The integration of familiarity and recollection information in short-term recognition: modeling speed-accuracy trade-off functions.

PubMed

Göthe, Katrin; Oberauer, Klaus

2008-05-01

Dual process models postulate familiarity and recollection as the basis of the recognition process. We investigated the time-course of integration of the two information sources to one recognition judgment in a working memory task. We tested 24 subjects with a response signal variant of the modified Sternberg recognition task (Oberauer, 2001) to isolate the time course of three different probe types indicating different combinations of familiarity and source information. We compared two mathematical models implementing different ways of integrating familiarity and recollection. Within each model, we tested three assumptions about the nature of the familiarity signal, with familiarity having (a) only positive values, indicating similarity of the probe with the memory list, (b) only negative values, indicating novelty, or (c) both positive and negative values. Both models provided good fits to the data. A model combining the outputs of both processes additively (Integration Model) gave an overall better fit to the data than a model based on a continuous familiarity signal and a probabilistic all-or-none recollection process (Dominance Model).

Evidence for the contribution of a threshold retrieval process to semantic memory.

PubMed

Kempnich, Maria; Urquhart, Josephine A; O'Connor, Akira R; Moulin, Chris J A

2017-10-01

It is widely held that episodic retrieval can recruit two processes: a threshold context retrieval process (recollection) and a continuous signal strength process (familiarity). Conversely the processes recruited during semantic retrieval are less well specified. We developed a semantic task analogous to single-item episodic recognition to interrogate semantic recognition receiver-operating characteristics (ROCs) for a marker of a threshold retrieval process. We fitted observed ROC points to three signal detection models: two models typically used in episodic recognition (unequal variance and dual-process signal detection models) and a novel dual-process recollect-to-reject (DP-RR) signal detection model that allows a threshold recollection process to aid both target identification and lure rejection. Given the nature of most semantic questions, we anticipated the DP-RR model would best fit the semantic task data. Experiment 1 (506 participants) provided evidence for a threshold retrieval process in semantic memory, with overall best fits to the DP-RR model. Experiment 2 (316 participants) found within-subjects estimates of episodic and semantic threshold retrieval to be uncorrelated. Our findings add weight to the proposal that semantic and episodic memory are served by similar dual-process retrieval systems, though the relationship between the two threshold processes needs to be more fully elucidated.

Intelligent Automatic Right-Left Sign Lamp Based on Brain Signal Recognition System

NASA Astrophysics Data System (ADS)

Winda, A.; Sofyan; Sthevany; Vincent, R. S.

2017-12-01

Comfort as a part of the human factor, plays important roles in nowadays advanced automotive technology. Many of the current technologies go in the direction of automotive driver assistance features. However, many of the driver assistance features still require physical movement by human to enable the features. In this work, the proposed method is used in order to make certain feature to be functioning without any physical movement, instead human just need to think about it in their mind. In this work, brain signal is recorded and processed in order to be used as input to the recognition system. Right-Left sign lamp based on the brain signal recognition system can potentially replace the button or switch of the specific device in order to make the lamp work. The system then will decide whether the signal is ‘Right’ or ‘Left’. The decision of the Right-Left side of brain signal recognition will be sent to a processing board in order to activate the automotive relay, which will be used to activate the sign lamp. Furthermore, the intelligent system approach is used to develop authorized model based on the brain signal. Particularly Support Vector Machines (SVMs)-based classification system is used in the proposed system to recognize the Left-Right of the brain signal. Experimental results confirm the effectiveness of the proposed intelligent Automatic brain signal-based Right-Left sign lamp access control system. The signal is processed by Linear Prediction Coefficient (LPC) and Support Vector Machines (SVMs), and the resulting experiment shows the training and testing accuracy of 100% and 80%, respectively.

Association of impaired facial affect recognition with basic facial and visual processing deficits in schizophrenia.

PubMed

Norton, Daniel; McBain, Ryan; Holt, Daphne J; Ongur, Dost; Chen, Yue

2009-06-15

Impaired emotion recognition has been reported in schizophrenia, yet the nature of this impairment is not completely understood. Recognition of facial emotion depends on processing affective and nonaffective facial signals, as well as basic visual attributes. We examined whether and how poor facial emotion recognition in schizophrenia is related to basic visual processing and nonaffective face recognition. Schizophrenia patients (n = 32) and healthy control subjects (n = 29) performed emotion discrimination, identity discrimination, and visual contrast detection tasks, where the emotionality, distinctiveness of identity, or visual contrast was systematically manipulated. Subjects determined which of two presentations in a trial contained the target: the emotional face for emotion discrimination, a specific individual for identity discrimination, and a sinusoidal grating for contrast detection. Patients had significantly higher thresholds (worse performance) than control subjects for discriminating both fearful and happy faces. Furthermore, patients' poor performance in fear discrimination was predicted by performance in visual detection and face identity discrimination. Schizophrenia patients require greater emotional signal strength to discriminate fearful or happy face images from neutral ones. Deficient emotion recognition in schizophrenia does not appear to be determined solely by affective processing but is also linked to the processing of basic visual and facial information.

Breaking cover: neural responses to slow and fast camouflage-breaking motion.

PubMed

Yin, Jiapeng; Gong, Hongliang; An, Xu; Chen, Zheyuan; Lu, Yiliang; Andolina, Ian M; McLoughlin, Niall; Wang, Wei

2015-08-22

Primates need to detect and recognize camouflaged animals in natural environments. Camouflage-breaking movements are often the only visual cue available to accomplish this. Specifically, sudden movements are often detected before full recognition of the camouflaged animal is made, suggesting that initial processing of motion precedes the recognition of motion-defined contours or shapes. What are the neuronal mechanisms underlying this initial processing of camouflaged motion in the primate visual brain? We investigated this question using intrinsic-signal optical imaging of macaque V1, V2 and V4, along with computer simulations of the neural population responses. We found that camouflaged motion at low speed was processed as a direction signal by both direction- and orientation-selective neurons, whereas at high-speed camouflaged motion was encoded as a motion-streak signal primarily by orientation-selective neurons. No population responses were found to be invariant to the camouflage contours. These results suggest that the initial processing of camouflaged motion at low and high speeds is encoded as direction and motion-streak signals in primate early visual cortices. These processes are consistent with a spatio-temporal filter mechanism that provides for fast processing of motion signals, prior to full recognition of camouflage-breaking animals. © 2015 The Authors.

Breaking cover: neural responses to slow and fast camouflage-breaking motion

PubMed Central

Yin, Jiapeng; Gong, Hongliang; An, Xu; Chen, Zheyuan; Lu, Yiliang; Andolina, Ian M.; McLoughlin, Niall; Wang, Wei

2015-01-01

Primates need to detect and recognize camouflaged animals in natural environments. Camouflage-breaking movements are often the only visual cue available to accomplish this. Specifically, sudden movements are often detected before full recognition of the camouflaged animal is made, suggesting that initial processing of motion precedes the recognition of motion-defined contours or shapes. What are the neuronal mechanisms underlying this initial processing of camouflaged motion in the primate visual brain? We investigated this question using intrinsic-signal optical imaging of macaque V1, V2 and V4, along with computer simulations of the neural population responses. We found that camouflaged motion at low speed was processed as a direction signal by both direction- and orientation-selective neurons, whereas at high-speed camouflaged motion was encoded as a motion-streak signal primarily by orientation-selective neurons. No population responses were found to be invariant to the camouflage contours. These results suggest that the initial processing of camouflaged motion at low and high speeds is encoded as direction and motion-streak signals in primate early visual cortices. These processes are consistent with a spatio-temporal filter mechanism that provides for fast processing of motion signals, prior to full recognition of camouflage-breaking animals. PMID:26269500

Automatic Speech Recognition from Neural Signals: A Focused Review.

PubMed

Herff, Christian; Schultz, Tanja

2016-01-01

Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e., patients suffering from locked-in syndrome). For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people. This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography). As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the Brain-to-text system.

Recollection is a continuous process: implications for dual-process theories of recognition memory.

PubMed

Mickes, Laura; Wais, Peter E; Wixted, John T

2009-04-01

Dual-process theory, which holds that recognition decisions can be based on recollection or familiarity, has long seemed incompatible with signal detection theory, which holds that recognition decisions are based on a singular, continuous memory-strength variable. Formal dual-process models typically regard familiarity as a continuous process (i.e., familiarity comes in degrees), but they construe recollection as a categorical process (i.e., recollection either occurs or does not occur). A continuous process is characterized by a graded relationship between confidence and accuracy, whereas a categorical process is characterized by a binary relationship such that high confidence is associated with high accuracy but all lower degrees of confidence are associated with chance accuracy. Using a source-memory procedure, we found that the relationship between confidence and source-recollection accuracy was graded. Because recollection, like familiarity, is a continuous process, dual-process theory is more compatible with signal detection theory than previously thought.

[A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

PubMed

Zhang, Y; Liu, A; Yu, K

1999-06-01

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Sun, Qi; Pi, Shaohua; Wu, Hongyan

2014-09-01

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.

PubMed

Khan, Adil Mehmood; Lee, Young-Koo; Lee, Sungyoung Y; Kim, Tae-Seong

2010-09-01

Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.

Signal processing method and system for noise removal and signal extraction

DOEpatents

Fu, Chi Yung; Petrich, Loren

2009-04-14

A signal processing method and system combining smooth level wavelet pre-processing together with artificial neural networks all in the wavelet domain for signal denoising and extraction. Upon receiving a signal corrupted with noise, an n-level decomposition of the signal is performed using a discrete wavelet transform to produce a smooth component and a rough component for each decomposition level. The n.sup.th level smooth component is then inputted into a corresponding neural network pre-trained to filter out noise in that component by pattern recognition in the wavelet domain. Additional rough components, beginning at the highest level, may also be retained and inputted into corresponding neural networks pre-trained to filter out noise in those components also by pattern recognition in the wavelet domain. In any case, an inverse discrete wavelet transform is performed on the combined output from all the neural networks to recover a clean signal back in the time domain.

Robot Command Interface Using an Audio-Visual Speech Recognition System

NASA Astrophysics Data System (ADS)

Ceballos, Alexánder; Gómez, Juan; Prieto, Flavio; Redarce, Tanneguy

In recent years audio-visual speech recognition has emerged as an active field of research thanks to advances in pattern recognition, signal processing and machine vision. Its ultimate goal is to allow human-computer communication using voice, taking into account the visual information contained in the audio-visual speech signal. This document presents a command's automatic recognition system using audio-visual information. The system is expected to control the laparoscopic robot da Vinci. The audio signal is treated using the Mel Frequency Cepstral Coefficients parametrization method. Besides, features based on the points that define the mouth's outer contour according to the MPEG-4 standard are used in order to extract the visual speech information.

On the Measurement of Criterion Noise in Signal Detection Theory: The Case of Recognition Memory

ERIC Educational Resources Information Center

Kellen, David; Klauer, Karl Christoph; Singmann, Henrik

2012-01-01

Traditional approaches within the framework of signal detection theory (SDT; Green & Swets, 1966), especially in the field of recognition memory, assume that the positioning of response criteria is not a noisy process. Recent work (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008) has challenged this assumption, arguing not only…

A continuous dual-process model of remember/know judgments.

PubMed

Wixted, John T; Mickes, Laura

2010-10-01

The dual-process theory of recognition memory holds that recognition decisions can be based on recollection or familiarity, and the remember/know procedure is widely used to investigate those 2 processes. Dual-process theory in general and the remember/know procedure in particular have been challenged by an alternative strength-based interpretation based on signal-detection theory, which holds that remember judgments simply reflect stronger memories than do know judgments. Although supported by a considerable body of research, the signal-detection account is difficult to reconcile with G. Mandler's (1980) classic "butcher-on-the-bus" phenomenon (i.e., strong, familiarity-based recognition). In this article, a new signal-detection model is proposed that does not deny either the validity of dual-process theory or the possibility that remember/know judgments can-when used in the right way-help to distinguish between memories that are largely recollection based from those that are largely familiarity based. It does, however, agree with all prior signal-detection-based critiques of the remember/know procedure, which hold that, as it is ordinarily used, the procedure mainly distinguishes strong memories from weak memories (not recollection from familiarity).

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

PubMed Central

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

2015-01-01

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

UHF Signal Processing and Pattern Recognition of Partial Discharge in Gas-Insulated Switchgear Using Chromatic Methodology

PubMed Central

Wang, Xiaohua; Li, Xi; Rong, Mingzhe; Xie, Dingli; Ding, Dan; Wang, Zhixiang

2017-01-01

The ultra-high frequency (UHF) method is widely used in insulation condition assessment. However, UHF signal processing algorithms are complicated and the size of the result is large, which hinders extracting features and recognizing partial discharge (PD) patterns. This article investigated the chromatic methodology that is novel in PD detection. The principle of chromatic methodologies in color science are introduced. The chromatic processing represents UHF signals sparsely. The UHF signals obtained from PD experiments were processed using chromatic methodology and characterized by three parameters in chromatic space (H, L, and S representing dominant wavelength, signal strength, and saturation, respectively). The features of the UHF signals were studied hierarchically. The results showed that the chromatic parameters were consistent with conventional frequency domain parameters. The global chromatic parameters can be used to distinguish UHF signals acquired by different sensors, and they reveal the propagation properties of the UHF signal in the L-shaped gas-insulated switchgear (GIS). Finally, typical PD defect patterns had been recognized by using novel chromatic parameters in an actual GIS tank and good performance of recognition was achieved. PMID:28106806

UHF Signal Processing and Pattern Recognition of Partial Discharge in Gas-Insulated Switchgear Using Chromatic Methodology.

PubMed

Wang, Xiaohua; Li, Xi; Rong, Mingzhe; Xie, Dingli; Ding, Dan; Wang, Zhixiang

2017-01-18

The ultra-high frequency (UHF) method is widely used in insulation condition assessment. However, UHF signal processing algorithms are complicated and the size of the result is large, which hinders extracting features and recognizing partial discharge (PD) patterns. This article investigated the chromatic methodology that is novel in PD detection. The principle of chromatic methodologies in color science are introduced. The chromatic processing represents UHF signals sparsely. The UHF signals obtained from PD experiments were processed using chromatic methodology and characterized by three parameters in chromatic space ( H , L , and S representing dominant wavelength, signal strength, and saturation, respectively). The features of the UHF signals were studied hierarchically. The results showed that the chromatic parameters were consistent with conventional frequency domain parameters. The global chromatic parameters can be used to distinguish UHF signals acquired by different sensors, and they reveal the propagation properties of the UHF signal in the L-shaped gas-insulated switchgear (GIS). Finally, typical PD defect patterns had been recognized by using novel chromatic parameters in an actual GIS tank and good performance of recognition was achieved.

Computer Vision for Artificially Intelligent Robotic Systems

NASA Astrophysics Data System (ADS)

Ma, Chialo; Ma, Yung-Lung

1987-04-01

In this paper An Acoustic Imaging Recognition System (AIRS) will be introduced which is installed on an Intelligent Robotic System and can recognize different type of Hand tools' by Dynamic pattern recognition. The dynamic pattern recognition is approached by look up table method in this case, the method can save a lot of calculation time and it is practicable. The Acoustic Imaging Recognition System (AIRS) is consist of four parts -- position control unit, pulse-echo signal processing unit, pattern recognition unit and main control unit. The position control of AIRS can rotate an angle of ±5 degree Horizental and Vertical seperately, the purpose of rotation is to find the maximum reflection intensity area, from the distance, angles and intensity of the target we can decide the characteristic of this target, of course all the decision is target, of course all the decision is processed bye the main control unit. In Pulse-Echo Signal Process Unit, we ultilize the correlation method, to overcome the limitation of short burst of ultrasonic, because the Correlation system can transmit large time bandwidth signals and obtain their resolution and increased intensity through pulse compression in the correlation receiver. The output of correlator is sampled and transfer into digital data by u law coding method, and this data together with delay time T, angle information OH, eV will be sent into main control unit for further analysis. The recognition process in this paper, we use dynamic look up table method, in this method at first we shall set up serval recognition pattern table and then the new pattern scanned by Transducer array will be devided into serval stages and compare with the sampling table. The comparison is implemented by dynamic programing and Markovian process. All the hardware control signals, such as optimum delay time for correlator receiver, horizental and vertical rotation angle for transducer plate, are controlled by the Main Control Unit, the Main Control Unit also handles the pattern recognition process. The distance from the target to the transducer plate is limitted by the power and beam angle of transducer elements, in this AIRS Model, we use a narrow beam transducer and it's input voltage is 50V p-p. A RobOt equipped with AIRS can not only measure the distance from the target but also recognize a three dimensional image of target from the image lab of Robot memory. Indexitems, Accoustic System, Supersonic transducer, Dynamic programming, Look-up-table, Image process, pattern Recognition, Quad Tree, Quadappoach.

Improved Performance Characteristics For Indium Antimonide Photovoltaic Detector Arrays Using A FET-Switched Multiplexing Technique

NASA Astrophysics Data System (ADS)

Ma, Yung-Lung; Ma, Chialo

1987-03-01

In this paper An Acoustic Imaging Recognition System (AIRS) will be introduced which is installed on an Intelligent Robotic System and can recognize different type of Hand tools' by Dynamic pattern recognition. The dynamic pattern recognition is approached by look up table method in this case, the method can save a lot of calculation time and it is practicable. The Acoustic Imaging Recognition System (AIRS) is consist of four parts _ position control unit, pulse-echo signal processing unit, pattern recognition unit and main control unit. The position control of AIRS can rotate an angle of ±5 degree Horizental and Vertical seperately, the purpose of rotation is to find the maximum reflection intensity area, from the distance, angles and intensity of the target we can decide the characteristic of this target, of course all the decision is target, of course all the decision is processed by the main control unit. In Pulse-Echo Signal Process Unit, we utilize the correlation method, to overcome the limitation of short burst of ultrasonic, because the Correlation system can transmit large time bandwidth signals and obtain their resolution and increased intensity through pulse compression in the correlation receiver. The output of correlator is sampled and transfer into digital data by p law coding method, and this data together with delay time T, angle information eH, eV will be sent into main control unit for further analysis. The recognition process in this paper, we use dynamic look up table method, in this method at first we shall set up serval recognition pattern table and then the new pattern scanned by Transducer array will be devided into serval stages and compare with the sampling table. The comparison is implemented by dynamic programing and Markovian process. All the hardware control signals, such as optimum delay time for correlator receiver, horizental and vertical rotation angle for transducer plate, are controlled by the Main Control Unit, the Main Control Unit also handles the pattern recognition process. The distance from the target to the transducer plate is limitted by the power and beam angle of transducer elements, in this AIRS Models, we use a narrow beam transducer and it's input voltage is 50V p-p. A Robot equipped with AIRS can not only measure the distance from the target but also recognize a three dimensional image of target from the image lab of Robot memory. Indexitems, Accoustic System, Supersonic transducer, Dynamic programming, Look-up-table, Image process, pattern Recognition, Quad Tree, Quadappoach.

Beyond ROC Curvature: Strength Effects and Response Time Data Support Continuous-Evidence Models of Recognition Memory

ERIC Educational Resources Information Center

Dube, Chad; Starns, Jeffrey J.; Rotello, Caren M.; Ratcliff, Roger

2012-01-01

A classic question in the recognition memory literature is whether retrieval is best described as a continuous-evidence process consistent with signal detection theory (SDT), or a threshold process consistent with many multinomial processing tree (MPT) models. Because receiver operating characteristics (ROCs) based on confidence ratings are…

Distributed Fusion in Sensor Networks with Information Genealogy

DTIC Science & Technology

2011-06-28

image processing [2], acoustic and speech recognition [3], multitarget tracking [4], distributed fusion [5], and Bayesian inference [6-7]. For...Adaptation for Distant-Talking Speech Recognition." in Proc Acoustics. Speech , and Signal Processing, 2004 |4| Y Bar-Shalom and T 1-. Fortmann...used in speech recognition and other classification applications [8]. But their use in underwater mine classification is limited. In this paper, we

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish.

PubMed

Vieira, Manuel; Fonseca, Paulo J; Amorim, M Clara P; Teixeira, Carlos J C

2015-12-01

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.

Individual recognition between mother and infant bats (Myotis)

NASA Technical Reports Server (NTRS)

Turner, D.; Shaughnessy, A.; Gould, E.

1972-01-01

The recognition process and the basis for that recognition, in brown bats, between mother and infant are analyzed. Two parameters, ultrasonic communication and olfactory stimuli, are investigated. The test animals were not allowed any visual contact. It was concluded that individual recognition between mother and infant occurred. However, it could not be determined if the recognition was based on ultrasonic signals or olfactory stimuli.

Effects of Pre-Experimental Knowledge on Recognition Memory

ERIC Educational Resources Information Center

Bird, Chris M.; Davies, Rachel A.; Ward, Jamie; Burgess, Neil

2011-01-01

The influence of pre-experimental autobiographical knowledge on recognition memory was investigated using as memoranda faces that were either personally known or unknown to the participant. Under a dual process theory, such knowledge boosted both recollection- and familiarity-based recognition judgements. Under an unequal variance signal detection…

Molecular Pathways for Immune Recognition of Preproinsulin Signal Peptide in Type 1 Diabetes.

PubMed

Kronenberg-Versteeg, Deborah; Eichmann, Martin; Russell, Mark A; de Ru, Arnoud; Hehn, Beate; Yusuf, Norkhairin; van Veelen, Peter A; Richardson, Sarah J; Morgan, Noel G; Lemberg, Marius K; Peakman, Mark

2018-04-01

The signal peptide region of preproinsulin (PPI) contains epitopes targeted by HLA-A-restricted (HLA-A0201, A2402) cytotoxic T cells as part of the pathogenesis of β-cell destruction in type 1 diabetes. We extended the discovery of the PPI epitope to disease-associated HLA-B*1801 and HLA-B*3906 (risk) and HLA-A*1101 and HLA-B*3801 (protective) alleles, revealing that four of six alleles present epitopes derived from the signal peptide region. During cotranslational translocation of PPI, its signal peptide is cleaved and retained within the endoplasmic reticulum (ER) membrane, implying it is processed for immune recognition outside of the canonical proteasome-directed pathway. Using in vitro translocation assays with specific inhibitors and gene knockout in PPI-expressing target cells, we show that PPI signal peptide antigen processing requires signal peptide peptidase (SPP). The intramembrane protease SPP generates cytoplasm-proximal epitopes, which are transporter associated with antigen processing (TAP), ER-luminal epitopes, which are TAP independent, each presented by different HLA class I molecules and N-terminal trimmed by ER aminopeptidase 1 for optimal presentation. In vivo, TAP expression is significantly upregulated and correlated with HLA class I hyperexpression in insulin-containing islets of patients with type 1 diabetes. Thus, PPI signal peptide epitopes are processed by SPP and loaded for HLA-guided immune recognition via pathways that are enhanced during disease pathogenesis. © 2018 by the American Diabetes Association.

The time course of individual face recognition: A pattern analysis of ERP signals.

PubMed

Nemrodov, Dan; Niemeier, Matthias; Mok, Jenkin Ngo Yin; Nestor, Adrian

2016-05-15

An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data. Specifically, we investigate the spatiotemporal profile of identity recognition across variation in emotional expression. To this end, we apply pattern classification to ERP signals both in time, for any single electrode, and in space, across multiple electrodes. Our results confirm the significance of traditional ERP components in face processing. At the same time though, they support the idea that the temporal profile of face recognition is incompletely described by such components. First, we show that signals associated with different facial identities can be discriminated from each other outside the scope of these components, as early as 70ms following stimulus presentation. Next, electrodes associated with traditional ERP components as well as, critically, those not associated with such components are shown to contribute information to stimulus discriminability. And last, the levels of ERP-based pattern discrimination are found to correlate with recognition accuracy across subjects confirming the relevance of these methods for bridging brain and behavior data. Altogether, the current results shed new light on the fine-grained time course of neural face processing and showcase the value of novel methods for pattern analysis to investigating fundamental aspects of visual recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

Noise tolerance in optical waveguide circuits for recognition of optical 16 quadrature amplitude modulation codes

NASA Astrophysics Data System (ADS)

Inoshita, Kensuke; Hama, Yoshimitsu; Kishikawa, Hiroki; Goto, Nobuo

2016-12-01

In photonic label routers, various optical signal processing functions are required; these include optical label extraction, recognition of the label, optical switching and buffering controlled by signals based on the label information and network routing tables, and label rewriting. Among these functions, we focus on photonic label recognition. We have proposed two kinds of optical waveguide circuits to recognize 16 quadrature amplitude modulation codes, i.e., recognition from the minimum output port and from the maximum output port. The recognition function was theoretically analyzed and numerically simulated by finite-difference beam-propagation method. We discuss noise tolerance in the circuit and show numerically simulated results to evaluate bit-error-rate (BER) characteristics against optical signal-to-noise ratio (OSNR). The OSNR required to obtain a BER less than 1.0×10-3 for the symbol rate of 2.5 GBaud was 14.5 and 27.0 dB for recognition from the minimum and maximum output, respectively.

A real time mobile-based face recognition with fisherface methods

NASA Astrophysics Data System (ADS)

Arisandi, D.; Syahputra, M. F.; Putri, I. L.; Purnamawati, S.; Rahmat, R. F.; Sari, P. P.

2018-03-01

Face Recognition is a field research in Computer Vision that study about learning face and determine the identity of the face from a picture sent to the system. By utilizing this face recognition technology, learning process about people’s identity between students in a university will become simpler. With this technology, student won’t need to browse student directory in university’s server site and look for the person with certain face trait. To obtain this goal, face recognition application use image processing methods consist of two phase, pre-processing phase and recognition phase. In pre-processing phase, system will process input image into the best image for recognition phase. Purpose of this pre-processing phase is to reduce noise and increase signal in image. Next, to recognize face phase, we use Fisherface Methods. This methods is chosen because of its advantage that would help system of its limited data. Therefore from experiment the accuracy of face recognition using fisherface is 90%.

Modeling the effect of channel number and interaction on consonant recognition in a cochlear implant peak-picking strategy.

PubMed

Verschuur, Carl

2009-03-01

Difficulties in speech recognition experienced by cochlear implant users may be attributed both to information loss caused by signal processing and to information loss associated with the interface between the electrode array and auditory nervous system, including cross-channel interaction. The objective of the work reported here was to attempt to partial out the relative contribution of these different factors to consonant recognition. This was achieved by comparing patterns of consonant feature recognition as a function of channel number and presence/absence of background noise in users of the Nucleus 24 device with normal hearing subjects listening to acoustic models that mimicked processing of that device. Additionally, in the acoustic model experiment, a simulation of cross-channel spread of excitation, or "channel interaction," was varied. Results showed that acoustic model experiments were highly correlated with patterns of performance in better-performing cochlear implant users. Deficits to consonant recognition in this subgroup could be attributed to cochlear implant processing, whereas channel interaction played a much smaller role in determining performance errors. The study also showed that large changes to channel number in the Advanced Combination Encoder signal processing strategy led to no substantial changes in performance.

Neural Dynamics Underlying Target Detection in the Human Brain

PubMed Central

Bansal, Arjun K.; Madhavan, Radhika; Agam, Yigal; Golby, Alexandra; Madsen, Joseph R.

2014-01-01

Sensory signals must be interpreted in the context of goals and tasks. To detect a target in an image, the brain compares input signals and goals to elicit the correct behavior. We examined how target detection modulates visual recognition signals by recording intracranial field potential responses from 776 electrodes in 10 epileptic human subjects. We observed reliable differences in the physiological responses to stimuli when a cued target was present versus absent. Goal-related modulation was particularly strong in the inferior temporal and fusiform gyri, two areas important for object recognition. Target modulation started after 250 ms post stimulus, considerably after the onset of visual recognition signals. While broadband signals exhibited increased or decreased power, gamma frequency power showed predominantly increases during target presence. These observations support models where task goals interact with sensory inputs via top-down signals that influence the highest echelons of visual processing after the onset of selective responses. PMID:24553944

Optical signal processing

NASA Technical Reports Server (NTRS)

Casasent, D.

1978-01-01

The article discusses several optical configurations used for signal processing. Electronic-to-optical transducers are outlined, noting fixed window transducers and moving window acousto-optic transducers. Folded spectrum techniques are considered, with reference to wideband RF signal analysis, fetal electroencephalogram analysis, engine vibration analysis, signal buried in noise, and spatial filtering. Various methods for radar signal processing are described, such as phased-array antennas, the optical processing of phased-array data, pulsed Doppler and FM radar systems, a multichannel one-dimensional optical correlator, correlations with long coded waveforms, and Doppler signal processing. Means for noncoherent optical signal processing are noted, including an optical correlator for speech recognition and a noncoherent optical correlator.

An approach to emotion recognition in single-channel EEG signals: a mother child interaction

NASA Astrophysics Data System (ADS)

Gómez, A.; Quintero, L.; López, N.; Castro, J.

2016-04-01

In this work, we perform a first approach to emotion recognition from EEG single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology. Single channel EEG signals are analyzed and processed using several window sizes by performing a statistical analysis over features in the time and frequency domains. Finally, a neural network obtained an average accuracy rate of 99% of classification in two emotional states such as happiness and sadness.

The Effect of Lexical Content on Dichotic Speech Recognition in Older Adults.

PubMed

Findlen, Ursula M; Roup, Christina M

2016-01-01

Age-related auditory processing deficits have been shown to negatively affect speech recognition for older adult listeners. In contrast, older adults gain benefit from their ability to make use of semantic and lexical content of the speech signal (i.e., top-down processing), particularly in complex listening situations. Assessment of auditory processing abilities among aging adults should take into consideration semantic and lexical content of the speech signal. The purpose of this study was to examine the effects of lexical and attentional factors on dichotic speech recognition performance characteristics for older adult listeners. A repeated measures design was used to examine differences in dichotic word recognition as a function of lexical and attentional factors. Thirty-five older adults (61-85 yr) with sensorineural hearing loss participated in this study. Dichotic speech recognition was evaluated using consonant-vowel-consonant (CVC) word and nonsense CVC syllable stimuli administered in the free recall, directed recall right, and directed recall left response conditions. Dichotic speech recognition performance for nonsense CVC syllables was significantly poorer than performance for CVC words. Dichotic recognition performance varied across response condition for both stimulus types, which is consistent with previous studies on dichotic speech recognition. Inspection of individual results revealed that five listeners demonstrated an auditory-based left ear deficit for one or both stimulus types. Lexical content of stimulus materials affects performance characteristics for dichotic speech recognition tasks in the older adult population. The use of nonsense CVC syllable material may provide a way to assess dichotic speech recognition performance while potentially lessening the effects of lexical content on performance (i.e., measuring bottom-up auditory function both with and without top-down processing). American Academy of Audiology.

Discrete-State and Continuous Models of Recognition Memory: Testing Core Properties under Minimal Assumptions

ERIC Educational Resources Information Center

Kellen, David; Klauer, Karl Christoph

2014-01-01

A classic discussion in the recognition-memory literature concerns the question of whether recognition judgments are better described by continuous or discrete processes. These two hypotheses are instantiated by the signal detection theory model (SDT) and the 2-high-threshold model, respectively. Their comparison has almost invariably relied on…

Reversing the picture superiority effect: a speed-accuracy trade-off study of recognition memory.

PubMed

Boldini, Angela; Russo, Riccardo; Punia, Sahiba; Avons, S E

2007-01-01

Speed-accuracy trade-off methods have been used to contrast single- and dual-process accounts of recognition memory. With these procedures, subjects are presented with individual test items and required to make recognition decisions under various time constraints. In three experiments, we presented words and pictures to be intentionally learned; test stimuli were always visually presented words. At test, we manipulated the interval between the presentation of each test stimulus and that of a response signal, thus controlling the amount of time available to retrieve target information. The standard picture superiority effect was significant in long response deadline conditions (i.e., > or = 2,000 msec). Conversely, a significant reverse picture superiority effect emerged at short response-signal deadlines (< 200 msec). The results are congruent with views suggesting that both fast familiarity and slower recollection processes contribute to recognition memory. Alternative accounts are also discussed.

The Systems Engineering Design of a Smart Forward Operating Base Surveillance System for Forward Operating Base Protection

DTIC Science & Technology

2013-06-01

fixed sensors located along the perimeter of the FOB. The video is analyzed for facial recognition to alert the Network Operations Center (NOC...the UAV is processed on board for facial recognition and video for behavior analysis is sent directly to the Network Operations Center (NOC). Video...captured by the fixed sensors are sent directly to the NOC for facial recognition and behavior analysis processing. The multi- directional signal

Dynamics Govern Specificity of a Protein-Protein Interface: Substrate Recognition by Thrombin.

PubMed

Fuchs, Julian E; Huber, Roland G; Waldner, Birgit J; Kahler, Ursula; von Grafenstein, Susanne; Kramer, Christian; Liedl, Klaus R

2015-01-01

Biomolecular recognition is crucial in cellular signal transduction. Signaling is mediated through molecular interactions at protein-protein interfaces. Still, specificity and promiscuity of protein-protein interfaces cannot be explained using simplistic static binding models. Our study rationalizes specificity of the prototypic protein-protein interface between thrombin and its peptide substrates relying solely on binding site dynamics derived from molecular dynamics simulations. We find conformational selection and thus dynamic contributions to be a key player in biomolecular recognition. Arising entropic contributions complement chemical intuition primarily reflecting enthalpic interaction patterns. The paradigm "dynamics govern specificity" might provide direct guidance for the identification of specific anchor points in biomolecular recognition processes and structure-based drug design.

Real-Time Reconfigurable Adaptive Speech Recognition Command and Control Apparatus and Method

NASA Technical Reports Server (NTRS)

Salazar, George A. (Inventor); Haynes, Dena S. (Inventor); Sommers, Marc J. (Inventor)

1998-01-01

An adaptive speech recognition and control system and method for controlling various mechanisms and systems in response to spoken instructions and in which spoken commands are effective to direct the system into appropriate memory nodes, and to respective appropriate memory templates corresponding to the voiced command is discussed. Spoken commands from any of a group of operators for which the system is trained may be identified, and voice templates are updated as required in response to changes in pronunciation and voice characteristics over time of any of the operators for which the system is trained. Provisions are made for both near-real-time retraining of the system with respect to individual terms which are determined not be positively identified, and for an overall system training and updating process in which recognition of each command and vocabulary term is checked, and in which the memory templates are retrained if necessary for respective commands or vocabulary terms with respect to an operator currently using the system. In one embodiment, the system includes input circuitry connected to a microphone and including signal processing and control sections for sensing the level of vocabulary recognition over a given period and, if recognition performance falls below a given level, processing audio-derived signals for enhancing recognition performance of the system.

A MUSIC-based method for SSVEP signal processing.

PubMed

Chen, Kun; Liu, Quan; Ai, Qingsong; Zhou, Zude; Xie, Sheng Quan; Meng, Wei

2016-03-01

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.

Signal detection with criterion noise: applications to recognition memory.

PubMed

Benjamin, Aaron S; Diaz, Michael; Wee, Serena

2009-01-01

A tacit but fundamental assumption of the theory of signal detection is that criterion placement is a noise-free process. This article challenges that assumption on theoretical and empirical grounds and presents the noisy decision theory of signal detection (ND-TSD). Generalized equations for the isosensitivity function and for measures of discrimination incorporating criterion variability are derived, and the model's relationship with extant models of decision making in discrimination tasks is examined. An experiment evaluating recognition memory for ensembles of word stimuli revealed that criterion noise is not trivial in magnitude and contributes substantially to variance in the slope of the isosensitivity function. The authors discuss how ND-TSD can help explain a number of current and historical puzzles in recognition memory, including the inconsistent relationship between manipulations of learning and the isosensitivity function's slope, the lack of invariance of the slope with manipulations of bias or payoffs, the effects of aging on the decision-making process in recognition, and the nature of responding in remember-know decision tasks. ND-TSD poses novel, theoretically meaningful constraints on theories of recognition and decision making more generally, and provides a mechanism for rapprochement between theories of decision making that employ deterministic response rules and those that postulate probabilistic response rules.

Detection and recognition of mechanical, digging and vehicle signals in the optical fiber pre-warning system

NASA Astrophysics Data System (ADS)

Tian, Qing; Yang, Dan; Zhang, Yuan; Qu, Hongquan

2018-04-01

This paper presents detection and recognition method to locate and identify harmful intrusions in the optical fiber pre-warning system (OFPS). Inspired by visual attention architecture (VAA), the process flow is divided into two parts, i.e., data-driven process and task-driven process. At first, data-driven process takes all the measurements collected by the system as input signals, which is handled by detection method to locate the harmful intrusion in both spatial domain and time domain. Then, these detected intrusion signals are taken over by task-driven process. Specifically, we get pitch period (PP) and duty cycle (DC) of the intrusion signals to identify the mechanical and manual digging (MD) intrusions respectively. For the passing vehicle (PV) intrusions, their strong low frequency component can be used as good feature. In generally, since the harmful intrusion signals only account for a small part of whole measurements, the data-driven process reduces the amount of input data for subsequent task-driven process considerably. Furthermore, the task-driven process determines the harmful intrusions orderly according to their severity, which makes a priority mechanism for the system as well as targeted processing for different harmful intrusion. At last, real experiments are performed to validate the effectiveness of this method.

Interspecific aggression and character displacement of competitor recognition in Hetaerina damselflies.

PubMed

Anderson, Christopher N; Grether, Gregory F

2010-02-22

In zones of sympatry between closely related species, species recognition errors in a competitive context can cause character displacement in agonistic signals and competitor recognition functions, just as species recognition errors in a mating context can cause character displacement in mating signals and mate recognition. These two processes are difficult to distinguish because the same traits can serve as both agonistic and mating signals. One solution is to test for sympatric shifts in recognition functions. We studied competitor recognition in Hetaerina damselflies by challenging territory holders with live tethered conspecific and heterospecific intruders. Heterospecific intruders elicited less aggression than conspecific intruders in species pairs with dissimilar wing coloration (H. occisa/H. titia, H. americana/H. titia) but not in species pairs with similar wing coloration (H. occisa/H. cruentata, H. americana/H. cruentata). Natural variation in the area of black wing pigmentation on H. titia intruders correlated negatively with heterospecific aggression. To directly examine the role of wing coloration, we blackened the wings of H. occisa or H. americana intruders and measured responses of conspecific territory holders. This treatment reduced territorial aggression at multiple sites where H. titia is present, but not at allopatric sites. These results provide strong evidence for agonistic character displacement.

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.

Effects of pre-experimental knowledge on recognition memory.

PubMed

Bird, Chris M; Davies, Rachel A; Ward, Jamie; Burgess, Neil

2011-01-01

The influence of pre-experimental autobiographical knowledge on recognition memory was investigated using as memoranda faces that were either personally known or unknown to the participant. Under a dual process theory, such knowledge boosted both recollection- and familiarity-based recognition judgements. Under an unequal variance signal detection model, pre-experimental knowledge increased both the variance and the separation of the target and foil memory strength distributions, boosting hits and correct rejections. Thus, pre-experimental knowledge has profound effects on the multiple, interacting processes that subserve recognition memory, and likely in the neural systems that underpin them.

Development of signal processing algorithms for ultrasonic detection of coal seam interfaces

NASA Technical Reports Server (NTRS)

Purcell, D. D.; Ben-Bassat, M.

1976-01-01

A pattern recognition system is presented for determining the thickness of coal remaining on the roof and floor of a coal seam. The system was developed to recognize reflected pulse echo signals that are generated by an acoustical transducer and reflected from the coal seam interface. The flexibility of the system, however, should enable it to identify pulse-echo signals generated by radar or other techniques. The main difference being the specific features extracted from the recorded data as a basis for pattern recognition.

Developing Signal-Pattern-Recognition Programs

NASA Technical Reports Server (NTRS)

Shelton, Robert O.; Hammen, David

2006-01-01

Pattern Interpretation and Recognition Application Toolkit Environment (PIRATE) is a block-oriented software system that aids the development of application programs that analyze signals in real time in order to recognize signal patterns that are indicative of conditions or events of interest. PIRATE was originally intended for use in writing application programs to recognize patterns in space-shuttle telemetry signals received at Johnson Space Center's Mission Control Center: application programs were sought to (1) monitor electric currents on shuttle ac power busses to recognize activations of specific power-consuming devices, (2) monitor various pressures and infer the states of affected systems by applying a Kalman filter to the pressure signals, (3) determine fuel-leak rates from sensor data, (4) detect faults in gyroscopes through analysis of system measurements in the frequency domain, and (5) determine drift rates in inertial measurement units by regressing measurements against time. PIRATE can also be used to develop signal-pattern-recognition software for different purposes -- for example, to monitor and control manufacturing processes.

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

PubMed

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

2018-06-01

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

Detection and recognition of targets by using signal polarization properties

NASA Astrophysics Data System (ADS)

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

1999-08-01

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

The recognition signal hypothesis for the adaptive evolution of religion : a phylogenetic test with Christian denominations.

PubMed

Matthews, Luke J

2012-06-01

Recent research on the evolution of religion has focused on whether religion is an unselected by-product of evolutionary processes or if it is instead an adaptation by natural selection. Adaptive hypotheses for religion include direct fitness benefits from improved health and indirect fitness benefits mediated by costly signals and/or cultural group selection. Herein, I propose that religious denominations achieve indirect fitness gains for members through the use of ecologically arbitrary beliefs, rituals, and moral rules that function as recognition markers of cultural inheritance analogous to kin and species recognition of genetic inheritance in biology. This recognition signal hypotheses could act in concert with either costly signaling or cultural group selection to produce evolutionarily altruistic behaviors within denominations. Using a cultural phylogenetic analysis, I show that a large set of religious behaviors among extant Christian denominations supports the prediction of the recognition signal hypothesis that characters change more frequently near historical schisms. By incorporating demographic data into the model, I show that more-distinctive denominations, as measured through dissimilar characteristics, appear to be protected from intrusion by nonmembers in mixed-denomination households, and that they may be experiencing greater biological growth of their populations even in the present day.

The effect of hearing aid technologies on listening in an automobile.

PubMed

Wu, Yu-Hsiang; Stangl, Elizabeth; Bentler, Ruth A; Stanziola, Rachel W

2013-06-01

Communication while traveling in an automobile often is very difficult for hearing aid users. This is because the automobile/road noise level is usually high, and listeners/drivers often do not have access to visual cues. Since the talker of interest usually is not located in front of the listener/driver, conventional directional processing that places the directivity beam toward the listener's front may not be helpful and, in fact, could have a negative impact on speech recognition (when compared to omnidirectional processing). Recently, technologies have become available in commercial hearing aids that are designed to improve speech recognition and/or listening effort in noisy conditions where talkers are located behind or beside the listener. These technologies include (1) a directional microphone system that uses a backward-facing directivity pattern (Back-DIR processing), (2) a technology that transmits audio signals from the ear with the better signal-to-noise ratio (SNR) to the ear with the poorer SNR (Side-Transmission processing), and (3) a signal processing scheme that suppresses the noise at the ear with the poorer SNR (Side-Suppression processing). The purpose of the current study was to determine the effect of (1) conventional directional microphones and (2) newer signal processing schemes (Back-DIR, Side-Transmission, and Side-Suppression) on listener's speech recognition performance and preference for communication in a traveling automobile. A single-blinded, repeated-measures design was used. Twenty-five adults with bilateral symmetrical sensorineural hearing loss aged 44 through 84 yr participated in the study. The automobile/road noise and sentences of the Connected Speech Test (CST) were recorded through hearing aids in a standard van moving at a speed of 70 mph on a paved highway. The hearing aids were programmed to omnidirectional microphone, conventional adaptive directional microphone, and the three newer schemes. CST sentences were presented from the side and back of the hearing aids, which were placed on the ears of a manikin. The recorded stimuli were presented to listeners via earphones in a sound-treated booth to assess speech recognition performance and preference with each programmed condition. Compared to omnidirectional microphones, conventional adaptive directional processing had a detrimental effect on speech recognition when speech was presented from the back or side of the listener. Back-DIR and Side-Transmission processing improved speech recognition performance (relative to both omnidirectional and adaptive directional processing) when speech was from the back and side, respectively. The performance with Side-Suppression processing was better than with adaptive directional processing when speech was from the side. The participants' preferences for a given processing scheme were generally consistent with speech recognition results. The finding that performance with adaptive directional processing was poorer than with omnidirectional microphones demonstrates the importance of selecting the correct microphone technology for different listening situations. The results also suggest the feasibility of using hearing aid technologies to provide a better listening experience for hearing aid users in automobiles. American Academy of Audiology.

Using Pattern Recognition and Discriminance Analysis to Predict Critical Events in Large Signal Databases

NASA Astrophysics Data System (ADS)

Feller, Jens; Feller, Sebastian; Mauersberg, Bernhard; Mergenthaler, Wolfgang

2009-09-01

Many applications in plant management require close monitoring of equipment performance, in particular with the objective to prevent certain critical events. At each point in time, the information available to classify the criticality of the process, is represented through the historic signal database as well as the actual measurement. This paper presents an approach to detect and predict critical events, based on pattern recognition and discriminance analysis.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

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

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2011-06-01

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

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

PubMed

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

2016-12-15

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

Some Memories are Odder than Others: Judgments of Episodic Oddity Violate Known Decision Rules

PubMed Central

O’Connor, Akira R.; Guhl, Emily N.; Cox, Justin C.; Dobbins, Ian G.

2011-01-01

Current decision models of recognition memory are based almost entirely on one paradigm, single item old/new judgments accompanied by confidence ratings. This task results in receiver operating characteristics (ROCs) that are well fit by both signal-detection and dual-process models. Here we examine an entirely new recognition task, the judgment of episodic oddity, whereby participants select the mnemonically odd members of triplets (e.g., a new item hidden among two studied items). Using the only two known signal-detection rules of oddity judgment derived from the sensory perception literature, the unequal variance signal-detection model predicted that an old item among two new items would be easier to discover than a new item among two old items. In contrast, four separate empirical studies demonstrated the reverse pattern: triplets with two old items were the easiest to resolve. This finding was anticipated by the dual-process approach as the presence of two old items affords the greatest opportunity for recollection. Furthermore, a bootstrap-fed Monte Carlo procedure using two independent datasets demonstrated that the dual-process parameters typically observed during single item recognition correctly predict the current oddity findings, whereas unequal variance signal-detection parameters do not. Episodic oddity judgments represent a case where dual- and single-process predictions qualitatively diverge and the findings demonstrate that novelty is “odder” than familiarity. PMID:22833695

Dynamics Govern Specificity of a Protein-Protein Interface: Substrate Recognition by Thrombin

PubMed Central

Fuchs, Julian E.; Huber, Roland G.; Waldner, Birgit J.; Kahler, Ursula; von Grafenstein, Susanne; Kramer, Christian; Liedl, Klaus R.

2015-01-01

Biomolecular recognition is crucial in cellular signal transduction. Signaling is mediated through molecular interactions at protein-protein interfaces. Still, specificity and promiscuity of protein-protein interfaces cannot be explained using simplistic static binding models. Our study rationalizes specificity of the prototypic protein-protein interface between thrombin and its peptide substrates relying solely on binding site dynamics derived from molecular dynamics simulations. We find conformational selection and thus dynamic contributions to be a key player in biomolecular recognition. Arising entropic contributions complement chemical intuition primarily reflecting enthalpic interaction patterns. The paradigm “dynamics govern specificity” might provide direct guidance for the identification of specific anchor points in biomolecular recognition processes and structure-based drug design. PMID:26496636

"Multiple partial recognitions in dynamic equilibrium" in the binding sites of proteins form the molecular basis of promiscuous recognition of structurally diverse ligands.

PubMed

Kohda, Daisuke

2018-04-01

Promiscuous recognition of ligands by proteins is as important as strict recognition in numerous biological processes. In living cells, many short, linear amino acid motifs function as targeting signals in proteins to specify the final destination of the protein transport. In general, the target signal is defined by a consensus sequence containing wild-characters, and hence represented by diverse amino acid sequences. The classical lock-and-key or induced-fit/conformational selection mechanism may not cover all aspects of the promiscuous recognition. On the basis of our crystallographic and NMR studies on the mitochondrial Tom20 protein-presequence interaction, we proposed a new hypothetical mechanism based on "a rapid equilibrium of multiple states with partial recognitions". This dynamic, multiple recognition mode enables the Tom20 receptor to recognize diverse mitochondrial presequences with nearly equal affinities. The plant Tom20 is evolutionally unrelated to the animal Tom20 in our study, but is a functional homolog of the animal/fungal Tom20. NMR studies by another research group revealed that the presequence binding by the plant Tom20 was not fully explained by simple interaction modes, suggesting the presence of a similar dynamic, multiple recognition mode. Circumstantial evidence also suggested that similar dynamic mechanisms may be applicable to other promiscuous recognitions of signal peptides by the SRP54/Ffh and SecA proteins.

New technique for real-time distortion-invariant multiobject recognition and classification

NASA Astrophysics Data System (ADS)

Hong, Rutong; Li, Xiaoshun; Hong, En; Wang, Zuyi; Wei, Hongan

2001-04-01

A real-time hybrid distortion-invariant OPR system was established to make 3D multiobject distortion-invariant automatic pattern recognition. Wavelet transform technique was used to make digital preprocessing of the input scene, to depress the noisy background and enhance the recognized object. A three-layer backpropagation artificial neural network was used in correlation signal post-processing to perform multiobject distortion-invariant recognition and classification. The C-80 and NOA real-time processing ability and the multithread programming technology were used to perform high speed parallel multitask processing and speed up the post processing rate to ROIs. The reference filter library was constructed for the distortion version of 3D object model images based on the distortion parameter tolerance measuring as rotation, azimuth and scale. The real-time optical correlation recognition testing of this OPR system demonstrates that using the preprocessing, post- processing, the nonlinear algorithm os optimum filtering, RFL construction technique and the multithread programming technology, a high possibility of recognition and recognition rate ere obtained for the real-time multiobject distortion-invariant OPR system. The recognition reliability and rate was improved greatly. These techniques are very useful to automatic target recognition.

Character displacement of Cercopithecini primate visual signals

PubMed Central

Allen, William L.; Stevens, Martin; Higham, James P.

2014-01-01

Animal visual signals have the potential to act as an isolating barrier to prevent interbreeding of populations through a role in species recognition. Within communities of competing species, species recognition signals are predicted to undergo character displacement, becoming more visually distinctive from each other, however this pattern has rarely been identified. Using computational face recognition algorithms to model primate face processing, we demonstrate that the face patterns of guenons (tribe: Cercopithecini) have evolved under selection to become more visually distinctive from those of other guenon species with whom they are sympatric. The relationship between the appearances of sympatric species suggests that distinguishing conspecifics from other guenon species has been a major driver of diversification in guenon face appearance. Visual signals that have undergone character displacement may have had an important role in the tribe’s radiation, keeping populations that became geographically separated reproductively isolated on secondary contact. PMID:24967517

Non-parallel coevolution of sender and receiver in the acoustic communication system of treefrogs.

PubMed

Schul, Johannes; Bush, Sarah L

2002-09-07

Advertisement calls of closely related species often differ in quantitative features such as the repetition rate of signal units. These differences are important in species recognition. Current models of signal-receiver coevolution predict two possible patterns in the evolution of the mechanism used by receivers to recognize the call: (i) classical sexual selection models (Fisher process, good genes/indirect benefits, direct benefits models) predict that close relatives use qualitatively similar signal recognition mechanisms tuned to different values of a call parameter; and (ii) receiver bias models (hidden preference, pre-existing bias models) predict that if different signal recognition mechanisms are used by sibling species, evidence of an ancestral mechanism will persist in the derived species, and evidence of a pre-existing bias will be detectable in the ancestral species. We describe qualitatively different call recognition mechanisms in sibling species of treefrogs. Whereas Hyla chrysoscelis uses pulse rate to recognize male calls, Hyla versicolor uses absolute measurements of pulse duration and interval duration. We found no evidence of either hidden preferences or pre-existing biases. The results are compared with similar data from katydids (Tettigonia sp.). In both taxa, the data are not adequately explained by current models of signal-receiver coevolution.

Relating memory to functional performance in normal aging to dementia using hierarchical Bayesian cognitive processing models.

PubMed

Shankle, William R; Pooley, James P; Steyvers, Mark; Hara, Junko; Mangrola, Tushar; Reisberg, Barry; Lee, Michael D

2013-01-01

Determining how cognition affects functional abilities is important in Alzheimer disease and related disorders. A total of 280 patients (normal or Alzheimer disease and related disorders) received a total of 1514 assessments using the functional assessment staging test (FAST) procedure and the MCI Screen. A hierarchical Bayesian cognitive processing model was created by embedding a signal detection theory model of the MCI Screen-delayed recognition memory task into a hierarchical Bayesian framework. The signal detection theory model used latent parameters of discriminability (memory process) and response bias (executive function) to predict, simultaneously, recognition memory performance for each patient and each FAST severity group. The observed recognition memory data did not distinguish the 6 FAST severity stages, but the latent parameters completely separated them. The latent parameters were also used successfully to transform the ordinal FAST measure into a continuous measure reflecting the underlying continuum of functional severity. Hierarchical Bayesian cognitive processing models applied to recognition memory data from clinical practice settings accurately translated a latent measure of cognition into a continuous measure of functional severity for both individuals and FAST groups. Such a translation links 2 levels of brain information processing and may enable more accurate correlations with other levels, such as those characterized by biomarkers.

Process dissociation between contextual retrieval and item recognition.

PubMed

Weis, Susanne; Specht, Karsten; Klaver, Peter; Tendolkar, Indira; Willmes, Klaus; Ruhlmann, Jürgen; Elger, Christian E; Fernández, Guillén

2004-12-22

We employed a source memory task in an event related fMRI study to dissociate MTL processes associated with either contextual retrieval or item recognition. To introduce context during study, stimuli (photographs of buildings and natural landscapes) were transformed into one of four single-color-scales: red, blue, yellow, or green. In the subsequent old/new recognition memory test, all stimuli were presented as gray scale photographs, and old-responses were followed by a four-alternative source judgment referring to the color in which the stimulus was presented during study. Our results suggest a clear-cut process dissociation within the human MTL. While an activity increase accompanies successful retrieval of contextual information, an activity decrease provides a familiarity signal that is sufficient for successful item recognition.

How landmark suitability shapes recognition memory signals for objects in the medial temporal lobes.

