WorldWideScience

Sample records for neural recognition molecule

  1. Backpropagation neural networks: pattern recognition

    OpenAIRE

    Studenikin, Oleg

    2005-01-01

    In this Master’s degree work artificial neural networks and back propagation learning algorithm for human faces and pattern recognition are analyzed. In the second part of work artificial neural networks and their architecture and structures models are analyzed. In the third part of article the backpropagation procedure and procedures theoretical learning principle are analyzed. In the fourth part different kinds of ANN methods and patterns extracting methods in recognition, learning and ...

  2. The neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Berezin, V; Bock, E; Poulsen, F M

    2000-01-01

    During the past year, the understanding of the structure and function of neural cell adhesion has advanced considerably. The three-dimensional structures of several of the individual modules of the neural cell adhesion molecule (NCAM) have been determined, as well as the structure of the complex...

  3. The neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Berezin, V; Bock, E; Poulsen, F M

    2000-01-01

    During the past year, the understanding of the structure and function of neural cell adhesion has advanced considerably. The three-dimensional structures of several of the individual modules of the neural cell adhesion molecule (NCAM) have been determined, as well as the structure of the complex...... between two identical fragments of the NCAM. Also during the past year, a link between homophilic cell adhesion and several signal transduction pathways has been proposed, connecting the event of cell surface adhesion to cellular responses such as neurite outgrowth. Finally, the stimulation of neurite...

  4. Hidden neural networks: application to speech recognition

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1998-01-01

    We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...

  5. Kannada character recognition system using neural network

    Science.gov (United States)

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

    2013-03-01

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

  6. Pattern recognition using chaotic neural networks

    OpenAIRE

    Tan, Z.; Hepburn, B. S.; Tucker, C.; Ali, M. K.

    1998-01-01

    Pattern recognition by chaotic neural networks is studied using a hyperchaotic neural network as model. Virtual basins of attraction are introduced around unstable periodic orbits which are then used as patterns. Search for periodic orbits in dynamical systems is treated as a process of pattern recognition. The role of synapses on patterns in chaotic networks is discussed. It is shown that distorted states having only limited information of the patterns are successfully recognized.

  7. Pattern recognition using chaotic neural networks

    Directory of Open Access Journals (Sweden)

    Z. Tan

    1998-01-01

    Full Text Available Pattern recognition by chaotic neural networks is studied using a hyperchaotic neural network as model. Virtual basins of attraction are introduced around unstable periodic orbits which are then used as patterns. Search for periodic orbits in dynamical systems is treated as a process of pattern recognition. The role of synapses on patterns in chaotic networks is discussed. It is shown that distorted states having only limited information of the patterns are successfully recognized.

  8. Designer molecule for molecular recognition and photoinduced ...

    Indian Academy of Sciences (India)

    amitava

    Designer molecule for molecular recognition and photoinduced energy/electron transfer processes p. A it. D. Central Salt and Marine Chemicals Research Institute (CSIR). Bhavnagar: 364002 Gujarat. Amitava Das. Bhavnagar: 364002, Gujarat. E-Mail: amitava@csmcri.org. IAS-2011 ...

  9. Structured neural networks for pattern recognition.

    Science.gov (United States)

    Murino, V

    1998-01-01

    This paper proposes a novel approach for the design of structures of neural networks for pattern recognition. The basic idea lies in subdividing the whole classification problem in smaller and simpler problems at different levels, each managed by appropriate components of a complex neural architecture. Three neural structures are presented and applied in a surveillance system aimed at monitoring a railway waiting room classifying potential dangerous situations. Each architecture is composed by nodes, which are actual multilayer perceptrons trained to discriminate between subsets of classes until a complete separation among the classes is achieved. This approach showed better performances with respect to a classical statistical classification procedures and to a single neural network.

  10. Target recognition based on convolutional neural network

    Science.gov (United States)

    Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

    2017-11-01

    One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

  11. Convolutional Neural Network for Image Recognition

    CERN Document Server

    Seifnashri, Sahand

    2015-01-01

    The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.

  12. Ribosome binding site recognition using neural networks

    Directory of Open Access Journals (Sweden)

    Márcio Ferreira da Silva Oliveira

    2004-01-01

    Full Text Available Pattern recognition is an important process for gene localization in genomes. The ribosome binding sites are signals that can help in the identification of a gene. It is difficult to find these signals in the genome through conventional methods because they are highly degenerated. Artificial Neural Networks is the approach used in this work to address this problem.

  13. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

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

    1998-10-01

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

  14. Human Face Recognition Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Răzvan-Daniel Albu

    2009-10-01

    Full Text Available In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.

  15. Convolutional neural networks and face recognition task

    Science.gov (United States)

    Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

    2017-09-01

    Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

  16. Insulator recognition based on convolution neural network

    Directory of Open Access Journals (Sweden)

    Yang Yanli

    2017-01-01

    Full Text Available Insulator fault detection plays an important role in maintaining the safety of transmission lines. Insulator recognition is a prerequisite for its fault detection. An insulator recognition algorithm based on convolution neural network (CNN is proposed. A dataset is established to train the constructed CNN. The correct rate is 98.52% for 1220 training samples and the accuracy rate of testing is 89.04% on 1305 samples. The classification result of the CNN is further used to segment the insulator image. The test results show that the proposed method can realize the effective segmentation of insulators.

  17. Hierarchical Neural Network Structures for Phoneme Recognition

    CERN Document Server

    Vasquez, Daniel; Minker, Wolfgang

    2013-01-01

    In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a  Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.

  18. Vehicle Color Recognition using Convolutional Neural Network

    OpenAIRE

    Rachmadi, Reza Fuad; Purnama, I Ketut Eddy

    2015-01-01

    Vehicle color information is one of the important elements in ITS (Intelligent Traffic System). In this paper, we present a vehicle color recognition method using convolutional neural network (CNN). Naturally, CNN is designed to learn classification method based on shape information, but we proved that CNN can also learn classification based on color distribution. In our method, we convert the input image to two different color spaces, HSV and CIE Lab, and run it to some CNN architecture. The...

  19. Handwritten Digits Recognition Using Neural Computing

    Directory of Open Access Journals (Sweden)

    Călin Enăchescu

    2009-12-01

    Full Text Available In this paper we present a method for the recognition of handwritten digits and a practical implementation of this method for real-time recognition. A theoretical framework for the neural networks used to classify the handwritten digits is also presented.The classification task is performed using a Convolutional Neural Network (CNN. CNN is a special type of multy-layer neural network, being trained with an optimized version of the back-propagation learning algorithm.CNN is designed to recognize visual patterns directly from pixel images with minimal preprocessing, being capable to recognize patterns with extreme variability (such as handwritten characters, and with robustness to distortions and simple geometric transformations.The main contributions of this paper are related to theoriginal methods for increasing the efficiency of the learning algorithm by preprocessing the images before the learning process and a method for increasing the precision and performance for real-time applications, by removing the non useful information from the background.By combining these strategies we have obtained an accuracy of 96.76%, using as training set the NIST (National Institute of Standards and Technology database.

  20. Neural pathways in tactile object recognition.

    Science.gov (United States)

    Deibert, E; Kraut, M; Kremen, S; Hart, J

    1999-04-22

    To define further the brain regions involved in tactile object recognition using functional MRI (fMRI) techniques. The neural substrates involved in tactile object recognition (TOR) have not been elucidated. Studies of nonhuman primates and humans suggest that basic motor and somatosensory mechanisms are involved at a peripheral level; however, the mechanisms of higher order object recognition have not been determined. The authors investigated 11 normal volunteers utilizing fMRI techniques in an attempt to determine the neural pathways involved in TOR. Each individual performed a behavioral paradigm with the activated condition involving identification of objects by touch, with identification of rough/smooth as the control. Data suggest that in a majority of individuals, TOR involves the calcarine and extrastriatal cortex, inferior parietal lobule, inferior frontal gyrus, and superior frontal gyrus-polar region. TOR may utilize visual systems to access an internal object representation. The parietal cortices and inferior frontal regions may be involved in a concomitant lexical strategy of naming the object being examined. Frontal polar activation likely serves a role in visuospatial working memory or in recognizing unusual representations of objects. Overall, these findings suggest that TOR could involve a network of cortical regions subserving somatosensory, motor, visual, and, at times, lexical processing. The primary finding suggests that in this normal study population, the visual cortices may be involved in the topographic spatial processing of TOR.

  1. Face recognition based on improved BP neural network

    Directory of Open Access Journals (Sweden)

    Yue Gaili

    2017-01-01

    Full Text Available In order to improve the recognition rate of face recognition, face recognition algorithm based on histogram equalization, PCA and BP neural network is proposed. First, the face image is preprocessed by histogram equalization. Then, the classical PCA algorithm is used to extract the features of the histogram equalization image, and extract the principal component of the image. And then train the BP neural network using the trained training samples. This improved BP neural network weight adjustment method is used to train the network because the conventional BP algorithm has the disadvantages of slow convergence, easy to fall into local minima and training process. Finally, the BP neural network with the test sample input is trained to classify and identify the face images, and the recognition rate is obtained. Through the use of ORL database face image simulation experiment, the analysis results show that the improved BP neural network face recognition method can effectively improve the recognition rate of face recognition.

  2. Granular neural networks, pattern recognition and bioinformatics

    CERN Document Server

    Pal, Sankar K; Ganivada, Avatharam

    2017-01-01

    This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinf...

  3. Applying Artificial Neural Networks for Face Recognition

    Directory of Open Access Journals (Sweden)

    Thai Hoang Le

    2011-01-01

    Full Text Available This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.

  4. Neural network based facial recognition system

    Science.gov (United States)

    Luebbers, Paul G.; Uwechue, Okechukwu A.; Pandya, Abhijit S.

    1994-03-01

    Researchers have for many years tried to develop machine recognition systems using video images of the human face as the input, with limited success. This paper presents a technique for recognizing individuals based on facial features using a novel multi-layer neural network architecture called `PWRNET'. We envision a real-time version of this technique to be used for high security applications. Two systems are proposed. One involves taking a grayscale video image and using it directly, the other involves decomposing the grayscale image into a series of binary images using the isodensity regions of the image. Isodensity regions are the areas within an image where the intensity is within a certain range. The binary image is produced by setting the pixels inside this intensity range to one, and the rest of the pixels in the image to zero. Features based on moments are subsequently extracted from these grayscale images. These features are then used for classification of the image. The classification is accomplished using an artificial neural network called `PWRNET', which produces a polynomial expression of the trained network. There is one neural network for each individual to be identified, with an output value which is either positive or negative identification. A detailed development of the design is presented, and identification for small population of individuals is presented. It is shown that the system is effective for variations in both scale and translation, which are considered to be reasonable variations for this type of facial identification.

  5. Chaotic Neural Network for Biometric Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Kushan Ahmadian

    2012-01-01

    Full Text Available Biometric pattern recognition emerged as one of the predominant research directions in modern security systems. It plays a crucial role in authentication of both real-world and virtual reality entities to allow system to make an informed decision on granting access privileges or providing specialized services. The major issues tackled by the researchers are arising from the ever-growing demands on precision and performance of security systems and at the same time increasing complexity of data and/or behavioral patterns to be recognized. In this paper, we propose to deal with both issues by introducing the new approach to biometric pattern recognition, based on chaotic neural network (CNN. The proposed method allows learning the complex data patterns easily while concentrating on the most important for correct authentication features and employs a unique method to train different classifiers based on each feature set. The aggregation result depicts the final decision over the recognized identity. In order to train accurate set of classifiers, the subspace clustering method has been used to overcome the problem of high dimensionality of the feature space. The experimental results show the superior performance of the proposed method.

  6. Handwritten Sindhi Character Recognition Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Shafique Ahmed Awan

    2018-01-01

    Full Text Available OCR (OpticalCharacter Recognition is a technology in which text image is used to understand and write text by machines. The work on languages containing isolated characters such as German, English, French and others is at its peak. The OCR and ICR (Intelligent Character Recognition research in Sindhi script is currently at in starting stages and not sufficient work have been cited in this area even though Sindhi language is rich in culture and history. This paper presents one of the initial steps in recognizing Sindhi handwritten characters. The isolated characters of Sindhi script written by thesubjects have been recognized. The various subjects were asked to write Sindhi characters in unconstrained form and then the written samples were collected and scanned through a flatbed scanner. The scanned documents were preprocessedwith the help of binary conversion, removing noise by pepper noise and the lines were segmented with the help of horizontal profile technique. The segmented lines were used to extract characters from scanned pages.This character segmentation was done by vertical projection. The extracted characters have been used to extract features so that the characters can be classified easily. Zoning was used for the feature extraction technique. For the classification, neural network has been used. The recognized characters converted into editable text with an average accuracy of 85%.

  7. Investigation of efficient features for image recognition by neural networks.

    Science.gov (United States)

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. Copyright © 2011 Elsevier Ltd. All rights reserved.

  8. Patterns recognition of electric brain activity using artificial neural networks

    Science.gov (United States)

    Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

    2017-04-01

    An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

  9. Face Recognition using Artificial Neural Network | Endeshaw | Zede ...

    African Journals Online (AJOL)

    Face recognition (FR) is one of the biometric methods to identify the individuals by the features of face. Two Face Recognition Systems (FRS) based on Artificial Neural Network (ANN) have been proposed in this paper based on feature extraction techniques. In the first system, Principal Component Analysis (PCA) has been ...

  10. Recognition of sign language gestures using neural networks

    Directory of Open Access Journals (Sweden)

    Simon Vamplew

    2007-04-01

    Full Text Available This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan hand gestures.

  11. Recognition of sign language gestures using neural networks

    OpenAIRE

    Simon Vamplew

    2007-01-01

    This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan) hand gestures.

  12. Convolutional Neural Networks for Handwritten Javanese Character Recognition

    OpenAIRE

    Dewa, Chandra Kusuma; Fadhilah, Amanda Lailatul; Afiahayati, A

    2018-01-01

    Convolutional neural network (CNN) is state-of-the-art method in object recognition task. Specialized for spatial input data type, CNN has special convolutional and pooling layers which enable hierarchical feature learning from the input space. For offline handwritten character recognition problem such as classifying character in MNIST database, CNN shows better classification result than any other methods. By leveraging the advantages of CNN over character recognition task, in this paper we ...

  13. Hand gesture recognition based on convolutional neural networks

    Science.gov (United States)

    Hu, Yu-lu; Wang, Lian-ming

    2017-11-01

    Hand gesture has been considered a natural, intuitive and less intrusive way for Human-Computer Interaction (HCI). Although many algorithms for hand gesture recognition have been proposed in literature, robust algorithms have been pursued. A recognize algorithm based on the convolutional neural networks is proposed to recognize ten kinds of hand gestures, which include rotation and turnover samples acquired from different persons. When 6000 hand gesture images were used as training samples, and 1100 as testing samples, a 98% recognition rate was achieved with the convolutional neural networks, which is higher than that with some other frequently-used recognition algorithms.

  14. Application of Recognition Tunneling in Single Molecule Identification

    Science.gov (United States)

    Zhao, Yanan

    Single molecule identification is one essential application area of nanotechnology. The application areas including DNA sequencing, peptide sequencing, early disease detection and other industrial applications such as quantitative and quantitative analysis of impurities, etc. The recognition tunneling technique we have developed shows that after functionalization of the probe and substrate of a conventional Scanning Tunneling Microscope with recognition molecules ("tethered molecule-pair" configuration), analyte molecules trapped in the gap that is formed by probe and substrate will bond with the reagent molecules. The stochastic bond formation/breakage fluctuations give insight into the nature of the intermolecular bonding at a single molecule-pair level. The distinct time domain and frequency domain features of tunneling signals were extracted from raw signals of analytes such as amino acids and their enantiomers. The Support Vector Machine (a machine-learning method) was used to do classification and predication based on the signal features generated by analytes, giving over 90% accuracy of separation of up to seven analytes. This opens up a new interface between chemistry and electronics with immediate implications for rapid Peptide/DNA sequencing and molecule identification at single molecule level.

  15. Very deep recurrent convolutional neural network for object recognition

    Science.gov (United States)

    Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri

    2017-03-01

    In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.

  16. Iris double recognition based on modified evolutionary neural network

    Science.gov (United States)

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

    2017-11-01

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

  17. Stimulated Deep Neural Network for Speech Recognition

    Science.gov (United States)

    2016-09-08

    approaches yield state-of-the-art performance in a range of tasks, including speech recognition . However, the parameters of the network are hard to analyze...advantage of the smoothness con- straints that stimulated training offers. The approaches are eval- uated on two large vocabulary speech recognition

  18. A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    Usham Dias

    2010-10-01

    Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

  19. Isolated Speech Recognition Using Artificial Neural Networks

    National Research Council Canada - National Science Library

    Polur, Prasad

    2001-01-01

    .... A small size vocabulary containing the words YES and NO is chosen. Spectral features using cepstral analysis are extracted per frame and imported to a feedforward neural network which uses a backpropagation with momentum training algorithm...

  20. Neural Network Based Indexing and Recognition of Power Quality Disturbances

    Directory of Open Access Journals (Sweden)

    Ram Awtar Gupta

    2011-08-01

    Full Text Available Power quality (PQ analysis has become imperative for utilities as well as for consumers due to huge cost burden of poor power quality. Accurate recognition of PQ disturbances is still a challenging task, whereas methods for its indexing are not much investigated yet. This paper expounds a system, which includes generation of unique patterns called signatures of various PQ disturbances using continuous wavelet transform (CWT and recognition of these signatures using feed-forward neural network. It is also corroborated that the size of signatures of PQ disturbances are proportional to its magnitude, so this feature of the signature is used for indexing the level of PQ disturbance in three sub-classes viz. high, medium, and low. Further, the effect of number of neurons used by neural network on the performance of recognition is also analyzed. Almost 100% accuracy of recognition substantiates the effectiveness of the proposed system.

  1. Automatic Speech Recognition from Neural Signals: A Focused Review

    Directory of Open Access Journals (Sweden)

    Christian Herff

    2016-09-01

    Full Text Available 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 emph{Brain-to-text} system.

  2. Three-dimensional fingerprint recognition by using convolution neural network

    Science.gov (United States)

    Tian, Qianyu; Gao, Nan; Zhang, Zonghua

    2018-01-01

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

  3. Fluid phase recognition molecules in neutrophil-dependent immune responses.

    Science.gov (United States)

    Jaillon, Sébastien; Ponzetta, Andrea; Magrini, Elena; Barajon, Isabella; Barbagallo, Marialuisa; Garlanda, Cecilia; Mantovani, Alberto

    2016-04-01

    The innate immune system comprises both a cellular and a humoral arm. Neutrophils are key effector cells of the immune and inflammatory responses and have emerged as a major source of humoral pattern recognition molecules (PRMs). These molecules, which include collectins, ficolins, and pentraxins, are specialised in the discrimination of self versus non-self and modified-self and share basic multifunctional properties including recognition and opsonisation of pathogens and apoptotic cells, activation and regulation of the complement cascade and tuning of inflammation. Neutrophils act as a reservoir of ready-made soluble PRMs, such as the long pentraxin PTX3, the peptidoglycan recognition protein PGRP-S, properdin and M-ficolin, which are stored in neutrophil granules and are involved in neutrophil effector functions. In addition, other soluble PRMs, such as members of the collectin family, are not expressed in neutrophils but can modulate neutrophil-dependent immune responses. Therefore, soluble PRMs are an essential part of the innate immune response and retain antibody-like effector functions. Here, we will review the expression and general function of soluble PRMs, focusing our attention on molecules involved in neutrophil effector functions. Copyright © 2016. Published by Elsevier Ltd.

  4. Invariant recognition drives neural representations of action sequences.

    Directory of Open Access Journals (Sweden)

    Andrea Tacchetti

    2017-12-01

    Full Text Available Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs, that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.

  5. Invariant recognition drives neural representations of action sequences.

    Science.gov (United States)

    Tacchetti, Andrea; Isik, Leyla; Poggio, Tomaso

    2017-12-01

    Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs), that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.

  6. PEAK TRACKING WITH A NEURAL NETWORK FOR SPECTRAL RECOGNITION

    NARCIS (Netherlands)

    COENEGRACHT, PMJ; METTING, HJ; VANLOO, EM; SNOEIJER, GJ; DOORNBOS, DA

    1993-01-01

    A peak tracking method based on a simulated feed-forward neural network with back-propagation is presented. The network uses the normalized UV spectra and peak areas measured in one chromatogram for peak recognition. It suffices to train the network with only one set of spectra recorded in one

  7. Strictly modular probabilistic neural networks for pattern recognition

    Czech Academy of Sciences Publication Activity Database

    Grim, Jiří; Just, P.; Pudil, Pavel

    2003-01-01

    Roč. 13, č. 6 (2003), s. 599-615 ISSN 1210-0552 R&D Projects: GA ČR GA402/01/0981 Institutional research plan: CEZ:AV0Z1075907 Keywords : neural networks * distribution mixtures * pattern recognition Subject RIV: BB - Applied Statistics, Operational Research

  8. Assembly Neural Network with Nearest-Neighbor Recognition Algorithm

    Czech Academy of Sciences Publication Activity Database

    Goltsev, A.; Húsek, Dušan; Frolov, A.

    2005-01-01

    Roč. 15, - (2005), s. 9-22 ISSN 1210-0552 R&D Projects: GA MŠk 1M0567 Grant - others:RFBR(RU) 02-01-00457 Keywords : assembly neural network * unsupervised learning * binary Hebbian rule * pattern recognition * texture segmentation * classification Subject RIV: BA - General Mathematics

  9. Recognition of decays of charged tracks with neural network techniques

    International Nuclear Information System (INIS)

    Stimpfl-Abele, G.

    1991-01-01

    We developed neural-network learning techniques for the recognition of decays of charged tracks using a feed-forward network with error back-propagation. Two completely different methods are described in detail and their efficiencies for several NN architectures are compared with conventional methods. Excellent results are obtained. (orig.)

  10. Neural Mechanisms and Information Processing in Recognition Systems

    Directory of Open Access Journals (Sweden)

    Mamiko Ozaki

    2014-10-01

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

  11. Hazardous Odor Recognition by CMAC Based Neural Networks

    Directory of Open Access Journals (Sweden)

    Bekir Karlık

    2009-09-01

    Full Text Available Electronic noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. Artificial neural networks (ANNs have been used to analyze complex data and to recognize patterns, and have shown promising results in recognition of volatile compounds and odors in electronic nose applications. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of unique sensor types. The odor sensing system should be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by a neural network or multivariate analysis. This paper describes the design, implementation and performance evaluations of the application developed for hazardous odor recognition using Cerebellar Model Articulation Controller (CMAC based neural networks.

  12. Neurite outgrowth induced by a synthetic peptide ligand of neural cell adhesion molecule requires fibroblast growth factor receptor activation

    DEFF Research Database (Denmark)

    Rønn, L C; Doherty, P; Holm, A

    2000-01-01

    The neural cell adhesion molecule NCAM is involved in axonal outgrowth and target recognition in the developing nervous system. In vitro, NCAM-NCAM binding has been shown to induce neurite outgrowth, presumably through an activation of fibroblast growth factor receptors (FGFRs). We have recently...

  13. Simple test system for single molecule recognition force microscopy

    International Nuclear Information System (INIS)

    Riener, Christian K.; Stroh, Cordula M.; Ebner, Andreas; Klampfl, Christian; Gall, Alex A.; Romanin, Christoph; Lyubchenko, Yuri L.; Hinterdorfer, Peter; Gruber, Hermann J.

    2003-01-01

    We have established an easy-to-use test system for detecting receptor-ligand interactions on the single molecule level using atomic force microscopy (AFM). For this, avidin-biotin, probably the best characterized receptor-ligand pair, was chosen. AFM sensors were prepared containing tethered biotin molecules at sufficiently low surface concentrations appropriate for single molecule studies. A biotin tether, consisting of a 6 nm poly(ethylene glycol) (PEG) chain and a functional succinimide group at the other end, was newly synthesized and covalently coupled to amine-functionalized AFM tips. In particular, PEG 800 diamine was glutarylated, the mono-adduct NH 2 -PEG-COOH was isolated by ion exchange chromatography and reacted with biotin succinimidylester to give biotin-PEG-COOH which was then activated as N-hydroxysuccinimide (NHS) ester to give the biotin-PEG-NHS conjugate which was coupled to the aminofunctionalized AFM tip. The motional freedom provided by PEG allows for free rotation of the biotin molecule on the AFM sensor and for specific binding to avidin which had been adsorbed to mica surfaces via electrostatic interactions. Specific avidin-biotin recognition events were discriminated from nonspecific tip-mica adhesion by their typical unbinding force (∼40 pN at 1.4 nN/s loading rate), unbinding length (<13 nm), the characteristic nonlinear force-distance relation of the PEG linker, and by specific block with excess of free d-biotin. The convenience of the test system allowed to evaluate, and compare, different methods and conditions of tip aminofunctionalization with respect to specific binding and nonspecific adhesion. It is concluded that this system is well suited as calibration or start-up kit for single molecule recognition force microscopy

  14. A New Method for Face Recognition Using Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Patrik Kamencay

    2017-01-01

    Full Text Available In this paper, the performance of the proposed Convolutional Neural Network (CNN with three well-known image recognition methods such as Principal Component Analysis (PCA, Local Binary Patterns Histograms (LBPH and K–Nearest Neighbour (KNN is tested. In our experiments, the overall recognition accuracy of the PCA, LBPH, KNN and proposed CNN is demonstrated. All the experiments were implemented on the ORL database and the obtained experimental results were shown and evaluated. This face database consists of 400 different subjects (40 classes/ 10 images for each class. The experimental result shows that the LBPH provide better results than PCA and KNN. These experimental results on the ORL database demonstrated the effectiveness of the proposed method for face recognition. For proposed CNN we have obtained a best recognition accuracy of 98.3 %. The proposed method based on CNN outperforms the state of the art methods.

  15. A Neural Model of Face Recognition: a Comprehensive Approach

    Science.gov (United States)

    Stara, Vera; Montesanto, Anna; Puliti, Paolo; Tascini, Guido; Sechi, Cristina

    Visual recognition of faces is an essential behavior of humans: we have optimal performance in everyday life and just such a performance makes us able to establish the continuity of actors in our social life and to quickly identify and categorize people. This remarkable ability justifies the general interest in face recognition of researchers belonging to different fields and specially of designers of biometrical identification systems able to recognize the features of person's faces in a background. Due to interdisciplinary nature of this topic in this contribute we deal with face recognition through a comprehensive approach with the purpose to reproduce some features of human performance, as evidenced by studies in psychophysics and neuroscience, relevant to face recognition. This approach views face recognition as an emergent phenomenon resulting from the nonlinear interaction of a number of different features. For this reason our model of face recognition has been based on a computational system implemented through an artificial neural network. This synergy between neuroscience and engineering efforts allowed us to implement a model that had a biological plausibility, performed the same tasks as human subjects, and gave a possible account of human face perception and recognition. In this regard the paper reports on an experimental study of performance of a SOM-based neural network in a face recognition task, with reference both to the ability to learn to discriminate different faces, and to the ability to recognize a face already encountered in training phase, when presented in a pose or with an expression differing from the one present in the training context.

  16. Recognition of Gestures using Artifical Neural Network

    Directory of Open Access Journals (Sweden)

    Marcel MORE

    2013-12-01

    Full Text Available Sensors for motion measurements are now becoming more widespread. Thanks to their parameters and affordability they are already used not only in the professional sector, but also in devices intended for daily use or entertainment. One of their applications is in control of devices by gestures. Systems that can determine type of gesture from measured motion have many uses. Some are for example in medical practice, but they are still more often used in devices such as cell phones, where they serve as a non-standard form of input. Today there are already several approaches for solving this problem, but building sufficiently reliable system is still a challenging task. In our project we are developing solution based on artificial neural network. In difference to other solutions, this one doesn’t require building model for each measuring system and thus it can be used in combination with various sensors just with minimal changes in his structure.

  17. Sign Language Recognition using Neural Networks

    Directory of Open Access Journals (Sweden)

    Sabaheta Djogic

    2014-11-01

    Full Text Available – Sign language plays a great role as communication media for people with hearing difficulties.In developed countries, systems are made for overcoming a problem in communication with deaf people. This encouraged us to develop a system for the Bosnian sign language since there is a need for such system. The work is done with the use of digital image processing methods providing a system that teaches a multilayer neural network using a back propagation algorithm. Images are processed by feature extraction methods, and by masking method the data set has been created. Training is done using cross validation method for better performance thus; an accuracy of 84% is achieved.

  18. End-to-End Neural Segmental Models for Speech Recognition

    Science.gov (United States)

    Tang, Hao; Lu, Liang; Kong, Lingpeng; Gimpel, Kevin; Livescu, Karen; Dyer, Chris; Smith, Noah A.; Renals, Steve

    2017-12-01

    Segmental models are an alternative to frame-based models for sequence prediction, where hypothesized path weights are based on entire segment scores rather than a single frame at a time. Neural segmental models are segmental models that use neural network-based weight functions. Neural segmental models have achieved competitive results for speech recognition, and their end-to-end training has been explored in several studies. In this work, we review neural segmental models, which can be viewed as consisting of a neural network-based acoustic encoder and a finite-state transducer decoder. We study end-to-end segmental models with different weight functions, including ones based on frame-level neural classifiers and on segmental recurrent neural networks. We study how reducing the search space size impacts performance under different weight functions. We also compare several loss functions for end-to-end training. Finally, we explore training approaches, including multi-stage vs. end-to-end training and multitask training that combines segmental and frame-level losses.

  19. Video-based face recognition via convolutional neural networks

    Science.gov (United States)

    Bao, Tianlong; Ding, Chunhui; Karmoshi, Saleem; Zhu, Ming

    2017-06-01

    Face recognition has been widely studied recently while video-based face recognition still remains a challenging task because of the low quality and large intra-class variation of video captured face images. In this paper, we focus on two scenarios of video-based face recognition: 1)Still-to-Video(S2V) face recognition, i.e., querying a still face image against a gallery of video sequences; 2)Video-to-Still(V2S) face recognition, in contrast to S2V scenario. A novel method was proposed in this paper to transfer still and video face images to an Euclidean space by a carefully designed convolutional neural network, then Euclidean metrics are used to measure the distance between still and video images. Identities of still and video images that group as pairs are used as supervision. In the training stage, a joint loss function that measures the Euclidean distance between the predicted features of training pairs and expanding vectors of still images is optimized to minimize the intra-class variation while the inter-class variation is guaranteed due to the large margin of still images. Transferred features are finally learned via the designed convolutional neural network. Experiments are performed on COX face dataset. Experimental results show that our method achieves reliable performance compared with other state-of-the-art methods.

