight junctions regulate mechanical tension applied to adherens junctions, which in turn impacts cytokinesis.This article has an associated First Person interview with the first author of the paper. © 2018. Published by The Company of Biologists Ltd.

The dorsomedial prefrontal cortex plays a causal role in mediating in-group advantage in emotion recognition: A TMS study.

PubMed

Gamond, L; Cattaneo, Z

2016-12-01

Consistent evidence suggests that emotional facial expressions are better recognized when the expresser and the perceiver belong to the same social group (in-group advantage). In this study, we used transcranial magnetic stimulation (TMS) to investigate the possible causal involvement of the dorsomedial prefrontal cortex (dmPFC) and of the right temporo-parietal junction (TPJ), two main nodes of the mentalizing neural network, in mediating the in-group advantage in emotion recognition. Participants performed an emotion discrimination task in a minimal (blue/green) group paradigm. We found that interfering with activity in the dmPFC significantly interfered with the effect of minimal group-membership on emotion recognition, reducing participants' ability to discriminate emotions expressed by in-group members. In turn, rTPJ mainly affected emotion discrimination per se, irrespective of group membership. Overall, our results point to a causal role of the dmPFC in mediating the in-group advantage in emotion recognition, favoring intragroup communication. Copyright © 2016 Elsevier Ltd. All rights reserved.

Diode/magnetic tunnel junction cell for fully scalable matrix-based biochip

NASA Astrophysics Data System (ADS)

Cardoso, F. A.; Ferreira, H. A.; Conde, J. P.; Chu, V.; Freitas, P. P.; Vidal, D.; Germano, J.; Sousa, L.; Piedade, M. S.; Costa, B. A.; Lemos, J. M.

2006-04-01

Magnetoresistive biochips have been recently introduced for the detection of biomolecular recognition. In this work, the detection site incorporates a thin-film diode in series with a magnetic tunnel junction (MTJ), leading to a matrix-based biochip that can be easily scaled up to screen large numbers of different target analytes. The fabricated 16×16 cell matrix integrates hydrogenated amorphous silicon (a-Si:H) diodes with aluminum oxide barrier MTJ. Each detection site also includes a U-shaped current line for magnetically assisted target concentration at probe sites. The biochip is being integrated in a portable, credit card size electronics control platform. Detection of 250 nm diameter magnetic nanoparticles by one of the matrix cells is demonstrated.

Genetic variants related to gap junctions and hormone secretion influence conception rates in cows

PubMed Central

Sugimoto, Mayumi; Sasaki, Shinji; Gotoh, Yusaku; Nakamura, Yuuki; Aoyagi, Yoshito; Kawahara, Takayoshi; Sugimoto, Yoshikazu

2013-01-01

The recent decline in fertility is a serious problem in the dairy industry. To overcome this problem, we performed a genome-wide association study using 384 Holsteins and identified four loci associated with conception rates. Two of them contained gap junction-related genes: PKP2 and CTTNBP2NL. Further analysis confirmed that PKP2 increased connexin 43, a gap junction protein, whereas CTTNBP2NL dephosphorylated connexin 43. Knockdown of PKP2 or overexpression of CTTNBP2NL inhibited embryo implantation in mice. The other two loci contained neuroendocrine-related genes: SETD6 and CACNB2. Additional experiments indicated that SETD6 is involved in the transcriptional regulation of gonadotropin-releasing hormone, whereas CACNB2 controlled the secretion of follicle-stimulating hormone in cattle. The total allele substitution effect of these genes on conception rate was 3.5%. Our findings reveal important roles for gap junction communication and the neuroendocrine system in conception and suggest unique selection methods to improve reproductive performance in the livestock industry. PMID:24218568

Genetic variants related to gap junctions and hormone secretion influence conception rates in cows.

PubMed

Sugimoto, Mayumi; Sasaki, Shinji; Gotoh, Yusaku; Nakamura, Yuuki; Aoyagi, Yoshito; Kawahara, Takayoshi; Sugimoto, Yoshikazu

2013-11-26

The recent decline in fertility is a serious problem in the dairy industry. To overcome this problem, we performed a genome-wide association study using 384 Holsteins and identified four loci associated with conception rates. Two of them contained gap junction-related genes: PKP2 and CTTNBP2NL. Further analysis confirmed that PKP2 increased connexin 43, a gap junction protein, whereas CTTNBP2NL dephosphorylated connexin 43. Knockdown of PKP2 or overexpression of CTTNBP2NL inhibited embryo implantation in mice. The other two loci contained neuroendocrine-related genes: SETD6 and CACNB2. Additional experiments indicated that SETD6 is involved in the transcriptional regulation of gonadotropin-releasing hormone, whereas CACNB2 controlled the secretion of follicle-stimulating hormone in cattle. The total allele substitution effect of these genes on conception rate was 3.5%. Our findings reveal important roles for gap junction communication and the neuroendocrine system in conception and suggest unique selection methods to improve reproductive performance in the livestock industry.

Stochastic Spiking Neural Networks Enabled by Magnetic Tunnel Junctions: From Nontelegraphic to Telegraphic Switching Regimes

NASA Astrophysics Data System (ADS)

Liyanagedera, Chamika M.; Sengupta, Abhronil; Jaiswal, Akhilesh; Roy, Kaushik

2017-12-01

Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-efficient cognitive intelligence. The computational model attempt to exploit the intrinsic device stochasticity of nanoelectronic synaptic or neural components to perform learning or inference. However, there has been limited analysis on the scaling effect of stochastic spin devices and its impact on the operation of such stochastic networks at the system level. This work attempts to explore the design space and analyze the performance of nanomagnet-based stochastic neuromorphic computing architectures for magnets with different barrier heights. We illustrate how the underlying network architecture must be modified to account for the random telegraphic switching behavior displayed by magnets with low barrier heights as they are scaled into the superparamagnetic regime. We perform a device-to-system-level analysis on a deep neural-network architecture for a digit-recognition problem on the MNIST data set.

What happens in Josephson junctions at high critical current densities

NASA Astrophysics Data System (ADS)

Massarotti, D.; Stornaiuolo, D.; Lucignano, P.; Caruso, R.; Galletti, L.; Montemurro, D.; Jouault, B.; Campagnano, G.; Arani, H. F.; Longobardi, L.; Parlato, L.; Pepe, G. P.; Rotoli, G.; Tagliacozzo, A.; Lombardi, F.; Tafuri, F.

2017-07-01

The impressive advances in material science and nanotechnology are more and more promoting the use of exotic barriers and/or superconductors, thus paving the way to new families of Josephson junctions. Semiconducting, ferromagnetic, topological insulator and graphene barriers are leading to unconventional and anomalous aspects of the Josephson coupling, which might be useful to respond to some issues on key problems of solid state physics. However, the complexity of the layout and of the competing physical processes occurring in the junctions is posing novel questions on the interpretation of their phenomenology. We classify some significant behaviors of hybrid and unconventional junctions in terms of their first imprinting, i.e., current-voltage curves, and propose a phenomenological approach to describe some features of junctions characterized by relatively high critical current densities Jc. Accurate arguments on the distribution of switching currents will provide quantitative criteria to understand physical processes occurring in high-Jc junctions. These notions are universal and apply to all kinds of junctions.

Synchronization of a Josephson junction array in terms of global variables

NASA Astrophysics Data System (ADS)

Vlasov, Vladimir; Pikovsky, Arkady

2013-08-01

We consider an array of Josephson junctions with a common LCR load. Application of the Watanabe-Strogatz approach [Physica DPDNPDT0167-278910.1016/0167-2789(94)90196-1 74, 197 (1994)] allows us to formulate the dynamics of the array via the global variables only. For identical junctions this is a finite set of equations, analysis of which reveals the regions of bistability of the synchronous and asynchronous states. For disordered arrays with distributed parameters of the junctions, the problem is formulated as an integro-differential equation for the global variables; here stability of the asynchronous states and the properties of the transition synchrony-asynchrony are established numerically.

Electronic single-molecule identification of carbohydrate isomers by recognition tunnelling

NASA Astrophysics Data System (ADS)

Im, Jongone; Biswas, Sovan; Liu, Hao; Zhao, Yanan; Sen, Suman; Biswas, Sudipta; Ashcroft, Brian; Borges, Chad; Wang, Xu; Lindsay, Stuart; Zhang, Peiming

2016-12-01

Carbohydrates are one of the four main building blocks of life, and are categorized as monosaccharides (sugars), oligosaccharides and polysaccharides. Each sugar can exist in two alternative anomers (in which a hydroxy group at C-1 takes different orientations) and each pair of sugars can form different epimers (isomers around the stereocentres connecting the sugars). This leads to a vast combinatorial complexity, intractable to mass spectrometry and requiring large amounts of sample for NMR characterization. Combining measurements of collision cross section with mass spectrometry (IM-MS) helps, but many isomers are still difficult to separate. Here, we show that recognition tunnelling (RT) can classify many anomers and epimers via the current fluctuations they produce when captured in a tunnel junction functionalized with recognition molecules. Most importantly, RT is a nanoscale technique utilizing sub-picomole quantities of analyte. If integrated into a nanopore, RT would provide a unique approach to sequencing linear polysaccharides.

Hidden Markov models for character recognition.

PubMed

Vlontzos, J A; Kung, S Y

1992-01-01

A hierarchical system for character recognition with hidden Markov model knowledge sources which solve both the context sensitivity problem and the character instantiation problem is presented. The system achieves 97-99% accuracy using a two-level architecture and has been implemented using a systolic array, thus permitting real-time (1 ms per character) multifont and multisize printed character recognition as well as handwriting recognition.

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

PubMed

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.

Identification of Biomolecular Building Blocks by Recognition Tunneling: Stride towards Nanopore Sequencing of Biomolecules

NASA Astrophysics Data System (ADS)

Sen, Suman

DNA, RNA and Protein are three pivotal biomolecules in human and other organisms, playing decisive roles in functionality, appearance, diseases development and other physiological phenomena. Hence, sequencing of these biomolecules acquires the prime interest in the scientific community. Single molecular identification of their building blocks can be done by a technique called Recognition Tunneling (RT) based on Scanning Tunneling Microscope (STM). A single layer of specially designed recognition molecule is attached to the STM electrodes, which trap the targeted molecules (DNA nucleoside monophosphates, RNA nucleoside monophosphates or amino acids) inside the STM nanogap. Depending on their different binding interactions with the recognition molecules, the analyte molecules generate stochastic signal trains accommodating their "electronic fingerprints". Signal features are used to detect the molecules using a machine learning algorithm and different molecules can be identified with significantly high accuracy. This, in turn, paves the way for rapid, economical nanopore sequencing platform, overcoming the drawbacks of Next Generation Sequencing (NGS) techniques. To read DNA nucleotides with high accuracy in an STM tunnel junction a series of nitrogen-based heterocycles were designed and examined to check their capabilities to interact with naturally occurring DNA nucleotides by hydrogen bonding in the tunnel junction. These recognition molecules are Benzimidazole, Imidazole, Triazole and Pyrrole. Benzimidazole proved to be best among them showing DNA nucleotide classification accuracy close to 99%. Also, Imidazole reader can read an abasic monophosphate (AP), a product from depurination or depyrimidination that occurs 10,000 times per human cell per day. In another study, I have investigated a new universal reader, 1-(2-mercaptoethyl)pyrene (Pyrene reader) based on stacking interactions, which should be more specific to the canonical DNA nucleosides. In addition, Pyrene reader showed higher DNA base-calling accuracy compare to Imidazole reader, the workhorse in our previous projects. In my other projects, various amino acids and RNA nucleoside monophosphates were also classified with significantly high accuracy using RT. Twenty naturally occurring amino acids and various RNA nucleosides (four canonical and two modified) were successfully identified. Thus, we envision nanopore sequencing biomolecules using Recognition Tunneling (RT) that should provide comprehensive betterment over current technologies in terms of time, chemical and instrumental cost and capability of de novo sequencing.

The neural basis of humour comprehension and humour appreciation: The roles of the temporoparietal junction and superior frontal gyrus.

PubMed

Campbell, Darren W; Wallace, Marc G; Modirrousta, Mandana; Polimeni, Joseph O; McKeen, Nancy A; Reiss, Jeffrey P

2015-12-01

Psychological well-being and social acumen benefit from the recognition of humourous intent and its enjoyment. The enjoyment of humour requires recognition, but humour recognition is not necessarily accompanied by humour enjoyment. Humour recognition is crucial during social interactions, while the associated enjoyment is less critical. Few neuroimaging studies have explicitly differentiated between the neural foundations of humour comprehension and humour appreciation. Among such studies, design limitations have obscured the specification of neural correlates to humour comprehension or appreciation. We implemented a trichotomous response option to address these design limitations. Twenty-four participants rated 120 comics (90 unaltered with humourous intent and 30 caption-altered without humourous intent) as either funny jokes (FJ), not funny jokes but intended to be funny (NFJ), or not intended to be funny or non-jokes (NJ). We defined humour comprehension by NFJ minus NJ and humour appreciation by FJ minus NFJ. We measured localized blood oxygen level dependent (BOLD) neural responses with a 3T MRI scanner. We tested for BOLD responses in humour comprehension brain regions of interest (ROIs), humour appreciation ROIs, and across the whole-brain. We found significant NFJ-NJ BOLD responses in our humour comprehension ROIs and significant FJ-NFJ BOLD responses in select humour appreciation ROIs. One key finding is that comprehension accuracy levels correlated with humour-comprehension responses in the left temporo-parietal junction (TPJ). This finding represents a novel and precise neural linkage to humour comprehension. A second key finding is that the superior frontal gyrus (SFG) was uniquely associated with humour-appreciation. The SFG response suggests that complex cognitive processing underlies humour appreciation and that current models of humour appreciation be revised. Finally, our research design provides an operational distinction between humour comprehension and appreciation and a sensitive measure of individual differences in humour comprehension accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

SQUID magnetometers for low-frequency applications

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

Ryhaenen, T.; Seppae, H.; Ilmoniemi, R.

1989-09-01

The authors present a novel formulation for SQUID operation, which enables them to evaluate and compare the sensitivity and applicability of different devices. SQUID magnetometers for low-frequency applications are analyzed, taking into account the coupling circuits and electronics. They discuss nonhysteretic and hysteretic single-junction rf SQUIDs, but the main emphasis is on the dynamics, sensitivity, and coupling considerations of dc-SQUID magnetometers. A short review of current ideas on thin-film, dc-SQUID design presents the problems in coupling and the basic limits of sensitivity. The fabrication technology of tunnel-junction devices is discussed with emphasis on how it limits critical current densities, specificmore » capacitances of junctions, minimum linewidths, conductor separations, etc. Properties of high-temperature superconductors are evaluated on the basis of recently published results on increased flux creep, low density of current carriers, and problems in fabricating reliable junctions. The optimization of electronics for different types of SQUIDs is presented. Finally, the most important low-frequency applications of SQUIDs in biomagnetism, metrology, geomagnetism, and some physics experiments demonstrate the various possibilities that state-of-the-art SQUIDs can provide.« less

Mechanisms and neural basis of object and pattern recognition: a study with chess experts.

PubMed

Bilalić, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-11-01

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and novices performing chess-related and -unrelated (visual) search tasks. As expected, the superiority of experts was limited to the chess-specific task, as there were no differences in a control task that used the same chess stimuli but did not require chess-specific recognition. The analysis of eye movements showed that experts immediately and exclusively focused on the relevant aspects in the chess task, whereas novices also examined irrelevant aspects. With random chess positions, when pattern knowledge could not be used to guide perception, experts nevertheless maintained an advantage. Experts' superior domain-specific parafoveal vision, a consequence of their knowledge about individual domain-specific symbols, enabled improved object recognition. Functional magnetic resonance imaging corroborated this differentiation between object and pattern recognition and showed that chess-specific object recognition was accompanied by bilateral activation of the occipitotemporal junction, whereas chess-specific pattern recognition was related to bilateral activations in the middle part of the collateral sulci. Using the expertise approach together with carefully chosen controls and multiple dependent measures, we identified object and pattern recognition as two essential cognitive processes in expert visual cognition, which may also help to explain the mechanisms of everyday perception.

Conic section function neural network circuitry for offline signature recognition.

PubMed

Erkmen, Burcu; Kahraman, Nihan; Vural, Revna A; Yildirim, Tulay

2010-04-01

In this brief, conic section function neural network (CSFNN) circuitry was designed for offline signature recognition. CSFNN is a unified framework for multilayer perceptron (MLP) and radial basis function (RBF) networks to make simultaneous use of advantages of both. The CSFNN circuitry architecture was developed using a mixed mode circuit implementation. The designed circuit system is problem independent. Hence, the general purpose neural network circuit system could be applied to various pattern recognition problems with different network sizes on condition with the maximum network size of 16-16-8. In this brief, CSFNN circuitry system has been applied to two different signature recognition problems. CSFNN circuitry was trained with chip-in-the-loop learning technique in order to compensate typical analog process variations. CSFNN hardware achieved highly comparable computational performances with CSFNN software for nonlinear signature recognition problems.

A comparative study of noise pollution levels in some selected areas in Ilorin Metropolis, Nigeria.

PubMed

Oyedepo, Olayinka S; Saadu, Abdullahi A

2009-11-01

The noise pollution is a major problem for the quality of life in urban areas. This study was conducted to compare the noise pollution levels at busy roads/road junctions, passengers loading parks, commercial, industrial and residential areas in Ilorin metropolis. A total number of 47-locations were selected within the metropolis. Statistical analysis shows significant difference (P < 0.05) in noise pollution levels between industrial areas and low density residential areas, industrial areas and high density areas, industrial areas and passengers loading parks, industrial areas and commercial areas, busy roads/road junctions and low density areas, passengers loading parks and commercial areas and commercial areas and low density areas. There is no significant difference (P > 0.05) in noise pollution levels between industrial areas and busy roads/road junctions, busy roads/road junctions and high density areas, busy roads/road junctions and passengers loading parks, busy roads/road junctions and commercial areas, passengers loading parks and high density areas, passengers loading parks and commercial areas and commercial areas and high density areas. The results show that Industrial areas have the highest noise pollution levels (110.2 dB(A)) followed by busy roads/Road junctions (91.5 dB(A)), Passengers loading parks (87.8 dB(A)) and Commercial areas (84.4 dB(A)). The noise pollution levels in Ilorin metropolis exceeded the recommended level by WHO at 34 of 47 measuring points. It can be concluded that the city is environmentally noise polluted and road traffic and industrial machineries are the major sources of it. Noting the noise emission standards, technical control measures, planning and promoting the citizens awareness about the high noise risk may help to relieve the noise problem in the metropolis.

Study on iron disilicide thermoelectric generator

NASA Astrophysics Data System (ADS)

Anderson, Gerald

1987-11-01

The first objective of the experimental work is to verify the characteristics of commercially available material. The Japanese company Komatsu Electronics Ltd., sells U-Shaped couples of FeSi2. Twenty-four couples are now in our laboratory. Each leg of the couple is made of one type (N or P) of material and the junction is placed directly into a flame. Being almost impossible to measure the hot junction temperature in the flame and to evaluate the heat flux going through the material between hot and cold junctions, we have designed an experimental assembly, suited to measure these values. The main problem is to obtain a good thermal contact for the hot junctions. If not, there is an important temperature drop between the hot source and the hot junction of the couple leading to wrong values of the characteristics.

Density matrix renormalization group study of Y-junction spin systems

NASA Astrophysics Data System (ADS)

Guo, Haihui

Junction systems are important to understand both from the fundamental and the practical point of view, as they are essential components in existing and future electronic and spintronic devices. With the continuous advance of technology, device size will eventual reach the atomic scale. Some of the most interesting and useful junction systems will be strongly correlated. We chose the Density Matrix Renormalization Group method to study two types of Y-junction systems, the Y and YDelta junctions, on strongly correlated spin chains. With new ideas coming from the quantum information field, we have made a very efficient. Y-junction DMRG algorithm, which improves the overall CUB cost from O(m6) to O(m4), where m is the number of states kept per block. We studied the ground state properties, the correlation length, and investigated the degeneracy problem on the Y and YDelta junctions. For the excited states, we researched the existence of magnon bound states for various conditions, and have shown that the bound state exists when the central coupling constant is small.

How can professionals carry out recognition towards children of parents with alcohol problems? A qualitative interview study.

PubMed

Werner, Anne; Malterud, Kirsti

2017-02-01

The aim of this study was to explore informal adult support experienced by children with parental alcohol problems to understand how professionals can show recognition in a similar way. We conducted a qualitative interview study with retrospective accounts from nine adults growing up with problem-drinking parents. Data were analysed with systematic text condensation. Goffman's concept "frame" offered a lens to study how supportive situations were defined and to understand opportunities and limitations for translation of recognition acts and attitudes to professional contexts. Analysis demonstrated frames of commonplace interaction where children experienced that adults recognised and responded to their needs. However, the silent support from an adult who recognised the problems without responding was an ambiguous frame. The child sometimes felt betrayed. Concentrating on frames of recognition which could be passed over to professional interactions, we noticed that participants called for a safe harbour, providing a sense of normality. Being with friends and their families, escaping difficulties at home without having to tell, was emphasised as important. Recognition was experienced when an adult with respect and dignity offered an open opportunity to address the problems, without pushing towards further communication. Our study indicates some specific lessons to be learnt about recognition for professional service providers from everyday situations. Frames of recognition, communicating availability and normality, and also unconditional confidentiality and safety when sharing problems may also be offered by professionals in public healthcare within their current frames of competency and time.

A Survey on Sentiment Classification in Face Recognition

NASA Astrophysics Data System (ADS)

Qian, Jingyu

2018-01-01

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

Adults' strategies for simple addition and multiplication: verbal self-reports and the operand recognition paradigm.

PubMed

Metcalfe, Arron W S; Campbell, Jamie I D

2011-05-01

Accurate measurement of cognitive strategies is important in diverse areas of psychological research. Strategy self-reports are a common measure, but C. Thevenot, M. Fanget, and M. Fayol (2007) proposed a more objective method to distinguish different strategies in the context of mental arithmetic. In their operand recognition paradigm, speed of recognition memory for problem operands after solving a problem indexes strategy (e.g., direct memory retrieval vs. a procedural strategy). Here, in 2 experiments, operand recognition time was the same following simple addition or multiplication, but, consistent with a wide variety of previous research, strategy reports indicated much greater use of procedures (e.g., counting) for addition than multiplication. Operation, problem size (e.g., 2 + 3 vs. 8 + 9), and operand format (digits vs. words) had interactive effects on reported procedure use that were not reflected in recognition performance. Regression analyses suggested that recognition time was influenced at least as much by the relative difficulty of the preceding problem as by the strategy used. The findings indicate that the operand recognition paradigm is not a reliable substitute for strategy reports and highlight the potential impact of difficulty-related carryover effects in sequential cognitive tasks.

Spectral behavior of integrated optics asymmetric y-junction used for optimizing a planar optics telescope beam combiner

NASA Astrophysics Data System (ADS)

Schanen-Duport, Isabelle; Persegol, Dominique; Collomb, Virginie; Minier, Vincent; Haguenauer, Pierre

2017-11-01

Astronomical aperture synthesis requires to combine beams coming from telescopes, with constraints on mechanical and thermal stability, accuracy on the measurement of the interferences visibility. One adapted way for solving the problem is integrated planar optics. A first two telescope beam combiner made by ion exchange technique on glass substrate and build with symmetric Y-junction provides laboratory white light interferograms simultaneously with photometric calibration. In order to increase the interferometric signal without loss of photometric output, we propose to replace symmetric Y-junctions by asymmetric ones. In this paper, we report the conception, the manufacturing and the characterization of asymmetric Y-junction realized by ion exchange on glass substrate. The specific application of astronomical interferometry required the characterization of such component in term of spectral behavior, so we report the simulation and the measurement of asymmetric Y-junction response versus wavelength.

Excimer laser annealing: A gold process for CZ silicon junction formation

NASA Technical Reports Server (NTRS)

Wong, David C.; Bottenberg, William R.; Byron, Stanley; Alexander, Paul

1987-01-01

A cold process using an excimer laser for junction formation in silicon has been evaluated as a way to avoid problems associated with thermal diffusion. Conventional thermal diffusion can cause bulk precipitation of SiOx and SiC or fail to completely activate the dopant, leaving a degenerate layer at the surface. Experiments were conducted to determine the feasibility of fabricating high quality p-n junctions using a pulsed excimer laser for junction formation at remelt temperature with ion-implanted surfaces. Solar-cell efficiency exceeding 16 percent was obtained using Czochralski single-crystal silicon without benefit of back surface field or surface passivation. Characterization shows that the formation of uniform, shallow junctions (approximately 0.25 micron) by excimer laser scanning preserves the minority carrier lifetime that leads to high current collection. However, the process is sensitive to initial surface conditions and handling parameters that drive the cost up.

Permutation coding technique for image recognition systems.

PubMed

Kussul, Ernst M; Baidyk, Tatiana N; Wunsch, Donald C; Makeyev, Oleksandr; Martín, Anabel

2006-11-01

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%.

Breakdown of autoresonance due to separatrix crossing in dissipative systems: From Josephson junctions to the three-wave problem.

PubMed

Chacón, Ricardo

2008-12-01

Optimal energy amplification via autoresonance in dissipative systems subjected to separatrix crossings is discussed through the universal model of a damped driven pendulum. Analytical expressions of the autoresonance responses and forces as well as the associated adiabatic invariants for the phase space regions separated by the underlying separatrix are derived from the energy-based theory of autoresonance. Additionally, applications to a single Josephson junction, topological solitons in Frenkel-Kontorova chains, as well as to the three-wave problem in dissipative media are discussed in detail from the autoresonance analysis.

Behavioral health needs and problem recognition by older adults receiving home-based aging services.

PubMed

Gum, Amber M; Petkus, Andrew; McDougal, Sarah J; Present, Melanie; King-Kallimanis, Bellinda; Schonfeld, Lawrence

2009-04-01

Older adults' recognition of a behavioral health need is one of the strongest predictors of their use of behavioral health services. Thus, study aims were to examine behavioral health problems in a sample of older adults receiving home-based aging services, their recognition of behavioral health problems, and covariates of problem recognition. The study design was cross-sectional. Older adults (n = 141) receiving home-based aging services completed interviews that included: Structured Clinical Interview for DSM-IV; Brief Symptom Inventory-18; attitudinal scales of stigma, expectations regarding aging, and thought suppression; behavioral health treatment experience; and questions about recognition of behavioral health problems. Thirty (21.9%) participants received an Axis I diagnosis (depressive, anxiety, or substance); another 17 (12.1%) were diagnosed with an adjustment disorder. Participants were more likely to recognize having a problem if they had an Axis I diagnosis, more distress on the BSI-18, family member or friend with a behavioral health problem, and greater thought suppression. In logistic regression, participants who identified a family member or friend with a behavioral health problem were more likely to identify having a behavioral health problem themselves. Findings suggest that older adults receiving home-based aging services who recognize behavioral health problems are more likely to have a psychiatric diagnosis or be experiencing significant distress, and they are more familiar with behavioral health problems in others. This familiarity may facilitate treatment planning; thus, older adults with behavioral health problems who do not report familiarity of problems in others likely require additional education. (c) 2008 John Wiley & Sons, Ltd.

Ultrasensitive mechanical crack-based sensor inspired by the spider sensory system

NASA Astrophysics Data System (ADS)

Kang, Daeshik; Pikhitsa, Peter V.; Choi, Yong Whan; Lee, Chanseok; Shin, Sung Soo; Piao, Linfeng; Park, Byeonghak; Suh, Kahp-Yang; Kim, Tae-Il; Choi, Mansoo

2014-12-01

Recently developed flexible mechanosensors based on inorganic silicon, organic semiconductors, carbon nanotubes, graphene platelets, pressure-sensitive rubber and self-powered devices are highly sensitive and can be applied to human skin. However, the development of a multifunctional sensor satisfying the requirements of ultrahigh mechanosensitivity, flexibility and durability remains a challenge. In nature, spiders sense extremely small variations in mechanical stress using crack-shaped slit organs near their leg joints. Here we demonstrate that sensors based on nanoscale crack junctions and inspired by the geometry of a spider's slit organ can attain ultrahigh sensitivity and serve multiple purposes. The sensors are sensitive to strain (with a gauge factor of over 2,000 in the 0-2 per cent strain range) and vibration (with the ability to detect amplitudes of approximately 10 nanometres). The device is reversible, reproducible, durable and mechanically flexible, and can thus be easily mounted on human skin as an electronic multipixel array. The ultrahigh mechanosensitivity is attributed to the disconnection-reconnection process undergone by the zip-like nanoscale crack junctions under strain or vibration. The proposed theoretical model is consistent with experimental data that we report here. We also demonstrate that sensors based on nanoscale crack junctions are applicable to highly selective speech pattern recognition and the detection of physiological signals. The nanoscale crack junction-based sensory system could be useful in diverse applications requiring ultrahigh displacement sensitivity.

Ultrasensitive mechanical crack-based sensor inspired by the spider sensory system.

PubMed

Kang, Daeshik; Pikhitsa, Peter V; Choi, Yong Whan; Lee, Chanseok; Shin, Sung Soo; Piao, Linfeng; Park, Byeonghak; Suh, Kahp-Yang; Kim, Tae-il; Choi, Mansoo

2014-12-11

Recently developed flexible mechanosensors based on inorganic silicon, organic semiconductors, carbon nanotubes, graphene platelets, pressure-sensitive rubber and self-powered devices are highly sensitive and can be applied to human skin. However, the development of a multifunctional sensor satisfying the requirements of ultrahigh mechanosensitivity, flexibility and durability remains a challenge. In nature, spiders sense extremely small variations in mechanical stress using crack-shaped slit organs near their leg joints. Here we demonstrate that sensors based on nanoscale crack junctions and inspired by the geometry of a spider's slit organ can attain ultrahigh sensitivity and serve multiple purposes. The sensors are sensitive to strain (with a gauge factor of over 2,000 in the 0-2 per cent strain range) and vibration (with the ability to detect amplitudes of approximately 10 nanometres). The device is reversible, reproducible, durable and mechanically flexible, and can thus be easily mounted on human skin as an electronic multipixel array. The ultrahigh mechanosensitivity is attributed to the disconnection-reconnection process undergone by the zip-like nanoscale crack junctions under strain or vibration. The proposed theoretical model is consistent with experimental data that we report here. We also demonstrate that sensors based on nanoscale crack junctions are applicable to highly selective speech pattern recognition and the detection of physiological signals. The nanoscale crack junction-based sensory system could be useful in diverse applications requiring ultrahigh displacement sensitivity.

Analog Approach to Constraint Satisfaction Enabled by Spin Orbit Torque Magnetic Tunnel Junctions.

PubMed

Wijesinghe, Parami; Liyanagedera, Chamika; Roy, Kaushik

2018-05-02

Boolean satisfiability (k-SAT) is an NP-complete (k ≥ 3) problem that constitute one of the hardest classes of constraint satisfaction problems. In this work, we provide a proof of concept hardware based analog k-SAT solver, that is built using Magnetic Tunnel Junctions (MTJs). The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog satisfiability (SAT) solver. In the presence of thermal noise, the MTJ based system can successfully solve Boolean satisfiability problems. Most importantly, our results exhibit that, the proposed MTJ based hardware SAT solver is capable of finding a solution to a significant fraction (at least 85%) of hard 3-SAT problems, within a time that has a polynomial relationship with the number of variables(<50).

Marketing/Sales Students' Understanding of What Counts as Sales

ERIC Educational Resources Information Center

Hoshower, Leon; Gupta, Ashok K.

2009-01-01

Improper sales revenue recognition is the single largest issue contributing to financial restatements. Understanding and applying the rules of sales revenue recognition is not just an accounting problem; it is a marketing problem, too. Thus, it is important that the sales force has a basic understanding of the rules of sales recognition and be…

Differentiating between self and others: an ALE meta-analysis of fMRI studies of self-recognition and theory of mind.

PubMed

van Veluw, Susanne J; Chance, Steven A

2014-03-01

The perception of self and others is a key aspect of social cognition. In order to investigate the neurobiological basis of this distinction we reviewed two classes of task that study self-awareness and awareness of others (theory of mind, ToM). A reliable task to measure self-awareness is the recognition of one's own face in contrast to the recognition of others' faces. False-belief tasks are widely used to identify neural correlates of ToM as a measure of awareness of others. We performed an activation likelihood estimation meta-analysis, using the fMRI literature on self-face recognition and false-belief tasks. The brain areas involved in performing false-belief tasks were the medial prefrontal cortex (MPFC), bilateral temporo-parietal junction, precuneus, and the bilateral middle temporal gyrus. Distinct self-face recognition regions were the right superior temporal gyrus, the right parahippocampal gyrus, the right inferior frontal gyrus/anterior cingulate cortex, and the left inferior parietal lobe. Overlapping brain areas were the superior temporal gyrus, and the more ventral parts of the MPFC. We confirmed that self-recognition in contrast to recognition of others' faces, and awareness of others involves a network that consists of separate, distinct neural pathways, but also includes overlapping regions of higher order prefrontal cortex where these processes may be combined. Insights derived from the neurobiology of disorders such as autism and schizophrenia are consistent with this notion.

The different faces of one’s self: an fMRI study into the recognition of current and past self-facial appearances

PubMed Central

Apps, Matthew A. J.; Tajadura-Jiménez, Ana; Turley, Grainne; Tsakiris, Manos

2013-01-01

Mirror self-recognition is often considered as an index of self-awareness. Neuroimaging studies have identified a neural circuit specialised for the recognition of one’s own current facial appearance. However, faces change considerably over a lifespan, highlighting the necessity for representations of one’s face to continually be updated. We used fMRI to investigate the different neural circuits involved in the recognition of the childhood and current, adult, faces of one’s self. Participants viewed images of either their own face as it currently looks morphed with the face of a familiar other or their childhood face morphed with the childhood face of the familiar other. Activity in areas which have a generalised selectivity for faces, including the inferior occipital gyrus, the superior parietal lobule and the inferior temporal gyrus, varied with the amount of current self in an image. Activity in areas involved in memory encoding and retrieval, including the hippocampus and the posterior cingulate gyrus, and areas involved in creating a sense of body ownership, including the temporo-parietal junction and the inferior parietal lobule, varied with the amount of childhood self in an image. We suggest that the recognition of one’s own past or present face is underpinned by different cognitive processes in distinct neural circuits. Current self-recognition engages areas involved in perceptual face processing, whereas childhood self-recognition recruits networks involved in body ownership and memory processing. PMID:22940117

The different faces of one's self: an fMRI study into the recognition of current and past self-facial appearances.

PubMed

Apps, Matthew A J; Tajadura-Jiménez, Ana; Turley, Grainne; Tsakiris, Manos

2012-11-15

Mirror self-recognition is often considered as an index of self-awareness. Neuroimaging studies have identified a neural circuit specialised for the recognition of one's own current facial appearance. However, faces change considerably over a lifespan, highlighting the necessity for representations of one's face to continually be updated. We used fMRI to investigate the different neural circuits involved in the recognition of the childhood and current, adult, faces of one's self. Participants viewed images of either their own face as it currently looks morphed with the face of a familiar other or their childhood face morphed with the childhood face of the familiar other. Activity in areas which have a generalised selectivity for faces, including the inferior occipital gyrus, the superior parietal lobule and the inferior temporal gyrus, varied with the amount of current self in an image. Activity in areas involved in memory encoding and retrieval, including the hippocampus and the posterior cingulate gyrus, and areas involved in creating a sense of body ownership, including the temporo-parietal junction and the inferior parietal lobule, varied with the amount of childhood self in an image. We suggest that the recognition of one's own past or present face is underpinned by different cognitive processes in distinct neural circuits. Current self-recognition engages areas involved in perceptual face processing, whereas childhood self-recognition recruits networks involved in body ownership and memory processing. Copyright © 2012 Elsevier Inc. All rights reserved.

Preferential V beta gene usage and lack of junctional sequence conservation among human T cell receptors specific for a tetanus toxin- derived peptide: evidence for a dominant role of a germline-encoded V region in antigen/major histocompatibility complex recognition

PubMed Central

1992-01-01

To investigate the structural and genetic basis of the T cell response to defined peptide/major histocompatibility (MHC) class II complexes in humans, we established a large panel of T cell clones (61) from donors of different HLA-DR haplotypes and reactive with a tetanus toxin- derived peptide (tt830-844) recognized in association with most DR molecules (universal peptide). By using a bacterial enterotoxin-based proliferation assay and cDNA sequencing, we found preferential use of a particular V beta region gene segment, V beta 2.1, in three of the individuals studied (64%, n = 58), irrespective of whether the peptide was presented by the DR6wcI, DR4w4, or DRw11.1 and DRw11.2 alleles, demonstrating that shared MHC class II antigens are not required for shared V beta gene use by T cell receptors (TCRs) specific for this peptide. V alpha gene use was more heterogeneous, with at least seven different V alpha segments derived from five distinct families encoding alpha chains able to pair with V beta 2.1 chains to form a tt830-844/DR- specific binding site. Several cases were found of clones restricted to different DR alleles that expressed identical V beta and (or very closely related) V alpha gene segments and that differed only in their junctional sequences. Thus, changes in the putative complementary determining region 3 (CDR3) of the TCR may, in certain cases, alter MHC specificity and maintain peptide reactivity. Finally, in contrast to what has been observed in other defined peptide/MHC systems, a striking heterogeneity was found in the junctional regions of both alpha and beta chains, even for TCRs with identical V alpha and/or V beta gene segments and the same restriction. Among 14 anti-tt830-844 clones using the V beta 2.1 gene segment, 14 unique V beta-D-J beta junctions were found, with no evident conservation in length and/or amino acid composition. One interpretation for this apparent lack of coselection of specific junctional sequences in the context of a common V element, V beta 2.1, is that this V region plays a dominant role in the recognition of the tt830-844/DR complex. PMID:1371303

Interprofessional, simulation-based technology-enhanced learning to improve physical health care in psychiatry: The recognition and assessment of medical problems in psychiatric settings course.

PubMed

Akroyd, Mike; Jordan, Gary; Rowlands, Paul

2016-06-01

People with serious mental illness have reduced life expectancy compared with a control population, much of which is accounted for by significant physical comorbidity. Frontline clinical staff in mental health often lack confidence in recognition, assessment and management of such 'medical' problems. Simulation provides one way for staff to practise these skills in a safe setting. We produced a multidisciplinary simulation course around recognition and assessment of medical problems in psychiatric settings. We describe an audit of strategic and design aspects of the recognition and assessment of medical problems in psychiatric settings course, using the Department of Health's 'Framework for Technology Enhanced Learning' as our audit standards. At the same time as highlighting areas where recognition and assessment of medical problems in psychiatric settings adheres to these identified principles, such as the strategic underpinning of the approach, and the means by which information is collected, reviewed and shared, it also helps us to identify areas where we can improve. © The Author(s) 2014.

Three-dimensional fingerprint recognition by using convolution neural network

NASA Astrophysics Data System (ADS)

Tian, Qianyu; Gao, Nan; Zhang, Zonghua

2018-01-01

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

Poster - Thur Eve - 57: Craniospinal irradiation with jagged-junction IMRT approach without beam edge matching for field junctions.

PubMed

Cao, F; Ramaseshan, R; Corns, R; Harrop, S; Nuraney, N; Steiner, P; Aldridge, S; Liu, M; Carolan, H; Agranovich, A; Karva, A

2012-07-01

Craniospinal irradiation were traditionally treated the central nervous system using two or three adjacent field sets. A intensity-modulated radiotherapy (IMRT) plan (Jagged-Junction IMRT) which overcomes problems associated with field junctions and beam edge matching, improves planning and treatment setup efficiencies with homogenous target dose distribution was developed. Jagged-Junction IMRT was retrospectively planned on three patients with prescription of 36 Gy in 20 fractions and compared to conventional treatment plans. Planning target volume (PTV) included the whole brain and spinal canal to the S3 vertebral level. The plan employed three field sets, each with a unique isocentre. One field set with seven fields treated the cranium. Two field sets treated the spine, each set using three fields. Fields from adjacent sets were overlapped and the optimization process smoothly integrated the dose inside the overlapped junction. For the Jagged-Junction IMRT plans vs conventional technique, average homogeneity index equaled 0.08±0.01 vs 0.12±0.02, and conformity number equaled 0.79±0.01 vs 0.47±0.12. The 95% isodose surface covered (99.5±0.3)% of the PTV vs (98.1±2.0)%. Both Jagged-Junction IMRT plans and the conventional plans had good sparing of the organs at risk. Jagged-Junction IMRT planning provided good dose homogeneity and conformity to the target while maintaining a low dose to the organs at risk. Jagged-Junction IMRT optimization smoothly distributed dose in the junction between field sets. Since there was no beam matching, this treatment technique is less likely to produce hot or cold spots at the junction in contrast to conventional techniques. © 2012 American Association of Physicists in Medicine.

dc properties of series-parallel arrays of Josephson junctions in an external magnetic field

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

Lewandowski, S.J.

1991-04-01

A detailed dc theory of superconducting multijunction interferometers has previously been developed by several authors for the case of parallel junction arrays. The theory is now extended to cover the case of a loop containing several junctions connected in series. The problem is closely associated with high-{ital T}{sub {ital c}} superconductors and their clusters of intrinsic Josephson junctions. These materials exhibit spontaneous interferometric effects, and there is no reason to assume that the intrinsic junctions form only parallel arrays. A simple formalism of phase states is developed in order to express the superconducting phase differences across the junctions forming amore » series array as functions of the phase difference across the weakest junction of the system, and to relate the differences in critical currents of the junctions to gaps in the allowed ranges of their phase functions. This formalism is used to investigate the energy states of the array, which in the case of different junctions are split and separated by energy barriers of height depending on the phase gaps. Modifications of the washboard model of a single junction are shown. Next a superconducting inductive loop containing a series array of two junctions is considered, and this model is used to demonstrate the transitions between phase states and the associated instabilities. Finally, the critical current of a parallel connection of two series arrays is analyzed and shown to be a multivalued function of the externally applied magnetic flux. The instabilities caused by the presence of intrinsic serial junctions in granular high-{ital T}{sub {ital c}} materials are pointed out as a potential source of additional noise.« less

Facilities for Passenger Movement to Decongest Underground Stations

NASA Astrophysics Data System (ADS)

Gupta, Sumana

2014-12-01

Underground rail network partially solves surface congestion problems in busy cities. Presently it is becoming overcrowded causing inconvenience to passengers at interchanges in the city cores especially during peak hours and at junction points. A conceptual model is suggested which can be adopted as an integral part of the under ground system to take care of this problem in particular and for greater sustainability of the entire transit system. The concept is to facilitate the passengers desiring interchange to avoid major junctions, to move between junctions, to reduce time of journey, to reduce detour according the situations through which a route moves. Primarily the model proposes additional connection between chosen stations mechanized with the help of travellators. The approach to decentralize the crowd can have several advantages. Firstly it allows smooth passenger dispersal. It helps in faster movement of passengers to destination. The model may be adopted in various situations with required modifications. This will result in accommodating more trips, comfortable journey and higher sustainability of the mass transit system. The problem and a feasible method of handling it have only been identified in this paper through review of the plans. An in-depth analysis for practical applicability of the proposed model in different stations has not been conducted. Feasibility study is necessary to be conducted before the implementation of the model at specific junctions. The concept proposed in the paper is different from the existing crowd handling methods and it provides a sustainable long term solution

Deficits in facial emotion recognition indicate behavioral changes and impaired self-awareness after moderate to severe traumatic brain injury.

PubMed

Spikman, Jacoba M; Milders, Maarten V; Visser-Keizer, Annemarie C; Westerhof-Evers, Herma J; Herben-Dekker, Meike; van der Naalt, Joukje

2013-01-01

Traumatic brain injury (TBI) is a leading cause of disability, specifically among younger adults. Behavioral changes are common after moderate to severe TBI and have adverse consequences for social and vocational functioning. It is hypothesized that deficits in social cognition, including facial affect recognition, might underlie these behavioral changes. Measurement of behavioral deficits is complicated, because the rating scales used rely on subjective judgement, often lack specificity and many patients provide unrealistically positive reports of their functioning due to impaired self-awareness. Accordingly, it is important to find performance based tests that allow objective and early identification of these problems. In the present study 51 moderate to severe TBI patients in the sub-acute and chronic stage were assessed with a test for emotion recognition (FEEST) and a questionnaire for behavioral problems (DEX) with a self and proxy rated version. Patients performed worse on the total score and on the negative emotion subscores of the FEEST than a matched group of 31 healthy controls. Patients also exhibited significantly more behavioral problems on both the DEX self and proxy rated version, but proxy ratings revealed more severe problems. No significant correlation was found between FEEST scores and DEX self ratings. However, impaired emotion recognition in the patients, and in particular of Sadness and Anger, was significantly correlated with behavioral problems as rated by proxies and with impaired self-awareness. This is the first study to find these associations, strengthening the proposed recognition of social signals as a condition for adequate social functioning. Hence, deficits in emotion recognition can be conceived as markers for behavioral problems and lack of insight in TBI patients. This finding is also of clinical importance since, unlike behavioral problems, emotion recognition can be objectively measured early after injury, allowing for early detection and treatment of these problems.

Control of neuronal morphology and connectivity: emerging developmental roles for gap junctional proteins.

PubMed

Baker, Michael W; Macagno, Eduardo R

2014-04-17

Recent evidence indicates that gap junction (GJ) proteins can play a critical role in controlling neuronal connectivity as well as cell morphology in the developing nervous system. GJ proteins may function analogously to cell adhesion molecules, mediating cellular recognition and selective neurite adhesion. Moreover, during synaptogenesis electrical synapses often herald the later establishment of chemical synapses, and thus may help facilitate activity-dependent sculpting of synaptic terminals. Recent findings suggest that the morphology and connectivity of embryonic leech neurons are fundamentally organized by the type and perhaps location of the GJ proteins they express. For example, ectopic expression in embryonic leech neurons of certain innexins that define small GJ-linked networks of cells leads to the novel coupling of the expressing cell into that network. Moreover, gap junctions appear to mediate interactions among homologous neurons that modulate process outgrowth and stability. We propose that the selective formation of GJs between developing neurons and perhaps glial cells in the CNS helps orchestrate not only cellular synaptic connectivity but also can have a pronounced effect on the arborization and morphology of those cells involved. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Adolescents' ability to read different emotional faces relates to their history of maltreatment and type of psychopathology.

PubMed

Leist, Tatyana; Dadds, Mark R

2009-04-01

Emotional processing styles appear to characterize various forms of psychopathology and environmental adversity in children. For example, autistic, anxious, high- and low-emotion conduct problem children, and children who have been maltreated, all appear to show specific deficits and strengths in recognizing the facial expressions of emotions. Until now, the relationships between emotion recognition, antisocial behaviour, emotional problems, callous-unemotional (CU) traits and early maltreatment have never been assessed simultaneously in one study, and the specific associations of emotion recognition to maltreatment and child characteristics are therefore unknown. We examined facial-emotion processing in a sample of 23 adolescents selected for high-risk status on the variables of interest. As expected, maltreatment and child characteristics showed unique associations. CU traits were uniquely related to impairments in fear recognition. Antisocial behaviour was uniquely associated with better fear recognition, but impaired anger recognition. Emotional problems were associated with better recognition of anger and sadness, but lower recognition of neutral faces. Maltreatment was predictive of superior recognition of fear and sadness. The findings are considered in terms of social information-processing theories of psychopathology. Implications for clinical interventions are discussed.

Faster Mass Spectrometry-based Protein Inference: Junction Trees are More Efficient than Sampling and Marginalization by Enumeration

PubMed Central

Serang, Oliver; Noble, William Stafford

2012-01-01

The problem of identifying the proteins in a complex mixture using tandem mass spectrometry can be framed as an inference problem on a graph that connects peptides to proteins. Several existing protein identification methods make use of statistical inference methods for graphical models, including expectation maximization, Markov chain Monte Carlo, and full marginalization coupled with approximation heuristics. We show that, for this problem, the majority of the cost of inference usually comes from a few highly connected subgraphs. Furthermore, we evaluate three different statistical inference methods using a common graphical model, and we demonstrate that junction tree inference substantially improves rates of convergence compared to existing methods. The python code used for this paper is available at http://noble.gs.washington.edu/proj/fido. PMID:22331862

Dissociative identity disorder (DID) in clinical practice - what you don't see may hurt you.

