Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

PubMed

Prahm, Cosima; Eckstein, Korbinian; Ortiz-Catalan, Max; Dorffner, Georg; Kaniusas, Eugenijus; Aszmann, Oskar C

2016-08-31

Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).

How much does language proficiency by non-native listeners influence speech audiometric tests in noise?

PubMed

Warzybok, Anna; Brand, Thomas; Wagener, Kirsten C; Kollmeier, Birger

2015-01-01

The current study investigates the extent to which the linguistic complexity of three commonly employed speech recognition tests and second language proficiency influence speech recognition thresholds (SRTs) in noise in non-native listeners. SRTs were measured for non-natives and natives using three German speech recognition tests: the digit triplet test (DTT), the Oldenburg sentence test (OLSA), and the Göttingen sentence test (GÖSA). Sixty-four non-native and eight native listeners participated. Non-natives can show native-like SRTs in noise only for the linguistically easy speech material (DTT). Furthermore, the limitation of phonemic-acoustical cues in digit triplets affects speech recognition to the same extent in non-natives and natives. For more complex and less familiar speech materials, non-natives, ranging from basic to advanced proficiency in German, require on average 3-dB better signal-to-noise ratio for the OLSA and 6-dB for the GÖSA to obtain 50% speech recognition compared to native listeners. In clinical audiology, SRT measurements with a closed-set speech test (i.e. DTT for screening or OLSA test for clinical purposes) should be used with non-native listeners rather than open-set speech tests (such as the GÖSA or HINT), especially if a closed-set version in the patient's own native language is available.

Real-time detecting and tracking ball with OpenCV and Kinect

NASA Astrophysics Data System (ADS)

Osiecki, Tomasz; Jankowski, Stanislaw

2016-09-01

This paper presents a way to detect and track ball with using the OpenCV and Kinect. Object and people recognition, tracking are more and more popular topics nowadays. Described solution makes it possible to detect ball based on the range, which is set by the user and capture information about ball position in three dimensions. It can be store in the computer and use for example to display trajectory of the ball.

The Auckland Optotypes: An open-access pictogram set for measuring recognition acuity.

PubMed

Hamm, Lisa M; Yeoman, Janice P; Anstice, Nicola; Dakin, Steven C

2018-03-01

When measuring recognition acuity in a research setting, the most widely used symbols are the Early Treatment of Diabetic Retinopathy Study (ETDRS) set of 10 Sloan letters. However, the symbols are not appropriate for patients unfamiliar with letters, and acuity for individual letters is variable. Alternative pictogram sets are available, but are generally comprised of fewer items. We set out to develop an open-access set of 10 pictograms that would elicit more consistent estimates of acuity across items than the ETDRS letters from visually normal adults. We measured monocular acuity for individual uncrowded optotypes within a newly designed set (The Auckland Optotype [TAO]), the ETDRS set, and Landolt Cs. Eleven visually normal adults were assessed on regular and vanishing formats of each set. Inter-optotype reliability and ability to detect subtle differences between participants were assessed using intraclass correlations (ICC) and fractional rank precision (FRP). The TAO vanishing set showed the strongest performance (ICC = 0.97, FRP = 0.90), followed by the other vanishing sets (Sloan ICC = 0.88, FRP = 0.74; Landolt ICC = 0.86, FRP = 0.80). Within the regular format, TAO again outperformed the existing sets (TAO ICC = 0.77, FRP = 0.75; Sloan ICC = 0.65, FRP = 0.64; Landolt ICC = 0.48, FRP = 0.63). For adults with normal visual acuity, the new optotypes (in both regular and vanishing formats) are more equally legible and sensitive to subtle individual differences than their Sloan counterparts. As this set does not require observers to be able to name Roman letters, and is freely available to use and modify, it may have wide application for measurement of acuity.

Temporal Sensitivity Measured Shortly After Cochlear Implantation Predicts 6-Month Speech Recognition Outcome.

PubMed

Erb, Julia; Ludwig, Alexandra Annemarie; Kunke, Dunja; Fuchs, Michael; Obleser, Jonas

2018-04-24

Psychoacoustic tests assessed shortly after cochlear implantation are useful predictors of the rehabilitative speech outcome. While largely independent, both spectral and temporal resolution tests are important to provide an accurate prediction of speech recognition. However, rapid tests of temporal sensitivity are currently lacking. Here, we propose a simple amplitude modulation rate discrimination (AMRD) paradigm that is validated by predicting future speech recognition in adult cochlear implant (CI) patients. In 34 newly implanted patients, we used an adaptive AMRD paradigm, where broadband noise was modulated at the speech-relevant rate of ~4 Hz. In a longitudinal study, speech recognition in quiet was assessed using the closed-set Freiburger number test shortly after cochlear implantation (t0) as well as the open-set Freiburger monosyllabic word test 6 months later (t6). Both AMRD thresholds at t0 (r = -0.51) and speech recognition scores at t0 (r = 0.56) predicted speech recognition scores at t6. However, AMRD and speech recognition at t0 were uncorrelated, suggesting that those measures capture partially distinct perceptual abilities. A multiple regression model predicting 6-month speech recognition outcome with deafness duration and speech recognition at t0 improved from adjusted R = 0.30 to adjusted R = 0.44 when AMRD threshold was added as a predictor. These findings identify AMRD thresholds as a reliable, nonredundant predictor above and beyond established speech tests for CI outcome. This AMRD test could potentially be developed into a rapid clinical temporal-resolution test to be integrated into the postoperative test battery to improve the reliability of speech outcome prognosis.

ChoiceKey: a real-time speech recognition program for psychology experiments with a small response set.

PubMed

Donkin, Christopher; Brown, Scott D; Heathcote, Andrew

2009-02-01

Psychological experiments often collect choice responses using buttonpresses. However, spoken responses are useful in many cases-for example, when working with special clinical populations, or when a paradigm demands vocalization, or when accurate response time measurements are desired. In these cases, spoken responses are typically collected using a voice key, which usually involves manual coding by experimenters in a tedious and error-prone manner. We describe ChoiceKey, an open-source speech recognition package for MATLAB. It can be optimized by training for small response sets and different speakers. We show ChoiceKey to be reliable with minimal training for most participants in experiments with two different responses. Problems presented by individual differences, and occasional atypical responses, are examined, and extensions to larger response sets are explored. The ChoiceKey source files and instructions may be downloaded as supplemental materials for this article from brm.psychonomic-journals.org/content/supplemental.

Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition.

PubMed

Bianne-Bernard, Anne-Laure; Menasri, Farès; Al-Hajj Mohamad, Rami; Mokbel, Chafic; Kermorvant, Christopher; Likforman-Sulem, Laurence

2011-10-01

This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.

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

NASA Astrophysics Data System (ADS)

Yao, Shoukui; Qin, Xiaojuan

2018-02-01

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

Developing open source, self-contained disease surveillance software applications for use in resource-limited settings

PubMed Central

2012-01-01

Background Emerging public health threats often originate in resource-limited countries. In recognition of this fact, the World Health Organization issued revised International Health Regulations in 2005, which call for significantly increased reporting and response capabilities for all signatory nations. Electronic biosurveillance systems can improve the timeliness of public health data collection, aid in the early detection of and response to disease outbreaks, and enhance situational awareness. Methods As components of its Suite for Automated Global bioSurveillance (SAGES) program, The Johns Hopkins University Applied Physics Laboratory developed two open-source, electronic biosurveillance systems for use in resource-limited settings. OpenESSENCE provides web-based data entry, analysis, and reporting. ESSENCE Desktop Edition provides similar capabilities for settings without internet access. Both systems may be configured to collect data using locally available cell phone technologies. Results ESSENCE Desktop Edition has been deployed for two years in the Republic of the Philippines. Local health clinics have rapidly adopted the new technology to provide daily reporting, thus eliminating the two-to-three week data lag of the previous paper-based system. Conclusions OpenESSENCE and ESSENCE Desktop Edition are two open-source software products with the capability of significantly improving disease surveillance in a wide range of resource-limited settings. These products, and other emerging surveillance technologies, can assist resource-limited countries compliance with the revised International Health Regulations. PMID:22950686

Hemi-methylated DNA opens a closed conformation of UHRF1 to facilitate its histone recognition

NASA Astrophysics Data System (ADS)

Fang, Jian; Cheng, Jingdong; Wang, Jiaolong; Zhang, Qiao; Liu, Mengjie; Gong, Rui; Wang, Ping; Zhang, Xiaodan; Feng, Yangyang; Lan, Wenxian; Gong, Zhou; Tang, Chun; Wong, Jiemin; Yang, Huirong; Cao, Chunyang; Xu, Yanhui

2016-04-01

UHRF1 is an important epigenetic regulator for maintenance DNA methylation. UHRF1 recognizes hemi-methylated DNA (hm-DNA) and trimethylation of histone H3K9 (H3K9me3), but the regulatory mechanism remains unknown. Here we show that UHRF1 adopts a closed conformation, in which a C-terminal region (Spacer) binds to the tandem Tudor domain (TTD) and inhibits H3K9me3 recognition, whereas the SET-and-RING-associated (SRA) domain binds to the plant homeodomain (PHD) and inhibits H3R2 recognition. Hm-DNA impairs the intramolecular interactions and promotes H3K9me3 recognition by TTD-PHD. The Spacer also facilitates UHRF1-DNMT1 interaction and enhances hm-DNA-binding affinity of the SRA. When TTD-PHD binds to H3K9me3, SRA-Spacer may exist in a dynamic equilibrium: either recognizes hm-DNA or recruits DNMT1 to chromatin. Our study reveals the mechanism for regulation of H3K9me3 and hm-DNA recognition by URHF1.

An open and configurable embedded system for EMG pattern recognition implementation for artificial arms.

PubMed

Jun Liu; Fan Zhang; Huang, He Helen

2014-01-01

Pattern recognition (PR) based on electromyographic (EMG) signals has been developed for multifunctional artificial arms for decades. However, assessment of EMG PR control for daily prosthesis use is still limited. One of the major barriers is the lack of a portable and configurable embedded system to implement the EMG PR control. This paper aimed to design an open and configurable embedded system for EMG PR implementation so that researchers can easily modify and optimize the control algorithms upon our designed platform and test the EMG PR control outside of the lab environments. The open platform was built on an open source embedded Linux Operating System running a high-performance Gumstix board. Both the hardware and software system framework were openly designed. The system was highly flexible in terms of number of inputs/outputs and calibration interfaces used. Such flexibility enabled easy integration of our embedded system with different types of commercialized or prototypic artificial arms. Thus far, our system was portable for take-home use. Additionally, compared with previously reported embedded systems for EMG PR implementation, our system demonstrated improved processing efficiency and high system precision. Our long-term goals are (1) to develop a wearable and practical EMG PR-based control for multifunctional artificial arms, and (2) to quantify the benefits of EMG PR-based control over conventional myoelectric prosthesis control in a home setting.

Development of an auditory situation awareness test battery for advanced hearing protectors and TCAPS: detection subtest of DRILCOM (detection-recognition/identification-localization-communication).

PubMed

Lee, Kichol; Casali, John G

2017-01-01

To design a test battery and conduct a proof-of-concept experiment of a test method that can be used to measure the detection performance afforded by military advanced hearing protection devices (HPDs) and tactical communication and protective systems (TCAPS). The detection test was conducted with each of the four loudspeakers located at front, right, rear and left of the participant. Participants wore 2 in-ear-type TCAPS, 1 earmuff-type TCAPS, a passive Combat Arms Earplug in its "open" or pass-through setting and an EB-15LE™ electronic earplug. Devices with electronic gain systems were tested under two gain settings: "unity" and "max". Testing without any device (open ear) was conducted as a control. Ten participants with audiometric requirements of 25 dBHL or better at 500, 1000, 2000, 4000, 8000 Hz in both ears. Detection task performance varied with different signals and speaker locations. The test identified performance differences among certain TCAPS and protectors, and the open ear. A computer-controlled detection subtest of the Detection-Recognition/Identification-Localisation-Communication (DRILCOM) test battery was designed and implemented. Tested in a proof-of-concept experiment, it showed statistically-significant sensitivity to device differences in detection effects with the small sample of participants (10). This result has important implications for selection and deployment of TCAPS and HPDs on soldiers and workers in dynamic situations.

Effects of low speed wind on the recognition/identification and pass-through communication tasks of auditory situation awareness afforded by military hearing protection/enhancement devices and tactical communication and protective systems.

PubMed

Lee, Kichol; Casali, John G

2016-01-01

To investigate the effect of controlled low-speed wind-noise on the auditory situation awareness performance afforded by military hearing protection/enhancement devices (HPED) and tactical communication and protective systems (TCAPS). Recognition/identification and pass-through communications tasks were separately conducted under three wind conditions (0, 5, and 10 mph). Subjects wore two in-ear-type TCAPS, one earmuff-type TCAPS, a Combat Arms Earplug in its 'open' or pass-through setting, and an EB-15LE electronic earplug. Devices with electronic gain systems were tested under two gain settings: 'unity' and 'max'. Testing without any device (open ear) was conducted as a control. Ten subjects were recruited from the student population at Virginia Tech. Audiometric requirements were 25 dBHL or better at 500, 1000, 2000, 4000, and 8000 Hz in both ears. Performance on the interaction of communication task-by-device was significantly different only in 0 mph wind speed. The between-device performance differences varied with azimuthal speaker locations. It is evident from this study that stable (non-gusting) wind speeds up to 10 mph did not significantly degrade recognition/identification task performance and pass-through communication performance of the group of HPEDs and TCAPS tested. However, the various devices performed differently as the test sound signal speaker location was varied and it appears that physical as well as electronic features may have contributed to this directional result.

Using sense-making theory to aid understanding of the recognition, assessment and management of pain in patients with dementia in acute hospital settings.

PubMed

Dowding, Dawn; Lichtner, Valentina; Allcock, Nick; Briggs, Michelle; James, Kirstin; Keady, John; Lasrado, Reena; Sampson, Elizabeth L; Swarbrick, Caroline; José Closs, S

2016-01-01

The recognition, assessment and management of pain in hospital settings is suboptimal, and is a particular challenge in patients with dementia. The existing process guiding pain assessment and management in clinical settings is based on the assumption that nurses follow a sequential linear approach to decision making. In this paper we re-evaluate this theoretical assumption drawing on findings from a study of pain recognition, assessment and management in patients with dementia. To provide a revised conceptual model of pain recognition, assessment and management based on sense-making theories of decision making. The research we refer to is an exploratory ethnographic study using nested case sites. Patients with dementia (n=31) were the unit of data collection, nested in 11 wards (vascular, continuing care, stroke rehabilitation, orthopaedic, acute medicine, care of the elderly, elective and emergency surgery), located in four NHS hospital organizations in the UK. Data consisted of observations of patients at bedside (170h in total); observations of the context of care; audits of patient hospital records; documentary analysis of artefacts; semi-structured interviews (n=56) and informal open conversations with staff and carers (family members). Existing conceptualizations of pain recognition, assessment and management do not fully explain how the decision process occurs in clinical practice. Our research indicates that pain recognition, assessment and management is not an individual cognitive activity; rather it is carried out by groups of individuals over time and within a specific organizational culture or climate, which influences both health care professional and patient behaviour. We propose a revised theoretical model of decision making related to pain assessment and management for patients with dementia based on theories of sense-making, which is reflective of the reality of clinical decision making in acute hospital wards. The revised model recognizes the salience of individual cognition as well as acknowledging that decisions are constructed through social interaction and organizational context. The model will be used in further research to develop decision support interventions to assist with the assessment and management of patients with dementia in acute hospital settings. Copyright © 2015. Published by Elsevier Ltd.

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

PubMed

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

2017-07-01

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

International recognition of basic medical education programmes.

PubMed

Karle, Hans

2008-01-01

This document aims to formulate a World Federation for Medical Education (WFME) policy and to open debate on the subject on international recognition of basic medical education institutions and programmes. We carried out a systematic review of international quality assurance of medical education and recognition methodology, including accreditation procedures and alternative quality assurance methods, with a focus on the role of the WFME in international recognition of basic medical education programmes. In order to further the intentions of the WFME, the Federation will: continue its activity to establish new Global Directories of Health Professions Education Institutions (GDHPEI); set up a planning working group to prepare the work of the international advisory committee for GDHPEI; develop a database of relevant accrediting and recognising agencies; continue its project on the promotion of proper national accreditation; establish a working group to develop principles to be used in the evaluation of medical schools and other health professions education institutions and their programmes for the purpose of international recognition, especially when proper accreditation is not feasible, and work with partners on training programmes for advisors and assessors. The new directory for medical schools, which will include qualitative information about basic medical education programmes, will provide a basis for the meta-recognition of medical schools' programmes by stimulating the establishment of national accreditation systems and other quality assurance instruments.

Picking Deep Filter Responses for Fine-Grained Image Recognition (Open Access Author’s Manuscript)

DTIC Science & Technology

2016-12-16

stages. Our method explores a unified framework based on two steps of deep filter response picking. The first picking step is to find distinctive... filters which respond to specific patterns significantly and consistently, and learn a set of part detectors via iteratively alternating between new...positive sample mining and part model retraining. The second picking step is to pool deep filter responses via spatially weighted combination of Fisher

Talker and lexical effects on audiovisual word recognition by adults with cochlear implants.

PubMed

Kaiser, Adam R; Kirk, Karen Iler; Lachs, Lorin; Pisoni, David B

2003-04-01

The present study examined how postlingually deafened adults with cochlear implants combine visual information from lipreading with auditory cues in an open-set word recognition task. Adults with normal hearing served as a comparison group. Word recognition performance was assessed using lexically controlled word lists presented under auditory-only, visual-only, and combined audiovisual presentation formats. Effects of talker variability were studied by manipulating the number of talkers producing the stimulus tokens. Lexical competition was investigated using sets of lexically easy and lexically hard test words. To assess the degree of audiovisual integration, a measure of visual enhancement, R(a), was used to assess the gain in performance provided in the audiovisual presentation format relative to the maximum possible performance obtainable in the auditory-only format. Results showed that word recognition performance was highest for audiovisual presentation followed by auditory-only and then visual-only stimulus presentation. Performance was better for single-talker lists than for multiple-talker lists, particularly under the audiovisual presentation format. Word recognition performance was better for the lexically easy than for the lexically hard words regardless of presentation format. Visual enhancement scores were higher for single-talker conditions compared to multiple-talker conditions and tended to be somewhat better for lexically easy words than for lexically hard words. The pattern of results suggests that information from the auditory and visual modalities is used to access common, multimodal lexical representations in memory. The findings are discussed in terms of the complementary nature of auditory and visual sources of information that specify the same underlying gestures and articulatory events in speech.

Talker and Lexical Effects on Audiovisual Word Recognition by Adults With Cochlear Implants

PubMed Central

Kaiser, Adam R.; Kirk, Karen Iler; Lachs, Lorin; Pisoni, David B.

2012-01-01

The present study examined how postlingually deafened adults with cochlear implants combine visual information from lipreading with auditory cues in an open-set word recognition task. Adults with normal hearing served as a comparison group. Word recognition performance was assessed using lexically controlled word lists presented under auditory-only, visual-only, and combined audiovisual presentation formats. Effects of talker variability were studied by manipulating the number of talkers producing the stimulus tokens. Lexical competition was investigated using sets of lexically easy and lexically hard test words. To assess the degree of audiovisual integration, a measure of visual enhancement, Ra, was used to assess the gain in performance provided in the audiovisual presentation format relative to the maximum possible performance obtainable in the auditory-only format. Results showed that word recognition performance was highest for audiovisual presentation followed by auditory-only and then visual-only stimulus presentation. Performance was better for single-talker lists than for multiple-talker lists, particularly under the audiovisual presentation format. Word recognition performance was better for the lexically easy than for the lexically hard words regardless of presentation format. Visual enhancement scores were higher for single-talker conditions compared to multiple-talker conditions and tended to be somewhat better for lexically easy words than for lexically hard words. The pattern of results suggests that information from the auditory and visual modalities is used to access common, multimodal lexical representations in memory. The findings are discussed in terms of the complementary nature of auditory and visual sources of information that specify the same underlying gestures and articulatory events in speech. PMID:14700380

Gratifications, collective self-esteem, online emotional openness, and traitlike communication apprehension as predictors of Facebook uses.

PubMed

Zhang, Yin; Tang, Leo Shing-Tung; Leung, Louis

2011-12-01

This study explores whether and how gratifications and psychological traits impact people's Facebook use. First, a factor analysis of an online survey (N= 437) outlined a unique set of gratifications obtained from the use of Facebook. Six aspects of gratifications (i.e., social surveillance, entertainment, recognition, emotional support, network extension, and maintenance) were identified. Results from regression analyses showed that psychological traits (i.e., collective self-esteem, online emotional openness, and traitlike communication apprehension) were strong predictors of most Facebook gratifications. Additionally, gratifications and, to a lesser extent, psychological traits significantly predicted Facebook usage, both in perceived importance and different indicators in the level of Facebook use.

Assessment of directionality performances: comparison between Freedom and CP810 sound processors.

PubMed

Razza, Sergio; Albanese, Greta; Ermoli, Lucilla; Zaccone, Monica; Cristofari, Eliana

2013-10-01

To compare speech recognition in noise for the Nucleus Freedom and CP810 sound processors using different directional settings among those available in the SmartSound portfolio. Single-subject, repeated measures study. Tertiary care referral center. Thirty-one monoaurally and binaurally implanted subjects (24 children and 7 adults) were enrolled. They were all experienced Nucleus Freedom sound processor users and achieved a 100% open set word recognition score in quiet listening conditions. Each patient was fitted with the Freedom and the CP810 processor. The program setting incorporated Adaptive Dynamic Range Optimization (ADRO) and adopted the directional algorithm BEAM (both devices) and ZOOM (only on CP810). Speech reception threshold (SRT) was assessed in a free-field layout, with disyllabic word list and interfering multilevel babble noise in the 3 different pre-processing configurations. On average, CP810 improved significantly patients' SRTs as compared to Freedom SP after 1 hour of use. Instead, no significant difference was observed in patients' SRT between the BEAM and the ZOOM algorithm fitted in the CP810 processor. The results suggest that hardware developments achieved in the design of CP810 allow an immediate and relevant directional advantage as compared to the previous-generation Freedom device.

Recognition of chemical entities: combining dictionary-based and grammar-based approaches.

PubMed

Akhondi, Saber A; Hettne, Kristina M; van der Horst, Eelke; van Mulligen, Erik M; Kors, Jan A

2015-01-01

The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokenization and better recognition of specific entity types is likely to further improve system performance.

Recognition of chemical entities: combining dictionary-based and grammar-based approaches

PubMed Central

2015-01-01

Background The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. Results The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. Conclusions We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokenization and better recognition of specific entity types is likely to further improve system performance. PMID:25810767

Masking release for words in amplitude-modulated noise as a function of modulation rate and task

PubMed Central

Buss, Emily; Whittle, Lisa N.; Grose, John H.; Hall, Joseph W.

2009-01-01

For normal-hearing listeners, masked speech recognition can improve with the introduction of masker amplitude modulation. The present experiments tested the hypothesis that this masking release is due in part to an interaction between the temporal distribution of cues necessary to perform the task and the probability of those cues temporally coinciding with masker modulation minima. Stimuli were monosyllabic words masked by speech-shaped noise, and masker modulation was introduced via multiplication with a raised sinusoid of 2.5–40 Hz. Tasks included detection, three-alternative forced-choice identification, and open-set identification. Overall, there was more masking release associated with the closed than the open-set tasks. The best rate of modulation also differed as a function of task; whereas low modulation rates were associated with best performance for the detection and three-alternative identification tasks, performance improved with modulation rate in the open-set task. This task-by-rate interaction was also observed when amplitude-modulated speech was presented in a steady masker, and for low- and high-pass filtered speech presented in modulated noise. These results were interpreted as showing that the optimal rate of amplitude modulation depends on the temporal distribution of speech cues and the information required to perform a particular task. PMID:19603883

Single-pixel non-imaging object recognition by means of Fourier spectrum acquisition

NASA Astrophysics Data System (ADS)

Chen, Huichao; Shi, Jianhong; Liu, Xialin; Niu, Zhouzhou; Zeng, Guihua

2018-04-01

Single-pixel imaging has emerged over recent years as a novel imaging technique, which has significant application prospects. In this paper, we propose and experimentally demonstrate a scheme that can achieve single-pixel non-imaging object recognition by acquiring the Fourier spectrum. In an experiment, a four-step phase-shifting sinusoid illumination light is used to irradiate the object image, the value of the light intensity is measured with a single-pixel detection unit, and the Fourier coefficients of the object image are obtained by a differential measurement. The Fourier coefficients are first cast into binary numbers to obtain the hash value. We propose a new method of perceptual hashing algorithm, which is combined with a discrete Fourier transform to calculate the hash value. The hash distance is obtained by calculating the difference of the hash value between the object image and the contrast images. By setting an appropriate threshold, the object image can be quickly and accurately recognized. The proposed scheme realizes single-pixel non-imaging perceptual hashing object recognition by using fewer measurements. Our result might open a new path for realizing object recognition with non-imaging.

Chronic methylphenidate-effects over circadian cycle of young and adult rats submitted to open-field and object recognition tests.

PubMed

Gomes, Karin M; Souza, Renan P; Valvassori, Samira S; Réus, Gislaine Z; Inácio, Cecília G; Martins, Márcio R; Comim, Clarissa M; Quevedo, João

2009-11-01

In this study age-, circadian rhythm- and methylphenidate administration- effect on open field habituation and object recognition were analyzed. Young and adult male Wistar rats were treated with saline or methylphenidate 2.0 mg/kg for 28 days. Experiments were performed during the light and the dark cycle. Locomotor activity was significantly altered by circadian cycle and methylphenidate treatment during the training session and by drug treatment during the testing session. Exploratory activity was significantly modulated by age during the training session and by age and drug treatment during the testing session. Object recognition memory was altered by cycle at the training session; by age 1.5 h later and by cycle and age 24 h after the training session. These results show that methylphenidate treatment was the major modulator factor on open-field test while cycle and age had an important effect on object recognition experiment.

Peer-to-Peer Recognition of Learning in Open Education

ERIC Educational Resources Information Center

Schmidt, Jan Philipp; Geith, Christine; Haklev, Stian; Thierstein, Joel

2009-01-01

Recognition in education is the acknowledgment of learning achievements. Accreditation is certification of such recognition by an institution, an organization, a government, a community, etc. There are a number of assessment methods by which learning can be evaluated (exam, practicum, etc.) for the purpose of recognition and accreditation, and…

The relationship between action anticipation and emotion recognition in athletes of open skill sports.

PubMed

Shih, Yu-Ling; Lin, Chia-Yen

2016-08-01

Action anticipation plays an important role in the successful performance of open skill sports, such as ball and combat sports. Evidence has shown that elite athletes of open sports excel in action anticipation. Most studies have targeted ball sports and agreed that information on body mechanics is one of the key determinants for successful action anticipation in open sports. However, less is known about combat sports, and whether facial emotions have an influence on athletes' action anticipation skill. It has been suggested that the understanding of intention in combat sports relies heavily on emotional context. Based on this suggestion, the present study compared the action anticipation performances of taekwondo athletes, weightlifting athletes, and non-athletes and then correlated these with their performances of emotion recognition. This study primarily found that accurate action anticipation does not necessarily rely on the dynamic information of movement, and that action anticipation performance is correlated with that of emotion recognition in taekwondo athletes, but not in weightlifting athletes. Our results suggest that the recognition of facial emotions plays a role in the action prediction in such combat sports as taekwondo.

The prediction of human exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames

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

Solovyev, V.V.; Salamov, A.A.; Lawrence, C.B.

1994-12-31

Discriminant analysis is applied to the problem of recognition 5`-, internal and 3`-exons in human DNA sequences. Specific recognition functions were developed for revealing exons of particular types. The method based on a splice site prediction algorithm that uses the linear Fisher discriminant to combine the information about significant triplet frequencies of various functional parts of splice site regions and preferences of oligonucleotide in protein coding and nation regions. The accuracy of our splice site recognition function is about 97%. A discriminant function for 5`-exon prediction includes hexanucleotide composition of upstream region, triplet composition around the ATG codon, ORF codingmore » potential, donor splice site potential and composition of downstream introit region. For internal exon prediction, we combine in a discriminant function the characteristics describing the 5`- intron region, donor splice site, coding region, acceptor splice site and Y-intron region for each open reading frame flanked by GT and AG base pairs. The accuracy of precise internal exon recognition on a test set of 451 exon and 246693 pseudoexon sequences is 77% with a specificity of 79% and a level of pseudoexon ORF prediction of 99.96%. The recognition quality computed at the level of individual nucleotides is 89%, for exon sequences and 98% for intron sequences. A discriminant function for 3`-exon prediction includes octanucleolide composition of upstream nation region, triplet composition around the stop codon, ORF coding potential, acceptor splice site potential and hexanucleotide composition of downstream region. We unite these three discriminant functions in exon predicting program FEX (find exons). FEX exactly predicts 70% of 1016 exons from the test of 181 complete genes with specificity 73%, and 89% exons are exactly or partially predicted. On the average, 85% of nucleotides were predicted accurately with specificity 91%.« less

Towards Seamless Validation of Land Cover Data

NASA Astrophysics Data System (ADS)

Chuprikova, Ekaterina; Liebel, Lukas; Meng, Liqiu

2018-05-01

This article demonstrates the ability of the Bayesian Network analysis for the recognition of uncertainty patterns associated with the fusion of various land cover data sets including GlobeLand30, CORINE (CLC2006, Germany) and land cover data derived from Volunteered Geographic Information (VGI) such as Open Street Map (OSM). The results of recognition are expressed as probability and uncertainty maps which can be regarded as a by-product of the GlobeLand30 data. The uncertainty information may guide the quality improvement of GlobeLand30 by involving the ground truth data, information with superior quality, the know-how of experts and the crowd intelligence. Such an endeavor aims to pave a way towards a seamless validation of global land cover data on the one hand and a targeted knowledge discovery in areas with higher uncertainty values on the other hand.

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.

Ordinal measures for iris recognition.

PubMed

Sun, Zhenan; Tan, Tieniu

2009-12-01

Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models.

Modeling open-set spoken word recognition in postlingually deafened adults after cochlear implantation: some preliminary results with the neighborhood activation model.

PubMed

Meyer, Ted A; Frisch, Stefan A; Pisoni, David B; Miyamoto, Richard T; Svirsky, Mario A

2003-07-01

Do cochlear implants provide enough information to allow adult cochlear implant users to understand words in ways that are similar to listeners with acoustic hearing? Can we use a computational model to gain insight into the underlying mechanisms used by cochlear implant users to recognize spoken words? The Neighborhood Activation Model has been shown to be a reasonable model of word recognition for listeners with normal hearing. The Neighborhood Activation Model assumes that words are recognized in relation to other similar-sounding words in a listener's lexicon. The probability of correctly identifying a word is based on the phoneme perception probabilities from a listener's closed-set consonant and vowel confusion matrices modified by the relative frequency of occurrence of the target word compared with similar-sounding words (neighbors). Common words with few similar-sounding neighbors are more likely to be selected as responses than less common words with many similar-sounding neighbors. Recent studies have shown that several of the assumptions of the Neighborhood Activation Model also hold true for cochlear implant users. Closed-set consonant and vowel confusion matrices were obtained from 26 postlingually deafened adults who use cochlear implants. Confusion matrices were used to represent input errors to the Neighborhood Activation Model. Responses to the different stimuli were then generated by the Neighborhood Activation Model after incorporating the frequency of occurrence counts of the stimuli and their neighbors. Model outputs were compared with obtained performance measures on the Consonant-Vowel Nucleus-Consonant word test. Information transmission analysis was used to assess whether the Neighborhood Activation Model was able to successfully generate and predict word and individual phoneme recognition by cochlear implant users. The Neighborhood Activation Model predicted Consonant-Vowel Nucleus-Consonant test words at levels similar to those correctly identified by the cochlear implant users. The Neighborhood Activation Model also predicted phoneme feature information well. The results obtained suggest that the Neighborhood Activation Model provides a reasonable explanation of word recognition by postlingually deafened adults after cochlear implantation. It appears that multichannel cochlear implants give cochlear implant users access to their mental lexicons in a manner that is similar to listeners with acoustic hearing. The lexical properties of the test stimuli used to assess performance are important to spoken-word recognition and should be included in further models of the word recognition process.

A comparison of image processing techniques for bird recognition.

PubMed

Nadimpalli, Uma D; Price, Randy R; Hall, Steven G; Bomma, Pallavi

2006-01-01

Bird predation is one of the major concerns for fish culture in open ponds. A novel method for dispersing birds is the use of autonomous vehicles. Image recognition software can improve their efficiency. Several image processing techniques for recognition of birds have been tested. A series of morphological operations were implemented. We divided images into 3 types, Type 1, Type 2, and Type 3, based on the level of difficulty of recognizing birds. Type 1 images were clear; Type 2 images were medium clear, and Type 3 images were unclear. Local thresholding has been implemented using HSV (Hue, Saturation, and Value), GRAY, and RGB (Red, Green, and Blue) color models on all three sections of images and results were tabulated. Template matching using normal correlation and artificial neural networks (ANN) are the other methods that have been developed in this study in addition to image morphology. Template matching produced satisfactory results irrespective of the difficulty level of images, but artificial neural networks produced accuracies of 100, 60, and 50% on Type 1, Type 2, and Type 3 images, respectively. Correct classification rate can be increased by further training. Future research will focus on testing the recognition algorithms in natural or aquacultural settings on autonomous boats. Applications of such techniques to industrial, agricultural, or related areas are additional future possibilities.

Type-specific proactive interference in patients with semantic and phonological STM deficits.

PubMed

Harris, Lara; Olson, Andrew; Humphreys, Glyn

2014-01-01

Prior neuropsychological evidence suggests that semantic and phonological components of short-term memory (STM) are functionally and neurologically distinct. The current paper examines proactive interference (PI) from semantic and phonological information in two STM-impaired patients, DS (semantic STM deficit) and AK (phonological STM deficit). In Experiment 1 probe recognition tasks with open and closed sets of stimuli were used. Phonological PI was assessed using nonword items, and semantic and phonological PI was assessed using words. In Experiment 2 phonological and semantic PI was elicited by an item recognition probe test with stimuli that bore phonological and semantic relations to the probes. The data suggested heightened phonological PI for the semantic STM patient, and exaggerated effects of semantic PI in the phonological STM case. The findings are consistent with an account of extremely rapid decay of activated type-specific representations in cases of severely impaired phonological and semantic STM.

Recognition-induced forgetting is not due to category-based set size.

PubMed

Maxcey, Ashleigh M

2016-01-01

What are the consequences of accessing a visual long-term memory representation? Previous work has shown that accessing a long-term memory representation via retrieval improves memory for the targeted item and hurts memory for related items, a phenomenon called retrieval-induced forgetting. Recently we found a similar forgetting phenomenon with recognition of visual objects. Recognition-induced forgetting occurs when practice recognizing an object during a two-alternative forced-choice task, from a group of objects learned at the same time, leads to worse memory for objects from that group that were not practiced. An alternative explanation of this effect is that category-based set size is inducing forgetting, not recognition practice as claimed by some researchers. This alternative explanation is possible because during recognition practice subjects make old-new judgments in a two-alternative forced-choice task, and are thus exposed to more objects from practiced categories, potentially inducing forgetting due to set-size. Herein I pitted the category-based set size hypothesis against the recognition-induced forgetting hypothesis. To this end, I parametrically manipulated the amount of practice objects received in the recognition-induced forgetting paradigm. If forgetting is due to category-based set size, then the magnitude of forgetting of related objects will increase as the number of practice trials increases. If forgetting is recognition induced, the set size of exemplars from any given category should not be predictive of memory for practiced objects. Consistent with this latter hypothesis, additional practice systematically improved memory for practiced objects, but did not systematically affect forgetting of related objects. These results firmly establish that recognition practice induces forgetting of related memories. Future directions and important real-world applications of using recognition to access our visual memories of previously encountered objects are discussed.

Public domain optical character recognition

NASA Astrophysics Data System (ADS)

Garris, Michael D.; Blue, James L.; Candela, Gerald T.; Dimmick, Darrin L.; Geist, Jon C.; Grother, Patrick J.; Janet, Stanley A.; Wilson, Charles L.

1995-03-01

A public domain document processing system has been developed by the National Institute of Standards and Technology (NIST). The system is a standard reference form-based handprint recognition system for evaluating optical character recognition (OCR), and it is intended to provide a baseline of performance on an open application. The system's source code, training data, performance assessment tools, and type of forms processed are all publicly available. The system recognizes the handprint entered on handwriting sample forms like the ones distributed with NIST Special Database 1. From these forms, the system reads hand-printed numeric fields, upper and lowercase alphabetic fields, and unconstrained text paragraphs comprised of words from a limited-size dictionary. The modular design of the system makes it useful for component evaluation and comparison, training and testing set validation, and multiple system voting schemes. The system contains a number of significant contributions to OCR technology, including an optimized probabilistic neural network (PNN) classifier that operates a factor of 20 times faster than traditional software implementations of the algorithm. The source code for the recognition system is written in C and is organized into 11 libraries. In all, there are approximately 19,000 lines of code supporting more than 550 subroutines. Source code is provided for form registration, form removal, field isolation, field segmentation, character normalization, feature extraction, character classification, and dictionary-based postprocessing. The recognition system has been successfully compiled and tested on a host of UNIX workstations. This paper gives an overview of the recognition system's software architecture, including descriptions of the various system components along with timing and accuracy statistics.

Face Recognition Vendor Test 2000: Appendices

DTIC Science & Technology

2001-02-01

DARPA), NAVSEA Crane Division and NAVSEA Dahlgren Division are sponsoring an evaluation of commercial off the shelf (COTS) facial recognition products...The purpose of these evaluations is to accurately gauge the capabilities of facial recognition biometric systems that are currently available for...or development efforts. Participation in these tests is open to all facial recognition systems on the US commercial market. The U.S. Government will

A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework.

PubMed

Wei, Shengjing; Chen, Xiang; Yang, Xidong; Cao, Shuai; Zhang, Xu

2016-04-19

Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). In this framework, a sign word was considered to be a combination of five common sign components, including hand shape, axis, orientation, rotation, and trajectory, and sign classification was implemented based on the recognition of five components. Especially, the proposed SLR framework consisted of two major parts. The first part was to obtain the component-based form of sign gestures and establish the code table of target sign gesture set using data from a reference subject. In the second part, which was designed for new users, component classifiers were trained using a training set suggested by the reference subject and the classification of unknown gestures was performed with a code matching method. Five subjects participated in this study and recognition experiments under different size of training sets were implemented on a target gesture set consisting of 110 frequently-used Chinese Sign Language (CSL) sign words. The experimental results demonstrated that the proposed framework can realize large-scale gesture set recognition with a small-scale training set. With the smallest training sets (containing about one-third gestures of the target gesture set) suggested by two reference subjects, (82.6 ± 13.2)% and (79.7 ± 13.4)% average recognition accuracy were obtained for 110 words respectively, and the average recognition accuracy climbed up to (88 ± 13.7)% and (86.3 ± 13.7)% when the training set included 50~60 gestures (about half of the target gesture set). The proposed framework can significantly reduce the user's training burden in large-scale gesture recognition, which will facilitate the implementation of a practical SLR system.

AutoMap User’s Guide

DTIC Science & Technology

2006-10-01

Hierarchy of Pre-Processing Techniques 3. NLP (Natural Language Processing) Utilities 3.1 Named-Entity Recognition 3.1.1 Example for Named-Entity... Recognition 3.2 Symbol RemovalN-Gram Identification: Bi-Grams 4. Stemming 4.1 Stemming Example 5. Delete List 5.1 Open a Delete List 5.1.1 Small...iterative and involves several key processes: • Named-Entity Recognition Named-Entity Recognition is an Automap feature that allows you to

Monitoring of piglets' open field activity and choice behaviour during the replay of maternal vocalization: a comparison between Observer and PID technique.

PubMed

Puppe, B; Schön, P C; Wendland, K

1999-07-01

The paper presents a new system for the automatic monitoring of open field activity and choice behaviour of medium-sized animals. Passive infrared motion detectors (PID) were linked on-line via a digital I/O interface to a personal computer provided with self-developed analysis software based on LabVIEW (PID technique). The set up was used for testing 18 one-week-old piglets (Sus scrofa) for their approach to their mother's nursing vocalization replayed through loudspeakers. The results were validated by comparison with a conventional Observer technique, a computer-aided direct observation. In most of the cases, no differences were seen between the Observer and PID technique regarding the percentage of stay in previously defined open field segments, the locomotor open field activity, and the choice behaviour. The results revealed that piglets are clearly attracted by their mother's nursing vocalization. The monitoring system presented in this study is thus suitable for detailed behavioural investigations of individual acoustic recognition. In general, the PID technique is a useful tool for research into the behaviour of individual animals in a restricted open field which does not rely on subjective analysis by a human observer.

An open access thyroid ultrasound image database

NASA Astrophysics Data System (ADS)

Pedraza, Lina; Vargas, Carlos; Narváez, Fabián.; Durán, Oscar; Muñoz, Emma; Romero, Eduardo

2015-01-01

Computer aided diagnosis systems (CAD) have been developed to assist radiologists in the detection and diagnosis of abnormalities and a large number of pattern recognition techniques have been proposed to obtain a second opinion. Most of these strategies have been evaluated using different datasets making their performance incomparable. In this work, an open access database of thyroid ultrasound images is presented. The dataset consists of a set of B-mode Ultrasound images, including a complete annotation and diagnostic description of suspicious thyroid lesions by expert radiologists. Several types of lesions as thyroiditis, cystic nodules, adenomas and thyroid cancers were included while an accurate lesion delineation is provided in XML format. The diagnostic description of malignant lesions was confirmed by biopsy. The proposed new database is expected to be a resource for the community to assess different CAD systems.

Eye movement analysis for activity recognition using electrooculography.

PubMed

Bulling, Andreas; Ward, Jamie A; Gellersen, Hans; Tröster, Gerhard

2011-04-01

In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.

Multi-Lingual Deep Neural Networks for Language Recognition

DTIC Science & Technology

2016-08-08

training configurations for the NIST 2011 and 2015 lan- guage recognition evaluations (LRE11 and LRE15). The best per- forming multi-lingual BN-DNN...very ef- fective approach in the NIST 2015 language recognition evaluation (LRE15) open training condition [4, 5]. In this work we evaluate the impact...language are summarized in Table 2. Two language recognition tasks are used for evaluating the multi-lingual bottleneck systems. The first is the NIST

Combining Open-domain and Biomedical Knowledge for Topic Recognition in Consumer Health Questions.

PubMed

Mrabet, Yassine; Kilicoglu, Halil; Roberts, Kirk; Demner-Fushman, Dina

2016-01-01

Determining the main topics in consumer health questions is a crucial step in their processing as it allows narrowing the search space to a specific semantic context. In this paper we propose a topic recognition approach based on biomedical and open-domain knowledge bases. In the first step of our method, we recognize named entities in consumer health questions using an unsupervised method that relies on a biomedical knowledge base, UMLS, and an open-domain knowledge base, DBpedia. In the next step, we cast topic recognition as a binary classification problem of deciding whether a named entity is the question topic or not. We evaluated our approach on a dataset from the National Library of Medicine (NLM), introduced in this paper, and another from the Genetic and Rare Disease Information Center (GARD). The combination of knowledge bases outperformed the results obtained by individual knowledge bases by up to 16.5% F1 and achieved state-of-the-art performance. Our results demonstrate that combining open-domain knowledge bases with biomedical knowledge bases can lead to a substantial improvement in understanding user-generated health content.

A beat-to-beat calculator for the diastolic pressure time index and the tension time index.

PubMed

Nose, Y; Tajimi, T; Watanabe, Y; Yokota, M; Akazawa, K; Nakamura, M

1987-01-01

We have developed a beat-to-beat calculator which can calculate in real-time the ratio of the diastolic pressure time index (DPTI), and the tension time index (TTI) as an index of the myocardial oxygen supply/demand balance. Physicians set up presumed value for the left ventricular endodiastolic pressure, a search area for the dicrotic notch, a threshold for the onset of the up-slope and the corresponding value of the calibration signal on the digital switches of the calculator. Next, the arterial pressure analog signal is input into the calculator. The calculator searches automatically for both the onset of the up-slope and the dicrotic notch. The arterial pressure curve is displayed beat-to-beat with the recognized onset and the dicrotic notch on the CRT to be confirmed by physicians. When physicians do not agree with the automatic recognition they can fit the automatic recognition to the observation. If the recognition of the onset is inadequate, the threshold can be re-adjusted to trigger the onset. If recognition of the dicrotic notch is inadequate, the physician can adjust the search-area. Therefore, physicians who operate the calculator can rely on the calculated DPTI/TTI. This calculator can continuously monitor the myocardial oxygen supply/demand balance in patients with acute myocardial infarction or just after open-heart surgery.

Modeling Open-Set Spoken Word Recognition in Postlingually Deafened Adults after Cochlear Implantation: Some Preliminary Results with the Neighborhood Activation Model

PubMed Central

Meyer, Ted A.; Frisch, Stefan A.; Pisoni, David B.; Miyamoto, Richard T.; Svirsky, Mario A.

2012-01-01

Hypotheses Do cochlear implants provide enough information to allow adult cochlear implant users to understand words in ways that are similar to listeners with acoustic hearing? Can we use a computational model to gain insight into the underlying mechanisms used by cochlear implant users to recognize spoken words? Background The Neighborhood Activation Model has been shown to be a reasonable model of word recognition for listeners with normal hearing. The Neighborhood Activation Model assumes that words are recognized in relation to other similar-sounding words in a listener’s lexicon. The probability of correctly identifying a word is based on the phoneme perception probabilities from a listener’s closed-set consonant and vowel confusion matrices modified by the relative frequency of occurrence of the target word compared with similar-sounding words (neighbors). Common words with few similar-sounding neighbors are more likely to be selected as responses than less common words with many similar-sounding neighbors. Recent studies have shown that several of the assumptions of the Neighborhood Activation Model also hold true for cochlear implant users. Methods Closed-set consonant and vowel confusion matrices were obtained from 26 postlingually deafened adults who use cochlear implants. Confusion matrices were used to represent input errors to the Neighborhood Activation Model. Responses to the different stimuli were then generated by the Neighborhood Activation Model after incorporating the frequency of occurrence counts of the stimuli and their neighbors. Model outputs were compared with obtained performance measures on the Consonant-Vowel Nucleus-Consonant word test. Information transmission analysis was used to assess whether the Neighborhood Activation Model was able to successfully generate and predict word and individual phoneme recognition by cochlear implant users. Results The Neighborhood Activation Model predicted Consonant-Vowel Nucleus-Consonant test words at levels similar to those correctly identified by the cochlear implant users. The Neighborhood Activation Model also predicted phoneme feature information well. Conclusion The results obtained suggest that the Neighborhood Activation Model provides a reasonable explanation of word recognition by postlingually deafened adults after cochlear implantation. It appears that multichannel cochlear implants give cochlear implant users access to their mental lexicons in a manner that is similar to listeners with acoustic hearing. The lexical properties of the test stimuli used to assess performance are important to spoken-word recognition and should be included in further models of the word recognition process. PMID:12851554

Specificity and multiplicity in the recognition of individuals: implications for the evolution of social behaviour.

PubMed

Wiley, R H

2013-02-01

Recognition of conspecifics occurs when individuals classify sets of conspecifics based on sensory input from them and associate these sets with different responses. Classification of conspecifics can vary in specificity (the number of individuals included in a set) and multiplicity (the number of sets differentiated). In other words, the information transmitted varies in complexity. Although recognition of conspecifics has been reported in a wide variety of organisms, few reports have addressed the specificity or multiplicity of this capability. This review discusses examples of these patterns, the mechanisms that can produce them, and the evolution of these mechanisms. Individual recognition is one end of a spectrum of specificity, and binary classification of conspecifics is one end of a spectrum of multiplicity. In some cases, recognition requires no more than simple forms of learning, such as habituation, yet results in individually specific recognition. In other cases, recognition of individuals involves complex associations of multiple cues with multiple previous experiences in particular contexts. Complex mechanisms for recognition are expected to evolve only when simpler mechanisms do not provide sufficient specificity and multiplicity to obtain the available advantages. In particular, the evolution of cooperation and deception is always promoted by specificity and multiplicity in recognition. Nevertheless, there is only one demonstration that recognition of specific individuals contributes to cooperation in animals other than primates. Human capacities for individual recognition probably have a central role in the evolution of complex forms of human cooperation and deception. Although relatively little studied, this capability probably rivals cognitive abilities for language. © 2012 The Author. Biological Reviews © 2012 Cambridge Philosophical Society.

Auditory Brainstem Implantation in Chinese Patients With Neurofibromatosis Type II: The Hong Kong Experience.

PubMed

Thong, Jiun Fong; Sung, John K K; Wong, Terence K C; Tong, Michael C F

2016-08-01

To describe our experience and outcomes of auditory brainstem implantation (ABI) in Chinese patients with Neurofibromatosis Type II (NF2). Retrospective case review. Tertiary referral center. Patients with NF2 who received ABIs. Between 1997 and 2014, eight patients with NF2 received 9 ABIs after translabyrinthine removal of their vestibular schwannomas. One patient did not have auditory response using the ABI after activation. Environmental sounds could be differentiated by six (75%) patients after 6 months of ABI use (mean score 46% [range 28-60%]), and by five (63%) patients after 1 year (mean score 57% [range 36-76%]) and 2 years of ABI use (mean score 48% [range 24-76%]). Closed-set word identification was possible in four (50%) patients after 6 months (mean score 39% [range 12-72%]), 1 year (mean score 68% [range 48-92%]), and 2 years of ABI use (mean score 62% [range 28-100%]). No patient demonstrated open-set sentence recognition in quiet in the ABI-only condition. However, the use of ABI together with lip-reading conferred an improvement over lip-reading alone in open-set sentence recognition scores in two (25%) patients after 6 months of ABI use (mean improvement 46%), and five (63%) patients after 1 year (mean improvement 25%) and 2 years of ABI use (mean improvement 28%). At 2 years postoperatively, three (38%) patients remained ABI users. This is the only published study to date examining ABI outcomes in Cantonese-speaking Chinese NF2 patients and the data seems to show poorer outcomes compared with English-speaking and other nontonal language-speaking NF2 patients. Environmental sound awareness and lip-reading enhancement are the main benefits observed in our patients. More work is needed to improve auditory implant speech-processing strategies for tonal languages and these advancements may yield better speech perception outcomes in the future.

Western-style diet induces insulin insensitivity and hyperactivity in adolescent male rats.

PubMed

Marwitz, Shannon E; Woodie, Lauren N; Blythe, Sarah N

2015-11-01

The prevalence of obesity in children and adolescents has increased rapidly over the past 30 years, as has the incidence of attention deficit hyperactivity disorder (ADHD). In 2012, it was found that overweight children have a twofold higher chance of developing ADHD than their normal weight counterparts. Previous work has documented learning and memory impairments linked to consumption of an energy-dense diet in rats, but the relationship between diet and ADHD-like behaviors has yet to be explored using animal models. Therefore, the purpose of this study was to explore the role of diet in the etiology of attention and hyperactivity disorders using a rat model of diet-induced obesity. Male Sprague-Dawley rats were fed either a control diet or a Western-style diet (WSD) for ten weeks, and specific physiological and behavioral effects were examined. Tail blood samples were collected to measure fasting blood glucose and insulin levels in order to assess insulin insensitivity. Rats also performed several behavioral tasks, including the open field task, novel object recognition test, and attentional set-shifting task. Rats exposed to a WSD had significantly higher fasting insulin levels than controls, but both groups had similar glucose levels. The quantitative insulin sensitivity check index (QUICKI) indicated the development of insulin resistance in WSD rats. Performance in the open field test indicated that WSD induced pronounced hyperactivity and impulsivity. Further, control diet animals were able to discriminate between old and novel objects, but the WSD animals were significantly impaired in object recognition. However, regardless of dietary condition, rats were able to perform the attentional set-shifting paradigm. While WSD impaired episodic memory and induced hyperactivity, attentional set-shifting capabilities are unaffected. With the increasing prevalence of both obesity and ADHD, understanding the potential links between the two conditions is of clinical relevance. Copyright © 2015 Elsevier Inc. All rights reserved.

Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation.

PubMed

Tormene, Paolo; Giorgino, Toni; Quaglini, Silvana; Stefanelli, Mario

2009-01-01

The purpose of this study was to assess the performance of a real-time ("open-end") version of the dynamic time warping (DTW) algorithm for the recognition of motor exercises. Given a possibly incomplete input stream of data and a reference time series, the open-end DTW algorithm computes both the size of the prefix of reference which is best matched by the input, and the dissimilarity between the matched portions. The algorithm was used to provide real-time feedback to neurological patients undergoing motor rehabilitation. We acquired a dataset of multivariate time series from a sensorized long-sleeve shirt which contains 29 strain sensors distributed on the upper limb. Seven typical rehabilitation exercises were recorded in several variations, both correctly and incorrectly executed, and at various speeds, totaling a data set of 840 time series. Nearest-neighbour classifiers were built according to the outputs of open-end DTW alignments and their global counterparts on exercise pairs. The classifiers were also tested on well-known public datasets from heterogeneous domains. Nonparametric tests show that (1) on full time series the two algorithms achieve the same classification accuracy (p-value =0.32); (2) on partial time series, classifiers based on open-end DTW have a far higher accuracy (kappa=0.898 versus kappa=0.447;p<10(-5)); and (3) the prediction of the matched fraction follows closely the ground truth (root mean square <10%). The results hold for the motor rehabilitation and the other datasets tested, as well. The open-end variant of the DTW algorithm is suitable for the classification of truncated quantitative time series, even in the presence of noise. Early recognition and accurate class prediction can be achieved, provided that enough variance is available over the time span of the reference. Therefore, the proposed technique expands the use of DTW to a wider range of applications, such as real-time biofeedback systems.

Recognition of the 3′ splice site RNA by the U2AF heterodimer involves a dynamic population shift

PubMed Central

Voith von Voithenberg, Lena; Sánchez-Rico, Carolina; Kang, Hyun-Seo; Madl, Tobias; Zanier, Katia; Barth, Anders; Warner, Lisa R.; Sattler, Michael; Lamb, Don C.

2016-01-01

An essential early step in the assembly of human spliceosomes onto pre-mRNA involves the recognition of regulatory RNA cis elements in the 3′ splice site by the U2 auxiliary factor (U2AF). The large (U2AF65) and small (U2AF35) subunits of the U2AF heterodimer contact the polypyrimidine tract (Py-tract) and the AG-dinucleotide, respectively. The tandem RNA recognition motif domains (RRM1,2) of U2AF65 adopt closed/inactive and open/active conformations in the free form and when bound to bona fide Py-tract RNA ligands. To investigate the molecular mechanism and dynamics of 3′ splice site recognition by U2AF65 and the role of U2AF35 in the U2AF heterodimer, we have combined single-pair FRET and NMR experiments. In the absence of RNA, the RRM1,2 domain arrangement is highly dynamic on a submillisecond time scale, switching between closed and open conformations. The addition of Py-tract RNA ligands with increasing binding affinity (strength) gradually shifts the equilibrium toward an open conformation. Notably, the protein–RNA complex is rigid in the presence of a strong Py-tract but exhibits internal motion with weak Py-tracts. Surprisingly, the presence of U2AF35, whose UHM domain interacts with U2AF65 RRM1, increases the population of the open arrangement of U2AF65 RRM1,2 in the absence and presence of a weak Py-tract. These data indicate that the U2AF heterodimer promotes spliceosome assembly by a dynamic population shift toward the open conformation of U2AF65 to facilitate the recognition of weak Py-tracts at the 3′ splice site. The structure and RNA binding of the heterodimer was unaffected by cancer-linked myelodysplastic syndrome mutants. PMID:27799531

Better Object Recognition and Naming Outcome With MRI-Guided Stereotactic Laser Amygdalohippocampotomy for Temporal Lobe Epilepsy

PubMed Central

Drane, Daniel L.; Loring, David W.; Voets, Natalie L.; Price, Michele; Ojemann, Jeffrey G.; Willie, Jon T.; Saindane, Amit M.; Phatak, Vaishali; Ivanisevic, Mirjana; Millis, Scott; Helmers, Sandra L.; Miller, John W.; Meador, Kimford J.; Gross, Robert E.

2015-01-01

SUMMARY OBJECTIVES Temporal lobe epilepsy (TLE) patients experience significant deficits in category-related object recognition and naming following standard surgical approaches. These deficits may result from a decoupling of core processing modules (e.g., language, visual processing, semantic memory), due to “collateral damage” to temporal regions outside the hippocampus following open surgical approaches. We predicted stereotactic laser amygdalohippocampotomy (SLAH) would minimize such deficits because it preserves white matter pathways and neocortical regions critical for these cognitive processes. METHODS Tests of naming and recognition of common nouns (Boston Naming Test) and famous persons were compared with nonparametric analyses using exact tests between a group of nineteen patients with medically-intractable mesial TLE undergoing SLAH (10 dominant, 9 nondominant), and a comparable series of TLE patients undergoing standard surgical approaches (n=39) using a prospective, non-randomized, non-blinded, parallel group design. RESULTS Performance declines were significantly greater for the dominant TLE patients undergoing open resection versus SLAH for naming famous faces and common nouns (F=24.3, p<.0001, η2=.57, & F=11.2, p<.001, η2=.39, respectively), and for the nondominant TLE patients undergoing open resection versus SLAH for recognizing famous faces (F=3.9, p<.02, η2=.19). When examined on an individual subject basis, no SLAH patients experienced any performance declines on these measures. In contrast, 32 of the 39 undergoing standard surgical approaches declined on one or more measures for both object types (p<.001, Fisher’s exact test). Twenty-one of 22 left (dominant) TLE patients declined on one or both naming tasks after open resection, while 11 of 17 right (non-dominant) TLE patients declined on face recognition. SIGNIFICANCE Preliminary results suggest 1) naming and recognition functions can be spared in TLE patients undergoing SLAH, and 2) the hippocampus does not appear to be an essential component of neural networks underlying name retrieval or recognition of common objects or famous faces. PMID:25489630

Early Postimplant Speech Perception and Language Skills Predict Long-Term Language and Neurocognitive Outcomes Following Pediatric Cochlear Implantation

PubMed Central

Kronenberger, William G.; Castellanos, Irina; Pisoni, David B.

2017-01-01

Purpose We sought to determine whether speech perception and language skills measured early after cochlear implantation in children who are deaf, and early postimplant growth in speech perception and language skills, predict long-term speech perception, language, and neurocognitive outcomes. Method Thirty-six long-term users of cochlear implants, implanted at an average age of 3.4 years, completed measures of speech perception, language, and executive functioning an average of 14.4 years postimplantation. Speech perception and language skills measured in the 1st and 2nd years postimplantation and open-set word recognition measured in the 3rd and 4th years postimplantation were obtained from a research database in order to assess predictive relations with long-term outcomes. Results Speech perception and language skills at 6 and 18 months postimplantation were correlated with long-term outcomes for language, verbal working memory, and parent-reported executive functioning. Open-set word recognition was correlated with early speech perception and language skills and long-term speech perception and language outcomes. Hierarchical regressions showed that early speech perception and language skills at 6 months postimplantation and growth in these skills from 6 to 18 months both accounted for substantial variance in long-term outcomes for language and verbal working memory that was not explained by conventional demographic and hearing factors. Conclusion Speech perception and language skills measured very early postimplantation, and early postimplant growth in speech perception and language, may be clinically relevant markers of long-term language and neurocognitive outcomes in users of cochlear implants. Supplemental materials https://doi.org/10.23641/asha.5216200 PMID:28724130

The Development of Auditory Perception in Children Following Auditory Brainstem Implantation

PubMed Central

Colletti, Liliana; Shannon, Robert V.; Colletti, Vittorio

2014-01-01

Auditory brainstem implants (ABI) can provide useful auditory perception and language development in deaf children who are not able to use a cochlear implant (CI). We prospectively followed-up a consecutive group of 64 deaf children up to 12 years following ABI implantation. The etiology of deafness in these children was: cochlear nerve aplasia in 49, auditory neuropathy in 1, cochlear malformations in 8, bilateral cochlear post-meningitic ossification in 3, NF2 in 2, and bilateral cochlear fractures due to a head injury in 1. Thirty five children had other congenital non-auditory disabilities. Twenty two children had previous CIs with no benefit. Fifty eight children were fitted with the Cochlear 24 ABI device and six with the MedEl ABI device and all children followed the same rehabilitation program. Auditory perceptual abilities were evaluated on the Categories of Auditory Performance (CAP) scale. No child was lost to follow-up and there were no exclusions from the study. All children showed significant improvement in auditory perception with implant experience. Seven children (11%) were able to achieve the highest score on the CAP test; they were able to converse on the telephone within 3 years of implantation. Twenty children (31.3%) achieved open set speech recognition (CAP score of 5 or greater) and 30 (46.9%) achieved a CAP level of 4 or greater. Of the 29 children without non-auditory disabilities, 18 (62%) achieved a CAP score of 5 or greater with the ABI. All children showed continued improvements in auditory skills over time. The long-term results of ABI implantation reveal significant auditory benefit in most children, and open set auditory recognition in many. PMID:25377987

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.

How similar are recognition memory and inductive reasoning?

PubMed

Hayes, Brett K; Heit, Evan

2013-07-01

Conventionally, memory and reasoning are seen as different types of cognitive activities driven by different processes. In two experiments, we challenged this view by examining the relationship between recognition memory and inductive reasoning involving multiple forms of similarity. A common study set (members of a conjunctive category) was followed by a test set containing old and new category members, as well as items that matched the study set on only one dimension. The study and test sets were presented under recognition or induction instructions. In Experiments 1 and 2, the inductive property being generalized was varied in order to direct attention to different dimensions of similarity. When there was no time pressure on decisions, patterns of positive responding were strongly affected by property type, indicating that different types of similarity were driving recognition and induction. By comparison, speeded judgments showed weaker property effects and could be explained by generalization based on overall similarity. An exemplar model, GEN-EX (GENeralization from EXamples), could account for both the induction and recognition data. These findings show that induction and recognition share core component processes, even when the tasks involve flexible forms of similarity.

Molecular Rift: Virtual Reality for Drug Designers.

PubMed

Norrby, Magnus; Grebner, Christoph; Eriksson, Joakim; Boström, Jonas

2015-11-23

Recent advances in interaction design have created new ways to use computers. One example is the ability to create enhanced 3D environments that simulate physical presence in the real world--a virtual reality. This is relevant to drug discovery since molecular models are frequently used to obtain deeper understandings of, say, ligand-protein complexes. We have developed a tool (Molecular Rift), which creates a virtual reality environment steered with hand movements. Oculus Rift, a head-mounted display, is used to create the virtual settings. The program is controlled by gesture-recognition, using the gaming sensor MS Kinect v2, eliminating the need for standard input devices. The Open Babel toolkit was integrated to provide access to powerful cheminformatics functions. Molecular Rift was developed with a focus on usability, including iterative test-group evaluations. We conclude with reflections on virtual reality's future capabilities in chemistry and education. Molecular Rift is open source and can be downloaded from GitHub.

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.

Modulation of the NMDA receptor by polyamines

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

Williams, K.; Romano, C.; Dichter, M.A.

1991-01-01

Results of recent biochemical and electrophysiological studies have suggested that a recognition site for polyamines exists as part of the NMDA receptor complex. The endogenous polyamines spermine and spermidine increase the binding of open-channel blockers and increase NMDA-elicited currents in cultured neutrons. These polyamines have been termed agonists at the polyamine recognition site. Studies of the effects of natural and synthetic polyamines on the binding of ({sup 3}H)MK-801 and on NMDA-elicited currents in cultured neurons have led to the identification of compounds classified as partial agonists, antagonists, and inverse agonists at the polyamine recognition site. Polyamines have also been foundmore » to affect the binding of ligands to the recognition sites for glutamate and glycine. However, these effects may be mediated at a site distinct from that at which polyamines act to modulate the binding of open-channel blockers. Endogenous polyamines may modulate excitatory synaptic transmission by acting at the polyamine recognition site of the NMDA receptor. This site could represent a novel therapeutic target for the treatment of ischemia-induced neurotoxicity, epilepsy, and neurodegenerative diseases.« less

Non-Cooperative Facial Recognition Video Dataset Collection Plan

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

Kimura, Marcia L.; Erikson, Rebecca L.; Lombardo, Nicholas J.

The Pacific Northwest National Laboratory (PNNL) will produce a non-cooperative (i.e. not posing for the camera) facial recognition video data set for research purposes to evaluate and enhance facial recognition systems technology. The aggregate data set consists of 1) videos capturing PNNL role players and public volunteers in three key operational settings, 2) photographs of the role players for enrolling in an evaluation database, and 3) ground truth data that documents when the role player is within various camera fields of view. PNNL will deliver the aggregate data set to DHS who may then choose to make it available tomore » other government agencies interested in evaluating and enhancing facial recognition systems. The three operational settings that will be the focus of the video collection effort include: 1) unidirectional crowd flow 2) bi-directional crowd flow, and 3) linear and/or serpentine queues.« less

Conflict and Criterion Setting in Recognition Memory

ERIC Educational Resources Information Center

Curran, Tim; DeBuse, Casey; Leynes, P. Andrew

2007-01-01

Recognition memory requires both retrieval processes and control processes such as criterion setting. Decision criteria were manipulated by offering different payoffs for correct "old" versus "new" responses. Criterion setting influenced the following late-occurring (1,000+ ms), conflict-sensitive event-related brain potential (ERP) components:…

Factors Affecting Open-Set Word Recognition in Adults with Cochlear Implants

PubMed Central

Holden, Laura K.; Finley, Charles C.; Firszt, Jill B.; Holden, Timothy A.; Brenner, Christine; Potts, Lisa G.; Gotter, Brenda D.; Vanderhoof, Sallie S.; Mispagel, Karen; Heydebrand, Gitry; Skinner, Margaret W.

2012-01-01

A monosyllabic word test was administered to 114 postlingually-deaf adult cochlear implant (CI) recipients at numerous intervals from two weeks to two years post-initial CI activation. Biographic/audiologic information, electrode position, and cognitive ability were examined to determine factors affecting CI outcomes. Results revealed that Duration of Severe-to-Profound Hearing Loss, Age at Implantation, CI Sound-field Threshold Levels, Percentage of Electrodes in Scala Vestibuli, Medio-lateral Electrode Position, Insertion Depth, and Cognition were among the factors that affected performance. Knowledge of how factors affect performance can influence counseling, device fitting, and rehabilitation for patients and may contribute to improved device design. PMID:23348845

Image Classification for Web Genre Identification

DTIC Science & Technology

2012-01-01

recognition and landscape detection using the computer vision toolkit OpenCV1. For facial recognition , we researched the possibilities of using the...method for connecting these names with a face/personal photo and logo respectively. [2] METHODOLOGY For this project, we focused primarily on facial

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.

Chemical entity recognition in patents by combining dictionary-based and statistical approaches

PubMed Central

Akhondi, Saber A.; Pons, Ewoud; Afzal, Zubair; van Haagen, Herman; Becker, Benedikt F.H.; Hettne, Kristina M.; van Mulligen, Erik M.; Kors, Jan A.

2016-01-01

We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small. Database URL: http://biosemantics.org/chemdner-patents PMID:27141091

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment.

PubMed

Lemaire, Edward D; Tundo, Marco D; Baddour, Natalie

2015-12-11

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated. The mobile versus immobile classification set had a sensitivity of 86.30% ± 7.2% and specificity of 98.96% ± 0.6%, while the second prediction set had a sensitivity of 88.35% ± 7.80% and specificity of 98.51% ± 0.62%. For the third classification set, sensitivity was 84.92% ± 6.38% and specificity was 98.17 ± 0.62. F1 scores for the first, second and third classification sets were 86.17 ± 6.3, 80.19 ± 6.36, and 78.42 ± 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

What's she doing in the kitchen? Context helps when actions are hard to recognize.

PubMed

Wurm, Moritz F; Schubotz, Ricarda I

2017-04-01

Specific spatial environments are often indicative of where certain actions may take place: In kitchens we prepare food, and in bathrooms we engage in personal hygiene, but not vice versa. In action recognition, contextual cues may constrain an observer's expectations toward actions that are more strongly associated with a particular context than others. Such cues should become particularly helpful when the action itself is difficult to recognize. However, to date only easily identifiable actions were investigated, and the effects of context on recognition were rather interfering than facilitatory. To test whether context also facilitates action recognition, we measured recognition performance of hardly identifiable actions that took place in compatible, incompatible, and neutral contextual settings. Action information was degraded by pixelizing the area of the object manipulation while the room in which the action took place remained fully visible. We found significantly higher accuracy for actions that took place in compatible compared to incompatible and neutral settings, indicating facilitation. Additionally, action recognition was slower in incompatible settings than in compatible and neutral settings, indicating interference. Together, our findings demonstrate that contextual information is effectively exploited during action observation, in particular when visual information about the action itself is sparse. Differential effects on speed and accuracy suggest that contexts modulate action recognition at different levels of processing. Our findings emphasize the importance of contextual information in comprehensive, ecologically valid models of action recognition.

Online medical symbol recognition using a Tablet PC

NASA Astrophysics Data System (ADS)

Kundu, Amlan; Hu, Qian; Boykin, Stanley; Clark, Cheryl; Fish, Randy; Jones, Stephen; Moore, Stephen

2011-01-01

In this paper we describe a scheme to enhance the usability of a Tablet PC's handwriting recognition system by including medical symbols that are not a part of the Tablet PC's symbol library. The goal of this work is to make handwriting recognition more useful for medical professionals accustomed to using medical symbols in medical records. To demonstrate that this new symbol recognition module is robust and expandable, we report results on both a medical symbol set and an expanded symbol test set which includes selected mathematical symbols.

Improved Open-Microphone Speech Recognition

NASA Astrophysics Data System (ADS)

Abrash, Victor

2002-12-01

Many current and future NASA missions make extreme demands on mission personnel both in terms of work load and in performing under difficult environmental conditions. In situations where hands are impeded or needed for other tasks, eyes are busy attending to the environment, or tasks are sufficiently complex that ease of use of the interface becomes critical, spoken natural language dialog systems offer unique input and output modalities that can improve efficiency and safety. They also offer new capabilities that would not otherwise be available. For example, many NASA applications require astronauts to use computers in micro-gravity or while wearing space suits. Under these circumstances, command and control systems that allow users to issue commands or enter data in hands-and eyes-busy situations become critical. Speech recognition technology designed for current commercial applications limits the performance of the open-ended state-of-the-art dialog systems being developed at NASA. For example, today's recognition systems typically listen to user input only during short segments of the dialog, and user input outside of these short time windows is lost. Mistakes detecting the start and end times of user utterances can lead to mistakes in the recognition output, and the dialog system as a whole has no way to recover from this, or any other, recognition error. Systems also often require the user to signal when that user is going to speak, which is impractical in a hands-free environment, or only allow a system-initiated dialog requiring the user to speak immediately following a system prompt. In this project, SRI has developed software to enable speech recognition in a hands-free, open-microphone environment, eliminating the need for a push-to-talk button or other signaling mechanism. The software continuously captures a user's speech and makes it available to one or more recognizers. By constantly monitoring and storing the audio stream, it provides the spoken dialog manager extra flexibility to recognize the signal with no audio gaps between recognition requests, as well as to rerecognize portions of the signal, or to rerecognize speech with different grammars, acoustic models, recognizers, start times, and so on. SRI expects that this new open-mic functionality will enable NASA to develop better error-correction mechanisms for spoken dialog systems, and may also enable new interaction strategies.

Improved Open-Microphone Speech Recognition

NASA Technical Reports Server (NTRS)

Abrash, Victor

2002-01-01

Many current and future NASA missions make extreme demands on mission personnel both in terms of work load and in performing under difficult environmental conditions. In situations where hands are impeded or needed for other tasks, eyes are busy attending to the environment, or tasks are sufficiently complex that ease of use of the interface becomes critical, spoken natural language dialog systems offer unique input and output modalities that can improve efficiency and safety. They also offer new capabilities that would not otherwise be available. For example, many NASA applications require astronauts to use computers in micro-gravity or while wearing space suits. Under these circumstances, command and control systems that allow users to issue commands or enter data in hands-and eyes-busy situations become critical. Speech recognition technology designed for current commercial applications limits the performance of the open-ended state-of-the-art dialog systems being developed at NASA. For example, today's recognition systems typically listen to user input only during short segments of the dialog, and user input outside of these short time windows is lost. Mistakes detecting the start and end times of user utterances can lead to mistakes in the recognition output, and the dialog system as a whole has no way to recover from this, or any other, recognition error. Systems also often require the user to signal when that user is going to speak, which is impractical in a hands-free environment, or only allow a system-initiated dialog requiring the user to speak immediately following a system prompt. In this project, SRI has developed software to enable speech recognition in a hands-free, open-microphone environment, eliminating the need for a push-to-talk button or other signaling mechanism. The software continuously captures a user's speech and makes it available to one or more recognizers. By constantly monitoring and storing the audio stream, it provides the spoken dialog manager extra flexibility to recognize the signal with no audio gaps between recognition requests, as well as to rerecognize portions of the signal, or to rerecognize speech with different grammars, acoustic models, recognizers, start times, and so on. SRI expects that this new open-mic functionality will enable NASA to develop better error-correction mechanisms for spoken dialog systems, and may also enable new interaction strategies.

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

PubMed

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

2016-01-14

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

Cell line name recognition in support of the identification of synthetic lethality in cancer from text

PubMed Central

Kaewphan, Suwisa; Van Landeghem, Sofie; Ohta, Tomoko; Van de Peer, Yves; Ginter, Filip; Pyysalo, Sampo

2016-01-01

Motivation: The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature. While several tools have previously been developed to address cell line recognition, it is unclear whether available systems can perform sufficiently well in realistic and broad-coverage applications such as extracting synthetically lethal genes from the cancer literature. In this study, we revisit the cell line name recognition task, evaluating both available systems and newly introduced methods on various resources to obtain a reliable tagger not tied to any specific subdomain. In support of this task, we introduce two text collections manually annotated for cell line names: the broad-coverage corpus Gellus and CLL, a focused target domain corpus. Results: We find that the best performance is achieved using NERsuite, a machine learning system based on Conditional Random Fields, trained on the Gellus corpus and supported with a dictionary of cell line names. The system achieves an F-score of 88.46% on the test set of Gellus and 85.98% on the independently annotated CLL corpus. It was further applied at large scale to 24 302 102 unannotated articles, resulting in the identification of 5 181 342 cell line mentions, normalized to 11 755 unique cell line database identifiers. Availability and implementation: The manually annotated datasets, the cell line dictionary, derived corpora, NERsuite models and the results of the large-scale run on unannotated texts are available under open licenses at http://turkunlp.github.io/Cell-line-recognition/. Contact: sukaew@utu.fi PMID:26428294

Robust Point Set Matching for Partial Face Recognition.

PubMed

Weng, Renliang; Lu, Jiwen; Tan, Yap-Peng

2016-03-01

Over the past three decades, a number of face recognition methods have been proposed in computer vision, and most of them use holistic face images for person identification. In many real-world scenarios especially some unconstrained environments, human faces might be occluded by other objects, and it is difficult to obtain fully holistic face images for recognition. To address this, we propose a new partial face recognition approach to recognize persons of interest from their partial faces. Given a pair of gallery image and probe face patch, we first detect keypoints and extract their local textural features. Then, we propose a robust point set matching method to discriminatively match these two extracted local feature sets, where both the textural information and geometrical information of local features are explicitly used for matching simultaneously. Finally, the similarity of two faces is converted as the distance between these two aligned feature sets. Experimental results on four public face data sets show the effectiveness of the proposed approach.

Better object recognition and naming outcome with MRI-guided stereotactic laser amygdalohippocampotomy for temporal lobe epilepsy.

PubMed

Drane, Daniel L; Loring, David W; Voets, Natalie L; Price, Michele; Ojemann, Jeffrey G; Willie, Jon T; Saindane, Amit M; Phatak, Vaishali; Ivanisevic, Mirjana; Millis, Scott; Helmers, Sandra L; Miller, John W; Meador, Kimford J; Gross, Robert E

2015-01-01

Patients with temporal lobe epilepsy (TLE) experience significant deficits in category-related object recognition and naming following standard surgical approaches. These deficits may result from a decoupling of core processing modules (e.g., language, visual processing, and semantic memory), due to "collateral damage" to temporal regions outside the hippocampus following open surgical approaches. We predicted that stereotactic laser amygdalohippocampotomy (SLAH) would minimize such deficits because it preserves white matter pathways and neocortical regions that are critical for these cognitive processes. Tests of naming and recognition of common nouns (Boston Naming Test) and famous persons were compared with nonparametric analyses using exact tests between a group of 19 patients with medically intractable mesial TLE undergoing SLAH (10 dominant, 9 nondominant), and a comparable series of TLE patients undergoing standard surgical approaches (n=39) using a prospective, nonrandomized, nonblinded, parallel-group design. Performance declines were significantly greater for the patients with dominant TLE who were undergoing open resection versus SLAH for naming famous faces and common nouns (F=24.3, p<0.0001, η2=0.57, and F=11.2, p<0.001, η2=0.39, respectively), and for the patients with nondominant TLE undergoing open resection versus SLAH for recognizing famous faces (F=3.9, p<0.02, η2=0.19). When examined on an individual subject basis, no SLAH patients experienced any performance declines on these measures. In contrast, 32 of the 39 patients undergoing standard surgical approaches declined on one or more measures for both object types (p<0.001, Fisher's exact test). Twenty-one of 22 left (dominant) TLE patients declined on one or both naming tasks after open resection, while 11 of 17 right (nondominant) TLE patients declined on face recognition. Preliminary results suggest (1) naming and recognition functions can be spared in TLE patients undergoing SLAH, and (2) the hippocampus does not appear to be an essential component of neural networks underlying name retrieval or recognition of common objects or famous faces. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

Open source OCR framework using mobile devices

NASA Astrophysics Data System (ADS)

Zhou, Steven Zhiying; Gilani, Syed Omer; Winkler, Stefan

2008-02-01

Mobile phones have evolved from passive one-to-one communication device to powerful handheld computing device. Today most new mobile phones are capable of capturing images, recording video, and browsing internet and do much more. Exciting new social applications are emerging on mobile landscape, like, business card readers, sing detectors and translators. These applications help people quickly gather the information in digital format and interpret them without the need of carrying laptops or tablet PCs. However with all these advancements we find very few open source software available for mobile phones. For instance currently there are many open source OCR engines for desktop platform but, to our knowledge, none are available on mobile platform. Keeping this in perspective we propose a complete text detection and recognition system with speech synthesis ability, using existing desktop technology. In this work we developed a complete OCR framework with subsystems from open source desktop community. This includes a popular open source OCR engine named Tesseract for text detection & recognition and Flite speech synthesis module, for adding text-to-speech ability.

Anti-HIV drugs nevirapine and efavirenz affect anxiety-related behavior and cognitive performance in mice.

PubMed

Romão, Pedro R T; Lemos, Joelson C; Moreira, Jeverson; de Chaves, Gisele; Moretti, Morgana; Castro, Adalberto A; Andrade, Vanessa M; Boeck, Carina R; Quevedo, João; Gavioli, Elaine C

2011-01-01

Nevirapine (NVP) and efavirenz (EFV) belong to the class of anti-HIV drugs called non-nucleoside reverse transcriptase inhibitors (NNRTIs), commonly used as part of highly active antiretroviral therapy (HAART). Although the HAART is able to bring down viral load to undetectable levels and restore immune function, their prolonged use causes several adverse effects. It has been demonstrated that both NVP and EFV are able to cross the blood-brain barrier, causing important central nervous system-related side effects. Thus, this study investigated the effects of chronic administration of EFV (10 mg/kg) and NVP (3.3 mg/kg) in mice submitted to two distinct series of experiments, which aimed to evaluate: (1) the emotional behavior (elevated plus-maze, forced swimming, and open-field test) and (2) the cognitive performance (object recognition and inhibitory avoidance test) of mice. Our results demonstrated that EFV, but not NVP, reduced the exploration to open arms in the elevated plus-maze test. Neither NVP nor EFV altered mouse behavior in the forced swimming and open-field tests. Both drugs reduced the recognition index in the object recognition test, but only EFV significantly impaired the aversive memory assessed in the inhibitory avoidance test 24 h after training. In conclusion, our findings point to a genuine anxiogenic-like effect to EFV, since it reduced exploration to open arms of elevated plus-maze test without affecting spontaneous locomotion. Additionally, both drugs impaired recognition memory, while only the treatment with EFV impaired significantly aversive memory.

Establishing Long Term Data Management Research Priorities via a Data Decadal Survey

NASA Astrophysics Data System (ADS)

Wilson, A.; Uhlir, P.; Meyer, C. B.; Robinson, E.

2013-12-01

We live in a time of unprecedented collection of and access to scientific data. Improvements in sensor technologies and modeling capabilities are constantly producing new data sources. Data sets are being used for unexpected purposes far from their point of origin, as research spans projects, discipline domains, and temporal and geographic boundaries. The nature of science is evolving, with more open science, open publications, and changes to the nature of peer review and data "publication". Data-intensive, or computational science, has been identified as a new research paradigm. There is recognition that the creation of a data set can be a contribution to science deserving of recognition comparable to other scientific publications. Federally funded projects are generally expected to make their data open and accessible to everyone. In this dynamic environment, scientific progress is ever more dependent on good data management practices and policies. Yet current data management and stewardship practices are insufficient. Data sets created at great, and often public, expense are at risk of being lost for technological or organizational reasons. Insufficient documentation and understanding of data can mean that the data are used incorrectly or not at all. Scientific results are being scrutinized and questioned, and occasionally retracted due to problems in data management. The volume of data is greatly increasing while funding for data management is meager and generally must be found within existing budgets. Many federal government agencies, including NASA, USGS, NOAA and NSF are already making efforts to address data management issues. Executive memos and directives give substantial impetus to those efforts, such as the May 9 Executive Order directing agencies to implement Open Data Policy requirements and regularly report their progress. However, these distributed efforts risk duplicating effort, lack a unifying, long-term strategic vision, and too often work in competition with other priorities of the research enterprise. This presentation will introduce the Data Decadal Survey, an initial concept created in collaboration between the Federation of Earth Science Information Partners (ESIP) and of the National Research Council's Board on Research Data and Information (BRDI). Consistent with Executive open data policies, the Survey will provide a coordinating platform to address overarching issues and identify research needs and funding priorities in scientific data management and stewardship for the long term. The Survey would address at the broadest level gaps in data management knowledge and practices that hold back scientific progress, and recommend a strategy to address them. The goal is to provide a long term strategic vision that will

Designing Capacity Building of Educators in Open Educational Resources Integration Leads to Transformational Change

ERIC Educational Resources Information Center

Karunanayaka, Shironica P.; Naidu, Som

2018-01-01

While there is growing recognition and acceptance of Open Educational Resources (OER) and Open Educational Practices (OEP) in teaching and learning, designing for their integration remains very challenging for educators. Adopting OER and OEP in their profession requires significant changes in practitioners' pedagogical thinking and practices,…

Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement.

PubMed

Jiang, Hanlun; Sheong, Fu Kit; Zhu, Lizhe; Gao, Xin; Bernauer, Julie; Huang, Xuhui

2015-07-01

Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems.

Early Identification of Skill Needs in Europe. CEDEFOP Reference Series.

ERIC Educational Resources Information Center

Schmidt, Susanne Liane, Ed.; Schomann, Klaus, Ed.; Tessaring, Manfred, Ed.

This document contains the following papers: "Early Recognition of Skill Needs in Europe: European Conference, Berlin, 30/31 May 2002" (Susanne Liane Schmidt, Klaus Schomann, Manfred Tessaring); "Welcome and Opening of the European Conference 'Early Recognition of Skill Needs in Europe,' 30 May 2002, Social Sciences Research Center…

Molecular dynamics simulations and docking enable to explore the biophysical factors controlling the yields of engineered nanobodies.

PubMed

Soler, Miguel A; de Marco, Ario; Fortuna, Sara

2016-10-10

Nanobodies (VHHs) have proved to be valuable substitutes of conventional antibodies for molecular recognition. Their small size represents a precious advantage for rational mutagenesis based on modelling. Here we address the problem of predicting how Camelidae nanobody sequences can tolerate mutations by developing a simulation protocol based on all-atom molecular dynamics and whole-molecule docking. The method was tested on two sets of nanobodies characterized experimentally for their biophysical features. One set contained point mutations introduced to humanize a wild type sequence, in the second the CDRs were swapped between single-domain frameworks with Camelidae and human hallmarks. The method resulted in accurate scoring approaches to predict experimental yields and enabled to identify the structural modifications induced by mutations. This work is a promising tool for the in silico development of single-domain antibodies and opens the opportunity to customize single functional domains of larger macromolecules.

Molecular dynamics simulations and docking enable to explore the biophysical factors controlling the yields of engineered nanobodies

NASA Astrophysics Data System (ADS)

Soler, Miguel A.; De Marco, Ario; Fortuna, Sara

2016-10-01

Nanobodies (VHHs) have proved to be valuable substitutes of conventional antibodies for molecular recognition. Their small size represents a precious advantage for rational mutagenesis based on modelling. Here we address the problem of predicting how Camelidae nanobody sequences can tolerate mutations by developing a simulation protocol based on all-atom molecular dynamics and whole-molecule docking. The method was tested on two sets of nanobodies characterized experimentally for their biophysical features. One set contained point mutations introduced to humanize a wild type sequence, in the second the CDRs were swapped between single-domain frameworks with Camelidae and human hallmarks. The method resulted in accurate scoring approaches to predict experimental yields and enabled to identify the structural modifications induced by mutations. This work is a promising tool for the in silico development of single-domain antibodies and opens the opportunity to customize single functional domains of larger macromolecules.

Human-assisted sound event recognition for home service robots.

PubMed

Do, Ha Manh; Sheng, Weihua; Liu, Meiqin

This paper proposes and implements an open framework of active auditory learning for a home service robot to serve the elderly living alone at home. The framework was developed to realize the various auditory perception capabilities while enabling a remote human operator to involve in the sound event recognition process for elderly care. The home service robot is able to estimate the sound source position and collaborate with the human operator in sound event recognition while protecting the privacy of the elderly. Our experimental results validated the proposed framework and evaluated auditory perception capabilities and human-robot collaboration in sound event recognition.

Automatic concept extraction from spoken medical reports.

PubMed

Happe, André; Pouliquen, Bruno; Burgun, Anita; Cuggia, Marc; Le Beux, Pierre

2003-07-01

The objective of this project is to investigate methods whereby a combination of speech recognition and automated indexing methods substitute for current transcription and indexing practices. We based our study on existing speech recognition software programs and on NOMINDEX, a tool that extracts MeSH concepts from medical text in natural language and that is mainly based on a French medical lexicon and on the UMLS. For each document, the process consists of three steps: (1) dictation and digital audio recording, (2) speech recognition, (3) automatic indexing. The evaluation consisted of a comparison between the set of concepts extracted by NOMINDEX after the speech recognition phase and the set of keywords manually extracted from the initial document. The method was evaluated on a set of 28 patient discharge summaries extracted from the MENELAS corpus in French, corresponding to in-patients admitted for coronarography. The overall precision was 73% and the overall recall was 90%. Indexing errors were mainly due to word sense ambiguity and abbreviations. A specific issue was the fact that the standard French translation of MeSH terms lacks diacritics. A preliminary evaluation of speech recognition tools showed that the rate of accurate recognition was higher than 98%. Only 3% of the indexing errors were generated by inadequate speech recognition. We discuss several areas to focus on to improve this prototype. However, the very low rate of indexing errors due to speech recognition errors highlights the potential benefits of combining speech recognition techniques and automatic indexing.

Action recognition using mined hierarchical compound features.

PubMed

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

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

Assessment of Self-Recognition in Young Children with Handicaps.

ERIC Educational Resources Information Center

Kelley, Michael F.; And Others

1988-01-01

Thirty young children with handicaps were assessed on five self-recognition mirror tasks. The set of tasks formed a reproducible scale, indicating that these tasks are an appropriate measure of self-recognition in this population. Data analysis suggested that stage of self-recognition is positively and significantly related to cognitive…

BMC Medicine editorial board members on open access publishing.

PubMed

Carmont, Michael R; Lawn, Stephen D; Stray-Pedersen, Babill; Shoenfeld, Yehuda; Meier, Pascal

2014-01-21

In recognition of Open Access week (21st-27th October 2013), we asked some BMC Medicine Editorial Board Members to share their views and experiences on open access publishing. In this short video, they highlight the benefits of visibility and dissemination of their research, and discuss the future directions for this model of publishing.

BMC medicine editorial board members on open access publishing

PubMed Central

2014-01-01

In recognition of Open Access week (21st-27th October 2013), we asked some BMC Medicine Editorial Board Members to share their views and experiences on open access publishing. In this short video, they highlight the benefits of visibility and dissemination of their research, and discuss the future directions for this model of publishing. PMID:24447778

Effects of Steady-State Noise on Verbal Working Memory in Young Adults

PubMed Central

Alt, Mary; DeDe, Gayle; Olson, Sarah; Shehorn, James

2015-01-01

Purpose We set out to examine the impact of perceptual, linguistic, and capacity demands on performance of verbal working-memory tasks. The Ease of Language Understanding model (Rönnberg et al., 2013) provides a framework for testing the dynamics of these interactions within the auditory-cognitive system. Methods Adult native speakers of English (n = 45) participated in verbal working-memory tasks requiring processing and storage of words involving different linguistic demands (closed/open set). Capacity demand ranged from 2 to 7 words per trial. Participants performed the tasks in quiet and in speech-spectrum-shaped noise. Separate groups of participants were tested at different signal-to-noise ratios. Word-recognition measures were obtained to determine effects of noise on intelligibility. Results Contrary to predictions, steady-state noise did not have an adverse effect on working-memory performance in every situation. Noise negatively influenced performance for the task with high linguistic demand. Of particular importance is the finding that the adverse effects of background noise were not confined to conditions involving declines in recognition. Conclusions Perceptual, linguistic, and cognitive demands can dynamically affect verbal working-memory performance even in a population of healthy young adults. Results suggest that researchers and clinicians need to carefully analyze task demands to understand the independent and combined auditory-cognitive factors governing performance in everyday listening situations. PMID:26384291

Effects of long-term voluntary exercise on learning and memory processes: dependency of the task and level of exercise.

PubMed

García-Capdevila, Sílvia; Portell-Cortés, Isabel; Torras-Garcia, Meritxell; Coll-Andreu, Margalida; Costa-Miserachs, David

2009-09-14

The effect of long-term voluntary exercise (running wheel) on anxiety-like behaviour (plus maze and open field) and learning and memory processes (object recognition and two-way active avoidance) was examined on Wistar rats. Because major individual differences in running wheel behaviour were observed, the data were analysed considering the exercising animals both as a whole and grouped according to the time spent in the running wheel (low, high, and very-high running). Although some variables related to anxiety-like behaviour seem to reflect an anxiogenic compatible effect, the view of the complete set of variables could be interpreted as an enhancement of defensive and risk assessment behaviours in exercised animals, without major differences depending on the exercise level. Effects on learning and memory processes were dependent on task and level of exercise. Two-way avoidance was not affected either in the acquisition or in the retention session, while the retention of object recognition task was affected. In this latter task, an enhancement in low running subjects and impairment in high and very-high running animals were observed.

The Immune System as a Model for Pattern Recognition and Classification

PubMed Central

Carter, Jerome H.

2000-01-01

Objective: To design a pattern recognition engine based on concepts derived from mammalian immune systems. Design: A supervised learning system (Immunos-81) was created using software abstractions of T cells, B cells, antibodies, and their interactions. Artificial T cells control the creation of B-cell populations (clones), which compete for recognition of “unknowns.” The B-cell clone with the “simple highest avidity” (SHA) or “relative highest avidity” (RHA) is considered to have successfully classified the unknown. Measurement: Two standard machine learning data sets, consisting of eight nominal and six continuous variables, were used to test the recognition capabilities of Immunos-81. The first set (Cleveland), consisting of 303 cases of patients with suspected coronary artery disease, was used to perform a ten-way cross-validation. After completing the validation runs, the Cleveland data set was used as a training set prior to presentation of the second data set, consisting of 200 unknown cases. Results: For cross-validation runs, correct recognition using SHA ranged from a high of 96 percent to a low of 63.2 percent. The average correct classification for all runs was 83.2 percent. Using the RHA metric, 11.2 percent were labeled “too close to determine” and no further attempt was made to classify them. Of the remaining cases, 85.5 percent were correctly classified. When the second data set was presented, correct classification occurred in 73.5 percent of cases when SHA was used and in 80.3 percent of cases when RHA was used. Conclusions: The immune system offers a viable paradigm for the design of pattern recognition systems. Additional research is required to fully exploit the nuances of immune computation. PMID:10641961

Recognition, Accreditation and Validation of Non-Formal and Informal Learning: Prospects for Lifelong Learning in Nepal

ERIC Educational Resources Information Center

Regmi, Kapil Dev

2009-01-01

This study was an exploration on the various issues related to recognition, accreditation and validation of non-formal and informal learning to open up avenues for lifelong learning and continuing education in Nepal. The perceptions, experiences, and opinions of Nepalese Development Activists, Educational Administrators, Policy Actors and…

Spaced Learning Enhances Subsequent Recognition Memory by Reducing Neural Repetition Suppression

ERIC Educational Resources Information Center

Xue, Gui; Mei, Leilei; Chen, Chuansheng; Lu, Zhong-Lin; Poldrack, Russell; Dong, Qi

2011-01-01

Spaced learning usually leads to better recognition memory as compared with massed learning, yet the underlying neural mechanisms remain elusive. One open question is whether the spacing effect is achieved by reducing neural repetition suppression. In this fMRI study, participants were scanned while intentionally memorizing 120 novel faces, half…

Neutral face classification using personalized appearance models for fast and robust emotion detection.

PubMed

Chiranjeevi, Pojala; Gopalakrishnan, Viswanath; Moogi, Pratibha

2015-09-01

Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning-based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, and so on, in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as user stays neutral for majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this paper, we propose a light-weight neutral versus emotion classification engine, which acts as a pre-processer to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at key emotion (KE) points using a statistical texture model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a statistical texture model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves emotion recognition (ER) accuracy and simultaneously reduces computational complexity of the ER system, as validated on multiple databases.

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.

Quantification of larval zebrafish motor function in multi-well plates using open-source MATLAB® applications

PubMed Central

Zhou, Yangzhong; Cattley, Richard T.; Cario, Clinton L.; Bai, Qing; Burton, Edward A.

2014-01-01

This article describes a method to quantify the movements of larval zebrafish in multi-well plates, using the open-source MATLAB® applications LSRtrack and LSRanalyze. The protocol comprises four stages: generation of high-quality, flatly-illuminated video recordings with exposure settings that facilitate object recognition; analysis of the resulting recordings using tools provided in LSRtrack to optimize tracking accuracy and motion detection; analysis of tracking data using LSRanalyze or custom MATLAB® scripts; implementation of validation controls. The method is reliable, automated and flexible, requires less than one hour of hands-on work for completion once optimized, and shows excellent signal:noise characteristics. The resulting data can be analyzed to determine: positional preference; displacement, velocity and acceleration; duration and frequency of movement events and rest periods. This approach is widely applicable to analyze spontaneous or stimulus-evoked zebrafish larval neurobehavioral phenotypes resulting from a broad array of genetic and environmental manipulations, in a multi-well plate format suitable for high-throughput applications. PMID:24901738

Quantification of larval zebrafish motor function in multiwell plates using open-source MATLAB applications.

PubMed

Zhou, Yangzhong; Cattley, Richard T; Cario, Clinton L; Bai, Qing; Burton, Edward A

2014-07-01

This article describes a method to quantify the movements of larval zebrafish in multiwell plates, using the open-source MATLAB applications LSRtrack and LSRanalyze. The protocol comprises four stages: generation of high-quality, flatly illuminated video recordings with exposure settings that facilitate object recognition; analysis of the resulting recordings using tools provided in LSRtrack to optimize tracking accuracy and motion detection; analysis of tracking data using LSRanalyze or custom MATLAB scripts; and implementation of validation controls. The method is reliable, automated and flexible, requires <1 h of hands-on work for completion once optimized and shows excellent signal:noise characteristics. The resulting data can be analyzed to determine the following: positional preference; displacement, velocity and acceleration; and duration and frequency of movement events and rest periods. This approach is widely applicable to the analysis of spontaneous or stimulus-evoked zebrafish larval neurobehavioral phenotypes resulting from a broad array of genetic and environmental manipulations, in a multiwell plate format suitable for high-throughput applications.

A validated set of tool pictures with matched objects and non-objects for laterality research.

PubMed

Verma, Ark; Brysbaert, Marc

2015-01-01

Neuropsychological and neuroimaging research has established that knowledge related to tool use and tool recognition is lateralized to the left cerebral hemisphere. Recently, behavioural studies with the visual half-field technique have confirmed the lateralization. A limitation of this research was that different sets of stimuli had to be used for the comparison of tools to other objects and objects to non-objects. Therefore, we developed a new set of stimuli containing matched triplets of tools, other objects and non-objects. With the new stimulus set, we successfully replicated the findings of no visual field advantage for objects in an object recognition task combined with a significant right visual field advantage for tools in a tool recognition task. The set of stimuli is available as supplemental data to this article.

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.

Geometry-based ensembles: toward a structural characterization of the classification boundary.

PubMed

Pujol, Oriol; Masip, David

2009-06-01

This paper introduces a novel binary discriminative learning technique based on the approximation of the nonlinear decision boundary by a piecewise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points-points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and nonlinear behavior is obtained. The simplicity of the method allows its extension to cope with some of today's machine learning challenges, such as online learning, large-scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database, comparing with several state-of-the-art classification techniques. Finally, we apply our technique in online and large-scale scenarios and in six real-life computer vision and pattern recognition problems: gender recognition based on face images, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease myocardial damage severity detection, old musical scores clef classification, and action recognition using 3D accelerometer data from a wearable device. The results are promising and this paper opens a line of research that deserves further attention.

Chemical entity recognition in patents by combining dictionary-based and statistical approaches.

PubMed

Akhondi, Saber A; Pons, Ewoud; Afzal, Zubair; van Haagen, Herman; Becker, Benedikt F H; Hettne, Kristina M; van Mulligen, Erik M; Kors, Jan A

2016-01-01

We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small.Database URL: http://biosemantics.org/chemdner-patents. © The Author(s) 2016. Published by Oxford University Press.

Speech recognition technology: an outlook for human-to-machine interaction.

PubMed

Erdel, T; Crooks, S

2000-01-01

Speech recognition, as an enabling technology in healthcare-systems computing, is a topic that has been discussed for quite some time, but is just now coming to fruition. Traditionally, speech-recognition software has been constrained by hardware, but improved processors and increased memory capacities are starting to remove some of these limitations. With these barriers removed, companies that create software for the healthcare setting have the opportunity to write more successful applications. Among the criticisms of speech-recognition applications are the high rates of error and steep training curves. However, even in the face of such negative perceptions, there remains significant opportunities for speech recognition to allow healthcare providers and, more specifically, physicians, to work more efficiently and ultimately spend more time with their patients and less time completing necessary documentation. This article will identify opportunities for inclusion of speech-recognition technology in the healthcare setting and examine major categories of speech-recognition software--continuous speech recognition, command and control, and text-to-speech. We will discuss the advantages and disadvantages of each area, the limitations of the software today, and how future trends might affect them.

View-invariant object recognition ability develops after discrimination, not mere exposure, at several viewing angles.

PubMed

Yamashita, Wakayo; Wang, Gang; Tanaka, Keiji

2010-01-01

One usually fails to recognize an unfamiliar object across changes in viewing angle when it has to be discriminated from similar distractor objects. Previous work has demonstrated that after long-term experience in discriminating among a set of objects seen from the same viewing angle, immediate recognition of the objects across 30-60 degrees changes in viewing angle becomes possible. The capability for view-invariant object recognition should develop during the within-viewing-angle discrimination, which includes two kinds of experience: seeing individual views and discriminating among the objects. The aim of the present study was to determine the relative contribution of each factor to the development of view-invariant object recognition capability. Monkeys were first extensively trained in a task that required view-invariant object recognition (Object task) with several sets of objects. The animals were then exposed to a new set of objects over 26 days in one of two preparatory tasks: one in which each object view was seen individually, and a second that required discrimination among the objects at each of four viewing angles. After the preparatory period, we measured the monkeys' ability to recognize the objects across changes in viewing angle, by introducing the object set to the Object task. Results indicated significant view-invariant recognition after the second but not first preparatory task. These results suggest that discrimination of objects from distractors at each of several viewing angles is required for the development of view-invariant recognition of the objects when the distractors are similar to the objects.

Effects of the medial or basolateral amygdala upon social anxiety and social recognition in mice.

PubMed

Wang, Yu; Zhao, Shanshan; Liu, Xu; Fu, Qunying

2014-01-01

Though social anxiety and social recognition have been studied extensively, the roles of the medial or basolateral amygdala in the control of social anxiety and social recognition remain to be determined. This study investigated the effects of excitotoxic bilateral medial or basolateral amygdala lesions upon social anxiety and social recognition in-mice. Animals at 9 weeks of age were given bilateral medial or basolateral amygdala lesions via infusion of N-methyl- D-aspartate and then were used for behavioral tests: anxiety-related tests (including open-field test, light-dark test, and elevated-plus maze test), social behavior test in a novel environment, social recognition test, and flavor recognition test. Medial or basolateral amygdala-lesioned mice showed lower levels of anxiety and increased social behaviors in a novel environment. Destruction of the medial or basolateral amygdala neurons impaired social recognition but not flavor recognition. The medial or basolateral amygdala is involved in the control of anxiety-related behavior (social anxiety and social behaviors) in mice. Moreover, both the medial and the basolateral amygdala are essential for social recognition but not flavor recognition in mice.

Standoff Human Identification Using Body Shape

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

Matzner, Shari; Heredia-Langner, Alejandro; Amidan, Brett G.

2015-09-01

The ability to identify individuals is a key component of maintaining safety and security in public spaces and around critical infrastructure. Monitoring an open space is challenging because individuals must be identified and re-identified from a standoff distance nonintrusively, making methods like fingerprinting and even facial recognition impractical. We propose using body shape features as a means for identification from standoff sensing, either complementing other identifiers or as an alternative. An important challenge in monitoring open spaces is reconstructing identifying features when only a partial observation is available, because of the view-angle limitations and occlusion or subject pose changes. Tomore » address this challenge, we investigated the minimum number of features required for a high probability of correct identification, and we developed models for predicting a key body feature—height—from a limited set of observed features. We found that any set of nine randomly selected body measurements was sufficient to correctly identify an individual in a dataset of 4426 subjects. For predicting height, anthropometric measures were investigated for correlation with height. Their correlation coefficients and associated linear models were reported. These results—a sufficient number of features for identification and height prediction from a single feature—contribute to developing systems for standoff identification when views of a subject are limited.« less

Two processes support visual recognition memory in rhesus monkeys.