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

    Science.gov (United States)

    Chao, Tien-Hsin

    1991-01-01

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

  1. Multiview fusion for activity recognition using deep neural networks

    Science.gov (United States)

    Kavi, Rahul; Kulathumani, Vinod; Rohit, Fnu; Kecojevic, Vlad

    2016-07-01

    Convolutional neural networks (ConvNets) coupled with long short term memory (LSTM) networks have been recently shown to be effective for video classification as they combine the automatic feature extraction capabilities of a neural network with additional memory in the temporal domain. This paper shows how multiview fusion can be applied to such a ConvNet LSTM architecture. Two different fusion techniques are presented. The system is first evaluated in the context of a driver activity recognition system using data collected in a multicamera driving simulator. These results show significant improvement in accuracy with multiview fusion and also show that deep learning performs better than a traditional approach using spatiotemporal features even without requiring any background subtraction. The system is also validated on another publicly available multiview action recognition dataset that has 12 action classes and 8 camera views.

  2. A hybrid neural network model in handwritten word recognition.

    Science.gov (United States)

    Chiang, J H

    1998-03-01

    A hybrid neural network model is developed and applied to handwritten word recognition. The word recognition system requires a module that assigns character class confidence values to segments of images of handwritten words. The module must accurately represent ambiguities between character classes and assign low confidence values to a wide variety of non-character segments resulting from erroneous segmentations. The proposed hybrid neural model is a cascaded system. The first stage is a self-organizing feature map algorithm (SOFM). The second stage maps distances into allograph membership values using a gradient descent learning algorithm. The third stage is a multi-layer feedforward network (MLFN). The new system performs better than the baseline system. Experiments were performed on a standard test set from the SUNY/USPS Database.

  3. Target Recognition Using Neural Networks for Model Deformation Measurements

    Science.gov (United States)

    Ross, Richard W.; Hibler, David L.

    1999-01-01

    Optical measurements provide a non-invasive method for measuring deformation of wind tunnel models. Model deformation systems use targets mounted or painted on the surface of the model to identify known positions, and photogrammetric methods are used to calculate 3-D positions of the targets on the model from digital 2-D images. Under ideal conditions, the reflective targets are placed against a dark background and provide high-contrast images, aiding in target recognition. However, glints of light reflecting from the model surface, or reduced contrast caused by light source or model smoothness constraints, can compromise accurate target determination using current algorithmic methods. This paper describes a technique using a neural network and image processing technologies which increases the reliability of target recognition systems. Unlike algorithmic methods, the neural network can be trained to identify the characteristic patterns that distinguish targets from other objects of similar size and appearance and can adapt to changes in lighting and environmental conditions.

  4. Dscam-Mediated Cell Recognition Regulates Neural Circuit Formation

    OpenAIRE

    Hattori, Daisuke; Millard, S. Sean; Wojtowicz, Woj M.; Zipursky, S. Lawrence

    2008-01-01

    The Dscam family of immunoglobulin cell surface proteins mediates recognition events between neurons that play an essential role in the establishment of neural circuits. The Drosophila Dscam1 locus encodes tens of thousands of cell surface proteins via alternative splicing. These isoforms exhibit exquisite isoform-specific binding in vitro that mediates homophilic repulsion in vivo. These properties provide the molecular basis for self-avoidance, an essential developmental mechanism that allo...

  5. Control Chart Pattern Recognition Using Artificial Neural Networks

    OpenAIRE

    SAĞIROĞLU, Şeref

    2000-01-01

    Precise and fast control chart pattern (CCP) recognition is important for monitoring process environments to achieve appropriate control and to produce high quality products. CCPs can exhibit six types of pattern: normal, cyclic, increasing trend, decreasing trend, upward shift and downward shift. Except for normal patterns, all other patterns indicate that the process being monitored is not functioning correctly and requires adjustment. This paper describes a new type of neural network for s...

  6. Iterative principles of recognition in probabilistic neural networks

    Czech Academy of Sciences Publication Activity Database

    Grim, Jiří; Hora, Jan

    2008-01-01

    Roč. 21, č. 6 (2008), s. 838-846 ISSN 0893-6080 R&D Projects: GA MŠk 1M0572; GA ČR GA102/07/1594 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Probabilistic neural networks * Distribution mixtures * EM algorithm * Recognition of numerals * Recurrent reasoning Subject RIV: IN - Informatics, Computer Science Impact factor: 2.656, year: 2008

  7. Automatic Pavement Crack Recognition Based on BP Neural Network

    OpenAIRE

    Li, Li; Sun, Lijun; Ning, Guobao; Tan, Shengguang

    2014-01-01

    A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN) is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possib...

  8. Pattern Recognition and Memory Mapping using Mirroring Neural Networks

    OpenAIRE

    Deepthi, Dasika Ratna; Eswaran, K.

    2008-01-01

    In this paper, we present a new kind of learning implementation to recognize the patterns using the concept of Mirroring Neural Network (MNN) which can extract information from distinct sensory input patterns and perform pattern recognition tasks. It is also capable of being used as an advanced associative memory wherein image data is associated with voice inputs in an unsupervised manner. Since the architecture is hierarchical and modular it has the potential of being used to devise learning...

  9. Hazardous Odor Recognition by CMAC Based Neural Networks

    OpenAIRE

    Bucak, İhsan Ömür; Karlık, Bekir

    2009-01-01

    Electronic noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. Artificial neural networks (ANNs) have been used to analyze complex data and to recognize patterns, and have shown promising results in recognition of volatile compounds and odors in electronic nose applications. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of unique sensor types. The odor sensing sy...

  10. Recognition of Tifinagh Characters with Missing Parts Using Neural Network

    OpenAIRE

    El Mahdi Barrah; Said Safi; Abdessamad Malaoui

    2016-01-01

    In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation resul...

  11. A model of traffic signs recognition with convolutional neural network

    Science.gov (United States)

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

    2016-10-01

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

  12. Neural networks for data compression and invariant image recognition

    Science.gov (United States)

    Gardner, Sheldon

    1989-01-01

    An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability.

  13. Finger vein recognition based on convolutional neural network

    Directory of Open Access Journals (Sweden)

    Meng Gesi

    2017-01-01

    Full Text Available Biometric Authentication Technology has been widely used in this information age. As one of the most important technology of authentication, finger vein recognition attracts our attention because of its high security, reliable accuracy and excellent performance. However, the current finger vein recognition system is difficult to be applied widely because its complicated image pre-processing and not representative feature vectors. To solve this problem, a finger vein recognition method based on the convolution neural network (CNN is proposed in the paper. The image samples are directly input into the CNN model to extract its feature vector so that we can make authentication by comparing the Euclidean distance between these vectors. Finally, the Deep Learning Framework Caffe is adopted to verify this method. The result shows that there are great improvements in both speed and accuracy rate compared to the previous research. And the model has nice robustness in illumination and rotation.

  14. Animal Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Tibor Trnovszky

    2017-01-01

    Full Text Available In this paper, the performances of well-known image recognition methods such as Principal Component Analysis (PCA, Linear Discriminant Analysis (LDA, Local Binary Patterns Histograms (LBPH and Support Vector Machine (SVM are tested and compared with proposed convolutional neural network (CNN for the recognition rate of the input animal images. In our experiments, the overall recognition accuracy of PCA, LDA, LBPH and SVM is demonstrated. Next, the time execution for animal recognition process is evaluated. The all experimental results on created animal database were conducted. This created animal database consist of 500 different subjects (5 classes/ 100 images for each class. The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set. For proposed CNN we have obtained a recognition accuracy of 98%. The proposed method based on CNN outperforms the state of the art methods.

  15. Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

    Science.gov (United States)

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

    2018-03-01

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

  16. Noisy Ocular Recognition Based on Three Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Min Beom Lee

    2017-12-01

    Full Text Available In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera, specular reflection (SR and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs. Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II training dataset (selected from the university of Beira iris (UBIRIS.v2 database, mobile iris challenge evaluation (MICHE database, and institute of automation of Chinese academy of sciences (CASIA-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

  17. ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Goh, Garrett B.; Siegel, Charles M.; Vishnu, Abhinav; Hodas, Nathan O.

    2017-12-08

    With access to large datasets, deep neural networks through representation learning have been able to identify patterns from raw data, achieving human-level accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and with a multitude of chemical properties of interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed from the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical tasks that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models, contemporary MLP models that trains on molecular fingerprints, and it matches the performance of the ConvGraph algorithm, the current state-of-the-art. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a universal “plug-and-play” deep neural network, which accelerates the deployment of deep neural networks for the prediction of novel small-molecule chemical properties.

  18. Efficient object recognition using boundary representation and wavelet neural network.

    Science.gov (United States)

    Pan, Hong; Xia, Liang-Zheng

    2008-12-01

    Wavelet neural networks combine the functions of time-frequency localization from the wavelet transform and of self-studying from the neural network, which make them particularly suitable for the classification of complex patterns. In this paper, an efficient object recognition method using boundary representation and the wavelet neural network is proposed. The method employs a wavelet neural network (WNN) to characterize the singularities of the object curvature representation and to perform the object classification at the same time and in an automatic way. The local time-frequency attributes of the singularities on the object boundary are detected by making a preliminary wavelet analysis of the curvature representation. Then, the discriminative scale-translation features of the singularities are stored as the initial scale-translation parameters of the wavelet nodes in the WNN. These parameters are trained to their optimum status during the learning stage. With our approach, as opposed to matching features by convolving the signal with wavelet functions at a large number of scales, the computational burden is significantly reduced. Only a few convolutions are performed at the optimum scale-translation grids during the classification, which makes it suitable for real-time recognition tasks. Compared with the artificial-neural-network-based approaches preceded by wavelet filter banks with fixed scale-translation parameters, the support vector machine (SVM) using traditional Fourier descriptors and K-nearest-neighbor ( K-NN) classifier based on the state-of-the-art shape descriptors, our scheme demonstrates superior and stable discrimination performance under various noisy and affine conditions.

  19. Spiking neural network-based control chart pattern recognition

    Directory of Open Access Journals (Sweden)

    Medhat H.A. Awadalla

    2012-03-01

    Full Text Available Due to an increasing competition in products, consumers have become more critical in choosing products. The quality of products has become more important. Statistical Process Control (SPC is usually used to improve the quality of products. Control charting plays the most important role in SPC. Control charts help to monitor the behavior of the process to determine whether it is stable or not. Unnatural patterns in control charts mean that there are some unnatural causes for variations in SPC. Spiking neural networks (SNNs are the third generation of artificial neural networks that consider time as an important feature for information representation and processing. In this paper, a spiking neural network architecture is proposed to be used for control charts pattern recognition (CCPR. Furthermore, enhancements to the SpikeProp learning algorithm are proposed. These enhancements provide additional learning rules for the synaptic delays, time constants and for the neurons thresholds. Simulated experiments have been conducted and the achieved results show a remarkable improvement in the overall performance compared with artificial neural networks.

  20. Infrared Spectral Classification with Artificial Neural Networks and Classical Pattern Recognition

    National Research Council Canada - National Science Library

    Mayfield, Howard

    2000-01-01

    .... Computer-assisted classification tools, including pattern recognition and artificial neural network techniques, have been applied to a collection of infrared spectra of organophosphorus compounds...

  1. Artificial neural network for bubbles pattern recognition on the images

    Science.gov (United States)

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

    2016-10-01

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

  2. Ficolins and FIBCD1: Soluble and membrane bound pattern recognition molecules with acetyl group selectivity

    DEFF Research Database (Denmark)

    Thomsen, Theresa; Schlosser, Anders; Holmskov, Uffe

    2011-01-01

    as pattern recognition molecules. Ficolins are soluble oligomeric proteins composed of trimeric collagen-like regions linked to fibrinogen-related domains (FReDs) that have the ability to sense molecular patterns on both pathogens and apoptotic cell surfaces and activate the complement system. The ficolins......D-containing molecules, and discusses structural resemblance but also diversity in recognition of acetylated ligands....

  3. Comparison of eye imaging pattern recognition using neural network

    Science.gov (United States)

    Bukhari, W. M.; Syed A., M.; Nasir, M. N. M.; Sulaima, M. F.; Yahaya, M. S.

    2015-05-01

    The beauty of eye recognition system that it is used in automatic identifying and verifies a human weather from digital images or video source. There are various behaviors of the eye such as the color of the iris, size of pupil and shape of the eye. This study represents the analysis, design and implementation of a system for recognition of eye imaging. All the eye images that had been captured from the webcam in RGB format must through several techniques before it can be input for the pattern and recognition processes. The result shows that the final value of weight and bias after complete training 6 eye images for one subject is memorized by the neural network system and be the reference value of the weight and bias for the testing part. The target classifies to 5 different types for 5 subjects. The eye images can recognize the subject based on the target that had been set earlier during the training process. When the values between new eye image and the eye image in the database are almost equal, it is considered the eye image is matched.

  4. Automatic Pavement Crack Recognition Based on BP Neural Network

    Directory of Open Access Journals (Sweden)

    Li Li

    2014-02-01

    Full Text Available A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possible and two sets of feature parameters are constructed to classify the crack images. Then a BPNN is adopted to distinguish pavement images between linear and alligator cracks to acquire high recognition accuracy. Besides, the linear cracks can be further classified into transversal and longitudinal cracks according to the direction angle. Finally, the proposed method is evaluated on the data of 400 pavement images obtained by the Automatic Road Analyzer (ARAN in Northern China and the results show that the proposed method seems to be a powerful tool for pavement crack recognition. The rates of correct classification for alligator, transversal and longitudinal cracks are 97.5%, 100% and 88.0%, respectively. Compared to some previous studies, the method proposed in this paper is effective for all three kinds of cracks and the results are also acceptable for engineering application.

  5. AN APPLICATION OF SPEAKER RECOGNITION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Murat CANER

    2006-02-01

    Full Text Available In this study an artificial neural network (ANN is implemented, which has been used frequently as an implementation model in recent years, to recognize speaker identification. Generally, recognition is consist of three stages that, processing of signal, obtaining attributes and comparing them. Speech samples are transformed into digital data according to voice card of PC. In the analysis of voice stage, recurrent periods and white noise of voice data are trimmed by hamming window method and voice attribute part of the digital data is obtained. For obtaining attribute of voice data LPC (linear predictive coding and DFT (discrete fourier transform methods are used. Of those 28 coefficents, that is used for speaker recognition, 16 were obtained by the analysis of DFT and 12 were obtained by the analysis of LPC. The parameters that represent speaker voice, is used for training and test of ANN. Multilayer perceptron model is used as an architecture of ANN and backpropagation algorithm is used for training method. Voices of "a" is taken from 7 different person and their attributes are found. ANN is trained with these features to find the speaker who is the owner of the sample voice. And then using the test data that is not used for training part, recognition achievement of ANN is tested. As a result, good results were obtained with low failure rate.

  6. Efficient unfolding pattern recognition in single molecule force spectroscopy data

    Directory of Open Access Journals (Sweden)

    Labudde Dirk

    2011-06-01

    Full Text Available Abstract Background Single-molecule force spectroscopy (SMFS is a technique that measures the force necessary to unfold a protein. SMFS experiments generate Force-Distance (F-D curves. A statistical analysis of a set of F-D curves reveals different unfolding pathways. Information on protein structure, conformation, functional states, and inter- and intra-molecular interactions can be derived. Results In the present work, we propose a pattern recognition algorithm and apply our algorithm to datasets from SMFS experiments on the membrane protein bacterioRhodopsin (bR. We discuss the unfolding pathways found in bR, which are characterised by main peaks and side peaks. A main peak is the result of the pairwise unfolding of the transmembrane helices. In contrast, a side peak is an unfolding event in the alpha-helix or other secondary structural element. The algorithm is capable of detecting side peaks along with main peaks. Therefore, we can detect the individual unfolding pathway as the sequence of events labeled with their occurrences and co-occurrences special to bR's unfolding pathway. We find that side peaks do not co-occur with one another in curves as frequently as main peaks do, which may imply a synergistic effect occurring between helices. While main peaks co-occur as pairs in at least 50% of curves, the side peaks co-occur with one another in less than 10% of curves. Moreover, the algorithm runtime scales well as the dataset size increases. Conclusions Our algorithm satisfies the requirements of an automated methodology that combines high accuracy with efficiency in analyzing SMFS datasets. The algorithm tackles the force spectroscopy analysis bottleneck leading to more consistent and reproducible results.

  7. Entity recognition from clinical texts via recurrent neural network.

    Science.gov (United States)

    Liu, Zengjian; Yang, Ming; Wang, Xiaolong; Chen, Qingcai; Tang, Buzhou; Wang, Zhe; Xu, Hua

    2017-07-05

    Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years. In recent years, recurrent neural network (RNN), one of deep learning methods that has shown great potential on many problems including named entity recognition, also has been gradually used for entity recognition from clinical texts. In this paper, we comprehensively investigate the performance of LSTM (long-short term memory), a representative variant of RNN, on clinical entity recognition and protected health information recognition. The LSTM model consists of three layers: input layer - generates representation of each word of a sentence; LSTM layer - outputs another word representation sequence that captures the context information of each word in this sentence; Inference layer - makes tagging decisions according to the output of LSTM layer, that is, outputting a label sequence. Experiments conducted on corpora of the 2010, 2012 and 2014 i2b2 NLP challenges show that LSTM achieves highest micro-average F1-scores of 85.81% on the 2010 i2b2 medical concept extraction, 92.29% on the 2012 i2b2 clinical event detection, and 94.37% on the 2014 i2b2 de-identification, which is considerably competitive with other state-of-the-art systems. LSTM that requires no hand-crafted feature has great potential on entity recognition from clinical texts. It outperforms traditional machine learning methods that suffer from fussy feature engineering. A possible future direction is how to integrate knowledge

  8. Differential theory of learning for efficient neural network pattern recognition

    Science.gov (United States)

    Hampshire, John B., II; Vijaya Kumar, Bhagavatula

    1993-09-01

    We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

  9. Convolutional neural networks with balanced batches for facial expressions recognition

    Science.gov (United States)

    Battini Sönmez, Elena; Cangelosi, Angelo

    2017-03-01

    This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.

  10. POLISH EMOTIONAL SPEECH RECOGNITION USING ARTIFICAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Paweł Powroźnik

    2014-11-01

    Full Text Available The article presents the issue of emotion recognition based on polish emotional speech analysis. The Polish database of emotional speech, prepared and shared by the Medical Electronics Division of the Lodz University of Technology, has been used for research. The following parameters extracted from sampled and normalised speech signal has been used for the analysis: energy of signal, speaker’s sex, average value of speech signal and both the minimum and maximum sample value for a given signal. As an emotional state a classifier fof our layers of artificial neural network has been used. The achieved results reach 50% of accuracy. Conducted researches focused on six emotional states: a neutral state, sadness, joy, anger, fear and boredom.

  11. A Chinese Named Entity Recognition System with Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Hui-Kang

    2017-01-01

    Full Text Available Named entity recognition (NER is a typical sequential labeling problem that plays an important role in natural language processing (NLP systems. In this paper, we discussed the details of applying a comprehensive model aggregating neural networks and conditional random field (CRF on Chinese NER tasks, and how to discovery character level features when implement a NER system in word level. We compared the difference between Chinese and English when modeling the character embeddings. We developed a NER system based on our analysis, it works well on the ACE 2004 and SIGHAN bakeoff 2006 MSRA dataset, and doesn’t rely on any gazetteers or handcraft features. We obtained F1 score of 82.3% on MSRA 2006.

  12. Face recognition: Eigenface, elastic matching, and neural nets

    International Nuclear Information System (INIS)

    Zhang, J.; Lades, M.

    1997-01-01

    This paper is a comparative study of three recently proposed algorithms for face recognition: eigenface, autoassociation and classification neural nets, and elastic matching. After these algorithms were analyzed under a common statistical decision framework, they were evaluated experimentally on four individual data bases, each with a moderate subject size, and a combined data base with more than a hundred different subjects. Analysis and experimental results indicate that the eigenface algorithm, which is essentially a minimum distance classifier, works well when lighting variation is small. Its performance deteriorates significantly as lighting variation increases. The elastic matching algorithm, on the other hand, is insensitive to lighting, face position, and expression variations and therefore is more versatile. The performance of the autoassociation and classification nets is upper bounded by that of the eigenface but is more difficult to implement in practice

  13. Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition

    Science.gov (United States)

    Popko, E. A.; Weinstein, I. A.

    2016-08-01

    Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.

  14. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  15. Neural Dissociation of Number from Letter Recognition and Its Relationship to Parietal Numerical Processing

    Science.gov (United States)

    Park, Joonkoo; Hebrank, Andrew; Polk, Thad A.; Park, Denise C.

    2012-01-01

    The visual recognition of letters dissociates from the recognition of numbers at both the behavioral and neural level. In this article, using fMRI, we investigate whether the visual recognition of numbers dissociates from letters, thereby establishing a double dissociation. In Experiment 1, participants viewed strings of consonants and Arabic…

  16. Neural Stem Cells Derived by Small Molecules Preserve Vision.

    Science.gov (United States)

    Lu, Bin; Morgans, Catherine W; Girman, Sergey; Luo, Jing; Zhao, Jiagang; Du, Hongjun; Lim, Sioklam; Ding, Sheng; Svendsen, Clive; Zhang, Kang; Wang, Shaomei

    2013-01-01

    The advances in stem cell biology hold a great potential to treat retinal degeneration. Importantly, specific cell types can be generated efficiently with small molecules and maintained stably over numerous passages. Here, we investigated whether neural stem cell (NSC) derived from human embryonic stem cells (hESC) by small molecules can preserve vision following grafting into the Royal College Surgeon (RCS) rats; a model for retinal degeneration. A cell suspension containing 3 × 10 4 NSCs or NSCs labeled with green fluorescent protein (GFP) was injected into the subretinal space or the vitreous cavity of RCS rats at postnatal day (P) 22; animals injected with cell-carry medium and those left untreated were used as controls. The efficacy of treatment was evaluated by testing optokinetic response, recording luminance threshold, and examining retinal histology. NSCs offered significant preservation of both photoreceptors and visual function. The grafted NSCs survived for long term without evidence of tumor formation. Functionally, NSC treated eyes had significantly better visual acuity and lower luminance threshold than controls. Morphologically, photoreceptors and retinal connections were well preserved. There was an increase in expression of cillary neurotrophic factor (CNTF) in Müller cells in the graft-protected retina. This study reveals that NSCs derived from hESC by small molecules can survive and preserve vision for long term following subretinal transplantation in the RCS rats. These cells migrate extensively in the subretinal space and inner retina; there is no evidence of tumor formation or unwanted changes after grafting into the eyes. The NSCs derived from hESC by small molecules can be generated efficiently and provide an unlimited supply of cells for the treatment of some forms of human outer retinal degenerative diseases. The capacity of NSCs migrating into inner retina offers a potential as a vehicle to delivery drugs/factors to treat inner retinal

  17. Neural cell adhesion molecule differentially interacts with isoforms of the fibroblast growth factor receptor

    DEFF Research Database (Denmark)

    Christensen, Claus; Berezin, Vladimir; Bock, Elisabeth

    2011-01-01

    The fibroblast growth factor receptor (FGFR) can be activated through direct interactions with various fibroblast growth factors or through a number of cell adhesion molecules, including the neural cell adhesion molecule (NCAM). We produced recombinant proteins comprising the ligand...

  18. Transmembrane neural cell-adhesion molecule (NCAM), but not glycosyl-phosphatidylinositol-anchored NCAM, down-regulates secretion of matrix metalloproteinases

    DEFF Research Database (Denmark)

    Edvardsen, K; Chen, W; Rucklidge, G

    1993-01-01

    proteinases, and proteinase inhibitors all participate in the construction, maintenance, and remodeling of extracellular matrix by cells. The neural cell-adhesion molecule (NCAM)-negative rat glioma cell line BT4Cn secretes substantial amounts of metalloproteinases, as compared with its NCAM-positive mother......) and interstitial collagenase (matrix metalloproteinase 1), indicating that cellular expression of the recognition molecule NCAM regulates the metabolism of the surrounding matrix....

  19. Neural correlates of recognition and naming of musical instruments.

    Science.gov (United States)

    Belfi, Amy M; Bruss, Joel; Karlan, Brett; Abel, Taylor J; Tranel, Daniel

    2016-10-01

    Retrieval of lexical (names) and conceptual (semantic) information is frequently impaired in individuals with neurological damage. One category of items that is often affected is musical instruments. However, distinct neuroanatomical correlates underlying lexical and conceptual knowledge for musical instruments have not been identified. We used a neuropsychological approach to explore the neural correlates of knowledge retrieval for musical instruments. A large sample of individuals with focal brain damage (N = 298), viewed pictures of 16 musical instruments and were asked to name and identify each instrument. Neuroanatomical data were analyzed with a proportional MAP-3 method to create voxelwise lesion proportion difference maps. Impaired naming (lexical retrieval) of musical instruments was associated with damage to the left temporal pole and inferior pre- and postcentral gyri. Impaired recognition (conceptual knowledge retrieval) of musical instruments was associated with a more broadly and bilaterally distributed network of regions, including ventromedial prefrontal cortices, occipital cortices, and superior temporal gyrus. The findings extend our understanding of how musical instruments are processed at neural system level, and elucidate factors that may explain why brain damage may or may not produce anomia or agnosia for musical instruments. Our findings also help inform broader understanding of category-related knowledge mapping in the brain, as musical instruments possess several characteristics that are similar to various other categories of items: They are inanimate and highly manipulable (similar to tools), produce characteristic sounds (similar to animals), and require fine-grained visual differentiation between each other (similar to people). (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  20. Carbohydrate Recognition by Boronolectins, Small Molecules, and Lectins

    Science.gov (United States)

    Jin, Shan; Cheng, Yunfeng; Reid, Suazette; Li, Minyong; Wang, Binghe

    2009-01-01

    Carbohydrates are known to mediate a large number of biological and pathological events. Small and macromolecules capable of carbohydrate recognition have great potentials as research tools, diagnostics, vectors for targeted delivery of therapeutic and imaging agents, and therapeutic agents. However, this potential is far from being realized. One key issue is the difficulty in the development of “binders” capable of specific recognition of carbohydrates of biological relevance. This review discusses systematically the general approaches that are available in developing carbohydrate sensors and “binders/receptors,” and their applications. The focus is on discoveries during the last five years. PMID:19291708

  1. SYSTEM DESIGN AND BEHAVIOR OF PATTERN RECOGNITION OF CONTROL CHART USING NEURAL NETWORK

    OpenAIRE

    Justin Yulius Halim

    2003-01-01

    Recognition of unnatural patterns in control charts is important to get more understanding about the process problem. Shewhart control chart can recognize shift pattern only, while the others cannot be detected by this chart. Neural networks were proposed by many researchers to achieve the better recognition of the patterns. A system of neural networks needs to be fitted to this problem by realizing the behaviors of control charts. Each behavior will affect the development of each part of the...

  2. A Novel Scheme for Speaker Recognition Using a Phonetically-Aware Deep Neural Network

    Science.gov (United States)

    2014-05-01

    A NOVEL SCHEME FOR SPEAKER RECOGNITION USING A PHONETICALLY -AWARE DEEP NEURAL NETWORK Yun Lei Nicolas Scheffer Luciana Ferrer Mitchell McLaren Speech...framework. Our use of a phonetically aware model is motivated by the fact that the speech content’s effect on the speech signal have been mostly ig...SUBTITLE A Novel Scheme for Speaker Recognition Using a Phonetically -Aware Deep Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT

  3. THE COMPARISON OF ALGORITHMS OF RECOGNITION OF IMAGES HOPFILD’S NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Anna Illarionovna Pavlova

    2016-05-01

    Full Text Available The main advantage of artificial neural networks (ANN in recognition of the cottages, is in their functioning like a human brain. The paper deals with image recognition neuron Hopfield’s networks, a comparative analysis of the recognition images by a projection’s method and the Hebb’s rule. For these purposes, was developed program with C# in Microsoft Visual Studio 2012. In this article to recognition for images with different levels of distortion were used. The analysis of results of recognition of images has shown that the method of projections allows to restore strongly distorted images (level of distortions up to 25–30 percent

  4. Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers

    NARCIS (Netherlands)

    Kruithof, M.C.; Bouma, H.; Fischer, N.M.; Schutte, K.

    2016-01-01

    Object recognition is important to understand the content of video and allow flexible querying in a large number of cameras, especially for security applications. Recent benchmarks show that deep convolutional neural networks are excellent approaches for object recognition. This paper describes an

  5. Fast Automatic Configuration of Artificial Neural Networks Used for Binary Patterns Recognition

    Directory of Open Access Journals (Sweden)

    Adrian Horzyk

    2001-01-01

    Full Text Available This paper presents a powerful method of an automatically generated architecture of neural networks used for binary pattems recognition, which can quickly and automatically reduce synapses in a way of minimally reducing a quality of recognition and a quality of generalization. Moreover, this method computes all weights in two runs over a leaming sequence, what makes this method very fast. First, the method calculates all binary features for each pattem and then weights are computed. Furthermore, there is a quality of generalization considered because it is one of the most important factors of recognition while using neural networks.

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

    Science.gov (United States)

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

    2017-03-01

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

  7. Fast recognition of single molecules based on single-event photon statistics

    International Nuclear Information System (INIS)

    Dong Shuangli; Huang Tao; Liu Yuan; Wang Jun; Zhang Guofeng; Xiao Liantuan; Jia Suotang

    2007-01-01

    Mandel's Q parameter, which is determined from single-event photon statistics, provides an alternative way to recognize single molecules with fluorescence detection, other than the second-order correlation function. It is shown that the Q parameter of an assumed ideal double-molecule fluorescence with the same average photon number as that of the sample fluorescence can act as the criterion for single-molecule recognition. The influence of signal-to-background ratio and the error estimates for photon statistics are also presented. We have applied this method to ascertain single Cy5 dye molecules within hundreds of milliseconds

  8. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    CERN Document Server

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  9. True and false recognition memories of odors induce distinct neural signatures

    Directory of Open Access Journals (Sweden)

    Jean-Pierre eRoyet

    2011-07-01

    Full Text Available Neural bases of human olfactory memory are poorly understood. Very few studies have examined neural substrates associated with correct odor recognition, and none has tackled neural networks associated with incorrect odor recognition. We investigated the neural basis of task performance during a yes-no odor recognition memory paradigm in young and elderly subjects using event-related functional magnetic resonance imaging. We explored four response categories: correct (Hit and incorrect (False Alarm recognition, as well as correct (CR and incorrect (Miss rejection, and we characterized corresponding brain responses using multivariate analysis and linear regression analysis. We hypothesized that areas of the medial temporal lobe were differentially involved depending on the accuracy of odor recognition. In young adults, we found that significant activity in the hippocampus and the parahippocampal gyrus was associated with correct (true recognition of odors, whereas the perirhinal cortex was associated with False Alarms and Misses. These findings are consistent with literature regarding hypothetical functional organization for memory processing. We also found that for correct recognition and rejection responses, the involvement of the hippocampus decreased when memory performances improved. In contrast to young individuals, elderly subjects were more prone to false memories and exhibited less specific activation patterns for the four response categories. Activation in the hippocampus and the parahippocampal gyrus was positively correlated with response bias scores for true and false recognition, demonstrating that conservative subjects produced an additional search effort leading to more activation of these two medial temporal lobe regions. These findings demonstrate that correct and incorrect recognition and rejection induce distinct neural signatures.