PubMed

Martin, Chris B; Sullivan, Jacqueline A; Wright, Jessey; Köhler, Stefan

2018-02-01

A role of perirhinal cortex (PrC) in recognition memory for objects has been well established. Contributions of parahippocampal cortex (PhC) to this function, while documented, remain less well understood. Here, we used fMRI to examine whether the organization of item-based recognition memory signals across these two structures is shaped by object category, independent of any difference in representing episodic context. Guided by research suggesting that PhC plays a critical role in processing landmarks, we focused on three categories of objects that differ from each other in their landmark suitability as confirmed with behavioral ratings (buildings > trees > aircraft). Participants made item-based recognition-memory decisions for novel and previously studied objects from these categories, which were matched in accuracy. Multi-voxel pattern classification revealed category-specific item-recognition memory signals along the long axis of PrC and PhC, with no sharp functional boundaries between these structures. Memory signals for buildings were observed in the mid to posterior extent of PhC, signals for trees in anterior to posterior segments of PhC, and signals for aircraft in mid to posterior aspects of PrC and the anterior extent of PhC. Notably, item-based memory signals for the category with highest landmark suitability ratings were observed only in those posterior segments of PhC that also allowed for classification of landmark suitability of objects when memory status was held constant. These findings provide new evidence in support of the notion that item-based memory signals for objects are not limited to PrC, and that the organization of these signals along the longitudinal axis that crosses PrC and PhC can be captured with reference to landmark suitability. Copyright © 2017 Elsevier Inc. All rights reserved.

Dynamic Spectral Structure Specifies Vowels for Adults and Children

PubMed Central

Nittrouer, Susan; Lowenstein, Joanna H.

2014-01-01

The dynamic specification account of vowel recognition suggests that formant movement between vowel targets and consonant margins is used by listeners to recognize vowels. This study tested that account by measuring contributions to vowel recognition of dynamic (i.e., time-varying) spectral structure and coarticulatory effects on stationary structure. Adults and children (four-and seven-year-olds) were tested with three kinds of consonant-vowel-consonant syllables: (1) unprocessed; (2) sine waves that preserved both stationary coarticulated and dynamic spectral structure; and (3) vocoded signals that primarily preserved that stationary, but not dynamic structure. Sections of two lengths were removed from syllable middles: (1) half the vocalic portion; and (2) all but the first and last three pitch periods. Adults performed accurately with unprocessed and sine-wave signals, as long as half the syllable remained; their recognition was poorer for vocoded signals, but above chance. Seven-year-olds performed more poorly than adults with both sorts of processed signals, but disproportionately worse with vocoded than sine-wave signals. Most four-year-olds were unable to recognize vowels at all with vocoded signals. Conclusions were that both dynamic and stationary coarticulated structures support vowel recognition for adults, but children attend to dynamic spectral structure more strongly because early phonological organization favors whole words. PMID:25536845

State Recognition of Bone Drilling Based on Acoustic Emission in Pedicle Screw Operation.

PubMed

Guan, Fengqing; Sun, Yu; Qi, Xiaozhi; Hu, Ying; Yu, Gang; Zhang, Jianwei

2018-05-09

Pedicle drilling is an important step in pedicle screw fixation and the most significant challenge in this operation is how to determine a key point in the transition region between cancellous and inner cortical bone. The purpose of this paper is to find a method to achieve the recognition for the key point. After acquiring acoustic emission (AE) signals during the drilling process, this paper proposed a novel frequency distribution-based algorithm (FDB) to analyze the AE signals in the frequency domain after certain processes. Then we select a specific frequency domain of the signal for standard operations and choose a fitting function to fit the obtained sequence. Characters of the fitting function are extracted as outputs for identification of different bone layers. The results, which are obtained by detecting force signal and direct measurement, are given in the paper. Compared with the results above, the results obtained by AE signals are distinguishable for different bone layers and are more accurate and precise. The results of the algorithm are trained and identified by a neural network and the recognition rate reaches 84.2%. The proposed method is proved to be efficient and can be used for bone layer identification in pedicle screw fixation.

Dynamic nanoplatforms in biosensor and membrane constitutional systems.

PubMed

Mahon, Eugene; Aastrup, Teodor; Barboiu, Mihail

2012-01-01

Molecular recognition in biological systems occurs mainly at interfacial environments such as membrane surfaces, enzyme active sites, or the interior of the DNA double helix. At the cell membrane surface, carbohydrate-protein recognition principles apply to a range of specific non-covalent interactions including immune response, cell proliferation, adhesion and death, cell-cell interaction and communication. Protein-protein recognition meanwhile accounts for signalling processes and ion channel structure. In this chapter we aim to describe such constitutional dynamic interfaces for biosensing and membrane transport applications. Constitutionally adaptive interfaces may mimic the recognition capabilities intrinsic to natural recognition processes. We present some recent examples of 2D and 3D constructed sensors and membranes of this type and describe their sensing and transport capabilities.

Optical Processing of Speckle Images with Bacteriorhodopsin for Pattern Recognition

NASA Technical Reports Server (NTRS)

Downie, John D.; Tucker, Deanne (Technical Monitor)

1994-01-01

Logarithmic processing of images with multiplicative noise characteristics can be utilized to transform the image into one with an additive noise distribution. This simplifies subsequent image processing steps for applications such as image restoration or correlation for pattern recognition. One particularly common form of multiplicative noise is speckle, for which the logarithmic operation not only produces additive noise, but also makes it of constant variance (signal-independent). We examine the optical transmission properties of some bacteriorhodopsin films here and find them well suited to implement such a pointwise logarithmic transformation optically in a parallel fashion. We present experimental results of the optical conversion of speckle images into transformed images with additive, signal-independent noise statistics using the real-time photochromic properties of bacteriorhodopsin. We provide an example of improved correlation performance in terms of correlation peak signal-to-noise for such a transformed speckle image.

Speech Recognition Using Multiple Features and Multiple Recognizers

DTIC Science & Technology

1991-12-03

6 2.1 Introduction ............................................... 6 2.2 Human Speech Communication Process...119 How to Setup ASRT.......................................... 119 How to Use Interactive Menus .................................. 120...recognize a word from an acoustic signal. The human ear and brain perform this type of recognition with incredible speed and precision. Even though

The recognition of extraterrestrial artificial signals

NASA Technical Reports Server (NTRS)

Seeger, C. L.

1980-01-01

Considerations in the design of receivers for the detection and recognition of artificial microwave signals of extraterrestrial origin are discussed. Following a review of the objectives of SETI and the probable reception and detection characteristics of extraterrestrial signals, means for the improvement of the sensitivity, signal-to-noise ratios and on-line data processing capabilities of SETI receivers are indicated. The characteristics of the signals likely to be present at the output of an ultra-low-noise microwave receiver are then examined, including the system background noise, terrestrial radiations, astrophysical radiations, accidental artificial radiations of terrestrial origin, and intentional radiations produced by humans and by extraterrestrial intelligence. The classes of extraterrestrial signals likely to be detected, beacons and leakage signals, are considered, and options in the specification of gating and thresholding for a high-spectral resolution, high-time-resolution signal discriminator are indicated. Possible tests for the nonhuman origin of a received signal are also pointed out.

The effect of hearing aid technologies on listening in an automobile

PubMed Central

Wu, Yu-Hsiang; Stangl, Elizabeth; Bentler, Ruth A.; Stanziola, Rachel W.

2014-01-01

Background Communication while traveling in an automobile often is very difficult for hearing aid users. This is because the automobile /road noise level is usually high, and listeners/drivers often do not have access to visual cues. Since the talker of interest usually is not located in front of the driver/listener, conventional directional processing that places the directivity beam toward the listener’s front may not be helpful, and in fact, could have a negative impact on speech recognition (when compared to omnidirectional processing). Recently, technologies have become available in commercial hearing aids that are designed to improve speech recognition and/or listening effort in noisy conditions where talkers are located behind or beside the listener. These technologies include (1) a directional microphone system that uses a backward-facing directivity pattern (Back-DIR processing), (2) a technology that transmits audio signals from the ear with the better signal-to-noise ratio (SNR) to the ear with the poorer SNR (Side-Transmission processing), and (3) a signal processing scheme that suppresses the noise at the ear with the poorer SNR (Side-Suppression processing). Purpose The purpose of the current study was to determine the effect of (1) conventional directional microphones and (2) newer signal processing schemes (Back-DIR, Side-Transmission, and Side-Suppression) on listener’s speech recognition performance and preference for communication in a traveling automobile. Research design A single-blinded, repeated-measures design was used. Study Sample Twenty-five adults with bilateral symmetrical sensorineural hearing loss aged 44 through 84 years participated in the study. Data Collection and Analysis The automobile/road noise and sentences of the Connected Speech Test (CST) were recorded through hearing aids in a standard van moving at a speed of 70 miles/hour on a paved highway. The hearing aids were programmed to omnidirectional microphone, conventional adaptive directional microphone, and the three newer schemes. CST sentences were presented from the side and back of the hearing aids, which were placed on the ears of a manikin. The recorded stimuli were presented to listeners via earphones in a sound treated booth to assess speech recognition performance and preference with each programmed condition. Results Compared to omnidirectional microphones, conventional adaptive directional processing had a detrimental effect on speech recognition when speech was presented from the back or side of the listener. Back-DIR and Side-Transmission processing improved speech recognition performance (relative to both omnidirectional and adaptive directional processing) when speech was from the back and side, respectively. The performance with Side-Suppression processing was better than with adaptive directional processing when speech was from the side. The participants’ preferences for a given processing scheme were generally consistent with speech recognition results. Conclusions The finding that performance with adaptive directional processing was poorer than with omnidirectional microphones demonstrates the importance of selecting the correct microphone technology for different listening situations. The results also suggest the feasibility of using hearing aid technologies to provide a better listening experience for hearing aid users in automobiles. PMID:23886425

Recognizing speech under a processing load: dissociating energetic from informational factors.

PubMed

Mattys, Sven L; Brooks, Joanna; Cooke, Martin

2009-11-01

Effects of perceptual and cognitive loads on spoken-word recognition have so far largely escaped investigation. This study lays the foundations of a psycholinguistic approach to speech recognition in adverse conditions that draws upon the distinction between energetic masking, i.e., listening environments leading to signal degradation, and informational masking, i.e., listening environments leading to depletion of higher-order, domain-general processing resources, independent of signal degradation. We show that severe energetic masking, such as that produced by background speech or noise, curtails reliance on lexical-semantic knowledge and increases relative reliance on salient acoustic detail. In contrast, informational masking, induced by a resource-depleting competing task (divided attention or a memory load), results in the opposite pattern. Based on this clear dissociation, we propose a model of speech recognition that addresses not only the mapping between sensory input and lexical representations, as traditionally advocated, but also the way in which this mapping interfaces with general cognition and non-linguistic processes.

Neuronal Spoken Word Recognition: The Time Course of Processing Variation in the Speech Signal

ERIC Educational Resources Information Center

Schild, Ulrike; Roder, Brigitte; Friedrich, Claudia K.

2012-01-01

Recent neurobiological studies revealed evidence for lexical representations that are not specified for the coronal place of articulation (PLACE; Friedrich, Eulitz, & Lahiri, 2006; Friedrich, Lahiri, & Eulitz, 2008). Here we tested when these types of underspecified representations influence neuronal speech recognition. In a unimodal…

Recognizing Speech under a Processing Load: Dissociating Energetic from Informational Factors

ERIC Educational Resources Information Center

Mattys, Sven L.; Brooks, Joanna; Cooke, Martin

2009-01-01

Effects of perceptual and cognitive loads on spoken-word recognition have so far largely escaped investigation. This study lays the foundations of a psycholinguistic approach to speech recognition in adverse conditions that draws upon the distinction between energetic masking, i.e., listening environments leading to signal degradation, and…

GRIM REAPER peptide binds to receptor kinase PRK5 to trigger cell death in Arabidopsis

PubMed Central

Wrzaczek, Michael; Vainonen, Julia P; Stael, Simon; Tsiatsiani, Liana; Help-Rinta-Rahko, Hanna; Gauthier, Adrien; Kaufholdt, David; Bollhöner, Benjamin; Lamminmäki, Airi; Staes, An; Gevaert, Kris; Tuominen, Hannele; Van Breusegem, Frank; Helariutta, Ykä; Kangasjärvi, Jaakko

2015-01-01

Recognition of extracellular peptides by plasma membrane-localized receptor proteins is commonly used in signal transduction. In plants, very little is known about how extracellular peptides are processed and activated in order to allow recognition by receptors. Here, we show that induction of cell death in planta by a secreted plant protein GRIM REAPER (GRI) is dependent on the activity of the type II metacaspase METACASPASE-9. GRI is cleaved by METACASPASE-9 in vitro resulting in the release of an 11 amino acid peptide. This peptide bound in vivo to the extracellular domain of the plasma membrane-localized, atypical leucine-rich repeat receptor-like kinase POLLEN-SPECIFIC RECEPTOR-LIKE KINASE 5 (PRK5) and was sufficient to induce oxidative stress/ROS-dependent cell death. This shows a signaling pathway in plants from processing and activation of an extracellular protein to recognition by its receptor. PMID:25398910

GRIM REAPER peptide binds to receptor kinase PRK5 to trigger cell death in Arabidopsis.

PubMed

Wrzaczek, Michael; Vainonen, Julia P; Stael, Simon; Tsiatsiani, Liana; Help-Rinta-Rahko, Hanna; Gauthier, Adrien; Kaufholdt, David; Bollhöner, Benjamin; Lamminmäki, Airi; Staes, An; Gevaert, Kris; Tuominen, Hannele; Van Breusegem, Frank; Helariutta, Ykä; Kangasjärvi, Jaakko

2015-01-02

Recognition of extracellular peptides by plasma membrane-localized receptor proteins is commonly used in signal transduction. In plants, very little is known about how extracellular peptides are processed and activated in order to allow recognition by receptors. Here, we show that induction of cell death in planta by a secreted plant protein GRIM REAPER (GRI) is dependent on the activity of the type II metacaspase METACASPASE-9. GRI is cleaved by METACASPASE-9 in vitro resulting in the release of an 11 amino acid peptide. This peptide bound in vivo to the extracellular domain of the plasma membrane-localized, atypical leucine-rich repeat receptor-like kinase POLLEN-SPECIFIC RECEPTOR-LIKE KINASE 5 (PRK5) and was sufficient to induce oxidative stress/ROS-dependent cell death. This shows a signaling pathway in plants from processing and activation of an extracellular protein to recognition by its receptor. © 2014 The Authors.

Signal Detection with Criterion Noise: Applications to Recognition Memory

ERIC Educational Resources Information Center

Benjamin, Aaron S.; Diaz, Michael; Wee, Serena

2009-01-01

A tacit but fundamental assumption of the theory of signal detection is that criterion placement is a noise-free process. This article challenges that assumption on theoretical and empirical grounds and presents the noisy decision theory of signal detection (ND-TSD). Generalized equations for the isosensitivity function and for measures of…

Dissecting the Signaling Mechanisms Underlying Recognition and Preference of Food Odors

PubMed Central

Harris, Gareth; Shen, Yu; Ha, Heonick; Donato, Alessandra; Wallis, Samuel; Zhang, Xiaodong

2014-01-01

Food is critical for survival. Many animals, including the nematode Caenorhabditis elegans, use sensorimotor systems to detect and locate preferred food sources. However, the signaling mechanisms underlying food-choice behaviors are poorly understood. Here, we characterize the molecular signaling that regulates recognition and preference between different food odors in C. elegans. We show that the major olfactory sensory neurons, AWB and AWC, play essential roles in this behavior. A canonical Gα-protein, together with guanylate cyclases and cGMP-gated channels, is needed for the recognition of food odors. The food-odor-evoked signal is transmitted via glutamatergic neurotransmission from AWC and through AMPA and kainate-like glutamate receptor subunits. In contrast, peptidergic signaling is required to generate preference between different food odors while being dispensable for the recognition of the odors. We show that this regulation is achieved by the neuropeptide NLP-9 produced in AWB, which acts with its putative receptor NPR-18, and by the neuropeptide NLP-1 produced in AWC. In addition, another set of sensory neurons inhibits food-odor preference. These mechanistic logics, together with a previously mapped neural circuit underlying food-odor preference, provide a functional network linking sensory response, transduction, and downstream receptors to process complex olfactory information and generate the appropriate behavioral decision essential for survival. PMID:25009271

Unsupervised pattern recognition methods in ciders profiling based on GCE voltammetric signals.

PubMed

Jakubowska, Małgorzata; Sordoń, Wanda; Ciepiela, Filip

2016-07-15

This work presents a complete methodology of distinguishing between different brands of cider and ageing degrees, based on voltammetric signals, utilizing dedicated data preprocessing procedures and unsupervised multivariate analysis. It was demonstrated that voltammograms recorded on glassy carbon electrode in Britton-Robinson buffer at pH 2 are reproducible for each brand. By application of clustering algorithms and principal component analysis visible homogenous clusters were obtained. Advanced signal processing strategy which included automatic baseline correction, interval scaling and continuous wavelet transform with dedicated mother wavelet, was a key step in the correct recognition of the objects. The results show that voltammetry combined with optimized univariate and multivariate data processing is a sufficient tool to distinguish between ciders from various brands and to evaluate their freshness. Copyright © 2016 Elsevier Ltd. All rights reserved.

SVD compression for magnetic resonance fingerprinting in the time domain.

PubMed

McGivney, Debra F; Pierre, Eric; Ma, Dan; Jiang, Yun; Saybasili, Haris; Gulani, Vikas; Griswold, Mark A

2014-12-01

Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.

SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain

PubMed Central

McGivney, Debra F.; Pierre, Eric; Ma, Dan; Jiang, Yun; Saybasili, Haris; Gulani, Vikas; Griswold, Mark A.

2016-01-01

Magnetic resonance fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition (SVD), which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously. PMID:25029380

Complex auditory behaviour emerges from simple reactive steering

NASA Astrophysics Data System (ADS)

Hedwig, Berthold; Poulet, James F. A.

2004-08-01

The recognition and localization of sound signals is fundamental to acoustic communication. Complex neural mechanisms are thought to underlie the processing of species-specific sound patterns even in animals with simple auditory pathways. In female crickets, which orient towards the male's calling song, current models propose pattern recognition mechanisms based on the temporal structure of the song. Furthermore, it is thought that localization is achieved by comparing the output of the left and right recognition networks, which then directs the female to the pattern that most closely resembles the species-specific song. Here we show, using a highly sensitive method for measuring the movements of female crickets, that when walking and flying each sound pulse of the communication signal releases a rapid steering response. Thus auditory orientation emerges from reactive motor responses to individual sound pulses. Although the reactive motor responses are not based on the song structure, a pattern recognition process may modulate the gain of the responses on a longer timescale. These findings are relevant to concepts of insect auditory behaviour and to the development of biologically inspired robots performing cricket-like auditory orientation.

Qualitatively similar processing for own- and other-race faces: Evidence from efficiency and equivalent input noise.

PubMed

Shafai, Fakhri; Oruc, Ipek

2018-02-01

The other-race effect is the finding of diminished performance in recognition of other-race faces compared to those of own-race. It has been suggested that the other-race effect stems from specialized expert processes being tuned exclusively to own-race faces. In the present study, we measured recognition contrast thresholds for own- and other-race faces as well as houses for Caucasian observers. We have factored face recognition performance into two invariant aspects of visual function: efficiency, which is related to neural computations and processing demanded by the task, and equivalent input noise, related to signal degradation within the visual system. We hypothesized that if expert processes are available only to own-race faces, this should translate into substantially greater recognition efficiencies for own-race compared to other-race faces. Instead, we found similar recognition efficiencies for both own- and other-race faces. The other-race effect manifested as increased equivalent input noise. These results argue against qualitatively distinct perceptual processes. Instead they suggest that for Caucasian observers, similar neural computations underlie recognition of own- and other-race faces. Copyright © 2018 Elsevier Ltd. All rights reserved.

An improved PSO-SVM model for online recognition defects in eddy current testing

NASA Astrophysics Data System (ADS)

Liu, Baoling; Hou, Dibo; Huang, Pingjie; Liu, Banteng; Tang, Huayi; Zhang, Wubo; Chen, Peihua; Zhang, Guangxin

2013-12-01

Accurate and rapid recognition of defects is essential for structural integrity and health monitoring of in-service device using eddy current (EC) non-destructive testing. This paper introduces a novel model-free method that includes three main modules: a signal pre-processing module, a classifier module and an optimisation module. In the signal pre-processing module, a kind of two-stage differential structure is proposed to suppress the lift-off fluctuation that could contaminate the EC signal. In the classifier module, multi-class support vector machine (SVM) based on one-against-one strategy is utilised for its good accuracy. In the optimisation module, the optimal parameters of classifier are obtained by an improved particle swarm optimisation (IPSO) algorithm. The proposed IPSO technique can improve convergence performance of the primary PSO through the following strategies: nonlinear processing of inertia weight, introductions of the black hole and simulated annealing model with extremum disturbance. The good generalisation ability of the IPSO-SVM model has been validated through adding additional specimen into the testing set. Experiments show that the proposed algorithm can achieve higher recognition accuracy and efficiency than other well-known classifiers and the superiorities are more obvious with less training set, which contributes to online application.

Wavelet-Based Signal and Image Processing for Target Recognition

NASA Astrophysics Data System (ADS)

Sherlock, Barry G.

2002-11-01

The PI visited NSWC Dahlgren, VA, for six weeks in May-June 2002 and collaborated with scientists in the G33 TEAMS facility, and with Marilyn Rudzinsky of T44 Technology and Photonic Systems Branch. During this visit the PI also presented six educational seminars to NSWC scientists on various aspects of signal processing. Several items from the grant proposal were completed, including (1) wavelet-based algorithms for interpolation of 1-d signals and 2-d images; (2) Discrete Wavelet Transform domain based algorithms for filtering of image data; (3) wavelet-based smoothing of image sequence data originally obtained for the CRITTIR (Clutter Rejection Involving Temporal Techniques in the Infra-Red) project. The PI visited the University of Stellenbosch, South Africa to collaborate with colleagues Prof. B.M. Herbst and Prof. J. du Preez on the use of wavelet image processing in conjunction with pattern recognition techniques. The University of Stellenbosch has offered the PI partial funding to support a sabbatical visit in Fall 2003, the primary purpose of which is to enable the PI to develop and enhance his expertise in Pattern Recognition. During the first year, the grant supported publication of 3 referred papers, presentation of 9 seminars and an intensive two-day course on wavelet theory. The grant supported the work of two students who functioned as research assistants.

Amplifying Electrochemical Indicators

NASA Technical Reports Server (NTRS)

Fan, Wenhong; Li, Jun; Han, Jie

2004-01-01

Dendrimeric reporter compounds have been invented for use in sensing and amplifying electrochemical signals from molecular recognition events that involve many chemical and biological entities. These reporter compounds can be formulated to target specific molecules or molecular recognition events. They can also be formulated to be, variously, hydrophilic or amphiphilic so that they are suitable for use at interfaces between (1) aqueous solutions and (2) electrodes connected to external signal-processing electronic circuits. The invention of these reporter compounds is expected to enable the development of highly miniaturized, low-power-consumption, relatively inexpensive, mass-producible sensor units for diverse applications.

Thunderstorm Hypothesis Reasoner

NASA Technical Reports Server (NTRS)

Mulvehill, Alice M.

1994-01-01

THOR is a knowledge-based system which incorporates techniques from signal processing, pattern recognition, and artificial intelligence (AI) in order to determine the boundary of small thunderstorms which develop and dissipate over the area encompassed by KSC and the Cape Canaveral Air Force Station. THOR interprets electric field mill data (derived from a network of electric field mills) by using heuristics and algorithms about thunderstorms that have been obtained from several domain specialists. THOR generates two forms of output: contour plots which visually describe the electric field activity over the network and a verbal interpretation of the activity. THOR uses signal processing and pattern recognition to detect signatures associated with noise or thunderstorm behavior in a near real time fashion from over 31 electrical field mills. THOR's AI component generates hypotheses identifying areas which are under a threat from storm activity, such as lightning. THOR runs on a VAX/VMS at the Kennedy Space Center. Its software is a coupling of C and FORTRAN programs, several signal processing packages, and an expert system development shell.

Dual-process models of associative recognition in young and older adults: evidence from receiver operating characteristics.

PubMed

Healy, Michael R; Light, Leah L; Chung, Christie

2005-07-01

In 3 experiments, young and older adults studied lists of unrelated word pairs and were given confidence-rated item and associative recognition tests. Several different models of recognition were fit to the confidence-rating data using techniques described by S. Macho (2002, 2004). Concordant with previous findings, item recognition data were best fit by an unequal-variance signal detection theory model for both young and older adults. For both age groups, associative recognition performance was best explained by models incorporating both recollection and familiarity components. Examination of parameter estimates supported the conclusion that recollection is reduced in old age, but inferences about age differences in familiarity were highly model dependent. Implications for dual-process models of memory in old age are discussed. ((c) 2005 APA, all rights reserved).

Chemical recognition of gases and gas mixtures with terahertz waves.

PubMed

Jacobsen, R H; Mittleman, D M; Nuss, M C

1996-12-15

A time-domain chemical-recognition system for classifying gases and analyzing gas mixtures is presented. We analyze the free induction decay exhibited by gases excited by far-infrared (terahertz) pulses in the time domain, using digital signal-processing techniques. A simple geometric picture is used for the classif ication of the waveforms measured for unknown gas species. We demonstrate how the recognition system can be used to determine the partial pressures of an ammonia-water gas mixture.

Chemical recognition of gases and gas mixtures with terahertz waves

NASA Astrophysics Data System (ADS)

Jacobsen, R. H.; Mittleman, D. M.; Nuss, M. C.

1996-12-01

A time-domain chemical-recognition system for classifying gases and analyzing gas mixtures is presented. We analyze the free induction decay exhibited by gases excited by far-infrared (terahertz) pulses in the time domain, using digital signal-processing techniques. A simple geometric picture is used for the classification of the waveforms measured for unknown gas species. We demonstrate how the recognition system can be used to determine the partial pressures of an ammonia-water gas mixture.

Door latching recognition apparatus and process

DOEpatents

Eakle, Jr., Robert F.

2012-05-15

An acoustic door latch detector is provided in which a sound recognition sensor is integrated into a door or door lock mechanism. The programmable sound recognition sensor can be trained to recognize the acoustic signature of the door and door lock mechanism being properly engaged and secured. The acoustic sensor will signal a first indicator indicating that proper closure was detected or sound an alarm condition if the proper acoustic signature is not detected within a predetermined time interval.

Automated information-analytical system for thunderstorm monitoring and early warning alarms using modern physical sensors and information technologies with elements of artificial intelligence

NASA Astrophysics Data System (ADS)

Boldyreff, Anton S.; Bespalov, Dmitry A.; Adzhiev, Anatoly Kh.

2017-05-01

Methods of artificial intelligence are a good solution for weather phenomena forecasting. They allow to process a large amount of diverse data. Recirculation Neural Networks is implemented in the paper for the system of thunderstorm events prediction. Large amounts of experimental data from lightning sensors and electric field mills networks are received and analyzed. The average recognition accuracy of sensor signals is calculated. It is shown that Recirculation Neural Networks is a promising solution in the forecasting of thunderstorms and weather phenomena, characterized by the high efficiency of the recognition elements of the sensor signals, allows to compress images and highlight their characteristic features for subsequent recognition.

Separating Mnemonic Process from Participant and Item Effects in the Assessment of ROC Asymmetries

ERIC Educational Resources Information Center

Pratte, Michael S.; Rouder, Jeffrey N.; Morey, Richard D.

2010-01-01

One of the most influential findings in the study of recognition memory is that receiver operating characteristic (ROC) curves are asymmetric about the negative diagonal. This result has led to the rejection of the equal-variance signal detection model of recognition memory and has provided motivation for more complex models, such as the…

Some Memories Are Odder than Others: Judgments of Episodic Oddity Violate Known Decision Rules

ERIC Educational Resources Information Center

O'Connor, Akira R.; Guhl, Emily N.; Cox, Justin C.; Dobbins, Ian G.

2011-01-01

Current decision models of recognition memory are based almost entirely on one paradigm, single item old/new judgments accompanied by confidence ratings. This task results in receiver operating characteristics (ROCs) that are well fit by both signal-detection and dual-process models. Here we examine an entirely new recognition task, the judgment…

Ultrasonic inspection of carbon fiber reinforced plastic by means of sample-recognition methods

NASA Technical Reports Server (NTRS)

Bilgram, R.

1985-01-01

In the case of carbon fiber reinforced plastic (CFRP), it has not yet been possible to detect nonlocal defects and material degradation related to aging with the aid of nondestructive inspection method. An approach for overcoming difficulties regarding such an inspection involves an extension of the ultrasonic inspection procedure on the basis of a use of signal processing and sample recognition methods. The basic concept involved in this approach is related to the realization that the ultrasonic signal contains information regarding the medium which is not utilized in conventional ultrasonic inspection. However, the analytical study of the phyiscal processes involved is very complex. For this reason, an empirical approach is employed to make use of the information which has not been utilized before. This approach uses reference signals which can be obtained with material specimens of different quality. The implementation of these concepts for the supersonic inspection of CFRP laminates is discussed.

Image-plane processing of visual information

NASA Technical Reports Server (NTRS)

Huck, F. O.; Fales, C. L.; Park, S. K.; Samms, R. W.

1984-01-01

Shannon's theory of information is used to optimize the optical design of sensor-array imaging systems which use neighborhood image-plane signal processing for enhancing edges and compressing dynamic range during image formation. The resultant edge-enhancement, or band-pass-filter, response is found to be very similar to that of human vision. Comparisons of traits in human vision with results from information theory suggest that: (1) Image-plane processing, like preprocessing in human vision, can improve visual information acquisition for pattern recognition when resolving power, sensitivity, and dynamic range are constrained. Improvements include reduced sensitivity to changes in lighter levels, reduced signal dynamic range, reduced data transmission and processing, and reduced aliasing and photosensor noise degradation. (2) Information content can be an appropriate figure of merit for optimizing the optical design of imaging systems when visual information is acquired for pattern recognition. The design trade-offs involve spatial response, sensitivity, and sampling interval.

Some-or-None Recollection: Evidence from Item and Source Memory

ERIC Educational Resources Information Center

Onyper, Serge V.; Zhang, Yaofei X.; Howard, Marc W.

2010-01-01

Dual-process theory hypothesizes that recognition memory depends on 2 distinguishable memory signals. Recollection reflects conscious recovery of detailed information about the learning episode. Familiarity reflects a memory signal that is not accompanied by a vivid conscious experience but nonetheless enables participants to distinguish recently…

Dissecting the signaling mechanisms underlying recognition and preference of food odors.

PubMed

Harris, Gareth; Shen, Yu; Ha, Heonick; Donato, Alessandra; Wallis, Samuel; Zhang, Xiaodong; Zhang, Yun

2014-07-09

Food is critical for survival. Many animals, including the nematode Caenorhabditis elegans, use sensorimotor systems to detect and locate preferred food sources. However, the signaling mechanisms underlying food-choice behaviors are poorly understood. Here, we characterize the molecular signaling that regulates recognition and preference between different food odors in C. elegans. We show that the major olfactory sensory neurons, AWB and AWC, play essential roles in this behavior. A canonical Gα-protein, together with guanylate cyclases and cGMP-gated channels, is needed for the recognition of food odors. The food-odor-evoked signal is transmitted via glutamatergic neurotransmission from AWC and through AMPA and kainate-like glutamate receptor subunits. In contrast, peptidergic signaling is required to generate preference between different food odors while being dispensable for the recognition of the odors. We show that this regulation is achieved by the neuropeptide NLP-9 produced in AWB, which acts with its putative receptor NPR-18, and by the neuropeptide NLP-1 produced in AWC. In addition, another set of sensory neurons inhibits food-odor preference. These mechanistic logics, together with a previously mapped neural circuit underlying food-odor preference, provide a functional network linking sensory response, transduction, and downstream receptors to process complex olfactory information and generate the appropriate behavioral decision essential for survival. Copyright © 2014 the authors 0270-6474/14/339389-15$15.00/0.

Benefits of adaptive FM systems on speech recognition in noise for listeners who use hearing aids.

PubMed

Thibodeau, Linda

2010-06-01

To compare the benefits of adaptive FM and fixed FM systems through measurement of speech recognition in noise with adults and students in clinical and real-world settings. Five adults and 5 students with moderate-to-severe hearing loss completed objective and subjective speech recognition in noise measures with the 2 types of FM processing. Sentence recognition was evaluated in a classroom for 5 competing noise levels ranging from 54 to 80 dBA while the FM microphone was positioned 6 in. from the signal loudspeaker to receive input at 84 dB SPL. The subjective measures included 2 classroom activities and 6 auditory lessons in a noisy, public aquarium. On the objective measures, adaptive FM processing resulted in significantly better speech recognition in noise than fixed FM processing for 68- and 73-dBA noise levels. On the subjective measures, all individuals preferred adaptive over fixed processing for half of the activities. Adaptive processing was also preferred by most (8-9) individuals for the remaining 4 activities. The adaptive FM processing resulted in significant improvements at the higher noise levels and was preferred by the majority of participants in most of the conditions.

Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement

PubMed Central

Guo, Shuxiang; Pang, Muye; Gao, Baofeng; Hirata, Hideyuki; Ishihara, Hidenori

2015-01-01

The surface electromyography (sEMG) technique is proposed for muscle activation detection and intuitive control of prostheses or robot arms. Motion recognition is widely used to map sEMG signals to the target motions. One of the main factors preventing the implementation of this kind of method for real-time applications is the unsatisfactory motion recognition rate and time consumption. The purpose of this paper is to compare eight combinations of four feature extraction methods (Root Mean Square (RMS), Detrended Fluctuation Analysis (DFA), Weight Peaks (WP), and Muscular Model (MM)) and two classifiers (Neural Networks (NN) and Support Vector Machine (SVM)), for the task of mapping sEMG signals to eight upper-limb motions, to find out the relation between these methods and propose a proper combination to solve this issue. Seven subjects participated in the experiment and six muscles of the upper-limb were selected to record sEMG signals. The experimental results showed that NN classifier obtained the highest recognition accuracy rate (88.7%) during the training process while SVM performed better in real-time experiments (85.9%). For time consumption, SVM took less time than NN during the training process but needed more time for real-time computation. Among the four feature extraction methods, WP had the highest recognition rate for the training process (97.7%) while MM performed the best during real-time tests (94.3%). The combination of MM and NN is recommended for strict real-time applications while a combination of MM and SVM will be more suitable when time consumption is not a key requirement. PMID:25894941

Perceptual learning for speech in noise after application of binary time-frequency masks

PubMed Central

Ahmadi, Mahnaz; Gross, Vauna L.; Sinex, Donal G.

2013-01-01

Ideal time-frequency (TF) masks can reject noise and improve the recognition of speech-noise mixtures. An ideal TF mask is constructed with prior knowledge of the target speech signal. The intelligibility of a processed speech-noise mixture depends upon the threshold criterion used to define the TF mask. The study reported here assessed the effect of training on the recognition of speech in noise after processing by ideal TF masks that did not restore perfect speech intelligibility. Two groups of listeners with normal hearing listened to speech-noise mixtures processed by TF masks calculated with different threshold criteria. For each group, a threshold criterion that initially produced word recognition scores between 0.56–0.69 was chosen for training. Listeners practiced with one set of TF-masked sentences until their word recognition performance approached asymptote. Perceptual learning was quantified by comparing word-recognition scores in the first and last training sessions. Word recognition scores improved with practice for all listeners with the greatest improvement observed for the same materials used in training. PMID:23464038

Memory Distortion and Its Avoidance: An Event-Related Potentials Study on False Recognition and Correct Rejection

PubMed Central

Beato, Maria Soledad

2016-01-01

Memory researchers have long been captivated by the nature of memory distortions and have made efforts to identify the neural correlates of true and false memories. However, the underlying mechanisms of avoiding false memories by correctly rejecting related lures remains underexplored. In this study, we employed a variant of the Deese/Roediger-McDermott paradigm to explore neural signatures of committing and avoiding false memories. ERP were obtained for True recognition, False recognition, Correct rejection of new items, and, more importantly, Correct rejection of related lures. With these ERP data, early-frontal, left-parietal, and late right-frontal old/new effects (associated with familiarity, recollection, and monitoring processes, respectively) were analysed. Results indicated that there were similar patterns for True and False recognition in all three old/new effects analysed in our study. Also, False recognition and Correct rejection of related lures activities seemed to share common underlying familiarity-based processes. The ERP similarities between False recognition and Correct rejection of related lures disappeared when recollection processes were examined because only False recognition presented a parietal old/new effect. This finding supported the view that actual false recollections underlie false memories, providing evidence consistent with previous behavioural research and with most ERP and neuroimaging studies. Later, with the onset of monitoring processes, False recognition and Correct rejection of related lures waveforms presented, again, clearly dissociated patterns. Specifically, False recognition and True recognition showed more positive going patterns than Correct rejection of related lures signal and Correct rejection of new items signature. Since False recognition and Correct rejection of related lures triggered familiarity-recognition processes, our results suggest that deciding which items are studied is based more on recollection processes, which are later supported by monitoring processes. Results are discussed in terms of Activation-Monitoring Framework and Fuzzy Trace-Theory, the most prominent explanatory theories of false memory raised with the Deese/Roediger-McDermott paradigm. PMID:27711125

Memory Distortion and Its Avoidance: An Event-Related Potentials Study on False Recognition and Correct Rejection.

PubMed

Cadavid, Sara; Beato, Maria Soledad

2016-01-01

Memory researchers have long been captivated by the nature of memory distortions and have made efforts to identify the neural correlates of true and false memories. However, the underlying mechanisms of avoiding false memories by correctly rejecting related lures remains underexplored. In this study, we employed a variant of the Deese/Roediger-McDermott paradigm to explore neural signatures of committing and avoiding false memories. ERP were obtained for True recognition, False recognition, Correct rejection of new items, and, more importantly, Correct rejection of related lures. With these ERP data, early-frontal, left-parietal, and late right-frontal old/new effects (associated with familiarity, recollection, and monitoring processes, respectively) were analysed. Results indicated that there were similar patterns for True and False recognition in all three old/new effects analysed in our study. Also, False recognition and Correct rejection of related lures activities seemed to share common underlying familiarity-based processes. The ERP similarities between False recognition and Correct rejection of related lures disappeared when recollection processes were examined because only False recognition presented a parietal old/new effect. This finding supported the view that actual false recollections underlie false memories, providing evidence consistent with previous behavioural research and with most ERP and neuroimaging studies. Later, with the onset of monitoring processes, False recognition and Correct rejection of related lures waveforms presented, again, clearly dissociated patterns. Specifically, False recognition and True recognition showed more positive going patterns than Correct rejection of related lures signal and Correct rejection of new items signature. Since False recognition and Correct rejection of related lures triggered familiarity-recognition processes, our results suggest that deciding which items are studied is based more on recollection processes, which are later supported by monitoring processes. Results are discussed in terms of Activation-Monitoring Framework and Fuzzy Trace-Theory, the most prominent explanatory theories of false memory raised with the Deese/Roediger-McDermott paradigm.

Implicit and Explicit Contributions to Object Recognition: Evidence from Rapid Perceptual Learning

PubMed Central

Hassler, Uwe; Friese, Uwe; Gruber, Thomas

2012-01-01

The present study investigated implicit and explicit recognition processes of rapidly perceptually learned objects by means of steady-state visual evoked potentials (SSVEP). Participants were initially exposed to object pictures within an incidental learning task (living/non-living categorization). Subsequently, degraded versions of some of these learned pictures were presented together with degraded versions of unlearned pictures and participants had to judge, whether they recognized an object or not. During this test phase, stimuli were presented at 15 Hz eliciting an SSVEP at the same frequency. Source localizations of SSVEP effects revealed for implicit and explicit processes overlapping activations in orbito-frontal and temporal regions. Correlates of explicit object recognition were additionally found in the superior parietal lobe. These findings are discussed to reflect facilitation of object-specific processing areas within the temporal lobe by an orbito-frontal top-down signal as proposed by bi-directional accounts of object recognition. PMID:23056558

Spatiotemporal dynamics underlying object completion in human ventral visual cortex.

PubMed

Tang, Hanlin; Buia, Calin; Madhavan, Radhika; Crone, Nathan E; Madsen, Joseph R; Anderson, William S; Kreiman, Gabriel

2014-08-06

Natural vision often involves recognizing objects from partial information. Recognition of objects from parts presents a significant challenge for theories of vision because it requires spatial integration and extrapolation from prior knowledge. Here we recorded intracranial field potentials of 113 visually selective electrodes from epilepsy patients in response to whole and partial objects. Responses along the ventral visual stream, particularly the inferior occipital and fusiform gyri, remained selective despite showing only 9%-25% of the object areas. However, these visually selective signals emerged ∼100 ms later for partial versus whole objects. These processing delays were particularly pronounced in higher visual areas within the ventral stream. This latency difference persisted when controlling for changes in contrast, signal amplitude, and the strength of selectivity. These results argue against a purely feedforward explanation of recognition from partial information, and provide spatiotemporal constraints on theories of object recognition that involve recurrent processing. Copyright © 2014 Elsevier Inc. All rights reserved.

Optical signal processing using photonic reservoir computing

NASA Astrophysics Data System (ADS)

Salehi, Mohammad Reza; Dehyadegari, Louiza

2014-10-01

As a new approach to recognition and classification problems, photonic reservoir computing has such advantages as parallel information processing, power efficient and high speed. In this paper, a photonic structure has been proposed for reservoir computing which is investigated using a simple, yet, non-partial noisy time series prediction task. This study includes the application of a suitable topology with self-feedbacks in a network of SOA's - which lends the system a strong memory - and leads to adjusting adequate parameters resulting in perfect recognition accuracy (100%) for noise-free time series, which shows a 3% improvement over previous results. For the classification of noisy time series, the rate of accuracy showed a 4% increase and amounted to 96%. Furthermore, an analytical approach was suggested to solve rate equations which led to a substantial decrease in the simulation time, which is an important parameter in classification of large signals such as speech recognition, and better results came up compared with previous works.

EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study

PubMed Central

2013-01-01

Background Several studies investigating the use of electromyographic (EMG) signals in robot-based stroke neuro-rehabilitation to enhance functional recovery. Here we explored whether a classical EMG-based patterns recognition approach could be employed to predict patients’ intentions while attempting to generate goal-directed movements in the horizontal plane. Methods Nine right-handed healthy subjects and seven right-handed stroke survivors performed reaching movements in the horizontal plane. EMG signals were recorded and used to identify the intended motion direction of the subjects. To this aim, a standard pattern recognition algorithm (i.e., Support Vector Machine, SVM) was used. Different tests were carried out to understand the role of the inter- and intra-subjects’ variability in affecting classifier accuracy. Abnormal muscular spatial patterns generating misclassification were evaluated by means of an assessment index calculated from the results achieved with the PCA, i.e., the so-called Coefficient of Expressiveness (CoE). Results Processing the EMG signals of the healthy subjects, in most of the cases we were able to build a static functional map of the EMG activation patterns for point-to-point reaching movements on the horizontal plane. On the contrary, when processing the EMG signals of the pathological subjects a good classification was not possible. In particular, patients’ aimed movement direction was not predictable with sufficient accuracy either when using the general map extracted from data of normal subjects and when tuning the classifier on the EMG signals recorded from each patient. Conclusions The experimental findings herein reported show that the use of EMG patterns recognition approach might not be practical to decode movement intention in subjects with neurological injury such as stroke. Rather than estimate motion from EMGs, future scenarios should encourage the utilization of these signals to detect and interpret the normal and abnormal muscle patterns and provide feedback on their correct recruitment. PMID:23855907

Advanced methods in NDE using machine learning approaches

NASA Astrophysics Data System (ADS)

Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

2018-04-01

Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability prediction based on big data becomes possible, even if components are used in different versions or configurations. This is the promise behind German Industry 4.0.

Very low-frequency signals support perceptual organization of implant-simulated speech for adults and children

PubMed Central

Nittrouer, Susan; Tarr, Eric; Bolster, Virginia; Caldwell-Tarr, Amanda; Moberly, Aaron C.; Lowenstein, Joanna H.

2014-01-01

Objective Using signals processed to simulate speech received through cochlear implants and low-frequency extended hearing aids, this study examined the proposal that low-frequency signals facilitate the perceptual organization of broader, spectrally degraded signals. Design In two experiments, words and sentences were presented in diotic and dichotic configurations as four-channel noise-vocoded signals (VOC-only), and as those signals combined with the acoustic signal below 250 Hz (LOW-plus). Dependent measures were percent correct recognition scores, and the difference between scores for the two processing conditions given as proportions of recognition scores for VOC-only. The influence of linguistic context was also examined. Study Sample Participants had normal hearing. In all, 40 adults, 40 7-year-olds, and 20 5-year-olds participated. Results Participants of all ages showed benefits of adding the low-frequency signal. The effect was greater for sentences than words, but no effect of configuration was found. The influence of linguistic context was similar across age groups, and did not contribute to the low-frequency effect. Listeners who scored more poorly with VOC-only stimuli showed greater low-frequency effects. Conclusion The benefit of adding a very low-frequency signal to a broader, spectrally degraded signal seems to derive from its facilitative influence on perceptual organization of the sensory input. PMID:24456179

A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface.

PubMed

Jiao, Yong; Zhang, Yu; Wang, Yu; Wang, Bei; Jin, Jing; Wang, Xingyu

2018-05-01

Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.

Use of Adaptive Digital Signal Processing to Improve Speech Communication for Normally Hearing aand Hearing-Impaired Subjects.

ERIC Educational Resources Information Center

Harris, Richard W.; And Others

1988-01-01

A two-microphone adaptive digital noise cancellation technique improved word-recognition ability for 20 normal and 12 hearing-impaired adults by reducing multitalker speech babble and speech spectrum noise 18-22 dB. Word recognition improvements averaged 37-50 percent for normal and 27-40 percent for hearing-impaired subjects. Improvement was best…

Design and application of pulse information acquisition and analysis system with dynamic recognition in traditional Chinese medicine.

PubMed

Zhang, Jian; Niu, Xin; Yang, Xue-zhi; Zhu, Qing-wen; Li, Hai-yan; Wang, Xuan; Zhang, Zhi-guo; Sha, Hong

2014-09-01

To design the pulse information which includes the parameter of pulse-position, pulse-number, pulse-shape and pulse-force acquisition and analysis system with function of dynamic recognition, and research the digitalization and visualization of some common cardiovascular mechanism of single pulse. To use some flexible sensors to catch the radial artery pressure pulse wave and utilize the high frequency B mode ultrasound scanning technology to synchronously obtain the information of radial extension and axial movement, by the way of dynamic images, then the gathered information was analyzed and processed together with ECG. Finally, the pulse information acquisition and analysis system was established which has the features of visualization and dynamic recognition, and it was applied to serve for ten healthy adults. The new system overcome the disadvantage of one-dimensional pulse information acquisition and process method which was common used in current research area of pulse diagnosis in traditional Chinese Medicine, initiated a new way of pulse diagnosis which has the new features of dynamic recognition, two-dimensional information acquisition, multiplex signals combination and deep data mining. The newly developed system could translate the pulse signals into digital, visual and measurable motion information of vessel.

Expanding the universe of cytokines and pattern recognition receptors: galectins and glycans in innate immunity.

PubMed

Cerliani, Juan P; Stowell, Sean R; Mascanfroni, Iván D; Arthur, Connie M; Cummings, Richard D; Rabinovich, Gabriel A

2011-02-01

Effective immunity relies on the recognition of pathogens and tumors by innate immune cells through diverse pattern recognition receptors (PRRs) that lead to initiation of signaling processes and secretion of pro- and anti-inflammatory cytokines. Galectins, a family of endogenous lectins widely expressed in infected and neoplastic tissues have emerged as part of the portfolio of soluble mediators and pattern recognition receptors responsible for eliciting and controlling innate immunity. These highly conserved glycan-binding proteins can control immune cell processes through binding to specific glycan structures on pathogens and tumors or by acting intracellularly via modulation of selective signaling pathways. Recent findings demonstrate that various galectin family members influence the fate and physiology of different innate immune cells including polymorphonuclear neutrophils, mast cells, macrophages, and dendritic cells. Moreover, several pathogens may actually utilize galectins as a mechanism of host invasion. In this review, we aim to highlight and integrate recent discoveries that have led to our current understanding of the role of galectins in host-pathogen interactions and innate immunity. Challenges for the future will embrace the rational manipulation of galectin-glycan interactions to instruct and shape innate immunity during microbial infections, inflammation, and cancer.

Saturation of recognition elements blocks evolution of new tRNA identities

PubMed Central

Saint-Léger, Adélaïde; Bello, Carla; Dans, Pablo D.; Torres, Adrian Gabriel; Novoa, Eva Maria; Camacho, Noelia; Orozco, Modesto; Kondrashov, Fyodor A.; Ribas de Pouplana, Lluís

2016-01-01

Understanding the principles that led to the current complexity of the genetic code is a central question in evolution. Expansion of the genetic code required the selection of new transfer RNAs (tRNAs) with specific recognition signals that allowed them to be matured, modified, aminoacylated, and processed by the ribosome without compromising the fidelity or efficiency of protein synthesis. We show that saturation of recognition signals blocks the emergence of new tRNA identities and that the rate of nucleotide substitutions in tRNAs is higher in species with fewer tRNA genes. We propose that the growth of the genetic code stalled because a limit was reached in the number of identity elements that can be effectively used in the tRNA structure. PMID:27386510

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

Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.

PubMed

Khan, Adil Mehmood; Siddiqi, Muhammad Hameed; Lee, Seok-Won

2013-09-27

Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.

Association of enhanced limbic response to threat with decreased cortical facial recognition memory response in schizophrenia

PubMed Central

Satterthwaite, Theodore D.; Wolf, Daniel H.; Loughead, James; Ruparel, Kosha; Valdez, Jeffrey N.; Siegel, Steven J.; Kohler, Christian G.; Gur, Raquel E.; Gur, Ruben C.

2014-01-01

Objective Recognition memory of faces is impaired in patients with schizophrenia, as is the neural processing of threat-related signals, but how these deficits interact to produce symptoms is unclear. Here we used an affective face recognition paradigm to examine possible interactions between cognitive and affective neural systems in schizophrenia. Methods fMRI (3T) BOLD response was examined in 21 controls and 16 patients during a two-choice recognition task using images of human faces. Each target face had previously been displayed with a threatening or non-threatening affect, but here were displayed with neutral affect. Responses to successful recognition and for the effect of previously threatening vs. non-threatening affect were evaluated, and correlations with total BPRS examined. Functional connectivity analyses examined the relationship between activation in the amygdala and cortical regions involved in recognition memory. Results Patients performed the task more slowly than controls. Controls recruited the expected cortical regions to a greater degree than patients, and patients with more severe symptoms demonstrated proportionally less recruitment. Increased symptoms were also correlated with augmented amygdala and orbitofrontal cortex response to threatening faces. Controls exhibited a negative correlation between activity in the amygdala and cortical regions involved in cognition, while patients showed a weakening of that relationship. Conclusions Increased symptoms were related to an enhanced threat response in limbic regions and a diminished recognition memory response in cortical regions, supporting a link between two brain systems often examined in isolation. This finding suggests that abnormal processing of threat-related signals in the environment may exacerbate cognitive impairment in schizophrenia. PMID:20194482

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals.

PubMed

Zhao, Weixiang; Sankaran, Shankar; Ibáñez, Ana M; Dandekar, Abhaya M; Davis, Cristina E

2009-08-04

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.

ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition.

PubMed

Zhang, Jianhai; Chen, Ming; Zhao, Shaokai; Hu, Sanqing; Shi, Zhiguo; Cao, Yu

2016-09-22

Electroencephalogram (EEG) signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI) environment. To improve the recognition performance, multi-channel EEG signals are usually used. A large set of EEG sensor channels will add to the computational complexity and cause users inconvenience. ReliefF-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP). Three strategies were employed to select the best channels in classifying four emotional states (joy, fear, sadness and relaxation). Furthermore, support vector machine (SVM) was used as a classifier to validate the performance of the channel selection results. The experimental results showed the effectiveness of our methods and the comparison with the similar strategies, based on the F-score, was given. Strategies to evaluate a channel as a unity gave better performance in channel reduction with an acceptable loss of accuracy. In the third strategy, after adjusting channels' weights according to their contribution to the classification accuracy, the number of channels was reduced to eight with a slight loss of accuracy (58.51% ± 10.05% versus the best classification accuracy 59.13% ± 11.00% using 19 channels). In addition, the study of selecting subject-independent channels, related to emotion processing, was also implemented. The sensors, selected subject-independently from frontal, parietal lobes, have been identified to provide more discriminative information associated with emotion processing, and are distributed symmetrically over the scalp, which is consistent with the existing literature. The results will make a contribution to the realization of a practical EEG-based emotion recognition system.

On a Quantum Model of Brain Activities

NASA Astrophysics Data System (ADS)

Fichtner, K.-H.; Fichtner, L.; Freudenberg, W.; Ohya, M.

2010-01-01

One of the main activities of the brain is the recognition of signals. A first attempt to explain the process of recognition in terms of quantum statistics was given in [6]. Subsequently, details of the mathematical model were presented in a (still incomplete) series of papers (cf. [7, 2, 5, 10]). In the present note we want to give a general view of the principal ideas of this approach. We will introduce the basic spaces and justify the choice of spaces and operations. Further, we bring the model face to face with basic postulates any statistical model of the recognition process should fulfill. These postulates are in accordance with the opinion widely accepted in psychology and neurology.

Neurofeedback Training for BCI Control

NASA Astrophysics Data System (ADS)

Neuper, Christa; Pfurtscheller, Gert

Brain-computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2-4].

Adaptive method of recognition of signals for one and two-frequency signal system in the telephony on the background of speech

NASA Astrophysics Data System (ADS)

Kuznetsov, Michael V.

2006-05-01

For reliable teamwork of various systems of automatic telecommunication including transferring systems of optical communication networks it is necessary authentic recognition of signals for one- or two-frequency service signal system. The analysis of time parameters of an accepted signal allows increasing reliability of detection and recognition of the service signal system on a background of speech.