PubMed

Leonard, David; Tiller, John

2016-02-01

To identify problems that interfere with the recognition, diagnosis and management of people with dissociative identity disorder (DID) presenting to psychiatric outpatient and inpatient services and suggest solutions. Problems and suggested solutions associated with clinical presentations and management of people with DID are outlined with references to relevant literature. Problems in the recognition and management of DID are described. These lead to delays in diagnosis and costly, inappropriate management, destructive to services, staff and patients alike. Problems include lack of understanding and experience and scepticism about the disorder, resulting in failure to provide appropriate treatment.Some suggestions to improve recognition and management are included. Better recognition, diagnosis and management of DID will lead to better and more cost effective outcomes. © The Royal Australian and New Zealand College of Psychiatrists 2015.

Left arm/left leg lead reversals at the cable junction box: A cause for an epidemic of errors.

PubMed

Velagapudi, Poonam; Turagam, Mohit K; Ritter, Sherry; Dohrmann, Mary L

Medical errors, especially due to misinterpretation of electrocardiograms (ECG), are extremely common in patients admitted to the hospital and significantly account for increased morbidity, mortality and health care costs in the United States. Inaccurate performance of an ECG can lead to invalid interpretation and in turn may lead to costly cardiovascular evaluation. We report a retrospective series of 58 sequential cases of ECG limb lead reversals in the ER due to inadvertent interchange in the lead cables at the point where they insert into the cable junction box of one ECG machine. This case series highlights recognition of ECG lead reversal originating in the ECG machine itself. This case series also demonstrates an ongoing need for education regarding standardization of ECG testing and for recognizing technical anomalies to deliver appropriate care for the patient. Copyright © 2016. Published by Elsevier Inc.

Low-Loss Photonic Reservoir Computing with Multimode Photonic Integrated Circuits.

PubMed

Katumba, Andrew; Heyvaert, Jelle; Schneider, Bendix; Uvin, Sarah; Dambre, Joni; Bienstman, Peter

2018-02-08

We present a numerical study of a passive integrated photonics reservoir computing platform based on multimodal Y-junctions. We propose a novel design of this junction where the level of adiabaticity is carefully tailored to capture the radiation loss in higher-order modes, while at the same time providing additional mode mixing that increases the richness of the reservoir dynamics. With this design, we report an overall average combination efficiency of 61% compared to the standard 50% for the single-mode case. We demonstrate that with this design, much more power is able to reach the distant nodes of the reservoir, leading to increased scaling prospects. We use the example of a header recognition task to confirm that such a reservoir can be used for bit-level processing tasks. The design itself is CMOS-compatible and can be fabricated through the known standard fabrication procedures.

The relationship between conduct symptoms and the recognition of emotions in non-clinical adolescents.

PubMed

Halász, József; Áspán, Nikoletta; Bozsik, Csilla; Gádoros, Júlia; Inántsy-Pap, Judit

2013-01-01

In adult individuals with antisocial personality disorder, impairment in the recognition of fear seems established. In adolescents with conduct disorder (antecedent of antisocial personality disorder), only sporadic data were assessed, but literature data indicate alterations in the recognition of emotions. The aim of the present study was to assess the relationship between emotion recognition and conduct symptoms in non-clinical adolescents. 53 adolescents participated in the study (13-16 years, boys, n=29, age 14.7±0.2 years; girls, n=24, age=14.7±0.2 years) after informed consent. The parent version of the Strengths and Difficulties Questionnaire was used to assess behavioral problems. The recognition of six basic emotions was established by the "Facial expressions of emotion-stimuli and tests", while Raven IQ measures were also performed. Compared to boys, girls showed significantly better performance in the recognition of disgust (p<0.035), while no significant difference occurred in the recognition of other emotions. In boys, Conduct Problems score was inversely correlated with the recognition of fear (Spearman R=-0.40, p<0.031) and overall emotion recognition (Spearman R=-0.44, p<0.015), while similar correlation was not present in girls. The relationship between the recognition of emotions and conduct problems might indicate an important mechanism in the development of antisocial behavior.

New processes and materials for ultraviolet detection with solid state devices

NASA Technical Reports Server (NTRS)

Chopra, D.

1977-01-01

The three major effects that degrade external responsivity of silicon from the 1/lambda theoretical curve for a quantum detector are: surface reflectance, surface recombination, and junction depth. Since the p-n junction must be very shallow, problems relating to surface are further enhanced. MOS type of processing is necessary. HCl oxides and numerous acid clean-ups are utilized in order to obtain a contamination free surface with low Qss levels. Stringent process controls such as CV shifts, spreading resistance measurements, thickness monitoring etc., are used to analyze the surface contaminations, surface mobile charges, surface concentrations, junction depth, oxide thickness etc. Low surface concentrations of 10 to the 18th atoms/cu cm are achieved by low temperature boron nitride depositions. Shallow junction depths of the order of a few tenths of a micron are achieved by low temperature controlled diffusions. In order to improve breakdown characteristics of these shallow junction devices, field plate and deep diffused p(+) ring geometries are used.

Solar cell circuit and method for manufacturing solar cells

NASA Technical Reports Server (NTRS)

Mardesich, Nick (Inventor)

2010-01-01

The invention is a novel manufacturing method for making multi-junction solar cell circuits that addresses current problems associated with such circuits by allowing the formation of integral diodes in the cells and allows for a large number of circuits to readily be placed on a single silicon wafer substrate. The standard Ge wafer used as the base for multi-junction solar cells is replaced with a thinner layer of Ge or a II-V semiconductor material on a silicon/silicon dioxide substrate. This allows high-voltage cells with multiple multi-junction circuits to be manufactured on a single wafer, resulting in less array assembly mass and simplified power management.

Selection of Two-Phase Flow Patterns at a Simple Junction in Microfluidic Devices

NASA Astrophysics Data System (ADS)

Engl, W.; Ohata, K.; Guillot, P.; Colin, A.; Panizza, P.

2006-04-01

We study the behavior of a confined stream made of two immiscible fluids when it reaches a T junction. Two flow patterns are witnessed: the stream is either directed in only one sidearm, yielding a preferential flow pathway for the dispersed phase, or splits between both. We show that the selection of these patterns is not triggered by the shape of the junction nor by capillary effects, but results from confinement. It can be anticipated in terms of the hydrodynamic properties of the flow. A simple model yielding universal behavior in terms of the relevant adimensional parameters of the problem is presented and discussed.

A gallium phosphide high-temperature bipolar junction transistor

NASA Technical Reports Server (NTRS)

Zipperian, T. E.; Dawson, L. R.; Chaffin, R. J.

1981-01-01

Preliminary results are reported on the development of a high temperature (350 C) gallium phosphide bipolar junction transistor (BJT) for geothermal and other energy applications. This four-layer p(+)n(-)pp(+) structure was formed by liquid phase epitaxy using a supercooling technique to insure uniform nucleation of the thin layers. Magnesium was used as the p-type dopant to avoid excessive out-diffusion into the lightly doped base. By appropriate choice of electrodes, the device may also be driven as an n-channel junction field-effect transistor. The initial design suffers from a series resistance problem which limits the transistor's usefulness at high temperatures.

A split recognition mode combined with cascade signal amplification strategy for highly specific, sensitive detection of microRNA.

PubMed

Wang, Rui; Wang, Lei; Zhao, Haiyan; Jiang, Wei

2016-12-15

MicroRNAs (miRNAs) are vital for many biological processes and have been regarded as cancer biomarkers. Specific and sensitive detection of miRNAs is essential for cancer diagnosis and therapy. Herein, a split recognition mode combined with cascade signal amplification strategy is developed for highly specific and sensitive detection of miRNA. The split recognition mode possesses two specific recognition processes, which are based on toehold-mediated strand displacement reaction (TSDR) and direct hybridization reaction. Two recognition probes, hairpin probe (HP) with overhanging toehold domain and assistant probe (AP), are specially designed. Firstly, the toehold domain of HP and AP recognize part of miRNA simultaneously, accompanied with TSDR to unfold the HP and form the stable DNA Y-shaped junction structure (YJS). Then, the AP in YJS can further act as primer to initiate strand displacement amplification, releasing numerous trigger sequences. Finally, the trigger sequences hybridize with padlock DNA to initiate circular rolling circle amplification and generate enhanced fluorescence responses. In this strategy, the dual recognition effect of split recognition mode guarantees the excellent selectivity to discriminate let-7b from high-homology sequences. Furthermore, the high amplification efficiency of cascade signal amplification guarantees a high sensitivity with the detection limit of 3.2 pM and the concentration of let-7b in total RNA sample extracted from Hela cells is determined. These results indicate our strategy will be a promising miRNA detection strategy in clinical diagnosis and disease treatment. Copyright © 2016 Elsevier B.V. All rights reserved.

A Complete OCR System for Tamil Magazine Documents

NASA Astrophysics Data System (ADS)

Kokku, Aparna; Chakravarthy, Srinivasa

We present a complete optical character recognition (OCR) system for Tamil magazines/documents. All the standard elements of OCR process like de-skewing, preprocessing, segmentation, character recognition, and reconstruction are implemented. Experience with OCR problems teaches that for most subtasks of OCR, there is no single technique that gives perfect results for every type of document image. We exploit the ability of neural networks to learn from experience in solving the problems of segmentation and character recognition. Text segmentation of Tamil newsprint poses a new challenge owing to its italic-like font type; problems that arise in recognition of touching and close characters are discussed. Character recognition efficiency varied from 94 to 97% for this type of font. The grouping of blocks into logical units and the determination of reading order within each logical unit helped us in reconstructing automatically the document image in an editable format.

Universal Readers Based on Hydrogen Bonding or π-π Stacking for Identification of DNA Nucleotides in Electron Tunnel Junctions.

PubMed

Biswas, Sovan; Sen, Suman; Im, JongOne; Biswas, Sudipta; Krstic, Predrag; Ashcroft, Brian; Borges, Chad; Zhao, Yanan; Lindsay, Stuart; Zhang, Peiming

2016-12-27

A reader molecule, which recognizes all the naturally occurring nucleobases in an electron tunnel junction, is required for sequencing DNA by a recognition tunneling (RT) technique, referred to as a universal reader. In the present study, we have designed a series of heterocyclic carboxamides based on hydrogen bonding and a large-sized pyrene ring based on a π-π stacking interaction as universal reader candidates. Each of these compounds was synthesized to bear a thiolated linker for attachment to metal electrodes and examined for their interactions with naturally occurring DNA nucleosides and nucleotides by 1 H NMR, ESI-MS, computational calculations, and surface plasmon resonance. RT measurements were carried out in a scanning tunnel microscope. All of these molecules generated electrical signals with DNA nucleotides in tunneling junctions under physiological conditions (phosphate buffered aqueous solution, pH 7.4). Using a support vector machine as a tool for data analysis, we found that these candidates distinguished among naturally occurring DNA nucleotides with the accuracy of pyrene (by π-π stacking interactions) > azole carboxamides (by hydrogen-bonding interactions). In addition, the pyrene reader operated efficiently in a larger tunnel junction. However, the azole carboxamide could read abasic (AP) monophosphate, a product from spontaneous base hydrolysis or an intermediate of base excision repair. Thus, we envision that sequencing DNA using both π-π stacking and hydrogen-bonding-based universal readers in parallel should generate more comprehensive genome sequences than sequencing based on either reader molecule alone.

fMRI characterization of visual working memory recognition.

PubMed

Rahm, Benjamin; Kaiser, Jochen; Unterrainer, Josef M; Simon, Juliane; Bledowski, Christoph

2014-04-15

Encoding and maintenance of information in visual working memory have been extensively studied, highlighting the crucial and capacity-limiting role of fronto-parietal regions. In contrast, the neural basis of recognition in visual working memory has remained largely unspecified. Cognitive models suggest that recognition relies on a matching process that compares sensory information with the mental representations held in memory. To characterize the neural basis of recognition we varied both the need for recognition and the degree of similarity between the probe item and the memory contents, while independently manipulating memory load to produce load-related fronto-parietal activations. fMRI revealed a fractionation of working memory functions across four distributed networks. First, fronto-parietal regions were activated independent of the need for recognition. Second, anterior parts of load-related parietal regions contributed to recognition but their activations were independent of the difficulty of matching in terms of sample-probe similarity. These results argue against a key role of the fronto-parietal attention network in recognition. Rather the third group of regions including bilateral temporo-parietal junction, posterior cingulate cortex and superior frontal sulcus reflected demands on matching both in terms of sample-probe-similarity and the number of items to be compared. Also, fourth, bilateral motor regions and right superior parietal cortex showed higher activation when matching provided clear evidence for a decision. Together, the segregation between the well-known fronto-parietal activations attributed to attentional operations in working memory from those regions involved in matching supports the theoretical view of separable attentional and mnemonic contributions to working memory. Yet, the close theoretical and empirical correspondence to perceptual decision making may call for an explicit consideration of decision making mechanisms in conceptions of working memory. Copyright © 2013 Elsevier Inc. All rights reserved.

Bridge Health Monitoring Using a Machine Learning Strategy

DOT National Transportation Integrated Search

2017-01-01

The goal of this project was to cast the SHM problem within a statistical pattern recognition framework. Techniques borrowed from speaker recognition, particularly speaker verification, were used as this discipline deals with problems very similar to...

Fermi edge singularity in a tunnel junction

NASA Astrophysics Data System (ADS)

Zhang, Jin; Sherkunov, Yury; D'Ambrumenil, Nicholas; Muzykantskii, Boris

2010-03-01

We present results on the non-equilibrium Fermi edge singularity (FES) problem in tunnel junctions. The FES, which is present in a Fermi gas subject to any sudden change of potential, manifests itself in the final state many body interaction between the electrons in the leads [1]. We establish a connection between the FES problem in a tunnel junction and the Full Counting Statistics (FCS) for the device [2]. We find that the exact profile of the changing potential (or the profile for the barrier opening and closing in the tunnel junction case) strongly affects the overlap between the initial and final state of the Fermi gas. We factorize the contribution to the FES into two approximately independent terms: one is connected with the short time opening process while the other is concerned with the long time asymptotic effect, namely the Anderson orthogonality catastrophe. We consider applications to a localized level coupled through a tunnel barrier to a 1D lead driven out of equilibrium [3]. References: [1] G. Mahan, Phys. Rev. 163, 1612 (1967); P. Nozieres and C. T. De Dominicis, Phys. Rev. 178, 1079 (1969); P. Anderson, Phys. Rev. Lett. 18, 1049 (1967) [2] J. Zhang, Y. Sherkunov, N. d'Ambrumenil, and B. Muzykantskii, ArXiv:0909.3427 [3] D. Abanin and L. Levitov, Phys. Rev. Lett. 94, 186803 (2005)

Prediction of static friction coefficient in rough contacts based on the junction growth theory

NASA Astrophysics Data System (ADS)

Spinu, S.; Cerlinca, D.

2017-08-01

The classic approach to the slip-stick contact is based on the framework advanced by Mindlin, in which localized slip occurs on the contact area when the local shear traction exceeds the product between the local pressure and the static friction coefficient. This assumption may be too conservative in the case of high tractions arising at the asperities tips in the contact of rough surfaces, because the shear traction may be allowed to exceed the shear strength of the softer material. Consequently, the classic frictional contact model is modified in this paper so that gross sliding occurs when the junctions formed between all contacting asperities are independently sheared. In this framework, when the contact tractions, normal and shear, exceed the hardness of the softer material on the entire contact area, the material of the asperities yields and the junction growth process ends in all contact regions, leading to gross sliding inception. This friction mechanism is implemented in a previously proposed numerical model for the Cattaneo-Mindlin slip-stick contact problem, which is modified to accommodate the junction growth theory. The frictionless normal contact problem is solved first, then the tangential force is gradually increased, until gross sliding inception. The contact problems in the normal and in the tangential direction are successively solved, until one is stabilized in relation to the other. The maximum tangential force leading to a non-vanishing stick area is the static friction force that can be sustained by the rough contact. The static friction coefficient is eventually derived as the ratio between the latter friction force and the normal force.

Toward open set recognition.

PubMed

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

2013-07-01

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

Numerical study of three-dimensional separation and flow control at a wing/body junction

NASA Technical Reports Server (NTRS)

Ash, Robert L.; Lakshmanan, Balakrishnan

1989-01-01

The problem of three-dimensional separation and flow control at a wing/body junction has been investigated numerically using a three-dimensional Navier-Stokes code. The numerical code employs an algebraic grid generation technique for generating the grid for unmodified junction and an elliptic grid generation technique for filleted fin junction. The results for laminar flow past a blunt fin/flat plate junction demonstrate that after grid refinement, the computations agree with experiment and reveal a strong dependency of the number of vortices at the junction on Mach number and Reynolds number. The numerical results for pressure distribution, particle paths and limiting streamlines for turbulent flow past a swept fin show a decrease in the peak pressure and in the extent of the separated flow region compared to the laminar case. The results for a filleted juncture indicate that the streamline patterns lose much of their vortical character with proper filleting. Fillets with a radius of three and one-half times the fin leading edge diameter or two times the incoming boundary layer thickness, significantly weaken the usual necklace interaction vortex for the Mach number and Reynolds number considered in the present study.

Kinematic evolution of the junction of the San Andreas, Garlock, and Big Pine faults, California

USGS Publications Warehouse

Bohannon, Robert G.; Howell, David G.

1982-01-01

If the San Andreas fault with about 300 km of right slip, the Carlock fault with about 60 km of left slip, and the Big Pine fault with about 15 km of left slip are considered to have been contemporaneously active, a space problem at their high-angle junctions becomes apparent. Large crustal masses converge in the area of the junctions as a result of the simultaneous large displacements on the faults. We present here a model in which an early straight north-northwest–trending San Andreas deforms to its present bent configuration in response to a westward displacement of crust north of the Garlock fault. During this deformation, the crust north of the Garlock in the vicinity of the junction undergoes north-south shortening, while the fault junction migrates along the trace of the San Andreas fault to the southeast relative to its original position. As a result of this migration, the Mojave area is displaced to the east relative to the original junction position. We suggest a similar history in mirror image for the Big Pine fault and the areas of crust adjacent to it.

GEN1 from a thermophilic fungus is functionally closely similar to non-eukaryotic junction-resolving enzymes.

PubMed

Freeman, Alasdair D J; Liu, Yijin; Déclais, Anne-Cécile; Gartner, Anton; Lilley, David M J

2014-12-12

Processing of Holliday junctions is essential in recombination. We have identified the gene for the junction-resolving enzyme GEN1 from the thermophilic fungus Chaetomium thermophilum and expressed the N-terminal 487-amino-acid section. The protein is a nuclease that is highly selective for four-way DNA junctions, cleaving 1nt 3' to the point of strand exchange on two strands symmetrically disposed about a diagonal axis. CtGEN1 binds to DNA junctions as a discrete homodimer with nanomolar affinity. Analysis of the kinetics of cruciform cleavage shows that cleavage of the second strand occurs an order of magnitude faster than the first cleavage so as to generate a productive resolution event. All these properties are closely similar to those described for bacterial, phage and mitochondrial junction-resolving enzymes. CtGEN1 is also similar in properties to the human enzyme but lacks the problems with aggregation that currently prevent detailed analysis of the latter protein. CtGEN1 is thus an excellent enzyme with which to engage in biophysical and structural analysis of eukaryotic GEN1. Copyright © 2014. Published by Elsevier Ltd.

Structure of an XPF endonuclease with and without DNA suggests a model for substrate recognition

PubMed Central

Newman, Matthew; Murray-Rust, Judith; Lally, John; Rudolf, Jana; Fadden, Andrew; Knowles, Philip P; White, Malcolm F; McDonald, Neil Q

2005-01-01

The XPF/Mus81 structure-specific endonucleases cleave double-stranded DNA (dsDNA) within asymmetric branched DNA substrates and play an essential role in nucleotide excision repair, recombination and genome integrity. We report the structure of an archaeal XPF homodimer alone and bound to dsDNA. Superposition of these structures reveals a large domain movement upon binding DNA, indicating how the (HhH)2 domain and the nuclease domain are coupled to allow the recognition of double-stranded/single-stranded DNA junctions. We identify two nonequivalent DNA-binding sites and propose a model in which XPF distorts the 3′ flap substrate in order to engage both binding sites and promote strand cleavage. The model rationalises published biochemical data and implies a novel role for the ERCC1 subunit of eukaryotic XPF complexes. PMID:15719018

Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner.

PubMed

An, Jhonghyun; Choi, Baehoon; Sim, Kwee-Bo; Kim, Euntai

2016-07-20

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation.

Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner

PubMed Central

An, Jhonghyun; Choi, Baehoon; Sim, Kwee-Bo; Kim, Euntai

2016-01-01

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation. PMID:27447640

Finding the optimal lengths for three branches at a junction.

PubMed

Woldenberg, M J; Horsfield, K

1983-09-21

This paper presents an exact analytical solution to the problem of locating the junction point between three branches so that the sum of the total costs of the branches is minimized. When the cost per unit length of each branch is known the angles between each pair of branches can be deduced following reasoning first introduced to biology by Murray. Assuming the outer ends of each branch are fixed, the location of the junction and the length of each branch are then deduced using plane geometry and trigonometry. The model has applications in determining the optimal cost of a branch or branches at a junction. Comparing the optimal to the actual cost of a junction is a new way to compare cost models for goodness of fit to actual junction geometry. It is an unambiguous measure and is superior to comparing observed and optimal angles between each daughter and the parent branch. We present data for 199 junctions in the pulmonary arteries of two human lungs. For the branches at each junction we calculated the best fitting value of x from the relationship that flow alpha (radius)x. We found that the value of x determined whether a junction was best fitted by a surface, volume, drag or power minimization model. While economy of explanation casts doubt that four models operate simultaneously, we found that optimality may still operate, since the angle to the major daughter is less than the angle to the minor daughter. Perhaps optimality combined with a space filling branching pattern governs the branching geometry of the pulmonary artery.

Double obstruction of ureter: A diagnostic challenge.

PubMed

Halder, Pankaj; Shukla, Ram Mohan; Mandal, Kartik Chandra; Mukhopadhyay, Biswanath; Barman, Shibsankar

2014-07-01

Isolated obstruction of the ureteropelvic junction and the vesico-ureteric junction are the two most common causes of hydronephrosis in a pediatric population.[1] They do not pose diagnostic difficulties when are present alone but when together can be difficult to diagnose. Here, we discuss the problems we faced when we encountered these two anomalies in the same ureter and the way in which we managed them. To assess the difficulties in diagnosis of pediatric patients who present with both ureteropelvic junction obstruction (UPJO) and vesico-ureteric junction obstruction (VUJO) in the ipsilateral ureter and their management protocol. This is a retrospective study. The study period is from 1 January 2004 to 31 December 2011. Out of 254 children who were diagnosed to have hydronephrosis due to UPJO in our institute, 5 patients (in the age range of 5 to 10 years) had both UPJO and VUJO in the ipsilateral ureter. The problems we faced in diagnosing the two conditions are mentioned with a literature review. Operative intervention was used in four out of five patients; none of the patients had an accurate diagnosis before surgery. All patients were suspected of having double obstruction during pyeloplasty when appropriate size double J stent could not be negotiated through the vesicoureteric junction into the bladder. Postoperative nephrostogram confirmed the diagnosis in all patients. Children with double obstruction of the ipsilateral ureter present as a diagnostic dilemma. Because of the rarity of this condition it can escape the eye of even an astute clinician. Early diagnosis can be made if this condition is kept in mind while treating any hydronephrosis due to UPJO or UVJO.

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Anatomical segregation of representations of personally familiar and famous people in the temporal and parietal cortices.

PubMed

Sugiura, Motoaki; Sassa, Yuko; Watanabe, Jobu; Akitsuki, Yuko; Maeda, Yasuhiro; Matsue, Yoshihiko; Kawashima, Ryuta

2009-10-01

Person recognition has been assumed to entail many types of person-specific cognitive responses, including retrieval of knowledge, episodic recollection, and emotional responses. To demonstrate the cortical correlates of this modular structure of multimodal person representation, we investigated neural responses preferential to personally familiar people and responses dependent on familiarity with famous people in the temporal and parietal cortices. During functional magnetic resonance imaging (fMRI) measurements, normal subjects recognized personally familiar names (personal) or famous names with high or low degrees of familiarity (high or low, respectively). Effects of familiarity with famous people (i.e., high-low) were identified in the bilateral angular gyri, the left supramarginal gyrus, the middle part of the bilateral posterior cingulate cortices, and the left precuneus. Activation preferentially relevant to personally familiar people (i.e., personal-high) was identified in the bilateral temporo-parietal junctions, the right anterolateral temporal cortices, posterior middle temporal gyrus, posterior cingulate cortex (with a peak in the posterodorsal part), and the left precuneus; these activation foci exhibited varying degrees of activation for high and low names. An equivalent extent of activation was observed for all familiar names in the bilateral temporal poles, the left orbito-insular junction, the middle temporal gyrus, and the anterior part of the posterior cingulate cortex. The results demonstrated that distinct cortical areas supported different types of cognitive responses, induced to different degrees during recognition of famous and personally familiar people, providing neuroscientific evidence for the modularity of multimodal person representation.

Model-based vision system for automatic recognition of structures in dental radiographs

NASA Astrophysics Data System (ADS)

Acharya, Raj S.; Samarabandu, Jagath K.; Hausmann, E.; Allen, K. A.

1991-07-01

X-ray diagnosis of destructive periodontal disease requires assessing serial radiographs by an expert to determine the change in the distance between cemento-enamel junction (CEJ) and the bone crest. To achieve this without the subjectivity of a human expert, a knowledge based system is proposed to automatically locate the two landmarks which are the CEJ and the level of alveolar crest at its junction with the periodontal ligament space. This work is a part of an ongoing project to automatically measure the distance between CEJ and the bone crest along a line parallel to the axis of the tooth. The approach presented in this paper is based on identifying a prominent feature such as the tooth boundary using local edge detection and edge thresholding to establish a reference and then using model knowledge to process sub-regions in locating the landmarks. Segmentation techniques invoked around these regions consists of a neural-network like hierarchical refinement scheme together with local gradient extraction, multilevel thresholding and ridge tracking. Recognition accuracy is further improved by first locating the easily identifiable parts of the bone surface and the interface between the enamel and the dentine and then extending these boundaries towards the periodontal ligament space and the tooth boundary respectively. The system is realized as a collection of tools (or knowledge sources) for pre-processing, segmentation, primary and secondary feature detection and a control structure based on the blackboard model to coordinate the activities of these tools.

Evolution of disorder in Mediator complex and its functional relevance

PubMed Central

Nagulapalli, Malini; Maji, Sourobh; Dwivedi, Nidhi; Dahiya, Pradeep; Thakur, Jitendra K.

2016-01-01

Mediator, an important component of eukaryotic transcriptional machinery, is a huge multisubunit complex. Though the complex is known to be conserved across all the eukaryotic kingdoms, the evolutionary topology of its subunits has never been studied. In this study, we profiled disorder in the Mediator subunits of 146 eukaryotes belonging to three kingdoms viz., metazoans, plants and fungi, and attempted to find correlation between the evolution of Mediator complex and its disorder. Our analysis suggests that disorder in Mediator complex have played a crucial role in the evolutionary diversification of complexity of eukaryotic organisms. Conserved intrinsic disordered regions (IDRs) were identified in only six subunits in the three kingdoms whereas unique patterns of IDRs were identified in other Mediator subunits. Acquisition of novel molecular recognition features (MoRFs) through evolution of new subunits or through elongation of the existing subunits was evident in metazoans and plants. A new concept of ‘junction-MoRF’ has been introduced. Evolutionary link between CBP and Med15 has been provided which explain the evolution of extended-IDR in CBP from Med15 KIX-IDR junction-MoRF suggesting role of junction-MoRF in evolution and modulation of protein–protein interaction repertoire. This study can be informative and helpful in understanding the conserved and flexible nature of Mediator complex across eukaryotic kingdoms. PMID:26590257

Relevance feedback-based building recognition

NASA Astrophysics Data System (ADS)

Li, Jing; Allinson, Nigel M.

2010-07-01

Building recognition is a nontrivial task in computer vision research which can be utilized in robot localization, mobile navigation, etc. However, existing building recognition systems usually encounter the following two problems: 1) extracted low level features cannot reveal the true semantic concepts; and 2) they usually involve high dimensional data which require heavy computational costs and memory. Relevance feedback (RF), widely applied in multimedia information retrieval, is able to bridge the gap between the low level visual features and high level concepts; while dimensionality reduction methods can mitigate the high-dimensional problem. In this paper, we propose a building recognition scheme which integrates the RF and subspace learning algorithms. Experimental results undertaken on our own building database show that the newly proposed scheme appreciably enhances the recognition accuracy.

Interprofessional, simulation-based technology-enhanced learning to improve physical healthcare in psychiatry: The RAMPPS course.

PubMed

Akroyd, Mike; Jordan, Gary; Rowlands, Paul

2016-06-01

People with serious mental illness have reduced life expectancy compared with a control population, much of which is accounted for by significant physical comorbidity. Frontline clinical staff in mental health often lack confidence in recognition, assessment and management of such 'medical' problems. Simulation provides one way for staff to practise these skills in a safe setting. We produced a multidisciplinary simulation course around recognition and assessment of medical problems in psychiatric settings. We describe an audit of strategic and design aspects of the recognition and assessment of medical problems in psychiatric settings, using the Department of Health's 'Framework for Technology Enhanced Learning' as our audit standards. At the same time, as highlighting areas where recognition and assessment of medical problems in psychiatric settings adheres to these identified principles, such as the strategic underpinning of the approach, and the means by which information is collected, reviewed and shared, it also helps us to identify areas where we can improve. © The Author(s) 2014.

Competition between Local Collisions and Collective Hydrodynamic Feedback Controls Traffic Flows in Microfluidic Networks

NASA Astrophysics Data System (ADS)

Belloul, M.; Engl, W.; Colin, A.; Panizza, P.; Ajdari, A.

2009-05-01

By studying the repartition of monodisperse droplets at a simple T junction, we show that the traffic of discrete fluid systems in microfluidic networks results from two competing mechanisms, whose significance is driven by confinement. Traffic is dominated by collisions occurring at the junction for small droplets and by collective hydrodynamic feedback for large ones. For each mechanism, we present simple models in terms of the pertinent dimensionless parameters of the problem.

Currents Induced by Injected Charge in Junction Detectors

PubMed Central

Gaubas, Eugenijus; Ceponis, Tomas; Kalesinskas, Vidas

2013-01-01

The problem of drifting charge-induced currents is considered in order to predict the pulsed operational characteristics in photo- and particle-detectors with a junction controlled active area. The direct analysis of the field changes induced by drifting charge in the abrupt junction devices with a plane-parallel geometry of finite area electrodes is presented. The problem is solved using the one-dimensional approach. The models of the formation of the induced pulsed currents have been analyzed for the regimes of partial and full depletion. The obtained solutions for the current density contain expressions of a velocity field dependence on the applied voltage, location of the injected surface charge domain and carrier capture parameters. The drift component of this current coincides with Ramo's expression. It has been illustrated, that the synchronous action of carrier drift, trapping, generation and diffusion can lead to a vast variety of possible current pulse waveforms. Experimental illustrations of the current pulse variations determined by either the rather small or large carrier density within the photo-injected charge domain are presented, based on a study of Si detectors. PMID:24036586

Crystal structure of a Fanconi anemia-associated nuclease homolog bound to 5' flap DNA: basis of interstrand cross-link repair by FAN1

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

Gwon, Gwang Hyeon; Kim, Youngran; Liu, Yaqi

2014-10-15

Fanconi anemia (FA) is an autosomal recessive genetic disorder caused by defects in any of 15 FA genes responsible for processing DNA interstrand cross-links (ICLs). The ultimate outcome of the FA pathway is resolution of cross-links, which requires structure-selective nucleases. FA-associated nuclease 1 (FAN1) is believed to be recruited to lesions by a monoubiquitinated FANCI–FANCD2 (ID) complex and participates in ICL repair. Here, we determined the crystal structure of Pseudomonas aeruginosa FAN1 (PaFAN1) lacking the UBZ (ubiquitin-binding zinc) domain in complex with 5' flap DNA. All four domains of the right-hand-shaped PaFAN1 are involved in DNA recognition, with each domainmore » playing a specific role in bending DNA at the nick. The six-helix bundle that binds the junction connects to the catalytic viral replication and repair (VRR) nuclease (VRR nuc) domain, enabling FAN1 to incise the scissile phosphate a few bases distant from the junction. The six-helix bundle also inhibits the cleavage of intact Holliday junctions. PaFAN1 shares several conserved features with other flap structure-selective nucleases despite structural differences. A clamping motion of the domains around the wedge helix, which acts as a pivot, facilitates nucleolytic cleavage. The PaFAN1 structure provides insights into how archaeal Holliday junction resolvases evolved to incise 5' flap substrates and how FAN1 integrates with the FA complex to participate in ICL repair.« less

Research and Implementation of Tibetan Word Segmentation Based on Syllable Methods

NASA Astrophysics Data System (ADS)

Jiang, Jing; Li, Yachao; Jiang, Tao; Yu, Hongzhi

2018-03-01

Tibetan word segmentation (TWS) is an important problem in Tibetan information processing, while abbreviated word recognition is one of the key and most difficult problems in TWS. Most of the existing methods of Tibetan abbreviated word recognition are rule-based approaches, which need vocabulary support. In this paper, we propose a method based on sequence tagging model for abbreviated word recognition, and then implement in TWS systems with sequence labeling models. The experimental results show that our abbreviated word recognition method is fast and effective and can be combined easily with the segmentation model. This significantly increases the effect of the Tibetan word segmentation.

Recognition and classification of oscillatory patterns of electric brain activity using artificial neural network approach

NASA Astrophysics Data System (ADS)

Pchelintseva, Svetlana V.; Runnova, Anastasia E.; Musatov, Vyacheslav Yu.; Hramov, Alexander E.

2017-03-01

In the paper we study the problem of recognition type of the observed object, depending on the generated pattern and the registered EEG data. EEG recorded at the time of displaying cube Necker characterizes appropriate state of brain activity. As an image we use bistable image Necker cube. Subject selects the type of cube and interpret it either as aleft cube or as the right cube. To solve the problem of recognition, we use artificial neural networks. In our paper to create a classifier we have considered a multilayer perceptron. We examine the structure of the artificial neural network and define cubes recognition accuracy.

Indonesian Sign Language Number Recognition using SIFT Algorithm

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Gap Junction Intercellular Communication: A Review of a Potential Platform to Modulate Craniofacial Tissue Engineering

PubMed Central

Rossello, Ricardo A.; Kohn, David H.

2009-01-01

Defects in craniofacial tissues, resulting from trauma, congenital abnormalities, oncologic resection or progressive deforming diseases, may result in aesthetic deformity, pain and reduced function. Restoring the structure, function and aesthetics of craniofacial tissues represents a substantial clinical problem in need of new solutions. More biologically-interactive biomaterials could potentially improve the treatment of craniofacial defects, and an understanding of developmental processes may help identify strategies and materials that can be used in tissue engineering. One such strategy that can potentially advance tissue engineering is cell–cell communication. Gap junction intercellular communication is the most direct way of achieving such signaling. Gap junction communication through connexin-mediated junctions, in particular connexin 43 (Cx43), plays a major role bone development. Given the important role of Cx43 in controlling development and differentiation, especially in bone cells, controlling the expression of Cx43 may provide control over cell-to-cell communication and may help overcome some of the challenges in craniofacial tissue engineering. Following a review of gap junctions in bone cells, the ability to enhance cell–cell communication and osteogenic differentiation via control of gap junctions is discussed, as is the potential utility of this approach in craniofacial tissue engineering. PMID:18481782

Image pattern recognition supporting interactive analysis and graphical visualization

NASA Technical Reports Server (NTRS)

Coggins, James M.

1992-01-01

Image Pattern Recognition attempts to infer properties of the world from image data. Such capabilities are crucial for making measurements from satellite or telescope images related to Earth and space science problems. Such measurements can be the required product itself, or the measurements can be used as input to a computer graphics system for visualization purposes. At present, the field of image pattern recognition lacks a unified scientific structure for developing and evaluating image pattern recognition applications. The overall goal of this project is to begin developing such a structure. This report summarizes results of a 3-year research effort in image pattern recognition addressing the following three principal aims: (1) to create a software foundation for the research and identify image pattern recognition problems in Earth and space science; (2) to develop image measurement operations based on Artificial Visual Systems; and (3) to develop multiscale image descriptions for use in interactive image analysis.

Magnetic tunnel junctions utilizing diamond-like carbon tunnel barriers

NASA Astrophysics Data System (ADS)

Cadieu, F. J.; Chen, Li; Li, Biao

2002-05-01

We have devised a method whereby thin particulate-free diamond-like carbon films can be made with good adhesion onto even room-temperature substrates. The method employs a filtered ionized carbon beam created by the vacuum impact of a high-energy, approximately 1 J per pulse, 248 nm excimer laser onto a carbon target. The resultant deposition beam can be steered and deflected by magnetic and electric fields to paint a specific substrate area. An important aspect of this deposition method is that the resultant films are particulate free and formed only as the result of atomic species impact. The vast majority of magnetic tunnel junctions utilizing thin metallic magnetic films have employed a thin oxidized layer of aluminum to form the tunnel barrier. This has presented reproducibility problems because the indicated optimal barrier thickness is only approximately 13 Å thick. Magnetic tunnel junctions utilizing Co and permalloy films made by evaporation and sputtering have been fabricated with an intervening diamond-like carbon tunnel barrier. The diamond-like carbon thickness profile has been tapered so that seven junctions with different barrier thickness can be formed at once. Magnetoresistive (MR) measurements made between successive permalloy strip ends include contributions from two junctions and from the permalloy and Co strips that act as current leads to the junctions. Magnetic tunnel junctions with thicker carbon barriers exhibit MR effects that are dominated by that of the permalloy strips. Since these tunnel barriers are formed without the need for oxygen, complete tunnel junctions can be formed with all high-vacuum processing.

Recognition Tunneling

PubMed Central

Lindsay, Stuart; He, Jin; Sankey, Otto; Hapala, Prokop; Jelinek, Pavel; Zhang, Peiming; Chang, Shuai; Huang, Shuo

2010-01-01

Single molecules in a tunnel junction can now be interrogated reliably using chemically-functionalized electrodes. Monitoring stochastic bonding fluctuations between a ligand bound to one electrode and its target bound to a second electrode (“tethered molecule-pair” configuration) gives insight into the nature of the intermolecular bonding at a single molecule-pair level, and defines the requirements for reproducible tunneling data. Simulations show that there is an instability in the tunnel gap at large currents, and this results in a multiplicity of contacts with a corresponding spread in the measured currents. At small currents (i.e. large gaps) the gap is stable, and functionalizing a pair of electrodes with recognition reagents (the “free analyte” configuration) can generate a distinct tunneling signal when an analyte molecule is trapped in the gap. This opens up a new interface between chemistry and electronics with immediate implications for rapid sequencing of single DNA molecules. PMID:20522930

Summary of 1971 pattern recognition program development

NASA Technical Reports Server (NTRS)

Whitley, S. L.

1972-01-01

Eight areas related to pattern recognition analysis at the Earth Resources Laboratory are discussed: (1) background; (2) Earth Resources Laboratory goals; (3) software problems/limitations; (4) operational problems/limitations; (5) immediate future capabilities; (6) Earth Resources Laboratory data analysis system; (7) general program needs and recommendations; and (8) schedule and milestones.

Control and Alcohol-Problem Recognition among College Students

ERIC Educational Resources Information Center

Simons, Raluca M.; Hahn, Austin M.; Simons, Jeffrey S.; Gaster, Sam

2015-01-01

Objective: This study examined negative control (ie, perceived lack of control over life outcomes) and need for control as predictors of alcohol-problem recognition, evaluations (good/bad), and expectancies (likely/unlikely) among college students. The study also explored the interaction between the need for control and alcohol consumption in…

Bayesian Analysis of Recognition Memory: The Case of the List-Length Effect

ERIC Educational Resources Information Center

Dennis, Simon; Lee, Michael D.; Kinnell, Angela

2008-01-01

Recognition memory experiments are an important source of empirical constraints for theories of memory. Unfortunately, standard methods for analyzing recognition memory data have problems that are often severe enough to prevent clear answers being obtained. A key example is whether longer lists lead to poorer recognition performance. The presence…

SRY, like HMG1, recognizes sharp angles in DNA.

PubMed Central

Ferrari, S; Harley, V R; Pontiggia, A; Goodfellow, P N; Lovell-Badge, R; Bianchi, M E

1992-01-01

HMG boxes are DNA binding domains present in chromatin proteins, general transcription factors for nucleolar and mitochondrial RNA polymerases, and gene- and tissue-specific transcriptional regulators. The HMG boxes of HMG1, an abundant component of chromatin, interact specifically with four-way junctions, DNA structures that are cross-shaped and contain angles of approximately 60 and 120 degrees between their arms. We show here also that the HMG box of SRY, the protein that determines the expression of male-specific genes in humans, recognizes four-way junction DNAs irrespective of their sequence. In addition, when SRY binds to linear duplex DNA containing its specific target AACAAAG, it produces a sharp bend. Therefore, the interaction between HMG boxes and DNA appears to be predominantly structure-specific. The production of the recognition of a kink in DNA can serve several distinct functions, such as the repair of DNA lesions, the folding of DNA segments with bound transcriptional factors into productive complexes or the wrapping of DNA in chromatin. Images PMID:1425584

Recognition, survival and persistence of Staphylococcus aureus in the model host Tenebrio molitor.

PubMed

Dorling, Jack; Moraes, Caroline; Rolff, Jens

2015-02-01

The degree of specificity of any given immune response to a parasite is governed by the complexity and variation of interactions between host and pathogen derived molecules. Here, we assess the extent to which recognition and immuno-resistance of cell wall mutants of the pathogen Staphylococcus aureus may contribute to establishment and maintenance of persistent infection in the model insect host, Tenebrio molitor. The cell surface of S. aureus is decorated with various molecules, including glycopolymers such as wall teichoic acid (WTA). WTA is covalently bound to peptidoglycan (PGN) and its absence has been associated with increased recognition of PGN by host receptors (PGRPs). WTA is also further modified by other molecules such as D-alanine (D-alanylation). Both the level of WTA expression and its D-alanylation were found to be important in the mediation of the host-parasite interaction in this model system. Specifically, WTA itself was seen to influence immune recognition, while D-alanylation of WTA was found to increase immuno-resistance and was associated with prolonged persistence of S. aureus in T. molitor. These results implicate WTA and its D-alanylation as important factors in the establishment and maintenance of persistent infection, affecting different critical junctions in the immune response; through potential evasion of recognition by PGRPs and resistance to humoral immune effectors during prolonged exposure to the immune system. This highlights a mechanism by which specificity in this host-parasite interaction may arise. Copyright © 2014 Elsevier Ltd. All rights reserved.

Pose-Invariant Face Recognition via RGB-D Images.

PubMed

Sang, Gaoli; Li, Jing; Zhao, Qijun

2016-01-01

Three-dimensional (3D) face models can intrinsically handle large pose face recognition problem. In this paper, we propose a novel pose-invariant face recognition method via RGB-D images. By employing depth, our method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images in the gallery can be rendered to the same view as the probe via depth. Meanwhile, depth is also used for similarity measure via frontalization and symmetric filling. Finally, both texture and depth contribute to the final identity estimation. Experiments on Bosphorus, CurtinFaces, Eurecom, and Kiwi databases demonstrate that the additional depth information has improved the performance of face recognition with large pose variations and under even more challenging conditions.

Linear Programming and Its Application to Pattern Recognition Problems

NASA Technical Reports Server (NTRS)

Omalley, M. J.

1973-01-01

Linear programming and linear programming like techniques as applied to pattern recognition problems are discussed. Three relatively recent research articles on such applications are summarized. The main results of each paper are described, indicating the theoretical tools needed to obtain them. A synopsis of the author's comments is presented with regard to the applicability or non-applicability of his methods to particular problems, including computational results wherever given.

Training Spiking Neural Models Using Artificial Bee Colony

PubMed Central

Vazquez, Roberto A.; Garro, Beatriz A.

2015-01-01

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

Reduction of the dimension of neural network models in problems of pattern recognition and forecasting

NASA Astrophysics Data System (ADS)

Nasertdinova, A. D.; Bochkarev, V. V.

2017-11-01

Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.

Development and fabrication of a solar cell junction processing system

NASA Technical Reports Server (NTRS)

Bunker, S.

1981-01-01

A solar cell junction processing system was developed and fabricated. A pulsed electron beam for the four inch wafers is being assembled and tested, wafers were successfully pulsed, and solar cells fabricated. Assembly of the transport locks is completed. The transport was operated successfully but not with sufficient reproducibility. An experiment test facility to examine potential scaleup problems associated with the proposed ion implanter design was constructed and operated. Cells were implanted and found to have efficiency identical to the normal Spire implant process.

Facial-affect recognition deficit as a predictor of different aspects of social-communication impairment in traumatic brain injury.

PubMed

Rigon, Arianna; Turkstra, Lyn S; Mutlu, Bilge; Duff, Melissa C

2018-05-01

To examine the relationship between facial-affect recognition and different aspects of self- and proxy-reported social-communication impairment following moderate-severe traumatic brain injury (TBI). Forty-six adults with chronic TBI (>6 months postinjury) and 42 healthy comparison (HC) adults were administered the La Trobe Communication Questionnaire (LCQ) Self and Other forms to assess different aspects of communication competence and the Emotion Recognition Test (ERT) to measure their ability to recognize facial affects. Individuals with TBI underperformed HC adults in the ERT and self-reported, as well as were reported by close others, as having more communication problems than did HC adults. TBI group ERT scores were significantly and negatively correlated with LCQ-Other (but not LCQ-Self) scores (i.e., participants with lower emotion-recognition scores were rated by close others as having more communication problems). Multivariate regression analysis revealed that adults with higher ERT scores self-reported more problems with disinhibition-impulsivity and partner sensitivity and had fewer other-reported problems with disinhibition-impulsivity and conversational effectiveness. Our findings support growing evidence that emotion-recognition deficits play a role in specific aspects of social-communication outcomes after TBI and should be considered in treatment planning. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Strain-balanced type-II superlattices for efficient multi-junction solar cells.

PubMed

Gonzalo, A; Utrilla, A D; Reyes, D F; Braza, V; Llorens, J M; Fuertes Marrón, D; Alén, B; Ben, T; González, D; Guzman, A; Hierro, A; Ulloa, J M

2017-06-21

Multi-junction solar cells made by assembling semiconductor materials with different bandgap energies have hold the record conversion efficiencies for many years and are currently approaching 50%. Theoretical efficiency limits make use of optimum designs with the right lattice constant-bandgap energy combination, which requires a 1.0-1.15 eV material lattice-matched to GaAs/Ge. Nevertheless, the lack of suitable semiconductor materials is hindering the achievement of the predicted efficiencies, since the only candidates were up to now complex quaternary and quinary alloys with inherent epitaxial growth problems that degrade carrier dynamics. Here we show how the use of strain-balanced GaAsSb/GaAsN superlattices might solve this problem. We demonstrate that the spatial separation of Sb and N atoms avoids the ubiquitous growth problems and improves crystal quality. Moreover, these new structures allow for additional control of the effective bandgap through the period thickness and provide a type-II band alignment with long carrier lifetimes. All this leads to a strong enhancement of the external quantum efficiency under photovoltaic conditions with respect to bulk layers of equivalent thickness. Our results show that GaAsSb/GaAsN superlattices with short periods are the ideal (pseudo)material to be integrated in new GaAs/Ge-based multi-junction solar cells that could approach the theoretical efficiency limit.

Coordinate Transformations in Object Recognition

ERIC Educational Resources Information Center

Graf, Markus

2006-01-01

A basic problem of visual perception is how human beings recognize objects after spatial transformations. Three central classes of findings have to be accounted for: (a) Recognition performance varies systematically with orientation, size, and position; (b) recognition latencies are sequentially additive, suggesting analogue transformation…

Document Form and Character Recognition using SVM

NASA Astrophysics Data System (ADS)

Park, Sang-Sung; Shin, Young-Geun; Jung, Won-Kyo; Ahn, Dong-Kyu; Jang, Dong-Sik

2009-08-01

Because of development of computer and information communication, EDI (Electronic Data Interchange) has been developing. There is OCR (Optical Character Recognition) of Pattern recognition technology for EDI. OCR contributed to changing many manual in the past into automation. But for the more perfect database of document, much manual is needed for excluding unnecessary recognition. To resolve this problem, we propose document form based character recognition method in this study. Proposed method is divided into document form recognition part and character recognition part. Especially, in character recognition, change character into binarization by using SVM algorithm and extract more correct feature value.