PubMed

Guderian, Sebastian; Brigham, Danielle; Mishkin, Mortimer

2011-11-29

A large body of evidence in humans suggests that recognition memory can be supported by both recollection and familiarity. Recollection-based recognition is characterized by the retrieval of contextual information about the episode in which an item was previously encountered, whereas familiarity-based recognition is characterized instead by knowledge only that the item had been encountered previously in the absence of any context. To date, it is unknown whether monkeys rely on similar mnemonic processes to perform recognition memory tasks. Here, we present evidence from the analysis of receiver operating characteristics, suggesting that visual recognition memory in rhesus monkeys also can be supported by two separate processes and that these processes have features considered to be characteristic of recollection and familiarity. Thus, the present study provides converging evidence across species for a dual process model of recognition memory and opens up the possibility of studying the neural mechanisms of recognition memory in nonhuman primates on tasks that are highly similar to the ones used in humans.

Two processes support visual recognition memory in rhesus monkeys

PubMed Central

Guderian, Sebastian; Brigham, Danielle; Mishkin, Mortimer

2011-01-01

A large body of evidence in humans suggests that recognition memory can be supported by both recollection and familiarity. Recollection-based recognition is characterized by the retrieval of contextual information about the episode in which an item was previously encountered, whereas familiarity-based recognition is characterized instead by knowledge only that the item had been encountered previously in the absence of any context. To date, it is unknown whether monkeys rely on similar mnemonic processes to perform recognition memory tasks. Here, we present evidence from the analysis of receiver operating characteristics, suggesting that visual recognition memory in rhesus monkeys also can be supported by two separate processes and that these processes have features considered to be characteristic of recollection and familiarity. Thus, the present study provides converging evidence across species for a dual process model of recognition memory and opens up the possibility of studying the neural mechanisms of recognition memory in nonhuman primates on tasks that are highly similar to the ones used in humans. PMID:22084079

Phonological mismatch makes aided speech recognition in noise cognitively taxing.

PubMed

Rudner, Mary; Foo, Catharina; Rönnberg, Jerker; Lunner, Thomas

2007-12-01

The working memory framework for Ease of Language Understanding predicts that speech processing becomes more effortful, thus requiring more explicit cognitive resources, when there is mismatch between speech input and phonological representations in long-term memory. To test this prediction, we changed the compression release settings in the hearing instruments of experienced users and allowed them to train for 9 weeks with the new settings. After training, aided speech recognition in noise was tested with both the trained settings and orthogonal settings. We postulated that training would lead to acclimatization to the trained setting, which in turn would involve establishment of new phonological representations in long-term memory. Further, we postulated that after training, testing with orthogonal settings would give rise to phonological mismatch, associated with more explicit cognitive processing. Thirty-two participants (mean=70.3 years, SD=7.7) with bilateral sensorineural hearing loss (pure-tone average=46.0 dB HL, SD=6.5), bilaterally fitted for more than 1 year with digital, two-channel, nonlinear signal processing hearing instruments and chosen from the patient population at the Linköping University Hospital were randomly assigned to 9 weeks training with new, fast (40 ms) or slow (640 ms), compression release settings in both channels. Aided speech recognition in noise performance was tested according to a design with three within-group factors: test occasion (T1, T2), test setting (fast, slow), and type of noise (unmodulated, modulated) and one between-group factor: experience setting (fast, slow) for two types of speech materials-the highly constrained Hagerman sentences and the less-predictable Hearing in Noise Test (HINT). Complex cognitive capacity was measured using the reading span and letter monitoring tests. PREDICTION: We predicted that speech recognition in noise at T2 with mismatched experience and test settings would be associated with more explicit cognitive processing and thus stronger correlations with complex cognitive measures, as well as poorer performance if complex cognitive capacity was exceeded. Under mismatch conditions, stronger correlations were found between performance on speech recognition with the Hagerman sentences and reading span, along with poorer speech recognition for participants with low reading span scores. No consistent mismatch effect was found with HINT. The mismatch prediction generated by the working memory framework for Ease of Language Understanding is supported for speech recognition in noise with the highly constrained Hagerman sentences but not the less-predictable HINT.

Effect of training on word-recognition performance in noise for young normal-hearing and older hearing-impaired listeners.

PubMed

Burk, Matthew H; Humes, Larry E; Amos, Nathan E; Strauser, Lauren E

2006-06-01

The objective of this study was to evaluate the effectiveness of a training program for hearing-impaired listeners to improve their speech-recognition performance within a background noise when listening to amplified speech. Both noise-masked young normal-hearing listeners, used to model the performance of elderly hearing-impaired listeners, and a group of elderly hearing-impaired listeners participated in the study. Of particular interest was whether training on an isolated word list presented by a standardized talker can generalize to everyday speech communication across novel talkers. Word-recognition performance was measured for both young normal-hearing (n = 16) and older hearing-impaired (n = 7) adults. Listeners were trained on a set of 75 monosyllabic words spoken by a single female talker over a 9- to 14-day period. Performance for the familiar (trained) talker was measured before and after training in both open-set and closed-set response conditions. Performance on the trained words of the familiar talker were then compared with those same words spoken by three novel talkers and to performance on a second set of untrained words presented by both the familiar and unfamiliar talkers. The hearing-impaired listeners returned 6 mo after their initial training to examine retention of the trained words as well as their ability to transfer any knowledge gained from word training to sentences containing both trained and untrained words. Both young normal-hearing and older hearing-impaired listeners performed significantly better on the word list in which they were trained versus a second untrained list presented by the same talker. Improvements on the untrained words were small but significant, indicating some generalization to novel words. The large increase in performance on the trained words, however, was maintained across novel talkers, pointing to the listener's greater focus on lexical memorization of the words rather than a focus on talker-specific acoustic characteristics. On return in 6 mo, listeners performed significantly better on the trained words relative to their initial baseline performance. Although the listeners performed significantly better on trained versus untrained words in isolation, once the trained words were embedded in sentences, no improvement in recognition over untrained words within the same sentences was shown. Older hearing-impaired listeners were able to significantly improve their word-recognition abilities through training with one talker and to the same degree as young normal-hearing listeners. The improved performance was maintained across talkers and across time. This might imply that training a listener using a standardized list and talker may still provide benefit when these same words are presented by novel talkers outside the clinic. However, training on isolated words was not sufficient to transfer to fluent speech for the specific sentence materials used within this study. Further investigation is needed regarding approaches to improve a hearing aid user's speech understanding in everyday communication situations.

Let the Doors of Learning Be Open to All--A Case for Recognition of Prior Learning

ERIC Educational Resources Information Center

Singh, A. M.

2011-01-01

Recognition of Prior Learning (RPL) is a process of evaluating an adult learners previous experience, skills, knowledge and informal learning and articulating it towards a formal qualification. Whilst RPL is enshrined in a number of international qualifications frameworks, there are certain barriers which have prevented its application and…

Mobility of Knowledge as a Recognition Challenge: Experiences from Sweden

ERIC Educational Resources Information Center

Andersson, Per; Fejes, Andreas

2010-01-01

This article focuses on the tensions between mobility, knowledge and recognition, and what the impact of migration could be on lifelong education and society. This is discussed with the case of Sweden as the starting point. The main issue in Sweden concerning migration is the admission of refugees. Sweden has had a relatively open policy…

Effects of perceptual similarity but not semantic association on false recognition in aging

PubMed Central

Gill, Emma

2017-01-01

This study investigated semantic and perceptual influences on false recognition in older and young adults in a variant on the Deese-Roediger-McDermott paradigm. In two experiments, participants encoded intermixed sets of semantically associated words, and sets of unrelated words. Each set was presented in a shared distinctive font. Older adults were no more likely to falsely recognize semantically associated lure words compared to unrelated lures also presented in studied fonts. However, they showed an increase in false recognition of lures which were related to studied items only by a shared font. This increased false recognition was associated with recollective experience. The data show that older adults do not always rely more on prior knowledge in episodic memory tasks. They converge with other findings suggesting that older adults may also be more prone to perceptually-driven errors. PMID:29302398

V2S: Voice to Sign Language Translation System for Malaysian Deaf People

NASA Astrophysics Data System (ADS)

Mean Foong, Oi; Low, Tang Jung; La, Wai Wan

The process of learning and understand the sign language may be cumbersome to some, and therefore, this paper proposes a solution to this problem by providing a voice (English Language) to sign language translation system using Speech and Image processing technique. Speech processing which includes Speech Recognition is the study of recognizing the words being spoken, regardless of whom the speaker is. This project uses template-based recognition as the main approach in which the V2S system first needs to be trained with speech pattern based on some generic spectral parameter set. These spectral parameter set will then be stored as template in a database. The system will perform the recognition process through matching the parameter set of the input speech with the stored templates to finally display the sign language in video format. Empirical results show that the system has 80.3% recognition rate.

76 FR 81404 - Information From Foreign Regions Applying for Recognition of Animal Health Status

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-28

.... APHIS-2007-0158] RIN 0579-AD30 Information From Foreign Regions Applying for Recognition of Animal... Recognition of Regions'' (referred to below as the regulations), set forth the process by which a foreign government may request recognition of the animal health status of a region. Section 92.2 of the regulations...

Analysis of the IJCNN 2011 UTL Challenge

DTIC Science & Technology

2012-01-13

large datasets from various application domains: handwriting recognition, image recognition, video processing, text processing, and ecology. The goal...http //clopinet.com/ul). We made available large datasets from various application domains handwriting recognition, image recognition, video...evaluation sets consist of 4096 examples each. Dataset Domain Features Sparsity Devel. Transf. AVICENNA Handwriting 120 0% 150205 50000 HARRY Video 5000 98.1

Recognition of facial emotions in neuropsychiatric disorders.

PubMed

Kohler, Christian G; Turner, Travis H; Gur, Raquel E; Gur, Ruben C

2004-04-01

Recognition of facial emotions represents an important aspect of interpersonal communication and is governed by select neural substrates. We present data on emotion recognition in healthy young adults utilizing a novel set of color photographs of evoked universal emotions. In addition, we review the recent literature on emotion recognition in psychiatric and neurologic disorders, and studies that compare different disorders.

Segmental Rescoring in Text Recognition

DTIC Science & Technology

2014-02-04

description relates to rescoring text hypotheses in text recognition based on segmental features. Offline printed text and handwriting recognition (OHR) can... Handwriting , College Park, Md., 2006, which is incorporated by reference here. For the set of training images 202, a character modeler 208 receives

Evidence for the Activation of Sensorimotor Information during Visual Word Recognition: The Body-Object Interaction Effect

ERIC Educational Resources Information Center

Siakaluk, Paul D.; Pexman, Penny M.; Aguilera, Laura; Owen, William J.; Sears, Christopher R.

2008-01-01

We examined the effects of sensorimotor experience in two visual word recognition tasks. Body-object interaction (BOI) ratings were collected for a large set of words. These ratings assess perceptions of the ease with which a human body can physically interact with a word's referent. A set of high BOI words (e.g., "mask") and a set of low BOI…

Speech emotion recognition methods: A literature review

NASA Astrophysics Data System (ADS)

Basharirad, Babak; Moradhaseli, Mohammadreza

2017-10-01

Recently, attention of the emotional speech signals research has been boosted in human machine interfaces due to availability of high computation capability. There are many systems proposed in the literature to identify the emotional state through speech. Selection of suitable feature sets, design of a proper classifications methods and prepare an appropriate dataset are the main key issues of speech emotion recognition systems. This paper critically analyzed the current available approaches of speech emotion recognition methods based on the three evaluating parameters (feature set, classification of features, accurately usage). In addition, this paper also evaluates the performance and limitations of available methods. Furthermore, it highlights the current promising direction for improvement of speech emotion recognition systems.

Learning to recognize rat social behavior: Novel dataset and cross-dataset application.

PubMed

Lorbach, Malte; Kyriakou, Elisavet I; Poppe, Ronald; van Dam, Elsbeth A; Noldus, Lucas P J J; Veltkamp, Remco C

2018-04-15

Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the performance of classifiers. With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings. Copyright © 2017 Elsevier B.V. All rights reserved.

An exemplar-familiarity model predicts short-term and long-term probe recognition across diverse forms of memory search.

PubMed

Nosofsky, Robert M; Cox, Gregory E; Cao, Rui; Shiffrin, Richard M

2014-11-01

Experiments were conducted to test a modern exemplar-familiarity model on its ability to account for both short-term and long-term probe recognition within the same memory-search paradigm. Also, making connections to the literature on attention and visual search, the model was used to interpret differences in probe-recognition performance across diverse conditions that manipulated relations between targets and foils across trials. Subjects saw lists of from 1 to 16 items followed by a single item recognition probe. In a varied-mapping condition, targets and foils could switch roles across trials; in a consistent-mapping condition, targets and foils never switched roles; and in an all-new condition, on each trial a completely new set of items formed the memory set. In the varied-mapping and all-new conditions, mean correct response times (RTs) and error proportions were curvilinear increasing functions of memory set size, with the RT results closely resembling ones from hybrid visual-memory search experiments reported by Wolfe (2012). In the consistent-mapping condition, new-probe RTs were invariant with set size, whereas old-probe RTs increased slightly with increasing study-test lag. With appropriate choice of psychologically interpretable free parameters, the model accounted well for the complete set of results. The work provides support for the hypothesis that a common set of processes involving exemplar-based familiarity may govern long-term and short-term probe recognition across wide varieties of memory- search conditions. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Neural Network and Letter Recognition.

NASA Astrophysics Data System (ADS)

Lee, Hue Yeon

Neural net architectures and learning algorithms that recognize hand written 36 alphanumeric characters are studied. The thin line input patterns written in 32 x 32 binary array are used. The system is comprised of two major components, viz. a preprocessing unit and a Recognition unit. The preprocessing unit in turn consists of three layers of neurons; the U-layer, the V-layer, and the C -layer. The functions of the U-layer is to extract local features by template matching. The correlation between the detected local features are considered. Through correlating neurons in a plane with their neighboring neurons, the V-layer would thicken the on-cells or lines that are groups of on-cells of the previous layer. These two correlations would yield some deformation tolerance and some of the rotational tolerance of the system. The C-layer then compresses data through the 'Gabor' transform. Pattern dependent choice of center and wavelengths of 'Gabor' filters is the cause of shift and scale tolerance of the system. Three different learning schemes had been investigated in the recognition unit, namely; the error back propagation learning with hidden units, a simple perceptron learning, and a competitive learning. Their performances were analyzed and compared. Since sometimes the network fails to distinguish between two letters that are inherently similar, additional ambiguity resolving neural nets are introduced on top of the above main neural net. The two dimensional Fourier transform is used as the preprocessing and the perceptron is used as the recognition unit of the ambiguity resolver. One hundred different person's handwriting sets are collected. Some of these are used as the training sets and the remainders are used as the test sets. The correct recognition rate of the system increases with the number of training sets and eventually saturates at a certain value. Similar recognition rates are obtained for the above three different learning algorithms. The minimum error rate, 4.9% is achieved for alphanumeric sets when 50 sets are trained. With the ambiguity resolver, it is reduced to 2.5%. In case that only numeral sets are trained and tested, 2.0% error rate is achieved. When only alphabet sets are considered, the error rate is reduced to 1.1%.

Evaluation of Image Segmentation and Object Recognition Algorithms for Image Parsing

DTIC Science & Technology

2013-09-01

generation of the features from the key points. OpenCV uses Euclidean distance to match the key points and has the option to use Manhattan distance...feature vector includes polarity and intensity information. Final step is matching the key points. In OpenCV , Euclidean distance or Manhattan...the code below is one way and OpenCV offers the function radiusMatch (a pair must have a distance less than a given maximum distance). OpenCV’s

Toward a legal framework that promotes and protects sex workers' health and human rights.

PubMed

Overs, Cheryl; Loff, Bebe

2013-06-14

Complex combinations of law, policy, and enforcement practices determine sex workers vulnerability to HIV and rights abuses. We identify "lack of recognition as a person before the law" as an important but undocumented barrier to accessing services and conclude that multi-faceted, setting-specific reform is needed-rather than a singular focus on decriminalization-if the health and human rights of sex workers are to be realized. Copyright © 2013 Overs and Loff. This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited.

Enhancement of face recognition learning in patients with brain injury using three cognitive training procedures.

PubMed

Powell, Jane; Letson, Susan; Davidoff, Jules; Valentine, Tim; Greenwood, Richard

2008-04-01

Twenty patients with impairments of face recognition, in the context of a broader pattern of cognitive deficits, were administered three new training procedures derived from contemporary theories of face processing to enhance their learning of new faces: semantic association (being given additional verbal information about the to-be-learned faces); caricaturing (presentation of caricatured versions of the faces during training and veridical versions at recognition testing); and part recognition (focusing patients on distinctive features during the training phase). Using a within-subjects design, each training procedure was applied to a different set of 10 previously unfamiliar faces and entailed six presentations of each face. In a "simple exposure" control procedure (SE), participants were given six presentations of another set of faces using the same basic protocol but with no further elaboration. Order of the four procedures was counterbalanced, and each condition was administered on a different day. A control group of 12 patients with similar levels of face recognition impairment were trained on all four sets of faces under SE conditions. Compared to the SE condition, all three training procedures resulted in more accurate discrimination between the 10 studied faces and 10 distractor faces in a post-training recognition test. This did not reflect any intrinsic lesser memorability of the faces used in the SE condition, as evidenced by the comparable performance across face sets by the control group. At the group level, the three experimental procedures were of similar efficacy, and associated cognitive deficits did not predict which technique would be most beneficial to individual patients; however, there was limited power to detect such associations. Interestingly, a pure prosopagnosic patient who was tested separately showed benefit only from the part recognition technique. Possible mechanisms for the observed effects, and implications for rehabilitation, are discussed.

HTM Spatial Pooler With Memristor Crossbar Circuits for Sparse Biometric Recognition.

PubMed

James, Alex Pappachen; Fedorova, Irina; Ibrayev, Timur; Kudithipudi, Dhireesha

2017-06-01

Hierarchical Temporal Memory (HTM) is an online machine learning algorithm that emulates the neo-cortex. The development of a scalable on-chip HTM architecture is an open research area. The two core substructures of HTM are spatial pooler and temporal memory. In this work, we propose a new Spatial Pooler circuit design with parallel memristive crossbar arrays for the 2D columns. The proposed design was validated on two different benchmark datasets, face recognition, and speech recognition. The circuits are simulated and analyzed using a practical memristor device model and 0.18 μm IBM CMOS technology model. The databases AR, YALE, ORL, and UFI, are used to test the performance of the design in face recognition. TIMIT dataset is used for the speech recognition.

Bio-recognitive photonics of a DNA-guided organic semiconductor

PubMed Central

Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-01

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA–DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an ‘inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA–DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition. PMID:26725969

Bio-recognitive photonics of a DNA-guided organic semiconductor.

PubMed

Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-04

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA-DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an 'inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA-DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition.

Bio-recognitive photonics of a DNA-guided organic semiconductor

NASA Astrophysics Data System (ADS)

Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-01

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA-DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an `inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA-DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition.

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.

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.

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

PubMed Central

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

2016-01-01

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

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

PubMed

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

2016-01-01

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

Appearance-based face recognition and light-fields.

PubMed

Gross, Ralph; Matthews, Iain; Baker, Simon

2004-04-01

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

Opening Up Without Cracking Up.

ERIC Educational Resources Information Center

Erickson, Maggie

In recognition of the fact that there are individual differences and preferences among teachers, the teachers at the Las Posas School in Camarillo, California were given the opportunity to use the methods they found most comfortable. This resulted in a variety of open classroom situations, with alternatives for parents, students, and teachers.…

Multilingual Videos for MOOCs and OER

ERIC Educational Resources Information Center

Valor Miró, Juan Daniel; Baquero-Arnal, Pau; Civera, Jorge; Turró, Carlos; Juan, Alfons

2018-01-01

Massive Open Online Courses (MOOCs) and Open Educational Resources (OER) are rapidly growing, but are not usually offered in multiple languages due to the lack of cost-effective solutions to translate the different objects comprising them and particularly videos. However, current state-of-the-art automatic speech recognition (ASR) and machine…

Open Book Professional Accountancy Examinations

ERIC Educational Resources Information Center

Rowlands, J. E.; Forsyth, D.

2006-01-01

This article describes the structure and rationale for an open-book approach in professional accountancy examinations. The concept of knowledge management and the recognition that some knowledge ought to be embedded in the minds of professional accountants while other knowledge ought to be readily accessible and capable of application forms the…

Changes in stroke awareness among undergraduate students after an educational intervention.

PubMed

Hwang, Lih-Lian; Lin, Huei-Chia; Tseng, Mei-Chiun

2010-06-01

This study investigated undergraduates'awareness about stroke, the effects of an educational intervention and the difference of measuring tests between recognition and recall. We chose a convenient sample from two classes. One of the classes, the recognition group, was tested by a close-ended questionnaire with multiple choices. The other class, the recall group, was tested via an open-ended questionnaire. Participants completed their pretest and first posttest before and right after the education intervention. Twelve weeks after the intervention, participants were tested again to assess the knowledge retention over time. Fifty six participants in the recognition group and 53 participants in the recall group completed all three tests. Before the intervention, all respondents in the recognition group could recognize three or more risk factors and at least one warning sign, but in the recall group were only 32% and 72% respectively. After the intervention, the mean scores of first posttest and second posttest were all significant higher than that of pretest in both groups (P less 0.001). Comparisons of mean score of same items in both groups, the mean score of recognition group was significantly higher than that of recall group at each test (all P less 0.001). The intervention improved participants'knowledge towards stroke, even twelve weeks later. Participants obtained higher scores with a close-ended questionnaire than those with an open-ended questionnaire.

Cochlear implantation in adults with asymmetric hearing loss.

PubMed

Firszt, Jill B; Holden, Laura K; Reeder, Ruth M; Cowdrey, Lisa; King, Sarah

2012-01-01

Bilateral severe to profound sensorineural hearing loss is a standard criterion for cochlear implantation. Increasingly, patients are implanted in one ear and continue to use a hearing aid in the nonimplanted ear to improve abilities such as sound localization and speech understanding in noise. Patients with severe to profound hearing loss in one ear and a more moderate hearing loss in the other ear (i.e., asymmetric hearing) are not typically considered candidates for cochlear implantation. Amplification in the poorer ear is often unsuccessful because of limited benefit, restricting the patient to unilateral listening from the better ear alone. The purpose of this study was to determine whether patients with asymmetric hearing loss could benefit from cochlear implantation in the poorer ear with continued use of a hearing aid in the better ear. Ten adults with asymmetric hearing between ears participated. In the poorer ear, all participants met cochlear implant candidacy guidelines; seven had postlingual onset, and three had pre/perilingual onset of severe to profound hearing loss. All had open-set speech recognition in the better-hearing ear. Assessment measures included word and sentence recognition in quiet, sentence recognition in fixed noise (four-talker babble) and in diffuse restaurant noise using an adaptive procedure, localization of word stimuli, and a hearing handicap scale. Participants were evaluated preimplant with hearing aids and postimplant with the implant alone, the hearing aid alone in the better ear, and bimodally (the implant and hearing aid in combination). Postlingual participants were evaluated at 6 mo postimplant, and pre/perilingual participants were evaluated at 6 and 12 mo postimplant. Data analysis compared the following results: (1) the poorer-hearing ear preimplant (with hearing aid) and postimplant (with cochlear implant); (2) the device(s) used for everyday listening pre- and postimplant; and (3) the hearing aid-alone and bimodal listening conditions postimplant. The postlingual participants showed significant improvements in speech recognition after 6 mo cochlear implant use in the poorer ear. Five postlingual participants had a bimodal advantage over the hearing aid-alone condition on at least one test measure. On average, the postlingual participants had significantly improved localization with bimodal input compared with the hearing aid-alone. Only one pre/perilingual participant had open-set speech recognition with the cochlear implant. This participant had better hearing than the other two pre/perilingual participants in both the poorer and better ear. Localization abilities were not significantly different between the bimodal and hearing aid-alone conditions for the pre/perilingual participants. Mean hearing handicap ratings improved postimplant for all participants indicating perceived benefit in everyday life with the addition of the cochlear implant. Patients with asymmetric hearing loss who are not typical cochlear implant candidates can benefit from using a cochlear implant in the poorer ear with continued use of a hearing aid in the better ear. For this group of 10, the 7 postlingually deafened participants showed greater benefits with the cochlear implant than the pre/perilingual participants; however, further study is needed to determine maximum benefit for those with early onset of hearing loss.

Cochlear Implantation in Adults with Asymmetric Hearing Loss

PubMed Central

Firszt, Jill B.; Holden, Laura K.; Reeder, Ruth M.; Cowdrey, Lisa; King, Sarah

2012-01-01

Objective Bilateral severe-to-profound sensorineural hearing loss is a standard criterion for cochlear implantation. Increasingly, patients are implanted in one ear and continue to use a hearing aid in the non-implanted ear to improve abilities such as sound localization and speech understanding in noise. Patients with severe-to-profound hearing loss in one ear and a more moderate hearing loss in the other ear (i.e., asymmetric hearing) are not typically considered candidates for cochlear implantation. Amplification in the poorer ear is often unsuccessful due to limited benefit, restricting the patient to unilateral listening from the better ear alone. The purpose of this study was to determine if patients with asymmetric hearing loss could benefit from cochlear implantation in the poorer ear with continued use of a hearing aid in the better ear. Design Ten adults with asymmetric hearing between ears participated. In the poorer ear, all participants met cochlear implant candidacy guidelines; seven had postlingual onset and three had pre/perilingual onset of severe-to-profound hearing loss. All had open-set speech recognition in the better hearing ear. Assessment measures included word and sentence recognition in quiet, sentence recognition in fixed noise (four-talker babble) and in diffuse restaurant noise using an adaptive procedure, localization of word stimuli and a hearing handicap scale. Participants were evaluated pre-implant with hearing aids and post-implant with the implant alone, the hearing aid alone in the better ear and bimodally (the implant and hearing aid in combination). Postlingual participants were evaluated at six months post-implant and pre/perilingual participants were evaluated at six and 12 months post-implant. Data analysis compared results 1) of the poorer hearing ear pre-implant (with hearing aid) and post-implant (with cochlear implant), 2) with the device(s) used for everyday listening pre- and post-implant and, 3) between the hearing aid-alone and bimodal listening conditions post-implant. Results The postlingual participants showed significant improvements in speech recognition after six months cochlear implant use in the poorer ear. Five postlingual participants had a bimodal advantage over the hearing aid-alone condition on at least one test measure. On average, the postlingual participants had significantly improved localization with bimodal input compared to the hearing aid-alone. Only one pre/perilingual participant had open-set speech recognition with the cochlear implant. This participant had better hearing than the other two pre/perilingual participants in both the poorer and better ear. Localization abilities were not significantly different between the bimodal and hearing aid-alone conditions for the pre/perilingual participants. Mean hearing handicap ratings improved post-implant for all participants indicating perceived benefit in everyday life with the addition of the cochlear implant. Conclusions Patients with asymmetric hearing loss who are not typical cochlear implant candidates can benefit from using a cochlear implant in the poorer ear with continued use of a hearing aid in the better ear. For this group of ten, the seven postlingually deafened participants showed greater benefits with the cochlear implant than the pre/perilingual participants; however, further study is needed to determine maximum benefit for those with early onset of hearing loss. PMID:22441359

Handwritten word preprocessing for database adaptation

NASA Astrophysics Data System (ADS)

Oprean, Cristina; Likforman-Sulem, Laurence; Mokbel, Chafic

2013-01-01

Handwriting recognition systems are typically trained using publicly available databases, where data have been collected in controlled conditions (image resolution, paper background, noise level,...). Since this is not often the case in real-world scenarios, classification performance can be affected when novel data is presented to the word recognition system. To overcome this problem, we present in this paper a new approach called database adaptation. It consists of processing one set (training or test) in order to adapt it to the other set (test or training, respectively). Specifically, two kinds of preprocessing, namely stroke thickness normalization and pixel intensity normalization are considered. The advantage of such approach is that we can re-use the existing recognition system trained on controlled data. We conduct several experiments with the Rimes 2011 word database and with a real-world database. We adapt either the test set or the training set. Results show that training set adaptation achieves better results than test set adaptation, at the cost of a second training stage on the adapted data. Accuracy of data set adaptation is increased by 2% to 3% in absolute value over no adaptation.

PubMedPortable: A Framework for Supporting the Development of Text Mining Applications.

PubMed

Döring, Kersten; Grüning, Björn A; Telukunta, Kiran K; Thomas, Philippe; Günther, Stefan

2016-01-01

Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified entities. The BioCreative community supports these developments with yearly open challenges, which led to a standardised XML text annotation format called BioC. PubMed provides access to the largest open biomedical literature repository, but there is no unified way of connecting its data to natural language processing tools. Therefore, an appropriate data environment is needed as a basis to combine different software solutions and to develop customised text mining applications. PubMedPortable builds a relational database and a full text index on PubMed citations. It can be applied either to the complete PubMed data set or an arbitrary subset of downloaded PubMed XML files. The software provides the infrastructure to combine stand-alone applications by exporting different data formats, e.g. BioC. The presented workflows show how to use PubMedPortable to retrieve, store, and analyse a disease-specific data set. The provided use cases are well documented in the PubMedPortable wiki. The open-source software library is small, easy to use, and scalable to the user's system requirements. It is freely available for Linux on the web at https://github.com/KerstenDoering/PubMedPortable and for other operating systems as a virtual container. The approach was tested extensively and applied successfully in several projects.

PubMedPortable: A Framework for Supporting the Development of Text Mining Applications

PubMed Central

Döring, Kersten; Grüning, Björn A.; Telukunta, Kiran K.; Thomas, Philippe; Günther, Stefan

2016-01-01

Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified entities. The BioCreative community supports these developments with yearly open challenges, which led to a standardised XML text annotation format called BioC. PubMed provides access to the largest open biomedical literature repository, but there is no unified way of connecting its data to natural language processing tools. Therefore, an appropriate data environment is needed as a basis to combine different software solutions and to develop customised text mining applications. PubMedPortable builds a relational database and a full text index on PubMed citations. It can be applied either to the complete PubMed data set or an arbitrary subset of downloaded PubMed XML files. The software provides the infrastructure to combine stand-alone applications by exporting different data formats, e.g. BioC. The presented workflows show how to use PubMedPortable to retrieve, store, and analyse a disease-specific data set. The provided use cases are well documented in the PubMedPortable wiki. The open-source software library is small, easy to use, and scalable to the user’s system requirements. It is freely available for Linux on the web at https://github.com/KerstenDoering/PubMedPortable and for other operating systems as a virtual container. The approach was tested extensively and applied successfully in several projects. PMID:27706202

BANNER: an executable survey of advances in biomedical named entity recognition.

PubMed

Leaman, Robert; Gonzalez, Graciela

2008-01-01

There has been an increasing amount of research on biomedical named entity recognition, the most basic text extraction problem, resulting in significant progress by different research teams around the world. This has created a need for a freely-available, open source system implementing the advances described in the literature. In this paper we present BANNER, an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field. BANNER is implemented in Java as a machine-learning system based on conditional random fields and includes a wide survey of the best techniques recently described in the literature. It is designed to maximize domain independence by not employing brittle semantic features or rule-based processing steps, and achieves significantly better performance than existing baseline systems. It is therefore useful to developers as an extensible NER implementation, to researchers as a standard for comparing innovative techniques, and to biologists requiring the ability to find novel entities in large amounts of text.

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.

Foreign Language Analysis and Recognition (FLARe)

DTIC Science & Technology

2016-10-08

10 7 Chinese CER ...Rates ( CERs ) were obtained with each feature set: (1) 19.2%, (2) 17.3%, and (3) 15.3%. Based on these results, a GMM-HMM speech recognition system...These systems were evaluated on the HUB4 and HKUST test partitions. Table 7 shows the CER obtained on each test set. Whereas including the HKUST data

Emotion Recognition Ability Test Using JACFEE Photos: A Validity/Reliability Study of a War Veterans' Sample and Their Offspring.

PubMed

Castro-Vale, Ivone; Severo, Milton; Carvalho, Davide; Mota-Cardoso, Rui

2015-01-01

Emotion recognition is very important for social interaction. Several mental disorders influence facial emotion recognition. War veterans and their offspring are subject to an increased risk of developing psychopathology. Emotion recognition is an important aspect that needs to be addressed in this population. To our knowledge, no test exists that is validated for use with war veterans and their offspring. The current study aimed to validate the JACFEE photo set to study facial emotion recognition in war veterans and their offspring. The JACFEE photo set was presented to 135 participants, comprised of 62 male war veterans and 73 war veterans' offspring. The participants identified the facial emotion presented from amongst the possible seven emotions that were tested for: anger, contempt, disgust, fear, happiness, sadness, and surprise. A loglinear model was used to evaluate whether the agreement between the intended and the chosen emotions was higher than the expected. Overall agreement between chosen and intended emotions was 76.3% (Cohen kappa = 0.72). The agreement ranged from 63% (sadness expressions) to 91% (happiness expressions). The reliability by emotion ranged from 0.617 to 0.843 and the overall JACFEE photo set Cronbach alpha was 0.911. The offspring showed higher agreement when compared with the veterans (RR: 41.52 vs 12.12, p < 0.001), which confirms the construct validity of the test. The JACFEE set of photos showed good validity and reliability indices, which makes it an adequate instrument for researching emotion recognition ability in the study sample of war veterans and their respective offspring.

Emotion Recognition Ability Test Using JACFEE Photos: A Validity/Reliability Study of a War Veterans' Sample and Their Offspring

PubMed Central

Castro-Vale, Ivone; Severo, Milton; Carvalho, Davide; Mota-Cardoso, Rui

2015-01-01

Emotion recognition is very important for social interaction. Several mental disorders influence facial emotion recognition. War veterans and their offspring are subject to an increased risk of developing psychopathology. Emotion recognition is an important aspect that needs to be addressed in this population. To our knowledge, no test exists that is validated for use with war veterans and their offspring. The current study aimed to validate the JACFEE photo set to study facial emotion recognition in war veterans and their offspring. The JACFEE photo set was presented to 135 participants, comprised of 62 male war veterans and 73 war veterans’ offspring. The participants identified the facial emotion presented from amongst the possible seven emotions that were tested for: anger, contempt, disgust, fear, happiness, sadness, and surprise. A loglinear model was used to evaluate whether the agreement between the intended and the chosen emotions was higher than the expected. Overall agreement between chosen and intended emotions was 76.3% (Cohen kappa = 0.72). The agreement ranged from 63% (sadness expressions) to 91% (happiness expressions). The reliability by emotion ranged from 0.617 to 0.843 and the overall JACFEE photo set Cronbach alpha was 0.911. The offspring showed higher agreement when compared with the veterans (RR: 41.52 vs 12.12, p < 0.001), which confirms the construct validity of the test. The JACFEE set of photos showed good validity and reliability indices, which makes it an adequate instrument for researching emotion recognition ability in the study sample of war veterans and their respective offspring. PMID:26147938

Cross-Modal Retrieval With CNN Visual Features: A New Baseline.

PubMed

Wei, Yunchao; Zhao, Yao; Lu, Canyi; Wei, Shikui; Liu, Luoqi; Zhu, Zhenfeng; Yan, Shuicheng

2017-02-01

Recently, convolutional neural network (CNN) visual features have demonstrated their powerful ability as a universal representation for various recognition tasks. In this paper, cross-modal retrieval with CNN visual features is implemented with several classic methods. Specifically, off-the-shelf CNN visual features are extracted from the CNN model, which is pretrained on ImageNet with more than one million images from 1000 object categories, as a generic image representation to tackle cross-modal retrieval. To further enhance the representational ability of CNN visual features, based on the pretrained CNN model on ImageNet, a fine-tuning step is performed by using the open source Caffe CNN library for each target data set. Besides, we propose a deep semantic matching method to address the cross-modal retrieval problem with respect to samples which are annotated with one or multiple labels. Extensive experiments on five popular publicly available data sets well demonstrate the superiority of CNN visual features for cross-modal retrieval.

A thorough experimental study of CH/π interactions in water: quantitative structure-stability relationships for carbohydrate/aromatic complexes.

PubMed

Jiménez-Moreno, Ester; Jiménez-Osés, Gonzalo; Gómez, Ana M; Santana, Andrés G; Corzana, Francisco; Bastida, Agatha; Jiménez-Barbero, Jesus; Asensio, Juan Luis

2015-11-13

CH/π interactions play a key role in a large variety of molecular recognition processes of biological relevance. However, their origins and structural determinants in water remain poorly understood. In order to improve our comprehension of these important interaction modes, we have performed a quantitative experimental analysis of a large data set comprising 117 chemically diverse carbohydrate/aromatic stacking complexes, prepared through a dynamic combinatorial approach recently developed by our group. The obtained free energies provide a detailed picture of the structure-stability relationships that govern the association process, opening the door to the rational design of improved carbohydrate-based ligands or carbohydrate receptors. Moreover, this experimental data set, supported by quantum mechanical calculations, has contributed to the understanding of the main driving forces that promote complex formation, underlining the key role played by coulombic and solvophobic forces on the stabilization of these complexes. This represents the most quantitative and extensive experimental study reported so far for CH/π complexes in water.

Speech Recognition and Parent Ratings From Auditory Development Questionnaires in Children Who Are Hard of Hearing.

PubMed

McCreery, Ryan W; Walker, Elizabeth A; Spratford, Meredith; Oleson, Jacob; Bentler, Ruth; Holte, Lenore; Roush, Patricia

2015-01-01

Progress has been made in recent years in the provision of amplification and early intervention for children who are hard of hearing. However, children who use hearing aids (HAs) may have inconsistent access to their auditory environment due to limitations in speech audibility through their HAs or limited HA use. The effects of variability in children's auditory experience on parent-reported auditory skills questionnaires and on speech recognition in quiet and in noise were examined for a large group of children who were followed as part of the Outcomes of Children with Hearing Loss study. Parent ratings on auditory development questionnaires and children's speech recognition were assessed for 306 children who are hard of hearing. Children ranged in age from 12 months to 9 years. Three questionnaires involving parent ratings of auditory skill development and behavior were used, including the LittlEARS Auditory Questionnaire, Parents Evaluation of Oral/Aural Performance in Children rating scale, and an adaptation of the Speech, Spatial, and Qualities of Hearing scale. Speech recognition in quiet was assessed using the Open- and Closed-Set Test, Early Speech Perception test, Lexical Neighborhood Test, and Phonetically Balanced Kindergarten word lists. Speech recognition in noise was assessed using the Computer-Assisted Speech Perception Assessment. Children who are hard of hearing were compared with peers with normal hearing matched for age, maternal educational level, and nonverbal intelligence. The effects of aided audibility, HA use, and language ability on parent responses to auditory development questionnaires and on children's speech recognition were also examined. Children who are hard of hearing had poorer performance than peers with normal hearing on parent ratings of auditory skills and had poorer speech recognition. Significant individual variability among children who are hard of hearing was observed. Children with greater aided audibility through their HAs, more hours of HA use, and better language abilities generally had higher parent ratings of auditory skills and better speech-recognition abilities in quiet and in noise than peers with less audibility, more limited HA use, or poorer language abilities. In addition to the auditory and language factors that were predictive for speech recognition in quiet, phonological working memory was also a positive predictor for word recognition abilities in noise. Children who are hard of hearing continue to experience delays in auditory skill development and speech-recognition abilities compared with peers with normal hearing. However, significant improvements in these domains have occurred in comparison to similar data reported before the adoption of universal newborn hearing screening and early intervention programs for children who are hard of hearing. Increasing the audibility of speech has a direct positive effect on auditory skill development and speech-recognition abilities and also may enhance these skills by improving language abilities in children who are hard of hearing. Greater number of hours of HA use also had a significant positive impact on parent ratings of auditory skills and children's speech recognition.

Image recognition and consistency of response

NASA Astrophysics Data System (ADS)

Haygood, Tamara M.; Ryan, John; Liu, Qing Mary A.; Bassett, Roland; Brennan, Patrick C.

2012-02-01

Purpose: To investigate the connection between conscious recognition of an image previously encountered in an experimental setting and consistency of response to the experimental question.
Materials and Methods: Twenty-four radiologists viewed 40 frontal chest radiographs and gave their opinion as to the position of a central venous catheter. One-to-three days later they again viewed 40 frontal chest radiographs and again gave their opinion as to the position of the central venous catheter. Half of the radiographs in the second set were repeated images from the first set and half were new. The radiologists were asked of each image whether it had been included in the first set. For this study, we are evaluating only the 20 repeated images. We used the Kruskal-Wallis test and Fisher's exact test to determine the relationship between conscious recognition of a previously interpreted image and consistency in interpretation of the image.
Results. There was no significant correlation between recognition of the image and consistency in response regarding the position of the central venous catheter. In fact, there was a trend in the opposite direction, with radiologists being slightly more likely to give a consistent response with respect to images they did not recognize than with respect to those they did recognize.
Conclusion: Radiologists' recognition of previously-encountered images in an observer-performance study does not noticeably color their interpretation on the second encounter.

Building Searchable Collections of Enterprise Speech Data.

ERIC Educational Resources Information Center

Cooper, James W.; Viswanathan, Mahesh; Byron, Donna; Chan, Margaret

The study has applied speech recognition and text-mining technologies to a set of recorded outbound marketing calls and analyzed the results. Since speaker-independent speech recognition technology results in a significantly lower recognition rate than that found when the recognizer is trained for a particular speaker, a number of post-processing…

The Wireless Ubiquitous Surveillance Testbed

DTIC Science & Technology

2003-03-01

c. Eye Patterns.............................................................................17 d. Facial Recognition ..................................................................19...27). ...........................................98 Table F.4. Facial Recognition Products. (After: Polemi, p. 25 and BiometriTech, 15 May 2002...it applies to homeland security. C. RESEARCH TASKS The main goals of this thesis are to: • Set up the biometric sensors and facial recognition surveillance

Towards Real-Time Speech Emotion Recognition for Affective E-Learning

ERIC Educational Resources Information Center

Bahreini, Kiavash; Nadolski, Rob; Westera, Wim

2016-01-01

This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILTWAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner's vocal intonations and facial expressions in order…

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

NASA Astrophysics Data System (ADS)

Babayan, Pavel; Smirnov, Sergey; Strotov, Valery

2017-10-01

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

Visual Word Recognition Across the Adult Lifespan

PubMed Central

Cohen-Shikora, Emily R.; Balota, David A.

2016-01-01

The current study examines visual word recognition in a large sample (N = 148) across the adult lifespan and across a large set of stimuli (N = 1187) in three different lexical processing tasks (pronunciation, lexical decision, and animacy judgments). Although the focus of the present study is on the influence of word frequency, a diverse set of other variables are examined as the system ages and acquires more experience with language. Computational models and conceptual theories of visual word recognition and aging make differing predictions for age-related changes in the system. However, these have been difficult to assess because prior studies have produced inconsistent results, possibly due to sample differences, analytic procedures, and/or task-specific processes. The current study confronts these potential differences by using three different tasks, treating age and word variables as continuous, and exploring the influence of individual differences such as vocabulary, vision, and working memory. The primary finding is remarkable stability in the influence of a diverse set of variables on visual word recognition across the adult age spectrum. This pattern is discussed in reference to previous inconsistent findings in the literature and implications for current models of visual word recognition. PMID:27336629

A practical approach for writer-dependent symbol recognition using a writer-independent symbol recognizer.

PubMed

LaViola, Joseph J; Zeleznik, Robert C

2007-11-01

We present a practical technique for using a writer-independent recognition engine to improve the accuracy and speed while reducing the training requirements of a writer-dependent symbol recognizer. Our writer-dependent recognizer uses a set of binary classifiers based on the AdaBoost learning algorithm, one for each possible pairwise symbol comparison. Each classifier consists of a set of weak learners, one of which is based on a writer-independent handwriting recognizer. During online recognition, we also use the n-best list of the writer-independent recognizer to prune the set of possible symbols and thus reduce the number of required binary classifications. In this paper, we describe the geometric and statistical features used in our recognizer and our all-pairs classification algorithm. We also present the results of experiments that quantify the effect incorporating a writer-independent recognition engine into a writer-dependent recognizer has on accuracy, speed, and user training time.

Driver fatigue detection based on eye state.

PubMed

Lin, Lizong; Huang, Chao; Ni, Xiaopeng; Wang, Jiawen; Zhang, Hao; Li, Xiao; Qian, Zhiqin

2015-01-01

Nowadays, more and more traffic accidents occur because of driver fatigue. In order to reduce and prevent it, in this study, a calculation method using PERCLOS (percentage of eye closure time) parameter characteristics based on machine vision was developed. It determined whether a driver's eyes were in a fatigue state according to the PERCLOS value. The overall workflow solutions included face detection and tracking, detection and location of the human eye, human eye tracking, eye state recognition, and driver fatigue testing. The key aspects of the detection system incorporated the detection and location of human eyes and driver fatigue testing. The simplified method of measuring the PERCLOS value of the driver was to calculate the ratio of the eyes being open and closed with the total number of frames for a given period. If the eyes were closed more than the set threshold in the total number of frames, the system would alert the driver. Through many experiments, it was shown that besides the simple detection algorithm, the rapid computing speed, and the high detection and recognition accuracies of the system, the system was demonstrated to be in accord with the real-time requirements of a driver fatigue detection system.

Intergenerational solidarity: the paradox of reciprocity imbalance in ageing welfare states.

PubMed

Thijssen, Peter

2016-12-01

In this article a new theoretical framework is applied to a research field that is somewhat fragmented, namely that of intergenerational solidarity in ageing welfare states. Inspired by utilitarian considerations many scholars tend to problematize the lack of reciprocity characterizing intergenerational exchanges. As some generations are longer old and more numerous they may receive excessive state-administered support of the younger generations, especially in a democratic setting. However, in reality there is limited empirical evidence of intergenerational conflict and theoretical explanations of this paradox are rare. An integrated and dynamical approach that incorporates Durkheim's solidarity theory, Honneth's intersubjective recognition theory, and the current work on reciprocal exchange is necessary in order to understand the survival of intergenerational solidarity in ageing welfare states. According to this model reciprocal recognition leading to the empathization of exchanges is the driving force of intergenerational solidarity in a prefigurative and democratized culture where the status of the young has risen dramatically. Hence, we come to the paradoxical conclusion that attempts to preserve intergenerational solidarity by openly denouncing excessive transfers and trying to bypass them institutionally sometimes might be counterproductive because they may erode their empathic underpinnings. © London School of Economics and Political Science 2016.

Natural selection underlies apparent stress-induced mutagenesis in a bacteriophage infection model.

PubMed

Yosef, Ido; Edgar, Rotem; Levy, Asaf; Amitai, Gil; Sorek, Rotem; Munitz, Ariel; Qimron, Udi

2016-04-18

The emergence of mutations following growth-limiting conditions underlies bacterial drug resistance, viral escape from the immune system and fundamental evolution-driven events. Intriguingly, whether mutations are induced by growth limitation conditions or are randomly generated during growth and then selected by growth limitation conditions remains an open question(1). Here, we show that bacteriophage T7 undergoes apparent stress-induced mutagenesis when selected for improved recognition of its host's receptor. In our unique experimental set-up, the growth limitation condition is physically and temporally separated from mutagenesis: growth limitation occurs while phage DNA is outside the host, and spontaneous mutations occur during phage DNA replication inside the host. We show that the selected beneficial mutations are not pre-existing and that the initial slow phage growth is enabled by the phage particle's low-efficiency DNA injection into the host. Thus, the phage particle allows phage populations to initially extend their host range without mutagenesis by virtue of residual recognition of the host receptor. Mutations appear during non-selective intracellular replication, and the frequency of mutant phages increases by natural selection acting on free phages, which are not capable of mutagenesis.

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

NASA Astrophysics Data System (ADS)

Zhu, Chunmei; Liu, Baojun; Li, Ping

Cough recognition provides important clinical information for the treatment of many respiratory diseases, but the assessment of cough frequency over a long period of time remains unsatisfied for either clinical or research purpose. In this paper, according to the advantage of dynamic time warping (DTW) and the characteristic of cough recognition, an attempt is made to adapt DTW as the recognition algorithm for cough recognition. The process of cough recognition based on mel frequency cepstral coefficients (MFCC) and DTW is introduced. Experiment results of testing samples from 3 subjects show that acceptable performances of cough recognition are obtained by DTW with a small training set.

Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton

PubMed Central

Corgnati, Lorenzo; Marini, Simone; Mazzei, Luca; Ottaviani, Ennio; Aliani, Stefano; Conversi, Alessandra; Griffa, Annalisa

2016-01-01

Marine plankton abundance and dynamics in the open and interior ocean is still an unknown field. The knowledge of gelatinous zooplankton distribution is especially challenging, because this type of plankton has a very fragile structure and cannot be directly sampled using traditional net based techniques. To overcome this shortcoming, Computer Vision techniques can be successfully used for the automatic monitoring of this group.This paper presents the GUARD1 imaging system, a low-cost stand-alone instrument for underwater image acquisition and recognition of gelatinous zooplankton, and discusses the performance of three different methodologies, Tikhonov Regularization, Support Vector Machines and Genetic Programming, that have been compared in order to select the one to be run onboard the system for the automatic recognition of gelatinous zooplankton. The performance comparison results highlight the high accuracy of the three methods in gelatinous zooplankton identification, showing their good capability in robustly selecting relevant features. In particular, Genetic Programming technique achieves the same performances of the other two methods by using a smaller set of features, thus being the most efficient in avoiding computationally consuming preprocessing stages, that is a crucial requirement for running on an autonomous imaging system designed for long lasting deployments, like the GUARD1. The Genetic Programming algorithm has been installed onboard the system, that has been operationally tested in a two-months survey in the Ligurian Sea, providing satisfactory results in terms of monitoring and recognition performances. PMID:27983638

Development and application of an algorithm to compute weighted multiple glycan alignments.

PubMed

Hosoda, Masae; Akune, Yukie; Aoki-Kinoshita, Kiyoko F

2017-05-01

A glycan consists of monosaccharides linked by glycosidic bonds, has branches and forms complex molecular structures. Databases have been developed to store large amounts of glycan-binding experiments, including glycan arrays with glycan-binding proteins. However, there are few bioinformatics techniques to analyze large amounts of data for glycans because there are few tools that can handle the complexity of glycan structures. Thus, we have developed the MCAW (Multiple Carbohydrate Alignment with Weights) tool that can align multiple glycan structures, to aid in the understanding of their function as binding recognition molecules. We have described in detail the first algorithm to perform multiple glycan alignments by modeling glycans as trees. To test our tool, we prepared several data sets, and as a result, we found that the glycan motif could be successfully aligned without any prior knowledge applied to the tool, and the known recognition binding sites of glycans could be aligned at a high rate amongst all our datasets tested. We thus claim that our tool is able to find meaningful glycan recognition and binding patterns using data obtained by glycan-binding experiments. The development and availability of an effective multiple glycan alignment tool opens possibilities for many other glycoinformatics analysis, making this work a big step towards furthering glycomics analysis. http://www.rings.t.soka.ac.jp. kkiyoko@soka.ac.jp. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

Usage Patterns of Open Genomic Data

ERIC Educational Resources Information Center

Xia, Jingfeng; Liu, Ying

2013-01-01

This paper uses Genome Expression Omnibus (GEO), a data repository in biomedical sciences, to examine the usage patterns of open data repositories. It attempts to identify the degree of recognition of data reuse value and understand how e-science has impacted a large-scale scholarship. By analyzing a list of 1,211 publications that cite GEO data…

A Summary of Project Open Horizons, Phase I: Implementation and Data Analysis.

ERIC Educational Resources Information Center

Grantham, Robert J.; Gordon, Myra

Project "Open Horizons," in Buffalo and Niagara Falls, New York, was born out of a recognition that minority adolescents in disadvantaged communities face serious social and personal problems in the area of career development. The originators of the project were seeking an effective methodology for exposing disadvantaged youth to a…

Capping the calix: How toluene completes cesium(i) coordination with calix[4]pyrrole

DOE PAGES

Ellis, Ross J.; Reinhart, Benjamin; Williams, Neil J.; ...