Diversity of Innate Immune Recognition Mechanism for Bacterial Polymeric meso-Diaminopimelic Acid-type Peptidoglycan in Insects

PubMed Central

Yu, Yang; Park, Ji-Won; Kwon, Hyun-Mi; Hwang, Hyun-Ok; Jang, In-Hwan; Masuda, Akiko; Kurokawa, Kenji; Nakayama, Hiroshi; Lee, Won-Jae; Dohmae, Naoshi; Zhang, Jinghai; Lee, Bok Luel

2010-01-01

In Drosophila, the synthesis of antimicrobial peptides in response to microbial infections is under the control of the Toll and immune deficiency (Imd) signaling pathway. The Toll signaling pathway responds mainly to the lysine-type peptidoglycan of Gram-positive bacteria and fungal β-1,3-glucan, whereas the Imd pathway responds to the meso-diaminopimelic acid (DAP)-type peptidoglycan of Gram-negative bacteria and certain Gram-positive bacilli. Recently we determined the activation mechanism of a Toll signaling pathway biochemically using a large beetle, Tenebrio molitor. However, DAP-type peptidoglycan recognition mechanism and its signaling pathway are still unclear in the fly and beetle. Here, we show that polymeric DAP-type peptidoglycan, but not its monomeric form, formed a complex with Tenebrio peptidoglycan recognition protein-SA, and this complex activated the three-step proteolytic cascade to produce processed Spätzle, a Toll receptor ligand, and induced Drosophila defensin-like antimicrobial peptide in Tenebrio larvae similarly to polymeric lysine-type peptidoglycan. Monomeric DAP-type peptidoglycan induced Drosophila diptericin-like antimicrobial peptide in Tenebrio hemocytes. In addition, both polymeric and monomeric DAP-type peptidoglycans induced expression of Tenebrio peptidoglycan recognition protein-SC2, which is DAP-type peptidoglycan-selective N-acetylmuramyl-l-alanine amidase that functions as a DAP-type peptidoglycan scavenger, appearing to function as a negative regulator of the DAP-type peptidoglycan signaling by cleaving DAP-type peptidoglycan in Tenebrio larvae. Taken together, these results demonstrate that molecular recognition mechanism for polymeric DAP-type peptidoglycan is different between Tenebrio larvae and Drosophila adults, providing biochemical evidences of biological diversity of innate immune responses in insects. PMID:20702416

Threat as a feature in visual semantic object memory.

PubMed

Calley, Clifford S; Motes, Michael A; Chiang, H-Sheng; Buhl, Virginia; Spence, Jeffrey S; Abdi, Hervé; Anand, Raksha; Maguire, Mandy; Estevez, Leonardo; Briggs, Richard; Freeman, Thomas; Kraut, Michael A; Hart, John

2013-08-01

Threatening stimuli have been found to modulate visual processes related to perception and attention. The present functional magnetic resonance imaging (fMRI) study investigated whether threat modulates visual object recognition of man-made and naturally occurring categories of stimuli. Compared with nonthreatening pictures, threatening pictures of real items elicited larger fMRI BOLD signal changes in medial visual cortices extending inferiorly into the temporo-occipital (TO) "what" pathways. This region elicited greater signal changes for threatening items compared to nonthreatening from both the natural-occurring and man-made stimulus supraordinate categories, demonstrating a featural component to these visual processing areas. Two additional loci of signal changes within more lateral inferior TO areas (bilateral BA18 and 19 as well as the right ventral temporal lobe) were detected for a category-feature interaction, with stronger responses to man-made (category) threatening (feature) stimuli than to natural threats. The findings are discussed in terms of visual recognition of processing efficiently or rapidly groups of items that confer an advantage for survival. Copyright © 2012 Wiley Periodicals, Inc.

Automated Target Acquisition, Recognition and Tracking (ATTRACT). Phase 1

NASA Technical Reports Server (NTRS)

Abdallah, Mahmoud A.

1995-01-01

The primary objective of phase 1 of this research project is to conduct multidisciplinary research that will contribute to fundamental scientific knowledge in several of the USAF critical technology areas. Specifically, neural networks, signal processing techniques, and electro-optic capabilities are utilized to solve problems associated with automated target acquisition, recognition, and tracking. To accomplish the stated objective, several tasks have been identified and were executed.

Face recognition increases during saccade preparation.

PubMed

Lin, Hai; Rizak, Joshua D; Ma, Yuan-ye; Yang, Shang-chuan; Chen, Lin; Hu, Xin-tian

2014-01-01

Face perception is integral to human perception system as it underlies social interactions. Saccadic eye movements are frequently made to bring interesting visual information, such as faces, onto the fovea for detailed processing. Just before eye movement onset, the processing of some basic features, such as the orientation, of an object improves at the saccade landing point. Interestingly, there is also evidence that indicates faces are processed in early visual processing stages similar to basic features. However, it is not known whether this early enhancement of processing includes face recognition. In this study, three experiments were performed to map the timing of face presentation to the beginning of the eye movement in order to evaluate pre-saccadic face recognition. Faces were found to be similarly processed as simple objects immediately prior to saccadic movements. Starting ∼ 120 ms before a saccade to a target face, independent of whether or not the face was surrounded by other faces, the face recognition gradually improved and the critical spacing of the crowding decreased as saccade onset was approaching. These results suggest that an upcoming saccade prepares the visual system for new information about faces at the saccade landing site and may reduce the background in a crowd to target the intended face. This indicates an important role of pre-saccadic eye movement signals in human face recognition.

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

NASA Astrophysics Data System (ADS)

Buryi, E. V.

1998-05-01

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

Decoding the patterns of ubiquitin recognition by ubiquitin-associated domains from free energy simulations.

PubMed

Bouvier, Benjamin

2014-01-07

Ubiquitin is a highly conserved, highly represented protein acting as a regulating signal in numerous cellular processes. It leverages a single hydrophobic binding patch to recognize and bind a large variety of protein domains with remarkable specificity, but can also self-assemble into chains of poly-diubiquitin units in which these interfaces are sequestered, profoundly altering the individual monomers' recognition characteristics. Despite numerous studies, the origins of this varied specificity and the competition between substrates for the binding of the ubiquitin interface patch remain under heated debate. This study uses enhanced sampling all-atom molecular dynamics to simulate the unbinding of complexes of mono- or K48-linked diubiquitin bound to several ubiquitin-associated domains, providing insights into the mechanism and free energetics of ubiquitin recognition and binding. The implications for the subtle tradeoff between the stability of the polyubiquitin signal and its easy recognition by target protein assemblies are discussed, as is the enhanced affinity of the latter for long polyubiquitin chains compared to isolated mono- or diubiquitin.

Multimodal neuroelectric interface development

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Wheeler, Kevin R.; Jorgensen, Charles C.; Rosipal, Roman; Clanton, Sam T.; Matthews, Bryan; Hibbs, Andrew D.; Matthews, Robert; Krupka, Michael

2003-01-01

We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies for computer interfaces using electroencephalogram (EEG) signals; 3) a flexible computation framework for neuroelectric interface research; and d) noncontact sensors, which measure electromyogram or EEG signals without resistive contact to the body.

The free-energy self: a predictive coding account of self-recognition.

PubMed

Apps, Matthew A J; Tsakiris, Manos

2014-04-01

Recognising and representing one's self as distinct from others is a fundamental component of self-awareness. However, current theories of self-recognition are not embedded within global theories of cortical function and therefore fail to provide a compelling explanation of how the self is processed. We present a theoretical account of the neural and computational basis of self-recognition that is embedded within the free-energy account of cortical function. In this account one's body is processed in a Bayesian manner as the most likely to be "me". Such probabilistic representation arises through the integration of information from hierarchically organised unimodal systems in higher-level multimodal areas. This information takes the form of bottom-up "surprise" signals from unimodal sensory systems that are explained away by top-down processes that minimise the level of surprise across the brain. We present evidence that this theoretical perspective may account for the findings of psychological and neuroimaging investigations into self-recognition and particularly evidence that representations of the self are malleable, rather than fixed as previous accounts of self-recognition might suggest. Copyright © 2013 Elsevier Ltd. All rights reserved.

The free-energy self: A predictive coding account of self-recognition

PubMed Central

Apps, Matthew A.J.; Tsakiris, Manos

2013-01-01

Recognising and representing one’s self as distinct from others is a fundamental component of self-awareness. However, current theories of self-recognition are not embedded within global theories of cortical function and therefore fail to provide a compelling explanation of how the self is processed. We present a theoretical account of the neural and computational basis of self-recognition that is embedded within the free-energy account of cortical function. In this account one’s body is processed in a Bayesian manner as the most likely to be “me”. Such probabilistic representation arises through the integration of information from hierarchically organised unimodal systems in higher-level multimodal areas. This information takes the form of bottom-up “surprise” signals from unimodal sensory systems that are explained away by top-down processes that minimise the level of surprise across the brain. We present evidence that this theoretical perspective may account for the findings of psychological and neuroimaging investigations into self-recognition and particularly evidence that representations of the self are malleable, rather than fixed as previous accounts of self-recognition might suggest. PMID:23416066

Context retrieval and description benefits for recognition of unfamiliar faces.

PubMed

Jones, Todd C; Robinson, Kealagh; Steel, Brenna C

2018-04-19

Describing unfamiliar faces during or immediately after their presentation in a study phase can produce better recognition memory performance compared with a view-only control condition. We treated descriptions as elaborative information that is part of the study context and investigated how context retrieval influences recognition memory. Following general dual-process theories, we hypothesized that recollection would be used to recall descriptions and that description recall would influence recognition decisions, including the level of recognition confidence. In four experiments description conditions produced higher hit rates and higher levels of recognition confidence than control conditions. Participants recalled descriptive content on some trials, and this context retrieval was linked to an increase in the recognition confidence level. Repeating study faces in description conditions increased recognition scores, recognition confidence level, and context retrieval. Estimates of recollection from Yonelinas' (1994) dual-process signal detection ROCs were, on average, very close to the measures of context recall. Description conditions also produced higher estimates of familiarity. Finally, we found evidence that participants engaged in description activity in some ostensibly view-only trials. An emphasis on the information participants use in making their recognition decisions can advance understanding on description effects when descriptions are part of the study trial context. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Listening Effort and Speech Recognition with Frequency Compression Amplification for Children and Adults with Hearing Loss.

PubMed

Brennan, Marc A; Lewis, Dawna; McCreery, Ryan; Kopun, Judy; Alexander, Joshua M

2017-10-01

Nonlinear frequency compression (NFC) can improve the audibility of high-frequency sounds by lowering them to a frequency where audibility is better; however, this lowering results in spectral distortion. Consequently, performance is a combination of the effects of increased access to high-frequency sounds and the detrimental effects of spectral distortion. Previous work has demonstrated positive benefits of NFC on speech recognition when NFC is set to improve audibility while minimizing distortion. However, the extent to which NFC impacts listening effort is not well understood, especially for children with sensorineural hearing loss (SNHL). To examine the impact of NFC on recognition and listening effort for speech in adults and children with SNHL. Within-subject, quasi-experimental study. Participants listened to amplified nonsense words that were (1) frequency-lowered using NFC, (2) low-pass filtered at 5 kHz to simulate the restricted bandwidth (RBW) of conventional hearing aid processing, or (3) low-pass filtered at 10 kHz to simulate extended bandwidth (EBW) amplification. Fourteen children (8-16 yr) and 14 adults (19-65 yr) with mild-to-severe SNHL. Participants listened to speech processed by a hearing aid simulator that amplified input signals to fit a prescriptive target fitting procedure. Participants were blinded to the type of processing. Participants' responses to each nonsense word were analyzed for accuracy and verbal-response time (VRT; listening effort). A multivariate analysis of variance and linear mixed model were used to determine the effect of hearing-aid signal processing on nonsense word recognition and VRT. Both children and adults identified the nonsense words and initial consonants better with EBW and NFC than with RBW. The type of processing did not affect the identification of the vowels or final consonants. There was no effect of age on recognition of the nonsense words, initial consonants, medial vowels, or final consonants. VRT did not change significantly with the type of processing or age. Both adults and children demonstrated improved speech recognition with access to the high-frequency sounds in speech. Listening effort as measured by VRT was not affected by access to high-frequency sounds. American Academy of Audiology

Software for biomedical engineering signal processing laboratory experiments.

PubMed

Tompkins, Willis J; Wilson, J

2009-01-01

In the early 1990's we developed a special computer program called UW DigiScope to provide a mechanism for anyone interested in biomedical digital signal processing to study the field without requiring any other instrument except a personal computer. There are many digital filtering and pattern recognition algorithms used in processing biomedical signals. In general, students have very limited opportunity to have hands-on access to the mechanisms of digital signal processing. In a typical course, the filters are designed non-interactively, which does not provide the student with significant understanding of the design constraints of such filters nor their actual performance characteristics. UW DigiScope 3.0 is the first major update since version 2.0 was released in 1994. This paper provides details on how the new version based on MATLAB! works with signals, including the filter design tool that is the programming interface between UW DigiScope and processing algorithms.

Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

PubMed Central

Yang, Zhutian; Wu, Zhilu; Yin, Zhendong; Quan, Taifan; Sun, Hongjian

2013-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches. PMID:23344380

Chemometrics.

ERIC Educational Resources Information Center

Delaney, Michael F.

1984-01-01

This literature review on chemometrics (covering December 1981 to December 1983) is organized under these headings: personal supermicrocomputers; education and books; statistics; modeling and parameter estimation; resolution; calibration; signal processing; image analysis; factor analysis; pattern recognition; optimization; artificial…

Association of enhanced limbic response to threat with decreased cortical facial recognition memory response in schizophrenia.

PubMed

Satterthwaite, Theodore D; Wolf, Daniel H; Loughead, James; Ruparel, Kosha; Valdez, Jeffrey N; Siegel, Steven J; Kohler, Christian G; Gur, Raquel E; Gur, Ruben C

2010-04-01

Recognition memory of faces is impaired in patients with schizophrenia, as is the neural processing of threat-related signals, but how these deficits interact to produce symptoms is unclear. The authors used an affective face recognition paradigm to examine possible interactions between cognitive and affective neural systems in schizophrenia. Blood-oxygen-level-dependent response was examined by means of functional magnetic resonance imaging (3 Tesla) in healthy comparison subjects (N=21) and in patients with schizophrenia (N=12) or schizoaffective disorder, depressed type (N=4), during a two-choice recognition task that used images of human faces. Each target face, previously displayed with a threatening or nonthreatening affect, was displayed with neutral affect. Responses to successful recognition and responses to the effect of previously threatening versus nonthreatening affect were evaluated, and correlations with symptom severity (total Brief Psychiatric Rating Scale score) were examined. Functional connectivity analyses examined the relationship between activation in the amygdala and cortical regions involved in recognition memory. Patients performed the task more slowly than healthy comparison subjects. Comparison subjects recruited the expected cortical regions to a greater degree than patients, and patients with more severe symptoms demonstrated proportionally less recruitment. Increased symptoms were also correlated with augmented amygdala and orbitofrontal cortex response to threatening faces. Comparison subjects exhibited a negative correlation between activity in the amygdala and cortical regions involved in cognition, while patients showed weakening of this relationship. Increased symptoms were related to an enhanced threat response in limbic regions and a diminished recognition memory response in cortical regions, supporting a link between these two brain systems that are often examined in isolation. This finding suggests that abnormal processing of threat-related signals in the environment may exacerbate cognitive impairment in schizophrenia.

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.

PubMed

Benatti, Simone; Casamassima, Filippo; Milosevic, Bojan; Farella, Elisabetta; Schönle, Philipp; Fateh, Schekeb; Burger, Thomas; Huang, Qiuting; Benini, Luca

2015-10-01

Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.

Aggression and courtship in Drosophila: pheromonal communication and sex recognition.

PubMed

Fernández, María Paz; Kravitz, Edward A

2013-11-01

Upon encountering a conspecific in the wild, males have to rapidly detect, integrate and process the most relevant signals to evoke an appropriate behavioral response. Courtship and aggression are the most important social behaviors in nature for procreation and survival: for males, making the right choice between the two depends on the ability to identify the sex of the other individual. In flies as in most species, males court females and attack other males. Although many sensory modalities are involved in sex recognition, chemosensory communication mediated by specific molecules that serve as pheromones plays a key role in helping males distinguish between courtship and aggression targets. The chemosensory signals used by flies include volatile and non-volatile compounds, detected by the olfactory and gustatory systems. Recently, several putative olfactory and gustatory receptors have been identified that play key roles in sex recognition, allowing investigators to begin to map the neuronal circuits that convey this sensory information to higher processing centers in the brain. Here, we describe how Drosophila melanogaster males use taste and smell to make correct behavioral choices.

Aggression and Courtship in Drosophila: Pheromonal Communication and Sex Recognition

PubMed Central

Fernández, María Paz; Kravitz, Edward A.

2013-01-01

Upon encountering a conspecific in the wild, males have to rapidly detect, integrate and process the most relevant signals to evoke an appropriate behavioral response. Courtship and aggression are the most important social behaviors in nature for procreation and survival: for males, making the right choice between the two depends on the ability to identify the sex of the other individual. In flies as in most species, males court females and attack other males. Although many sensory modalities are involved in sex recognition, chemosensory communication mediated by specific molecules that serve as pheromones plays a key role in helping males distinguish between courtship and aggression targets. The chemosensory signals used by flies include volatile and non-volatile compounds, detected by the olfactory and gustatory systems. Recently, several putative olfactory and gustatory receptors have been identified that play key roles in sex recognition, allowing investigators to begin to map the neuronal circuits that convey this sensory information to higher processing centers in the brain. Here, we describe how Drosophila melanogaster males use taste and smell to make correct behavioral choices. PMID:24043358

Identification and classification of failure modes in laminated composites by using a multivariate statistical analysis of wavelet coefficients

NASA Astrophysics Data System (ADS)

Baccar, D.; Söffker, D.

2017-11-01

Acoustic Emission (AE) is a suitable method to monitor the health of composite structures in real-time. However, AE-based failure mode identification and classification are still complex to apply due to the fact that AE waves are generally released simultaneously from all AE-emitting damage sources. Hence, the use of advanced signal processing techniques in combination with pattern recognition approaches is required. In this paper, AE signals generated from laminated carbon fiber reinforced polymer (CFRP) subjected to indentation test are examined and analyzed. A new pattern recognition approach involving a number of processing steps able to be implemented in real-time is developed. Unlike common classification approaches, here only CWT coefficients are extracted as relevant features. Firstly, Continuous Wavelet Transform (CWT) is applied to the AE signals. Furthermore, dimensionality reduction process using Principal Component Analysis (PCA) is carried out on the coefficient matrices. The PCA-based feature distribution is analyzed using Kernel Density Estimation (KDE) allowing the determination of a specific pattern for each fault-specific AE signal. Moreover, waveform and frequency content of AE signals are in depth examined and compared with fundamental assumptions reported in this field. A correlation between the identified patterns and failure modes is achieved. The introduced method improves the damage classification and can be used as a non-destructive evaluation tool.

Get rich quick: the signal to respond procedure reveals the time course of semantic richness effects during visual word recognition.

PubMed

Hargreaves, Ian S; Pexman, Penny M

2014-05-01

According to several current frameworks, semantic processing involves an early influence of language-based information followed by later influences of object-based information (e.g., situated simulations; Santos, Chaigneau, Simmons, & Barsalou, 2011). In the present study we examined whether these predictions extend to the influence of semantic variables in visual word recognition. We investigated the time course of semantic richness effects in visual word recognition using a signal-to-respond (STR) paradigm fitted to a lexical decision (LDT) and a semantic categorization (SCT) task. We used linear mixed effects to examine the relative contributions of language-based (number of senses, ARC) and object-based (imageability, number of features, body-object interaction ratings) descriptions of semantic richness at four STR durations (75, 100, 200, and 400ms). Results showed an early influence of number of senses and ARC in the SCT. In both LDT and SCT, object-based effects were the last to influence participants' decision latencies. We interpret our results within a framework in which semantic processes are available to influence word recognition as a function of their availability over time, and of their relevance to task-specific demands. Copyright © 2014 Elsevier B.V. All rights reserved.

Working group organizational meeting

NASA Technical Reports Server (NTRS)

1982-01-01

Scene radiation and atmospheric effects, mathematical pattern recognition and image analysis, information evaluation and utilization, and electromagnetic measurements and signal handling are considered. Research issues in sensors and signals, including radar (SAR) reflectometry, SAR processing speed, registration, including overlay of SAR and optical imagery, entire system radiance calibration, and lack of requirements for both sensors and systems, etc. were discussed.

Hands-free device control using sound picked up in the ear canal

NASA Astrophysics Data System (ADS)

Chhatpar, Siddharth R.; Ngia, Lester; Vlach, Chris; Lin, Dong; Birkhimer, Craig; Juneja, Amit; Pruthi, Tarun; Hoffman, Orin; Lewis, Tristan

2008-04-01

Hands-free control of unmanned ground vehicles is essential for soldiers, bomb disposal squads, and first responders. Having their hands free for other equipment and tasks allows them to be safer and more mobile. Currently, the most successful hands-free control devices are speech-command based. However, these devices use external microphones, and in field environments, e.g., war zones and fire sites, their performance suffers because of loud ambient noise: typically above 90dBA. This paper describes the development of technology using the ear as an output source that can provide excellent command recognition accuracy even in noisy environments. Instead of picking up speech radiating from the mouth, this technology detects speech transmitted internally through the ear canal. Discreet tongue movements also create air pressure changes within the ear canal, and can be used for stealth control. A patented earpiece was developed with a microphone pointed into the ear canal that captures these signals generated by tongue movements and speech. The signals are transmitted from the earpiece to an Ultra-Mobile Personal Computer (UMPC) through a wired connection. The UMPC processes the signals and utilizes them for device control. The processing can include command recognition, ambient noise cancellation, acoustic echo cancellation, and speech equalization. Successful control of an iRobot PackBot has been demonstrated with both speech (13 discrete commands) and tongue (5 discrete commands) signals. In preliminary tests, command recognition accuracy was 95% with speech control and 85% with tongue control.

Effects of distinctive encoding on correct and false memory: a meta-analytic review of costs and benefits and their origins in the DRM paradigm.

PubMed

Huff, Mark J; Bodner, Glen E; Fawcett, Jonathan M

2015-04-01

We review and meta-analyze how distinctive encoding alters encoding and retrieval processes and, thus, affects correct and false recognition in the Deese-Roediger-McDermott (DRM) paradigm. Reductions in false recognition following distinctive encoding (e.g., generation), relative to a nondistinctive read-only control condition, reflected both impoverished relational encoding and use of a retrieval-based distinctiveness heuristic. Additional analyses evaluated the costs and benefits of distinctive encoding in within-subjects designs relative to between-group designs. Correct recognition was design independent, but in a within design, distinctive encoding was less effective at reducing false recognition for distinctively encoded lists but more effective for nondistinctively encoded lists. Thus, distinctive encoding is not entirely "cost free" in a within design. In addition to delineating the conditions that modulate the effects of distinctive encoding on recognition accuracy, we discuss the utility of using signal detection indices of memory information and memory monitoring at test to separate encoding and retrieval processes.

Surface EMG signals based motion intent recognition using multi-layer ELM

NASA Astrophysics Data System (ADS)

Wang, Jianhui; Qi, Lin; Wang, Xiao

2017-11-01

The upper-limb rehabilitation robot is regard as a useful tool to help patients with hemiplegic to do repetitive exercise. The surface electromyography (sEMG) contains motion information as the electric signals are generated and related to nerve-muscle motion. These sEMG signals, representing human's intentions of active motions, are introduced into the rehabilitation robot system to recognize upper-limb movements. Traditionally, the feature extraction is an indispensable part of drawing significant information from original signals, which is a tedious task requiring rich and related experience. This paper employs a deep learning scheme to extract the internal features of the sEMG signals using an advanced Extreme Learning Machine based auto-encoder (ELMAE). The mathematical information contained in the multi-layer structure of the ELM-AE is used as the high-level representation of the internal features of the sEMG signals, and thus a simple ELM can post-process the extracted features, formulating the entire multi-layer ELM (ML-ELM) algorithm. The method is employed for the sEMG based neural intentions recognition afterwards. The case studies show the adopted deep learning algorithm (ELM-AE) is capable of yielding higher classification accuracy compared to the Principle Component Analysis (PCA) scheme in 5 different types of upper-limb motions. This indicates the effectiveness and the learning capability of the ML-ELM in such motion intent recognition applications.

What pharmacological interventions indicate concerning the role of the perirhinal cortex in recognition memory

PubMed Central

Brown, M.W.; Barker, G.R.I.; Aggleton, J.P.; Warburton, E.C.

2012-01-01

Findings of pharmacological studies that have investigated the involvement of specific regions of the brain in recognition memory are reviewed. The particular emphasis of the review concerns what such studies indicate concerning the role of the perirhinal cortex in recognition memory. Most of the studies involve rats and most have investigated recognition memory for objects. Pharmacological studies provide a large body of evidence supporting the essential role of the perirhinal cortex in the acquisition, consolidation and retrieval of object recognition memory. Such studies provide increasingly detailed evidence concerning both the neurotransmitter systems and the underlying intracellular mechanisms involved in recognition memory processes. They have provided evidence in support of synaptic weakening as a major synaptic plastic process within perirhinal cortex underlying object recognition memory. They have also supplied confirmatory evidence that that there is more than one synaptic plastic process involved. The demonstrated necessity to long-term recognition memory of intracellular signalling mechanisms related to synaptic modification within perirhinal cortex establishes a central role for the region in the information storage underlying such memory. Perirhinal cortex is thereby established as an information storage site rather than solely a processing station. Pharmacological studies have also supplied new evidence concerning the detailed roles of other regions, including the hippocampus and the medial prefrontal cortex in different types of recognition memory tasks that include a spatial or temporal component. In so doing, they have also further defined the contribution of perirhinal cortex to such tasks. To date it appears that the contribution of perirhinal cortex to associative and temporal order memory reflects that in simple object recognition memory, namely that perirhinal cortex provides information concerning objects and their prior occurrence (novelty/familiarity). PMID:22841990

The Electrophysiological Signature of Remember-Know Is Confounded with Memory Strength and Cannot Be Interpreted as Evidence for Dual-process Theory of Recognition.

PubMed

Brezis, Noam; Bronfman, Zohar Z; Yovel, Galit; Goshen-Gottstein, Yonatan

2017-02-01

The quantity and nature of the processes underlying recognition memory remains an open question. A majority of behavioral, neuropsychological, and brain studies have suggested that recognition memory is supported by two dissociable processes: recollection and familiarity. It has been conversely argued, however, that recollection and familiarity map onto a single continuum of mnemonic strength and hence that recognition memory is mediated by a single process. Previous electrophysiological studies found marked dissociations between recollection and familiarity, which have been widely held as corroborating the dual-process account. However, it remains unknown whether a strength interpretation can likewise apply for these findings. Here we describe an ERP study, using a modified remember-know (RK) procedure, which allowed us to control for mnemonic strength. We find that ERPs of high and low mnemonic strength mimicked the electrophysiological distinction between R and K responses, in a lateral positive component (LPC), 500-1000 msec poststimulus onset. Critically, when contrasting strength with RK experience, by comparing weak R to strong K responses, the electrophysiological signal mapped onto strength, not onto subjective RK experience. Invoking the LPC as support for dual-process accounts may, therefore, be amiss.

Computational Burden Resulting from Image Recognition of High Resolution Radar Sensors

PubMed Central

López-Rodríguez, Patricia; Fernández-Recio, Raúl; Bravo, Ignacio; Gardel, Alfredo; Lázaro, José L.; Rufo, Elena

2013-01-01

This paper presents a methodology for high resolution radar image generation and automatic target recognition emphasizing the computational cost involved in the process. In order to obtain focused inverse synthetic aperture radar (ISAR) images certain signal processing algorithms must be applied to the information sensed by the radar. From actual data collected by radar the stages and algorithms needed to obtain ISAR images are revised, including high resolution range profile generation, motion compensation and ISAR formation. Target recognition is achieved by comparing the generated set of actual ISAR images with a database of ISAR images generated by electromagnetic software. High resolution radar image generation and target recognition processes are burdensome and time consuming, so to determine the most suitable implementation platform the analysis of the computational complexity is of great interest. To this end and since target identification must be completed in real time, computational burden of both processes the generation and comparison with a database is explained separately. Conclusions are drawn about implementation platforms and calculation efficiency in order to reduce time consumption in a possible future implementation. PMID:23609804

Computational burden resulting from image recognition of high resolution radar sensors.

PubMed

López-Rodríguez, Patricia; Fernández-Recio, Raúl; Bravo, Ignacio; Gardel, Alfredo; Lázaro, José L; Rufo, Elena

2013-04-22

This paper presents a methodology for high resolution radar image generation and automatic target recognition emphasizing the computational cost involved in the process. In order to obtain focused inverse synthetic aperture radar (ISAR) images certain signal processing algorithms must be applied to the information sensed by the radar. From actual data collected by radar the stages and algorithms needed to obtain ISAR images are revised, including high resolution range profile generation, motion compensation and ISAR formation. Target recognition is achieved by comparing the generated set of actual ISAR images with a database of ISAR images generated by electromagnetic software. High resolution radar image generation and target recognition processes are burdensome and time consuming, so to determine the most suitable implementation platform the analysis of the computational complexity is of great interest. To this end and since target identification must be completed in real time, computational burden of both processes the generation and comparison with a database is explained separately. Conclusions are drawn about implementation platforms and calculation efficiency in order to reduce time consumption in a possible future implementation.

Techniques of EMG signal analysis: detection, processing, classification and applications

PubMed Central

Hussain, M.S.; Mohd-Yasin, F.

2006-01-01

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications. PMID:16799694

Cell-cell recognition and social networking in bacteria

PubMed Central

Troselj, Vera; Cao, Pengbo; Wall, Daniel

2018-01-01

SUMMARY The ability to recognize self and to recognize partnering cells allows microorganisms to build social networks that perform functions beyond the capabilities of the individual. In bacteria, recognition typically involves genetic determinants that provide cell surface receptors or diffusible signaling chemicals to identify proximal cells at the molecular level that can participate in cooperative processes. Social networks also rely on discriminating mechanisms to exclude competing cells from joining and exploiting their groups. In addition to their appropriate genotypes, cell-cell recognition also requires compatible phenotypes, which vary according to environmental cues or exposures as well as stochastic processes that leads to heterogeneity and potential disharmony in the population. Understanding how bacteria identify their social partners and how they synchronize their behaviors to conduct multicellular functions is an expanding field of research. Here we review recent progress in the field and contrast the various strategies used in recognition and behavioral networking. PMID:29194914

Acoustic Signal Processing in Photorefractive Optical Systems.

NASA Astrophysics Data System (ADS)

Zhou, Gan

This thesis discusses applications of the photorefractive effect in the context of acoustic signal processing. The devices and systems presented here illustrate the ideas and optical principles involved in holographic processing of acoustic information. The interest in optical processing stems from the similarities between holographic optical systems and contemporary models for massively parallel computation, in particular, neural networks. An initial step in acoustic processing is the transformation of acoustic signals into relevant optical forms. A fiber-optic transducer with photorefractive readout transforms acoustic signals into optical images corresponding to their short-time spectrum. The device analyzes complex sound signals and interfaces them with conventional optical correlators. The transducer consists of 130 multimode optical fibers sampling the spectral range of 100 Hz to 5 kHz logarithmically. A physical model of the human cochlea can help us understand some characteristics of human acoustic transduction and signal representation. We construct a life-sized cochlear model using elastic membranes coupled with two fluid-filled chambers, and use a photorefractive novelty filter to investigate its response. The detection sensitivity is determined to be 0.3 angstroms per root Hz at 2 kHz. Qualitative agreement is found between the model response and physiological data. Delay lines map time-domain signals into space -domain and permit holographic processing of temporal information. A parallel optical delay line using dynamic beam coupling in a rotating photorefractive crystal is presented. We experimentally demonstrate a 64 channel device with 0.5 seconds of time-delay and 167 Hz bandwidth. Acoustic signal recognition is described in a photorefractive system implementing the time-delay neural network model. The system consists of a photorefractive optical delay-line and a holographic correlator programmed in a LiNbO_3 crystal. We demonstrate the recognition of synthesized chirps as well as spoken words. A photorefractive ring resonator containing an optical delay line can learn temporal information through self-organization. We experimentally investigate a system that learns by itself and picks out the most-frequently -presented signals from the input. We also give results demonstrating the separation of two orthogonal temporal signals into two competing ring resonators.

Signal propagation in cortical networks: a digital signal processing approach.

PubMed

Rodrigues, Francisco Aparecido; da Fontoura Costa, Luciano

2009-01-01

This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks.

Phylogenetic analysis and expression profiling of the pattern recognition receptors: Insights into molecular recognition of invading pathogens in Manduca sexta.

PubMed

Zhang, Xiufeng; He, Yan; Cao, Xiaolong; Gunaratna, Ramesh T; Chen, Yun-ru; Blissard, Gary; Kanost, Michael R; Jiang, Haobo

2015-07-01

Pattern recognition receptors (PRRs) detect microbial pathogens and trigger innate immune responses. Previous biochemical studies have elucidated the physiological functions of eleven PRRs in Manduca sexta but our understanding of the recognition process is still limited, lacking genomic perspectives. While 34 C-type lectin-domain proteins and 16 Toll-like receptors are reported in the companion papers, we present here 120 other putative PRRs identified through the genome annotation. These include 76 leucine-rich repeat (LRR) proteins, 14 peptidoglycan recognition proteins, 6 EGF/Nim-domain proteins, 5 β-1,3-glucanase-related proteins, 4 galectins, 4 fibrinogen-related proteins, 3 thioester proteins, 5 immunoglobulin-domain proteins, 2 hemocytins, and 1 Reeler. Sequence alignment and phylogenetic analysis reveal the evolution history of a diverse repertoire of proteins for pathogen recognition. While functions of insect LRR proteins are mostly unknown, their structure diversification is phenomenal: In addition to the Toll homologs, 22 LRR proteins with a signal peptide are expected to be secreted; 18 LRR proteins lacking signal peptides may be cytoplasmic; 36 LRRs with a signal peptide and a transmembrane segment may be non-Toll receptors on the surface of cells. Expression profiles of the 120 genes in 52 tissue samples reflect complex regulation in various developmental stages and physiological states, including some likely by Rel family transcription factors via κB motifs in the promoter regions. This collection of information is expected to facilitate future biochemical studies detailing their respective roles in this model insect. Copyright © 2015 Elsevier Ltd. All rights reserved.

Phylogenetic analysis and expression profiling of the pattern recognition receptors: insights into molecular recognition of invading pathogens in Manduca sexta

PubMed Central

Zhang, Xiufeng; He, Yan; Cao, Xiaolong; Gunaratna, Ramesh T.; Chen, Yun-ru; Blissard, Gary; Kanost, Michael R.; Jiang, Haobo

2015-01-01

Pattern recognition receptors (PRRs) detect microbial pathogens and trigger innate immune responses. Previous biochemical studies have elucidated the physiological functions of eleven PRRs in Manduca sexta but our understanding of the recognition process is still limited, lacking genomic perspectives. While 34 C-type lectin-domain proteins and 16 Toll-like receptors are reported in the companion papers, we present here 120 other putative PRRs identified through the genome annotation. These include 76 leucine-rich repeat (LRR) proteins, 14 peptidoglycan recognition proteins, 6 EGF/Nim-domain proteins, 5 β-1,3-glucanase-related proteins, 4 galectins, 4 fibrinogen-related proteins, 3 thioester proteins, 5 immunoglobulin-domain proteins, 2 hemocytins, and 1 Reeler. Sequence alignment and phylogenetic analysis reveal the evolution history of a diverse repertoire of proteins for pathogen recognition. While functions of insect LRR proteins are mostly unknown, their structure diversification is phenomenal: In addition to the Toll homologs, 22 LRR proteins with a signal peptide are expected to be secreted; 18 LRR proteins lacking signal peptides may be cytoplasmic; 36 LRRs with a signal peptide and a transmembrane segment may be non-Toll receptors on the surface of cells. Expression profiles of the 120 genes in 52 tissue samples reflect complex regulation in various developmental stages and physiological states, including some likely by Rel family transcription factors via κB motifs in the promoter regions. This collection of information is expected to facilitate future biochemical studies detailing their respective roles in this model insect. PMID:25701384

Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review.

PubMed

Naik, Ganesh R; Arjunan, Sridhar; Kumar, Dinesh

2011-06-01

The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.

Parallel effects of processing fluency and positive affect on familiarity-based recognition decisions for faces.

PubMed

Duke, Devin; Fiacconi, Chris M; Köhler, Stefan

2014-01-01

According to attribution models of familiarity assessment, people can use a heuristic in recognition-memory decisions, in which they attribute the subjective ease of processing of a memory probe to a prior encounter with the stimulus in question. Research in social cognition suggests that experienced positive affect may be the proximal cue that signals fluency in various experimental contexts. In the present study, we compared the effects of positive affect and fluency on recognition-memory judgments for faces with neutral emotional expression. We predicted that if positive affect is indeed the critical cue that signals processing fluency at retrieval, then its manipulation should produce effects that closely mirror those produced by manipulations of processing fluency. In two experiments, we employed a masked-priming procedure in combination with a Remember-Know (RK) paradigm that aimed to separate familiarity- from recollection-based memory decisions. In addition, participants performed a prime-discrimination task that allowed us to take inter-individual differences in prime awareness into account. We found highly similar effects of our priming manipulations of processing fluency and of positive affect. In both cases, the critical effect was specific to familiarity-based recognition responses. Moreover, in both experiments it was reflected in a shift toward a more liberal response bias, rather than in changed discrimination. Finally, in both experiments, the effect was found to be related to prime awareness; it was present only in participants who reported a lack of such awareness on the prime-discrimination task. These findings add to a growing body of evidence that points not only to a role of fluency, but also of positive affect in familiarity assessment. As such they are consistent with the idea that fluency itself may be hedonically marked.

On the measurement of criterion noise in signal detection theory: the case of recognition memory.

PubMed

Kellen, David; Klauer, Karl Christoph; Singmann, Henrik

2012-07-01

Traditional approaches within the framework of signal detection theory (SDT; Green & Swets, 1966), especially in the field of recognition memory, assume that the positioning of response criteria is not a noisy process. Recent work (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008) has challenged this assumption, arguing not only for the existence of criterion noise but also for its large magnitude and substantive contribution to individuals' performance. A review of these recent approaches for the measurement of criterion noise in SDT identifies several shortcomings and confoundings. A reanalysis of Benjamin et al.'s (2009) data sets as well as the results from a new experimental method indicate that the different forms of criterion noise proposed in the recognition memory literature are of very low magnitudes, and they do not provide a significant improvement over the account already given by traditional SDT without criterion noise. Copyright 2012 APA, all rights reserved.

The effects of digital signal processing features on children's speech recognition and loudness perception.

PubMed

Crukley, Jeffery; Scollie, Susan D

2014-03-01

The purpose of this study was to determine the effects of hearing instruments set to Desired Sensation Level version 5 (DSL v5) hearing instrument prescription algorithm targets and equipped with directional microphones and digital noise reduction (DNR) on children's sentence recognition in noise performance and loudness perception in a classroom environment. Ten children (ages 8-17 years) with stable, congenital sensorineural hearing losses participated in the study. Participants were fitted bilaterally with behind-the-ear hearing instruments set to DSL v5 prescriptive targets. Sentence recognition in noise was evaluated using the Bamford-Kowal-Bench Speech in Noise Test (Niquette et al., 2003). Loudness perception was evaluated using a modified version of the Contour Test of Loudness Perception (Cox, Alexander, Taylor, & Gray, 1997). Children's sentence recognition in noise performance was significantly better when using directional microphones alone or in combination with DNR than when using omnidirectional microphones alone or in combination with DNR. Children's loudness ratings for sounds above 72 dB SPL were lowest when fitted with the DSL v5 Noise prescription combined with directional microphones. DNR use showed no effect on loudness ratings. Use of the DSL v5 Noise prescription with a directional microphone improved sentence recognition in noise performance and reduced loudness perception ratings for loud sounds relative to a typical clinical reference fitting with the DSL v5 Quiet prescription with no digital signal processing features enabled. Potential clinical strategies are discussed.

When fear forms memories: threat of shock and brain potentials during encoding and recognition.

PubMed

Weymar, Mathias; Bradley, Margaret M; Hamm, Alfons O; Lang, Peter J

2013-03-01

The anticipation of highly aversive events is associated with measurable defensive activation, and both animal and human research suggests that stress-inducing contexts can facilitate memory. Here, we investigated whether encoding stimuli in the context of anticipating an aversive shock affects recognition memory. Event-related potentials (ERPs) were measured during a recognition test for words that were encoded in a font color that signaled threat or safety. At encoding, cues signaling threat of shock, compared to safety, prompted enhanced P2 and P3 components. Correct recognition of words encoded in the context of threat, compared to safety, was associated with an enhanced old-new ERP difference (500-700 msec; centro-parietal), and this difference was most reliable for emotional words. Moreover, larger old-new ERP differences when recognizing emotional words encoded in a threatening context were associated with better recognition, compared to words encoded in safety. Taken together, the data indicate enhanced memory for stimuli encoded in a context in which an aversive event is merely anticipated, which could assist in understanding effects of anxiety and stress on memory processes. Copyright © 2012 Elsevier Ltd. All rights reserved.

[Research on Control System of an Exoskeleton Upper-limb Rehabilitation Robot].

PubMed

Wang, Lulu; Hu, Xin; Hu, Jie; Fang, Youfang; He, Rongrong; Yu, Hongliu

2016-12-01

In order to help the patients with upper-limb disfunction go on rehabilitation training,this paper proposed an upper-limb exoskeleton rehabilitation robot with four degrees of freedom(DOF),and realized two control schemes,i.e.,voice control and electromyography control.The hardware and software design of the voice control system was completed based on RSC-4128 chips,which realized the speech recognition technology of a specific person.Besides,this study adapted self-made surface eletromyogram(sEMG)signal extraction electrodes to collect sEMG signals and realized pattern recognition by conducting sEMG signals processing,extracting time domain features and fixed threshold algorithm.In addition,the pulse-width modulation(PWM)algorithm was used to realize the speed adjustment of the system.Voice control and electromyography control experiments were then carried out,and the results showed that the mean recognition rate of the voice control and electromyography control reached 93.1%and 90.9%,respectively.The results proved the feasibility of the control system.This study is expected to lay a theoretical foundation for the further improvement of the control system of the upper-limb rehabilitation robot.

Discrimination techniques employing both reflective and thermal multispectral signals. [for remote sensor technology

NASA Technical Reports Server (NTRS)

Malila, W. A.; Crane, R. B.; Richardson, W.

1973-01-01

Recent improvements in remote sensor technology carry implications for data processing. Multispectral line scanners now exist that can collect data simultaneously and in registration in multiple channels at both reflective and thermal (emissive) wavelengths. Progress in dealing with two resultant recognition processing problems is discussed: (1) More channels mean higher processing costs; to combat these costs, a new and faster procedure for selecting subsets of channels has been developed. (2) Differences between thermal and reflective characteristics influence recognition processing; to illustrate the magnitude of these differences, some explanatory calculations are presented. Also introduced, is a different way to process multispectral scanner data, namely, radiation balance mapping and related procedures. Techniques and potentials are discussed and examples presented.

Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-Based fMRI

ERIC Educational Resources Information Center

Davis, Tyler; Love, Bradley C.; Preston, Alison R.

2012-01-01

Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and…

Smart Sensing and Recognition Based on Models of Neural Networks

DTIC Science & Technology

1990-11-15

9P-o ,yY𔄃-’. AD-A230 701 University of Pensylvania Philadelphia, PA 19104-6390 SMART SENSING AND RECOGNITION BASED ON MODELS OF NEURAL NETWORKS ... networks , photonic 1 implementations, nonlinear dynamical signal processing 9 ABSTRACT (Continue on reverse if necessary and identify by block number...not develop in isolation but in synergism with sensory organs and their feature forming networks . This means that development of artificial pattern

Background characterization techniques for target detection using scene metrics and pattern recognition

NASA Astrophysics Data System (ADS)

Noah, Paul V.; Noah, Meg A.; Schroeder, John W.; Chernick, Julian A.

1990-09-01

The U.S. Army has a requirement to develop systems for the detection and identification of ground targets in a clutter environment. Autonomous Homing Munitions (AHM) using infrared, visible, millimeter wave and other sensors are being investigated for this application. Advanced signal processing and computational approaches using pattern recognition and artificial intelligence techniques combined with multisensor data fusion have the potential to meet the Army's requirements for next generation ARM.

Fusing Sensor Paradigms to Acquire Chemical Information: An Integrative Role for Smart Biopolymeric Hydrogels

PubMed Central

Kim, Eunkyoung; Liu, Yi; Ben-Yoav, Hadar; Winkler, Thomas E.; Yan, Kun; Shi, Xiaowen; Shen, Jana; Kelly, Deanna L.; Ghodssi, Reza; Bentley, William E.

2017-01-01

The Information Age transformed our lives but it has had surprisingly little impact on the way chemical information (e.g., from our biological world) is acquired, analyzed and communicated. Sensor systems are poised to change this situation by providing rapid access to chemical information. This access will be enabled by technological advances from various fields: biology enables the synthesis, design and discovery of molecular recognition elements as well as the generation of cell-based signal processors; physics and chemistry are providing nano-components that facilitate the transmission and transduction of signals rich with chemical information; microfabrication is yielding sensors capable of receiving these signals through various modalities; and signal processing analysis enhances the extraction of chemical information. The authors contend that integral to the development of functional sensor systems will be materials that (i) enable the integrative and hierarchical assembly of various sensing components (for chemical recognition and signal transduction) and (ii) facilitate meaningful communication across modalities. It is suggested that stimuli-responsive self-assembling biopolymers can perform such integrative functions, and redox provides modality-spanning communication capabilities. Recent progress toward the development of electrochemical sensors to manage schizophrenia is used to illustrate the opportunities and challenges for enlisting sensors for chemical information processing. PMID:27616350

Application of pattern recognition techniques to acousto-ultrasonic testing of Kevlar composite panels

NASA Astrophysics Data System (ADS)

Hinton, Yolanda L.

An acousto-ultrasonic evaluation of panels fabricated from woven Kevlar and PVB/phenolic resin is being conducted. The panels were fabricated with various simulated defects. They were examined by pulsing with one acoustic emission sensor, and detecting the signal with another sensor, on the same side of the panel at a fixed distance. The acoustic emission signals were filtered through high (400-600 KHz), low (100-300 KHz) and wide (100-1200 KHz) bandpass filters. Acoustic emission signal parameters, including amplitude, counts, rise time, duration, 'energy', rms, and counts to peak, were recorded. These were statistically analyzed to determine which of the AE parameters best characterize the simulated defects. The wideband filtered acoustic emission signal was also digitized and recorded for further processing. Seventy-one features of the signals in both the time and frequency domains were calculated and compared to determine which subset of these features uniquely characterize the defects in the panels. The objective of the program is to develop a database of AE signal parameters and features to be used in pattern recognition as an inspection tool for material fabricated from these materials.

Protein phosphorylation in plant immunity: insights into the regulation of pattern recognition receptor-mediated signaling

PubMed Central

Park, Chang-Jin; Caddell, Daniel F.; Ronald, Pamela C.

2012-01-01

Plants are continuously challenged by pathogens including viruses, bacteria, and fungi. The plant immune system recognizes invading pathogens and responds by activating an immune response. These responses occur rapidly and often involve post-translational modifications (PTMs) within the proteome. Protein phosphorylation is a common and intensively studied form of these PTMs and regulates many plant processes including plant growth, development, and immunity. Most well-characterized pattern recognition receptors (PRRs), including Xanthomonas resistance 21, flagellin sensitive 2, and elongation factor-Tu receptor, possess intrinsic protein kinase activity and regulate downstream signaling through phosphorylation events. Here, we focus on the phosphorylation events of plant PRRs that play important roles in the immune response. We also discuss the role of phosphorylation in regulating mitogen-associated protein kinase cascades and transcription factors in plant immune signaling. PMID:22876255

Colocalization recognition-activated cascade signal amplification strategy for ultrasensitive detection of transcription factors.

PubMed

Zhu, Desong; Wang, Lei; Xu, Xiaowen; Jiang, Wei

2017-03-15

Transcription factors (TFs) bind to specific double-stranded DNA (dsDNA) sequences in the regulatory regions of genes to regulate the process of gene transcription. Their expression levels sensitively reflect cell developmental situation and disease state. TFs have become potential diagnostic markers and therapeutic targets of cancers and some other diseases. Hence, high sensitive detection of TFs is of vital importance for early diagnosis of diseases and drugs development. The traditional exonucleases-assisted signal amplification methods suffered from the false positives caused by incomplete digestion of excess recognition probes. Herein, based on a new recognition way-colocalization recognition (CR)-activated dual signal amplification, an ultrasensitive fluorescent detection strategy for TFs was developed. TFs-induced the colocalization of three split recognition components resulted in noticeable increases of local effective concentrations and hybridization of three split components, which activated the subsequent cascade signal amplification including strand displacement amplification (SDA) and exponential rolling circle amplification (ERCA). This strategy eliminated the false positive influence and achieved ultra-high sensitivity towards the purified NF-κB p50 with detection limit of 2.0×10 -13 M. Moreover, NF-κB p50 can be detected in as low as 0.21ngμL -1 HeLa cell nuclear extracts. In addition, this proposed strategy could be used for the screening of NF-κB p50 activity inhibitors and potential anti-NF-κB p50 drugs. Finally, our proposed strategy offered a potential method for reliable detection of TFs in medical diagnosis and treatment research of cancers and other related diseases. Copyright © 2016 Elsevier B.V. All rights reserved.

Chemiresistive and Gravimetric Dual-Mode Gas Sensor toward Target Recognition and Differentiation.

PubMed

Chen, Yan; Zhang, Hao; Feng, Zhihong; Zhang, Hongxiang; Zhang, Rui; Yu, Yuanyuan; Tao, Jin; Zhao, Hongyuan; Guo, Wenlan; Pang, Wei; Duan, Xuexin; Liu, Jing; Zhang, Daihua

2016-08-24

We demonstrate a dual-mode gas sensor for simultaneous and independent acquisition of electrical and mechanical signals from the same gas adsorption event. The device integrates a graphene field-effect transistor (FET) with a piezoelectric resonator in a seamless manner by leveraging multiple structural and functional synergies. Dual signals resulting from independent physical processes, i.e., mass attachment and charge transfer can reflect intrinsic properties of gas molecules and potentially enable target recognition and quantification at the same time. Fabrication of the device is based on standard Integrated Circuit (IC) foundry processes and fully compatible with system-on-a-chip (SoC) integration to achieve extremely small form factors. In addition, the ability of simultaneous measurements of mass adsorption and charge transfer guides us to a more precise understanding of the interactions between graphene and various gas molecules. Besides its practical functions, the device serves as an effective tool to quantitatively investigate the physical processes and sensing mechanisms for a large library of sensing materials and target analytes.

Neural-scaled entropy predicts the effects of nonlinear frequency compression on speech perception

PubMed Central

Rallapalli, Varsha H.; Alexander, Joshua M.

2015-01-01

The Neural-Scaled Entropy (NSE) model quantifies information in the speech signal that has been altered beyond simple gain adjustments by sensorineural hearing loss (SNHL) and various signal processing. An extension of Cochlear-Scaled Entropy (CSE) [Stilp, Kiefte, Alexander, and Kluender (2010). J. Acoust. Soc. Am. 128(4), 2112–2126], NSE quantifies information as the change in 1-ms neural firing patterns across frequency. To evaluate the model, data from a study that examined nonlinear frequency compression (NFC) in listeners with SNHL were used because NFC can recode the same input information in multiple ways in the output, resulting in different outcomes for different speech classes. Overall, predictions were more accurate for NSE than CSE. The NSE model accurately described the observed degradation in recognition, and lack thereof, for consonants in a vowel-consonant-vowel context that had been processed in different ways by NFC. While NSE accurately predicted recognition of vowel stimuli processed with NFC, it underestimated them relative to a low-pass control condition without NFC. In addition, without modifications, it could not predict the observed improvement in recognition for word final /s/ and /z/. Findings suggest that model modifications that include information from slower modulations might improve predictions across a wider variety of conditions. PMID:26627780

The Contribution of Brainstem and Cerebellar Pathways to Auditory Recognition

PubMed Central

McLachlan, Neil M.; Wilson, Sarah J.

2017-01-01

The cerebellum has been known to play an important role in motor functions for many years. More recently its role has been expanded to include a range of cognitive and sensory-motor processes, and substantial neuroimaging and clinical evidence now points to cerebellar involvement in most auditory processing tasks. In particular, an increase in the size of the cerebellum over recent human evolution has been attributed in part to the development of speech. Despite this, the auditory cognition literature has largely overlooked afferent auditory connections to the cerebellum that have been implicated in acoustically conditioned reflexes in animals, and could subserve speech and other auditory processing in humans. This review expands our understanding of auditory processing by incorporating cerebellar pathways into the anatomy and functions of the human auditory system. We reason that plasticity in the cerebellar pathways underpins implicit learning of spectrotemporal information necessary for sound and speech recognition. Once learnt, this information automatically recognizes incoming auditory signals and predicts likely subsequent information based on previous experience. Since sound recognition processes involving the brainstem and cerebellum initiate early in auditory processing, learnt information stored in cerebellar memory templates could then support a range of auditory processing functions such as streaming, habituation, the integration of auditory feature information such as pitch, and the recognition of vocal communications. PMID:28373850

Department of Cybernetic Acoustics

NASA Astrophysics Data System (ADS)

The development of the theory, instrumentation and applications of methods and systems for the measurement, analysis, processing and synthesis of acoustic signals within the audio frequency range, particularly of the speech signal and the vibro-acoustic signal emitted by technical and industrial equipments treated as noise and vibration sources was discussed. The research work, both theoretical and experimental, aims at applications in various branches of science, and medicine, such as: acoustical diagnostics and phoniatric rehabilitation of pathological and postoperative states of the speech organ; bilateral ""man-machine'' speech communication based on the analysis, recognition and synthesis of the speech signal; vibro-acoustical diagnostics and continuous monitoring of the state of machines, technical equipments and technological processes.

ESARR: enhanced situational awareness via road sign recognition

NASA Astrophysics Data System (ADS)

Perlin, V. E.; Johnson, D. B.; Rohde, M. M.; Lupa, R. M.; Fiorani, G.; Mohammad, S.

2010-04-01

The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). Sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.

A complex mTOR response in habituation paradigms for a social signal in adult songbirds.

PubMed

Ahmadiantehrani, Somayeh; Gores, Elisa O; London, Sarah E

2018-06-01

Nonassociative learning is considered simple because it depends on presentation of a single stimulus, but it likely reflects complex molecular signaling. To advance understanding of the molecular mechanisms of one form of nonassociative learning, habituation, for ethologically relevant signals we examined song recognition learning in adult zebra finches. These colonial songbirds learn the unique song of individuals, which helps establish and maintain mate and other social bonds, and informs appropriate behavioral interactions with specific birds. We leveraged prior work demonstrating behavioral habituation for individual songs, and extended the molecular framework correlated with this behavior by investigating the mechanistic Target of Rapamycin (mTOR) signaling cascade. We hypothesized that mTOR may contribute to habituation because it integrates a variety of upstream signals and enhances associative learning, and it crosstalks with another cascade previously associated with habituation, ERK/ZENK. To begin probing for a possible role for mTOR in song recognition learning, we used a combination of song playback paradigms and bidirectional dysregulation of mTORC1 activation. We found that mTOR demonstrates the molecular signatures of a habituation mechanism, and that its manipulation reveals the complexity of processes that may be invoked during nonassociative learning. These results thus expand the molecular targets for habituation studies and raise new questions about neural processing of complex natural signals. © 2018 Ahmadiantehrani et al.; Published by Cold Spring Harbor Laboratory Press.