Effects of Problem-Based Learning on Recognition Learning and Transfer Accounting for GPA and Goal Orientation

ERIC Educational Resources Information Center

Bergstrom, Cassendra M.; Pugh, Kevin J.; Phillips, Michael M.; Machlev, Moshe

2016-01-01

Conflicting research results have stirred controversy over the effectiveness of problem-based learning (PBL) compared to direct instruction at fostering content learning, particularly for novices. We addressed this by investigating effectiveness with respect to recognition learning and transfer and conducting an aptitude-treatment interaction…

Basics of identification measurement technology

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Speech Recognition in Noise by Children with and without Dyslexia: How is it Related to Reading?

PubMed

Nittrouer, Susan; Krieg, Letitia M; Lowenstein, Joanna H

2018-06-01

Developmental dyslexia is commonly viewed as a phonological deficit that makes it difficult to decode written language. But children with dyslexia typically exhibit other problems, as well, including poor speech recognition in noise. The purpose of this study was to examine whether the speech-in-noise problems of children with dyslexia are related to their reading problems, and if so, if a common underlying factor might explain both. The specific hypothesis examined was that a spectral processing disorder results in these children receiving smeared signals, which could explain both the diminished sensitivity to phonological structure - leading to reading problems - and the speech recognition in noise difficulties. The alternative hypothesis tested in this study was that children with dyslexia simply have broadly based language deficits. Ninety-seven children between the ages of 7 years; 10 months and 12 years; 9 months participated: 46 with dyslexia and 51 without dyslexia. Children were tested on two dependent measures: word reading and recognition in noise with two types of sentence materials: as unprocessed (UP) signals, and as spectrally smeared (SM) signals. Data were collected for four predictor variables: phonological awareness, vocabulary, grammatical knowledge, and digit span. Children with dyslexia showed deficits on both dependent and all predictor variables. Their scores for speech recognition in noise were poorer than those of children without dyslexia for both the UP and SM signals, but by equivalent amounts across signal conditions indicating that they were not disproportionately hindered by spectral distortion. Correlation analyses on scores from children with dyslexia showed that reading ability and speech-in-noise recognition were only mildly correlated, and each skill was related to different underlying abilities. No substantial evidence was found to support the suggestion that the reading and speech recognition in noise problems of children with dyslexia arise from a single factor that could be defined as a spectral processing disorder. The reading and speech recognition in noise deficits of these children appeared to be largely independent. Copyright © 2018 Elsevier Ltd. All rights reserved.

Ballistic One-Dimensional InAs Nanowire Cross-Junction Interconnects.

PubMed

Gooth, Johannes; Borg, Mattias; Schmid, Heinz; Schaller, Vanessa; Wirths, Stephan; Moselund, Kirsten; Luisier, Mathieu; Karg, Siegfried; Riel, Heike

2017-04-12

Coherent interconnection of quantum bits remains an ongoing challenge in quantum information technology. Envisioned hardware to achieve this goal is based on semiconductor nanowire (NW) circuits, comprising individual NW devices that are linked through ballistic interconnects. However, maintaining the sensitive ballistic conduction and confinement conditions across NW intersections is a nontrivial problem. Here, we go beyond the characterization of a single NW device and demonstrate ballistic one-dimensional (1D) quantum transport in InAs NW cross-junctions, monolithically integrated on Si. Characteristic 1D conductance plateaus are resolved in field-effect measurements across up to four NW-junctions in series. The 1D ballistic transport and sub-band splitting is preserved for both crossing-directions. We show that the 1D modes of a single injection terminal can be distributed into multiple NW branches. We believe that NW cross-junctions are well-suited as cross-directional communication links for the reliable transfer of quantum information as required for quantum computational systems.

Generalized Israel junction conditions for a fourth-order brane world

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

Balcerzak, Adam; Dabrowski, Mariusz P.

2008-01-15

We discuss a general fourth-order theory of gravity on the brane. In general, the formulation of the junction conditions (except for Euler characteristics such as Gauss-Bonnet term) leads to the higher powers of the delta function and requires regularization. We suggest the way to avoid such a problem by imposing the metric and its first derivative to be regular at the brane, while the second derivative to have a kink, the third derivative of the metric to have a step function discontinuity, and no sooner as the fourth derivative of the metric to give the delta function contribution to themore » field equations. Alternatively, we discuss the reduction of the fourth-order gravity to the second-order theory by introducing an extra tensor field. We formulate the appropriate junction conditions on the brane. We prove the equivalence of both theories. In particular, we prove the equivalence of the junction conditions with different assumptions related to the continuity of the metric along the brane.« less

What is the evidence for retrieval problems in the elderly?

PubMed

White, N; Cunningham, W R

1982-01-01

To determine whether older adults experience particular problems with retrieval, groups of young and elderly adults were given free recall and recognition tests of supraspan lists of unrelated words. Analysis of number of words correctly recalled and recognized yielded a significant age by retention test interaction: greater age differences were observed for recall than for recognition. In a second analysis of words recalled and recognized, corrected for guessing, the interaction disappeared. It was concluded that previous interpretations that age by retention test interactions are indicative of retrieval problems of the elderly may have been confounded by methodological problems. Furthermore, it was suggested that researchers in aging and memory need to be explicit in identifying their underlying models of error processes when analyzing recognition scores: different error models may lead to different results and interpretations.

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

NASA Technical Reports Server (NTRS)

Knasel, T. Michael

1996-01-01

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

Iris recognition in the presence of ocular disease

PubMed Central

Aslam, Tariq Mehmood; Tan, Shi Zhuan; Dhillon, Baljean

2009-01-01

Iris recognition systems are among the most accurate of all biometric technologies with immense potential for use in worldwide security applications. This study examined the effect of eye pathology on iris recognition and in particular whether eye disease could cause iris recognition systems to fail. The experiment involved a prospective cohort of 54 patients with anterior segment eye disease who were seen at the acute referral unit of the Princess Alexandra Eye Pavilion in Edinburgh. Iris camera images were obtained from patients before treatment was commenced and again at follow-up appointments after treatment had been given. The principal outcome measure was that of mathematical difference in the iris recognition templates obtained from patients' eyes before and after treatment of the eye disease. Results showed that the performance of iris recognition was remarkably resilient to most ophthalmic disease states, including corneal oedema, iridotomies (laser puncture of iris) and conjunctivitis. Problems were, however, encountered in some patients with acute inflammation of the iris (iritis/anterior uveitis). The effects of a subject developing anterior uveitis may cause current recognition systems to fail. Those developing and deploying iris recognition should be aware of the potential problems that this could cause to this key biometric technology. PMID:19324690

Iris recognition in the presence of ocular disease.

PubMed

Aslam, Tariq Mehmood; Tan, Shi Zhuan; Dhillon, Baljean

2009-05-06

Iris recognition systems are among the most accurate of all biometric technologies with immense potential for use in worldwide security applications. This study examined the effect of eye pathology on iris recognition and in particular whether eye disease could cause iris recognition systems to fail. The experiment involved a prospective cohort of 54 patients with anterior segment eye disease who were seen at the acute referral unit of the Princess Alexandra Eye Pavilion in Edinburgh. Iris camera images were obtained from patients before treatment was commenced and again at follow-up appointments after treatment had been given. The principal outcome measure was that of mathematical difference in the iris recognition templates obtained from patients' eyes before and after treatment of the eye disease. Results showed that the performance of iris recognition was remarkably resilient to most ophthalmic disease states, including corneal oedema, iridotomies (laser puncture of iris) and conjunctivitis. Problems were, however, encountered in some patients with acute inflammation of the iris (iritis/anterior uveitis). The effects of a subject developing anterior uveitis may cause current recognition systems to fail. Those developing and deploying iris recognition should be aware of the potential problems that this could cause to this key biometric technology.

A pattern recognition approach to transistor array parameter variance

NASA Astrophysics Data System (ADS)

da F. Costa, Luciano; Silva, Filipi N.; Comin, Cesar H.

2018-06-01

The properties of semiconductor devices, including bipolar junction transistors (BJTs), are known to vary substantially in terms of their parameters. In this work, an experimental approach, including pattern recognition concepts and methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), was used to experimentally investigate the variation among BJTs belonging to integrated circuits known as transistor arrays. It was shown that a good deal of the devices variance can be captured using only two PCA axes. It was also verified that, though substantially small variation of parameters is observed for BJT from the same array, larger variation arises between BJTs from distinct arrays, suggesting the consideration of device characteristics in more critical analog designs. As a consequence of its supervised nature, LDA was able to provide a substantial separation of the BJT into clusters, corresponding to each transistor array. In addition, the LDA mapping into two dimensions revealed a clear relationship between the considered measurements. Interestingly, a specific mapping suggested by the PCA, involving the total harmonic distortion variation expressed in terms of the average voltage gain, yielded an even better separation between the transistor array clusters. All in all, this work yielded interesting results from both semiconductor engineering and pattern recognition perspectives.

Evolution of disorder in Mediator complex and its functional relevance.

PubMed

Nagulapalli, Malini; Maji, Sourobh; Dwivedi, Nidhi; Dahiya, Pradeep; Thakur, Jitendra K

2016-02-29

Mediator, an important component of eukaryotic transcriptional machinery, is a huge multisubunit complex. Though the complex is known to be conserved across all the eukaryotic kingdoms, the evolutionary topology of its subunits has never been studied. In this study, we profiled disorder in the Mediator subunits of 146 eukaryotes belonging to three kingdoms viz., metazoans, plants and fungi, and attempted to find correlation between the evolution of Mediator complex and its disorder. Our analysis suggests that disorder in Mediator complex have played a crucial role in the evolutionary diversification of complexity of eukaryotic organisms. Conserved intrinsic disordered regions (IDRs) were identified in only six subunits in the three kingdoms whereas unique patterns of IDRs were identified in other Mediator subunits. Acquisition of novel molecular recognition features (MoRFs) through evolution of new subunits or through elongation of the existing subunits was evident in metazoans and plants. A new concept of 'junction-MoRF' has been introduced. Evolutionary link between CBP and Med15 has been provided which explain the evolution of extended-IDR in CBP from Med15 KIX-IDR junction-MoRF suggesting role of junction-MoRF in evolution and modulation of protein-protein interaction repertoire. This study can be informative and helpful in understanding the conserved and flexible nature of Mediator complex across eukaryotic kingdoms. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Establishment of a universal and rational gene detection strategy through three-way junction-based remote transduction.

PubMed

Tang, Yidan; Lu, Baiyang; Zhu, Zhentong; Li, Bingling

2018-01-21

The polymerase chain reaction and many isothermal amplifications are able to achieve super gene amplification. Unfortunately, most commonly-used transduction methods, such as dye staining and Taqman-like probing, still suffer from shortcomings including false signals or difficult probe design, or are incompatible with multi-analysis. Here a universal and rational gene detection strategy has been established by translating isothermal amplicons to enzyme-free strand displacement circuits via three-way junction-based remote transduction. An assistant transduction probe was imported to form a partial hybrid with the target single-stranded nucleic acid. After systematic optimization the hybrid could serve as an associative trigger to activate a downstream circuit detector via a strand displacement reaction across the three-way junction. By doing so, the detection selectivity can be double-guaranteed through both amplicon-transducer recognition and the amplicon-circuit reaction. A well-optimized circuit can be immediately applied to a new target detection through simply displacing only 10-12 nt on only one component, according to the target. More importantly, this property for the first time enables multi-analysis and logic-analysis in a single reaction, sharing a single fluorescence reporter. In an applicable model, trace amounts of Cronobacter and Enterobacteria genes have been clearly distinguished from samples with no bacteria or one bacterium, with ultra-high sensitivity and selectivity.

Genotype-specific signal generation based on digestion of 3-way DNA junctions: application to KRAS variation detection.

PubMed

Amicarelli, Giulia; Adlerstein, Daniel; Shehi, Erlet; Wang, Fengfei; Makrigiorgos, G Mike

2006-10-01

Genotyping methods that reveal single-nucleotide differences are useful for a wide range of applications. We used digestion of 3-way DNA junctions in a novel technology, OneCutEventAmplificatioN (OCEAN) that allows sequence-specific signal generation and amplification. We combined OCEAN with peptide-nucleic-acid (PNA)-based variant enrichment to detect and simultaneously genotype v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) codon 12 sequence variants in human tissue specimens. We analyzed KRAS codon 12 sequence variants in 106 lung cancer surgical specimens. We conducted a PNA-PCR reaction that suppresses wild-type KRAS amplification and genotyped the product with a set of OCEAN reactions carried out in fluorescence microplate format. The isothermal OCEAN assay enabled a 3-way DNA junction to form between the specific target nucleic acid, a fluorescently labeled "amplifier", and an "anchor". The amplifier-anchor contact contains the recognition site for a restriction enzyme. Digestion produces a cleaved amplifier and generation of a fluorescent signal. The cleaved amplifier dissociates from the 3-way DNA junction, allowing a new amplifier to bind and propagate the reaction. The system detected and genotyped KRAS sequence variants down to approximately 0.3% variant-to-wild-type alleles. PNA-PCR/OCEAN had a concordance rate with PNA-PCR/sequencing of 93% to 98%, depending on the exact implementation. Concordance rate with restriction endonuclease-mediated selective-PCR/sequencing was 89%. OCEAN is a practical and low-cost novel technology for sequence-specific signal generation. Reliable analysis of KRAS sequence alterations in human specimens circumvents the requirement for sequencing. Application is expected in genotyping KRAS codon 12 sequence variants in surgical specimens or in bodily fluids, as well as single-base variations and sequence alterations in other genes.

Structural and thermodynamic analysis of modified nucleosides in self-assembled DNA cross-tiles.

PubMed

Hakker, Lauren; Marchi, Alexandria N; Harris, Kimberly A; LaBean, Thomas H; Agris, Paul F

2014-01-01

DNA Holliday junctions are important natural strand-exchange structures that form during homologous recombination. Immobile four-arm junctions, analogs to Holliday junctions, have been designed to self-assemble into cross-tile structures by maximizing Watson-Crick base pairing and fixed crossover points. The cross-tiles, self-assembled from base pair recognition between designed single-stranded DNAs, form higher order lattice structures through cohesion of self-associating sticky ends. These cross-tiles have 16 unpaired nucleosides in the central loop at the junction of the four duplex stems. The importance of the centralized unpaired nucleosides to the structure's thermodynamic stability and self-assembly is unknown. Cross-tile DNA nanostructures were designed and constructed from nine single-stranded DNAs with four shell strands, four arms, and a central loop containing 16 unpaired bases. The 16 unpaired bases were either 2'-deoxyribothymidines, 2'-O-methylribouridines, or abasic 1',2'-dideoxyribonucleosides. Thermodynamic profiles and structural base-stacking contributions were assessed using UV absorption spectroscopy during thermal denaturation and circular dichroism spectroscopy, respectively, and the resulting structures were observed by atomic force microscopy. There were surprisingly significant changes in the thermodynamic and structural properties of lattice formation as a result of altering only the 16 unpaired, centralized nucleosides. The 16 unpaired 2'-O-methyluridines were stabilizing and produced uniform tubular structures. In contrast, the abasic nucleosides were destabilizing producing a mixture of structures. These results strongly indicate the importance of a small number of centrally located unpaired nucleosides within the structures. Since minor modifications lead to palpable changes in lattice formation, DNA cross-tiles present an easily manipulated structure convenient for applications in biomedical and biosensing devices.

Introduction and Overview of the Vicens-Reddy Speech Recognition System.

ERIC Educational Resources Information Center

Kameny, Iris; Ritea, H.

The Vicens-Reddy System is unique in the sense that it approaches the problem of speech recognition as a whole, rather than treating particular aspects of the problems as in previous attempts. For example, where earlier systems treated only segmentation of speech into phoneme groups, or detected phonemes in a given context, the Vicens-Reddy System…

Correlates of Problem Recognition and Intentions to Change among Caregivers of Abused and Neglected Children

ERIC Educational Resources Information Center

Littell, Julia H.; Girvin, Heather

2006-01-01

Objective: To identify individual, family, and caseworker characteristics associated with problem recognition (PR) and intentions to change (ITC) in a sample of caregivers who received in-home child welfare services following substantiated reports of child abuse or neglect. Methods: Caregivers were interviewed at 4 weeks, 16 weeks, and 1 year…

Semisupervised kernel marginal Fisher analysis for face recognition.

PubMed

Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

2013-01-01

Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.

HolT Hunter: Software for Identifying and Characterizing Low-Strain DNA Holliday Triangles

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

Sherman W. B.

2012-06-05

Synthetic DNA nanostructures are most commonly held together via Holliday junctions. These junctions allow for a wide variety of different angles between the double helices they connect. Nevertheless, only constructs with a very limited selection of angles have been built, to date, because of the computational complexity of identifying structures that fit together with low strain at odd angles. I have developed an algorithm that finds over 95% of the possible solutions by breaking the problem down into two portions. First, there is a problem of how smooth rods can form triangles by lying across one another. This problem ismore » easily handled by numerical computation. Second, there is the question of how distorted DNA double helices would need to be to fit onto the rod structure. This strain is calculated directly. The algorithm has been implemented in a Mathematica 8 notebook called Holliday Triangle Hunter. A large database of solutions has been identified. Additional interface software is available to facilitate drawing and viewing models.« less

Possible microplate generation at RRR triple junctions due to the non-circular finite motion of plates relative to each other

NASA Astrophysics Data System (ADS)

Cronin, V. S.

2012-12-01

First generation ideas of the kinematic stability of triple junctions lead to the common belief that the geometry of ridge-ridge-ridge (RRR) triple junctions remains constant over time under conditions of symmetric spreading. Given constant relative motion between each plate pair -- that is, the pole of plate relative motion is fixed to both plates in each pair during finite motion, as assumed in many accounts of plate kinematics -- there would be no boundary mismatch at the triple junction and no apparent kinematic reason why a microplate might develop there. But if, in a given RRR triple junction, the finite motion of one plate as observed from the other plate is not circular (as is generally the case, given the three-plate problem of plate kinematics), the geometry of the ridges and the triple junction will vary with time (Cronin, 1992, Tectonophys 207, 287-301). To explore the possible consequences of non-circular finite motion between plates at an RRR triple junction, a simple model was coded based on the cycloid finite-motion model (e.g., Cronin, 1987, Geology 15, 1006-1009) using NNR-MORVEL56 velocities for individual plates (Argus et al., 2011, G3 12, doi: 10.1029/2011GC003751). Initial assumptions include a spherical Earth, symmetric spreading, and constant angular velocities during the modeled finite time interval. The assumed-constant angular velocity vectors constitute a reference frame for observing finite plate motion. Typical results are [1] that the triple junction migrates relative to a coordinate system fixed to the angular-velocity vectors, [2] ridge axes rotates relative to each other, and [3] a boundary mismatch develops at the synthetic triple junction that might result in microplate nucleation. In a model simulating the Galapagos triple junction between the Cocos, Nazca and Pacific plates whose initial state did not include the Galapagos microplate, the mismatch gap was as much as ~3.4 km during 3 Myr of model displacement (see figure). The centroid of the synthetic triple junction translates ~81 km toward azimuth ~352° in 3 Myr. Of course, the details will vary as different angular velocity vectors are used; however, modeling indicates that non-circular finite relative motion between adjacent plates generally results in boundary mismatches and rotation of ridge segments relative to each other at RRR triple junctions. Left: synthetic Galapagos triple junction at initial model time, without a microplate. Right: synthetic triple junction after 3 Myr displacement, illustrating the resulting boundary mismatch (gap) and rotated ridge axes.

Modeling single molecule junction mechanics as a probe of interface bonding

NASA Astrophysics Data System (ADS)

Hybertsen, Mark S.

2017-03-01

Using the atomic force microscope based break junction approach, applicable to metal point contacts and single molecule junctions, measurements can be repeated thousands of times resulting in rich data sets characterizing the properties of an ensemble of nanoscale junction structures. This paper focuses on the relationship between the measured force extension characteristics including bond rupture and the properties of the interface bonds in the junction. A set of exemplary model junction structures has been analyzed using density functional theory based calculations to simulate the adiabatic potential surface that governs the junction elongation. The junction structures include representative molecules that bond to the electrodes through amine, methylsulfide, and pyridine links. The force extension characteristics are shown to be most effectively analyzed in a scaled form with maximum sustainable force and the distance between the force zero and force maximum as scale factors. Widely used, two parameter models for chemical bond potential energy versus bond length are found to be nearly identical in scaled form. Furthermore, they fit well to the present calculations of N-Au and S-Au donor-acceptor bonds, provided no other degrees of freedom are allowed to relax. Examination of the reduced problem of a single interface, but including relaxation of atoms proximal to the interface bond, shows that a single-bond potential form renormalized by an effective harmonic potential in series fits well to the calculated results. This allows relatively accurate extraction of the interface bond energy. Analysis of full junction models shows cooperative effects that go beyond the mechanical series inclusion of the second bond in the junction, the spectator bond that does not rupture. Calculations for a series of diaminoalkanes as a function of molecule length indicate that the most important cooperative effect is due to the interactions between the dipoles induced by the donor-acceptor bond formation at the junction interfaces. The force extension characteristic of longer molecules such as diaminooctane, where the dipole interaction effects drop to a negligible level, accurately fit to the renormalized single-bond potential form. The results suggest that measured force extension characteristics for single molecule junctions could be analyzed with a modified potential form that accounts for the energy stored in deformable mechanical components in series.

Modeling single molecule junction mechanics as a probe of interface bonding

DOE PAGES

Hybertsen, Mark S.

2017-03-07

Using the atomic force microscope based break junction approach, applicable to metal point contacts and single molecule junctions, measurements can be repeated thousands of times resulting in rich data sets characterizing the properties of an ensemble of nanoscale junction structures. This paper focuses on the relationship between the measured force extension characteristics including bond rupture and the properties of the interface bonds in the junction. We analyzed a set of exemplary model junction structures using density functional theory based calculations to simulate the adiabatic potential surface that governs the junction elongation. The junction structures include representative molecules that bond tomore » the electrodes through amine, methylsulfide, and pyridine links. The force extension characteristics are shown to be most effectively analyzed in a scaled form with maximum sustainable force and the distance between the force zero and force maximum as scale factors. Widely used, two parameter models for chemical bond potential energy versus bond length are found to be nearly identical in scaled form. Furthermore, they fit well to the present calculations of N–Au and S–Au donor-acceptor bonds, provided no other degrees of freedom are allowed to relax. Examination of the reduced problem of a single interface, but including relaxation of atoms proximal to the interface bond, shows that a single-bond potential form renormalized by an effective harmonic potential in series fits well to the calculated results. This, then, allows relatively accurate extraction of the interface bond energy. Analysis of full junction models shows cooperative effects that go beyond the mechanical series inclusion of the second bond in the junction, the spectator bond that does not rupture. Calculations for a series of diaminoalkanes as a function of molecule length indicate that the most important cooperative effect is due to the interactions between the dipoles induced by the donor-acceptor bond formation at the junction interfaces. The force extension characteristic of longer molecules such as diaminooctane, where the dipole interaction effects drop to a negligible level, accurately fit to the renormalized single-bond potential form. Our results suggest that measured force extension characteristics for single molecule junctions could be analyzed with a modified potential form that accounts for the energy stored in deformable mechanical components in series.« less

Modeling single molecule junction mechanics as a probe of interface bonding

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

Hybertsen, Mark S.

Using the atomic force microscope based break junction approach, applicable to metal point contacts and single molecule junctions, measurements can be repeated thousands of times resulting in rich data sets characterizing the properties of an ensemble of nanoscale junction structures. This paper focuses on the relationship between the measured force extension characteristics including bond rupture and the properties of the interface bonds in the junction. We analyzed a set of exemplary model junction structures using density functional theory based calculations to simulate the adiabatic potential surface that governs the junction elongation. The junction structures include representative molecules that bond tomore » the electrodes through amine, methylsulfide, and pyridine links. The force extension characteristics are shown to be most effectively analyzed in a scaled form with maximum sustainable force and the distance between the force zero and force maximum as scale factors. Widely used, two parameter models for chemical bond potential energy versus bond length are found to be nearly identical in scaled form. Furthermore, they fit well to the present calculations of N–Au and S–Au donor-acceptor bonds, provided no other degrees of freedom are allowed to relax. Examination of the reduced problem of a single interface, but including relaxation of atoms proximal to the interface bond, shows that a single-bond potential form renormalized by an effective harmonic potential in series fits well to the calculated results. This, then, allows relatively accurate extraction of the interface bond energy. Analysis of full junction models shows cooperative effects that go beyond the mechanical series inclusion of the second bond in the junction, the spectator bond that does not rupture. Calculations for a series of diaminoalkanes as a function of molecule length indicate that the most important cooperative effect is due to the interactions between the dipoles induced by the donor-acceptor bond formation at the junction interfaces. The force extension characteristic of longer molecules such as diaminooctane, where the dipole interaction effects drop to a negligible level, accurately fit to the renormalized single-bond potential form. Our results suggest that measured force extension characteristics for single molecule junctions could be analyzed with a modified potential form that accounts for the energy stored in deformable mechanical components in series.« less

Emotion Knowledge and Attentional Differences in Preschoolers Showing Context-Inappropriate Anger.

PubMed

Locke, Robin L; Lang, Nichole J

2016-08-01

Some children show anger inappropriate for the situation based on the predominant incentives, which is called context-inappropriate anger. Children need to attend to and interpret situational incentives for appropriate emotional responses. We examined associations of context-inappropriate anger with emotion recognition and attention problems in 43 preschoolers (42% male; M age = 55.1 months, SD = 4.1). Parents rated context-inappropriate anger across situations. Teachers rated attention problems using the Child Behavior Checklist-Teacher Report Form. Emotion recognition was ability to recognize emotional faces using the Emotion Matching Test. Anger perception bias was indicated by anger to non-anger situations using an adapted Affect Knowledge Test. 28% of children showed context-inappropriate anger, which correlated with lower emotion recognition (β = -.28) and higher attention problems (β = .36). Higher attention problems correlated with more anger perception bias (β = .32). This cross-sectional, correlational study provides preliminary findings that children with context-inappropriate anger showed more attention problems, which suggests that both "problems" tend to covary and associate with deficits or biases in emotion knowledge. © The Author(s) 2016.

Post processing for offline Chinese handwritten character string recognition

NASA Astrophysics Data System (ADS)

Wang, YanWei; Ding, XiaoQing; Liu, ChangSong

2012-01-01

Offline Chinese handwritten character string recognition is one of the most important research fields in pattern recognition. Due to the free writing style, large variability in character shapes and different geometric characteristics, Chinese handwritten character string recognition is a challenging problem to deal with. However, among the current methods over-segmentation and merging method which integrates geometric information, character recognition information and contextual information, shows a promising result. It is found experimentally that a large part of errors are segmentation error and mainly occur around non-Chinese characters. In a Chinese character string, there are not only wide characters namely Chinese characters, but also narrow characters like digits and letters of the alphabet. The segmentation error is mainly caused by uniform geometric model imposed on all segmented candidate characters. To solve this problem, post processing is employed to improve recognition accuracy of narrow characters. On one hand, multi-geometric models are established for wide characters and narrow characters respectively. Under multi-geometric models narrow characters are not prone to be merged. On the other hand, top rank recognition results of candidate paths are integrated to boost final recognition of narrow characters. The post processing method is investigated on two datasets, in total 1405 handwritten address strings. The wide character recognition accuracy has been improved lightly and narrow character recognition accuracy has been increased up by 10.41% and 10.03% respectively. It indicates that the post processing method is effective to improve recognition accuracy of narrow characters.

The Recognition of Web Pages' Hyperlinks by People with Intellectual Disabilities: An Evaluation Study

ERIC Educational Resources Information Center

Rocha, Tania; Bessa, Maximino; Goncalves, Martinho; Cabral, Luciana; Godinho, Francisco; Peres, Emanuel; Reis, Manuel C.; Magalhaes, Luis; Chalmers, Alan

2012-01-01

Background: One of the most mentioned problems of web accessibility, as recognized in several different studies, is related to the difficulty regarding the perception of what is or is not clickable in a web page. In particular, a key problem is the recognition of hyperlinks by a specific group of people, namely those with intellectual…

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

Brane junctions in the Randall-Sundrum scenario

NASA Astrophysics Data System (ADS)

Csáki, Csaba; Shirman, Yuri

2000-01-01

We present static solutions to Einstein's equations corresponding to branes at various angles intersecting in a single 3-brane. Such configurations may be useful for building models with localized gravity via the Randall-Sundrum mechanism. We find that such solutions may exist only if the mechanical forces acting on the junction exactly cancel. In addition to this constraint there are further conditions that the parameters of the theory have to satisfy. We find that at least one of these involves only the brane tensions and cosmological constants, and thus cannot have a dynamical origin. We present these conditions in detail for two simple examples. We discuss the nature of the cosmological constant problem in the framework of these scenarios, and outline the desired features of the brane configurations which may bring us closer towards a resolution of the cosmological constant problem.

Hybrid generative-discriminative approach to age-invariant face recognition

NASA Astrophysics Data System (ADS)

Sajid, Muhammad; Shafique, Tamoor

2018-03-01

Age-invariant face recognition is still a challenging research problem due to the complex aging process involving types of facial tissues, skin, fat, muscles, and bones. Most of the related studies that have addressed the aging problem are focused on generative representation (aging simulation) or discriminative representation (feature-based approaches). Designing an appropriate hybrid approach taking into account both the generative and discriminative representations for age-invariant face recognition remains an open problem. We perform a hybrid matching to achieve robustness to aging variations. This approach automatically segments the eyes, nose-bridge, and mouth regions, which are relatively less sensitive to aging variations compared with the rest of the facial regions that are age-sensitive. The aging variations of age-sensitive facial parts are compensated using a demographic-aware generative model based on a bridged denoising autoencoder. The age-insensitive facial parts are represented by pixel average vector-based local binary patterns. Deep convolutional neural networks are used to extract relative features of age-sensitive and age-insensitive facial parts. Finally, the feature vectors of age-sensitive and age-insensitive facial parts are fused to achieve the recognition results. Extensive experimental results on morphological face database II (MORPH II), face and gesture recognition network (FG-NET), and Verification Subset of cross-age celebrity dataset (CACD-VS) demonstrate the effectiveness of the proposed method for age-invariant face recognition well.

Techniques for Connecting Superconducting Thin Films

NASA Technical Reports Server (NTRS)

Mester, John; Gwo, Dz-Hung

2006-01-01

Several improved techniques for connecting superconducting thin films on substrates have been developed. The techniques afford some versatility for tailoring the electronic and mechanical characteristics of junctions between superconductors in experimental electronic devices. The techniques are particularly useful for making superconducting or alternatively normally conductive junctions (e.g., Josephson junctions) between patterned superconducting thin films in order to exploit electron quantum-tunneling effects. The techniques are applicable to both low-Tc and high-Tc superconductors (where Tc represents the superconducting- transition temperature of a given material), offering different advantages for each. Most low-Tc superconductors are metallic, and heretofore, connections among them have been made by spot welding. Most high-Tc superconductors are nonmetallic and cannot be spot welded. These techniques offer alternatives to spot welding of most low-Tc superconductors and additional solutions to problems of connecting most high-Tc superconductors.

Constructive autoassociative neural network for facial recognition.

PubMed

Fernandes, Bruno J T; Cavalcanti, George D C; Ren, Tsang I

2014-01-01

Autoassociative artificial neural networks have been used in many different computer vision applications. However, it is difficult to define the most suitable neural network architecture because this definition is based on previous knowledge and depends on the problem domain. To address this problem, we propose a constructive autoassociative neural network called CANet (Constructive Autoassociative Neural Network). CANet integrates the concepts of receptive fields and autoassociative memory in a dynamic architecture that changes the configuration of the receptive fields by adding new neurons in the hidden layer, while a pruning algorithm removes neurons from the output layer. Neurons in the CANet output layer present lateral inhibitory connections that improve the recognition rate. Experiments in face recognition and facial expression recognition show that the CANet outperforms other methods presented in the literature.

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

PubMed Central

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

2018-01-01

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

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

PubMed

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

2018-05-22

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

Identification of cloud fields by the nonparametric algorithm of pattern recognition from normalized video data recorded with the AVHRR instrument

NASA Astrophysics Data System (ADS)

Protasov, Konstantin T.; Pushkareva, Tatyana Y.; Artamonov, Evgeny S.

2002-02-01

The problem of cloud field recognition from the NOAA satellite data is urgent for solving not only meteorological problems but also for resource-ecological monitoring of the Earth's underlying surface associated with the detection of thunderstorm clouds, estimation of the liquid water content of clouds and the moisture of the soil, the degree of fire hazard, etc. To solve these problems, we used the AVHRR/NOAA video data that regularly displayed the situation in the territory. The complexity and extremely nonstationary character of problems to be solved call for the use of information of all spectral channels, mathematical apparatus of testing statistical hypotheses, and methods of pattern recognition and identification of the informative parameters. For a class of detection and pattern recognition problems, the average risk functional is a natural criterion for the quality and the information content of the synthesized decision rules. In this case, to solve efficiently the problem of identifying cloud field types, the informative parameters must be determined by minimization of this functional. Since the conditional probability density functions, representing mathematical models of stochastic patterns, are unknown, the problem of nonparametric reconstruction of distributions from the leaning samples arises. To this end, we used nonparametric estimates of distributions with the modified Epanechnikov kernel. The unknown parameters of these distributions were determined by minimization of the risk functional, which for the learning sample was substituted by the empirical risk. After the conditional probability density functions had been reconstructed for the examined hypotheses, a cloudiness type was identified using the Bayes decision rule.

[Pharmacology of glutamate sensitive synapses (I). Glutamate agonists (author's transl)].

PubMed

Shinozaki, H

1982-04-01

The actions of kainic acid, quisqualic acid, and ibotenic acid on the crayfish neuromuscular junction were described, and it was particularly interesting that the discrepancy between glutamate responses and EJPs was revealed by the use of kainic acid. On the other hand, there is increasing evidence showing that glutamate is an excitatory transmitter at the crayfish neuromuscular junction. At this stage, we are unable as yet to definitively support or reject glutamate's candidacy as the excitatory transmitter at the crayfish neuromuscular junction. The discrepancy revealed by the use of kainic acid may bring up some questions. Certainly, the differential action of kainic acid on the glutamate current and the excitatory synaptic current opens to doubt the transmitter role of glutamate. In the case of the study on a transmitter role for a substance of doubt status, the value of pharmacological studies seems to be greater in disproving than in asserting such the role. However, we have to consider the matter of the extra-junctional receptor postulated on the crayfish postsynaptic membrane as one of the major problems for pharmacological identification.

Electron transport in graphene/graphene side-contact junction by plane-wave multiple-scattering method

DOE PAGES

Li, Xiang-Guo; Chu, Iek-Heng; Zhang, X. -G.; ...

2015-05-28

Electron transport in graphene is along the sheet but junction devices are often made by stacking different sheets together in a “side-contact” geometry which causes the current to flow perpendicular to the sheets within the device. Such geometry presents a challenge to first-principles transport methods. We solve this problem by implementing a plane-wave-based multiple-scattering theory for electron transport. In this study, this implementation improves the computational efficiency over the existing plane-wave transport code, scales better for parallelization over large number of nodes, and does not require the current direction to be along a lattice axis. As a first application, wemore » calculate the tunneling current through a side-contact graphene junction formed by two separate graphene sheets with the edges overlapping each other. We find that transport properties of this junction depend strongly on the AA or AB stacking within the overlapping region as well as the vacuum gap between two graphene sheets. Finally, such transport behaviors are explained in terms of carbon orbital orientation, hybridization, and delocalization as the geometry is varied.« less

Measure synchronization in a spin-orbit-coupled bosonic Josephson junction

NASA Astrophysics Data System (ADS)

Wang, Wen-Yuan; Liu, Jie; Fu, Li-Bin

2015-11-01

We present measure synchronization (MS) in a bosonic Josephson junction with spin-orbit coupling. The two atomic hyperfine states are coupled by a Raman dressing scheme, and they are regarded as two orientations of a pseudo-spin-1 /2 system. A feature specific to a spin-orbit-coupled (SOC) bosonic Josephson junction is that the transition from non-MS to MS dynamics can be modulated by Raman laser intensity, even in the absence of interspin atomic interaction. A phase diagram of non-MS and MS dynamics as functions of Raman laser intensity and Josephson tunneling amplitude is presented. Taking into account interspin atomic interactions, the system exhibits MS breaking dynamics resulting from the competition between intraspin and interspin atomic interactions. When interspin atomic interactions dominate in the competition, the system always exhibits MS dynamics. For interspin interaction weaker than intraspin interaction, a window for non-MS dynamics is present. Since SOC Bose-Einstein condensates provide a powerful platform for studies on physical problems in various fields, the study of MS dynamics is valuable in researching the collective coherent dynamical behavior in a spin-orbit-coupled bosonic Josephson junction.

Joint association of sleep problems and psychosocial working conditions with registered long-term sickness absence. A Danish cohort study.

PubMed

Madsen, Ida Eh; Larsen, Ann D; Thorsen, Sannie V; Pejtersen, Jan H; Rugulies, Reiner; Sivertsen, Børge

2016-07-01

Sleep problems and adverse psychosocial working conditions are associated with increased risk of long-term sickness absence. Because sleep problems affect role functioning they may also exacerbate any effects of psychosocial working conditions and vice versa. We examined whether sleep problems and psychosocial working conditions interact in their associations with long-term sickness absence. We linked questionnaire data from participants to two surveys of random samples of the Danish working population (N=10 752) with registries on long-term sick leave during five years after questionnaire response. We defined sleep problems by self-reported symptoms and/or register data on hypnotics purchases of hypnotics. Psychosocial working conditions included quantitative and emotional demands, influence, supervisor recognition and social support, leadership quality, and social support from colleagues. Using time-to-event models, we calculated hazard ratios (HR) and differences and examined interaction as departure from multiplicativity and additivity. During 40 165 person-years of follow-up, we identified 2313 episodes of long-terms sickness absence. Sleep problems predicted risk of long-term sickness absence [HR 1.54, 95% confidence interval (95% CI) 1.38-1.73]. This association was statistically significantly stronger among participants with high quantitative demands and weaker among those with high supervisor recognition (P<0.0001). High quantitative demands exacerbated the association of sleep problems with risk of long-term sickness absence whereas high supervisor recognition buffered this association. To prevent long-term sickness absence among employees with sleep problems, workplace modifications focusing on quantitative demands and supervisor recognition may be considered. Workplace interventions for these factors may more effectively prevent sickness absence when targeted at this group. The efficacy and effectiveness of such interventions needs to be established in future studies.

The Relative Success of Recognition-Based Inference in Multichoice Decisions

ERIC Educational Resources Information Center

McCloy, Rachel; Beaman, C. Philip; Smith, Philip T.

2008-01-01

The utility of an "ecologically rational" recognition-based decision rule in multichoice decision problems is analyzed, varying the type of judgment required (greater or lesser). The maximum size and range of a counterintuitive advantage associated with recognition-based judgment (the "less-is-more effect") is identified for a range of cue…

Multi-layer sparse representation for weighted LBP-patches based facial expression recognition.

PubMed

Jia, Qi; Gao, Xinkai; Guo, He; Luo, Zhongxuan; Wang, Yi

2015-03-19

In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.

State Decision-Makers Guide for Hazardous Waste Management: Defining Hazardous Wastes, Problem Recognition, Land Use, Facility Operations, Conceptual Framework, Policy Issues, Transportation.

ERIC Educational Resources Information Center

Corson, Alan; And Others

Presented are key issues to be addressed by state, regional, and local governments and agencies in creating effective hazardous waste management programs. Eight chapters broadly frame the topics which state-level decision makers should consider. These chapters include: (1) definition of hazardous waste; (2) problem definition and recognition; (3)…

Sleep promotes analogical transfer in problem solving.

PubMed

Monaghan, Padraic; Sio, Ut Na; Lau, Sum Wai; Woo, Hoi Kei; Linkenauger, Sally A; Ormerod, Thomas C

2015-10-01

Analogical problem solving requires using a known solution from one problem to apply to a related problem. Sleep is known to have profound effects on memory and information restructuring, and so we tested whether sleep promoted such analogical transfer, determining whether improvement was due to subjective memory for problems, subjective recognition of similarity across related problems, or by abstract generalisation of structure. In Experiment 1, participants were exposed to a set of source problems. Then, after a 12-h period involving sleep or wake, they attempted target problems structurally related to the source problems but with different surface features. Experiment 2 controlled for time of day effects by testing participants either in the morning or the evening. Sleep improved analogical transfer, but effects were not due to improvements in subjective memory or similarity recognition, but rather effects of structural generalisation across problems. Copyright © 2015 Elsevier B.V. All rights reserved.

Integrated Spintronic Platforms for Biomolecular Recognition Detection

NASA Astrophysics Data System (ADS)

Martins, V. C.; Cardoso, F. A.; Loureiro, J.; Mercier, M.; Germano, J.; Cardoso, S.; Ferreira, R.; Fonseca, L. P.; Sousa, L.; Piedade, M. S.; Freitas, P. P.

2008-06-01

This paper covers recent developments in magnetoresistive based biochip platforms fabricated at INESC-MN, and their application to the detection and quantification of pathogenic waterborn microorganisms in water samples for human consumption. Such platforms are intended to give response to the increasing concern related to microbial contaminated water sources. The presented results concern the development of biological active DNA chips and protein chips and the demonstration of the detection capability of the present platforms. Two platforms are described, one including spintronic sensors only (spin-valve based or magnetic tunnel junction based), and the other, a fully scalable platform where each probe site consists of a MTJ in series with a thin film diode (TFD). Two microfluidic systems are described, for cell separation and concentration, and finally, the read out and control integrated electronics are described, allowing the realization of bioassays with a portable point of care unit. The present platforms already allow the detection of complementary biomolecular target recognition with 1 pM concentration.

Bilateral Theta-Burst TMS to Influence Global Gestalt Perception

PubMed Central

Ritzinger, Bernd; Huberle, Elisabeth; Karnath, Hans-Otto

2012-01-01

While early and higher visual areas along the ventral visual pathway in the inferotemporal cortex are critical for the recognition of individual objects, the neural representation of human perception of complex global visual scenes remains under debate. Stroke patients with a selective deficit in the perception of a complex global Gestalt with intact recognition of individual objects – a deficit termed simultanagnosia – greatly helped to study this question. Interestingly, simultanagnosia typically results from bilateral lesions of the temporo-parietal junction (TPJ). The present study aimed to verify the relevance of this area for human global Gestalt perception. We applied continuous theta-burst TMS either unilaterally (left or right) or bilateral simultaneously over TPJ. Healthy subjects were presented with hierarchically organized visual stimuli that allowed parametrical degrading of the object at the global level. Identification of the global Gestalt was significantly modulated only for the bilateral TPJ stimulation condition. Our results strengthen the view that global Gestalt perception in the human brain involves TPJ and is co-dependent on both hemispheres. PMID:23110106

Bilateral theta-burst TMS to influence global gestalt perception.

PubMed

Ritzinger, Bernd; Huberle, Elisabeth; Karnath, Hans-Otto

2012-01-01

While early and higher visual areas along the ventral visual pathway in the inferotemporal cortex are critical for the recognition of individual objects, the neural representation of human perception of complex global visual scenes remains under debate. Stroke patients with a selective deficit in the perception of a complex global Gestalt with intact recognition of individual objects - a deficit termed simultanagnosia - greatly helped to study this question. Interestingly, simultanagnosia typically results from bilateral lesions of the temporo-parietal junction (TPJ). The present study aimed to verify the relevance of this area for human global Gestalt perception. We applied continuous theta-burst TMS either unilaterally (left or right) or bilateral simultaneously over TPJ. Healthy subjects were presented with hierarchically organized visual stimuli that allowed parametrical degrading of the object at the global level. Identification of the global Gestalt was significantly modulated only for the bilateral TPJ stimulation condition. Our results strengthen the view that global Gestalt perception in the human brain involves TPJ and is co-dependent on both hemispheres.

Mechanism of intermediate filament recognition by plakin repeat domains revealed by envoplakin targeting of vimentin

NASA Astrophysics Data System (ADS)

Fogl, Claudia; Mohammed, Fiyaz; Al-Jassar, Caezar; Jeeves, Mark; Knowles, Timothy J.; Rodriguez-Zamora, Penelope; White, Scott A.; Odintsova, Elena; Overduin, Michael; Chidgey, Martyn

2016-03-01

Plakin proteins form critical connections between cell junctions and the cytoskeleton; their disruption within epithelial and cardiac muscle cells cause skin-blistering diseases and cardiomyopathies. Envoplakin has a single plakin repeat domain (PRD) which recognizes intermediate filaments through an unresolved mechanism. Herein we report the crystal structure of envoplakin's complete PRD fold, revealing binding determinants within its electropositive binding groove. Four of its five internal repeats recognize negatively charged patches within vimentin via five basic determinants that are identified by nuclear magnetic resonance spectroscopy. Mutations of the Lys1901 or Arg1914 binding determinants delocalize heterodimeric envoplakin from intracellular vimentin and keratin filaments in cultured cells. Recognition of vimentin is abolished when its residues Asp112 or Asp119 are mutated. The latter slot intermediate filament rods into basic PRD domain grooves through electrosteric complementarity in a widely applicable mechanism. Together this reveals how plakin family members form dynamic linkages with cytoskeletal frameworks.

Engineering a robust DNA split proximity circuit with minimized circuit leakage

PubMed Central

Ang, Yan Shan; Tong, Rachel; Yung, Lin-Yue Lanry

2016-01-01

DNA circuit is a versatile and highly-programmable toolbox which can potentially be used for the autonomous sensing of dynamic events, such as biomolecular interactions. However, the experimental implementation of in silico circuit designs has been hindered by the problem of circuit leakage. Here, we systematically analyzed the sources and characteristics of various types of leakage in a split proximity circuit which was engineered to spatially probe for target sites held within close proximity. Direct evidence that 3′-truncated oligonucleotides were the major impurity contributing to circuit leakage was presented. More importantly, a unique strategy of translocating a single nucleotide between domains, termed ‘inter-domain bridging’, was introduced to eliminate toehold-independent leakages while enhancing the strand displacement kinetics across a three-way junction. We also analyzed the dynamics of intermediate complexes involved in the circuit computation in order to define the working range of domain lengths for the reporter toehold and association region respectively. The final circuit design was successfully implemented on a model streptavidin-biotin system and demonstrated to be robust against both circuit leakage and biological interferences. We anticipate that this simple signal transduction strategy can be used to probe for diverse biomolecular interactions when used in conjunction with specific target recognition moieties. PMID:27207880

Representations of the language recognition problem for a theorem prover

NASA Technical Reports Server (NTRS)

Minker, J.; Vanderbrug, G. J.

1972-01-01

Two representations of the language recognition problem for a theorem prover in first order logic are presented and contrasted. One of the representations is based on the familiar method of generating sentential forms of the language, and the other is based on the Cocke parsing algorithm. An augmented theorem prover is described which permits recognition of recursive languages. The state-transformation method developed by Cordell Green to construct problem solutions in resolution-based systems can be used to obtain the parse tree. In particular, the end-order traversal of the parse tree is derived in one of the representations. An inference system, termed the cycle inference system, is defined which makes it possible for the theorem prover to model the method on which the representation is based. The general applicability of the cycle inference system to state space problems is discussed. Given an unsatisfiable set S, where each clause has at most one positive literal, it is shown that there exists an input proof. The clauses for the two representations satisfy these conditions, as do many state space problems.

Modular Nuclease-Responsive DNA Three-Way Junction-Based Dynamic Assembly of a DNA Device and Its Sensing Application.

PubMed

Zhu, Jing; Wang, Lei; Xu, Xiaowen; Wei, Haiping; Jiang, Wei

2016-04-05

Here, we explored a modular strategy for rational design of nuclease-responsive three-way junctions (TWJs) and fabricated a dynamic DNA device in a "plug-and-play" fashion. First, inactivated TWJs were designed, which contained three functional domains: the inaccessible toehold and branch migration domains, the specific sites of nucleases, and the auxiliary complementary sequence. The actions of different nucleases on their specific sites in TWJs caused the close proximity of the same toehold and branch migration domains, resulting in the activation of the TWJs and the formation of a universal trigger for the subsequent dynamic assembly. Second, two hairpins (H1 and H2) were introduced, which could coexist in a metastable state, initially to act as the components for the dynamic assembly. Once the trigger initiated the opening of H1 via TWJs-driven strand displacement, the cascade hybridization of hairpins immediately switched on, resulting in the formation of the concatemers of H1/H2 complex appending numerous integrated G-quadruplexes, which were used to obtain label-free signal readout. The inherent modularity of this design allowed us to fabricate a flexible DNA dynamic device and detect multiple nucleases through altering the recognition pattern slightly. Taking uracil-DNA glycosylase and CpG methyltransferase M.SssI as models, we successfully realized the butt joint between the uracil-DNA glycosylase and M.SssI recognition events and the dynamic assembly process. Furthermore, we achieved ultrasensitive assay of nuclease activity and the inhibitor screening. The DNA device proposed here will offer an adaptive and flexible tool for clinical diagnosis and anticancer drug discovery.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

PubMed Central

2018-01-01

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

A Sarsa(λ)-based control model for real-time traffic light coordination.