2017-05-04

The role of solvent in molecular recognition systems is under-researched and often ignored, especially when the solvent is considered “non-interacting”. This study concerns the role of toluene solvent in cesium(I) recognition by calix[4]pyrrole. We show that π-donor interactions bind toluene molecules onto the open face of the cation-receptor complex, thus “capping the calix.” As a result, by characterizing this unusual aromatically-saturated complex, we show how “non-interacting” aromatic solvents can directly coordinate receptor-bound cations and thus influence recognition.

Capping the calix: How toluene completes cesium(i) coordination with calix[4]pyrrole

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

Ellis, Ross J.; Reinhart, Benjamin; Williams, Neil J.

The role of solvent in molecular recognition systems is under-researched and often ignored, especially when the solvent is considered “non-interacting”. This study concerns the role of toluene solvent in cesium(I) recognition by calix[4]pyrrole. We show that π-donor interactions bind toluene molecules onto the open face of the cation-receptor complex, thus “capping the calix.” As a result, by characterizing this unusual aromatically-saturated complex, we show how “non-interacting” aromatic solvents can directly coordinate receptor-bound cations and thus influence recognition.

Near-infrared face recognition utilizing open CV software

NASA Astrophysics Data System (ADS)

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

2014-06-01

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

Cultural differences in self-recognition: the early development of autonomous and related selves?

PubMed

Ross, Josephine; Yilmaz, Mandy; Dale, Rachel; Cassidy, Rose; Yildirim, Iraz; Suzanne Zeedyk, M

2017-05-01

Fifteen- to 18-month-old infants from three nationalities were observed interacting with their mothers and during two self-recognition tasks. Scottish interactions were characterized by distal contact, Zambian interactions by proximal contact, and Turkish interactions by a mixture of contact strategies. These culturally distinct experiences may scaffold different perspectives on self. In support, Scottish infants performed best in a task requiring recognition of the self in an individualistic context (mirror self-recognition), whereas Zambian infants performed best in a task requiring recognition of the self in a less individualistic context (body-as-obstacle task). Turkish infants performed similarly to Zambian infants on the body-as-obstacle task, but outperformed Zambians on the mirror self-recognition task. Verbal contact (a distal strategy) was positively related to mirror self-recognition and negatively related to passing the body-as-obstacle task. Directive action and speech (proximal strategies) were negatively related to mirror self-recognition. Self-awareness performance was best predicted by cultural context; autonomous settings predicted success in mirror self-recognition, and related settings predicted success in the body-as-obstacle task. These novel data substantiate the idea that cultural factors may play a role in the early expression of self-awareness. More broadly, the results highlight the importance of moving beyond the mark test, and designing culturally sensitive tests of self-awareness. © 2016 John Wiley & Sons Ltd.

Character context: a shape descriptor for Arabic handwriting recognition

NASA Astrophysics Data System (ADS)

Mudhsh, Mohammed; Almodfer, Rolla; Duan, Pengfei; Xiong, Shengwu

2017-11-01

In the handwriting recognition field, designing good descriptors are substantial to obtain rich information of the data. However, the handwriting recognition research of a good descriptor is still an open issue due to unlimited variation in human handwriting. We introduce a "character context descriptor" that efficiently dealt with the structural characteristics of Arabic handwritten characters. First, the character image is smoothed and normalized, then the character context descriptor of 32 feature bins is built based on the proposed "distance function." Finally, a multilayer perceptron with regularization is used as a classifier. On experimentation with a handwritten Arabic characters database, the proposed method achieved a state-of-the-art performance with recognition rate equal to 98.93% and 99.06% for the 66 and 24 classes, respectively.

The benefits of mystery in nature on attention: assessing the impacts of presentation duration

PubMed Central

Szolosi, Andrew M.; Watson, Jason M.; Ruddell, Edward J.

2014-01-01

Although research has provided prodigious evidence in support of the cognitive benefits that natural settings have over urban settings, all nature is not equal. Within nature, natural settings that contain mystery are often among the most preferred nature scenes. With the prospect of acquiring new information, scenes of this type could more effectively elicit a person's sense of fascination, enabling that person to rest the more effortful forms of attention. The present study examined the direct cognitive benefits that mystery in nature has on attention. Settings of this sort presumably evoke a form of attention that is undemanding or effortless. In order to investigate that notion, participants (n = 144) completed a Recognition Memory Task (RMT) that evaluated recognition performance based on the presence of mystery and presentation duration (300 ms, 1 s, 5 s, and 10 s). Results revealed that with additional viewing time, images perceived high in mystery achieved greater improvements in recognition performance when compared to those images perceived low in mystery. Tests for mediation showed that the effect mystery had on recognition performance occurred through perceptions of fascination. Implications of these and other findings are discussed in the context of Attention Restoration Theory. PMID:25505441

Pattern Recognition Control Design

NASA Technical Reports Server (NTRS)

Gambone, Elisabeth A.

2018-01-01

Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

Deletion of the GluA1 AMPA receptor subunit impairs recency-dependent object recognition memory

PubMed Central

Sanderson, David J.; Hindley, Emma; Smeaton, Emily; Denny, Nick; Taylor, Amy; Barkus, Chris; Sprengel, Rolf; Seeburg, Peter H.; Bannerman, David M.

2011-01-01

Deletion of the GluA1 AMPA receptor subunit impairs short-term spatial recognition memory. It has been suggested that short-term recognition depends upon memory caused by the recent presentation of a stimulus that is independent of contextual–retrieval processes. The aim of the present set of experiments was to test whether the role of GluA1 extends to nonspatial recognition memory. Wild-type and GluA1 knockout mice were tested on the standard object recognition task and a context-independent recognition task that required recency-dependent memory. In a first set of experiments it was found that GluA1 deletion failed to impair performance on either of the object recognition or recency-dependent tasks. However, GluA1 knockout mice displayed increased levels of exploration of the objects in both the sample and test phases compared to controls. In contrast, when the time that GluA1 knockout mice spent exploring the objects was yoked to control mice during the sample phase, it was found that GluA1 deletion now impaired performance on both the object recognition and the recency-dependent tasks. GluA1 deletion failed to impair performance on a context-dependent recognition task regardless of whether object exposure in knockout mice was yoked to controls or not. These results demonstrate that GluA1 is necessary for nonspatial as well as spatial recognition memory and plays an important role in recency-dependent memory processes. PMID:21378100

75 FR 21666 - Canadian Standards Association; Application for Expansion of Recognition

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-26

... provides its final decision on the application. These notices set forth the NRTL's scope of recognition or... . Each NRTL's scope of recognition has three elements: (1) The type of products the NRTL may test, with.... OSHA will publish a public notice of this final decision in the Federal Register. Authority and...

Optical coherence tomography in the diagnosis of dysplasia and adenocarcinoma in Barret's esophagus

NASA Astrophysics Data System (ADS)

Gladkova, N. D.; Zagaynova, E. V.; Zuccaro, G.; Kareta, M. V.; Feldchtein, F. I.; Balalaeva, I. V.; Balandina, E. B.

2007-02-01

Statistical analysis of endoscopic optical coherence tomography (EOCT) surveillance of 78 patients with Barrett's esophagus (BE) is presented in this study. The sensitivity of OCT device in retrospective open detection of early malignancy (including high grade dysplasia and intramucosal adenocarcinoma (IMAC)) was 75%, specificity 82%, diagnostic accuracy - 80%, positive predictive value- 60%, negative predictive value- 87%. In the open recognition of IMAC sensitivity was 81% and specificity were 85% each. Results of a blind recognition with the same material were similar: sensitivity - 77%, specificity 85%, diagnostic accuracy - 82%, positive predictive value- 70%, negative predictive value- 87%. As the endoscopic detection of early malignancy is problematic, OCT holds great promise in enhancing the diagnostic capability of clinical GI endoscopy.

A Prospective Examination of Clinician and Supervisor Turnover Within the Context of Implementation of Evidence-Based Practices in a Publicly-Funded Mental Health System.

PubMed

Beidas, Rinad S; Marcus, Steven; Wolk, Courtney Benjamin; Powell, Byron; Aarons, Gregory A; Evans, Arthur C; Hurford, Matthew O; Hadley, Trevor; Adams, Danielle R; Walsh, Lucia M; Babbar, Shaili; Barg, Frances; Mandell, David S

2016-09-01

Staff turnover rates in publicly-funded mental health settings are high. We investigated staff and organizational predictors of turnover in a sample of individuals working in an urban public mental health system that has engaged in a system-level effort to implement evidence-based practices. Additionally, we interviewed staff to understand reasons for turnover. Greater staff burnout predicted increased turnover, more openness toward new practices predicted retention, and more professional recognition predicted increased turnover. Staff reported leaving their organizations because of personal, organizational, and financial reasons; just over half of staff that left their organization stayed in the public mental health sector. Implications include an imperative to focus on turnover, with a particular emphasis on ameliorating staff burnout.

History of Combat Pay

DTIC Science & Technology

2011-08-01

distribution of risks, the equal eligibility criteria and monetary compensation of HFP and IDP failed to equitably recognize the dire risks of war zones...notions of equity . The wide distribution of risks receiving special pay may also dilute the impact of recognition on servicemember morale. In 2003, the...recognition for the latent risks of low-intensity conflicts as the hazards of open war. Equalization of special pay among individuals exposed to risk

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

PubMed Central

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-01-01

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches. PMID:27792136

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-10-25

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

Emotional face processing deficit in schizophrenia: A replication study in a South African Xhosa population.

PubMed

Leppänen, J M; Niehaus, D J H; Koen, L; Du Toit, E; Schoeman, R; Emsley, R

2006-06-01

Schizophrenia is associated with a deficit in the recognition of negative emotions from facial expressions. The present study examined the universality of this finding by studying facial expression recognition in African Xhosa population. Forty-four Xhosa patients with schizophrenia and forty healthy controls were tested with a computerized task requiring rapid perceptual discrimination of matched positive (i.e. happy), negative (i.e. angry), and neutral faces. Patients were equally accurate as controls in recognizing happy faces but showed a marked impairment in recognition of angry faces. The impairment was particularly pronounced for high-intensity (open-mouth) angry faces. Patients also exhibited more false happy and angry responses to neutral faces than controls. No correlation between level of education or illness duration and emotion recognition was found but the deficit in the recognition of negative emotions was more pronounced in familial compared to non-familial cases of schizophrenia. These findings suggest that the deficit in the recognition of negative facial expressions may constitute a universal neurocognitive marker of schizophrenia.

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.

Electromagnetic versus electrical coupling of personal frequency modulation (FM) receivers to cochlear implant sound processors.

PubMed

Schafer, Erin C; Romine, Denise; Musgrave, Elizabeth; Momin, Sadaf; Huynh, Christy

2013-01-01

Previous research has suggested that electrically coupled frequency modulation (FM) systems substantially improved speech-recognition performance in noise in individuals with cochlear implants (CIs). However, there is limited evidence to support the use of electromagnetically coupled (neck loop) FM receivers with contemporary CI sound processors containing telecoils. The primary goal of this study was to compare speech-recognition performance in noise and subjective ratings of adolescents and adults using one of three contemporary CI sound processors coupled to electromagnetically and electrically coupled FM receivers from Oticon. A repeated-measures design was used to compare speech-recognition performance in noise and subjective ratings without and with the FM systems across three test sessions (Experiment 1) and to compare performance at different FM-gain settings (Experiment 2). Descriptive statistics were used in Experiment 3 to describe output differences measured through a CI sound processor. Experiment 1 included nine adolescents or adults with unilateral or bilateral Advanced Bionics Harmony (n = 3), Cochlear Nucleus 5 (n = 3), and MED-EL OPUS 2 (n = 3) CI sound processors. In Experiment 2, seven of the original nine participants were tested. In Experiment 3, electroacoustic output was measured from a Nucleus 5 sound processor when coupled to the electromagnetically coupled Oticon Arc neck loop and electrically coupled Oticon R2. In Experiment 1, participants completed a field trial with each FM receiver and three test sessions that included speech-recognition performance in noise and a subjective rating scale. In Experiment 2, participants were tested in three receiver-gain conditions. Results in both experiments were analyzed using repeated-measures analysis of variance. Experiment 3 involved electroacoustic-test measures to determine the monitor-earphone output of the CI alone and CI coupled to the two FM receivers. The results in Experiment 1 suggested that both FM receivers provided significantly better speech-recognition performance in noise than the CI alone; however, the electromagnetically coupled receiver provided significantly better speech-recognition performance in noise and better ratings in some situations than the electrically coupled receiver when set to the same gain. In Experiment 2, the primary analysis suggested significantly better speech-recognition performance in noise for the neck-loop versus electrically coupled receiver, but a second analysis, using the best performance across gain settings for each device, revealed no significant differences between the two FM receivers. Experiment 3 revealed monitor-earphone output differences in the Nucleus 5 sound processor for the two FM receivers when set to the +8 setting used in Experiment 1 but equal output when the electrically coupled device was set to a +16 gain setting and the electromagnetically coupled device was set to the +8 gain setting. Individuals with contemporary sound processors may show more favorable speech-recognition performance in noise electromagnetically coupled FM systems (i.e., Oticon Arc), which is most likely related to the input processing and signal processing pathway within the CI sound processor for direct input versus telecoil input. Further research is warranted to replicate these findings with a larger sample size and to develop and validate a more objective approach to fitting FM systems to CI sound processors. American Academy of Audiology.

Processing dynamic facial affect in frequent cannabis-users: evidence of deficits in the speed of identifying emotional expressions.

PubMed

Platt, Bradley; Kamboj, Sunjeev; Morgan, Celia J A; Curran, H Valerie

2010-11-01

While heavy cannabis-users seem to show various cognitive impairments, it remains unclear whether they also experience significant deficits in affective functioning. Evidence of such deficits may contribute to our understanding of the interpersonal difficulties in cannabis-users, and the link between cannabis-use and psychological disorders (Moore et al., 2007). Emotion recognition performance of heavy cannabis-users and non-using controls was compared. A measure of emotion recognition was used in which participants identified facial expressions as they changed from neutral (open-mouth) to gradually more intense expressions of sadness, neutral, anger or happiness (open or closed mouth). Reaction times and accuracy were recorded as the facial expressions changed. Participants also completed measures of 'theory of mind,' depression and impulsivity. Cannabis-users were significantly slower than controls at identifying all three emotional expressions. There was no difference between groups in identifying facial expressions changing from open-mouth neutral expressions to closed-mouth neutral expressions suggesting that differences in emotion recognition were not due to a general slowing of reaction times. Cannabis-users were also significantly more liberal in their response criterion for recognising sadness. Heavy cannabis-use may be associated with affect recognition deficits. In particular, a greater intensity of emotion expression was required before identification of positive and negative emotions. This was found using stimuli which simulated dynamic changes in emotion expression, and in turn, suggests that cannabis-users may experience generalised problems in decoding basic emotions during social interactions. The implications of these findings are discussed for vulnerability to psychological and interpersonal difficulties in cannabis-users. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

When false recognition is out of control: the case of facial conjunctions.

PubMed

Jones, Todd C; Bartlett, James C

2009-03-01

In three experiments, a dual-process approach to face recognition memory is examined, with a specific focus on the idea that a recollection process can be used to retrieve configural information of a studied face. Subjects could avoid, with confidence, a recognition error to conjunction lure faces (each a reconfiguration of features from separate studied faces) or feature lure faces (each based on a set of old features and a set of new features) by recalling a studied configuration. In Experiment 1, study repetition (one vs. eight presentations) was manipulated, and in Experiments 2 and 3, retention interval over a short number of trials (0-20) was manipulated. Different measures converged on the conclusion that subjects were unable to use a recollection process to retrieve configural information in an effort to temper recognition errors for conjunction or feature lure faces. A single process, familiarity, appears to be the sole process underlying recognition of conjunction and feature faces, and familiarity contributes, perhaps in whole, to discrimination of old from conjunction faces.

Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition.

PubMed

Yuan, Chunfeng; Li, Xi; Hu, Weiming; Ling, Haibin; Maybank, Stephen J

2014-02-01

In this paper, we present a new geometric-temporal representation for visual action recognition based on local spatio-temporal features. First, we propose a modified covariance descriptor under the log-Euclidean Riemannian metric to represent the spatio-temporal cuboids detected in the video sequences. Compared with previously proposed covariance descriptors, our descriptor can be measured and clustered in Euclidian space. Second, to capture the geometric-temporal contextual information, we construct a directional pyramid co-occurrence matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a video. DPCM characterizes the co-occurrence statistics of local features as well as the spatio-temporal positional relationships among the concurrent features. These statistics provide strong descriptive power for action recognition. To use DPCM for action recognition, we propose a directional pyramid co-occurrence matching kernel to measure the similarity of videos. The proposed method achieves the state-of-the-art performance and improves on the recognition performance of the bag-of-visual-words (BOVWs) models by a large margin on six public data sets. For example, on the KTH data set, it achieves 98.78% accuracy while the BOVW approach only achieves 88.06%. On both Weizmann and UCF CIL data sets, the highest possible accuracy of 100% is achieved.

Vital signs for vital people: an exploratory study into the role of the Healthcare Assistant in recognising, recording and responding to the acutely ill patient in the general ward setting.

PubMed

James, Jayne; Butler-Williams, Carole; Hunt, Julian; Cox, Helen

2010-07-01

To examine the contribution of the Healthcare Assistant (HCA) as the recogniser, responder and recorder of acutely ill patients within the general ward setting. Concerns have been highlighted regarding the recognition and management of the acutely ill patient within the general ward setting. The contribution of the HCA role to this process has been given limited attention. A postal survey of HCAs was piloted and conducted within two district general hospitals. Open and closed questions were used. Results suggest that on a regular basis HCAs are caring for acutely ill patients. Contextual issues and inaccuracies in some aspects of patient assessment were highlighted. It would appear normal communication channels and hierarchies were bypassed when patients' safety was of concern. Educational needs were identified including scenario-based learning and the importance of ensuring mandatory training is current. HCAs play a significant role in the detection and monitoring of acutely ill patients. Acknowledgement is needed of the contextual factors in the general ward setting which may influence the quality of this process. The educational needs identified by this study can assist managers to improve clinical supervision and educational input in order to improve the quality of care for acutely ill patients.

Gesture Analysis for Astronomy Presentation Software

NASA Astrophysics Data System (ADS)

Robinson, Marc A.

Astronomy presentation software in a planetarium setting provides a visually stimulating way to introduce varied scientific concepts, including computer science concepts, to a wide audience. However, the underlying computational complexity and opportunities for discussion are often overshadowed by the brilliance of the presentation itself. To bring this discussion back out into the open, a method needs to be developed to make the computer science applications more visible. This thesis introduces the GAAPS system, which endeavors to implement free-hand gesture-based control of astronomy presentation software, with the goal of providing that talking point to begin the discussion of computer science concepts in a planetarium setting. The GAAPS system incorporates gesture capture and analysis in a unique environment presenting unique challenges, and introduces a novel algorithm called a Bounding Box Tree to create and select features for this particular gesture data. This thesis also analyzes several different machine learning techniques to determine a well-suited technique for the classification of this particular data set, with an artificial neural network being chosen as the implemented algorithm. The results of this work will allow for the desired introduction of computer science discussion into the specific setting used, as well as provide for future work pertaining to gesture recognition with astronomy presentation software.

Virtual faces expressing emotions: an initial concomitant and construct validity study.

PubMed

Joyal, Christian C; Jacob, Laurence; Cigna, Marie-Hélène; Guay, Jean-Pierre; Renaud, Patrice

2014-01-01

Facial expressions of emotions represent classic stimuli for the study of social cognition. Developing virtual dynamic facial expressions of emotions, however, would open-up possibilities, both for fundamental and clinical research. For instance, virtual faces allow real-time Human-Computer retroactions between physiological measures and the virtual agent. The goal of this study was to initially assess concomitants and construct validity of a newly developed set of virtual faces expressing six fundamental emotions (happiness, surprise, anger, sadness, fear, and disgust). Recognition rates, facial electromyography (zygomatic major and corrugator supercilii muscles), and regional gaze fixation latencies (eyes and mouth regions) were compared in 41 adult volunteers (20 ♂, 21 ♀) during the presentation of video clips depicting real vs. virtual adults expressing emotions. Emotions expressed by each set of stimuli were similarly recognized, both by men and women. Accordingly, both sets of stimuli elicited similar activation of facial muscles and similar ocular fixation times in eye regions from man and woman participants. Further validation studies can be performed with these virtual faces among clinical populations known to present social cognition difficulties. Brain-Computer Interface studies with feedback-feedforward interactions based on facial emotion expressions can also be conducted with these stimuli.

The MUSOS (MUsic SOftware System) Toolkit: A computer-based, open source application for testing memory for melodies.

PubMed

Rainsford, M; Palmer, M A; Paine, G

2018-04-01

Despite numerous innovative studies, rates of replication in the field of music psychology are extremely low (Frieler et al., 2013). Two key methodological challenges affecting researchers wishing to administer and reproduce studies in music cognition are the difficulty of measuring musical responses, particularly when conducting free-recall studies, and access to a reliable set of novel stimuli unrestricted by copyright or licensing issues. In this article, we propose a solution for these challenges in computer-based administration. We present a computer-based application for testing memory for melodies. Created using the software Max/MSP (Cycling '74, 2014a), the MUSOS (Music Software System) Toolkit uses a simple modular framework configurable for testing common paradigms such as recall, old-new recognition, and stem completion. The program is accompanied by a stimulus set of 156 novel, copyright-free melodies, in audio and Max/MSP file formats. Two pilot tests were conducted to establish the properties of the accompanying stimulus set that are relevant to music cognition and general memory research. By using this software, a researcher without specialist musical training may administer and accurately measure responses from common paradigms used in the study of memory for music.

Robust kernel collaborative representation for face recognition

NASA Astrophysics Data System (ADS)

Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong

2015-05-01

One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.

Automatic voice recognition using traditional and artificial neural network approaches

NASA Technical Reports Server (NTRS)

Botros, Nazeih M.

1989-01-01

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

Voice gender discrimination provides a measure of more than pitch-related perception in cochlear implant users.

PubMed

Li, Tianhao; Fu, Qian-Jie

2011-08-01

(1) To investigate whether voice gender discrimination (VGD) could be a useful indicator of the spectral and temporal processing abilities of individual cochlear implant (CI) users; (2) To examine the relationship between VGD and speech recognition with CI when comparable acoustic cues are used for both perception processes. VGD was measured using two talker sets with different inter-gender fundamental frequencies (F(0)), as well as different acoustic CI simulations. Vowel and consonant recognition in quiet and noise were also measured and compared with VGD performance. Eleven postlingually deaf CI users. The results showed that (1) mean VGD performance differed for different stimulus sets, (2) VGD and speech recognition performance varied among individual CI users, and (3) individual VGD performance was significantly correlated with speech recognition performance under certain conditions. VGD measured with selected stimulus sets might be useful for assessing not only pitch-related perception, but also spectral and temporal processing by individual CI users. In addition to improvements in spectral resolution and modulation detection, the improvement in higher modulation frequency discrimination might be particularly important for CI users in noisy environments.

Driver face recognition as a security and safety feature

NASA Astrophysics Data System (ADS)

Vetter, Volker; Giefing, Gerd-Juergen; Mai, Rudolf; Weisser, Hubert

1995-09-01

We present a driver face recognition system for comfortable access control and individual settings of automobiles. The primary goals are the prevention of car thefts and heavy accidents caused by unauthorized use (joy-riders), as well as the increase of safety through optimal settings, e.g. of the mirrors and the seat position. The person sitting on the driver's seat is observed automatically by a small video camera in the dashboard. All he has to do is to behave cooperatively, i.e. to look into the camera. A classification system validates his access. Only after a positive identification, the car can be used and the driver-specific environment (e.g. seat position, mirrors, etc.) may be set up to ensure the driver's comfort and safety. The driver identification system has been integrated in a Volkswagen research car. Recognition results are presented.

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

PubMed

Janko, Vito; Luštrek, Mitja

2017-12-29

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

Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization.

PubMed

Dai, Hong-Jie; Lai, Po-Ting; Chang, Yung-Chun; Tsai, Richard Tzong-Han

2015-01-01

The functions of chemical compounds and drugs that affect biological processes and their particular effect on the onset and treatment of diseases have attracted increasing interest with the advancement of research in the life sciences. To extract knowledge from the extensive literatures on such compounds and drugs, the organizers of BioCreative IV administered the CHEMical Compound and Drug Named Entity Recognition (CHEMDNER) task to establish a standard dataset for evaluating state-of-the-art chemical entity recognition methods. This study introduces the approach of our CHEMDNER system. Instead of emphasizing the development of novel feature sets for machine learning, this study investigates the effect of various tag schemes on the recognition of the names of chemicals and drugs by using conditional random fields. Experiments were conducted using combinations of different tokenization strategies and tag schemes to investigate the effects of tag set selection and tokenization method on the CHEMDNER task. This study presents the performance of CHEMDNER of three more representative tag schemes-IOBE, IOBES, and IOB12E-when applied to a widely utilized IOB tag set and combined with the coarse-/fine-grained tokenization methods. The experimental results thus reveal that the fine-grained tokenization strategy performance best in terms of precision, recall and F-scores when the IOBES tag set was utilized. The IOBES model with fine-grained tokenization yielded the best-F-scores in the six chemical entity categories other than the "Multiple" entity category. Nonetheless, no significant improvement was observed when a more representative tag schemes was used with the coarse or fine-grained tokenization rules. The best F-scores that were achieved using the developed system on the test dataset of the CHEMDNER task were 0.833 and 0.815 for the chemical documents indexing and the chemical entity mention recognition tasks, respectively. The results herein highlight the importance of tag set selection and the use of different tokenization strategies. Fine-grained tokenization combined with the tag set IOBES most effectively recognizes chemical and drug names. To the best of the authors' knowledge, this investigation is the first comprehensive investigation use of various tag set schemes combined with different tokenization strategies for the recognition of chemical entities.

Auditory Perception in an Open Space: Detection and Recognition

DTIC Science & Technology

2015-06-01

recognition ranges of most sounds were approximately 100–200 m. Therefore, it may be hypothesized that this range makes up the soundscape or the range of the... soundscapes . Acta Acustica united with Acustica. 2003;89:287–295. Delaney ME. Range predictions for siren sources. Teddington (UK): National...management of park soundscapes : a review. Applied Acoustics. 2008;69:77–92. Mirabella A, Goldstein D. The effects of ambient noise upon signal detection

State Recognition of High Voltage Isolation Switch Based on Background Difference and Iterative Search

NASA Astrophysics Data System (ADS)

Xu, Jiayuan; Yu, Chengtao; Bo, Bin; Xue, Yu; Xu, Changfu; Chaminda, P. R. Dushantha; Hu, Chengbo; Peng, Kai

2018-03-01

The automatic recognition of the high voltage isolation switch by remote video monitoring is an effective means to ensure the safety of the personnel and the equipment. The existing methods mainly include two ways: improving monitoring accuracy and adopting target detection technology through equipment transformation. Such a method is often applied to specific scenarios, with limited application scope and high cost. To solve this problem, a high voltage isolation switch state recognition method based on background difference and iterative search is proposed in this paper. The initial position of the switch is detected in real time through the background difference method. When the switch starts to open and close, the target tracking algorithm is used to track the motion trajectory of the switch. The opening and closing state of the switch is determined according to the angle variation of the switch tracking point and the center line. The effectiveness of the method is verified by experiments on different switched video frames of switching states. Compared with the traditional methods, this method is more robust and effective.

A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text

PubMed Central

Yu, Jian

2017-01-01

Medical entity recognition, a basic task in the language processing of clinical data, has been extensively studied in analyzing admission notes in alphabetic languages such as English. However, much less work has been done on nonstructural texts that are written in Chinese, or in the setting of differentiation of Chinese drug names between traditional Chinese medicine and Western medicine. Here, we propose a novel cascade-type Chinese medication entity recognition approach that aims at integrating the sentence category classifier from a support vector machine and the conditional random field-based medication entity recognition. We hypothesized that this approach could avoid the side effects of abundant negative samples and improve the performance of the named entity recognition from admission notes written in Chinese. Therefore, we applied this approach to a test set of 324 Chinese-written admission notes with manual annotation by medical experts. Our data demonstrated that this approach had a score of 94.2% in precision, 92.8% in recall, and 93.5% in F-measure for the recognition of traditional Chinese medicine drug names and 91.2% in precision, 92.6% in recall, and 91.7% F-measure for the recognition of Western medicine drug names. The differences in F-measure were significant compared with those in the baseline systems. PMID:29065612

Speech recognition and parent-ratings from auditory development questionnaires in children who are hard of hearing

PubMed Central

McCreery, Ryan W.; Walker, Elizabeth A.; Spratford, Meredith; Oleson, Jacob; Bentler, Ruth; Holte, Lenore; Roush, Patricia

2015-01-01

Objectives Progress has been made in recent years in the provision of amplification and early intervention for children who are hard of hearing. However, children who use hearing aids (HA) may have inconsistent access to their auditory environment due to limitations in speech audibility through their HAs or limited HA use. The effects of variability in children’s auditory experience on parent-report auditory skills questionnaires and on speech recognition in quiet and in noise were examined for a large group of children who were followed as part of the Outcomes of Children with Hearing Loss study. Design Parent ratings on auditory development questionnaires and children’s speech recognition were assessed for 306 children who are hard of hearing. Children ranged in age from 12 months to 9 years of age. Three questionnaires involving parent ratings of auditory skill development and behavior were used, including the LittlEARS Auditory Questionnaire, Parents Evaluation of Oral/Aural Performance in Children Rating Scale, and an adaptation of the Speech, Spatial and Qualities of Hearing scale. Speech recognition in quiet was assessed using the Open and Closed set task, Early Speech Perception Test, Lexical Neighborhood Test, and Phonetically-balanced Kindergarten word lists. Speech recognition in noise was assessed using the Computer-Assisted Speech Perception Assessment. Children who are hard of hearing were compared to peers with normal hearing matched for age, maternal educational level and nonverbal intelligence. The effects of aided audibility, HA use and language ability on parent responses to auditory development questionnaires and on children’s speech recognition were also examined. Results Children who are hard of hearing had poorer performance than peers with normal hearing on parent ratings of auditory skills and had poorer speech recognition. Significant individual variability among children who are hard of hearing was observed. Children with greater aided audibility through their HAs, more hours of HA use and better language abilities generally had higher parent ratings of auditory skills and better speech recognition abilities in quiet and in noise than peers with less audibility, more limited HA use or poorer language abilities. In addition to the auditory and language factors that were predictive for speech recognition in quiet, phonological working memory was also a positive predictor for word recognition abilities in noise. Conclusions Children who are hard of hearing continue to experience delays in auditory skill development and speech recognition abilities compared to peers with normal hearing. However, significant improvements in these domains have occurred in comparison to similar data reported prior to the adoption of universal newborn hearing screening and early intervention programs for children who are hard of hearing. Increasing the audibility of speech has a direct positive effect on auditory skill development and speech recognition abilities, and may also enhance these skills by improving language abilities in children who are hard of hearing. Greater number of hours of HA use also had a significant positive impact on parent ratings of auditory skills and children’s speech recognition. PMID:26731160

Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

PubMed

Chowdhury, Alok Kumar; Tjondronegoro, Dian; Chandran, Vinod; Trost, Stewart G

2017-09-01

To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

Pattern Recognition Control Design

NASA Technical Reports Server (NTRS)

Gambone, Elisabeth

2016-01-01

Spacecraft control algorithms must know the expected spacecraft response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach can be used to investigate the relationship between the control effector commands and the spacecraft responses. Instead of supplying the approximated vehicle properties and the effector performance characteristics, a database of information relating the effector commands and the desired vehicle response can be used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands can be analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center (Ref. 1) to analyze flight dynamics Monte Carlo data sets through pattern recognition methods can be used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands is established, it can be used in place of traditional control laws and gains set. This pattern recognition approach can be compared with traditional control algorithms to determine the potential benefits and uses.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

PubMed Central

Arruti, Andoni; Cearreta, Idoia; Álvarez, Aitor; Lazkano, Elena; Sierra, Basilio

2014-01-01

Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested. PMID:25279686

Ball-scale based hierarchical multi-object recognition in 3D medical images

NASA Astrophysics Data System (ADS)

Bağci, Ulas; Udupa, Jayaram K.; Chen, Xinjian

2010-03-01

This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searches or optimization. That is, the goal is to place the model in a single shot close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. This is achieved via the following set of key ideas: (a) A semi-automatic way of constructing a multi-object shape model assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship between objects in the training images and their intensity patterns captured in b-scale images. (c) A hierarchical mechanism of positioning the model, in a one-shot way, in a given image from a knowledge of the learnt pose relationship and the b-scale image of the given image to be segmented. The evaluation results on a set of 20 routine clinical abdominal female and male CT data sets indicate the following: (1) Incorporating a large number of objects improves the recognition accuracy dramatically. (2) The recognition algorithm can be thought as a hierarchical framework such that quick replacement of the model assembly is defined as coarse recognition and delineation itself is known as finest recognition. (3) Scale yields useful information about the relationship between the model assembly and any given image such that the recognition results in a placement of the model close to the actual pose without doing any elaborate searches or optimization. (4) Effective object recognition can make delineation most accurate.

Benchmark data sets for structure-based computational target prediction.

PubMed

Schomburg, Karen T; Rarey, Matthias

2014-08-25

Structure-based computational target prediction methods identify potential targets for a bioactive compound. Methods based on protein-ligand docking so far face many challenges, where the greatest probably is the ranking of true targets in a large data set of protein structures. Currently, no standard data sets for evaluation exist, rendering comparison and demonstration of improvements of methods cumbersome. Therefore, we propose two data sets and evaluation strategies for a meaningful evaluation of new target prediction methods, i.e., a small data set consisting of three target classes for detailed proof-of-concept and selectivity studies and a large data set consisting of 7992 protein structures and 72 drug-like ligands allowing statistical evaluation with performance metrics on a drug-like chemical space. Both data sets are built from openly available resources, and any information needed to perform the described experiments is reported. We describe the composition of the data sets, the setup of screening experiments, and the evaluation strategy. Performance metrics capable to measure the early recognition of enrichments like AUC, BEDROC, and NSLR are proposed. We apply a sequence-based target prediction method to the large data set to analyze its content of nontrivial evaluation cases. The proposed data sets are used for method evaluation of our new inverse screening method iRAISE. The small data set reveals the method's capability and limitations to selectively distinguish between rather similar protein structures. The large data set simulates real target identification scenarios. iRAISE achieves in 55% excellent or good enrichment a median AUC of 0.67 and RMSDs below 2.0 Å for 74% and was able to predict the first true target in 59 out of 72 cases in the top 2% of the protein data set of about 8000 structures.

Image preprocessing study on KPCA-based face recognition

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Dehua

2015-12-01

Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

Social Recognition Memory: The Effect of Other People's Responses for Previously Seen and Unseen Items

ERIC Educational Resources Information Center

Wright, Daniel B.; Mathews, Sorcha A.; Skagerberg, Elin M.

2005-01-01

When people discuss their memories, what one person says can influence what another personal reports. In 3 studies, participants were shown sets of stimuli and then given recognition memory tests to measure the effect of one person's response on another's. The 1st study (n=24) used word recognition with participant-confederate pairs and found that…

Character recognition using a neural network model with fuzzy representation

NASA Technical Reports Server (NTRS)

Tavakoli, Nassrin; Seniw, David

1992-01-01

The degree to which digital images are recognized correctly by computerized algorithms is highly dependent upon the representation and the classification processes. Fuzzy techniques play an important role in both processes. In this paper, the role of fuzzy representation and classification on the recognition of digital characters is investigated. An experimental Neural Network model with application to character recognition was developed. Through a set of experiments, the effect of fuzzy representation on the recognition accuracy of this model is presented.

iFER: facial expression recognition using automatically selected geometric eye and eyebrow features

NASA Astrophysics Data System (ADS)

Oztel, Ismail; Yolcu, Gozde; Oz, Cemil; Kazan, Serap; Bunyak, Filiz

2018-03-01

Facial expressions have an important role in interpersonal communications and estimation of emotional states or intentions. Automatic recognition of facial expressions has led to many practical applications and became one of the important topics in computer vision. We present a facial expression recognition system that relies on geometry-based features extracted from eye and eyebrow regions of the face. The proposed system detects keypoints on frontal face images and forms a feature set using geometric relationships among groups of detected keypoints. Obtained feature set is refined and reduced using the sequential forward selection (SFS) algorithm and fed to a support vector machine classifier to recognize five facial expression classes. The proposed system, iFER (eye-eyebrow only facial expression recognition), is robust to lower face occlusions that may be caused by beards, mustaches, scarves, etc. and lower face motion during speech production. Preliminary experiments on benchmark datasets produced promising results outperforming previous facial expression recognition studies using partial face features, and comparable results to studies using whole face information, only slightly lower by ˜ 2.5 % compared to the best whole face facial recognition system while using only ˜ 1 / 3 of the facial region.

Constructing Flexible, Configurable, ETL Pipelines for the Analysis of "Big Data" with Apache OODT

NASA Astrophysics Data System (ADS)

Hart, A. F.; Mattmann, C. A.; Ramirez, P.; Verma, R.; Zimdars, P. A.; Park, S.; Estrada, A.; Sumarlidason, A.; Gil, Y.; Ratnakar, V.; Krum, D.; Phan, T.; Meena, A.

2013-12-01

A plethora of open source technologies for manipulating, transforming, querying, and visualizing 'big data' have blossomed and matured in the last few years, driven in large part by recognition of the tremendous value that can be derived by leveraging data mining and visualization techniques on large data sets. One facet of many of these tools is that input data must often be prepared into a particular format (e.g.: JSON, CSV), or loaded into a particular storage technology (e.g.: HDFS) before analysis can take place. This process, commonly known as Extract-Transform-Load, or ETL, often involves multiple well-defined steps that must be executed in a particular order, and the approach taken for a particular data set is generally sensitive to the quantity and quality of the input data, as well as the structure and complexity of the desired output. When working with very large, heterogeneous, unstructured or semi-structured data sets, automating the ETL process and monitoring its progress becomes increasingly important. Apache Object Oriented Data Technology (OODT) provides a suite of complementary data management components called the Process Control System (PCS) that can be connected together to form flexible ETL pipelines as well as browser-based user interfaces for monitoring and control of ongoing operations. The lightweight, metadata driven middleware layer can be wrapped around custom ETL workflow steps, which themselves can be implemented in any language. Once configured, it facilitates communication between workflow steps and supports execution of ETL pipelines across a distributed cluster of compute resources. As participants in a DARPA-funded effort to develop open source tools for large-scale data analysis, we utilized Apache OODT to rapidly construct custom ETL pipelines for a variety of very large data sets to prepare them for analysis and visualization applications. We feel that OODT, which is free and open source software available through the Apache Software Foundation, is particularly well suited to developing and managing arbitrary large-scale ETL processes both for the simplicity and flexibility of its wrapper framework, as well as the detailed provenance information it exposes throughout the process. Our experience using OODT to manage processing of large-scale data sets in domains as diverse as radio astronomy, life sciences, and social network analysis demonstrates the flexibility of the framework, and the range of potential applications to a broad array of big data ETL challenges.

The neural correlates of gist-based true and false recognition

PubMed Central

Gutchess, Angela H.; Schacter, Daniel L.

2012-01-01

When information is thematically related to previously studied information, gist-based processes contribute to false recognition. Using functional MRI, we examined the neural correlates of gist-based recognition as a function of increasing numbers of studied exemplars. Sixteen participants incidentally encoded small, medium, and large sets of pictures, and we compared the neural response at recognition using parametric modulation analyses. For hits, regions in middle occipital, middle temporal, and posterior parietal cortex linearly modulated their activity according to the number of related encoded items. For false alarms, visual, parietal, and hippocampal regions were modulated as a function of the encoded set size. The present results are consistent with prior work in that the neural regions supporting veridical memory also contribute to false memory for related information. The results also reveal that these regions respond to the degree of relatedness among similar items, and implicate perceptual and constructive processes in gist-based false memory. PMID:22155331

The within-subjects design in the study of facial expressions.

PubMed

Yik, Michelle; Widen, Sherri C; Russell, James A

2013-01-01

The common within-subjects design of studies on the recognition of emotion from facial expressions allows the judgement of one face to be influenced by previous faces, thus introducing the potential for artefacts. The present study (N=344) showed that the canonical "disgust face" was judged as disgusted, provided that the preceding set of faces included "anger expressions", but was judged as angry when the preceding set of faces excluded anger but instead included persons who looked sad or about to be sick. Chinese observers showed lower recognition of the "disgust face" than did American observers. Chinese observers also showed lower recognition of the "fear face" when responding in Chinese than in English.

Age- and sex-related disturbance in a battery of sensorimotor and cognitive tasks in Kunming mice.

PubMed

Chen, Gui-Hai; Wang, Yue-Ju; Zhang, Li-Qun; Zhou, Jiang-Ning

2004-12-15

A battery of tasks, i.e. beam walking, open field, tightrope, radial six-arm water maze (RAWM), novel-object recognition and olfactory discrimination, was used to determine whether there was age- and sex-related memory deterioration in Kunming (KM) mice, and whether these tasks are independent or correlated with each other. Two age groups of KM mice were used: a younger group (7-8 months old, 12 males and 11 females) and an older group (17-18 months old, 12 males and 12 females). The results showed that the spatial learning ability and memory in the RAWM were lower in older female KM mice relative to younger female mice and older male mice. Consistent with this, in the novel-object recognition task, a non-spatial cognitive task, older female mice but not older male mice had impairment of short-term memory. In olfactory discrimination, another non-spatial task, the older mice retained this ability. Interestingly, female mice performed better than males, especially in the younger group. The older females exhibited sensorimotor impairment in the tightrope task and low locomotor activity in the open-field task. Moreover, older mice spent a longer time in the peripheral squares of the open-field than younger ones. The non-spatial cognitive performance in the novel-object recognition and olfactory discrimination tasks was related to performance in the open-field, whereas the spatial cognitive performance in the RAWM was not related to performance in any of the three sensorimotor tasks. These results suggest that disturbance of spatial learning and memory, as well as selective impairment of non-spatial learning and memory, existed in older female KM mice.

Evidence for the activation of sensorimotor information during visual word recognition: the body-object interaction effect.

PubMed

Siakaluk, Paul D; Pexman, Penny M; Aguilera, Laura; Owen, William J; Sears, Christopher R

2008-01-01

We examined the effects of sensorimotor experience in two visual word recognition tasks. Body-object interaction (BOI) ratings were collected for a large set of words. These ratings assess perceptions of the ease with which a human body can physically interact with a word's referent. A set of high BOI words (e.g., mask) and a set of low BOI words (e.g., ship) were created, matched on imageability and concreteness. Facilitatory BOI effects were observed in lexical decision and phonological lexical decision tasks: responses were faster for high BOI words than for low BOI words. We discuss how our findings may be accounted for by (a) semantic feedback within the visual word recognition system, and (b) an embodied view of cognition (e.g., Barsalou's perceptual symbol systems theory), which proposes that semantic knowledge is grounded in sensorimotor interactions with the environment.

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

PubMed Central

Janko, Vito

2017-01-01

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

Age-related differences in emotion recognition ability: a cross-sectional study.

PubMed

Mill, Aire; Allik, Jüri; Realo, Anu; Valk, Raivo

2009-10-01

Experimental studies indicate that recognition of emotions, particularly negative emotions, decreases with age. However, there is no consensus at which age the decrease in emotion recognition begins, how selective this is to negative emotions, and whether this applies to both facial and vocal expression. In the current cross-sectional study, 607 participants ranging in age from 18 to 84 years (mean age = 32.6 +/- 14.9 years) were asked to recognize emotions expressed either facially or vocally. In general, older participants were found to be less accurate at recognizing emotions, with the most distinctive age difference pertaining to a certain group of negative emotions. Both modalities revealed an age-related decline in the recognition of sadness and -- to a lesser degree -- anger, starting at about 30 years of age. Although age-related differences in the recognition of expression of emotion were not mediated by personality traits, 2 of the Big 5 traits, openness and conscientiousness, made an independent contribution to emotion-recognition performance. Implications of age-related differences in facial and vocal emotion expression and early onset of the selective decrease in emotion recognition are discussed in terms of previous findings and relevant theoretical models.

Cherry recognition in natural environment based on the vision of picking robot

NASA Astrophysics Data System (ADS)

Zhang, Qirong; Chen, Shanxiong; Yu, Tingzhong; Wang, Yan

2017-04-01

In order to realize the automatic recognition of cherry in the natural environment, this paper designed a robot vision system recognition method. The first step of this method is to pre-process the cherry image by median filtering. The second step is to identify the colour of the cherry through the 0.9R-G colour difference formula, and then use the Otsu algorithm for threshold segmentation. The third step is to remove noise by using the area threshold. The fourth step is to remove the holes in the cherry image by morphological closed and open operation. The fifth step is to obtain the centroid and contour of cherry by using the smallest external rectangular and the Hough transform. Through this recognition process, we can successfully identify 96% of the cherry without blocking and adhesion.

Door Security using Face Detection and Raspberry Pi

NASA Astrophysics Data System (ADS)

Bhutra, Venkatesh; Kumar, Harshav; Jangid, Santosh; Solanki, L.

2018-03-01

With the world moving towards advanced technologies, security forms a crucial part in daily life. Among the many techniques used for this purpose, Face Recognition stands as effective means of authentication and security. This paper deals with the user of principal component and security. PCA is a statistical approach used to simplify a data set. The minimum Euclidean distance found from the PCA technique is used to recognize the face. Raspberry Pi a low cost ARM based computer on a small circuit board, controls the servo motor and other sensors. The servo-motor is in turn attached to the doors of home and opens up when the face is recognized. The proposed work has been done using a self-made training database of students from B.K. Birla Institute of Engineering and Technology, Pilani, Rajasthan, India.

A prospective examination of clinician and supervisor turnover within the context of implementation of evidence-based practices in a publicly-funded mental health system

PubMed Central

Marcus, Steven; Wolk, Courtney Benjamin; Powell, Byron; Aarons, Gregory A.; Evans, Arthur C.; Hurford, Matthew O.; Hadley, Trevor; Adams, Danielle R.; Walsh, Lucia M.; Babbar, Shaili; Barg, Frances; Mandell, David S.

2015-01-01

Staff turnover rates in publicly-funded mental health settings are high. We investigated staff and organizational predictors of turnover in a sample of individuals working in an urban public mental health system that has engaged in a system-level effort to implement evidence-based practices. Additionally, we interviewed staff to understand reasons for turnover. Greater staff burnout predicted increased turnover, more openness toward new practices predicted retention, and more professional recognition predicted increased turnover. Staff reported leaving their organizations because of personal, organizational, and financial reasons; just over half of staff that left their organization stayed in the public mental health sector. Implications include an imperative to focus on turnover, with a particular emphasis on ameliorating staff burnout. PMID:26179469

Consistency of response and image recognition, pulmonary nodules

PubMed Central

Liu, M A Q; Galvan, E; Bassett, R; Murphy, W A; Matamoros, A; Marom, E M

2014-01-01

Objective: To investigate the effect of recognition of a previously encountered radiograph on consistency of response in localized pulmonary nodules. Methods: 13 radiologists interpreted 40 radiographs each to locate pulmonary nodules. A few days later, they again interpreted 40 radiographs. Half of the images in the second set were new. We asked the radiologists whether each image had been in the first set. We used Fisher's exact test and Kruskal–Wallis test to evaluate the correlation between recognition of an image and consistency in its interpretation. We evaluated the data using all possible recognition levels—definitely, probably or possibly included vs definitely, probably or possibly not included by collapsing the recognition levels into two and by eliminating the “possibly included” and “possibly not included” scores. Results: With all but one of six methods of looking at the data, there was no significant correlation between consistency in interpretation and recognition of the image. When the possibly included and possibly not included scores were eliminated, there was a borderline statistical significance (p = 0.04) with slightly greater consistency in interpretation of recognized than that of non-recognized images. Conclusion: We found no convincing evidence that radiologists' recognition of images in an observer performance study affects their interpretation on a second encounter. Advances in knowledge: Conscious recognition of chest radiographs did not result in a greater degree of consistency in the tested interpretation than that in the interpretation of images that were not recognized. PMID:24697724

Neural-Network Object-Recognition Program

NASA Technical Reports Server (NTRS)

Spirkovska, L.; Reid, M. B.

1993-01-01

HONTIOR computer program implements third-order neural network exhibiting invariance under translation, change of scale, and in-plane rotation. Invariance incorporated directly into architecture of network. Only one view of each object needed to train network for two-dimensional-translation-invariant recognition of object. Also used for three-dimensional-transformation-invariant recognition by training network on only set of out-of-plane rotated views. Written in C language.

Attenuation of cadmium-induced decline in spatial, habituation and recognition memory by long-term administration of almond and walnut supplementation: Role of cholinergic function.

PubMed

Batool, Zehra; Agha, Faiza; Ahmad, Saara; Liaquat, Laraib; Tabassum, Saiqa; Khaliq, Saima; Anis, Lubna; Sajid, Irfan; Emad, Shaista; Perveen, Tahira; Haider, Saida

2017-01-01

Excessive exposure of cadmium which is regarded as a neurotoxin can stimulate aging process by inducing abnormality in neuronal function. It has been reported that supplementation of almond and walnut attenuate age-related memory loss. Present study was designed to investigate the weekly administration of cadmium for one month on learning and memory function with relation to cholinergic activity. Cadmium was administered at the dose of 50 mg/kg/week. Whereas, almond and walnut was supplemented at the dose of 400 mg/kg/day along with cadmium administration to separate set of rats. At the end of experiment, memory function was assessed by Morris water maze, open field test and novel object recognition test. Results of the present study showed that cadmium administration significantly reduced memory retention. Reduced acetylcholine levels and elevated acetyl cholinesterase activity were also observed in frontal cortex and hippocampus of cadmium treated rats. Malondialdehyde levels were also significantly increased following the administration of cadmium. Daily supplementation of almond and walnut for 28 days significantly attenuated cadmium-induced memory impairment in rats. Results of the present study are discussed in term of cholinergic activity in cadmium-induced memory loss and its attenuation by nuts supplementation in rats.

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

NASA Technical Reports Server (NTRS)

Thadani, S. G.

1977-01-01

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

The effect of forced choice on facial emotion recognition: a comparison to open verbal classification of emotion labels

PubMed Central

Limbrecht-Ecklundt, Kerstin; Scheck, Andreas; Jerg-Bretzke, Lucia; Walter, Steffen; Hoffmann, Holger; Traue, Harald C.