Infrared sensor for hot spot recognition for a small satellite mission

NASA Astrophysics Data System (ADS)

Skrbek, W.; Bachmann, K.; Lorenz, E.; Neidhardt, M.; Peschel, M.; Walter, I.; Zender, B.

1996-11-01

High temperature events strongly influence the environmental processes. Therefore, their observation is an important constituent of the global monitoring network. Unfortunately the current remote sensing systems are not able to deliver the necessary information about the world wide burn out of vegetation and its consequences. For global observations a dedicated system of small satellites is required. The main components of the corresponding instrumentation are the infrared channels. The proposed HSRS (HOT SPOT RECOGNITION SENSOR) has to demonstrate the possibilities of an such instrumentation and its feasibility for small satellites. The main drawbacks of the HSRS design are the handling of the hot spot recognition in the subpixel area and of the saturation in the case of larger hot areas by a suitable signal processing hardware.

Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-Based fMRI

PubMed Central

Davis, Tyler; Love, Bradley C.; Preston, Alison R.

2012-01-01

Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and adjust their representations to support behavior in future encounters. Many techniques that are available to understand the neural basis of category learning assume that the multiple processes that subserve it can be neatly separated between different trials of an experiment. Model-based functional magnetic resonance imaging offers a promising tool to separate multiple, simultaneously occurring processes and bring the analysis of neuroimaging data more in line with category learning’s dynamic and multifaceted nature. We use model-based imaging to explore the neural basis of recognition and entropy signals in the medial temporal lobe and striatum that are engaged while participants learn to categorize novel stimuli. Consistent with theories suggesting a role for the anterior hippocampus and ventral striatum in motivated learning in response to uncertainty, we find that activation in both regions correlates with a model-based measure of entropy. Simultaneously, separate subregions of the hippocampus and striatum exhibit activation correlated with a model-based recognition strength measure. Our results suggest that model-based analyses are exceptionally useful for extracting information about cognitive processes from neuroimaging data. Models provide a basis for identifying the multiple neural processes that contribute to behavior, and neuroimaging data can provide a powerful test bed for constraining and testing model predictions. PMID:22746951

Neurocomputational account of memory and perception: Thresholded and graded signals in the hippocampus.

PubMed

Elfman, Kane W; Aly, Mariam; Yonelinas, Andrew P

2014-12-01

Recent evidence suggests that the hippocampus, a region critical for long-term memory, also supports certain forms of high-level visual perception. A seemingly paradoxical finding is that, unlike the thresholded hippocampal signals associated with memory, the hippocampus produces graded, strength-based signals in perception. This article tests a neurocomputational model of the hippocampus, based on the complementary learning systems framework, to determine if the same model can account for both memory and perception, and whether it produces the appropriate thresholded and strength-based signals in these two types of tasks. The simulations showed that the hippocampus, and most prominently the CA1 subfield, produced graded signals when required to discriminate between highly similar stimuli in a perception task, but generated thresholded patterns of activity in recognition memory. A threshold was observed in recognition memory because pattern completion occurred for only some trials and completely failed to occur for others; conversely, in perception, pattern completion always occurred because of the high degree of item similarity. These results offer a neurocomputational account of the distinct hippocampal signals associated with perception and memory, and are broadly consistent with proposals that CA1 functions as a comparator of expected versus perceived events. We conclude that the hippocampal computations required for high-level perceptual discrimination are congruous with current neurocomputational models that account for recognition memory, and fit neatly into a broader description of the role of the hippocampus for the processing of complex relational information. © 2014 Wiley Periodicals, Inc.

Evaluative conditioning of positive and negative valence affects P1 and N1 in verbal processing.

PubMed

Kuchinke, Lars; Fritsch, Nathalie; Müller, Christina J

2015-10-22

The present study examined the effect of contextual learning on the neural processing of previously meaningless pseudowords. During an evaluative conditioning session on 5 consecutive days, participants learned to associate 120 pseudowords with either positive, neutral or negative pictures. In a second session, participants were presented all conditioned pseudowords again together with 40 new pseudowords in a recognition memory task while their event-related potentials (ERPs) were recorded. The behavioral data confirm successful learning of pseudoword valence. At the neural level, early modulations of the ERPs are visible at the P1 and the N1 components discriminating between positively and negatively conditioned pseudowords. Differences to new pseudowords were visible at later processing stages as indicated by modulations of the LPC. These results support a contextual learning hypothesis that is able to explain very early emotional ERP modulations in visual word recognition. Source localization indicates a role of medial-frontal brain regions as a likely origin of these early valence discrimination signals which are discussed to promote top-down signals to sensory processing. Copyright © 2015. Published by Elsevier B.V.

Neural networks and applications tutorial

NASA Astrophysics Data System (ADS)

Guyon, I.

1991-09-01

The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

Asynchronous Data-Driven Classification of Weapon Systems

DTIC Science & Technology

2009-10-01

Classification of Weapon SystemsF Xin Jin† Kushal Mukherjee† Shalabh Gupta† Asok Ray † Shashi Phoha† Thyagaraju Damarla‡ xuj103@psu.edu kum162@psu.edu szg107...Orlando, FL. [8] A. Ray , “Symbolic dynamic analysis of complex systems for anomaly detection,” Signal Processing, vol. 84, no. 7, pp. 1115–1130, July...2004. [9] S. Gupta and A. Ray , “Symbolic dynamic filtering for data-driven pat- tern recognition,” PATTERN RECOGNITION: Theory and Application

Perceptual Decoding Processes for Language in a Visual Mode and for Language in an Auditory Mode.

ERIC Educational Resources Information Center

Myerson, Rosemarie Farkas

The purpose of this paper is to gain insight into the nature of the reading process through an understanding of the general nature of sensory processing mechanisms which reorganize and restructure input signals for central recognition, and an understanding of how the grammar of the language functions in defining the set of possible sentences in…

Effects of input processing and type of personal frequency modulation system on speech-recognition performance of adults with cochlear implants.

PubMed

Wolfe, Jace; Schafer, Erin; Parkinson, Aaron; John, Andrew; Hudson, Mary; Wheeler, Julie; Mucci, Angie

2013-01-01

The objective of this study was to compare speech recognition in quiet and in noise for cochlear implant recipients using two different types of personal frequency modulation (FM) systems (directly coupled [direct auditory input] versus induction neckloop) with each of two sound processors (Cochlear Nucleus Freedom versus Cochlear Nucleus 5). Two different experiments were conducted within this study. In both these experiments, mixing of the FM signal within the Freedom processor was implemented via the same scheme used clinically for the Freedom sound processor. In Experiment 1, the aforementioned comparisons were conducted with the Nucleus 5 programmed so that the microphone and FM signals were mixed and then the mixed signals were subjected to autosensitivity control (ASC). In Experiment 2, comparisons between the two FM systems and processors were conducted again with the Nucleus 5 programmed to provide a more complex multistage implementation of ASC during the preprocessing stage. This study was a within-subject, repeated-measures design. Subjects were recruited from the patient population at the Hearts for Hearing Foundation in Oklahoma City, OK. Fifteen subjects participated in Experiment 1, and 16 subjects participated in Experiment 2. Subjects were adults who had used either unilateral or bilateral cochlear implants for at least 1 year. In this experiment, no differences were found in speech recognition in quiet obtained with the two different FM systems or the various sound-processor conditions. With each sound processor, speech recognition in noise was better with the directly coupled direct auditory input system relative to the neckloop system. The multistage ASC processing of the Nucleus 5 sound processor provided better performance than the single-stage approach for the Nucleus 5 and the Nucleus Freedom sound processor. Speech recognition in noise is substantially affected by the type of sound processor, FM system, and implementation of ASC used by a Cochlear implant recipient.

Automated feature detection and identification in digital point-ordered signals

DOEpatents

Oppenlander, Jane E.; Loomis, Kent C.; Brudnoy, David M.; Levy, Arthur J.

1998-01-01

A computer-based automated method to detect and identify features in digital point-ordered signals. The method is used for processing of non-destructive test signals, such as eddy current signals obtained from calibration standards. The signals are first automatically processed to remove noise and to determine a baseline. Next, features are detected in the signals using mathematical morphology filters. Finally, verification of the features is made using an expert system of pattern recognition methods and geometric criteria. The method has the advantage that standard features can be, located without prior knowledge of the number or sequence of the features. Further advantages are that standard features can be differentiated from irrelevant signal features such as noise, and detected features are automatically verified by parameters extracted from the signals. The method proceeds fully automatically without initial operator set-up and without subjective operator feature judgement.

Intra-pulse modulation recognition using short-time ramanujan Fourier transform spectrogram

NASA Astrophysics Data System (ADS)

Ma, Xiurong; Liu, Dan; Shan, Yunlong

2017-12-01

Intra-pulse modulation recognition under negative signal-to-noise ratio (SNR) environment is a research challenge. This article presents a robust algorithm for the recognition of 5 types of radar signals with large variation range in the signal parameters in low SNR using the combination of the Short-time Ramanujan Fourier transform (ST-RFT) and pseudo-Zernike moments invariant features. The ST-RFT provides the time-frequency distribution features for 5 modulations. The pseudo-Zernike moments provide invariance properties that are able to recognize different modulation schemes on different parameter variation conditions from the ST-RFT spectrograms. Simulation results demonstrate that the proposed algorithm achieves the probability of successful recognition (PSR) of over 90% when SNR is above -5 dB with large variation range in the signal parameters: carrier frequency (CF) for all considered signals, hop size (HS) for frequency shift keying (FSK) signals, and the time-bandwidth product for Linear Frequency Modulation (LFM) signals.

Statistical process control using optimized neural networks: a case study.

PubMed

Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

2014-09-01

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Eicosanoid-mediated immunity in insects

USDA-ARS?s Scientific Manuscript database

Eicosanoid is a collective term for oxygenated metabolites of C20 polyunsaturated fatty acids. As seen in mammals, eicosanoids play crucial roles in mediating various physiological processes, including immune responses, in insects. Upon microbial pathogen infection, non-self recognition signals are ...

[Analysis and research of brain-computer interface experiments for imaging left-right hands movement].

PubMed

Wu, Yazhou; He, Qinghua; Huang, Hua; Zhang, Ling; Zhuo, Yu; Xie, Qi; Wu, Baoming

2008-10-01

This is a research carried out to explore a pragmatic way of BCI based imaging movement, i. e. to extract the feature of EEG for reflecting different thinking by searching suitable methods of signal extraction and recognition algorithm processing, to boost the recognition rate of communication for BCI system, and finally to establish a substantial theory and experimental support for BCI application. In this paper, different mental tasks for imaging left-right hands movement from 6 subjects were studied in three different time sections (hint keying at 2s, 1s and 0s after appearance of arrow). Then we used wavelet analysis and Feed-forward Back-propagation Neural Network (BP-NN) method for processing and analyzing the experimental data of off-line. Delay time delta t2, delta t1 and delta t0 for all subjects in the three different time sections were analyzed. There was significant difference between delta to and delta t2 or delta t1 (P<0.05), but no significant difference was noted between delta t2 and delta t1 (P>0.05). The average results of recognition rate were 65%, 86.67% and 72%, respectively. There were obviously different features for imaging left-right hands movement about 0.5-1s before actual movement; these features displayed significant difference. We got higher recognition rate of communication under the hint keying at about 1s after the appearance of arrow. These showed the feasibility of using the feature signals extracted from the project as the external control signals for BCI system, and demon strated that the project provided new ideas and methods for feature extraction and classification of mental tasks for BCI.

Optical information-processing systems and architectures II; Proceedings of the Meeting, San Diego, CA, July 9-13, 1990

NASA Astrophysics Data System (ADS)

Javidi, Bahram

The present conference discusses topics in the fields of neural networks, acoustooptic signal processing, pattern recognition, phase-only processing, nonlinear signal processing, image processing, optical computing, and optical information processing. Attention is given to the optical implementation of an inner-product neural associative memory, optoelectronic associative recall via motionless-head/parallel-readout optical disk, a compact real-time acoustooptic image correlator, a multidimensional synthetic estimation filter, and a light-efficient joint transform optical correlator. Also discussed are a high-resolution spatial light modulator, compact real-time interferometric Fourier-transform processors, a fast decorrelation algorithm for permutation arrays, the optical interconnection of optical modules, and carry-free optical binary adders.

Instrument-independent analysis of music by means of the continuous wavelet transform

NASA Astrophysics Data System (ADS)

Olmo, Gabriella; Dovis, Fabio; Benotto, Paolo; Calosso, Claudio; Passaro, Pierluigi

1999-10-01

This paper deals with the problem of automatic recognition of music. Segments of digitized music are processed by means of a Continuous Wavelet Transform, properly chosen so as to match the spectral characteristics of the signal. In order to achieve a good time-scale representation of the signal components a novel wavelet has been designed suited to the musical signal features. particular care has been devoted towards an efficient implementation, which operates in the frequency domain, and includes proper segmentation and aliasing reduction techniques to make the analysis of long signals feasible. The method achieves very good performance in terms of both time and frequency selectivity, and can yield the estimate and the localization in time of both the fundamental frequency and the main harmonics of each tone. The analysis is used as a preprocessing step for a recognition algorithm, which we show to be almost independent on the instrument reproducing the sounds. Simulations are provided to demonstrate the effectiveness of the proposed method.

Microscopic prediction of speech recognition for listeners with normal hearing in noise using an auditory model.

PubMed

Jürgens, Tim; Brand, Thomas

2009-11-01

This study compares the phoneme recognition performance in speech-shaped noise of a microscopic model for speech recognition with the performance of normal-hearing listeners. "Microscopic" is defined in terms of this model twofold. First, the speech recognition rate is predicted on a phoneme-by-phoneme basis. Second, microscopic modeling means that the signal waveforms to be recognized are processed by mimicking elementary parts of human's auditory processing. The model is based on an approach by Holube and Kollmeier [J. Acoust. Soc. Am. 100, 1703-1716 (1996)] and consists of a psychoacoustically and physiologically motivated preprocessing and a simple dynamic-time-warp speech recognizer. The model is evaluated while presenting nonsense speech in a closed-set paradigm. Averaged phoneme recognition rates, specific phoneme recognition rates, and phoneme confusions are analyzed. The influence of different perceptual distance measures and of the model's a-priori knowledge is investigated. The results show that human performance can be predicted by this model using an optimal detector, i.e., identical speech waveforms for both training of the recognizer and testing. The best model performance is yielded by distance measures which focus mainly on small perceptual distances and neglect outliers.

Simulation of Biomimetic Recognition between Polymers and Surfaces

NASA Astrophysics Data System (ADS)

Golumbfskie, Aaron J.; Pande, Vijay S.; Chakraborty, Arup K.

1999-10-01

Many biological processes, such as transmembrane signaling and pathogen-host interactions, are initiated by a protein recognizing a specific pattern of binding sites on part of a membrane or cell surface. By recognition, we imply that the polymer quickly finds and then adsorbs strongly on the pattern-matched region and not on others. The development of synthetic systems that can mimic such recognition between polymers and surfaces could have significant impact on advanced applications such as the development of sensors, molecular-scale separation processes, and synthetic viral inhibition agents. Attempting to affect recognition in synthetic systems by copying the detailed chemistries to which nature has been led over millenia of evolution does not seem practical for most applications. This leads us to the following question: Are there any universal strategies that can affect recognition between polymers and surfaces? Such generic strategies may be easier to implement in abiotic applications. We describe results that suggest that biomimetic recognition between synthetic polymers and surfaces is possible by exploiting certain generic strategies, and we elucidate the kinetic mechanisms by which this occurs. Our results suggest convenient model systems for experimental studies of dynamics in free energy landscapes characteristic of frustrated systems.

[False recognition of faces associated with fronto-temporal dementia with prosopagnosia].

PubMed

Verstichel, P

2005-09-01

The association of prosopagnosia and false recognition of faces is unusual and contributes to our understanding of the generation of facial familiarity. A 67-year-old man with a left prefrontal traumatic lesion, developed a temporal variety of fronto-temporal dementia (semantic dementia) with amyotrophic lateral sclerosis. Cerebral imagery demonstrated a bilateral, temporal anterior atrophy predominating in the right hemisphere. The main cognitive signs consisted in severe difficulties to recognize faces of familiar people (prosopagnosia), associated with systematic false recognition of unfamiliar people. Neuropsychological testing indicated that the prosopagnosia probably resulted from the association of an associative/mnemonic mechanism (inability to activate the Face Recognition Units (FRU) from the visual input) and a semantic mechanism (degradation of semantic/biographical information or deconnexion between FRU and this information). At the early stage of the disease, the patient could activate residual semantic information about individuals from their names, but after a 4-year course, he failed to do so. This worsening could be attributed to the extension of the degenerative lesions to the left temporal lobe. Familiar and unfamiliar faces triggered a marked feeling of knowing. False recognition concerned all the unfamiliar faces, and the patient claimed spontaneously that they corresponded to actors, but he could not provide any additional information about their specific identities. The coexistence of prosopagnosia and false recognition suggests the existence of different interconnected systems processing face recognition, one intended to identification of individuals, and the other producing the sense of familiarity. Dysfunctions at different stages of one or the other of these two processes could result in distortions in the feeling of knowing. From this case and others reported in literature, we propose to complete the classical model of face processing by adding a pathway linked to limbic system and frontal structures. This later pathway could normally emit signals for familiarity, essentially autonomic, in response to the familiar faces. These signals, primitively unconscious, secondly reach consciousness and are then integrated by a central supervisor system which evaluates and verifies identity-specific biographical information in order to make a decision about the sense of familiarity.

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

PubMed

Gordon-Salant, Sandra; Cole, Stacey Samuels

2016-01-01

This study aimed to determine if younger and older listeners with normal hearing who differ on working memory span perform differently on speech recognition tests in noise. Older adults typically exhibit poorer speech recognition scores in noise than younger adults, which is attributed primarily to poorer hearing sensitivity and more limited working memory capacity in older than younger adults. Previous studies typically tested older listeners with poorer hearing sensitivity and shorter working memory spans than younger listeners, making it difficult to discern the importance of working memory capacity on speech recognition. This investigation controlled for hearing sensitivity and compared speech recognition performance in noise by younger and older listeners who were subdivided into high and low working memory groups. Performance patterns were compared for different speech materials to assess whether or not the effect of working memory capacity varies with the demands of the specific speech test. The authors hypothesized that (1) normal-hearing listeners with low working memory span would exhibit poorer speech recognition performance in noise than those with high working memory span; (2) older listeners with normal hearing would show poorer speech recognition scores than younger listeners with normal hearing, when the two age groups were matched for working memory span; and (3) an interaction between age and working memory would be observed for speech materials that provide contextual cues. Twenty-eight older (61 to 75 years) and 25 younger (18 to 25 years) normal-hearing listeners were assigned to groups based on age and working memory status. Northwestern University Auditory Test No. 6 words and Institute of Electrical and Electronics Engineers sentences were presented in noise using an adaptive procedure to measure the signal-to-noise ratio corresponding to 50% correct performance. Cognitive ability was evaluated with two tests of working memory (Listening Span Test and Reading Span Test) and two tests of processing speed (Paced Auditory Serial Addition Test and The Letter Digit Substitution Test). Significant effects of age and working memory capacity were observed on the speech recognition measures in noise, but these effects were mediated somewhat by the speech signal. Specifically, main effects of age and working memory were revealed for both words and sentences, but the interaction between the two was significant for sentences only. For these materials, effects of age were observed for listeners in the low working memory groups only. Although all cognitive measures were significantly correlated with speech recognition in noise, working memory span was the most important variable accounting for speech recognition performance. The results indicate that older adults with high working memory capacity are able to capitalize on contextual cues and perform as well as young listeners with high working memory capacity for sentence recognition. The data also suggest that listeners with normal hearing and low working memory capacity are less able to adapt to distortion of speech signals caused by background noise, which requires the allocation of more processing resources to earlier processing stages. These results indicate that both younger and older adults with low working memory capacity and normal hearing are at a disadvantage for recognizing speech in noise.

Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Lu, Weizhao; Huang, Chunhui; Hou, Kun; Shi, Liting; Zhao, Huihui; Li, Zhengmei; Qiu, Jianfeng

2018-05-01

In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged.

Comparison of formant detection methods used in speech processing applications

NASA Astrophysics Data System (ADS)

Belean, Bogdan

2013-11-01

The paper describes time frequency representations of speech signal together with the formant significance in speech processing applications. Speech formants can be used in emotion recognition, sex discrimination or diagnosing different neurological diseases. Taking into account the various applications of formant detection in speech signal, two methods for detecting formants are presented. First, the poles resulted after a complex analysis of LPC coefficients are used for formants detection. The second approach uses the Kalman filter for formant prediction along the speech signal. Results are presented for both approaches on real life speech spectrograms. A comparison regarding the features of the proposed methods is also performed, in order to establish which method is more suitable in case of different speech processing applications.

Automatic welding detection by an intelligent tool pipe inspection

NASA Astrophysics Data System (ADS)

Arizmendi, C. J.; Garcia, W. L.; Quintero, M. A.

2015-07-01

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called “smart pig” in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

Learning target masks in infrared linescan imagery

NASA Astrophysics Data System (ADS)

Fechner, Thomas; Rockinger, Oliver; Vogler, Axel; Knappe, Peter

1997-04-01

In this paper we propose a neural network based method for the automatic detection of ground targets in airborne infrared linescan imagery. Instead of using a dedicated feature extraction stage followed by a classification procedure, we propose the following three step scheme: In the first step of the recognition process, the input image is decomposed into its pyramid representation, thus obtaining a multiresolution signal representation. At the lowest three levels of the Laplacian pyramid a neural network filter of moderate size is trained to indicate the target location. The last step consists of a fusion process of the several neural network filters to obtain the final result. To perform this fusion we use a belief network to combine the various filter outputs in a statistical meaningful way. In addition, the belief network allows the integration of further knowledge about the image domain. By applying this multiresolution recognition scheme, we obtain a nearly scale- and rotational invariant target recognition with a significantly decreased false alarm rate compared with a single resolution target recognition scheme.

Pupil size changes during recognition memory.

PubMed

Otero, Samantha C; Weekes, Brendan S; Hutton, Samuel B

2011-10-01

Pupils dilate to a greater extent when participants view old compared to new items during recognition memory tests. We report three experiments investigating the cognitive processes associated with this pupil old/new effect. Using a remember/know procedure, we found that the effect occurred for old items that were both remembered and known at recognition, although it was attenuated for known compared to remembered items. In Experiment 2, the pupil old/new effect was observed when items were presented acoustically, suggesting the effect does not depend on low-level visual processes. The pupil old/new effect was also greater for items encoded under deep compared to shallow orienting instructions, suggesting it may reflect the strength of the underlying memory trace. Finally, the pupil old/new effect was also found when participants falsely recognized items as being old. We propose that pupils respond to a strength-of-memory signal and suggest that pupillometry provides a useful technique for exploring the underlying mechanisms of recognition memory. Copyright © 2011 Society for Psychophysiological Research.

Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems.

PubMed

Gao, Lei; Bourke, A K; Nelson, John

2014-06-01

Physical activity has a positive impact on people's well-being and it had been shown to decrease the occurrence of chronic diseases in the older adult population. To date, a substantial amount of research studies exist, which focus on activity recognition using inertial sensors. Many of these studies adopt a single sensor approach and focus on proposing novel features combined with complex classifiers to improve the overall recognition accuracy. In addition, the implementation of the advanced feature extraction algorithms and the complex classifiers exceed the computing ability of most current wearable sensor platforms. This paper proposes a method to adopt multiple sensors on distributed body locations to overcome this problem. The objective of the proposed system is to achieve higher recognition accuracy with "light-weight" signal processing algorithms, which run on a distributed computing based sensor system comprised of computationally efficient nodes. For analysing and evaluating the multi-sensor system, eight subjects were recruited to perform eight normal scripted activities in different life scenarios, each repeated three times. Thus a total of 192 activities were recorded resulting in 864 separate annotated activity states. The methods for designing such a multi-sensor system required consideration of the following: signal pre-processing algorithms, sampling rate, feature selection and classifier selection. Each has been investigated and the most appropriate approach is selected to achieve a trade-off between recognition accuracy and computing execution time. A comparison of six different systems, which employ single or multiple sensors, is presented. The experimental results illustrate that the proposed multi-sensor system can achieve an overall recognition accuracy of 96.4% by adopting the mean and variance features, using the Decision Tree classifier. The results demonstrate that elaborate classifiers and feature sets are not required to achieve high recognition accuracies on a multi-sensor system. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures

PubMed Central

Pi, Yiming

2017-01-01

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar. PMID:29267249

Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures.

PubMed

Zhou, Zhi; Cao, Zongjie; Pi, Yiming

2017-12-21

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar.

Audio-based, unsupervised machine learning reveals cyclic changes in earthquake mechanisms in the Geysers geothermal field, California

NASA Astrophysics Data System (ADS)

Holtzman, B. K.; Paté, A.; Paisley, J.; Waldhauser, F.; Repetto, D.; Boschi, L.

2017-12-01

The earthquake process reflects complex interactions of stress, fracture and frictional properties. New machine learning methods reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Our methods are based closely on those developed for music information retrieval and voice recognition, using the spectrogram instead of the waveform directly. Unsupervised learning involves identification of patterns based on differences among signals without any additional information provided to the algorithm. Clustering of 46,000 earthquakes of $0.3

What does voice-processing technology support today?

PubMed Central

Nakatsu, R; Suzuki, Y

1995-01-01

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

CAVIAR: a 45k neuron, 5M synapse, 12G connects/s AER hardware sensory-processing- learning-actuating system for high-speed visual object recognition and tracking.

PubMed

Serrano-Gotarredona, Rafael; Oster, Matthias; Lichtsteiner, Patrick; Linares-Barranco, Alejandro; Paz-Vicente, Rafael; Gomez-Rodriguez, Francisco; Camunas-Mesa, Luis; Berner, Raphael; Rivas-Perez, Manuel; Delbruck, Tobi; Liu, Shih-Chii; Douglas, Rodney; Hafliger, Philipp; Jimenez-Moreno, Gabriel; Civit Ballcels, Anton; Serrano-Gotarredona, Teresa; Acosta-Jimenez, Antonio J; Linares-Barranco, Bernabé

2009-09-01

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies.

A Stochastic Detection and Retrieval Model for the Study of Metacognition

ERIC Educational Resources Information Center

Jang, Yoonhee; Wallsten, Thomas S.; Huber, David E.

2012-01-01

We present a signal detection-like model termed the stochastic detection and retrieval model (SDRM) for use in studying metacognition. Focusing on paradigms that relate retrieval (e.g., recall or recognition) and confidence judgments, the SDRM measures (1) variance in the retrieval process, (2) variance in the confidence process, (3) the extent to…

[Recognition of walking stance phase and swing phase based on moving window].

PubMed

Geng, Xiaobo; Yang, Peng; Wang, Xinran; Geng, Yanli; Han, Yu

2014-04-01

Wearing transfemoral prosthesis is the only way to complete daily physical activity for amputees. Motion pattern recognition is important for the control of prosthesis, especially in the recognizing swing phase and stance phase. In this paper, it is reported that surface electromyography (sEMG) signal is used in swing and stance phase recognition. sEMG signal of related muscles was sampled by Infiniti of a Canadian company. The sEMG signal was then filtered by weighted filtering window and analyzed by height permitted window. The starting time of stance phase and swing phase is determined through analyzing special muscles. The sEMG signal of rectus femoris was used in stance phase recognition and sEMG signal of tibialis anterior is used in swing phase recognition. In a certain tolerating range, the double windows theory, including weighted filtering window and height permitted window, can reach a high accuracy rate. Through experiments, the real walking consciousness of the people was reflected by sEMG signal of related muscles. Using related muscles to recognize swing and stance phase is reachable. The theory used in this paper is useful for analyzing sEMG signal and actual prosthesis control.

Basic perceptual changes that alter meaning and neural correlates of recognition memory

PubMed Central

Gao, Chuanji; Hermiller, Molly S.; Voss, Joel L.; Guo, Chunyan

2015-01-01

It is difficult to pinpoint the border between perceptual and conceptual processing, despite their treatment as distinct entities in many studies of recognition memory. For instance, alteration of simple perceptual characteristics of a stimulus can radically change meaning, such as the color of bread changing from white to green. We sought to better understand the role of perceptual and conceptual processing in memory by identifying the effects of changing a basic perceptual feature (color) on behavioral and neural correlates of memory in circumstances when this change would be expected to either change the meaning of a stimulus or to have no effect on meaning (i.e., to influence conceptual processing or not). Abstract visual shapes (“squiggles”) were colorized during study and presented during test in either the same color or a different color. Those squiggles that subjects found to resemble meaningful objects supported behavioral measures of conceptual priming, whereas meaningless squiggles did not. Further, changing color from study to test had a selective effect on behavioral correlates of priming for meaningful squiggles, indicating that color change altered conceptual processing. During a recognition memory test, color change altered event-related brain potential (ERP) correlates of memory for meaningful squiggles but not for meaningless squiggles. Specifically, color change reduced the amplitude of frontally distributed N400 potentials (FN400), implying that these potentials indicated conceptual processing during recognition memory that was sensitive to color change. In contrast, color change had no effect on FN400 correlates of recognition for meaningless squiggles, which were overall smaller in amplitude than for meaningful squiggles (further indicating that these potentials signal conceptual processing during recognition). Thus, merely changing the color of abstract visual shapes can alter their meaning, changing behavioral and neural correlates of memory. These findings are relevant to understanding similarities and distinctions between perceptual and conceptual processing as well as the functional interpretation of neural correlates of recognition memory. PMID:25717298

Different importance of the volatile and non-volatile fractions of an olfactory signature for individual social recognition in rats versus mice and short-term versus long-term memory.

PubMed

Noack, Julia; Richter, Karin; Laube, Gregor; Haghgoo, Hojjat Allah; Veh, Rüdiger W; Engelmann, Mario

2010-11-01

When tested in the olfactory cued social recognition/discrimination test, rats and mice differ in their retention of a recognition memory for a previously encountered conspecific juvenile: Rats are able to recognize a given juvenile for approximately 45 min only whereas mice show not only short-term, but also long-term recognition memory (≥ 24 h). Here we modified the social recognition/social discrimination procedure to investigate the neurobiological mechanism(s) underlying the species differences. We presented a conspecific juvenile repeatedly to the experimental subjects and monitored the investigation duration as a measure for recognition. Presentation of only the volatile fraction of the juvenile olfactory signature was sufficient for both short- and long-term recognition in mice but not rats. Applying additional volatile, mono-molecular odours to the "to be recognized" juveniles failed to affect short-term memory in both species, but interfered with long-term recognition in mice. Finally immunocytochemical analysis of c-Fos as a marker for cellular activation, revealed that juvenile exposure stimulated areas involved in the processing of olfactory signals in both the main and the accessory olfactory bulb in mice. In rats, we measured an increased c-Fos synthesis almost exclusively in cells of the accessory olfactory bulb. Our data suggest that the species difference in the retention of social recognition memory is based on differences in the processing of the volatile versus non-volatile fraction of the individuals' olfactory signature. The non-volatile fraction is sufficient for retaining a short-term social memory only. Long-term social memory - as observed in mice - requires a processing of both the volatile and non-volatile fractions of the olfactory signature. Copyright © 2010 Elsevier Inc. All rights reserved.

Signal processing techniques for damage detection with piezoelectric wafer active sensors and embedded ultrasonic structural radar

NASA Astrophysics Data System (ADS)

Yu, Lingyu; Bao, Jingjing; Giurgiutiu, Victor

2004-07-01

Embedded ultrasonic structural radar (EUSR) algorithm is developed for using piezoelectric wafer active sensor (PWAS) array to detect defects within a large area of a thin-plate specimen. Signal processing techniques are used to extract the time of flight of the wave packages, and thereby to determine the location of the defects with the EUSR algorithm. In our research, the transient tone-burst wave propagation signals are generated and collected by the embedded PWAS. Then, with signal processing, the frequency contents of the signals and the time of flight of individual frequencies are determined. This paper starts with an introduction of embedded ultrasonic structural radar algorithm. Then we will describe the signal processing methods used to extract the time of flight of the wave packages. The signal processing methods being used include the wavelet denoising, the cross correlation, and Hilbert transform. Though hardware device can provide averaging function to eliminate the noise coming from the signal collection process, wavelet denoising is included to ensure better signal quality for the application in real severe environment. For better recognition of time of flight, cross correlation method is used. Hilbert transform is applied to the signals after cross correlation in order to extract the envelope of the signals. Signal processing and EUSR are both implemented by developing a graphical user-friendly interface program in LabView. We conclude with a description of our vision for applying EUSR signal analysis to structural health monitoring and embedded nondestructive evaluation. To this end, we envisage an automatic damage detection application utilizing embedded PWAS, EUSR, and advanced signal processing.

Visual Biopsy by Hydrogen Peroxide-Induced Signal Amplification.

PubMed

Zhao, Wenjie; Yang, Sheng; Yang, Jinfeng; Li, Jishan; Zheng, Jing; Qing, Zhihe; Yang, Ronghua

2016-11-01

Visual biopsy has attracted special interest by surgeons due to its simplicity and practicality; however, the limited sensitivity of the technology makes it difficult to achieve an early diagnosis. To circumvent this problem, herein, we report a visual signal amplification strategy for establishing a marker-recognizable biopsy that enables early cancer diagnosis. In our proposed approach, hydrogen peroxide (H 2 O 2 ) was selected as a potential underlying marker for its compact relationship in cancer progression. For selective recognition of H 2 O 2 in the process of visual biopsy, a benzylbenzeneboronic acid pinacol ester-decorated copolymer, namely, PMPC-Bpe, was synthesized, affording the final formation of the H 2 O 2 -responsive micelles in which amylose was trapped. The presence of H 2 O 2 activates the boronate ester recognition site and induces it releasing abundant indicator amylose, leading to signal amplification. The indicator came across the solution of KI/I 2 added to the sample, and the formative amylose-KI/I 2 complex has a distinct blue color at 574 nm for visual amplification detection. The feasibility of the proposed method is demonstrated by visualizing the H 2 O 2 content of cancer at different stages and three kinds of actual cancerous samples. As far as we know, this is the first paradigm to rationally design a signaling amplification-based molecular recognizable biopsy for visual and sensitive disease identification, which will extend new possibilities for marker-recognition and signal amplification-based biopsy in disease progressing.

Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

PubMed

Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

2015-01-01

Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients

PubMed Central

2015-01-01

Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations. PMID:25885272

Single-trial EEG-informed fMRI reveals spatial dependency of BOLD signal on early and late IC-ERP amplitudes during face recognition.

PubMed

Wirsich, Jonathan; Bénar, Christian; Ranjeva, Jean-Philippe; Descoins, Médéric; Soulier, Elisabeth; Le Troter, Arnaud; Confort-Gouny, Sylviane; Liégeois-Chauvel, Catherine; Guye, Maxime

2014-10-15

Simultaneous EEG-fMRI has opened up new avenues for improving the spatio-temporal resolution of functional brain studies. However, this method usually suffers from poor EEG quality, especially for evoked potentials (ERPs), due to specific artifacts. As such, the use of EEG-informed fMRI analysis in the context of cognitive studies has particularly focused on optimizing narrow ERP time windows of interest, which ignores the rich diverse temporal information of the EEG signal. Here, we propose to use simultaneous EEG-fMRI to investigate the neural cascade occurring during face recognition in 14 healthy volunteers by using the successive ERP peaks recorded during the cognitive part of this process. N170, N400 and P600 peaks, commonly associated with face recognition, were successfully and reproducibly identified for each trial and each subject by using a group independent component analysis (ICA). For the first time we use this group ICA to extract several independent components (IC) corresponding to the sequence of activation and used single-trial peaks as modulation parameters in a general linear model (GLM) of fMRI data. We obtained an occipital-temporal-frontal stream of BOLD signal modulation, in accordance with the three successive IC-ERPs providing an unprecedented spatio-temporal characterization of the whole cognitive process as defined by BOLD signal modulation. By using this approach, the pattern of EEG-informed BOLD modulation provided improved characterization of the network involved than the fMRI-only analysis or the source reconstruction of the three ERPs; the latter techniques showing only two regions in common localized in the occipital lobe. Copyright © 2014 Elsevier Inc. All rights reserved.

The role of the human hippocampus in familiarity-based and recollection-based recognition memory

PubMed Central

Wixted, John T.; Squire, Larry R.

2010-01-01

The ability to recognize a previously encountered stimulus is dependent on the structures of the medial temporal lobe and is thought to be supported by two processes, recollection and familiarity. A focus of research in recent years concerns the extent to which these two processes depend on the hippocampus and on the other structures of the medial temporal lobe. One view holds that the hippocampus is important for both processes, whereas a different view holds that the hippocampus supports only the recollection process and the perirhinal cortex supports the familiarity process. One approach has been to study patients with hippocampal lesions and to contrast old/new recognition (which can be supported by familiarity) to free recall (which is supported by recollection). Despite some early case studies suggesting otherwise, several group studies have now shown that hippocampal patients exhibit comparable impairments on old/new recognition and free recall. These findings suggest that the hippocampus is important for both recollection and familiarity. Neuroimaging studies and Receiver Operating Characteristic analyses also initially suggested that the hippocampus was specialized for recollection, but these studies involved a strength confound (strong memories have been compared to weak memories). When steps are taken to compare strong recollection-based memories with strong familiarity-based memories, or otherwise control for memory strength, evidence for a familiarity signal (as well as a recollection signal) is evident in the hippocampus. These findings suggest that the functional organization of the medial temporal lobe is probably best understood in terms unrelated to the distinction between recollection and familiarity. PMID:20412819

Short temporal asynchrony disrupts visual object recognition

PubMed Central

Singer, Jedediah M.; Kreiman, Gabriel

2014-01-01

Humans can recognize objects and scenes in a small fraction of a second. The cascade of signals underlying rapid recognition might be disrupted by temporally jittering different parts of complex objects. Here we investigated the time course over which shape information can be integrated to allow for recognition of complex objects. We presented fragments of object images in an asynchronous fashion and behaviorally evaluated categorization performance. We observed that visual recognition was significantly disrupted by asynchronies of approximately 30 ms, suggesting that spatiotemporal integration begins to break down with even small deviations from simultaneity. However, moderate temporal asynchrony did not completely obliterate recognition; in fact, integration of visual shape information persisted even with an asynchrony of 100 ms. We describe the data with a concise model based on the dynamic reduction of uncertainty about what image was presented. These results emphasize the importance of timing in visual processing and provide strong constraints for the development of dynamical models of visual shape recognition. PMID:24819738

Recall and recognition hypermnesia for Socratic stimuli.

PubMed

Kazén, Miguel; Solís-Macías, Víctor M

2016-01-01

In two experiments, we investigate hypermnesia, net memory improvements with repeated testing of the same material after a single study trial. In the first experiment, we found hypermnesia across three trials for the recall of word solutions to Socratic stimuli (dictionary-like definitions of concepts) replicating Erdelyi, Buschke, and Finkelstein and, for the first time using these materials, for their recognition. In the second experiment, we had two "yes/no" recognition groups, a Socratic stimuli group presented with concrete and abstract verbal materials and a word-only control group. Using signal detection measures, we found hypermnesia for concrete Socratic stimuli-and stable performance for abstract stimuli across three recognition tests. The control group showed memory decrements across tests. We interpret these findings with the alternative retrieval pathways (ARP) hypothesis, contrasting it with alternative theories of hypermnesia, such as depth of processing, generation and retrieve-recognise. We conclude that recognition hypermnesia for concrete Socratic stimuli is a reliable phenomenon, which we found in two experiments involving both forced-choice and yes/no recognition procedures.

Compact holographic optical neural network system for real-time pattern recognition

NASA Astrophysics Data System (ADS)

Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

1996-08-01

One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

Hippocampal Protein Kinase C Signaling Mediates the Short-Term Memory Impairment Induced by Delta9-Tetrahydrocannabinol.

PubMed

Busquets-Garcia, Arnau; Gomis-González, Maria; Salgado-Mendialdúa, Victòria; Galera-López, Lorena; Puighermanal, Emma; Martín-García, Elena; Maldonado, Rafael; Ozaita, Andrés

2018-04-01

Cannabis affects cognitive performance through the activation of the endocannabinoid system, and the molecular mechanisms involved in this process are poorly understood. Using the novel object-recognition memory test in mice, we found that the main psychoactive component of cannabis, delta9-tetrahydrocannabinol (THC), alters short-term object-recognition memory specifically involving protein kinase C (PKC)-dependent signaling. Indeed, the systemic or intra-hippocampal pre-treatment with the PKC inhibitors prevented the short-term, but not the long-term, memory impairment induced by THC. In contrast, systemic pre-treatment with mammalian target of rapamycin complex 1 inhibitors, known to block the amnesic-like effects of THC on long-term memory, did not modify such a short-term cognitive deficit. Immunoblot analysis revealed a transient increase in PKC signaling activity in the hippocampus after THC treatment. Thus, THC administration induced the phosphorylation of a specific Ser residue in the hydrophobic-motif at the C-terminal tail of several PKC isoforms. This significant immunoreactive band that paralleled cognitive performance did not match in size with the major PKC isoforms expressed in the hippocampus except for PKCθ. Moreover, THC transiently enhanced the phosphorylation of the postsynaptic calmodulin-binding protein neurogranin in a PKC dependent manner. These data demonstrate that THC alters short-term object-recognition memory through hippocampal PKC/neurogranin signaling.

Individual recognition based on communication behaviour of male fowl.

PubMed

Smith, Carolynn L; Taubert, Jessica; Weldon, Kimberly; Evans, Christopher S

2016-04-01

Correctly directing social behaviour towards a specific individual requires an ability to discriminate between conspecifics. The mechanisms of individual recognition include phenotype matching and familiarity-based recognition. Communication-based recognition is a subset of familiarity-based recognition wherein the classification is based on behavioural or distinctive signalling properties. Male fowl (Gallus gallus) produce a visual display (tidbitting) upon finding food in the presence of a female. Females typically approach displaying males. However, males may tidbit without food. We used the distinctiveness of the visual display and the unreliability of some males to test for communication-based recognition in female fowl. We manipulated the prior experience of the hens with the males to create two classes of males: S(+) wherein the tidbitting signal was paired with a food reward to the female, and S (-) wherein the tidbitting signal occurred without food reward. We then conducted a sequential discrimination test with hens using a live video feed of a familiar male. The results of the discrimination tests revealed that hens discriminated between categories of males based on their signalling behaviour. These results suggest that fowl possess a communication-based recognition system. This is the first demonstration of live-to-video transfer of recognition in any species of bird. Copyright © 2016 Elsevier B.V. All rights reserved.

Neurobiological mechanisms associated with facial affect recognition deficits after traumatic brain injury.

PubMed

Neumann, Dawn; McDonald, Brenna C; West, John; Keiski, Michelle A; Wang, Yang

2016-06-01

The neurobiological mechanisms that underlie facial affect recognition deficits after traumatic brain injury (TBI) have not yet been identified. Using functional magnetic resonance imaging (fMRI), study aims were to 1) determine if there are differences in brain activation during facial affect processing in people with TBI who have facial affect recognition impairments (TBI-I) relative to people with TBI and healthy controls who do not have facial affect recognition impairments (TBI-N and HC, respectively); and 2) identify relationships between neural activity and facial affect recognition performance. A facial affect recognition screening task performed outside the scanner was used to determine group classification; TBI patients who performed greater than one standard deviation below normal performance scores were classified as TBI-I, while TBI patients with normal scores were classified as TBI-N. An fMRI facial recognition paradigm was then performed within the 3T environment. Results from 35 participants are reported (TBI-I = 11, TBI-N = 12, and HC = 12). For the fMRI task, TBI-I and TBI-N groups scored significantly lower than the HC group. Blood oxygenation level-dependent (BOLD) signals for facial affect recognition compared to a baseline condition of viewing a scrambled face, revealed lower neural activation in the right fusiform gyrus (FG) in the TBI-I group than the HC group. Right fusiform gyrus activity correlated with accuracy on the facial affect recognition tasks (both within and outside the scanner). Decreased FG activity suggests facial affect recognition deficits after TBI may be the result of impaired holistic face processing. Future directions and clinical implications are discussed.

Rapid update of discrete Fourier transform for real-time signal processing

NASA Astrophysics Data System (ADS)

Sherlock, Barry G.; Kakad, Yogendra P.

2001-10-01

In many identification and target recognition applications, the incoming signal will have properties that render it amenable to analysis or processing in the Fourier domain. In such applications, however, it is usually essential that the identification or target recognition be performed in real time. An important constraint upon real-time processing in the Fourier domain is the time taken to perform the Discrete Fourier Transform (DFT). Ideally, a new Fourier transform should be obtained after the arrival of every new data point. However, the Fast Fourier Transform (FFT) algorithm requires on the order of N log2 N operations, where N is the length of the transform, and this usually makes calculation of the transform for every new data point computationally prohibitive. In this paper, we develop an algorithm to update the existing DFT to represent the new data series that results when a new signal point is received. Updating the DFT in this way uses less computational order by a factor of log2 N. The algorithm can be modified to work in the presence of data window functions. This is a considerable advantage, because windowing is often necessary to reduce edge effects that occur because the implicit periodicity of the Fourier transform is not exhibited by the real-world signal. Versions are developed in this paper for use with the boxcar window, the split triangular, Hanning, Hamming, and Blackman windows. Generalization of these results to 2D is also presented.

Cerebral Laterality in Color Information Processing.

ERIC Educational Resources Information Center

Berry, Louis H.

This study investigated the interaction between hemispheric specialization and pictorial recognition memory for pictures presented in three different color modes: realistic color, non-realistic color, and monochrome (back and white). The study was also designed to confirm the efficacy of applying signal detection analysis to color recognition…

High resolution ultrasonic spectroscopy system for nondestructive evaluation

NASA Technical Reports Server (NTRS)

Chen, C. H.

1991-01-01

With increased demand for high resolution ultrasonic evaluation, computer based systems or work stations become essential. The ultrasonic spectroscopy method of nondestructive evaluation (NDE) was used to develop a high resolution ultrasonic inspection system supported by modern signal processing, pattern recognition, and neural network technologies. The basic system which was completed consists of a 386/20 MHz PC (IBM AT compatible), a pulser/receiver, a digital oscilloscope with serial and parallel communications to the computer, an immersion tank with motor control of X-Y axis movement, and the supporting software package, IUNDE, for interactive ultrasonic evaluation. Although the hardware components are commercially available, the software development is entirely original. By integrating signal processing, pattern recognition, maximum entropy spectral analysis, and artificial neural network functions into the system, many NDE tasks can be performed. The high resolution graphics capability provides visualization of complex NDE problems. The phase 3 efforts involve intensive marketing of the software package and collaborative work with industrial sectors.

A Portable and Autonomous Magnetic Detection Platform for Biosensing

PubMed Central

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

2009-01-01

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

Modeling the Interaction between Quinolinate and the Receptor for Advanced Glycation End Products (RAGE): Relevance for Early Neuropathological Processes

PubMed Central

Serratos, Iris N.; Castellanos, Pilar; Pastor, Nina; Millán-Pacheco, César; Rembao, Daniel; Pérez-Montfort, Ruy; Cabrera, Nallely; Reyes-Espinosa, Francisco; Díaz-Garrido, Paulina; López-Macay, Ambar; Martínez-Flores, Karina; López-Reyes, Alberto; Sánchez-García, Aurora; Cuevas, Elvis; Santamaria, Abel

2015-01-01

The receptor for advanced glycation end products (RAGE) is a pattern-recognition receptor involved in neurodegenerative and inflammatory disorders. RAGE induces cellular signaling upon binding to a variety of ligands. Evidence suggests that RAGE up-regulation is involved in quinolinate (QUIN)-induced toxicity. We investigated the QUIN-induced toxic events associated with early noxious responses, which might be linked to signaling cascades leading to cell death. The extent of early cellular damage caused by this receptor in the rat striatum was characterized by image processing methods. To document the direct interaction between QUIN and RAGE, we determined the binding constant (Kb) of RAGE (VC1 domain) with QUIN through a fluorescence assay. We modeled possible binding sites of QUIN to the VC1 domain for both rat and human RAGE. QUIN was found to bind at multiple sites to the VC1 dimer, each leading to particular mechanistic scenarios for the signaling evoked by QUIN binding, some of which directly alter RAGE oligomerization. This work contributes to the understanding of the phenomenon of RAGE-QUIN recognition, leading to the modulation of RAGE function. PMID:25757085

Nonlinear Real-Time Optical Signal Processing

DTIC Science & Technology

1990-09-01

pattern recognition. Additional work concerns the relationship of parallel computation paradigms to optical computing and halftone screen techniques...paradigms to optical computing and halftone screen techniques for implementing general nonlinear functions. 3\\ 2 Research Progress This section...Vol. 23, No. 8, pp. 34-57, 1986. 2.4 Nonlinear Optical Processing with Halftones : Degradation and Compen- sation Models This paper is concerned with

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS).

PubMed

Latifoğlu, Fatma; Polat, Kemal; Kara, Sadik; Güneş, Salih

2008-02-01

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

Auditory orientation in crickets: Pattern recognition controls reactive steering

NASA Astrophysics Data System (ADS)

Poulet, James F. A.; Hedwig, Berthold

2005-10-01

Many groups of insects are specialists in exploiting sensory cues to locate food resources or conspecifics. To achieve orientation, bees and ants analyze the polarization pattern of the sky, male moths orient along the females' odor plume, and cicadas, grasshoppers, and crickets use acoustic signals to locate singing conspecifics. In comparison with olfactory and visual orientation, where learning is involved, auditory processing underlying orientation in insects appears to be more hardwired and genetically determined. In each of these examples, however, orientation requires a recognition process identifying the crucial sensory pattern to interact with a localization process directing the animal's locomotor activity. Here, we characterize this interaction. Using a sensitive trackball system, we show that, during cricket auditory behavior, the recognition process that is tuned toward the species-specific song pattern controls the amplitude of auditory evoked steering responses. Females perform small reactive steering movements toward any sound patterns. Hearing the male's calling song increases the gain of auditory steering within 2-5 s, and the animals even steer toward nonattractive sound patterns inserted into the speciesspecific pattern. This gain control mechanism in the auditory-to-motor pathway allows crickets to pursue species-specific sound patterns temporarily corrupted by environmental factors and may reflect the organization of recognition and localization networks in insects. localization | phonotaxis

Relating Memory To Functional Performance In Normal Aging to Dementia Using Hierarchical Bayesian Cognitive Processing Models

PubMed Central

Shankle, William R.; Pooley, James P.; Steyvers, Mark; Hara, Junko; Mangrola, Tushar; Reisberg, Barry; Lee, Michael D.

2012-01-01

Determining how cognition affects functional abilities is important in Alzheimer’s disease and related disorders (ADRD). 280 patients (normal or ADRD) received a total of 1,514 assessments using the Functional Assessment Staging Test (FAST) procedure and the MCI Screen (MCIS). A hierarchical Bayesian cognitive processing (HBCP) model was created by embedding a signal detection theory (SDT) model of the MCIS delayed recognition memory task into a hierarchical Bayesian framework. The SDT model used latent parameters of discriminability (memory process) and response bias (executive function) to predict, simultaneously, recognition memory performance for each patient and each FAST severity group. The observed recognition memory data did not distinguish the six FAST severity stages, but the latent parameters completely separated them. The latent parameters were also used successfully to transform the ordinal FAST measure into a continuous measure reflecting the underlying continuum of functional severity. HBCP models applied to recognition memory data from clinical practice settings accurately translated a latent measure of cognition to a continuous measure of functional severity for both individuals and FAST groups. Such a translation links two levels of brain information processing, and may enable more accurate correlations with other levels, such as those characterized by biomarkers. PMID:22407225

Defense Against Pathogens: Structural Insights into the Mechanism of Chitin Induced Activation of Innate Immunity.

PubMed

Squeglia, Flavia; Berisio, Rita; Shibuya, Naoto; Kaku, Hanae

2017-11-24

Pattern recognition receptors on the plant cell surface mediate the recognition of microbe-associated molecular patterns, in a process which activates downstream immune signaling. These receptors are plasma membrane-localized kinases which need to be autophosphorylated to activate downstream responses. Perception of attacks from fungi occurs through recognition of chitin, a polymer of an N-acetylglucosamine which is a characteristic component of the cell walls of fungi. This process is regulated in Arabidopsis by chitin elicitor receptor kinase CERK1. A more complex process characterizes rice, in which regulation of chitin perception is operated by a complex composed of OsCERK1, a homolog of CERK1, and the chitin elicitor binding protein OsCEBiP. Recent literature has provided a mechanistic description of the complex regulation of activation of innate immunity in rice and an advance in the structural description of molecular players involved in this process. This review describes the current status of the understanding of molecular events involved in innate immunity activation in rice. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Relating the Content and Confidence of Recognition Judgments

PubMed Central

Selmeczy, Diana; Dobbins, Ian G.

2014-01-01

The Remember/Know procedure, developed by Tulving (1985) to capture the distinction between the conscious correlates of episodic and semantic retrieval, has spurned considerable research and debate. However, only a handful of reports have examined the recognition content beyond this dichotomous simplification. To address this, we collected participants’ written justifications in support of ordinary old/new recognition decisions accompanied by confidence ratings using a 3-point scale (high/medium/low). Unlike prior research, we did not provide the participants with any descriptions of Remembering or Knowing and thus, if the justifications mapped well onto theory, they would do so spontaneously. Word frequency analysis (unigrams, bigrams, and trigrams), independent ratings, and machine learning techniques (Support Vector Machine - SVM) converged in demonstrating that the linguistic content of high and medium confidence recognition differs in a manner consistent with dual process theories of recognition. For example, the use of ‘I remember’, particularly when combined with temporal or perceptual information (e.g., ‘when’, ‘saw’, ‘distinctly’), was heavily associated with high confidence recognition. Conversely, participants also used the absence of remembering for personally distinctive materials as support for high confidence new reports (‘would have remembered’). Thus, participants afford a special status to the presence or absence of remembering and use this actively as a basis for high confidence during recognition judgments. Additionally, the pattern of classification successes and failures of a SVM was well anticipated by the Dual Process Signal Detection model of recognition and inconsistent with a single process, strictly unidimensional approach. “One might think that memory should have something to do with remembering, and remembering is a conscious experience.”(Tulving, 1985, p. 1) PMID:23957366

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.