PubMed

Zhou, Xiaoke; Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

Word Spotting and Recognition with Embedded Attributes.

PubMed

Almazán, Jon; Gordo, Albert; Fornés, Alicia; Valveny, Ernest

2014-12-01

This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.

An application of viola jones method for face recognition for absence process efficiency

NASA Astrophysics Data System (ADS)

Rizki Damanik, Rudolfo; Sitanggang, Delima; Pasaribu, Hendra; Siagian, Hendrik; Gulo, Frisman

2018-04-01

Absence was a list of documents that the company used to record the attendance time of each employee. The most common problem in a fingerprint machine is the identification of a slow sensor or a sensor not recognizing a finger. The employees late to work because they get difficulties at fingerprint system, they need about 3 – 5 minutes to absence when the condition of finger is wet or not fit. To overcome this problem, this research tried to utilize facial recognition for attendance process. The method used for facial recognition was Viola Jones. Through the processing phase of the RGB face image was converted into a histogram equalization face image for the next stage of recognition. The result of this research was the absence process could be done less than 1 second with a maximum slope of ± 700 and a distance of 20-200 cm. After implement facial recognition the process of absence is more efficient, just take less 1 minute to absence.

Multi-template image matching using alpha-rooted biquaternion phase correlation with application to logo recognition

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2011-06-01

Hypercomplex approaches are seeing increased application to signal and image processing problems. The use of multicomponent hypercomplex numbers, such as quaternions, enables the simultaneous co-processing of multiple signal or image components. This joint processing capability can provide improved exploitation of the information contained in the data, thereby leading to improved performance in detection and recognition problems. In this paper, we apply hypercomplex processing techniques to the logo image recognition problem. Specifically, we develop an image matcher by generalizing classical phase correlation to the biquaternion case. We further incorporate biquaternion Fourier domain alpha-rooting enhancement to create Alpha-Rooted Biquaternion Phase Correlation (ARBPC). We present the mathematical properties which justify use of ARBPC as an image matcher. We present numerical performance results of a logo verification problem using real-world logo data, demonstrating the performance improvement obtained using the hypercomplex approach. We compare results of the hypercomplex approach to standard multi-template matching approaches.

Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression.

PubMed

Gao, Guangwei; Yang, Jian; Jing, Xiaoyuan; Huang, Pu; Hua, Juliang; Yue, Dong

2016-01-01

In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructed error image, the so-called nuclear norm-based matrix regression has been demonstrated to be effective for robust face recognition with continuous occlusions. However, the recognition performance of nuclear norm-based matrix regression degrades greatly in the face of the small sample size problem. An alternative solution to tackle this problem is performing matrix regression on each patch and then integrating the outputs from all patches. However, it is difficult to set an optimal patch size across different databases. To fully utilize the complementary information from different patch scales for the final decision, we propose a multi-scale patch-based matrix regression scheme based on which the ensemble of multi-scale outputs can be achieved optimally. Extensive experiments on benchmark face databases validate the effectiveness and robustness of our method, which outperforms several state-of-the-art patch-based face recognition algorithms.

Comment on “Frequency-domain stimulated and spontaneous light emission signals at molecular junctions” [J. Chem. Phys. 141, 074107 (2014)

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

Galperin, Michael; Ratner, Mark A.; Nitzan, Abraham

2015-04-07

We discuss the derivation of the optical response in molecular junctions presented by U. Harbola et al. [J. Chem. Phys. 141, 074107 (2014)], which questions some terms in the theory of Raman scattering in molecular junctions developed in our earlier publications. We show that the terms considered in our theory represent the correct contribution to calculated Raman scattering and are in fact identical to those considered by Harbola et al. We also indicate drawbacks of the presented approach in treating the quantum transport part of the problem.

Mechanism of T7 RNAP pausing and termination at the T7 concatemer junction: a local change in transcription bubble structure drives a large change in transcription complex architecture.

PubMed

Nayak, Dhananjaya; Siller, Sylvester; Guo, Qing; Sousa, Rui

2008-02-15

The T7RNA polymerase (RNAP) elongation complex (EC) pauses and is destabilized at a unique 8 nucleotide (nt) sequence found at the junction of the head-to-tail concatemers of T7 genomic DNA generated during T7 DNA replication. The paused EC may recruit the T7 DNA processing machinery, which cleaves the concatemerized DNA within this 8 nt concatemer junction (CJ). Pausing of the EC at the CJ involves structural changes in both the RNAP and transcription bubble. However, these structural changes have not been fully defined, nor is it understood how the CJ sequence itself causes the EC to change its structure, to pause, and to become less stable. Here we use solution and RNAP-tethered chemical nucleases to probe the CJ transcript and changes in the EC structure as the polymerase pauses and terminates at the CJ. Together with extensive mutational scanning of regions of the polymerase that are likely to be involved in recognition of the CJ, we are able to develop a description of the events that occur as the EC transcribes through the CJ and subsequently pauses. In this process, a local change in the structure of the transcription bubble drives a large change in the architecture of the EC. This altered EC structure may then serve as the signal that recruits the processing machinery to the CJ.

Gap junctional intercellular communication dysfunction mediates the cognitive impairment induced by cerebral ischemia-reperfusion injury: PI3K/Akt pathway involved.

PubMed

Zhou, Shujun; Fang, Zheng; Wang, Gui; Wu, Song

2017-01-01

Cerebral ischemia/reperfusion (I/R) injury causes hippocampal apoptosis and cognitive impairment, and the dysfunction of gap junction intercellular communication (GJIC) may contribute to the cognitive impairment. We aim to examine the impact of cerebral I/R injury on cognitive impairment, the role of GJIC dysfunction in the rat hippocampus and the involvement of the phosphatidylinositol 3 kinase (PI3K)/protein kinase B (Akt) pathway. Rats were subjected to a cerebral I/R procedure and underwent cognitive assessment with the novel object recognition and Morris Water Maze tasks. The distance of Lucifer Yellow dye transfer and the Cx43 protein were examined to measure GJIC. Neural apoptosis was assessed with the terminal deoxynucleotide-transferase-mediated dUTP-digoxigenin nick end labeling (TUNEL) method. After rats received inhibitors of the PI3K/Akt pathway, GJIC and cognitive ability were measured again. GJIC promotion by ZP123 significantly reversed cognitive impairment and hippocampal apoptosis induced by cerebral I/R, while the inhibition of GJIC by octanol significantly facilitated cognitive impairment and hippocampal apoptosis. The phosphorylation of Akt was enhanced by cerebral I/R and octanol but inhibited by ZP123. The inhibition of the PI3K/Akt pathway significantly suppressed GJIC and cognitive impairment. The PI3K/Akt pathway is involved in cognitive impairment caused by gap junctional communication dysfunction in the rat hippocampus after ischemia-reperfusion injury.

The role of lateral occipitotemporal junction and area MT/V5 in the visual analysis of upper-limb postures.

PubMed

Peigneux, P; Salmon, E; van der Linden, M; Garraux, G; Aerts, J; Delfiore, G; Degueldre, C; Luxen, A; Orban, G; Franck, G

2000-06-01

Humans, like numerous other species, strongly rely on the observation of gestures of other individuals in their everyday life. It is hypothesized that the visual processing of human gestures is sustained by a specific functional architecture, even at an early prelexical cognitive stage, different from that required for the processing of other visual entities. In the present PET study, the neural basis of visual gesture analysis was investigated with functional neuroimaging of brain activity during naming and orientation tasks performed on pictures of either static gestures (upper-limb postures) or tridimensional objects. To prevent automatic object-related cerebral activation during the visual processing of postures, only intransitive postures were selected, i. e., symbolic or meaningless postures which do not imply the handling of objects. Conversely, only intransitive objects which cannot be handled were selected to prevent gesture-related activation during their visual processing. Results clearly demonstrate a significant functional segregation between the processing of static intransitive postures and the processing of intransitive tridimensional objects. Visual processing of objects elicited mainly occipital and fusiform gyrus activity, while visual processing of postures strongly activated the lateral occipitotemporal junction, encroaching upon area MT/V5, involved in motion analysis. These findings suggest that the lateral occipitotemporal junction, working in association with area MT/V5, plays a prominent role in the high-level perceptual analysis of gesture, namely the construction of its visual representation, available for subsequent recognition or imitation. Copyright 2000 Academic Press.

How and why does the immunological synapse form? Physical chemistry meets cell biology.

PubMed

Chakraborty, Arup K

2002-03-05

During T lymphocyte (T cell) recognition of an antigen, a highly organized and specific pattern of membrane proteins forms in the junction between the T cell and the antigen-presenting cell (APC). This specialized cell-cell junction is called the immunological synapse. It is several micrometers large and forms over many minutes. A plethora of experiments are being performed to study the mechanisms that underlie synapse formation and the way in which information transfer occurs across the synapse. The wealth of experimental data that is beginning to emerge must be understood within a mechanistic framework if it is to prove useful in developing modalities to control the immune response. Quantitative models can complement experiments in the quest for such a mechanistic understanding by suggesting experimentally testable hypotheses. Here, a quantitative synapse assembly model is described. The model uses concepts developed in physical chemistry and cell biology and is able to predict the spatiotemporal evolution of cell shape and receptor protein patterns observed during synapse formation. Attention is directed to how the juxtaposition of model predictions and experimental data has led to intriguing hypotheses regarding the role of null and self peptides during synapse assembly, as well as correlations between T cell effector functions and the robustness of synapse assembly. We remark on some ways in which synergistic experiments and modeling studies can improve current models, and we take steps toward a better understanding of information transfer across the T cell-APC junction.

Leveraging Automatic Speech Recognition Errors to Detect Challenging Speech Segments in TED Talks

ERIC Educational Resources Information Center

Mirzaei, Maryam Sadat; Meshgi, Kourosh; Kawahara, Tatsuya

2016-01-01

This study investigates the use of Automatic Speech Recognition (ASR) systems to epitomize second language (L2) listeners' problems in perception of TED talks. ASR-generated transcripts of videos often involve recognition errors, which may indicate difficult segments for L2 listeners. This paper aims to discover the root-causes of the ASR errors…

Quantum-statistical theory of microwave detection using superconducting tunnel junctions

NASA Astrophysics Data System (ADS)

Deviatov, I. A.; Kuzmin, L. S.; Likharev, K. K.; Migulin, V. V.; Zorin, A. B.

1986-09-01

A quantum-statistical theory of microwave and millimeter-wave detection using superconducting tunnel junctions is developed, with a rigorous account of quantum, thermal, and shot noise arising from fluctuation sources associated with the junctions, signal source, and matching circuits. The problem of the noise characterization in the quantum sensitivity range is considered and a general noise parameter Theta(N) is introduced. This parameter is shown to be an adequate figure of merit for most receivers of interest while some devices can require a more complex characterization. Analytical expressions and/or numerically calculated plots for Theta(N) are presented for the most promising detection modes including the parametric amplification, heterodyne mixing, and quadratic videodetection, using both the quasiparticle-current and the Cooper-pair-current nonlinearities. Ultimate minimum values of Theta(N) for each detection mode are compared and found to be in agreement with limitations imposed by the quantum-mechanical uncertainty principle.

The SPS interference problem-electronic system effects and mitigation techniques

NASA Technical Reports Server (NTRS)

Juroshek, J. R.

1980-01-01

The potential for interference between solar power satellites (SPS) and other Earth satellite operations was examined along with interference problems involving specific electronic devices. Conclusions indicate that interference is likely in the 2500 MHz to 2690 MHz direct broadcast satellite band adjacent to SPS. Estimates of the adjacent channel noise from SPS in this band are as high as -124 dBc/4 kHz and -100 dBc/MHz, where dBc represents decibels relative to the total power in the fundamental. A second potential problem is the 7350 MHz, 3d harmonic from SPS that falls within the 7300 MHz to 7450 MHz space to Earth, government, satellite assignment. Catastrophic failures can be produced in integrated circuits when the microwave power levels coupled into inputs and power leads reach 1 to 100 watts. The failures are typically due to bonding wire melting, metallization failures, and junction shorting. Nondestructive interaction or interference, however, generally occurs with coupled power levels of the order of 10 milliwatts. This integration is due to the rectification of microwave energy by the numerous pn junctions within these circuits.

Probabilistic Open Set Recognition

NASA Astrophysics Data System (ADS)

Jain, Lalit Prithviraj

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

Social emotion recognition, social functioning, and attempted suicide in late-life depression.

PubMed

Szanto, Katalin; Dombrovski, Alexandre Y; Sahakian, Barbara J; Mulsant, Benoit H; Houck, Patricia R; Reynolds, Charles F; Clark, Luke

2012-03-01

: Lack of feeling connected and poor social problem solving have been described in suicide attempters. However, cognitive substrates of this apparent social impairment in suicide attempters remain unknown. One possible deficit, the inability to recognize others' complex emotional states has been observed not only in disorders characterized by prominent social deficits (autism-spectrum disorders and frontotemporal dementia) but also in depression and normal aging. This study assessed the relationship between social emotion recognition, problem solving, social functioning, and attempted suicide in late-life depression. : There were 90 participants: 24 older depressed suicide attempters, 38 nonsuicidal depressed elders, and 28 comparison subjects with no psychiatric history. We compared performance on the Reading the Mind in the Eyes test and measures of social networks, social support, social problem solving, and chronic interpersonal difficulties in these three groups. : Suicide attempters committed significantly more errors in social emotion recognition and showed poorer global cognitive performance than elders with no psychiatric history. Attempters had restricted social networks: they were less likely to talk to their children, had fewer close friends, and did not engage in volunteer activities, compared to nonsuicidal depressed elders and those with no psychiatric history. They also reported a pattern of struggle against others and hostility in relationships, felt a lack of social support, perceived social problems as impossible to resolve, and displayed a careless/impulsive approach to problems. : Suicide attempts in depressed elders were associated with poor social problem solving, constricted social networks, and disruptive interpersonal relationships. Impaired social emotion recognition in the suicide attempter group was related.

An accurate two-dimensional LBIC solution for bipolar transistors

NASA Astrophysics Data System (ADS)

Benarab, A.; Baudrand, H.; Lescure, M.; Boucher, J.

1988-05-01

A complete solution of the diffusion problem of carriers generated by a located light beam in the emitter and base region of a bipolar structure is presented. Green's function method and moment method are used to solve the 2-D diffusion equation in these regions. From the Green's functions solution of these equations, the light beam induced currents (LBIC) in the different junctions of the structure due to an extended generation represented by a rectangular light spot; are thus decided. The equations of these currents depend both on the parameters which characterise the structure, surface states, dimensions of the emitter and the base region, and the characteristics of the light spot, that is to say, the width and the wavelength. Curves illustrating the variation of the various LBIC in the base region junctions as a function of the impact point of the light beam ( x0) for different values of these parameters are discussed. In particular, the study of the base-emitter currents when the light beam is swept right across the sample illustrates clearly a good geometrical definition of the emitter region up to base end of the emitter-base space-charge areas and a "whirl" lateral diffusion beneath this region, (i.e. the diffusion of the generated carriers near the surface towards the horizontal base-emitter junction and those created beneath this junction towards the lateral (B-E) junctions).

Die attach dimension and material on thermal conductivity study for high power COB LED

NASA Astrophysics Data System (ADS)

Sarukunaselan, K.; Ong, N. R.; Sauli, Z.; Mahmed, N.; Kirtsaeng, S.; Sakuntasathien, S.; Suppiah, S.; Alcain, J. B.; Retnasamy, V.

2017-09-01

High power LED began to gain popularity in the semiconductor market due to its efficiency and luminance. Nonetheless, along with the increased in efficiency, there was an increased in the junction temperature too. The alleviating junction temperature is undesirable since the performances and lifetime will be degraded over time. Therefore, it is crucial to solve this thermal problem by maximizing the heat dissipation to the ambience. Improvising the die attach (DA) layer would be the best option because this layer is sandwiched between the chip (heat source) and the substrate (channel to the ambient). In this paper, the impact of thickness and thermal conductivity onto the junction temperature and Von Mises stress is analyzed. Results obtained showed that the junction temperature is directly proportional to the thickness but the stress was inversely proportional to the thickness of the DA. The thermal conductivity of the materials did affect the junction temperature as there was not much changes once the thermal conductivity reached 20W/mK. However, no significant changes were observed on the Von Mises stress caused by the thermal conductivity. Material with the second highest thermal conductivity had the lowest stress, whereas the highest conductivity material had the highest stress value at 20 µm. Overall, silver sinter provided the best thermal dissipation compared to the other materials.

Poor self-recognition of disordered eating among girls with bulimic-type eating disorders: cause for concern?

PubMed

Gratwick-Sarll, Kassandra; Bentley, Caroline; Harrison, Carmel; Mond, Jonathan

2016-08-01

Bulimic-type eating disorders are common among young women and associated with high levels of distress and disability and low uptake of mental health care. We examined self-recognition of disordered eating and factors associated with this among female adolescents with bulimic-type eating disorders (n = 139) recruited from a large, population-based sample. A vignette of a fictional character with bulimia nervosa was presented, followed by a series of questions addressing the nature and treatment of the problem described. One of these questions required participants to indicate whether they currently had a problem such as the one described. Self-report measures of eating disorder symptoms, general psychological distress and quality of life were also completed. More than half of participants (58%) did not believe that they currently had a problem with their eating. In multivariable analysis, impairment in emotional well-being and self-induced vomiting were the only variables independently associated with self-recognition. Participants who recognized a problem with their eating were more likely to have sought treatment for an eating problem than those who did not. Recognition of disordered eating among adolescents with bulimic-type eating disorders may be poor and this may be a factor in low uptake of mental health care. Health promotion efforts may need to address the misconception that only bulimic-type disorders involving self-induced vomiting are pathological. © 2014 Wiley Publishing Asia Pty Ltd.

Overview of depression: epidemiology and implications for community nursing practice.

PubMed

Lazarou, Chrystalleni; Kouta, Christiana; Kapsou, Margarita; Kaite, Charis

2011-01-01

Depressive disorders are among the most common psychological conditions currently affecting individuals living in the Westernized world. Yet, available data indicate that fewer than one third of adults with depression obtain appropriate professional treatment. This is attributed, among other reasons, to the under-recognition of the problem by health professionals, including district nurses. In order to improve recognition of the problem, it is imperative for nurses and especially those working in community settings, to appreciate the importance of prompt diagnosis which presumes both an understanding and knowledge of basic aspects of the problem and, an understanding of their role in dealing with depression. This overview presents epidemiological data and identifies the potential consequences of depression on daily functioning and other aspects of life among adults in Westernized countries, aiming to raise awareness and sensitize district nurses about the issue The article discusses how the role of district nurses can be enhanced to improve recognition rates.

A modified active appearance model based on an adaptive artificial bee colony.

PubMed

Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; 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.

Human Activity Recognition from Body Sensor Data using Deep Learning.

PubMed

Hassan, Mohammad Mehedi; Huda, Shamsul; Uddin, Md Zia; Almogren, Ahmad; Alrubaian, Majed

2018-04-16

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

Face sketch recognition based on edge enhancement via deep learning

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Parallel and distributed computation for fault-tolerant object recognition

NASA Technical Reports Server (NTRS)

Wechsler, Harry

1988-01-01

The distributed associative memory (DAM) model is suggested for distributed and fault-tolerant computation as it relates to object recognition tasks. The fault-tolerance is with respect to geometrical distortions (scale and rotation), noisy inputs, occulsion/overlap, and memory faults. An experimental system was developed for fault-tolerant structure recognition which shows the feasibility of such an approach. The approach is futher extended to the problem of multisensory data integration and applied successfully to the recognition of colored polyhedral objects.

Deep kernel learning method for SAR image target recognition

NASA Astrophysics Data System (ADS)

Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

2017-10-01

With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

Infrared Ship Classification Using A New Moment Pattern Recognition Concept

NASA Astrophysics Data System (ADS)

Casasent, David; Pauly, John; Fetterly, Donald

1982-03-01

An analysis of the statistics of the moments and the conventional invariant moments shows that the variance of the latter become quite large as the order of the moments and the degree of invariance increases. Moreso, the need to whiten the error volume increases with the order and degree, but so does the computational load associated with computing the whitening operator. We thus advance a new estimation approach to the use of moments in pattern recog-nition that overcomes these problems. This work is supported by experimental verification and demonstration on an infrared ship pattern recognition problem. The computational load associated with our new algorithm is also shown to be very low.

A Taxonomy of 3D Occluded Objects Recognition Techniques

NASA Astrophysics Data System (ADS)

Soleimanizadeh, Shiva; Mohamad, Dzulkifli; Saba, Tanzila; Al-ghamdi, Jarallah Saleh

2016-03-01

The overall performances of object recognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring object recognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

PubMed Central

Siddiqi, Muhammad Hameed; Lee, Sungyoung; Lee, Young-Koo; Khan, Adil Mehmood; Truc, Phan Tran Ho

2013-01-01

Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER. PMID:24316568

3D face recognition based on multiple keypoint descriptors and sparse representation.

PubMed

Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

2014-01-01

Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm.

Effects of health and safety problem recognition on small business facility investment

PubMed Central

2013-01-01

Objectives This study involved a survey of the facility investment experiences, which was designed to recognize the importance of health and safety problems, and industrial accident prevention. Ultimately, we hope that small scale industries will create effective industrial accident prevention programs and facility investments. Methods An individual survey of businesses’ present physical conditions, recognition of the importance of the health and safety problems, and facility investment experiences for preventing industrial accidents was conducted. The survey involved 1,145 business operators or management workers in small business places with fewer than 50 workers in six industrial complexes. Results Regarding the importance of occupational health and safety problems (OHS), 54.1% said it was “very important”. Received technical and financial support, and industrial accidents that occurred during the past three years were recognized as highly important for OHS. In an investigation regarding facility investment experiences for industrial accident prevention, the largest factors were business size, greater numbers of industrial accidents, greater technical and financial support received, and greater recognition of the importance of the OHS. The related variables that decided facility investment for industry accident prevention in a logistic regression analysis were the experiences of business facilities where industrial accidents occurred during the past three years, received technical and financial support, and recognition of the OHS. Those considered very important were shown to be highly significant. Conclusions Recognition of health and safety issues was higher when small businesses had experienced industrial accidents or received financial support. The investment in industrial accidents was greater when health and safety issues were recognized as important. Therefore, the goal of small business health and safety projects is to prioritize health and safety issues in terms of business management and recognition of importance. Therefore, currently various support projects are being conducted. However, there are issues regarding the limitations of the target businesses and inadequacies in maintenance and follow-up. Overall, it is necessary to provide various incentives for onsite participation that can lead to increased recognition of health and safety issues and practical investments, while perfecting maintenance and follow up measures by thoroughly revising existing operating systems. PMID:24472180

Effects of health and safety problem recognition on small business facility investment.

PubMed

Park, Jisu; Jeong, Harin; Hong, Sujin; Park, Jong-Tae; Kim, Dae-Sung; Kim, Jongseo; Kim, Hae-Joon

2013-10-23

This study involved a survey of the facility investment experiences, which was designed to recognize the importance of health and safety problems, and industrial accident prevention. Ultimately, we hope that small scale industries will create effective industrial accident prevention programs and facility investments. An individual survey of businesses' present physical conditions, recognition of the importance of the health and safety problems, and facility investment experiences for preventing industrial accidents was conducted. The survey involved 1,145 business operators or management workers in small business places with fewer than 50 workers in six industrial complexes. Regarding the importance of occupational health and safety problems (OHS), 54.1% said it was "very important". Received technical and financial support, and industrial accidents that occurred during the past three years were recognized as highly important for OHS. In an investigation regarding facility investment experiences for industrial accident prevention, the largest factors were business size, greater numbers of industrial accidents, greater technical and financial support received, and greater recognition of the importance of the OHS. The related variables that decided facility investment for industry accident prevention in a logistic regression analysis were the experiences of business facilities where industrial accidents occurred during the past three years, received technical and financial support, and recognition of the OHS. Those considered very important were shown to be highly significant. Recognition of health and safety issues was higher when small businesses had experienced industrial accidents or received financial support. The investment in industrial accidents was greater when health and safety issues were recognized as important. Therefore, the goal of small business health and safety projects is to prioritize health and safety issues in terms of business management and recognition of importance. Therefore, currently various support projects are being conducted. However, there are issues regarding the limitations of the target businesses and inadequacies in maintenance and follow-up. Overall, it is necessary to provide various incentives for onsite participation that can lead to increased recognition of health and safety issues and practical investments, while perfecting maintenance and follow up measures by thoroughly revising existing operating systems.

Recognition of handprinted characters for automated cartography A progress report

NASA Technical Reports Server (NTRS)

Lybanon, M.; Brown, R. M.; Gronmeyer, L. K.

1980-01-01

A research program for developing handwritten character recognition techniques is reported. The generation of cartographic/hydrographic manuscripts is overviewed. The performance of hardware/software systems is discussed, along with future research problem areas and planned approaches.

Content analysis in information flows

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

Grusho, Alexander A.; Faculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow; Grusho, Nick A.

The paper deals with architecture of content recognition system. To analyze the problem the stochastic model of content recognition in information flows was built. We proved that under certain conditions it is possible to solve correctly a part of the problem with probability 1, viewing a finite section of the information flow. That means that good architecture consists of two steps. The first step determines correctly certain subsets of contents, while the second step may demand much more time for true decision.

Domain Regeneration for Cross-Database Micro-Expression Recognition

NASA Astrophysics Data System (ADS)

Zong, Yuan; Zheng, Wenming; Huang, Xiaohua; Shi, Jingang; Cui, Zhen; Zhao, Guoying

2018-05-01

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.

Emotional System for Military Target Identification

DTIC Science & Technology

2009-10-01

algorithm [23], and used it to solve a facial recognition problem. In other works [24,25], we explored the potential of using emotional neural...other application areas, such as security ( facial recognition ) and medical (blood cell identification), can be also efficiently used in military...Application of an emotional neural network to facial recognition . Neural Computing and Applications, 18(4), 309-320. [25] Khashman, A. (2009). Blood cell

Temporary banding of the gastroesophageal junction in the critically ill neonate with esophageal atresia and tracheoesophageal fistula.

PubMed

Fagelman, K M; Boyarsky, A

1985-09-01

Patients with esophageal atresia and a distal tracheoesophageal fistula with associated conditions contributing to decreased pulmonary compliance present special problems in management. In the face of positive pressure ventilation, the fistula acts as a vent preventing adequate ventilatory effort from reaching the lungs. A thoracic approach to ligate or divide the fistula carries with it a high mortality rate. A technique is described whereby a silicone rubber band is applied to the gastroesophageal junction to effectively occlude the esophagus. It is designed so that is can be adjusted or removed, without operative intervention, according to the patient's clinical course and growth.

Interactive object recognition assistance: an approach to recognition starting from target objects

NASA Astrophysics Data System (ADS)

Geisler, Juergen; Littfass, Michael

1999-07-01

Recognition of target objects in remotely sensed imagery required detailed knowledge about the target object domain as well as about mapping properties of the sensing system. The art of object recognition is to combine both worlds appropriately and to provide models of target appearance with respect to sensor characteristics. Common approaches to support interactive object recognition are either driven from the sensor point of view and address the problem of displaying images in a manner adequate to the sensing system. Or they focus on target objects and provide exhaustive encyclopedic information about this domain. Our paper discusses an approach to assist interactive object recognition based on knowledge about target objects and taking into account the significance of object features with respect to characteristics of the sensed imagery, e.g. spatial and spectral resolution. An `interactive recognition assistant' takes the image analyst through the interpretation process by indicating step-by-step the respectively most significant features of objects in an actual set of candidates. The significance of object features is expressed by pregenerated trees of significance, and by the dynamic computation of decision relevance for every feature at each step of the recognition process. In the context of this approach we discuss the question of modeling and storing the multisensorial/multispectral appearances of target objects and object classes as well as the problem of an adequate dynamic human-machine-interface that takes into account various mental models of human image interpretation.

Emotion recognition in girls with conduct problems.

PubMed

Schwenck, Christina; Gensthaler, Angelika; Romanos, Marcel; Freitag, Christine M; Schneider, Wolfgang; Taurines, Regina

2014-01-01

A deficit in emotion recognition has been suggested to underlie conduct problems. Although several studies have been conducted on this topic so far, most concentrated on male participants. The aim of the current study was to compare recognition of morphed emotional faces in girls with conduct problems (CP) with elevated or low callous-unemotional (CU+ vs. CU-) traits and a matched healthy developing control group (CG). Sixteen girls with CP-CU+, 16 girls with CP-CU- and 32 controls (mean age: 13.23 years, SD=2.33 years) were included. Video clips with morphed faces were presented in two runs to assess emotion recognition. Multivariate analysis of variance with the factors group and run was performed. Girls with CP-CU- needed more time than the CG to encode sad, fearful, and happy faces and they correctly identified sadness less often. Girls with CP-CU+ outperformed the other groups in the identification of fear. Learning effects throughout runs were the same for all groups except that girls with CP-CU- correctly identified fear less often in the second run compared to the first run. Results need to be replicated with comparable tasks, which might result in subgroup-specific therapeutic recommendations.

A model of traffic signs recognition with convolutional neural network

NASA Astrophysics Data System (ADS)

Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing

2016-10-01

In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.

A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

PubMed Central

Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control. PMID:24592183

Coherent conversion of the sunlight spectrum

NASA Technical Reports Server (NTRS)

Gustafson, T. K.

1982-01-01

Efforts related to the utilization of tunneling junctions for the conversion of radiative power to electrical power are reported. The theoretical foundations for these particular devices is presented along with a discussion of many of the practical problems associated with the implementation of such devices.

A Novel Junctional Tether Weave Technique for Adult Spinal Deformity: 2-Dimensional Operative Video.

PubMed

Buell, Thomas J; Mullin, Jeffrey P; Nguyen, James H; Taylor, Davis G; Garces, Juanita; Mazur, Marcus D; Buchholz, Avery L; Shaffrey, Mark E; Yen, Chun-Po; Shaffrey, Christopher I; Smith, Justin S

2018-06-05

Proximal junctional kyphosis (PJK) is a common problem after multilevel spine instrumentation for adult spinal deformity. Various anti-PJK techniques such as junctional tethers for ligamentous augmentation have been proposed. We present an operative video demonstrating technical nuances of junctional tether "weave" application. A 70-yr-old male with prior L2-S1 instrumented fusion presented with worsening back pain and posture. Imaging demonstrated pathological loss of lumbar lordosis (flat back deformity), proximal junctional failure, and pseudarthrosis. The patient had severe global and segmental sagittal malalignment, with sagittal vertical axis (SVA, C7-plumbline) measuring 22.3 cm, pelvic incidence (PI) 55°, lumbar lordosis (LL) 8° in kyphosis, pelvic tilt (PT) 30°, and thoracic kyphosis (TK) 6°. The patient gave informed consent for surgery and use of imaging for medical publication. Briefly, surgery first involved re-instrumentation with bilateral pedicle screws from T10 to S1. After right-sided iliac screw fixation (left-sided iliac screw fixation was not performed due to extensive prior iliac crest bone graft harvesting), we then completed a L2-3 Smith-Petersen osteotomy, extended L4 pedicle subtraction osteotomy, and L3-4 interbody arthrodesis with a 12° lordotic cage (9 × 14 × 40 mm). Cobalt Chromium rods were placed spanning the instrumentation bilaterally, and accessory supplemental rods spanning the PSO were attached. An anti-PJK junctional tether "weave" was then implemented using 4.5 mm polyethylene tape (Mersilene tape [Ethicon, Somerville, New Jersey]). Postoperative imaging demonstrated improved alignment (SVA 2.8 cm, PI 55°, LL 53°, PT 25°, TK 45°) and no significant neurological complications occurred during convalescence or at 6 mo postop.

Data-driven indexing mechanism for the recognition of polyhedral objects

NASA Astrophysics Data System (ADS)

McLean, Stewart; Horan, Peter; Caelli, Terry M.

1992-02-01

This paper is concerned with the problem of searching large model databases. To date, most object recognition systems have concentrated on the problem of matching using simple searching algorithms. This is quite acceptable when the number of object models is small. However, in the future, general purpose computer vision systems will be required to recognize hundreds or perhaps thousands of objects and, in such circumstances, efficient searching algorithms will be needed. The problem of searching a large model database is one which must be addressed if future computer vision systems are to be at all effective. In this paper we present a method we call data-driven feature-indexed hypothesis generation as one solution to the problem of searching large model databases.

The macrophage cytoskeleton acts as a contact sensor upon interaction with Entamoeba histolytica to trigger IL-1β secretion

PubMed Central

Moreau, France; Gorman, Hayley

2017-01-01

Entamoeba histolytica (Eh) is the causative agent of amebiasis, one of the major causes of dysentery-related morbidity worldwide. Recent studies have underlined the importance of the intercellular junction between Eh and host cells as a determinant in the pathogenesis of amebiasis. Despite the fact that direct contact and ligation between Eh surface Gal-lectin and EhCP-A5 with macrophage α5β1 integrin are absolute requirements for NLRP3 inflammasome activation and IL-1β release, many other undefined molecular events and downstream signaling occur at the interface of Eh and macrophage. In this study, we investigated the molecular events at the intercellular junction that lead to recognition of Eh through modulation of the macrophage cytoskeleton. Upon Eh contact with macrophages key cytoskeletal-associated proteins were rapidly post-translationally modified only with live Eh but not with soluble Eh proteins or fragments. Eh ligation with macrophages rapidly activated caspase-6 dependent cleavage of the cytoskeletal proteins talin, Pyk2 and paxillin and caused robust release of the pro-inflammatory cytokine, IL-1β. Macrophage cytoskeletal cleavages were dependent on Eh cysteine proteinases EhCP-A1 and EhCP-A4 but not EhCP-A5 based on pharmacological blockade of Eh enzyme inhibitors and EhCP-A5 deficient parasites. These results unravel a model where the intercellular junction between macrophages and Eh form an area of highly interacting proteins that implicate the macrophage cytoskeleton as a sensor for Eh contact that leads downstream to subsequent inflammatory immune responses. PMID:28837696

Cold rescue of the thermolabile tailspike intermediate at the junction between productive folding and off-pathway aggregation.

PubMed Central

Betts, S. D.; King, J.

1998-01-01

Off-pathway intermolecular interactions between partially folded polypeptide chains often compete with correct intramolecular interactions, resulting in self-association of folding intermediates into the inclusion body state. Intermediates for both productive folding and off-pathway aggregation of the parallel beta-coil tailspike trimer of phage P22 have been identified in vivo and in vitro using native gel electrophoresis in the cold. Aggregation of folding intermediates was suppressed when refolding was initiated and allowed to proceed for a short period at 0 degrees C prior to warming to 20 degrees C. Yields of refolded tailspike trimers exceeding 80% were obtained using this temperature-shift procedure, first described by Xie and Wetlaufer (1996, Protein Sci 5:517-523). We interpret this as due to stabilization of the thermolabile monomeric intermediate at the junction between productive folding and off-pathway aggregation. Partially folded monomers, a newly identified dimer, and the protrimer folding intermediates were populated in the cold. These species were electrophoretically distinguished from the multimeric intermediates populated on the aggregation pathway. The productive protrimer intermediate is disulfide bonded (Robinson AS, King J, 1997, Nat Struct Biol 4:450-455), while the multimeric aggregation intermediates are not disulfide bonded. The partially folded dimer appears to be a precursor to the disulfide-bonded protrimer. The results support a model in which the junctional partially folded monomeric intermediate acquires resistance to aggregation in the cold by folding further to a conformation that is activated for correct recognition and subunit assembly. PMID:9684883

Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning

NASA Astrophysics Data System (ADS)

Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

2016-07-01

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

Crystal structure of the Mus81-Eme1 complex.

PubMed

Chang, Jeong Ho; Kim, Jeong Joo; Choi, Jung Min; Lee, Jung Hoon; Cho, Yunje

2008-04-15

The Mus81-Eme1 complex is a structure-specific endonuclease that plays an important role in rescuing stalled replication forks and resolving the meiotic recombination intermediates in eukaryotes. We have determined the crystal structure of the Mus81-Eme1 complex. Both Mus81 and Eme1 consist of a central nuclease domain, two repeats of the helix-hairpin-helix (HhH) motif at their C-terminal region, and a linker helix. While each domain structure resembles archaeal XPF homologs, the overall structure is significantly different from those due to the structure of a linker helix. We show that a flexible intradomain linker that formed with 36 residues in the nuclease domain of Eme1 is essential for the recognition of DNA. We identified several basic residues lining the outer surface of the active site cleft of Mus81 that are involved in the interaction with a flexible arm of a nicked Holliday junction (HJ). These interactions might contribute to the optimal positioning of the opposite junction across the nick into the catalytic site, which provided the basis for the "nick and counternick" mechanism of Mus81-Eme1 and for the nicked HJ to be the favored in vitro substrate of this enzyme.

Group A Streptococcus tissue invasion by CD44-mediated cell signalling

NASA Astrophysics Data System (ADS)

Cywes, Colette; Wessels, Michael R.

2001-12-01

Streptococcus pyogenes (also known as group A Streptococcus, GAS), the agent of streptococcal sore throat and invasive soft-tissue infections, attaches to human pharyngeal or skin epithelial cells through specific recognition of its hyaluronic acid capsular polysaccharide by the hyaluronic-acid-binding protein CD44 (refs 1, 2). Because ligation of CD44 by hyaluronic acid can induce epithelial cell movement on extracellular matrix, we investigated whether molecular mimicry by the GAS hyaluronic acid capsule might induce similar cellular responses. Here we show that CD44-dependent GAS binding to polarized monolayers of human keratinocytes induced marked cytoskeletal rearrangements manifested by membrane ruffling and disruption of intercellular junctions. Transduction of the signal induced by GAS binding to CD44 on the keratinocyte surface involved Rac1 and the cytoskeleton linker protein ezrin, as well as tyrosine phosphorylation of cellular proteins. Studies of bacterial translocation in two models of human skin indicated that cell signalling triggered by interaction of the GAS capsule with CD44 opened intercellular junctions and promoted tissue penetration by GAS through a paracellular route. These results support a model of host cytoskeleton manipulation and tissue invasion by an extracellular bacterial pathogen.

Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning.

PubMed

Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

2016-07-13

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

Object recognition of real targets using modelled SAR images

NASA Astrophysics Data System (ADS)

Zherdev, D. A.

2017-12-01

In this work the problem of recognition is studied using SAR images. The algorithm of recognition is based on the computation of conjugation indices with vectors of class. The support subspaces for each class are constructed by exception of the most and the less correlated vectors in a class. In the study we examine the ability of a significant feature vector size reduce that leads to recognition time decrease. The images of targets form the feature vectors that are transformed using pre-trained convolutional neural network (CNN).

Gait recognition based on integral outline

NASA Astrophysics Data System (ADS)

Ming, Guan; Fang, Lv

2017-02-01

Biometric identification technology replaces traditional security technology, which has become a trend, and gait recognition also has become a hot spot of research because its feature is difficult to imitate and theft. This paper presents a gait recognition system based on integral outline of human body. The system has three important aspects: the preprocessing of gait image, feature extraction and classification. Finally, using a method of polling to evaluate the performance of the system, and summarizing the problems existing in the gait recognition and the direction of development in the future.

Sediment problems in urban areas

USGS Publications Warehouse

Guy, Harold P.

1970-01-01

One obstacle to a scientific recognition and an engineering solution to sediment-related environmental problems is that such problems are bound in conflicting and generally undefinable political and institutional restraints. Also, some of the difficulty may involve the fact that the scientist or engineer, because of his relatively narrow field of investigation, cannot always completely envision the less desirable effects of his work and communicate alternative solutions to the public. For example, the highway and motor-vehicle engineers have learned how to provide the means by which one can transport himself from one point to another with such great efficiency that a person's employment in this country is now commonly more than 5 miles from his residence. However, providing such efficient personal transport has created numerous serious environmental problems. Obstacles to recognition of and action to control sediment problems in and around urban areas are akin to other environmental problems with respect to the many scientific, engineering, economic, and social aspects.

Robust Radio Broadcast Monitoring Using a Multi-Band Spectral Entropy Signature

NASA Astrophysics Data System (ADS)

Camarena-Ibarrola, Antonio; Chávez, Edgar; Tellez, Eric Sadit

Monitoring media broadcast content has deserved a lot of attention lately from both academy and industry due to the technical challenge involved and its economic importance (e.g. in advertising). The problem pose a unique challenge from the pattern recognition point of view because a very high recognition rate is needed under non ideal conditions. The problem consist in comparing a small audio sequence (the commercial ad) with a large audio stream (the broadcast) searching for matches.

Degraded character recognition based on gradient pattern

NASA Astrophysics Data System (ADS)

Babu, D. R. Ramesh; Ravishankar, M.; Kumar, Manish; Wadera, Kevin; Raj, Aakash

2010-02-01

Degraded character recognition is a challenging problem in the field of Optical Character Recognition (OCR). The performance of an optical character recognition depends upon printed quality of the input documents. Many OCRs have been designed which correctly identifies the fine printed documents. But, very few reported work has been found on the recognition of the degraded documents. The efficiency of the OCRs system decreases if the input image is degraded. In this paper, a novel approach based on gradient pattern for recognizing degraded printed character is proposed. The approach makes use of gradient pattern of an individual character for recognition. Experiments were conducted on character image that is either digitally written or a degraded character extracted from historical documents and the results are found to be satisfactory.

Combinatorial approaches to gene recognition.

PubMed

Roytberg, M A; Astakhova, T V; Gelfand, M S

1997-01-01

Recognition of genes via exon assembly approaches leads naturally to the use of dynamic programming. We consider the general graph-theoretical formulation of the exon assembly problem and analyze in detail some specific variants: multicriterial optimization in the case of non-linear gene-scoring functions; context-dependent schemes for scoring exons and related procedures for exon filtering; and highly specific recognition of arbitrary gene segments, oligonucleotide probes and polymerase chain reaction (PCR) primers.

Methods and means of diagnostics of oncological diseases on the basis of pattern recognition: intelligent morphological systems - problems and solutions

NASA Astrophysics Data System (ADS)

Nikitaev, V. G.

2017-01-01

The development of methods of pattern recognition in modern intelligent systems of clinical cancer diagnosis are discussed. The histological (morphological) diagnosis - primary diagnosis for medical setting with cancer are investigated. There are proposed: interactive methods of recognition and structure of intellectual morphological complexes based on expert training-diagnostic and telemedicine systems. The proposed approach successfully implemented in clinical practice.

Does the cost function matter in Bayes decision rule?

PubMed

Schlü ter, Ralf; Nussbaum-Thom, Markus; Ney, Hermann

2012-02-01

In many tasks in pattern recognition, such as automatic speech recognition (ASR), optical character recognition (OCR), part-of-speech (POS) tagging, and other string recognition tasks, we are faced with a well-known inconsistency: The Bayes decision rule is usually used to minimize string (symbol sequence) error, whereas, in practice, we want to minimize symbol (word, character, tag, etc.) error. When comparing different recognition systems, we do indeed use symbol error rate as an evaluation measure. The topic of this work is to analyze the relation between string (i.e., 0-1) and symbol error (i.e., metric, integer valued) cost functions in the Bayes decision rule, for which fundamental analytic results are derived. Simple conditions are derived for which the Bayes decision rule with integer-valued metric cost function and with 0-1 cost gives the same decisions or leads to classes with limited cost. The corresponding conditions can be tested with complexity linear in the number of classes. The results obtained do not make any assumption w.r.t. the structure of the underlying distributions or the classification problem. Nevertheless, the general analytic results are analyzed via simulations of string recognition problems with Levenshtein (edit) distance cost function. The results support earlier findings that considerable improvements are to be expected when initial error rates are high.

Addressing the issue of insufficient information in data-based bridge health monitoring : final report.

DOT National Transportation Integrated Search

2015-11-01

One of the most efficient ways to solve the damage detection problem using the statistical pattern recognition : approach is that of exploiting the methods of outlier analysis. Cast within the pattern recognition framework, : damage detection assesse...

Microcomputers and Preschoolers.

ERIC Educational Resources Information Center

Evans, Dina

Preschool children can benefit by working with microcomputers. Thinking skills are enhanced by software games that focus on logic, memory, problem solving, and pattern recognition. Counting, sequencing, and matching games develop mathematics skills, and word games focusing on basic letter symbol and word recognition develop language skills.…

Mental health problems of undocumented migrants in the Netherlands: A qualitative exploration of recognition, recording, and treatment by general practitioners.

PubMed

Teunissen, Erik; Van Bavel, Eric; Van Den Driessen Mareeuw, Francine; Macfarlane, Anne; Van Weel-Baumgarten, Evelyn; Van Den Muijsenbergh, Maria; Van Weel, Chris

2015-06-01

To explore the views and experiences of general practitioners (GPs) in relation to recognition, recording, and treatment of mental health problems of undocumented migrants (UMs), and to gain insight in the reasons for under-registration of mental health problems in the electronic medical records. Qualitative study design with semi-structured interviews using a topic guide. Sixteen GPs in the Netherlands with clinical expertise in the care of UMs. GPs recognized many mental health problems in UMs. Barriers that prevented them from recording these problems and from delivering appropriate care were their low consultation rates, physical presentation of mental health problems, high number of other problems, the UM's lack of trust towards health care professionals, and cultural differences in health beliefs and language barriers. Referrals to mental health care organizations were often seen as problematic by GPs. To overcome these barriers, GPs provided personalized care as far as possible, referred to other primary care professionals such as social workers or mental health care nurses in their practice, and were a little less restrictive in prescribing psychotropics than guidelines recommended. GPs experienced a variety of barriers in engaging with UMs when identifying or suspecting mental health problems. This explains why there is a gap between the high recognition of mental health problems and the low recording of these problems in general practice files. It is recommended that GPs address mental health problems more actively, strive for continuity of care in order to gain trust of the UMs, and look for opportunities to provide mental care that is accessible and acceptable for UMs.

Visible light electroluminescent diodes of indium-gallium phosphide

NASA Technical Reports Server (NTRS)

Clough, R.; Richman, D.; Tietjen, J.

1970-01-01

Vapor deposition and acceptor impurity diffusion techniques are used to prepare indium-gallium phosphide junctions. Certain problems in preparation are overcome by altering gas flow conditions and by increasing the concentration of phosphine in the gas. A general formula is given for the alloy's composition.

Report on Project to Characterize Multi-Junction Solar Cells in the Stratosphere using Low-Cost Balloon and Communication Technologies

NASA Technical Reports Server (NTRS)

Mirza, Ali; Sant, David; Woodyard, James R.; Johnston, Richard R.; Brown, William J.

2002-01-01

Balloon, control and communication technologies are under development in our laboratory for testing multi-junction solar cells in the stratosphere to achieve near AM0 conditions. One flight, Suntracker I, has been carried out reported earlier. We report on our efforts in preparation for a second flight, Suntracker II, that was aborted due to hardware problems. The package for Suntracker I system has been modified to include separate electronics and battery packs for the 70 centimeter and 2 meter systems. The collimator control system and motor gearboxes have been redesigned to address problems with the virtual stops and backlash. Surface mount technology on a printed circuit board was used in place of the through-hole prototype circuit in efforts to reduce weight and size, and improve reliability. A mobile base station has been constructed that includes a 35' tower with a two axis rotator and multi-element yagi antennas. Modifications in Suntracker I and the factors that lead to aborting Suntracker II are discussed.

TOPLHA and ALOHA: comparison between Lower Hybrid wave coupling codes

NASA Astrophysics Data System (ADS)

Meneghini, Orso; Hillairet, J.; Goniche, M.; Bilato, R.; Voyer, D.; Parker, R.

2008-11-01

TOPLHA and ALOHA are wave coupling simulation tools for LH antennas. Both codes are able to account for realistic 3D antenna geometries and use a 1D plasma model. In the framework of a collaboration between MIT and CEA laboratories, the two codes have been extensively compared. In TOPLHA the EM problem is self consistently formulated by means of a set of multiple coupled integral equations having as domain the triangles of the meshed antenna surface. TOPLHA currently uses the FELHS code for modeling the plasma response. ALOHA instead uses a mode matching approach and its own plasma model. Comparisons have been done for several plasma scenarios on different antenna designs: an array of independent waveguides, a multi-junction antenna and a passive/active multi-junction antenna. When simulating the same geometry and plasma conditions the two codes compare remarkably well both for the reflection coefficients and for the launched spectra. The different approach of the two codes to solve the same problem strengthens the confidence in the final results.