  10. Slit molecules prevent entrance of trunk neural crest cells in developing gut.

    Science.gov (United States)

    Zuhdi, Nora; Ortega, Blanca; Giovannone, Dion; Ra, Hannah; Reyes, Michelle; Asención, Viviana; McNicoll, Ian; Ma, Le; de Bellard, Maria Elena

    2015-04-01

    Neural crest cells emerge from the dorsal neural tube early in development and give rise to sensory and sympathetic ganglia, adrenal cells, teeth, melanocytes and especially enteric nervous system. Several inhibitory molecules have been shown to play important roles in neural crest migration, among them are the chemorepulsive Slit1-3. It was known that Slits chemorepellants are expressed at the entry to the gut, and thus could play a role in the differential ability of vagal but not trunk neural crest cells to invade the gut and form enteric ganglia. Especially since trunk neural crest cells express Robo receptor while vagal do not. Thus, although we know that Robo mediates migration along the dorsal pathway in neural crest cells, we do not know if it is responsible in preventing their entry into the gut. The goal of this study was to further corroborate a role for Slit molecules in keeping trunk neural crest cells away from the gut. We observed that when we silenced Robo receptor in trunk neural crest, the sympathoadrenal (somites 18-24) were capable of invading gut mesenchyme in larger proportion than more rostral counterparts. The more rostral trunk neural crest tended not to migrate beyond the ventral aorta, suggesting that there are other repulsive molecules keeping them away from the gut. Interestingly, we also found that when we silenced Robo in sacral neural crest they did not wait for the arrival of vagal crest but entered the gut and migrated rostrally, suggesting that Slit molecules are the ones responsible for keeping them waiting at the hindgut mesenchyme. These combined results confirm that Slit molecules are responsible for keeping the timeliness of colonization of the gut by neural crest cells. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    JIDE JULIUS POPOOLA

    2014-04-01

    Full Text Available In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the problem of how to accurately and promptly recognize a modulation format. In addressing these challenges, the development of automatic modulation recognition systems that can classify a radio signal’s modulation format has received worldwide attention. Decision-theoretic methods and pattern recognition solutions are the two typical automatic modulation recognition approaches. While decision-theoretic approaches use probabilistic or likelihood functions, pattern recognition uses feature-based methods. This study applies the pattern recognition approach based on statistical parameters, using an artificial neural network to classify five different digital modulation formats. The paper deals with automatic recognition of both inter-and intra-classes of digitally modulated signals in contrast to most of the existing algorithms in literature that deal with either inter-class or intra-class modulation format recognition. The results of this study show that accurate and prompt modulation recognition is possible beyond the lower bound of 5 dB commonly acclaimed in literature. The other significant contribution of this paper is the usage of the Python programming language which reduces computational complexity that characterizes other automatic modulation recognition classifiers developed using the conventional MATLAB neural network toolbox.

  12. The Neural Cell Adhesion Molecule NCAM2/OCAM/RNCAM, a Close Relative to NCAM

    DEFF Research Database (Denmark)

    Kulahin, Nikolaj; Walmod, Peter

    2008-01-01

    molecule (NCAM) is a well characterized, ubiquitously expressed CAM that is highly expressed in the nervous system. In addition to mediating cell adhesion, NCAM participates in a multitude of cellular events, including survival, migration, and differentiation of cells, outgrowth of neurites, and formation......Cell adhesion molecules (CAMs) constitute a large class of plasma membrane-anchored proteins that mediate attachment between neighboring cells and between cells and the surrounding extracellular matrix (ECM). However, CAMs are more than simple mediators of cell adhesion. The neural cell adhesion...... and plasticity of synapses. NCAM shares an overall sequence identity of approximately 44% with the neural cell adhesion molecule 2 (NCAM2), a protein also known as olfactory cell adhesion molecule (OCAM) and Rb-8 neural cell adhesion molecule (RNCAM), and the region-for-region sequence homology between the two...

  13. Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

    Science.gov (United States)

    Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

    2016-10-01

    Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

  14. Unstable Morse Code recognition system with back propagation neural network for person with disabilities.

    Science.gov (United States)

    Fuh, D T; Luo, C H

    2001-01-01

    A Morse code auto-recognition system is limited by stable typing speed and stable typing ratio from long to short intervals. For an unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough for applications. This paper adopted a neural network to recognize unstable Morse codes. From an experiment on a teenager with cerebral palsy, the neural network has an average recognition rate up to 93.2%. The recognition rate from an amputee aged 40, who used a prosthesis for typing, it is 97.2% on average. When we compare this to 99.2% for the recognition rate from a skilled expert, the result is quite promising. The neural network has successfully overcome the difficulty of analysing a severely unstable Morse code time series. Since the human typing speed is quite slow in comparison to signal processing by the computer, it also makes it possible to use a neural network for real-time signal recognition.

  15. Interactions of the humoral pattern recognition molecule PTX3 with the complement system

    DEFF Research Database (Denmark)

    Doni, Andrea; Garlanda, Cecilia; Bottazzi, Barbara

    2012-01-01

    The innate immune system comprises a cellular and a humoral arm. The long pentraxin PTX3 is a fluid phase pattern recognition molecule, which acts as an essential component of the humoral arm of innate immunity. PTX3 has antibody-like properties including interactions with complement components...

  16. New domains of neural cell-adhesion molecule L1 implicated in X-linked hydrocephalus and MASA syndrome

    Energy Technology Data Exchange (ETDEWEB)

    Jouet, M.; Kenwick, S. [Univ. of Cambridge (United Kingdom); Moncla, A. [Hopital d`Enfants de la Timone, Marseillas (United Kingdom)] [and others

    1995-06-01

    The neural cell-adhesion molecule L1 is involved in intercellular recognition and neuronal migration in the CNS. Recently, we have shown that mutations in the gene encoding L1 are responsible for three related disorders; X-linked hydrocephalus, MASA (mental retardation, aphasia, shuffling gait, and adducted thumbs) syndrome, and spastic paraplegia type I (SPG1). These three disorders represent a clinical spectrum that varies not only between families but sometimes also within families. To date, 14 independent L1 mutations have been reported and shown to be disease causing. Here we report nine novel L1 mutations in X-linked hydrocephalus and MASA-syndrome families, including the first examples of mutations affecting the fibronectin type III domains of the molecule. They are discussed in relation both to phenotypes and to the insights that they provide into L1 function. 39 refs., 5 figs., 3 tabs.

  17. Neural cell adhesion molecules in rodent brains isolated by monoclonal antibodies with cross-species reactivity.

    OpenAIRE

    Chuong, C M; McClain, D A; Streit, P; Edelman, G M

    1982-01-01

    Previous studies in this laboratory have led to the identification and purification of a chicken cell surface protein named "neural cell adhesion molecule" (N-CAM) that is involved in neural cell-cell and neurite-neurite interactions. In the present investigation, we have found that a similar molecule exists in the mouse and have confirmed that it is also present in rat neural tissue. A monoclonal antibody to chicken N-CAM that crossreacted with mouse and rat brains and an independently deriv...

  18. The neural correlates of gist-based true and false recognition

    Science.gov (United States)

    Gutchess, Angela H.; Schacter, Daniel L.

    2012-01-01

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

  19. Neuregulin-1 is a chemoattractant and chemokinetic molecule for trunk neural crest cells.

    Science.gov (United States)

    de Bellard, Maria Elena; Ortega, Blanca; Sao, Sothy; Kim, Lino; Herman, Joshua; Zuhdi, Nora

    2018-03-08

    Trunk neural crest cells migrate rapidly along characteristic pathways within the developing vertebrate embryo. Proper trunk neural crest cell migration is necessary for the morphogenesis of much of the peripheral nervous system, melanocytes, and the adrenal medulla. Numerous molecules help guide trunk neural crest cell migration throughout the early embryo. Here, we show that the trophic factor NRG1 is a chemoattractant through in vitro chemotaxis assays and in vivo silencing via a DN-erbB receptor. Interestingly, we also observed changes in migratory responses consistent with a chemokinetic effect of NRG1 in trunk neural crest velocity. NRG1 is a trunk neural crest cell chemoattractant and chemokinetic molecule. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

  20. Optimization of multilayer neural network parameters for speaker recognition

    Science.gov (United States)

    Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka

    2016-05-01

    This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.

  1. The application of the neural network on Morse code recognition for users with physical impairments.

    Science.gov (United States)

    Yang, C H; Yang, C H; Chuang, L Y; Truong, T K

    2001-01-01

    Morse code is a simple, speedy and low cost means of communication composed of a series of dots, dashes and space intervals. Each tone element (either a dot, dash or space interval) is transmitted by sending a signal for a defined length of time. This poses a challenge as the automatic recognition of Morse code is dependent upon maintaining a stable typing rate. In this paper, a suitable adaptive automatic recognition method, combining the least-mean-square (LMS) algorithm with a neural network, was applied to this problem. The method presented in this paper is divided into five modules: space recognition, tone recognition, learning process, adaptive processing and character recognition. Statistical analyses demonstrated that the proposed method elicited a better recognition rate in comparison with other methods in the literature.

  2. SYSTEM DESIGN AND BEHAVIOR OF PATTERN RECOGNITION OF CONTROL CHART USING NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Justin Yulius Halim

    2003-01-01

    Full Text Available Recognition of unnatural patterns in control charts is important to get more understanding about the process problem. Shewhart control chart can recognize shift pattern only, while the others cannot be detected by this chart. Neural networks were proposed by many researchers to achieve the better recognition of the patterns. A system of neural networks needs to be fitted to this problem by realizing the behaviors of control charts. Each behavior will affect the development of each part of the system. Some problem need to be pointed also when dealing with the patterns of the control chart.

  3. Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

    Directory of Open Access Journals (Sweden)

    Md. Abdullah-al-mamun

    2015-08-01

    Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

  4. Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

    Science.gov (United States)

    Wang, Runchun; Thakur, Chetan Singh; Cohen, Gregory; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, Andre

    2017-06-01

    We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. We have now scaled this approach up to build a pattern recognition system by combining identical neural cores together. As a proof of concept, we have developed a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture and hardware optimisations presented offer high-speed and resource-efficient means for performing high-speed, neuromorphic, and massively parallel pattern recognition and classification tasks.

  5. The use of global image characteristics for neural network pattern recognitions

    Science.gov (United States)

    Kulyas, Maksim O.; Kulyas, Oleg L.; Loshkarev, Aleksey S.

    2017-04-01

    The recognition system is observed, where the information is transferred by images of symbols generated by a television camera. For descriptors of objects the coefficients of two-dimensional Fourier transformation generated in a special way. For solution of the task of classification the one-layer neural network trained on reference images is used. Fast learning of a neural network with a single neuron calculation of coefficients is applied.

  6. How Deep Neural Networks Can Improve Emotion Recognition on Video Data

    Science.gov (United States)

    2016-09-25

    HOW DEEP NEURAL NETWORKS CAN IMPROVE EMOTION RECOGNITION ON VIDEO DATA Pooya Khorrami1 , Tom Le Paine1, Kevin Brady2, Charlie Dagli2, Thomas S...decades, emotion recognition has remained one of the of the most important problems in the field of human computer interaction. A large portion of the...benefits of using ei- ther a CNN or a temporal model like an RNN or Long Short Term Memory (LSTM) network individually, very few works [14, 15] have

  7. Definition of new 3D invariants. Applications to pattern recognition problems with neural networks

    International Nuclear Information System (INIS)

    Proriol, J.

    1996-01-01

    We propose a definition of new 3D invariants. Usual pattern recognition methods use 2D descriptions of 3D objects, we propose a 2D approximation of the defined 3D invariants which can be used with neural networks to solve pattern recognition problems. We describe some methods to use the 2 D approximants. This work is an extension of previous 3D invariants used to solve some high energy physics problems. (author)

  8. Fast Automatic Configuration of Artificial Neural Networks Used for Binary Patterns Recognition

    OpenAIRE

    Adrian Horzyk

    2001-01-01

    This paper presents a powerful method of an automatically generated architecture of neural networks used for binary pattems recognition, which can quickly and automatically reduce synapses in a way of minimally reducing a quality of recognition and a quality of generalization. Moreover, this method computes all weights in two runs over a leaming sequence, what makes this method very fast. First, the method calculates all binary features for each pattem and then weights are computed. Furthermo...

  9. Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Zorins Aleksejs

    2016-12-01

    Full Text Available The article presents an introductory analysis of relevant research topic for Latvian deaf society, which is the development of the Latvian Sign Language Recognition System. More specifically the data preprocessing methods are discussed in the paper and several approaches are shown with a focus on systems based on artificial neural networks, which are one of the most successful solutions for sign language recognition task.

  10. From neural-based object recognition toward microelectronic eyes

    Science.gov (United States)

    Sheu, Bing J.; Bang, Sa Hyun

    1994-01-01

    Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

  11. Recognition of malignant processes with neural nets from ESR spectra of serum albumin

    Energy Technology Data Exchange (ETDEWEB)

    Seidel, P. [Inst. of Medical Physics and Biophysics, Univ. Leipzig (Germany); Gurachevsky, A.; Muravsky, V.; Schnurr, K.; Seibt, G. [Medinnovation GmbH, Wildau (Germany); Matthes, G. [Inst. of Transfusion Medicine, Univ. Hospital Leipzig (Germany)

    2005-07-01

    Cancer diseases are the focus of intense research due to their frequent occurrence. It is known from the literature that serum proteins are changed in the case of malignant processes. Changes of albumin conformation, transport efficiency, and binding characteristics can be determined by electron spin resonance spectroscopy (ESR). The present study analysed the binding/dissociation function of albumin with an ESR method using 16-doxyl stearate spin probe as reporter molecule and ethanol as modifier of hydrophobic interactions. Native and frozen plasma of healthy donors (608 samples), patients with malignant diseases (423 samples), and patients with benign conditions (221 samples) were analysed. The global specificity was 91% and the sensitivity 96%. In look-back samples of 27 donors, a malignant process could be detected up to 30 months before clinical diagnosis. To recognise different entities of malignant diseases from the ESR spectra, Artificial neural networks were implemented. For 48 female donors with breast cancer, the recognition specificity was 85%. Other carcinoma entities (22 colon, 18 prostate, 12 stomach) were recognised with specificities between 75% and 84%. Should these specificity values be reproduced in larger studies, the described method could be used as a new specific tumour marker for the early detection of malignant processes. Since transmission of cancer via blood transfusion cannot be excluded as yet, the described ESR method could also be used as a quality test for plasma products. (orig.)

  12. Recognition of malignant processes with neural nets from ESR spectra of serum albumin

    International Nuclear Information System (INIS)

    Seidel, P.; Gurachevsky, A.; Muravsky, V.; Schnurr, K.; Seibt, G.; Matthes, G.

    2005-01-01

    Cancer diseases are the focus of intense research due to their frequent occurrence. It is known from the literature that serum proteins are changed in the case of malignant processes. Changes of albumin conformation, transport efficiency, and binding characteristics can be determined by electron spin resonance spectroscopy (ESR). The present study analysed the binding/dissociation function of albumin with an ESR method using 16-doxyl stearate spin probe as reporter molecule and ethanol as modifier of hydrophobic interactions. Native and frozen plasma of healthy donors (608 samples), patients with malignant diseases (423 samples), and patients with benign conditions (221 samples) were analysed. The global specificity was 91% and the sensitivity 96%. In look-back samples of 27 donors, a malignant process could be detected up to 30 months before clinical diagnosis. To recognise different entities of malignant diseases from the ESR spectra, Artificial neural networks were implemented. For 48 female donors with breast cancer, the recognition specificity was 85%. Other carcinoma entities (22 colon, 18 prostate, 12 stomach) were recognised with specificities between 75% and 84%. Should these specificity values be reproduced in larger studies, the described method could be used as a new specific tumour marker for the early detection of malignant processes. Since transmission of cancer via blood transfusion cannot be excluded as yet, the described ESR method could also be used as a quality test for plasma products. (orig.)

  13. Hierarchical singleton-type recurrent neural fuzzy networks for noisy speech recognition.

    Science.gov (United States)

    Juang, Chia-Feng; Chiou, Chyi-Tian; Lai, Chun-Lung

    2007-05-01

    This paper proposes noisy speech recognition using hierarchical singleton-type recurrent neural fuzzy networks (HSRNFNs). The proposed HSRNFN is a hierarchical connection of two singleton-type recurrent neural fuzzy networks (SRNFNs), where one is used for noise filtering and the other for recognition. The SRNFN is constructed by recurrent fuzzy if-then rules with fuzzy singletons in the consequences, and their recurrent properties make them suitable for processing speech patterns with temporal characteristics. In n words recognition, n SRNFNs are created for modeling n words, where each SRNFN receives the current frame feature and predicts the next one of its modeling word. The prediction error of each SRNFN is used as recognition criterion. In filtering, one SRNFN is created, and each SRNFN recognizer is connected to the same SRNFN filter, which filters noisy speech patterns in the feature domain before feeding them to the SRNFN recognizer. Experiments with Mandarin word recognition under different types of noise are performed. Other recognizers, including multilayer perceptron (MLP), time-delay neural networks (TDNNs), and hidden Markov models (HMMs), are also tested and compared. These experiments and comparisons demonstrate good results with HSRNFN for noisy speech recognition tasks.

  14. Application of deep convolutional neural networks for ocean front recognition

    Science.gov (United States)

    Lima, Estanislau; Sun, Xin; Yang, Yuting; Dong, Junyu

    2017-10-01

    Ocean fronts have been a subject of study for many years, a variety of methods and algorithms have been proposed to address the problem of ocean fronts. However, all these existing ocean front recognition methods are built upon human expertise in defining the front based on subjective thresholds of relevant physical variables. This paper proposes a deep learning approach for ocean front recognition that is able to automatically recognize the front. We first investigated four existing deep architectures, i.e., AlexNet, CaffeNet, GoogLeNet, and VGGNet, for the ocean front recognition task using remote sensing (RS) data. We then propose a deep network with fewer layers compared to existing architecture for the front recognition task. This network has a total of five learnable layers. In addition, we extended the proposed network to recognize and classify the front into strong and weak ones. We evaluated and analyzed the proposed network with two strategies of exploiting the deep model: full-training and fine-tuning. Experiments are conducted on three different RS image datasets, which have different properties. Experimental results show that our model can produce accurate recognition results.

  15. Musical expertise affects neural bases of letter recognition.

    Science.gov (United States)

    Proverbio, Alice Mado; Manfredi, Mirella; Zani, Alberto; Adorni, Roberta

    2013-02-01

    It is known that early music learning (playing of an instrument) modifies functional brain structure (both white and gray matter) and connectivity, especially callosal transfer, motor control/coordination and auditory processing. We compared visual processing of notes and words in 15 professional musicians and 15 controls by recording their synchronized bioelectrical activity (ERPs) in response to words and notes. We found that musical training in childhood (from age ~8 years) modifies neural mechanisms of word reading, whatever the genetic predisposition, which was unknown. While letter processing was strongly left-lateralized in controls, the fusiform (BA37) and inferior occipital gyri (BA18) were activated in both hemispheres in musicians for both word and music processing. The evidence that the neural mechanism of letter processing differed in musicians and controls (being absolutely bilateral in musicians) suggests that musical expertise modifies the neural mechanisms of letter reading. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks.

    Science.gov (United States)

    Segler, Marwin H S; Kogej, Thierry; Tyrchan, Christian; Waller, Mark P

    2018-01-24

    In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus , the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.

  17. Optical implementation of a feature-based neural network with application to automatic target recognition

    Science.gov (United States)

    Chao, Tien-Hsin; Stoner, William W.

    1993-01-01

    An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.

  18. Automatic target recognition using a feature-based optical neural network

    Science.gov (United States)

    Chao, Tien-Hsin

    1992-01-01

    An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

  19. Mexican sign language recognition using normalized moments and artificial neural networks

    Science.gov (United States)

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

    2014-09-01

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

  20. The novel carbohydrate epitope L3 is shared by some neural cell adhesion molecules.

    Science.gov (United States)

    Kücherer, A; Faissner, A; Schachner, M

    1987-06-01

    The monoclonal L3 antibody reacts with an N-glycosidically linked carbohydrate structure on at least nine glycoproteins of adult mouse brain. Three out of the L3 epitope-carrying glycoproteins could be identified as the neural cell adhesion molecules L1 and myelin-associated glycoprotein, and the novel adhesion molecule on glia. Expression of the L3 carbohydrate epitope is regulated independently of the protein backbone of these three glycoproteins. Based on the observation that out of three functionally characterized L3 epitope-carrying glycoproteins three fulfill the operational definition of an adhesion molecule, we would like to suggest that they form a new family of adhesion molecules that is distinct from the L2/HNK-1 carbohydrate epitope family of neural cell adhesion molecules. Interestingly, some members in each family appear to be unique to one family while other members belong to the two families.

  1. Automatic Target Recognition Using Nonlinear Autoregressive Neural Networks

    Science.gov (United States)

    2014-03-27

    International Conference on Natural Computation, 97-101. Loeffelholz, B., Bednar, E., & Bauer, K. W. (2009). Predicting NBA Games Using Neural Networks... Communications (ICDIPC2013), 611-620. Roth, B. D. (2013, December 13). Data Collection Methodologies. (M. R. Ward, Interviewer) 65 Skidmore, T

  2. Fish species recognition using computer vision and a neural network

    NARCIS (Netherlands)

    Storbeck, F.; Daan, B.

    2001-01-01

    A system is described to recognize fish species by computer vision and a neural network program. The vision system measures a number of features of fish as seen by a camera perpendicular to a conveyor belt. The features used here are the widths and heights at various locations along the fish. First

  3. Fault diagnosis with neural networks. Part 1: Trajectory recognition

    Directory of Open Access Journals (Sweden)

    Enrique Eduardo Tarifa

    2007-01-01

    Full Text Available The present investigation was focused on formulating a method for designing a fault diagnosis system for chemical plants by using artificial neural networks. Fault diagnosis is aimed at identifying a fault which affects a given process by analysing the signs supplied by process sensors. Neuronal networks are mathematical models which try to imitate the functioning of the human brain. A neural network is defined by its structure and the learning method used. The difficulty with diagnosing faults lies in recognising the tralectories (temporal series of data followed by process variables when a fault affects the process; when tralectories are recognised, the associated fault is also identified. The theory so developed recommended an optimised structure and training method for the neural networks to use. Both the proposed structure and the training method were tested by carrying out comparative studies between traditional structures and a training method. The results showed the superiority of the neural networks designed and trained with the method proposed in this work. Except for simple processes, fault diagnosis is a more complex problem than simply identifying tralectories, because a fault may cause an infinite set of tralectories (i.e. flow. The fundaments established in this work are thus used in Part II, where the analysis is extended to recognise flows.

  4. A neural network for the Bragg synthetic curves recognition

    International Nuclear Information System (INIS)

    Reynoso V, M.R.; Vega C, J.J.; Fernandez A, J.; Belmont M, E.; Policroniades R, R.; Moreno B, E.

    1996-01-01

    A ionization chamber was employed named Bragg curve spectroscopy. The Bragg peak amplitude is a monotone growing function of Z, which permits to identify elements through their measurement. A better technique for this measurement is to improve the use of neural networks with the purpose of the identification of the Bragg curve. (Author)

  5. Low-power Appliance Recognition using Recurrent Neural Networks

    NARCIS (Netherlands)

    Rizky Pratama, Azkario; Simanjuntak, Frans Juanda; Lazovik, Aliaksandr; Aiello, Marco

    2018-01-01

    Indoor energy consumption can be understood by breaking overall power consumption down into individual components and appliance activations. The clas- sification of components of energy usage is known as load disaggregation or ap- pliance recognition. Most of the previous efforts address the

  6. Neural Correlates of Music Recognition in Down Syndrome

    Science.gov (United States)

    Virji-Babul, N.; Moiseev, A.; Sun, W.; Feng, T.; Moiseeva, N.; Watt, K. J.; Huotilainen, M.

    2013-01-01

    The brain mechanisms that subserve music recognition remain unclear despite increasing interest in this process. Here we report the results of a magnetoencephalography experiment to determine the temporal dynamics and spatial distribution of brain regions activated during listening to a familiar and unfamiliar instrumental melody in control adults…

  7. Cascading neural networks for upper-body gesture recognition

    CSIR Research Space (South Africa)

    Mangera, R

    2014-01-01

    Full Text Available Gesture recognition has many applications ranging from health care to entertainment. However for it to be a feasible method of human-computer interaction it is essential that only intentional movements are interpreted and that the system can work...

  8. Control chart pattern recognition using an optimized neural network and efficient features.

    Science.gov (United States)

    Ebrahimzadeh, Ata; Ranaee, Vahid

    2010-07-01

    Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This study investigates the design of an accurate system for control chart pattern (CCP) recognition from two aspects. First, an efficient system is introduced that includes two main modules: the feature extraction module and the classifier module. The feature extraction module uses the entropies of the wavelet packets. These are applied for the first time in this area. In the classifier module several neural networks, such as the multilayer perceptron and radial basis function, are investigated. Using an experimental study, we choose the best classifier in order to recognize the CCPs. Second, we propose a hybrid heuristic recognition system based on particle swarm optimization to improve the generalization performance of the classifier. The results obtained clearly confirm that further improvements in terms of recognition accuracy can be achieved by the proposed recognition system. 2010 ISA. Published by Elsevier Ltd. All rights reserved.

  9. A modular neural network classifier for the recognition of occluded characters in automatic license plate reading

    NARCIS (Netherlands)

    Nijhuis, JAG; Broersma, A; Spaanenburg, L; Ruan, D; Dhondt, P; Kerre, EE

    2002-01-01

    Occlusion is the most common reason for lowered recognition yield in free-flow license-plate reading systems. (Non-)occluded characters can readily be learned in separate neural networks but not together. Even a small proportion of occluded characters in the training set will already significantly

  10. Behavioral and Physiological Neural Network Analyses: A Common Pathway toward Pattern Recognition and Prediction

    Science.gov (United States)

    Ninness, Chris; Lauter, Judy L.; Coffee, Michael; Clary, Logan; Kelly, Elizabeth; Rumph, Marilyn; Rumph, Robin; Kyle, Betty; Ninness, Sharon K.

    2012-01-01

    Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Machine Learning Repository. Data for this study…

  11. Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts

    Science.gov (United States)

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

    2010-01-01

    Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

  12. NEURAL NETWORK­BASED CONTEXT DRIVEN RECOGNITION OF ON­LINE CURSIVE SCRIP

    NARCIS (Netherlands)

    Neskovic, P.; Cooper, L.N.

    2004-01-01

    Most of the state­of­the­art systems for cursive script recognition are based on a combination of neural networks (NN) and hidden Markov models (HMMs) 1;2 . The post­processing stage is almost exclusively modeled using HMMs and the dynamic programming (DP) technique (the Viterbi algorithm) is used

  13. Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition

    NARCIS (Netherlands)

    Leon Rincon, Carlos; Moreno, José Fernando; Cely, Jorge

    2017-01-01

    The balance sheet is a snapshot that portraits the financial position of a firm at a specific point of time. Under the reasonable assumption that the financial position of a firm is unique and representative, we use a basic artificial neural network pattern recognition method on Colombian banks’

  14. Assay for the pattern recognition molecule collectin liver 1 (CL-L1)

    DEFF Research Database (Denmark)

    Axelgaard, Esben; Jensenius, Jens Christian; Jensen, Lisbeth

    Collectin liver 1 (also termed collectin 10 and CL-L1) is a C-type lectin that functions as a pattern recognition molecule (PRM) in the innate immune system1. We have produced antibodies against CL-L1 and have developed a sandwich-type time-resolved immuno-fluorometric assay (TRIFMA) for the meas......Collectin liver 1 (also termed collectin 10 and CL-L1) is a C-type lectin that functions as a pattern recognition molecule (PRM) in the innate immune system1. We have produced antibodies against CL-L1 and have developed a sandwich-type time-resolved immuno-fluorometric assay (TRIFMA...

  15. Higher-order neural network software for distortion invariant object recognition

    Science.gov (United States)

    Reid, Max B.; Spirkovska, Lilly

    1991-01-01

    The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

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

    Science.gov (United States)

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

    2017-11-01

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

  17. Banknote recognition: investigating processing and cognition framework using competitive neural network.

    Science.gov (United States)

    Oyedotun, Oyebade K; Khashman, Adnan

    2017-02-01

    Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria's Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.

  18. The pattern-recognition molecule mannan-binding lectin (MBL) in the pathophysiology of diabetic nephropathy

    DEFF Research Database (Denmark)

    Axelgaard, Esben; Thiel, Steffen; Hansen, Troels Krarup

    The pattern-recognition molecule mannan-binding lectin (MBL) in the pathophysiology of diabetic nephropathy Esben Axelgaard*; Steffen Thiel*; Jakob Appel Østergaard† and Troels Krarup Hansen† *Department of Biomedicine, Aarhus University, Wilhelm Meyer´s Allé 4, 8000 Aarhus C, Denmark. †Department...... of Clinical Medicine, Aarhus University and The Danish Diabetes Academy, Nørrebrogade 44, build. 3, 8000 Aarhus C, Denmark The complement system is part of the innate immune system and is an important part of the first line of defence against pathogens. Mannan-binding lectin (MBL) is one of the pattern-recognition...... mechanisms are proposed, 1) the formation of neoepitopes for MBL pattern recognition on host cells would enable lectin pathway activation and 2) inactivation of complement regulatory proteins by glycation that may exaggerate complement attack on host cells. MBL initiates the lectin pathway through binding...

  19. An object recognition using structured light and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Byeong Gab; Kim, Dong Gi; Kang, E Sok [Chungnam National Univ., Taejon (Korea, Republic of); Yoon, Ji Sup [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1997-12-31

    This paper presents a 3D image processing which uses neural networks to combine a 2D vision camera and a laser slit beam. A laser slit beam from laser source is slitted by a set of cylindrical lenses and the line image of the networks allow to get the 3D image parameters such as the size, the position and the orientation from the line image without knowing the camera intrinsic parameters. (author). 7 refs., 3 tabs., 5 figs.

  20. Optimal Recognition Method of Human Activities Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Oniga Stefan

    2015-12-01

    Full Text Available The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.

  1. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  2. Multi-Lingual Deep Neural Networks for Language Recognition

    Science.gov (United States)

    2016-08-08

    technique to artificially add phono- tactic variability to speech for example to account for formal and informal versions of language dialect (see [4...Switchboard English shows the impact of language se- lection and the amount of training data on overall BN-DNN perfor- mance. Index Terms: language recognition...low dimensional representation of a waveform that contains speaker, language , session and channel information . With a sufficient number of recorded

  3. Three-dimensional object representation and invariant recognition using continuous distance transform neural networks.

    Science.gov (United States)

    Tseng, Y H; Hwang, J N; Sheehan, F H

    1997-01-01

    3D object recognition under partial object viewing is a difficult pattern recognition task. In this paper, we introduce a neural-network solution that is robust to partial viewing of objects and noise corruption. This method directly utilizes the acquired 3D data and requires no feature extraction. The object is first parametrically represented by a continuous distance transform neural network (CDTNN) trained by the surface points of the exemplar object. The CDTNN maps any 3D coordinate into a value that corresponds to the distance between the point and the nearest surface point of the object. Therefore, a mismatch between the exemplar object and an unknown object can be easily computed. When encountered with deformed objects, this mismatch information can be backpropagated through the CDTNN to iteratively determine the deformation in terms of affine transform. Application to 3D heart contour delineation and invariant recognition of 3D rigid-body objects is presented.