2013-01-01

Objective: This article includes the examination of potential methodological problems of the application of a forced choice response format in facial emotion recognition. Methodology: 33 subjects were presented with validated facial stimuli. The task was to make a decision about which emotion was shown. In addition, the subjective certainty concerning the decision was recorded. Results: The detection rates are 68% for fear, 81% for sadness, 85% for anger, 87% for surprise, 88% for disgust, and 94% for happiness, and are thus well above the random probability. Conclusion: This study refutes the concern that the use of forced choice formats may not adequately reflect actual recognition performance. The use of standardized tests to examine emotion recognition ability leads to valid results and can be used in different contexts. For example, the images presented here appear suitable for diagnosing deficits in emotion recognition in the context of psychological disorders and for mapping treatment progress. PMID:23798981

Leadership for the 1970s. Human Relations in the Military Environment

DTIC Science & Technology

1978-08-01

gleaned from humanistic response to the recognition of problem symptoms. The commander becomes known as a caring person, which opens communications...plagued by the deviant behavior described above. This delinquency was countered by opening communication channels, adopting a more humanistic approach...trade-off between mission accomplishment and the humanistic treatment of people prob- lens. Most conflicts can be resolved in favor of the

Modern Clinical Research on LSD

PubMed Central

Liechti, Matthias E

2017-01-01

All modern clinical studies using the classic hallucinogen lysergic acid diethylamide (LSD) in healthy subjects or patients in the last 25 years are reviewed herein. There were five recent studies in healthy participants and one in patients. In a controlled setting, LSD acutely induced bliss, audiovisual synesthesia, altered meaning of perceptions, derealization, depersonalization, and mystical experiences. These subjective effects of LSD were mediated by the 5-HT2A receptor. LSD increased feelings of closeness to others, openness, trust, and suggestibility. LSD impaired the recognition of sad and fearful faces, reduced left amygdala reactivity to fearful faces, and enhanced emotional empathy. LSD increased the emotional response to music and the meaning of music. LSD acutely produced deficits in sensorimotor gating, similar to observations in schizophrenia. LSD had weak autonomic stimulant effects and elevated plasma cortisol, prolactin, and oxytocin levels. Resting-state functional magnetic resonance studies showed that LSD acutely reduced the integrity of functional brain networks and increased connectivity between networks that normally are more dissociated. LSD increased functional thalamocortical connectivity and functional connectivity of the primary visual cortex with other brain areas. The latter effect was correlated with subjective hallucinations. LSD acutely induced global increases in brain entropy that were associated with greater trait openness 14 days later. In patients with anxiety associated with life-threatening disease, anxiety was reduced for 2 months after two doses of LSD. In medical settings, no complications of LSD administration were observed. These data should contribute to further investigations of the therapeutic potential of LSD in psychiatry. PMID:28447622

Modern Clinical Research on LSD.

PubMed

Liechti, Matthias E

2017-10-01

All modern clinical studies using the classic hallucinogen lysergic acid diethylamide (LSD) in healthy subjects or patients in the last 25 years are reviewed herein. There were five recent studies in healthy participants and one in patients. In a controlled setting, LSD acutely induced bliss, audiovisual synesthesia, altered meaning of perceptions, derealization, depersonalization, and mystical experiences. These subjective effects of LSD were mediated by the 5-HT 2A receptor. LSD increased feelings of closeness to others, openness, trust, and suggestibility. LSD impaired the recognition of sad and fearful faces, reduced left amygdala reactivity to fearful faces, and enhanced emotional empathy. LSD increased the emotional response to music and the meaning of music. LSD acutely produced deficits in sensorimotor gating, similar to observations in schizophrenia. LSD had weak autonomic stimulant effects and elevated plasma cortisol, prolactin, and oxytocin levels. Resting-state functional magnetic resonance studies showed that LSD acutely reduced the integrity of functional brain networks and increased connectivity between networks that normally are more dissociated. LSD increased functional thalamocortical connectivity and functional connectivity of the primary visual cortex with other brain areas. The latter effect was correlated with subjective hallucinations. LSD acutely induced global increases in brain entropy that were associated with greater trait openness 14 days later. In patients with anxiety associated with life-threatening disease, anxiety was reduced for 2 months after two doses of LSD. In medical settings, no complications of LSD administration were observed. These data should contribute to further investigations of the therapeutic potential of LSD in psychiatry.

Democracy and sustainable development--what is the alternative to cost-benefit analysis?

PubMed

Söderbaum, Peter

2006-04-01

Cost-benefit analysis (CBA) is part of neoclassical economics, a specific paradigm, or theoretical perspective. In searching for alternatives to CBA, competing theoretical frameworks in economics appear to be a natural starting point. Positional analysis (PA) as an alternative to CBA is built on institutional theory and a different set of assumptions about human beings, organizations, markets, etc. Sustainable development (SD) is a multidimensional concept that includes social and ecological dimensions in addition to monetary aspects. If the political commitment to SD in the European Union and elsewhere is taken seriously, then approaches to decision making should be chosen that 1st open the door for multidimensional analysis rather than close it. Sustainable development suggests a direction for development in a broad sense but is still open to different interpretations. Each such interpretation is political in kind, and a 2nd criterion for judging different approaches is whether they are ideologically open rather than closed. Although methods for decision making have traditionally been connected with mathematical objective functions and optimization, the purpose of PA is to illuminate a decision situation in a many-sided way with respect to possibly relevant ideological orientations, alternatives, and consequences. Decisions are understood in terms of matching the ideological orientation of each decision maker with the expected effects profile of each alternative considered. Appropriateness and pattern recognition are other concepts in understanding this process.

Voice gender discrimination provides a measure of more than pitch-related perception in cochlear implant users

PubMed Central

Li, Tianhao; Fu, Qian-Jie

2013-01-01

Objectives (1) To investigate whether voice gender discrimination (VGD) could be a useful indicator of the spectral and temporal processing abilities of individual cochlear implant (CI) users; (2) To examine the relationship between VGD and speech recognition with CI when comparable acoustic cues are used for both perception processes. Design VGD was measured using two talker sets with different inter-gender fundamental frequencies (F0), as well as different acoustic CI simulations. Vowel and consonant recognition in quiet and noise were also measured and compared with VGD performance. Study sample Eleven postlingually deaf CI users. Results The results showed that (1) mean VGD performance differed for different stimulus sets, (2) VGD and speech recognition performance varied among individual CI users, and (3) individual VGD performance was significantly correlated with speech recognition performance under certain conditions. Conclusions VGD measured with selected stimulus sets might be useful for assessing not only pitch-related perception, but also spectral and temporal processing by individual CI users. In addition to improvements in spectral resolution and modulation detection, the improvement in higher modulation frequency discrimination might be particularly important for CI users in noisy environments. PMID:21696330

[Research of electroencephalography representational emotion recognition based on deep belief networks].

PubMed

Yang, Hao; Zhang, Junran; Jiang, Xiaomei; Liu, Fei

2018-04-01

In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accuracy and stability have a better trend. In three experiments with different time points, single subject can achieve the consistent results of classification by using DBN (the mean standard deviation is1.44%), and the experimental results show that the system has steady performance and good repeatability. According to our research, the characteristic of DE has a better classification result than other characteristics. Furthermore, the Beta band and the Gamma band in the emotional recognition model have higher classification accuracy. To sum up, the performances of classifiers have a promotion by using the deep learning algorithm, which has a reference for establishing a more accurate system of emotional recognition. Meanwhile, we can trace through the results of recognition to find out the brain regions and frequency band that are related to the emotions, which can help us to understand the emotional mechanism better. This study has a high academic value and practical significance, so further investigation still needs to be done.

Secondary iris recognition method based on local energy-orientation feature

NASA Astrophysics Data System (ADS)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing

2015-01-01

This paper proposes a secondary iris recognition based on local features. The application of the energy-orientation feature (EOF) by two-dimensional Gabor filter to the extraction of the iris goes before the first recognition by the threshold of similarity, which sets the whole iris database into two categories-a correctly recognized class and a class to be recognized. Therefore, the former are accepted and the latter are transformed by histogram to achieve an energy-orientation histogram feature (EOHF), which is followed by a second recognition with the chi-square distance. The experiment has proved that the proposed method, because of its higher correct recognition rate, could be designated as the most efficient and effective among its companion studies in iris recognition algorithms.

The Development of the Speaker Independent ARM Continuous Speech Recognition System

DTIC Science & Technology

1992-01-01

spokeTi airborne reconnaissance reports u-ing a speech recognition system based on phoneme-level hidden Markov models (HMMs). Previous versions of the ARM...will involve automatic selection from multiple model sets, corresponding to different speaker types, and that the most rudimen- tary partition of a...The vocabulary size for the ARM task is 497 words. These words are related to the phoneme-level symbols corresponding to the models in the model set

A Valepotriate Fraction of Valeriana glechomifolia Shows Sedative and Anxiolytic Properties and Impairs Recognition But Not Aversive Memory in Mice

PubMed Central

Maurmann, Natasha; Reolon, Gustavo Kellermann; Rech, Sandra Beatriz; Fett-Neto, Arthur Germano; Roesler, Rafael

2011-01-01

Plants of the genus Valeriana (Valerianaceae) are used in traditional medicine as a mild sedative, antispasmodic and tranquilizer in many countries. This study was undertaken to explore the neurobehavioral effects of systemic administration of a valepotriate extract fraction of known quantitative composition of Valeriana glechomifolia (endemic of southern Brazil) in mice. Adult animals were treated with a single intraperitoneal injection of valepotriate fraction (VF) in the concentrations of 1, 3 or 10 mg kg−1, or with vehicle in the pre-training period before each behavioral test. During the exploration of an open field, mice treated with 10 mg kg−1 of VF showed reduced locomotion and exploratory behavior. Although overall habituation sessions for locomotion and exploratory behavior among vehicle control and doses of VF were not affected, comparison between open-field and habituation sessions within each treatment showed that VF administration at 1 and 10 mg kg−1 impaired habituation. In the elevated plus-maze test, mice treated with VF (10 mg kg−1) showed a significant increase in the percentage of time spent in the open arms without significant effects in the number of total arm entries. VF at 3 mg kg−1 produced an impairment of novel-object recognition memory. In contrast, VF did not affect fear-related memory assessed in an inhibitory avoidance task. The results indicate that VF can have sedative effects and affect behavioral parameters related to recognition memory. PMID:20047889

A Voice Enabled Procedure Browser for the International Space Station

NASA Technical Reports Server (NTRS)

Rayner, Manny; Chatzichrisafis, Nikos; Hockey, Beth Ann; Farrell, Kim; Renders, Jean-Michel

2005-01-01

Clarissa, an experimental voice enabled procedure browser that has recently been deployed on the International Space Station (ISS), is to the best of our knowledge the first spoken dialog system in space. This paper gives background on the system and the ISS procedures, then discusses the research developed to address three key problems: grammar-based speech recognition using the Regulus toolkit; SVM based methods for open microphone speech recognition; and robust side-effect free dialogue management for handling undos, corrections and confirmations.

Speak-up culture in an intensive care unit in Hong Kong: a cross-sectional survey exploring the communication openness perceptions of Chinese doctors and nurses

PubMed Central

Ng, George Wing Yiu; Pun, Jack Kwok Hung; So, Eric Hang Kwong; Chiu, Wendy Wai Hang; Leung, Avis Siu Ha; Stone, Yuk Han; Lam, Chung Ling; Lai, Sarah Pui Wa; Leung, Rowlina Pui Wah; Luk, Hing Wah; Leung, Anne Kit Hung; Au Yeung, Kin Wah; Lai, Kang Yiu; Slade, Diana; Chan, Engle Angela

2017-01-01

Objectives Despite growing recognition of the importance of speaking up to protect patient safety in critical care, little research has been performed in this area in an intensive care unit (ICU) context. This study explored the communication openness perceptions of Chinese doctors and nurses and identified their perceptions of issues in ICU communication, their reasons for speaking up and the possible factors and strategies involved in promoting the practice of speaking up. Design A mixed-methods design with quantitative and sequential qualitative components was used. Setting and participants Eighty ICU staff members from a large public hospital in Hong Kong completed a questionnaire regarding their perceptions of communication openness. Ten clinicians whose survey responses indicated support for open communication were then interviewed about their speak-up practices. Results The participating ICU staff members had similar perceptions of their openness to communication. However, the doctors responded more positively than the nurses to many aspects of communication openness. The two groups also had different perceptions of speaking up. The interviewed ICU staff members who indicated a high level of communication openness reported that their primary reasons for speaking up were to seek and clarify information, which was achieved by asking questions. Other factors perceived to influence the motivation to speak up included seniority, relationships and familiarity with patient cases. Conclusions Creating an atmosphere of safety and equality in which team members feel confident in expressing their personal views without fear of reprisal or embarrassment is necessary to encourage ICU staff members, regardless of their position, to speak up. Because harmony and saving face is valued in Chinese culture, training nurses and doctors to speak up by focusing on human factors and values rather than simply addressing conflict management is desirable in this context. PMID:28801406

Gabor Jets for Clutter Rejection in Infrared Imagery

DTIC Science & Technology

2004-12-01

application of a suitable model like Gabor Jets in facial recognition is well motivated by the observation that some low level, spatial-frequency...set. This is a simplified form of the Gabor Jet procedure and will not require any elastic graph matching procedures used in facial recognition . Another...motivation for employing Gabor jets as a post processing clutter rejecter is attributed to the great deal of research in facial recognition , invariant

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.

Pen-chant: Acoustic emissions of handwriting and drawing

NASA Astrophysics Data System (ADS)

Seniuk, Andrew G.

The sounds generated by a writing instrument ('pen-chant') provide a rich and underutilized source of information for pattern recognition. We examine the feasibility of recognition of handwritten cursive text, exclusively through an analysis of acoustic emissions. We design and implement a family of recognizers using a template matching approach, with templates and similarity measures derived variously from: smoothed amplitude signal with fixed resolution, discrete sequence of magnitudes obtained from peaks in the smoothed amplitude signal, and ordered tree obtained from a scale space signal representation. Test results are presented for recognition of isolated lowercase cursive characters and for whole words. We also present qualitative results for recognizing gestures such as circling, scratch-out, check-marks, and hatching. Our first set of results, using samples provided by the author, yield recognition rates of over 70% (alphabet) and 90% (26 words), with a confidence of +/-8%, based solely on acoustic emissions. Our second set of results uses data gathered from nine writers. These results demonstrate that acoustic emissions are a rich source of information, usable---on their own or in conjunction with image-based features---to solve pattern recognition problems. In future work, this approach can be applied to writer identification, handwriting and gesture-based computer input technology, emotion recognition, and temporal analysis of sketches.

The influence of speech rate and accent on access and use of semantic information.

PubMed

Sajin, Stanislav M; Connine, Cynthia M

2017-04-01

Circumstances in which the speech input is presented in sub-optimal conditions generally lead to processing costs affecting spoken word recognition. The current study indicates that some processing demands imposed by listening to difficult speech can be mitigated by feedback from semantic knowledge. A set of lexical decision experiments examined how foreign accented speech and word duration impact access to semantic knowledge in spoken word recognition. Results indicate that when listeners process accented speech, the reliance on semantic information increases. Speech rate was not observed to influence semantic access, except in the setting in which unusually slow accented speech was presented. These findings support interactive activation models of spoken word recognition in which attention is modulated based on speech demands.

Experimental study on GMM-based speaker recognition

NASA Astrophysics Data System (ADS)

Ye, Wenxing; Wu, Dapeng; Nucci, Antonio

2010-04-01

Speaker recognition plays a very important role in the field of biometric security. In order to improve the recognition performance, many pattern recognition techniques have be explored in the literature. Among these techniques, the Gaussian Mixture Model (GMM) is proved to be an effective statistic model for speaker recognition and is used in most state-of-the-art speaker recognition systems. The GMM is used to represent the 'voice print' of a speaker through modeling the spectral characteristic of speech signals of the speaker. In this paper, we implement a speaker recognition system, which consists of preprocessing, Mel-Frequency Cepstrum Coefficients (MFCCs) based feature extraction, and GMM based classification. We test our system with TIDIGITS data set (325 speakers) and our own recordings of more than 200 speakers; our system achieves 100% correct recognition rate. Moreover, we also test our system under the scenario that training samples are from one language but test samples are from a different language; our system also achieves 100% correct recognition rate, which indicates that our system is language independent.

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

PubMed Central

Tamati, Terrin N.; Pisoni, David B.

2015-01-01

Background Natural variability in speech is a significant challenge to robust successful spoken word recognition. In everyday listening environments, listeners must quickly adapt and adjust to multiple sources of variability in both the signal and listening environments. High-variability speech may be particularly difficult to understand for non-native listeners, who have less experience with the second language (L2) phonological system and less detailed knowledge of sociolinguistic variation of the L2. Purpose The purpose of this study was to investigate the effects of high-variability sentences on non-native speech recognition and to explore the underlying sources of individual differences in speech recognition abilities of non-native listeners. Research Design Participants completed two sentence recognition tasks involving high-variability and low-variability sentences. They also completed a battery of behavioral tasks and self-report questionnaires designed to assess their indexical processing skills, vocabulary knowledge, and several core neurocognitive abilities. Study Sample Native speakers of Mandarin (n = 25) living in the United States recruited from the Indiana University community participated in the current study. A native comparison group consisted of scores obtained from native speakers of English (n = 21) in the Indiana University community taken from an earlier study. Data Collection and Analysis Speech recognition in high-variability listening conditions was assessed with a sentence recognition task using sentences from PRESTO (Perceptually Robust English Sentence Test Open-Set) mixed in 6-talker multitalker babble. Speech recognition in low-variability listening conditions was assessed using sentences from HINT (Hearing In Noise Test) mixed in 6-talker multitalker babble. Indexical processing skills were measured using a talker discrimination task, a gender discrimination task, and a forced-choice regional dialect categorization task. Vocabulary knowledge was assessed with the WordFam word familiarity test, and executive functioning was assessed with the BRIEF-A (Behavioral Rating Inventory of Executive Function – Adult Version) self-report questionnaire. Scores from the non-native listeners on behavioral tasks and self-report questionnaires were compared with scores obtained from native listeners tested in a previous study and were examined for individual differences. Results Non-native keyword recognition scores were significantly lower on PRESTO sentences than on HINT sentences. Non-native listeners’ keyword recognition scores were also lower than native listeners’ scores on both sentence recognition tasks. Differences in performance on the sentence recognition tasks between non-native and native listeners were larger on PRESTO than on HINT, although group differences varied by signal-to-noise ratio. The non-native and native groups also differed in the ability to categorize talkers by region of origin and in vocabulary knowledge. Individual non-native word recognition accuracy on PRESTO sentences in multitalker babble at more favorable signal-to-noise ratios was found to be related to several BRIEF-A subscales and composite scores. However, non-native performance on PRESTO was not related to regional dialect categorization, talker and gender discrimination, or vocabulary knowledge. Conclusions High-variability sentences in multitalker babble were particularly challenging for non-native listeners. Difficulty under high-variability testing conditions was related to lack of experience with the L2, especially L2 sociolinguistic information, compared with native listeners. Individual differences among the non-native listeners were related to weaknesses in core neurocognitive abilities affecting behavioral control in everyday life. PMID:25405842

Recognition of face and non-face stimuli in autistic spectrum disorder.

PubMed

Arkush, Leo; Smith-Collins, Adam P R; Fiorentini, Chiara; Skuse, David H

2013-12-01

The ability to remember faces is critical for the development of social competence. From childhood to adulthood, we acquire a high level of expertise in the recognition of facial images, and neural processes become dedicated to sustaining competence. Many people with autism spectrum disorder (ASD) have poor face recognition memory; changes in hairstyle or other non-facial features in an otherwise familiar person affect their recollection skills. The observation implies that they may not use the configuration of the inner face to achieve memory competence, but bolster performance in other ways. We aimed to test this hypothesis by comparing the performance of a group of high-functioning unmedicated adolescents with ASD and a matched control group on a "surprise" face recognition memory task. We compared their memory for unfamiliar faces with their memory for images of houses. To evaluate the role that is played by peripheral cues in assisting recognition memory, we cropped both sets of pictures, retaining only the most salient central features. ASD adolescents had poorer recognition memory for faces than typical controls, but their recognition memory for houses was unimpaired. Cropping images of faces did not disproportionately influence their recall accuracy, relative to controls. House recognition skills (cropped and uncropped) were similar in both groups. In the ASD group only, performance on both sets of task was closely correlated, implying that memory for faces and other complex pictorial stimuli is achieved by domain-general (non-dedicated) cognitive mechanisms. Adolescents with ASD apparently do not use domain-specialized processing of inner facial cues to support face recognition memory. © 2013 International Society for Autism Research, Wiley Periodicals, Inc.

pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis.

PubMed

Giannakopoulos, Theodoros

2015-01-01

Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library.

pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis

PubMed Central

Giannakopoulos, Theodoros

2015-01-01

Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library. PMID:26656189

Exploratory behavior and recognition memory in medial septal electrolytic, neuro- and immunotoxic lesioned rats.

PubMed

Dashniani, M G; Burjanadze, M A; Naneishvili, T L; Chkhikvishvili, N C; Beselia, G V; Kruashvili, L B; Pochkhidze, N O; Chighladze, M R

2015-01-01

In the present study, the effect of the medial septal (MS) lesions on exploratory activity in the open field and the spatial and object recognition memory has been investigated. This experiment compares three types of MS lesions: electrolytic lesions that destroy cells and fibers of passage, neurotoxic - ibotenic acid lesions that spare fibers of passage but predominantly affect the septal noncholinergic neurons, and immunotoxin - 192 IgG-saporin infusions that only eliminate cholinergic neurons. The main results are: the MS electrolytic lesioned rats were impaired in habituating to the environment in the repeated spatial environment, but rats with immuno- or neurotoxic lesions of the MS did not differ from control ones; the MS electrolytic and ibotenic acid lesioned rats showed an increase in their exploratory activity to the objects and were impaired in habituating to the objects in the repeated spatial environment; rats with immunolesions of the MS did not differ from control rats; electrolytic lesions of the MS disrupt spatial recognition memory; rats with immuno- or neurotoxic lesions of the MS were normal in detecting spatial novelty; all of the MS-lesioned and control rats clearly reacted to the object novelty by exploring the new object more than familiar ones. Results observed across lesion techniques indicate that: (i) the deficits after nonselective damage of MS are limited to a subset of cognitive processes dependent on the hippocampus, (ii) MS is substantial for spatial, but not for object recognition memory - the object recognition memory can be supported outside the septohippocampal system; (iii) the selective loss of septohippocampal cholinergic or noncholinergic projections does not disrupt the function of the hippocampus to a sufficient extent to impair spatial recognition memory; (iv) there is dissociation between the two major components (cholinergic and noncholinergic) of the septohippocampal pathway in exploratory behavior assessed in the open field - the memory exhibited by decrements in exploration of repeated object presentations is affected by either electrolytic or ibotenic lesions, but not saporin.

Measuring Reading Performance Informally.

ERIC Educational Resources Information Center

Powell, William R.

To improve the accuracy of the informal reading inventory (IRI), a differential set of criteria is necessary for both word recognition and comprehension scores for different levels and reading conditions. In initial evaluation, word recognition scores should reflect only errors of insertions, omissions, mispronunciations, substitiutions, unkown…

Molecular recognition and colorimetric detection of cholera toxin by poly(diacetylene) liposomes incorporating G{sub m1} ganglioside

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

Pan, J.J.; Charych, D.

1997-03-19

Molecular recognition sites on cell membranes serve as the main communication channels between the inside of a cell and its surroundings. Upon receptor binding, cellular messages such as ion channel opening or activation of enzymes are triggered. In this report, we demonstrate that artificial cell membranes made from conjugated lipid polymers (poly(diacetylene)) can, on a simple level, mimic membrane processes of molecular recognition and signal transduction. The ganglioside GM1 was incorporated into poly(diacetylene) liposomes. Molecular recognition of cholera toxin at the interface of the liposome resulted in a change of the membrane color due to conformational charges in the conjugatedmore » (ene-yne) polymer backbone. The `colored liposomes` might be used as simple colorimetric sensors for drug screening or as new tools to study membrane-membrane or membrane-receptor interactions. 21 refs., 3 figs.« less

PCANet: A Simple Deep Learning Baseline for Image Classification?

PubMed

Chan, Tsung-Han; Jia, Kui; Gao, Shenghua; Lu, Jiwen; Zeng, Zinan; Ma, Yi

2015-12-01

In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.

Subject-specific and pose-oriented facial features for face recognition across poses.

PubMed

Lee, Ping-Han; Hsu, Gee-Sern; Wang, Yun-Wen; Hung, Yi-Ping

2012-10-01

Most face recognition scenarios assume that frontal faces or mug shots are available for enrollment to the database, faces of other poses are collected in the probe set. Given a face from the probe set, one needs to determine whether a match in the database exists. This is under the assumption that in forensic applications, most suspects have their mug shots available in the database, and face recognition aims at recognizing the suspects when their faces of various poses are captured by a surveillance camera. This paper considers a different scenario: given a face with multiple poses available, which may or may not include a mug shot, develop a method to recognize the face with poses different from those captured. That is, given two disjoint sets of poses of a face, one for enrollment and the other for recognition, this paper reports a method best for handling such cases. The proposed method includes feature extraction and classification. For feature extraction, we first cluster the poses of each subject's face in the enrollment set into a few pose classes and then decompose the appearance of the face in each pose class using Embedded Hidden Markov Model, which allows us to define a set of subject-specific and pose-priented (SSPO) facial components for each subject. For classification, an Adaboost weighting scheme is used to fuse the component classifiers with SSPO component features. The proposed method is proven to outperform other approaches, including a component-based classifier with local facial features cropped manually, in an extensive performance evaluation study.

EPA New England Opens Nomination Period for Annual Environmental Merit Awards

EPA Pesticide Factsheets

The U.S. Environmental Protection Agency office in New England is accepting nominations for New England people, organizations, government entities or businesses whose environmental achievements during the past year deserve recognition.

Effectiveness of feature and classifier algorithms in character recognition systems

NASA Astrophysics Data System (ADS)

Wilson, Charles L.

1993-04-01

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

Improving Mobile Phone Speech Recognition by Personalized Amplification: Application in People with Normal Hearing and Mild-to-Moderate Hearing Loss.

PubMed

Kam, Anna Chi Shan; Sung, John Ka Keung; Lee, Tan; Wong, Terence Ka Cheong; van Hasselt, Andrew

In this study, the authors evaluated the effect of personalized amplification on mobile phone speech recognition in people with and without hearing loss. This prospective study used double-blind, within-subjects, repeated measures, controlled trials to evaluate the effectiveness of applying personalized amplification based on the hearing level captured on the mobile device. The personalized amplification settings were created using modified one-third gain targets. The participants in this study included 100 adults of age between 20 and 78 years (60 with age-adjusted normal hearing and 40 with hearing loss). The performance of the participants with personalized amplification and standard settings was compared using both subjective and speech-perception measures. Speech recognition was measured in quiet and in noise using Cantonese disyllabic words. Subjective ratings on the quality, clarity, and comfortableness of the mobile signals were measured with an 11-point visual analog scale. Subjective preferences of the settings were also obtained by a paired-comparison procedure. The personalized amplification application provided better speech recognition via the mobile phone both in quiet and in noise for people with hearing impairment (improved 8 to 10%) and people with normal hearing (improved 1 to 4%). The improvement in speech recognition was significantly better for people with hearing impairment. When the average device output level was matched, more participants preferred to have the individualized gain than not to have it. The personalized amplification application has the potential to improve speech recognition for people with mild-to-moderate hearing loss, as well as people with normal hearing, in particular when listening in noisy environments.

The benefits of remote microphone technology for adults with cochlear implants.

PubMed

Fitzpatrick, Elizabeth M; Séguin, Christiane; Schramm, David R; Armstrong, Shelly; Chénier, Josée

2009-10-01

Cochlear implantation has become a standard practice for adults with severe to profound hearing loss who demonstrate limited benefit from hearing aids. Despite the substantial auditory benefits provided by cochlear implants, many adults experience difficulty understanding speech in noisy environments and in other challenging listening conditions such as television. Remote microphone technology may provide some benefit in these situations; however, little is known about whether these systems are effective in improving speech understanding in difficult acoustic environments for this population. This study was undertaken with adult cochlear implant recipients to assess the potential benefits of remote microphone technology. The objectives were to examine the measurable and perceived benefit of remote microphone devices during television viewing and to assess the benefits of a frequency-modulated system for speech understanding in noise. Fifteen adult unilateral cochlear implant users were fit with remote microphone devices in a clinical environment. The study used a combination of direct measurements and patient perceptions to assess speech understanding with and without remote microphone technology. The direct measures involved a within-subject repeated-measures design. Direct measures of patients' speech understanding during television viewing were collected using their cochlear implant alone and with their implant device coupled to an assistive listening device. Questionnaires were administered to document patients' perceptions of benefits during the television-listening tasks. Speech recognition tests of open-set sentences in noise with and without remote microphone technology were also administered. Participants showed improved speech understanding for television listening when using remote microphone devices coupled to their cochlear implant compared with a cochlear implant alone. This benefit was documented both when listening to news and talk show recordings. Questionnaire results also showed statistically significant differences between listening with a cochlear implant alone and listening with a remote microphone device. Participants judged that remote microphone technology provided them with better comprehension, more confidence, and greater ease of listening. Use of a frequency-modulated system coupled to a cochlear implant also showed significant improvement over a cochlear implant alone for open-set sentence recognition in +10 and +5 dB signal to noise ratios. Benefits were measured during remote microphone use in focused-listening situations in a clinical setting, for both television viewing and speech understanding in noise in the audiometric sound suite. The results suggest that adult cochlear implant users should be counseled regarding the potential for enhanced speech understanding in difficult listening environments through the use of remote microphone technology.

Open architecture of smart sensor suites

NASA Astrophysics Data System (ADS)

Müller, Wilmuth; Kuwertz, Achim; Grönwall, Christina; Petersson, Henrik; Dekker, Rob; Reinert, Frank; Ditzel, Maarten

2017-10-01

Experiences from recent conflicts show the strong need for smart sensor suites comprising different multi-spectral imaging sensors as core elements as well as additional non-imaging sensors. Smart sensor suites should be part of a smart sensor network - a network of sensors, databases, evaluation stations and user terminals. Its goal is to optimize the use of various information sources for military operations such as situation assessment, intelligence, surveillance, reconnaissance, target recognition and tracking. Such a smart sensor network will enable commanders to achieve higher levels of situational awareness. Within the study at hand, an open system architecture was developed in order to increase the efficiency of sensor suites. The open system architecture for smart sensor suites, based on a system-of-systems approach, enables combining different sensors in multiple physical configurations, such as distributed sensors, co-located sensors combined in a single package, tower-mounted sensors, sensors integrated in a mobile platform, and trigger sensors. The architecture was derived from a set of system requirements and relevant scenarios. Its mode of operation is adaptable to a series of scenarios with respect to relevant objects of interest, activities to be observed, available transmission bandwidth, etc. The presented open architecture is designed in accordance with the NATO Architecture Framework (NAF). The architecture allows smart sensor suites to be part of a surveillance network, linked e.g. to a sensor planning system and a C4ISR center, and to be used in combination with future RPAS (Remotely Piloted Aircraft Systems) for supporting a more flexible dynamic configuration of RPAS payloads.

Optical add/drop filter for wavelength division multiplexed systems

DOEpatents

Deri, Robert J.; Strand, Oliver T.; Garrett, Henry E.

2002-01-01

An optical add/drop filter for wavelength division multiplexed systems and construction methods are disclosed. The add/drop filter includes a first ferrule having a first pre-formed opening for receiving a first optical fiber; an interference filter oriented to pass a first set of wavelengths along the first optical fiber and reflect a second set of wavelengths; and, a second ferrule having a second pre-formed opening for receiving the second optical fiber, and the reflected second set of wavelengths. A method for constructing the optical add/drop filter consists of the steps of forming a first set of openings in a first ferrule; inserting a first set of optical fibers into the first set of openings; forming a first set of guide pin openings in the first ferrule; dividing the first ferrule into a first ferrule portion and a second ferrule portion; forming an interference filter on the first ferrule portion; inserting guide pins through the first set of guide pin openings in the first ferrule portion and second ferrule portion to passively align the first set of optical fibers; removing material such that light reflected from the interference filter from the first set of optical fibers is accessible; forming a second set of openings in a second ferrule; inserting a second set of optical fibers into the second set of openings; and positioning the second ferrule with respect to the first ferrule such that the second set of optical fibers receive the light reflected from the interference filter.

An ERP analysis of recognition and categorization decisions in a prototype-distortion task.

PubMed

Tunney, Richard J; Fernie, Gordon; Astle, Duncan E

2010-04-12

Theories of categorization make different predictions about the underlying processes used to represent categories. Episodic theories suggest that categories are represented in memory by storing previously encountered exemplars in memory. Prototype theories suggest that categories are represented in the form of a prototype independently of memory. A number of studies that show dissociations between categorization and recognition are often cited as evidence for the prototype account. These dissociations have compared recognition judgements made to one set of items to categorization judgements to a different set of items making a clear interpretation difficult. Instead of using different stimuli for different tests this experiment compares the processes by which participants make decisions about category membership in a prototype-distortion task and with recognition decisions about the same set of stimuli by examining the Event Related Potentials (ERPs) associated with them. Sixty-three participants were asked to make categorization or recognition decisions about stimuli that either formed an artificial category or that were category non-members. We examined the ERP components associated with both kinds of decision for pre-exposed and control participants. In contrast to studies using different items we observed no behavioural differences between the two kinds of decision; participants were equally able to distinguish category members from non-members, regardless of whether they were performing a recognition or categorisation judgement. Interestingly, this did not interact with prior-exposure. However, the ERP data demonstrated that the early visual evoked response that discriminated category members from non-members was modulated by which judgement participants performed and whether they had been pre-exposed to category members. We conclude from this that any differences between categorization and recognition reflect differences in the information that participants focus on in the stimuli to make the judgements at test, rather than any differences in encoding or process.

An ERP Analysis of Recognition and Categorization Decisions in a Prototype-Distortion Task

PubMed Central

Tunney, Richard J.; Fernie, Gordon; Astle, Duncan E.

2010-01-01

Background Theories of categorization make different predictions about the underlying processes used to represent categories. Episodic theories suggest that categories are represented in memory by storing previously encountered exemplars in memory. Prototype theories suggest that categories are represented in the form of a prototype independently of memory. A number of studies that show dissociations between categorization and recognition are often cited as evidence for the prototype account. These dissociations have compared recognition judgements made to one set of items to categorization judgements to a different set of items making a clear interpretation difficult. Instead of using different stimuli for different tests this experiment compares the processes by which participants make decisions about category membership in a prototype-distortion task and with recognition decisions about the same set of stimuli by examining the Event Related Potentials (ERPs) associated with them. Method Sixty-three participants were asked to make categorization or recognition decisions about stimuli that either formed an artificial category or that were category non-members. We examined the ERP components associated with both kinds of decision for pre-exposed and control participants. Conclusion In contrast to studies using different items we observed no behavioural differences between the two kinds of decision; participants were equally able to distinguish category members from non-members, regardless of whether they were performing a recognition or categorisation judgement. Interestingly, this did not interact with prior-exposure. However, the ERP data demonstrated that the early visual evoked response that discriminated category members from non-members was modulated by which judgement participants performed and whether they had been pre-exposed to category members. We conclude from this that any differences between categorization and recognition reflect differences in the information that participants focus on in the stimuli to make the judgements at test, rather than any differences in encoding or process. PMID:20404932

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.

Smartphone based face recognition tool for the blind.

PubMed

Kramer, K M; Hedin, D S; Rolkosky, D J

2010-01-01

The inability to identify people during group meetings is a disadvantage for blind people in many professional and educational situations. To explore the efficacy of face recognition using smartphones in these settings, we have prototyped and tested a face recognition tool for blind users. The tool utilizes Smartphone technology in conjunction with a wireless network to provide audio feedback of the people in front of the blind user. Testing indicated that the face recognition technology can tolerate up to a 40 degree angle between the direction a person is looking and the camera's axis and a 96% success rate with no false positives. Future work will be done to further develop the technology for local face recognition on the smartphone in addition to remote server based face recognition.

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.

Facial emotion recognition in Williams syndrome and Down syndrome: A matching and developmental study.

PubMed

Martínez-Castilla, Pastora; Burt, Michael; Borgatti, Renato; Gagliardi, Chiara

2015-01-01

In this study both the matching and developmental trajectories approaches were used to clarify questions that remain open in the literature on facial emotion recognition in Williams syndrome (WS) and Down syndrome (DS). The matching approach showed that individuals with WS or DS exhibit neither proficiency for the expression of happiness nor specific impairments for negative emotions. Instead, they present the same pattern of emotion recognition as typically developing (TD) individuals. Thus, the better performance on the recognition of positive compared to negative emotions usually reported in WS and DS is not specific of these populations but seems to represent a typical pattern. Prior studies based on the matching approach suggested that the development of facial emotion recognition is delayed in WS and atypical in DS. Nevertheless, and even though performance levels were lower in DS than in WS, the developmental trajectories approach used in this study evidenced that not only individuals with DS but also those with WS present atypical development in facial emotion recognition. Unlike in the TD participants, where developmental changes were observed along with age, in the WS and DS groups, the development of facial emotion recognition was static. Both individuals with WS and those with DS reached an early maximum developmental level due to cognitive constraints.

Crowd Sourcing Data Collection through Amazon Mechanical Turk

DTIC Science & Technology

2013-09-01

The first recognition study consisted of a Panel Study using a simple detection protocol, in which participants were presented with vignettes and, for...variability than the crowdsourcing data set, hewing more closely to the year 1 verbs of interest and simple description grammar . The DT:PS data were...Study RT: PS Recognition Task: Panel Study RT: RT Recognition Task: Round Table S3 Amazon Simple Storage Service SVPA Single Verb Present /Absent

From scores to face templates: a model-based approach.

PubMed

Mohanty, Pranab; Sarkar, Sudeep; Kasturi, Rangachar

2007-12-01

Regeneration of templates from match scores has security and privacy implications related to any biometric authentication system. We propose a novel paradigm to reconstruct face templates from match scores using a linear approach. It proceeds by first modeling the behavior of the given face recognition algorithm by an affine transformation. The goal of the modeling is to approximate the distances computed by a face recognition algorithm between two faces by distances between points, representing these faces, in an affine space. Given this space, templates from an independent image set (break-in) are matched only once with the enrolled template of the targeted subject and match scores are recorded. These scores are then used to embed the targeted subject in the approximating affine (non-orthogonal) space. Given the coordinates of the targeted subject in the affine space, the original template of the targeted subject is reconstructed using the inverse of the affine transformation. We demonstrate our ideas using three, fundamentally different, face recognition algorithms: Principal Component Analysis (PCA) with Mahalanobis cosine distance measure, Bayesian intra-extrapersonal classifier (BIC), and a feature-based commercial algorithm. To demonstrate the independence of the break-in set with the gallery set, we select face templates from two different databases: Face Recognition Grand Challenge (FRGC) and Facial Recognition Technology (FERET) Database (FERET). With an operational point set at 1 percent False Acceptance Rate (FAR) and 99 percent True Acceptance Rate (TAR) for 1,196 enrollments (FERET gallery), we show that at most 600 attempts (score computations) are required to achieve a 73 percent chance of breaking in as a randomly chosen target subject for the commercial face recognition system. With similar operational set up, we achieve a 72 percent and 100 percent chance of breaking in for the Bayesian and PCA based face recognition systems, respectively. With three different levels of score quantization, we achieve 69 percent, 68 percent and 49 percent probability of break-in, indicating the robustness of our proposed scheme to score quantization. We also show that the proposed reconstruction scheme has 47 percent more probability of breaking in as a randomly chosen target subject for the commercial system as compared to a hill climbing approach with the same number of attempts. Given that the proposed template reconstruction method uses distinct face templates to reconstruct faces, this work exposes a more severe form of vulnerability than a hill climbing kind of attack where incrementally different versions of the same face are used. Also, the ability of the proposed approach to reconstruct actual face templates of the users increases privacy concerns in biometric systems.

Frequency Interference in Children' Recognition of Sentence Information

ERIC Educational Resources Information Center

Levin, Joel R.; And Others

1978-01-01

Children listened to sentences under two instructional sets (imagery or repetition) and answered multiple choice alternatives--either identical or similar in meaning to correct information in the sentences; and including or not including previously presented irrelevant information. The sources of interference predicted from recognition memory…

Robust representation and recognition of facial emotions using extreme sparse learning.

PubMed

Shojaeilangari, Seyedehsamaneh; Yau, Wei-Yun; Nandakumar, Karthik; Li, Jun; Teoh, Eam Khwang

2015-07-01

Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition systems have been evaluated on laboratory controlled data, which is not representative of the environment faced in real-world applications. To robustly recognize the facial emotions in real-world natural situations, this paper proposes an approach called extreme sparse learning, which has the ability to jointly learn a dictionary (set of basis) and a nonlinear classification model. The proposed approach combines the discriminative power of extreme learning machine with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. In addition, this paper presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve the state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases.

Palm vein recognition based on directional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lee, Jen-Chun; Chang, Chien-Ping; Chen, Wei-Kuei

2014-04-01

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D IMFs), from finer to large scale. A DEMD-based 2 linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the 2LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based 2LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition.

Machine-printed Arabic OCR

NASA Astrophysics Data System (ADS)

Hassibi, Khosrow M.

1994-02-01

This paper presents a brief overview of our research in the development of an OCR system for recognition of machine-printed texts in languages that use the Arabic alphabet. The cursive nature of machine-printed Arabic makes the segmentation of words into letters a challenging problem. In our approach, through a novel preliminary segmentation technique, a word is broken into pieces where each piece may not represent a valid letter in general. Neural networks trained on a training sample set of about 500 Arabic text images are used for recognition of these pieces. The rules governing the alphabet and character-level contextual information are used for recombining these pieces into valid letters. Higher-level contextual analysis schemes including the use of an Arabic lexicon and n-grams is also under development and are expected to improve the word recognition accuracy. The segmentation, recognition, and contextual analysis processes are closely integrated using a feedback scheme. The details of preparation of the training set and some recent results on training of the networks will be presented.

Combining point context and dynamic time warping for online gesture recognition

NASA Astrophysics Data System (ADS)

Mao, Xia; Li, Chen

2017-05-01

Previous gesture recognition methods usually focused on recognizing gestures after the entire gesture sequences were obtained. However, in many practical applications, a system has to identify gestures before they end to give instant feedback. We present an online gesture recognition approach that can realize early recognition of unfinished gestures with low latency. First, a curvature buffer-based point context (CBPC) descriptor is proposed to extract the shape feature of a gesture trajectory. The CBPC descriptor is a complete descriptor with a simple computation, and thus has its superiority in online scenarios. Then, we introduce an online windowed dynamic time warping algorithm to realize online matching between the ongoing gesture and the template gestures. In the algorithm, computational complexity is effectively decreased by adding a sliding window to the accumulative distance matrix. Lastly, the experiments are conducted on the Australian sign language data set and the Kinect hand gesture (KHG) data set. Results show that the proposed method outperforms other state-of-the-art methods especially when gesture information is incomplete.

PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications.

PubMed

Pasquier, C; Promponas, V J; Hamodrakas, S J

2001-08-15

A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLASS.

[Test set for the evaluation of hearing and speech development after cochlear implantation in children].

PubMed

Lamprecht-Dinnesen, A; Sick, U; Sandrieser, P; Illg, A; Lesinski-Schiedat, A; Döring, W H; Müller-Deile, J; Kiefer, J; Matthias, K; Wüst, A; Konradi, E; Riebandt, M; Matulat, P; Von Der Haar-Heise, S; Swart, J; Elixmann, K; Neumann, K; Hildmann, A; Coninx, F; Meyer, V; Gross, M; Kruse, E; Lenarz, T

2002-10-01

Since autumn 1998 the multicenter interdisciplinary study group "Test Materials for CI Children" has been compiling a uniform examination tool for evaluation of speech and hearing development after cochlear implantation in childhood. After studying the relevant literature, suitable materials were checked for practical applicability, modified and provided with criteria for execution and break-off. For data acquisition, observation forms for preparation of a PC-version were developed. The evaluation set contains forms for master data with supplements relating to postoperative processes. The hearing tests check supra-threshold hearing with loudness scaling for children, speech comprehension in silence (Mainz and Göttingen Test for Speech Comprehension in Childhood) and phonemic differentiation (Oldenburg Rhyme Test for Children), the central auditory processes of detection, discrimination, identification and recognition (modification of the "Frankfurt Functional Hearing Test for Children") and audiovisual speech perception (Open Paragraph Tracking, Kiel Speech Track Program). The materials for speech and language development comprise phonetics-phonology, lexicon and semantics (LOGO Pronunciation Test), syntax and morphology (analysis of spontaneous speech), language comprehension (Reynell Scales), communication and pragmatics (observation forms). The MAIS and MUSS modified questionnaires are integrated. The evaluation set serves quality assurance and permits factor analysis as well as controls for regularity through the multicenter comparison of long-term developmental trends after cochlear implantation.

Determination of longevities, chamber building rates and growth functions for Operculina complanata from long term cultivation experiments

NASA Astrophysics Data System (ADS)

Woeger, Julia; Kinoshita, Shunichi; Wolfgang, Eder; Briguglio, Antonino; Hohenegger, Johann

2016-04-01

Operculina complanata was collected in 20 and 50 m depth around the Island of Sesoko belonging to Japans southernmost prefecture Okinawa in a series of monthly sampling over a period of 16 months (Apr.2014-July2015). A minimum of 8 specimens (4 among the smallest and 4 among the largest) per sampling were cultured in a long term experiment that was set up to approximate conditions in the field as closely as possible. A set up allowing recognition of individual specimens enabled consistent documentation of chamber formation, which in combination with μ-CT-scanning after the investigation period permitted the assignment of growth steps to specific time periods. These data were used to fit various mathematical models to describe growth (exponential-, logistic-, generalized logistic-, Gompertz-function) and chamber building rate (Michaelis-Menten-, Bertalanffy- function) of Operculina complanata. The mathematically retrieved maximum lifespan and mean chamber building rate found in cultured Operculina complanata were further compared to first results obtained by the simultaneously conducted "natural laboratory approach". Even though these comparisons hint at a somewhat stunted growth and truncated life spans of Operculina complanata in culture, they represent a possibility to assess and improve the quality of further cultivation set ups, opening new prospects to a better understanding of the their theoretical niches.

Recognition of edible oil by using BP neural network and laser induced fluorescence spectrum

NASA Astrophysics Data System (ADS)

Mu, Tao-tao; Chen, Si-ying; Zhang, Yin-chao; Guo, Pan; Chen, He; Zhang, Hong-yan; Liu, Xiao-hua; Wang, Yuan; Bu, Zhi-chao

2013-09-01

In order to accomplish recognition of the different edible oil we set up a laser induced fluorescence spectrum system in the laboratory based on Laser induced fluorescence spectrum technology, and then collect the fluorescence spectrum of different edible oil by using that system. Based on this, we set up a fluorescence spectrum database of different cooking oil. It is clear that there are three main peak position of different edible oil from fluorescence spectrum chart. Although the peak positions of all cooking oil were almost the same, the relative intensity of different edible oils was totally different. So it could easily accomplish that oil recognition could take advantage of the difference of relative intensity. Feature invariants were extracted from the spectrum data, which were chosen from the fluorescence spectrum database randomly, before distinguishing different cooking oil. Then back propagation (BP) neural network was established and trained by the chosen data from the spectrum database. On that basis real experiment data was identified by BP neural network. It was found that the overall recognition rate could reach as high as 83.2%. Experiments showed that the laser induced fluorescence spectrum of different cooking oil was very different from each other, which could be used to accomplish the oil recognition. Laser induced fluorescence spectrum technology, combined BP neural network，was fast, high sensitivity, non-contact, and high recognition rate. It could become a new technique to accomplish the edible oil recognition and quality detection.

Sentence Recognition Prediction for Hearing-impaired Listeners in Stationary and Fluctuation Noise With FADE

PubMed Central

Schädler, Marc René; Warzybok, Anna; Meyer, Bernd T.; Brand, Thomas

2016-01-01

To characterize the individual patient’s hearing impairment as obtained with the matrix sentence recognition test, a simulation Framework for Auditory Discrimination Experiments (FADE) is extended here using the Attenuation and Distortion (A+D) approach by Plomp as a blueprint for setting the individual processing parameters. FADE has been shown to predict the outcome of both speech recognition tests and psychoacoustic experiments based on simulations using an automatic speech recognition system requiring only few assumptions. It builds on the closed-set matrix sentence recognition test which is advantageous for testing individual speech recognition in a way comparable across languages. Individual predictions of speech recognition thresholds in stationary and in fluctuating noise were derived using the audiogram and an estimate of the internal level uncertainty for modeling the individual Plomp curves fitted to the data with the Attenuation (A-) and Distortion (D-) parameters of the Plomp approach. The “typical” audiogram shapes from Bisgaard et al with or without a “typical” level uncertainty and the individual data were used for individual predictions. As a result, the individualization of the level uncertainty was found to be more important than the exact shape of the individual audiogram to accurately model the outcome of the German Matrix test in stationary or fluctuating noise for listeners with hearing impairment. The prediction accuracy of the individualized approach also outperforms the (modified) Speech Intelligibility Index approach which is based on the individual threshold data only. PMID:27604782

A Benchmark and Comparative Study of Video-Based Face Recognition on COX Face Database.

PubMed

Huang, Zhiwu; Shan, Shiguang; Wang, Ruiping; Zhang, Haihong; Lao, Shihong; Kuerban, Alifu; Chen, Xilin

2015-12-01

Face recognition with still face images has been widely studied, while the research on video-based face recognition is inadequate relatively, especially in terms of benchmark datasets and comparisons. Real-world video-based face recognition applications require techniques for three distinct scenarios: 1) Videoto-Still (V2S); 2) Still-to-Video (S2V); and 3) Video-to-Video (V2V), respectively, taking video or still image as query or target. To the best of our knowledge, few datasets and evaluation protocols have benchmarked for all the three scenarios. In order to facilitate the study of this specific topic, this paper contributes a benchmarking and comparative study based on a newly collected still/video face database, named COX(1) Face DB. Specifically, we make three contributions. First, we collect and release a largescale still/video face database to simulate video surveillance with three different video-based face recognition scenarios (i.e., V2S, S2V, and V2V). Second, for benchmarking the three scenarios designed on our database, we review and experimentally compare a number of existing set-based methods. Third, we further propose a novel Point-to-Set Correlation Learning (PSCL) method, and experimentally show that it can be used as a promising baseline method for V2S/S2V face recognition on COX Face DB. Extensive experimental results clearly demonstrate that video-based face recognition needs more efforts, and our COX Face DB is a good benchmark database for evaluation.

Unilateral Hearing Loss: Understanding Speech Recognition and Localization Variability-Implications for Cochlear Implant Candidacy.

PubMed

Firszt, Jill B; Reeder, Ruth M; Holden, Laura K

At a minimum, unilateral hearing loss (UHL) impairs sound localization ability and understanding speech in noisy environments, particularly if the loss is severe to profound. Accompanying the numerous negative consequences of UHL is considerable unexplained individual variability in the magnitude of its effects. Identification of covariables that affect outcome and contribute to variability in UHLs could augment counseling, treatment options, and rehabilitation. Cochlear implantation as a treatment for UHL is on the rise yet little is known about factors that could impact performance or whether there is a group at risk for poor cochlear implant outcomes when hearing is near-normal in one ear. The overall goal of our research is to investigate the range and source of variability in speech recognition in noise and localization among individuals with severe to profound UHL and thereby help determine factors relevant to decisions regarding cochlear implantation in this population. The present study evaluated adults with severe to profound UHL and adults with bilateral normal hearing. Measures included adaptive sentence understanding in diffuse restaurant noise, localization, roving-source speech recognition (words from 1 of 15 speakers in a 140° arc), and an adaptive speech-reception threshold psychoacoustic task with varied noise types and noise-source locations. There were three age-sex-matched groups: UHL (severe to profound hearing loss in one ear and normal hearing in the contralateral ear), normal hearing listening bilaterally, and normal hearing listening unilaterally. Although the normal-hearing-bilateral group scored significantly better and had less performance variability than UHLs on all measures, some UHL participants scored within the range of the normal-hearing-bilateral group on all measures. The normal-hearing participants listening unilaterally had better monosyllabic word understanding than UHLs for words presented on the blocked/deaf side but not the open/hearing side. In contrast, UHLs localized better than the normal-hearing unilateral listeners for stimuli on the open/hearing side but not the blocked/deaf side. This suggests that UHLs had learned strategies for improved localization on the side of the intact ear. The UHL and unilateral normal-hearing participant groups were not significantly different for speech in noise measures. UHL participants with childhood rather than recent hearing loss onset localized significantly better; however, these two groups did not differ for speech recognition in noise. Age at onset in UHL adults appears to affect localization ability differently than understanding speech in noise. Hearing thresholds were significantly correlated with speech recognition for UHL participants but not the other two groups. Auditory abilities of UHLs varied widely and could be explained only in part by hearing threshold levels. Age at onset and length of hearing loss influenced performance on some, but not all measures. Results support the need for a revised and diverse set of clinical measures, including sound localization, understanding speech in varied environments, and careful consideration of functional abilities as individuals with severe to profound UHL are being considered potential cochlear implant candidates.