Recognition of neural brain activity patterns correlated with complex motor activity

NASA Astrophysics Data System (ADS)

Kurkin, Semen; Musatov, Vyacheslav Yu.; Runnova, Anastasia E.; Grubov, Vadim V.; Efremova, Tatyana Yu.; Zhuravlev, Maxim O.

2018-04-01

In this paper, based on the apparatus of artificial neural networks, a technique for recognizing and classifying patterns corresponding to imaginary movements on electroencephalograms (EEGs) obtained from a group of untrained subjects was developed. The works on the selection of the optimal type, topology, training algorithms and neural network parameters were carried out from the point of view of the most accurate and fast recognition and classification of patterns on multi-channel EEGs associated with the imagination of movements. The influence of the number and choice of the analyzed channels of a multichannel EEG on the quality of recognition of imaginary movements was also studied, and optimal configurations of electrode arrangements were obtained. The effect of pre-processing of EEG signals is analyzed from the point of view of improving the accuracy of recognition of imaginary movements.

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

PubMed

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

2008-10-01

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

A fast, robust pattern recognition asystem for low light level image registration and its application to retinal imaging

NASA Astrophysics Data System (ADS)

Wade, Alex Robert; Fitzke, Frederick W.

1998-08-01

We describe an image processing system which we have developed to align autofluorescence and high-magnification images taken with a laser scanning ophthalmoscope. The low signal to noise ratio of these images makes pattern recognition a non-trivial task. However, once n images are aligned and averaged, the noise levels drop by a factor of n and the image quality is improved. We include examples of autofluorescence images and images of the cone photoreceptor mosaic obtained using this system.

Voice technology and BBN

NASA Technical Reports Server (NTRS)

Wolf, Jared J.

1977-01-01

The following research was discussed: (1) speech signal processing; (2) automatic speech recognition; (3) continuous speech understanding; (4) speaker recognition; (5) speech compression; (6) subjective and objective evaluation of speech communication system; (7) measurement of the intelligibility and quality of speech when degraded by noise or other masking stimuli; (8) speech synthesis; (9) instructional aids for second-language learning and for training of the deaf; and (10) investigation of speech correlates of psychological stress. Experimental psychology, control systems, and human factors engineering, which are often relevant to the proper design and operation of speech systems are described.

Perceptual organization of speech signals by children with and without dyslexia

PubMed Central

Nittrouer, Susan; Lowenstein, Joanna H.

2013-01-01

Developmental dyslexia is a condition in which children encounter difficulty learning to read in spite of adequate instruction. Although considerable effort has been expended trying to identify the source of the problem, no single solution has been agreed upon. The current study explored a new hypothesis, that developmental dyslexia may be due to faulty perceptual organization of linguistically relevant sensory input. To test that idea, sentence-length speech signals were processed to create either sine-wave or noise-vocoded analogs. Seventy children between 8 and 11 years of age, with and without dyslexia participated. Children with dyslexia were selected to have phonological awareness deficits, although those without such deficits were retained in the study. The processed sentences were presented for recognition, and measures of reading, phonological awareness, and expressive vocabulary were collected. Results showed that children with dyslexia, regardless of phonological subtype, had poorer recognition scores than children without dyslexia for both kinds of degraded sentences. Older children with dyslexia recognized the sine-wave sentences better than younger children with dyslexia, but no such effect of age was found for the vocoded materials. Recognition scores were used as predictor variables in regression analyses with reading, phonological awareness, and vocabulary measures used as dependent variables. Scores for both sorts of sentence materials were strong predictors of performance on all three dependent measures when all children were included, but only performance for the sine-wave materials explained significant proportions of variance when only children with dyslexia were included. Finally, matching young, typical readers with older children with dyslexia on reading abilities did not mitigate the group difference in recognition of vocoded sentences. Conclusions were that children with dyslexia have difficulty organizing linguistically relevant sensory input, but learn to do so for the structure preserved by sine-wave signals before they do so for other sorts of signal structure. These perceptual organization deficits could account for difficulties acquiring refined linguistic representations, including those of a phonological nature, although ramifications are different across affected children. PMID:23702597

Perceptual organization of speech signals by children with and without dyslexia.

PubMed

Nittrouer, Susan; Lowenstein, Joanna H

2013-08-01

Developmental dyslexia is a condition in which children encounter difficulty learning to read in spite of adequate instruction. Although considerable effort has been expended trying to identify the source of the problem, no single solution has been agreed upon. The current study explored a new hypothesis, that developmental dyslexia may be due to faulty perceptual organization of linguistically relevant sensory input. To test that idea, sentence-length speech signals were processed to create either sine-wave or noise-vocoded analogs. Seventy children between 8 and 11 years of age, with and without dyslexia participated. Children with dyslexia were selected to have phonological awareness deficits, although those without such deficits were retained in the study. The processed sentences were presented for recognition, and measures of reading, phonological awareness, and expressive vocabulary were collected. Results showed that children with dyslexia, regardless of phonological subtype, had poorer recognition scores than children without dyslexia for both kinds of degraded sentences. Older children with dyslexia recognized the sine-wave sentences better than younger children with dyslexia, but no such effect of age was found for the vocoded materials. Recognition scores were used as predictor variables in regression analyses with reading, phonological awareness, and vocabulary measures used as dependent variables. Scores for both sorts of sentence materials were strong predictors of performance on all three dependent measures when all children were included, but only performance for the sine-wave materials explained significant proportions of variance when only children with dyslexia were included. Finally, matching young, typical readers with older children with dyslexia on reading abilities did not mitigate the group difference in recognition of vocoded sentences. Conclusions were that children with dyslexia have difficulty organizing linguistically relevant sensory input, but learn to do so for the structure preserved by sine-wave signals before they do so for other sorts of signal structure. These perceptual organization deficits could account for difficulties acquiring refined linguistic representations, including those of a phonological nature, although ramifications are different across affected children. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Effect of Asymmetrical Signal Degradation on Binaural Speech Recognition in Children and Adults.

ERIC Educational Resources Information Center

Rothpletz, Ann M.; Tharpe, Anne Marie; Grantham, D. Wesley

2004-01-01

To determine the effect of asymmetrical signal degradation on binaural speech recognition, 28 children and 14 adults were administered a sentence recognition task amidst multitalker babble. There were 3 listening conditions: (a) monaural, with mild degradation in 1 ear; (b) binaural, with mild degradation in both ears (symmetric degradation); and…

The role of multimodal signals in species recognition between tree-killing bark beetles in a narrow sympatric zone.

Treesearch

Deepa S. Pureswaran; Richard W. Hofstetter; Brian Sullivan; Kristen A. Potter

2016-01-01

When related species coexist, selection pressure should favor evolution of species recognition mechanisms to prevent interspecific pairing and wasteful reproductive encounters. We investigated the potential role of pheromone and acoustic signals in species recognition between two species of tree-killing bark beetles, the southern pine beetle, Dendroctonus frontalis...

Generating Control Commands From Gestures Sensed by EMG

NASA Technical Reports Server (NTRS)

Wheeler, Kevin R.; Jorgensen, Charles

2006-01-01

An effort is under way to develop noninvasive neuro-electric interfaces through which human operators could control systems as diverse as simple mechanical devices, computers, aircraft, and even spacecraft. The basic idea is to use electrodes on the surface of the skin to acquire electromyographic (EMG) signals associated with gestures, digitize and process the EMG signals to recognize the gestures, and generate digital commands to perform the actions signified by the gestures. In an experimental prototype of such an interface, the EMG signals associated with hand gestures are acquired by use of several pairs of electrodes mounted in sleeves on a subject s forearm (see figure). The EMG signals are sampled and digitized. The resulting time-series data are fed as input to pattern-recognition software that has been trained to distinguish gestures from a given gesture set. The software implements, among other things, hidden Markov models, which are used to recognize the gestures as they are being performed in real time. Thus far, two experiments have been performed on the prototype interface to demonstrate feasibility: an experiment in synthesizing the output of a joystick and an experiment in synthesizing the output of a computer or typewriter keyboard. In the joystick experiment, the EMG signals were processed into joystick commands for a realistic flight simulator for an airplane. The acting pilot reached out into the air, grabbed an imaginary joystick, and pretended to manipulate the joystick to achieve left and right banks and up and down pitches of the simulated airplane. In the keyboard experiment, the subject pretended to type on a numerical keypad, and the EMG signals were processed into keystrokes. The results of the experiments demonstrate the basic feasibility of this method while indicating the need for further research to reduce the incidence of errors (including confusion among gestures). Topics that must be addressed include the numbers and arrangements of electrodes needed to acquire sufficient data; refinements in the acquisition, filtering, and digitization of EMG signals; and methods of training the pattern- recognition software. The joystick and keyboard simulations were chosen for the initial experiments because they are familiar to many computer users. It is anticipated that, ultimately, interfaces would utilize EMG signals associated with movements more nearly natural than those associated with joysticks or keyboards. Future versions of the pattern-recognition software are planned to be capable of adapting to the preferences and day-today variations in EMG outputs of individual users; this capability for adaptation would also make it possible to select gestures that, to a given user, feel the most nearly natural for generating control signals for a given task (provided that there are enough properly positioned electrodes to acquire the EMG signals from the muscles involved in the gestures).

Testosterone reduces conscious detection of signals serving social correction: implications for antisocial behavior.

PubMed

van Honk, Jack; Schutter, Dennis J L G

2007-08-01

Elevated levels of testosterone have repeatedly been associated with antisocial behavior, but the psychobiological mechanisms underlying this effect are unknown. However, testosterone is evidently capable of altering the processing of facial threat, and facial signals of fear and anger serve sociality through their higher-level empathy-provoking and socially corrective properties. We investigated the hypothesis that testosterone predisposes people to antisocial behavior by reducing conscious recognition of facial threat. In a within-subjects design, testosterone (0.5 mg) or placebo was administered to 16 female volunteers. Afterward, a task with morphed stimuli indexed their sensitivity for consciously recognizing the facial expressions of threat (disgust, fear, and anger) and nonthreat (surprise, sadness, and happiness). Testosterone induced a significant reduction in the conscious recognition of facial threat overall. Separate analyses for the three categories of threat faces indicated that this effect was reliable for angry facial expressions exclusively. This testosterone-induced impairment in the conscious detection of the socially corrective facial signal of anger may predispose individuals to antisocial behavior.

Multimodal Neuroelectric Interface Development

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Wheeler, Kevin R.; Jorgensen, Charles C.; Totah, Joseph (Technical Monitor)

2001-01-01

This project aims to improve performance of NASA missions by developing multimodal neuroelectric technologies for augmented human-system interaction. Neuroelectric technologies will add completely new modes of interaction that operate in parallel with keyboards, speech, or other manual controls, thereby increasing the bandwidth of human-system interaction. We recently demonstrated the feasibility of real-time electromyographic (EMG) pattern recognition for a direct neuroelectric human-computer interface. We recorded EMG signals from an elastic sleeve with dry electrodes, while a human subject performed a range of discrete gestures. A machine-teaming algorithm was trained to recognize the EMG patterns associated with the gestures and map them to control signals. Successful applications now include piloting two Class 4 aircraft simulations (F-15 and 757) and entering data with a "virtual" numeric keyboard. Current research focuses on on-line adaptation of EMG sensing and processing and recognition of continuous gestures. We are also extending this on-line pattern recognition methodology to electroencephalographic (EEG) signals. This will allow us to bypass muscle activity and draw control signals directly from the human brain. Our system can reliably detect P-rhythm (a periodic EEG signal from motor cortex in the 10 Hz range) with a lightweight headset containing saline-soaked sponge electrodes. The data show that EEG p-rhythm can be modulated by real and imaginary motions. Current research focuses on using biofeedback to train of human subjects to modulate EEG rhythms on demand, and to examine interactions of EEG-based control with EMG-based and manual control. Viewgraphs on these neuroelectric technologies are also included.

Signal Processing for Radar Target Tracking and Identification

DTIC Science & Technology

1996-12-01

Computes the likelihood for various potential jump moves. 12. matrix_mult.m: Parallel implementation of linear algebra ... Elementary Lineary Algebra with Applications, John Wiley k Sons, Inc., New York, 1987. [9] A. K. Bhattacharyya, and D. L. Sengupta, Radar Cross...Miller, ’Target Tracking and Recognition Using Jump-Diffusion Processes," ARO’s 11th Army Conf. on Applied Mathemat- ics and Computing, June 8-11

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

DTIC Science & Technology

2014-09-30

repeating pulse-like signals were investigated. Software prototypes were developed and integrated into distinct streams of reseach ; projects...to study complex sound archives spanning large spatial and temporal scales. A new post processing method for detection and classifcation was also...false positive rates. HK-ANN was successfully tested for a large minke whale dataset, but could easily be used on other signal types. Various

Physical signals for protein-DNA recognition

NASA Astrophysics Data System (ADS)

Cao, Xiao-Qin; Zeng, Jia; Yan, Hong

2009-09-01

This paper discovers consensus physical signals around eukaryotic splice sites, transcription start sites, and replication origin start and end sites on a genome-wide scale based on their DNA flexibility profiles calculated by three different flexibility models. These salient physical signals are localized highly rigid and flexible DNAs, which may play important roles in protein-DNA recognition by the sliding search mechanism. The found physical signals lead us to a detailed hypothetical view of the search process in which a DNA-binding protein first finds a genomic region close to the target site from an arbitrary starting location by three-dimensional (3D) hopping and intersegment transfer mechanisms for long distances, and subsequently uses the one-dimensional (1D) sliding mechanism facilitated by the localized highly rigid DNAs to accurately locate the target flexible binding site within 30 bp (base pair) short distances. Guided by these physical signals, DNA-binding proteins rapidly search the entire genome to recognize a specific target site from the 3D to 1D pathway. Our findings also show that current promoter prediction programs (PPPs) based on DNA physical properties may suffer from lots of false positives because other functional sites such as splice sites and replication origins have similar physical signals as promoters do.

Autophagic control of RLR signaling

PubMed Central

Tal, Michal Caspi; Iwasaki, Akiko

2013-01-01

Innate immunity to viral infection is initiated within the infected cells through the recognition of unique viral signatures by pattern recognition receptors (PRRs) that mediate the induction of potent antiviral factor, type I interferons (IFNs). Infection with RNA viruses is recognized by the members of the retinoic acid inducible gene I (RIG-I)-like receptor (RLR) family in the cytosol. Our recent study demonstrates that IFN production in response to RNA viral ligands is increased in the absence of autophagy. The process of autophagy functions as an internal clean-up crew within the cell, shuttling damaged cellular organelles and long-lived proteins to the lysosomes for degradation. Our data show that the absence of autophagy leads to the amplification of RLR signaling in two ways. First, in the absence of autophagy, mitochondria accumulate within the cell leading to the build up of mitochondrial associated protein, IPS-1, a key signaling protein for RLRs. Second, damaged mitochondria that are not degraded in the absence of autophagy provide a source of reactive oxygen species (ROS), which amplify RLR signaling in Atg5 knockout cells. Our study provides the first link between ROS and cytosolic signaling mediated by the RLRs, and suggests the importance of autophagy in the regulation of signaling emanating from mitochondria. PMID:19571662

The bag-of-frames approach to audio pattern recognition: a sufficient model for urban soundscapes but not for polyphonic music.

PubMed

Aucouturier, Jean-Julien; Defreville, Boris; Pachet, François

2007-08-01

The "bag-of-frames" approach (BOF) to audio pattern recognition represents signals as the long-term statistical distribution of their local spectral features. This approach has proved nearly optimal for simulating the auditory perception of natural and human environments (or soundscapes), and is also the most predominent paradigm to extract high-level descriptions from music signals. However, recent studies show that, contrary to its application to soundscape signals, BOF only provides limited performance when applied to polyphonic music signals. This paper proposes to explicitly examine the difference between urban soundscapes and polyphonic music with respect to their modeling with the BOF approach. First, the application of the same measure of acoustic similarity on both soundscape and music data sets confirms that the BOF approach can model soundscapes to near-perfect precision, and exhibits none of the limitations observed in the music data set. Second, the modification of this measure by two custom homogeneity transforms reveals critical differences in the temporal and statistical structure of the typical frame distribution of each type of signal. Such differences may explain the uneven performance of BOF algorithms on soundscapes and music signals, and suggest that their human perception rely on cognitive processes of a different nature.

Neural Global Pattern Similarity Underlies True and False Memories.

PubMed

Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui

2016-06-22

The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.

Visual information processing; Proceedings of the Meeting, Orlando, FL, Apr. 20-22, 1992

NASA Technical Reports Server (NTRS)

Huck, Friedrich O. (Editor); Juday, Richard D. (Editor)

1992-01-01

Topics discussed in these proceedings include nonlinear processing and communications; feature extraction and recognition; image gathering, interpolation, and restoration; image coding; and wavelet transform. Papers are presented on noise reduction for signals from nonlinear systems; driving nonlinear systems with chaotic signals; edge detection and image segmentation of space scenes using fractal analyses; a vision system for telerobotic operation; a fidelity analysis of image gathering, interpolation, and restoration; restoration of images degraded by motion; and information, entropy, and fidelity in visual communication. Attention is also given to image coding methods and their assessment, hybrid JPEG/recursive block coding of images, modified wavelets that accommodate causality, modified wavelet transform for unbiased frequency representation, and continuous wavelet transform of one-dimensional signals by Fourier filtering.

Sub-Audible Speech Recognition Based upon Electromyographic Signals

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C. (Inventor); Agabon, Shane T. (Inventor); Lee, Diana D. (Inventor)

2012-01-01

Method and system for processing and identifying a sub-audible signal formed by a source of sub-audible sounds. Sequences of samples of sub-audible sound patterns ("SASPs") for known words/phrases in a selected database are received for overlapping time intervals, and Signal Processing Transforms ("SPTs") are formed for each sample, as part of a matrix of entry values. The matrix is decomposed into contiguous, non-overlapping two-dimensional cells of entries, and neural net analysis is applied to estimate reference sets of weight coefficients that provide sums with optimal matches to reference sets of values. The reference sets of weight coefficients are used to determine a correspondence between a new (unknown) word/phrase and a word/phrase in the database.

Vector coding of wavelet-transformed images

NASA Astrophysics Data System (ADS)

Zhou, Jun; Zhi, Cheng; Zhou, Yuanhua

1998-09-01

Wavelet, as a brand new tool in signal processing, has got broad recognition. Using wavelet transform, we can get octave divided frequency band with specific orientation which combines well with the properties of Human Visual System. In this paper, we discuss the classified vector quantization method for multiresolution represented image.

Neuromorphic Optical Signal Processing and Image Understanding for Automated Target Recognition

DTIC Science & Technology

1989-12-01

34 Stochastic Learning Machine " Neuromorphic Target Identification * Cognitive Networks 3. Conclusions ..... ................ .. 12 4. Publications...16 5. References ...... ................... . 17 6. Appendices ....... .................. 18 I. Optoelectronic Neural Networks and...Learning Machines. II. Stochastic Optical Learning Machine. III. Learning Network for Extrapolation AccesFon For and Radar Target Identification

The Effectiveness of Clear Speech as a Masker

ERIC Educational Resources Information Center

Calandruccio, Lauren; Van Engen, Kristin; Dhar, Sumitrajit; Bradlow, Ann R.

2010-01-01

Purpose: It is established that speaking clearly is an effective means of enhancing intelligibility. Because any signal-processing scheme modeled after known acoustic-phonetic features of clear speech will likely affect both target and competing speech, it is important to understand how speech recognition is affected when a competing speech signal…

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

PubMed Central

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

2016-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches. PMID:26927111

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning.

PubMed

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

2016-02-25

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches.

SAR processing using SHARC signal processing systems

NASA Astrophysics Data System (ADS)

Huxtable, Barton D.; Jackson, Christopher R.; Skaron, Steve A.

1998-09-01

Synthetic aperture radar (SAR) is uniquely suited to help solve the Search and Rescue problem since it can be utilized either day or night and through both dense fog or thick cloud cover. Other papers in this session, and in this session in 1997, describe the various SAR image processing algorithms that are being developed and evaluated within the Search and Rescue Program. All of these approaches to using SAR data require substantial amounts of digital signal processing: for the SAR image formation, and possibly for the subsequent image processing. In recognition of the demanding processing that will be required for an operational Search and Rescue Data Processing System (SARDPS), NASA/Goddard Space Flight Center and NASA/Stennis Space Center are conducting a technology demonstration utilizing SHARC multi-chip modules from Boeing to perform SAR image formation processing.

Automated speech understanding: the next generation

NASA Astrophysics Data System (ADS)

Picone, J.; Ebel, W. J.; Deshmukh, N.

1995-04-01

Modern speech understanding systems merge interdisciplinary technologies from Signal Processing, Pattern Recognition, Natural Language, and Linguistics into a unified statistical framework. These systems, which have applications in a wide range of signal processing problems, represent a revolution in Digital Signal Processing (DSP). Once a field dominated by vector-oriented processors and linear algebra-based mathematics, the current generation of DSP-based systems rely on sophisticated statistical models implemented using a complex software paradigm. Such systems are now capable of understanding continuous speech input for vocabularies of several thousand words in operational environments. The current generation of deployed systems, based on small vocabularies of isolated words, will soon be replaced by a new technology offering natural language access to vast information resources such as the Internet, and provide completely automated voice interfaces for mundane tasks such as travel planning and directory assistance.

Exploring multiple feature combination strategies with a recurrent neural network architecture for off-line handwriting recognition

NASA Astrophysics Data System (ADS)

Mioulet, L.; Bideault, G.; Chatelain, C.; Paquet, T.; Brunessaux, S.

2015-01-01

The BLSTM-CTC is a novel recurrent neural network architecture that has outperformed previous state of the art algorithms in tasks such as speech recognition or handwriting recognition. It has the ability to process long term dependencies in temporal signals in order to label unsegmented data. This paper describes different ways of combining features using a BLSTM-CTC architecture. Not only do we explore the low level combination (feature space combination) but we also explore high level combination (decoding combination) and mid-level (internal system representation combination). The results are compared on the RIMES word database. Our results show that the low level combination works best, thanks to the powerful data modeling of the LSTM neurons.

The process of spoken word recognition in the face of signal degradation.

PubMed

Farris-Trimble, Ashley; McMurray, Bob; Cigrand, Nicole; Tomblin, J Bruce

2014-02-01

Though much is known about how words are recognized, little research has focused on how a degraded signal affects the fine-grained temporal aspects of real-time word recognition. The perception of degraded speech was examined in two populations with the goal of describing the time course of word recognition and lexical competition. Thirty-three postlingually deafened cochlear implant (CI) users and 57 normal hearing (NH) adults (16 in a CI-simulation condition) participated in a visual world paradigm eye-tracking task in which their fixations to a set of phonologically related items were monitored as they heard one item being named. Each degraded-speech group was compared with a set of age-matched NH participants listening to unfiltered speech. CI users and the simulation group showed a delay in activation relative to the NH listeners, and there is weak evidence that the CI users showed differences in the degree of peak and late competitor activation. In general, though, the degraded-speech groups behaved statistically similarly with respect to activation levels. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Models of Recognition, Repetition Priming, and Fluency : Exploring a New Framework

ERIC Educational Resources Information Center

Berry, Christopher J.; Shanks, David R.; Speekenbrink, Maarten; Henson, Richard N. A.

2012-01-01

We present a new modeling framework for recognition memory and repetition priming based on signal detection theory. We use this framework to specify and test the predictions of 4 models: (a) a single-system (SS) model, in which one continuous memory signal drives recognition and priming; (b) a multiple-systems-1 (MS1) model, in which completely…

A unique dual recognition hairpin probe mediated fluorescence amplification method for sensitive detection of uracil-DNA glycosylase and endonuclease IV activities.

PubMed

Wu, Yushu; Yan, Ping; Xu, Xiaowen; Jiang, Wei

2016-03-07

Uracil-DNA glycosylase (UDG) and endonuclease IV (Endo IV) play cooperative roles in uracil base-excision repair (UBER) and inactivity of either will interrupt the UBER to cause disease. Detection of UDG and Endo IV activities is crucial to evaluate the UBER process in fundamental research and diagnostic application. Here, a unique dual recognition hairpin probe mediated fluorescence amplification method was developed for sensitively and selectively detecting UDG and Endo IV activities. For detecting UDG activity, the uracil base in the probe was excised by the target enzyme to generate an apurinic/apyrimidinic (AP) site, achieving the UDG recognition. Then, the AP site was cleaved by a tool enzyme Endo IV, releasing a primer to trigger rolling circle amplification (RCA) reaction. Finally, the RCA reaction produced numerous repeated G-quadruplex sequences, which interacted with N-methyl-mesoporphyrin IX to generate an enhanced fluorescence signal. Alternatively, for detecting Endo IV activity, the uracil base in the probe was first converted into an AP site by a tool enzyme UDG. Next, the AP site was cleaved by the target enzyme, achieving the Endo IV recognition. The signal was then generated and amplified in the same way as those in the UDG activity assay. The detection limits were as low as 0.00017 U mL(-1) for UDG and 0.11 U mL(-1) for Endo IV, respectively. Moreover, UDG and Endo IV can be well distinguished from their analogs. This method is beneficial for properly evaluating the UBER process in function studies and disease prognoses.

Stable Odor Recognition by a neuro-adaptive Electronic Nose

PubMed Central

Martinelli, Eugenio; Magna, Gabriele; Polese, Davide; Vergara, Alexander; Schild, Detlev; Di Natale, Corrado

2015-01-01

Sensitivity, selectivity and stability are decisive properties of sensors. In chemical gas sensors odor recognition can be severely compromised by poor signal stability, particularly in real life applications where the sensors are exposed to unpredictable sequences of odors under changing external conditions. Although olfactory receptor neurons in the nose face similar stimulus sequences under likewise changing conditions, odor recognition is very stable and odorants can be reliably identified independently from past odor perception. We postulate that appropriate pre-processing of the output signals of chemical sensors substantially contributes to the stability of odor recognition, in spite of marked sensor instabilities. To investigate this hypothesis, we use an adaptive, unsupervised neural network inspired by the glomerular input circuitry of the olfactory bulb. Essentially the model reduces the effect of the sensors’ instabilities by utilizing them via an adaptive multicompartment feed-forward inhibition. We collected and analyzed responses of a 4 × 4 gas sensor array to a number of volatile compounds applied over a period of 18 months, whereby every sensor was sampled episodically. The network conferred excellent stability to the compounds’ identification and was clearly superior over standard classifiers, even when one of the sensors exhibited random fluctuations or stopped working at all. PMID:26043043

Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface.

PubMed

Chang, G C; Kang, W J; Luh, J J; Cheng, C K; Lai, J S; Chen, J J; Kuo, T S

1996-10-01

The purpose of this study was to develop a real-time electromyogram (EMG) discrimination system to provide control commands for man-machine interface applications. A host computer with a plug-in data acquisition and processing board containing a TMS320 C31 floating-point digital signal processor was used to attain real-time EMG classification. Two-channel EMG signals were collected by two pairs of surface electrodes located bilaterally between the sternocleidomastoid and the upper trapezius. Five motions of the neck and shoulders were discriminated for each subject. The zero-crossing rate was employed to detect the onset of muscle contraction. The cepstral coefficients, derived from autoregressive coefficients and estimated by a recursive least square algorithm, were used as the recognition features. These features were then discriminated using a modified maximum likelihood distance classifier. The total response time of this EMG discrimination system was achieved about within 0.17 s. Four able bodied and two C5/6 quadriplegic subjects took part in the experiment, and achieved 95% mean recognition rate in discrimination between the five specific motions. The response time and the reliability of recognition indicate that this system has the potential to discriminate body motions for man-machine interface applications.

The Tandem CARDs of NOD2: Intramolecular Interactions and Recognition of RIP2

PubMed Central

Fridh, Veronica; Rittinger, Katrin

2012-01-01

Caspase recruitment domains (CARDs) are homotypic protein interaction modules that link the stimulus-dependent assembly of large signaling platforms such as inflammasomes to the activation of downstream effectors that often include caspases and kinases and thereby play an important role in the regulation of inflammatory and apoptotic signaling pathways. NOD2 belongs to the NOD-like (NLR) family of intracellular pattern recognition receptors (PRR) and induces activation of the NF-κB pathway in response to the recognition of bacterial components. This process requires the specific recognition of the CARD of the protein kinase RIP2 by the tandem CARDs of NOD2. Here we demonstrate that the tandem CARDs of NOD2 are engaged in an intramolecular interaction that is important for the structural stability of this region. Using a combination of ITC and pull-down experiments we identify distinct surface areas that are involved in the intramolecular tandem CARD interaction and the interaction with the downstream effector RIP2. Our findings indicate that while CARDa of NOD2 might be the primary binding partner of RIP2 the two CARDs of NOD2 do not act independently of one another but may cooperate to from a binding surface that is distinct from that of single CARDs. PMID:22470564

Talker and accent variability effects on spoken word recognition

NASA Astrophysics Data System (ADS)

Nyang, Edna E.; Rogers, Catherine L.; Nishi, Kanae

2003-04-01

A number of studies have shown that words in a list are recognized less accurately in noise and with longer response latencies when they are spoken by multiple talkers, rather than a single talker. These results have been interpreted as support for an exemplar-based model of speech perception, in which it is assumed that detailed information regarding the speaker's voice is preserved in memory and used in recognition, rather than being eliminated via normalization. In the present study, the effects of varying both accent and talker are investigated using lists of words spoken by (a) a single native English speaker, (b) six native English speakers, (c) three native English speakers and three Japanese-accented English speakers. Twelve /hVd/ words were mixed with multi-speaker babble at three signal-to-noise ratios (+10, +5, and 0 dB) to create the word lists. Native English-speaking listeners' percent-correct recognition for words produced by native English speakers across the three talker conditions (single talker native, multi-talker native, and multi-talker mixed native and non-native) and three signal-to-noise ratios will be compared to determine whether sources of speaker variability other than voice alone add to the processing demands imposed by simple (i.e., single accent) speaker variability in spoken word recognition.

Déjà vu experiences in healthy subjects are unrelated to laboratory tests of recollection and familiarity for word stimuli.

PubMed

O'Connor, Akira R; Moulin, Chris J A

2013-01-01

Recent neuropsychological and neuroscientific research suggests that people who experience more déjà vu display characteristic patterns in normal recognition memory. We conducted a large individual differences study (n = 206) to test these predictions using recollection and familiarity parameters recovered from a standard memory task. Participants reported déjà vu frequency and a number of its correlates, and completed a recognition memory task analogous to a Remember-Know procedure. The individual difference measures replicated an established correlation between déjà vu frequency and frequency of travel, and recognition performance showed well-established word frequency and accuracy effects. Contrary to predictions, no relationships were found between déjà vu frequency and recollection or familiarity memory parameters from the recognition test. We suggest that déjà vu in the healthy population reflects a mismatch between errant memory signaling and memory monitoring processes not easily characterized by standard recognition memory task performance.

Déjà vu experiences in healthy subjects are unrelated to laboratory tests of recollection and familiarity for word stimuli

PubMed Central

O’Connor, Akira R.; Moulin, Chris J. A.

2013-01-01

Recent neuropsychological and neuroscientific research suggests that people who experience more déjà vu display characteristic patterns in normal recognition memory. We conducted a large individual differences study (n = 206) to test these predictions using recollection and familiarity parameters recovered from a standard memory task. Participants reported déjà vu frequency and a number of its correlates, and completed a recognition memory task analogous to a Remember-Know procedure. The individual difference measures replicated an established correlation between déjà vu frequency and frequency of travel, and recognition performance showed well-established word frequency and accuracy effects. Contrary to predictions, no relationships were found between déjà vu frequency and recollection or familiarity memory parameters from the recognition test. We suggest that déjà vu in the healthy population reflects a mismatch between errant memory signaling and memory monitoring processes not easily characterized by standard recognition memory task performance. PMID:24409159

Hidden Markov models in automatic speech recognition

NASA Astrophysics Data System (ADS)

Wrzoskowicz, Adam

1993-11-01

This article describes a method for constructing an automatic speech recognition system based on hidden Markov models (HMMs). The author discusses the basic concepts of HMM theory and the application of these models to the analysis and recognition of speech signals. The author provides algorithms which make it possible to train the ASR system and recognize signals on the basis of distinct stochastic models of selected speech sound classes. The author describes the specific components of the system and the procedures used to model and recognize speech. The author discusses problems associated with the choice of optimal signal detection and parameterization characteristics and their effect on the performance of the system. The author presents different options for the choice of speech signal segments and their consequences for the ASR process. The author gives special attention to the use of lexical, syntactic, and semantic information for the purpose of improving the quality and efficiency of the system. The author also describes an ASR system developed by the Speech Acoustics Laboratory of the IBPT PAS. The author discusses the results of experiments on the effect of noise on the performance of the ASR system and describes methods of constructing HMM's designed to operate in a noisy environment. The author also describes a language for human-robot communications which was defined as a complex multilevel network from an HMM model of speech sounds geared towards Polish inflections. The author also added mandatory lexical and syntactic rules to the system for its communications vocabulary.

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

The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis.

PubMed

Jing, Lan; Guo, Dandan; Hu, Wenjie; Niu, Xiaofan

2017-03-11

Many plant pathogen secretory proteins are known to be elicitors or pathogenic factors,which play an important role in the host-pathogen interaction process. Bioinformatics approaches make possible the large scale prediction and analysis of secretory proteins from the Puccinia helianthi transcriptome. The internet-based software SignalP v4.1, TargetP v1.01, Big-PI predictor, TMHMM v2.0 and ProtComp v9.0 were utilized to predict the signal peptides and the signal peptide-dependent secreted proteins among the 35,286 ORFs of the P. helianthi transcriptome. 908 ORFs (accounting for 2.6% of the total proteins) were identified as putative secretory proteins containing signal peptides. The length of the majority of proteins ranged from 51 to 300 amino acids (aa), while the signal peptides were from 18 to 20 aa long. Signal peptidase I (SpI) cleavage sites were found in 463 of these putative secretory signal peptides. 55 proteins contained the lipoprotein signal peptide recognition site of signal peptidase II (SpII). Out of 908 secretory proteins, 581 (63.8%) have functions related to signal recognition and transduction, metabolism, transport and catabolism. Additionally, 143 putative secretory proteins were categorized into 27 functional groups based on Gene Ontology terms, including 14 groups in biological process, seven in cellular component, and six in molecular function. Gene ontology analysis of the secretory proteins revealed an enrichment of hydrolase activity. Pathway associations were established for 82 (9.0%) secretory proteins. A number of cell wall degrading enzymes and three homologous proteins specific to Phytophthora sojae effectors were also identified, which may be involved in the pathogenicity of the sunflower rust pathogen. This investigation proposes a new approach for identifying elicitors and pathogenic factors. The eventual identification and characterization of 908 extracellularly secreted proteins will advance our understanding of the molecular mechanisms of interactions between sunflower and rust pathogen and will enhance our ability to intervene in disease states.

What happens to the motor theory of perception when the motor system is damaged?

PubMed

Stasenko, Alena; Garcea, Frank E; Mahon, Bradford Z

2013-09-01

Motor theories of perception posit that motor information is necessary for successful recognition of actions. Perhaps the most well known of this class of proposals is the motor theory of speech perception, which argues that speech recognition is fundamentally a process of identifying the articulatory gestures (i.e. motor representations) that were used to produce the speech signal. Here we review neuropsychological evidence from patients with damage to the motor system, in the context of motor theories of perception applied to both manual actions and speech. Motor theories of perception predict that patients with motor impairments will have impairments for action recognition. Contrary to that prediction, the available neuropsychological evidence indicates that recognition can be spared despite profound impairments to production. These data falsify strong forms of the motor theory of perception, and frame new questions about the dynamical interactions that govern how information is exchanged between input and output systems.

What happens to the motor theory of perception when the motor system is damaged?

PubMed Central

Stasenko, Alena; Garcea, Frank E.; Mahon, Bradford Z.

2016-01-01

Motor theories of perception posit that motor information is necessary for successful recognition of actions. Perhaps the most well known of this class of proposals is the motor theory of speech perception, which argues that speech recognition is fundamentally a process of identifying the articulatory gestures (i.e. motor representations) that were used to produce the speech signal. Here we review neuropsychological evidence from patients with damage to the motor system, in the context of motor theories of perception applied to both manual actions and speech. Motor theories of perception predict that patients with motor impairments will have impairments for action recognition. Contrary to that prediction, the available neuropsychological evidence indicates that recognition can be spared despite profound impairments to production. These data falsify strong forms of the motor theory of perception, and frame new questions about the dynamical interactions that govern how information is exchanged between input and output systems. PMID:26823687

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

Oxytocin increases bias, but not accuracy, in face recognition line-ups.

PubMed

Bate, Sarah; Bennetts, Rachel; Parris, Benjamin A; Bindemann, Markus; Udale, Robert; Bussunt, Amanda

2015-07-01

Previous work indicates that intranasal inhalation of oxytocin improves face recognition skills, raising the possibility that it may be used in security settings. However, it is unclear whether oxytocin directly acts upon the core face-processing system itself or indirectly improves face recognition via affective or social salience mechanisms. In a double-blind procedure, 60 participants received either an oxytocin or placebo nasal spray before completing the One-in-Ten task-a standardized test of unfamiliar face recognition containing target-present and target-absent line-ups. Participants in the oxytocin condition outperformed those in the placebo condition on target-present trials, yet were more likely to make false-positive errors on target-absent trials. Signal detection analyses indicated that oxytocin induced a more liberal response bias, rather than increasing accuracy per se. These findings support a social salience account of the effects of oxytocin on face recognition and indicate that oxytocin may impede face recognition in certain scenarios. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Real-time object recognition in multidimensional images based on joined extended structural tensor and higher-order tensor decomposition methods

NASA Astrophysics Data System (ADS)

Cyganek, Boguslaw; Smolka, Bogdan

2015-02-01

In this paper a system for real-time recognition of objects in multidimensional video signals is proposed. Object recognition is done by pattern projection into the tensor subspaces obtained from the factorization of the signal tensors representing the input signal. However, instead of taking only the intensity signal the novelty of this paper is first to build the Extended Structural Tensor representation from the intensity signal that conveys information on signal intensities, as well as on higher-order statistics of the input signals. This way the higher-order input pattern tensors are built from the training samples. Then, the tensor subspaces are built based on the Higher-Order Singular Value Decomposition of the prototype pattern tensors. Finally, recognition relies on measurements of the distance of a test pattern projected into the tensor subspaces obtained from the training tensors. Due to high-dimensionality of the input data, tensor based methods require high memory and computational resources. However, recent achievements in the technology of the multi-core microprocessors and graphic cards allows real-time operation of the multidimensional methods as is shown and analyzed in this paper based on real examples of object detection in digital images.

A two-step recognition of signal sequences determines the translocation efficiency of proteins.

PubMed Central

Belin, D; Bost, S; Vassalli, J D; Strub, K

1996-01-01

The cytosolic and secreted, N-glycosylated, forms of plasminogen activator inhibitor-2 (PAI-2) are generated by facultative translocation. To study the molecular events that result in the bi-topological distribution of proteins, we determined in vitro the capacities of several signal sequences to bind the signal recognition particle (SRP) during targeting, and to promote vectorial transport of murine PAI-2 (mPAI-2). Interestingly, the six signal sequences we compared (mPAI-2 and three mutated derivatives thereof, ovalbumin and preprolactin) were found to have the differential activities in the two events. For example, the mPAI-2 signal sequence first binds SRP with moderate efficiency and secondly promotes the vectorial transport of only a fraction of the SRP-bound nascent chains. Our results provide evidence that the translocation efficiency of proteins can be controlled by the recognition of their signal sequences at two steps: during SRP-mediated targeting and during formation of a committed translocation complex. This second recognition may occur at several time points during the insertion/translocation step. In conclusion, signal sequences have a more complex structure than previously anticipated, allowing for multiple and independent interactions with the translocation machinery. Images PMID:8599930

A two-step recognition of signal sequences determines the translocation efficiency of proteins.

PubMed

Belin, D; Bost, S; Vassalli, J D; Strub, K

1996-02-01

The cytosolic and secreted, N-glycosylated, forms of plasminogen activator inhibitor-2 (PAI-2) are generated by facultative translocation. To study the molecular events that result in the bi-topological distribution of proteins, we determined in vitro the capacities of several signal sequences to bind the signal recognition particle (SRP) during targeting, and to promote vectorial transport of murine PAI-2 (mPAI-2). Interestingly, the six signal sequences we compared (mPAI-2 and three mutated derivatives thereof, ovalbumin and preprolactin) were found to have the differential activities in the two events. For example, the mPAI-2 signal sequence first binds SRP with moderate efficiency and secondly promotes the vectorial transport of only a fraction of the SRP-bound nascent chains. Our results provide evidence that the translocation efficiency of proteins can be controlled by the recognition of their signal sequences at two steps: during SRP-mediated targeting and during formation of a committed translocation complex. This second recognition may occur at several time points during the insertion/translocation step. In conclusion, signal sequences have a more complex structure than previously anticipated, allowing for multiple and independent interactions with the translocation machinery.

Destabilization of strigolactone receptor DWARF14 by binding of ligand and E3-ligase signaling effector DWARF3

PubMed Central

Zhao, Li-Hua; Zhou, X Edward; Yi, Wei; Wu, Zhongshan; Liu, Yue; Kang, Yanyong; Hou, Li; de Waal, Parker W; Li, Suling; Jiang, Yi; Scaffidi, Adrian; Flematti, Gavin R; Smith, Steven M; Lam, Vinh Q; Griffin, Patrick R; Wang, Yonghong; Li, Jiayang; Melcher, Karsten; Xu, H Eric

2015-01-01

Strigolactones (SLs) are endogenous hormones and exuded signaling molecules in plant responses to low levels of mineral nutrients. Key mediators of the SL signaling pathway in rice include the α/β-fold hydrolase DWARF 14 (D14) and the F-box component DWARF 3 (D3) of the ubiquitin ligase SCFD3 that mediate ligand-dependent degradation of downstream signaling repressors. One perplexing feature is that D14 not only functions as the SL receptor but is also an active enzyme that slowly hydrolyzes diverse natural and synthetic SLs including GR24, preventing the crystallization of a binary complex of D14 with an intact SL as well as the ternary D14/SL/D3 complex. Here we overcome these barriers to derive a structural model of D14 bound to intact GR24 and identify the interface that is required for GR24-mediated D14-D3 interaction. The mode of GR24-mediated signaling, including ligand recognition, hydrolysis by D14, and ligand-mediated D14-D3 interaction, is conserved in structurally diverse SLs. More importantly, D14 is destabilized upon the binding of ligands and D3, thus revealing an unusual mechanism of SL recognition and signaling, in which the hormone, the receptor, and the downstream effectors are systematically destabilized during the signal transduction process. PMID:26470846

Reconstruction of audio waveforms from spike trains of artificial cochlea models

PubMed Central

Zai, Anja T.; Bhargava, Saurabh; Mesgarani, Nima; Liu, Shih-Chii

2015-01-01

Spiking cochlea models describe the analog processing and spike generation process within the biological cochlea. Reconstructing the audio input from the artificial cochlea spikes is therefore useful for understanding the fidelity of the information preserved in the spikes. The reconstruction process is challenging particularly for spikes from the mixed signal (analog/digital) integrated circuit (IC) cochleas because of multiple non-linearities in the model and the additional variance caused by random transistor mismatch. This work proposes an offline method for reconstructing the audio input from spike responses of both a particular spike-based hardware model called the AEREAR2 cochlea and an equivalent software cochlea model. This method was previously used to reconstruct the auditory stimulus based on the peri-stimulus histogram of spike responses recorded in the ferret auditory cortex. The reconstructed audio from the hardware cochlea is evaluated against an analogous software model using objective measures of speech quality and intelligibility; and further tested in a word recognition task. The reconstructed audio under low signal-to-noise (SNR) conditions (SNR < –5 dB) gives a better classification performance than the original SNR input in this word recognition task. PMID:26528113

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

PubMed

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

1993-01-01

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

Problems Associated with Statistical Pattern Recognition of Acoustic Emission Signals in a Compact Tension Fatigue Specimen

NASA Technical Reports Server (NTRS)

Hinton, Yolanda L.

1999-01-01

Acoustic emission (AE) data were acquired during fatigue testing of an aluminum 2024-T4 compact tension specimen using a commercially available AE system. AE signals from crack extension were identified and separated from noise spikes, signals that reflected from the specimen edges, and signals that saturated the instrumentation. A commercially available software package was used to train a statistical pattern recognition system to classify the signals. The software trained a network to recognize signals with a 91-percent accuracy when compared with the researcher's interpretation of the data. Reasons for the discrepancies are examined and it is postulated that additional preprocessing of the AE data to focus on the extensional wave mode and eliminate other effects before training the pattern recognition system will result in increased accuracy.

Computing with competition in biochemical networks.

PubMed

Genot, Anthony J; Fujii, Teruo; Rondelez, Yannick

2012-11-16

Cells rely on limited resources such as enzymes or transcription factors to process signals and make decisions. However, independent cellular pathways often compete for a common molecular resource. Competition is difficult to analyze because of its nonlinear global nature, and its role remains unclear. Here we show how decision pathways such as transcription networks may exploit competition to process information. Competition for one resource leads to the recognition of convex sets of patterns, whereas competition for several resources (overlapping or cascaded regulons) allows even more general pattern recognition. Competition also generates surprising couplings, such as correlating species that share no resource but a common competitor. The mechanism we propose relies on three primitives that are ubiquitous in cells: multiinput motifs, competition for a resource, and positive feedback loops.

RIG-I in RNA virus recognition

PubMed Central

Kell, Alison M.; Gale, Michael

2015-01-01

Antiviral immunity is initiated upon host recognition of viral products via non-self molecular patterns known as pathogen-associated molecular patterns (PAMPs). Such recognition initiates signaling cascades that induce intracellular innate immune defenses and an inflammatory response that facilitates development of the acquired immune response. The retinoic acid-inducible gene I (RIG-I) and the RIG-I-like receptor (RLR) protein family are key cytoplasmic pathogen recognition receptors that are implicated in the recognition of viruses across genera and virus families, including functioning as major sensors of RNA viruses, and promoting recognition of some DNA viruses. RIG-I, the charter member of the RLR family, is activated upon binding to PAMP RNA. Activated RIG-I signals by interacting with the adapter protein MAVS leading to a signaling cascade that activates the transcription factors IRF3 and NF-κB. These actions induce the expression of antiviral gene products and the production of type I and III interferons that lead to an antiviral state in the infected cell and surrounding tissue. RIG-I signaling is essential for the control of infection by many RNA viruses. Recently, RIG-I crosstalk with other pathogen recognition receptors and components of the inflammasome has been described. In this review, we discuss the current knowledge regarding the role of RIG-I in recognition of a variety of virus families and its role in programming the adaptive immune response through cross-talk with parallel arms of the innate immune system, including how RIG-I can be leveraged for antiviral therapy. PMID:25749629

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

PubMed

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-12-02

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

PubMed Central

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-01-01

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works. PMID:27918414

Cognitive Algorithms for Signal Processing

DTIC Science & Technology

2011-03-18

Analysis of Millennial Spiritual Issues,” Zygon, Journal of Science and Religion , 43(4), 797-821, 2008. [46] R. Linnehan, C. Mutz, L.I. Perlovsky, B...dimensions of X and Y : (a) true ‘smile’ and ‘frown’ patterns are shown without clutter; (b) actual image available for recognition (signal is below...clutter in 2 dimensions of X(n) = (X, Y ), is given by l(X(n)|m = clutter) = 1/ (X •  Y ), X = (Xmax-Xmin),  Y = (Ymax-Ymin); (6) 13 Minimal

A nonlinear heartbeat dynamics model approach for personalized emotion recognition.

PubMed

Valenza, Gaetano; Citi, Luca; Lanatà, Antonio; Scilingo, Enzo Pasquale; Barbieri, Riccardo

2013-01-01

Emotion recognition based on autonomic nervous system signs is one of the ambitious goals of affective computing. It is well-accepted that standard signal processing techniques require relative long-time series of multivariate records to ensure reliability and robustness of recognition and classification algorithms. In this work, we present a novel methodology able to assess cardiovascular dynamics during short-time (i.e. < 10 seconds) affective stimuli, thus overcoming some of the limitations of current emotion recognition approaches. We developed a personalized, fully parametric probabilistic framework based on point-process theory where heartbeat events are modelled using a 2(nd)-order nonlinear autoregressive integrative structure in order to achieve effective performances in short-time affective assessment. Experimental results show a comprehensive emotional characterization of 4 subjects undergoing a passive affective elicitation using a sequence of standardized images gathered from the international affective picture system. Each picture was identified by the IAPS arousal and valence scores as well as by a self-reported emotional label associating a subjective positive or negative emotion. Results show a clear classification of two defined levels of arousal, valence and self-emotional state using features coming from the instantaneous spectrum and bispectrum of the considered RR intervals, reaching up to 90% recognition accuracy.

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

NASA Astrophysics Data System (ADS)

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

1999-05-01

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

Real-time processing of ASL signs: Delayed first language acquisition affects organization of the mental lexicon

PubMed Central

Lieberman, Amy M.; Borovsky, Arielle; Hatrak, Marla; Mayberry, Rachel I.

2014-01-01

Sign language comprehension requires visual attention to the linguistic signal and visual attention to referents in the surrounding world, whereas these processes are divided between the auditory and visual modalities for spoken language comprehension. Additionally, the age-onset of first language acquisition and the quality and quantity of linguistic input and for deaf individuals is highly heterogeneous, which is rarely the case for hearing learners of spoken languages. Little is known about how these modality and developmental factors affect real-time lexical processing. In this study, we ask how these factors impact real-time recognition of American Sign Language (ASL) signs using a novel adaptation of the visual world paradigm in deaf adults who learned sign from birth (Experiment 1), and in deaf individuals who were late-learners of ASL (Experiment 2). Results revealed that although both groups of signers demonstrated rapid, incremental processing of ASL signs, only native-signers demonstrated early and robust activation of sub-lexical features of signs during real-time recognition. Our findings suggest that the organization of the mental lexicon into units of both form and meaning is a product of infant language learning and not the sensory and motor modality through which the linguistic signal is sent and received. PMID:25528091

A self-teaching image processing and voice-recognition-based, intelligent and interactive system to educate visually impaired children

NASA Astrophysics Data System (ADS)

Iqbal, Asim; Farooq, Umar; Mahmood, Hassan; Asad, Muhammad Usman; Khan, Akrama; Atiq, Hafiz Muhammad

2010-02-01

A self teaching image processing and voice recognition based system is developed to educate visually impaired children, chiefly in their primary education. System comprises of a computer, a vision camera, an ear speaker and a microphone. Camera, attached with the computer system is mounted on the ceiling opposite (on the required angle) to the desk on which the book is placed. Sample images and voices in the form of instructions and commands of English, Urdu alphabets, Numeric Digits, Operators and Shapes are already stored in the database. A blind child first reads the embossed character (object) with the help of fingers than he speaks the answer, name of the character, shape etc into the microphone. With the voice command of a blind child received by the microphone, image is taken by the camera which is processed by MATLAB® program developed with the help of Image Acquisition and Image processing toolbox and generates a response or required set of instructions to child via ear speaker, resulting in self education of a visually impaired child. Speech recognition program is also developed in MATLAB® with the help of Data Acquisition and Signal Processing toolbox which records and process the command of the blind child.

Calcium Signals: The Lead Currency of Plant Information Processing

PubMed Central

Kudla, Jörg; Batistič, Oliver; Hashimoto, Kenji

2010-01-01

Ca2+ signals are core transducers and regulators in many adaptation and developmental processes of plants. Ca2+ signals are represented by stimulus-specific signatures that result from the concerted action of channels, pumps, and carriers that shape temporally and spatially defined Ca2+ elevations. Cellular Ca2+ signals are decoded and transmitted by a toolkit of Ca2+ binding proteins that relay this information into downstream responses. Major transduction routes of Ca2+ signaling involve Ca2+-regulated kinases mediating phosphorylation events that orchestrate downstream responses or comprise regulation of gene expression via Ca2+-regulated transcription factors and Ca2+-responsive promoter elements. Here, we review some of the remarkable progress that has been made in recent years, especially in identifying critical components functioning in Ca2+ signal transduction, both at the single-cell and multicellular level. Despite impressive progress in our understanding of the processing of Ca2+ signals during the past years, the elucidation of the exact mechanistic principles that underlie the specific recognition and conversion of the cellular Ca2+ currency into defined changes in protein–protein interaction, protein phosphorylation, and gene expression and thereby establish the specificity in stimulus response coupling remain to be explored. PMID:20354197

Dynamic relation between working memory capacity and speech recognition in noise during the first 6 months of hearing aid use.

PubMed

Ng, Elaine H N; Classon, Elisabet; Larsby, Birgitta; Arlinger, Stig; Lunner, Thomas; Rudner, Mary; Rönnberg, Jerker

2014-11-23

The present study aimed to investigate the changing relationship between aided speech recognition and cognitive function during the first 6 months of hearing aid use. Twenty-seven first-time hearing aid users with symmetrical mild to moderate sensorineural hearing loss were recruited. Aided speech recognition thresholds in noise were obtained in the hearing aid fitting session as well as at 3 and 6 months postfitting. Cognitive abilities were assessed using a reading span test, which is a measure of working memory capacity, and a cognitive test battery. Results showed a significant correlation between reading span and speech reception threshold during the hearing aid fitting session. This relation was significantly weakened over the first 6 months of hearing aid use. Multiple regression analysis showed that reading span was the main predictor of speech recognition thresholds in noise when hearing aids were first fitted, but that the pure-tone average hearing threshold was the main predictor 6 months later. One way of explaining the results is that working memory capacity plays a more important role in speech recognition in noise initially rather than after 6 months of use. We propose that new hearing aid users engage working memory capacity to recognize unfamiliar processed speech signals because the phonological form of these signals cannot be automatically matched to phonological representations in long-term memory. As familiarization proceeds, the mismatch effect is alleviated, and the engagement of working memory capacity is reduced. © The Author(s) 2014.