  4. Application of Synapses Dilution Method for Pattern Recognition Optimation Using Hopfield Model Neural Network

    International Nuclear Information System (INIS)

    Wicaksana, D.S.

    1997-01-01

    Human's neural network consist of thousands of neurons, each of which has only one input, and more than one output. these neurons are linked together through junctions called synapses, which have different strength from one to another, to configure specific information pattern. Using their functions and capabilities, we are able to improve the performance of neuman-type computers in the future. This is because of the capabilities to parallely process information, especially for voice and image pattern recognitions, instead of serial process as in Neuman-type computers. This paper explains how to simplify hopfield model neural network by using synapse dilution without reducing the capability of its pattern recognition. the dilution is done by using two ways: sequence, and random. Both ways are followed by either intact or distorted pattern recognitions

  5. Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy

    Directory of Open Access Journals (Sweden)

    Tran Hoai Linh

    2014-09-01

    Full Text Available The paper presents a new system for ECG (ElectroCardioGraphy signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron, modified TSK (Takagi-Sugeno-Kang and the SVM (Support Vector Machine, will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston’s Beth Israel Hospital Arrhythmia Database. The results will be compared with individual base classifiers’ performances and with other integration methods to show the high quality of the proposed solution

  6. A comparison study between MLP and convolutional neural network models for character recognition

    Science.gov (United States)

    Ben Driss, S.; Soua, M.; Kachouri, R.; Akil, M.

    2017-05-01

    Optical Character Recognition (OCR) systems have been designed to operate on text contained in scanned documents and images. They include text detection and character recognition in which characters are described then classified. In the classification step, characters are identified according to their features or template descriptions. Then, a given classifier is employed to identify characters. In this context, we have proposed the unified character descriptor (UCD) to represent characters based on their features. Then, matching was employed to ensure the classification. This recognition scheme performs a good OCR Accuracy on homogeneous scanned documents, however it cannot discriminate characters with high font variation and distortion.3 To improve recognition, classifiers based on neural networks can be used. The multilayer perceptron (MLP) ensures high recognition accuracy when performing a robust training. Moreover, the convolutional neural network (CNN), is gaining nowadays a lot of popularity for its high performance. Furthermore, both CNN and MLP may suffer from the large amount of computation in the training phase. In this paper, we establish a comparison between MLP and CNN. We provide MLP with the UCD descriptor and the appropriate network configuration. For CNN, we employ the convolutional network designed for handwritten and machine-printed character recognition (Lenet-5) and we adapt it to support 62 classes, including both digits and characters. In addition, GPU parallelization is studied to speed up both of MLP and CNN classifiers. Based on our experimentations, we demonstrate that the used real-time CNN is 2x more relevant than MLP when classifying characters.

  7. New approach to ECG's features recognition involving neural network

    International Nuclear Information System (INIS)

    Babloyantz, A.; Ivanov, V.V.; Zrelov, P.V.

    2001-01-01

    A new approach for the detection of slight changes in the form of the ECG signal is proposed. It is based on the approximation of raw ECG data inside each RR-interval by the expansion in polynomials of special type and on the classification of samples represented by sets of expansion coefficients using a layered feed-forward neural network. The transformation applied provides significantly simpler data structure, stability to noise and to other accidental factors. A by-product of the method is the compression of ECG data with factor 5

  8. Single-molecule spectroscopy of amino acids and peptides by recognition tunnelling

    Science.gov (United States)

    Zhao, Yanan; Ashcroft, Brian; Zhang, Peiming; Liu, Hao; Sen, Suman; Song, Weisi; Im, Jongone; Gyarfas, Brett; Manna, Saikat; Biswas, Sovan; Borges, Chad; Lindsay, Stuart

    2014-06-01

    The human proteome has millions of protein variants due to alternative RNA splicing and post-translational modifications, and variants that are related to diseases are frequently present in minute concentrations. For DNA and RNA, low concentrations can be amplified using the polymerase chain reaction, but there is no such reaction for proteins. Therefore, the development of single-molecule protein sequencing is a critical step in the search for protein biomarkers. Here, we show that single amino acids can be identified by trapping the molecules between two electrodes that are coated with a layer of recognition molecules, then measuring the electron tunnelling current across the junction. A given molecule can bind in more than one way in the junction, and we therefore use a machine-learning algorithm to distinguish between the sets of electronic `fingerprints' associated with each binding motif. With this recognition tunnelling technique, we are able to identify D and L enantiomers, a methylated amino acid, isobaric isomers and short peptides. The results suggest that direct electronic sequencing of single proteins could be possible by sequentially measuring the products of processive exopeptidase digestion, or by using a molecular motor to pull proteins through a tunnel junction integrated with a nanopore.

  9. Tifinagh Character Recognition Using Geodesic Distances, Decision Trees & Neural Networks

    OpenAIRE

    O.BENCHAREF; M.FAKIR; B. MINAOUI; B.BOUIKHALENE

    2011-01-01

    The recognition of Tifinagh characters cannot be perfectly carried out using the conventional methods which are based on the invariance, this is due to the similarity that exists between some characters which differ from each other only by size or rotation, hence the need to come up with new methods to remedy this shortage. In this paper we propose a direct method based on the calculation of what is called Geodesic Descriptors which have shown significant reliability vis-à-vis the change of s...

  10. Neural ECM molecules in axonal and synaptic homeostatic plasticity.

    Science.gov (United States)

    Frischknecht, Renato; Chang, Kae-Jiun; Rasband, Matthew N; Seidenbecher, Constanze I

    2014-01-01

    Neural circuits can express different forms of plasticity. So far, Hebbian synaptic plasticity was considered the most important plastic phenomenon, but over the last decade, homeostatic mechanisms gained more interest because they can explain how a neuronal network maintains stable baseline function despite multiple plastic challenges, like developmental plasticity, learning, or lesion. Such destabilizing influences can be counterbalanced by the mechanisms of homeostatic plasticity, which restore the stability of neuronal circuits. Synaptic scaling is a mechanism in which neurons can detect changes in their own firing rates through a set of molecular sensors that then regulate receptor trafficking to scale the accumulation of glutamate receptors at synaptic sites. Additional homeostatic mechanisms allow local changes in synaptic activation to generate local synaptic adaptations and network-wide changes in activity, which lead to adjustments in the balance between excitation and inhibition. The molecular pathways underlying these forms of homeostatic plasticity are currently under intense investigation, and it becomes clear that the extracellular matrix (ECM) of the brain, which surrounds individual neurons and integrates them into the tissue, is an important element in these processes. As a highly dynamic structure, which can be remodeled and degraded in an activity-dependent manner and in concerted action of neurons and glial cells, it can on one hand promote structural and functional plasticity and on the other hand stabilize neural microcircuits. This chapter highlights the composition of brain ECM with particular emphasis on perisynaptic and axonal matrix formations and its involvement in plastic and adaptive processes of the central nervous system.

  11. A neural cell adhesion molecule-derived peptide reduces neuropathological signs and cognitive impairment induced by Abeta25-35

    DEFF Research Database (Denmark)

    Klementiev, B; Novikova, T; Novitskaya, V

    2007-01-01

    death and brain atrophy in response to Abeta25-35. Finally, the Abeta25-35-administration led to a reduced short-term memory as determined by the social recognition test. A synthetic peptide termed FGL derived from the neural cell adhesion molecule (NCAM) was able to prevent or, if already manifest......, strongly reduce all investigated signs of Abeta25-35-induced neuropathology and cognitive impairment. The FGL peptide was recently demonstrated to be able to cross the blood-brain-barrier. Accordingly, we found that the beneficial effects of FGL were achieved not only by intracisternal, but also...... and cognitive impairment involves the modulation of intracellular signal-transduction mediated through GSK3beta....

  12. Voice reinstatement modulates neural indices of continuous word recognition.

    Science.gov (United States)

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

    2014-09-01

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

  13. Realization for Chinese vehicle license plate recognition based on computer vision and fuzzy neural network

    Science.gov (United States)

    Yang, Yun; Zhang, Weigang; Guo, Pan

    2010-07-01

    The proposed approach in this paper is divided into three steps namely the location of plate, the segmentation of the characters and the recognition of the characters. The location algorithm is firstly consisted of two video captures to get high quality images, and estimates the size of vehicle plate in these images via parallel binocular stereo vision algorithm. Then the segmentation method extracts the edge of vehicle plate based on second generation non-orthogonal Haar wavelet transformation, and locates the vehicle plate according to the estimated result in the first step. Finally, the recognition algorithm is realized based on the Radial Basis Function Fuzzy Neural Network. Experiments have been conducted for real images. The results show this method can decrease the error recognition rate of Chinese license plate recognition.

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

    Science.gov (United States)

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

    2018-03-01

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

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

    Directory of Open Access Journals (Sweden)

    T. Pasupathi

    2014-05-01

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

  16. Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

    Science.gov (United States)

    Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

    2017-01-01

    Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

  17. Transfection of glioma cells with the neural-cell adhesion molecule NCAM

    DEFF Research Database (Denmark)

    Edvardsen, K; Pedersen, P H; Bjerkvig, R

    1994-01-01

    The tumor growth and the invasive capacity of a rat glioma cell line (BT4Cn) were studied after transfection with the human transmembrane 140-kDa isoform of the neural-cell adhesion molecule, NCAM. After s.c. injection, the NCAM-transfected cells showed a slower growth rate than the parent cell...

  18. Age-related changes in expression of neural cell adhesion molecule (NCAM) in heart

    DEFF Research Database (Denmark)

    Gaardsvoll, H; Krog, L; Zhernosekov, D

    1993-01-01

    The neural cell adhesion molecule (NCAM) has been implicated in cellular interactions involved in cardiac morphogenesis and innervation. In this study, expression of NCAM mRNA and protein was characterized in rat heart during postnatal development and aging (postnatal days 1, 10, 40, 270, and 730...

  19. The neural cell adhesion molecule binds to fibroblast growth factor receptor 2

    DEFF Research Database (Denmark)

    Christensen, Claus; Lauridsen, Jes B; Berezin, Vladimir

    2006-01-01

    The neural cell adhesion molecule (NCAM) can bind to and activate fibroblast growth factor receptor 1 (FGFR1). However, there are four major FGFR isoforms (FGFR1-FGFR4), and it is not known whether NCAM also interacts directly with the other three FGFR isoforms. In this study, we show by surface...

  20. Biosynthesis of the neural cell adhesion molecule: characterization of polypeptide C

    DEFF Research Database (Denmark)

    Nybroe, O; Albrechtsen, M; Dahlin, J

    1985-01-01

    The biosynthesis of the neural cell adhesion molecule (N-CAM) was studied in primary cultures of rat cerebral glial cells, cerebellar granule neurons, and skeletal muscle cells. The three cell types produced different N-CAM polypeptide patterns. Glial cells synthesized a 135,000 Mr polypeptide B...

  1. Lexical decoder for continuous speech recognition: sequential neural network approach

    International Nuclear Information System (INIS)

    Iooss, Christine

    1991-01-01

    The work presented in this dissertation concerns the study of a connectionist architecture to treat sequential inputs. In this context, the model proposed by J.L. Elman, a recurrent multilayers network, is used. Its abilities and its limits are evaluated. Modifications are done in order to treat erroneous or noisy sequential inputs and to classify patterns. The application context of this study concerns the realisation of a lexical decoder for analytical multi-speakers continuous speech recognition. Lexical decoding is completed from lattices of phonemes which are obtained after an acoustic-phonetic decoding stage relying on a K Nearest Neighbors search technique. Test are done on sentences formed from a lexicon of 20 words. The results are obtained show the ability of the proposed connectionist model to take into account the sequentiality at the input level, to memorize the context and to treat noisy or erroneous inputs. (author) [fr

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

    Science.gov (United States)

    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

  3. Fine-grained vehicle type recognition based on deep convolution neural networks

    Directory of Open Access Journals (Sweden)

    Hongcai CHEN

    2017-12-01

    Full Text Available Public security and traffic department put forward higher requirements for real-time performance and accuracy of vehicle type recognition in complex traffic scenes. Aiming at the problems of great plice forces occupation, low retrieval efficiency, and lacking of intelligence for dealing with false license, fake plate vehicles and vehicles without plates, this paper proposes a vehicle type fine-grained recognition method based GoogleNet deep convolution neural networks. The filter size and numbers of convolution neural network are designed, the activation function and vehicle type classifier are optimally selected, and a new network framework is constructed for vehicle type fine-grained recognition. The experimental results show that the proposed method has 97% accuracy for vehicle type fine-grained recognition and has greater improvement than the original GoogleNet model. Moreover, the new model effectively reduces the number of training parameters, and saves computer memory. Fine-grained vehicle type recognition can be used in intelligent traffic management area, and has important theoretical research value and practical significance.

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

    Science.gov (United States)

    Xing, Weiwei; Li, Ying; Zhang, Shunli

    2018-01-01

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

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

    Science.gov (United States)

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

    2010-11-01

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

  6. Classification of remotely sensed data using OCR-inspired neural network techniques. [Optical Character Recognition

    Science.gov (United States)

    Kiang, Richard K.

    1992-01-01

    Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

  7. Genetic Optimization of Neural Networks for Person Recognition based on the Iris

    Directory of Open Access Journals (Sweden)

    Oscar Castillo

    2012-06-01

    Full Text Available This paper describes the application of modular neural network architectures for person recognition using the human iris images as a biometric measure. The iris database was obtained from the Institute of Automation of the Academy of Sciences China (CASIA. We show simulation results with the modular neural network approach, its optimization using genetic algorithms, and the integration with different methods, such as: the gating network method, type-1 fuzzy integration and optimized fuzzy integration using genetic algorithms. Simulation results show a good identification rate using fuzzy integrators and the best structure found by the genetic algorithm.

  8. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  9. Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

    Energy Technology Data Exchange (ETDEWEB)

    Nikiforov, M P; Guo, S; Kalinin, S V; Jesse, S [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN 37831 (United States); Reukov, V V; Thompson, G L; Vertegel, A A, E-mail: sergei2@ornl.go [Department of Bioengineering, Clemson University, Clemson, SC 29634 (United States)

    2009-10-07

    Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

  10. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  11. Neural network recognition of nuclear power plant transients

    International Nuclear Information System (INIS)

    Bartlett, E.B.; Danofsky, R.; Adams, J.; AlJundi, T.; Basu, A.; Dhanwada, C.; Kerr, J.; Kim, K.; Lanc, T.

    1993-01-01

    The objective of this report is to describe results obtained during the first year of funding that will lead to the development of an artificial neural network (ANN) fault - diagnostic system for the real - time classification of operational transients at nuclear power plants. The ultimate goal of this three-year project is to design, build, and test a prototype diagnostic adviser for use in the control room or technical support center at Duane Arnold Energy Center (DAEC); such a prototype could be integrated into the plant process computer or safety - parameter display system. The adviser could then warn and inform plant operators and engineers of plant component failures in a timely manner. This report describes the work accomplished in the first of three scheduled years for the project. Included herein is a summary of the first year's results as, well as individual descriptions of each of the major topics undertaken by the researchers. Also included are reprints of the articles written under this funding as well as those that were published during the funded period

  12. Assay for the pattern recognition molecule collectin liver 1 (CL-L1)

    DEFF Research Database (Denmark)

    Axelgaard, Esben; Jensenius, Jens Christian; Jensen, Lisbeth

    Collectin liver 1 (also termed collectin 10 and CL-L1) is a C-type lectin that functions as a pattern recognition molecule (PRM) in the innate immune system1. We have produced antibodies against CL-L1 and have developed a sandwich-type time-resolved immuno-fluorometric assay (TRIFMA...... to co-purify with MASPs, possibly rendering it a role in complement. CL-L1 showed binding activity towards mannose-TSK beads in a Ca2+-dependent manner. This binding could be inhibited by mannose and glucose, but not by galactose, indicating that CL-L1 binds via its carbohydrate-recognition domain (CRD)....

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

    International Nuclear Information System (INIS)

    Avci, E.

    2007-01-01

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

  14. Recognition of underground nuclear explosion and natural earthquake based on neural network

    International Nuclear Information System (INIS)

    Yang Hong; Jia Weimin

    2000-01-01

    Many features are extracted to improve the identified rate and reliability of underground nuclear explosion and natural earthquake. But how to synthesize these characters is the key of pattern recognition. Based on the improved Delta algorithm, features of underground nuclear explosion and natural earthquake are inputted into BP neural network, and friendship functions are constructed to identify the output values. The identified rate is up to 92.0%, which shows that: the way is feasible

  15. Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?

    OpenAIRE

    Khorrami, Pooya; Paine, Tom Le; Huang, Thomas S.

    2015-01-01

    Despite being the appearance-based classifier of choice in recent years, relatively few works have examined how much convolutional neural networks (CNNs) can improve performance on accepted expression recognition benchmarks and, more importantly, examine what it is they actually learn. In this work, not only do we show that CNNs can achieve strong performance, but we also introduce an approach to decipher which portions of the face influence the CNN's predictions. First, we train a zero-bias ...

  16. Control chart pattern recognition using K-MICA clustering and neural networks.

    Science.gov (United States)

    Ebrahimzadeh, Ataollah; Addeh, Jalil; Rahmani, Zahra

    2012-01-01

    Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory

    Science.gov (United States)

    Xue, Gui; Love, Bradley C.; Preston, Alison R.; Poldrack, Russell A.

    2014-01-01

    Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. PMID:24872552

  18. End-to-End Neural Optical Music Recognition of Monophonic Scores

    Directory of Open Access Journals (Sweden)

    Jorge Calvo-Zaragoza

    2018-04-01

    Full Text Available Optical Music Recognition is a field of research that investigates how to computationally decode music notation from images. Despite the efforts made so far, there are hardly any complete solutions to the problem. In this work, we study the use of neural networks that work in an end-to-end manner. This is achieved by using a neural model that combines the capabilities of convolutional neural networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature of the problem. Thanks to the use of the the so-called Connectionist Temporal Classification loss function, these models can be directly trained from input images accompanied by their corresponding transcripts into music symbol sequences. We also present the Printed Music Scores dataset, containing more than 80,000 monodic single-staff real scores in common western notation, that is used to train and evaluate the neural approach. In our experiments, it is demonstrated that this formulation can be carried out successfully. Additionally, we study several considerations about the codification of the output musical sequences, the convergence and scalability of the neural models, as well as the ability of this approach to locate symbols in the input score.

  19. A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

    Directory of Open Access Journals (Sweden)

    Daniela Sánchez

    2017-01-01

    Full Text Available A grey wolf optimizer for modular neural network (MNN with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition.

  20. Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network.

    Science.gov (United States)

    Yao, Kun; Herr, John E; Brown, Seth N; Parkhill, John

    2017-06-15

    Neural networks are being used to make new types of empirical chemical models as inexpensive as force fields, but with accuracy similar to the ab initio methods used to build them. In this work, we present a neural network that predicts the energies of molecules as a sum of intrinsic bond energies. The network learns the total energies of the popular GDB9 database to a competitive MAE of 0.94 kcal/mol on molecules outside of its training set, is naturally linearly scaling, and applicable to molecules consisting of thousands of bonds. More importantly, it gives chemical insight into the relative strengths of bonds as a function of their molecular environment, despite only being trained on total energy information. We show that the network makes predictions of relative bond strengths in good agreement with measured trends and human predictions. A Bonds-in-Molecules Neural Network (BIM-NN) learns heuristic relative bond strengths like expert synthetic chemists, and compares well with ab initio bond order measures such as NBO analysis.

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

    Science.gov (United States)

    Petrulis, Aras

    2009-06-25

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

  2. The nuclear fuel rod character recognition system based on neural network technique

    International Nuclear Information System (INIS)

    Kim, Woong-Ki; Park, Soon-Yong; Lee, Yong-Bum; Kim, Seung-Ho; Lee, Jong-Min; Chien, Sung-Il.

    1994-01-01

    The nuclear fuel rods should be discriminated and managed systematically by numeric characters which are printed at the end part of each rod in the process of producing fuel assembly. The characters are used to examine manufacturing process of the fuel rods in the inspection process of irradiated fuel rod. Therefore automatic character recognition is one of the most important technologies to establish automatic manufacturing process of fuel assembly. In the developed character recognition system, mesh feature set extracted from each character written in the fuel rod is employed to train a neural network based on back-propagation algorithm as a classifier for character recognition system. Performance evaluation has been achieved on a test set which is not included in a training character set. (author)

  3. Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks

    KAUST Repository

    Younis, Sohaib

    2018-03-13

    Herbaria worldwide are housing a treasure of hundreds of millions of herbarium specimens, which are increasingly being digitized and thereby more accessible to the scientific community. At the same time, deep-learning algorithms are rapidly improving pattern recognition from images and these techniques are more and more being applied to biological objects. In this study, we are using digital images of herbarium specimens in order to identify taxa and traits of these collection objects by applying convolutional neural networks (CNN). Images of the 1000 species most frequently documented by herbarium specimens on GBIF have been downloaded and combined with morphological trait data, preprocessed and divided into training and test datasets for species and trait recognition. Good performance in both domains suggests substantial potential of this approach for supporting taxonomy and natural history collection management. Trait recognition is also promising for applications in functional ecology.

  4. Static sign language recognition using 1D descriptors and neural networks

    Science.gov (United States)

    Solís, José F.; Toxqui, Carina; Padilla, Alfonso; Santiago, César

    2012-10-01

    A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificial neural networks is presented in this work. The 1D descriptors were computed by two methods, first one consists in a correlation rotational operator.1 and second is based on contour analysis of hand shape. One of the main problems in sign language recognition is segmentation; most of papers report a special color in gloves or background for hand shape analysis. In order to avoid the use of gloves or special clothing, a thermal imaging camera was used to capture images. Static signs were picked up from 1 to 9 digits of American Sign Language, a multilayer perceptron reached 100% recognition with cross-validation.

  5. Priming for novel object associations: Neural differences from object item priming and equivalent forms of recognition.

    Science.gov (United States)

    Gomes, Carlos Alexandre; Figueiredo, Patrícia; Mayes, Andrew

    2016-04-01

    The neural substrates of associative and item priming and recognition were investigated in a functional magnetic resonance imaging study over two separate sessions. In the priming session, participants decided which object of a pair was bigger during both study and test phases. In the recognition session, participants saw different object pairs and performed the same size-judgement task followed by an associative recognition memory task. Associative priming was accompanied by reduced activity in the right middle occipital gyrus as well as in bilateral hippocampus. Object item priming was accompanied by reduced activity in extensive priming-related areas in the bilateral occipitotemporofrontal cortex, as well as in the perirhinal cortex, but not in the hippocampus. Associative recognition was characterized by activity increases in regions linked to recollection, such as the hippocampus, posterior cingulate cortex, anterior medial frontal gyrus and posterior parahippocampal cortex. Item object priming and recognition recruited broadly overlapping regions (e.g., bilateral middle occipital and prefrontal cortices, left fusiform gyrus), even though the BOLD response was in opposite directions. These regions along with the precuneus, where both item priming and recognition were accompanied by activation, have been found to respond to object familiarity. The minimal structural overlap between object associative priming and recollection-based associative recognition suggests that they depend on largely different stimulus-related information and that the different directions of the effects indicate distinct retrieval mechanisms. In contrast, item priming and familiarity-based recognition seemed mainly based on common memory information, although the extent of common processing between priming and familiarity remains unclear. Further implications of these findings are discussed. © 2015 Wiley Periodicals, Inc.

  6. Chinese Herbal Medicine Image Recognition and Retrieval by Convolutional Neural Network.

    Science.gov (United States)

    Sun, Xin; Qian, Huinan

    2016-01-01

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

  7. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    Marwin H. S. Segler

    2017-12-01

    Full Text Available In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria, it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.

  8. Neural Cell Adhesion Molecules of the Immunoglobulin Superfamily Regulate Synapse Formation, Maintenance, and Function.

    Science.gov (United States)

    Sytnyk, Vladimir; Leshchyns'ka, Iryna; Schachner, Melitta

    2017-05-01

    Immunoglobulin superfamily adhesion molecules are among the most abundant proteins in vertebrate and invertebrate nervous systems. Prominent family members are the neural cell adhesion molecules NCAM and L1, which were the first to be shown to be essential not only in development but also in synaptic function and as key regulators of synapse formation, synaptic activity, plasticity, and synaptic vesicle recycling at distinct developmental and activity stages. In addition to interacting with each other, adhesion molecules interact with ion channels and cytokine and neurotransmitter receptors. Mutations in their genes are linked to neurological disorders associated with abnormal development and synaptic functioning. This review presents an overview of recent studies on these molecules and their crucial impact on neurological disorders. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Novel roles for immune molecules in neural development: Implications for neurodevelopmental disoders

    Directory of Open Access Journals (Sweden)

    Paula A Garay

    2010-09-01

    Full Text Available Although the brain has classically been considered "immune-privileged," current research suggests extensive communication between the nervous and the immune systems in both health and disease. Recent studies demonstrate that immune molecules are present at the right place and time to modulate the development and function of the healthy and diseased CNS. Indeed, immune molecules play integral roles in the CNS throughout neural development, including affecting neurogenesis, neuronal migration, axon guidance, synapse formation, activity-dependent refinement of circuits, and synaptic plasticity. Moreover, the roles of individual immune molecules in the nervous system may change over development. This review focuses on the effects of immune molecules on neuronal connections in the mammalian central nervous system—specifically the roles for MHCI and its receptors, complement, and cytokines on the function, refinement, and plasticity of cortical and hippocampal synapses and their relationship to neurodevelopmental disorders. These functions for immune molecules during neural development suggest that they could also mediate pathological responses to chronic elevations of cytokines in neurodevelopmental disorders, including autism spectrum disorders (ASD and schizophrenia.

  10. Neural activity during emotion recognition after combined cognitive plus social cognitive training in schizophrenia.

    Science.gov (United States)

    Hooker, Christine I; Bruce, Lori; Fisher, Melissa; Verosky, Sara C; Miyakawa, Asako; Vinogradov, Sophia

    2012-08-01

    Cognitive remediation training has been shown to improve both cognitive and social cognitive deficits in people with schizophrenia, but the mechanisms that support this behavioral improvement are largely unknown. One hypothesis is that intensive behavioral training in cognition and/or social cognition restores the underlying neural mechanisms that support targeted skills. However, there is little research on the neural effects of cognitive remediation training. This study investigated whether a 50 h (10-week) remediation intervention which included both cognitive and social cognitive training would influence neural function in regions that support social cognition. Twenty-two stable, outpatient schizophrenia participants were randomized to a treatment condition consisting of auditory-based cognitive training (AT) [Brain Fitness Program/auditory module ~60 min/day] plus social cognition training (SCT) which was focused on emotion recognition [~5-15 min per day] or a placebo condition of non-specific computer games (CG) for an equal amount of time. Pre and post intervention assessments included an fMRI task of positive and negative facial emotion recognition, and standard behavioral assessments of cognition, emotion processing, and functional outcome. There were no significant intervention-related improvements in general cognition or functional outcome. fMRI results showed the predicted group-by-time interaction. Specifically, in comparison to CG, AT+SCT participants had a greater pre-to-post intervention increase in postcentral gyrus activity during emotion recognition of both positive and negative emotions. Furthermore, among all participants, the increase in postcentral gyrus activity predicted behavioral improvement on a standardized test of emotion processing (MSCEIT: Perceiving Emotions). Results indicate that combined cognition and social cognition training impacts neural mechanisms that support social cognition skills. Copyright © 2012 Elsevier B.V. All

  11. Differential expression of the neural cell adhesion molecule NCAM 140 in human pituitary tumors

    OpenAIRE

    Aletsee-Ufrecht, M. C.; Langley, O. K.; Gratzl, O.; Gratzl, Manfred

    1990-01-01

    We have analyzed the expression of the intracellular marker protein neuron specific enolase (NSE), synaptophysin (SYN) and of the cell surface marker NCAM (neural cell adhesion molecule) in both normal human hypophysis and in pituitary adenomas in order to explore their potential use as diagnostic tools. All adenomas (4 prolactinomas, 3 growth hormone (GH) producing adenomas and 4 inactive adenomas) showed SYN and NSE immunoreactivity on tissue sections and this was confirmed by immunoblots. ...

  12. Polysialic Acid/Neural Cell Adhesion Molecule Modulates the Formation of Ductular Reactions in Liver Injury

    OpenAIRE

    Tsuchiya, Atsunori; Lu, Wei-Yu; Weinhold, Birgit; Boulter, Luke; Stutchfield, Benjamin M.; Williams, Michael J.; Guest, Rachel V.; Minnis-Lyons, Sarah E.; MacKinnon, Alison C.; Schwarzer, David; Ichida, Takafumi; Nomoto, Minoru; Aoyagi, Yutaka; Gerardy-Schahn, Rita; Forbes, Stuart J.

    2014-01-01

    In severe liver injury, ductular reactions (DRs) containing bipotential hepatic progenitor cells (HPCs) branch from the portal tract. Neural cell adhesion molecule (NCAM) marks bile ducts and DRs, but not mature hepatocytes. NCAM mediates interactions between cells and surrounding matrix; however, its role in liver development and regeneration is undefined. Polysialic acid (polySia), a unique posttranslational modifier of NCAM, is produced by the enzymes, ST8SiaII and ST8SiaIV, and weakens NC...

  13. Neural cell adhesion molecule induces intracellular signaling via multiple mechanisms of Ca2+ homeostasis

    DEFF Research Database (Denmark)

    Kiryushko, Darya; Korshunova, Irina; Berezin, Vladimir

    2006-01-01

    The neural cell adhesion molecule (NCAM) plays a pivotal role in the development of the nervous system, promoting neuronal differentiation via homophilic (NCAM-NCAM) as well as heterophilic (NCAM-fibroblast growth factor receptor [FGFR]) interactions. NCAM-induced intracellular signaling has been...... with the Src-family kinases, were also involved in neuritogenesis induced by physiological, homophilic NCAM interactions. Thus, unanticipated mechanisms of Ca2+ homeostasis are shown to be activated by NCAM and to contribute to neuronal differentiation....

  14. Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

    Science.gov (United States)

    Islam, Kh Tohidul; Raj, Ram Gopal

    2017-01-01

    Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’ indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications. PMID:28406471

  15. Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition

    Science.gov (United States)

    Yin, Xi; Liu, Xiaoming

    2018-02-01

    This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weight to each side task, which is a crucial problem in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the variations from the learnt identity features. Extensive experiments on the entire Multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in Multi-PIE for face recognition. Our approach is also applicable to in-the-wild datasets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.

  16. Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks.

    Science.gov (United States)

    Wei, Qikang; Chen, Tao; Xu, Ruifeng; He, Yulan; Gui, Lin

    2016-01-01

    The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V.Database URL: http://219.223.252.210:8080/SS/cdr.html. © The Author(s) 2016. Published by Oxford University Press.