Document recognition serving people with disabilities

NASA Astrophysics Data System (ADS)

Fruchterman, James R.

2007-01-01

Document recognition advances have improved the lives of people with print disabilities, by providing accessible documents. This invited paper provides perspectives on the author's career progression from document recognition professional to social entrepreneur applying this technology to help people with disabilities. Starting with initial thoughts about optical character recognition in college, it continues with the creation of accurate omnifont character recognition that did not require training. It was difficult to make a reading machine for the blind in a commercial setting, which led to the creation of a nonprofit social enterprise to deliver these devices around the world. This network of people with disabilities scanning books drove the creation of Bookshare.org, an online library of scanned books. Looking forward, the needs for improved document recognition technology to further lower the barriers to reading are discussed. Document recognition professionals should be proud of the positive impact their work has had on some of society's most disadvantaged communities.

Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

NASA Astrophysics Data System (ADS)

Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang

2018-03-01

Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.

An individual differences approach to the suggestibility of memory over time.

PubMed

Frost, Peter; Nussbaum, Gregory; Loconto, Taylor; Syke, Richard; Warren, Casey; Muise, Christina

2013-04-01

We examined how certain personality traits might relate to the formation of suggestive memory over time. We hypothesised that compliance and trust relate to initial acceptance of misinformation as memory, whereas fantasy proneness might relate to integration of misinformation into memory after later intervals (relative to the time of exposure to misinformation). Participants watched an excerpt from a movie--the simulated eyewitness event. They next answered a recall test that included embedded misinformation about the movie. Participants then answered a yes/no recognition test. A week later, participants answered a second yes/no recognition test about the movie (each yes/no recognition test included different questions). Before both recognition tests, participants were warned about the misinformation shown during recall and were asked to base their answer on the movie excerpt only. After completing the second recognition test, participants answered questions from the Neuroticism Extroversion Openness Personality Inventory-3 (McCrae, Costa, & Martin, 2005) and Creative Experiences Questionnaire (Merckelbach, Horselenberg, & Muris, 2001). While compliance correlated with misinformation effects immediately after exposure to misinformation, fantasy-prone personality accounted for more of the variability in false recognition rates than compliance after a 1-week interval.

Improved Hip-Based Individual Recognition Using Wearable Motion Recording Sensor

NASA Astrophysics Data System (ADS)

Gafurov, Davrondzhon; Bours, Patrick

In todays society the demand for reliable verification of a user identity is increasing. Although biometric technologies based on fingerprint or iris can provide accurate and reliable recognition performance, they are inconvenient for periodic or frequent re-verification. In this paper we propose a hip-based user recognition method which can be suitable for implicit and periodic re-verification of the identity. In our approach we use a wearable accelerometer sensor attached to the hip of the person, and then the measured hip motion signal is analysed for identity verification purposes. The main analyses steps consists of detecting gait cycles in the signal and matching two sets of detected gait cycles. Evaluating the approach on a hip data set consisting of 400 gait sequences (samples) from 100 subjects, we obtained equal error rate (EER) of 7.5% and identification rate at rank 1 was 81.4%. These numbers are improvements by 37.5% and 11.2% respectively of the previous study using the same data set.

Recognition of complex human behaviours using 3D imaging for intelligent surveillance applications

NASA Astrophysics Data System (ADS)

Yao, Bo; Lepley, Jason J.; Peall, Robert; Butler, Michael; Hagras, Hani

2016-10-01

We introduce a system that exploits 3-D imaging technology as an enabler for the robust recognition of the human form. We combine this with pose and feature recognition capabilities from which we can recognise high-level human behaviours. We propose a hierarchical methodology for the recognition of complex human behaviours, based on the identification of a set of atomic behaviours, individual and sequential poses (e.g. standing, sitting, walking, drinking and eating) that provides a framework from which we adopt time-based machine learning techniques to recognise complex behaviour patterns.

Fuzzy Logic-Based Audio Pattern Recognition

NASA Astrophysics Data System (ADS)

Malcangi, M.

2008-11-01

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

Iris recognition based on key image feature extraction.

PubMed

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

2008-01-01

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

Reverse Transfers and other Regular Irregulars.

ERIC Educational Resources Information Center

Clark, Dean O.

1982-01-01

Postsecondary education needs to encourage open recognition of reverse (four-year college to two-year college) and lateral (two-year to two-year college) transfers not recognized in traditional articulation efforts, and develop constructive programs for them. (MSE)

Development and validation of an Argentine set of facial expressions of emotion.

PubMed

Vaiman, Marcelo; Wagner, Mónica Anna; Caicedo, Estefanía; Pereno, Germán Leandro

2017-02-01

Pictures of facial expressions of emotion are used in a wide range of experiments. The last decade has seen an increase in the number of studies presenting local sets of emotion stimuli. However, only a few existing sets contain pictures of Latin Americans, despite the growing attention emotion research is receiving in this region. Here we present the development and validation of the Universidad Nacional de Cordoba, Expresiones de Emociones Faciales (UNCEEF), a Facial Action Coding System (FACS)-verified set of pictures of Argentineans expressing the six basic emotions, plus neutral expressions. FACS scores, recognition rates, Hu scores, and discrimination indices are reported. Evidence of convergent validity was obtained using the Pictures of Facial Affect in an Argentine sample. However, recognition accuracy was greater for UNCEEF. The importance of local sets of emotion pictures is discussed.

Spatial, Hysteretic, and Adaptive Host-Guest Chemistry in a Metal-Organic Framework with Open Watson-Crick Sites.

PubMed

Cai, Hong; Li, Mian; Lin, Xiao-Rong; Chen, Wei; Chen, Guang-Hui; Huang, Xiao-Chun; Li, Dan

2015-09-01

Biological and artificial molecules and assemblies capable of supramolecular recognition, especially those with nucleobase pairing, usually rely on autonomous or collective binding to function. Advanced site-specific recognition takes advantage of cooperative spatial effects, as in local folding in protein-DNA binding. Herein, we report a new nucleobase-tagged metal-organic framework (MOF), namely ZnBTCA (BTC=benzene-1,3,5-tricarboxyl, A=adenine), in which the exposed Watson-Crick faces of adenine residues are immobilized periodically on the interior crystalline surface. Systematic control experiments demonstrated the cooperation of the open Watson-Crick sites and spatial effects within the nanopores, and thermodynamic and kinetic studies revealed a hysteretic host-guest interaction attributed to mild chemisorption. We further exploited this behavior for adenine-thymine binding within the constrained pores, and a globally adaptive response of the MOF host was observed. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Utilization of satellite data for inventorying prairie ponds and lakes

USGS Publications Warehouse

Work, E.A.; Gilmer, D.S.

1976-01-01

By using data acquired by LANDSAT-1 (formerly ERTS- 1), studies were conducted in extracting information necessary for formulating management decisions relating to migratory waterfowl. Management decisions are based in part on an assessment ofhabitat characteristics, specifically numbers, distribution, and quality of ponds and lakes in the prime breeding range. This paper reports on a study concerned with mapping open surface water features in the glaciated prairies. Emphasis was placed on the recognition of these features based upon water's uniquely low radiance in a single nearinfrared waveband. The results of this recognition were thematic maps and statistics relating to open surface water. In a related effort, the added information content of multiple spectral wavebands was used for discriminating surface water at a level of detail finer than the virtual resolution of the data. The basic theory of this technique and some preliminary results are described.

Structural basis for dynamic mechanism of nitrate/nitrite antiport by NarK

NASA Astrophysics Data System (ADS)

Fukuda, Masahiro; Takeda, Hironori; Kato, Hideaki E.; Doki, Shintaro; Ito, Koichi; Maturana, Andrés D.; Ishitani, Ryuichiro; Nureki, Osamu

2015-05-01

NarK belongs to the nitrate/nitrite porter (NNP) family in the major facilitator superfamily (MFS) and plays a central role in nitrate uptake across the membrane in diverse organisms, including archaea, bacteria, fungi and plants. Although previous studies provided insight into the overall structure and the substrate recognition of NarK, its molecular mechanism, including the driving force for nitrate transport, remained elusive. Here we demonstrate that NarK is a nitrate/nitrite antiporter, using an in vitro reconstituted system. Furthermore, we present the high-resolution crystal structures of NarK from Escherichia coli in the nitrate-bound occluded, nitrate-bound inward-open and apo inward-open states. The integrated structural, functional and computational analyses reveal the nitrate/nitrite antiport mechanism of NarK, in which substrate recognition is coupled to the transport cycle by the concomitant movement of the transmembrane helices and the key tyrosine and arginine residues in the substrate-binding site.

How to responsibly acknowledge research work in the era of big data and biobanks: ethical aspects of the Bioresource Research Impact Factor (BRIF).

PubMed

Howard, Heidi Carmen; Mascalzoni, Deborah; Mabile, Laurence; Houeland, Gry; Rial-Sebbag, Emmanuelle; Cambon-Thomsen, Anne

2018-04-01

Currently, a great deal of biomedical research in fields such as epidemiology, clinical trials and genetics is reliant on vast amounts of biological and phenotypic information collected and assembled in biobanks. While many resources are being invested to ensure that comprehensive and well-organised biobanks are able to provide increased access to, and sharing of biomedical samples and information, many barriers and challenges remain to such responsible and extensive sharing. Germane to the discussion herein is the barrier to collecting and sharing bioresources related to the lack of proper recognition of researchers and clinicians who developed the bioresource. Indeed, the efforts and resources invested to set up and sustain a bioresource can be enormous and such work should be easily traced and properly recognised. However, there is currently no such system that systematically and accurately traces and attributes recognition to those doing this work or the bioresource institution itself. As a beginning of a solution to the "recognition problem", the Bioresource Research Impact Factor/Framework (BRIF) initiative was proposed almost a decade and a half ago and is currently under further development. With the ultimate aim of increasing awareness and understanding of the BRIF, in this article, we contribute the following: (1) a review of the objectives and functions of the BRIF including the description of two tools that will help in the deployment of the BRIF, the CoBRA (Citation of BioResources in journal Articles) guideline, and the Open Journal of Bioresources (OJB); (2) the results of a small empirical study on stakeholder awareness of the BRIF and (3) a brief analysis of the ethical dimensions of the BRIF which allow it to be a positive contribution to responsible biobanking.

Some Computational Analyses of the PBK Test: Effects of Frequency and Lexical Density on Spoken Word Recognition

PubMed Central

Meyer, Ted A.; Pisoni, David B.

2012-01-01

Objective The Phonetically Balanced Kindergarten (PBK) Test (Haskins, Reference Note 2) has been used for almost 50 yr to assess spoken word recognition performance in children with hearing impairments. The test originally consisted of four lists of 50 words, but only three of the lists (lists 1, 3, and 4) were considered “equivalent” enough to be used clinically with children. Our goal was to determine if the lexical properties of the different PBK lists could explain any differences between the three “equivalent” lists and the fourth PBK list (List 2) that has not been used in clinical testing. Design Word frequency and lexical neighborhood frequency and density measures were obtained from a computerized database for all of the words on the four lists from the PBK Test as well as the words from a single PB-50 (Egan, 1948) word list. Results The words in the “easy” PBK list (List 2) were of higher frequency than the words in the three “equivalent” lists. Moreover, the lexical neighborhoods of the words on the “easy” list contained fewer phonetically similar words than the neighborhoods of the words on the other three “equivalent” lists. Conclusions It is important for researchers to consider word frequency and lexical neighborhood frequency and density when constructing word lists for testing speech perception. The results of this computational analysis of the PBK Test provide additional support for the proposal that spoken words are recognized “relationally” in the context of other phonetically similar words in the lexicon. Implications of using open-set word recognition tests with children with hearing impairments are discussed with regard to the specific vocabulary and information processing demands of the PBK Test. PMID:10466571

Photoproduction of Mesons

NASA Astrophysics Data System (ADS)

Schmieden, Hartmut; Klein, Friedrich

2017-01-01

B.1 is one of the experimental projects within the CRC16. It aims at the systematic investigation of the photoproduction of mesons off nucleons in order to understand reaction mechanisms and the relevant degrees of freedom in resonance formation. Of particular interest is the photoproduction of mesons heavier than the pion and resonances involving hidden or open strangeness. Essential hardware contributions have been made to the experimental programme of the CRC16 through tagging systems, and photon-beam polarisation and polarimetry. A new experiment has been set up within the framework of the BGO-OD collaboration. This combines a forward magnetic spectrometer with a central BGO calorimeter with charged particle recognition and identification. The BGO-OD experiment enables reconstruction of complex final states composed of both charged and neutral particles, complementary to the existing CBELSA/TAPS calorimeter which is optimised for multi-photon final states. Selected results of the 12-year CRC period are presented from both experiments.

KSC-06pd0472

NASA Image and Video Library

2006-03-10

KENNEDY SPACE CENTER, FLA. - During opening ceremonies of the 2006 FIRST Robotics Regional Competition held March 9-11 at the University of Central Florida in Orlando, Kennedy Space Center Director Jim Kennedy talks to the participants. The FIRST Robotics Competition challenges teams of young people and their mentors to solve a common problem in a six-week timeframe using a standard "kit of parts" and a common set of rules. Teams build robots from the parts and enter them in a series of competitions. FIRST, which is based on "For Inspiration and Recognition of Science and Technology," redefines winning for these students. Teams are rewarded for excellence in design, demonstrated team spirit, gracious professionalism and maturity, and ability to overcome obstacles. Scoring the most points is a secondary goal. Winning means building partnerships that last. NASA and the University of Central Florida are co-sponsors of the regional event, which this year included more than 50 teams. Photo credit: NASA/Kim Shiflett

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...

Face recognition system using multiple face model of hybrid Fourier feature under uncontrolled illumination variation.

PubMed

Hwang, Wonjun; Wang, Haitao; Kim, Hyunwoo; Kee, Seok-Cheol; Kim, Junmo

2011-04-01

The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illumination-insensitive image, called an "integral normalized gradient image," by normalizing and integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated; FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.

Structural insights into the regulation and the recognition of histone marks by the SET domain of NSD1

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

Morishita, Masayo; Di Luccio, Eric, E-mail: eric.diluccio@gmail.com

2011-08-26

Highlights: {yields} NSD1, NSD2/MMSET/WHSC1, and NSD3/WHSC1L1 are histone methyltransferases linked to numerous cancers. {yields} Little is known about the NSD pathways and HMTase inhibitors are sorely needed in the epigenetic therapy of cancers. {yields} We investigate the regulation and the recognition of histone marks by the SET domain of NSD1. {yields} A unique and key mechanism is driven by a loop at the interface of the SET and postSET region. {yields} Implications for developing specific and selective HMTase inhibitors are presented. -- Abstract: The development of epigenetic therapies fuels cancer hope. DNA-methylation inhibitors, histone-deacetylase and histone-methyltransferase (HMTase) inhibitors are beingmore » developed as the utilization of epigenetic targets is emerging as an effective and valuable approach to chemotherapy as well as chemoprevention of cancer. The nuclear receptor binding SET domain (NSD) protein is a family of three HMTases, NSD1, NSD2/MMSET/WHSC1, and NSD3/WHSC1L1 that are critical in maintaining the chromatin integrity. A growing number of studies have reported alterations or amplifications of NSD1, NSD2, or NSD3 in numerous carcinogenic events. Reducing NSDs activity through specific lysine-HMTase inhibitors appears promising to help suppressing cancer growth. However, little is known about the NSD pathways and our understanding of the histone lysine-HMTase mechanism is partial. To shed some light on both the recognition and the regulation of epigenetic marks by the SET domain of the NSD family, we investigate the structural mechanisms of the docking of the histone-H4 tail on the SET domain of NSD1. Our finding exposes a key regulatory and recognition mechanism driven by the flexibility of a loop at the interface of the SET and postSET region. Finally, we prospect the special value of this regulatory region for developing specific and selective NSD inhibitors for the epigenetic therapy of cancers.« less

Cross-View Action Recognition via Transferable Dictionary Learning.

PubMed

Zheng, Jingjing; Jiang, Zhuolin; Chellappa, Rama

2016-05-01

Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation. In the second approach, we additionally learn a common dictionary shared by different views to model view-shared features. This approach represents the videos in each view using a view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from the different views of the same action to have the similar sparse representations. The learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labeled videos exist in the target view. The extensive experiments using three public datasets demonstrate that the proposed approach outperforms recently developed approaches for cross-view action recognition.

Cucurbituril mediated single molecule detection and identification via recognition tunneling.

PubMed

Xiao, Bohuai; Liang, Feng; Liu, Simin; Im, JongOne; Li, Yunchuan; Liu, Jing; Zhang, Bintian; Zhou, Jianghao; He, Jin; Chang, Shuai

2018-06-08

Recognition tunneling (RT) is an emerging technique for investigating single molecules in a tunnel junction. We have previously demonstrated its capability of single molecule detection and identification, as well as probing the dynamics of intermolecular bonding at the single molecule level. Here by introducing cucurbituril as a new class of recognition molecule, we demonstrate a powerful platform for electronically investigating the host-guest chemistry at single molecule level. In this report, we first investigated the single molecule electrical properties of cucurbituril in a tunnel junction. Then we studied two model guest molecules, aminoferrocene and amantadine, which were encapsulated by cucurbituril. Small differences in conductance and lifetime can be recognized between the host-guest complexes with the inclusion of different guest molecules. By using a machine learning algorithm to classify the RT signals in a hyper dimensional space, the accuracy of guest molecule recognition can be significantly improved, suggesting the possibility of using cucurbituril molecule for single molecule identification. This work enables a new class of recognition molecule for RT technique and opens the door for detecting a vast variety of small molecules by electrical measurements.

Implementation of a Peltier-based cooling device for localized deep cortical deactivation during in vivo object recognition testing

NASA Astrophysics Data System (ADS)

Marra, Kyle; Graham, Brett; Carouso, Samantha; Cox, David

2012-02-01

While the application of local cortical cooling has recently become a focus of neurological research, extended localized deactivation deep within brain structures is still unexplored. Using a wirelessly controlled thermoelectric (Peltier) device and water-based heat sink, we have achieved inactivating temperatures (<20 C) at greater depths (>8 mm) than previously reported. After implanting the device into Long Evans rats' basolateral amygdala (BLA), an inhibitory brain center that controls anxiety and fear, we ran an open field test during which anxiety-driven behavioral tendencies were observed to decrease during cooling, thus confirming the device's effect on behavior. Our device will next be implanted in the rats' temporal association cortex (TeA) and recordings from our signal-tracing multichannel microelectrodes will measure and compare activated and deactivated neuronal activity so as to isolate and study the TeA signals responsible for object recognition. Having already achieved a top performing computational face-recognition system, the lab will utilize this TeA activity data to generalize its computational efforts of face recognition to achieve general object recognition.

Exploiting Hidden Layer Responses of Deep Neural Networks for Language Recognition

DTIC Science & Technology

2016-09-08

trained DNNs. We evaluated this ap- proach in NIST 2015 language recognition evaluation. The per- formances achieved by the proposed approach are very...activations, used in direct DNN-LID. Results from the LID experiments support our hypothesis. The LID experiments are performed on NIST Language Recognition...of-the-art I- vector system [3, 10, 11] in evaluation (eval) set of NIST LRE 2015. Combination of proposed technique and state-of-the-art I-vector

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

PubMed

Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

2017-01-01

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition

PubMed Central

Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

2017-01-01

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition. PMID:28937987

Error Rates in Users of Automatic Face Recognition Software

PubMed Central

White, David; Dunn, James D.; Schmid, Alexandra C.; Kemp, Richard I.

2015-01-01

In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated ‘candidate lists’ selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers–who use the system in their daily work–and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced “facial examiners” outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems–potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems. PMID:26465631

Fusion of smartphone motion sensors for physical activity recognition.

PubMed

Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J M

2014-06-10

For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.

Mechanistic Study of Human Glucose Transport Mediated by GLUT1.

PubMed

Fu, Xuegang; Zhang, Gang; Liu, Ran; Wei, Jing; Zhang-Negrerie, Daisy; Jian, Xiaodong; Gao, Qingzhi

2016-03-28

The glucose transporter 1 (GLUT1) belongs to the major facilitator superfamily (MFS) and is responsible for the constant uptake of glucose. However, the molecular mechanism of sugar transport remains obscure. In this study, homology modeling and molecular dynamics (MD) simulations in lipid bilayers were performed to investigate the combination of the alternate and multisite transport mechanism of glucose with GLUT1 in atomic detail. To explore the substrate recognition mechanism, the outward-open state human GLUT1 homology model was generated based on the template of xylose transporter XylE (PDB ID: 4GBZ), which shares up to 29% sequence identity and 49% similarity with GLUT1. Through the MD simulation study of glucose across lipid bilayer with both the outward-open GLUT1 and the GLUT1 inward-open crystal structure, we investigated six different conformational states and identified four key binding sites in both exofacial and endofacial loops that are essential for glucose recognition and transport. The study further revealed that four flexible gates consisting of W65/Y292/Y293-M420/TM10b-W388 might play important roles in the transport cycle. The study showed that some side chains close to the central ligand binding site underwent larger position changes. These conformational interchanges formed gated networks within an S-shaped central channel that permitted staged ligand diffusion across the transporter. This study provides new inroads for the understanding of GLUT1 ligand recognition paradigm and configurational features which are important for molecular, structural, and physiological research of the MFS members, especially for GLUT1-targeted drug design and discovery.

Effect of Vowel Context on the Recognition of Initial Consonants in Kannada.

PubMed

Kalaiah, Mohan Kumar; Bhat, Jayashree S

2017-09-01

The present study was carried out to investigate the effect of vowel context on the recognition of Kannada consonants in quiet for young adults. A total of 17 young adults with normal hearing in both ears participated in the study. The stimuli included consonant-vowel syllables, spoken by 12 native speakers of Kannada. Consonant recognition task was carried out as a closed-set (fourteen-alternative forced-choice). The present study showed an effect of vowel context on the perception of consonants. Maximum consonant recognition score was obtained in the /o/ vowel context, followed by the /a/ and /u/ vowel contexts, and then the /e/ context. Poorest consonant recognition score was obtained in the vowel context /i/. Vowel context has an effect on the recognition of Kannada consonants, and the vowel effect was unique for Kannada consonants.

Towards fully analog hardware reservoir computing for speech recognition

NASA Astrophysics Data System (ADS)

Smerieri, Anteo; Duport, François; Paquot, Yvan; Haelterman, Marc; Schrauwen, Benjamin; Massar, Serge

2012-09-01

Reservoir computing is a very recent, neural network inspired unconventional computation technique, where a recurrent nonlinear system is used in conjunction with a linear readout to perform complex calculations, leveraging its inherent internal dynamics. In this paper we show the operation of an optoelectronic reservoir computer in which both the nonlinear recurrent part and the readout layer are implemented in hardware for a speech recognition application. The performance obtained is close to the one of to state-of-the-art digital reservoirs, while the analog architecture opens the way to ultrafast computation.

Developing a Natural User Interface and Facial Recognition System With OpenCV and the Microsoft Kinect

NASA Technical Reports Server (NTRS)

Gutensohn, Michael

2018-01-01

The task for this project was to design, develop, test, and deploy a facial recognition system for the Kennedy Space Center Augmented/Virtual Reality Lab. This system will serve as a means of user authentication as part of the NUI of the lab. The overarching goal is to create a seamless user interface that will allow the user to initiate and interact with AR and VR experiences without ever needing to use a mouse or keyboard at any step in the process.

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.

Morphological Influences on the Recognition of Monosyllabic Monomorphemic Words

ERIC Educational Resources Information Center

Baayen, R. H.; Feldman, L. B.; Schreuder, R.

2006-01-01

Balota et al. [Balota, D., Cortese, M., Sergent-Marshall, S., Spieler, D., & Yap, M. (2004). Visual word recognition for single-syllable words. "Journal of Experimental Psychology: General, 133," 283-316] studied lexical processing in word naming and lexical decision using hierarchical multiple regression techniques for a large data set of…

[GNU Pattern: open source pattern hunter for biological sequences based on SPLASH algorithm].

PubMed

Xu, Ying; Li, Yi-xue; Kong, Xiang-yin

2005-06-01

To construct a high performance open source software engine based on IBM SPLASH algorithm for later research on pattern discovery. Gpat, which is based on SPLASH algorithm, was developed by using open source software. GNU Pattern (Gpat) software was developped, which efficiently implemented the core part of SPLASH algorithm. Full source code of Gpat was also available for other researchers to modify the program under the GNU license. Gpat is a successful implementation of SPLASH algorithm and can be used as a basic framework for later research on pattern recognition in biological sequences.

Speech recognition features for EEG signal description in detection of neonatal seizures.

PubMed

Temko, A; Boylan, G; Marnane, W; Lightbody, G

2010-01-01

In this work, features which are usually employed in automatic speech recognition (ASR) are used for the detection of neonatal seizures in newborn EEG. Three conventional ASR feature sets are compared to the feature set which has been previously developed for this task. The results indicate that the thoroughly-studied spectral envelope based ASR features perform reasonably well on their own. Additionally, the SVM Recursive Feature Elimination routine is applied to all extracted features pooled together. It is shown that ASR features consistently appear among the top-rank features.

Model driven mobile care for patients with type 1 diabetes.

PubMed

Skrøvseth, Stein Olav; Arsand, Eirik; Godtliebsen, Fred; Joakimsen, Ragnar M

2012-01-01

We gathered a data set from 30 patients with type 1 diabetes by giving the patients a mobile phone application, where they recorded blood glucose measurements, insulin injections, meals, and physical activity. Using these data as a learning data set, we describe a new approach of building a mobile feedback system for these patients based on periodicities, pattern recognition, and scale-space trends. Most patients have important patterns for periodicities and trends, though better resolution of input variables is needed to provide useful feedback using pattern recognition.

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

PubMed

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

2014-03-01

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

Sentence Recognition Prediction for Hearing-impaired Listeners in Stationary and Fluctuation Noise With FADE: Empowering the Attenuation and Distortion Concept by Plomp With a Quantitative Processing Model.

PubMed

Kollmeier, Birger; Schädler, Marc René; Warzybok, Anna; Meyer, Bernd T; Brand, Thomas

2016-09-07

To characterize the individual patient's hearing impairment as obtained with the matrix sentence recognition test, a simulation Framework for Auditory Discrimination Experiments (FADE) is extended here using the Attenuation and Distortion (A+D) approach by Plomp as a blueprint for setting the individual processing parameters. FADE has been shown to predict the outcome of both speech recognition tests and psychoacoustic experiments based on simulations using an automatic speech recognition system requiring only few assumptions. It builds on the closed-set matrix sentence recognition test which is advantageous for testing individual speech recognition in a way comparable across languages. Individual predictions of speech recognition thresholds in stationary and in fluctuating noise were derived using the audiogram and an estimate of the internal level uncertainty for modeling the individual Plomp curves fitted to the data with the Attenuation (A-) and Distortion (D-) parameters of the Plomp approach. The "typical" audiogram shapes from Bisgaard et al with or without a "typical" level uncertainty and the individual data were used for individual predictions. As a result, the individualization of the level uncertainty was found to be more important than the exact shape of the individual audiogram to accurately model the outcome of the German Matrix test in stationary or fluctuating noise for listeners with hearing impairment. The prediction accuracy of the individualized approach also outperforms the (modified) Speech Intelligibility Index approach which is based on the individual threshold data only. © The Author(s) 2016.

Validating the Implementation Climate Scale (ICS) in Child Welfare Organizations

PubMed Central

Ehrhart, Mark G.; Torres, Elisa M.; Wright, Lisa A.; Martinez, Sandra Y.; Aarons, Gregory A.

2015-01-01

There is increasing emphasis on the use of evidence-based practices (EBPs) in child welfare settings and growing recognition of the importance of the organizational environment, and the organization’s climate in particular, for how employees perceive and support EBP implementation. Recently, Ehrhart, Aarons, and Farahnak (2014) reported on the development and validation of a measure of EBP implementation climate, the Implementation Climate Scale (ICS), in a sample of mental health clinicians. The ICS consists of 18 items and measures six critical dimensions of implementation climate: focus on EBP, educational support for EBP, recognition for EBP, rewards for EBP, selection or EBP, and selection for openness. The goal of the current study is to extend this work by providing evidence for the factor structure, reliability, and validity of the ICS in a sample of child welfare service providers. Survey data were collected from 215 child welfare providers across three states, 12 organizations, and 43 teams. Confirmatory factor analysis demonstrated good fit to the six-factor model and the alpha reliabilities for the overall measure and its subscales was acceptable. In addition, there was general support for the invariance of the factor structure across the child welfare and mental health sectors. In conclusion, this study provides evidence for the factor structure, reliability, and validity of the ICS measure for use in child welfare service organizations. PMID:26563643

Research on multi - channel interactive virtual assembly system for power equipment under the “VR+” era

NASA Astrophysics Data System (ADS)

Ren, Yilong; Duan, Xitong; Wu, Lei; He, Jin; Xu, Wu

2017-06-01

With the development of the “VR+” era, the traditional virtual assembly system of power equipment has been unable to satisfy our growing needs. In this paper, based on the analysis of the traditional virtual assembly system of electric power equipment and the application of VR technology in the virtual assembly system of electric power equipment in our country, this paper puts forward the scheme of establishing the virtual assembly system of power equipment: At first, we should obtain the information of power equipment, then we should using OpenGL and multi texture technology to build 3D solid graphics library. After the completion of three-dimensional modeling, we can use the dynamic link library DLL package three-dimensional solid graphics generation program to realize the modularization of power equipment model library and power equipment model library generated hidden algorithm. After the establishment of 3D power equipment model database, we set up the virtual assembly system of 3D power equipment to separate the assembly operation of the power equipment from the space. At the same time, aiming at the deficiency of the traditional gesture recognition algorithm, we propose a gesture recognition algorithm based on improved PSO algorithm for BP neural network data glove. Finally, the virtual assembly system of power equipment can really achieve multi-channel interaction function.

The I/O transform of a chemical sensor

PubMed Central

Katta, Nalin; Meier, Douglas C.; Benkstein, Kurt D.; Semancik, Steve; Raman, Baranidharan

2016-01-01

A number of sensing technologies, using a variety of transduction principles, have been proposed for non-invasive chemical sensing. A fundamental problem common to all these sensing technologies is determining what features of the transducer's signal constitute a chemical fingerprint that allows for precise analyte recognition. Of particular importance is the need to extract features that are robust with respect to the sensor's age or stimulus intensity. Here, using pulsed stimulus delivery, we show that a sensor's operation can be modeled as a linear input-output (I/O) transform. The I/O transform is unique for each analyte and can be used to precisely predict a temperature-programmed chemiresistor's response to the analyte given the recent stimulus history (i.e. state of an analyte delivery valve being open or closed). We show that the analyte specific I/O transforms are to a certain degree stimulus intensity invariant and can remain consistent even when the sensor has undergone considerable aging. Significantly, the I/O transforms for a given analyte are highly conserved across sensors of equal manufacture, thereby allowing training data obtained from one sensor to be used for recognition of the same set of chemical species with another sensor. Hence, this proposed approach facilitates decoupling of the signal processing algorithms from the chemical transducer, a key advance necessary for achieving long-term, non-invasive chemical sensing. PMID:27932855

Validating the Implementation Climate Scale (ICS) in child welfare organizations.

PubMed

Ehrhart, Mark G; Torres, Elisa M; Wright, Lisa A; Martinez, Sandra Y; Aarons, Gregory A

2016-03-01

There is increasing emphasis on the use of evidence-based practices (EBPs) in child welfare settings and growing recognition of the importance of the organizational environment, and the organization's climate in particular, for how employees perceive and support EBP implementation. Recently, Ehrhart, Aarons, and Farahnak (2014) reported on the development and validation of a measure of EBP implementation climate, the Implementation Climate Scale (ICS), in a sample of mental health clinicians. The ICS consists of 18 items and measures six critical dimensions of implementation climate: focus on EBP, educational support for EBP, recognition for EBP, rewards for EBP, selection or EBP, and selection for openness. The goal of the current study is to extend this work by providing evidence for the factor structure, reliability, and validity of the ICS in a sample of child welfare service providers. Survey data were collected from 215 child welfare providers across three states, 12 organizations, and 43 teams. Confirmatory factor analysis demonstrated good fit to the six-factor model and the alpha reliabilities for the overall measure and its subscales was acceptable. In addition, there was general support for the invariance of the factor structure across the child welfare and mental health sectors. In conclusion, this study provides evidence for the factor structure, reliability, and validity of the ICS measure for use in child welfare service organizations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Towards a smart glove: arousal recognition based on textile Electrodermal Response.

PubMed

Valenza, Gaetano; Lanata, Antonio; Scilingo, Enzo Pasquale; De Rossi, Danilo

2010-01-01

This paper investigates the possibility of using Electrodermal Response, acquired by a sensing fabric glove with embedded textile electrodes, as reliable means for emotion recognition. Here, all the essential steps for an automatic recognition system are described, from the recording of physiological data set to a feature-based multiclass classification. Data were collected from 35 healthy volunteers during arousal elicitation by means of International Affective Picture System (IAPS) pictures. Experimental results show high discrimination after twenty steps of cross validation.

Center for Corporate Climate Leadership Goal Setting

EPA Pesticide Factsheets

EPA provides tools and recognition for companies setting aggressive GHG reduction goals, which can galvanize reduction efforts at a company and often leads to the identification of many additional reduction opportunities.

Research on Attribute Reduction in Hoisting Motor State Recognition of Quayside Container Crane

NASA Astrophysics Data System (ADS)

Li, F.; Tang, G.; Hu, X.

2017-07-01

In view of too many attributes in hoisting motor state recognition of quayside container crane. Attribute reduction method based on discernibility matrix is introduced to attribute reduction of lifting motor state information table. A method of attribute reduction based on the combination of rough set and genetic algorithm is proposed to deal with the hoisting motor state decision table. Under the condition that the information system's decision-making ability is unchanged, the redundant attribute is deleted. Which reduces the complexity and computation of the recognition process of the hoisting motor. It is possible to realize the fast state recognition.

Mexican sign language recognition using normalized moments and artificial neural networks

NASA Astrophysics Data System (ADS)

Solís-V., J.-Francisco; Toxqui-Quitl, Carina; Martínez-Martínez, David; H.-G., Margarita

2014-09-01

This work presents a framework designed for the Mexican Sign Language (MSL) recognition. A data set was recorded with 24 static signs from the MSL using 5 different versions, this MSL dataset was captured using a digital camera in incoherent light conditions. Digital Image Processing was used to segment hand gestures, a uniform background was selected to avoid using gloved hands or some special markers. Feature extraction was performed by calculating normalized geometric moments of gray scaled signs, then an Artificial Neural Network performs the recognition using a 10-fold cross validation tested in weka, the best result achieved 95.83% of recognition rate.

Probing Conformational Changes and Interfacial Recognition Site of Lipases With Surfactants and Inhibitors.

PubMed

Mateos-Diaz, E; Amara, S; Roussel, A; Longhi, S; Cambillau, C; Carrière, F

2017-01-01

Structural studies on lipases by X-ray crystallography have revealed conformational changes occurring in the presence of surfactants/inhibitors and the pivotal role played by a molecular "lid" of variable size and structure depending on the enzyme. Besides controlling the access to the enzyme active site, the lid is involved in lipase activation, formation of the interfacial recognition site (IRS), and substrate docking within the active site. The combined use of surfactants and inhibitors has been critical for a better understanding of lipase structure-function relationships. An overview of crystal structures of lipases in complex with surfactants and inhibitors reveals common structural features and shows how surfactants monomers interact with the lid in its open conformation. The location of surfactants, inhibitors, and hydrophobic residues exposed upon lid opening provides insights into the IRS of lipases. The mechanism by which surfactants promote the lid opening can be further investigated in solution by site-directed spin labeling of lipase coupled to electron paramagnetic resonance spectroscopy. These experimental approaches are illustrated here by results obtained with mammalian digestive lipases, fungal lipases, and cutinases. © 2017 Elsevier Inc. All rights reserved.

HIGH-PRECISION BIOLOGICAL EVENT EXTRACTION: EFFECTS OF SYSTEM AND OF DATA

PubMed Central

Cohen, K. Bretonnel; Verspoor, Karin; Johnson, Helen L.; Roeder, Chris; Ogren, Philip V.; Baumgartner, William A.; White, Elizabeth; Tipney, Hannah; Hunter, Lawrence

2013-01-01

We approached the problems of event detection, argument identification, and negation and speculation detection in the BioNLP’09 information extraction challenge through concept recognition and analysis. Our methodology involved using the OpenDMAP semantic parser with manually written rules. The original OpenDMAP system was updated for this challenge with a broad ontology defined for the events of interest, new linguistic patterns for those events, and specialized coordination handling. We achieved state-of-the-art precision for two of the three tasks, scoring the highest of 24 teams at precision of 71.81 on Task 1 and the highest of 6 teams at precision of 70.97 on Task 2. We provide a detailed analysis of the training data and show that a number of trigger words were ambiguous as to event type, even when their arguments are constrained by semantic class. The data is also shown to have a number of missing annotations. Analysis of a sampling of the comparatively small number of false positives returned by our system shows that major causes of this type of error were failing to recognize second themes in two-theme events, failing to recognize events when they were the arguments to other events, failure to recognize nontheme arguments, and sentence segmentation errors. We show that specifically handling coordination had a small but important impact on the overall performance of the system. The OpenDMAP system and the rule set are available at http://bionlp.sourceforge.net. PMID:25937701

75 FR 1418 - Implementation of Open Government Directive

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-11

... three high-value data sets by January 22, 2010, and an Open Government Plan by April 7, 2010. While the... and publish high-value data sets and draft an Open Government Plan, and the NRC is now inviting public...-value data sets as soon as possible to assure consideration for purposes of the Open Government...

A Review of Subsequence Time Series Clustering

PubMed Central

Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

A review of subsequence time series clustering.

PubMed

Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Feasibility and Efficacy of Brief Computerized Training to Improve Emotion Recognition in Premanifest and Early-Symptomatic Huntington's Disease.

PubMed

Kempnich, Clare L; Wong, Dana; Georgiou-Karistianis, Nellie; Stout, Julie C

2017-04-01

Deficits in the recognition of negative emotions emerge before clinical diagnosis in Huntington's disease (HD). To address emotion recognition deficits, which have been shown in schizophrenia to be improved by computerized training, we conducted a study of the feasibility and efficacy of computerized training of emotion recognition in HD. We randomly assigned 22 individuals with premanifest or early symptomatic HD to the training or control group. The training group used a self-guided online training program, MicroExpression Training Tool (METT), twice weekly for 4 weeks. All participants completed measures of emotion recognition at baseline and post-training time-points. Participants in the training group also completed training adherence measures. Participants in the training group completed seven of the eight sessions on average. Results showed a significant group by time interaction, indicating that METT training was associated with improved accuracy in emotion recognition. Although sample size was small, our study demonstrates that emotion recognition remediation using the METT is feasible in terms of training adherence. The evidence also suggests METT may be effective in premanifest or early-symptomatic HD, opening up a potential new avenue for intervention. Further study with a larger sample size is needed to replicate these findings, and to characterize the durability and generalizability of these improvements, and their impact on functional outcomes in HD. (JINS, 2017, 23, 314-321).

Corona Phase Molecular Recognition (CoPhMoRe) to Enable New Nanosensor Interfaces

NASA Astrophysics Data System (ADS)

Strano, Michael

2015-03-01

Our lab at MIT has been interested in how the 1D and 2D electronic structures of carbon nanotubes and graphene respectively can be utilized to advance new concepts in molecular detection. We introduce CoPhMoRe or corona phase molecular recognition as a method of discovering synthetic antibodies, or nanotube-templated recognition sites from a heteropolymer library. We show that certain synthetic heteropolymers, once constrained onto a single-walled carbon nanotube by chemical adsorption, also form a new corona phase that exhibits highly selective recognition for specific molecules. To prove the generality of this phenomenon, we report three examples of heteropolymers-nanotube recognition complexes for riboflavin, L-thyroxine and estradiol. The platform opens new opportunities to create synthetic recognition sites for molecular detection. We have also extended this molecular recognition technique to neurotransmitters, producing the first fluorescent sensor for dopamine. Another area of advancement in biosensor development is the use of near infrared fluorescent carbon nanotube sensors for in-vivo detection. Here, we show that PEG-ligated d(AAAT)7 DNA wrapped SWNT are selective for nitric oxide, a vasodilator of blood vessels, and can be tail vein injected into mice and localized within the viable mouse liver. We use an SJL mouse model to study liver inflammation in vivo using the spatially and spectrally resolved nIR signature of the localized SWNT sensors.

Integrated segmentation and recognition of connected Ottoman script

NASA Astrophysics Data System (ADS)

Yalniz, Ismet Zeki; Altingovde, Ismail Sengor; Güdükbay, Uğur; Ulusoy, Özgür

2009-11-01

We propose a novel context-sensitive segmentation and recognition method for connected letters in Ottoman script. This method first extracts a set of segments from a connected script and determines the candidate letters to which extracted segments are most similar. Next, a function is defined for scoring each different syntactically correct sequence of these candidate letters. To find the candidate letter sequence that maximizes the score function, a directed acyclic graph is constructed. The letters are finally recognized by computing the longest path in this graph. Experiments using a collection of printed Ottoman documents reveal that the proposed method provides >90% precision and recall figures in terms of character recognition. In a further set of experiments, we also demonstrate that the framework can be used as a building block for an information retrieval system for digital Ottoman archives.

Conditions for positive and negative recencies in running memory-span recognition.

PubMed

Ruiz, R Marcos; Elosúa, M Rosa

2013-10-01

A positive recency effect in a running-span recognition procedure was obtained in Experiment 1 for hits and for intratrial false alarms. In running recall procedures, recency does not fit well with an active updating hypothesis. In Experiment 2, in which the beginning of the target set was marked with a cue upon presentation, the recency effects disappeared. In Experiments 3 and 4 participants were forced to maintain 2 items in memory until the last one was presented for recognition. These three items were the target set. When the last item presentation was uncertain-because of the variable length list-an unexpected negative recency effect appeared. An explanation for this change from positive to negative recency is offered based on the sharing of attentional resources put forward by others for similar procedures. © 2013.

Development of a written music-recognition system using Java and open source technologies

NASA Astrophysics Data System (ADS)

Loibner, Gernot; Schwarzl, Andreas; Kovač, Matthias; Paulus, Dietmar; Pölzleitner, Wolfgang

2005-10-01

We report on the development of a software system to recognize and interpret printed music. The overall goal is to scan printed music sheets, analyze and recognize the notes, timing, and written text, and derive the all necessary information to use the computers MIDI sound system to play the music. This function is primarily useful for musicians who want to digitize printed music for editing purposes. There exist a number of commercial systems that offer such a functionality. However, on testing these systems, we were astonished on how weak they behave in their pattern recognition parts. Although we submitted very clear and rather flawless scanning input, none of these systems was able to e.g. recognize all notes, staff lines, and systems. They all require a high degree of interaction, post-processing, and editing to get a decent digital version of the hard copy material. In this paper we focus on the pattern recognition area. In a first approach we tested more or less standard methods of adaptive thresholding, blob detection, line detection, and corner detection to find the notes, staff lines, and candidate objects subject to OCR. Many of the objects on this type of material can be learned in a training phase. None of the commercial systems we saw offers the option to train special characters or unusual signatures. A second goal in this project is to use a modern software engineering platform. We were interested in how well Java and open source technologies are suitable for pattern recognition and machine vision. The scanning of music served as a case-study.

Transgenerational Effects of Prenatal Bisphenol A on Social Recognition

PubMed Central

Wolstenholme, Jennifer T.; Goldsby, Jessica A.; Rissman, Emilie F.

2014-01-01

Bisphenol A (BPA) is a man-made endocrine disrupting compound used to manufacture polycarbonate plastics. It is found in plastic bottles, canned food linings, thermal receipts and other commonly used items. Over 93% of people have detectable BPA levels in their urine. Epidemiological studies report correlations between BPA levels during pregnancy and activity, anxiety, and depression in children. We fed female mice control or BPA–containing diets that produced plasma BPA concentrations similar to concentrations in humans. Females were mated and at birth, pups were fostered to control dams to limit BPA exposure to gestation in the first generation. Sibling pairs were bred to the third generation with no further BPA exposure. First (F1) and third (F3) generation juveniles were tested for social recognition and in the open field. Adult F3 mice were tested for olfactory discrimination. In both generations, BPA exposed juvenile mice displayed higher levels of investigation than controls in a social recognition task. In F3 BPA exposed mice, dishabituation to a novel female was impaired. In the open field, no differences were noted in F1 mice, while in F3, BPA lineage mice were more active than controls. No impairments were detected in F3 mice, all were able to discriminate different male urine pools and urine from water. No sex differences were found in any task. These results demonstrate that BPA exposure during gestation has long lasting, transgenerational effects on social recognition and activity in mice. These findings show that BPA exposure has transgenerational actions on behavior and have implications for human neurodevelopmental behavioral disorders. PMID:24100195

Transgenerational effects of prenatal bisphenol A on social recognition.

PubMed

Wolstenholme, Jennifer T; Goldsby, Jessica A; Rissman, Emilie F

2013-11-01

Bisphenol A (BPA) is a man-made endocrine disrupting compound used to manufacture polycarbonate plastics. It is found in plastic bottles, canned food linings, thermal receipts and other commonly used items. Over 93% of people have detectable BPA levels in their urine. Epidemiological studies report correlations between BPA levels during pregnancy and activity, anxiety, and depression in children. We fed female mice control or BPA-containing diets that produced plasma BPA concentrations similar to concentrations in humans. Females were mated and at birth, pups were fostered to control dams to limit BPA exposure to gestation in the first generation. Sibling pairs were bred to the third generation with no further BPA exposure. First (F1) and third (F3) generation juveniles were tested for social recognition and in the open field. Adult F3 mice were tested for olfactory discrimination. In both generations, BPA exposed juvenile mice displayed higher levels of investigation than controls in a social recognition task. In F3 BPA exposed mice, dishabituation to a novel female was impaired. In the open field, no differences were noted in F1 mice, while in F3, BPA lineage mice were more active than controls. No impairments were detected in F3 mice, all were able to discriminate different male urine pools and urine from water. No sex differences were found in any task. These results demonstrate that BPA exposure during gestation has long lasting, transgenerational effects on social recognition and activity in mice. These findings show that BPA exposure has transgenerational actions on behavior and have implications for human neurodevelopmental behavioral disorders. © 2013.

Design and development of an ancient Chinese document recognition system

NASA Astrophysics Data System (ADS)

Peng, Liangrui; Xiu, Pingping; Ding, Xiaoqing

2003-12-01

The digitization of ancient Chinese documents presents new challenges to OCR (Optical Character Recognition) research field due to the large character set of ancient Chinese characters, variant font types, and versatile document layout styles, as these documents are historical reflections to the thousands of years of Chinese civilization. After analyzing the general characteristics of ancient Chinese documents, we present a solution for recognition of ancient Chinese documents with regular font-types and layout-styles. Based on the previous work on multilingual OCR in TH-OCR system, we focus on the design and development of two key technologies which include character recognition and page segmentation. Experimental results show that the developed character recognition kernel of 19,635 Chinese characters outperforms our original traditional Chinese recognition kernel; Benchmarked test on printed ancient Chinese books proves that the proposed system is effective for regular ancient Chinese documents.

Activity recognition from minimal distinguishing subsequence mining

NASA Astrophysics Data System (ADS)

Iqbal, Mohammad; Pao, Hsing-Kuo

2017-08-01

Human activity recognition is one of the most important research topics in the era of Internet of Things. To separate different activities given sensory data, we utilize a Minimal Distinguishing Subsequence (MDS) mining approach to efficiently find distinguishing patterns among different activities. We first transform the sensory data into a series of sensor triggering events and operate the MDS mining procedure afterwards. The gap constraints are also considered in the MDS mining. Given the multi-class nature of most activity recognition tasks, we modify the MDS mining approach from a binary case to a multi-class one to fit the need for multiple activity recognition. We also study how to select the best parameter set including the minimal and the maximal support thresholds in finding the MDSs for effective activity recognition. Overall, the prediction accuracy is 86.59% on the van Kasteren dataset which consists of four different activities for recognition.

Low-contrast underwater living fish recognition using PCANet

NASA Astrophysics Data System (ADS)

Sun, Xin; Yang, Jianping; Wang, Changgang; Dong, Junyu; Wang, Xinhua

2018-04-01

Quantitative and statistical analysis of ocean creatures is critical to ecological and environmental studies. And living fish recognition is one of the most essential requirements for fishery industry. However, light attenuation and scattering phenomenon are present in the underwater environment, which makes underwater images low-contrast and blurry. This paper tries to design a robust framework for accurate fish recognition. The framework introduces a two stage PCA Network to extract abstract features from fish images. On a real-world fish recognition dataset, we use a linear SVM classifier and set penalty coefficients to conquer data unbalanced issue. Feature visualization results show that our method can avoid the feature distortion in boundary regions of underwater image. Experiments results show that the PCA Network can extract discriminate features and achieve promising recognition accuracy. The framework improves the recognition accuracy of underwater living fishes and can be easily applied to marine fishery industry.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Kannada character recognition system using neural network

NASA Astrophysics Data System (ADS)

Kumar, Suresh D. S.; Kamalapuram, Srinivasa K.; Kumar, Ajay B. R.

2013-03-01

Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. As there is no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India. In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten Kannada character is resized into 20x30 Pixel. The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.

[Creating language model of the forensic medicine domain for developing a autopsy recording system by automatic speech recognition].

PubMed

Niijima, H; Ito, N; Ogino, S; Takatori, T; Iwase, H; Kobayashi, M

2000-11-01

For the purpose of practical use of speech recognition technology for recording of forensic autopsy, a language model of the speech recording system, specialized for the forensic autopsy, was developed. The language model for the forensic autopsy by applying 3-gram model was created, and an acoustic model for Japanese speech recognition by Hidden Markov Model in addition to the above were utilized to customize the speech recognition engine for forensic autopsy. A forensic vocabulary set of over 10,000 words was compiled and some 300,000 sentence patterns were made to create the forensic language model, then properly mixing with a general language model to attain high exactitude. When tried by dictating autopsy findings, this speech recognition system was proved to be about 95% of recognition rate that seems to have reached to the practical usability in view of speech recognition software, though there remains rooms for improving its hardware and application-layer software.

Spaced Learning Enhances Subsequent Recognition Memory by Reducing Neural Repetition Suppression

PubMed Central

Xue, Gui; Mei, Leilei; Chen, Chuansheng; Lu, Zhong-Lin; Poldrack, Russell; Dong, Qi

2012-01-01

Spaced learning usually leads to better recognition memory as compared with massed learning, yet the underlying neural mechanisms remain elusive. One open question is whether the spacing effect is achieved by reducing neural repetition suppression. In this fMRI study, participants were scanned while intentionally memorizing 120 novel faces, half under the massed learning condition (i.e., four consecutive repetitions with jittered interstimulus interval) and the other half under the spaced learning condition (i.e., the four repetitions were interleaved). Recognition memory tests afterward revealed a significant spacing effect: Participants recognized more items learned under the spaced learning condition than under the massed learning condition. Successful face memory encoding was associated with stronger activation in the bilateral fusiform gyrus, which showed a significant repetition suppression effect modulated by subsequent memory status and spaced learning. Specifically, remembered faces showed smaller repetition suppression than forgotten faces under both learning conditions, and spaced learning significantly reduced repetition suppression. These results suggest that spaced learning enhances recognition memory by reducing neural repetition suppression. PMID:20617892

Spaced learning enhances subsequent recognition memory by reducing neural repetition suppression.