Crystal structures of the SAM-III/SMK riboswitch reveal the SAM-dependent translation inhibition mechanism

PubMed Central

Lu, Changrui; Smith, Angela M; Fuchs, Ryan T; Ding, Fang; Rajashankar, Kanagalaghatta; Henkin, Tina M; Ke, Ailong

2011-01-01

Three distinct classes of S-adenosyl-l-methionine (SAM)-responsive riboswitches have been identified that regulate bacterial gene expression at the levels of transcription attenuation or translation inhibition. The SMK box (SAM-III) translational riboswitch has been identified in the SAM synthetase gene in members of the Lactobacillales. Here we report the 2.2-Å crystal structure of the Enterococcus faecalis SMK box riboswitch. The Y-shaped riboswitch organizes its conserved nucleotides around a three-way junction for SAM recognition. The Shine-Dalgarno sequence, which is sequestered by base-pairing with the anti–Shine-Dalgarno sequence in response to SAM binding, also directly participates in SAM recognition. The riboswitch makes extensive interactions with the adenosine and sulfonium moieties of SAM but does not appear to recognize the tail of the methionine moiety. We captured a structural snapshot of the SMK box riboswitch sampling the near-cognate ligand S-adenosyl-l-homocysteine (SAH) in which SAH was found to adopt an alternative conformation and fails to make several key interactions. PMID:18806797

Crystal structures of the SAM-III/S[subscript MK] riboswitch reveal the SAM-dependent translation inhibition mechanism

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

Lu, C.; Smith, A.M.; Fuchs, R.T.

2010-01-07

Three distinct classes of S-adenosyl-L-methionine (SAM)-responsive riboswitches have been identified that regulate bacterial gene expression at the levels of transcription attenuation or translation inhibition. The SMK box (SAM-III) translational riboswitch has been identified in the SAM synthetase gene in members of the Lactobacillales. Here we report the 2.2-{angstrom} crystal structure of the Enterococcus faecalis SMK box riboswitch. The Y-shaped riboswitch organizes its conserved nucleotides around a three-way junction for SAM recognition. The Shine-Dalgarno sequence, which is sequestered by base-pairing with the anti-Shine-Dalgarno sequence in response to SAM binding, also directly participates in SAM recognition. The riboswitch makes extensive interactions withmore » the adenosine and sulfonium moieties of SAM but does not appear to recognize the tail of the methionine moiety. We captured a structural snapshot of the SMK box riboswitch sampling the near-cognate ligand S-adenosyl-L-homocysteine (SAH) in which SAH was found to adopt an alternative conformation and fails to make several key interactions.« less

Structural basis of RNA folding and recognition in an AMP-RNA aptamer complex.

PubMed

Jiang, F; Kumar, R A; Jones, R A; Patel, D J

1996-07-11

The catalytic properties of RNA and its well known role in gene expression and regulation are the consequence of its unique solution structures. Identification of the structural determinants of ligand recognition by RNA molecules is of fundamental importance for understanding the biological functions of RNA, as well as for the rational design of RNA Sequences with specific catalytic activities. Towards this latter end, Szostak et al. used in vitro selection techniques to isolate RNA sequences ('aptamers') containing a high-affinity binding site for ATP, the universal currency of cellular energy, and then used this motif to engineer ribozymes with polynucleotide kinase activity. Here we present the solution structure, as determined by multidimensional NMR spectroscopy and molecular dynamics calculations, of both uniformly and specifically 13C-, 15N-labelled 40-mer RNA containing the ATP-binding motif complexed with AMP. The aptamer adopts an L-shaped structure with two nearly orthogonal stems, each capped proximally by a G x G mismatch pair, binding the AMP ligand at their junction in a GNRA-like motif.

Deep diode arrays for X-ray detection

NASA Technical Reports Server (NTRS)

Zemel, J. N.

1984-01-01

Temperature gradient zone melting process was used to form p-n junctions in bulk of high purity silicon wafers. These diodes were patterned to form arrays for X-ray spectrometers. The whole fabrication processes for these X-ray detectors are reviewed in detail. The p-n junctions were evaluated by (1) the dark diode I-V measurements, (2) the diode C sub I - V measurements, and (3) the MOS C-V measurements. The results showed that these junctions were linearly graded in charge distribution with low reverse bias leakage current flowing through them (few nA at -10 volts). The X-ray detection experiments showed that an FWHM of 500 eV was obtained from these diodes with a small bias of just -5 volts (for X-ray source Fe55). A theoretical model was proposed to explain the extra peaks found in the energy spectra and a very interesting point - cross talk effect was pointed out. This might be a solution to the problem of making really high resolution X-ray spectrometers.

(abstract) PV Technology for Low Intensity, Low Temperature (LILT) Applications

NASA Technical Reports Server (NTRS)

Stella, Paul M.; Pool, Frederick S.; Nicolet, Marc A.; Iles, Peter A.

1994-01-01

As a result of the recent NASA emphasis on smaller, lower cost space missions, PV is now being considered for a number of missions operating at solar distances of 3 AU or greater. In the past, many of these missions would utilize an RTG (radioisotope thermoelectric generator). Historically, silicon solar cell behavior at these distances has been compromised by a number of mechanisms including shunting, nonohmic back contacts, and the 'broken knee' curve shape. The former two can usually be neglected for modern silicon cells, but the latter has not been eliminated. This problem has been identified with localized diffusion at the top contact/silicon interface which leads to structural changes at the local junction. This is believed to create a resistive metal-semiconductor-like (MSL) interface in parallel with the junction which results in the characteristic forms of the LILT (low intensity, low temperature) 'broken knee'. This paper discusses a TaSiN contact barrier that will prevent the MSL structure in the junction.

PV Technology for Low Intensity, Low Temperature (LILT) Applications

NASA Technical Reports Server (NTRS)

Stella, Paul M.; Pool, Frederick S.; Nicolet, Marc A.; Iles, Peter A.

1994-01-01

As a result of the recent NASA emphasis on smaller, lower cost space missions, PV is now being considered for a number of missions operating at solar distances of 3 AU or greater. In the past, many of these missions would utilize an RTG (radioisotope thermoelectric generator). Historically, silicon solar cell behavior at these distances has been compromised by a number of mechanisms including shunting, nonohmic back contacts, and the 'broken knee' curve shape. The former two can usually be neglected for modern silicon cells, but the latter has not been eliminated. This problem has been identified with localized diffusion at the top contact/silicon interface which leads to structural changes at the local junction. This is believed to create a resistive metal-semiconductor-like (MSL) interface in parallel with the junction which results in the characteristic forms of the LILT (low intensity, low temperature) 'broken knee'. This paper discusses a TaSiN contact barrier that will prevent the MSL structure in the junction.

Coherent and dissipative transport in a Josephson junction between fermionic superfluids of 6Li atoms

NASA Astrophysics Data System (ADS)

Neri, Elettra; Scazza, Francesco; Roati, Giacomo

2018-04-01

Quantum systems out of equilibrium offer the possibility of understanding intriguing and challenging problems in modern physics. Studying transport properties is not only valuable to unveil fundamental properties of quantum matter but it is also an excellent tool for developing new quantum devices which inherently employ quantum-mechanical effects. In this contribution, we present our experimental studies on quantum transport using ultracold Fermi gases of 6Li atoms. We realize the analogous of a Josephson junction by bisecting fermionic superfluids by a thin optical barrier. We observe coherent dynamics in both the population and in the relative phase between the two reservoirs. For critical parameters, the superfluid dynamics exhibits both coherent and resistive flow due to phase-slippage events manifesting as vortices propagating into the bulk. We uncover also a regime of strong dissipation where the junction operation is irreversibly affected by vortex proliferation. Our studies open new directions for investigating dissipation and superfluid transport in strongly correlated fermionic systems.

Chinese Herbal Medicine Image Recognition and Retrieval by Convolutional Neural Network

PubMed Central

Sun, Xin; Qian, Huinan

2016-01-01

Chinese herbal medicine image recognition and retrieval have great potential of practical applications. Several previous studies have focused on the recognition with hand-crafted image features, but there are two limitations in them. Firstly, most of these hand-crafted features are low-level image representation, which is easily affected by noise and background. Secondly, the medicine images are very clean without any backgrounds, which makes it difficult to use in practical applications. Therefore, designing high-level image representation for recognition and retrieval in real world medicine images is facing a great challenge. Inspired by the recent progress of deep learning in computer vision, we realize that deep learning methods may provide robust medicine image representation. In this paper, we propose to use the Convolutional Neural Network (CNN) for Chinese herbal medicine image recognition and retrieval. For the recognition problem, we use the softmax loss to optimize the recognition network; then for the retrieval problem, we fine-tune the recognition network by adding a triplet loss to search for the most similar medicine images. To evaluate our method, we construct a public database of herbal medicine images with cluttered backgrounds, which has in total 5523 images with 95 popular Chinese medicine categories. Experimental results show that our method can achieve the average recognition precision of 71% and the average retrieval precision of 53% over all the 95 medicine categories, which are quite promising given the fact that the real world images have multiple pieces of occluded herbal and cluttered backgrounds. Besides, our proposed method achieves the state-of-the-art performance by improving previous studies with a large margin. PMID:27258404

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem

PubMed Central

Liu, Xunying; Zhang, Chao; Woodland, Phil; Fonteneau, Elisabeth

2017-01-01

There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental ‘machine states’, generated as the ASR analysis progresses over time, to the incremental ‘brain states’, measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain. PMID:28945744

Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen Identification

PubMed Central

Tcheng, David K.; Nayak, Ashwin K.; Fowlkes, Charless C.; Punyasena, Surangi W.

2016-01-01

Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based “pollen spotting” model to segment pollen grains from the slide background. We next tested ARLO’s ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems. PMID:26867017

3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation

PubMed Central

Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

2014-01-01

Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm. PMID:24940876

Diagnosing plant problems

Treesearch

Cheryl A. Smith

2008-01-01

Diagnosing Christmas tree problems can be a challenge, requiring a basic knowledge of plant culture and physiology, the effect of environmental influences on plant health, and the ability to identify the possible causes of plant problems. Developing a solution or remedy to the problem depends on a proper diagnosis, a process that requires recognition of a problem and...

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

PubMed Central

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

Gender recognition from vocal source

NASA Astrophysics Data System (ADS)

Sorokin, V. N.; Makarov, I. S.

2008-07-01

Efficiency of automatic recognition of male and female voices based on solving the inverse problem for glottis area dynamics and for waveform of the glottal airflow volume velocity pulse is studied. The inverse problem is regularized through the use of analytical models of the voice excitation pulse and of the dynamics of the glottis area, as well as the model of one-dimensional glottal airflow. Parameters of these models and spectral parameters of the volume velocity pulse are considered. The following parameters are found to be most promising: the instant of maximum glottis area, the maximum derivative of the area, the slope of the spectrum of the glottal airflow volume velocity pulse, the amplitude ratios of harmonics of this spectrum, and the pitch. On the plane of the first two main components in the space of these parameters, an almost twofold decrease in the classification error relative to that for the pitch alone is attained. The male voice recognition probability is found to be 94.7%, and the female voice recognition probability is 95.9%.

Prevalence and Recognition of Depressive Disorder in Three Medical Outpatient Departments of General Hospitals in Beijing, China.

PubMed

Liu, Chang; Liu, Meiyan; Jiang, Ronghuan; Ma, Hong; Wu, Xiamin; Luan, Shuxin; He, Yanling; Wei, Jing; Bai, Wenpei

2016-07-01

This purpose of this study was to explore the prevalence and recognition of depressive disorders in cardiology, gastroenterology, and neurology outpatient departments of general hospitals. Patients screened with a Hospital Anxiety and Depression Scale score of 8 or higher were interviewed by psychiatrists using Mini-International Neuropsychiatric Interview (MINI). Prevalence of depressive disorders within the cohort was determined, sociodemographic data were analyzed for correlations to a depression diagnosis, and comparisons between the surveys and the clinical diagnosis were done to assess recognition of depressive disorders by physicians. Of the patients screened for this study (1552 cases), 12.8% were diagnosed with depressive disorders by MINI, with major depressive disorder, depression due to general medical conditions, and dysthymia having prevalence values of 10.8%, 1.4%, and 0.6%, respectively. As compared with MINI, physicians only recognized 27.6% of any of the depressive disorders. Among the complaints examined, both mood problems and sleeping problems predicted the probability of recognition.

An evolution based biosensor receptor DNA sequence generation algorithm.

PubMed

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

2010-01-01

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

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-01-01

Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at . Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition. PMID:17570145

Development of an Autonomous Face Recognition Machine.

DTIC Science & Technology

1986-12-08

This approach, like Baron’s, would be a very time consuming task. The problem of locating a face in Bromley’s work was the least complex of the three...top level design and the development and design decisions that were made in developing the Autonomous Face Recognition Machine (AFRM). The chapter is...images within a digital image. The second sectio examines the algorithm used in performing face recognition. The decision to divide the development

Study on Performance of Intersection Around The Underpass Using Micro Simulation Program

NASA Astrophysics Data System (ADS)

Arliansyah, J.; Bawono, R. T.

2018-03-01

In order to overcome the problems of congestion at the major intersections in Palembang City, there have been grade separation constructions in the form of flyover or underpass. One of them is the intersection of Patal Pusri Underpass. The smooth traffic due to the underpass construction needs to be balanced by the arrangement management at the intersections located close to the underpass to get the best network performance since both affect each other performance. The Taman Kenten and Seduduk Putih junctions which are just 569 and 226 meters from the underpass of Patal Pusri has to be analyzed for its needs of management and traffic arrangements to reduce its impact on the performance of the intersection of Patal Pusri or vice versa. Some alternatives of management and traffic arrangements were developed to get the best solutions and the Vissim 8.00 microsimulation program was used to evaluate the performance of intersections in the network where the compared parameters are the value of the queue length and average delay. The results of the analysis and the conducted modeling show that the best solution to optimize the performance of the two junctions are to make geometric changes and diversion of traffic flow at Seduduk Putih Junction and making geometric changes at Taman Kenten Junction.

Reasoning on the Self-Organizing Incremental Associative Memory for Online Robot Path Planning

NASA Astrophysics Data System (ADS)

Kawewong, Aram; Honda, Yutaro; Tsuboyama, Manabu; Hasegawa, Osamu

Robot path-planning is one of the important issues in robotic navigation. This paper presents a novel robot path-planning approach based on the associative memory using Self-Organizing Incremental Neural Networks (SOINN). By the proposed method, an environment is first autonomously divided into a set of path-fragments by junctions. Each fragment is represented by a sequence of preliminarily generated common patterns (CPs). In an online manner, a robot regards the current path as the associative path-fragments, each connected by junctions. The reasoning technique is additionally proposed for decision making at each junction to speed up the exploration time. Distinct from other methods, our method does not ignore the important information about the regions between junctions (path-fragments). The resultant number of path-fragments is also less than other method. Evaluation is done via Webots physical 3D-simulated and real robot experiments, where only distance sensors are available. Results show that our method can represent the environment effectively; it enables the robot to solve the goal-oriented navigation problem in only one episode, which is actually less than that necessary for most of the Reinforcement Learning (RL) based methods. The running time is proved finite and scales well with the environment. The resultant number of path-fragments matches well to the environment.

Studies of recognition with multitemporal remote sensor data

NASA Technical Reports Server (NTRS)

Malila, W. A.; Hieber, R. H.; Cicone, R. C.

1975-01-01

Characteristics of multitemporal data and their use in recognition processing were investigated. Principal emphasis was on satellite data collected by the LANDSAT multispectral scanner and on temporal changes throughout a growing season. The effects of spatial misregistration on recognition performance with multitemporal data were examined. A capability to compute probabilities of detection and false alarm was developed and used with simulated distributions for misregistered pixels. Wheat detection was found to be degraded and false alarms increased by misregistration effects. Multitemporal signature characteristics and multitemporal recognition processing were studied to gain insights into problems associated with this approach and possible improvements. Recognition performance with one multitemporal data set displayed marked improvements over results from single-time data.

Iris recognition based on robust principal component analysis

NASA Astrophysics Data System (ADS)

Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

2014-11-01

Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

Recognition of facial emotion and affective prosody in children with ASD (+ADHD) and their unaffected siblings.

PubMed

Oerlemans, Anoek M; van der Meer, Jolanda M J; van Steijn, Daphne J; de Ruiter, Saskia W; de Bruijn, Yvette G E; de Sonneville, Leo M J; Buitelaar, Jan K; Rommelse, Nanda N J

2014-05-01

Autism is a highly heritable and clinically heterogeneous neuropsychiatric disorder that frequently co-occurs with other psychopathologies, such as attention-deficit/hyperactivity disorder (ADHD). An approach to parse heterogeneity is by forming more homogeneous subgroups of autism spectrum disorder (ASD) patients based on their underlying, heritable cognitive vulnerabilities (endophenotypes). Emotion recognition is a likely endophenotypic candidate for ASD and possibly for ADHD. Therefore, this study aimed to examine whether emotion recognition is a viable endophenotypic candidate for ASD and to assess the impact of comorbid ADHD in this context. A total of 90 children with ASD (43 with and 47 without ADHD), 79 ASD unaffected siblings, and 139 controls aged 6-13 years, were included to test recognition of facial emotion and affective prosody. Our results revealed that the recognition of both facial emotion and affective prosody was impaired in children with ASD and aggravated by the presence of ADHD. The latter could only be partly explained by typical ADHD cognitive deficits, such as inhibitory and attentional problems. The performance of unaffected siblings could overall be considered at an intermediate level, performing somewhat worse than the controls and better than the ASD probands. Our findings suggest that emotion recognition might be a viable endophenotype in ASD and a fruitful target in future family studies of the genetic contribution to ASD and comorbid ADHD. Furthermore, our results suggest that children with comorbid ASD and ADHD are at highest risk for emotion recognition problems.

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition.

PubMed

Janidarmian, Majid; Roshan Fekr, Atena; Radecka, Katarzyna; Zilic, Zeljko

2017-03-07

Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

Operator for object recognition and scene analysis by estimation of set occupancy with noisy and incomplete data sets

NASA Astrophysics Data System (ADS)

Rees, S. J.; Jones, Bryan F.

1992-11-01

Once feature extraction has occurred in a processed image, the recognition problem becomes one of defining a set of features which maps sufficiently well onto one of the defined shape/object models to permit a claimed recognition. This process is usually handled by aggregating features until a large enough weighting is obtained to claim membership, or an adequate number of located features are matched to the reference set. A requirement has existed for an operator or measure capable of a more direct assessment of membership/occupancy between feature sets, particularly where the feature sets may be defective representations. Such feature set errors may be caused by noise, by overlapping of objects, and by partial obscuration of features. These problems occur at the point of acquisition: repairing the data would then assume a priori knowledge of the solution. The technique described in this paper offers a set theoretical measure for partial occupancy defined in terms of the set of minimum additions to permit full occupancy and the set of locations of occupancy if such additions are made. As is shown, this technique permits recognition of partial feature sets with quantifiable degrees of uncertainty. A solution to the problems of obscuration and overlapping is therefore available.

Tensor manifold-based extreme learning machine for 2.5-D face recognition

NASA Astrophysics Data System (ADS)

Chong, Lee Ying; Ong, Thian Song; Teoh, Andrew Beng Jin

2018-01-01

We explore the use of the Gabor regional covariance matrix (GRCM), a flexible matrix-based descriptor that embeds the Gabor features in the covariance matrix, as a 2.5-D facial descriptor and an effective means of feature fusion for 2.5-D face recognition problems. Despite its promise, matching is not a trivial problem for GRCM since it is a special instance of a symmetric positive definite (SPD) matrix that resides in non-Euclidean space as a tensor manifold. This implies that GRCM is incompatible with the existing vector-based classifiers and distance matchers. Therefore, we bridge the gap of the GRCM and extreme learning machine (ELM), a vector-based classifier for the 2.5-D face recognition problem. We put forward a tensor manifold-compliant ELM and its two variants by embedding the SPD matrix randomly into reproducing kernel Hilbert space (RKHS) via tensor kernel functions. To preserve the pair-wise distance of the embedded data, we orthogonalize the random-embedded SPD matrix. Hence, classification can be done using a simple ridge regressor, an integrated component of ELM, on the random orthogonal RKHS. Experimental results show that our proposed method is able to improve the recognition performance and further enhance the computational efficiency.

Text recognition and correction for automated data collection by mobile devices

NASA Astrophysics Data System (ADS)

Ozarslan, Suleyman; Eren, P. Erhan

2014-03-01

Participatory sensing is an approach which allows mobile devices such as mobile phones to be used for data collection, analysis and sharing processes by individuals. Data collection is the first and most important part of a participatory sensing system, but it is time consuming for the participants. In this paper, we discuss automatic data collection approaches for reducing the time required for collection, and increasing the amount of collected data. In this context, we explore automated text recognition on images of store receipts which are captured by mobile phone cameras, and the correction of the recognized text. Accordingly, our first goal is to evaluate the performance of the Optical Character Recognition (OCR) method with respect to data collection from store receipt images. Images captured by mobile phones exhibit some typical problems, and common image processing methods cannot handle some of them. Consequently, the second goal is to address these types of problems through our proposed Knowledge Based Correction (KBC) method used in support of the OCR, and also to evaluate the KBC method with respect to the improvement on the accurate recognition rate. Results of the experiments show that the KBC method improves the accurate data recognition rate noticeably.

Development of coffee maker service robot using speech and face recognition systems using POMDP

NASA Astrophysics Data System (ADS)

Budiharto, Widodo; Meiliana; Santoso Gunawan, Alexander Agung

2016-07-01

There are many development of intelligent service robot in order to interact with user naturally. This purpose can be done by embedding speech and face recognition ability on specific tasks to the robot. In this research, we would like to propose Intelligent Coffee Maker Robot which the speech recognition is based on Indonesian language and powered by statistical dialogue systems. This kind of robot can be used in the office, supermarket or restaurant. In our scenario, robot will recognize user's face and then accept commands from the user to do an action, specifically in making a coffee. Based on our previous work, the accuracy for speech recognition is about 86% and face recognition is about 93% in laboratory experiments. The main problem in here is to know the intention of user about how sweetness of the coffee. The intelligent coffee maker robot should conclude the user intention through conversation under unreliable automatic speech in noisy environment. In this paper, this spoken dialog problem is treated as a partially observable Markov decision process (POMDP). We describe how this formulation establish a promising framework by empirical results. The dialog simulations are presented which demonstrate significant quantitative outcome.

Bilevel Model-Based Discriminative Dictionary Learning for Recognition.

PubMed

Zhou, Pan; Zhang, Chao; Lin, Zhouchen

2017-03-01

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the l 0 or l 1 norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn-Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.

A Grassmann graph embedding framework for gait analysis

NASA Astrophysics Data System (ADS)

Connie, Tee; Goh, Michael Kah Ong; Teoh, Andrew Beng Jin

2014-12-01

Gait recognition is important in a wide range of monitoring and surveillance applications. Gait information has often been used as evidence when other biometrics is indiscernible in the surveillance footage. Building on recent advances of the subspace-based approaches, we consider the problem of gait recognition on the Grassmann manifold. We show that by embedding the manifold into reproducing kernel Hilbert space and applying the mechanics of graph embedding on such manifold, significant performance improvement can be obtained. In this work, the gait recognition problem is studied in a unified way applicable for both supervised and unsupervised configurations. Sparse representation is further incorporated in the learning mechanism to adaptively harness the local structure of the data. Experiments demonstrate that the proposed method can tolerate variations in appearance for gait identification effectively.

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

NASA Astrophysics Data System (ADS)

Guo, Shuhang; Wang, Jian; Wang, Tong

2017-09-01

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

Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques

USDA-ARS?s Scientific Manuscript database

The combination of gas chromatography and pattern recognition (GC/PR) analysis is a powerful tool for investigating complicated biological problems. Clustering, mapping, discriminant development, etc. are necessary to analyze realistically large chromatographic data sets and to seek meaningful relat...

Black and White Parents' Willingness to Seek Help for Children's Internalizing and Externalizing Symptoms.

PubMed

Thurston, Idia B; Hardin, Robin; Decker, Kristina; Arnold, Trisha; Howell, Kathryn H; Phares, Vicky

2018-01-01

Understanding social and environmental factors that contribute to parental help-seeking intentions is an important step in addressing service underutilization for children in need of treatment. This study examined factors that contribute to parents' intentions to seek formal and informal help for child psychopathology (anxiety and attention-deficit/hyperactivity disorder [ADHD]). A total of 251 parents (N = 128 mothers, N = 123 fathers; 49% Black, 51% White) read 3 vignettes describing children with anxiety, ADHD, and no diagnosis. Measures of problem recognition, perceived barriers, and formal (pediatricians, psychologists, teachers) and informal (religious leaders, family/friends, self-help) help seeking were completed. Four separate hierarchical logistic regression models were used to examine parental help-seeking likelihood from formal and informal sources for internalizing and externalizing symptoms. Predictors were socioeconomic status, parent race, age, and sex, parent problem recognition (via study vignettes), and perceived barriers to mental health service utilization. Mothers were more likely than fathers to seek help from pediatricians, psychologists, teachers, and religious leaders for child anxiety and pediatricians, religious leaders, and self-help resources for child ADHD. Black parents were more likely to seek help from religious leaders and White parents were more likely to use self-help resources. Problem recognition was associated with greater intentions to seek help from almost all formal and informal sources (except from friends/family). Understanding factors that contribute to parental help seeking for child psychopathology is critical for increasing service utilization and reducing the negative effects of mental health problems. This study highlights the importance of decreasing help-seeking barriers and increasing problem recognition to improve health equity. © 2017 Wiley Periodicals, Inc.

Optimal pattern synthesis for speech recognition based on principal component analysis

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Race, Ethnicity, and Eating Disorder Recognition by Peers

PubMed Central

Sala, Margarita; Reyes-Rodríguez, Mae Lynn; Bulik, Cynthia M.; Bardone-Cone, Anna

2013-01-01

We investigated racial/ethnic stereotyping in the recognition and referral of eating disorders with 663 university students. We explored responses to problem and eating disorder recognition, and health care referral after reading a vignette concerning a patient of different race/ethnic background presenting with eating disorders. A series of three 4 × 3 ANOVAs revealed significant main effects for eating disorder across all three outcome variables. There were no significant main effects across the four different race/ethnicity conditions and no significant race by condition interactions. Lack of general eating disorder recognition and health care referral by student participants were found. PMID:24044598

FaceIt: face recognition from static and live video for law enforcement

NASA Astrophysics Data System (ADS)

Atick, Joseph J.; Griffin, Paul M.; Redlich, A. N.

1997-01-01

Recent advances in image and pattern recognition technology- -especially face recognition--are leading to the development of a new generation of information systems of great value to the law enforcement community. With these systems it is now possible to pool and manage vast amounts of biometric intelligence such as face and finger print records and conduct computerized searches on them. We review one of the enabling technologies underlying these systems: the FaceIt face recognition engine; and discuss three applications that illustrate its benefits as a problem-solving technology and an efficient and cost effective investigative tool.

A study of fuzzy logic ensemble system performance on face recognition problem

NASA Astrophysics Data System (ADS)

Polyakova, A.; Lipinskiy, L.

2017-02-01

Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.

Handwritten digits recognition based on immune network

NASA Astrophysics Data System (ADS)

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

2011-11-01

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

Transfer Learning for Activity Recognition: A Survey

PubMed Central

Cook, Diane; Feuz, Kyle D.; Krishnan, Narayanan C.

2013-01-01

Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed. PMID:24039326

Image registration under translation and rotation in two-dimensional planes using Fourier slice theorem.

PubMed

Pohit, M; Sharma, J

2015-05-10

Image recognition in the presence of both rotation and translation is a longstanding problem in correlation pattern recognition. Use of log polar transform gives a solution to this problem, but at a cost of losing the vital phase information from the image. The main objective of this paper is to develop an algorithm based on Fourier slice theorem for measuring the simultaneous rotation and translation of an object in a 2D plane. The algorithm is applicable for any arbitrary object shift for full 180° rotation.

Parent Recognition and Responses to Developmental Concerns in Young Children

ERIC Educational Resources Information Center

Marshall, Jennifer; Coulter, Martha L.; Gorski, Peter A.; Ewing, Aldenise

2016-01-01

This mixed-methods study examined influences, factors, and processes associated with parental recognition and appraisal of developmental concerns among 23 English- and Spanish-speaking parents of young children with signs of developmental or behavioral problems. Participants shared their experiences through in-depth interviews or focus groups and…

Pattern Recognition by Retina-Like Devices.

ERIC Educational Resources Information Center

Weiman, Carl F. R.; Rothstein, Jerome

This study has investigated some pattern recognition capabilities of devices consisting of arrays of cooperating elements acting in parallel. The problem of recognizing straight lines in general position on the quadratic lattice has been completely solved by applying parallel acting algorithms to a special code for lines on the lattice. The…

Concept Recognition in an Automatic Text-Processing System for the Life Sciences.

ERIC Educational Resources Information Center

Vleduts-Stokolov, Natasha

1987-01-01

Describes a system developed for the automatic recognition of biological concepts in titles of scientific articles; reports results of several pilot experiments which tested the system's performance; analyzes typical ambiguity problems encountered by the system; describes a disambiguation technique that was developed; and discusses future plans…

The Army word recognition system

NASA Technical Reports Server (NTRS)

Hadden, David R.; Haratz, David

1977-01-01

The application of speech recognition technology in the Army command and control area is presented. The problems associated with this program are described as well as as its relevance in terms of the man/machine interactions, voice inflexions, and the amount of training needed to interact with and utilize the automated system.

Ovulation, In Vivo Emotion Regulation Problems, and Sexual Risk Recognition Deficits

ERIC Educational Resources Information Center

Walsh, Kate; DiLillo, David

2013-01-01

Objective: To examine associations between menstrual cycle phase, negative mood, sexual risk recognition deficits (assessed via an analogue risk vignette), and in vivo emotion dysregulation. Participants: Participants were 714 college women recruited between February 2007 and December 2009. Methods: Participants were randomly assigned to a…

A "Situational" and "Coorientational" Measure of Specialized Magazine Editors' Perceptions of Readers.

ERIC Educational Resources Information Center

Jeffers, Dennis W.

A study was undertaken of specialized magazine editors' perceptions of audience characteristics as well as the perceived role of their publications. Specifically, the study examines the relationship between the editors' perceptions of reader problem recognition, level of involvement, constraint recognition, and possession of reference criteria and…

Neural correlates underlying the comprehension of deceitful and ironic communicative intentions.

PubMed

Bosco, Francesca M; Parola, Alberto; Valentini, Maria C; Morese, Rosalba

2017-09-01

Neuroimaging studies have shown that a left fronto-temporo-parietal cerebral network is recruited in the comprehension of both deceitful and ironic speech acts. However, no studies to date have directly compared neural activation during the comprehension of these pragmatic phenomena. We used fMRI to investigate the existence of common and specific neural circuits underlying the comprehension of the same speech act, uttered with different communicative intentions, i.e., of being sincere, deceitful or ironic. In particular, the novelty of the present study is that it explores the existence of a specific cerebral area involved in the recognition of irony versus deceit. We presented 23 healthy participants with 48 context stories each followed by a target sentence. For each story we designed different versions eliciting, respectively, different pragmatic interpretations of the same target sentence - literal, deceitful or ironic-. We kept the semantic and syntactic complexity of the target sentence constant across the conditions. Our results showed that the recognition of ironic communicative intention activated the left temporo-parietal junction (lTPJ), the left inferior frontal gyrus (lIFG), the left middle frontal gyrus (lMFG), the left middle temporal gyrus (lMTG), and the left dorsolateral prefrontal cortex (lDLPFC). Comprehension of deceitful communicative intention activated the lIFG, the lMFG, and the lDLPFC. fMRI analysis revealed that a left fronto-temporal network-including the inferior frontal gyrus (IFG), the dorsolateral prefrontal cortex (DLPFC) and the middle frontal gyrus (MFG)-is activated in both irony and deceit recognition. The original result of the present investigation is that the lMTG was found to be more active in the comprehension of ironic versus deceitful communicative intention, thus suggesting its specific role in irony recognition. To conclude, our results showed that common cerebral areas are recruited in the comprehension of both pragmatic phenomena, while the lMTG has a key role in the recognition of ironic versus deceitful communicative intention. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cross-domain expression recognition based on sparse coding and transfer learning

NASA Astrophysics Data System (ADS)

Yang, Yong; Zhang, Weiyi; Huang, Yong

2017-05-01

Traditional facial expression recognition methods usually assume that the training set and the test set are independent and identically distributed. However, in actual expression recognition applications, the conditions of independent and identical distribution are hardly satisfied for the training set and test set because of the difference of light, shade, race and so on. In order to solve this problem and improve the performance of expression recognition in the actual applications, a novel method based on transfer learning and sparse coding is applied to facial expression recognition. First of all, a common primitive model, that is, the dictionary is learnt. Then, based on the idea of transfer learning, the learned primitive pattern is transferred to facial expression and the corresponding feature representation is obtained by sparse coding. The experimental results in CK +, JAFFE and NVIE database shows that the transfer learning based on sparse coding method can effectively improve the expression recognition rate in the cross-domain expression recognition task and is suitable for the practical facial expression recognition applications.

A novel junctional adhesion molecule A (CgJAM-A-L) from oyster (Crassostrea gigas) functions as pattern recognition receptor and opsonin.

PubMed

Liu, Conghui; Wang, Mengqiang; Jiang, Shuai; Wang, Lingling; Chen, Hao; Liu, Zhaoqun; Qiu, Limei; Song, Linsheng

2016-02-01

Junctional adhesion molecule (JAM), a subfamily of immunoglobulin superfamily (IgSF) with a couple of immunoglobulin domains, can act as regulator in homeostasis and inflammation of vertebrates. In the present study, a structural homolog of JAM-A (designated CgJAM-A-L) was screened out from oyster, Crassostrea gigas, through a search of JAM-A D1 domain (N-terminal Ig domain in JAM-A). The cDNA of CgJAM-A-L was of 1188 bp encoding a predicted polypeptide of 395 amino acids. The immunoreactive area of CgJAM-A-L mainly distributed over the plasma membrane of hemocytes. After Vibro splendidus or tumor necrosis factor (CgTNF-1) stimulation, the mRNA transcripts of CgJAM-A-L in hemocytes increased significantly by 4.46-fold and 9.00-fold (p < 0.01) of those in control group, respectively. The recombinant CgJAM-A-L protein (rCgJAM-A-L) could bind multiple PAMPs including lipopolysaccharides (LPS), peptidoglycan (PGN), lipoteichoic acid (LTA), mannose (MAN), β-glucan (GLU) and poly(I:C), and various microorganisms including Micrococcus luteus, Staphylococcus aureus, Escherichia coli, Vibro anguillarum, V. splendidus, Pastoris pastoris and Yarrowia lipolytica. The phagocytic rates of oyster hemocytes towards Gram-negative bacteria V. anguillarum and yeast P. pastoris were significantly enhanced after the incubation of rCgJAM-A-L, and even increased more significantly after the pre-incubation of rCgJAM-A-L with microbes (p < 0.01). The results collectively indicated that CgJAM-A-L functioned as an important pattern recognition receptor (PRR) and opsonin in the immune defense against invading pathogen in oyster. Moreover, as the most primitive specie with homolog of JAMs, the information of CgJAM-A-L in oyster would provide useful clues for the evolutionary study of JAMs and immunoglobulins. Copyright © 2015 Elsevier Ltd. All rights reserved.

Bridging the gap: from biometrics to forensics.

PubMed

Jain, Anil K; Ross, Arun

2015-08-05

Biometric recognition, or simply biometrics, refers to automated recognition of individuals based on their behavioural and biological characteristics. The success of fingerprints in forensic science and law enforcement applications, coupled with growing concerns related to border control, financial fraud and cyber security, has generated a huge interest in using fingerprints, as well as other biological traits, for automated person recognition. It is, therefore, not surprising to see biometrics permeating various segments of our society. Applications include smartphone security, mobile payment, border crossing, national civil registry and access to restricted facilities. Despite these successful deployments in various fields, there are several existing challenges and new opportunities for person recognition using biometrics. In particular, when biometric data is acquired in an unconstrained environment or if the subject is uncooperative, the quality of the ensuing biometric data may not be amenable for automated person recognition. This is particularly true in crime-scene investigations, where the biological evidence gleaned from a scene may be of poor quality. In this article, we first discuss how biometrics evolved from forensic science and how its focus is shifting back to its origin in order to address some challenging problems. Next, we enumerate the similarities and differences between biometrics and forensics. We then present some applications where the principles of biometrics are being successfully leveraged into forensics in order to solve critical problems in the law enforcement domain. Finally, we discuss new collaborative opportunities for researchers in biometrics and forensics, in order to address hitherto unsolved problems that can benefit society at large. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Bridging the gap: from biometrics to forensics

PubMed Central

Jain, Anil K.; Ross, Arun

2015-01-01

Biometric recognition, or simply biometrics, refers to automated recognition of individuals based on their behavioural and biological characteristics. The success of fingerprints in forensic science and law enforcement applications, coupled with growing concerns related to border control, financial fraud and cyber security, has generated a huge interest in using fingerprints, as well as other biological traits, for automated person recognition. It is, therefore, not surprising to see biometrics permeating various segments of our society. Applications include smartphone security, mobile payment, border crossing, national civil registry and access to restricted facilities. Despite these successful deployments in various fields, there are several existing challenges and new opportunities for person recognition using biometrics. In particular, when biometric data is acquired in an unconstrained environment or if the subject is uncooperative, the quality of the ensuing biometric data may not be amenable for automated person recognition. This is particularly true in crime-scene investigations, where the biological evidence gleaned from a scene may be of poor quality. In this article, we first discuss how biometrics evolved from forensic science and how its focus is shifting back to its origin in order to address some challenging problems. Next, we enumerate the similarities and differences between biometrics and forensics. We then present some applications where the principles of biometrics are being successfully leveraged into forensics in order to solve critical problems in the law enforcement domain. Finally, we discuss new collaborative opportunities for researchers in biometrics and forensics, in order to address hitherto unsolved problems that can benefit society at large. PMID:26101280

Biometric identification

NASA Astrophysics Data System (ADS)

Syryamkim, V. I.; Kuznetsov, D. N.; Kuznetsova, A. S.

2018-05-01

Image recognition is an information process implemented by some information converter (intelligent information channel, recognition system) having input and output. The input of the system is fed with information about the characteristics of the objects being presented. The output of the system displays information about which classes (generalized images) the recognized objects are assigned to. When creating and operating an automated system for pattern recognition, a number of problems are solved, while for different authors the formulations of these tasks, and the set itself, do not coincide, since it depends to a certain extent on the specific mathematical model on which this or that recognition system is based. This is the task of formalizing the domain, forming a training sample, learning the recognition system, reducing the dimensionality of space.

Effects of mutation at the D-JH junction on affinity, specificity, and idiotypy of anti-progesterone antibody DB3.

PubMed

He, Mingyue; Hamon, Maureen; Liu, Hong; Corper, Adam L; Taussig, Michael J

2006-09-01

The crystal structures of the Fab' fragment of the anti-progesterone monoclonal antibody DB3 and its complexes with steroid haptens have shown that the D-JH junctional residue TrpH100 is a key contributor to binding site interactions with ligands. The indole group of TrpH100 also undergoes a significant conformational change between the bound and unliganded states, effectively opening and closing the combining site pocket. In order to explore the effect of substitutions at this position on steroid recognition, we have carried out mutagenesis on a construct encoding a three-domain single-chain fragment (VH/K) of DB3 expressed in Escherichia coli. TrpH100 was replaced by 13 different amino acids or deleted, and the functional and antigenic properties of the mutated fragments were analyzed. Most substitutions, including small, hydrophobic, hydrophilic, neutral, and negatively charged side chains, were reduced or abolished binding to free progesterone, although binding to progesterone-BSA was partially retained. The reduction in antigen binding was paralleled by alteration of the idiotype associated with the DB3 combining site. In contrast, the replacement of TrpH100 by Arg produced a mutant that retained wild-type antibody affinity and idiotype, but with altered specificity. Significant changes in this mutant included increased relative affinities of 10(4)-fold for progesterone-3-carboxymethyloxime and 10-fold for aetiocholanolone. Our results demonstrate an essential role for the junctional residue H100 in determining steroid-binding specificity and combining site idiotype and show that these properties can be changed by a single amino acid substitution at this position.

Pannexin-1 channels show distinct morphology and no gap junction characteristics in mammalian cells.

PubMed

Beckmann, Anja; Grissmer, Alexander; Krause, Elmar; Tschernig, Thomas; Meier, Carola

2016-03-01

Pannexins (Panx) are proteins with a similar membrane topology to connexins, the integral membrane protein of gap junctions. Panx1 channels are generally of major importance in a large number of system and cellular processes and their function has been thoroughly characterized. In contrast, little is known about channel structure and subcellular distribution. We therefore determine the subcellular localization of Panx1 channels in cultured cells and aim at the identification of channel morphology in vitro. Using freeze-fracture replica immunolabeling on EYFP-Panx1-overexpressing HEK 293 cells, large particles were identified in plasma membranes, which were immunogold-labeled using either GFP or Panx1 antibodies. There was no labeling or particles in the nuclear membranes of these cells, pointing to plasma membrane localization of Panx1-EYFP channels. The assembly of particles was irregular, this being in contrast to the regular pattern of gap junctions. The fact that no counterparts were identified on apposing cells, which would have been indicative of intercellular signaling, supported the idea of Panx1 channels within one membrane. Control cells (transfected with EYFP only, non-transfected) were devoid of both particles and immunogold labeling. Altogether, this study provides the first demonstration of Panx1 channel morphology and assembly in intact cells. The identification of Panx1 channels as large particles within the plasma membrane provides the knowledge required to enable recognition of Panx1 channels in tissues in future studies. Thus, these results open up new avenues for the detailed analysis of the subcellular localization of Panx1 and of its nearest neighbors such as purinergic receptors in vivo.

Single-molecule force-conductance spectroscopy of hydrogen-bonded complexes

NASA Astrophysics Data System (ADS)

Pirrotta, Alessandro; De Vico, Luca; Solomon, Gemma C.; Franco, Ignacio

2017-03-01

The emerging ability to study physical properties at the single-molecule limit highlights the disparity between what is observable in an ensemble of molecules and the heterogeneous contributions of its constituent parts. A particularly convenient platform for single-molecule studies are molecular junctions where forces and voltages can be applied to individual molecules, giving access to a series of electromechanical observables that can form the basis of highly discriminating multidimensional single-molecule spectroscopies. Here, we computationally examine the ability of force and conductance to inform about molecular recognition events at the single-molecule limit. For this, we consider the force-conductance characteristics of a prototypical class of hydrogen bonded bimolecular complexes sandwiched between gold electrodes. The complexes consist of derivatives of a barbituric acid and a Hamilton receptor that can form up to six simultaneous hydrogen bonds. The simulations combine classical molecular dynamics of the mechanical deformation of the junction with non-equilibrium Green's function computations of the electronic transport. As shown, in these complexes hydrogen bonds mediate transport either by directly participating as a possible transport pathway or by stabilizing molecular conformations with enhanced conductance properties. Further, we observe that force-conductance correlations can be very sensitive to small changes in the chemical structure of the complexes and provide detailed information about the behavior of single molecules that cannot be gleaned from either measurement alone. In fact, there are regions during the elongation that are only mechanically active, others that are only conductance active, and regions where both force and conductance changes as the complex is mechanically manipulated. The implication is that force and conductance provide complementary information about the evolution of molecules in junctions that can be used to interrogate basic structure-transport relations at the single-molecule limit.

Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

NASA Astrophysics Data System (ADS)

Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.

2018-04-01

This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images.

PubMed

Rakotomamonjy, Alain; Petitjean, Caroline; Salaün, Mathieu; Thiberville, Luc

2014-06-01

To assess the feasibility of lung cancer diagnosis using fibered confocal fluorescence microscopy (FCFM) imaging technique and scattering features for pattern recognition. FCFM imaging technique is a new medical imaging technique for which interest has yet to be established for diagnosis. This paper addresses the problem of lung cancer detection using FCFM images and, as a first contribution, assesses the feasibility of computer-aided diagnosis through these images. Towards this aim, we have built a pattern recognition scheme which involves a feature extraction stage and a classification stage. The second contribution relies on the features used for discrimination. Indeed, we have employed the so-called scattering transform for extracting discriminative features, which are robust to small deformations in the images. We have also compared and combined these features with classical yet powerful features like local binary patterns (LBP) and their variants denoted as local quinary patterns (LQP). We show that scattering features yielded to better recognition performances than classical features like LBP and their LQP variants for the FCFM image classification problems. Another finding is that LBP-based and scattering-based features provide complementary discriminative information and, in some situations, we empirically establish that performance can be improved when jointly using LBP, LQP and scattering features. In this work we analyze the joint capability of FCFM images and scattering features for lung cancer diagnosis. The proposed method achieves a good recognition rate for such a diagnosis problem. It also performs well when used in conjunction with other features for other classical medical imaging classification problems. Copyright © 2014 Elsevier B.V. All rights reserved.

Combining heterogenous features for 3D hand-held object recognition

NASA Astrophysics Data System (ADS)

Lv, Xiong; Wang, Shuang; Li, Xiangyang; Jiang, Shuqiang

2014-10-01

Object recognition has wide applications in the area of human-machine interaction and multimedia retrieval. However, due to the problem of visual polysemous and concept polymorphism, it is still a great challenge to obtain reliable recognition result for the 2D images. Recently, with the emergence and easy availability of RGB-D equipment such as Kinect, this challenge could be relieved because the depth channel could bring more information. A very special and important case of object recognition is hand-held object recognition, as hand is a straight and natural way for both human-human interaction and human-machine interaction. In this paper, we study the problem of 3D object recognition by combining heterogenous features with different modalities and extraction techniques. For hand-craft feature, although it reserves the low-level information such as shape and color, it has shown weakness in representing hiconvolutionalgh-level semantic information compared with the automatic learned feature, especially deep feature. Deep feature has shown its great advantages in large scale dataset recognition but is not always robust to rotation or scale variance compared with hand-craft feature. In this paper, we propose a method to combine hand-craft point cloud features and deep learned features in RGB and depth channle. First, hand-held object segmentation is implemented by using depth cues and human skeleton information. Second, we combine the extracted hetegerogenous 3D features in different stages using linear concatenation and multiple kernel learning (MKL). Then a training model is used to recognize 3D handheld objects. Experimental results validate the effectiveness and gerneralization ability of the proposed method.

Gait Recognition Based on Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Sokolova, A.; Konushin, A.

2017-05-01

In this work we investigate the problem of people recognition by their gait. For this task, we implement deep learning approach using the optical flow as the main source of motion information and combine neural feature extraction with the additional embedding of descriptors for representation improvement. In order to find the best heuristics, we compare several deep neural network architectures, learning and classification strategies. The experiments were made on two popular datasets for gait recognition, so we investigate their advantages and disadvantages and the transferability of considered methods.

OPTICAL INFORMATION PROCESSING: Synthesis of an object recognition system based on the profile of the envelope of a laser pulse in pulsed lidars

NASA Astrophysics Data System (ADS)

Buryi, E. V.

1998-05-01

The main problems in the synthesis of an object recognition system, based on the principles of operation of neuron networks, are considered. Advantages are demonstrated of a hierarchical structure of the recognition algorithm. The use of reading of the amplitude spectrum of signals as information tags is justified and a method is developed for determination of the dimensionality of the tag space. Methods are suggested for ensuring the stability of object recognition in the optical range. It is concluded that it should be possible to recognise perspectives of complex objects.

Optical and digital pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 13-15, 1987

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Editor); Schenker, Paul (Editor)

1987-01-01

The papers presented in this volume provide an overview of current research in both optical and digital pattern recognition, with a theme of identifying overlapping research problems and methodologies. Topics discussed include image analysis and low-level vision, optical system design, object analysis and recognition, real-time hybrid architectures and algorithms, high-level image understanding, and optical matched filter design. Papers are presented on synthetic estimation filters for a control system; white-light correlator character recognition; optical AI architectures for intelligent sensors; interpreting aerial photographs by segmentation and search; and optical information processing using a new photopolymer.

Influence of Contact Angle Boundary Condition on CFD Simulation of T-Junction

NASA Astrophysics Data System (ADS)

Arias, S.; Montlaur, A.