  17. Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network.

    Science.gov (United States)

    Islam, Kh Tohidul; Raj, Ram Gopal

    2017-04-13

    Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are 'traffic light ahead' or 'pedestrian crossing' indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications.

  18. Direct Reprogramming of Mouse Fibroblasts to Neural Stem Cells by Small Molecules

    Directory of Open Access Journals (Sweden)

    Yan-Chuang Han

    2016-01-01

    Full Text Available Although it is possible to generate neural stem cells (NSC from somatic cells by reprogramming technologies with transcription factors, clinical utilization of patient-specific NSC for the treatment of human diseases remains elusive. The risk hurdles are associated with viral transduction vectors induced mutagenesis, tumor formation from undifferentiated stem cells, and transcription factors-induced genomic instability. Here we describe a viral vector-free and more efficient method to induce mouse fibroblasts into NSC using small molecules. The small molecule-induced neural stem (SMINS cells closely resemble NSC in morphology, gene expression patterns, self-renewal, excitability, and multipotency. Furthermore, the SMINS cells are able to differentiate into astrocytes, functional neurons, and oligodendrocytes in vitro and in vivo. Thus, we have established a novel way to efficiently induce neural stem cells (iNSC from fibroblasts using only small molecules without altering the genome. Such chemical induction removes the risks associated with current techniques such as the use of viral vectors or the induction of oncogenic factors. This technique may, therefore, enable NSC to be utilized in various applications within clinical medicine.

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

    Directory of Open Access Journals (Sweden)

    Chuanji eGao

    2015-02-01

    Full Text Available 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 correlates of memory for meaningful squiggles but not for meaningless squiggles. Specifically, color change reduced the amplitude of frontally distributed N400 potentials (FN400, indicating 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

  20. Arabic Handwritten Digit Recognition Based on Restricted Boltzmann Machine and Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Ali A. Alani

    2017-11-01

    Full Text Available Handwritten digit recognition is an open problem in computer vision and pattern recognition, and solving this problem has elicited increasing interest. The main challenge of this problem is the design of an efficient method that can recognize the handwritten digits that are submitted by the user via digital devices. Numerous studies have been proposed in the past and in recent years to improve handwritten digit recognition in various languages. Research on handwritten digit recognition in Arabic is limited. At present, deep learning algorithms are extremely popular in computer vision and are used to solve and address important problems, such as image classification, natural language processing, and speech recognition, to provide computers with sensory capabilities that reach the ability of humans. In this study, we propose a new approach for Arabic handwritten digit recognition by use of restricted Boltzmann machine (RBM and convolutional neural network (CNN deep learning algorithms. In particular, we propose an Arabic handwritten digit recognition approach that works in two phases. First, we use the RBM, which is a deep learning technique that can extract highly useful features from raw data, and which has been utilized in several classification problems as a feature extraction technique in the feature extraction phase. Then, the extracted features are fed to an efficient CNN architecture with a deep supervised learning architecture for the training and testing process. In the experiment, we used the CMATERDB 3.3.1 Arabic handwritten digit dataset for training and testing the proposed method. Experimental results show that the proposed method significantly improves the accuracy rate, with accuracy reaching 98.59%. Finally, comparison of our results with those of other studies on the CMATERDB 3.3.1 Arabic handwritten digit dataset shows that our approach achieves the highest accuracy rate.

  1. End-to-End Multimodal Emotion Recognition Using Deep Neural Networks

    Science.gov (United States)

    Tzirakis, Panagiotis; Trigeorgis, George; Nicolaou, Mihalis A.; Schuller, Bjorn W.; Zafeiriou, Stefanos

    2017-12-01

    Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using auditory and visual modalities. To capture the emotional content for various styles of speaking, robust features need to be extracted. To this purpose, we utilize a Convolutional Neural Network (CNN) to extract features from the speech, while for the visual modality a deep residual network (ResNet) of 50 layers. In addition to the importance of feature extraction, a machine learning algorithm needs also to be insensitive to outliers while being able to model the context. To tackle this problem, Long Short-Term Memory (LSTM) networks are utilized. The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.

  2. Cross-cultural differences in the neural correlates of specific and general recognition.

    Science.gov (United States)

    Paige, Laura E; Ksander, John C; Johndro, Hunter A; Gutchess, Angela H

    2017-06-01

    Research suggests that culture influences how people perceive the world, which extends to memory specificity, or how much perceptual detail is remembered. The present study investigated cross-cultural differences (Americans vs East Asians) at the time of encoding in the neural correlates of specific versus general memory formation. Participants encoded photos of everyday items in the scanner and 48 h later completed a surprise recognition test. The recognition test consisted of same (i.e., previously seen in scanner), similar (i.e., same name, different features), or new photos (i.e., items not previously seen in scanner). For Americans compared to East Asians, we predicted greater activation in the hippocampus and right fusiform for specific memory at recognition, as these regions were implicated previously in encoding perceptual details. Results revealed that East Asians activated the left fusiform and left hippocampus more than Americans for specific versus general memory. Follow-up analyses ruled out alternative explanations of retrieval difficulty and familiarity for this pattern of cross-cultural differences at encoding. Results overall suggest that culture should be considered as another individual difference that affects memory specificity and modulates neural regions underlying these processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. NATO Advanced Research Workshop on Chemosensors of Ion and Molecule Recognition

    CERN Document Server

    Czarnik, A

    1997-01-01

    In the broad field of supramolecular chemistry, the design and hence the use of chemosensors for ion and molecule recognition have developed at an extroardinary rate. This imaginative and creative area which involves the interface of different disciplines, e.g. organic and inorganic chemistry, physical chemistry, biology, medicine, environmental science, is not only fundamental in nature. It is also clear that progress is most rewarding for several new sensor applications deriving from the specific signal delivered by the analyte-probe interaction. Indeed, if calcium sensing in real time for biological purposes is actually possible, owing to the emergence of efficient fluorescent receptors, other elements can also be specifically detected, identified and finally titrated using tailored chemosensors. Pollutants such as heavy metals or radionuclides are among the main targets since their detection and removal could be envisioned at very low concentrations with, in addition, sensors displaying specific and stron...

  4. Complement activating soluble pattern recognition molecules with collagen-like regions, mannan-binding lectin, ficolins and associated proteins

    DEFF Research Database (Denmark)

    Thiel, Steffen

    2007-01-01

    Mannan-binding lectin (MBL), L-ficolin, M-ficolin and H-ficolin are all complement activating soluble pattern recognition molecules with recognition domains linked to collagen-like regions. All four may form complexes with four structurally related proteins, the three MBL-associated serine...... proteases (MASPs), MASP-1, MASP-2 and MASP-3, and a smaller MBL-associated protein (MAp19). The four recognition molecules recognize patterns of carbohydrate or acetyl-group containing ligands. After binding to the relevant targets all four are able to activate the complement system. We thus have a system...... where four different and/or overlapping patterns of microbial origin or patterns of altered-self may be recognized, but in all cases the signalling molecules, the MASPs, are shared. MASP-1 and MASP-3 are formed from one gene, MASP1/3, by alternative splicing generating two different mRNAs from a single...

  5. Detection and recognition of bridge crack based on convolutional neural network

    Directory of Open Access Journals (Sweden)

    Honggong LIU

    2016-10-01

    Full Text Available Aiming at the backward artificial visual detection status of bridge crack in China, which has a great danger coefficient, a digital and intelligent detection method of improving the diagnostic efficiency and reducing the risk coefficient is studied. Combing with machine vision and convolutional neural network technology, Raspberry Pi is used to acquire and pre-process image, and the crack image is analyzed; the processing algorithm which has the best effect in detecting and recognizing is selected; the convolutional neural network(CNN for crack classification is optimized; finally, a new intelligent crack detection method is put forward. The experimental result shows that the system can find all cracks beyond the maximum limit, and effectively identify the type of fracture, and the recognition rate is above 90%. The study provides reference data for engineering detection.

  6. Automatic modulation format recognition for the next generation optical communication networks using artificial neural networks

    Science.gov (United States)

    Guesmi, Latifa; Hraghi, Abir; Menif, Mourad

    2015-03-01

    A new technique for Automatic Modulation Format Recognition (AMFR) in next generation optical communication networks is presented. This technique uses the Artificial Neural Network (ANN) in conjunction with the features of Linear Optical Sampling (LOS) of the detected signal at high bit rates using direct detection or coherent detection. The use of LOS method for this purpose mainly driven by the increase of bit rates which enables the measurement of eye diagrams. The efficiency of this technique is demonstrated under different transmission impairments such as chromatic dispersion (CD) in the range of -500 to 500 ps/nm, differential group delay (DGD) in the range of 0-15 ps and the optical signal tonoise ratio (OSNR) in the range of 10-30 dB. The results of numerical simulation for various modulation formats demonstrate successful recognition from a known bit rates with a higher estimation accuracy, which exceeds 99.8%.

  7. Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    C. S. Chin

    2017-01-01

    Full Text Available The control of biofouling on marine vessels is challenging and costly. Early detection before hull performance is significantly affected is desirable, especially if “grooming” is an option. Here, a system is described to detect marine fouling at an early stage of development. In this study, an image of fouling can be transferred wirelessly via a mobile network for analysis. The proposed system utilizes transfer learning and deep convolutional neural network (CNN to perform image recognition on the fouling image by classifying the detected fouling species and the density of fouling on the surface. Transfer learning using Google’s Inception V3 model with Softmax at last layer was carried out on a fouling database of 10 categories and 1825 images. Experimental results gave acceptable accuracies for fouling detection and recognition.

  8. Modeling the Process of Color Image Recognition Using ART2 Neural Network

    Directory of Open Access Journals (Sweden)

    Todor Petkov

    2015-09-01

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

  9. The 3-D image recognition based on fuzzy neural network technology

    Science.gov (United States)

    Hirota, Kaoru; Yamauchi, Kenichi; Murakami, Jun; Tanaka, Kei

    1993-01-01

    Three dimensional stereoscopic image recognition system based on fuzzy-neural network technology was developed. The system consists of three parts; preprocessing part, feature extraction part, and matching part. Two CCD color camera image are fed to the preprocessing part, where several operations including RGB-HSV transformation are done. A multi-layer perception is used for the line detection in the feature extraction part. Then fuzzy matching technique is introduced in the matching part. The system is realized on SUN spark station and special image input hardware system. An experimental result on bottle images is also presented.

  10. Automatic Recognition of fMRI-derived Functional Networks using 3D Convolutional Neural Networks.

    Science.gov (United States)

    Zhao, Yu; Dong, Qinglin; Zhang, Shu; Zhang, Wei; Chen, Hanbo; Jiang, Xi; Guo, Lei; Hu, Xintao; Han, Junwei; Liu, Tianming

    2017-06-15

    Current fMRI data modeling techniques such as Independent Component Analysis (ICA) and Sparse Coding methods can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications. However, this task is still a challenging and open problem due to the tremendous variability of various types of functional brain networks and the presence of various sources of noises. In recognition of the fact that convolutional neural networks (CNN) has superior capability of representing spatial patterns with huge variability and dealing with large noises, in this paper, we design, apply and evaluate a deep 3D CNN framework for automatic, effective and accurate classification and recognition of large number of functional brain networks reconstructed by sparse representation of whole-brain fMRI signals. Our extensive experimental results based on the Human Connectome Project (HCP) fMRI data showed that the proposed deep 3D CNN can effectively and robustly perform functional networks classification and recognition tasks, while maintaining a high tolerance for mistakenly labelled training instances. Our work provides a new deep learning approach for modeling functional connectomes based on fMRI data.

  11. Neural correlates of auditory recognition memory in primate lateral prefrontal cortex.

    Science.gov (United States)

    Plakke, B; Ng, C-W; Poremba, A

    2013-08-06

    The neural underpinnings of working and recognition memory have traditionally been studied in the visual domain and these studies pinpoint the lateral prefrontal cortex (lPFC) as a primary region for visual memory processing (Miller et al., 1996; Ranganath et al., 2004; Kennerley and Wallis, 2009). Herein, we utilize single-unit recordings for the same region in monkeys (Macaca mulatta) but investigate a second modality examining auditory working and recognition memory during delayed matching-to-sample (DMS) performance. A large portion of neurons in the dorsal and ventral banks of the principal sulcus (area 46, 46/9) show DMS event-related activity to one or more of the following task events: auditory cues, memory delay, decision wait time, response, and/or reward portions. Approximately 50% of the neurons show evidence of auditory-evoked activity during the task and population activity demonstrated encoding of recognition memory in the form of match enhancement. However, neither robust nor sustained delay activity was observed. The neuronal responses during the auditory DMS task are similar in many respects to those found within the visual working memory domain, which supports the hypothesis that the lPFC, particularly area 46, functionally represents key pieces of information for recognition memory inclusive of decision-making, but regardless of modality. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  12. Traffic Command Gesture Recognition for Virtual Urban Scenes Based on a Spatiotemporal Convolution Neural Network

    Directory of Open Access Journals (Sweden)

    Chunyong Ma

    2018-01-01

    Full Text Available Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton (TPCGS dataset collected from 10 volunteers. Subsequently, convolution operations on the locational change of each skeletal point were performed to extract temporal features, analyze the relative positions of skeletal points, and extract spatial features. After temporal and spatial features based on the three-dimensional positional information of traffic police skeleton points were extracted, the ST-CNN model classified positional information into eight types of Chinese traffic police gestures. The test accuracy of the ST-CNN model was 96.67%. In addition, a virtual urban traffic scene in which real-time command tests were carried out was set up, and a real-time test accuracy rate of 93.0% was achieved. The proposed ST-CNN model ensured a high level of accuracy and robustness. The ST-CNN model recognized traffic command gestures, and such recognition was found to control vehicles in virtual traffic environments, which enriches the interactive mode of the virtual city scene. Traffic command gesture recognition contributes to smart city construction.

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

    Directory of Open Access Journals (Sweden)

    Min Peng

    2017-10-01

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

  14. Deep neural networks rival the representation of primate IT cortex for core visual object recognition.

    Directory of Open Access Journals (Sweden)

    Charles F Cadieu

    2014-12-01

    Full Text Available The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition. This remarkable performance is mediated by the representation formed in inferior temporal (IT cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs. It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of "kernel analysis" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.

  15. Improving Pattern Recognition and Neural Network Algorithms with Applications to Solar Panel Energy Optimization

    Science.gov (United States)

    Zamora Ramos, Ernesto

    Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize solar panel energy production. Our contributions detail an improved non-linear pre-processing technique to enhance poorly illuminated images based on modifications to the standard histogram equalization for an image. While the original motivation was to improve nocturnal navigation, the results have applications in surveillance, search and rescue, medical imaging enhancing, and many others. We created a vision system for precise camera distance positioning motivated to correctly locate the robot for capture of solar panel images for classification. The classification algorithm marks solar panels as clean or dirty for later processing. Our algorithm extends past image classification and, based on historical and experimental data, it identifies the optimal moment in which to perform maintenance on marked solar panels as to minimize the energy and profit loss. In order to improve upon the classification algorithm, we delved into feedforward neural networks because of their recent advancements, proven universal approximation and classification capabilities, and excellent recognition rates. We explore state-of-the-art neural network training techniques offering pointers and insights, culminating on the implementation of a complete library with support for modern deep learning architectures

  16. Triple Effect of Mimetic Peptides Interfering with Neural Cell Adhesion Molecule Homophilic Cis Interactions

    DEFF Research Database (Denmark)

    Li, S. Z.; Kolkova, Kateryna; Rudenko, Olga

    2005-01-01

    on neurite extension and adhesion. To evaluate how interference of these mimetic peptides with NCAM homophilic interactions in cis influences NCAM binding in trans, we employed a coculture system in which PC12-E2 cells were grown on monolayers of fibroblasts with or without NCAM expression and the rate...... of neurite outgrowth subsequently was analyzed. P2, but not P1-B, induced neurite outgrowth in the absence of NCAM binding in trans. When PC12-E2 cells were grown on monolayers of NCAM-expressing fibroblasts, the effect of both P1-B and P2 on neurite outgrowth was dependent on peptide concentrations. P1-B......The neural cell adhesion molecule (NCAM) is pivotal in neural development, regeneration, and learning. Here we characterize two peptides, termed P1-B and P2, derived from the homophilic binding sites in the first two N-terminal immunoglobulin (Ig) modules of NCAM, with regard to their effects...

  17. Identification of Correlated GRACE Monthly Harmonic Coefficients Using Pattern Recognition and Neural Networks

    Science.gov (United States)

    Piretzidis, D.; Sra, G.; Sideris, M. G.

    2016-12-01

    This study explores new methods for identifying correlation errors in harmonic coefficients derived from monthly solutions of the Gravity Recovery and Climate Experiment (GRACE) satellite mission using pattern recognition and neural network algorithms. These correlation errors are evidenced in the differences between monthly solutions and can be suppressed using a de-correlation filter. In all studies so far, the implementation of the de-correlation filter starts from a specific minimum order (i.e., 11 for RL04 and 38 for RL05) until the maximum order of the monthly solution examined. This implementation method has two disadvantages, namely, the omission of filtering correlated coefficients of order less than the minimum order and the filtering of uncorrelated coefficients of order higher than the minimum order. In the first case, the filtered solution is not completely free of correlated errors, whereas the second case results in a monthly solution that suffers from loss of geophysical signal. In the present study, a new method of implementing the de-correlation filter is suggested, by identifying and filtering only the coefficients that show indications of high correlation. Several numerical and geometric properties of the harmonic coefficient series of all orders are examined. Extreme cases of both correlated and uncorrelated coefficients are selected, and their corresponding properties are used to train a two-layer feed-forward neural network. The objective of the neural network is to identify and quantify the correlation by providing the probability of an order of coefficients to be correlated. Results show good performance of the neural network, both in the validation stage of the training procedure and in the subsequent use of the trained network to classify independent coefficients. The neural network is also capable of identifying correlated coefficients even when a small number of training samples and neurons are used (e.g.,100 and 10, respectively).

  18. Facial Expression Recognition By Using Fisherface Methode With Backpropagation Neural Network

    Directory of Open Access Journals (Sweden)

    Zaenal Abidin

    2011-01-01

    Full Text Available Abstract— In daily lives, especially in interpersonal communication, face often used for expression. Facial expressions give information about the emotional state of the person. A facial expression is one of the behavioral characteristics. The components of a basic facial expression analysis system are face detection, face data extraction, and facial expression recognition. Fisherface method with backpropagation artificial neural network approach can be used for facial expression recognition. This method consists of two-stage process, namely PCA and LDA. PCA is used to reduce the dimension, while the LDA is used for features extraction of facial expressions. The system was tested with 2 databases namely JAFFE database and MUG database. The system correctly classified the expression with accuracy of 86.85%, and false positive 25 for image type I of JAFFE, for image type II of JAFFE 89.20% and false positive 15,  for type III of JAFFE 87.79%, and false positive for 16. The image of MUG are 98.09%, and false positive 5. Keywords— facial expression, fisherface method, PCA, LDA, backpropagation neural network.

  19. Multiresolution stroke sketch adaptive representation and neural network processing system for gray-level image recognition

    Science.gov (United States)

    Meystel, Alexander M.; Rybak, Ilya A.; Bhasin, Sanjay

    1992-11-01

    This paper describes a method for multiresolutional representation of gray-level images as hierarchial sets of strokes characterizing forms of objects with different degrees of generalization depending on the context of the image. This method transforms the original image into a hierarchical graph which allows for efficient coding in order to store, retrieve, and recognize the image. The method which is described is based upon finding the resolution levels for each image which minimizes the computations required. This becomes possible because of the use of a special image representation technique called Multiresolutional Attentional Representation for Recognition, based upon a feature which the authors call a stroke. This feature turns out to be efficient in the process of finding the appropriate system of resolutions and construction of the relational graph. Multiresolutional Attentional Representation for Recognition (MARR) is formed by a multi-layer neural network with recurrent inhibitory connections between neurons, the receptive fields of which are selectively tuned to detect the orientation of local contrasts in parts of the image with appropriate degree of generalization. This method simulates the 'coarse-to-fine' algorithm which an artist usually uses, making at attentional sketch of real images. The method, algorithms, and neural network architecture in this system can be used in many machine-vision systems with AI properties; in particular, robotic vision. We expect that systems with MARR can become a component of intelligent control systems for autonomous robots. Their architectures are mostly multiresolutional and match well with the multiple resolutions of the MARR structure.

  20. Neural correlates of the self-reference effect: evidence from evaluation and recognition processes.

    Science.gov (United States)

    Yaoi, Ken; Osaka, Mariko; Osaka, Naoyuki

    2015-01-01

    The self-reference effect (SRE) is defined as better recall or recognition performance when the memorized materials refer to the self. Recently, a number of neuroimaging studies using self-referential and other-referential tasks have reported that self- and other-referential judgments basically show greater activation in common brain regions, specifically in the medial prefrontal cortex (MPFC) when compared with nonmentalizing judgments, but that a ventral-to-dorsal gradient in MPFC emerges from a direct comparison between self- and other-judgments. However, most of these previous studies could not provide an adequate explanation for the neural basis of SRE because they did not directly compare brain activation for recognition/recall of the words referenced to the self with another person. Here, we used an event-related functional magnetic resonance imaging (fMRI) that measured brain activity during processing of references to the self and another, and for recognition of self and other referenced words. Results from the fMRI evaluation task indicated greater activation in ventromedial prefrontal cortex (VMPFC) in the self-referential condition. While in the recognition task, VMPFC, posterior cingulate cortex (PCC) and bilateral angular gyrus (AG) showed greater activation when participants correctly recognized self-referenced words versus other-referenced words. These data provide evidence that the self-referenced words evoked greater activation in the self-related region (VMPFC) and memory-related regions (PCC and AG) relative to another person in the retrieval phase, and that the words remained as a stronger memory trace that supports recognition.

  1. Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

    Science.gov (United States)

    Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

    2018-04-01

    Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

  2. Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

    Directory of Open Access Journals (Sweden)

    Abdullahi Abubakar Mas’ud

    2016-07-01

    Full Text Available In order to investigate how artificial neural networks (ANNs have been applied for partial discharge (PD pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1 determining the optimum weights in training the ANN; (2 using PD data captured over long stressing period in training the ANN; (3 ANN recognizing different PD degradation levels; (4 using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5 understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6 developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

  3. Crystal structure of the Ig1 domain of the neural cell adhesion molecule NCAM2 displays domain swapping

    DEFF Research Database (Denmark)

    Rasmussen, Kim Krighaar; Kulahin, Nikolaj; Kristensen, Ole

    2008-01-01

    The crystal structure of the first immunoglobulin (Ig1) domain of neural cell adhesion molecule 2 (NCAM2/OCAM/RNCAM) is presented at a resolution of 2.7 A. NCAM2 is a member of the immunoglobulin superfamily of cell adhesion molecules (IgCAMs). In the structure, two Ig domains interact by domain...

  4. Age-related changes in expression of the neural cell adhesion molecule in skeletal muscle

    DEFF Research Database (Denmark)

    Andersson, A M; Olsen, M; Zhernosekov, D

    1993-01-01

    Neural cell adhesion molecule (NCAM) is expressed by muscle and involved in muscle-neuron and muscle-muscle cell interactions. The expression in muscle is regulated during myogenesis and by the state of innervation. In aged muscle, both neurogenic and myogenic degenerative processes occur. We here...... been demonstrated in muscle cell lines and in primary cultures of muscle cells during formation of myotubes in vitro, and this switch in NCAM mRNA classes has been suggested to correlate with myogenesis.(ABSTRACT TRUNCATED AT 250 WORDS)...

  5. Neural cell adhesion molecule (NCAM) and prealbumin in cerebrospinal fluid from depressed patients

    DEFF Research Database (Denmark)

    Jørgensen, Ole Steen

    1988-01-01

    The size of the soluble form of the human cerebrospinal fluid (CSF) neural cell adhesion molecule, NCAM-sol, was by gel permeation chromatography estimated to 160-250 kDa. Within the CSF the concentration of NCAM-sol was found about 15-25% increased in lumbar fluid and 25% increased in ventricular...... fluid, both compared to cisternal fluid. Whereas prealbumin was found evenly distributed in CSF, albumin was relatively enriched in lumbar fluid. The concentrations of NCAM-sol and prealbumin were measured in lumbar CSF from psychiatric patients. Prealbumin was increased 7.2% and NCAM-sol was decreased...

  6. Neural cell adhesion molecule (NCAM) and prealbumin in cerebrospinal fluid from depressed patients

    DEFF Research Database (Denmark)

    Jørgensen, Ole Steen

    1988-01-01

    The size of the soluble form of the human cerebrospinal fluid (CSF) neural cell adhesion molecule, NCAM-sol, was by gel permeation chromatography estimated to 160-250 kDa. Within the CSF the concentration of NCAM-sol was found about 15-25% increased in lumbar fluid and 25% increased in ventricula...... 15.1% in depressed patients. The changes were partially normalized during recovery from the depression. The findings can be explained by hypothesizing that endogenous depression is associated with an increased choroid plexus activity and CSF production....

  7. The pattern recognition molecule deleted in malignant brain tumors 1 (DMBT1) and synthetic mimics inhibit liposomal nucleic acid delivery

    DEFF Research Database (Denmark)

    Lund Hansen, Pernille; Blaich, Stephanie; End, Caroline

    2011-01-01

    Liposomal nucleic acid delivery is a preferred option for therapeutic settings. The cellular pattern recognition molecule DMBT1, secreted at high levels in various diseases, and synthetic mimics efficiently inhibit liposomal nucleic acid delivery to human cells. These findings may have relevance ...

  8. The innate pattern recognition molecule Ficolin-1 is secreted by monocytes/macrophages and is circulating in human plasma

    DEFF Research Database (Denmark)

    Honoré, Christian; Rørvig, Sara; Munthe-Fog, Lea

    2008-01-01

    Ficolin-1 (M-Ficolin) is a pattern recognition molecule of the complement system that is expressed by myeloid cells and type II alveolar epithelial cells. Ficolin-1 has been shown to localize in the secretory granules of these cells and attached to cell surfaces, but whether Ficolin-1 exists...

  9. Automatic recognition of alertness and drowsiness from EEG by an artificial neural network.

    Science.gov (United States)

    Vuckovic, Aleksandra; Radivojevic, Vlada; Chen, Andrew C N; Popovic, Dejan

    2002-06-01

    We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum EEG recordings in an arbitrary subject. This novel method uses time series of interhemispheric and intrahemispheric cross spectral densities of full spectrum EEG as the input to an artificial neural network (ANN) with two discrete outputs: drowsy and alert. The experimental data were collected from 17 subjects. Two experts in EEG interpretation visually inspected the data and provided the necessary expertise for the training of an ANN. We selected the following three ANNs as potential candidates: (1) the linear network with Widrow-Hoff (WH) algorithm; (2) the non-linear ANN with the Levenberg-Marquardt (LM) rule; and (3) the Learning Vector Quantization (LVQ) neural network. We showed that the LVQ neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule. Classification properties of LVQ were validated using the data recorded in 12 healthy volunteer subjects, yet whose EEG recordings have not been used for the training of the ANN. The statistics were used as a measure of potential applicability of the LVQ: the t-distribution showed that matching between the human assessment and the network output was 94.37+/-1.95%. This result suggests that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training.

  10. Structure and Mutagenesis of Neural Cell Adhesion Molecule Domains Evidence for Flexibility in the Placement of Polysialic Acid Attachment Sites

    Energy Technology Data Exchange (ETDEWEB)

    Foley, Deirdre A.; Swartzentruber, Kristin G.; Lavie, Arnon; Colley, Karen J. (UICM)

    2010-11-09

    The addition of {alpha}2,8-polysialic acid to the N-glycans of the neural cell adhesion molecule, NCAM, is critical for brain development and plays roles in synaptic plasticity, learning and memory, neuronal regeneration, and the growth and invasiveness of cancer cells. Our previous work indicates that the polysialylation of two N-glycans located on the fifth immunoglobulin domain (Ig5) of NCAM requires the presence of specific sequences in the adjacent fibronectin type III repeat (FN1). To understand the relationship of these two domains, we have solved the crystal structure of the NCAM Ig5-FN1 tandem. Unexpectedly, the structure reveals that the sites of Ig5 polysialylation are on the opposite face from the FN1 residues previously found to be critical for N-glycan polysialylation, suggesting that the Ig5-FN1 domain relationship may be flexible and/or that there is flexibility in the placement of Ig5 glycosylation sites for polysialylation. To test the latter possibility, new Ig5 glycosylation sites were engineered and their polysialylation tested. We observed some flexibility in glycosylation site location for polysialylation and demonstrate that the lack of polysialylation of a glycan attached to Asn-423 may be in part related to a lack of terminal processing. The data also suggest that, although the polysialyltransferases do not require the Ig5 domain for NCAM recognition, their ability to engage with this domain is necessary for polysialylation to occur on Ig5 N-glycans.

  11. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

    Science.gov (United States)

    Sladojevic, Srdjan; Arsenovic, Marko; Anderla, Andras; Culibrk, Dubravko; Stefanovic, Darko

    2016-01-01

    The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

  12. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features.

    Science.gov (United States)

    Zhou, Liangji; Li, Qingwu; Huo, Guanying; Zhou, Yan

    2017-01-01

    As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases.

  13. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

    Science.gov (United States)

    Sladojevic, Srdjan; Arsenovic, Marko; Culibrk, Dubravko; Stefanovic, Darko

    2016-01-01

    The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

  14. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

    Directory of Open Access Journals (Sweden)

    Srdjan Sladojevic

    2016-01-01

    Full Text Available The latest generation of convolutional neural networks (CNNs has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

  15. Gait pattern recognition in cerebral palsy patients using neural network modelling

    International Nuclear Information System (INIS)

    Muhammad, J.; Gibbs, S.; Abboud, R.; Anand, S.