PubMed

Xue, Gui; Mei, Leilei; Chen, Chuansheng; Lu, Zhong-Lin; Poldrack, Russell; Dong, Qi

2011-07-01

Spaced learning usually leads to better recognition memory as compared with massed learning, yet the underlying neural mechanisms remain elusive. One open question is whether the spacing effect is achieved by reducing neural repetition suppression. In this fMRI study, participants were scanned while intentionally memorizing 120 novel faces, half under the massed learning condition (i.e., four consecutive repetitions with jittered interstimulus interval) and the other half under the spaced learning condition (i.e., the four repetitions were interleaved). Recognition memory tests afterward revealed a significant spacing effect: Participants recognized more items learned under the spaced learning condition than under the massed learning condition. Successful face memory encoding was associated with stronger activation in the bilateral fusiform gyrus, which showed a significant repetition suppression effect modulated by subsequent memory status and spaced learning. Specifically, remembered faces showed smaller repetition suppression than forgotten faces under both learning conditions, and spaced learning significantly reduced repetition suppression. These results suggest that spaced learning enhances recognition memory by reducing neural repetition suppression.

Early Decomposition in Visual Word Recognition: Dissociating Morphology, Form, and Meaning

ERIC Educational Resources Information Center

Marslen-Wilson, William D.; Bozic, Mirjana; Randall, Billi

2008-01-01

The role of morphological, semantic, and form-based factors in the early stages of visual word recognition was investigated across different SOAs in a masked priming paradigm, focusing on English derivational morphology. In a first set of experiments, stimulus pairs co-varying in morphological decomposability and in semantic and orthographic…

Distractor Plausibility and Criterion Placement in Recognition

ERIC Educational Resources Information Center

Benjamin, Aaron S.; Bawa, Sameer

2004-01-01

To set an optimal decision criterion on a test of recognition, a subject must estimate the degree to which they can discriminate previously studied from unstudied stimuli. To do so accurately, the subject must assess not only their mastery of the material but also the extent to which the distractors yield mnemonic evidence that makes them…

Between Private and Public: Recognition, Revolution and Political Renewal

ERIC Educational Resources Information Center

Stillwaggon, James

2011-01-01

This paper deals with some issues underlying the role of education in the preparation of students for democratic participation. Throughout, I maintain two basic ideas: first, that a political action undertaken to obtain practical ends reflects a set of privately held values whose recognition is therefore essential to any idea of the political;…

76 FR 64175 - Loans in Areas Having Special Flood Hazards; Interagency Questions and Answers Regarding Flood...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-17

... such as logos and special characters. Identifying information that you provide, such as phone numbers... are further made in recognition of the position, set out in the revisions to proposed question and...-day notice period. However, in recognition of standard provisions in many contracts entered into...

Segment-based acoustic models for continuous speech recognition

NASA Astrophysics Data System (ADS)

Ostendorf, Mari; Rohlicek, J. R.

1993-07-01

This research aims to develop new and more accurate stochastic models for speaker-independent continuous speech recognition, by extending previous work in segment-based modeling and by introducing a new hierarchical approach to representing intra-utterance statistical dependencies. These techniques, which are more costly than traditional approaches because of the large search space associated with higher order models, are made feasible through rescoring a set of HMM-generated N-best sentence hypotheses. We expect these different modeling techniques to result in improved recognition performance over that achieved by current systems, which handle only frame-based observations and assume that these observations are independent given an underlying state sequence. In the fourth quarter of the project, we have completed the following: (1) ported our recognition system to the Wall Street Journal task, a standard task in the ARPA community; (2) developed an initial dependency-tree model of intra-utterance observation correlation; and (3) implemented baseline language model estimation software. Our initial results on the Wall Street Journal task are quite good and represent significantly improved performance over most HMM systems reporting on the Nov. 1992 5k vocabulary test set.

A multidisciplinary study of earth resources imagery of Australia, Antarctica and Papua, New Guinea

NASA Technical Reports Server (NTRS)

Fisher, N. H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. A thirteen category recognition map was prepared, showing forest, water, grassland, and exposed rock types. Preliminary assessment of classification accuracies showed that water, forest, meadow, and Niobrara shale were the most accurately mapped classes. Unsatisfactory results, were obtained in an attempt to discrimate sparse forest cover over different substrates. As base elevation varied from 7,000 to 13,000 ft, with an atmospheric visibility of 48 km, no changes in water and forest recognition were observed. Granodiorite recognition accuracy decreased monotonically as base elevation increased, even though the training set location was at 10,000 ft elevation. For snow varying in base elevation from 9400 to 8420 ft, recognition decreases from 99% at the 9400 ft training set elevation to 88% at 8420 ft. Calculations of expected radiance at the ERTS sensor from snow reflectance measured at the site and from Turner model calculations of irradiance, transmission and path radiance, reveal that snow signals should not be clipped, assuming that calculations and ERTS calibration constants were correct.

Unsupervised learning of structure in spectroscopic cubes

NASA Astrophysics Data System (ADS)

Araya, M.; Mendoza, M.; Solar, M.; Mardones, D.; Bayo, A.

2018-07-01

We consider the problem of analyzing the structure of spectroscopic cubes using unsupervised machine learning techniques. We propose representing the target's signal as a homogeneous set of volumes through an iterative algorithm that separates the structured emission from the background while not overestimating the flux. Besides verifying some basic theoretical properties, the algorithm is designed to be tuned by domain experts, because its parameters have meaningful values in the astronomical context. Nevertheless, we propose a heuristic to automatically estimate the signal-to-noise ratio parameter of the algorithm directly from data. The resulting light-weighted set of samples (≤ 1% compared to the original data) offer several advantages. For instance, it is statistically correct and computationally inexpensive to apply well-established techniques of the pattern recognition and machine learning domains; such as clustering and dimensionality reduction algorithms. We use ALMA science verification data to validate our method, and present examples of the operations that can be performed by using the proposed representation. Even though this approach is focused on providing faster and better analysis tools for the end-user astronomer, it also opens the possibility of content-aware data discovery by applying our algorithm to big data.

Mechanism of molecular recognition on molecular imprinted monolith by capillary electrochromatography.

PubMed

Liu, Zhao-Sheng; Xu, Yan-Li; Yan, Chao; Gao, Ru-Yu

2005-09-16

The recognition mechanism of molecularly imprinted polymer (MIP) in capillary electrochromatography (CEC) is complicated since it possesses a hybrid process, which comprises the features of chromatographic retention, electrophoretic migration and molecular imprinting. For an understanding of the molecular recognition of MIP in CEC, a monolithic MIP in a capillary with 1,1'-binaphthyl-2,2'-diamine (BNA) imprinting was prepared by in situ copolymerization of imprinted molecule, methacrylic acid and ethylene glycol dimethacrylate in porogenic solvent, a mixture of toluene-isooctane. Strong recognition ability and high column performance (theory plates was 43,000 plates/m) of BNA were achieved on this monolithic MIP in CEC mode. In addition, BNA and its structural analogue, 1,1'-bi-2, 2'-naphthol, differing in functional groups, were used as model compounds to study imprinting effect on the resultant BNA-imprinted monolithic column, a reference column without imprinting of BNA and a open capillary. The effects of organic modifier concentration, pH value of buffer, salt concentration of buffer and column temperature on the retention and recognition of two compounds were investigated. The results showed that the molecular recognition on MIP monolith in CEC mode mainly derived from imprinting cavities on BNA-imprinted polymer other than chromatographic retention and electrophoretic migration.

Software for Partly Automated Recognition of Targets

NASA Technical Reports Server (NTRS)

Opitz, David; Blundell, Stuart; Bain, William; Morris, Matthew; Carlson, Ian; Mangrich, Mark; Selinsky, T.

2002-01-01

The Feature Analyst is a computer program for assisted (partially automated) recognition of targets in images. This program was developed to accelerate the processing of high-resolution satellite image data for incorporation into geographic information systems (GIS). This program creates an advanced user interface that embeds proprietary machine-learning algorithms in commercial image-processing and GIS software. A human analyst provides samples of target features from multiple sets of data, then the software develops a data-fusion model that automatically extracts the remaining features from selected sets of data. The program thus leverages the natural ability of humans to recognize objects in complex scenes, without requiring the user to explain the human visual recognition process by means of lengthy software. Two major subprograms are the reactive agent and the thinking agent. The reactive agent strives to quickly learn the user's tendencies while the user is selecting targets and to increase the user's productivity by immediately suggesting the next set of pixels that the user may wish to select. The thinking agent utilizes all available resources, taking as much time as needed, to produce the most accurate autonomous feature-extraction model possible.

3D abnormal behavior recognition in power generation

NASA Astrophysics Data System (ADS)

Wei, Zhenhua; Li, Xuesen; Su, Jie; Lin, Jie

2011-06-01

So far most research of human behavior recognition focus on simple individual behavior, such as wave, crouch, jump and bend. This paper will focus on abnormal behavior with objects carrying in power generation. Such as using mobile communication device in main control room, taking helmet off during working and lying down in high place. Taking account of the color and shape are fixed, we adopted edge detecting by color tracking to recognize object in worker. This paper introduces a method, which using geometric character of skeleton and its angle to express sequence of three-dimensional human behavior data. Then adopting Semi-join critical step Hidden Markov Model, weighing probability of critical steps' output to reduce the computational complexity. Training model for every behavior, mean while select some skeleton frames from 3D behavior sample to form a critical step set. This set is a bridge linking 2D observation behavior with 3D human joints feature. The 3D reconstruction is not required during the 2D behavior recognition phase. In the beginning of recognition progress, finding the best match for every frame of 2D observed sample in 3D skeleton set. After that, 2D observed skeleton frames sample will be identified as a specifically 3D behavior by behavior-classifier. The effectiveness of the proposed algorithm is demonstrated with experiments in similar power generation environment.

Face recognition system for set-top box-based intelligent TV.

PubMed

Lee, Won Oh; Kim, Yeong Gon; Hong, Hyung Gil; Park, Kang Ryoung

2014-11-18

Despite the prevalence of smart TVs, many consumers continue to use conventional TVs with supplementary set-top boxes (STBs) because of the high cost of smart TVs. However, because the processing power of a STB is quite low, the smart TV functionalities that can be implemented in a STB are very limited. Because of this, negligible research has been conducted regarding face recognition for conventional TVs with supplementary STBs, even though many such studies have been conducted with smart TVs. In terms of camera sensors, previous face recognition systems have used high-resolution cameras, cameras with high magnification zoom lenses, or camera systems with panning and tilting devices that can be used for face recognition from various positions. However, these cameras and devices cannot be used in intelligent TV environments because of limitations related to size and cost, and only small, low cost web-cameras can be used. The resulting face recognition performance is degraded because of the limited resolution and quality levels of the images. Therefore, we propose a new face recognition system for intelligent TVs in order to overcome the limitations associated with low resource set-top box and low cost web-cameras. We implement the face recognition system using a software algorithm that does not require special devices or cameras. Our research has the following four novelties: first, the candidate regions in a viewer's face are detected in an image captured by a camera connected to the STB via low processing background subtraction and face color filtering; second, the detected candidate regions of face are transmitted to a server that has high processing power in order to detect face regions accurately; third, in-plane rotations of the face regions are compensated based on similarities between the left and right half sub-regions of the face regions; fourth, various poses of the viewer's face region are identified using five templates obtained during the initial user registration stage and multi-level local binary pattern matching. Experimental results indicate that the recall; precision; and genuine acceptance rate were about 95.7%; 96.2%; and 90.2%, respectively.

The effects of memory load and stimulus relevance on the EEG during a visual selective memory search task: an ERP and ERD/ERS study.

PubMed

Gomarus, H Karin; Althaus, Monika; Wijers, Albertus A; Minderaa, Ruud B

2006-04-01

Psychophysiological correlates of selective attention and working memory were investigated in a group of 18 healthy children using a visually presented selective memory search task. Subjects had to memorize one (load1) or 3 (load3) letters (memory set) and search for these among a recognition set consisting of 4 letters only if the letters appeared in the correct (relevant) color. Event-related potentials (ERPs) as well as alpha and theta event-related synchronization and desynchronization (ERD/ERS) were derived from the EEG that was recorded during the task. In the ERP to the memory set, a prolonged load-related positivity was found. In response to the recognition set, effects of relevance were manifested in an early frontal positivity and a later frontal negativity. Effects of load were found in a search-related negativity within the attended category and a suppression of the P3-amplitude. Theta ERS was most pronounced for the most difficult task condition during the recognition set, whereas alpha ERD showed a load-effect only during memorization. The manipulation of stimulus relevance and memory load affected both ERP components and ERD/ERS. The present paradigm may supply a useful method for studying processes of selective attention and working memory and can be used to examine group differences between healthy controls and children showing psychopathology.

Training a whole-book LSTM-based recognizer with an optimal training set

NASA Astrophysics Data System (ADS)

Soheili, Mohammad Reza; Yousefi, Mohammad Reza; Kabir, Ehsanollah; Stricker, Didier

2018-04-01

Despite the recent progress in OCR technologies, whole-book recognition, is still a challenging task, in particular in case of old and historical books, that the unknown font faces or low quality of paper and print contributes to the challenge. Therefore, pre-trained recognizers and generic methods do not usually perform up to required standards, and usually the performance degrades for larger scale recognition tasks, such as of a book. Such reportedly low error-rate methods turn out to require a great deal of manual correction. Generally, such methodologies do not make effective use of concepts such redundancy in whole-book recognition. In this work, we propose to train Long Short Term Memory (LSTM) networks on a minimal training set obtained from the book to be recognized. We show that clustering all the sub-words in the book, and using the sub-word cluster centers as the training set for the LSTM network, we can train models that outperform any identical network that is trained with randomly selected pages of the book. In our experiments, we also show that although the sub-word cluster centers are equivalent to about 8 pages of text for a 101- page book, a LSTM network trained on such a set performs competitively compared to an identical network that is trained on a set of 60 randomly selected pages of the book.

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

PubMed Central

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

2012-01-01

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

Recognition of Modified Conditioning Sounds by Competitively Trained Guinea Pigs

PubMed Central

Ojima, Hisayuki; Horikawa, Junsei

2016-01-01

The guinea pig (GP) is an often-used species in hearing research. However, behavioral studies are rare, especially in the context of sound recognition, because of difficulties in training these animals. We examined sound recognition in a social competitive setting in order to examine whether this setting could be used as an easy model. Two starved GPs were placed in the same training arena and compelled to compete for food after hearing a conditioning sound (CS), which was a repeat of almost identical sound segments. Through a 2-week intensive training, animals were trained to demonstrate a set of distinct behaviors solely to the CS. Then, each of them was subjected to generalization tests for recognition of sounds that had been modified from the CS in spectral, fine temporal and tempo (i.e., intersegment interval, ISI) dimensions. Results showed that they discriminated between the CS and band-rejected test sounds but had no preference for a particular frequency range for the recognition. In contrast, sounds modified in the fine temporal domain were largely perceived to be in the same category as the CS, except for the test sound generated by fully reversing the CS in time. Animals also discriminated sounds played at different tempos. Test sounds with ISIs shorter than that of the multi-segment CS were discriminated from the CS, while test sounds with ISIs longer than that of the CS segments were not. For the shorter ISIs, most animals initiated apparently positive food-access behavior as they did in response to the CS, but discontinued it during the sound-on period probably because of later recognition of tempo. Interestingly, the population range and mean of the delay time before animals initiated the food-access behavior were very similar among different ISI test sounds. This study, for the first time, demonstrates a wide aspect of sound discrimination abilities of the GP and will provide a way to examine tempo perception mechanisms using this animal species. PMID:26858617

Automatic anatomy recognition via multiobject oriented active shape models.

PubMed

Chen, Xinjian; Udupa, Jayaram K; Alavi, Abass; Torigian, Drew A

2010-12-01

This paper studies the feasibility of developing an automatic anatomy recognition (AAR) system in clinical radiology and demonstrates its operation on clinical 2D images. The anatomy recognition method described here consists of two main components: (a) multiobject generalization of OASM and (b) object recognition strategies. The OASM algorithm is generalized to multiple objects by including a model for each object and assigning a cost structure specific to each object in the spirit of live wire. The delineation of multiobject boundaries is done in MOASM via a three level dynamic programming algorithm, wherein the first level is at pixel level which aims to find optimal oriented boundary segments between successive landmarks, the second level is at landmark level which aims to find optimal location for the landmarks, and the third level is at the object level which aims to find optimal arrangement of object boundaries over all objects. The object recognition strategy attempts to find that pose vector (consisting of translation, rotation, and scale component) for the multiobject model that yields the smallest total boundary cost for all objects. The delineation and recognition accuracies were evaluated separately utilizing routine clinical chest CT, abdominal CT, and foot MRI data sets. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF and FPVF). The recognition accuracy was assessed (1) in terms of the size of the space of the pose vectors for the model assembly that yielded high delineation accuracy, (2) as a function of the number of objects and objects' distribution and size in the model, (3) in terms of the interdependence between delineation and recognition, and (4) in terms of the closeness of the optimum recognition result to the global optimum. When multiple objects are included in the model, the delineation accuracy in terms of TPVF can be improved to 97%-98% with a low FPVF of 0.1%-0.2%. Typically, a recognition accuracy of > or = 90% yielded a TPVF > or = 95% and FPVF < or = 0.5%. Over the three data sets and over all tested objects, in 97% of the cases, the optimal solutions found by the proposed method constituted the true global optimum. The experimental results showed the feasibility and efficacy of the proposed automatic anatomy recognition system. Increasing the number of objects in the model can significantly improve both recognition and delineation accuracy. More spread out arrangement of objects in the model can lead to improved recognition and delineation accuracy. Including larger objects in the model also improved recognition and delineation. The proposed method almost always finds globally optimum solutions.

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

PubMed Central

Mehta, Dhwani; Siddiqui, Mohammad Faridul Haque

2018-01-01

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

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

PubMed

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

2018-02-01

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

Open Source, Open Standards, and Health Care Information Systems

PubMed Central

2011-01-01

Recognition of the improvements in patient safety, quality of patient care, and efficiency that health care information systems have the potential to bring has led to significant investment. Globally the sale of health care information systems now represents a multibillion dollar industry. As policy makers, health care professionals, and patients, we have a responsibility to maximize the return on this investment. To this end we analyze alternative licensing and software development models, as well as the role of standards. We describe how licensing affects development. We argue for the superiority of open source licensing to promote safer, more effective health care information systems. We claim that open source licensing in health care information systems is essential to rational procurement strategy. PMID:21447469

Open source, open standards, and health care information systems.

PubMed

Reynolds, Carl J; Wyatt, Jeremy C

2011-02-17

Recognition of the improvements in patient safety, quality of patient care, and efficiency that health care information systems have the potential to bring has led to significant investment. Globally the sale of health care information systems now represents a multibillion dollar industry. As policy makers, health care professionals, and patients, we have a responsibility to maximize the return on this investment. To this end we analyze alternative licensing and software development models, as well as the role of standards. We describe how licensing affects development. We argue for the superiority of open source licensing to promote safer, more effective health care information systems. We claim that open source licensing in health care information systems is essential to rational procurement strategy.

Non literal language comprehension in a large sample of first episode psychosis patients in adulthood.

PubMed

Perlini, Cinzia; Bellani, Marcella; Finos, Livio; Lasalvia, Antonio; Bonetto, Chiara; Scocco, Paolo; D'Agostino, Armando; Torresani, Stefano; Imbesi, Massimiliano; Bellini, Francesca; Konze, Angela; Veronese, Angela; Ruggeri, Mirella; Brambilla, Paolo

2017-11-10

To date no data still exist on the comprehension of figurative language in the early phases of psychosis. The aim of this study is to investigate for the first time the comprehension of metaphors and idioms at the onset of the illness. Two-hundred-twenty eight (228) first episode psychosis (FEP) patients (168 NAP, non-affective psychosis; 60 AP, affective psychosis) and 70 healthy controls (HC) were assessed. Groups were contrasted on: a) type of stimulus (metaphors vs idioms) and b) type of response (OPEN = spontaneous explanations vs CLOSED = multiple choice answer). Moreover, a machine learning (ML) approach was adopted to classifying participants. Both NAP and AP had a poorer performance on OPEN metaphors and idioms compared to HC, with worse results on spontaneous interpretation of idioms than metaphors. No differences were observed between NAP and AP in CLOSED tasks. The ML approach points at CLOSED idioms as the best discriminating variable, more relevant than the set of pre-frontal and IQ scores. Deficits in non-figurative language may represent a core feature of psychosis. The possibility to identify linguistic features discriminating FEP may support the early recognition of patients at risk to develop psychosis, guiding provision of personalized and timely interventions. Copyright © 2017 Elsevier B.V. All rights reserved.

Reduced set averaging of face identity in children and adolescents with autism.

PubMed

Rhodes, Gillian; Neumann, Markus F; Ewing, Louise; Palermo, Romina

2015-01-01

Individuals with autism have difficulty abstracting and updating average representations from their diet of faces. These averages function as perceptual norms for coding faces, and poorly calibrated norms may contribute to face recognition difficulties in autism. Another kind of average, known as an ensemble representation, can be abstracted from briefly glimpsed sets of faces. Here we show for the first time that children and adolescents with autism also have difficulty abstracting ensemble representations from sets of faces. On each trial, participants saw a study set of four identities and then indicated whether a test face was present. The test face could be a set average or a set identity, from either the study set or another set. Recognition of set averages was reduced in participants with autism, relative to age- and ability-matched typically developing participants. This difference, which actually represents more accurate responding, indicates weaker set averaging and thus weaker ensemble representations of face identity in autism. Our finding adds to the growing evidence for atypical abstraction of average face representations from experience in autism. Weak ensemble representations may have negative consequences for face processing in autism, given the importance of ensemble representations in dealing with processing capacity limitations.

Increasing the object recognition distance of compact open air on board vision system

NASA Astrophysics Data System (ADS)

Kirillov, Sergey; Kostkin, Ivan; Strotov, Valery; Dmitriev, Vladimir; Berdnikov, Vadim; Akopov, Eduard; Elyutin, Aleksey

2016-10-01

The aim of this work was developing an algorithm eliminating the atmospheric distortion and improves image quality. The proposed algorithm is entirely software without using additional hardware photographic equipment. . This algorithm does not required preliminary calibration. It can work equally effectively with the images obtained at a distances from 1 to 500 meters. An algorithm for the open air images improve designed for Raspberry Pi model B on-board vision systems is proposed. The results of experimental examination are given.

Human target acquisition performance

NASA Astrophysics Data System (ADS)

Teaney, Brian P.; Du Bosq, Todd W.; Reynolds, Joseph P.; Thompson, Roger; Aghera, Sameer; Moyer, Steven K.; Flug, Eric; Espinola, Richard; Hixson, Jonathan

2012-06-01

The battlefield has shifted from armored vehicles to armed insurgents. Target acquisition (identification, recognition, and detection) range performance involving humans as targets is vital for modern warfare. The acquisition and neutralization of armed insurgents while at the same time minimizing fratricide and civilian casualties is a mounting concern. U.S. Army RDECOM CERDEC NVESD has conducted many experiments involving human targets for infrared and reflective band sensors. The target sets include human activities, hand-held objects, uniforms & armament, and other tactically relevant targets. This paper will define a set of standard task difficulty values for identification and recognition associated with human target acquisition performance.

Folk Dance Pattern Recognition Over Depth Images Acquired via Kinect Sensor

NASA Astrophysics Data System (ADS)

Protopapadakis, E.; Grammatikopoulou, A.; Doulamis, A.; Grammalidis, N.

2017-02-01

The possibility of accurate recognition of folk dance patterns is investigated in this paper. System inputs are raw skeleton data, provided by a low cost sensor. In particular, data were obtained by monitoring three professional dancers, using a Kinect II sensor. A set of six traditional Greek dances (without their variations) consists the investigated data. A two-step process was adopted. At first, the most descriptive skeleton data were selected using a combination of density based and sparse modelling algorithms. Then, the representative data served as training set for a variety of classifiers.

An automatic speech recognition system with speaker-independent identification support

NASA Astrophysics Data System (ADS)

Caranica, Alexandru; Burileanu, Corneliu

2015-02-01

The novelty of this work relies on the application of an open source research software toolkit (CMU Sphinx) to train, build and evaluate a speech recognition system, with speaker-independent support, for voice-controlled hardware applications. Moreover, we propose to use the trained acoustic model to successfully decode offline voice commands on embedded hardware, such as an ARMv6 low-cost SoC, Raspberry PI. This type of single-board computer, mainly used for educational and research activities, can serve as a proof-of-concept software and hardware stack for low cost voice automation systems.

Biologically inspired emotion recognition from speech

NASA Astrophysics Data System (ADS)

Caponetti, Laura; Buscicchio, Cosimo Alessandro; Castellano, Giovanna

2011-12-01

Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM) recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC) and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.

Static facial expression recognition with convolution neural networks

NASA Astrophysics Data System (ADS)

Zhang, Feng; Chen, Zhong; Ouyang, Chao; Zhang, Yifei

2018-03-01

Facial expression recognition is a currently active research topic in the fields of computer vision, pattern recognition and artificial intelligence. In this paper, we have developed a convolutional neural networks (CNN) for classifying human emotions from static facial expression into one of the seven facial emotion categories. We pre-train our CNN model on the combined FER2013 dataset formed by train, validation and test set and fine-tune on the extended Cohn-Kanade database. In order to reduce the overfitting of the models, we utilized different techniques including dropout and batch normalization in addition to data augmentation. According to the experimental result, our CNN model has excellent classification performance and robustness for facial expression recognition.

Pornographic image recognition and filtering using incremental learning in compressed domain

NASA Astrophysics Data System (ADS)

Zhang, Jing; Wang, Chao; Zhuo, Li; Geng, Wenhao

2015-11-01

With the rapid development and popularity of the network, the openness, anonymity, and interactivity of networks have led to the spread and proliferation of pornographic images on the Internet, which have done great harm to adolescents' physical and mental health. With the establishment of image compression standards, pornographic images are mainly stored with compressed formats. Therefore, how to efficiently filter pornographic images is one of the challenging issues for information security. A pornographic image recognition and filtering method in the compressed domain is proposed by using incremental learning, which includes the following steps: (1) low-resolution (LR) images are first reconstructed from the compressed stream of pornographic images, (2) visual words are created from the LR image to represent the pornographic image, and (3) incremental learning is adopted to continuously adjust the classification rules to recognize the new pornographic image samples after the covering algorithm is utilized to train and recognize the visual words in order to build the initial classification model of pornographic images. The experimental results show that the proposed pornographic image recognition method using incremental learning has a higher recognition rate as well as costing less recognition time in the compressed domain.

Effect of nitrogen narcosis on free recall and recognition memory in open water.

PubMed

Hobbs, M; Kneller, W

2009-01-01

Previous research has demonstrated that nitrogen narcosis causes decrements in memory performance but the precise aspect of memory impaired is not clear in the literature. The present research investigated the effect of narcosis on free recall and recognition memory by appling signal detection theory (SDT) to the analysis of the recognition data. Using a repeated measures design, the free recall and recognition memory of 20 divers was tested in four learning-recall conditions: shallow-shallow (SS), deep-deep (DD), shallow-deep (SD) and deep-shallow (DS). The data was collected in the ocean offDahab, Egypt with shallow water representing a depth of 0-10m (33ft) and deep water 37-40m (121-131ft). The presence of narcosis was independently indexed with subjective ratings. In comparison to the SS condition there was a clear impairment of free recall in the DD and DS conditions, but not the SD condition. Recognition memory remained unaffected by narcosis. It was concluded narcosis-induced memory decrements cannot be explained as simply an impairment of input into long term memory or of self-guided search and it is suggested instead that narcosis acts to reduce the level of processing/encoding of information.

A Novel Locally Linear KNN Method With Applications to Visual Recognition.

PubMed

Liu, Qingfeng; Liu, Chengjun

2017-09-01

A locally linear K Nearest Neighbor (LLK) method is presented in this paper with applications to robust visual recognition. Specifically, the concept of an ideal representation is first presented, which improves upon the traditional sparse representation in many ways. The objective function based on a host of criteria for sparsity, locality, and reconstruction is then optimized to derive a novel representation, which is an approximation to the ideal representation. The novel representation is further processed by two classifiers, namely, an LLK-based classifier and a locally linear nearest mean-based classifier, for visual recognition. The proposed classifiers are shown to connect to the Bayes decision rule for minimum error. Additional new theoretical analysis is presented, such as the nonnegative constraint, the group regularization, and the computational efficiency of the proposed LLK method. New methods such as a shifted power transformation for improving reliability, a coefficients' truncating method for enhancing generalization, and an improved marginal Fisher analysis method for feature extraction are proposed to further improve visual recognition performance. Extensive experiments are implemented to evaluate the proposed LLK method for robust visual recognition. In particular, eight representative data sets are applied for assessing the performance of the LLK method for various visual recognition applications, such as action recognition, scene recognition, object recognition, and face recognition.

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.

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

NASA Astrophysics Data System (ADS)

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

1997-03-01

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

Enhancement Of Reading Accuracy By Multiple Data Integration

NASA Astrophysics Data System (ADS)

Lee, Kangsuk

1989-07-01

In this paper, a multiple sensor integration technique with neural network learning algorithms is presented which can enhance the reading accuracy of the hand-written numerals. Many document reading applications involve hand-written numerals in a predetermined location on a form, and in many cases, critical data is redundantly described. The amount of a personal check is one such case which is written redundantly in numerals and in alphabetical form. Information from two optical character recognition modules, one specialized for digits and one for words, is combined to yield an enhanced recognition of the amount. The combination can be accomplished by a decision tree with "if-then" rules, but by simply fusing two or more sets of sensor data in a single expanded neural net, the same functionality can be expected with a much reduced system cost. Experimental results of fusing two neural nets to enhance overall recognition performance using a controlled data set are presented.

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

NASA Astrophysics Data System (ADS)

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

2006-08-01

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

Control of adaptive immunity by the innate immune system.

PubMed

Iwasaki, Akiko; Medzhitov, Ruslan

2015-04-01

Microbial infections are recognized by the innate immune system both to elicit immediate defense and to generate long-lasting adaptive immunity. To detect and respond to vastly different groups of pathogens, the innate immune system uses several recognition systems that rely on sensing common structural and functional features associated with different classes of microorganisms. These recognition systems determine microbial location, viability, replication and pathogenicity. Detection of these features by recognition pathways of the innate immune system is translated into different classes of effector responses though specialized populations of dendritic cells. Multiple mechanisms for the induction of immune responses are variations on a common design principle wherein the cells that sense infections produce one set of cytokines to induce lymphocytes to produce another set of cytokines, which in turn activate effector responses. Here we discuss these emerging principles of innate control of adaptive immunity.

Computer-assisted visual interactive recognition and its prospects of implementation over the Internet

NASA Astrophysics Data System (ADS)

Zou, Jie; Gattani, Abhishek

2005-01-01

When completely automated systems don't yield acceptable accuracy, many practical pattern recognition systems involve the human either at the beginning (pre-processing) or towards the end (handling rejects). We believe that it may be more useful to involve the human throughout the recognition process rather than just at the beginning or end. We describe a methodology of interactive visual recognition for human-centered low-throughput applications, Computer Assisted Visual InterActive Recognition (CAVIAR), and discuss the prospects of implementing CAVIAR over the Internet. The novelty of CAVIAR is image-based interaction through a domain-specific parameterized geometrical model, which reduces the semantic gap between humans and computers. The user may interact with the computer anytime that she considers its response unsatisfactory. The interaction improves the accuracy of the classification features by improving the fit of the computer-proposed model. The computer makes subsequent use of the parameters of the improved model to refine not only its own statistical model-fitting process, but also its internal classifier. The CAVIAR methodology was applied to implement a flower recognition system. The principal conclusions from the evaluation of the system include: 1) the average recognition time of the CAVIAR system is significantly shorter than that of the unaided human; 2) its accuracy is significantly higher than that of the unaided machine; 3) it can be initialized with as few as one training sample per class and still achieve high accuracy; and 4) it demonstrates a self-learning ability. We have also implemented a Mobile CAVIAR system, where a pocket PC, as a client, connects to a server through wireless communication. The motivation behind a mobile platform for CAVIAR is to apply the methodology in a human-centered pervasive environment, where the user can seamlessly interact with the system for classifying field-data. Deploying CAVIAR to a networked mobile platform poses the challenge of classifying field images and programming under constraints of display size, network bandwidth, processor speed, and memory size. Editing of the computer-proposed model is performed on the handheld while statistical model fitting and classification take place on the server. The possibility that the user can easily take several photos of the object poses an interesting information fusion problem. The advantage of the Internet is that the patterns identified by different users can be pooled together to benefit all peer users. When users identify patterns with CAVIAR in a networked setting, they also collect training samples and provide opportunities for machine learning from their intervention. CAVIAR implemented over the Internet provides a perfect test bed for, and extends, the concept of Open Mind Initiative proposed by David Stork. Our experimental evaluation focuses on human time, machine and human accuracy, and machine learning. We devoted much effort to evaluating the use of our image-based user interface and on developing principles for the evaluation of interactive pattern recognition system. The Internet architecture and Mobile CAVIAR methodology have many applications. We are exploring in the directions of teledermatology, face recognition, and education.

Heterogeneous compute in computer vision: OpenCL in OpenCV

NASA Astrophysics Data System (ADS)

Gasparakis, Harris

2014-02-01

We explore the relevance of Heterogeneous System Architecture (HSA) in Computer Vision, both as a long term vision, and as a near term emerging reality via the recently ratified OpenCL 2.0 Khronos standard. After a brief review of OpenCL 1.2 and 2.0, including HSA features such as Shared Virtual Memory (SVM) and platform atomics, we identify what genres of Computer Vision workloads stand to benefit by leveraging those features, and we suggest a new mental framework that replaces GPU compute with hybrid HSA APU compute. As a case in point, we discuss, in some detail, popular object recognition algorithms (part-based models), emphasizing the interplay and concurrent collaboration between the GPU and CPU. We conclude by describing how OpenCL has been incorporated in OpenCV, a popular open source computer vision library, emphasizing recent work on the Transparent API, to appear in OpenCV 3.0, which unifies the native CPU and OpenCL execution paths under a single API, allowing the same code to execute either on CPU or on a OpenCL enabled device, without even recompiling.

The Affordance of Speech Recognition Technology for EFL Learning in an Elementary School Setting

ERIC Educational Resources Information Center

Liaw, Meei-Ling

2014-01-01

This study examined the use of speech recognition (SR) technology to support a group of elementary school children's learning of English as a foreign language (EFL). SR technology has been used in various language learning contexts. Its application to EFL teaching and learning is still relatively recent, but a solid understanding of its…

Pattern Recognition Receptors in Innate Immunity, Host Defense, and Immunopathology

ERIC Educational Resources Information Center

Suresh, Rahul; Mosser, David M.

2013-01-01

Infection by pathogenic microbes initiates a set of complex interactions between the pathogen and the host mediated by pattern recognition receptors. Innate immune responses play direct roles in host defense during the early stages of infection, and they also exert a profound influence on the generation of the adaptive immune responses that ensue.…

Analysis Of The IJCNN 2011 UTL Challenge

DTIC Science & Technology

2012-01-13

large datasets from various application domains: handwriting recognition, image recognition, video processing, text processing, and ecology. The goal...validation and final evaluation sets consist of 4096 examples each. Dataset Domain Features Sparsity Devel. Transf. AVICENNA Handwriting 120 0% 150205...documents [3]. Transfer learning methods could accelerate the application of handwriting recognizers to historical manuscript by reducing the need for

Apps, iPads, and Literacy: Examining the Feasibility of Speech Recognition in a First-Grade Classroom

ERIC Educational Resources Information Center

Baker, Elizabeth A.

2017-01-01

Informed by sociocultural and systems theory tenets, this study used ethnographic research methods to examine the feasibility of using speech recognition (SR) technology to support struggling readers in an early elementary classroom setting. Observations of eight first graders were conducted as they participated in a structured SR-supported…

Differences between Children and Adults in the Recognition of Enjoyment Smiles

ERIC Educational Resources Information Center

Del Giudice, Marco; Colle, Livia

2007-01-01

The authors investigated the differences between 8-year-olds (n = 80) and adults (n = 80) in recognition of felt versus faked enjoyment smiles by using a newly developed picture set that is based on the Facial Action Coding System. The authors tested the effect of different facial action units (AUs) on judgments of smile authenticity. Multiple…

Facilitating Recognition Memory: The Use of Distinctive Contexts in Study Materials and Tests.

ERIC Educational Resources Information Center

Marlin, Carol A.; And Others

The effects of distinctive background settings on children's recognition memory for subjects and objects of related sentences was examined. As a follow-up to a study by Levin, Ghatala, and Truman (1979), the effects of presenting distinctive background contexts in sentences and multiple-choice tests were separated from the effects of providing…

Detailed Phonetic Labeling of Multi-language Database for Spoken Language Processing Applications

DTIC Science & Technology

2015-03-01

which contains about 60 interfering speakers as well as background music in a bar. The top panel is again clean training /noisy testing settings, and...recognition system for Mandarin was developed and tested. Character recognition rates as high as 88% were obtained, using an approximately 40 training ...Tool_ComputeFeat.m) .............................................................................................................. 50 6.3. Training

26 CFR 1.367(a)-6T - Transfer of foreign branch with previously deducted losses (temporary).

Code of Federal Regulations, 2011 CFR

2011-04-01

... the recognition of the gain realized on the transfer. Paragraph (c) of this section sets forth rules concerning the character of, and limitations on, the gain required to be recognized. Paragraph (d) of this... section. Finally, paragraph (g) of this section defines the term foreign branch. (b) Recognition of gain...

A text input system developed by using lips image recognition based LabVIEW for the seriously disabled.

PubMed

Chen, S C; Shao, C L; Liang, C K; Lin, S W; Huang, T H; Hsieh, M C; Yang, C H; Luo, C H; Wuo, C M

2004-01-01

In this paper, we present a text input system for the seriously disabled by using lips image recognition based on LabVIEW. This system can be divided into the software subsystem and the hardware subsystem. In the software subsystem, we adopted the technique of image processing to recognize the status of mouth-opened or mouth-closed depending the relative distance between the upper lip and the lower lip. In the hardware subsystem, parallel port built in PC is used to transmit the recognized result of mouth status to the Morse-code text input system. Integrating the software subsystem with the hardware subsystem, we implement a text input system by using lips image recognition programmed in LabVIEW language. We hope the system can help the seriously disabled to communicate with normal people more easily.

A Compact Prototype of an Optical Pattern Recognition System

NASA Technical Reports Server (NTRS)

Jin, Y.; Liu, H. K.; Marzwell, N. I.

1996-01-01

In the Technology 2006 Case Studies/Success Stories presentation, we will describe and demonstrate a prototype of a compact optical pattern recognition system as an example of a successful technology transfer and continuuing development of state-of-the-art know-how by the close collaboration among government, academia, and small business via the NASA SBIR program. The prototype consists of a complete set of optical pattern recognition hardware with multi-channel storage and retrieval capability that is compactly configured inside a portable 1'X 2'X 3' aluminum case.

Recognition of blurred images by the method of moments.

PubMed

Flusser, J; Suk, T; Saic, S

1996-01-01

The article is devoted to the feature-based recognition of blurred images acquired by a linear shift-invariant imaging system against an image database. The proposed approach consists of describing images by features that are invariant with respect to blur and recognizing images in the feature space. The PSF identification and image restoration are not required. A set of symmetric blur invariants based on image moments is introduced. A numerical experiment is presented to illustrate the utilization of the invariants for blurred image recognition. Robustness of the features is also briefly discussed.

Reflections of the Gifted by the Gifted on the gifted

ERIC Educational Resources Information Center

Wittek, M. J.

1973-01-01

A 20-item open-ended questionnaire to assess self perception of gifted children in grades 5 through 7 indicates characteristics of high motivation, recognition of pride in special status, high competition for school honors, and strong reaction to parental pressure for highachievement. (MC)

Maritime Threat Detection using Plan Recognition

DTIC Science & Technology

2012-11-01

logic with a probabilistic interpretation to represent expert domain knowledge [13]. We used Alchemy [14] to implement MLN-BR. It interfaces with the...Domingos, P., & Lowd, D. (2009). Markov logic: An interface layer for AI. Morgan & Claypool. [14] Alchemy (2011). Alchemy ─ Open source AI. [http

Unilateral Hearing Loss: Understanding Speech Recognition and Localization Variability - Implications for Cochlear Implant Candidacy

PubMed Central

Firszt, Jill B.; Reeder, Ruth M.; Holden, Laura K.

2016-01-01

Objectives At a minimum, unilateral hearing loss (UHL) impairs sound localization ability and understanding speech in noisy environments, particularly if the loss is severe to profound. Accompanying the numerous negative consequences of UHL is considerable unexplained individual variability in the magnitude of its effects. Identification of co-variables that affect outcome and contribute to variability in UHLs could augment counseling, treatment options, and rehabilitation. Cochlear implantation as a treatment for UHL is on the rise yet little is known about factors that could impact performance or whether there is a group at risk for poor cochlear implant outcomes when hearing is near-normal in one ear. The overall goal of our research is to investigate the range and source of variability in speech recognition in noise and localization among individuals with severe to profound UHL and thereby help determine factors relevant to decisions regarding cochlear implantation in this population. Design The present study evaluated adults with severe to profound UHL and adults with bilateral normal hearing. Measures included adaptive sentence understanding in diffuse restaurant noise, localization, roving-source speech recognition (words from 1 of 15 speakers in a 140° arc) and an adaptive speech-reception threshold psychoacoustic task with varied noise types and noise-source locations. There were three age-gender-matched groups: UHL (severe to profound hearing loss in one ear and normal hearing in the contralateral ear), normal hearing listening bilaterally, and normal hearing listening unilaterally. Results Although the normal-hearing-bilateral group scored significantly better and had less performance variability than UHLs on all measures, some UHL participants scored within the range of the normal-hearing-bilateral group on all measures. The normal-hearing participants listening unilaterally had better monosyllabic word understanding than UHLs for words presented on the blocked/deaf side but not the open/hearing side. In contrast, UHLs localized better than the normal hearing unilateral listeners for stimuli on the open/hearing side but not the blocked/deaf side. This suggests that UHLs had learned strategies for improved localization on the side of the intact ear. The UHL and unilateral normal hearing participant groups were not significantly different for speech-in-noise measures. UHL participants with childhood rather than recent hearing loss onset localized significantly better; however, these two groups did not differ for speech recognition in noise. Age at onset in UHL adults appears to affect localization ability differently than understanding speech in noise. Hearing thresholds were significantly correlated with speech recognition for UHL participants but not the other two groups. Conclusions Auditory abilities of UHLs varied widely and could be explained only in part by hearing threshold levels. Age at onset and length of hearing loss influenced performance on some, but not all measures. Results support the need for a revised and diverse set of clinical measures, including sound localization, understanding speech in varied environments and careful consideration of functional abilities as individuals with severe to profound UHL are being considered potential cochlear implant candidates. PMID:28067750

[Explicit memory for type font of words in source monitoring and recognition tasks].

PubMed

Hatanaka, Yoshiko; Fujita, Tetsuya

2004-02-01

We investigated whether people can consciously remember type fonts of words by methods of examining explicit memory; source-monitoring and old/new-recognition. We set matched, non-matched, and non-studied conditions between the study and the test words using two kinds of type fonts; Gothic and MARU. After studying words in one way of encoding, semantic or physical, subjects in a source-monitoring task made a three way discrimination between new words, Gothic words, and MARU words (Exp. 1). Subjects in an old/new-recognition task indicated whether test words were previously presented or not (Exp. 2). We compared the source judgments with old/new recognition data. As a result, these data showed conscious recollection for type font of words on the source monitoring task and dissociation between source monitoring and old/new recognition performance.

Active Multimodal Sensor System for Target Recognition and Tracking

PubMed Central

Zhang, Guirong; Zou, Zhaofan; Liu, Ziyue; Mao, Jiansen

2017-01-01

High accuracy target recognition and tracking systems using a single sensor or a passive multisensor set are susceptible to external interferences and exhibit environmental dependencies. These difficulties stem mainly from limitations to the available imaging frequency bands, and a general lack of coherent diversity of the available target-related data. This paper proposes an active multimodal sensor system for target recognition and tracking, consisting of a visible, an infrared, and a hyperspectral sensor. The system makes full use of its multisensor information collection abilities; furthermore, it can actively control different sensors to collect additional data, according to the needs of the real-time target recognition and tracking processes. This level of integration between hardware collection control and data processing is experimentally shown to effectively improve the accuracy and robustness of the target recognition and tracking system. PMID:28657609

Fuzzy Set Methods for Object Recognition in Space Applications

NASA Technical Reports Server (NTRS)

Keller, James M. (Editor)

1992-01-01

Progress on the following four tasks is described: (1) fuzzy set based decision methodologies; (2) membership calculation; (3) clustering methods (including derivation of pose estimation parameters), and (4) acquisition of images and testing of algorithms.

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.

Recognition memory reveals just how CONTRASTIVE contrastive accenting really is

PubMed Central

Fraundorf, Scott H.; Watson, Duane G.; Benjamin, Aaron S.

2010-01-01

The effects of pitch accenting on memory were investigated in three experiments. Participants listened to short recorded discourses that contained contrast sets with two items (e.g. British scientists and French scientists); a continuation specified one item from the set. Pitch accenting on the critical word in the continuation was manipulated between non-contrastive (H* in the ToBI system) and contrastive (L+H*). On subsequent recognition memory tests, the L+H* accent increased hits to correct statements and correct rejections of the contrast item (Experiments 1–3), but did not impair memory for other parts of the discourse (Experiment 2). L+H* also did not facilitate correct rejections of lures not in the contrast set (Experiment 3), indicating that contrastive accents do not simply strengthen the representation of the target item. These results suggest comprehenders use pitch accenting to encode and update information about multiple elements in a contrast set. PMID:20835405

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

NASA Technical Reports Server (NTRS)

Zhang, Yuhan; Lu, Dr. Thomas

2010-01-01

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

The Visible Human Data Sets (VHD) and Insight Toolkit (ITk): Experiments in Open Source Software

PubMed Central

Ackerman, Michael J.; Yoo, Terry S.

2003-01-01

From its inception in 1989, the Visible Human Project was designed as an experiment in open source software. In 1994 and 1995 the male and female Visible Human data sets were released by the National Library of Medicine (NLM) as open source data sets. In 2002 the NLM released the first version of the Insight Toolkit (ITk) as open source software. PMID:14728278

A Joint Gaussian Process Model for Active Visual Recognition with Expertise Estimation in Crowdsourcing

PubMed Central

Long, Chengjiang; Hua, Gang; Kapoor, Ashish

2015-01-01

We present a noise resilient probabilistic model for active learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noise and the expertise level of each individual labeler with two levels of flip models. Expectation propagation is adopted for efficient approximate Bayesian inference of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to estimate both the global label noise and the expertise of each individual labeler. The probabilistic nature of our model immediately allows the adoption of the prediction entropy for active selection of data samples to be labeled, and active selection of high quality labelers based on their estimated expertise to label the data. We apply the proposed model for four visual recognition tasks, i.e., object category recognition, multi-modal activity recognition, gender recognition, and fine-grained classification, on four datasets with real crowd-sourced labels from the Amazon Mechanical Turk. The experiments clearly demonstrate the efficacy of the proposed model. In addition, we extend the proposed model with the Predictive Active Set Selection Method to speed up the active learning system, whose efficacy is verified by conducting experiments on the first three datasets. The results show our extended model can not only preserve a higher accuracy, but also achieve a higher efficiency. PMID:26924892

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

Influence of gender in the recognition of basic facial expressions: A critical literature review

PubMed Central

Forni-Santos, Larissa; Osório, Flávia L

2015-01-01

AIM: To conduct a systematic literature review about the influence of gender on the recognition of facial expressions of six basic emotions. METHODS: We made a systematic search with the search terms (face OR facial) AND (processing OR recognition OR perception) AND (emotional OR emotion) AND (gender or sex) in PubMed, PsycINFO, LILACS, and SciELO electronic databases for articles assessing outcomes related to response accuracy and latency and emotional intensity. The articles selection was performed according to parameters set by COCHRANE. The reference lists of the articles found through the database search were checked for additional references of interest. RESULTS: In respect to accuracy, women tend to perform better than men when all emotions are considered as a set. Regarding specific emotions, there seems to be no gender-related differences in the recognition of happiness, whereas results are quite heterogeneous in respect to the remaining emotions, especially sadness, anger, and disgust. Fewer articles dealt with the parameters of response latency and emotional intensity, which hinders the generalization of their findings, especially in the face of their methodological differences. CONCLUSION: The analysis of the studies conducted to date do not allow for definite conclusions concerning the role of the observer’s gender in the recognition of facial emotion, mostly because of the absence of standardized methods of investigation. PMID:26425447

Gene/protein name recognition based on support vector machine using dictionary as features.

PubMed

Mitsumori, Tomohiro; Fation, Sevrani; Murata, Masaki; Doi, Kouichi; Doi, Hirohumi

2005-01-01

Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

Spoken Language Processing in the Clarissa Procedure Browser

NASA Technical Reports Server (NTRS)

Rayner, M.; Hockey, B. A.; Renders, J.-M.; Chatzichrisafis, N.; Farrell, K.

2005-01-01

Clarissa, an experimental voice enabled procedure browser that has recently been deployed on the International Space Station, is as far as we know the first spoken dialog system in space. We describe the objectives of the Clarissa project and the system's architecture. In particular, we focus on three key problems: grammar-based speech recognition using the Regulus toolkit; methods for open mic speech recognition; and robust side-effect free dialogue management for handling undos, corrections and confirmations. We first describe the grammar-based recogniser we have build using Regulus, and report experiments where we compare it against a class N-gram recogniser trained off the same 3297 utterance dataset. We obtained a 15% relative improvement in WER and a 37% improvement in semantic error rate. The grammar-based recogniser moreover outperforms the class N-gram version for utterances of all lengths from 1 to 9 words inclusive. The central problem in building an open-mic speech recognition system is being able to distinguish between commands directed at the system, and other material (cross-talk), which should be rejected. Most spoken dialogue systems make the accept/reject decision by applying a threshold to the recognition confidence score. NASA shows how a simple and general method, based on standard approaches to document classification using Support Vector Machines, can give substantially better performance, and report experiments showing a relative reduction in the task-level error rate by about 25% compared to the baseline confidence threshold method. Finally, we describe a general side-effect free dialogue management architecture that we have implemented in Clarissa, which extends the "update semantics'' framework by including task as well as dialogue information in the information state. We show that this enables elegant treatments of several dialogue management problems, including corrections, confirmations, querying of the environment, and regression testing.

Face averages enhance user recognition for smartphone security.

PubMed

Robertson, David J; Kramer, Robin S S; Burton, A Mike

2015-01-01

Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual's 'face-average'--a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user's face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. We propose that this representation could significantly improve recognition rates in everyday settings.