2018-03-01

In this work, we study the influence of the contact angle boundary condition on 3D CFD simulations of the bubble generation process occurring in a capillary T-junction. Numerical simulations have been performed with the commercial Computational Fluid Dynamics solver ANSYS Fluent v15.0.7. Experimental results serve as a reference to validate numerical results for four independent parameters: the bubble generation frequency, volume, velocity and length. CFD simulations accurately reproduce experimental results both from qualitative and quantitative points of view. Numerical results are very sensitive to the gas-liquid-wall contact angle boundary conditions, confirming that this is a fundamental parameter to obtain accurate CFD results for simulations of this kind of problems.

The in-phase states of Josephson junctions stacks as attractors

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

Hristov, I.; Dimova, S.; Hristova, R.

2014-11-12

The aim of this investigation is to show that the coherent, in-phase states of intrinsic Josephson junctions stacks are attractors of the stacks' states when the applied external magnetic field h{sub e} and the external current γ vary within certain domains. Mathematically the problem is to find the solutions of the system of perturbed sine-Gordon equations for fixed other parameters and zero or random initial conditions. We determine the region in the plane (h{sub e}, γ), where the in-phase states are attractors of the stack's states for arbitrary initial perturbations. This is important, because the in-phase states are required formore » achieving terahertz radiation from the Josephson stacks.« less

Speech Recognition Technology for Disabilities Education

ERIC Educational Resources Information Center

Tang, K. Wendy; Kamoua, Ridha; Sutan, Victor; Farooq, Omer; Eng, Gilbert; Chu, Wei Chern; Hou, Guofeng

2005-01-01

Speech recognition is an alternative to traditional methods of interacting with a computer, such as textual input through a keyboard. An effective system can replace or reduce the reliability on standard keyboard and mouse input. This can especially assist dyslexic students who have problems with character or word use and manipulation in a textual…

A Multimodal Approach to Emotion Recognition Ability in Autism Spectrum Disorders

ERIC Educational Resources Information Center

Jones, Catherine R. G.; Pickles, Andrew; Falcaro, Milena; Marsden, Anita J. S.; Happe, Francesca; Scott, Sophie K.; Sauter, Disa; Tregay, Jenifer; Phillips, Rebecca J.; Baird, Gillian; Simonoff, Emily; Charman, Tony

2011-01-01

Background: Autism spectrum disorders (ASD) are characterised by social and communication difficulties in day-to-day life, including problems in recognising emotions. However, experimental investigations of emotion recognition ability in ASD have been equivocal, hampered by small sample sizes, narrow IQ range and over-focus on the visual modality.…

Computing with Connections in Visual Recognition of Origami Objects.

ERIC Educational Resources Information Center

Sabbah, Daniel

1985-01-01

Summarizes an initial foray in tackling artificial intelligence problems using a connectionist approach. The task chosen is visual recognition of Origami objects, and the questions answered are how to construct a connectionist network to represent and recognize projected Origami line drawings and the advantages such an approach would have. (30…

Prediction of Word Recognition in the First Half of Grade 1

ERIC Educational Resources Information Center

Snel, M. J.; Aarnoutse, C. A. J.; Terwel, J.; van Leeuwe, J. F. J.; van der Veld, W. M.

2016-01-01

Early detection of reading problems is important to prevent an enduring lag in reading skills. We studied the relationship between speed of word recognition (after six months of grade 1 education) and four kindergarten pre-literacy skills: letter knowledge, phonological awareness and naming speed for both digits and letters. Our sample consisted…

Appearance-based representative samples refining method for palmprint recognition

NASA Astrophysics Data System (ADS)

Wen, Jiajun; Chen, Yan

2012-07-01

The sparse representation can deal with the lack of sample problem due to utilizing of all the training samples. However, the discrimination ability will degrade when more training samples are used for representation. We propose a novel appearance-based palmprint recognition method. We aim to find a compromise between the discrimination ability and the lack of sample problem so as to obtain a proper representation scheme. Under the assumption that the test sample can be well represented by a linear combination of a certain number of training samples, we first select the representative training samples according to the contributions of the samples. Then we further refine the training samples by an iteration procedure, excluding the training sample with the least contribution to the test sample for each time. Experiments on PolyU multispectral palmprint database and two-dimensional and three-dimensional palmprint database show that the proposed method outperforms the conventional appearance-based palmprint recognition methods. Moreover, we also explore and find out the principle of the usage for the key parameters in the proposed algorithm, which facilitates to obtain high-recognition accuracy.

An improved finger-vein recognition algorithm based on template matching

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Syntactic/semantic techniques for feature description and character recognition

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

Gonzalez, R.C.

1983-01-01

The Pattern Analysis Branch, Mapping, Charting and Geodesy (MC/G) Division, of the Naval Ocean Research and Development Activity (NORDA) has been involved over the past several years in the development of algorithms and techniques for computer recognition of free-form handprinted symbols as they appear on the Defense Mapping Agency (DMA) maps and charts. NORDA has made significant contributions to the automation of MC/G through advancing the state of the art in such information extraction techniques. In particular, new concepts in character (symbol) skeletonization, rugged feature measurements, and expert system-oriented decision logic have allowed the development of a very high performancemore » Handprinted Symbol Recognition (HSR) system for identifying depth soundings from naval smooth sheets (accuracies greater than 99.5%). The study reported in this technical note is part of NORDA's continuing research and development in pattern and shape analysis as it applies to Navy and DMA ocean/environment problems. The issue addressed in this technical note deals with emerging areas of syntactic and semantic techniques in pattern recognition as they might apply to the free-form symbol problem.« less

Pose Invariant Face Recognition Based on Hybrid Dominant Frequency Features

NASA Astrophysics Data System (ADS)

Wijaya, I. Gede Pasek Suta; Uchimura, Keiichi; Hu, Zhencheng

Face recognition is one of the most active research areas in pattern recognition, not only because the face is a human biometric characteristics of human being but also because there are many potential applications of the face recognition which range from human-computer interactions to authentication, security, and surveillance. This paper presents an approach to pose invariant human face image recognition. The proposed scheme is based on the analysis of discrete cosine transforms (DCT) and discrete wavelet transforms (DWT) of face images. From both the DCT and DWT domain coefficients, which describe the facial information, we build compact and meaningful features vector, using simple statistical measures and quantization. This feature vector is called as the hybrid dominant frequency features. Then, we apply a combination of the L2 and Lq metric to classify the hybrid dominant frequency features to a person's class. The aim of the proposed system is to overcome the high memory space requirement, the high computational load, and the retraining problems of previous methods. The proposed system is tested using several face databases and the experimental results are compared to a well-known Eigenface method. The proposed method shows good performance, robustness, stability, and accuracy without requiring geometrical normalization. Furthermore, the purposed method has low computational cost, requires little memory space, and can overcome retraining problem.

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

PubMed

Liu, Yi-Hung; Chen, Yen-Ting

2007-01-01

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

Ear recognition from one sample per person.

PubMed

Chen, Long; Mu, Zhichun; Zhang, Baoqing; Zhang, Yi

2015-01-01

Biometrics has the advantages of efficiency and convenience in identity authentication. As one of the most promising biometric-based methods, ear recognition has received broad attention and research. Previous studies have achieved remarkable performance with multiple samples per person (MSPP) in the gallery. However, most conventional methods are insufficient when there is only one sample per person (OSPP) available in the gallery. To solve the OSPP problem by maximizing the use of a single sample, this paper proposes a hybrid multi-keypoint descriptor sparse representation-based classification (MKD-SRC) ear recognition approach based on 2D and 3D information. Because most 3D sensors capture 3D data accessorizing the corresponding 2D data, it is sensible to use both types of information. First, the ear region is extracted from the profile. Second, keypoints are detected and described for both the 2D texture image and 3D range image. Then, the hybrid MKD-SRC algorithm is used to complete the recognition with only OSPP in the gallery. Experimental results on a benchmark dataset have demonstrated the feasibility and effectiveness of the proposed method in resolving the OSPP problem. A Rank-one recognition rate of 96.4% is achieved for a gallery of 415 subjects, and the time involved in the computation is satisfactory compared to conventional methods.

Intelligent data processing of an ultrasonic sensor system for pattern recognition improvements

NASA Astrophysics Data System (ADS)

Na, Seung You; Park, Min-Sang; Hwang, Won-Gul; Kee, Chang-Doo

1999-05-01

Though conventional time-of-flight ultrasonic sensor systems are popular due to the advantages of low cost and simplicity, the usage of the sensors is rather narrowly restricted within object detection and distance readings. There is a strong need to enlarge the amount of environmental information for mobile applications to provide intelligent autonomy. Wide sectors of such neighboring object recognition problems can be satisfactorily handled with coarse vision data such as sonar maps instead of accurate laser or optic measurements. For the usage of object pattern recognition, ultrasonic senors have inherent shortcomings of poor directionality and specularity which result in low spatial resolution and indistinctiveness of object patterns. To resolve these problems an array of increased number of sensor elements has been used for large objects. In this paper we propose a method of sensor array system with improved recognition capability using electronic circuits accompanying the sensor array and neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. Relying upon the known sensor characteristics, a set of different return signals from neighboring senors is manipulated to provide an enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

Social behavior following traumatic brain injury and its association with emotion recognition, understanding of intentions, and cognitive flexibility.

PubMed

Milders, Maarten; Ietswaart, Magdalena; Crawford, John R; Currie, David

2008-03-01

Although the adverse consequences of changes in social behavior following traumatic brain injury (TBI) are well documented, relatively little is known about possible underlying neuropsychological deficits. Following a model originally developed for social behavior deficits in schizophrenia, we investigated whether impairments in emotion recognition, understanding of other people's intentions ("theory of mind"), and cognitive flexibility soon after first TBI or 1 year later were associated with self and proxy ratings of behavior following TBI. Each of the three functions was assessed with two separate tests, and ratings of behavior were collected on three questionnaires. Patients with TBI (n = 33) were impaired in emotion recognition, "theory of mind," and cognitive flexibility compared with matched orthopedic controls (n = 34). Proxy ratings showed increases in behavioral problems 1 year following injury in the TBI group but not in the control group. However, test performance was not associated with questionnaire data. Severity of the impairments in emotion recognition, understanding intention, and flexibility were unrelated to the severity of behavioral problems following TBI. These findings failed to confirm the used model for social behavior deficits and may cast doubt on the alleged link between deficits in emotion recognition or theory of mind and social functioning.

A framework for the recognition of 3D faces and expressions

NASA Astrophysics Data System (ADS)

Li, Chao; Barreto, Armando

2006-04-01

Face recognition technology has been a focus both in academia and industry for the last couple of years because of its wide potential applications and its importance to meet the security needs of today's world. Most of the systems developed are based on 2D face recognition technology, which uses pictures for data processing. With the development of 3D imaging technology, 3D face recognition emerges as an alternative to overcome the difficulties inherent with 2D face recognition, i.e. sensitivity to illumination conditions and orientation positioning of the subject. But 3D face recognition still needs to tackle the problem of deformation of facial geometry that results from the expression changes of a subject. To deal with this issue, a 3D face recognition framework is proposed in this paper. It is composed of three subsystems: an expression recognition system, a system for the identification of faces with expression, and neutral face recognition system. A system for the recognition of faces with one type of expression (happiness) and neutral faces was implemented and tested on a database of 30 subjects. The results proved the feasibility of this framework.

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

PubMed

Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

2013-08-01

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

General tensor discriminant analysis and gabor features for gait recognition.

PubMed

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2007-10-01

The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.

Reduced junction temperature and enhanced performance of high power light-emitting diodes using reduced graphene oxide pattern

NASA Astrophysics Data System (ADS)

Han, Nam; Jung, Eunjin; Han, Min; Deul Ryu, Beo; Bok Ko, Kang; Park, Young Jae; Cuong, TranViet; Cho, Jaehee; Kim, Hyunsoo; Hong, Chang-Hee

2015-07-01

Thermal management has become a crucial area for further development of high-power light-emitting didoes (LEDs) due to the high operating current densities that are required and result in additional joule heating. This increased joule heating negatively affects the performance of the LEDs since it greatly decreases both the optical performance and the lifetime. To circumvent this problem, a reduced graphene oxide (rGO) layer can be inserted to act as a heat spreader. In this study, current-voltage and light-output-current measurements are systematically performed at different temperatures from 30 to 190 °C to investigate the effect that the embedded rGO pattern has on the device performance. At a high temperature and high operating current, the junction temperature (Tj) is 23% lower and the external quantum efficiency (EQE) is 24% higher for the rGO embedded LEDs relative to those of conventional LEDs. In addition, the thermal activation energy of the rGO embedded LEDs exhibits a 30% enhancement as a function of the temperature at a bias of  -5 V. This indicates that the rGO pattern plays an essential role in decreasing the junction temperature and results in a favorable performance in terms of the temperature of the high power GaN-based LED junction.

Using information content and base frequencies to distinguish mutations from genetic polymorphisms in splice junction recognition sites.

PubMed

Rogan, P K; Schneider, T D

1995-01-01

Predicting the effects of nucleotide substitutions in human splice sites has been based on analysis of consensus sequences. We used a graphic representation of sequence conservation and base frequency, the sequence logo, to demonstrate that a change in a splice acceptor of hMSH2 (a gene associated with familial nonpolyposis colon cancer) probably does not reduce splicing efficiency. This confirms a population genetic study that suggested that this substitution is a genetic polymorphism. The information theory-based sequence logo is quantitative and more sensitive than the corresponding splice acceptor consensus sequence for detection of true mutations. Information analysis may potentially be used to distinguish polymorphisms from mutations in other types of transcriptional, translational, or protein-coding motifs.

Critical current of SF-NFS Josephson junctions

NASA Astrophysics Data System (ADS)

Soloviev, I. I.; Klenov, N. V.; Bakursky, S. V.; Kupriyanov, M. Yu.; Golubov, A. A.

2015-02-01

The properties of SF-NFS sandwiches composed of two superconducting (S) electrodes separated by a weak-link region formed by a normal-metal (N) step with the thickness d N situated on the top of a lower S electrode and a ferromagnetic (F) layer with the thickness d F deposited onto the step and the remaining free surface of the lower electrode have been studied theoretically. It has been shown in the approximation of linearized semiclassical Usadel equations that the two-dimensional problem in the weak-link region can be reduced to two one-dimensional problems in its SFS and SNFS segments. The spatial distributions of the critical current density J c in the segments as a function of the layer thickness d F have been calculated. The dependences of the critical current I c of the structure on the magnitude of the magnetization vector M of the ferromagnetic layer have been found for various directions of the magnetization within the junction plane. It has been shown that these dependences are affected considerably by both the orientation of M and the spatial distribution of J c.

Electrical and Structural Analysis on the Formation of n-type Junction in Germanium

NASA Astrophysics Data System (ADS)

Aziz, Umar Abdul; Nadhirah Mohamad Rashid, Nur; Rahmah Aid, Siti; Centeno, Anthony; Ikenoue, Hiroshi; Xie, Fang

2017-05-01

Germanium (Ge) has re-emerged as a potential candidate to replace silicon (Si) as a substrate, due to its higher carrier mobility properties that are the key point for the realization of devices high drive current. However, the fabrication process of Ge is confronted with many problems such as low dopant electrical activation and the utilization of heavy n-type dopant atoms during ion implantation. These problems result in more damage and defects that can affect dopant activation. This paper reports the electrical and structural analysis on the formation of n-type junction in Ge substrate by ion implantation, followed by excimer laser annealing (ELA) using KrF laser. ELA parameters such as laser fluences were varied from 100 - 2000 mJ/cm2 and shot number between 1 - 1000 to obtain the optimized parameter of ELA with a high degree of damage and defect removal. Low resistance with a high degree of crystallinity is obtained for the samples annealed with less than five shot number. Higher shot number with high laser fluence, shows a high degree of ablation damage.

Silicide/Silicon Hetero-Junction Structure for Thermoelectric Applications.

PubMed

Jun, Dongsuk; Kim, Soojung; Choi, Wonchul; Kim, Junsoo; Zyung, Taehyoung; Jang, Moongyu

2015-10-01

We fabricated silicide/silicon hetero-junction structured thermoelectric device by CMOS process for the reduction of thermal conductivity with the scatterings of phonons at silicide/silicon interfaces. Electrical conductivities, Seebeck coefficients, power factors, and temperature differences are evaluated using the steady state analysis method. Platinum silicide/silicon multilayered structure showed an enhanced Seebeck coefficient and power factor characteristics, which was considered for p-leg element. Also, erbium silicide/silicon structure showed an enhanced Seebeck coefficient, which was considered for an n-leg element. Silicide/silicon multilayered structure is promising for thermoelectric applications by reducing thermal conductivity with an enhanced Seebeck coefficient. However, because of the high thermal conductivity of the silicon packing during thermal gradient is not a problem any temperature difference. Therefore, requires more testing and analysis in order to overcome this problem. Thermoelectric generators are devices that based on the Seebeck effect, convert temperature differences into electrical energy. Although thermoelectric phenomena have been used for heating and cooling applications quite extensively, it is only in recent years that interest has increased in energy generation.

Transdiagnostic deviant facial recognition for implicit negative emotion in autism and schizophrenia.

PubMed

Ciaramidaro, Angela; Bölte, Sven; Schlitt, Sabine; Hainz, Daniela; Poustka, Fritz; Weber, Bernhard; Freitag, Christine; Walter, Henrik

2018-02-01

Impaired facial affect recognition (FAR) is observed in schizophrenia and autism spectrum disorder (ASD) and has been linked to amygdala and fusiform gyrus dysfunction. ASD patient's impairments seem to be more pronounced during implicit rather than explicit FAR, whereas for schizophrenia data are inconsistent. However, there are no studies comparing both patient groups in an identical design. The aim of this three-group study was to identify (i) whether FAR alterations are equally present in both groups, (ii) whether they are present rather during implicit or explicit FAR, (iii) and whether they are conveyed by similar or disorder-specific neural mechanisms. Using fMRI, we investigated neural activation during explicit and implicit negative and neutral FAR in 33 young-adult individuals with ASD, 20 subjects with paranoid-schizophrenia and 25 IQ- and gender-matched controls individuals. Differences in activation patterns between each clinical group and controls, respectively were found exclusively for implicit FAR in amygdala and fusiform gyrus. In addition, the ASD group additionally showed reduced activations in medial prefrontal cortex (PFC), bilateral dorso-lateral PFC, ventro-lateral PFC, posterior-superior temporal sulcus and left temporo-parietal junction. Although subjects with ASD showed more widespread altered activation patterns, a direct comparison between both patient groups did not show disorder-specific deficits in neither patient group. In summary, our findings are consistent with a common neural deficit during implicit negative facial affect recognition in schizophrenia and autism spectrum disorders. Copyright © 2017 Elsevier B.V. and ECNP. All rights reserved.

Recognition Tunneling of Canonical and Modified RNA Nucleotides for Their Identification with the Aid of Machine Learning.

PubMed

Im, JongOne; Sen, Suman; Lindsay, Stuart; Zhang, Peiming

2018-06-28

In the present study, we demonstrate a tunneling nanogap technique to identify individual RNA nucleotides, which can be used as a mechanism to read the nucleobases for direct sequencing of RNA in a solid-state nanopore. The tunneling nanogap is composed of two electrodes separated by a distance of <3 nm and functionalized with a recognition molecule. When a chemical entity is captured in the gap, it generates electron tunneling currents, a process we call recognition tunneling (RT). Using RT nanogaps created in a scanning tunneling microscope (STM), we acquired the electron tunneling signals for the canonical and two modified RNA nucleotides. To call the individual RNA nucleotides from the RT data, we adopted a machine learning algorithm, support vector machine (SVM), for the data analysis. Through the SVM, we were able to identify the individual RNA nucleotides and distinguish them from their DNA counterparts with reasonably high accuracy. Since each RNA nucleoside contains a hydroxyl group at the 2'-position of its sugar ring in an RNA strand, it allows for the formation of a tunneling junction at a larger nanogap compared to the DNA nucleoside in a DNA strand, which lacks the 2' hydroxyl group. It also proves advantageous for the manufacture of RT devices. This study is a proof-of-principle demonstration for the development of an RT nanopore device for directly sequencing single RNA molecules, including those bearing modifications.

Cost-sensitive learning for emotion robust speaker recognition.

PubMed

Li, Dongdong; Yang, Yingchun; Dai, Weihui

2014-01-01

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.

Cost-Sensitive Learning for Emotion Robust Speaker Recognition

PubMed Central

Li, Dongdong; Yang, Yingchun

2014-01-01

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved. PMID:24999492

Fingerprint recognition system by use of graph matching

NASA Astrophysics Data System (ADS)

Shen, Wei; Shen, Jun; Zheng, Huicheng

2001-09-01

Fingerprint recognition is an important subject in biometrics to identify or verify persons by physiological characteristics, and has found wide applications in different domains. In the present paper, we present a finger recognition system that combines singular points and structures. The principal steps of processing in our system are: preprocessing and ridge segmentation, singular point extraction and selection, graph representation, and finger recognition by graphs matching. Our fingerprint recognition system is implemented and tested for many fingerprint images and the experimental result are satisfactory. Different techniques are used in our system, such as fast calculation of orientation field, local fuzzy dynamical thresholding, algebraic analysis of connections and fingerprints representation and matching by graphs. Wed find that for fingerprint database that is not very large, the recognition rate is very high even without using a prior coarse category classification. This system works well for both one-to-few and one-to-many problems.

Review of chart recognition in document images

NASA Astrophysics Data System (ADS)

Liu, Yan; Lu, Xiaoqing; Qin, Yeyang; Tang, Zhi; Xu, Jianbo

2013-01-01

As an effective information transmitting way, chart is widely used to represent scientific statistics datum in books, research papers, newspapers etc. Though textual information is still the major source of data, there has been an increasing trend of introducing graphs, pictures, and figures into the information pool. Text recognition techniques for documents have been accomplished using optical character recognition (OCR) software. Chart recognition techniques as a necessary supplement of OCR for document images are still an unsolved problem due to the great subjectiveness and variety of charts styles. This paper reviews the development process of chart recognition techniques in the past decades and presents the focuses of current researches. The whole process of chart recognition is presented systematically, which mainly includes three parts: chart segmentation, chart classification, and chart Interpretation. In each part, the latest research work is introduced. In the last, the paper concludes with a summary and promising future research direction.

Face Recognition Using Local Quantized Patterns and Gabor Filters

NASA Astrophysics Data System (ADS)

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

2015-05-01

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

Detection and recognition of targets by using signal polarization properties

NASA Astrophysics Data System (ADS)

Ponomaryov, Volodymyr I.; Peralta-Fabi, Ricardo; Popov, Anatoly V.; Babakov, Mikhail F.

1999-08-01

The quality of radar target recognition can be enhanced by exploiting its polarization signatures. A specialized X-band polarimetric radar was used for target recognition in experimental investigations. The following polarization characteristics connected to the object geometrical properties were investigated: the amplitudes of the polarization matrix elements; an anisotropy coefficient; depolarization coefficient; asymmetry coefficient; the energy of a backscattering signal; object shape factor. A large quantity of polarimetric radar data was measured and processed to form a database of different object and different weather conditions. The histograms of polarization signatures were approximated by a Nakagami distribution, then used for real- time target recognition. The Neyman-Pearson criterion was used for the target detection, and the criterion of the maximum of a posterior probability was used for recognition problem. Some results of experimental verification of pattern recognition and detection of objects with different electrophysical and geometrical characteristics urban in clutter are presented in this paper.

New approach for logo recognition

NASA Astrophysics Data System (ADS)

Chen, Jingying; Leung, Maylor K. H.; Gao, Yongsheng

2000-03-01

The problem of logo recognition is of great interest in the document domain, especially for document database. By recognizing the logo we obtain semantic information about the document which may be useful in deciding whether or not to analyze the textual components. In order to develop a logo recognition method that is efficient to compute and product intuitively reasonable results, we investigate the Line Segment Hausdorff Distance on logo recognition. Researchers apply Hausdorff Distance to measure the dissimilarity of two point sets. It has been extended to match two sets of line segments. The new approach has the advantage to incorporate structural and spatial information to compute the dissimilarity. The added information can conceptually provide more and better distinctive capability for recognition. The proposed technique has been applied on line segments of logos with encouraging results that support the concept experimentally. This might imply a new way for logo recognition.

Examination of soldier target recognition with direct view optics

NASA Astrophysics Data System (ADS)

Long, Frederick H.; Larkin, Gabriella; Bisordi, Danielle; Dorsey, Shauna; Marianucci, Damien; Goss, Lashawnta; Bastawros, Michael; Misiuda, Paul; Rodgers, Glenn; Mazz, John P.

2017-10-01

Target recognition and identification is a problem of great military and scientific importance. To examine the correlation between target recognition and optical magnification, ten U.S. Army soldiers were tasked with identifying letters on targets at 800 and 1300 meters away. Letters were used since they are a standard method for measuring visual acuity. The letters were approximately 90 cm high, which is the size of a well-known rifle. Four direct view optics with angular magnifications of 1.5x, 4x, 6x, and 9x were used. The goal of this approach was to measure actual probabilities for correct target identification. Previous scientific literature suggests that target recognition can be modeled as a linear response problem in angular frequency space using the established values for the contrast sensitivity function for a healthy human eye and the experimentally measured modulation transfer function of the optic. At the 9x magnification, the soldiers could identify the letters with almost no errors (i.e., 97% probability of correct identification). At lower magnification, errors in letter identification were more frequent. The identification errors were not random but occurred most frequently with a few pairs of letters (e.g., O and Q), which is consistent with the literature for letter recognition. In addition, in the small subject sample of ten soldiers, there was considerable variation in the observer recognition capability at 1.5x and a range of 800 meters. This can be directly attributed to the variation in the observer visual acuity.

A multi-view face recognition system based on cascade face detector and improved Dlib

NASA Astrophysics Data System (ADS)

Zhou, Hongjun; Chen, Pei; Shen, Wei

2018-03-01

In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

Computers Are for Kids: Designing Software Programs to Avoid Problems of Learning.

ERIC Educational Resources Information Center

Grimes, Lynn

1981-01-01

Procedures for programing computers to deal with handicapped students, problems in selective attention, visual discrimination, reaction time differences, short term memory, transfer and generalization, recognition of mistakes, and social skills are discussed. (CL)

Comparing mental health literacy and physical health literacy: an exploratory study.

PubMed

Wickstead, Robert; Furnham, Adrian

2017-10-01

This study compared mental health and physical health literacy using five health problems from each area. The aim was to determine whether the same group had better physical than mental health literacy Method: A sample of 263 participants completed an online questionnaire requiring them to name a problem/illness described in 10 vignettes and suggest treatment options. Five vignettes described mental health problems (anxiety, bipolar-disorder, depression, OCPD and schizophrenia) and five physical problems (angina, COPD, diabetes, a heart attack, and sinusitis). Participants were also asked to rate their sympathy and estimates of prevalence for each disorder. Recognition of the mental health disorders was superior compared recognition of the physical disorders. Analysis of treatment beliefs, sympathy and prevalence ratings also showed significant differences between disorders. Results highlight the importance of education and the lack of public knowledge regarding major physical health conditions.

Spinal cord injuries among paragliders in Norway.

PubMed

Rekand, T; Schaanning, E E; Varga, V; Schattel, U; Gronning, M

2008-06-01

A national retrospective descriptive study. To study the clinical effects of spinal cord injuries (SCIs) caused by paragliding accidents in Norway. Spinal cord units at Haukeland University Hospital, Sunnaas Rehabilitation Hospital and St Olav Hospital in Norway. We studied the medical files for nine patients with SCI caused by paragliding accidents to evaluate the circumstances of the accidents, and clinical effects of injury. We obtained the data from hospital patient files at all three spinal units in Norway and crosschecked them through the Norwegian Paragliding Association's voluntary registry for injuries. All patients were hospitalized from 1997 to 2006, eight men and one woman, with mean age 30.7 years. The causes of the accidents were landing problems combined with unexpected wind whirls, technical problems and limited experience with unexpected events. All patients contracted fractures in the thoracolumbal junction of the spine, most commonly at the L1 level. At clinical follow-up, all patients presented clinically incomplete SCI (American Spinal Injury Association impairment scores B-D). Their main health problems differed widely, ranging from urinary and sexual disturbances to neuropathic pain and loss of motor functioning. Only three patients returned to full-time employment after rehabilitation. Paragliding accidents cause spinal fractures predominantly in the thoracolumbal junction with subsequent SCIs and increased morbidity. All patients experienced permanent health problems that influenced daily activities and required long-time clinical follow-up and medical intervention. Better education in landing techniques and understanding of aerodynamics may reduce the risk of paragliding accidents.

Combining convolutional neural networks and Hough Transform for classification of images containing lines

NASA Astrophysics Data System (ADS)

Sheshkus, Alexander; Limonova, Elena; Nikolaev, Dmitry; Krivtsov, Valeriy

2017-03-01

In this paper, we propose an expansion of convolutional neural network (CNN) input features based on Hough Transform. We perform morphological contrasting of source image followed by Hough Transform, and then use it as input for some convolutional filters. Thus, CNNs computational complexity and the number of units are not affected. Morphological contrasting and Hough Transform are the only additional computational expenses of introduced CNN input features expansion. Proposed approach was demonstrated on the example of CNN with very simple structure. We considered two image recognition problems, that were object classification on CIFAR-10 and printed character recognition on private dataset with symbols taken from Russian passports. Our approach allowed to reach noticeable accuracy improvement without taking much computational effort, which can be extremely important in industrial recognition systems or difficult problems utilising CNNs, like pressure ridge analysis and classification.

Face recognition via edge-based Gabor feature representation for plastic surgery-altered images

NASA Astrophysics Data System (ADS)

Chude-Olisah, Chollette C.; Sulong, Ghazali; Chude-Okonkwo, Uche A. K.; Hashim, Siti Z. M.

2014-12-01

Plastic surgery procedures on the face introduce skin texture variations between images of the same person (intra-subject), thereby making the task of face recognition more difficult than in normal scenario. Usually, in contemporary face recognition systems, the original gray-level face image is used as input to the Gabor descriptor, which translates to encoding some texture properties of the face image. The texture-encoding process significantly degrades the performance of such systems in the case of plastic surgery due to the presence of surgically induced intra-subject variations. Based on the proposition that the shape of significant facial components such as eyes, nose, eyebrow, and mouth remains unchanged after plastic surgery, this paper employs an edge-based Gabor feature representation approach for the recognition of surgically altered face images. We use the edge information, which is dependent on the shapes of the significant facial components, to address the plastic surgery-induced texture variation problems. To ensure that the significant facial components represent useful edge information with little or no false edges, a simple illumination normalization technique is proposed for preprocessing. Gabor wavelet is applied to the edge image to accentuate on the uniqueness of the significant facial components for discriminating among different subjects. The performance of the proposed method is evaluated on the Georgia Tech (GT) and the Labeled Faces in the Wild (LFW) databases with illumination and expression problems, and the plastic surgery database with texture changes. Results show that the proposed edge-based Gabor feature representation approach is robust against plastic surgery-induced face variations amidst expression and illumination problems and outperforms the existing plastic surgery face recognition methods reported in the literature.

Thermal-to-visible face recognition using partial least squares.

PubMed

Hu, Shuowen; Choi, Jonghyun; Chan, Alex L; Schwartz, William Robson

2015-03-01

Although visible face recognition has been an active area of research for several decades, cross-modal face recognition has only been explored by the biometrics community relatively recently. Thermal-to-visible face recognition is one of the most difficult cross-modal face recognition challenges, because of the difference in phenomenology between the thermal and visible imaging modalities. We address the cross-modal recognition problem using a partial least squares (PLS) regression-based approach consisting of preprocessing, feature extraction, and PLS model building. The preprocessing and feature extraction stages are designed to reduce the modality gap between the thermal and visible facial signatures, and facilitate the subsequent one-vs-all PLS-based model building. We incorporate multi-modal information into the PLS model building stage to enhance cross-modal recognition. The performance of the proposed recognition algorithm is evaluated on three challenging datasets containing visible and thermal imagery acquired under different experimental scenarios: time-lapse, physical tasks, mental tasks, and subject-to-camera range. These scenarios represent difficult challenges relevant to real-world applications. We demonstrate that the proposed method performs robustly for the examined scenarios.

Recognition of the pro-mutagenic base uracil by family B DNA polymerases from archaea.

PubMed

Shuttleworth, Gillian; Fogg, Mark J; Kurpiewski, Michael R; Jen-Jacobson, Linda; Connolly, Bernard A

2004-03-26

Archaeal family B DNA polymerases contain a specialised pocket that binds tightly to template-strand uracil, causing the stalling of DNA replication. The mechanism of this unique "template-strand proof-reading" has been studied using equilibrium binding measurements, DNA footprinting, van't Hoff analysis and calorimetry. Binding assays have shown that the polymerase preferentially binds to uracil in single as opposed to double-stranded DNA. Tightest binding is observed using primer-templates that contain uracil four bases in front of the primer-template junction, corresponding to the observed stalling position. Ethylation interference analysis of primer-templates shows that the two phosphates, immediately flanking the uracil (NpUpN), are important for binding; contacts are also made to phosphates in the primer-strand. Microcalorimetry and van't Hoff analysis have given a fuller understanding of the thermodynamic parameters involved in uracil recognition. All the results are consistent with a "read-ahead" mechanism, in which the replicating polymerase scans the template, ahead of the replication fork, for the presence of uracil and halts polymerisation on detecting this base. Post-stalling events, serving to eliminate uracil, await full elucidation.

A Portable and Autonomous Magnetic Detection Platform for Biosensing

PubMed Central

Germano, José; Martins, Verónica C.; Cardoso, Filipe A.; Almeida, Teresa M.; Sousa, Leonel; Freitas, Paulo P.; Piedade, Moisés S.

2009-01-01

This paper presents a prototype of a platform for biomolecular recognition detection. The system is based on a magnetoresistive biochip that performs biorecognition assays by detecting magnetically tagged targets. All the electronic circuitry for addressing, driving and reading out signals from spin-valve or magnetic tunnel junctions sensors is implemented using off-the-shelf components. Taking advantage of digital signal processing techniques, the acquired signals are processed in real time and transmitted to a digital analyzer that enables the user to control and follow the experiment through a graphical user interface. The developed platform is portable and capable of operating autonomously for nearly eight hours. Experimental results show that the noise level of the described platform is one order of magnitude lower than the one presented by the previously used measurement set-up. Experimental results also show that this device is able to detect magnetic nanoparticles with a diameter of 250 nm at a concentration of about 40 fM. Finally, the biomolecular recognition detection capabilities of the platform are demonstrated by performing a hybridization assay using complementary and non-complementary probes and a magnetically tagged 20mer single stranded DNA target. PMID:22408516

The Effect of the Visual Context in the Recognition of Symbolic Gestures

PubMed Central

Villarreal, Mirta F.; Fridman, Esteban A.; Leiguarda, Ramón C.

2012-01-01

Background To investigate, by means of fMRI, the influence of the visual environment in the process of symbolic gesture recognition. Emblems are semiotic gestures that use movements or hand postures to symbolically encode and communicate meaning, independently of language. They often require contextual information to be correctly understood. Until now, observation of symbolic gestures was studied against a blank background where the meaning and intentionality of the gesture was not fulfilled. Methodology/Principal Findings Normal subjects were scanned while observing short videos of an individual performing symbolic gesture with or without the corresponding visual context and the context scenes without gestures. The comparison between gestures regardless of the context demonstrated increased activity in the inferior frontal gyrus, the superior parietal cortex and the temporoparietal junction in the right hemisphere and the precuneus and posterior cingulate bilaterally, while the comparison between context and gestures alone did not recruit any of these regions. Conclusions/Significance These areas seem to be crucial for the inference of intentions in symbolic gestures observed in their natural context and represent an interrelated network formed by components of the putative human neuron mirror system as well as the mentalizing system. PMID:22363406

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

PubMed

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

2004-04-01

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

Pathways to help-seeking in bulimia nervosa and binge eating problems: a concept mapping approach.

PubMed

Hepworth, Natasha; Paxton, Susan J

2007-09-01

To conduct an in-depth study, using concept mapping, of three factors related to help-seeking for bulimia nervosa and binge eating: problem recognition, barriers to help-seeking, and prompts to help-seeking. Semistructured interviews were conducted to elicit information about help-seeking with 63 women (18-62 years) with past or present bulimic behaviors. Using Leximancer software, factors identified as associated with problem recognition were Changes in Behavior, Interference with Life Roles, Comments about Changes and Psychological Problems. Salient barriers to help-seeking were Fear of Stigma, Low Mental Health Literacy/Perception of Need, Shame, Fear of Change and Cost. Prompts to help-seeking were increased Symptom Severity, Psychological Distress, Interference with Life Roles, Health Problems, and Desire to Get Better. Results highlighted the need for awareness campaigns to reduce both self and perceived stigma by others towards bulimic behaviors, and the need to enhance awareness of available interventions for people ready to engage in treatment, to increase help-seeking. (c) 2007 by Wiley Periodicals, Inc.

Recognition of plant parts with problem-specific algorithms

NASA Astrophysics Data System (ADS)

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

1994-06-01

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

Development of a Curriculum for Long-Term Care Nurses to Improve Recognition of Depression in Dementia

ERIC Educational Resources Information Center

Williams, Christine L.; Molinari, Victor; Bond, Jennifer; Smith, Michael; Hyer, Kathryn; Malphurs, Julie

2006-01-01

There is increasing recognition of the severe consequences of depression in long-term care residents with dementia. Most health care providers are unprepared to recognize and to manage the complexity of depression in dementia. Targeted educational initiatives in nursing homes are needed to address this growing problem. This paper describes the…

The Application of Recognition-Primed Decision Theory to Decisions Made in an Outdoor Education Context

ERIC Educational Resources Information Center

Boyes, Mike; Potter, Tom

2015-01-01

This research examined the decisions that highly experienced outdoor leaders made on backpacking expeditions conducted by a tertiary institution in the Southern Alps of New Zealand. The purpose of the research was to document decision problems and explore them as Recognition-Primed Decisions (RPD) within naturalistic decision making (NDM)…

Common and Unique Impairments in Facial-Expression Recognition in Pervasive Developmental Disorder-Not Otherwise Specified and Asperger's Disorder

ERIC Educational Resources Information Center

Uono, Shota; Sato, Wataru; Toichi, Motomi

2013-01-01

This study was designed to identify specific difficulties and associated features related to the problems with social interaction experienced by individuals with pervasive developmental disorder-not otherwise specified (PDD-NOS) using an emotion-recognition task. We compared individuals with PDD-NOS or Asperger's disorder (ASP) and typically…

Leadership Styles of Principals in Successful Title I Elementary Schools

ERIC Educational Resources Information Center

Gonzales, Cameron

2016-01-01

The problem addressed in the dissertation is the relationship between high poverty and low academic achievement that persists in spite of efforts to change it. In one Western state, a small proportion of the schools that are eligible for Title I funds, a measure of poverty, have achieved recognition for high student achievement. The recognition,…

Machine Learning Through Signature Trees. Applications to Human Speech.

ERIC Educational Resources Information Center

White, George M.

A signature tree is a binary decision tree used to classify unknown patterns. An attempt was made to develop a computer program for manipulating signature trees as a general research tool for exploring machine learning and pattern recognition. The program was applied to the problem of speech recognition to test its effectiveness for a specific…

Overview of radar intra-pulse modulation recognition

NASA Astrophysics Data System (ADS)

Zang, Hanlin; Li, Yanling

2018-05-01

This paper introduces the current radar intra-pulse modulation method, describes the status quo and development direction of the intentional modulation and unintentional modulation in the pulse, and summarizes the existing problems and prospects for the future. Looking forward to the future, and providing a reference direction for the research on radar signal recognition in the next step.

Hyperspectral face recognition with spatiospectral information fusion and PLS regression.

PubMed

Uzair, Muhammad; Mahmood, Arif; Mian, Ajmal

2015-03-01

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

Relationships between alexithymia, affect recognition, and empathy after traumatic brain injury.

PubMed

Neumann, Dawn; Zupan, Barbra; Malec, James F; Hammond, Flora

2014-01-01

To determine (1) alexithymia, affect recognition, and empathy differences in participants with and without traumatic brain injury (TBI); (2) the amount of affect recognition variance explained by alexithymia; and (3) the amount of empathy variance explained by alexithymia and affect recognition. Sixty adults with moderate-to-severe TBI; 60 age and gender-matched controls. Participants were evaluated for alexithymia (difficulty identifying feelings, difficulty describing feelings, and externally-oriented thinking); facial and vocal affect recognition; and affective and cognitive empathy (empathic concern and perspective-taking, respectively). Participants with TBI had significantly higher alexithymia; poorer facial and vocal affect recognition; and lower empathy scores. For TBI participants, facial and vocal affect recognition variances were significantly explained by alexithymia (12% and 8%, respectively); however, the majority of the variances were accounted for by externally-oriented thinking alone. Affect recognition and alexithymia significantly accounted for 16.5% of cognitive empathy. Again, the majority of the variance was primarily explained by externally-oriented thinking. Affect recognition and alexithymia did not explain affective empathy. Results suggest that people who have a tendency to avoid thinking about emotions (externally-oriented thinking) are more likely to have problems recognizing others' emotions and assuming others' points of view. Clinical implications are discussed.

Towards discrete wavelet transform-based human activity recognition

NASA Astrophysics Data System (ADS)

Khare, Manish; Jeon, Moongu

2017-06-01

Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

View-Invariant Gait Recognition Through Genetic Template Segmentation

NASA Astrophysics Data System (ADS)

Isaac, Ebenezer R. H. P.; Elias, Susan; Rajagopalan, Srinivasan; Easwarakumar, K. S.

2017-08-01

Template-based model-free approach provides by far the most successful solution to the gait recognition problem in literature. Recent work discusses how isolating the head and leg portion of the template increase the performance of a gait recognition system making it robust against covariates like clothing and carrying conditions. However, most involve a manual definition of the boundaries. The method we propose, the genetic template segmentation (GTS), employs the genetic algorithm to automate the boundary selection process. This method was tested on the GEI, GEnI and AEI templates. GEI seems to exhibit the best result when segmented with our approach. Experimental results depict that our approach significantly outperforms the existing implementations of view-invariant gait recognition.

Incorporating Duration Information in Activity Recognition

NASA Astrophysics Data System (ADS)

Chaurasia, Priyanka; Scotney, Bryan; McClean, Sally; Zhang, Shuai; Nugent, Chris

Activity recognition has become a key issue in smart home environments. The problem involves learning high level activities from low level sensor data. Activity recognition can depend on several variables; one such variable is duration of engagement with sensorised items or duration of intervals between sensor activations that can provide useful information about personal behaviour. In this paper a probabilistic learning algorithm is proposed that incorporates episode, time and duration information to determine inhabitant identity and the activity being undertaken from low level sensor data. Our results verify that incorporating duration information consistently improves the accuracy.

Why Organizations Continue to Create Patient Information Leaflets with Readability and Usability Problems: An Exploratory Study

ERIC Educational Resources Information Center

Gal, Iddo; Prigat, Ayelet

2005-01-01

Readability and usability problems with patient information leaflets continue to be reported despite long-standing recognition of their existence and the availability of guidelines for developing health education materials. This exploratory study examined possible causes for such problems, based on interviews with professionals who developed…

Sino-US cooperation in water saving technologies: essential international problems

USDA-ARS?s Scientific Manuscript database

The United States and China share many agricultural problems, but one of great importance is the need to produce more crop yield in the face of water scarcity. Common recognition of this problem led to the development of a joint Sino-US Water Saving Technologies Flagship project within the larger US...

Teaching Problem-Solving Competency in Business Studies at Secondary School Level

ERIC Educational Resources Information Center

Meintjes, Aloe; Henrico, Alfred; Kroon, Japie

2015-01-01

The high unemployment rate in South Africa compels potential entrepreneurs to start their own businesses in order to survive. Often this is with little or no formal training or education in entrepreneurship. Since problem recognition and problem-solving are amongst the most crucial competencies required for a successful entrepreneurial career,…

The development of cross-cultural recognition of vocal emotion during childhood and adolescence.

PubMed

Chronaki, Georgia; Wigelsworth, Michael; Pell, Marc D; Kotz, Sonja A

2018-06-14

Humans have an innate set of emotions recognised universally. However, emotion recognition also depends on socio-cultural rules. Although adults recognise vocal emotions universally, they identify emotions more accurately in their native language. We examined developmental trajectories of universal vocal emotion recognition in children. Eighty native English speakers completed a vocal emotion recognition task in their native language (English) and foreign languages (Spanish, Chinese, and Arabic) expressing anger, happiness, sadness, fear, and neutrality. Emotion recognition was compared across 8-to-10, 11-to-13-year-olds, and adults. Measures of behavioural and emotional problems were also taken. Results showed that although emotion recognition was above chance for all languages, native English speaking children were more accurate in recognising vocal emotions in their native language. There was a larger improvement in recognising vocal emotion from the native language during adolescence. Vocal anger recognition did not improve with age for the non-native languages. This is the first study to demonstrate universality of vocal emotion recognition in children whilst supporting an "in-group advantage" for more accurate recognition in the native language. Findings highlight the role of experience in emotion recognition, have implications for child development in modern multicultural societies and address important theoretical questions about the nature of emotions.

Fine-grained recognition of plants from images.

PubMed

Šulc, Milan; Matas, Jiří

2017-01-01

Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition "in the wild". We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition "in the wild". The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition "in the wild" where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.

Hierarchical equations of motion method applied to nonequilibrium heat transport in model molecular junctions: Transient heat current and high-order moments of the current operator

NASA Astrophysics Data System (ADS)

Song, Linze; Shi, Qiang

2017-02-01

We present a theoretical approach to study nonequilibrium quantum heat transport in molecular junctions described by a spin-boson type model. Based on the Feynman-Vernon path integral influence functional formalism, expressions for the average value and high-order moments of the heat current operators are derived, which are further obtained directly from the auxiliary density operators (ADOs) in the hierarchical equations of motion (HEOM) method. Distribution of the heat current is then derived from the high-order moments. As the HEOM method is nonperturbative and capable of treating non-Markovian system-environment interactions, the method can be applied to various problems of nonequilibrium quantum heat transport beyond the weak coupling regime.

Stochastic first passage time accelerated with CUDA

NASA Astrophysics Data System (ADS)

Pierro, Vincenzo; Troiano, Luigi; Mejuto, Elena; Filatrella, Giovanni

2018-05-01

The numerical integration of stochastic trajectories to estimate the time to pass a threshold is an interesting physical quantity, for instance in Josephson junctions and atomic force microscopy, where the full trajectory is not accessible. We propose an algorithm suitable for efficient implementation on graphical processing unit in CUDA environment. The proposed approach for well balanced loads achieves almost perfect scaling with the number of available threads and processors, and allows an acceleration of about 400× with a GPU GTX980 respect to standard multicore CPU. This method allows with off the shell GPU to challenge problems that are otherwise prohibitive, as thermal activation in slowly tilted potentials. In particular, we demonstrate that it is possible to simulate the switching currents distributions of Josephson junctions in the timescale of actual experiments.

Radiation Damage Workshop

NASA Technical Reports Server (NTRS)

Stella, P. M.

1984-01-01

The availability of data regarding the radiation behavior of GaAs and silicon solar cells is discussed as well as efforts to provide sufficient information. Other materials are considered too immature for reasonable radiation evaluation. The lack of concern over the possible catastrophic radiation degradation in cascade cells is a potentially serious problem. Lithium counterdoping shows potential for removing damage in irradiated P-type material, although initial efficiencies are not comparable to current state of the art. The possibility of refining the lithium doping method to maintain high initial efficiencies and combining it with radiation tolerant structures such as thin BSF cells or vertical junction cells could provide a substantial improvement in EOL efficiencies. Laser annealing of junctions, either those formed ion implantation or diffusion, may not only improve initial cell performance but might also reduce the radiation degradation rate.

Development of SIS Mixers for 1 THz

NASA Technical Reports Server (NTRS)

Zmuidzinas, J.; Kooi, J.; Chattopadhyay, G.; Bumble, B.; LeDuc, H. G.; Stern, J. A.

1998-01-01

SIS heterodyne mixer technology based on niobium tunnel junctions has now been pushed to frequencies over 1 THz, clearly demonstrating that the SIS junctions are capable of mixing at frequencies up to twice the energy gap frequency (4 Delta/h). However, the performance degrades rapidly above the gap frequency of niobium (2 Delta/h approx. 700 GHz) due to substantial ohmic losses in the on-chip tuning circuit. To solve this problem, the tuning circuit should be fabricated using a superconducting film with a larger energy gap, such as NbN; unfortunately, NbN films often have a substantial excess surface resistance in the submillimeter band. In contrast, the SIS mixer measurements we present in this paper indicate that the losses for NbTiN thin films can be quite low.