    2015-01-01

    Interpretation of gait data obtained from modern 3D gait analysis is a challenging and time consuming task. The aim of this study was to create neural network models which can recognise the gait patterns from pre- and post-treatment and the normal ones. Neural network is a method which works on the principle of learning from experience and then uses the obtained knowledge to predict the unknown. Methods: Twenty-eight patients with cerebral palsy were recruited as subjects whose gait was analysed in pre- and post-treatment. A group of twenty-six normal subjects also participated in this study as control group. All subjects gait was analysed using Vicon Nexus to obtain the gait parameters and kinetic and kinematic parameters of hip, knee and ankle joints in three planes of both limbs. The gait data was used as input to create neural network models. A total of approximately 300 trials were split into 70% and 30% to train and test the models, respectively. Different models were built using different parameters. The gait was categorised as three patterns, i.e., normal, pre- and post-treatments. Result: The results showed that the models using all parameters or using the joint angles and moments could predict the gait patterns with approximately 95% accuracy. Some of the models e.g., the models using joint power and moments, had lower rate in recognition of gait patterns with approximately 70-90% successful ratio. Conclusion: Neural network model can be used in clinical practice to recognise the gait pattern for cerebral palsy patients. (author)

  16. The long pentraxin PTX3: a paradigm for humoral pattern recognition molecules.

    Science.gov (United States)

    Mantovani, Alberto; Valentino, Sonia; Gentile, Stefania; Inforzato, Antonio; Bottazzi, Barbara; Garlanda, Cecilia

    2013-05-01

    Pattern recognition molecules (PRMs) are components of the humoral arm of innate immunity; they recognize pathogen-associated molecular patterns (PAMP) and are functional ancestors of antibodies, promoting complement activation, opsonization, and agglutination. In addition, several PRMs have a regulatory function on inflammation. Pentraxins are a family of evolutionarily conserved PRMs characterized by a cyclic multimeric structure. On the basis of structure, pentraxins have been operationally divided into short and long families. C-reactive protein (CRP) and serum amyloid P component are prototypes of the short pentraxin family, while pentraxin 3 (PTX3) is a prototype of the long pentraxins. PTX3 is produced by somatic and immune cells in response to proinflammatory stimuli and Toll-like receptor engagement, and it interacts with several ligands and exerts multifunctional properties. Unlike CRP, PTX3 gene organization and regulation have been conserved in evolution, thus allowing its pathophysiological roles to be evaluated in genetically modified animals. Here we will briefly review the general properties of CRP and PTX3 as prototypes of short and long pentraxins, respectively, emphasizing in particular the functional role of PTX3 as a prototypic PRM with antibody-like properties. © 2013 New York Academy of Sciences.

  17. EMG-based facial gesture recognition through versatile elliptic basis function neural network.

    Science.gov (United States)

    Hamedi, Mahyar; Salleh, Sh-Hussain; Astaraki, Mehdi; Noor, Alias Mohd

    2013-07-17

    Recently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures. The effectiveness of different facial EMG time-domain features was also explored to introduce the most discriminating. In this study, EMGs of ten facial gestures were recorded from ten subjects using three pairs of surface electrodes in a bi-polar configuration. The signals were filtered and segmented into distinct portions prior to feature extraction. Ten different time-domain features, namely, Integrated EMG, Mean Absolute Value, Mean Absolute Value Slope, Maximum Peak Value, Root Mean Square, Simple Square Integral, Variance, Mean Value, Wave Length, and Sign Slope Changes were extracted from the EMGs. The statistical relationships between these features were investigated by Mutual Information measure. Then, the feature combinations including two to ten single features were formed based on the feature rankings appointed by Minimum-Redundancy-Maximum-Relevance (MRMR) and Recognition Accuracy (RA) criteria. In the last step, VEBFNN was employed to classify the facial gestures. The effectiveness of single features as well as the feature sets on the system performance was examined by considering the two major metrics, recognition accuracy and training time. Finally, the proposed classifier was assessed and compared with conventional methods support vector machines and multilayer perceptron neural network. The average classification results showed that the best performance for recognizing facial gestures among all single/multi-features was achieved by Maximum Peak Value with 87.1% accuracy. Moreover, the results proved a

  18. A hybrid neural network system for prediction and recognition of promoter regions in human genome.

    Science.gov (United States)

    Chen, Chuan-Bo; Li, Tao

    2005-05-01

    This paper proposes a high specificity and sensitivity algorithm called PromPredictor for recognizing promoter regions in the human genome. PromPredictor extracts compositional features and CpG islands information from genomic sequence, feeding these features as input for a hybrid neural network system (HNN) and then applies the HNN for prediction. It combines a novel promoter recognition model, coding theory, feature selection and dimensionality reduction with machine learning algorithm. Evaluation on Human chromosome 22 was approximately 66% in sensitivity and approximately 48% in specificity. Comparison with two other systems revealed that our method had superior sensitivity and specificity in predicting promoter regions. PromPredictor is written in MATLAB and requires Matlab to run. PromPredictor is freely available at http://www.whtelecom.com/Prompredictor.htm.

  19. Intelligent visual recognition and classification of cork tiles with neural networks.

    Science.gov (United States)

    Georgieva, Antoniya; Jordanov, Ivan

    2009-04-01

    An intelligent machine vision system is investigated and used for pattern recognition and classification of seven different types of cork tiles. The system includes image acquisition with a charge-coupled device (CCD) camera, texture feature generation (co-occurrence matrices and Laws' masks), analysis and processing of the feature vectors [linear discriminant analysis (LDA) and principal component analysis (PCA)], and cork tiles classification with feedforward neural networks (NN), employing our GLP(tau) S (genetic low-discrepancy search) hybrid global optimization method. In addition, the same NN are trained with backpropagation (BP) and the obtained results are compared with the ones from GLP(tau) S . The NN generalization abilities are discussed and assessed with respect to the NN architectures and the texture feature sets. The reported results are very encouraging with testing rate reaching up to 95%.

  20. A NEW RECOGNITION TECHNIQUE NAMED SOMP BASED ON PALMPRINT USING NEURAL NETWORK BASED SELF ORGANIZING MAPS

    Directory of Open Access Journals (Sweden)

    A. S. Raja

    2012-08-01

    Full Text Available The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Palmprint has become a new class of human biometrics for passive identification with uniqueness and stability. This is considered to be reliable due to the lack of expressions and the lesser effect of aging. In this manuscript a new Palmprint based biometric system based on neural networks self organizing maps (SOM is presented. The method is named as SOMP. The paper shows that the proposed SOMP method improves the performance and robustness of recognition. The proposed method is applied to a variety of datasets and the results are shown.

  1. MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition

    Directory of Open Access Journals (Sweden)

    King Hann Lim

    2012-01-01

    Full Text Available Lyapunov theory-based radial basis function neural network (RBFNN is developed for traffic sign recognition in this paper to perform multiple inputs multiple outputs (MIMO classification. Multidimensional input is inserted into RBF nodes and these nodes are linked with multiple weights. An iterative weight adaptation scheme is hence designed with regards to the Lyapunov stability theory to obtain a set of optimum weights. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Weight gain is formed later to obey the Lyapunov stability theory. Detail analysis and discussion on the proposed classifier’s properties are included in the paper. The performance comparisons between the proposed classifier and some existing conventional techniques are evaluated using traffic sign patterns. Simulation results reveal that our proposed system achieved better performance with lower number of training iterations.

  2. Korean letter handwritten recognition using deep convolutional neural network on android platform

    Science.gov (United States)

    Purnamawati, S.; Rachmawati, D.; Lumanauw, G.; Rahmat, R. F.; Taqyuddin, R.

    2018-03-01

    Currently, popularity of Korean culture attracts many people to learn everything about Korea, particularly its language. To acquire Korean Language, every single learner needs to be able to understand Korean non-Latin character. A digital approach needs to be carried out in order to make Korean learning process easier. This study is done by using Deep Convolutional Neural Network (DCNN). DCNN performs the recognition process on the image based on the model that has been trained such as Inception-v3 Model. Subsequently, re-training process using transfer learning technique with the trained and re-trained value of model is carried though in order to develop a new model with a better performance without any specific systemic errors. The testing accuracy of this research results in 86,9%.

  3. Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

    Directory of Open Access Journals (Sweden)

    Mario Sansone

    2013-01-01

    Full Text Available Computer systems for Electrocardiogram (ECG analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units or in prompt detection of dangerous events (e.g., ventricular fibrillation. Together with clinical applications (arrhythmia detection and heart rate variability analysis, ECG is currently being investigated in biometrics (human identification, an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.

  4. Face Recognition Using MLP and RBF Neural Network with Gabor and Discrete Wavelet Transform Characterization: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Fatma Zohra Chelali

    2015-01-01

    Full Text Available Face recognition has received a great attention from a lot of researchers in computer vision, pattern recognition, and human machine computer interfaces in recent years. Designing a face recognition system is a complex task due to the wide variety of illumination, pose, and facial expression. A lot of approaches have been developed to find the optimal space in which face feature descriptors are well distinguished and separated. Face representation using Gabor features and discrete wavelet has attracted considerable attention in computer vision and image processing. We describe in this paper a face recognition system using artificial neural networks like multilayer perceptron (MLP and radial basis function (RBF where Gabor and discrete wavelet based feature extraction methods are proposed for the extraction of features from facial images using two facial databases: the ORL and computer vision. Good recognition rate was obtained using Gabor and DWT parameterization with MLP classifier applied for computer vision dataset.

  5. HONTIOR - HIGHER-ORDER NEURAL NETWORK FOR TRANSFORMATION INVARIANT OBJECT RECOGNITION

    Science.gov (United States)

    Spirkovska, L.

    1994-01-01

    Neural networks have been applied in numerous fields, including transformation invariant object recognition, wherein an object is recognized despite changes in the object's position in the input field, size, or rotation. One of the more successful neural network methods used in invariant object recognition is the higher-order neural network (HONN) method. With a HONN, known relationships are exploited and the desired invariances are built directly into the architecture of the network, eliminating the need for the network to learn invariance to transformations. This results in a significant reduction in the training time required, since the network needs to be trained on only one view of each object, not on numerous transformed views. Moreover, one hundred percent accuracy is guaranteed for images characterized by the built-in distortions, providing noise is not introduced through pixelation. The program HONTIOR implements a third-order neural network having invariance to translation, scale, and in-plane rotation built directly into the architecture, Thus, for 2-D transformation invariance, the network needs only to be trained on just one view of each object. HONTIOR can also be used for 3-D transformation invariant object recognition by training the network only on a set of out-of-plane rotated views. Historically, the major drawback of HONNs has been that the size of the input field was limited to the memory required for the large number of interconnections in a fully connected network. HONTIOR solves this problem by coarse coding the input images (coding an image as a set of overlapping but offset coarser images). Using this scheme, large input fields (4096 x 4096 pixels) can easily be represented using very little virtual memory (30Mb). The HONTIOR distribution consists of three main programs. The first program contains the training and testing routines for a third-order neural network. The second program contains the same training and testing procedures as the

  6. GAUSSIAN MIXTURE MODELS FOR ADAPTATION OF DEEP NEURAL NETWORK ACOUSTIC MODELS IN AUTOMATIC SPEECH RECOGNITION SYSTEMS

    Directory of Open Access Journals (Sweden)

    Natalia A. Tomashenko

    2016-11-01

    Full Text Available Subject of Research. We study speaker adaptation of deep neural network (DNN acoustic models in automatic speech recognition systems. The aim of speaker adaptation techniques is to improve the accuracy of the speech recognition system for a particular speaker. Method. A novel method for training and adaptation of deep neural network acoustic models has been developed. It is based on using an auxiliary GMM (Gaussian Mixture Models model and GMMD (GMM-derived features. The principle advantage of the proposed GMMD features is the possibility of performing the adaptation of a DNN through the adaptation of the auxiliary GMM. In the proposed approach any methods for the adaptation of the auxiliary GMM can be used, hence, it provides a universal method for transferring adaptation algorithms developed for GMMs to DNN adaptation.Main Results. The effectiveness of the proposed approach was shown by means of one of the most common adaptation algorithms for GMM models – MAP (Maximum A Posteriori adaptation. Different ways of integration of the proposed approach into state-of-the-art DNN architecture have been proposed and explored. Analysis of choosing the type of the auxiliary GMM model is given. Experimental results on the TED-LIUM corpus demonstrate that, in an unsupervised adaptation mode, the proposed adaptation technique can provide, approximately, a 11–18% relative word error reduction (WER on different adaptation sets, compared to the speaker-independent DNN system built on conventional features, and a 3–6% relative WER reduction compared to the SAT-DNN trained on fMLLR adapted features.

  7. Connectivity strategies for higher-order neural networks applied to pattern recognition

    Science.gov (United States)

    Spirkovska, Lilly; Reid, Max B.

    1990-01-01

    Different strategies for non-fully connected HONNs (higher-order neural networks) are discussed, showing that by using such strategies an input field of 128 x 128 pixels can be attained while still achieving in-plane rotation and translation-invariant recognition. These techniques allow HONNs to be used with the larger input scenes required for practical pattern-recognition applications. The number of interconnections that must be stored has been reduced by a factor of approximately 200,000 in a T/C case and about 2000 in a Space Shuttle/F-18 case by using regional connectivity. Third-order networks have been simulated using several connection strategies. The method found to work best is regional connectivity. The main advantages of this strategy are the following: (1) it considers features of various scales within the image and thus gets a better sample of what the image looks like; (2) it is invariant to shape-preserving geometric transformations, such as translation and rotation; (3) the connections are predetermined so that no extra computations are necessary during run time; and (4) it does not require any extra storage for recording which connections were formed.

  8. Modeling activity-dependent plasticity in BCM spiking neural networks with application to human behavior recognition.

    Science.gov (United States)

    Meng, Yan; Jin, Yaochu; Yin, Jun

    2011-12-01

    Spiking neural networks (SNNs) are considered to be computationally more powerful than conventional NNs. However, the capability of SNNs in solving complex real-world problems remains to be demonstrated. In this paper, we propose a substantial extension of the Bienenstock, Cooper, and Munro (BCM) SNN model, in which the plasticity parameters are regulated by a gene regulatory network (GRN). Meanwhile, the dynamics of the GRN is dependent on the activation levels of the BCM neurons. We term the whole model "GRN-BCM." To demonstrate its computational power, we first compare the GRN-BCM with a standard BCM, a hidden Markov model, and a reservoir computing model on a complex time series classification problem. Simulation results indicate that the GRN-BCM significantly outperforms the compared models. The GRN-BCM is then applied to two widely used datasets for human behavior recognition. Comparative results on the two datasets suggest that the GRN-BCM is very promising for human behavior recognition, although the current experiments are still limited to the scenarios in which only one object is moving in the considered video sequences.

  9. Feature Set Evaluation for Offline Handwriting Recognition Systems: Application to the Recurrent Neural Network Model.

    Science.gov (United States)

    Chherawala, Youssouf; Roy, Partha Pratim; Cheriet, Mohamed

    2016-12-01

    The performance of handwriting recognition systems is dependent on the features extracted from the word image. A large body of features exists in the literature, but no method has yet been proposed to identify the most promising of these, other than a straightforward comparison based on the recognition rate. In this paper, we propose a framework for feature set evaluation based on a collaborative setting. We use a weighted vote combination of recurrent neural network (RNN) classifiers, each trained with a particular feature set. This combination is modeled in a probabilistic framework as a mixture model and two methods for weight estimation are described. The main contribution of this paper is to quantify the importance of feature sets through the combination weights, which reflect their strength and complementarity. We chose the RNN classifier because of its state-of-the-art performance. Also, we provide the first feature set benchmark for this classifier. We evaluated several feature sets on the IFN/ENIT and RIMES databases of Arabic and Latin script, respectively. The resulting combination model is competitive with state-of-the-art systems.

  10. Deep neural network features for horses identity recognition using multiview horses' face pattern

    Science.gov (United States)

    Jarraya, Islem; Ouarda, Wael; Alimi, Adel M.

    2017-03-01

    To control the state of horses in the born, breeders needs a monitoring system with a surveillance camera that can identify and distinguish between horses. We proposed in [5] a method of horse's identification at a distance using the frontal facial biometric modality. Due to the change of views, the face recognition becomes more difficult. In this paper, the number of images used in our THoDBRL'2015 database (Tunisian Horses DataBase of Regim Lab) is augmented by adding other images of other views. Thus, we used front, right and left profile face's view. Moreover, we suggested an approach for multiview face recognition. First, we proposed to use the Gabor filter for face characterization. Next, due to the augmentation of the number of images, and the large number of Gabor features, we proposed to test the Deep Neural Network with the auto-encoder to obtain the more pertinent features and to reduce the size of features vector. Finally, we performed the proposed approach on our THoDBRL'2015 database and we used the linear SVM for classification.

  11. Fluid pipeline system leak detection based on neural network and pattern recognition

    International Nuclear Information System (INIS)

    Tang Xiujia

    1998-01-01

    The mechanism of the stress wave propagation along the pipeline system of NPP, caused by turbulent ejection from pipeline leakage, is researched. A series of characteristic index are described in time domain or frequency domain, and compress numerical algorithm is developed for original data compression. A back propagation neural networks (BPNN) with the input matrix composed by stress wave characteristics in time domain or frequency domain is first proposed to classify various situations of the pipeline, in order to detect the leakage in the fluid flow pipelines. The capability of the new method had been demonstrated by experiments and finally used to design a handy instrument for the pipeline leakage detection. Usually a pipeline system has many inner branches and often in adjusting dynamic condition, it is difficult for traditional pipeline diagnosis facilities to identify the difference between inner pipeline operation and pipeline fault. The author first proposed pipeline wave propagation identification by pattern recognition to diagnose pipeline leak. A series of pattern primitives such as peaks, valleys, horizon lines, capstan peaks, dominant relations, slave relations, etc., are used to extract features of the negative pressure wave form. The context-free grammar of symbolic representation of the negative wave form is used, and a negative wave form parsing system with application to structural pattern recognition based on the representation is first proposed to detect and localize leaks of the fluid pipelines

  12. American Sign Language Alphabet Recognition Using a Neuromorphic Sensor and an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Miguel Rivera-Acosta

    2017-09-01

    Full Text Available This paper reports the design and analysis of an American Sign Language (ASL alphabet translation system implemented in hardware using a Field-Programmable Gate Array. The system process consists of three stages, the first being the communication with the neuromorphic camera (also called Dynamic Vision Sensor, DVS sensor using the Universal Serial Bus protocol. The feature extraction of the events generated by the DVS is the second part of the process, consisting of a presentation of the digital image processing algorithms developed in software, which aim to reduce redundant information and prepare the data for the third stage. The last stage of the system process is the classification of the ASL alphabet, achieved with a single artificial neural network implemented in digital hardware for higher speed. The overall result is the development of a classification system using the ASL signs contour, fully implemented in a reconfigurable device. The experimental results consist of a comparative analysis of the recognition rate among the alphabet signs using the neuromorphic camera in order to prove the proper operation of the digital image processing algorithms. In the experiments performed with 720 samples of 24 signs, a recognition accuracy of 79.58% was obtained.

  13. New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.

    Science.gov (United States)

    Jiang, Quansheng; Shen, Yehu; Li, Hua; Xu, Fengyu

    2018-01-24

    Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

  14. Theoretical Investigation of Optical Detection and Recognition of Single Biological Molecules Using Coherent Dynamics of Exciton-Plasmon Coupling.

    Science.gov (United States)

    Sadeghi, S M; Hood, B; Patty, K D; Mao, C-B

    2013-08-20

    We use quantum coherence in a system consisting of one metallic nanorod and one semi-conductor quantum dot to investigate a plasmonic nanosensor capable of digital optical detection and recognition of single biological molecules. In such a sensor the adsorption of a specific molecule to the nanorod turns off the emission of the system when it interacts with an optical pulse having a certain intensity and temporal width. The proposed quantum sensors can count the number of molecules of the same type or differentiate between molecule types with digital optical signals that can be measured with high certainty. We show that these sensors are based on the ultrafast upheaval of coherent dynamics of the system and the removal of coherent blockage of energy transfer from the quantum dot to the nanorod once the adsorption process has occurred.

  15. Folding and ligand recognition of the TPP riboswitch aptamer at single-molecule resolution.

    Science.gov (United States)

    Haller, Andrea; Altman, Roger B; Soulière, Marie F; Blanchard, Scott C; Micura, Ronald

    2013-03-12

    Thiamine pyrophosphate (TPP)-sensitive mRNA domains are the most prevalent riboswitches known. Despite intensive investigation, the complex ligand recognition and concomitant folding processes in the TPP riboswitch that culminate in the regulation of gene expression remain elusive. Here, we used single-molecule fluorescence resonance energy transfer imaging to probe the folding landscape of the TPP aptamer domain in the absence and presence of magnesium and TPP. To do so, distinct labeling patterns were used to sense the dynamics of the switch helix (P1) and the two sensor arms (P2/P3 and P4/P5) of the aptamer domain. The latter structural elements make interdomain tertiary contacts (L5/P3) that span a region immediately adjacent to the ligand-binding site. In each instance, conformational dynamics of the TPP riboswitch were influenced by ligand binding. The P1 switch helix, formed by the 5' and 3' ends of the aptamer domain, adopts a predominantly folded structure in the presence of Mg(2+) alone. However, even at saturating concentrations of Mg(2+) and TPP, the P1 helix, as well as distal regions surrounding the TPP-binding site, exhibit an unexpected degree of residual dynamics and disperse kinetic behaviors. Such plasticity results in a persistent exchange of the P3/P5 forearms between open and closed configurations that is likely to facilitate entry and exit of the TPP ligand. Correspondingly, we posit that such features of the TPP aptamer domain contribute directly to the mechanism of riboswitch-mediated translational regulation.

  16. Self-Organizing Neural Integration of Pose-Motion Features for Human Action Recognition

    Directory of Open Access Journals (Sweden)

    German Ignacio Parisi

    2015-06-01

    Full Text Available The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented towards human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR networks that obtain progressively generalized representations of sensory inputs and learn inherent spatiotemporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best 21 results for a public benchmark of domestic daily actions.

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

    Directory of Open Access Journals (Sweden)

    David L Butler

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

  18. Biosynthesis of the neural cell adhesion molecule: characterization of polypeptide C

    DEFF Research Database (Denmark)

    Nybroe, O; Albrechtsen, M; Dahlin, J

    1985-01-01

    The biosynthesis of the neural cell adhesion molecule (N-CAM) was studied in primary cultures of rat cerebral glial cells, cerebellar granule neurons, and skeletal muscle cells. The three cell types produced different N-CAM polypeptide patterns. Glial cells synthesized a 135,000 Mr polypeptide B...... and a 115,000 Mr polypeptide C, whereas neurons expressed a 200,000 Mr polypeptide A as well as polypeptide B. Skeletal muscle cells produced polypeptide B. The polypeptides synthesized by the three cell types were immunochemically identical. The membrane association of polypeptide C was investigated...... with methods that distinguish peripheral and integral membrane proteins. Polypeptide C was found to be a peripheral membrane protein, whereas polypeptides A and B were integral membrane proteins with cytoplasmic domains of approximately 50,000 and approximately 25,000 Mr, respectively. The affinity...

  19. An fMRI comparison of neural activity associated with recognition of familiar melodies in younger and older adults

    Directory of Open Access Journals (Sweden)

    Ritu eSikka

    2015-10-01

    Full Text Available Several studies of semantic memory in non-musical domains involving recognition of items from long-term memory have shown an age-related shift from the medial temporal lobe structures to the frontal lobe. However, the effects of aging on musical semantic memory remain unexamined. We compared activation associated with recognition of familiar melodies in younger and older adults. Recognition follows successful retrieval from the musical lexicon that comprises a lifetime of learned musical phrases. We used the sparse-sampling technique in fMRI to determine the neural correlates of melody recognition by comparing activation when listening to familiar versus unfamiliar melodies, and to identify age differences. Recognition-related cortical activation was detected in the right superior temporal, bilateral inferior and superior frontal, left middle orbitofrontal, bilateral precentral, and left supramarginal gyri. Region-of-interest analysis showed greater activation for younger adults in the left superior temporal gyrus and for older adults in the left superior frontal, left angular, and bilateral superior parietal regions. Our study provides powerful evidence for these musical memory networks due to a large sample (N = 40 that includes older adults. This study is the first to investigate the neural basis of melody recognition in older adults and to compare the findings to younger adults.

  20. Survivin Improves Reprogramming Efficiency of Human Neural Progenitors by Single Molecule OCT4

    Directory of Open Access Journals (Sweden)

    Shixin Zhou

    2016-01-01

    Full Text Available Induced pluripotent stem (iPS cells have been generated from human somatic cells by ectopic expression of four Yamanaka factors. Here, we report that Survivin, an apoptosis inhibitor, can enhance iPS cells generation from human neural progenitor cells (NPCs together with one factor OCT4 (1F-OCT4-Survivin. Compared with 1F-OCT4, Survivin accelerates the process of reprogramming from human NPCs. The neurocyte-originated induced pluripotent stem (NiPS cells generated from 1F-OCT4-Survivin resemble human embryonic stem (hES cells in morphology, surface markers, global gene expression profiling, and epigenetic status. Survivin keeps high expression in both iPS and ES cells. During the process of NiPS cell to neural cell differentiation, the expression of Survivin is rapidly decreased in protein level. The mechanism of Survivin promotion of reprogramming efficiency from NPCs may be associated with stabilization of β-catenin in WNT signaling pathway. This hypothesis is supported by experiments of RT-PCR, chromatin immune-precipitation, and Western blot in human ES cells. Our results showed overexpression of Survivin could improve the efficiency of reprogramming from NPCs to iPS cells by one factor OCT4 through stabilization of the key molecule, β-catenin.

  1. Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.

    Science.gov (United States)

    Kasabov, Nikola; Dhoble, Kshitij; Nuntalid, Nuttapod; Indiveri, Giacomo

    2013-05-01

    On-line learning and recognition of spatio- and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as Spike-Timing Dependent Plasticity (STDP) and its variant Spike Driven Synaptic Plasticity (SDSP), can also be used to learn spatio-temporal representations, but they usually require many iterations in an unsupervised or semi-supervised mode of learning. This paper introduces a new class of eSNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode. The paper also introduces a new model called deSNN, that utilises rank-order learning and SDSP spike-time learning in unsupervised, supervised, or semi-supervised modes. The SDSP learning is used to evolve dynamically the network changing connection weights that capture spatio-temporal spike data clusters both during training and during recall. The new deSNN model is first illustrated on simple examples and then applied on two case study applications: (1) moving object recognition using address-event representation (AER) with data collected using a silicon retina device; (2) EEG SSTD recognition for brain-computer interfaces. The deSNN models resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank-order or STDP learning. The reason is that the deSNN makes use of both the information contained in the order of the first input spikes

  2. Pharmacological approach for targeting dysfunctional brain plasticity: Focus on neural cell adhesion molecule (NCAM).

    Science.gov (United States)

    Aonurm-Helm, Anu; Jaako, Külli; Jürgenson, Monika; Zharkovsky, Alexander

    2016-11-01

    Brain plasticity refers to the ability of the brain to undergo functionally relevant adaptations in response to external and internal stimuli. Alterations in brain plasticity have been associated with several neuropsychiatric disorders, and current theories suggest that dysfunctions in neuronal circuits and synaptogenesis have a major impact in the development of these diseases. Among the molecules that regulate brain plasticity, neural cell adhesion molecule (NCAM) and its polysialylated form PSA-NCAM have been of particular interest for years because alterations in NCAM and PSA-NCAM levels have been associated with memory impairment, depression, autistic spectrum disorders and schizophrenia. In this review, we discuss the roles of NCAM and PSA-NCAM in the regulation of brain plasticity and, in particular, their roles in the mechanisms of depression. We also demonstrate that the NCAM-mimetic peptides FGL and Enreptin are able to restore disrupted neuronal plasticity. FGL peptide has also been demonstrated to ameliorate the symptoms of depressive-like behavior in NCAM-deficient mice and therefore, may be considered a new drug candidate for the treatment of depression as well as other neuropsychiatric disorders with disrupted neuroplasticity. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2018-03-01

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

  4. Neural androgen receptors modulate gene expression and social recognition but not social investigation

    Directory of Open Access Journals (Sweden)

    Sara A Karlsson

    2016-03-01

    Full Text Available The role of sex and androgen receptors (ARs for social preference and social memory is rather unknown. In this study of mice we compared males, females and males lacking ARs specifically in the nervous system, ARNesDel, with respect to social preference, assessed with the three-chambered apparatus test, and social recognition, assessed with the social discrimination procedure. In the social discrimination test we also evaluated the tentative importance of the sex of the stimulus animal. Novel object recognition and olfaction were investigated to complement the results from the social tests. Gene expression analysis was performed to reveal molecules involved in the effects of sex and androgens on social behaviors. All three test groups showed social preference in the three-chambered apparatus test. In both social tests an AR-independent sexual dimorphism was seen in the persistence of social investigation of female conspecifics, whereas the social interest towards male stimuli mice was similar in all groups. Male and female controls recognized conspecifics independent of their sex, whereas ARNesDel males recognized female but not male stimuli mice. Moreover, the non-social behaviors were not affected by AR deficiency. The gene expression analyses of hypothalamus and amygdala indicated that Oxtr, Cd38, Esr1, Cyp19a1, Ucn3, Crh and Gtf2i were differentially expressed between the three groups. In conclusion, our results suggest that ARs are required for recognition of male but not female conspecifics, while being dispensable for social investigation towards both sexes. In addition, the AR seems to regulate genes related to oxytocin, estrogen and William’s syndrome.

  5. The Neural Cell Adhesion Molecule-Derived Peptide FGL Facilitates Long-Term Plasticity in the Dentate Gyrus in Vivo

    Science.gov (United States)

    Dallerac, Glenn; Zerwas, Meike; Novikova, Tatiana; Callu, Delphine; Leblanc-Veyrac, Pascale; Bock, Elisabeth; Berezin, Vladimir; Rampon, Claire; Doyere, Valerie

    2011-01-01

    The neural cell adhesion molecule (NCAM) is known to play a role in developmental and structural processes but also in synaptic plasticity and memory of the adult animal. Recently, FGL, a NCAM mimetic peptide that binds to the Fibroblast Growth Factor Receptor 1 (FGFR-1), has been shown to have a beneficial impact on normal memory functioning, as…

  6. An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ahmadi Majid

    2003-01-01

    Full Text Available This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF neural network with a hybrid learning algorithm (HLA has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.

  7. Neural Correlates of Word Recognition: A Systematic Comparison of Natural Reading and Rapid Serial Visual Presentation.

    Science.gov (United States)

    Kornrumpf, Benthe; Niefind, Florian; Sommer, Werner; Dimigen, Olaf

    2016-09-01

    Neural correlates of word recognition are commonly studied with (rapid) serial visual presentation (RSVP), a condition that eliminates three fundamental properties of natural reading: parafoveal preprocessing, saccade execution, and the fast changes in attentional processing load occurring from fixation to fixation. We combined eye-tracking and EEG to systematically investigate the impact of all three factors on brain-electric activity during reading. Participants read lists of words either actively with eye movements (eliciting fixation-related potentials) or maintained fixation while the text moved passively through foveal vision at a matched pace (RSVP-with-flankers paradigm, eliciting ERPs). The preview of the upcoming word was manipulated by changing the number of parafoveally visible letters. Processing load was varied by presenting words of varying lexical frequency. We found that all three factors have strong interactive effects on the brain's responses to words: Once a word was fixated, occipitotemporal N1 amplitude decreased monotonically with the amount of parafoveal information available during the preceding fixation; hence, the N1 component was markedly attenuated under reading conditions with preview. Importantly, this preview effect was substantially larger during active reading (with saccades) than during passive RSVP with flankers, suggesting that the execution of eye movements facilitates word recognition by increasing parafoveal preprocessing. Lastly, we found that the N1 component elicited by a word also reflects the lexical processing load imposed by the previously inspected word. Together, these results demonstrate that, under more natural conditions, words are recognized in a spatiotemporally distributed and interdependent manner across multiple eye fixations, a process that is mediated by active motor behavior.