A speech processing study using an acoustic model of a multiple-channel cochlear implant

NASA Astrophysics Data System (ADS)

Xu, Ying

1998-10-01

A cochlear implant is an electronic device designed to provide sound information for adults and children who have bilateral profound hearing loss. The task of representing speech signals as electrical stimuli is central to the design and performance of cochlear implants. Studies have shown that the current speech- processing strategies provide significant benefits to cochlear implant users. However, the evaluation and development of speech-processing strategies have been complicated by hardware limitations and large variability in user performance. To alleviate these problems, an acoustic model of a cochlear implant with the SPEAK strategy is implemented in this study, in which a set of acoustic stimuli whose psychophysical characteristics are as close as possible to those produced by a cochlear implant are presented on normal-hearing subjects. To test the effectiveness and feasibility of this acoustic model, a psychophysical experiment was conducted to match the performance of a normal-hearing listener using model- processed signals to that of a cochlear implant user. Good agreement was found between an implanted patient and an age-matched normal-hearing subject in a dynamic signal discrimination experiment, indicating that this acoustic model is a reasonably good approximation of a cochlear implant with the SPEAK strategy. The acoustic model was then used to examine the potential of the SPEAK strategy in terms of its temporal and frequency encoding of speech. It was hypothesized that better temporal and frequency encoding of speech can be accomplished by higher stimulation rates and a larger number of activated channels. Vowel and consonant recognition tests were conducted on normal-hearing subjects using speech tokens processed by the acoustic model, with different combinations of stimulation rate and number of activated channels. The results showed that vowel recognition was best at 600 pps and 8 activated channels, but further increases in stimulation rate and channel numbers were not beneficial. Manipulations of stimulation rate and number of activated channels did not appreciably affect consonant recognition. These results suggest that overall speech performance may improve by appropriately increasing stimulation rate and number of activated channels. Future revision of this acoustic model is necessary to provide more accurate amplitude representation of speech.

Radar transponder apparatus and signal processing technique

DOEpatents

Axline, Jr., Robert M.; Sloan, George R.; Spalding, Richard E.

1996-01-01

An active, phase-coded, time-grating transponder and a synthetic-aperture radar (SAR) and signal processor means, in combination, allow the recognition and location of the transponder (tag) in the SAR image and allow communication of information messages from the transponder to the SAR. The SAR is an illuminating radar having special processing modifications in an image-formation processor to receive an echo from a remote transponder, after the transponder receives and retransmits the SAR illuminations, and to enhance the transponder's echo relative to surrounding ground clutter by recognizing special transponder modulations from phase-shifted from the transponder retransmissions. The remote radio-frequency tag also transmits information to the SAR through a single antenna that also serves to receive the SAR illuminations. Unique tag-modulation and SAR signal processing techniques, in combination, allow the detection and precise geographical location of the tag through the reduction of interfering signals from ground clutter, and allow communication of environmental and status information from said tag to be communicated to said SAR.

Radar transponder apparatus and signal processing technique

DOEpatents

Axline, R.M. Jr.; Sloan, G.R.; Spalding, R.E.

1996-01-23

An active, phase-coded, time-grating transponder and a synthetic-aperture radar (SAR) and signal processor means, in combination, allow the recognition and location of the transponder (tag) in the SAR image and allow communication of information messages from the transponder to the SAR. The SAR is an illuminating radar having special processing modifications in an image-formation processor to receive an echo from a remote transponder, after the transponder receives and retransmits the SAR illuminations, and to enhance the transponder`s echo relative to surrounding ground clutter by recognizing special transponder modulations from phase-shifted from the transponder retransmissions. The remote radio-frequency tag also transmits information to the SAR through a single antenna that also serves to receive the SAR illuminations. Unique tag-modulation and SAR signal processing techniques, in combination, allow the detection and precise geographical location of the tag through the reduction of interfering signals from ground clutter, and allow communication of environmental and status information from said tag to be communicated to said SAR. 4 figs.

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. R.; Landgrebe, David

1991-01-01

Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. Rasoul; Landgrebe, David

1990-01-01

Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issue. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

Achromatical Optical Correlator

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Liu, Hua-Kuang

1989-01-01

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

EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals.

PubMed

Zhao, Jiaduo; Gong, Weiguo; Tang, Yuzhen; Li, Weihong

2016-01-20

In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector's false alarms.

Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.

PubMed

Kim, Ju-Won; Park, Seunghee

2018-01-02

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

Heterogeneous Sensor Webs for Automated Target Recognition and Tracking in Urban Terrain

DTIC Science & Technology

2012-04-09

Seto, E. Martin , A. Yang, P. Yan, R. Gravina, I. Lin, C. Wang, M. Roy, V. Shia, R. Bajcsy, “Opportunistic strategies for lightweight signal...processing for body sensor networks,” PETRAE , 2010. 10. Dheeraj Singaraju, Roberto Tron, Ehsan Elhamifar, Allen Yang, and Shankar Sastry. On the Lagrangian

Automatic target recognition apparatus and method

DOEpatents

Baumgart, Chris W.; Ciarcia, Christopher A.

2000-01-01

An automatic target recognition apparatus (10) is provided, having a video camera/digitizer (12) for producing a digitized image signal (20) representing an image containing therein objects which objects are to be recognized if they meet predefined criteria. The digitized image signal (20) is processed within a video analysis subroutine (22) residing in a computer (14) in a plurality of parallel analysis chains such that the objects are presumed to be lighter in shading than the background in the image in three of the chains and further such that the objects are presumed to be darker than the background in the other three chains. In two of the chains the objects are defined by surface texture analysis using texture filter operations. In another two of the chains the objects are defined by background subtraction operations. In yet another two of the chains the objects are defined by edge enhancement processes. In each of the analysis chains a calculation operation independently determines an error factor relating to the probability that the objects are of the type which should be recognized, and a probability calculation operation combines the results of the analysis chains.

Recognition Imaging with a DNA Aptamer

PubMed Central

Lin, Liyun; Wang, Hongda; Liu, Yan; Yan, Hao; Lindsay, Stuart

2006-01-01

We have used a DNA-aptamer tethered to an atomic force microscope probe to carry out recognition imaging of IgE molecules attached to a mica substrate. The recognition was efficient (∼90%) and specific, being blocked by injection of IgE molecules in solution, and not being interfered with by high concentrations of a second protein. The signal/noise ratio of the recognition signal was better than that obtained with antibodies, despite the fact that the average force required to break the aptamer-protein bonds was somewhat smaller. PMID:16513776

Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation

PubMed Central

Song, Zhibin; Zhang, Songyuan

2016-01-01

Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used in the application of bilateral rehabilitation, where hemiplegic patients can train their affected limbs following the motion of unaffected limbs via some rehabilitation devices. Traditional methods to process the sEMG focused on motion pattern recognition, namely, discrete patterns, which are not satisfactory for use in bilateral rehabilitation. In order to overcome this problem, in this paper, we built a relationship between sEMG signals and human motion in elbow flexion and extension on the sagittal plane. During the conducted experiments, four participants were required to perform elbow flexion and extension on the sagittal plane smoothly with only an inertia sensor in their hands, where forearm dynamics were not considered. In these circumstances, sEMG signals were weak compared to those with heavy loads or high acceleration. The contrastive experimental results show that continuous motion can also be obtained within an acceptable precision range. PMID:27775573

Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation.

PubMed

Song, Zhibin; Zhang, Songyuan

2016-10-19

Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used in the application of bilateral rehabilitation, where hemiplegic patients can train their affected limbs following the motion of unaffected limbs via some rehabilitation devices. Traditional methods to process the sEMG focused on motion pattern recognition, namely, discrete patterns, which are not satisfactory for use in bilateral rehabilitation. In order to overcome this problem, in this paper, we built a relationship between sEMG signals and human motion in elbow flexion and extension on the sagittal plane. During the conducted experiments, four participants were required to perform elbow flexion and extension on the sagittal plane smoothly with only an inertia sensor in their hands, where forearm dynamics were not considered. In these circumstances, sEMG signals were weak compared to those with heavy loads or high acceleration. The contrastive experimental results show that continuous motion can also be obtained within an acceptable precision range.

Automated transformation-invariant shape recognition through wavelet multiresolution

NASA Astrophysics Data System (ADS)

Brault, Patrice; Mounier, Hugues

2001-12-01

We present here new results in Wavelet Multi-Resolution Analysis (W-MRA) applied to shape recognition in automatic vehicle driving applications. Different types of shapes have to be recognized in this framework. They pertain to most of the objects entering the sensors field of a car. These objects can be road signs, lane separation lines, moving or static obstacles, other automotive vehicles, or visual beacons. The recognition process must be invariant to global, affine or not, transformations which are : rotation, translation and scaling. It also has to be invariant to more local, elastic, deformations like the perspective (in particular with wide angle camera lenses), and also like deformations due to environmental conditions (weather : rain, mist, light reverberation) or optical and electrical signal noises. To demonstrate our method, an initial shape, with a known contour, is compared to the same contour altered by rotation, translation, scaling and perspective. The curvature computed for each contour point is used as a main criterion in the shape matching process. The original part of this work is to use wavelet descriptors, generated with a fast orthonormal W-MRA, rather than Fourier descriptors, in order to provide a multi-resolution description of the contour to be analyzed. In such way, the intrinsic spatial localization property of wavelet descriptors can be used and the recognition process can be speeded up. The most important part of this work is to demonstrate the potential performance of Wavelet-MRA in this application of shape recognition.

Signal recognition and parameter estimation of BPSK-LFM combined modulation

NASA Astrophysics Data System (ADS)

Long, Chao; Zhang, Lin; Liu, Yu

2015-07-01

Intra-pulse analysis plays an important role in electronic warfare. Intra-pulse feature abstraction focuses on primary parameters such as instantaneous frequency, modulation, and symbol rate. In this paper, automatic modulation recognition and feature extraction for combined BPSK-LFM modulation signals based on decision theoretic approach is studied. The simulation results show good recognition effect and high estimation precision, and the system is easy to be realized.

One-Dimensional Signal Extraction Of Paper-Written ECG Image And Its Archiving

NASA Astrophysics Data System (ADS)

Zhang, Zhi-ni; Zhang, Hong; Zhuang, Tian-ge

1987-10-01

A method for converting paper-written electrocardiograms to one dimensional (1-D) signals for archival storage on floppy disk is presented here. Appropriate image processing techniques were employed to remove the back-ground noise inherent to ECG recorder charts and to reconstruct the ECG waveform. The entire process consists of (1) digitization of paper-written ECGs with an image processing system via a TV camera; (2) image preprocessing, including histogram filtering and binary image generation; (3) ECG feature extraction and ECG wave tracing, and (4) transmission of the processed ECG data to IBM-PC compatible floppy disks for storage and retrieval. The algorithms employed here may also be used in the recognition of paper-written EEG or EMG and may be useful in robotic vision.

Infant information processing and family history of specific language impairment: converging evidence for RAP deficits from two paradigms

PubMed Central

Choudhury, Naseem; Leppanen, Paavo H.T.; Leevers, Hilary J.; Benasich, April A.

2007-01-01

An infant’s ability to process auditory signals presented in rapid succession (i.e. rapid auditory processing abilities [RAP]) has been shown to predict differences in language outcomes in toddlers and preschool children. Early deficits in RAP abilities may serve as a behavioral marker for language-based learning disabilities. The purpose of this study is to determine if performance on infant information processing measures designed to tap RAP and global processing skills differ as a function of family history of specific language impairment (SLI) and/or the particular demand characteristics of the paradigm used. Seventeen 6- to 9-month-old infants from families with a history of specific language impairment (FH+) and 29 control infants (FH−) participated in this study. Infants’ performance on two different RAP paradigms (head-turn procedure [HT] and auditory-visual habituation/recognition memory [AVH/RM]) and on a global processing task (visual habituation/recognition memory [VH/RM]) was assessed at 6 and 9 months. Toddler language and cognitive skills were evaluated at 12 and 16 months. A number of significant group differences were seen: FH+ infants showed significantly poorer discrimination of fast rate stimuli on both RAP tasks, took longer to habituate on both habituation/recognition memory measures, and had lower novelty preference scores on the visual habituation/recognition memory task. Infants’ performance on the two RAP measures provided independent but converging contributions to outcome. Thus, different mechanisms appear to underlie performance on operantly conditioned tasks as compared to habituation/recognition memory paradigms. Further, infant RAP processing abilities predicted to 12- and 16-month language scores above and beyond family history of SLI. The results of this study provide additional support for the validity of infant RAP abilities as a behavioral marker for later language outcome. Finally, this is the first study to use a battery of infant tasks to demonstrate multi-modal processing deficits in infants at risk for SLI. PMID:17286846

Aging selectively impairs recollection in recognition memory for pictures: Evidence from modeling and ROC curves

PubMed Central

Howard, Marc W.; Bessette-Symons, Brandy; Zhang, Yaofei; Hoyer, William J.

2006-01-01

Younger and older adults were tested on recognition memory for pictures. The Yonelinas high threshold (YHT) model, a formal implementation of two-process theory, fit the response distribution data of both younger and older adults significantly better than a normal unequal variance signal detection model. Consistent with this finding, non-linear zROC curves were obtained for both groups. Estimates of recollection from the YHT model were significantly higher for younger than older adults. This deficit was not a consequence of a general decline in memory; older adults showed comparable overall accuracy and in fact a non-significant increase in their familiarity scores. Implications of these results for theories of recognition memory and the mnemonic deficit associated with aging are discussed. PMID:16594795

Protein tyrosine phosphatase receptor type R deficient mice exhibit increased exploration in a new environment and impaired novel object recognition memory.

PubMed

Erkens, Mirthe; Bakker, Brenda; van Duijn, Lucette M; Hendriks, Wiljan J A J; Van der Zee, Catharina E E M

2014-05-15

Mouse gene Ptprr encodes multiple protein tyrosine phosphatase receptor type R (PTPRR) isoforms that negatively regulate mitogen-activated protein kinase (MAPK) signaling pathways. In the mouse brain, PTPRR proteins are expressed in cerebellum, olfactory bulb, hippocampus, amygdala and perirhinal cortex but their precise role in these regions remains to be determined. Here, we evaluated phenotypic consequences of loss of PTPRR activity and found that basal smell was normal for Ptprr(-/-) mice. Also, spatial learning and fear-associated contextual learning were unaffected. PTPRR deficiency, however, resulted in impaired novel object recognition and a striking increase in exploratory activity in a new environment. The data corroborate the importance of proper control of MAPK signaling in cerebral functions and put forward PTPRR as a novel target to modulate synaptic processes. Copyright © 2014 Elsevier B.V. All rights reserved.

Defect Inspection of Flip Chip Solder Bumps Using an Ultrasonic Transducer

PubMed Central

Su, Lei; Shi, Tielin; Xu, Zhensong; Lu, Xiangning; Liao, Guanglan

2013-01-01

Surface mount technology has spurred a rapid decrease in the size of electronic packages, where solder bump inspection of surface mount packages is crucial in the electronics manufacturing industry. In this study we demonstrate the feasibility of using a 230 MHz ultrasonic transducer for nondestructive flip chip testing. The reflected time domain signal was captured when the transducer scanning the flip chip, and the image of the flip chip was generated by scanning acoustic microscopy. Normalized cross-correlation was used to locate the center of solder bumps for segmenting the flip chip image. Then five features were extracted from the signals and images. The support vector machine was adopted to process the five features for classification and recognition. The results show the feasibility of this approach with high recognition rate, proving that defect inspection of flip chip solder bumps using the ultrasonic transducer has high potential in microelectronics packaging.

Assembly of the Human Signal Recognition Particle

NASA Astrophysics Data System (ADS)

Menichelli, Elena; Nagai, Kiyoshi

Large RNA-protein complexes (ribonucleoprotein particles or RNPs) control fundamental biological processes. Their correct assembly is essential for function and occurs by the ordered addition of proteins to the RNA. A good model system for studying RNP assembly is provided by the Signal Recognition Particle (SRP), an RNP conserved from bacteria to humans, with different degrees of complexity. Human SRP, composed of a single RNA molecule and six pro teins, is responsible for the co-translational targeting of secretory and membrane proteins to the endoplasmic reticulum membrane. In vitro studies reveal that the SRP proteins need to be added to the RNA sequentially. If the order of addition is altered, non-native particles are formed. The sequential association of proteins causes conformational changes in the RNA, allowing binding of other proteins. The in vivo assembly is regulated by the translocation of precursors between different cellular compartments. In this chapter we review the current understanding of the human SRP assembly mechanism.

Structural health monitoring feature design by genetic programming

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Todd, Michael D.

2014-09-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

Two Dimensional Processing Of Speech And Ecg Signals Using The Wigner-Ville Distribution

NASA Astrophysics Data System (ADS)

Boashash, Boualem; Abeysekera, Saman S.

1986-12-01

The Wigner-Ville Distribution (WVD) has been shown to be a valuable tool for the analysis of non-stationary signals such as speech and Electrocardiogram (ECG) data. The one-dimensional real data are first transformed into a complex analytic signal using the Hilbert Transform and then a 2-dimensional image is formed using the Wigner-Ville Transform. For speech signals, a contour plot is determined and used as a basic feature. for a pattern recognition algorithm. This method is compared with the classical Short Time Fourier Transform (STFT) and is shown, to be able to recognize isolated words better in a noisy environment. The same method together with the concept of instantaneous frequency of the signal is applied to the analysis of ECG signals. This technique allows one to classify diseased heart-beat signals. Examples are shown.

A miniature electronic nose system based on an MWNT-polymer microsensor array and a low-power signal-processing chip.

PubMed

Chiu, Shih-Wen; Wu, Hsiang-Chiu; Chou, Ting-I; Chen, Hsin; Tang, Kea-Tiong

2014-06-01

This article introduces a power-efficient, miniature electronic nose (e-nose) system. The e-nose system primarily comprises two self-developed chips, a multiple-walled carbon nanotube (MWNT)-polymer based microsensor array, and a low-power signal-processing chip. The microsensor array was fabricated on a silicon wafer by using standard photolithography technology. The microsensor array comprised eight interdigitated electrodes surrounded by SU-8 "walls," which restrained the material-solvent liquid in a defined area of 650 × 760 μm(2). To achieve a reliable sensor-manufacturing process, we used a two-layer deposition method, coating the MWNTs and polymer film as the first and second layers, respectively. The low-power signal-processing chip included array data acquisition circuits and a signal-processing core. The MWNT-polymer microsensor array can directly connect with array data acquisition circuits, which comprise sensor interface circuitry and an analog-to-digital converter; the signal-processing core consists of memory and a microprocessor. The core executes the program, classifying the odor data received from the array data acquisition circuits. The low-power signal-processing chip was designed and fabricated using the Taiwan Semiconductor Manufacturing Company 0.18-μm 1P6M standard complementary metal oxide semiconductor process. The chip consumes only 1.05 mW of power at supply voltages of 1 and 1.8 V for the array data acquisition circuits and the signal-processing core, respectively. The miniature e-nose system, which used a microsensor array, a low-power signal-processing chip, and an embedded k-nearest-neighbor-based pattern recognition algorithm, was developed as a prototype that successfully recognized the complex odors of tincture, sorghum wine, sake, whisky, and vodka.

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

PubMed

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

2015-01-01

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

A functional glycoprotein competitive recognition and signal amplification strategy for carbohydrate-protein interaction profiling and cell surface carbohydrate expression evaluation

NASA Astrophysics Data System (ADS)

Wang, Yangzhong; Chen, Zhuhai; Liu, Yang; Li, Jinghong

2013-07-01

A simple and sensitive carbohydrate biosensor has been suggested as a potential tool for accurate analysis of cell surface carbohydrate expression as well as carbohydrate-based therapeutics for a variety of diseases and infections. In this work, a sensitive biosensor for carbohydrate-lectin profiling and in situ cell surface carbohydrate expression was designed by taking advantage of a functional glycoprotein of glucose oxidase acting as both a multivalent recognition unit and a signal amplification probe. Combining the gold nanoparticle catalyzed luminol electrogenerated chemiluminescence and nanocarrier for active biomolecules, the number of cell surface carbohydrate groups could be conveniently read out. The apparent dissociation constant between GOx@Au probes and Con A was detected to be 1.64 nM and was approximately 5 orders of magnitude smaller than that of mannose and Con A, which would arise from the multivalent effect between the probe and Con A. Both glycoproteins and gold nanoparticles contribute to the high affinity between carbohydrates and lectin. The as-proposed biosensor exhibits excellent analytical performance towards the cytosensing of K562 cells with a detection limit of 18 cells, and the mannose moieties on a single K562 cell were determined to be 1.8 × 1010. The biosensor can also act as a useful tool for antibacterial drug screening and mechanism investigation. This strategy integrates the excellent biocompatibility and multivalent recognition of glycoproteins as well as the significant enzymatic catalysis and gold nanoparticle signal amplification, and avoids the cell pretreatment and labelling process. This would contribute to the glycomic analysis and the understanding of complex native glycan-related biological processes.A simple and sensitive carbohydrate biosensor has been suggested as a potential tool for accurate analysis of cell surface carbohydrate expression as well as carbohydrate-based therapeutics for a variety of diseases and infections. In this work, a sensitive biosensor for carbohydrate-lectin profiling and in situ cell surface carbohydrate expression was designed by taking advantage of a functional glycoprotein of glucose oxidase acting as both a multivalent recognition unit and a signal amplification probe. Combining the gold nanoparticle catalyzed luminol electrogenerated chemiluminescence and nanocarrier for active biomolecules, the number of cell surface carbohydrate groups could be conveniently read out. The apparent dissociation constant between GOx@Au probes and Con A was detected to be 1.64 nM and was approximately 5 orders of magnitude smaller than that of mannose and Con A, which would arise from the multivalent effect between the probe and Con A. Both glycoproteins and gold nanoparticles contribute to the high affinity between carbohydrates and lectin. The as-proposed biosensor exhibits excellent analytical performance towards the cytosensing of K562 cells with a detection limit of 18 cells, and the mannose moieties on a single K562 cell were determined to be 1.8 × 1010. The biosensor can also act as a useful tool for antibacterial drug screening and mechanism investigation. This strategy integrates the excellent biocompatibility and multivalent recognition of glycoproteins as well as the significant enzymatic catalysis and gold nanoparticle signal amplification, and avoids the cell pretreatment and labelling process. This would contribute to the glycomic analysis and the understanding of complex native glycan-related biological processes. Electronic supplementary information (ESI) available: Experimental details; characterization of probes; the influence of electrolyte pH; probe concentration and glucose concentration on the electrode ECL effect. See DOI: 10.1039/c3nr01598j

Method of synthesized phase objects for pattern recognition with rotation invariance

NASA Astrophysics Data System (ADS)

Ostroukh, Alexander P.; Butok, Alexander M.; Shvets, Rostislav A.; Yezhov, Pavel V.; Kim, Jin-Tae; Kuzmenko, Alexander V.

2015-11-01

We present a development of the method of synthesized phase objects (SPO-method) [1] for the rotation-invariant pattern recognition. For the standard method of recognition and the SPO-method, the comparison of the parameters of correlation signals for a number of amplitude objects is executed at the realization of a rotation in an optical-digital correlator with the joint Fourier transformation. It is shown that not only the invariance relative to a rotation at a realization of the joint correlation for synthesized phase objects (SP-objects) but also the main advantage of the method of SP-objects over the reference one such as the unified δ-like recognition signal with the largest possible signal-to-noise ratio independent of the type of an object are attained.

[Perception of emotional intonation of noisy speech signal with different acoustic parameters by adults of different age and gender].

PubMed

Dmitrieva, E S; Gel'man, V Ia

2011-01-01

The listener-distinctive features of recognition of different emotional intonations (positive, negative and neutral) of male and female speakers in the presence or absence of background noise were studied in 49 adults aged 20-79 years. In all the listeners noise produced the most pronounced decrease in recognition accuracy for positive emotional intonation ("joy") as compared to other intonations, whereas it did not influence the recognition accuracy of "anger" in 65-79-year-old listeners. The higher emotion recognition rates of a noisy signal were observed for speech emotional intonations expressed by female speakers. Acoustic characteristics of noisy and clear speech signals underlying perception of speech emotional prosody were found for adult listeners of different age and gender.

Applications of Wavelet Transform and Fuzzy Neural Network on Power Quality Recognition

NASA Astrophysics Data System (ADS)

Liao, Chiung-Chou; Yang, Hong-Tzer; Lin, Ying-Chun

2008-10-01

The wavelet transform coefficients (WTCs) contain plenty of information needed for transient event identification of power quality (PQ) events. However, adopting WTCs directly has the drawbacks of taking a longer time and too much memory for the recognition system. To solve the abovementioned recognition problems and to effectively reduce the number of features representing power transients, spectrum energies of WTCs in different scales are calculated by Parseval's Theorem. Through the proposed approach, features of the original power signals can be reserved and not influenced by occurring points of PQ events. The fuzzy neural classification systems are then used for signal recognition and fuzzy rule construction. Success rates of recognizing PQ events from noise-riding signals are proven to be feasible in power system applications in this paper.

Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

PubMed Central

Lee, Boon-Giin; Lee, Boon-Leng; Chung, Wan-Young

2014-01-01

Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals. PMID:25264954

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment

PubMed Central

Rundo, Francesco; Ortis, Alessandro

2018-01-01

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG “combo” pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting “clean” PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach. PMID:29385774

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment.

PubMed

Rundo, Francesco; Conoci, Sabrina; Ortis, Alessandro; Battiato, Sebastiano

2018-01-30

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG "combo" pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting "clean" PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.

Discovery of a novel site of opioid action at the innate immune pattern-recognition receptor TLR4 and its role in addiction.

PubMed

Jacobsen, Jonathan Henry W; Watkins, Linda R; Hutchinson, Mark R

2014-01-01

Opioids have historically, and continue to be, an integral component of pain management. However, despite pharmacokinetic and dynamic optimization over the past 100 years, opioids continue to produce many undesirable side effects such as tolerance, reward, and dependence. As such, opioids are liable for addiction. Traditionally, opioid addiction was viewed as a solely neuronal process, and while substantial headway has been made into understanding the molecular and cellular mechanisms mediating this process, research has however, been relatively ambivalent to how the rest of the central nervous system (CNS) responds to opioids. Evidence over the past 20 years has clearly demonstrated the importance of the immunocompetent cells of the CNS (glia) in many aspects of opioid pharmacology. Particular focus has been placed on microglia and astrocytes, who in response to opioids, become activated and release inflammatory mediators. Importantly, the mechanism underlying immune activation is beginning to be elucidated. Evidence suggests an innate immune pattern-recognition receptor (toll-like receptor 4) as an integral component underlying opioid-induced glial activation. The subsequent proinflammatory response may be viewed akin to neurotransmission creating a process termed central immune signaling. Translationally, we are beginning to appreciate the importance of central immune signaling as it contributes to many behavioral actions of addiction including reward, withdrawal, and craving. As such, the aim of this chapter is to review and integrate the neuronal and central immune signaling perspective of addiction. © 2014 Elsevier Inc. All rights reserved.

Controlling a human-computer interface system with a novel classification method that uses electrooculography signals.

PubMed

Wu, Shang-Lin; Liao, Lun-De; Lu, Shao-Wei; Jiang, Wei-Ling; Chen, Shi-An; Lin, Chin-Teng

2013-08-01

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.

Multimodal fusion of polynomial classifiers for automatic person recgonition

NASA Astrophysics Data System (ADS)

Broun, Charles C.; Zhang, Xiaozheng

2001-03-01

With the prevalence of the information age, privacy and personalization are forefront in today's society. As such, biometrics are viewed as essential components of current evolving technological systems. Consumers demand unobtrusive and non-invasive approaches. In our previous work, we have demonstrated a speaker verification system that meets these criteria. However, there are additional constraints for fielded systems. The required recognition transactions are often performed in adverse environments and across diverse populations, necessitating robust solutions. There are two significant problem areas in current generation speaker verification systems. The first is the difficulty in acquiring clean audio signals in all environments without encumbering the user with a head- mounted close-talking microphone. Second, unimodal biometric systems do not work with a significant percentage of the population. To combat these issues, multimodal techniques are being investigated to improve system robustness to environmental conditions, as well as improve overall accuracy across the population. We propose a multi modal approach that builds on our current state-of-the-art speaker verification technology. In order to maintain the transparent nature of the speech interface, we focus on optical sensing technology to provide the additional modality-giving us an audio-visual person recognition system. For the audio domain, we use our existing speaker verification system. For the visual domain, we focus on lip motion. This is chosen, rather than static face or iris recognition, because it provides dynamic information about the individual. In addition, the lip dynamics can aid speech recognition to provide liveness testing. The visual processing method makes use of both color and edge information, combined within Markov random field MRF framework, to localize the lips. Geometric features are extracted and input to a polynomial classifier for the person recognition process. A late integration approach, based on a probabilistic model, is employed to combine the two modalities. The system is tested on the XM2VTS database combined with AWGN in the audio domain over a range of signal-to-noise ratios.

Visual and auditory socio-cognitive perception in unilateral temporal lobe epilepsy in children and adolescents: a prospective controlled study.

PubMed

Laurent, Agathe; Arzimanoglou, Alexis; Panagiotakaki, Eleni; Sfaello, Ignacio; Kahane, Philippe; Ryvlin, Philippe; Hirsch, Edouard; de Schonen, Scania

2014-12-01

A high rate of abnormal social behavioural traits or perceptual deficits is observed in children with unilateral temporal lobe epilepsy. In the present study, perception of auditory and visual social signals, carried by faces and voices, was evaluated in children or adolescents with temporal lobe epilepsy. We prospectively investigated a sample of 62 children with focal non-idiopathic epilepsy early in the course of the disorder. The present analysis included 39 children with a confirmed diagnosis of temporal lobe epilepsy. Control participants (72), distributed across 10 age groups, served as a control group. Our socio-perceptual evaluation protocol comprised three socio-visual tasks (face identity, facial emotion and gaze direction recognition), two socio-auditory tasks (voice identity and emotional prosody recognition), and three control tasks (lip reading, geometrical pattern and linguistic intonation recognition). All 39 patients also benefited from a neuropsychological examination. As a group, children with temporal lobe epilepsy performed at a significantly lower level compared to the control group with regards to recognition of facial identity, direction of eye gaze, and emotional facial expressions. We found no relationship between the type of visual deficit and age at first seizure, duration of epilepsy, or the epilepsy-affected cerebral hemisphere. Deficits in socio-perceptual tasks could be found independently of the presence of deficits in visual or auditory episodic memory, visual non-facial pattern processing (control tasks), or speech perception. A normal FSIQ did not exempt some of the patients from an underlying deficit in some of the socio-perceptual tasks. Temporal lobe epilepsy not only impairs development of emotion recognition, but can also impair development of perception of other socio-perceptual signals in children with or without intellectual deficiency. Prospective studies need to be designed to evaluate the results of appropriate re-education programs in children presenting with deficits in social cue processing.

Design of a compact low-power human-computer interaction equipment for hand motion

NASA Astrophysics Data System (ADS)

Wu, Xianwei; Jin, Wenguang

2017-01-01

Human-Computer Interaction (HCI) raises demand of convenience, endurance, responsiveness and naturalness. This paper describes a design of a compact wearable low-power HCI equipment applied to gesture recognition. System combines multi-mode sense signals: the vision sense signal and the motion sense signal, and the equipment is equipped with the depth camera and the motion sensor. The dimension (40 mm × 30 mm) and structure is compact and portable after tight integration. System is built on a module layered framework, which contributes to real-time collection (60 fps), process and transmission via synchronous confusion with asynchronous concurrent collection and wireless Blue 4.0 transmission. To minimize equipment's energy consumption, system makes use of low-power components, managing peripheral state dynamically, switching into idle mode intelligently, pulse-width modulation (PWM) of the NIR LEDs of the depth camera and algorithm optimization by the motion sensor. To test this equipment's function and performance, a gesture recognition algorithm is applied to system. As the result presents, general energy consumption could be as low as 0.5 W.

Left prefrontal cortex control of novel occurrences during recollection: a psychopharmacological study using scopolamine and event-related fMRI.

PubMed

Bozzali, M; MacPherson, S E; Dolan, R J; Shallice, T

2006-10-15

Recollection and familiarity represent two processes involved in episodic memory retrieval. We investigated how scopolamine (an antagonist of acetylcholine muscarinic receptors) influenced brain activity during memory retrieval, using a paradigm that separated recollection and familiarity. Eighteen healthy volunteers were recruited in a randomized, placebo-controlled, double-blind design using event-related fMRI. Participants were required to perform a verbal recognition memory task within the scanner, either under placebo or scopolamine conditions. Depending on the subcondition, participants were required to make a simple recognition decision (old/new items) or base their decision on more specific information related to prior experience (target/non-target/new items). We show a drug modulation in left prefrontal and perirhinal cortex during recollection. Such an effect was specifically driven by novelty and showed an inverse correlation with accuracy performance. Additionally, we show a direct correlation between drug-related signal change in left prefrontal and perirhinal cortices. We discuss the findings in terms of acetylcholine mediation of the familiarity/novelty signal through perirhinal cortex and the control of the relative signal strength through prefrontal cortex.

Itk tyrosine kinase substrate docking is mediated by a nonclassical SH2 domain surface of PLCgamma1.

PubMed

Min, Lie; Joseph, Raji E; Fulton, D Bruce; Andreotti, Amy H

2009-12-15

Interleukin-2 tyrosine kinase (Itk) is a Tec family tyrosine kinase that mediates signaling processes after T cell receptor engagement. Activation of Itk requires recruitment to the membrane via its pleckstrin homology domain, phosphorylation of Itk by the Src kinase, Lck, and binding of Itk to the SLP-76/LAT adapter complex. After activation, Itk phosphorylates and activates phospholipase C-gamma1 (PLC-gamma1), leading to production of two second messengers, DAG and IP(3). We have previously shown that phosphorylation of PLC-gamma1 by Itk requires a direct, phosphotyrosine-independent interaction between the Src homology 2 (SH2) domain of PLC-gamma1 and the kinase domain of Itk. We now define this docking interface using a combination of mutagenesis and NMR spectroscopy and show that disruption of the Itk/PLCgamma1 docking interaction attenuates T cell signaling. The binding surface on PLCgamma1 that mediates recognition by Itk highlights a nonclassical binding activity of the well-studied SH2 domain providing further evidence that SH2 domains participate in important signaling interactions beyond recognition of phosphotyrosine.

Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe -decay searches

NASA Astrophysics Data System (ADS)

Caldwell, A.; Cossavella, F.; Majorovits, B.; Palioselitis, D.; Volynets, O.

2015-07-01

A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variations of efficiencies as a function of used training set. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like evaluation samples from calibration measurements is estimated to be 5 %. This uncertainty is due to differences between signal and calibration samples.

[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.

Robust Indoor Human Activity Recognition Using Wireless Signals.

PubMed

Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

2015-07-15

Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Subject-independent emotion recognition based on physiological signals: a three-stage decision method.

PubMed

Chen, Jing; Hu, Bin; Wang, Yue; Moore, Philip; Dai, Yongqiang; Feng, Lei; Ding, Zhijie

2017-12-20

Collaboration between humans and computers has become pervasive and ubiquitous, however current computer systems are limited in that they fail to address the emotional component. An accurate understanding of human emotions is necessary for these computers to trigger proper feedback. Among multiple emotional channels, physiological signals are synchronous with emotional responses; therefore, analyzing physiological changes is a recognized way to estimate human emotions. In this paper, a three-stage decision method is proposed to recognize four emotions based on physiological signals in the multi-subject context. Emotion detection is achieved by using a stage-divided strategy in which each stage deals with a fine-grained goal. The decision method consists of three stages. During the training process, the initial stage transforms mixed training subjects to separate groups, thus eliminating the effect of individual differences. The second stage categorizes four emotions into two emotion pools in order to reduce recognition complexity. The third stage trains a classifier based on emotions in each emotion pool. During the testing process, a test case or test trial will be initially classified to a group followed by classification into an emotion pool in the second stage. An emotion will be assigned to the test trial in the final stage. In this paper we consider two different ways of allocating four emotions into two emotion pools. A comparative analysis is also carried out between the proposal and other methods. An average recognition accuracy of 77.57% was achieved on the recognition of four emotions with the best accuracy of 86.67% to recognize the positive and excited emotion. Using differing ways of allocating four emotions into two emotion pools, we found there is a difference in the effectiveness of a classifier on learning each emotion. When compared to other methods, the proposed method demonstrates a significant improvement in recognizing four emotions in the multi-subject context. The proposed three-stage decision method solves a crucial issue which is 'individual differences' in multi-subject emotion recognition and overcomes the suboptimal performance with respect to direct classification of multiple emotions. Our study supports the observation that the proposed method represents a promising methodology for recognizing multiple emotions in the multi-subject context.

Variations in Recollection: The Effects of Complexity on Source Recognition

ERIC Educational Resources Information Center

Parks, Colleen M.; Murray, Linda J.; Elfman, Kane; Yonelinas, Andrew P.

2011-01-01

Whether recollection is a threshold or signal detection process is highly controversial, and the controversy has centered in part on the shape of receiver operating characteristics (ROCs) and z-transformed ROCs (zROCs). U-shaped zROCs observed in tests thought to rely heavily on recollection, such as source memory tests, have provided evidence in…

The nucleosome: orchestrating DNA damage signaling and repair within chromatin.

PubMed

Agarwal, Poonam; Miller, Kyle M

2016-10-01

DNA damage occurs within the chromatin environment, which ultimately participates in regulating DNA damage response (DDR) pathways and repair of the lesion. DNA damage activates a cascade of signaling events that extensively modulates chromatin structure and organization to coordinate DDR factor recruitment to the break and repair, whilst also promoting the maintenance of normal chromatin functions within the damaged region. For example, DDR pathways must avoid conflicts between other DNA-based processes that function within the context of chromatin, including transcription and replication. The molecular mechanisms governing the recognition, target specificity, and recruitment of DDR factors and enzymes to the fundamental repeating unit of chromatin, i.e., the nucleosome, are poorly understood. Here we present our current view of how chromatin recognition by DDR factors is achieved at the level of the nucleosome. Emerging evidence suggests that the nucleosome surface, including the nucleosome acidic patch, promotes the binding and activity of several DNA damage factors on chromatin. Thus, in addition to interactions with damaged DNA and histone modifications, nucleosome recognition by DDR factors plays a key role in orchestrating the requisite chromatin response to maintain both genome and epigenome integrity.

Mechanisms and evolution of plant resistance to aphids.

PubMed

Züst, Tobias; Agrawal, Anurag A

2016-01-06

Aphids are important herbivores of both wild and cultivated plants. Plants rely on unique mechanisms of recognition, signalling and defence to cope with the specialized mode of phloem feeding by aphids. Aspects of the molecular mechanisms underlying aphid-plant interactions are beginning to be understood. Recent advances include the identification of aphid salivary proteins involved in host plant manipulation, and plant receptors involved in aphid recognition. However, a complete picture of aphid-plant interactions requires consideration of the ecological outcome of these mechanisms in nature, and the evolutionary processes that shaped them. Here we identify general patterns of resistance, with a special focus on recognition, phytohormonal signalling, secondary metabolites and induction of plant resistance. We discuss how host specialization can enable aphids to co-opt both the phytohormonal responses and defensive compounds of plants for their own benefit at a local scale. In response, systemically induced resistance in plants is common and often involves targeted responses to specific aphid species or even genotypes. As co-evolutionary adaptation between plants and aphids is ongoing, the stealthy nature of aphid feeding makes both the mechanisms and outcomes of these interactions highly distinct from those of other herbivore-plant interactions.

Multimedia Classifier

NASA Astrophysics Data System (ADS)

Costache, G. N.; Gavat, I.

2004-09-01

Along with the aggressive growing of the amount of digital data available (text, audio samples, digital photos and digital movies joined all in the multimedia domain) the need for classification, recognition and retrieval of this kind of data became very important. In this paper will be presented a system structure to handle multimedia data based on a recognition perspective. The main processing steps realized for the interesting multimedia objects are: first, the parameterization, by analysis, in order to obtain a description based on features, forming the parameter vector; second, a classification, generally with a hierarchical structure to make the necessary decisions. For audio signals, both speech and music, the derived perceptual features are the melcepstral (MFCC) and the perceptual linear predictive (PLP) coefficients. For images, the derived features are the geometric parameters of the speaker mouth. The hierarchical classifier consists generally in a clustering stage, based on the Kohonnen Self-Organizing Maps (SOM) and a final stage, based on a powerful classification algorithm called Support Vector Machines (SVM). The system, in specific variants, is applied with good results in two tasks: the first, is a bimodal speech recognition which uses features obtained from speech signal fused to features obtained from speaker's image and the second is a music retrieval from large music database.

Non-Cooperative Target Recognition by Means of Singular Value Decomposition Applied to Radar High Resolution Range Profiles †

PubMed Central

López-Rodríguez, Patricia; Escot-Bocanegra, David; Fernández-Recio, Raúl; Bravo, Ignacio

2015-01-01

Radar high resolution range profiles are widely used among the target recognition community for the detection and identification of flying targets. In this paper, singular value decomposition is applied to extract the relevant information and to model each aircraft as a subspace. The identification algorithm is based on angle between subspaces and takes place in a transformed domain. In order to have a wide database of radar signatures and evaluate the performance, simulated range profiles are used as the recognition database while the test samples comprise data of actual range profiles collected in a measurement campaign. Thanks to the modeling of aircraft as subspaces only the valuable information of each target is used in the recognition process. Thus, one of the main advantages of using singular value decomposition, is that it helps to overcome the notable dissimilarities found in the shape and signal-to-noise ratio between actual and simulated profiles due to their difference in nature. Despite these differences, the recognition rates obtained with the algorithm are quite promising. PMID:25551484

Assessing spoken word recognition in children who are deaf or hard of hearing: a translational approach.

PubMed

Kirk, Karen Iler; Prusick, Lindsay; French, Brian; Gotch, Chad; Eisenberg, Laurie S; Young, Nancy

2012-06-01

Under natural conditions, listeners use both auditory and visual speech cues to extract meaning from speech signals containing many sources of variability. However, traditional clinical tests of spoken word recognition routinely employ isolated words or sentences produced by a single talker in an auditory-only presentation format. The more central cognitive processes used during multimodal integration, perceptual normalization, and lexical discrimination that may contribute to individual variation in spoken word recognition performance are not assessed in conventional tests of this kind. In this article, we review our past and current research activities aimed at developing a series of new assessment tools designed to evaluate spoken word recognition in children who are deaf or hard of hearing. These measures are theoretically motivated by a current model of spoken word recognition and also incorporate "real-world" stimulus variability in the form of multiple talkers and presentation formats. The goal of this research is to enhance our ability to estimate real-world listening skills and to predict benefit from sensory aid use in children with varying degrees of hearing loss. American Academy of Audiology.

Binding Affinity of Glycoconjugates to BACILLUS Spores and Toxins

NASA Astrophysics Data System (ADS)

Rasol, Aveen; Eassa, Souzan; Tarasenko, Olga

2010-04-01

Early recognition of Bacillus cereus group species is important since they can cause food-borne illnesses and deadly diseases in humans. Glycoconjugates (GCs) are carbohydrates covalently linked to non-sugar moieties including lipids, proteins or other entities. GCs are involved in recognition and signaling processes intrinsic to biochemical functions in cells. They also stimulate cell-cell adhesion and subsequent recognition and activation of receptors. We have demonstrated that GCs are involved in Bacillus cereus spore recognition. In the present study, we have investigated whether GCs possess the ability to bind and recognize B. cereus spores and Bacillus anthracis recombinant single toxins (sTX) and complex toxins (cTX). The affinity of GCs to spores + sTX and spores + cTX toxins was studied in the binding essay. Our results demonstrated that GC9 and GC10 were able to selectively bind to B. cereus spores and B. anthracis toxins. Different binding affinities for GCs were found toward Bacillus cereus spores + sTX and spores + cTX. Dilution of GCs does not impede the recognition and binding. Developed method provides a tool for simultaneous recognition and targeting of spores, bacteria toxins, and/or other entities.

The time course of auditory-visual processing of speech and body actions: evidence for the simultaneous activation of an extended neural network for semantic processing.

PubMed

Meyer, Georg F; Harrison, Neil R; Wuerger, Sophie M

2013-08-01

An extensive network of cortical areas is involved in multisensory object and action recognition. This network draws on inferior frontal, posterior temporal, and parietal areas; activity is modulated by familiarity and the semantic congruency of auditory and visual component signals even if semantic incongruences are created by combining visual and auditory signals representing very different signal categories, such as speech and whole body actions. Here we present results from a high-density ERP study designed to examine the time-course and source location of responses to semantically congruent and incongruent audiovisual speech and body actions to explore whether the network involved in action recognition consists of a hierarchy of sequentially activated processing modules or a network of simultaneously active processing sites. We report two main results:1) There are no significant early differences in the processing of congruent and incongruent audiovisual action sequences. The earliest difference between congruent and incongruent audiovisual stimuli occurs between 240 and 280 ms after stimulus onset in the left temporal region. Between 340 and 420 ms, semantic congruence modulates responses in central and right frontal areas. Late differences (after 460 ms) occur bilaterally in frontal areas.2) Source localisation (dipole modelling and LORETA) reveals that an extended network encompassing inferior frontal, temporal, parasaggital, and superior parietal sites are simultaneously active between 180 and 420 ms to process auditory–visual action sequences. Early activation (before 120 ms) can be explained by activity in mainly sensory cortices. . The simultaneous activation of an extended network between 180 and 420 ms is consistent with models that posit parallel processing of complex action sequences in frontal, temporal and parietal areas rather than models that postulate hierarchical processing in a sequence of brain regions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Pedestrian recognition using automotive radar sensors

NASA Astrophysics Data System (ADS)

Bartsch, A.; Fitzek, F.; Rasshofer, R. H.

2012-09-01

The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insights into the object classification process. The impact of raw radar data properties can be directly observed in every layer of the classification system by avoiding machine learning and tracking. This gives information on the limiting factors of raw radar data in terms of classification decision making. To accomplish the very challenging distinction between pedestrians and static objects, five significant and stable object features from the spatial distribution and Doppler information are found. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. The impact of the pedestrian's direction of movement, occlusion, antenna beam elevation angle, linear vehicle movement, and other factors are investigated and discussed. The results show that under real life conditions, radar only based pedestrian recognition is limited due to insufficient Doppler frequency and spatial resolution as well as antenna side lobe effects.

Improved Reconstruction of Radio Holographic Signal for Forward Scatter Radar Imaging

PubMed Central

Hu, Cheng; Liu, Changjiang; Wang, Rui; Zeng, Tao

2016-01-01

Forward scatter radar (FSR), as a specially configured bistatic radar, is provided with the capabilities of target recognition and classification by the Shadow Inverse Synthetic Aperture Radar (SISAR) imaging technology. This paper mainly discusses the reconstruction of radio holographic signal (RHS), which is an important procedure in the signal processing of FSR SISAR imaging. Based on the analysis of signal characteristics, the method for RHS reconstruction is improved in two parts: the segmental Hilbert transformation and the reconstruction of mainlobe RHS. In addition, a quantitative analysis of the method’s applicability is presented by distinguishing between the near field and far field in forward scattering. Simulation results validated the method’s advantages in improving the accuracy of RHS reconstruction and imaging. PMID:27164114

Pattern recognition of native plant communities: Manitou Colorado test site

NASA Technical Reports Server (NTRS)

Driscoll, R. S.

1972-01-01

Optimum channel selection among 12 channels of multispectral scanner imagery identified six as providing the best information about 11 vegetation classes and two nonvegetation classes at the Manitou Experimental Forest. Intensive preprocessing of the scanner signals was required to eliminate a serious scan angle effect. Final processing of the normalized data provided acceptable recognition results of generalized plant community types. Serious errors occurred with attempts to classify specific community types within upland grassland areas. The consideration of the convex mixtures concept (effects of amounts of live plant cover, exposed soil, and plant litter cover on apparent scene radiances) significantly improved the classification of some of the grassland classes.

Weak Long-Range Correlated Motions in a Surface Patch of Ubiquitin Involved in Molecular Recognition

PubMed Central

2011-01-01

Long-range correlated motions in proteins are candidate mechanisms for processes that require information transfer across protein structures, such as allostery and signal transduction. However, the observation of backbone correlations between distant residues has remained elusive, and only local correlations have been revealed using residual dipolar couplings measured by NMR spectroscopy. In this work, we experimentally identified and characterized collective motions spanning four β-strands separated by up to 15 Å in ubiquitin. The observed correlations link molecular recognition sites and result from concerted conformational changes that are in part mediated by the hydrogen-bonding network. PMID:21634390

Pattern-recognition techniques applied to performance monitoring of the DSS 13 34-meter antenna control assembly

NASA Technical Reports Server (NTRS)

Mellstrom, J. A.; Smyth, P.

1991-01-01

The results of applying pattern recognition techniques to diagnose fault conditions in the pointing system of one of the Deep Space network's large antennas, the DSS 13 34-meter structure, are discussed. A previous article described an experiment whereby a neural network technique was used to identify fault classes by using data obtained from a simulation model of the Deep Space Network (DSN) 70-meter antenna system. Described here is the extension of these classification techniques to the analysis of real data from the field. The general architecture and philosophy of an autonomous monitoring paradigm is described and classification results are discussed and analyzed in this context. Key features of this approach include a probabilistic time-varying context model, the effective integration of signal processing and system identification techniques with pattern recognition algorithms, and the ability to calibrate the system given limited amounts of training data. Reported here are recognition accuracies in the 97 to 98 percent range for the particular fault classes included in the experiments.

Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods

NASA Astrophysics Data System (ADS)

Xing, Y. F.; Wang, Y. S.; Shi, L.; Guo, H.; Chen, H.

2016-01-01

According to the human perceptional characteristics, a method combined by the optimal wavelet-packet transform and artificial neural network, so-called OWPT-ANN model, for psychoacoustical recognition is presented. Comparisons of time-frequency analysis methods are performed, and an OWPT with 21 critical bands is designed for feature extraction of a sound, as is a three-layer back-propagation ANN for sound quality (SQ) recognition. Focusing on the loudness and sharpness, the OWPT-ANN model is applied on vehicle noises under different working conditions. Experimental verifications show that the OWPT can effectively transfer a sound into a time-varying energy pattern as that in the human auditory system. The errors of loudness and sharpness of vehicle noise from the OWPT-ANN are all less than 5%, which suggest a good accuracy of the OWPT-ANN model in SQ recognition. The proposed methodology might be regarded as a promising technique for signal processing in the human-hearing related fields in engineering.

Primary Stability Recognition of the Newly Designed Cementless Femoral Stem Using Digital Signal Processing

PubMed Central

Salleh, Sh-Hussain; Hamedi, Mahyar; Zulkifly, Ahmad Hafiz; Lee, Muhammad Hisyam; Mohd Noor, Alias; Harris, Arief Ruhullah A.; Abdul Majid, Norazman

2014-01-01

Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant. We measured strain with triaxial rosette at the metaphyseal region and micromotion with linear variable direct transducer proximally and distally using composite femora. The root mean squares technique is used to feed the classifier which provides maximum likelihood estimation of amplitude, and radial basis function is used as the kernel parameter which mapped the datasets into separable hyperplanes. The results showed 100% pattern recognition accuracy using SVM for both strain and micromotion. This indicates that DSP could be applied in determining the femoral stem primary stability with high pattern recognition accuracy in biomechanical testing. PMID:24800230

Primary stability recognition of the newly designed cementless femoral stem using digital signal processing.

PubMed

Baharuddin, Mohd Yusof; Salleh, Sh-Hussain; Hamedi, Mahyar; Zulkifly, Ahmad Hafiz; Lee, Muhammad Hisyam; Mohd Noor, Alias; Harris, Arief Ruhullah A; Abdul Majid, Norazman

2014-01-01

Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant. We measured strain with triaxial rosette at the metaphyseal region and micromotion with linear variable direct transducer proximally and distally using composite femora. The root mean squares technique is used to feed the classifier which provides maximum likelihood estimation of amplitude, and radial basis function is used as the kernel parameter which mapped the datasets into separable hyperplanes. The results showed 100% pattern recognition accuracy using SVM for both strain and micromotion. This indicates that DSP could be applied in determining the femoral stem primary stability with high pattern recognition accuracy in biomechanical testing.

On the Use of Evolutionary Algorithms to Improve the Robustness of Continuous Speech Recognition Systems in Adverse Conditions

NASA Astrophysics Data System (ADS)

Selouani, Sid-Ahmed; O'Shaughnessy, Douglas

2003-12-01

Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR) systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT) in the mel-frequency domain with a genetic algorithm (GA) to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs) varying from 16 dB to[InlineEquation not available: see fulltext.] dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.

Intracellular Zn(2+) signaling in the dentate gyrus is required for object recognition memory.

PubMed

Takeda, Atsushi; Tamano, Haruna; Ogawa, Taisuke; Takada, Shunsuke; Nakamura, Masatoshi; Fujii, Hiroaki; Ando, Masaki

2014-11-01

The role of perforant pathway-dentate granule cell synapses in cognitive behavior was examined focusing on synaptic Zn(2+) signaling in the dentate gyrus. Object recognition memory was transiently impaired when extracellular Zn(2+) levels were decreased by injection of clioquinol and N,N,N',N'-tetrakis-(2-pyridylmethyl) ethylendediamine. To pursue the effect of the loss and/or blockade of Zn(2+) signaling in dentate granule cells, ZnAF-2DA (100 pmol, 0.1 mM/1 µl), an intracellular Zn(2+) chelator, was locally injected into the dentate molecular layer of rats. ZnAF-2DA injection, which was estimated to chelate intracellular Zn(2+) signaling only in the dentate gyrus, affected object recognition memory 1 h after training without affecting intracellular Ca(2+) signaling in the dentate molecular layer. In vivo dentate gyrus long-term potentiation (LTP) was affected under the local perfusion of the recording region (the dentate granule cell layer) with 0.1 mM ZnAF-2DA, but not with 1-10 mM CaEDTA, an extracellular Zn(2+) chelator, suggesting that the blockade of intracellular Zn(2+) signaling in dentate granule cells affects dentate gyrus LTP. The present study demonstrates that intracellular Zn(2+) signaling in the dentate gyrus is required for object recognition memory, probably via dentate gyrus LTP expression. Copyright © 2014 Wiley Periodicals, Inc.

Scene Analysis: Non-Linear Spatial Filtering for Automatic Target Detection.

DTIC Science & Technology

1982-12-01

In this thesis, a method for two-dimensional pattern recognition was developed and tested. The method included a global search scheme for candidate...test global switch TYPEO Creating negative video file only.W 11=0 12=256 13=512 14=768 GO 70 2 1 TYPE" Creating negative and horizontally flipped video...purpose was to develop a base of image processing software for the AFIT Digital Signal Processing Laboratory NOVA- ECLIPSE minicomputer system, for

The Effects of Probe Similarity on Retrieval and Comparison Processes in Associative Recognition.