Constraint-based stereo matching

NASA Technical Reports Server (NTRS)

Kuan, D. T.

1987-01-01

The major difficulty in stereo vision is the correspondence problem that requires matching features in two stereo images. Researchers describe a constraint-based stereo matching technique using local geometric constraints among edge segments to limit the search space and to resolve matching ambiguity. Edge segments are used as image features for stereo matching. Epipolar constraint and individual edge properties are used to determine possible initial matches between edge segments in a stereo image pair. Local edge geometric attributes such as continuity, junction structure, and edge neighborhood relations are used as constraints to guide the stereo matching process. The result is a locally consistent set of edge segment correspondences between stereo images. These locally consistent matches are used to generate higher-level hypotheses on extended edge segments and junctions to form more global contexts to achieve global consistency.

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

Recognition of face identity and emotion in expressive specific language impairment.

PubMed

Merkenschlager, A; Amorosa, H; Kiefl, H; Martinius, J

2012-01-01

To study face and emotion recognition in children with mostly expressive specific language impairment (SLI-E). A test movie to study perception and recognition of faces and mimic-gestural expression was applied to 24 children diagnosed as suffering from SLI-E and an age-matched control group of normally developing children. Compared to a normal control group, the SLI-E children scored significantly worse in both the face and expression recognition tasks with a preponderant effect on emotion recognition. The performance of the SLI-E group could not be explained by reduced attention during the test session. We conclude that SLI-E is associated with a deficiency in decoding non-verbal emotional facial and gestural information, which might lead to profound and persistent problems in social interaction and development. Copyright © 2012 S. Karger AG, Basel.

Preoperative and Postoperative CT Scan Assessment of Pterygomaxillary Junction in Patients Undergoing Le Fort I Osteotomy: Comparison of Pterygomaxillary Dysjunction Technique and Trimble Technique-A Pilot Study.

PubMed

Dadwal, Himani; Shanmugasundaram, S; Krishnakumar Raja, V B

2015-09-01

To determine the rate of complications and occurrence of pterygoid plate fractures comparing two techniques of Le Fort I osteotomy i.e., Classic Pterygomaxillary Dysjunction technique and Trimble technique and to know whether the dimensions of pterygomaxillary junction [determined preoperatively by computed tomography (CT) scan] have any influence on pterygomaxillary separation achieved during surgery. The study group consisted of eight South Indian patients with maxillary excess. A total of 16 sides were examined by CT. Preoperative CT was analyzed for all the patients. The thickness and width of the pterygomaxillary junction and the distance of the greater palatine canal from the pterygomaxillary junction was noted. Pterygomaxillary dysjunction was achieved by two techniques, the classic pterygomaxillary dysjunction technique (Group I) and Trimble technique (Group II). Patients were selected randomly and equally for both the techniques. Dysjunction was analyzed by postoperative CT. The average thickness of the pterygomaxillary junction on 16 sides was 4.5 ± 1.2 mm. Untoward pterygoid plate fractures occurred in Group I in 3 sides out of 8. In Trimble technique (Group II), no pterygoid plate fractures were noted. The average width of the pterygomaxillary junction was 7.8 ± 1.5 mm, distance of the greater palatine canal from pterygomaxillary junction was 7.4 ± 1.6 mm and the length of fusion of pterygomaxillary junction was 8.0 ± 1.9 mm. The Le Fort I osteotomy has become a standard procedure for correcting various dentofacial deformities. In an attempt to make Le Fort I osteotomy safer and avoid the problems associated with sectioning with an osteotome between the maxillary tuberosity and the pterygoid plates, Trimble suggested sectioning across the posterior aspect of the maxillary tuberosity itself. In our study, comparison between the classic pterygomaxillary dysjunction technique and the Trimble technique was made by using postoperative CT scan. It was found that unfavorable pterygoid plate fractures occurred only in dysjunction group and not in Trimble technique group. Preoperative CT scan assessment was done for all the patients to determine the dimension of the pterygomaxillary region. Preoperative CT scan proved to be helpful in not only determining the dimensions of the pterygomaxillary region but we also found out that thickness of the pterygomaxillary junction was an important parameter which may influence the separation at the pterygomaxillary region. No untoward fractures of the pterygoid plates were seen in Trimble technique (Group II) which makes it a safer technique than classic dysjunction technique. It was noted that pterygoid plate fractures occurred in patients in whom the thickness of the pterygomaxillary junction was <3.6 mm (preoperatively). Therefore, preoperative evaluation is important, on the basis of which we can decide upon the technique to be selected for safer and acceptable separation of pterygomaxillary region.

Development of high efficiency (14 percent) solar cell array module

NASA Technical Reports Server (NTRS)

Iles, P. A.; Khemthong, S.; Olah, S.; Sampson, W. J.; Ling, K. S.

1980-01-01

Most effort was concentrated on development of procedures to provide large area (3 in. diameter) high efficiency (16.5 percent AM1, 28 C) P+NN+ solar cells. Intensive tests with 3 in. slices gave consistently lower efficiency (13.5 percent). The problems were identified as incomplete formation of and optimum back surface field (BSF), and interaction of the BSF process and the shallow P+ junction. The problem was shown not to be caused by reduced quality of silicon near the edges of the larger slices.

Bounded solutions in a T-shaped waveguide and the spectral properties of the Dirichlet ladder

NASA Astrophysics Data System (ADS)

Nazarov, S. A.

2014-08-01

The Dirichlet problem is considered on the junction of thin quantum waveguides (of thickness h ≪ 1) in the shape of an infinite two-dimensional ladder. Passage to the limit as h → +0 is discussed. It is shown that the asymptotically correct transmission conditions at nodes of the corresponding one-dimensional quantum graph are Dirichlet conditions rather than the conventional Kirchhoff transmission conditions. The result is obtained by analyzing bounded solutions of a problem in the T-shaped waveguide that the boundary layer phenomenon.

Transfer learning for visual categorization: a survey.

PubMed

Shao, Ling; Zhu, Fan; Li, Xuelong

2015-05-01

Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition.

Neural network for intelligent query of an FBI forensic database

NASA Astrophysics Data System (ADS)

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

1997-02-01

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

Parental Recognition of Developmental Problems in Toddlers with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Chawarska, Katarzyna; Paul, Rhea; Klin, Ami; Hannigen, Sarah; Dichtel, Laura E.; Volkmar, Fred

2007-01-01

Symptoms of Autism Spectrum Disorders (ASD) begin to manifest during the first 2 years; there is limited evidence regarding type and timing of symptom onset. We examined factors related to parental age of recognition (AOR) of early abnormalities and the association between AOR and diagnosis and levels of functioning at 2 and 4 years in 75 toddlers…

Teaching Emotion Recognition Skills to Young Children with Autism: A Randomised Controlled Trial of an Emotion Training Programme

ERIC Educational Resources Information Center

Williams, Beth T.; Gray, Kylie M.; Tonge, Bruce J.

2012-01-01

Background: Children with autism have difficulties in emotion recognition and a number of interventions have been designed to target these problems. However, few emotion training interventions have been trialled with young children with autism and co-morbid ID. This study aimed to evaluate the efficacy of an emotion training programme for a group…

Word Recognition and Nonword Repetition in Children with Language Disorders: The Effects of Neighborhood Density, Lexical Frequency, and Phonotactic Probability

ERIC Educational Resources Information Center

Rispens, Judith; Baker, Anne; Duinmeijer, Iris

2015-01-01

Purpose: The effects of neighborhood density (ND) and lexical frequency on word recognition and the effects of phonotactic probability (PP) on nonword repetition (NWR) were examined to gain insight into processing at the lexical and sublexical levels in typically developing (TD) children and children with developmental language problems. Method:…

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

PubMed

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

2017-06-09

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

Multiclassifier information fusion methods for microarray pattern recognition

NASA Astrophysics Data System (ADS)

Braun, Jerome J.; Glina, Yan; Judson, Nicholas; Herzig-Marx, Rachel

2004-04-01

This paper addresses automatic recognition of microarray patterns, a capability that could have a major significance for medical diagnostics, enabling development of diagnostic tools for automatic discrimination of specific diseases. The paper presents multiclassifier information fusion methods for microarray pattern recognition. The input space partitioning approach based on fitness measures that constitute an a-priori gauging of classification efficacy for each subspace is investigated. Methods for generation of fitness measures, generation of input subspaces and their use in the multiclassifier fusion architecture are presented. In particular, two-level quantification of fitness that accounts for the quality of each subspace as well as the quality of individual neighborhoods within the subspace is described. Individual-subspace classifiers are Support Vector Machine based. The decision fusion stage fuses the information from mulitple SVMs along with the multi-level fitness information. Final decision fusion stage techniques, including weighted fusion as well as Dempster-Shafer theory based fusion are investigated. It should be noted that while the above methods are discussed in the context of microarray pattern recognition, they are applicable to a broader range of discrimination problems, in particular to problems involving a large number of information sources irreducible to a low-dimensional feature space.

Research and Development of Target Recognition and Location Crawling Platform based on Binocular Vision

NASA Astrophysics Data System (ADS)

Xu, Weidong; Lei, Zhu; Yuan, Zhang; Gao, Zhenqing

2018-03-01

The application of visual recognition technology in industrial robot crawling and placing operation is one of the key tasks in the field of robot research. In order to improve the efficiency and intelligence of the material sorting in the production line, especially to realize the sorting of the scattered items, the robot target recognition and positioning crawling platform based on binocular vision is researched and developed. The images were collected by binocular camera, and the images were pretreated. Harris operator was used to identify the corners of the images. The Canny operator was used to identify the images. Hough-chain code recognition was used to identify the images. The target image in the image, obtain the coordinates of each vertex of the image, calculate the spatial position and posture of the target item, and determine the information needed to capture the movement and transmit it to the robot control crawling operation. Finally, In this paper, we use this method to experiment the wrapping problem in the express sorting process The experimental results show that the platform can effectively solve the problem of sorting of loose parts, so as to achieve the purpose of efficient and intelligent sorting.

Activity inference for Ambient Intelligence through handling artifacts in a healthcare environment.

PubMed

Martínez-Pérez, Francisco E; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C; Rodríguez, Marcela D

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user.

Activity Inference for Ambient Intelligence Through Handling Artifacts in a Healthcare Environment

PubMed Central

Martínez-Pérez, Francisco E.; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C.; Rodríguez, Marcela D.

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user. PMID:22368512

In-the-wild facial expression recognition in extreme poses

NASA Astrophysics Data System (ADS)

Yang, Fei; Zhang, Qian; Zheng, Chi; Qiu, Guoping

2018-04-01

In the computer research area, facial expression recognition is a hot research problem. Recent years, the research has moved from the lab environment to in-the-wild circumstances. It is challenging, especially under extreme poses. But current expression detection systems are trying to avoid the pose effects and gain the general applicable ability. In this work, we solve the problem in the opposite approach. We consider the head poses and detect the expressions within special head poses. Our work includes two parts: detect the head pose and group it into one pre-defined head pose class; do facial expression recognize within each pose class. Our experiments show that the recognition results with pose class grouping are much better than that of direct recognition without considering poses. We combine the hand-crafted features, SIFT, LBP and geometric feature, with deep learning feature as the representation of the expressions. The handcrafted features are added into the deep learning framework along with the high level deep learning features. As a comparison, we implement SVM and random forest to as the prediction models. To train and test our methodology, we labeled the face dataset with 6 basic expressions.

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

NASA Astrophysics Data System (ADS)

Kim, Sungho

2017-06-01

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

[Analysis of the factors related to the needs of patients with cancer].

PubMed

Lee, Jung A; Lee, Sun Hee; Park, Jong Hyock; Park, Jae Hyun; Kim, Sung Gyeong; Seo, Ju Hyun

2010-05-01

Limited research has investigated the specific needs of patients with cancer. This study was performed to explore patients needs and the related factors. The data were collected by 1 National Cancer Center and 9 regional cancer centers in Korea. An interview survey was performed with using a structured questionnaire for the subjects (2,661 patients who gave written informed consent to participate) survey 4 months after diagnosis and review of medical records. Data were analyzed using t-test, ANOVA and multiple regression analysis. When comparing the relating factors related with patient needs to the sociodemographic characteristics, the female group showed a higher level of recognition for physical symptoms, social support needs. The younger group showed a significantly higher level of recognition for health care staff, psychological problems, information and education, social support, hospital services needs. In addition, the higher educated group showed a higher level of recognition for health care staff, physical symptoms, social support needs. The higher income and office workers group showed a higher level of recognition for hospital services needs. When comparing the relating factors related with patient needs to the cancer, the breast cancer group showed a higher level of recognition for all needs excluding physical symptoms, accessibility and financial support needs. The combined radiotherapy with surgery and chemotherapy group showed a higher level of recognition for psychological problems, information and education, social support needs. This study showed that needs on patient with cancer was significantly influenced by female, higher education, lower income, having religion, office worker, liver cancer, breast cancer, colon cancer, chemotherapy, and combined therapy.

Multiple template-based image matching using alpha-rooted quaternion phase correlation

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2010-04-01

In computer vision applications, image matching performed on quality-degraded imagery is difficult due to image content distortion and noise effects. State-of-the art keypoint based matchers, such as SURF and SIFT, work very well on clean imagery. However, performance can degrade significantly in the presence of high noise and clutter levels. Noise and clutter cause the formation of false features which can degrade recognition performance. To address this problem, previously we developed an extension to the classical amplitude and phase correlation forms, which provides improved robustness and tolerance to image geometric misalignments and noise. This extension, called Alpha-Rooted Phase Correlation (ARPC), combines Fourier domain-based alpha-rooting enhancement with classical phase correlation. ARPC provides tunable parameters to control the alpha-rooting enhancement. These parameter values can be optimized to tradeoff between high narrow correlation peaks, and more robust wider, but smaller peaks. Previously, we applied ARPC in the radon transform domain for logo image recognition in the presence of rotational image misalignments. In this paper, we extend ARPC to incorporate quaternion Fourier transforms, thereby creating Alpha-Rooted Quaternion Phase Correlation (ARQPC). We apply ARQPC to the logo image recognition problem. We use ARQPC to perform multiple-reference logo template matching by representing multiple same-class reference templates as quaternion-valued images. We generate recognition performance results on publicly-available logo imagery, and compare recognition results to results generated from standard approaches. We show that small deviations in reference templates of sameclass logos can lead to improved recognition performance using the joint matching inherent in ARQPC.

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

PubMed

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

2018-02-22

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

Pattern Recognition Using Artificial Neural Network: A Review

NASA Astrophysics Data System (ADS)

Kim, Tai-Hoon

Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, artificial neural network techniques theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.

The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping.

PubMed

Bahlmann, Claus; Burkhardt, Hans

2004-03-01

In this paper, we give a comprehensive description of our writer-independent online handwriting recognition system frog on hand. The focus of this work concerns the presentation of the classification/training approach, which we call cluster generative statistical dynamic time warping (CSDTW). CSDTW is a general, scalable, HMM-based method for variable-sized, sequential data that holistically combines cluster analysis and statistical sequence modeling. It can handle general classification problems that rely on this sequential type of data, e.g., speech recognition, genome processing, robotics, etc. Contrary to previous attempts, clustering and statistical sequence modeling are embedded in a single feature space and use a closely related distance measure. We show character recognition experiments of frog on hand using CSDTW on the UNIPEN online handwriting database. The recognition accuracy is significantly higher than reported results of other handwriting recognition systems. Finally, we describe the real-time implementation of frog on hand on a Linux Compaq iPAQ embedded device.

Current trends in small vocabulary speech recognition for equipment control

NASA Astrophysics Data System (ADS)

Doukas, Nikolaos; Bardis, Nikolaos G.

2017-09-01

Speech recognition systems allow human - machine communication to acquire an intuitive nature that approaches the simplicity of inter - human communication. Small vocabulary speech recognition is a subset of the overall speech recognition problem, where only a small number of words need to be recognized. Speaker independent small vocabulary recognition can find significant applications in field equipment used by military personnel. Such equipment may typically be controlled by a small number of commands that need to be given quickly and accurately, under conditions where delicate manual operations are difficult to achieve. This type of application could hence significantly benefit by the use of robust voice operated control components, as they would facilitate the interaction with their users and render it much more reliable in times of crisis. This paper presents current challenges involved in attaining efficient and robust small vocabulary speech recognition. These challenges concern feature selection, classification techniques, speaker diversity and noise effects. A state machine approach is presented that facilitates the voice guidance of different equipment in a variety of situations.

Utterance independent bimodal emotion recognition in spontaneous communication

NASA Astrophysics Data System (ADS)

Tao, Jianhua; Pan, Shifeng; Yang, Minghao; Li, Ya; Mu, Kaihui; Che, Jianfeng

2011-12-01

Emotion expressions sometimes are mixed with the utterance expression in spontaneous face-to-face communication, which makes difficulties for emotion recognition. This article introduces the methods of reducing the utterance influences in visual parameters for the audio-visual-based emotion recognition. The audio and visual channels are first combined under a Multistream Hidden Markov Model (MHMM). Then, the utterance reduction is finished by finding the residual between the real visual parameters and the outputs of the utterance related visual parameters. This article introduces the Fused Hidden Markov Model Inversion method which is trained in the neutral expressed audio-visual corpus to solve the problem. To reduce the computing complexity the inversion model is further simplified to a Gaussian Mixture Model (GMM) mapping. Compared with traditional bimodal emotion recognition methods (e.g., SVM, CART, Boosting), the utterance reduction method can give better results of emotion recognition. The experiments also show the effectiveness of our emotion recognition system when it was used in a live environment.

A Theoretical Framework for Studying Adolescent Contraceptive Use.

ERIC Educational Resources Information Center

Urberg, Kathryn A.

1982-01-01

Presents a theoretical framework for viewing adolescent contraceptive usage. The problem-solving process is used for developmentally examining the competencies that must be present for effective contraceptive use, including: problem recognition, motivation, generation of alternatives, decision making and implementation. Each aspect is discussed…

How Captain Amerika uses neural networks to fight crime

NASA Technical Reports Server (NTRS)

Rogers, Steven K.; Kabrisky, Matthew; Ruck, Dennis W.; Oxley, Mark E.

1994-01-01

Artificial neural network models can make amazing computations. These models are explained along with their application in problems associated with fighting crime. Specific problems addressed are identification of people using face recognition, speaker identification, and fingerprint and handwriting analysis (biometric authentication).

Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from spike trains by network model simulation.

PubMed

Onizuka, Miho; Hoang, Huu; Kawato, Mitsuo; Tokuda, Isao T; Schweighofer, Nicolas; Katori, Yuichi; Aihara, Kazuyuki; Lang, Eric J; Toyama, Keisuke

2013-11-01

The inferior olive (IO) possesses synaptic glomeruli, which contain dendritic spines from neighboring neurons and presynaptic terminals, many of which are inhibitory and GABAergic. Gap junctions between the spines electrically couple neighboring neurons whereas the GABAergic synaptic terminals are thought to act to decrease the effectiveness of this coupling. Thus, the glomeruli are thought to be important for determining the oscillatory and synchronized activity displayed by IO neurons. Indeed, the tendency to display such activity patterns is enhanced or reduced by the local administration of the GABA-A receptor blocker picrotoxin (PIX) or the gap junction blocker carbenoxolone (CBX), respectively. We studied the functional roles of the glomeruli by solving the inverse problem of estimating the inhibitory (gi) and gap-junctional conductance (gc) using an IO network model. This model was built upon a prior IO network model, in which the individual neurons consisted of soma and dendritic compartments, by adding a glomerular compartment comprising electrically coupled spines that received inhibitory synapses. The model was used in the forward mode to simulate spike data under PIX and CBX conditions for comparison with experimental data consisting of multi-electrode recordings of complex spikes from arrays of Purkinje cells (complex spikes are generated in a one-to-one manner by IO spikes and thus can substitute for directly measuring IO spike activity). The spatiotemporal firing dynamics of the experimental and simulation spike data were evaluated as feature vectors, including firing rates, local variation, auto-correlogram, cross-correlogram, and minimal distance, and were contracted onto two-dimensional principal component analysis (PCA) space. gc and gi were determined as the solution to the inverse problem such that the simulation and experimental spike data were closely matched in the PCA space. The goodness of the match was confirmed by an analysis of variance (ANOVA) of the PCA scores between the experimental and simulation spike data. In the PIX condition, gi was found to decrease to approximately half its control value. CBX caused an approximately 30% decrease in gc from control levels. These results support the hypothesis that the glomeruli are control points for determining the spatiotemporal characteristics of olivocerebellar activity and thus may shape its ability to convey signals to the cerebellum that may be used for motor learning or motor control purposes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Computer discrimination procedures applicable to aerial and ERTS multispectral data

NASA Technical Reports Server (NTRS)

Richardson, A. J.; Torline, R. J.; Allen, W. A.

1970-01-01

Two statistical models are compared in the classification of crops recorded on color aerial photographs. A theory of error ellipses is applied to the pattern recognition problem. An elliptical boundary condition classification model (EBC), useful for recognition of candidate patterns, evolves out of error ellipse theory. The EBC model is compared with the minimum distance to the mean (MDM) classification model in terms of pattern recognition ability. The pattern recognition results of both models are interpreted graphically using scatter diagrams to represent measurement space. Measurement space, for this report, is determined by optical density measurements collected from Kodak Ektachrome Infrared Aero Film 8443 (EIR). The EBC model is shown to be a significant improvement over the MDM model.

Image quality assessment for video stream recognition systems

NASA Astrophysics Data System (ADS)

Chernov, Timofey S.; Razumnuy, Nikita P.; Kozharinov, Alexander S.; Nikolaev, Dmitry P.; Arlazarov, Vladimir V.

2018-04-01

Recognition and machine vision systems have long been widely used in many disciplines to automate various processes of life and industry. Input images of optical recognition systems can be subjected to a large number of different distortions, especially in uncontrolled or natural shooting conditions, which leads to unpredictable results of recognition systems, making it impossible to assess their reliability. For this reason, it is necessary to perform quality control of the input data of recognition systems, which is facilitated by modern progress in the field of image quality evaluation. In this paper, we investigate the approach to designing optical recognition systems with built-in input image quality estimation modules and feedback, for which the necessary definitions are introduced and a model for describing such systems is constructed. The efficiency of this approach is illustrated by the example of solving the problem of selecting the best frames for recognition in a video stream for a system with limited resources. Experimental results are presented for the system for identity documents recognition, showing a significant increase in the accuracy and speed of the system under simulated conditions of automatic camera focusing, leading to blurring of frames.

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

PubMed Central

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-01-01

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies. PMID:29695113

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor.

PubMed

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-04-24

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

High-throughput determination of RNA structure by proximity ligation.

PubMed

Ramani, Vijay; Qiu, Ruolan; Shendure, Jay

2015-09-01

We present an unbiased method to globally resolve RNA structures through pairwise contact measurements between interacting regions. RNA proximity ligation (RPL) uses proximity ligation of native RNA followed by deep sequencing to yield chimeric reads with ligation junctions in the vicinity of structurally proximate bases. We apply RPL in both baker's yeast (Saccharomyces cerevisiae) and human cells and generate contact probability maps for ribosomal and other abundant RNAs, including yeast snoRNAs, the RNA subunit of the signal recognition particle and the yeast U2 spliceosomal RNA homolog. RPL measurements correlate with established secondary structures for these RNA molecules, including stem-loop structures and long-range pseudoknots. We anticipate that RPL will complement the current repertoire of computational and experimental approaches in enabling the high-throughput determination of secondary and tertiary RNA structures.

Genitourinary Tuberculosis: A Rare Cause of Obstructive Uropathy in Pregnancy

PubMed Central

Duryea, Elaine L.; Sheffield, Jeanne S.

2014-01-01

Background. A rare but morbid form of extrapulmonary tuberculosis (TB), genitourinary TB is an important cause of obstructive uropathy and is likely underdiagnosed in pregnancy. Case. A 30-year-old primigravida undergoing treatment for active pulmonary TB presented with anuria at 13-14-weeks gestation. Bilateral ureteral strictures above the level of the ureterovesicular junctions were seen on imaging studies. Given her pulmonary disease, her obstructive uropathy was attributed to genitourinary TB. Bilateral percutaneous nephrostomy tubes were placed during pregnancy with successful ureteral reimplantation postpartum. Conclusion. Genitourinary TB should be considered as an etiology of urinary tract pathology during pregnancy, especially in foreign-born and immunocompromised persons. Early recognition resulting in prompt treatment can prevent further deterioration of maternal renal function and optimize pregnancy outcomes. PMID:25045558

Incidents Prediction in Road Junctions Using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Hajji, Tarik; Alami Hassani, Aicha; Ouazzani Jamil, Mohammed

2018-05-01

The implementation of an incident detection system (IDS) is an indispensable operation in the analysis of the road traffics. However the IDS may, in no case, represent an alternative to the classical monitoring system controlled by the human eye. The aim of this work is to increase detection and prediction probability of incidents in camera-monitored areas. Knowing that, these areas are monitored by multiple cameras and few supervisors. Our solution is to use Artificial Neural Networks (ANN) to analyze moving objects trajectories on captured images. We first propose a modelling of the trajectories and their characteristics, after we develop a learning database for valid and invalid trajectories, and then we carry out a comparative study to find the artificial neural network architecture that maximizes the rate of valid and invalid trajectories recognition.

Cell-derived microparticles: new targets in the therapeutic management of disease.

PubMed

Roseblade, Ariane; Luk, Frederick; Rawling, Tristan; Ung, Alison; Grau, Georges E R; Bebawy, Mary

2013-01-01

Intercellular communication is essential to maintain vital physiological activities and to regulate the organism's phenotype. There are a number of ways in which cells communicate with one another. This can occur via autocrine signaling, endocrine signaling or by the transfer of molecular mediators across gap junctions. More recently communication via microvesicular shedding has gained important recognition as a significant pathway by which cells can coordinate the spread and dominance of selective traits within a population. Through this communication apparatus, cells can now acquire and secure a survival advantage, particularly in the context of malignant disease. This review aims to highlight some of the functions and implications of microparticles in physiology of various disease states, and present a novel therapeutic strategy through the regulation of microparticle production.

Recognition Using Hybrid Classifiers.

PubMed

Osadchy, Margarita; Keren, Daniel; Raviv, Dolev

2016-04-01

A canonical problem in computer vision is category recognition (e.g., find all instances of human faces, cars etc., in an image). Typically, the input for training a binary classifier is a relatively small sample of positive examples, and a huge sample of negative examples, which can be very diverse, consisting of images from a large number of categories. The difficulty of the problem sharply increases with the dimension and size of the negative example set. We propose to alleviate this problem by applying a "hybrid" classifier, which replaces the negative samples by a prior, and then finds a hyperplane which separates the positive samples from this prior. The method is extended to kernel space and to an ensemble-based approach. The resulting binary classifiers achieve an identical or better classification rate than SVM, while requiring far smaller memory and lower computational complexity to train and apply.

Comparison of the scanned pages of the contractual documents

NASA Astrophysics Data System (ADS)

Andreeva, Elena; Arlazarov, Vladimir V.; Manzhikov, Temudzhin; Slavin, Oleg

2018-04-01

In this paper the problem statement is given to compare the digitized pages of the official papers. Such problem appears during the comparison of two customer copies signed at different times between two parties with a view to find the possible modifications introduced on the one hand. This problem is a practically significant in the banking sector during the conclusion of contracts in a paper format. The method of comparison based on the recognition, which consists in the comparison of two bag-of-words, which are the recognition result of the master and test pages, is suggested. The described experiments were conducted using the OCR Tesseract and the siamese neural network. The advantages of the suggested method are the steady operation of the comparison algorithm and the high exacting precision, and one of the disadvantages is the dependence on the chosen OCR.

Enhanced iris recognition method based on multi-unit iris images

NASA Astrophysics Data System (ADS)

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 averaged equal error rate of iris recognition using the proposed method was 4.3006%, which is lower than that of other methods.

Current-Induced Transistor Sensorics with Electrogenic Cells

PubMed Central

Fromherz, Peter

2016-01-01

The concepts of transistor recording of electroactive cells are considered, when the response is determined by a current-induced voltage in the electrolyte due to cellular activity. The relationship to traditional transistor recording, with an interface-induced response due to interactions with the open gate oxide, is addressed. For the geometry of a cell-substrate junction, the theory of a planar core-coat conductor is described with a one-compartment approximation. The fast electrical relaxation of the junction and the slow change of ion concentrations are pointed out. On that basis, various recording situations are considered and documented by experiments. For voltage-gated ion channels under voltage clamp, the effects of a changing extracellular ion concentration and the enhancement/depletion of ion conductances in the adherent membrane are addressed. Inhomogeneous ion conductances are crucial for transistor recording of neuronal action potentials. For a propagating action potential, the effects of an axon-substrate junction and the surrounding volume conductor are distinguished. Finally, a receptor-transistor-sensor is described, where the inhomogeneity of a ligand–activated ion conductance is achieved by diffusion of the agonist and inactivation of the conductance. Problems with regard to a development of reliable biosensors are mentioned. PMID:27120627

A Physics-Based Engineering Approach to Predict the Cross Section for Advanced SRAMs

NASA Astrophysics Data System (ADS)

Li, Lei; Zhou, Wanting; Liu, Huihua

2012-12-01

This paper presents a physics-based engineering approach to estimate the heavy ion induced upset cross section for 6T SRAM cells from layout and technology parameters. The new approach calculates the effects of radiation with junction photocurrent, which is derived based on device physics. The new and simple approach handles the problem by using simple SPICE simulations. At first, the approach uses a standard SPICE program on a typical PC to predict the SPICE-simulated curve of the collected charge vs. its affected distance from the drain-body junction with the derived junction photocurrent. And then, the SPICE-simulated curve is used to calculate the heavy ion induced upset cross section with a simple model, which considers that the SEU cross section of a SRAM cell is more related to a “radius of influence” around a heavy ion strike than to the physical size of a diffusion node in the layout for advanced SRAMs in nano-scale process technologies. The calculated upset cross section based on this method is in good agreement with the test results for 6T SRAM cells processed using 90 nm process technology.

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

NASA Astrophysics Data System (ADS)

Wang, Chao; Wang, Yunhong; Zhang, Zhaoxiang

2013-10-01

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

Higher-Order Neural Networks Applied to 2D and 3D Object Recognition

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly; Reid, Max B.

1994-01-01

A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.

Facial emotion recognition in paranoid schizophrenia and autism spectrum disorder.

PubMed

Sachse, Michael; Schlitt, Sabine; Hainz, Daniela; Ciaramidaro, Angela; Walter, Henrik; Poustka, Fritz; Bölte, Sven; Freitag, Christine M

2014-11-01

Schizophrenia (SZ) and autism spectrum disorder (ASD) share deficits in emotion processing. In order to identify convergent and divergent mechanisms, we investigated facial emotion recognition in SZ, high-functioning ASD (HFASD), and typically developed controls (TD). Different degrees of task difficulty and emotion complexity (face, eyes; basic emotions, complex emotions) were used. Two Benton tests were implemented in order to elicit potentially confounding visuo-perceptual functioning and facial processing. Nineteen participants with paranoid SZ, 22 with HFASD and 20 TD were included, aged between 14 and 33 years. Individuals with SZ were comparable to TD in all obtained emotion recognition measures, but showed reduced basic visuo-perceptual abilities. The HFASD group was impaired in the recognition of basic and complex emotions compared to both, SZ and TD. When facial identity recognition was adjusted for, group differences remained for the recognition of complex emotions only. Our results suggest that there is a SZ subgroup with predominantly paranoid symptoms that does not show problems in face processing and emotion recognition, but visuo-perceptual impairments. They also confirm the notion of a general facial and emotion recognition deficit in HFASD. No shared emotion recognition deficit was found for paranoid SZ and HFASD, emphasizing the differential cognitive underpinnings of both disorders. Copyright © 2014 Elsevier B.V. All rights reserved.

Two-Dimensional Grammars And Their Applications To Artificial Intelligence

NASA Astrophysics Data System (ADS)

Lee, Edward T.

1987-05-01

During the past several years, the concepts and techniques of two-dimensional grammars1,2 have attracted growing attention as promising avenues of approach to problems in picture generation as well as in picture description3 representation, recognition, transformation and manipulation. Two-dimensional grammar techniques serve the purpose of exploiting the structure or underlying relationships in a picture. This approach attempts to describe a complex picture in terms of their components and their relative positions. This resembles the way a sentence is described in terms of its words and phrases, and the terms structural picture recognition, linguistic picture recognition, or syntactic picture recognition are often used. By using this approach, the problem of picture recognition becomes similar to that of phrase recognition in a language. However, describing pictures using a string grammar (one-dimensional grammar), the only relation between sub-pictures and/or primitives is the concatenation; that is each picture or primitive can be connected only at the left or right. This one-dimensional relation has not been very effective in describing two-dimensional pictures. A natural generaliza-tion is to use two-dimensional grammars. In this paper, two-dimensional grammars and their applications to artificial intelligence are presented. Picture grammars and two-dimensional grammars are introduced and illustrated by examples. In particular, two-dimensional grammars for generating all possible squares and all possible rhombuses are presented. The applications of two-dimensional grammars to solving region filling problems are discussed. An algorithm for region filling using two-dimensional grammars is presented together with illustrative examples. The advantages of using this algorithm in terms of computation time are also stated. A high-level description of a two-level picture generation system is proposed. The first level is the picture primitive generation using two-dimensional grammars. The second level is picture generation using either string description or entity-relationship (ER) diagram description. Illustrative examples are also given. The advantages of ER diagram description together with its comparison to string description are also presented. The results obtained in this paper may have useful applications in artificial intelligence, robotics, expert systems, picture processing, pattern recognition, knowledge engineering and pictorial database design. Furthermore, examples related to satellite surveillance and identifications are also included.

Building Hierarchical Representations for Oracle Character and Sketch Recognition.

PubMed

Jun Guo; Changhu Wang; Roman-Rangel, Edgar; Hongyang Chao; Yong Rui

2016-01-01

In this paper, we study oracle character recognition and general sketch recognition. First, a data set of oracle characters, which are the oldest hieroglyphs in China yet remain a part of modern Chinese characters, is collected for analysis. Second, typical visual representations in shape- and sketch-related works are evaluated. We analyze the problems suffered when addressing these representations and determine several representation design criteria. Based on the analysis, we propose a novel hierarchical representation that combines a Gabor-related low-level representation and a sparse-encoder-related mid-level representation. Extensive experiments show the effectiveness of the proposed representation in both oracle character recognition and general sketch recognition. The proposed representation is also complementary to convolutional neural network (CNN)-based models. We introduce a solution to combine the proposed representation with CNN-based models, and achieve better performances over both approaches. This solution has beaten humans at recognizing general sketches.

Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors

PubMed Central

Hong, Hyung Gil; Lee, Min Beom; Park, Kang Ryoung

2017-01-01

Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods. PMID:28587269

Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors.

PubMed

Hong, Hyung Gil; Lee, Min Beom; Park, Kang Ryoung

2017-06-06

Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.

Intelligent fault recognition strategy based on adaptive optimized multiple centers

NASA Astrophysics Data System (ADS)

Zheng, Bo; Li, Yan-Feng; Huang, Hong-Zhong

2018-06-01

For the recognition principle based optimized single center, one important issue is that the data with nonlinear separatrix cannot be recognized accurately. In order to solve this problem, a novel recognition strategy based on adaptive optimized multiple centers is proposed in this paper. This strategy recognizes the data sets with nonlinear separatrix by the multiple centers. Meanwhile, the priority levels are introduced into the multi-objective optimization, including recognition accuracy, the quantity of optimized centers, and distance relationship. According to the characteristics of various data, the priority levels are adjusted to ensure the quantity of optimized centers adaptively and to keep the original accuracy. The proposed method is compared with other methods, including support vector machine (SVM), neural network, and Bayesian classifier. The results demonstrate that the proposed strategy has the same or even better recognition ability on different distribution characteristics of data.

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

PubMed

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.

A depictive neural model for the representation of motion verbs.

PubMed

Rao, Sunil; Aleksander, Igor

2011-11-01

In this paper, we present a depictive neural model for the representation of motion verb semantics in neural models of visual awareness. The problem of modelling motion verb representation is shown to be one of function application, mapping a set of given input variables defining the moving object and the path of motion to a defined output outcome in the motion recognition context. The particular function-applicative implementation and consequent recognition model design presented are seen as arising from a noun-adjective recognition model enabling the recognition of colour adjectives as applied to a set of shapes representing objects to be recognised. The presence of such a function application scheme and a separately implemented position identification and path labelling scheme are accordingly shown to be the primitives required to enable the design and construction of a composite depictive motion verb recognition scheme. Extensions to the presented design to enable the representation of transitive verbs are also discussed.

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

PubMed

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

2003-01-01

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

Crowd motion segmentation and behavior recognition fusing streak flow and collectiveness

NASA Astrophysics Data System (ADS)

Gao, Mingliang; Jiang, Jun; Shen, Jin; Zou, Guofeng; Fu, Guixia

2018-04-01

Crowd motion segmentation and crowd behavior recognition are two hot issues in computer vision. A number of methods have been proposed to tackle these two problems. Among the methods, flow dynamics is utilized to model the crowd motion, with little consideration of collective property. Moreover, the traditional crowd behavior recognition methods treat the local feature and dynamic feature separately and overlook the interconnection of topological and dynamical heterogeneity in complex crowd processes. A crowd motion segmentation method and a crowd behavior recognition method are proposed based on streak flow and crowd collectiveness. The streak flow is adopted to reveal the dynamical property of crowd motion, and the collectiveness is incorporated to reveal the structure property. Experimental results show that the proposed methods improve the crowd motion segmentation accuracy and the crowd recognition rates compared with the state-of-the-art methods.

Emotion recognition impairment in traumatic brain injury compared with schizophrenia spectrum: similar deficits with different origins.

PubMed

Mancuso, Mauro; Magnani, Nadia; Cantagallo, Anna; Rossi, Giulia; Capitani, Donatella; Galletti, Vania; Cardamone, Giuseppe; Robertson, Ian Hamilton

2015-02-01

The aim of our study was to identify the common and separate mechanisms that might underpin emotion recognition impairment in patients with traumatic brain injury (TBI) and schizophrenia (Sz) compared with healthy controls (HCs). We recruited 21 Sz outpatients, 24 severe TBI outpatients, and 38 HCs, and we used eye-tracking to compare facial emotion processing performance. Both Sz and TBI patients were significantly poorer at recognizing facial emotions compared with HC. Sz patients showed a different way of exploring the Pictures of Facial Affects stimuli and were significantly worse in recognition of neutral expressions. Selective or sustained attention deficits in TBI may reduce efficient emotion recognition, whereas in Sz, there is a more strategic deficit underlying the observed problem. There would seem to be scope for adjustment of effective rehabilitative training focused on emotion recognition.

Information-based approach to performance estimation and requirements allocation in multisensor fusion for target recognition

NASA Astrophysics Data System (ADS)

Harney, Robert C.

1997-03-01

A novel methodology offering the potential for resolving two of the significant problems of implementing multisensor target recognition systems, i.e., the rational selection of a specific sensor suite and optimal allocation of requirements among sensors, is presented. Based on a sequence of conjectures (and their supporting arguments) concerning the relationship of extractable information content to recognition performance of a sensor system, a set of heuristics (essentially a reformulation of Johnson's criteria applicable to all sensor and data types) is developed. An approach to quantifying the information content of sensor data is described. Coupling this approach with the widely accepted Johnson's criteria for target recognition capabilities results in a quantitative method for comparing the target recognition ability of diverse sensors (imagers, nonimagers, active, passive, electromagnetic, acoustic, etc.). Extension to describing the performance of multiple sensors is straightforward. The application of the technique to sensor selection and requirements allocation is discussed.

The recognition of facial emotion expressions in Parkinson's disease.

PubMed

Assogna, Francesca; Pontieri, Francesco E; Caltagirone, Carlo; Spalletta, Gianfranco

2008-11-01

A limited number of studies in Parkinson's Disease (PD) suggest a disturbance of recognition of facial emotion expressions. In particular, disgust recognition impairment has been reported in unmedicated and medicated PD patients. However, the results are rather inconclusive in the definition of the degree and the selectivity of emotion recognition impairment, and an associated impairment of almost all basic facial emotions in PD is also described. Few studies have investigated the relationship with neuropsychiatric and neuropsychological symptoms with mainly negative results. This inconsistency may be due to many different problems, such as emotion assessment, perception deficit, cognitive impairment, behavioral symptoms, illness severity and antiparkinsonian therapy. Here we review the clinical characteristics and neural structures involved in the recognition of specific facial emotion expressions, and the plausible role of dopamine transmission and dopamine replacement therapy in these processes. It is clear that future studies should be directed to clarify all these issues.

Finger vein recognition based on finger crease location

NASA Astrophysics Data System (ADS)

Lu, Zhiying; Ding, Shumeng; Yin, Jing

2016-07-01

Finger vein recognition technology has significant advantages over other methods in terms of accuracy, uniqueness, and stability, and it has wide promising applications in the field of biometric recognition. We propose using finger creases to locate and extract an object region. Then we use linear fitting to overcome the problem of finger rotation in the plane. The method of modular adaptive histogram equalization (MAHE) is presented to enhance image contrast and reduce computational cost. To extract the finger vein features, we use a fusion method, which can obtain clear and distinguishable vein patterns under different conditions. We used the Hausdorff average distance algorithm to examine the recognition performance of the system. The experimental results demonstrate that MAHE can better balance the recognition accuracy and the expenditure of time compared with three other methods. Our resulting equal error rate throughout the total procedure was 3.268% in a database of 153 finger vein images.

The biometric-based module of smart grid system

NASA Astrophysics Data System (ADS)

Engel, E.; Kovalev, I. V.; Ermoshkina, A.

2015-10-01

Within Smart Grid concept the flexible biometric-based module base on Principal Component Analysis (PCA) and selective Neural Network is developed. The formation of the selective Neural Network the biometric-based module uses the method which includes three main stages: preliminary processing of the image, face localization and face recognition. Experiments on the Yale face database show that (i) selective Neural Network exhibits promising classification capability for face detection, recognition problems; and (ii) the proposed biometric-based module achieves near real-time face detection, recognition speed and the competitive performance, as compared to some existing subspaces-based methods.

Handwritten Word Recognition Using Multi-view Analysis

NASA Astrophysics Data System (ADS)

de Oliveira, J. J.; de A. Freitas, C. O.; de Carvalho, J. M.; Sabourin, R.

This paper brings a contribution to the problem of efficiently recognizing handwritten words from a limited size lexicon. For that, a multiple classifier system has been developed that analyzes the words from three different approximation levels, in order to get a computational approach inspired on the human reading process. For each approximation level a three-module architecture composed of a zoning mechanism (pseudo-segmenter), a feature extractor and a classifier is defined. The proposed application is the recognition of the Portuguese handwritten names of the months, for which a best recognition rate of 97.7% was obtained, using classifier combination.

Remote sensing techniques applied to multispectral recognition of the Aranjuez pilot zone

NASA Technical Reports Server (NTRS)

Lemos, G. L.; Salinas, J.; Rebollo, M.

1977-01-01

A rectangular (7 x 14 km) area 40 km S of Madrid was remote-sensed with a three-stage recognition process. Ground truth was established in the first phase, airborne sensing with a multispectral scanner and photographic cameras were used in the second phase, and Landsat satellite data were obtained in the third phase. Agronomic and hydrological photointerpretation problems are discussed. Color, black/white, and labeled areas are displayed for crop recognition in the land-use survey; turbidity, concentrations of pollutants and natural chemicals, and densitometry of the water are considered in the evaluation of water resources.

Report of Defense Science Board Task Force on Industry-to-Industry International Armaments Cooperation. Phase II. Japan

DTIC Science & Technology

1984-06-01

TEMPERATURE MAT’LS IMAGE RECOGNITION ROCKET PROPULSION SPEECH RECOGNITION/TRANSLATION COMPUTER-AIDED DESIGN ARTIFICIAL INTELLIGENCE PRODUCTION TECHNOLOGY...planning, intelligence exchange, and logistics. While not called out in the Guidelines, any further standardization in equipments and interoperability...COST AND TIME THAN DEVELCPING THEM -ESTABLISHMENT OF PRODUCTIVE LONG-TERM BUSINESS RELATIONSH IPS WITH JAPANESE COMPAN IES * PROBLEM -POSSIBILITY OF

Viewpoint Invariant Gesture Recognition and 3D Hand Pose Estimation Using RGB-D

ERIC Educational Resources Information Center

Doliotis, Paul

2013-01-01

The broad application domain of the work presented in this thesis is pattern classification with a focus on gesture recognition and 3D hand pose estimation. One of the main contributions of the proposed thesis is a novel method for 3D hand pose estimation using RGB-D. Hand pose estimation is formulated as a database retrieval problem. The proposed…

Automated Target Acquisition, Recognition and Tracking (ATTRACT). Phase 1

NASA Technical Reports Server (NTRS)

Abdallah, Mahmoud A.

1995-01-01

The primary objective of phase 1 of this research project is to conduct multidisciplinary research that will contribute to fundamental scientific knowledge in several of the USAF critical technology areas. Specifically, neural networks, signal processing techniques, and electro-optic capabilities are utilized to solve problems associated with automated target acquisition, recognition, and tracking. To accomplish the stated objective, several tasks have been identified and were executed.

Effects of An Integrated Format for Reading Instruction on the Comprehension and Word-Recognition Performance of Fourth- and Fifth-Grade Students Who Exhibit Severe Reading Problems.

ERIC Educational Resources Information Center

Parmer, Lavada Jacumin; Thames, Dana G.; Kazelskis, Richard

A study examined the effectiveness of an integrated language arts instructional format for teaching reading compared with the effectiveness of the typical traditional reading program. The study investigated the effectiveness of approaches that are representative of both viewpoints of the reading process (i.e., word recognition and the construction…

Plasticized Polymer Interlayer for Low-Temperature Fabrication of a High-Quality Silver Nanowire-Based Flexible Transparent and Conductive Film

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

Jo, Wonhee; Kang, Hong Suk; Choi, Jaeho

Silver nanowires (AgNWs) are one of the most promising materials to replace commercially available indium tin oxide in flexible transparent conductive films (TCFs); however, there are still numerous problems originating from poor AgNW junction formation and improper AgNW embedment into transparent substrates. To mitigate these problems, high-temperature processes have been adopted; however, unwanted substrate deformation prevents the use of these processes for the formation of flexible TCFs. In this work, we present a novel poly(methyl methacrylate) interlayer plasticized by dibutyl phthalate for low-temperature fabrication of AgNW-based TCFs, which does not cause any substrate deformation. By exploiting the viscoelastic properties ofmore » the plasticized interlayer near the lowered glass-transition temperature, a monolithic junction of AgNWs on the interlayer and embedment of the interconnected AgNWs into the interlayer are achieved in a single-step pressing. The resulting AgNW-TCFs are highly transparent (~92% at a wavelength of 550 nm), highly conductive (<90 Ω/sq), and environmentally and mechanically robust. Therefore, the plasticized interlayer provides a simple and effective route to fabricate high-quality AgNW-based TCFs.« less

Local pulmonary structure classification for computer-aided nodule detection

NASA Astrophysics Data System (ADS)

Bahlmann, Claus; Li, Xianlin; Okada, Kazunori

2006-03-01

We propose a new method of classifying the local structure types, such as nodules, vessels, and junctions, in thoracic CT scans. This classification is important in the context of computer aided detection (CAD) of lung nodules. The proposed method can be used as a post-process component of any lung CAD system. In such a scenario, the classification results provide an effective means of removing false positives caused by vessels and junctions thus improving overall performance. As main advantage, the proposed solution transforms the complex problem of classifying various 3D topological structures into much simpler 2D data clustering problem, to which more generic and flexible solutions are available in literature, and which is better suited for visualization. Given a nodule candidate, first, our solution robustly fits an anisotropic Gaussian to the data. The resulting Gaussian center and spread parameters are used to affine-normalize the data domain so as to warp the fitted anisotropic ellipsoid into a fixed-size isotropic sphere. We propose an automatic method to extract a 3D spherical manifold, containing the appropriate bounding surface of the target structure. Scale selection is performed by a data driven entropy minimization approach. The manifold is analyzed for high intensity clusters, corresponding to protruding structures. Techniques involve EMclustering with automatic mode number estimation, directional statistics, and hierarchical clustering with a modified Bhattacharyya distance. The estimated number of high intensity clusters explicitly determines the type of pulmonary structures: nodule (0), attached nodule (1), vessel (2), junction (>3). We show accurate classification results for selected examples in thoracic CT scans. This local procedure is more flexible and efficient than current state of the art and will help to improve the accuracy of general lung CAD systems.