  8. Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers

    Science.gov (United States)

    Kruithof, Maarten C.; Bouma, Henri; Fischer, Noëlle M.; Schutte, Klamer

    2016-10-01

    Object recognition is important to understand the content of video and allow flexible querying in a large number of cameras, especially for security applications. Recent benchmarks show that deep convolutional neural networks are excellent approaches for object recognition. This paper describes an approach of domain transfer, where features learned from a large annotated dataset are transferred to a target domain where less annotated examples are available as is typical for the security and defense domain. Many of these networks trained on natural images appear to learn features similar to Gabor filters and color blobs in the first layer. These first-layer features appear to be generic for many datasets and tasks while the last layer is specific. In this paper, we study the effect of copying all layers and fine-tuning a variable number. We performed an experiment with a Caffe-based network on 1000 ImageNet classes that are randomly divided in two equal subgroups for the transfer from one to the other. We copy all layers and vary the number of layers that is fine-tuned and the size of the target dataset. We performed additional experiments with the Keras platform on CIFAR-10 dataset to validate general applicability. We show with both platforms and both datasets that the accuracy on the target dataset improves when more target data is used. When the target dataset is large, it is beneficial to freeze only a few layers. For a large target dataset, the network without transfer learning performs better than the transfer network, especially if many layers are frozen. When the target dataset is small, it is beneficial to transfer (and freeze) many layers. For a small target dataset, the transfer network boosts generalization and it performs much better than the network without transfer learning. Learning time can be reduced by freezing many layers in a network.

  9. A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

    Science.gov (United States)

    Zhu, Yanan; Ouyang, Qi; Mao, Youdong

    2017-07-21

    Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs. We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly "knowledgeable". Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features. The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.

  10. Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano’s Continuous Note Recognition

    Directory of Open Access Journals (Sweden)

    YuKang Jia

    2017-01-01

    Full Text Available Long Short-Term Memory (LSTM is a kind of Recurrent Neural Networks (RNN relating to time series, which has achieved good performance in speech recogniton and image recognition. Long Short-Term Memory Projection (LSTMP is a variant of LSTM to further optimize speed and performance of LSTM by adding a projection layer. As LSTM and LSTMP have performed well in pattern recognition, in this paper, we combine them with Connectionist Temporal Classification (CTC to study piano’s continuous note recognition for robotics. Based on the Beijing Forestry University music library, we conduct experiments to show recognition rates and numbers of iterations of LSTM with a single layer, LSTMP with a single layer, and Deep LSTM (DLSTM, LSTM with multilayers. As a result, the single layer LSTMP proves performing much better than the single layer LSTM in both time and the recognition rate; that is, LSTMP has fewer parameters and therefore reduces the training time, and, moreover, benefiting from the projection layer, LSTMP has better performance, too. The best recognition rate of LSTMP is 99.8%. As for DLSTM, the recognition rate can reach 100% because of the effectiveness of the deep structure, but compared with the single layer LSTMP, DLSTM needs more training time.

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

    Directory of Open Access Journals (Sweden)

    I. Topalova

    2005-03-01

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

  12. Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Xiaolong Zhai

    2017-07-01

    Full Text Available Hand movement classification based on surface electromyography (sEMG pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types and ~2.99% (amputee, 10 movement types increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.

  13. Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

    Science.gov (United States)

    Han, Bing; Taha, Tarek M

    2010-04-01

    There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

  14. Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters

    Directory of Open Access Journals (Sweden)

    Amir LAKZIAN

    2010-09-01

    Full Text Available This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC, -33 kPa, and permanent wilting point (PWP, -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN, Artificial Neural Network (ANN and pedotransfer functions (PTF were used to predict the soil water at each sampling location. Mean square deviation (MSD and its components, index of agreement (d, root mean square difference (RMSD and normalized RMSD (RMSDr were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.

  15. Recognition

    DEFF Research Database (Denmark)

    Gimmler, Antje

    2017-01-01

    In this article, I shall examine the cognitive, heuristic and theoretical functions of the concept of recognition. To evaluate both the explanatory power and the limitations of a sociological concept, the theory construction must be analysed and its actual productivity for sociological theory must...... be evaluated. In the first section, I will introduce the concept of recognition as a travelling concept playing a role both on the intellectual stage and in real life. In the second section, I will concentrate on the presentation of Honneth’s theory of recognition, emphasizing the construction of the concept...... and its explanatory power. Finally, I will discuss Honneth’s concept in relation to the critique that has been raised, addressing the debate between Honneth and Fraser. In a short conclusion, I will return to the question of the explanatory power of the concept of recognition....

  16. Exploring both sequence detection and restriction endonuclease cleavage kinetics by recognition site via single-molecule microfluidic trapping.

    Science.gov (United States)

    Xu, Weilin; Muller, Susan J

    2011-02-07

    We demonstrate the feasibility of a single-molecule microfluidic approach to both sequence detection and obtaining kinetic information for restriction endonucleases on dsDNA. In this method, a microfluidic stagnation point flow is designed to trap, hold, and linearize double-stranded (ds) genomic DNA to which a restriction endonuclease has been pre-bound sequence-specifically. By introducing the cofactor magnesium, we determine the binding location of the enzyme by the cleavage process of dsDNA as in optical restriction mapping, however here the DNA need not be immobilized on a surface. We note that no special labeling of the enzyme is required, which makes it simpler than our previous scheme using stagnation point flows for sequence detection. Our accuracy in determining the location of the recognition site is comparable to or better than other single molecule techniques due to the fidelity with which we can control the linearization of the DNA molecules. In addition, since the cleavage process can be followed in real time, information about the cleavage kinetics, and subtle differences in binding and cleavage frequencies among the recognition sites, may also be obtained. Data for the five recognition sites for the type II restriction endonuclease EcoRI on λ-DNA are presented as a model system. While the roles of the varying fluid velocity and tension along the chain backbone on the measured kinetics remain to be determined, we believe this new method holds promise for a broad range of studies of DNA-protein interactions, including the kinetics of other DNA cleavage processes, the dissociation of a restriction enzyme from the cleaved substrate, and other macromolecular cleavage processes.

  17. The Prion Protein Controls Polysialylation of Neural Cell Adhesion Molecule 1 during Cellular Morphogenesis.

    Directory of Open Access Journals (Sweden)

    Mohadeseh Mehrabian

    Full Text Available Despite its multi-faceted role in neurodegenerative diseases, the physiological function of the prion protein (PrP has remained elusive. On the basis of its evolutionary relationship to ZIP metal ion transporters, we considered that PrP may contribute to the morphogenetic reprogramming of cells underlying epithelial-to-mesenchymal transitions (EMT. Consistent with this hypothesis, PrP transcription increased more than tenfold during EMT, and stable PrP-deficient cells failed to complete EMT in a mammalian cell model. A global comparative proteomics analysis identified the neural cell adhesion molecule 1 (NCAM1 as a candidate mediator of this impairment, which led to the observation that PrP-deficient cells fail to undergo NCAM1 polysialylation during EMT. Surprisingly, this defect was caused by a perturbed transcription of the polysialyltransferase ST8SIA2 gene. Proteomics data pointed toward β-catenin as a transcriptional regulator affected in PrP-deficient cells. Indeed, pharmacological blockade or siRNA-based knockdown of β-catenin mimicked PrP-deficiency in regards to NCAM1 polysialylation. Our data established the existence of a PrP-ST8SIA2-NCAM signaling loop, merged two mature fields of investigation and offer a simple model for explaining phenotypes linked to PrP.

  18. Effect of human colorectal carcinogenesis on the neural cell adhesion molecule expression and polysialylation.

    Science.gov (United States)

    Fernández-Briera, A; García-Parceiro, I; Cuevas, E; Gil-Martín, E

    2010-01-01

    Although downregulation of neural cell adhesion molecule (NCAM) has been correlated with poor prognosis in colorectal cancer (CRC), it is also possible that colon cancer spreading comes from reducing tumor cell adhesion through NCAM polysialylation, as occurs in lung carcinoma or Wilms' tumor. To prove this hypothesis, we have performed a prospective study on tumor and control specimens from 39 CRC patients, which were immunostained for NCAM and PSA (polysialic acid) expression. Tumor versus control expression of NCAM and PSA epitopes in tissue specimens, as well as correlation between tumor expression and clinicopathological features, were statistically analyzed. Results showed a low constitutive expression of NCAM and PSA (PSA-NCAM) in control tissue, which reached a statistically significant increase in the tumor tissue. Likewise, the presence and number of lymph node metastases at surgery were correlated with NCAM expression and PSA/NCAM coexpression. These data highlight the importance of taking into account PSA-associated epitopes when dealing with NCAM cell expression studies in tumor development and progression. The analysis of PSA and NCAM expression in CRC suggests a new way, other than downregulation of NCAM, in order to escape contact inhibition and promote cell tumor spreading in colorectal cancer. Copyright 2010 S. Karger AG, Basel.

  19. DMBT1 functions as pattern-recognition molecule for poly-sulfated and poly-phosphorylated ligands

    DEFF Research Database (Denmark)

    End, Caroline; Bikker, Floris; Renner, Marcus

    2009-01-01

    Deleted in malignant brain tumors 1 (DMBT1) is a secreted glycoprotein displaying a broad bacterial-binding spectrum. Recent functional and genetic studies linked DMBT1 to the suppression of LPS-induced TLR4-mediated NF-kappaB activation and to the pathogenesis of Crohn's disease. Here, we aimed ...... that DMBT1 functions as pattern-recognition molecule for poly-sulfated and poly-phosphorylated ligands providing a molecular basis for its broad bacterial-binding specificity and its inhibitory effects on LPS-induced TLR4-mediated NF-kappaB activation....

  20. Neural correlates of own- and other-race face recognition in children: a functional near-infrared spectroscopy study.

    Science.gov (United States)

    Ding, Xiao Pan; Fu, Genyue; Lee, Kang

    2014-01-15

    The present study used the functional Near-infrared Spectroscopy (fNIRS) methodology to investigate the neural correlates of elementary school children's own- and other-race face processing. An old-new paradigm was used to assess children's recognition ability of own- and other-race faces. FNIRS data revealed that other-race faces elicited significantly greater [oxy-Hb] changes than own-race faces in the right middle frontal gyrus and inferior frontal gyrus regions (BA9) and the left cuneus (BA18). With increased age, the [oxy-Hb] activity differences between own- and other-race faces, or the neural other-race effect (NORE), underwent significant changes in these two cortical areas: at younger ages, the neural response to the other-race faces was modestly greater than that to the own-race faces, but with increased age, the neural response to the own-race faces became increasingly greater than that to the other-race faces. Moreover, these areas had strong regional functional connectivity with a swath of the cortical regions in terms of the neural other-race effect that also changed with increased age. We also found significant and positive correlations between the behavioral other-race effect (reaction time) and the neural other-race effect in the right middle frontal gyrus and inferior frontal gyrus regions (BA9). These results taken together suggest that children, like adults, devote different amounts of neural resources to processing own- and other-race faces, but the size and direction of the neural other-race effect and associated functional regional connectivity change with increased age. © 2013.

  1. Organic Zeolite Analogues Based on Multi-Component Liquid Crystals: Recognition and Transformation of Molecules within Constrained Environments

    Directory of Open Access Journals (Sweden)

    Yasuhiro Ishida

    2011-01-01

    Full Text Available In liquid crystals (LCs, molecules are confined in peculiar environments, where ordered alignment and certain mobility are realized at the same time. Considering these characteristics, the idea of “controlling molecular events within LC media” seems reasonable. As a suitable system for investigating this challenge, we have recently developed a new class of ionic LCs; the salts of amphiphilic carboxylic acids with 2-amino alcohols, or those of carboxylic acids with amphiphilic 2-amino alcohols, have a strong tendency to exhibit thermotropic LC phases. Because of the noncovalent nature of the interaction between molecules, one of the two components can easily be exchanged with, or transformed into, another molecule, without distorting the original LC architecture. In addition, both components are common organic molecules, and a variety of compounds are easily available. Taking advantage of these characteristics, we have succeeded in applying two‑component LCs as chiral media for molecular recognition and reactions. This review presents an overview of our recent studies, together with notable reports related to this field.

  2. Organic Zeolite Analogues Based on Multi-Component Liquid Crystals: Recognition and Transformation of Molecules within Constrained Environments.

    Science.gov (United States)

    Ishida, Yasuhiro

    2011-01-11

    In liquid crystals (LCs), molecules are confined in peculiar environments, where ordered alignment and certain mobility are realized at the same time. Considering these characteristics, the idea of "controlling molecular events within LC media" seems reasonable. As a suitable system for investigating this challenge, we have recently developed a new class of ionic LCs; the salts of amphiphilic carboxylic acids with 2-amino alcohols, or those of carboxylic acids with amphiphilic 2-amino alcohols, have a strong tendency to exhibit thermotropic LC phases. Because of the noncovalent nature of the interaction between molecules, one of the two components can easily be exchanged with, or transformed into, another molecule, without distorting the original LC architecture. In addition, both components are common organic molecules, and a variety of compounds are easily available. Taking advantage of these characteristics, we have succeeded in applying two‑component LCs as chiral media for molecular recognition and reactions. This review presents an overview of our recent studies, together with notable reports related to this field.

  3. The neural cell adhesion molecule L1 is distinct from the N-CAM related group of surface antigens BSP-2 and D2

    DEFF Research Database (Denmark)

    Faissner, A; Kruse, J; Goridis, C

    1984-01-01

    The neural cell adhesion molecule L1 and the group of N-CAM related molecules, BSP-2 and D2 antigen, are immunochemically distinct molecular species. The two groups of surface molecules are also functionally distinct entities, since inhibition of Ca2+-independent adhesion among early post-natal m...

  4. Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition.

    Science.gov (United States)

    Jauregi Unanue, Iñigo; Zare Borzeshi, Ehsan; Piccardi, Massimo

    2017-12-01

    Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings". (i) To create a highly accurate DNR and CCE system that avoids conventional, time-consuming feature engineering. (ii) To create richer, more specialized word embeddings by using health domain datasets such as MIMIC-III. (iii) To evaluate our systems over three contemporary datasets. Two deep learning methods, namely the Bidirectional LSTM and the Bidirectional LSTM-CRF, are evaluated. A CRF model is set as the baseline to compare the deep learning systems to a traditional machine learning approach. The same features are used for all the models. We have obtained the best results with the Bidirectional LSTM-CRF model, which has outperformed all previously proposed systems. The specialized embeddings have helped to cover unusual words in DrugBank and MedLine, but not in the i2b2/VA dataset. We present a state-of-the-art system for DNR and CCE. Automated word embeddings has allowed us to avoid costly feature engineering and achieve higher accuracy. Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. H-1 and N-15 resonance assignment of the second fibronectin type III module of the neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Kiselyov, Vladislav V; Berezin, Vladimir; Bock, Elisabeth

    2008-01-01

    We report here the NMR assignment of the second fibronectin type III module of the neural cell adhesion molecule (NCAM). This module has previously been shown to interact with the fibroblast growth factor receptor (FGFR), and the FGFR-binding site was mapped by NMR to the FG-loop region of the mo......We report here the NMR assignment of the second fibronectin type III module of the neural cell adhesion molecule (NCAM). This module has previously been shown to interact with the fibroblast growth factor receptor (FGFR), and the FGFR-binding site was mapped by NMR to the FG-loop region...... of the module. The FG-loop region also contains a putative nucleotide-binding motif, which was shown by NMR to interact with ATP. Furthermore, ATP was demonstrated to inhibit binding of the second F3 module of NCAM to FGFR....

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

    DEFF Research Database (Denmark)

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

    2014-01-01

    and demonstrated that CD56(dim) NK-cells and both CD4(+) and CD8(+) subsets of activated T-cells were recognized by ficolin-1. In contrast we did not detect binding of ficolin-1 to CD56(bright) NK-cells, NKT-cells, resting T-cells or B-cells. Furthermore, we showed that the protein-lymphocyte interaction occurred......Ficolin-1 is a soluble pattern recognition molecule synthesized by myeloid cells and capable of activating the lectin pathway of complement on the surface of pathogens. It is tethered to the membranes of monocytes and granulocytes; however, the biological significance of cell-associated ficolin-1...... is unknown. Recognition of healthy host cells by a pattern recognition molecule constitutes a potential hazard to self cells and tissues, emphasizing the importance of further elucidating the reported self-recognition. In the current study we investigated the potential recognition of lymphocytes by ficolin-1...

  7. Recognition and detection of seismic phases by artificial neural network detector; Jinko neural network ni yoru jishinha no ninshiki to kenshutsu

    Energy Technology Data Exchange (ETDEWEB)

    Yamazaki, K.; Wang, W. [Tokyo Gakugei University, Tokyo (Japan)

    1997-05-27

    Initial parts of P-waves, medium or high in intensity, are detected using an artificial neural network (ANN). The ANN is the generic name given to information processing systems of the non-Neumann type configured to human brain in point of information processing function, and is packaged into computers in the form of software capable of parallel processing, self-organizing, learning, etc. In this paper, a hierarchical ANN-assisted seismic motion recognition system is constructed on the basis of an error reverse propagation algorithm. It is reported here, with a remark that this study wants much more data from tests for the evaluation of the quality of the recognition, that P-wave recognition has been achieved. When this technique is applied to the S-wave, much more real-time information will become available. For the improvement of the system, a number of problems have to be solved, including the establishment of automatic refurbishment through adaptation-and-learning and configuration that incorporates frequency-related matters. It is found that this system is effective in seismic wave phase recognition but that it is not suitable for precision measurement. 7 refs., 4 figs.

  8. Collectin liver 1 and collectin kidney 1 and other complement-associated pattern recognition molecules in systemic lupus erythematosus

    DEFF Research Database (Denmark)

    Troldborg, A; Thiel, S; Jensen, L

    2015-01-01

    The objective of this study was to explore the involvement of collectin liver 1 (CL-L1) and collectin kidney 1 (CL-K1) and other pattern recognition molecules (PRMs) of the lectin pathway of the complement system in a cross-sectional cohort of systemic lupus erythematosus (SLE) patients. Concentr......The objective of this study was to explore the involvement of collectin liver 1 (CL-L1) and collectin kidney 1 (CL-K1) and other pattern recognition molecules (PRMs) of the lectin pathway of the complement system in a cross-sectional cohort of systemic lupus erythematosus (SLE) patients...... patients negative for anti-dsDNA antibodies (P = 0·02). In a cross-sectional cohort of SLE patients, we found differences in the plasma concentrations of CL-L1, CL-K1, M-ficolin and H-ficolin compared to a group of healthy controls. Alterations in plasma concentrations of the PRMs of the lectin pathway...

  9. Regulation of Neural Cell Adhesion Molecule Polysialylation: Evidence for Nontranscriptional Control and Sensitivity to an Intracellular Pool of Calcium

    OpenAIRE

    Brusés, Juan L.; Rutishauser, Urs

    1998-01-01

    The up- and downregulation of polysialic acid–neural cell adhesion molecule (PSA–NCAM) expression on motorneurons during development is associated respectively with target innervation and synaptogenesis, and is regulated at the level of PSA enzymatic biosynthesis involving specific polysialyltransferase activity. The purpose of this study has been to describe the cellular mechanisms by which that regulation might occur. It has been found that developmental regulation of PSA synthesis by cilia...

  10. New method for recognition of sterol signalling molecules: Methinium salts as receptors for sulphated steroids

    Czech Academy of Sciences Publication Activity Database

    Kejík, Z.; Bříza, T.; Králová, Jarmila; Mikula, I.; Poučková, P.; Martásek, P.; Král, V.

    2015-01-01

    Roč. 94, February 2015 (2015), s. 15-20 ISSN 1878-5867 R&D Projects: GA TA ČR(CZ) TE01020028; GA ČR(CZ) GAP303/11/1291; GA MŠk(CZ) LH14008; GA MŠk(CZ) CZ.1.07/2.300/30.0060; GA MŠk(CZ) ED1.1.00/02.0109 Institutional support: RVO:68378050 Keywords : Polymethinium salts * Sulphated sterols * Molecular recognition * Synthetic receptors Subject RIV: EB - Genetics ; Molecular Biology

  11. A neural network multi-task learning approach to biomedical named entity recognition.

    Science.gov (United States)

    Crichton, Gamal; Pyysalo, Sampo; Chiu, Billy; Korhonen, Anna

    2017-08-15

    Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use them together to improve NER performance. To investigate this, we develop supervised, multi-task, convolutional neural network models and apply them to a large number of varied existing biomedical named entity datasets. Additionally, we investigated the effect of dataset size on performance in both single- and multi-task settings. We present a single-task model for NER, a Multi-output multi-task model and a Dependent multi-task model. We apply the three models to 15 biomedical datasets containing multiple named entities including Anatomy, Chemical, Disease, Gene/Protein and Species. Each dataset represent a task. The results from the single-task model and the multi-task models are then compared for evidence of benefits from Multi-task Learning. With the Multi-output multi-task model we observed an average F-score improvement of 0.8% when compared to the single-task model from an average baseline of 78.4%. Although there was a significant drop in performance on one dataset, performance improves significantly for five datasets by up to 6.3%. For the Dependent multi-task model we observed an average improvement of 0.4% when compared to the single-task model. There were no significant drops in performance on any dataset, and performance improves significantly for six datasets by up to 1.1%. The dataset size experiments found that as dataset size decreased, the multi-output model's performance increased compared to the single-task model's. Using 50, 25 and 10% of the training data resulted in an average drop of approximately 3.4, 8 and 16.7% respectively for the single-task model but approximately 0.2, 3.0 and 9.8% for the multi-task model. Our

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

    Directory of Open Access Journals (Sweden)

    Mahesh Jangid

    2018-02-01

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

  13. Impact of negative emotion on the neural correlates of long-term recognition in younger and older adults

    Directory of Open Access Journals (Sweden)

    Grégoria eKalpouzos

    2012-09-01

    Full Text Available Some studies have suggested that the memory advantage for negative emotional information over neutral information (negativity effect is reduced in aging. Besides the fact that most findings are based on immediate retrieval, the neural underpinnings of long-term emotional memory in aging have so far not been investigated. To address these issues, we assessed recognition of neutral and negative scenes after one- and 3-week retention intervals in younger and older adults using fMRI. We further used an event-related design in order to disentangle successful, false and true recognition. This study revealed 4 key findings: 1 Increased retention interval induced an increased rate of false recognitions for negative scenes, cancelling out the negativity effect (present for hit rates only on discrimination in both younger and older adults; 2 In younger, but not older, adults, reduced activity of the medial temporal lobe was observed over time for neutral scenes, but not for negative scenes, where stable or increased activity was seen; 3 Engagement of amygdala was observed in older adults after a 3-week delay during successful recognition of negative scenes (hits versus misses in comparison with neutral scenes, which may indicate engagement of automatic processes, but engagement of ventrolateral prefrontal cortex was unrelated to amygdala activity and performance; and 4 After 3 weeks, but not after one week, true recognition of negative scenes was characterized by more activity in left hippocampus and lateral occipito-temporal regions (hits versus false alarms. As these regions are known to be related to consolidation mechanisms, the observed pattern may indicate the presence of delayed consolidation of true memories. Nonetheless, older adults’ low performance in discrimination of negative scenes could reflect the fact that overall, after long delays of retention, they rely more on general information rather than on perceptual detail in making

  14. Neuritogenic and survival-promoting effects of the P2 peptide derived from a homophilic binding site in the neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Pedersen, Martin V; Køhler, Lene B; Ditlevsen, Dorte K

    2004-01-01

    The neural cell adhesion molecule (NCAM) plays a pivotal role in neural development, regeneration, and plasticity. NCAM mediates adhesion and subsequent signal transduction through NCAM-NCAM binding. Recently, a peptide ligand termed P2 corresponding to a 12-amino-acid sequence in the FG loop...

  15. Laser Controlled Synthesis of Noble Metal Nanoparticle Arrays for Low Concentration Molecule Recognition

    Directory of Open Access Journals (Sweden)

    Enza Fazio

    2014-12-01

    Full Text Available Nanostructured gold and silver thin films were grown by pulsed laser deposition.Performing the process in an ambient gas (Ar leads to the nucleation and growth ofnanoparticles in the ablation plasma and their self-organization on the substrate. Thedependence of surface nanostructuring of the films on the deposition parameters is discussedconsidering in particular the number of laser pulses and the ambient gas nature and pressure.The performance of the deposited thin films as substrates for surface-enhanced Ramanspectroscopy (SERS was tested against the detection of molecules at a low concentration.Taking Raman maps on micrometer-sized areas, the spatial homogeneity of the substrateswith respect to the SERS signal was tested.

  16. An electrochemical impedance sensor based on a small molecule modified Au electrode for the recognition of a trinucleotide repeat.

    Science.gov (United States)

    He, Hanping; Peng, Xiaoqian; Huang, Min; Chang, Gang; Zhang, Xiuhua; Wang, Shengfu

    2014-11-07

    A small molecule modified sensor was developed for the detection of XGG trinucleotide repeats (X = C, T) by electrochemical impedance spectroscopy. The sensor (NCD/MPA/Au) was fabricated by immobilizing the nucleic acid recognition molecule (NCD) on the surface of a gold electrode through a condensation reaction between the amino-terminal end of the NCD linker and carboxylic groups in 3-mercaptopropionic acid that were self-assembled on the electrode surface. After the sensor was incubated with trinucleotide repeats, electrochemical impedance spectroscopy was performed using [Fe(CN)6](3-/4-) as redox marker ions. XGG repeats (X = C, T) could be selectively detected based on the differences in charge transfer resistance (ΔRct) even in the presence of other trinucleotide repeats. The relationship between ΔRct and lg [concentration of CGG repeats] for the sensor was linear from 1 nM to 1 μM, enabling the quantification of the number of repeats. The electrochemical impedance sensor provides a simple and rapid method to detect trinucleotide repeats without requiring labelling and immobilizations of DNA, making it promising for the early diagnosis of neurodegenerative diseases; the sensor may be further extended to the detection of other special sequences of DNA.

  17. Perceived Parenting Mediates Serotonin Transporter Gene (5-HTTLPR) and Neural System Function during Facial Recognition: A Pilot Study

    Science.gov (United States)

    Nishikawa, Saori

    2015-01-01

    This study examined changes in prefrontal oxy-Hb levels measured by NIRS (Near-Infrared Spectroscopy) during a facial-emotion recognition task in healthy adults, testing a mediational/moderational model of these variables. Fifty-three healthy adults (male = 35, female = 18) aged between 22 to 37 years old (mean age = 24.05 years old) provided saliva samples, completed a EMBU questionnaire (Swedish acronym for Egna Minnen Beträffande Uppfostran [My memories of upbringing]), and participated in a facial-emotion recognition task during NIRS recording. There was a main effect of maternal rejection on RoxH (right frontal activation during an ambiguous task), and a gene × environment (G×E) interaction on RoxH, suggesting that individuals who carry the SL or LL genotype and who endorse greater perceived maternal rejection show less right frontal activation than SL/LL carriers with lower perceived maternal rejection. Finally, perceived parenting style played a mediating role in right frontal activation via the 5-HTTLPR genotype. Early-perceived parenting might influence neural activity in an uncertain situation i.e. rating ambiguous faces among individuals with certain genotypes. This preliminary study makes a small contribution to the mapping of an influence of gene and behaviour on the neural system. More such attempts should be made in order to clarify the links. PMID:26418317

  18. Perceived Parenting Mediates Serotonin Transporter Gene (5-HTTLPR) and Neural System Function during Facial Recognition: A Pilot Study.

    Science.gov (United States)

    Nishikawa, Saori; Toshima, Tamotsu; Kobayashi, Masao

    2015-01-01

    This study examined changes in prefrontal oxy-Hb levels measured by NIRS (Near-Infrared Spectroscopy) during a facial-emotion recognition task in healthy adults, testing a mediational/moderational model of these variables. Fifty-three healthy adults (male = 35, female = 18) aged between 22 to 37 years old (mean age = 24.05 years old) provided saliva samples, completed a EMBU questionnaire (Swedish acronym for Egna Minnen Beträffande Uppfostran [My memories of upbringing]), and participated in a facial-emotion recognition task during NIRS recording. There was a main effect of maternal rejection on RoxH (right frontal activation during an ambiguous task), and a gene × environment (G × E) interaction on RoxH, suggesting that individuals who carry the SL or LL genotype and who endorse greater perceived maternal rejection show less right frontal activation than SL/LL carriers with lower perceived maternal rejection. Finally, perceived parenting style played a mediating role in right frontal activation via the 5-HTTLPR genotype. Early-perceived parenting might influence neural activity in an uncertain situation i.e. rating ambiguous faces among individuals with certain genotypes. This preliminary study makes a small contribution to the mapping of an influence of gene and behaviour on the neural system. More such attempts should be made in order to clarify the links.

  19. Perceived Parenting Mediates Serotonin Transporter Gene (5-HTTLPR and Neural System Function during Facial Recognition: A Pilot Study.

    Directory of Open Access Journals (Sweden)

    Saori Nishikawa

    Full Text Available This study examined changes in prefrontal oxy-Hb levels measured by NIRS (Near-Infrared Spectroscopy during a facial-emotion recognition task in healthy adults, testing a mediational/moderational model of these variables. Fifty-three healthy adults (male = 35, female = 18 aged between 22 to 37 years old (mean age = 24.05 years old provided saliva samples, completed a EMBU questionnaire (Swedish acronym for Egna Minnen Beträffande Uppfostran [My memories of upbringing], and participated in a facial-emotion recognition task during NIRS recording. There was a main effect of maternal rejection on RoxH (right frontal activation during an ambiguous task, and a gene × environment (G × E interaction on RoxH, suggesting that individuals who carry the SL or LL genotype and who endorse greater perceived maternal rejection show less right frontal activation than SL/LL carriers with lower perceived maternal rejection. Finally, perceived parenting style played a mediating role in right frontal activation via the 5-HTTLPR genotype. Early-perceived parenting might influence neural activity in an uncertain situation i.e. rating ambiguous faces among individuals with certain genotypes. This preliminary study makes a small contribution to the mapping of an influence of gene and behaviour on the neural system. More such attempts should be made in order to clarify the links.

  20. PERFORMANCE EVALUATION OF VARIANCES IN BACKPROPAGATION NEURAL NETWORK USED FOR HANDWRITTEN CHARACTER RECOGNITION

    OpenAIRE

    Vairaprakash Gurusamy *1 & K.Nandhini2

    2017-01-01

    A Neural Network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain.Back propagation was created by generalizing the Widrow-Hoff learning rule to multiple-layer networks and nonlinear differentiable transfer functions. The term back pro...

  1. The role of phosphatidylinositol 3-kinase in neural cell adhesion molecule-mediated neuronal differentiation and survival

    DEFF Research Database (Denmark)

    Ditlevsen, Dorte K; Køhler, Lene B; Pedersen, Martin V

    2003-01-01

    The neural cell adhesion molecule, NCAM, is known to stimulate neurite outgrowth from primary neurones and PC12 cells presumably through signalling pathways involving the fibroblast growth factor receptor (FGFR), protein kinase A (PKA), protein kinase C (PKC), the Ras-mitogen activated protein...... that phosphatidylinositol 3-kinase (PI3K) is required for NCAM-mediated neurite outgrowth from PC12-E2 cells and from cerebellar and dopaminergic neurones in primary culture, and that the thr/ser kinase Akt/protein kinase B (PKB) is phosphorylated downstream of PI3K after stimulation with C3. Moreover, we present data...