PubMed

Zhang, Qiong; Walsh, Matthew M; Anderson, John R

2017-02-01

In this study, we investigated the information processing stages underlying associative recognition. We recorded EEG data while participants performed a task that involved deciding whether a probe word triple matched any previously studied triple. We varied the similarity between probes and studied triples. According to a model of associative recognition developed in the Adaptive Control of Thought-Rational cognitive architecture, probe similarity affects the duration of the retrieval stage: Retrieval is fastest when the probe is similar to a studied triple. This effect may be obscured, however, by the duration of the comparison stage, which is fastest when the probe is not similar to the retrieved triple. Owing to the opposing effects of probe similarity on retrieval and comparison, overall RTs provide little information about each stage's duration. As such, we evaluated the model using a novel approach that decomposes the EEG signal into a sequence of latent states and provides information about the durations of the underlying information processing stages. The approach uses a hidden semi-Markov model to identify brief sinusoidal peaks (called bumps) that mark the onsets of distinct cognitive stages. The analysis confirmed that probe type has opposite effects on retrieval and comparison stages.

Hetero-enzyme-based two-round signal amplification strategy for trace detection of aflatoxin B1 using an electrochemical aptasensor.

PubMed

Zheng, Wanli; Teng, Jun; Cheng, Lin; Ye, Yingwang; Pan, Daodong; Wu, Jingjing; Xue, Feng; Liu, Guodong; Chen, Wei

2016-06-15

An electrochemical aptasensor for trace detection of aflatoxin B1 (AFB1) was developed by using an aptamer as the recognition unit while adopting the telomerase and EXO III based two-round signal amplification strategy as the signal enhancement units. The telomerase amplification was used to elongate the ssDNA probes on the surface of gold nanoparticles, by which the signal response range of the signal-off model electrochemical aptasensor could be correspondingly enlarged. Then, the EXO III amplification was used to hydrolyze the 3'-end of the dsDNA after the recognition of target AFB1, which caused the release of bounded AFB1 into the sensing system, where it participated in the next recognition-sensing cycle. With this two-round signal amplified electrochemical aptasensor, target AFB1 was successfully measured at trace concentrations with excellent detection limit of 0.6*10(-4)ppt and satisfied specificity due to the excellent affinity of the aptamer against AFB1. Based on this designed two-round signal amplification strategy, both the sensing range and detection limit were greatly improved. This proposed ultrasensitive electrochemical aptasensor method was also validated by comparison with the classic instrumental methods. Importantly, this hetero-enzyme based two-round signal amplified electrochemical aptasensor offers a great promising protocol for ultrasensitive detection of AFB1 and other mycotoxins by replacing the core recognition sequence of the aptamer. Copyright © 2016 Elsevier B.V. All rights reserved.

Motion-based signaling in sympatric species of Australian agamid lizards.

PubMed

Ramos, Jose A; Peters, Richard A

2017-08-01

Signaling species occurring in sympatry are often exposed to similar environmental constraints, so similar adaptations to enhance signal efficacy are expected. However, potentially opposing selective pressures might be present to ensure species recognition. Here, we analyzed the movement-based signals of two pairs of sympatric lizard species to consider how reliable communication is maintained while avoiding misidentification. Our novel approach allows us to quantify signal contrast with plant motion noise at any site we measure, including those utilized by other species. Ctenophorus caudicinctus and Gowidon longirostris differed in display complexity and motor pattern use. They also differed in overall morphology, but their signal contrast scores are strikingly similar. These results demonstrate similar adaptations to their shared environment while maintaining species recognition cues. In contrast, Ctenophorus fordi and Ctenophorus pictus are much closer in appearance, but C. pictus produces considerably higher signal contrast scores, which we suggest is attributable to the absence of territoriality in C. fordi. Taken together, our data provide evidence for adaptation to the local environment in movement-based signals, while also meeting species recognition requirements, but the selective pressure to deal with local conditions is mediated by signal function.

Visual speech information: a help or hindrance in perceptual processing of dysarthric speech.

PubMed

Borrie, Stephanie A

2015-03-01

This study investigated the influence of visual speech information on perceptual processing of neurologically degraded speech. Fifty listeners identified spastic dysarthric speech under both audio (A) and audiovisual (AV) conditions. Condition comparisons revealed that the addition of visual speech information enhanced processing of the neurologically degraded input in terms of (a) acuity (percent phonemes correct) of vowels and consonants and (b) recognition (percent words correct) of predictive and nonpredictive phrases. Listeners exploited stress-based segmentation strategies more readily in AV conditions, suggesting that the perceptual benefit associated with adding visual speech information to the auditory signal-the AV advantage-has both segmental and suprasegmental origins. Results also revealed that the magnitude of the AV advantage can be predicted, to some degree, by the extent to which an individual utilizes syllabic stress cues to inform word recognition in AV conditions. Findings inform the development of a listener-specific model of speech perception that applies to processing of dysarthric speech in everyday communication contexts.

Infrared and visible fusion face recognition based on NSCT domain

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Zhang, Shuai; Liu, Guodong; Xiong, Jinquan

2018-01-01

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In this paper, a novel fusion algorithm in non-subsampled contourlet transform (NSCT) domain is proposed for Infrared and visible face fusion recognition. Firstly, NSCT is used respectively to process the infrared and visible face images, which exploits the image information at multiple scales, orientations, and frequency bands. Then, to exploit the effective discriminant feature and balance the power of high-low frequency band of NSCT coefficients, the local Gabor binary pattern (LGBP) and Local Binary Pattern (LBP) are applied respectively in different frequency parts to obtain the robust representation of infrared and visible face images. Finally, the score-level fusion is used to fuse the all the features for final classification. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. Experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition.

[Surface electromyography signal classification using gray system theory].

PubMed

Xie, Hongbo; Ma, Congbin; Wang, Zhizhong; Huang, Hai

2004-12-01

A new method based on gray correlation was introduced to improve the identification rate in artificial limb. The electromyography (EMG) signal was first transformed into time-frequency domain by wavelet transform. Singular value decomposition (SVD) was then used to extract feature vector from the wavelet coefficient for pattern recognition. The decision was made according to the maximum gray correlation coefficient. Compared with neural network recognition, this robust method has an almost equivalent recognition rate but much lower computation costs and less training samples.

Likelihood ratio decisions in memory: three implied regularities.

PubMed

Glanzer, Murray; Hilford, Andrew; Maloney, Laurence T

2009-06-01

We analyze four general signal detection models for recognition memory that differ in their distributional assumptions. Our analyses show that a basic assumption of signal detection theory, the likelihood ratio decision axis, implies three regularities in recognition memory: (1) the mirror effect, (2) the variance effect, and (3) the z-ROC length effect. For each model, we present the equations that produce the three regularities and show, in computed examples, how they do so. We then show that the regularities appear in data from a range of recognition studies. The analyses and data in our study support the following generalization: Individuals make efficient recognition decisions on the basis of likelihood ratios.

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.

PubMed

Yildirim, Özal

2018-05-01

Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures. In this study, a new model for deep bidirectional LSTM network-based wavelet sequences called DBLSTM-WS was proposed for classifying electrocardiogram (ECG) signals. For this purpose, a new wavelet-based layer is implemented to generate ECG signal sequences. The ECG signals were decomposed into frequency sub-bands at different scales in this layer. These sub-bands are used as sequences for the input of LSTM networks. New network models that include unidirectional (ULSTM) and bidirectional (BLSTM) structures are designed for performance comparisons. Experimental studies have been performed for five different types of heartbeats obtained from the MIT-BIH arrhythmia database. These five types are Normal Sinus Rhythm (NSR), Ventricular Premature Contraction (VPC), Paced Beat (PB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The results show that the DBLSTM-WS model gives a high recognition performance of 99.39%. It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks. This proposed network structure is an important approach that can be applied to similar signal processing problems. Copyright © 2018 Elsevier Ltd. All rights reserved.

Temporally flexible feedback signal to foveal cortex for peripheral object recognition

PubMed Central

Fan, Xiaoxu; Wang, Lan; Shao, Hanyu; Kersten, Daniel; He, Sheng

2016-01-01

Recent studies have shown that information from peripherally presented images is present in the human foveal retinotopic cortex, presumably because of feedback signals. We investigated this potential feedback signal by presenting noise in fovea at different object–noise stimulus onset asynchronies (SOAs), whereas subjects performed a discrimination task on peripheral objects. Results revealed a selective impairment of performance when foveal noise was presented at 250-ms SOA, but only for tasks that required comparing objects’ spatial details, suggesting a task- and stimulus-dependent foveal processing mechanism. Critically, the temporal window of foveal processing was shifted when mental rotation was required for the peripheral objects, indicating that the foveal retinotopic processing is not automatically engaged at a fixed time following peripheral stimulation; rather, it occurs at a stage when detailed information is required. Moreover, fMRI measurements using multivoxel pattern analysis showed that both image and object category-relevant information of peripheral objects was represented in the foveal cortex. Taken together, our results support the hypothesis of a temporally flexible feedback signal to the foveal retinotopic cortex when discriminating objects in the visual periphery. PMID:27671651

Information Theoretic Extraction of EEG Features for Monitoring Subject Attention

NASA Technical Reports Server (NTRS)

Principe, Jose C.

2000-01-01

The goal of this project was to test the applicability of information theoretic learning (feasibility study) to develop new brain computer interfaces (BCI). The difficulty to BCI comes from several aspects: (1) the effective data collection of signals related to cognition; (2) the preprocessing of these signals to extract the relevant information; (3) the pattern recognition methodology to detect reliably the signals related to cognitive states. We only addressed the two last aspects in this research. We started by evaluating an information theoretic measure of distance (Bhattacharyya distance) for BCI performance with good predictive results. We also compared several features to detect the presence of event related desynchronization (ERD) and synchronization (ERS), and concluded that at least for now the bandpass filtering is the best compromise between simplicity and performance. Finally, we implemented several classifiers for temporal - pattern recognition. We found out that the performance of temporal classifiers is superior to static classifiers but not by much. We conclude by stating that the future of BCI should be found in alternate approaches to sense, collect and process the signals created by populations of neurons. Towards this goal, cross-disciplinary teams of neuroscientists and engineers should be funded to approach BCIs from a much more principled view point.

A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices.

PubMed

Chen, Chieh-Li; Chuang, Chun-Te

2017-08-26

In the new-generation wearable Electrocardiogram (ECG) system, signal processing with low power consumption is required to transmit data when detecting dangerous rhythms and to record signals when detecting abnormal rhythms. The QRS complex is a combination of three of the graphic deflection seen on a typical ECG. This study proposes a real-time QRS detection and R point recognition method with low computational complexity while maintaining a high accuracy. The enhancement of QRS segments and restraining of P and T waves are carried out by the proposed ECG signal transformation, which also leads to the elimination of baseline wandering. In this study, the QRS fiducial point is determined based on the detected crests and troughs of the transformed signal. Subsequently, the R point can be recognized based on four QRS waveform templates and preliminary heart rhythm classification can be also achieved at the same time. The performance of the proposed approach is demonstrated using the benchmark of the MIT-BIH Arrhythmia Database, where the QRS detected sensitivity (Se) and positive prediction (+P) are 99.82% and 99.81%, respectively. The result reveals the approach's advantage of low computational complexity, as well as the feasibility of the real-time application on a mobile phone and an embedded system.

Detection of Terrorist Preparations by an Artificial Intelligence Expert System Employing Fuzzy Signal Detection Theory

DTIC Science & Technology

2004-10-25

FUSEDOT does not require facial recognition , or video surveillance of public areas, both of which are apparently a component of TIA ([26], pp...does not use fuzzy signal detection. Involves facial recognition and video surveillance of public areas. Involves monitoring the content of voice...fuzzy signal detection, which TIA does not. Second, FUSEDOT would be easier to develop, because it does not require the development of facial

Mechanism for recognition of polyubiquitin chains: balancing affinity through interplay between multivalent binding and dynamics.

PubMed

Markin, Craig J; Xiao, Wei; Spyracopoulos, Leo

2010-08-18

RAP80 plays a key role in signal transduction in the DNA damage response by recruiting proteins to DNA damage foci by binding K63-polyubiquitin chains with two tandem ubiquitin-interacting motifs (tUIM). It is generally recognized that the typically weak interaction between ubiquitin (Ub) and various recognition motifs is intensified by themes such as tandem recognition motifs and Ub polymerization to achieve biological relevance. However, it remains an intricate problem to develop a detailed molecular mechanism to describe the process that leads to amplification of the Ub signal. A battery of solution-state NMR methods and molecular dynamics simulations were used to demonstrate that RAP80-tUIM employs mono- and multivalent interactions with polyUb chains to achieve enhanced affinity in comparison to monoUb interactions for signal amplification. The enhanced affinity is balanced by unfavorable entropic effects that include partial quenching of rapid reorientation between individual UIM domains and individual Ub domains in the bound state. For the RAP80-tUIM-polyUb interaction, increases in affinity with increasing chain length are a result of increased numbers of mono- and multivalent binding sites in the longer polyUb chains. The mono- and multivalent interactions are characterized by intrinsically weak binding and fast off-rates; these weak interactions with fast kinetics may be an important factor underlying the transient nature of protein-protein interactions that comprise DNA damage foci.

QWT: Retrospective and New Applications

NASA Astrophysics Data System (ADS)

Xu, Yi; Yang, Xiaokang; Song, Li; Traversoni, Leonardo; Lu, Wei

Quaternion wavelet transform (QWT) achieves much attention in recent years as a new image analysis tool. In most cases, it is an extension of the real wavelet transform and complex wavelet transform (CWT) by using the quaternion algebra and the 2D Hilbert transform of filter theory, where analytic signal representation is desirable to retrieve phase-magnitude description of intrinsically 2D geometric structures in a grayscale image. In the context of color image processing, however, it is adapted to analyze the image pattern and color information as a whole unit by mapping sequential color pixels to a quaternion-valued vector signal. This paper provides a retrospective of QWT and investigates its potential use in the domain of image registration, image fusion, and color image recognition. It is indicated that it is important for QWT to induce the mechanism of adaptive scale representation of geometric features, which is further clarified through two application instances of uncalibrated stereo matching and optical flow estimation. Moreover, quaternionic phase congruency model is defined based on analytic signal representation so as to operate as an invariant feature detector for image registration. To achieve better localization of edges and textures in image fusion task, we incorporate directional filter bank (DFB) into the quaternion wavelet decomposition scheme to greatly enhance the direction selectivity and anisotropy of QWT. Finally, the strong potential use of QWT in color image recognition is materialized in a chromatic face recognition system by establishing invariant color features. Extensive experimental results are presented to highlight the exciting properties of QWT.

The Consonant-Weighted Envelope Difference Index (cEDI): A Proposed Technique for Quantifying Envelope Distortion

ERIC Educational Resources Information Center

Hoover, Eric C.; Souza, Pamela E.; Gallun, Frederick J.

2012-01-01

Purpose: The benefits of amplitude compression in hearing aids may be limited by distortion resulting from rapid gain adjustment. To evaluate this, it is convenient to quantify distortion by using a metric that is sensitive to the changes in the processed signal that decrease consonant recognition, such as the Envelope Difference Index (EDI;…

Terrestrial implications of mathematical modeling developed for space biomedical research

NASA Technical Reports Server (NTRS)

Lujan, Barbara F.; White, Ronald J.; Leonard, Joel I.; Srinivasan, R. Srini

1988-01-01

This paper summarizes several related research projects supported by NASA which seek to apply computer models to space medicine and physiology. These efforts span a wide range of activities, including mathematical models used for computer simulations of physiological control systems; power spectral analysis of physiological signals; pattern recognition models for detection of disease processes; and computer-aided diagnosis programs.

Applicability of mathematical modeling to problems of environmental physiology

NASA Technical Reports Server (NTRS)

White, Ronald J.; Lujan, Barbara F.; Leonard, Joel I.; Srinivasan, R. Srini

1988-01-01

The paper traces the evolution of mathematical modeling and systems analysis from terrestrial research to research related to space biomedicine and back again to terrestrial research. Topics covered include: power spectral analysis of physiological signals; pattern recognition models for detection of disease processes; and, computer-aided diagnosis programs used in conjunction with a special on-line biomedical computer library.

Flexible Piezoelectric Sensor-Based Gait Recognition.

PubMed

Cha, Youngsu; Kim, Hojoon; Kim, Doik

2018-02-05

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

Intrahippocampal injection of Cortistatin-14 impairs recognition memory consolidation in mice through activation of sst2, ghrelin and GABAA/B receptors.

PubMed

Jiang, Jinhong; Peng, Yali; He, Zhen; Wei, Lijuan; Jin, Weidong; Wang, Xiaoli; Chang, Min

2017-07-01

Cortistatin-14 (CST-14), a neuropeptide related to somatostatin, is primarily localized within the cortex and hippocampus. In the hippocampus, CST-14 inhibits CA1 neuronal pyramidal cell firing and co-exists with GABA. However, its role in cognitive is still not clarified. The first aim of our study was to elucidate the role of CST-14 signaling in consolidation and reconsolidation of recognition memory in mice, using novel object recognition task. The results showed that central CST-14 induced in impairment of long-term and short-term recognition memory, indicating memory consolidation impairment effect. Similarly, we found that CST-14 did not impaired long-term and short-term reconsolidation recognition memory. To further investigate the underlying mechanisms of CST-14 in memory process, we used cyclosomatostatin (c-SOM, a selective sst 1-5 receptor antagonist), cyanamid154806 (a selective sst 2 receptor antagonist), ODN-8 (a high affinity and selectivity compound for sst 3 receptor), [d-Lys 3 ]GHRP-6 (a selective ghrelin receptor antagonist), picrotoxin (PTX, a GABA A receptor antagonist), and sacolfen (a GABA B receptor antagonist) to research its effects in recognition. Our results firstly indicated that the memory-impairing effects of CST-14 were significantly reversed by c-SOM, cyanamid154806, [d-Lys 3 ]GHRP-6, PTX and sacolfen, but not ODN-8, suggesting that the blockage of recognition memory consolidation induced by CST-14 involves sst 2 , ghrelin and GABA system. The present study provides a potential strategy to regulate memory processes, providing new evidence that reconsolidation is not a simple reiteration of consolidation. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection and inhibition of bacterial cell-cell communication.

PubMed

Rice, Scott A; McDougald, Diane; Givskov, Michael; Kjelleberg, Staffan

2008-01-01

Bacteria communicate with other members of their community through the secretion and perception of small chemical cues or signals. The recognition of a signal normally leads to the expression of a large suite of genes, which in some bacteria are involved in the regulation of virulence factors, and as a result, these signaling compounds are key regulatory factors in many disease processes. Thus, it is of interest when studying pathogens to understand the mechanisms used to control the expression of virulence genes so that strategies might be devised for the control of those pathogens. Clearly, the ability to interfere with this process of signaling represents a novel approach for the treatment of bacterial infections. There is a broad range of compounds that bacteria can use for signaling purposes, including fatty acids, peptides, N-acylated homoserine lactones, and the signals collectively called autoinducer 2 (AI-2). This chapter will focus on the latter two signaling systems as they are present in a range of medically relevant bacteria, and here we describe assays for determining whether an organism produces a particular signal and assays that can be used to identify inhibitors of the signaling cascade. Lastly, the signal detection and inhibition assays will be directly linked to the expression of virulence factors of specific pathogens.

Hands-free human-machine interaction with voice

NASA Astrophysics Data System (ADS)

Juang, B. H.

2004-05-01

Voice is natural communication interface between a human and a machine. The machine, when placed in today's communication networks, may be configured to provide automation to save substantial operating cost, as demonstrated in AT&T's VRCP (Voice Recognition Call Processing), or to facilitate intelligent services, such as virtual personal assistants, to enhance individual productivity. These intelligent services often need to be accessible anytime, anywhere (e.g., in cars when the user is in a hands-busy-eyes-busy situation or during meetings where constantly talking to a microphone is either undersirable or impossible), and thus call for advanced signal processing and automatic speech recognition techniques which support what we call ``hands-free'' human-machine communication. These techniques entail a broad spectrum of technical ideas, ranging from use of directional microphones and acoustic echo cancellatiion to robust speech recognition. In this talk, we highlight a number of key techniques that were developed for hands-free human-machine communication in the mid-1990s after Bell Labs became a unit of Lucent Technologies. A video clip will be played to demonstrate the accomplishement.

Investigating Strength and Frequency Effects in Recognition Memory Using Type-2 Signal Detection Theory

ERIC Educational Resources Information Center

Higham, Philip A.; Perfect, Timothy J.; Bruno, Davide

2009-01-01

Criterion- versus distribution-shift accounts of frequency and strength effects in recognition memory were investigated with Type-2 signal detection receiver operating characteristic (ROC) analysis, which provides a measure of metacognitive monitoring. Experiment 1 demonstrated a frequency-based mirror effect, with a higher hit rate and lower…

Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.

PubMed

Li, Mi; Xu, Hongpei; Liu, Xingwang; Lu, Shengfu

2018-04-27

Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier. The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands. This paper provided better frequency bands and channels reference for emotion recognition based on EEG.

Applications of Hilbert Spectral Analysis for Speech and Sound Signals

NASA Technical Reports Server (NTRS)

Huang, Norden E.

2003-01-01

A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

Molecular and functional profiling of histamine receptor-mediated calcium ion signals in different cell lines.

PubMed

Meisenberg, Annika; Kaschuba, Dagmar; Balfanz, Sabine; Jordan, Nadine; Baumann, Arnd

2015-10-01

Calcium ions (Ca(2+)) play a pivotal role in cellular physiology. Often Ca(2+)-dependent processes are studied in commonly available cell lines. To induce Ca(2+) signals on demand, cells may need to be equipped with additional proteins. A prominent group of membrane proteins evoking Ca(2+) signals are G-protein coupled receptors (GPCRs). These proteins register external signals such as photons, odorants, and neurotransmitters and convey ligand recognition into cellular responses, one of which is Ca(2+) signaling. To avoid receptor cross-talk or cross-activation with introduced proteins, the repertoire of cell-endogenous receptors must be known. Here we examined the presence of histamine receptors in six cell lines frequently used as hosts to study cellular signaling processes. In a concentration-dependent manner, histamine caused a rise in intracellular Ca(2+) in HeLa, HEK 293, and COS-1 cells. The concentration for half-maximal activation (EC50) was in the low micromolar range. In individual cells, transient Ca(2+) signals and Ca(2+) oscillations were uncovered. The results show that (i) HeLa, HEK 293, and COS-1 cells express sufficient amounts of endogenous receptors to study cellular Ca(2+) signaling processes directly and (ii) these cell lines are suitable for calibrating Ca(2+) biosensors in situ based on histamine receptor evoked responses. Copyright © 2015 Elsevier Inc. All rights reserved.

Dual sensitivity mode system for monitoring processes and sensors

DOEpatents

Wilks, Alan D.; Wegerich, Stephan W.; Gross, Kenneth C.

2000-01-01

A method and system for analyzing a source of data. The system and method involves initially training a system using a selected data signal, calculating at least two levels of sensitivity using a pattern recognition methodology, activating a first mode of alarm sensitivity to monitor the data source, activating a second mode of alarm sensitivity to monitor the data source and generating a first alarm signal upon the first mode of sensitivity detecting an alarm condition and a second alarm signal upon the second mode of sensitivity detecting an associated alarm condition. The first alarm condition and second alarm condition can be acted upon by an operator and/or analyzed by a specialist or computer program.

Recognition of digital characteristics based new improved genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Meng; Xu, Guoqiang; Lin, Zihao

2017-08-01

In the field of digital signal processing, Estimating the characteristics of signal modulation parameters is an significant research direction. The paper determines the set of eigenvalue which can show the difference of the digital signal modulation based on the deep research of the new improved genetic algorithm. Firstly take them as the best gene pool; secondly, The best gene pool will be changed in the genetic evolvement by selecting, overlapping and eliminating each other; Finally, Adapting the strategy of futher enhance competition and punishment to more optimizer the gene pool and ensure each generation are of high quality gene. The simulation results show that this method not only has the global convergence, stability and faster convergence speed.

From perception to activation: the molecular-genetic and biochemical landscape of disease resistance signaling in plants.

PubMed

Knepper, Caleb; Day, Brad

2010-01-01

More than 60 years ago, H.H. Flor proposed the "Gene-for-Gene" hypothesis, which described the genetic relationship between host plants and pathogens. In the decades that followed Flor's seminal work, our understanding of the plant-pathogen interaction has evolved into a sophisticated model, detailing the molecular genetic and biochemical processes that control host-range, disease resistance signaling and susceptibility. The interaction between plants and microbes is an intimate exchange of signals that has evolved for millennia, resulting in the modification and adaptation of pathogen virulence strategies and host recognition elements. In total, plants have evolved mechanisms to combat the ever-changing landscape of biotic interactions bombarding their environment, while in parallel, plant pathogens have co-evolved mechanisms to sense and adapt to these changes. On average, the typical plant is susceptible to attack by dozens of microbial pathogens, yet in most cases, remains resistant to many of these challenges. The sum of research in our field has revealed that these interactions are regulated by multiple layers of intimately linked signaling networks. As an evolved model of Flor's initial observations, the current paradigm in host-pathogen interactions is that pathogen effector molecules, in large part, drive the recognition, activation and subsequent physiological responses in plants that give rise to resistance and susceptibility. In this Chapter, we will discuss our current understanding of the association between plants and microbial pathogens, detailing the pressures placed on both host and microbe to either maintain disease resistance, or induce susceptibility and disease. From recognition to transcriptional reprogramming, we will review current data and literature that has advanced the classical model of the Gene-for-Gene hypothesis to our current understanding of basal and effector triggered immunity.

Facing the Problem: Impaired Emotion Recognition During Multimodal Social Information Processing in Borderline Personality Disorder.

PubMed

Niedtfeld, Inga; Defiebre, Nadine; Regenbogen, Christina; Mier, Daniela; Fenske, Sabrina; Kirsch, Peter; Lis, Stefanie; Schmahl, Christian

2017-04-01

Previous research has revealed alterations and deficits in facial emotion recognition in patients with borderline personality disorder (BPD). During interpersonal communication in daily life, social signals such as speech content, variation in prosody, and facial expression need to be considered simultaneously. We hypothesized that deficits in higher level integration of social stimuli contribute to difficulties in emotion recognition in BPD, and heightened arousal might explain this effect. Thirty-one patients with BPD and thirty-one healthy controls were asked to identify emotions in short video clips, which were designed to represent different combinations of the three communication channels: facial expression, speech content, and prosody. Skin conductance was recorded as a measure of sympathetic arousal, while controlling for state dissociation. Patients with BPD showed lower mean accuracy scores than healthy control subjects in all conditions comprising emotional facial expressions. This was true for the condition with facial expression only, and for the combination of all three communication channels. Electrodermal responses were enhanced in BPD only in response to auditory stimuli. In line with the major body of facial emotion recognition studies, we conclude that deficits in the interpretation of facial expressions lead to the difficulties observed in multimodal emotion processing in BPD.

Eyes and ears: Using eye tracking and pupillometry to understand challenges to speech recognition.

PubMed

Van Engen, Kristin J; McLaughlin, Drew J

2018-05-04

Although human speech recognition is often experienced as relatively effortless, a number of common challenges can render the task more difficult. Such challenges may originate in talkers (e.g., unfamiliar accents, varying speech styles), the environment (e.g. noise), or in listeners themselves (e.g., hearing loss, aging, different native language backgrounds). Each of these challenges can reduce the intelligibility of spoken language, but even when intelligibility remains high, they can place greater processing demands on listeners. Noisy conditions, for example, can lead to poorer recall for speech, even when it has been correctly understood. Speech intelligibility measures, memory tasks, and subjective reports of listener difficulty all provide critical information about the effects of such challenges on speech recognition. Eye tracking and pupillometry complement these methods by providing objective physiological measures of online cognitive processing during listening. Eye tracking records the moment-to-moment direction of listeners' visual attention, which is closely time-locked to unfolding speech signals, and pupillometry measures the moment-to-moment size of listeners' pupils, which dilate in response to increased cognitive load. In this paper, we review the uses of these two methods for studying challenges to speech recognition. Copyright © 2018. Published by Elsevier B.V.

Distinct Modes of Macrophage Recognition for Apoptotic and Necrotic Cells Are Not Specified Exclusively by Phosphatidylserine Exposure

PubMed Central

Cocco, Regina E.; Ucker, David S.

2001-01-01

The distinction between physiological (apoptotic) and pathological (necrotic) cell deaths reflects mechanistic differences in cellular disintegration and is of functional significance with respect to the outcomes that are triggered by the cell corpses. Mechanistically, apoptotic cells die via an active and ordered pathway; necrotic deaths, conversely, are chaotic and passive. Macrophages and other phagocytic cells recognize and engulf these dead cells. This clearance is believed to reveal an innate immunity, associated with inflammation in cases of pathological but not physiological cell deaths. Using objective and quantitative measures to assess these processes, we find that macrophages bind and engulf native apoptotic and necrotic cells to similar extents and with similar kinetics. However, recognition of these two classes of dying cells occurs via distinct and noncompeting mechanisms. Phosphatidylserine, which is externalized on both apoptotic and necrotic cells, is not a specific ligand for the recognition of either one. The distinct modes of recognition for these different corpses are linked to opposing responses from engulfing macrophages. Necrotic cells, when recognized, enhance proinflammatory responses of activated macrophages, although they are not sufficient to trigger macrophage activation. In marked contrast, apoptotic cells profoundly inhibit phlogistic macrophage responses; this represents a cell-associated, dominant-acting anti-inflammatory signaling activity acquired posttranslationally during the process of physiological cell death. PMID:11294896

New Insight into the Role of Reactive Oxygen Species (ROS) in Cellular Signal-Transduction Processes.

PubMed

Russell, Eileen G; Cotter, Thomas G

2015-01-01

Reactive oxygen species (ROS) were once considered to be deleterious agents, contributing to a vast range of pathologies. But, now their protective effects are being appreciated. Both their damaging and beneficial effects are initiated when they target distinct molecules and consequently begin functioning as part of complex signal-transduction pathways. The recognition of ROS as signaling mediators has driven a wealth of research into their roles in both normal and pathophysiological states. The present review assesses the relevant recent literature to outline the current perspectives on redox-signaling mechanisms, physiological implications, and therapeutic strategies. This study highlights that a more fundamental knowledge about many aspects of redox signaling will allow better targeting of ROS, which would in turn improve prophylactic and pharmacotherapy for redox-associated diseases. Copyright © 2015 Elsevier Inc. All rights reserved.

An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition.

PubMed

Lozano-Diez, Alicia; Zazo, Ruben; Toledano, Doroteo T; Gonzalez-Rodriguez, Joaquin

2017-01-01

Language recognition systems based on bottleneck features have recently become the state-of-the-art in this research field, showing its success in the last Language Recognition Evaluation (LRE 2015) organized by NIST (U.S. National Institute of Standards and Technology). This type of system is based on a deep neural network (DNN) trained to discriminate between phonetic units, i.e. trained for the task of automatic speech recognition (ASR). This DNN aims to compress information in one of its layers, known as bottleneck (BN) layer, which is used to obtain a new frame representation of the audio signal. This representation has been proven to be useful for the task of language identification (LID). Thus, bottleneck features are used as input to the language recognition system, instead of a classical parameterization of the signal based on cepstral feature vectors such as MFCCs (Mel Frequency Cepstral Coefficients). Despite the success of this approach in language recognition, there is a lack of studies analyzing in a systematic way how the topology of the DNN influences the performance of bottleneck feature-based language recognition systems. In this work, we try to fill-in this gap, analyzing language recognition results with different topologies for the DNN used to extract the bottleneck features, comparing them and against a reference system based on a more classical cepstral representation of the input signal with a total variability model. This way, we obtain useful knowledge about how the DNN configuration influences bottleneck feature-based language recognition systems performance.

Verbal Memory Functioning in Adolescents and Young Adults with Costello Syndrome: Evidence for Relative Preservation in Recognition Memory

PubMed Central

Schwartz, David D.; Katzenstein, Jennifer M.; Hopkins, Elisabeth; Stabley, Deborah L.; Sol-Church, Katia; Gripp, Karen W.; Axelrad, Marni E.

2013-01-01

Costello syndrome (CS) is a rare genetic disorder caused by germline mutations in the HRAS proto-oncogene which belongs to the family of syndromes called rasopathies. HRAS plays a key role in synaptic long-term potentiation (LTP) and memory formation. Prior research has found impaired recall memory in CS despite enhancement in LTP that would predict memory preservation. Based on findings in other rasopathies, we hypothesized that the memory deficit in CS would be specific to recall, and that recognition memory would show relative preservation. Memory was tested using word-list learning and story memory tasks with both recall and recognition trials, a design that allowed us to examine these processes separately. Participants were 11 adolescents and young adults with molecularly confirmed CS, all of whom fell in the mild to moderate range of intellectual disability. Results indicated a clear dissociation between verbal recall, which was impaired (M = 69 ± 14), and recognition memory, which was relatively intact (M = 86 ± 14). Story recognition was highly correlated with listening comprehension (r = .986), which also fell in the low-average range (M = 80 ± 12.9). Performance on other measures of linguistic ability and academic skills was impaired. The findings suggest relatively preserved recognition memory that also provides some support for verbal comprehension. This is the first report of relatively normal performance in a cognitive domain in CS. Further research is needed to better understand the mechanisms by which altered RAS-MAPK signaling affects neuronal plasticity and memory processes in the brain. PMID:23918324

New partner proteins containing novel internal recognition motif for human Glutaminase Interacting Protein (hGIP)

PubMed Central

Zencir, Sevil; Banerjee, Monimoy; Dobson, Melanie J.; Ayaydin, Ferhan; Fodor, Elfrieda Ayaydin; Topcu, Zeki; Mohanty, Smita

2013-01-01

Regulation of gene expression in cells is mediated by protein-protein, DNA-protein and receptor-ligand interactions. PDZ (PSD-95/Discs-large/ZO-1) domains are protein–protein interaction modules. PDZ-containing proteins function in the organization of multi-protein complexes controlling spatial and temporal fidelity of intracellular signaling pathways. In general, PDZ proteins possess multiple domains facilitating distinct interactions. The human Glutaminase Interacting Protein (hGIP) is an unusual PDZ protein comprising entirely of a single PDZ domain and plays pivotal roles in many cellular processes through its interaction with the C-terminus of partner proteins. Here, we report the identification by yeast two-hybrid screening of two new hGIP-interacting partners, DTX1 and STAU1. Both proteins lack the typical C-terminal PDZ recognition motif but contain a novel internal hGIP recognition motif recently identified in a phage display library screen. Fluorescence resonance energy transfer and confocal microscopy analysis confirmed the in vivo association of hGIP with DTX1 and STAU1 in mammalian cells validating the previous discovery of S/T-X-V/L-D as a consensus internal motif for hGIP recognition. Similar to hGIP, DTX1 and STAU1 have been implicated in neuronal function. Identification of these new interacting partners furthers our understanding of GIP-regulated signaling cascades and these interactions may represent potential new drug targets in humans. PMID:23395680

Evolution of Novel Signal Traits in the Absence of Female Preferences in Neoconocephalus Katydids (Orthoptera, Tettigoniidae)

PubMed Central

Bush, Sarah L.; Schul, Johannes

2010-01-01

Background Significance Communication signals that function to bring together the sexes are important for maintaining reproductive isolation in many taxa. Changes in male calls are often attributed to sexual selection, in which female preferences initiate signal divergence. Natural selection can also influence signal traits if calls attract predators or parasitoids, or if calling is energetically costly. Neutral evolution is often neglected in the context of acoustic communication. Methodology/Principal Findings We describe a signal trait that appears to have evolved in the absence of either sexual or natural selection. In the katydid genus Neoconocephalus, calls with a derived pattern in which pulses are grouped into pairs have evolved five times independently. We have previously shown that in three of these species, females require the double pulse pattern for call recognition, and hence the recognition system of the females is also in a derived state. Here we describe the remaining two species and find that although males produce the derived call pattern, females use the ancestral recognition mechanism in which no pulse pattern is required. Females respond equally well to the single and double pulse calls, indicating that the derived trait is selectively neutral in the context of mate recognition. Conclusions/Significance These results suggest that 1) neutral changes in signal traits could be important in the diversification of communication systems, and 2) males rather than females may be responsible for initiating signal divergence. PMID:20805980

Cochlear implant microphone location affects speech recognition in diffuse noise.

PubMed

Kolberg, Elizabeth R; Sheffield, Sterling W; Davis, Timothy J; Sunderhaus, Linsey W; Gifford, René H

2015-01-01

Despite improvements in cochlear implants (CIs), CI recipients continue to experience significant communicative difficulty in background noise. Many potential solutions have been proposed to help increase signal-to-noise ratio in noisy environments, including signal processing and external accessories. To date, however, the effect of microphone location on speech recognition in noise has focused primarily on hearing aid users. The purpose of this study was to (1) measure physical output for the T-Mic as compared with the integrated behind-the-ear (BTE) processor mic for various source azimuths, and (2) to investigate the effect of CI processor mic location for speech recognition in semi-diffuse noise with speech originating from various source azimuths as encountered in everyday communicative environments. A repeated-measures, within-participant design was used to compare performance across listening conditions. A total of 11 adults with Advanced Bionics CIs were recruited for this study. Physical acoustic output was measured on a Knowles Experimental Mannequin for Acoustic Research (KEMAR) for the T-Mic and BTE mic, with broadband noise presented at 0 and 90° (directed toward the implant processor). In addition to physical acoustic measurements, we also assessed recognition of sentences constructed by researchers at Texas Instruments, the Massachusetts Institute of Technology, and the Stanford Research Institute (TIMIT sentences) at 60 dBA for speech source azimuths of 0, 90, and 270°. Sentences were presented in a semi-diffuse restaurant noise originating from the R-SPACE 8-loudspeaker array. Signal-to-noise ratio was determined individually to achieve approximately 50% correct in the unilateral implanted listening condition with speech at 0°. Performance was compared across the T-Mic, 50/50, and the integrated BTE processor mic. The integrated BTE mic provided approximately 5 dB attenuation from 1500-4500 Hz for signals presented at 0° as compared with 90° (directed toward the processor). The T-Mic output was essentially equivalent for sources originating from 0 and 90°. Mic location also significantly affected sentence recognition as a function of source azimuth, with the T-Mic yielding the highest performance for speech originating from 0°. These results have clinical implications for (1) future implant processor design with respect to mic location, (2) mic settings for implant recipients, and (3) execution of advanced speech testing in the clinic. American Academy of Audiology.

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

PubMed

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

2017-05-01

Children with hearing loss experience significant difficulty understanding speech in noisy and reverberant situations. Adaptive noise management technologies, such as fully adaptive directional microphones and digital noise reduction, have the potential to improve communication in noise for children with hearing aids. However, there are no published studies evaluating the potential benefits children receive from the use of adaptive noise management technologies in simulated real-world environments as well as in daily situations. The objective of this study was to compare speech recognition, speech intelligibility ratings (SIRs), and sound preferences of children using hearing aids equipped with and without adaptive noise management technologies. A single-group, repeated measures design was used to evaluate performance differences obtained in four simulated environments. In each simulated environment, participants were tested in a basic listening program with minimal noise management features, a manual program designed for that scene, and the hearing instruments' adaptive operating system that steered hearing instrument parameterization based on the characteristics of the environment. Twelve children with mild to moderately severe sensorineural hearing loss. Speech recognition and SIRs were evaluated in three hearing aid programs with and without noise management technologies across two different test sessions and various listening environments. Also, the participants' perceptual hearing performance in daily real-world listening situations with two of the hearing aid programs was evaluated during a four- to six-week field trial that took place between the two laboratory sessions. On average, the use of adaptive noise management technology improved sentence recognition in noise for speech presented in front of the participant but resulted in a decrement in performance for signals arriving from behind when the participant was facing forward. However, the improvement with adaptive noise management exceeded the decrement obtained when the signal arrived from behind. Most participants reported better subjective SIRs when using adaptive noise management technologies, particularly when the signal of interest arrived from in front of the listener. In addition, most participants reported a preference for the technology with an automatically switching, adaptive directional microphone and adaptive noise reduction in real-world listening situations when compared to conventional, omnidirectional microphone use with minimal noise reduction processing. Use of the adaptive noise management technologies evaluated in this study improves school-age children's speech recognition in noise for signals arriving from the front. Although a small decrement in speech recognition in noise was observed for signals arriving from behind the listener, most participants reported a preference for use of noise management technology both when the signal arrived from in front and from behind the child. The results of this study suggest that adaptive noise management technologies should be considered for use with school-age children when listening in academic and social situations. American Academy of Audiology

Sound vibration signal processing for detection and identification detonation (knock) to optimize performance Otto engine

NASA Astrophysics Data System (ADS)

Sujono, A.; Santoso, B.; Juwana, W. E.

2016-03-01

Problems of detonation (knock) on Otto engine (petrol engine) is completely unresolved problem until now, especially if want to improve the performance. This research did sound vibration signal processing engine with a microphone sensor, for the detection and identification of detonation. A microphone that can be mounted is not attached to the cylinder block, that's high temperature, so that its performance will be more stable, durable and inexpensive. However, the method of analysis is not very easy, because a lot of noise (interference). Therefore the use of new methods of pattern recognition, through filtration, and the regression function normalized envelope. The result is quite good, can achieve a success rate of about 95%.

Empirical modeling for intelligent, real-time manufacture control

NASA Technical Reports Server (NTRS)

Xu, Xiaoshu

1994-01-01

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation.

PubMed

Segreto, Tiziana; Caggiano, Alessandra; Karam, Sara; Teti, Roberto

2017-12-12

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

PubMed Central

Segreto, Tiziana; Karam, Sara; Teti, Roberto

2017-01-01

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions. PMID:29231864

Research on oral test modeling based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

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

Estradiol-induced object recognition memory consolidation is dependent on activation of mTOR signaling in the dorsal hippocampus

PubMed Central

Fortress, Ashley M.; Fan, Lu; Orr, Patrick T.; Zhao, Zaorui; Frick, Karyn M.

2013-01-01

The mammalian target of rapamycin (mTOR) signaling pathway is an important regulator of protein synthesis and is essential for various forms of hippocampal memory. Here, we asked whether the enhancement of object recognition memory consolidation produced by dorsal hippocampal infusion of 17β-estradiol (E2) is dependent on mTOR signaling in the dorsal hippocampus, and whether E2-induced mTOR signaling is dependent on dorsal hippocampal phosphatidylinositol 3-kinase (PI3K) and extracellular signal-regulated kinase (ERK) activation. We first demonstrated that the enhancement of object recognition induced by E2 was blocked by dorsal hippocampal inhibition of ERK, PI3K, or mTOR activation. We then showed that an increase in dorsal hippocampal ERK phosphorylation 5 min after intracerebroventricular (ICV) E2 infusion was also blocked by dorsal hippocampal infusion of the three cell signaling inhibitors. Next, we found that ICV infusion of E2 increased phosphorylation of the downstream mTOR targets S6K (Thr-421) and 4E-BP1 in the dorsal hippocampus 5 min after infusion, and that this phosphorylation was blocked by dorsal hippocampal infusion of inhibitors of ERK, PI3K, and mTOR. Collectively, these data demonstrate for the first time that activation of the dorsal hippocampal mTOR signaling pathway is necessary for E2 to enhance object recognition memory consolidation and that E2-induced mTOR activation is dependent on upstream activation of ERK and PI3K signaling. PMID:23422279

Novel Strategy for Discrimination of Transcription Factor Binding Motifs Employing Mathematical Neural Network

NASA Astrophysics Data System (ADS)

Sugimoto, Asuka; Sumi, Takuya; Kang, Jiyoung; Tateno, Masaru

2017-07-01

Recognition in biological macromolecular systems, such as DNA-protein recognition, is one of the most crucial problems to solve toward understanding the fundamental mechanisms of various biological processes. Since specific base sequences of genome DNA are discriminated by proteins, such as transcription factors (TFs), finding TF binding motifs (TFBMs) in whole genome DNA sequences is currently a central issue in interdisciplinary biophysical and information sciences. In the present study, a novel strategy to create a discriminant function for discrimination of TFBMs by constituting mathematical neural networks (NNs) is proposed, together with a method to determine the boundary of signals (TFBMs) and noise in the NN-score (output) space. This analysis also leads to the mathematical limitation of discrimination in the recognition of features representing TFBMs, in an information geometrical manifold. Thus, the present strategy enables the identification of the whole space of TFBMs, right up to the noise boundary.

Transfer Learning for Improved Audio-Based Human Activity Recognition.

PubMed

Ntalampiras, Stavros; Potamitis, Ilyas

2018-06-25

Human activities are accompanied by characteristic sound events, the processing of which might provide valuable information for automated human activity recognition. This paper presents a novel approach addressing the case where one or more human activities are associated with limited audio data, resulting in a potentially highly imbalanced dataset. Data augmentation is based on transfer learning; more specifically, the proposed method: (a) identifies the classes which are statistically close to the ones associated with limited data; (b) learns a multiple input, multiple output transformation; and (c) transforms the data of the closest classes so that it can be used for modeling the ones associated with limited data. Furthermore, the proposed framework includes a feature set extracted out of signal representations of diverse domains, i.e., temporal, spectral, and wavelet. Extensive experiments demonstrate the relevance of the proposed data augmentation approach under a variety of generative recognition schemes.

Extracting semantics from audio-visual content: the final frontier in multimedia retrieval.

PubMed

Naphade, M R; Huang, T S

2002-01-01

Multimedia understanding is a fast emerging interdisciplinary research area. There is tremendous potential for effective use of multimedia content through intelligent analysis. Diverse application areas are increasingly relying on multimedia understanding systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases, and smart sensors. We review the state-of-the-art techniques in multimedia retrieval. In particular, we discuss how multimedia retrieval can be viewed as a pattern recognition problem. We discuss how reliance on powerful pattern recognition and machine learning techniques is increasing in the field of multimedia retrieval. We review the state-of-the-art multimedia understanding systems with particular emphasis on a system for semantic video indexing centered around multijects and multinets. We discuss how semantic retrieval is centered around concepts and context and the various mechanisms for modeling concepts and context.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

NASA Astrophysics Data System (ADS)

Fernández Pozo, Rubén; Blanco Murillo, Jose Luis; Hernández Gómez, Luis; López Gonzalo, Eduardo; Alcázar Ramírez, José; Toledano, Doroteo T.

2009-12-01

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Early Detection of Severe Apnoea through Voice Analysis and Automatic Speaker Recognition Techniques

NASA Astrophysics Data System (ADS)

Fernández, Ruben; Blanco, Jose Luis; Díaz, David; Hernández, Luis A.; López, Eduardo; Alcázar, José

This study is part of an on-going collaborative effort between the medical and the signal processing communities to promote research on applying voice analysis and Automatic Speaker Recognition techniques (ASR) for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based diagnosis could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we present and discuss the possibilities of using generative Gaussian Mixture Models (GMMs), generally used in ASR systems, to model distinctive apnoea voice characteristics (i.e. abnormal nasalization). Finally, we present experimental findings regarding the discriminative power of speaker recognition techniques applied to severe apnoea detection. We have achieved an 81.25 % correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Activation of Entorhinal Cortical Projections to the Dentate Gyrus Underlies Social Memory Retrieval.

PubMed

Leung, Celeste; Cao, Feng; Nguyen, Robin; Joshi, Krutika; Aqrabawi, Afif J; Xia, Shuting; Cortez, Miguel A; Snead, O Carter; Kim, Jun Chul; Jia, Zhengping

2018-05-22

Social interactions are essential to our mental health, and a deficit in social interactions is a hallmark characteristic of numerous brain disorders. Various subregions within the medial temporal lobe have been implicated in social memory, but the underlying mechanisms that tune these neural circuits remain unclear. Here, we demonstrate that optical activation of excitatory entorhinal cortical perforant projections to the dentate gyrus (EC-DG) is necessary and sufficient for social memory retrieval. We further show that inducible disruption of p21-activated kinase (PAK) signaling, a key pathway important for cytoskeletal reorganization, in the EC-DG circuit leads to impairments in synaptic function and social recognition memory, and, importantly, optogenetic activation of the EC-DG terminals reverses the social memory deficits in the transgenic mice. These results provide compelling evidence that activation of the EC-DG pathway underlies social recognition memory recall and that PAK signaling may play a critical role in modulating this process. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

NASA Astrophysics Data System (ADS)

Jiao, Jing; Yue, Jianhai; Pei, Di

2017-10-01

Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.

EMG-based speech recognition using hidden markov models with global control variables.

PubMed

Lee, Ki-Seung

2008-03-01

It is well known that a strong relationship exists between human voices and the movement of articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The sequence of EMG signals for each word is modelled by a hidden Markov model (HMM) framework. The main objective of the work involves building a model for state observation density when multichannel observation sequences are given. The proposed model reflects the dependencies between each of the EMG signals, which are described by introducing a global control variable. We also develop an efficient model training method, based on a maximum likelihood criterion. In a preliminary study, 60 isolated words were used as recognition variables. EMG signals were acquired from three articulatory facial muscles. The findings indicate that such a system may have the capacity to recognize speech signals with an accuracy of up to 87.07%, which is superior to the independent probabilistic model.

Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

PubMed

Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

Random Deep Belief Networks for Recognizing Emotions from Speech Signals

PubMed Central

Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition. PMID:28356908

Speech Perception in Noise by Children With Cochlear Implants

PubMed Central

Caldwell, Amanda; Nittrouer, Susan

2013-01-01

Purpose Common wisdom suggests that listening in noise poses disproportionately greater difficulty for listeners with cochlear implants (CIs) than for peers with normal hearing (NH). The purpose of this study was to examine phonological, language, and cognitive skills that might help explain speech-in-noise abilities for children with CIs. Method Three groups of kindergartners (NH, hearing aid wearers, and CI users) were tested on speech recognition in quiet and noise and on tasks thought to underlie the abilities that fit into the domains of phonological awareness, general language, and cognitive skills. These last measures were used as predictor variables in regression analyses with speech-in-noise scores as dependent variables. Results Compared to children with NH, children with CIs did not perform as well on speech recognition in noise or on most other measures, including recognition in quiet. Two surprising results were that (a) noise effects were consistent across groups and (b) scores on other measures did not explain any group differences in speech recognition. Conclusions Limitations of implant processing take their primary toll on recognition in quiet and account for poor speech recognition and language/phonological deficits in children with CIs. Implications are that teachers/clinicians need to teach language/phonology directly and maximize signal-to-noise levels in the classroom. PMID:22744138

Modeling Fan Effects on the Time Course of Associative Recognition

ERIC Educational Resources Information Center

Schneider, Darryl W.; Anderson, John R.

2012-01-01

We investigated the time course of associative recognition using the response signal procedure, whereby a stimulus is presented and followed after a variable lag by a signal indicating that an immediate response is required. More specifically, we examined the effects of associative fan (the number of associations that an item has with other items…

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals.

PubMed

Zhuang, Ning; Zeng, Ying; Yang, Kai; Zhang, Chi; Tong, Li; Yan, Bin

2018-03-12

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods.

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

PubMed Central

Zeng, Ying; Yang, Kai; Tong, Li; Yan, Bin

2018-01-01

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. PMID:29534515

Real-time edge tracking using a tactile sensor

NASA Technical Reports Server (NTRS)

Berger, Alan D.; Volpe, Richard; Khosla, Pradeep K.

1989-01-01

Object recognition through the use of input from multiple sensors is an important aspect of an autonomous manipulation system. In tactile object recognition, it is necessary to determine the location and orientation of object edges and surfaces. A controller is proposed that utilizes a tactile sensor in the feedback loop of a manipulator to track along edges. In the control system, the data from the tactile sensor is first processed to find edges. The parameters of these edges are then used to generate a control signal to a hybrid controller. Theory is presented for tactile edge detection and an edge tracking controller. In addition, experimental verification of the edge tracking controller is presented.

Complexity of Danger: The Diverse Nature of Damage-associated Molecular Patterns*

PubMed Central

Schaefer, Liliana

2014-01-01

In reply to internal or external danger stimuli, the body orchestrates an inflammatory response. The endogenous triggers of this process are the damage-associated molecular patterns (DAMPs). DAMPs represent a heterogeneous group of molecules that draw their origin either from inside the various compartments of the cell or from the extracellular space. Following interaction with pattern recognition receptors in cross-talk with various non-immune receptors, DAMPs determine the downstream signaling outcome of septic and aseptic inflammatory responses. In this review, the diverse nature, structural characteristics, and signaling pathways elicited by DAMPs will be critically evaluated. PMID:25391648

Separable spectro-temporal Gabor filter bank features: Reducing the complexity of robust features for automatic speech recognition.

PubMed

Schädler, Marc René; Kollmeier, Birger

2015-04-01

To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [J. Acoust. Soc. Am. 131, 4134-4151 (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor filter bank. A feature set that is extracted with these separate spectral and temporal modulation filter banks was introduced, the separate Gabor filter bank (SGBFB) features, and evaluated on the CHiME (Computational Hearing in Multisource Environments) keywords-in-noise recognition task. From the perspective of robust ASR, the results showed that spectral and temporal processing can be performed independently and are not required to interact with each other. Using SGBFB features permitted the signal-to-noise ratio (SNR) to be lowered by 1.2 dB while still performing as well as the GBFB-based reference system, which corresponds to a relative improvement of the word error rate by 12.8%. Additionally, the real time factor of the spectro-temporal processing could be reduced by more than an order of magnitude. Compared to human listeners, the SNR needed to be 13 dB higher when using Mel-frequency cepstral coefficient features, 11 dB higher when using GBFB features, and 9 dB higher when using SGBFB features to achieve the same recognition performance.

Goal-seeking neural net for recall and recognition

NASA Astrophysics Data System (ADS)

Omidvar, Omid M.

1990-07-01

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

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.

Modeling of biologically motivated self-learning equivalent-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for image fragments clustering and recognition

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.

2018-03-01

The biologically-motivated self-learning equivalence-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for fragments images clustering and recognition will be discussed. We shall consider these neural structures and their spatial-invariant equivalental models (SIEMs) based on proposed equivalent two-dimensional functions of image similarity and the corresponding matrix-matrix (or tensor) procedures using as basic operations of continuous logic and nonlinear processing. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalent weighing of input patterns. We show that these SL_EC_RMNSs have several advantages, such as the self-study and self-identification of features and signs of the similarity of fragments, ability to clustering and recognize of image fragments with best efficiency and strong mutual correlation. The proposed combined with learning-recognition clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively continuous logic and nonlinear processing algorithms and to k-average method or method the winner takes all (WTA). The experimental results confirmed that fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an images of different dimensions (a reference array) and fragments with diferent dimensions for clustering is carried out. The experiments, using the software environment Mathcad showed that the proposed method is universal, has a significant convergence, the small number of iterations is easily, displayed on the matrix structure, and confirmed its prospects. Thus, to understand the mechanisms of self-learning equivalence-convolutional clustering, accompanying her to the competitive processes in neurons, and the neural auto-encoding-decoding and recognition principles with the use of self-learning cluster patterns is very important which used the algorithm and the principles of non-linear processing of two-dimensional spatial functions of images comparison. The experimental results show that such models can be successfully used for auto- and hetero-associative recognition. Also they can be used to explain some mechanisms, known as "the reinforcementinhibition concept". Also we demonstrate a real model experiments, which confirm that the nonlinear processing by equivalent function allow to determine the neuron-winners and customize the weight matrix. At the end of the report, we will show how to use the obtained results and to propose new more efficient hardware architecture of SL_EC_RMNS based on matrix-tensor multipliers. Also we estimate the parameters and performance of such architectures.