DFT treatment of transport through Anderson junction: exact results and approximations

NASA Astrophysics Data System (ADS)

Burke, Kieron

2012-02-01

Since the pioneering break-junction experiments of Reed and Tour measuring the conductance of dithiolated benzene between gold leads, many researchers in physics and chemistry have been calculating conductance for such systems using density functional theory (DFT). Off resonance, the predicted current is often 10-100 times larger than that measured. This error is often ascribed to the application of ground-state DFT to a non-equilibrium problem. I will argue that, in fact, this is largely due to errors in the density functional approximations in popular use, rather than necessarily errors in the methodology. A stark illustration of this principle is the ability of DFT to reproduce the exact transmission through an Anderson junction at zero-temperature and weak bias, including the Kondo plateau, but only if the exact ground-state density functional is used. In fact, this case can be used to reverse-engineer the exact functional for this problem. Popular approximations can also be tested, including both smooth and discontinuous functionals of the density, as well as symmetry-broken approaches. [4pt] [1] Kondo effect given exactly by density functional theory, J. P. Bergfield, Z. Liu, K. Burke, and C. A. Stafford, arXiv:1106.3104; [0pt] [2] Broadening of the Derivative Discontinuity in Density Functional Theory, F. Evers, and P. Schmitteckert, arXiv:1106.3658; [0pt] [3] DFT-based transport calculations, Friedel's sum rule and the Kondo effect, P. Tr"oster, P. Schmitteckert, and F. Evers, arXiv:1106.3669; [0pt] [4] Towards a description of the Kondo effect using time-dependent density functional theory, G. Stefanucci, and S. Kurth, arXiv:1106.3728.

Heterogeneous iris image hallucination using sparse representation on a learned heterogeneous patch dictionary

NASA Astrophysics Data System (ADS)

Li, Yung-Hui; Zheng, Bo-Ren; Ji, Dai-Yan; Tien, Chung-Hao; Liu, Po-Tsun

2014-09-01

Cross sensor iris matching may seriously degrade the recognition performance because of the sensor mis-match problem of iris images between the enrollment and test stage. In this paper, we propose two novel patch-based heterogeneous dictionary learning method to attack this problem. The first method applies the latest sparse representation theory while the second method tries to learn the correspondence relationship through PCA in heterogeneous patch space. Both methods learn the basic atoms in iris textures across different image sensors and build connections between them. After such connections are built, at test stage, it is possible to hallucinate (synthesize) iris images across different sensors. By matching training images with hallucinated images, the recognition rate can be successfully enhanced. The experimental results showed the satisfied results both visually and in terms of recognition rate. Experimenting with an iris database consisting of 3015 images, we show that the EER is decreased 39.4% relatively by the proposed method.

The location and recognition of anti-counterfeiting code image with complex background

NASA Astrophysics Data System (ADS)

Ni, Jing; Liu, Quan; Lou, Ping; Han, Ping

2017-07-01

The order of cigarette market is a key issue in the tobacco business system. The anti-counterfeiting code, as a kind of effective anti-counterfeiting technology, can identify counterfeit goods, and effectively maintain the normal order of market and consumers' rights and interests. There are complex backgrounds, light interference and other problems in the anti-counterfeiting code images obtained by the tobacco recognizer. To solve these problems, the paper proposes a locating method based on Susan operator, combined with sliding window and line scanning,. In order to reduce the interference of background and noise, we extract the red component of the image and convert the color image into gray image. For the confusing characters, recognition results correction based on the template matching method has been adopted to improve the recognition rate. In this method, the anti-counterfeiting code can be located and recognized correctly in the image with complex background. The experiment results show the effectiveness and feasibility of the approach.

Drosophila Kette coordinates myoblast junction dissolution and the ratio of Scar-to-WASp during myoblast fusion

PubMed Central

Hamp, Julia; Löwer, Andreas; Dottermusch-Heidel, Christine; Beck, Lothar; Moussian, Bernard; Flötenmeyer, Matthias

2016-01-01

ABSTRACT The fusion of founder cells and fusion-competent myoblasts (FCMs) is crucial for muscle formation in Drosophila. Characteristic events of myoblast fusion include the recognition and adhesion of myoblasts, and the formation of branched F-actin by the Arp2/3 complex at the site of cell–cell contact. At the ultrastructural level, these events are reflected by the appearance of finger-like protrusions and electron-dense plaques that appear prior to fusion. Severe defects in myoblast fusion are caused by the loss of Kette (a homolog of Nap1 and Hem-2, also known as NCKAP1 and NCKAP1L, respectively), a member of the regulatory complex formed by Scar or WAVE proteins (represented by the single protein, Scar, in flies). kette mutants form finger-like protrusions, but the electron-dense plaques are extended. Here, we show that the electron-dense plaques in wild-type and kette mutant myoblasts resemble other electron-dense structures that are known to function as cellular junctions. Furthermore, analysis of double mutants and attempts to rescue the kette mutant phenotype with N-cadherin, wasp and genes of members of the regulatory Scar complex revealed that Kette has two functions during myoblast fusion. First, Kette controls the dissolution of electron-dense plaques. Second, Kette controls the ratio of the Arp2/3 activators Scar and WASp in FCMs. PMID:27521427

Automated array assembly, phase 2

NASA Technical Reports Server (NTRS)

Carbajal, B. G.

1979-01-01

Tasks of scaling up the tandem junction cell (TJC) from 2 cm x 2 cm to 6.2 cm and the assembly of several modules using these large area TJC's are described. The scale-up of the TJC was based on using the existing process and doing the necessary design activities to increase the cell area to an acceptably large area. The design was carried out using available device models. The design was verified and sample large area TJCs were fabricated. Mechanical and process problems occurred causing a schedule slippage that resulted in contract expiration before enough large-area TJCs were fabricated to populate the sample tandem junction modules (TJM). A TJM design was carried out in which the module interconnects served to augment the current collecting buses on the cell. No sample TJMs were assembled due to a shortage of large-area TJCs.

Applying the Network Simulation Method for testing chaos in a resistively and capacitively shunted Josephson junction model

NASA Astrophysics Data System (ADS)

Bellver, Fernando Gimeno; Garratón, Manuel Caravaca; Soto Meca, Antonio; López, Juan Antonio Vera; Guirao, Juan L. G.; Fernández-Martínez, Manuel

In this paper, we explore the chaotic behavior of resistively and capacitively shunted Josephson junctions via the so-called Network Simulation Method. Such a numerical approach establishes a formal equivalence among physical transport processes and electrical networks, and hence, it can be applied to efficiently deal with a wide range of differential systems. The generality underlying that electrical equivalence allows to apply the circuit theory to several scientific and technological problems. In this work, the Fast Fourier Transform has been applied for chaos detection purposes and the calculations have been carried out in PSpice, an electrical circuit software. Overall, it holds that such a numerical approach leads to quickly computationally solve Josephson differential models. An empirical application regarding the study of the Josephson model completes the paper.

Progress Report on the Development of Child Abuse Prevention, Identification, and Treatment Systems in Eastern Europe

ERIC Educational Resources Information Center

Lewis, Owen; Sargent, John; Chaffin, Mark; Friedrich, William N.; Cunningham, Nicholas; Cantor, Pamela; Coffey, Pamela Sumner; Villani, Susan; Beard, Philip R.; Clifft, Mary Ann; Greenspun, David

2004-01-01

Problem: After the Soviet Union dissolved in 1989, it became apparent that there was little recognition of the problems of child abuse and neglect, professionally, legally, or societally. There were no effective systems or laws in place to deal with these problems. Method: Beginning in 1995 the Children's Mental Health Alliance, in conjunction…

Application of the SNoW machine learning paradigm to a set of transportation imaging problems

NASA Astrophysics Data System (ADS)

Paul, Peter; Burry, Aaron M.; Wang, Yuheng; Kozitsky, Vladimir

2012-01-01

Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.

Combining feature extraction and classification for fNIRS BCIs by regularized least squares optimization.

PubMed

Heger, Dominic; Herff, Christian; Schultz, Tanja

2014-01-01

In this paper, we show that multiple operations of the typical pattern recognition chain of an fNIRS-based BCI, including feature extraction and classification, can be unified by solving a convex optimization problem. We formulate a regularized least squares problem that learns a single affine transformation of raw HbO(2) and HbR signals. We show that this transformation can achieve competitive results in an fNIRS BCI classification task, as it significantly improves recognition of different levels of workload over previously published results on a publicly available n-back data set. Furthermore, we visualize the learned models and analyze their spatio-temporal characteristics.

Single molecule junction conductance and binding geometry

NASA Astrophysics Data System (ADS)

Kamenetska, Maria

This Thesis addresses the fundamental problem of controlling transport through a metal-organic interface by studying electronic and mechanical properties of single organic molecule-metal junctions. Using a Scanning Tunneling Microscope (STM) we image, probe energy-level alignment and perform STM-based break junction (BJ) measurements on molecules bound to a gold surface. Using Scanning Tunneling Microscope-based break-junction (STM-BJ) techniques, we explore the effect of binding geometry on single-molecule conductance by varying the structure of the molecules, metal-molecule binding chemistry and by applying sub-nanometer manipulation control to the junction. These experiments are performed both in ambient conditions and in ultra high vacuum (UHV) at cryogenic temperatures. First, using STM imaging and scanning tunneling spectroscopy (STS) measurements we explore binding configurations and electronic properties of an amine-terminated benzene derivative on gold. We find that details of metal-molecule binding affect energy-level alignment at the interface. Next, using the STM-BJ technique, we form and rupture metal-molecule-metal junctions ˜104 times to obtain conductance-vs-extension curves and extract most likely conductance values for each molecule. With these measurements, we demonstrated that the control of junction conductance is possible through a choice of metal-molecule binding chemistry and sub-nanometer positioning. First, we show that molecules terminated with amines, sulfides and phosphines bind selectively on gold and therefore demonstrate constant conductance levels even as the junction is elongated and the metal-molecule attachment point is modified. Such well-defined conductance is also obtained with paracyclophane molecules which bind to gold directly through the pi system. Next, we are able to create metal-molecule-metal junctions with more than one reproducible conductance signatures that can be accessed by changing junction geometry. In the case of pyridine-linked molecules, conductance can be reliably switched between two distinct conductance states using sub-nanometer mechanical manipulation. Using a methyl sulfide linker attached to an oligoene backbone, we are able to create a 3-nm-long molecular potentiometer, whose resistance can be tuned exponentially with Angstom-scale modulations in metal-molecule configuration. These experiments points to a new paradigm for attaining reproducible electrical characteristics of metal-organic devices which involves controlling linker-metal chemistry rather than fabricating identically structured metal-molecule interfaces. By choosing a linker group which is either insensitive to or responds reproducibly to changes in metal-molecule configuration, one can design single molecule devices with functionality more complex than a simple resistor. These ambient temperature experiments were combined with UHV conductance measurements performed in a commercial STM on amine-terminated benzene derivatives which conduct through a non-resonant tunneling mechanism, at temperatures varying from 5 to 300 Kelvin. Our results indicate that while amine-gold binding remains selective irrespective of environment, conductance is not temperature independent, in contrast to what is expected for a tunneling mechanism. Furthermore, using temperature-dependent measurements in ambient conditions we find that HOMO-conducting amines and LUMO-conducting pyridines show opposite dependence of conductance on temperature. These results indicate that energy-level alignment between the molecule and the electrodes changes as a result of varying electrode structure at different temperatures. We find that temperature can serve as a knob with which to tune transport properties of single molecule-metal junctions.

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

NASA Astrophysics Data System (ADS)

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.

Intelligent Facial Recognition Systems: Technology advancements for security applications

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

Beer, C.L.

1993-07-01

Insider problems such as theft and sabotage can occur within the security and surveillance realm of operations when unauthorized people obtain access to sensitive areas. A possible solution to these problems is a means to identify individuals (not just credentials or badges) in a given sensitive area and provide full time personnel accountability. One approach desirable at Department of Energy facilities for access control and/or personnel identification is an Intelligent Facial Recognition System (IFRS) that is non-invasive to personnel. Automatic facial recognition does not require the active participation of the enrolled subjects, unlike most other biological measurement (biometric) systems (e.g.,more » fingerprint, hand geometry, or eye retinal scan systems). It is this feature that makes an IFRS attractive for applications other than access control such as emergency evacuation verification, screening, and personnel tracking. This paper discusses current technology that shows promising results for DOE and other security applications. A survey of research and development in facial recognition identified several companies and universities that were interested and/or involved in the area. A few advanced prototype systems were also identified. Sandia National Laboratories is currently evaluating facial recognition systems that are in the advanced prototype stage. The initial application for the evaluation is access control in a controlled environment with a constant background and with cooperative subjects. Further evaluations will be conducted in a less controlled environment, which may include a cluttered background and subjects that are not looking towards the camera. The outcome of the evaluations will help identify areas of facial recognition systems that need further development and will help to determine the effectiveness of the current systems for security applications.« less

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

NASA Astrophysics Data System (ADS)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

Executive Control and Dimensions of Problem Behaviors in Preschool Children

ERIC Educational Resources Information Center

Espy, Kimberly Andrews; Sheffield, Tiffany D.; Wiebe, Sandra A.; Clark, Caron A. C.; Moehr, Matthew J.

2011-01-01

Background: Despite the widespread recognition of the importance of executive control (EC) in externalizing psychopathology, the relation between EC and problem behavior has not been well characterized, particularly in typically developing preschoolers. Method: Using the sample, battery of laboratory tasks, and latent variable modeling methods…

Emotion Processing in Parkinson’s Disease: A Three-Level Study on Recognition, Representation, and Regulation

PubMed Central

Enrici, Ivan; Adenzato, Mauro; Ardito, Rita B.; Mitkova, Antonia; Cavallo, Marco; Zibetti, Maurizio; Lopiano, Leonardo; Castelli, Lorys

2015-01-01

Background Parkinson’s disease (PD) is characterised by well-known motor symptoms, whereas the presence of cognitive non-motor symptoms, such as emotional disturbances, is still underestimated. One of the major problems in studying emotion deficits in PD is an atomising approach that does not take into account different levels of emotion elaboration. Our study addressed the question of whether people with PD exhibit difficulties in one or more specific dimensions of emotion processing, investigating three different levels of analyses, that is, recognition, representation, and regulation. Methodology Thirty-two consecutive medicated patients with PD and 25 healthy controls were enrolled in the study. Participants performed a three-level analysis assessment of emotional processing using quantitative standardised emotional tasks: the Ekman 60-Faces for emotion recognition, the full 36-item version of the Reading the Mind in the Eyes (RME) for emotion representation, and the 20-item Toronto Alexithymia Scale (TAS-20) for emotion regulation. Principal Findings Regarding emotion recognition, patients obtained significantly worse scores than controls in the total score of Ekman 60-Faces but not in any other basic emotions. For emotion representation, patients obtained significantly worse scores than controls in the RME experimental score but no in the RME gender control task. Finally, on emotion regulation, PD and controls did not perform differently at TAS-20 and no specific differences were found on TAS-20 subscales. The PD impairments on emotion recognition and representation do not correlate with dopamine therapy, disease severity, or with the duration of illness. These results are independent from other cognitive processes, such as global cognitive status and executive function, or from psychiatric status, such as depression, anxiety or apathy. Conclusions These results may contribute to better understanding of the emotional problems that are often seen in patients with PD and the measures used to test these problems, in particular on the use of different versions of the RME task. PMID:26110271

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

PubMed

Osowski, Stanislaw; Siwek, Krzysztof; Siroic, Robert

2011-03-01

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

Applications of Wavelet Transform and Fuzzy Neural Network on Power Quality Recognition

NASA Astrophysics Data System (ADS)

Liao, Chiung-Chou; Yang, Hong-Tzer; Lin, Ying-Chun

2008-10-01

The wavelet transform coefficients (WTCs) contain plenty of information needed for transient event identification of power quality (PQ) events. However, adopting WTCs directly has the drawbacks of taking a longer time and too much memory for the recognition system. To solve the abovementioned recognition problems and to effectively reduce the number of features representing power transients, spectrum energies of WTCs in different scales are calculated by Parseval's Theorem. Through the proposed approach, features of the original power signals can be reserved and not influenced by occurring points of PQ events. The fuzzy neural classification systems are then used for signal recognition and fuzzy rule construction. Success rates of recognizing PQ events from noise-riding signals are proven to be feasible in power system applications in this paper.

Performance evaluation of MLP and RBF feed forward neural network for the recognition of off-line handwritten characters

NASA Astrophysics Data System (ADS)

Rishi, Rahul; Choudhary, Amit; Singh, Ravinder; Dhaka, Vijaypal Singh; Ahlawat, Savita; Rao, Mukta

2010-02-01

In this paper we propose a system for classification problem of handwritten text. The system is composed of preprocessing module, supervised learning module and recognition module on a very broad level. The preprocessing module digitizes the documents and extracts features (tangent values) for each character. The radial basis function network is used in the learning and recognition modules. The objective is to analyze and improve the performance of Multi Layer Perceptron (MLP) using RBF transfer functions over Logarithmic Sigmoid Function. The results of 35 experiments indicate that the Feed Forward MLP performs accurately and exhaustively with RBF. With the change in weight update mechanism and feature-drawn preprocessing module, the proposed system is competent with good recognition show.

Evaluating deep learning architectures for Speech Emotion Recognition.

PubMed

Fayek, Haytham M; Lech, Margaret; Cavedon, Lawrence

2017-08-01

Speech Emotion Recognition (SER) can be regarded as a static or dynamic classification problem, which makes SER an excellent test bed for investigating and comparing various deep learning architectures. We describe a frame-based formulation to SER that relies on minimal speech processing and end-to-end deep learning to model intra-utterance dynamics. We use the proposed SER system to empirically explore feed-forward and recurrent neural network architectures and their variants. Experiments conducted illuminate the advantages and limitations of these architectures in paralinguistic speech recognition and emotion recognition in particular. As a result of our exploration, we report state-of-the-art results on the IEMOCAP database for speaker-independent SER and present quantitative and qualitative assessments of the models' performances. Copyright © 2017 Elsevier Ltd. All rights reserved.

Body-Based Gender Recognition Using Images from Visible and Thermal Cameras

PubMed Central

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-01

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems. PMID:26828487

Body-Based Gender Recognition Using Images from Visible and Thermal Cameras.

PubMed

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-27

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems.

Static sign language recognition using 1D descriptors and neural networks

NASA Astrophysics Data System (ADS)

Solís, José F.; Toxqui, Carina; Padilla, Alfonso; Santiago, César

2012-10-01

A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificial neural networks is presented in this work. The 1D descriptors were computed by two methods, first one consists in a correlation rotational operator.1 and second is based on contour analysis of hand shape. One of the main problems in sign language recognition is segmentation; most of papers report a special color in gloves or background for hand shape analysis. In order to avoid the use of gloves or special clothing, a thermal imaging camera was used to capture images. Static signs were picked up from 1 to 9 digits of American Sign Language, a multilayer perceptron reached 100% recognition with cross-validation.

Gender interactions in the recognition of emotions and conduct symptoms in adolescents.

PubMed

Halász, József; Aspán, Nikoletta; Bozsik, Csilla; Gádoros, Júlia; Inántsy-Pap, Judit

2014-01-01

According to literature data, impairment in the recognition of emotions might be related to antisocial developmental pathway. In the present study, the relationship between gender-specific interaction of emotion recognition and conduct symptoms were studied in non-clinical adolescents. After informed consent, 29 boys and 24 girls (13-16 years, 14 ± 0.1 years) participated in the study. The parent version of the Strengths and Difficulties Questionnaire was used to assess behavioral problems. The recognition of basic emotions was analyzed according to both the gender of the participants and the gender of the stimulus faces via the "Facial Expressions of Emotion- Stimuli and Tests". Girls were significantly better than boys in the recognition of disgust, irrespective from the gender of the stimulus faces, albeit both genders were significantly better in the recognition of disgust in the case of male stimulus faces compared to female stimulus faces. Both boys and girls were significantly better in the recognition of sadness in the case of female stimulus faces compared to male stimulus faces. There was no gender effect (neither participant nor stimulus faces) in the recognition of other emotions. Conduct scores in boys were inversely correlated with the recognition of fear in male stimulus faces (R=-0.439, p<0.05) and with overall emotion recognition in male stimulus faces (R=-0.558, p<0.01). In girls, conduct scores were shown a tendency for positive correlation with disgust recognition in female stimulus faces (R=0.376, p<0.07). A gender-specific interaction between the recognition of emotions and antisocial developmentalpathway is suggested.

The familial basis of facial emotion recognition deficits in adolescents with conduct disorder and their unaffected relatives.

PubMed

Sully, K; Sonuga-Barke, E J S; Fairchild, G

2015-07-01

There is accumulating evidence of impairments in facial emotion recognition in adolescents with conduct disorder (CD). However, the majority of studies in this area have only been able to demonstrate an association, rather than a causal link, between emotion recognition deficits and CD. To move closer towards understanding the causal pathways linking emotion recognition problems with CD, we studied emotion recognition in the unaffected first-degree relatives of CD probands, as well as those with a diagnosis of CD. Using a family-based design, we investigated facial emotion recognition in probands with CD (n = 43), their unaffected relatives (n = 21), and healthy controls (n = 38). We used the Emotion Hexagon task, an alternative forced-choice task using morphed facial expressions depicting the six primary emotions, to assess facial emotion recognition accuracy. Relative to controls, the CD group showed impaired recognition of anger, fear, happiness, sadness and surprise (all p < 0.005). Similar to probands with CD, unaffected relatives showed deficits in anger and happiness recognition relative to controls (all p < 0.008), with a trend toward a deficit in fear recognition. There were no significant differences in performance between the CD probands and the unaffected relatives following correction for multiple comparisons. These results suggest that facial emotion recognition deficits are present in adolescents who are at increased familial risk for developing antisocial behaviour, as well as those who have already developed CD. Consequently, impaired emotion recognition appears to be a viable familial risk marker or candidate endophenotype for CD.

Prevalence of face recognition deficits in middle childhood.

PubMed

Bennetts, Rachel J; Murray, Ebony; Boyce, Tian; Bate, Sarah

2017-02-01

Approximately 2-2.5% of the adult population is believed to show severe difficulties with face recognition, in the absence of any neurological injury-a condition known as developmental prosopagnosia (DP). However, to date no research has attempted to estimate the prevalence of face recognition deficits in children, possibly because there are very few child-friendly, well-validated tests of face recognition. In the current study, we examined face and object recognition in a group of primary school children (aged 5-11 years), to establish whether our tests were suitable for children and to provide an estimate of face recognition difficulties in children. In Experiment 1 (n = 184), children completed a pre-existing test of child face memory, the Cambridge Face Memory Test-Kids (CFMT-K), and a bicycle test with the same format. In Experiment 2 (n = 413), children completed three-alternative forced-choice matching tasks with faces and bicycles. All tests showed good psychometric properties. The face and bicycle tests were well matched for difficulty and showed a similar developmental trajectory. Neither the memory nor the matching tests were suitable to detect impairments in the youngest groups of children, but both tests appear suitable to screen for face recognition problems in middle childhood. In the current sample, 1.2-5.2% of children showed difficulties with face recognition; 1.2-4% showed face-specific difficulties-that is, poor face recognition with typical object recognition abilities. This is somewhat higher than previous adult estimates: It is possible that face matching tests overestimate the prevalence of face recognition difficulties in children; alternatively, some children may "outgrow" face recognition difficulties.

Psychosocial work factors and sleep problems: findings from the French national SIP survey.

PubMed

Chazelle, Emilie; Chastang, Jean-François; Niedhammer, Isabelle

2016-04-01

This study aimed at exploring the cross-sectional and prospective associations between psychosocial work factors and sleep problems. The study population consisted of a national representative sample of the French working population (SIP survey). The sample sizes were 7506 and 3555 for the cross-sectional and prospective analyses. Sleep problems were defined by either sleep disturbances or insufficient sleep duration at least several times a week. Psychosocial work factors included classical (job strain model factors) and emergent factors (recognition, insecurity, role/ethical conflict, emotional demands, work-life imbalance, etc.). Occupational factors related to working time/hours and physical work environment were also included as well as covariates related to factors outside work. Statistical analyses were performed using weighted Poisson regression analysis. In the cross-sectional analyses, psychological demands, low social support, low recognition, emotional demands, perception of danger, work-life imbalance and night work were found to be associated with sleep problems. In the prospective analyses, psychological demands and night work were predictive of sleep problems. Using a less conservative method, more factors were found to be associated with sleep problems. Dose-response associations were observed, showing that the more frequent the exposure to these factors, the higher the risk of sleep problems. No effect of repeated exposure was found on sleep problems. Classical and emergent psychosocial work factors were associated with sleep problems. More prospective studies and prevention policies may be needed.

Centre-based restricted nearest feature plane with angle classifier for face recognition

NASA Astrophysics Data System (ADS)

Tang, Linlin; Lu, Huifen; Zhao, Liang; Li, Zuohua

2017-10-01

An improved classifier based on the nearest feature plane (NFP), called the centre-based restricted nearest feature plane with the angle (RNFPA) classifier, is proposed for the face recognition problems here. The famous NFP uses the geometrical information of samples to increase the number of training samples, but it increases the computation complexity and it also has an inaccuracy problem coursed by the extended feature plane. To solve the above problems, RNFPA exploits a centre-based feature plane and utilizes a threshold of angle to restrict extended feature space. By choosing the appropriate angle threshold, RNFPA can improve the performance and decrease computation complexity. Experiments in the AT&T face database, AR face database and FERET face database are used to evaluate the proposed classifier. Compared with the original NFP classifier, the nearest feature line (NFL) classifier, the nearest neighbour (NN) classifier and some other improved NFP classifiers, the proposed one achieves competitive performance.

Odor Recognition vs. Classification in Artificial Olfaction

NASA Astrophysics Data System (ADS)

Raman, Baranidharan; Hertz, Joshua; Benkstein, Kurt; Semancik, Steve

2011-09-01

Most studies in chemical sensing have focused on the problem of precise identification of chemical species that were exposed during the training phase (the recognition problem). However, generalization of training to predict the chemical composition of untrained gases based on their similarity with analytes in the training set (the classification problem) has received very limited attention. These two analytical tasks pose conflicting constraints on the system. While correct recognition requires detection of molecular features that are unique to an analyte, generalization to untrained chemicals requires detection of features that are common across a desired class of analytes. A simple solution that addresses both issues simultaneously can be obtained from biological olfaction, where the odor class and identity information are decoupled and extracted individually over time. Mimicking this approach, we proposed a hierarchical scheme that allowed initial discrimination between broad chemical classes (e.g. contains oxygen) followed by finer refinements using additional data into sub-classes (e.g. ketones vs. alcohols) and, eventually, specific compositions (e.g. ethanol vs. methanol) [1]. We validated this approach using an array of temperature-controlled chemiresistors. We demonstrated that a small set of training analytes is sufficient to allow generalization to novel chemicals and that the scheme provides robust categorization despite aging. Here, we provide further characterization of this approach.

Training the max-margin sequence model with the relaxed slack variables.

PubMed

Niu, Lingfeng; Wu, Jianmin; Shi, Yong

2012-09-01

Sequence models are widely used in many applications such as natural language processing, information extraction and optical character recognition, etc. We propose a new approach to train the max-margin based sequence model by relaxing the slack variables in this paper. With the canonical feature mapping definition, the relaxed problem is solved by training a multiclass Support Vector Machine (SVM). Compared with the state-of-the-art solutions for the sequence learning, the new method has the following advantages: firstly, the sequence training problem is transformed into a multiclassification problem, which is more widely studied and already has quite a few off-the-shelf training packages; secondly, this new approach reduces the complexity of training significantly and achieves comparable prediction performance compared with the existing sequence models; thirdly, when the size of training data is limited, by assigning different slack variables to different microlabel pairs, the new method can use the discriminative information more frugally and produces more reliable model; last but not least, by employing kernels in the intermediate multiclass SVM, nonlinear feature space can be easily explored. Experimental results on the task of named entity recognition, information extraction and handwritten letter recognition with the public datasets illustrate the efficiency and effectiveness of our method. Copyright © 2012 Elsevier Ltd. All rights reserved.

Classification and recognition of dynamical models: the role of phase, independent components, kernels and optimal transport.

PubMed

Bissacco, Alessandro; Chiuso, Alessandro; Soatto, Stefano

2007-11-01

We address the problem of performing decision tasks, and in particular classification and recognition, in the space of dynamical models in order to compare time series of data. Motivated by the application of recognition of human motion in image sequences, we consider a class of models that include linear dynamics, both stable and marginally stable (periodic), both minimum and non-minimum phase, driven by non-Gaussian processes. This requires extending existing learning and system identification algorithms to handle periodic modes and nonminimum phase behavior, while taking into account higher-order statistics of the data. Once a model is identified, we define a kernel-based cord distance between models that includes their dynamics, their initial conditions as well as input distribution. This is made possible by a novel kernel defined between two arbitrary (non-Gaussian) distributions, which is computed by efficiently solving an optimal transport problem. We validate our choice of models, inference algorithm, and distance on the tasks of human motion synthesis (sample paths of the learned models), and recognition (nearest-neighbor classification in the computed distance). However, our work can be applied more broadly where one needs to compare historical data while taking into account periodic trends, non-minimum phase behavior, and non-Gaussian input distributions.

Target recognition and scene interpretation in image/video understanding systems based on network-symbolic models

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2004-08-01

Vision is only a part of a system that converts visual information into knowledge structures. These structures drive the vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, which is an interpretation of visual information in terms of these knowledge models. These mechanisms provide a reliable recognition if the object is occluded or cannot be recognized as a whole. It is hard to split the entire system apart, and reliable solutions to the target recognition problems are possible only within the solution of a more generic Image Understanding Problem. Brain reduces informational and computational complexities, using implicit symbolic coding of features, hierarchical compression, and selective processing of visual information. Biologically inspired Network-Symbolic representation, where both systematic structural/logical methods and neural/statistical methods are parts of a single mechanism, is the most feasible for such models. It converts visual information into relational Network-Symbolic structures, avoiding artificial precise computations of 3-dimensional models. Network-Symbolic Transformations derive abstract structures, which allows for invariant recognition of an object as exemplar of a class. Active vision helps creating consistent models. Attention, separation of figure from ground and perceptual grouping are special kinds of network-symbolic transformations. Such Image/Video Understanding Systems will be reliably recognizing targets.

[Neurological manifestations of Behçet's disease].

PubMed

Noel, N; Drier, A; Wechsler, B; Piette, J-C; De Paz, R; Dormont, D; Cacoub, P; Saadoun, D

2014-02-01

Neurological manifestations of Behçet's disease (BD) occur in 5.3 to more than 50% of patients. They are divided into two major forms: "parenchymal" lesions, which include mainly meningoencephalitis as opposed to "extra-parenchymal" lesions (i.e. cerebral venous thrombosis and arterial aneurysms). Myelitis or peripheral neuropathy is exceptional. The neuro-Behçet syndrome (NBS) should be considered in the setting of neurological manifestations, particularly headache and pyramidal signs, in a young man diagnosed with BD. However, its recognition may be difficult when neurological manifestations are the presenting features of BD (one third of cases), and requires a thorough knowledge of clinical manifestations and morphological lesions. Thus, parenchymal NB lesions classically exhibit inflammatory characteristics on MRI and are located at the meso-diencephalic junction and in the brainstem, rarely with a supratentorial extension. Meningitis is not systematically associated, and may be absent in about 30% of cases. The pathogenesis of these lesions is incompletely understood, but inflammatory infiltrates include mainly neutrophils and activated T cells (mainly Th17). Differential diagnoses include infectious diseases (herpes, listeria, tuberculosis), and inflammatory diseases (i.e. multiple sclerosis and sarcoidosis). A prompt recognition of NBS should lead to initiate adequate therapies in order to limit the risk of sequelae, relapses or death. Copyright © 2013. Published by Elsevier SAS.

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras.

PubMed

Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

2017-03-16

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

Degraded Impairment of Emotion Recognition in Parkinson's Disease Extends from Negative to Positive Emotions.

PubMed

Lin, Chia-Yao; Tien, Yi-Min; Huang, Jong-Tsun; Tsai, Chon-Haw; Hsu, Li-Chuan

2016-01-01

Because of dopaminergic neurodegeneration, patients with Parkinson's disease (PD) show impairment in the recognition of negative facial expressions. In the present study, we aimed to determine whether PD patients with more advanced motor problems would show a much greater deficit in recognition of emotional facial expressions than a control group and whether impairment of emotion recognition would extend to positive emotions. Twenty-nine PD patients and 29 age-matched healthy controls were recruited. Participants were asked to discriminate emotions in Experiment  1 and identify gender in Experiment  2. In Experiment  1, PD patients demonstrated a recognition deficit for negative (sadness and anger) and positive faces. Further analysis showed that only PD patients with high motor dysfunction performed poorly in recognition of happy faces. In Experiment  2, PD patients showed an intact ability for gender identification, and the results eliminated possible abilities in the functions measured in Experiment  2 as alternative explanations for the results of Experiment  1. We concluded that patients' ability to recognize emotions deteriorated as the disease progressed. Recognition of negative emotions was impaired first, and then the impairment extended to positive emotions.

Degraded Impairment of Emotion Recognition in Parkinson's Disease Extends from Negative to Positive Emotions

PubMed Central

Tien, Yi-Min; Huang, Jong-Tsun

2016-01-01

Because of dopaminergic neurodegeneration, patients with Parkinson's disease (PD) show impairment in the recognition of negative facial expressions. In the present study, we aimed to determine whether PD patients with more advanced motor problems would show a much greater deficit in recognition of emotional facial expressions than a control group and whether impairment of emotion recognition would extend to positive emotions. Twenty-nine PD patients and 29 age-matched healthy controls were recruited. Participants were asked to discriminate emotions in Experiment  1 and identify gender in Experiment  2. In Experiment  1, PD patients demonstrated a recognition deficit for negative (sadness and anger) and positive faces. Further analysis showed that only PD patients with high motor dysfunction performed poorly in recognition of happy faces. In Experiment  2, PD patients showed an intact ability for gender identification, and the results eliminated possible abilities in the functions measured in Experiment  2 as alternative explanations for the results of Experiment  1. We concluded that patients' ability to recognize emotions deteriorated as the disease progressed. Recognition of negative emotions was impaired first, and then the impairment extended to positive emotions. PMID:27555668

Chinese character recognition based on Gabor feature extraction and CNN

NASA Astrophysics Data System (ADS)

Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan

2018-03-01

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

Super-resolution method for face recognition using nonlinear mappings on coherent features.

PubMed

Huang, Hua; He, Huiting

2011-01-01

Low-resolution (LR) of face images significantly decreases the performance of face recognition. To address this problem, we present a super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image. Canonical correlation analysis is applied to establish the coherent subspaces between the principal component analysis (PCA) based features of high-resolution (HR) and LR face images. Then, a nonlinear mapping between HR/LR features can be built by radial basis functions (RBFs) with lower regression errors in the coherent feature space than in the PCA feature space. Thus, we can compute super-resolved coherent features corresponding to an input LR image according to the trained RBF model efficiently and accurately. And, face identity can be obtained by feeding these super-resolved features to a simple NN classifier. Extensive experiments on the Facial Recognition Technology, University of Manchester Institute of Science and Technology, and Olivetti Research Laboratory databases show that the proposed method outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.

Word-level recognition of multifont Arabic text using a feature vector matching approach

NASA Astrophysics Data System (ADS)

Erlandson, Erik J.; Trenkle, John M.; Vogt, Robert C., III

1996-03-01

Many text recognition systems recognize text imagery at the character level and assemble words from the recognized characters. An alternative approach is to recognize text imagery at the word level, without analyzing individual characters. This approach avoids the problem of individual character segmentation, and can overcome local errors in character recognition. A word-level recognition system for machine-printed Arabic text has been implemented. Arabic is a script language, and is therefore difficult to segment at the character level. Character segmentation has been avoided by recognizing text imagery of complete words. The Arabic recognition system computes a vector of image-morphological features on a query word image. This vector is matched against a precomputed database of vectors from a lexicon of Arabic words. Vectors from the database with the highest match score are returned as hypotheses for the unknown image. Several feature vectors may be stored for each word in the database. Database feature vectors generated using multiple fonts and noise models allow the system to be tuned to its input stream. Used in conjunction with database pruning techniques, this Arabic recognition system has obtained promising word recognition rates on low-quality multifont text imagery.

Attention and Encoding in Physics Learning and Problem Solving

ERIC Educational Resources Information Center

Feil, Adam John

2009-01-01

This dissertation presents several studies designed to probe the mental representations that physics experts and novices form when interacting with typical instructional materials, such as diagrams and problem statements. By using recognition tasks and a change detection task, the mental representations of experts and novices are studied in a more…

Forging Institutional Links: Making Quality Circles Work in the U.S.

ERIC Educational Resources Information Center

Cole, Robert E.; Tachiki, Dennis S.

1984-01-01

The authors discuss the top three problems reported to hinder the spread of quality circles and evaluate selected solutions to them. The top problems include middle management resistance, lack of top management support, and employee resistance or apathy. Solutions discussed include social recognition and economic rewards. (CT)

Synthesis Road Map Problems in Organic Chemistry

ERIC Educational Resources Information Center

Schaller, Chris P.; Graham, Kate J.; Jones, T. Nicholas

2014-01-01

Road map problems ask students to integrate their knowledge of organic reactions with pattern recognition skills to "fill in the blanks" in the synthesis of an organic compound. Students are asked to identify familiar organic reactions in unfamiliar contexts. A practical context, such as a medicinally useful target compound, helps…

Aspects of the Cognitive Model of Physics Problem Solving.

ERIC Educational Resources Information Center

Brekke, Stewart E.

Various aspects of the cognitive model of physics problem solving are discussed in detail including relevant cues, encoding, memory, and input stimuli. The learning process involved in the recognition of familiar and non-familiar sensory stimuli is highlighted. Its four components include selection, acquisition, construction, and integration. The…

The mean-square error optimal linear discriminant function and its application to incomplete data vectors

NASA Technical Reports Server (NTRS)

Walker, H. F.

1979-01-01

In many pattern recognition problems, data vectors are classified although one or more of the data vector elements are missing. This problem occurs in remote sensing when the ground is obscured by clouds. Optimal linear discrimination procedures for classifying imcomplete data vectors are discussed.

Transport through correlated systems with density functional theory

NASA Astrophysics Data System (ADS)

Kurth, S.; Stefanucci, G.

2017-10-01

We present recent advances in density functional theory (DFT) for applications in the field of quantum transport, with particular emphasis on transport through strongly correlated systems. We review the foundations of the popular Landauer-Büttiker(LB)  +  DFT approach. This formalism, when using approximations to the exchange-correlation (xc) potential with steps at integer occupation, correctly captures the Kondo plateau in the zero bias conductance at zero temperature but completely fails to capture the transition to the Coulomb blockade (CB) regime as the temperature increases. To overcome the limitations of LB  +  DFT, the quantum transport problem is treated from a time-dependent (TD) perspective using TDDFT, an exact framework to deal with nonequilibrium situations. The steady-state limit of TDDFT shows that in addition to an xc potential in the junction, there also exists an xc correction to the applied bias. Open shell molecules in the CB regime provide the most striking examples of the importance of the xc bias correction. Using the Anderson model as guidance we estimate these corrections in the limit of zero bias. For the general case we put forward a steady-state DFT which is based on one-to-one correspondence between the pair of basic variables, steady density on and steady current across the junction and the pair local potential on and bias across the junction. Like TDDFT, this framework also leads to both an xc potential in the junction and an xc correction to the bias. Unlike TDDFT, these potentials are independent of history. We highlight the universal features of both xc potential and xc bias corrections for junctions in the CB regime and provide an accurate parametrization for the Anderson model at arbitrary temperatures and interaction strengths, thus providing a unified DFT description for both Kondo and CB regimes and the transition between them.

Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer.

PubMed

Liu, Maolin; Li, Huaiyu; Wang, Yuan; Li, Fei; Chen, Xiuwan

2018-04-01

Accelerometers, gyroscopes and magnetometers in smartphones are often used to recognize human motions. Since it is difficult to distinguish between vertical motions and horizontal motions in the data provided by these built-in sensors, the vertical motion recognition accuracy is relatively low. The emergence of a built-in barometer in smartphones improves the accuracy of motion recognition in the vertical direction. However, there is a lack of quantitative analysis and modelling of the barometer signals, which is the basis of barometer's application to motion recognition, and a problem of imbalanced data also exists. This work focuses on using the barometers inside smartphones for vertical motion recognition in multi-floor buildings through modelling and feature extraction of pressure signals. A novel double-windows pressure feature extraction method, which adopts two sliding time windows of different length, is proposed to balance recognition accuracy and response time. Then, a random forest classifier correlation rule is further designed to weaken the impact of imbalanced data on recognition accuracy. The results demonstrate that the recognition accuracy can reach 95.05% when pressure features and the improved random forest classifier are adopted. Specifically, the recognition accuracy of the stair and elevator motions is significantly improved with enhanced response time. The proposed approach proves effective and accurate, providing a robust strategy for increasing accuracy of vertical motions.

Conductance and thermopower in molecular nanojunctions

NASA Astrophysics Data System (ADS)

Sen, Arijit

2013-02-01

Electronic transport through short channels in a molecular junction is an intricate quantum scattering problem [1]. To garner insight on how the structure and the electrical properties of a nanoscale junction are correlated is thus of both fundamental and technological interest [1-3]. As observed experimentally in the last couple of years by several independent research groups [4-5], a two-terminal molecular junction comprising of a simple alkane chain with varying length can exhibit high as well as low conductance. However, what causes the simultaneous unveiling of multiple conductances remained largely obscure. We have recently demonstrated [6] that the binary conductance in these heterostructures is due mainly to two distinct electrode orientations that control the electrode-molecule coupling as well as the tunneling strength through quantum interference following diversity in the electrode band structures. Our detailed analysis on the transmission spectra indicates that even a single-molecule nanojunction can potentially serve as a realistic double-quantum-dot kind of system to yield tunable Fano resonance, as often desired for nanoscale switching. In this talk, I intend to give a brief account of molecular electronics and its future applications along with the challenges and possibilities in the current perspective. A few deliberations may as well include how the inter-dot tunneling strength may affect the non-equilibrium charge transport and thermoelectricity in a myriad of molecular junctions based on different molecular conformations and electrode structures. Finally, I shall try to touch upon the effect of electron-phonon interaction on the nanoscale charge transport, and also, the phonon-mediated thermal transport in molecular nanodevices.

Patients affected by endemic pemphigus foliaceus in Colombia, South America exhibit autoantibodies to optic nerve sheath envelope cell junctions.

PubMed

Abreu-Velez, Ana Maria; Gao, Wendy; Howard, Michael S

2018-01-01

The majority of the patients affected by a new variant of endemic pemphigus foliaceus in El Bagre, Colombia (El Bagre EPF or pemphigus Abreu-Manu), have experienced vision problems; we have previously reported several ocular abnormalities. Here, we aimed to investigate reactivity to optic nerves in these patients. We utilized bovine, rat and mouse optic nerves, and performed immunofluorescence and confocal microscopy to test for optical nerve autoreactivity. We tested 45 patients affected by this disease and 45 controls from the endemic area matched by age, sex and work activity. Overall, 37 of the 45 patient sera reacted to the optic nerve envelope that is composed of leptomeninges; the reactivity was polyclonal and present mostly at the cell junctions (P < 0.001). The immune response was directed against optic nerve sheath cell junctions and the vessels inside it, as well as other molecules inside the nerve. No control cases were positive. Of interest, all the patient autoantibodies co-localized with commercial antibodies to desmoplakins I-II, myocardium-enriched zonula occludens-1- associated protein (MYZAP), armadillo repeat gene deleted in velo-cardio-facial syndrome (ARVCF), and plakophilin-4 (p0071) from Progen Biotechnik (P < 0.001). We conclude that the majority of the patients affected by pemphigus Abreu-Manu have autoantibodies to optic nerve sheath envelope cell junctions. These antibodies also co-localize with armadillo repeat gene deleted in velo-cardio-facial syndrome, p0071 and desmoplakins I-II. The clinical significance of our findings remains unknown.

Vortices in Long Josephson Junctions.

DTIC Science & Technology

1987-11-01

of the very low impedance vortex flow transistor and toward determination of its potential for high frequency applications. Capability for higher...version. New progress was made toward solution of the problems of high frequency testing of the very low impedance vortex flow transistor and towards... measurable transresistance ’". out to frequencies of about 10% of the theoretical transit time cutoff fre- quency. Capability for higher frequency testing

Multirotor micro air vehicle autonomous landing system based on image markers recognition

NASA Astrophysics Data System (ADS)

Skoczylas, Marcin; Gadomer, Lukasz; Walendziuk, Wojciech

2017-08-01

In this paper the idea of an autonomic drone landing system which bases on different markers detection, is presented. The issue of safe autonomic drone landing is one of the major aspects connected with drone missions. The idea of the proposed system is to detect the landing place, marked with an image called marker, using one of the image recognition algorithms, and heading during the landing procedure to this place. Choosing the proper marker, which allows the greatest quality of the recognition system, is the main problem faced in this paper. Seven markers are tested and compared. The achieved results are described and discussed.

A new method of edge detection for object recognition

USGS Publications Warehouse

Maddox, Brian G.; Rhew, Benjamin

2004-01-01

Traditional edge detection systems function by returning every edge in an input image. This can result in a large amount of clutter and make certain vectorization algorithms less accurate. Accuracy problems can then have a large impact on automated object recognition systems that depend on edge information. A new method of directed edge detection can be used to limit the number of edges returned based on a particular feature. This results in a cleaner image that is easier for vectorization. Vectorized edges from this process could then feed an object recognition system where the edge data would also contain information as to what type of feature it bordered.

Convolutional neural networks and face recognition task

NASA Astrophysics Data System (ADS)

Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

2017-09-01

Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

Assessment and pharmacologic treatment of sleep disturbance in autism.

PubMed

Johnson, Kyle P; Malow, Beth A

2008-10-01

Like children with other developmental disabilities, children with autism spectrum disorders suffer with sleep problems at a higher rate than do typically developing children. There is a growing recognition that addressing these sleep problems may improve daytime functioning and decrease family stress. Presented here is a discussion of the sleep problems experienced by children with autism spectrum disorders, focusing on appropriate assessment and pharmacologic treatment.

Exploring 3D Human Action Recognition: from Offline to Online.

PubMed

Liu, Zhenyu; Li, Rui; Tan, Jianrong

2018-02-20

With the introduction of cost-effective depth sensors, a tremendous amount of research has been devoted to studying human action recognition using 3D motion data. However, most existing methods work in an offline fashion, i.e., they operate on a segmented sequence. There are a few methods specifically designed for online action recognition, which continually predicts action labels as a stream sequence proceeds. In view of this fact, we propose a question: can we draw inspirations and borrow techniques or descriptors from existing offline methods, and then apply these to online action recognition? Note that extending offline techniques or descriptors to online applications is not straightforward, since at least two problems-including real-time performance and sequence segmentation-are usually not considered in offline action recognition. In this paper, we give a positive answer to the question. To develop applicable online action recognition methods, we carefully explore feature extraction, sequence segmentation, computational costs, and classifier selection. The effectiveness of the developed methods is validated on the MSR 3D Online Action dataset and the MSR Daily Activity 3D dataset.

The recognition of graphical patterns invariant to geometrical transformation of the models

NASA Astrophysics Data System (ADS)

Ileană, Ioan; Rotar, Corina; Muntean, Maria; Ceuca, Emilian

2010-11-01

In case that a pattern recognition system is used for images recognition (in robot vision, handwritten recognition etc.), the system must have the capacity to identify an object indifferently of its size or position in the image. The problem of the invariance of recognition can be approached in some fundamental modes. One may apply the similarity criterion used in associative recall. The original pattern is replaced by a mathematical transform that assures some invariance (e.g. the value of two-dimensional Fourier transformation is translation invariant, the value of Mellin transformation is scale invariant). In a different approach the original pattern is represented through a set of features, each of them being coded indifferently of the position, orientation or position of the pattern. Generally speaking, it is easy to obtain invariance in relation with one transformation group, but is difficult to obtain simultaneous invariance at rotation, translation and scale. In this paper we analyze some methods to achieve invariant recognition of images, particularly for digit images. A great number of experiments are due and the conclusions are underplayed in the paper.

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

PubMed

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.

Robust kernel representation with statistical local features for face recognition.

PubMed

Yang, Meng; Zhang, Lei; Shiu, Simon Chi-Keung; Zhang, David

2013-06-01

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.

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

NASA Astrophysics Data System (ADS)

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.