Open ended intelligence: the individuation of intelligent agents

NASA Astrophysics Data System (ADS)

Weinbaum Weaver, David; Veitas, Viktoras

2017-03-01

Artificial general intelligence is a field of research aiming to distil the principles of intelligence that operate independently of a specific problem domain and utilise these principles in order to synthesise systems capable of performing any intellectual task a human being is capable of and beyond. While "narrow" artificial intelligence which focuses on solving specific problems such as speech recognition, text comprehension, visual pattern recognition and robotic motion has shown impressive breakthroughs lately, understanding general intelligence remains elusive. We propose a paradigm shift from intelligence perceived as a competence of individual agents defined in relation to an a priori given problem domain or a goal, to intelligence perceived as a formative process of self-organisation. We call this process open-ended intelligence. Starting with a brief introduction of the current conceptual approach, we expose a number of serious limitations that are traced back to the ontological roots of the concept of intelligence. Open-ended intelligence is then developed as an abstraction of the process of human cognitive development, so its application can be extended to general agents and systems. We introduce and discuss three facets of the idea: the philosophical concept of individuation, sense-making and the individuation of general cognitive agents. We further show how open-ended intelligence can be framed in terms of a distributed, self-organising network of interacting elements and how such process is scalable. The framework highlights an important relation between coordination and intelligence and a new understanding of values.

The New Environment for Development Evaluation

ERIC Educational Resources Information Center

Picciotto, Robert

2007-01-01

The millennium development goals have created new challenges for development evaluation. The main unit of account has shifted to the country level. Evaluation ownership must move from donor agencies to developing countries. The recognition that rich countries have development obligations is opening up evaluation frontiers beyond aid. A…

Learning the moves: the effect of familiarity and facial motion on person recognition across large changes in viewing format.

PubMed

Roark, Dana A; O'Toole, Alice J; Abdi, Hervé; Barrett, Susan E

2006-01-01

Familiarity with a face or person can support recognition in tasks that require generalization to novel viewing contexts. Using naturalistic viewing conditions requiring recognition of people from face or whole body gait stimuli, we investigated the effects of familiarity, facial motion, and direction of learning/test transfer on person recognition. Participants were familiarized with previously unknown people from gait videos and were tested on faces (experiment 1a) or were familiarized with faces and were tested with gait videos (experiment 1b). Recognition was more accurate when learning from the face and testing with the gait videos, than when learning from the gait videos and testing with the face. The repetition of a single stimulus, either the face or gait, produced strong recognition gains across transfer conditions. Also, the presentation of moving faces resulted in better performance than that of static faces. In experiment 2, we investigated the role of facial motion further by testing recognition with static profile images. Motion provided no benefit for recognition, indicating that structure-from-motion is an unlikely source of the motion advantage found in the first set of experiments.

A Delphi approach to reach consensus on primary care guidelines regarding youth violence prevention.

PubMed

De Vos, Edward; Spivak, Howard; Hatmaker-Flanigan, Elizabeth; Sege, Robert D

2006-10-01

Anticipatory guidance is a cornerstone of modern pediatric practice. In recognition of its importance for child well being, injury prevention counseling is a standard element of that guidance. Over the last 20 years, there has been growing recognition that intentional injury or violence is one of the leading causes of morbidity and mortality among youth. The US Surgeon General identified youth violence as a major public health issue and a top priority. Yet, only recently has the scope of injury prevention counseling been expanded to include violence. Pediatric health care providers agree that youth violence-prevention counseling should be provided, yet the number of topics available, the already lengthy list of other anticipatory guidance topics to be covered, developmental considerations, and the evidence base make the selection of an agreed-on set a considerable challenge. The purpose of this study was to systematically identify and prioritize specific counseling topics in violence prevention that could be integrated into anticipatory guidance best practice. A modified electronic Delphi process was used to gain consensus among 50 national multidisciplinary violence-prevention experts. Participants were unaware of other participants' identities. The process consisted of 4 serial rounds of inquiry beginning with a broad open-ended format for the generation of anticipatory guidance and screening topics across 5 age groups (infant, toddler, school age, adolescent, and all ages). Each subsequent round narrowed the list of topics toward the development of a manageable set of essential topics for screening and counseling about positive youth development and violence prevention. Forty-seven unique topics were identified, spanning birth to age 21 years. Topics cover 4 broad categories (building blocks): physical safety, parent centered, child centered, and community connection. Participants placed topics into their developmentally appropriate visit-based schedule and made suggestions for an appropriate topic reinforcement schedule. The resulting schedule provides topics for introduction and reinforcement at each visit. The Delphi technique proved a useful approach for accessing expert opinion, for analyzing and synthesizing results, for achieving consensus, and for setting priorities among the numerous anticipatory guidance and assessment topics relevant for raising resilient, violence-free youth.

Sensory, Cognitive, and Sensorimotor Learning Effects in Recognition Memory for Music.

PubMed

Mathias, Brian; Tillmann, Barbara; Palmer, Caroline

2016-08-01

Recent research suggests that perception and action are strongly interrelated and that motor experience may aid memory recognition. We investigated the role of motor experience in auditory memory recognition processes by musicians using behavioral, ERP, and neural source current density measures. Skilled pianists learned one set of novel melodies by producing them and another set by perception only. Pianists then completed an auditory memory recognition test during which the previously learned melodies were presented with or without an out-of-key pitch alteration while the EEG was recorded. Pianists indicated whether each melody was altered from or identical to one of the original melodies. Altered pitches elicited a larger N2 ERP component than original pitches, and pitches within previously produced melodies elicited a larger N2 than pitches in previously perceived melodies. Cortical motor planning regions were more strongly activated within the time frame of the N2 following altered pitches in previously produced melodies compared with previously perceived melodies, and larger N2 amplitudes were associated with greater detection accuracy following production learning than perception learning. Early sensory (N1) and later cognitive (P3a) components elicited by pitch alterations correlated with predictions of sensory echoic and schematic tonality models, respectively, but only for the perception learning condition, suggesting that production experience alters the extent to which performers rely on sensory and tonal recognition cues. These findings provide evidence for distinct time courses of sensory, schematic, and motoric influences within the same recognition task and suggest that learned auditory-motor associations influence responses to out-of-key pitches.

A dynamical pattern recognition model of gamma activity in auditory cortex

PubMed Central

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

2012-01-01

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

Automatic recognition of ship types from infrared images using superstructure moment invariants

NASA Astrophysics Data System (ADS)

Li, Heng; Wang, Xinyu

2007-11-01

Automatic object recognition is an active area of interest for military and commercial applications. In this paper, a system addressing autonomous recognition of ship types in infrared images is proposed. Firstly, an approach of segmentation based on detection of salient features of the target with subsequent shadow removing is proposed, as is the base of the subsequent object recognition. Considering the differences between the shapes of various ships mainly lie in their superstructures, we then use superstructure moment functions invariant to translation, rotation and scale differences in input patterns and develop a robust algorithm of obtaining ship superstructure. Subsequently a back-propagation neural network is used as a classifier in the recognition stage and projection images of simulated three-dimensional ship models are used as the training sets. Our recognition model was implemented and experimentally validated using both simulated three-dimensional ship model images and real images derived from video of an AN/AAS-44V Forward Looking Infrared(FLIR) sensor.

Understanding the impact of Fc glycosylation on its conformational changes by molecular dynamics simulations and bioinformatics.

PubMed

Zhang, Yubo

2015-12-01

N-linked glycosylation of Fc at N297 plays an important role in its effector function, aberrance of which would cause disease pathogenesis. Here, we performed all-atom molecular dynamics simulations to explore the effects of Fc glycosylation on its dynamics behaviors. Firstly, equilibrium simulations suggested that Fc deglycosylation was able to induce residual flexibility in its CH2 domain. Besides, the free energy landscape revealed three minimum energy wells in deglycosylated Fc, representing its "open", "semi-closed" and "closed" states. However, we could only observe the "open" state of glycosylated Fc. Supportively, principal component analysis emphasized the prominent motion of delyclosylated Fc and dynamically depicted how it changed from the "open" state to its "closed" state. Secondly, we studied the recognition mechanism of the Fc binding to its partners. Energy decomposition analysis identified key residues of Fc to recognize its two partners P13 and P34. Evidently, electrostatic potential surfaces showed that electrostatic attraction helped to stabilize the interaction between Fc and its partners. Also, relative binding free energies explained different binding affinities in Fc-P13 and Fc-P34. Collectively, these results together provided the structural basis for understanding conformational changes of deglycosylated Fc and the recognition mechanism of the Fc binding to its partners.

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.

Tracking the footsteps: a constructivist grounded theory of the clinical reasoning processes that registered nurses use to recognise delirium.

PubMed

El Hussein, Mohamed; Hirst, Sandra

2016-02-01

To construct a grounded theory that explains the clinical reasoning processes that registered nurses use to recognise delirium while caring for older adults in acute care settings. Delirium is often under-recognised in acute care settings; this may stem from underdeveloped clinical reasoning processes. Little is known about registered nurses' clinical reasoning processes in complex situations such as delirium recognition. Seventeen registered nurses working in acute care settings were interviewed. Concurrent data collection and analysis, constant comparative analysis and theoretical sampling were conducted in 2013-2014. A grounded theory approach was used to analyse interview data about the clinical reasoning processes of registered nurse in acute hospital settings. The core category that emerged from data was 'Tracking the footsteps'. This refers to the common clinical reasoning processes that registered nurses in this study used to recognise delirium in older adults in acute care settings. It depicted the process of continuously trying to catch the state of delirium in older adults. Understanding the clinical reasoning processes that contribute to delirium under-recognition provides a strategy by which this problem can be brought to the forefront of awareness and intervention by registered nurses. Registered nurses could draw from the various processes identified in this research to develop their clinical reasoning practice to enhance their effective assessment strategies. Delirium recognition by registered nurses will contribute to quality care to older adults. © 2016 John Wiley & Sons Ltd.

Processing statistics: an examination of focused and distributed attention using event related potentials.

PubMed

Baijal, Shruti; Nakatani, Chie; van Leeuwen, Cees; Srinivasan, Narayanan

2013-06-07

Human observers show remarkable efficiency in statistical estimation; they are able, for instance, to estimate the mean size of visual objects, even if their number exceeds the capacity limits of focused attention. This ability has been understood as the result of a distinct mode of attention, i.e. distributed attention. Compared to the focused attention mode, working memory representations under distributed attention are proposed to be more compressed, leading to reduced working memory loads. An alternate proposal is that distributed attention uses less structured, feature-level representations. These would fill up working memory (WM) more, even when target set size is low. Using event-related potentials, we compared WM loading in a typical distributed attention task (mean size estimation) to that in a corresponding focused attention task (object recognition), using a measure called contralateral delay activity (CDA). Participants performed both tasks on 2, 4, or 8 different-sized target disks. In the recognition task, CDA amplitude increased with set size; notably, however, in the mean estimation task the CDA amplitude was high regardless of set size. In particular for set-size 2, the amplitude was higher in the mean estimation task than in the recognition task. The result showed that the task involves full WM loading even with a low target set size. This suggests that in the distributed attention mode, representations are not compressed, but rather less structured than under focused attention conditions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Developing a Chinese Version of an Author Recognition Test for College Students in Taiwan

ERIC Educational Resources Information Center

Chen, Su-Yen; Fang, Sheng-Ping

2015-01-01

This study set out to develop a Chinese Author Recognition Test (CART) that might be used as a measure of objective print exposure for college students in Taiwan. We found that there is a linkage between print exposure and general reading achievement for college students. We also found that, among self-reported reading habits, comparative reading…

A Freely-Available Authoring System for Browser-Based CALL with Speech Recognition

ERIC Educational Resources Information Center

O'Brien, Myles

2017-01-01

A system for authoring browser-based CALL material incorporating Google speech recognition has been developed and made freely available for download. The system provides a teacher with a simple way to set up CALL material, including an optional image, sound or video, which will elicit spoken (and/or typed) answers from the user and check them…

Parent Pathways: Recognition and Responses to Developmental Delays in Young Children: A Mixed-Methods Exploratory Study

ERIC Educational Resources Information Center

Marshall, Jennifer Tess

2013-01-01

The importance of early recognition and intervention for developmental delays is increasingly acknowledged, yet high rates of under-enrollment and 1-3 year delays in entry to the public early intervention system continue. Much research has examined developmental screening in health and child care settings, but less well understood is what prompts…

Adult Trade Apprentices: Exploring the Significance of Recognition of Prior Learning and Skill Sets for Earlier Completion. Research Report

ERIC Educational Resources Information Center

Hargreaves, Jo; Blomberg, Davinia

2015-01-01

The nature of apprenticeships is changing. Increasing proportions of adult apprentices are prompting demand for various alternative pathways to completion. One option for an alternative pathway to accelerate completion is the use of recognition of prior learning (RPL) to identify existing skills and knowledge in combination with gap training. This…

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.

Weighted score-level feature fusion based on Dempster-Shafer evidence theory for action recognition

NASA Astrophysics Data System (ADS)

Zhang, Guoliang; Jia, Songmin; Li, Xiuzhi; Zhang, Xiangyin

2018-01-01

The majority of human action recognition methods use multifeature fusion strategy to improve the classification performance, where the contribution of different features for specific action has not been paid enough attention. We present an extendible and universal weighted score-level feature fusion method using the Dempster-Shafer (DS) evidence theory based on the pipeline of bag-of-visual-words. First, the partially distinctive samples in the training set are selected to construct the validation set. Then, local spatiotemporal features and pose features are extracted from these samples to obtain evidence information. The DS evidence theory and the proposed rule of survival of the fittest are employed to achieve evidence combination and calculate optimal weight vectors of every feature type belonging to each action class. Finally, the recognition results are deduced via the weighted summation strategy. The performance of the established recognition framework is evaluated on Penn Action dataset and a subset of the joint-annotated human metabolome database (sub-JHMDB). The experiment results demonstrate that the proposed feature fusion method can adequately exploit the complementarity among multiple features and improve upon most of the state-of-the-art algorithms on Penn Action and sub-JHMDB datasets.

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.

A Random Forest-based ensemble method for activity recognition.

PubMed

Feng, Zengtao; Mo, Lingfei; Li, Meng

2015-01-01

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

Hybrid generative-discriminative human action recognition by combining spatiotemporal words with supervised topic models

NASA Astrophysics Data System (ADS)

Sun, Hao; Wang, Cheng; Wang, Boliang

2011-02-01

We present a hybrid generative-discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. A video sequence is represented as a collection of spatiotemporal words by extracting space-time interest points and describing these points using both shape and motion cues. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure that is most relevant to action categorization. The proposed algorithm retains most of the desirable properties of generative learning while increasing the classification performance though a discriminative setting. It has also been extended to exploit both labeled data and unlabeled data to learn human actions under a unified framework. We test our algorithm on three challenging data sets: the KTH human motion data set, the Weizmann human action data set, and a ballet data set. Our results are either comparable to or significantly better than previously published results on these data sets and reflect the promise of hybrid generative-discriminative learning approaches.

Still-to-video face recognition in unconstrained environments

NASA Astrophysics Data System (ADS)

Wang, Haoyu; Liu, Changsong; Ding, Xiaoqing

2015-02-01

Face images from video sequences captured in unconstrained environments usually contain several kinds of variations, e.g. pose, facial expression, illumination, image resolution and occlusion. Motion blur and compression artifacts also deteriorate recognition performance. Besides, in various practical systems such as law enforcement, video surveillance and e-passport identification, only a single still image per person is enrolled as the gallery set. Many existing methods may fail to work due to variations in face appearances and the limit of available gallery samples. In this paper, we propose a novel approach for still-to-video face recognition in unconstrained environments. By assuming that faces from still images and video frames share the same identity space, a regularized least squares regression method is utilized to tackle the multi-modality problem. Regularization terms based on heuristic assumptions are enrolled to avoid overfitting. In order to deal with the single image per person problem, we exploit face variations learned from training sets to synthesize virtual samples for gallery samples. We adopt a learning algorithm combining both affine/convex hull-based approach and regularizations to match image sets. Experimental results on a real-world dataset consisting of unconstrained video sequences demonstrate that our method outperforms the state-of-the-art methods impressively.

B-cell Ligand Processing Pathways Detected by Large-scale Comparative Analysis

PubMed Central

Towfic, Fadi; Gupta, Shakti; Honavar, Vasant; Subramaniam, Shankar

2012-01-01

The initiation of B-cell ligand recognition is a critical step for the generation of an immune response against foreign bodies. We sought to identify the biochemical pathways involved in the B-cell ligand recognition cascade and sets of ligands that trigger similar immunological responses. We utilized several comparative approaches to analyze the gene coexpression networks generated from a set of microarray experiments spanning 33 different ligands. First, we compared the degree distributions of the generated networks. Second, we utilized a pairwise network alignment algorithm, BiNA, to align the networks based on the hubs in the networks. Third, we aligned the networks based on a set of KEGG pathways. We summarized our results by constructing a consensus hierarchy of pathways that are involved in B cell ligand recognition. The resulting pathways were further validated through literature for their common physiological responses. Collectively, the results based on our comparative analyses of degree distributions, alignment of hubs, and alignment based on KEGG pathways provide a basis for molecular characterization of the immune response states of B-cells and demonstrate the power of comparative approaches (e.g., gene coexpression network alignment algorithms) in elucidating biochemical pathways involved in complex signaling events in cells. PMID:22917187

Software for Partly Automated Recognition of Targets

NASA Technical Reports Server (NTRS)

Opitz, David; Blundell, Stuart; Bain, William; Morris, Matthew; Carlson, Ian; Mangrich, Mark

2003-01-01

The Feature Analyst is a computer program for assisted (partially automated) recognition of targets in images. This program was developed to accelerate the processing of high-resolution satellite image data for incorporation into geographic information systems (GIS). This program creates an advanced user interface that embeds proprietary machine-learning algorithms in commercial image-processing and GIS software. A human analyst provides samples of target features from multiple sets of data, then the software develops a data-fusion model that automatically extracts the remaining features from selected sets of data. The program thus leverages the natural ability of humans to recognize objects in complex scenes, without requiring the user to explain the human visual recognition process by means of lengthy software. Two major subprograms are the reactive agent and the thinking agent. The reactive agent strives to quickly learn the user s tendencies while the user is selecting targets and to increase the user s productivity by immediately suggesting the next set of pixels that the user may wish to select. The thinking agent utilizes all available resources, taking as much time as needed, to produce the most accurate autonomous feature-extraction model possible.

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.

Recognition of voice commands using adaptation of foreign language speech recognizer via selection of phonetic transcriptions

NASA Astrophysics Data System (ADS)

Maskeliunas, Rytis; Rudzionis, Vytautas

2011-06-01

In recent years various commercial speech recognizers have become available. These recognizers provide the possibility to develop applications incorporating various speech recognition techniques easily and quickly. All of these commercial recognizers are typically targeted to widely spoken languages having large market potential; however, it may be possible to adapt available commercial recognizers for use in environments where less widely spoken languages are used. Since most commercial recognition engines are closed systems the single avenue for the adaptation is to try set ways for the selection of proper phonetic transcription methods between the two languages. This paper deals with the methods to find the phonetic transcriptions for Lithuanian voice commands to be recognized using English speech engines. The experimental evaluation showed that it is possible to find phonetic transcriptions that will enable the recognition of Lithuanian voice commands with recognition accuracy of over 90%.

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.

Three-dimensional object recognition using similar triangles and decision trees

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

1993-01-01

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

Integrated system for automated financial document processing

NASA Astrophysics Data System (ADS)

Hassanein, Khaled S.; Wesolkowski, Slawo; Higgins, Ray; Crabtree, Ralph; Peng, Antai

1997-02-01

A system was developed that integrates intelligent document analysis with multiple character/numeral recognition engines in order to achieve high accuracy automated financial document processing. In this system, images are accepted in both their grayscale and binary formats. A document analysis module starts by extracting essential features from the document to help identify its type (e.g. personal check, business check, etc.). These features are also utilized to conduct a full analysis of the image to determine the location of interesting zones such as the courtesy amount and the legal amount. These fields are then made available to several recognition knowledge sources such as courtesy amount recognition engines and legal amount recognition engines through a blackboard architecture. This architecture allows all the available knowledge sources to contribute incrementally and opportunistically to the solution of the given recognition query. Performance results on a test set of machine printed business checks using the integrated system are also reported.

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.

An Individual Finger Gesture Recognition System Based on Motion-Intent Analysis Using Mechanomyogram Signal

PubMed Central

Ding, Huijun; He, Qing; Zhou, Yongjin; Dan, Guo; Cui, Song

2017-01-01

Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human–computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results. PMID:29167655

Scene recognition based on integrating active learning with dictionary learning

NASA Astrophysics Data System (ADS)

Wang, Chengxi; Yin, Xueyan; Yang, Lin; Gong, Chengrong; Zheng, Caixia; Yi, Yugen

2018-04-01

Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.

MicroRNA-132 regulates recognition memory and synaptic plasticity in the perirhinal cortex

PubMed Central

Scott, Helen L; Tamagnini, Francesco; Narduzzo, Katherine E; Howarth, Joanna L; Lee, Youn-Bok; Wong, Liang-Fong; Brown, Malcolm W; Warburton, Elizabeth C; Bashir, Zafar I; Uney, James B

2012-01-01

Evidence suggests that the acquisition of recognition memory depends upon CREB-dependent long-lasting changes in synaptic plasticity in the perirhinal cortex. The CREB-responsive microRNA miR-132 has been shown to regulate synaptic transmission and we set out to investigate a role for this microRNA in recognition memory and its underlying plasticity mechanisms. To this end we mediated the specific overexpression of miR-132 selectively in the rat perirhinal cortex and demonstrated impairment in short-term recognition memory. This functional deficit was associated with a reduction in both long-term depression and long-term potentiation. These results confirm that microRNAs are key coordinators of the intracellular pathways that mediate experience-dependent changes in the brain. In addition, these results demonstrate a role for miR-132 in the neuronal mechanisms underlying the formation of short-term recognition memory. PMID:22845676

Magendie and Luschka: Holes in the 4th ventricle.

PubMed

Engelhardt, Eliasz

2016-01-01

Cerebrospinal fluid (CSF) is a complex liquid formed mainly by the choroid plexuses. After filling the ventricular system where it circulates, CSF flows out to the subarachnoid spaces through openings in the 4 th ventricle. Following numerous studies on CSF pathways, these openings were first discovered in the 19 th century by two notable researchers, François Magendie and Hubert von Luschka, who described the median and lateral openings subsequently named after them. Even after the studies of Axel Key and Gustav Magnus Retzius confirming these openings, their existence was questioned by many anatomists, yet acknowledged by others. Finally gaining the acceptance of all, recognition of the holes endures to the present day. Interest in these openings may be attributed to the several congenital or acquired pathological conditions that may affect them, usually associated with hydrocephalus. We report some historical aspects of these apertures and their discoverers.

Discriminability effect on Garner interference: evidence from recognition of facial identity and expression

PubMed Central

Wang, Yamin; Fu, Xiaolan; Johnston, Robert A.; Yan, Zheng

2013-01-01

Using Garner’s speeded classification task existing studies demonstrated an asymmetric interference in the recognition of facial identity and facial expression. It seems that expression is hard to interfere with identity recognition. However, discriminability of identity and expression, a potential confounding variable, had not been carefully examined in existing studies. In current work, we manipulated discriminability of identity and expression by matching facial shape (long or round) in identity and matching mouth (opened or closed) in facial expression. Garner interference was found either from identity to expression (Experiment 1) or from expression to identity (Experiment 2). Interference was also found in both directions (Experiment 3) or in neither direction (Experiment 4). The results support that Garner interference tends to occur under condition of low discriminability of relevant dimension regardless of facial property. Our findings indicate that Garner interference is not necessarily related to interdependent processing in recognition of facial identity and expression. The findings also suggest that discriminability as a mediating factor should be carefully controlled in future research. PMID:24391609

Pesticide Emergency Resources

Science.gov Websites

National Pesticide Information Center 1.800.858.7378 npic@ace.orst.edu We're open from 8:00AM to 12 Plants Pest Control Identify Your Pest Learn About Your Pest Control Your Pest Integrated Pest Management Home Page Emergency Resources Related Topics: Pesticide Incidents Recognition and Management of

Cultural Competence and School Counselor Training: A Collective Case Study

ERIC Educational Resources Information Center

Nelson, Judith A.; Bustamante, Rebecca; Sawyer, Cheryl; Sloan, Eva D.

2015-01-01

This collective case study investigated the experiences of bilingual counselors-in-training who assessed school-wide cultural competence in public schools. Analysis and interpretation of data resulted in the identification of 5 themes: eye-opening experiences, recognition of strengths, the role of school leaders, road maps for change, and…

Mathematics Competency Test: User's Manual.

ERIC Educational Resources Information Center

Vernon, P. E.; And Others

The Mathematics Competency Test is a 46-question written test assessing mathematics achievement for groups or individuals aged 11 to adult. It is suitable for use with groups or individuals in school, college and workplace contexts. The questions are open-ended and require constructed responses rather than recognition of a correct answer in a…

GED Revision Opens Path to Higher Ed.

ERIC Educational Resources Information Center

Gewertz, Catherine

2011-01-01

The General Educational Development program, or GED, is undergoing the biggest revamping in its 69-year history, driven by mounting recognition that young adults' future success depends on getting more than a high-school-level education. Potent forces have converged to stoke the GED's redesign. A labor market that increasingly seeks some…

Effects of the HN gene c-terminal extensions on the Newcastle disease virus virulence

USDA-ARS?s Scientific Manuscript database

The hemagglutinin-neuraminidase (HN) of Newcastle disease virus (NDV) is a multifunctional protein that has receptor recognition, neuraminidase and fusion promotion activities. Sequence analysis revealed that the HN gene of many extremely low virulence NDV strains encodes a larger open reading frame...

Operational Considerations for Opening a Branch Campus Abroad

ERIC Educational Resources Information Center

Harding, Lawrence M.; Lammey, Robert W.

2011-01-01

Universities have been attracted to the creation of international branch campuses (IBCs) for many reasons, including cultural immersion of students and faculty and global brand recognition for a university seeking to enhance its reputation and strengthen its academic standards. This chapter provides specific advice for how IBCs can negotiate entry…

45 CFR 73.735-505 - Acceptance of awards and prizes.

Code of Federal Regulations, 2010 CFR

2010-10-01

...) Employees may accept awards, including cash awards, given in recognition of a meritorious public... employee in the performance of his or her offical duties, advice about the acceptance of it should be... trophies, entertainment, rewards, and prizes given to competitors in contests or events which are open to...

45 CFR 73.735-505 - Acceptance of awards and prizes.

Code of Federal Regulations, 2011 CFR

2011-10-01

...) Employees may accept awards, including cash awards, given in recognition of a meritorious public... employee in the performance of his or her offical duties, advice about the acceptance of it should be... trophies, entertainment, rewards, and prizes given to competitors in contests or events which are open to...

Changing Conceptions of Employee Compensation

ERIC Educational Resources Information Center

Dixon, Mark R.; Hayes, Linda J.

2004-01-01

This paper reviews and discusses many differing forms of incentive compensation systems that are being used in today's organizations. The review traces the roots of bonus compensation from individual piece-work plans through the adoption of organization-wide gain sharing plans to the growing recognition of open-book management. Reasons for the…

78 FR 71611 - Appraisal Subcommittee; Notice of Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-29

... that the Appraisal Subcommittee (ASC) will meet in open session for its regular meeting: Location: Federal Reserve Board--International Square location, 1850 K Street NW., 4th Floor, Washington, DC 20006... Compliance Review(s) Staff Service Recognition How To Attend and Observe an ASC Meeting If you plan to attend...

[Vis-NIR spectroscopic pattern recognition combined with SG smoothing applied to breed screening of transgenic sugarcane].

PubMed

Liu, Gui-Song; Guo, Hao-Song; Pan, Tao; Wang, Ji-Hua; Cao, Gan

2014-10-01

Based on Savitzky-Golay (SG) smoothing screening, principal component analysis (PCA) combined with separately supervised linear discriminant analysis (LDA) and unsupervised hierarchical clustering analysis (HCA) were used for non-destructive visible and near-infrared (Vis-NIR) detection for breed screening of transgenic sugarcane. A random and stability-dependent framework of calibration, prediction, and validation was proposed. A total of 456 samples of sugarcane leaves planting in the elongating stage were collected from the field, which was composed of 306 transgenic (positive) samples containing Bt and Bar gene and 150 non-transgenic (negative) samples. A total of 156 samples (negative 50 and positive 106) were randomly selected as the validation set; the remaining samples (negative 100 and positive 200, a total of 300 samples) were used as the modeling set, and then the modeling set was subdivided into calibration (negative 50 and positive 100, a total of 150 samples) and prediction sets (negative 50 and positive 100, a total of 150 samples) for 50 times. The number of SG smoothing points was ex- panded, while some modes of higher derivative were removed because of small absolute value, and a total of 264 smoothing modes were used for screening. The pairwise combinations of first three principal components were used, and then the optimal combination of principal components was selected according to the model effect. Based on all divisions of calibration and prediction sets and all SG smoothing modes, the SG-PCA-LDA and SG-PCA-HCA models were established, the model parameters were optimized based on the average prediction effect for all divisions to produce modeling stability. Finally, the model validation was performed by validation set. With SG smoothing, the modeling accuracy and stability of PCA-LDA, PCA-HCA were signif- icantly improved. For the optimal SG-PCA-LDA model, the recognition rate of positive and negative validation samples were 94.3%, 96.0%; and were 92.5%, 98.0% for the optimal SG-PCA-LDA model, respectively. Vis-NIR spectro- scopic pattern recognition combined with SG smoothing could be used for accurate recognition of transgenic sugarcane leaves, and provided a convenient screening method for transgenic sugarcane breeding.

Homing endonucleases: from basics to therapeutic applications.

PubMed

Marcaida, Maria J; Muñoz, Inés G; Blanco, Francisco J; Prieto, Jesús; Montoya, Guillermo

2010-03-01

Homing endonucleases (HE) are double-stranded DNAses that target large recognition sites (12-40 bp). HE-encoding sequences are usually embedded in either introns or inteins. Their recognition sites are extremely rare, with none or only a few of these sites present in a mammalian-sized genome. However, these enzymes, unlike standard restriction endonucleases, tolerate some sequence degeneracy within their recognition sequence. Several members of this enzyme family have been used as templates to engineer tools to cleave DNA sequences that differ from their original wild-type targets. These custom HEs can be used to stimulate double-strand break homologous recombination in cells, to induce the repair of defective genes with very low toxicity levels. The use of tailored HEs opens up new possibilities for gene therapy in patients with monogenic diseases that can be treated ex vivo. This review provides an overview of recent advances in this field.

Presentation of lipid antigens to T cells.

PubMed

Mori, Lucia; De Libero, Gennaro

2008-04-15

T cells specific for lipid antigens participate in regulation of the immune response during infections, tumor immunosurveillance, allergy and autoimmune diseases. T cells recognize lipid antigens as complexes formed with CD1 antigen-presenting molecules, thus resembling recognition of MHC-peptide complexes. The biophysical properties of lipids impose unique mechanisms for their delivery, internalization into antigen-presenting cells, membrane trafficking, processing, and loading of CD1 molecules. Each of these steps is controlled at molecular and celular levels and determines lipid immunogenicity. Lipid antigens may derive from microbes and from the cellular metabolism, thus allowing the immune system to survey a large repertoire of immunogenic molecules. Recognition of lipid antigens facilitates the detection of infectious agents and the initiation of responses involved in immunoregulation and autoimmunity. This review focuses on the presentation mechanisms and specific recognition of self and bacterial lipid antigens and discusses the important open issues.

Door recognition in cluttered building interiors using imagery and lidar data

NASA Astrophysics Data System (ADS)

Díaz-Vilariño, L.; Martínez-Sánchez, J.; Lagüela, S.; Armesto, J.; Khoshelham, K.

2014-06-01

Building indoors reconstruction is an active research topic due to the importance of the wide range of applications to which they can be subjected, from architecture and furniture design, to movies and video games editing, or even crime scene investigation. Among the constructive elements defining the inside of a building, doors are important entities in applications like routing and navigation, and their automated recognition is advantageous e.g. in case of large multi-storey buildings with many office rooms. The inherent complexity of the automation of the recognition process is increased by the presence of clutter and occlusions, difficult to avoid in indoor scenes. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors using information acquired in the form of point clouds and images. The methodology goes in depth with door detection and labelling as either opened, closed or furniture (false positive)

Protein-targeted corona phase molecular recognition

PubMed Central

Bisker, Gili; Dong, Juyao; Park, Hoyoung D.; Iverson, Nicole M.; Ahn, Jiyoung; Nelson, Justin T.; Landry, Markita P.; Kruss, Sebastian; Strano, Michael S.

2016-01-01

Corona phase molecular recognition (CoPhMoRe) uses a heteropolymer adsorbed onto and templated by a nanoparticle surface to recognize a specific target analyte. This method has not yet been extended to macromolecular analytes, including proteins. Herein we develop a variant of a CoPhMoRe screening procedure of single-walled carbon nanotubes (SWCNT) and use it against a panel of human blood proteins, revealing a specific corona phase that recognizes fibrinogen with high selectivity. In response to fibrinogen binding, SWCNT fluorescence decreases by >80% at saturation. Sequential binding of the three fibrinogen nodules is suggested by selective fluorescence quenching by isolated sub-domains and validated by the quenching kinetics. The fibrinogen recognition also occurs in serum environment, at the clinically relevant fibrinogen concentrations in the human blood. These results open new avenues for synthetic, non-biological antibody analogues that recognize biological macromolecules, and hold great promise for medical and clinical applications. PMID:26742890

WW Domains of the Yes-Kinase-Associated-Protein (YAP) Transcriptional Regulator Behave as Independent Units with Different Binding Preferences for PPxY Motif-Containing Ligands

PubMed Central

Iglesias-Bexiga, Manuel; Castillo, Francisco; Cobos, Eva S.; Oka, Tsutomu; Sudol, Marius; Luque, Irene

2015-01-01

YAP is a WW domain-containing effector of the Hippo tumor suppressor pathway, and the object of heightened interest as a potent oncogene and stemness factor. YAP has two major isoforms that differ in the number of WW domains they harbor. Elucidating the degree of co-operation between these WW domains is important for a full understanding of the molecular function of YAP. We present here a detailed biophysical study of the structural stability and binding properties of the two YAP WW domains aimed at investigating the relationship between both domains in terms of structural stability and partner recognition. We have carried out a calorimetric study of the structural stability of the two YAP WW domains, both isolated and in a tandem configuration, and their interaction with a set of functionally relevant ligands derived from PTCH1 and LATS kinases. We find that the two YAP WW domains behave as independent units with different binding preferences, suggesting that the presence of the second WW domain might contribute to modulate target recognition between the two YAP isoforms. Analysis of structural models and phage-display studies indicate that electrostatic interactions play a critical role in binding specificity. Together, these results are relevant to understand of YAP function and open the door to the design of highly specific ligands of interest to delineate the functional role of each WW domain in YAP signaling. PMID:25607641

[Scholarships for a science in crisis: the JAE as sponsor for macroscopic anatomy (1912-1931)].

PubMed

Velasco Morgado, Raúl

2010-01-01

Beside the creation of national research institutions, the patronage work of the JAE (through scholarships and recognition given to Spanish scientists in the first third of the 20th century) was important in opening the door to the silver age of Spanish science. In the morphologic sciences, macroscopic anatomy was an almost closed science and in crisis with regard to the microscopic sciences and embryology. Despite this setting, the JAE chose to promote this science, importing European anatomical pedagogy and including the technologies and philosophy of the new dynamic anatomy under way on the continent. In this paper, we analyze the grantholders listed in the JAE archives and the studies that they published by them. We conclude that the utilization of these grants played an important role in promoting the international exchange necessary for the reform of a science in crisis, with anatomical pedagogy and technology being the major protagonists of this renewal.

Knock detection system to improve petrol engine performance, using microphone sensor

NASA Astrophysics Data System (ADS)

Sujono, Agus; Santoso, Budi; Juwana, Wibawa Endra

2017-01-01

An increase of power and efficiency of spark ignition engines (petrol engines) are always faced with the problem of knock. Even the characteristics of the engine itself are always determined from the occurrence of knock. Until today, this knocking problem has not been solved completely. Knock is caused by principal factors that are influenced by the engine rotation, the load or opening the throttle and spark advance (ignition timing). In this research, the engine is mounted on the engine test bed (ETB) which is equipped with the necessary sensors. Knock detection using a new method, which is based on pattern recognition, which through the knock sound detection by using a microphone sensor, active filter, the regression of the normalized envelope function, and the calculation of the Euclidean distance is used for identifying knock. This system is implemented with a microcontroller which uses fuzzy logic controller ignition (FLIC), which aims to set proper spark advance, in accordance with operating conditions. This system can improve the engine performance for approximately 15%.

Silicon nanowires: where mechanics and optics meet at the nanoscale.

PubMed

Ramos, Daniel; Gil-Santos, Eduardo; Malvar, Oscar; Llorens, Jose M; Pini, Valerio; San Paulo, Alvaro; Calleja, Montserrat; Tamayo, Javier

2013-12-06

Mechanical transducers based on nanowires promise revolutionary advances in biological sensing and force microscopy/spectroscopy. A crucial step is the development of simple and non-invasive techniques able to detect displacements with subpicometer sensitivity per unit bandwidth. Here, we design suspended tapered silicon nanowires supporting a range of optical resonances that confine and efficiently scatter light in the visible range. Then, we develop an optical method for efficiently coupling the evanescent field to the regular interference pattern generated by an incoming laser beam and the reflected beam from the substrate underneath the nanowire. This optomechanical coupling is here applied to measure the displacement of 50 nm wide nanowires with sensitivity on the verge of 1 fm/Hz(1/2) at room temperature with a simple laser interferometry set-up. This method opens the door to the measurement of the Brownian motion of ultrashort nanowires for the detection of single biomolecular recognition events in liquids, and single molecule spectroscopy in vacuum.

KSC-06pd0464

NASA Image and Video Library

2006-03-10

KENNEDY SPACE CENTER, FLA. - During opening ceremonies of the 2006 FIRST Robotics Regional Competition held March 9-11 at the University of Central Florida in Orlando, Florida Governor Jeb Bush poses with recipients of the Governor's Award trophy. The FIRST Robotics Competition challenges teams of young people and their mentors to solve a common problem in a six-week timeframe using a standard "kit of parts" and a common set of rules. Teams build robots from the parts and enter them in a series of competitions. FIRST, which is based on "For Inspiration and Recognition of Science and Technology," redefines winning for these students. Teams are rewarded for excellence in design, demonstrated team spirit, gracious professionalism and maturity, and ability to overcome obstacles. Scoring the most points is a secondary goal. Winning means building partnerships that last. NASA and the University of Central Florida are co-sponsors of the regional event, which this year included more than 50 teams. Photo credit: NASA/Kim Shiflett

KSC-06pd0462

NASA Image and Video Library

2006-03-10

KENNEDY SPACE CENTER, FLA. - Opening ceremonies of the 2006 FIRST Robotics Regional Competition held March 9-11 at the University of Central Florida in Orlando included Florida Governor Jeb Bush (center). At left is Sam Mallikarjunan from Rockledge High School, and at right is Stephanie Alphonso from Freedom High School in Orlando. The FIRST Robotics Competition challenges teams of young people and their mentors to solve a common problem in a six-week timeframe using a standard "kit of parts" and a common set of rules. Teams build robots from the parts and enter them in a series of competitions. FIRST, which is based on "For Inspiration and Recognition of Science and Technology," redefines winning for these students. Teams are rewarded for excellence in design, demonstrated team spirit, gracious professionalism and maturity, and ability to overcome obstacles. Scoring the most points is a secondary goal. Winning means building partnerships that last. NASA and the University of Central Florida are co-sponsors of the regional event, which this year included more than 50 teams. Photo credit: NASA/Kim Shiflett

KSC-06pd0463

NASA Image and Video Library

2006-03-10

KENNEDY SPACE CENTER, FLA. - During opening ceremonies of the 2006 FIRST Robotics Regional Competition held March 9-11 at the University of Central Florida in Orlando, Florida Governor Jeb Bush receives the inaugural Governor's Award trophy from Sam Mallikarjunan from Rockledge High School and Stephanie Alphonso from Freedom High School in Orlando. The FIRST Robotics Competition challenges teams of young people and their mentors to solve a common problem in a six-week timeframe using a standard "kit of parts" and a common set of rules. Teams build robots from the parts and enter them in a series of competitions. FIRST, which is based on "For Inspiration and Recognition of Science and Technology," redefines winning for these students. Teams are rewarded for excellence in design, demonstrated team spirit, gracious professionalism and maturity, and ability to overcome obstacles. Scoring the most points is a secondary goal. Winning means building partnerships that last. NASA and the University of Central Florida are co-sponsors of the regional event, which this year included more than 50 teams. Photo credit: NASA/Kim Shiflett

Developing Local Board of Health Guidelines to Promote Healthy Food Access — King County, Washington, 2010–2012

PubMed Central

Johnson, Donna B.; Krieger, James; MacDougall, Erin; Payne, Elizabeth; Chan, Nadine L.

2015-01-01

Policies that change environments are important tools for preventing chronic diseases, including obesity. Boards of health often have authority to adopt such policies, but few do so. This study assesses 1) how one local board of health developed a policy approach for healthy food access through vending machine guidelines (rather than regulations) and 2) the impact of the approach. Using a case study design guided by “three streams” policy theory and RE-AIM, we analyzed data from a focus group, interviews, and policy documents. The guidelines effectively supported institutional policy development in several settings. Recognition of the problem of chronic disease and the policy solution of vending machine guidelines created an opening for the board to influence nutrition environments. Institutions identified a need for support in adopting vending machine policies. Communities could benefit from the study board’s approach to using nonregulatory evidence-based guidelines as a policy tool. PMID:25927606

Smart Secure Homes: A Survey of Smart Home Technologies that Sense, Assess, and Respond to Security Threats.

PubMed

Dahmen, Jessamyn; Cook, Diane J; Wang, Xiaobo; Honglei, Wang

2017-08-01

Smart home design has undergone a metamorphosis in recent years. The field has evolved from designing theoretical smart home frameworks and performing scripted tasks in laboratories. Instead, we now find robust smart home technologies that are commonly used by large segments of the population in a variety of settings. Recent smart home applications are focused on activity recognition, health monitoring, and automation. In this paper, we take a look at another important role for smart homes: security. We first explore the numerous ways smart homes can and do provide protection for their residents. Next, we provide a comparative analysis of the alternative tools and research that has been developed for this purpose. We investigate not only existing commercial products that have been introduced but also discuss the numerous research that has been focused on detecting and identifying potential threats. Finally, we close with open challenges and ideas for future research that will keep individuals secure and healthy while in their own homes.

A Phosphorylated Cytoplasmic Autoantigen, GW182, Associates with a Unique Population of Human mRNAs within Novel Cytoplasmic Speckles

PubMed Central

Eystathioy, Theophany; Chan, Edward K. L.; Tenenbaum, Scott A.; Keene, Jack D.; Griffith, Kevin; Fritzler, Marvin J.

2002-01-01

A novel human cellular structure has been identified that contains a unique autoimmune antigen and multiple messenger RNAs. This complex was discovered using an autoimmune serum from a patient with motor and sensory neuropathy and contains a protein of 182 kDa. The gene and cDNA encoding the protein indicated an open reading frame with glycine-tryptophan (GW) repeats and a single RNA recognition motif. Both the patient's serum and a rabbit serum raised against the recombinant GW protein costained discrete cytoplasmic speckles designated as GW bodies (GWBs) that do not overlap with the Golgi complex, endosomes, lysosomes, or peroxisomes. The mRNAs associated with GW182 represent a clustered set of transcripts that are presumed to reside within the GW complexes. We propose that the GW ribonucleoprotein complex is involved in the posttranscriptional regulation of gene expression by sequestering a specific subset of gene transcripts involved in cell growth and homeostasis. PMID:11950943

Thermodynamic Modeling of Donor Splice Site Recognition in pre-mRNA

NASA Astrophysics Data System (ADS)

Aalberts, Daniel P.; Garland, Jeffrey A.

2004-03-01

When eukaryotic genes are edited by the spliceosome, the first step in intron recognition is the binding of a U1 snRNA with the donor (5') splice site. We model this interaction thermodynamically to identify splice sites. Applied to a set of 65 annotated genes, our Finding with Binding method achieves a significant separation between real and false sites. Analyzing binding patterns allows us to discard a large number of decoy sites. Our results improve statistics-based methods for donor site recognition, demonstrating the promise of physical modeling to find functional elements in the genome.

Thermodynamic modeling of donor splice site recognition in pre-mRNA

NASA Astrophysics Data System (ADS)

Garland, Jeffrey A.; Aalberts, Daniel P.

2004-04-01

When eukaryotic genes are edited by the spliceosome, the first step in intron recognition is the binding of a U1 small nuclear RNA with the donor ( 5' ) splice site. We model this interaction thermodynamically to identify splice sites. Applied to a set of 65 annotated genes, our “finding with binding” method achieves a significant separation between real and false sites. Analyzing binding patterns allows us to discard a large number of decoy sites. Our results improve statistics-based methods for donor site recognition, demonstrating the promise of physical modeling to find functional elements in the genome.

Subauditory Speech Recognition based on EMG/EPG Signals

NASA Technical Reports Server (NTRS)

Jorgensen, Charles; Lee, Diana Dee; Agabon, Shane; Lau, Sonie (Technical Monitor)

2003-01-01

Sub-vocal electromyogram/electro palatogram (EMG/EPG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub-vocally pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

Learning and Recognition of Clothing Genres From Full-Body Images.

PubMed

Hidayati, Shintami C; You, Chuang-Wen; Cheng, Wen-Huang; Hua, Kai-Lung

2018-05-01

According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory. In addition, the corresponding salient visual features of each style element were identified and formulated with variables that can be computationally derived with various computer vision algorithms. To evaluate the performance of our algorithm, a dataset containing 3250 full-body shots crawled from popular online stores was built. Recognition results show that our proposed algorithms achieved promising overall precision, recall, and -score of 88.76%, 88.53%, and 88.64% for recognizing upperwear genres, and 88.21%, 88.17%, and 88.19% for recognizing lowerwear genres, respectively. The effectiveness of each style element and its visual features on recognizing clothing genres was demonstrated through a set of experiments involving different sets of style elements or features. In summary, our experimental results demonstrate the effectiveness of the proposed method in clothing genre recognition.

Automated speech recognition for time recording in out-of-hospital emergency medicine-an experimental approach.

PubMed

Gröschel, J; Philipp, F; Skonetzki, St; Genzwürker, H; Wetter, Th; Ellinger, K

2004-02-01

Precise documentation of medical treatment in emergency medical missions and for resuscitation is essential from a medical, legal and quality assurance point of view [Anästhesiologie und Intensivmedizin, 41 (2000) 737]. All conventional methods of time recording are either too inaccurate or elaborate for routine application. Automated speech recognition may offer a solution. A special erase programme for the documentation of all time events was developed. Standard speech recognition software (IBM ViaVoice 7.0) was adapted and installed on two different computer systems. One was a stationary PC (500MHz Pentium III, 128MB RAM, Soundblaster PCI 128 Soundcard, Win NT 4.0), the other was a mobile pen-PC that had already proven its value during emergency missions [Der Notarzt 16, p. 177] (Fujitsu Stylistic 2300, 230Mhz MMX Processor, 160MB RAM, embedded soundcard ESS 1879 chipset, Win98 2nd ed.). On both computers two different microphones were tested. One was a standard headset that came with the recognition software, the other was a small microphone (Lavalier-Kondensatormikrofon EM 116 from Vivanco), that could be attached to the operators collar. Seven women and 15 men spoke a text with 29 phrases to be recognised. Two emergency physicians tested the system in a simulated emergency setting using the collar microphone and the pen-PC with an analogue wireless connection. Overall recognition was best for the PC with a headset (89%) followed by the pen-PC with a headset (85%), the PC with a microphone (84%) and the pen-PC with a microphone (80%). Nevertheless, the difference was not statistically significant. Recognition became significantly worse (89.5% versus 82.3%, P<0.0001 ) when numbers had to be recognised. The gender of speaker and the number of words in a sentence had no influence. Average recognition in the simulated emergency setting was 75%. At no time did false recognition appear. Time recording with automated speech recognition seems to be possible in emergency medical missions. Although results show an average recognition of only 75%, it is possible that missing elements may be reconstructed more precisely. Future technology should integrate a secure wireless connection between microphone and mobile computer. The system could then prove its value for real out-of-hospital emergencies.

An analysis of possible applications of fuzzy set theory to the actuarial credibility theory

NASA Technical Reports Server (NTRS)

Ostaszewski, Krzysztof; Karwowski, Waldemar

1992-01-01

In this work, we review the basic concepts of actuarial credibility theory from the point of view of introducing applications of the fuzzy set-theoretic method. We show how the concept of actuarial credibility can be modeled through the fuzzy set membership functions and how fuzzy set methods, especially fuzzy pattern recognition, can provide an alternative tool for estimating credibility.

Recognition of oxytocin by capillary electrochromatography with monolithic tetrapeptide-imprinted polymer used as the stationary phase.

PubMed

Zheng, Chao; Liu, Zhaosheng; Gao, Ruyu; Zhang, Lihua; Zhang, Yukui

2007-07-01

Using YPLG (Tyr-Pro-Leu-Gly), a tetrapeptide, as the template, an imprinted monolithic column was prepared and applied to the selective recognition of oxytocin based on the epitope approach and capillary electrochromatography (CEC). By optimizing the polymerization solution in terms of functional monomer, cross-linking reagent, porogen, and imprinted template via CEC evaluations of synthesized columns, an imprinted monolith with good recognition capacity (the imprinting factors for YPLG and oxytocin were 4.499 and 4.013, respectively) and high column efficiency (theoretical plates for YPLG and oxytocin were 22,995 plates/m and 16,952 plates/m, respectively) was achieved. In addition, the effects of various experimental parameters on the recognition of oxytocin, including the organic modifier content, the buffer concentration, and the pH value, were studied systematically. Furthermore, a mixture of oxytocin and other proteins was analyzed using this monolithic CEC column, and oxytocin was eluted much more slowly than other large biomolecules, which demonstrated the high selective recognition ability of such an imprinted monolith for oxytocin with PLG (Pro-Leu-Gly) as the epitope. Figure Separation of a mixture of oxytocin, BSA, bovine hemoglobin, ovalbumin, and lysozyme on the open column, the blank monolithic column, and the monolithic YPLG-imprinted column.

A Longitudinal Investigation of Visual Event-Related Potentials in the First Year of Life

ERIC Educational Resources Information Center

Webb, Sara J.; Long, Jeffrey D.; Nelson, Charles A.

2005-01-01

The goal of the current study was to assess general maturational changes in the ERP in the same sample of infants from 4 to 12 months of age. All participants were tested in two experimental manipulations at each age: a test of facial recognition and one of object recognition. Two sets of analyses were undertaken. First, growth curve modeling with…

Black Ink and Red Ink (BIRI) Testing: A Testing Method to Evaluate Both Recall and Recognition Learning in Accelerated Adult-Learning Courses

ERIC Educational Resources Information Center

Rodgers, Joseph Lee; Rodgers, Jacci L.

2011-01-01

We propose, develop, and evaluate the black ink-red ink (BIRI) method of testing. This approach uses two different methods within the same test administration setting, one that matches recognition learning and the other that matches recall learning. Students purposively define their own tradeoff between the two approaches. Evaluation of the method…

Speech Recognition Scores of White and Black Student-Teacher Listeners for Black and White First Grade Speakers. Final Technical Report.

ERIC Educational Resources Information Center

Nober, E. Harris; Seymour, Harry N.

In order to investigate the possible consequences of dialectical differences in the classroom setting relative to the low income black and white first grade child and the prospective white middle-class teacher, 25 black and 25 white university listeners yielded speech recognition scores for 48 black and 48 white five-year-old urban school-children…

Ease of Access to List Items in Short-Term Memory Depends on the Order of the Recognition Probes

ERIC Educational Resources Information Center

Lange, Elke B.; Cerella, John; Verhaeghen, Paul

2011-01-01

We report data from 4 experiments using a recognition design with multiple probes to be matched to specific study positions. Items could be accessed rapidly, independent of set size, when the test order matched the study order (forward condition). When the order of testing was random, backward, or in a prelearned irregular sequence (reordered…