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.

Four-Channel Biosignal Analysis and Feature Extraction for Automatic Emotion Recognition

NASA Astrophysics Data System (ADS)

Kim, Jonghwa; André, Elisabeth

This paper investigates the potential of physiological signals as a reliable channel for automatic recognition of user's emotial state. For the emotion recognition, little attention has been paid so far to physiological signals compared to audio-visual emotion channels such as facial expression or speech. All essential stages of automatic recognition system using biosignals are discussed, from recording physiological dataset up to feature-based multiclass classification. Four-channel biosensors are used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to search the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by emotion recognition results.

Recognition of Human Histocompatibility Leukocyte Antigen (HLA)-E Complexed with HLA Class I Signal Sequence–derived Peptides by CD94/NKG2 Confers Protection from Natural Killer Cell–mediated Lysis

PubMed Central

Borrego, Francisco; Ulbrecht, Matthias; Weiss, Elisabeth H.; Coligan, John E.; Brooks, Andrew G.

1998-01-01

Human histocompatibility leukocyte antigen (HLA)-E is a nonclassical HLA class I molecule, the gene for which is transcribed in most tissues. It has recently been reported that this molecule binds peptides derived from the signal sequence of HLA class I proteins; however, no function for HLA-E has yet been described. We show that natural killer (NK) cells can recognize target cells expressing HLA-E molecules on the cell surface and this interaction results in inhibition of the lytic process. Furthermore, HLA-E recognition is mediated primarily through the CD94/NKG2-A heterodimer, as CD94-specific, but not killer cell inhibitory receptor (KIR)–specific mAbs block HLA-E–mediated protection of target cells. Cell surface HLA-E could be increased by incubation with synthetic peptides corresponding to residues 3–11 from the signal sequences of a number of HLA class I molecules; however, only peptides which contained a Met at position 2 were capable of conferring resistance to NK-mediated lysis, whereas those having Thr at position 2 had no effect. Interestingly, HLA class I molecules previously correlated with CD94/NKG2 recognition all have Met at residue 4 of the signal sequence (position 2 of the HLA-E binding peptide), whereas those which have been reported not to interact with CD94/NKG2 have Thr at this position. Thus, these data show a function for HLA-E and suggest an alternative explanation for the apparent broad reactivity of CD94/NKG2 with HLA class I molecules; that CD94/NKG2 interacts with HLA-E complexed with signal sequence peptides derived from “protective” HLA class I alleles rather than directly interacting with classical HLA class I proteins. PMID:9480992

Carbonic anhydrase activation enhances object recognition memory in mice through phosphorylation of the extracellular signal-regulated kinase in the cortex and the hippocampus.

PubMed

Canto de Souza, Lucas; Provensi, Gustavo; Vullo, Daniela; Carta, Fabrizio; Scozzafava, Andrea; Costa, Alessia; Schmidt, Scheila Daiane; Passani, Maria Beatrice; Supuran, Claudiu T; Blandina, Patrizio

2017-05-15

Rats injected with by d-phenylalanine, a carbonic anhydrase (CA) activator, enhanced spatial learning, whereas rats given acetazolamide, a CA inhibitor, exhibited impairments of fear memory consolidation. However, the related mechanisms are unclear. We investigated if CAs are involved in a non-spatial recognition memory task assessed using the object recognition test (ORT). Systemic administration of acetazolamide to male CD1 mice caused amnesia in the ORT and reduced CA activity in brain homogenates, while treatment with d-phenylalanine enhanced memory and increased CA activity. We provided also the first evidence that d-phenylalanine administration rapidly activated extracellular signal-regulated kinase (ERK) pathways, a critical step for memory formation, in the cortex and the hippocampus, two brain areas involved in memory processing. Effects elicited by d-phenylalanine were completely blunted by co-administration of acetazolamide, but not of 1-N-(4-sulfamoylphenyl-ethyl)-2,4,6-trimethylpyridinium perchlorate (C18), a CA inhibitor that, differently from acetazolamide, does not cross the blood brain barrier. Our results strongly suggest that brain but not peripheral CAs activation potentiates memory as a result of ERK pathway enhanced activation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals.

PubMed

Vanrell, Sebastian Rodrigo; Milone, Diego Humberto; Rufiner, Hugo Leonardo

2017-07-03

Unobtrusive activity monitoring can provide valuable information for medical and sports applications. In recent years, human activity recognition has moved to wearable sensors to deal with unconstrained scenarios. Accelerometers are the preferred sensors due to their simplicity and availability. Previous studies have examined several \\azul{classic} techniques for extracting features from acceleration signals, including time-domain, time-frequency, frequency-domain, and other heuristic features. Spectral and temporal features are the preferred ones and they are generally computed from acceleration components, leaving the acceleration magnitude potential unexplored. In this study, based on homomorphic analysis, a new type of feature extraction stage is proposed in order to exploit discriminative activity information present in acceleration signals. Homomorphic analysis can isolate the information about whole body dynamics and translate it into a compact representation, called cepstral coefficients. Experiments have explored several configurations of the proposed features, including size of representation, signals to be used, and fusion with other features. Cepstral features computed from acceleration magnitude obtained one of the highest recognition rates. In addition, a beneficial contribution was found when time-domain and moving pace information was included in the feature vector. Overall, the proposed system achieved a recognition rate of 91.21% on the publicly available SCUT-NAA dataset. To the best of our knowledge, this is the highest recognition rate on this dataset.

Interface Prostheses With Classifier-Feedback-Based User Training.

PubMed

Fang, Yinfeng; Zhou, Dalin; Li, Kairu; Liu, Honghai

2017-11-01

It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.

Implementation theory of distortion-invariant pattern recognition for optical and digital signal processing systems

NASA Astrophysics Data System (ADS)

Lhamon, Michael Earl

A pattern recognition system which uses complex correlation filter banks requires proportionally more computational effort than single-real valued filters. This introduces increased computation burden but also introduces a higher level of parallelism, that common computing platforms fail to identify. As a result, we consider algorithm mapping to both optical and digital processors. For digital implementation, we develop computationally efficient pattern recognition algorithms, referred to as, vector inner product operators that require less computational effort than traditional fast Fourier methods. These algorithms do not need correlation and they map readily onto parallel digital architectures, which imply new architectures for optical processors. These filters exploit circulant-symmetric matrix structures of the training set data representing a variety of distortions. By using the same mathematical basis as with the vector inner product operations, we are able to extend the capabilities of more traditional correlation filtering to what we refer to as "Super Images". These "Super Images" are used to morphologically transform a complicated input scene into a predetermined dot pattern. The orientation of the dot pattern is related to the rotational distortion of the object of interest. The optical implementation of "Super Images" yields feature reduction necessary for using other techniques, such as artificial neural networks. We propose a parallel digital signal processor architecture based on specific pattern recognition algorithms but general enough to be applicable to other similar problems. Such an architecture is classified as a data flow architecture. Instead of mapping an algorithm to an architecture, we propose mapping the DSP architecture to a class of pattern recognition algorithms. Today's optical processing systems have difficulties implementing full complex filter structures. Typically, optical systems (like the 4f correlators) are limited to phase-only implementation with lower detection performance than full complex electronic systems. Our study includes pseudo-random pixel encoding techniques for approximating full complex filtering. Optical filter bank implementation is possible and they have the advantage of time averaging the entire filter bank at real time rates. Time-averaged optical filtering is computational comparable to billions of digital operations-per-second. For this reason, we believe future trends in high speed pattern recognition will involve hybrid architectures of both optical and DSP elements.

Nature and extent of person recognition impairments associated with Capgras syndrome in Lewy body dementia

PubMed Central

Fiacconi, Chris M.; Barkley, Victoria; Finger, Elizabeth C.; Carson, Nicole; Duke, Devin; Rosenbaum, R. Shayna; Gilboa, Asaf; Köhler, Stefan

2014-01-01

Patients with Capgras syndrome (CS) adopt the delusional belief that persons well-known to them have been replaced by an imposter. Several current theoretical models of CS attribute such misidentification problems to deficits in covert recognition processes related to the generation of appropriate affective autonomic signals. These models assume intact overt recognition processes for the imposter and, more broadly, for other individuals. As such, it has been suggested that CS could reflect the “mirror-image” of prosopagnosia. The purpose of the current study was to determine whether overt person recognition abilities are indeed always spared in CS. Furthermore, we examined whether CS might be associated with any impairments in overt affective judgments of facial expressions. We pursued these goals by studying a patient with Dementia with Lewy bodies (DLB) who showed clear signs of CS, and by comparing him to another patient with DLB who did not experience CS, as well as to a group of healthy control participants. Clinical magnetic resonance imaging scans revealed medial prefrontal cortex (mPFC) atrophy that appeared to be uniquely associated with the presence CS. We assessed overt person recognition with three fame recognition tasks, using faces, voices, and names as cues. We also included measures of confidence and probed pertinent semantic knowledge. In addition, participants rated the intensity of fearful facial expressions. We found that CS was associated with overt person recognition deficits when probed with faces and voices, but not with names. Critically, these deficits were not present in the DLB patient without CS. In addition, CS was associated with impairments in overt judgments of affect intensity. Taken together, our findings cast doubt on the traditional view that CS is the mirror-image of prosopagnosia and that it spares overt recognition abilities. These findings can still be accommodated by models of CS that emphasize deficits in autonomic responding, to the extent that the potential role of interoceptive awareness in overt judgments is taken into account. PMID:25309399

Morphological and population genomic evidence that human faces have evolved to signal individual identity.

PubMed

Sheehan, Michael J; Nachman, Michael W

2014-09-16

Facial recognition plays a key role in human interactions, and there has been great interest in understanding the evolution of human abilities for individual recognition and tracking social relationships. Individual recognition requires sufficient cognitive abilities and phenotypic diversity within a population for discrimination to be possible. Despite the importance of facial recognition in humans, the evolution of facial identity has received little attention. Here we demonstrate that faces evolved to signal individual identity under negative frequency-dependent selection. Faces show elevated phenotypic variation and lower between-trait correlations compared with other traits. Regions surrounding face-associated single nucleotide polymorphisms show elevated diversity consistent with frequency-dependent selection. Genetic variation maintained by identity signalling tends to be shared across populations and, for some loci, predates the origin of Homo sapiens. Studies of human social evolution tend to emphasize cognitive adaptations, but we show that social evolution has shaped patterns of human phenotypic and genetic diversity as well.

A mechatronics platform to study prosthetic hand control using EMG signals.

PubMed

Geethanjali, P

2016-09-01

In this paper, a low-cost mechatronics platform for the design and development of robotic hands as well as a surface electromyogram (EMG) pattern recognition system is proposed. This paper also explores various EMG classification techniques using a low-cost electronics system in prosthetic hand applications. The proposed platform involves the development of a four channel EMG signal acquisition system; pattern recognition of acquired EMG signals; and development of a digital controller for a robotic hand. Four-channel surface EMG signals, acquired from ten healthy subjects for six different movements of the hand, were used to analyse pattern recognition in prosthetic hand control. Various time domain features were extracted and grouped into five ensembles to compare the influence of features in feature-selective classifiers (SLR) with widely considered non-feature-selective classifiers, such as neural networks (NN), linear discriminant analysis (LDA) and support vector machines (SVM) applied with different kernels. The results divulged that the average classification accuracy of the SVM, with a linear kernel function, outperforms other classifiers with feature ensembles, Hudgin's feature set and auto regression (AR) coefficients. However, the slight improvement in classification accuracy of SVM incurs more processing time and memory space in the low-level controller. The Kruskal-Wallis (KW) test also shows that there is no significant difference in the classification performance of SLR with Hudgin's feature set to that of SVM with Hudgin's features along with AR coefficients. In addition, the KW test shows that SLR was found to be better in respect to computation time and memory space, which is vital in a low-level controller. Similar to SVM, with a linear kernel function, other non-feature selective LDA and NN classifiers also show a slight improvement in performance using twice the features but with the drawback of increased memory space requirement and time. This prototype facilitated the study of various issues of pattern recognition and identified an efficient classifier, along with a feature ensemble, in the implementation of EMG controlled prosthetic hands in a laboratory setting at low-cost. This platform may help to motivate and facilitate prosthetic hand research in developing countries.

Structural basis of UGUA recognition by the Nudix protein CFIm25 and implications for a regulatory role in mRNA 3′ processing

PubMed Central

Yang, Qin; Gilmartin, Gregory M.; Doublié, Sylvie

2010-01-01

Human Cleavage Factor Im (CFIm) is an essential component of the pre-mRNA 3′ processing complex that functions in the regulation of poly(A) site selection through the recognition of UGUA sequences upstream of the poly(A) site. Although the highly conserved 25 kDa subunit (CFIm25) of the CFIm complex possesses a characteristic α/β/α Nudix fold, CFIm25 has no detectable hydrolase activity. Here we report the crystal structures of the human CFIm25 homodimer in complex with UGUAAA and UUGUAU RNA sequences. CFIm25 is the first Nudix protein to be reported to bind RNA in a sequence-specific manner. The UGUA sequence contributes to binding specificity through an intramolecular G:A Watson–Crick/sugar-edge base interaction, an unusual pairing previously found to be involved in the binding specificity of the SAM-III riboswitch. The structures, together with mutational data, suggest a novel mechanism for the simultaneous sequence-specific recognition of two UGUA elements within the pre-mRNA. Furthermore, the mutually exclusive binding of RNA and the signaling molecule Ap4A (diadenosine tetraphosphate) by CFIm25 suggests a potential role for small molecules in the regulation of mRNA 3′ processing. PMID:20479262

Structural basis of UGUA recognition by the Nudix protein CFI(m)25 and implications for a regulatory role in mRNA 3' processing.

PubMed

Yang, Qin; Gilmartin, Gregory M; Doublié, Sylvie

2010-06-01

Human Cleavage Factor Im (CFI(m)) is an essential component of the pre-mRNA 3' processing complex that functions in the regulation of poly(A) site selection through the recognition of UGUA sequences upstream of the poly(A) site. Although the highly conserved 25 kDa subunit (CFI(m)25) of the CFI(m) complex possesses a characteristic alpha/beta/alpha Nudix fold, CFI(m)25 has no detectable hydrolase activity. Here we report the crystal structures of the human CFI(m)25 homodimer in complex with UGUAAA and UUGUAU RNA sequences. CFI(m)25 is the first Nudix protein to be reported to bind RNA in a sequence-specific manner. The UGUA sequence contributes to binding specificity through an intramolecular G:A Watson-Crick/sugar-edge base interaction, an unusual pairing previously found to be involved in the binding specificity of the SAM-III riboswitch. The structures, together with mutational data, suggest a novel mechanism for the simultaneous sequence-specific recognition of two UGUA elements within the pre-mRNA. Furthermore, the mutually exclusive binding of RNA and the signaling molecule Ap(4)A (diadenosine tetraphosphate) by CFI(m)25 suggests a potential role for small molecules in the regulation of mRNA 3' processing.

Development of precursors recognition methods in vector signals

NASA Astrophysics Data System (ADS)

Kapralov, V. G.; Elagin, V. V.; Kaveeva, E. G.; Stankevich, L. A.; Dremin, M. M.; Krylov, S. V.; Borovov, A. E.; Harfush, H. A.; Sedov, K. S.

2017-10-01

Precursor recognition methods in vector signals of plasma diagnostics are presented. Their requirements and possible options for their development are considered. In particular, the variants of using symbolic regression for building a plasma disruption prediction system are discussed. The initial data preparation using correlation analysis and symbolic regression is discussed. Special attention is paid to the possibility of using algorithms in real time.

Estradiol-Induced Object Recognition Memory Consolidation Is Dependent on Activation of mTOR Signaling in the Dorsal Hippocampus

ERIC Educational Resources Information Center

Fortress, Ashley M.; Fan, Lu; Orr, Patrick T.; Zhao, Zaorui; Frick, Karyn M.

2013-01-01

The mammalian target of rapamycin (mTOR) signaling pathway is an important regulator of protein synthesis and is essential for various forms of hippocampal memory. Here, we asked whether the enhancement of object recognition memory consolidation produced by dorsal hippocampal infusion of 17[Beta]-estradiol (E[subscript 2]) is dependent on mTOR…

STATUS OF THE SYSTEM OF SIGNALING PATTERN RECOGNITION RECEPTORS OF MONOCYTES AND GRANULOCYTES IN COSMONAUTS' PERIPHERAL BLOOD BEFORE AND AFTER LONG-DURATION MISSIONS TO THE INTERNATIONAL SPACE STATION.

PubMed

Ponomarev, S A; Berendeeva, T A; Kalinin, S A; Muranova, A V

The system of signaling pattern recognition receptors was studied in 8 cosmonauts aged 35 to 56 years before and after (R+) long-duration missions to the International space station. Peripheral blood samples were analyzed for the content of monocytes and granulocytes that express the signaling pattern recognition Toll- like (TLR) receptors localized as on cell surface (TLR1, TLR2, TLR4, TLR5, TLR6), so inside cells (TLR3, TLR8, TLR9). In parallel, serum concentrations of TLR2 (HSP60) and TLR4 ligands (HSP70, HMGB1) were measured. The results of investigations showed growth of HSP60, HSP70 and HMGB1 concentrations on R+1. In the;majority of cosmonauts increases in endogenous ligands were followed by growth in the number of both monocytes and granulocytes that express TLR2 1 TLR4. This consistency gives ground to assume that changes in the system of signaling pattern recognition receptors can stem .from the predominantly endogenous ligands' response to the effects of long-duration space flight on human organism.

How a mycoparasite employs g-protein signaling: using the example of trichoderma.

PubMed

Omann, Markus; Zeilinger, Susanne

2010-01-01

Mycoparasitic Trichoderma spp. act as potent biocontrol agents against a number of plant pathogenic fungi, whereupon the mycoparasitic attack includes host recognition followed by infection structure formation and secretion of lytic enzymes and antifungal metabolites leading to the host's death. Host-derived signals are suggested to be recognized by receptors located on the mycoparasite's cell surface eliciting an internal signal transduction cascade which results in the transcription of mycoparasitism-relevant genes. Heterotrimeric G proteins of fungi transmit signals originating from G-protein-coupled receptors mainly to the cAMP and the MAP kinase pathways resulting in regulation of downstream effectors. Components of the G-protein signaling machinery such as Gα subunits and G-protein-coupled receptors were recently shown to play crucial roles in Trichoderma mycoparasitism as they govern processes such as the production of extracellular cell wall lytic enzymes, the secretion of antifungal metabolites, and the formation of infection structures.

How a Mycoparasite Employs G-Protein Signaling: Using the Example of Trichoderma

PubMed Central

Omann, Markus; Zeilinger, Susanne

2010-01-01

Mycoparasitic Trichoderma spp. act as potent biocontrol agents against a number of plant pathogenic fungi, whereupon the mycoparasitic attack includes host recognition followed by infection structure formation and secretion of lytic enzymes and antifungal metabolites leading to the host's death. Host-derived signals are suggested to be recognized by receptors located on the mycoparasite's cell surface eliciting an internal signal transduction cascade which results in the transcription of mycoparasitism-relevant genes. Heterotrimeric G proteins of fungi transmit signals originating from G-protein-coupled receptors mainly to the cAMP and the MAP kinase pathways resulting in regulation of downstream effectors. Components of the G-protein signaling machinery such as Gα subunits and G-protein-coupled receptors were recently shown to play crucial roles in Trichoderma mycoparasitism as they govern processes such as the production of extracellular cell wall lytic enzymes, the secretion of antifungal metabolites, and the formation of infection structures. PMID:21637351

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

PubMed Central

Mala, S.; Latha, K.

2014-01-01

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

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

PubMed

Mala, S; Latha, K

2014-01-01

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

Quantifying facial expression signal and intensity use during development.

PubMed

Rodger, Helen; Lao, Junpeng; Caldara, Roberto

2018-06-12

Behavioral studies investigating facial expression recognition during development have applied various methods to establish by which age emotional expressions can be recognized. Most commonly, these methods employ static images of expressions at their highest intensity (apex) or morphed expressions of different intensities, but they have not previously been compared. Our aim was to (a) quantify the intensity and signal use for recognition of six emotional expressions from early childhood to adulthood and (b) compare both measures and assess their functional relationship to better understand the use of different measures across development. Using a psychophysical approach, we isolated the quantity of signal necessary to recognize an emotional expression at full intensity and the quantity of expression intensity (using neutral expression image morphs of varying intensities) necessary for each observer to recognize the six basic emotions while maintaining performance at 75%. Both measures revealed that fear and happiness were the most difficult and easiest expressions to recognize across age groups, respectively, a pattern already stable during early childhood. The quantity of signal and intensity needed to recognize sad, angry, disgust, and surprise expressions decreased with age. Using a Bayesian update procedure, we then reconstructed the response profiles for both measures. This analysis revealed that intensity and signal processing are similar only during adulthood and, therefore, cannot be straightforwardly compared during development. Altogether, our findings offer novel methodological and theoretical insights and tools for the investigation of the developing affective system. Copyright © 2018 Elsevier Inc. All rights reserved.

Development of an automated ultrasonic testing system

NASA Astrophysics Data System (ADS)

Shuxiang, Jiao; Wong, Brian Stephen

2005-04-01

Non-Destructive Testing is necessary in areas where defects in structures emerge over time due to wear and tear and structural integrity is necessary to maintain its usability. However, manual testing results in many limitations: high training cost, long training procedure, and worse, the inconsistent test results. A prime objective of this project is to develop an automatic Non-Destructive testing system for a shaft of the wheel axle of a railway carriage. Various methods, such as the neural network, pattern recognition methods and knowledge-based system are used for the artificial intelligence problem. In this paper, a statistical pattern recognition approach, Classification Tree is applied. Before feature selection, a thorough study on the ultrasonic signals produced was carried out. Based on the analysis of the ultrasonic signals, three signal processing methods were developed to enhance the ultrasonic signals: Cross-Correlation, Zero-Phase filter and Averaging. The target of this step is to reduce the noise and make the signal character more distinguishable. Four features: 1. The Auto Regressive Model Coefficients. 2. Standard Deviation. 3. Pearson Correlation 4. Dispersion Uniformity Degree are selected. And then a Classification Tree is created and applied to recognize the peak positions and amplitudes. Searching local maximum is carried out before feature computing. This procedure reduces much computation time in the real-time testing. Based on this algorithm, a software package called SOFRA was developed to recognize the peaks, calibrate automatically and test a simulated shaft automatically. The automatic calibration procedure and the automatic shaft testing procedure are developed.

Deficiency of activated STAT1 in head and neck cancer cells mediates TAP1-dependent escape from cytotoxic T lymphocytes

PubMed Central

Leibowitz, Michael S.; Filho, Pedro A. Andrade; Ferrone, Soldano; Ferris, Robert L.

2012-01-01

Squamous cell carcinoma of the head and neck (SCCHN) cells can escape recognition by tumor antigen (TA)-specific cytotoxic T lymphocytes (CTL) by downregulation of antigen processing machinery (APM) components, such as the transporter associated with antigen processing (TAP)-1/2 heterodimer. APM component upregulation by interferon gamma (IFN-γ) restores SCCHN cell recognition and susceptibility to lysis by CTL, but the mechanism underlying TAP1/2 downregulation in SCCHN cells is not known. Because IFN-γ activates signal transducer and activator of transcription (STAT)-1, we investigated phosphorylated (p)-STAT1 as a mediator of low basal TAP1/2 expression in SCCHN cells. SCCHN cells were found to express basal total STAT1 but low to undetectable levels of activated STAT1. The association of increased pSTAT1 levels and APM components likely reflects a cause–effect relationship, since STAT1 knockdown significantly reduced both IFN-γ-mediated APM component expression and TA-specific CTL recognition of IFN-γ-treated SCCHN cells. On the other hand, since oncogenic pSTAT3 is overexpressed in SCCHN cells and was found to heterodimerize with pSTAT1, we also tested whether pSTAT3 and pSTAT1:pSTAT3 heterodimers inhibited IFN-γ-induced STAT1 activation and APM component expression. First, STAT3 activation or depletion did not affect basal or IFN-γ-induced expression of pSTAT1 and APM components or recognition of SCCHN cells by TA-specific CTL. Second, pSTAT1:pSTAT3 heterodimers did not interfere with IFN-γ-induced STAT1 binding to the TAP1 promoter or APM protein expression. These findings demonstrate that APM component downregulation is regulated primarily by an IFN-γ-pSTAT1-mediated signaling pathway, independent of oncogenic STAT3 overexpression in SCCHN cells. PMID:21207025

Double Fourier analysis for Emotion Identification in Voiced Speech

NASA Astrophysics Data System (ADS)

Sierra-Sosa, D.; Bastidas, M.; Ortiz P., D.; Quintero, O. L.

2016-04-01

We propose a novel analysis alternative, based on two Fourier Transforms for emotion recognition from speech. Fourier analysis allows for display and synthesizes different signals, in terms of power spectral density distributions. A spectrogram of the voice signal is obtained performing a short time Fourier Transform with Gaussian windows, this spectrogram portraits frequency related features, such as vocal tract resonances and quasi-periodic excitations during voiced sounds. Emotions induce such characteristics in speech, which become apparent in spectrogram time-frequency distributions. Later, the signal time-frequency representation from spectrogram is considered an image, and processed through a 2-dimensional Fourier Transform in order to perform the spatial Fourier analysis from it. Finally features related with emotions in voiced speech are extracted and presented.

Self-organizing neural network models for visual pattern recognition.

PubMed

Fukushima, K

1987-01-01

Two neural network models for visual pattern recognition are discussed. The first model, called a "neocognitron", is a hierarchical multilayered network which has only afferent synaptic connections. It can acquire the ability to recognize patterns by "learning-without-a-teacher": the repeated presentation of a set of training patterns is sufficient, and no information about the categories of the patterns is necessary. The cells of the highest stage eventually become "gnostic cells", whose response shows the final result of the pattern-recognition of the network. Pattern recognition is performed on the basis of similarity in shape between patterns, and is not affected by deformation, nor by changes in size, nor by shifts in the position of the stimulus pattern. The second model has not only afferent but also efferent synaptic connections, and is endowed with the function of selective attention. The afferent and the efferent signals interact with each other in the hierarchical network: the efferent signals, that is, the signals for selective attention, have a facilitating effect on the afferent signals, and at the same time, the afferent signals gate efferent signal flow. When a complex figure, consisting of two patterns or more, is presented to the model, it is segmented into individual patterns, and each pattern is recognized separately. Even if one of the patterns to which the models is paying selective attention is affected by noise or defects, the model can "recall" the complete pattern from which the noise has been eliminated and the defects corrected.

Approximated mutual information training for speech recognition using myoelectric signals.

PubMed

Guo, Hua J; Chan, A D C

2006-01-01

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

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

Calcium-dependent oligomerization of CAR proteins at cell membrane modulates ABA signaling

PubMed Central

Diaz, Maira; Sanchez-Barrena, Maria Jose; Gonzalez-Rubio, Juana Maria; Rodriguez, Lesia; Fernandez, Daniel; Antoni, Regina; Yunta, Cristina; Belda-Palazon, Borja; Gonzalez-Guzman, Miguel; Peirats-Llobet, Marta; Menendez, Margarita; Boskovic, Jasminka; Marquez, Jose A.; Rodriguez, Pedro L.; Albert, Armando

2016-01-01

Regulation of ion transport in plants is essential for cell function. Abiotic stress unbalances cell ion homeostasis, and plants tend to readjust it, regulating membrane transporters and channels. The plant hormone abscisic acid (ABA) and the second messenger Ca2+ are central in such processes, as they are involved in the regulation of protein kinases and phosphatases that control ion transport activity in response to environmental stimuli. The identification and characterization of the molecular mechanisms underlying the effect of ABA and Ca2+ signaling pathways on membrane function are central and could provide opportunities for crop improvement. The C2-domain ABA-related (CAR) family of small proteins is involved in the Ca2+-dependent recruitment of the pyrabactin resistance 1/PYR1-like (PYR/PYL) ABA receptors to the membrane. However, to fully understand CAR function, it is necessary to define a molecular mechanism that integrates Ca2+ sensing, membrane interaction, and the recognition of the PYR/PYL interacting partners. We present structural and biochemical data showing that CARs are peripheral membrane proteins that functionally cluster on the membrane and generate strong positive membrane curvature in a Ca2+-dependent manner. These features represent a mechanism for the generation, stabilization, and/or specific recognition of membrane discontinuities. Such structures may act as signaling platforms involved in the recruitment of PYR/PYL receptors and other signaling components involved in cell responses to stress. PMID:26719420

Calcium-dependent oligomerization of CAR proteins at cell membrane modulates ABA signaling.

PubMed

Diaz, Maira; Sanchez-Barrena, Maria Jose; Gonzalez-Rubio, Juana Maria; Rodriguez, Lesia; Fernandez, Daniel; Antoni, Regina; Yunta, Cristina; Belda-Palazon, Borja; Gonzalez-Guzman, Miguel; Peirats-Llobet, Marta; Menendez, Margarita; Boskovic, Jasminka; Marquez, Jose A; Rodriguez, Pedro L; Albert, Armando

2016-01-19

Regulation of ion transport in plants is essential for cell function. Abiotic stress unbalances cell ion homeostasis, and plants tend to readjust it, regulating membrane transporters and channels. The plant hormone abscisic acid (ABA) and the second messenger Ca(2+) are central in such processes, as they are involved in the regulation of protein kinases and phosphatases that control ion transport activity in response to environmental stimuli. The identification and characterization of the molecular mechanisms underlying the effect of ABA and Ca(2+) signaling pathways on membrane function are central and could provide opportunities for crop improvement. The C2-domain ABA-related (CAR) family of small proteins is involved in the Ca(2+)-dependent recruitment of the pyrabactin resistance 1/PYR1-like (PYR/PYL) ABA receptors to the membrane. However, to fully understand CAR function, it is necessary to define a molecular mechanism that integrates Ca(2+) sensing, membrane interaction, and the recognition of the PYR/PYL interacting partners. We present structural and biochemical data showing that CARs are peripheral membrane proteins that functionally cluster on the membrane and generate strong positive membrane curvature in a Ca(2+)-dependent manner. These features represent a mechanism for the generation, stabilization, and/or specific recognition of membrane discontinuities. Such structures may act as signaling platforms involved in the recruitment of PYR/PYL receptors and other signaling components involved in cell responses to stress.

Are there differential deficits in facial emotion recognition between paranoid and non-paranoid schizophrenia? A signal detection analysis.

PubMed

Huang, Charles Lung-Cheng; Hsiao, Sigmund; Hwu, Hai-Gwo; Howng, Shen-Long

2013-10-30

This study assessed facial emotion recognition abilities in subjects with paranoid and non-paranoid schizophrenia (NPS) using signal detection theory. We explore the differential deficits in facial emotion recognition in 44 paranoid patients with schizophrenia (PS) and 30 non-paranoid patients with schizophrenia (NPS), compared to 80 healthy controls. We used morphed faces with different intensities of emotion and computed the sensitivity index (d') of each emotion. The results showed that performance differed between the schizophrenia and healthy controls groups in the recognition of both negative and positive affects. The PS group performed worse than the healthy controls group but better than the NPS group in overall performance. Performance differed between the NPS and healthy controls groups in the recognition of all basic emotions and neutral faces; between the PS and healthy controls groups in the recognition of angry faces; and between the PS and NPS groups in the recognition of happiness, anger, sadness, disgust, and neutral affects. The facial emotion recognition impairment in schizophrenia may reflect a generalized deficit rather than a negative-emotion specific deficit. The PS group performed worse than the control group, but better than the NPS group in facial expression recognition, with differential deficits between PS and NPS patients. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A Feasibility Study for Perioperative Ventricular Tachycardia Prognosis and Detection and Noise Detection Using a Neural Network and Predictive Linear Operators

NASA Technical Reports Server (NTRS)

Moebes, T. A.

1994-01-01

To locate the accessory pathway(s) in preexicitation syndromes, epicardial and endocardial ventricular mapping is performed during anterograde ventricular activation via accessory pathway(s) from data originally received in signal form. As the number of channels increases, it is pertinent that more automated detection of coherent/incoherent signals is achieved as well as the prediction and prognosis of ventricular tachywardia (VT). Today's computers and computer program algorithms are not good in simple perceptual tasks such as recognizing a pattern or identifying a sound. This discrepancy, among other things, has been a major motivating factor in developing brain-based, massively parallel computing architectures. Neural net paradigms have proven to be effective at pattern recognition tasks. In signal processing, the picking of coherent/incoherent signals represents a pattern recognition task for computer systems. The picking of signals representing the onset ot VT also represents such a computer task. We attacked this problem by defining four signal attributes for each potential first maximal arrival peak and one signal attribute over the entire signal as input to a back propagation neural network. One attribute was the predicted amplitude value after the maximum amplitude over a data window. Then, by using a set of known (user selected) coherent/incoherent signals, and signals representing the onset of VT, we trained the back propagation network to recognize coherent/incoherent signals, and signals indicating the onset of VT. Since our output scheme involves a true or false decision, and since the output unit computes values between 0 and 1, we used a Fuzzy Arithmetic approach to classify data as coherent/incoherent signals. Furthermore, a Mean-Square Error Analysis was used to determine system stability. The neural net based picking coherent/incoherent signal system achieved high accuracy on picking coherent/incoherent signals on different patients. The system also achieved a high accuracy of picking signals which represent the onset of VT, that is, VT immediately followed these signals. A special binary representation of the input and output data allowed the neural network to train very rapidly as compared to another standard decimal or normalized representations of the data.

Scene Context Dependency of Pattern Constancy of Time Series Imagery

NASA Technical Reports Server (NTRS)

Woodell, Glenn A.; Jobson, Daniel J.; Rahman, Zia-ur

2008-01-01

A fundamental element of future generic pattern recognition technology is the ability to extract similar patterns for the same scene despite wide ranging extraneous variables, including lighting, turbidity, sensor exposure variations, and signal noise. In the process of demonstrating pattern constancy of this kind for retinex/visual servo (RVS) image enhancement processing, we found that the pattern constancy performance depended somewhat on scene content. Most notably, the scene topography and, in particular, the scale and extent of the topography in an image, affects the pattern constancy the most. This paper will explore these effects in more depth and present experimental data from several time series tests. These results further quantify the impact of topography on pattern constancy. Despite this residual inconstancy, the results of overall pattern constancy testing support the idea that RVS image processing can be a universal front-end for generic visual pattern recognition. While the effects on pattern constancy were significant, the RVS processing still does achieve a high degree of pattern constancy over a wide spectrum of scene content diversity, and wide ranging extraneousness variations in lighting, turbidity, and sensor exposure.

Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System.

PubMed

Kwon, Yea-Hoon; Shin, Sae-Byuk; Kim, Shin-Dug

2018-04-30

The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN) model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG) and galvanic skin response (GSR) signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models.

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

PubMed Central

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Oliva, Aude

2017-01-01

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100 ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250 ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain. PMID:27039703

Embodiment of Learning in Electro-Optical Signal Processors

NASA Astrophysics Data System (ADS)

Hermans, Michiel; Antonik, Piotr; Haelterman, Marc; Massar, Serge

2016-09-01

Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing. In particular, it has recently been demonstrated, using the artificial intelligence algorithm known as reservoir computing, that photonic implementations of such systems solve complex tasks such as speech recognition. Here, we show how the backpropagation algorithm can be physically implemented on the same electro-optical delay-coupled architecture used for computation with only minor changes to the original design. We find that, compared to when the backpropagation algorithm is not used, the error rate of the resulting computing device, evaluated on three benchmark tasks, decreases considerably. This demonstrates that electro-optical analog computers can embody a large part of their own training process, allowing them to be applied to new, more difficult tasks.

Embodiment of Learning in Electro-Optical Signal Processors.

PubMed

Hermans, Michiel; Antonik, Piotr; Haelterman, Marc; Massar, Serge

2016-09-16

Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing. In particular, it has recently been demonstrated, using the artificial intelligence algorithm known as reservoir computing, that photonic implementations of such systems solve complex tasks such as speech recognition. Here, we show how the backpropagation algorithm can be physically implemented on the same electro-optical delay-coupled architecture used for computation with only minor changes to the original design. We find that, compared to when the backpropagation algorithm is not used, the error rate of the resulting computing device, evaluated on three benchmark tasks, decreases considerably. This demonstrates that electro-optical analog computers can embody a large part of their own training process, allowing them to be applied to new, more difficult tasks.

Learning to perceptually organize speech signals in native fashion.

PubMed

Nittrouer, Susan; Lowenstein, Joanna H

2010-03-01

The ability to recognize speech involves sensory, perceptual, and cognitive processes. For much of the history of speech perception research, investigators have focused on the first and third of these, asking how much and what kinds of sensory information are used by normal and impaired listeners, as well as how effective amounts of that information are altered by "top-down" cognitive processes. This experiment focused on perceptual processes, asking what accounts for how the sensory information in the speech signal gets organized. Two types of speech signals processed to remove properties that could be considered traditional acoustic cues (amplitude envelopes and sine wave replicas) were presented to 100 listeners in five groups: native English-speaking (L1) adults, 7-, 5-, and 3-year-olds, and native Mandarin-speaking adults who were excellent second-language (L2) users of English. The L2 adults performed more poorly than L1 adults with both kinds of signals. Children performed more poorly than L1 adults but showed disproportionately better performance for the sine waves than for the amplitude envelopes compared to both groups of adults. Sentence context had similar effects across groups, so variability in recognition was attributed to differences in perceptual organization of the sensory information, presumed to arise from native language experience.

The study of infrared target recognition at sea background based on visual attention computational model

NASA Astrophysics Data System (ADS)

Wang, Deng-wei; Zhang, Tian-xu; Shi, Wen-jun; Wei, Long-sheng; Wang, Xiao-ping; Ao, Guo-qing

2009-07-01

Infrared images at sea background are notorious for the low signal-to-noise ratio, therefore, the target recognition of infrared image through traditional methods is very difficult. In this paper, we present a novel target recognition method based on the integration of visual attention computational model and conventional approach (selective filtering and segmentation). The two distinct techniques for image processing are combined in a manner to utilize the strengths of both. The visual attention algorithm searches the salient regions automatically, and represented them by a set of winner points, at the same time, demonstrated the salient regions in terms of circles centered at these winner points. This provides a priori knowledge for the filtering and segmentation process. Based on the winner point, we construct a rectangular region to facilitate the filtering and segmentation, then the labeling operation will be added selectively by requirement. Making use of the labeled information, from the final segmentation result we obtain the positional information of the interested region, label the centroid on the corresponding original image, and finish the localization for the target. The cost time does not depend on the size of the image but the salient regions, therefore the consumed time is greatly reduced. The method is used in the recognition of several kinds of real infrared images, and the experimental results reveal the effectiveness of the algorithm presented in this paper.

Human engineer's guide to auditory displays. Volume 1. Elements of perception and memory affecting auditory displays

NASA Astrophysics Data System (ADS)

Mulligan, B. E.; Goodman, L. S.; McBride, D. K.; Mitchell, T. M.; Crosby, T. N.

1984-08-01

This work reviews the areas of auditory attention, recognition, memory and auditory perception of patterns, pitch, and loudness. The review was written from the perspective of human engineering and focuses primarily on auditory processing of information contained in acoustic signals. The impetus for this effort was to establish a data base to be utilized in the design and evaluation of acoustic displays.

Examination on the use of acoustic emission for monitoring metal forging process: A study using simulation technique

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

Mullins, W.M.; Irwin, R.D.; Malas, J.C. III

The aim of this study is to determine the feasibility of using acoustic emission as a monitoring technique for metal forging operations. From the sensor development paradigm proposed by McClean et al. the most likely approach to determining feasibility for application is through signal recognition. For this reason, signature prediction and analysis was chosen to determine the suitability for forging applications.

Pattern-Recognition Algorithm for Locking Laser Frequency

NASA Technical Reports Server (NTRS)

Karayan, Vahag; Klipstein, William; Enzer, Daphna; Yates, Philip; Thompson, Robert; Wells, George

2006-01-01

A computer program serves as part of a feedback control system that locks the frequency of a laser to one of the spectral peaks of cesium atoms in an optical absorption cell. The system analyzes a saturation absorption spectrum to find a target peak and commands a laser-frequency-control circuit to minimize an error signal representing the difference between the laser frequency and the target peak. The program implements an algorithm consisting of the following steps: Acquire a saturation absorption signal while scanning the laser through the frequency range of interest. Condition the signal by use of convolution filtering. Detect peaks. Match the peaks in the signal to a pattern of known spectral peaks by use of a pattern-recognition algorithm. Add missing peaks. Tune the laser to the desired peak and thereafter lock onto this peak. Finding and locking onto the desired peak is a challenging problem, given that the saturation absorption signal includes noise and other spurious signal components; the problem is further complicated by nonlinearity and shifting of the voltage-to-frequency correspondence. The pattern-recognition algorithm, which is based on Hausdorff distance, is what enables the program to meet these challenges.

The Assessment of Recognition Memory Using the Remember/Know Procedure in Amnestic Mild Cognitive Impairment and Probable Alzheimer's Disease

ERIC Educational Resources Information Center

Hudon, Carol; Belleville, Sylvie; Gauthier, Serge

2009-01-01

This study used the Remember/Know (R/K) procedure combined with signal detection analyses to assess recognition memory in 20 elders with amnestic mild cognitive impairment (aMCI), 10 patients with probable Alzheimer's disease (AD) as well as matched healthy older adults. Signal detection analyses first indicated that aMCI and control participants…

Low Temperature Performance of High-Speed Neural Network Circuits

NASA Technical Reports Server (NTRS)

Duong, T.; Tran, M.; Daud, T.; Thakoor, A.

1995-01-01

Artificial neural networks, derived from their biological counterparts, offer a new and enabling computing paradigm specially suitable for such tasks as image and signal processing with feature classification/object recognition, global optimization, and adaptive control. When implemented in fully parallel electronic hardware, it offers orders of magnitude speed advantage. Basic building blocks of the new architecture are the processing elements called neurons implemented as nonlinear operational amplifiers with sigmoidal transfer function, interconnected through weighted connections called synapses implemented using circuitry for weight storage and multiply functions either in an analog, digital, or hybrid scheme.

Habitat-dependent olfactory discrimination in three-spined sticklebacks (Gasterosteus aculeatus).

PubMed

Hiermes, Meike; Mehlis, Marion; Rick, Ingolf P; Bakker, Theo C M

2015-07-01

The ability to recognize conspecifics is indispensible for differential treatment of particular individuals in social contexts like grouping behavior. The advantages of grouping are multifarious, and there exist numerous additional benefits of joining aggregations of conspecifics. Recognition is based on different signals and transmitted via multiple channels, among others the olfactory channel. The sensory system or the combination of sensory modalities used in recognition processes is highly dependent on the availability and effectiveness of modalities, which are a function of the environmental conditions. Using F1-generations of six three-spined stickleback (Gasterosteus aculeatus) populations from two habitat types (tea-stained and clear-water lakes) from the Outer Hebrides, Scotland, we investigated whether individuals are able to recognize members of their own population solely based on olfactory cues and whether the habitat type an individual originated from had an influence on its recognition abilities. When given the choice (own vs. foreign population) sticklebacks from tea-stained lakes significantly preferred the odor of their own population, whereas fish from clear-water habitats did not show any preference. Moreover, fish from the two habitat types differed significantly in their recognition abilities, indicating that olfactory communication is better developed when visual signaling is disturbed. Thus, the observed odor preferences appear to be the consequence of different selective constraints and adaptations as a result of the differences in environmental conditions that have acted on the parental generations. These adaptations are likely genetically based as the differences are present in the F1-generation that had been reared under identical laboratory conditions.

Planar optical waveguide based sandwich assay sensors and processes for the detection of biological targets including protein markers, pathogens and cellular debris

DOEpatents

Martinez, Jennifer S [Santa Fe, NM; Swanson, Basil I [Los Alamos, NM; Grace, Karen M [Los Alamos, NM; Grace, Wynne K [Los Alamos, NM; Shreve, Andrew P [Santa Fe, NM

2009-06-02

An assay element is described including recognition ligands bound to a film on a single mode planar optical waveguide, the film from the group of a membrane, a polymerized bilayer membrane, and a self-assembled monolayer containing polyethylene glycol or polypropylene glycol groups therein and an assay process for detecting the presence of a biological target is described including injecting a biological target-containing sample into a sensor cell including the assay element, with the recognition ligands adapted for binding to selected biological targets, maintaining the sample within the sensor cell for time sufficient for binding to occur between selected biological targets within the sample and the recognition ligands, injecting a solution including a reporter ligand into the sensor cell; and, interrogating the sample within the sensor cell with excitation light from the waveguide, the excitation light provided by an evanescent field of the single mode penetrating into the biological target-containing sample to a distance of less than about 200 nanometers from the waveguide thereby exciting the fluorescent-label in any bound reporter ligand within a distance of less than about 200 nanometers from the waveguide and resulting in a detectable signal.

On the recognition of emotional vocal expressions: motivations for a holistic approach.

PubMed

Esposito, Anna; Esposito, Antonietta M

2012-10-01

Human beings seem to be able to recognize emotions from speech very well and information communication technology aims to implement machines and agents that can do the same. However, to be able to automatically recognize affective states from speech signals, it is necessary to solve two main technological problems. The former concerns the identification of effective and efficient processing algorithms capable of capturing emotional acoustic features from speech sentences. The latter focuses on finding computational models able to classify, with an approximation as good as human listeners, a given set of emotional states. This paper will survey these topics and provide some insights for a holistic approach to the automatic analysis, recognition and synthesis of affective states.

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

PubMed

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

2016-08-01

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

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

Glucose enhancement of a facial recognition task in young adults.

PubMed

Metzger, M M

2000-02-01

Numerous studies have reported that glucose administration enhances memory processes in both elderly and young adult subjects. Although these studies have utilized a variety of procedures and paradigms, investigations of both young and elderly subjects have typically used verbal tasks (word list recall, paragraph recall, etc.). In the present study, the effect of glucose consumption on a nonverbal, facial recognition task in young adults was examined. Lemonade sweetened with either glucose (50 g) or saccharin (23.7 mg) was consumed by college students (mean age of 21.1 years) 15 min prior to a facial recognition task. The task consisted of a familiarization phase in which subjects were presented with "target" faces, followed immediately by a recognition phase in which subjects had to identify the targets among a random array of familiar target and novel "distractor" faces. Statistical analysis indicated that there were no differences on hit rate (target identification) for subjects who consumed either saccharin or glucose prior to the test. However, further analyses revealed that subjects who consumed glucose committed significantly fewer false alarms and had (marginally) higher d-prime scores (a signal detection measure) compared to subjects who consumed saccharin prior to the test. These results parallel a previous report demonstrating glucose enhancement of a facial recognition task in probable Alzheimer's patients; however, this is believed to be the first demonstration of glucose enhancement for a facial recognition task in healthy, young adults.

An articulatorily constrained, maximum entropy approach to speech recognition and speech coding

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

Hogden, J.

Hidden Markov models (HMM`s) are among the most popular tools for performing computer speech recognition. One of the primary reasons that HMM`s typically outperform other speech recognition techniques is that the parameters used for recognition are determined by the data, not by preconceived notions of what the parameters should be. This makes HMM`s better able to deal with intra- and inter-speaker variability despite the limited knowledge of how speech signals vary and despite the often limited ability to correctly formulate rules describing variability and invariance in speech. In fact, it is often the case that when HMM parameter values aremore » constrained using the limited knowledge of speech, recognition performance decreases. However, the structure of an HMM has little in common with the mechanisms underlying speech production. Here, the author argues that by using probabilistic models that more accurately embody the process of speech production, he can create models that have all the advantages of HMM`s, but that should more accurately capture the statistical properties of real speech samples--presumably leading to more accurate speech recognition. The model he will discuss uses the fact that speech articulators move smoothly and continuously. Before discussing how to use articulatory constraints, he will give a brief description of HMM`s. This will allow him to highlight the similarities and differences between HMM`s and the proposed technique.« less

Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Musatov, Vyachaslav Yu.

2018-02-01

In present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.

Electrophysiological signals associated with fluency of different levels of processing reveal multiple contributions to recognition memory.

PubMed

Li, Bingbing; Taylor, Jason R; Wang, Wei; Gao, Chuanji; Guo, Chunyan

2017-08-01

Processing fluency appears to influence recognition memory judgements, and the manipulation of fluency, if misattributed to an effect of prior exposure, can result in illusory memory. Although it is well established that fluency induced by masked repetition priming leads to increased familiarity, manipulations of conceptual fluency have produced conflicting results, variously affecting familiarity or recollection. Some recent studies have found that masked conceptual priming increases correct recollection (Taylor & Henson, 2012), and the magnitude of this behavioural effect correlates with analogous fMRI BOLD priming effects in brain regions associated with recollection (Taylor, Buratto, & Henson, 2013). However, the neural correlates and time-courses of masked repetition and conceptual priming were not compared directly in previous studies. The present study used event-related potentials (ERPs) to identify and compare the electrophysiological correlates of masked repetition and conceptual priming and investigate how they contribute to recognition memory. Behavioural results were consistent with previous studies: Repetition primes increased familiarity, whereas conceptual primes increased correct recollection. Masked repetition and conceptual priming also decreased the latency of late parietal component (LPC). Masked repetition priming was associated with an early P200 effect and a later parietal maximum N400 effect, whereas masked conceptual priming was only associated with a central-parietal maximum N400 effect. In addition, the topographic distributions of the N400 repetition priming and conceptual priming effects were different. These results suggest that fluency at different levels of processing is associated with different ERP components, and contributes differentially to subjective recognition memory experiences. Copyright © 2017 Elsevier Inc. All rights reserved.

Nanovesicle-based bioelectronic nose platform mimicking human olfactory signal transduction.

PubMed

Jin, Hye Jun; Lee, Sang Hun; Kim, Tae Hyun; Park, Juhun; Song, Hyun Seok; Park, Tai Hyun; Hong, Seunghun

2012-05-15

We developed a nanovesicle-based bioelectronic nose (NBN) that could recognize a specific odorant and mimic the receptor-mediated signal transmission of human olfactory systems. To build an NBN, we combined a single-walled carbon nanotube-based field effect transistor with cell-derived nanovesicles containing human olfactory receptors and calcium ion signal pathways. Importantly, the NBN took advantages of cell signal pathways for sensing signal amplification, enabling ≈ 100 times better sensitivity than that of previous bioelectronic noses based on only olfactory receptor protein and carbon nanotube transistors. The NBN sensors exhibited a human-like selectivity with single-carbon-atomic resolution and a high sensitivity of 1 fM detection limit. Moreover, this sensor platform could mimic a receptor-meditated cellular signal transmission in live cells. This sensor platform can be utilized for the study of molecular recognition and biological processes occurring at cell membranes and also for various practical applications such as food screening and medical diagnostics. Copyright © 2012 Elsevier B.V. All rights reserved.

Overview of the NASA SETI Program

NASA Technical Reports Server (NTRS)

Oliver, B. M.

1986-01-01

The NASA Search of Extraterrestrial Intelligence (SETI) program plan is to scan the microwave window from 1 to 10 GHz with existing radio telescopes and sophisticated signal processing equipment looking for narrow band features that might represent artificial signals. A microwave spectrometer was built and is being field tested. A pattern recognition computer to search for drifting continuous wave signals and pulse trains in the output spectra is being designed. Equipment to characterize the radio frequency interference environment was also built. The plan is to complete the hardware and software by FY-88. Then, with increased funding, this equipment will be replicated in Very Large Scale Integration form. Observations, both a complete sky survey and a search fo nearby solar type stars, will begin in about 1990. The hypothesis that very powerful signals exist or that signals are being beamed at us will be tested. To detect the kinds of signals radiated at distances of 100 light years will require a collecting area kilometers in diameter.

Influence of signal processing strategy in auditory abilities.

PubMed

Melo, Tatiana Mendes de; Bevilacqua, Maria Cecília; Costa, Orozimbo Alves; Moret, Adriane Lima Mortari

2013-01-01

The signal processing strategy is a parameter that may influence the auditory performance of cochlear implant and is important to optimize this parameter to provide better speech perception, especially in difficult listening situations. To evaluate the individual's auditory performance using two different signal processing strategy. Prospective study with 11 prelingually deafened children with open-set speech recognition. A within-subjects design was used to compare performance with standard HiRes and HiRes 120 in three different moments. During test sessions, subject's performance was evaluated by warble-tone sound-field thresholds, speech perception evaluation, in quiet and in noise. In the silence, children S1, S4, S5, S7 showed better performance with the HiRes 120 strategy and children S2, S9, S11 showed better performance with the HiRes strategy. In the noise was also observed that some children performed better using the HiRes 120 strategy and other with HiRes. Not all children presented the same pattern of response to the different strategies used in this study, which reinforces the need to look at optimizing cochlear implant clinical programming.

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

PubMed Central

Shinozaki, Takahiro

2018-01-01

Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data. PMID:29425248

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

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

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

Measuring the effects of spectral smearing and enhancement on speech recognition in noise for adults and children

PubMed Central

Nittrouer, Susan; Tarr, Eric; Wucinich, Taylor; Moberly, Aaron C.; Lowenstein, Joanna H.

2015-01-01

Broadened auditory filters associated with sensorineural hearing loss have clearly been shown to diminish speech recognition in noise for adults, but far less is known about potential effects for children. This study examined speech recognition in noise for adults and children using simulated auditory filters of different widths. Specifically, 5 groups (20 listeners each) of adults or children (5 and 7 yrs), were asked to recognize sentences in speech-shaped noise. Seven-year-olds listened at 0 dB signal-to-noise ratio (SNR) only; 5-yr-olds listened at +3 or 0 dB SNR; and adults listened at 0 or −3 dB SNR. Sentence materials were processed both to smear the speech spectrum (i.e., simulate broadened filters), and to enhance the spectrum (i.e., simulate narrowed filters). Results showed: (1) Spectral smearing diminished recognition for listeners of all ages; (2) spectral enhancement did not improve recognition, and in fact diminished it somewhat; and (3) interactions were observed between smearing and SNR, but only for adults. That interaction made age effects difficult to gauge. Nonetheless, it was concluded that efforts to diagnose the extent of broadening of auditory filters and to develop techniques to correct this condition could benefit patients with hearing loss, especially children. PMID:25920851