  2. Recognition of In-Ear Microphone Speech Data Using Multi-Layer Neural Networks

    National Research Council Canada - National Science Library

    Bulbuller, Gokhan

    2006-01-01

    .... In this study, a speech recognition system is presented, specifically an isolated word recognizer which uses speech collected from the external auditory canals of the subjects via an in-ear microphone...

  3. Neural correlates of auditory recognition memory in the primate dorsal temporal pole

    OpenAIRE

    Ng, Chi-Wing; Plakke, Bethany; Poremba, Amy

    2013-01-01

    Temporal pole (TP) cortex is associated with higher-order sensory perception and/or recognition memory, as human patients with damage in this region show impaired performance during some tasks requiring recognition memory (Olson et al. 2007). The underlying mechanisms of TP processing are largely based on examination of the visual nervous system in humans and monkeys, while little is known about neuronal activity patterns in the auditory portion of this region, dorsal TP (dTP; Poremba et al. ...

  4. Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

    Science.gov (United States)

    Srimani, P. K.; Parimala, Y. G.

    2011-12-01

    A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

  5. Colorimetric sensor arrays based on pattern recognition for the detection of nitroaromatic molecules

    International Nuclear Information System (INIS)

    Lu, Wei; Dong, Xiao; Qiu, Lili; Yan, Zequn; Meng, Zihui; Xue, Min; He, Xuan; Liu, Xueyong

    2017-01-01

    , the sensor array can illustrate the influence of the nitryl quantity and generate a separate response region of nitroaromatics for pattern recognition with 95.25% of variance explained in the measurements by the first three principal components (PCs). The statistical analysis endowed the cross-reactive array with better classification and identification ability and this novel detection platform provided a wider applied range among other harmful chemicals in a simple sensor array with customized functionality.

  6. Colorimetric sensor arrays based on pattern recognition for the detection of nitroaromatic molecules

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Wei; Dong, Xiao [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Qiu, Lili, E-mail: qiulili@bit.edu.cn [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Yan, Zequn [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Meng, Zihui, E-mail: m_zihui@yahoo.com [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Xue, Min [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); He, Xuan; Liu, Xueyong [Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900 (China)

    2017-03-15

    , the sensor array can illustrate the influence of the nitryl quantity and generate a separate response region of nitroaromatics for pattern recognition with 95.25% of variance explained in the measurements by the first three principal components (PCs). The statistical analysis endowed the cross-reactive array with better classification and identification ability and this novel detection platform provided a wider applied range among other harmful chemicals in a simple sensor array with customized functionality.

  7. A Parallel Supercomputer Implementation of a Biological Inspired Neural Network and its use for Pattern Recognition

    International Nuclear Information System (INIS)

    De Ladurantaye, Vincent; Lavoie, Jean; Bergeron, Jocelyn; Parenteau, Maxime; Lu Huizhong; Pichevar, Ramin; Rouat, Jean

    2012-01-01

    A parallel implementation of a large spiking neural network is proposed and evaluated. The neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher (ODLM). Scalability, speed and performance are compared for 2 implementations: Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) running on clusters of multicore supercomputers and NVIDIA graphical processing units respectively. A global spiking list that represents at each instant the state of the neural network is described. This list indexes each neuron that fires during the current simulation time so that the influence of their spikes are simultaneously processed on all computing units. Our implementation shows a good scalability for very large networks. A complex and large spiking neural network has been implemented in parallel with success, thus paving the road towards real-life applications based on networks of spiking neurons. MPI offers a better scalability than CUDA, while the CUDA implementation on a GeForce GTX 285 gives the best cost to performance ratio. When running the neural network on the GTX 285, the processing speed is comparable to the MPI implementation on RQCHP's Mammouth parallel with 64 notes (128 cores).

  8. Study on pattern recognition of Raman spectrum based on fuzzy neural network

    Science.gov (United States)

    Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing

    2017-10-01

    Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

  9. Successful combination of computationally inexpensive GIAO 13C NMR calculations and artificial neural network pattern recognition: a new strategy for simple and rapid detection of structural misassignments.

    Science.gov (United States)

    Sarotti, Ariel M

    2013-08-07

    GIAO NMR chemical shift calculations coupled with trained artificial neural networks (ANNs) have been shown to provide a powerful strategy for simple, rapid and reliable identification of structural misassignments of organic compounds using only one set of both computational and experimental data. The geometry optimization, usually the most time-consuming step in the overall procedure, was carried out using computationally inexpensive methods (MM+, AM1 or HF/3-21G) and the NMR shielding constants at the affordable mPW1PW91/6-31G(d) level of theory. As low quality NMR prediction is typically obtained with such protocols, the decision making was foreseen as a problem of pattern recognition. Thus, given a set of statistical parameters computed after correlation between experimental and calculated chemical shifts the classification was done using the knowledge derived from trained ANNs. The training process was carried out with a set of 200 molecules chosen to provide a wide array of chemical functionalities and molecular complexity, and the results were validated with a set of 26 natural products that had been incorrectly assigned along with their 26 revised structures. The high prediction effectiveness observed makes this method a suitable test for rapid identification of structural misassignments, preventing not only the publication of wrong structures but also avoiding the consequences of such a mistake.

  10. MHC class I molecules with superenhanced CD8 binding properties bypass the requirement for cognate TCR recognition and nonspecifically activate CTLs

    NARCIS (Netherlands)

    L. Wooldridge (Linda); M. Clement (Mathew); A. Lissina (Anna); E.S.J. Edwards (Emily); K. Ladell (Kristin); J. Ekeruche (Julia); R.E. Hewitt (Rachel); B. Laugel (Bruno); E. Gostick (Emma); D.K. Cole (David); J.E.M.A. Debets (Reno); C.A. Berrevoets (Cor); J.J. Miles (John); S.R. Burrows (Scott); D.A. Price (David); A.K. Sewell (Andrew)

    2010-01-01

    textabstractCD8+CTLs are essential for effective immune defense against intracellular microbes and neoplasia. CTLs recognize short peptide fragments presented in association with MHC class I (MHCI) molecules on the surface of infected or dysregulated cells. Ag recognition involves the binding of

  11. Molecular Recognition of Azelaic Acid and Related Molecules with DNA Polymerase I Investigated by Molecular Modeling Calculations.

    Science.gov (United States)

    Shawon, Jakaria; Khan, Akib Mahmud; Rahman, Adhip; Hoque, Mohammad Mazharol; Khan, Mohammad Abdul Kader; Sarwar, Mohammed G; Halim, Mohammad A

    2016-10-01

    Molecular recognition has central role on the development of rational drug design. Binding affinity and interactions are two key components which aid to understand the molecular recognition in drug-receptor complex and crucial for structure-based drug design in medicinal chemistry. Herein, we report the binding affinity and the nonbonding interactions of azelaic acid and related compounds with the receptor DNA polymerase I (2KFN). Quantum mechanical calculation was employed to optimize the modified drugs using B3LYP/6-31G(d,p) level of theory. Charge distribution, dipole moment and thermodynamic properties such as electronic energy, enthalpy and free energy of these optimized drugs are also explored to evaluate how modifications impact the drug properties. Molecular docking calculation was performed to evaluate the binding affinity and nonbonding interactions between designed molecules and the receptor protein. We notice that all modified drugs are thermodynamically more stable and some of them are more chemically reactive than the unmodified drug. Promise in enhancing hydrogen bonds is found in case of fluorine-directed modifications as well as in the addition of trifluoroacetyl group. Fluorine participates in forming fluorine bonds and also stimulates alkyl, pi-alkyl interactions in some drugs. Designed drugs revealed increased binding affinity toward 2KFN. A1, A2 and A3 showed binding affinities of -8.7, -8.6 and -7.9 kcal/mol, respectively against 2KFN compared to the binding affinity -6.7 kcal/mol of the parent drug. Significant interactions observed between the drugs and Thr358 and Asp355 residues of 2KFN. Moreover, designed drugs demonstrated improved pharmacokinetic properties. This study disclosed that 9-octadecenoic acid and drugs containing trifluoroacetyl and trifluoromethyl groups are the best 2KFN inhibitors. Overall, these results can be useful for the design of new potential candidates against DNA polymerase I.

  12. Multilingual vocal emotion recognition and classification using back propagation neural network

    Science.gov (United States)

    Kayal, Apoorva J.; Nirmal, Jagannath

    2016-03-01

    This work implements classification of different emotions in different languages using Artificial Neural Networks (ANN). Mel Frequency Cepstral Coefficients (MFCC) and Short Term Energy (STE) have been considered for creation of feature set. An emotional speech corpus consisting of 30 acted utterances per emotion has been developed. The emotions portrayed in this work are Anger, Joy and Neutral in each of English, Marathi and Hindi languages. Different configurations of Artificial Neural Networks have been employed for classification purposes. The performance of the classifiers has been evaluated by False Negative Rate (FNR), False Positive Rate (FPR), True Positive Rate (TPR) and True Negative Rate (TNR).

  13. Enhanced resolution of molecular recognition to distinguish structurally similar molecules by different conformational responses of a protein upon ligand binding.

    Science.gov (United States)

    Higuchi, Mariko; Fujii, Jumpei; Yonetani, Yoshiteru; Kitao, Akio; Go, Nobuhiro

    2011-01-01

    MutT distinguishes substrate 8-oxo-dGTP from dGTP and also 8-oxo-dGMP from dGMP despite small differences of chemical structures between them. In this paper we show by the method of molecular dynamics simulation that the transition between conformational substates of MutT is a key mechanism for a high-resolution molecular recognition of the differences between the very similar chemical compounds. (1) The native state MutT has two conformational substates with similar free energies, each characterized by either open or closed of two loops surrounding the substrate binding active site. Between the two substates, the open substate is more stable in free MutT and in dGMP-MutT complex, and the closed substate is more stable in 8-oxo-dGMP-MutT complex. (2) Conformational fluctuation of the open substate is much larger than that of the closed substate. An estimate of associated entropy difference was found to be consistent with the experimentally found difference of entropy contribution to the binding free energies of the two molecules. (3) A hydrogen bond between H7 atom of 8-oxo-dGMP and the sidechain of Asn119 plays a crucial role for maintaining the closed substate in 8-oxo-dGMP-MutT complex. When this hydrogen bond is absent in the H7-deficient dGMP-MutT complex, the closed substate is no more maintained and transition to the more entropically-favored open substate is induced. (4) Thus, this mechanism of the hydrogen bond controlling the relative stabilities of the drastically different two conformational substates enhances the resolution to recognize the small difference of the chemical structures between the two molecules, dGMP and 8-oxo-dGMP. Copyright © 2010 Elsevier Inc. All rights reserved.

  14. Pattern recognition

    CERN Document Server

    Theodoridis, Sergios

    2003-01-01

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

  15. A Deep Convolutional Neural Network for Location Recognition and Geometry based Information

    NARCIS (Netherlands)

    Bidoia, Francesco; Sabatelli, Matthia; Shantia, Amir; Wiering, Marco A.; Schomaker, Lambert; De Marsico, Maria; Sanniti di Baja, Gabriella; Fred, Ana

    2018-01-01

    In this paper we propose a new approach to Deep Neural Networks (DNNs) based on the particular needs of navigation tasks. To investigate these needs we created a labeled image dataset of a test environment and we compare classical computer vision approaches with the state of the art in image

  16. Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition

    NARCIS (Netherlands)

    Pawara, Pornntiwa; Okafor, Emmanuel; Surinta, Olarik; Schomaker, Lambertus; Wiering, Marco

    2017-01-01

    The use of machine learning and computer vision methods for recognizing different plants from images has attracted lots of attention from the community. This paper aims at comparing local feature descriptors and bags of visual words with different classifiers to deep convolutional neural networks

  17. Expression of polysialylated neural cell adhesion molecules on adult stem cells after neuronal differentiation of inner ear spiral ganglion neurons

    Energy Technology Data Exchange (ETDEWEB)

    Park, Kyoung Ho [Department of Otolaryngology Head and Neck Surgery, College of Medicine, Catholic University, Seoul (Korea, Republic of); Yeo, Sang Won, E-mail: swyeo@catholic.ac.kr [Department of Otolaryngology Head and Neck Surgery, College of Medicine, Catholic University, Seoul (Korea, Republic of); Troy, Frederic A., E-mail: fatroy@ucdavis.edu [Department of Biochemistry and Molecular Medicine, University of California, School of Medicine, Davis, CA 95616 (United States); Xiamen University, School of Medicine, Xiamen City (China)

    2014-10-17

    Highlights: • PolySia expressed on neurons primarily during early stages of neuronal development. • PolySia–NCAM is expressed on neural stem cells from adult guinea pig spiral ganglion. • PolySia is a biomarker that modulates neuronal differentiation in inner ear stem cells. - Abstract: During brain development, polysialylated (polySia) neural cell adhesion molecules (polySia–NCAMs) modulate cell–cell adhesive interactions involved in synaptogenesis, neural plasticity, myelination, and neural stem cell (NSC) proliferation and differentiation. Our findings show that polySia–NCAM is expressed on NSC isolated from adult guinea pig spiral ganglion (GPSG), and in neurons and Schwann cells after differentiation of the NSC with epidermal, glia, fibroblast growth factors (GFs) and neurotrophins. These differentiated cells were immunoreactive with mAb’s to polySia, NCAM, β-III tubulin, nestin, S-100 and stained with BrdU. NSC could regenerate and be differentiated into neurons and Schwann cells. We conclude: (1) polySia is expressed on NSC isolated from adult GPSG and on neurons and Schwann cells differentiated from these NSC; (2) polySia is expressed on neurons primarily during the early stage of neuronal development and is expressed on Schwann cells at points of cell–cell contact; (3) polySia is a functional biomarker that modulates neuronal differentiation in inner ear stem cells. These new findings suggest that replacement of defective cells in the inner ear of hearing impaired patients using adult spiral ganglion neurons may offer potential hope to improve the quality of life for patients with auditory dysfunction and impaired hearing disorders.

  18. Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition

    NARCIS (Netherlands)

    van de Wolfshaar, Jos; Karaaba, Mahir; Wiering, Marco

    2015-01-01

    Social behavior and many cultural etiquettes are influenced by gender. There are numerous potential applications of automatic face gender recognition such as human-computer interaction systems, content based image search, video surveillance and more. The immense increase of images that are uploaded

  19. Cell recognition molecule L1 promotes embryonic stem cell differentiation through the regulation of cell surface glycosylation

    Energy Technology Data Exchange (ETDEWEB)

    Li, Ying [Department of Biochemistry and Molecular Biology, Dalian Medical University, Dalian 116044 (China); Department of Clinical Laboratory, Second Affiliated Hospital of Dalian Medical University, Dalian 116023 (China); Huang, Xiaohua [Department of Biochemistry and Molecular Biology, Dalian Medical University, Dalian 116044 (China); Department of Clinical Biochemistry, College of Laboratory Medicine, Dalian Medical University, Dalian 116044 (China); An, Yue [Department of Clinical Laboratory, Second Affiliated Hospital of Dalian Medical University, Dalian 116023 (China); Ren, Feng [Department of Biochemistry and Molecular Biology, Dalian Medical University, Dalian 116044 (China); Yang, Zara Zhuyun; Zhu, Hongmei; Zhou, Lei [The Key Laboratory of Stem Cell and Regenerative Medicine, Institute of Molecular and Clinical Medicine, Kunming Medical University, Kunming 650228 (China); Department of Anatomy and Developmental Biology, Monash University, Clayton 3800 (Australia); He, Xiaowen; Schachner, Melitta [Keck Center for Collaborative Neuroscience and Department of Cell Biology and Neuroscience, Rutgers University, New Brunswick, NJ (United States); Xiao, Zhicheng, E-mail: zhicheng.xiao@monash.edu [The Key Laboratory of Stem Cell and Regenerative Medicine, Institute of Molecular and Clinical Medicine, Kunming Medical University, Kunming 650228 (China); Department of Anatomy and Developmental Biology, Monash University, Clayton 3800 (Australia); Ma, Keli, E-mail: makeli666@aliyun.com [Department of Biochemistry and Molecular Biology, Dalian Medical University, Dalian 116044 (China); Li, Yali, E-mail: yalilipaper@gmail.com [Department of Biochemistry and Molecular Biology, Dalian Medical University, Dalian 116044 (China); Department of Anatomy, National University of Singapore, Singapore 119078 (Singapore)

    2013-10-25

    Highlights: •Down-regulating FUT9 and ST3Gal4 expression blocks L1-induced neuronal differentiation of ESCs. •Up-regulating FUT9 and ST3Gal4 expression in L1-ESCs depends on the activation of PLCγ. •L1 promotes ESCs to differentiate into neuron through regulating cell surface glycosylation. -- Abstract: Cell recognition molecule L1 (CD171) plays an important role in neuronal survival, migration, differentiation, neurite outgrowth, myelination, synaptic plasticity and regeneration after injury. Our previous study has demonstrated that overexpressing L1 enhances cell survival and proliferation of mouse embryonic stem cells (ESCs) through promoting the expression of FUT9 and ST3Gal4, which upregulates cell surface sialylation and fucosylation. In the present study, we examined whether sialylation and fucosylation are involved in ESC differentiation through L1 signaling. RNA interference analysis showed that L1 enhanced differentiation of ESCs into neurons through the upregulation of FUT9 and ST3Gal4. Furthermore, blocking the phospholipase Cγ (PLCγ) signaling pathway with either a specific PLCγ inhibitor or knockdown PLCγ reduced the expression levels of both FUT9 and ST3Gal4 mRNAs and inhibited L1-mediated neuronal differentiation. These results demonstrate that L1 promotes neuronal differentiation from ESCs through the L1-mediated enhancement of FUT9 and ST3Gal4 expression.

  20. Atomic force microscopy imaging and single molecule recognition force spectroscopy of coat proteins on the surface of Bacillus subtilis spore.

    Science.gov (United States)

    Tang, Jilin; Krajcikova, Daniela; Zhu, Rong; Ebner, Andreas; Cutting, Simon; Gruber, Hermann J; Barak, Imrich; Hinterdorfer, Peter

    2007-01-01

    Coat assembly in Bacillus subtilis serves as a tractable model for the study of the self-assembly process of biological structures and has a significant potential for use in nano-biotechnological applications. In the present study, the morphology of B. subtilis spores was investigated by magnetically driven dynamic force microscopy (MAC mode atomic force microscopy) under physiological conditions. B. subtilis spores appeared as prolate structures, with a length of 0.6-3 microm and a width of about 0.5-2 microm. The spore surface was mainly covered with bump-like structures with diameters ranging from 8 to 70 nm. Besides topographical explorations, single molecule recognition force spectroscopy (SMRFS) was used to characterize the spore coat protein CotA. This protein was specifically recognized by a polyclonal antibody directed against CotA (anti-CotA), the antibody being covalently tethered to the AFM tip via a polyethylene glycol linker. The unbinding force between CotA and anti-CotA was determined as 55 +/- 2 pN. From the high-binding probability of more than 20% in force-distance cycles it is concluded that CotA locates in the outer surface of B. subtilis spores. Copyright (c) 2007 John Wiley & Sons, Ltd.

  1. Collectin liver 1 and collectin kidney 1 and other complement-associated pattern recognition molecules in systemic lupus erythematosus.

    Science.gov (United States)

    Troldborg, A; Thiel, S; Jensen, L; Hansen, S; Laska, M J; Deleuran, B; Jensenius, J C; Stengaard-Pedersen, K

    2015-11-01

    The objective of this study was to explore the involvement of collectin liver 1 (CL-L1) and collectin kidney 1 (CL-K1) and other pattern recognition molecules (PRMs) of the lectin pathway of the complement system in a cross-sectional cohort of systemic lupus erythematosus (SLE) patients. Concentrations in plasma of CL-L1, CL-K1, mannan-binding lectin (MBL), M-ficolin, H-ficolin and L-ficolin were determined in 58 patients with SLE and 65 healthy controls using time-resolved immunoflourometric assays. The SLE patients' demographic, diagnostic, clinical and biochemical data and collection of plasma samples were performed prospectively during 4 months. CL-L1, CL-K1 and M-ficolin plasma concentrations were lower in SLE patients than healthy controls (P-values PRMs of the lectin pathway in SLE patients and associations to key elements of the disease support the hypothesis that the lectin pathway plays a role in the pathogenesis of SLE. © 2015 British Society for Immunology.

  2. Cell recognition molecule L1 promotes embryonic stem cell differentiation through the regulation of cell surface glycosylation

    International Nuclear Information System (INIS)

    Li, Ying; Huang, Xiaohua; An, Yue; Ren, Feng; Yang, Zara Zhuyun; Zhu, Hongmei; Zhou, Lei; He, Xiaowen; Schachner, Melitta; Xiao, Zhicheng; Ma, Keli; Li, Yali

    2013-01-01

    Highlights: •Down-regulating FUT9 and ST3Gal4 expression blocks L1-induced neuronal differentiation of ESCs. •Up-regulating FUT9 and ST3Gal4 expression in L1-ESCs depends on the activation of PLCγ. •L1 promotes ESCs to differentiate into neuron through regulating cell surface glycosylation. -- Abstract: Cell recognition molecule L1 (CD171) plays an important role in neuronal survival, migration, differentiation, neurite outgrowth, myelination, synaptic plasticity and regeneration after injury. Our previous study has demonstrated that overexpressing L1 enhances cell survival and proliferation of mouse embryonic stem cells (ESCs) through promoting the expression of FUT9 and ST3Gal4, which upregulates cell surface sialylation and fucosylation. In the present study, we examined whether sialylation and fucosylation are involved in ESC differentiation through L1 signaling. RNA interference analysis showed that L1 enhanced differentiation of ESCs into neurons through the upregulation of FUT9 and ST3Gal4. Furthermore, blocking the phospholipase Cγ (PLCγ) signaling pathway with either a specific PLCγ inhibitor or knockdown PLCγ reduced the expression levels of both FUT9 and ST3Gal4 mRNAs and inhibited L1-mediated neuronal differentiation. These results demonstrate that L1 promotes neuronal differentiation from ESCs through the L1-mediated enhancement of FUT9 and ST3Gal4 expression

  3. The humoral pattern recognition molecule PTX3 is a key component of innate immunity against urinary tract infection.

    Science.gov (United States)

    Jaillon, Sébastien; Moalli, Federica; Ragnarsdottir, Bryndis; Bonavita, Eduardo; Puthia, Manoj; Riva, Federica; Barbati, Elisa; Nebuloni, Manuela; Cvetko Krajinovic, Lidija; Markotic, Alemka; Valentino, Sonia; Doni, Andrea; Tartari, Silvia; Graziani, Giorgio; Montanelli, Alessandro; Delneste, Yves; Svanborg, Catharina; Garlanda, Cecilia; Mantovani, Alberto

    2014-04-17

    Immunity in the urinary tract has distinct and poorly understood pathophysiological characteristics and urinary tract infections (UTIs) are important causes of morbidity and mortality. We investigated the role of the soluble pattern recognition molecule pentraxin 3 (PTX3), a key component of the humoral arm of innate immunity, in UTIs. PTX3-deficient mice showed defective control of UTIs and exacerbated inflammation. Expression of PTX3 was induced in uroepithelial cells by uropathogenic Escherichia coli (UPEC) in a Toll-like receptor 4 (TLR4)- and MyD88-dependent manner. PTX3 enhanced UPEC phagocytosis and phagosome maturation by neutrophils. PTX3 was detected in urine of UTI patients and amounts correlated with disease severity. In cohorts of UTI-prone patients, PTX3 gene polymorphisms correlated with susceptibility to acute pyelonephritis and cystitis. These results suggest that PTX3 is an essential component of innate resistance against UTIs. Thus, the cellular and humoral arms of innate immunity exert complementary functions in mediating resistance against UTIs. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Recognition of normal-abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients.

    Science.gov (United States)

    Maknickas, Vykintas; Maknickas, Algirdas

    2017-07-31

    Intensive care unit patients are heavily monitored, and several clinically-relevant parameters are routinely extracted from high resolution signals. The goal of the 2016 PhysioNet/CinC Challenge was to encourage the creation of an intelligent system that fused information from different phonocardiographic signals to create a robust set of normal/abnormal signal detections. Deep convolutional neural networks and mel-frequency spectral coefficients were used for recognition of normal-abnormal phonocardiographic signals of the human heart. This technique was developed using the PhysioNet.org Heart Sound database and was submitted for scoring on the challenge test set. The current entry for the proposed approach obtained an overall score of 84.15% in the last phase of the challenge, which provided the sixth official score and differs from the best score of 86.02% by just 1.87%.

  5. ShcA regulates neurite outgrowth stimulated by neural cell adhesion molecule but not by fibroblast growth factor 2: evidence for a distinct fibroblast growth factor receptor response to neural cell adhesion molecule activation

    DEFF Research Database (Denmark)

    Hinsby, Anders M; Lundfald, Line; Ditlevsen, Dorte K

    2004-01-01

    Homophilic binding in trans of the neural cell adhesion molecule (NCAM) mediates adhesion between cells and leads, via activation of intracellular signaling cascades, to neurite outgrowth in primary neurons as well as in the neuronal cell line PC12. NCAM mediates neurite extension in PC12 cells....... Here, we investigated the involvement of adaptor proteins in NCAM and fibroblast growth factor 2 (FGF2)-mediated neurite outgrowth in the PC12-E2 cell line. We found that both FGFR substrate-2 and Grb2 play important roles in NCAM as well as in FGF2-stimulated events. In contrast, the docking protein...... ShcA was pivotal to neurite outgrowth induced by NCAM, but not by FGF2, in PC12 cells. Moreover, in rat cerebellar granule neurons, phosphorylation of ShcA was stimulated by an NCAM mimicking peptide, but not by FGF2. This activation was blocked by inhibitors of both FGFR and Fyn, indicating that NCAM...

  6. Recognition of Roasted Coffee Bean Levels using Image Processing and Neural Network

    Science.gov (United States)

    Nasution, T. H.; Andayani, U.

    2017-03-01

    The coffee beans roast levels have some characteristics. However, some people cannot recognize the coffee beans roast level. In this research, we propose to design a method to recognize the coffee beans roast level of images digital by processing the image and classifying with backpropagation neural network. The steps consist of how to collect the images data with image acquisition, pre-processing, feature extraction using Gray Level Co-occurrence Matrix (GLCM) method and finally normalization of data extraction using decimal scaling features. The values of decimal scaling features become an input of classifying in backpropagation neural network. We use the method of backpropagation to recognize the coffee beans roast levels. The results showed that the proposed method is able to identify the coffee roasts beans level with an accuracy of 97.5%.

  7. Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition

    Science.gov (United States)

    Baskaran, Subbiah; Ramachandran, Narayanan; Noever, David

    1998-01-01

    The use of probabilistic (PNN) and multilayer feed forward (MLFNN) neural networks are investigated for calibration of multi-hole pressure probes and the prediction of associated flow angularity patterns in test flow fields. Both types of networks are studied in detail for their calibration and prediction characteristics. The current formalism can be applied to any multi-hole probe, however the test results for the most commonly used five-hole Cone and Prism probe types alone are reported in this article.

  8. A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural Network

    Directory of Open Access Journals (Sweden)

    Jing Xu

    2015-10-01

    Full Text Available In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition (IEEMD and Probabilistic Neural Network (PNN is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function (IMF components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method.

  9. Gait Phases Recognition from Accelerations and Ground Reaction Forces: Application of Neural Networks

    Directory of Open Access Journals (Sweden)

    S. Rafajlović

    2009-06-01

    Full Text Available The goal of this study was to test the applicability of accelerometer as the sensor for assessment of the walking. We present here the comparison of gait phases detected from the data recorded by force sensing resistors mounted in the shoe insoles, non-processed acceleration and processed acceleration perpendicular to the direction of the foot. The gait phases in all three cases were detected by means of a neural network. The output from the neural network was the gait phase, while the inputs were data from the sensors. The results show that the errors were in the ranges: 30 ms (2.7% – force sensors; 150 ms (13.6% – nonprocessed acceleration, and 120 ms (11% – processed acceleration data. This result suggests that it is possible to use the accelerometer as the gait phase detector, however, with the knowledge that the gait phases are time shifted for about 100 ms with respect the neural network predicted times.

  10. Human Age Recognition by Electrocardiogram Signal Based on Artificial Neural Network

    Science.gov (United States)

    Dasgupta, Hirak

    2016-12-01

    The objective of this work is to make a neural network function approximation model to detect human age from the electrocardiogram (ECG) signal. The input vectors of the neural network are the Katz fractal dimension of the ECG signal, frequencies in the QRS complex, male or female (represented by numeric constant) and the average of successive R-R peak distance of a particular ECG signal. The QRS complex has been detected by short time Fourier transform algorithm. The successive R peak has been detected by, first cutting the signal into periods by auto-correlation method and then finding the absolute of the highest point in each period. The neural network used in this problem consists of two layers, with Sigmoid neuron in the input and linear neuron in the output layer. The result shows the mean of errors as -0.49, 1.03, 0.79 years and the standard deviation of errors as 1.81, 1.77, 2.70 years during training, cross validation and testing with unknown data sets, respectively.

  11. Application of neural classifier to risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography

    Science.gov (United States)

    Wydrzyński, Jacek; Jankowski, Stanisław; Piątkowska-Janko, Ewa

    2008-01-01

    This paper presents the application of neural networks to the risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography. This work is based on dataset obtained from the Medical University of Warsaw. The studies were performed on one multiclass classifier and on binary classifiers. For each case the optimal number of hidden neurons was found. The effect of data preparation: normalization and the proper selection of parameters was considered, as well as the influence of applied filters. The best neural classifier contains 5 hidden neurons, the input ECG signal is represented by 8 parameters. The neural network classifier had high rate of successful recognitions up to 90% performed on the test data set.

  12. Neural basis of distorted self-face recognition in social anxiety disorder

    Directory of Open Access Journals (Sweden)

    Min-Kyeong Kim

    2016-01-01

    Conclusion: Patients with SAD have a positive point of view of their own face and experience self-relevance for the attractively transformed self-faces. This distorted cognition may be based on dysfunctions in the frontal and inferior parietal regions. The abnormal engagement of the fronto-parietal attentional network during processing face stimuli in non-social situations may be linked to distorted self-recognition in SAD.

  13. Neural correlates of auditory recognition memory in the primate dorsal temporal pole.

    Science.gov (United States)

    Ng, Chi-Wing; Plakke, Bethany; Poremba, Amy

    2014-02-01

    Temporal pole (TP) cortex is associated with higher-order sensory perception and/or recognition memory, as human patients with damage in this region show impaired performance during some tasks requiring recognition memory (Olson et al. 2007). The underlying mechanisms of TP processing are largely based on examination of the visual nervous system in humans and monkeys, while little is known about neuronal activity patterns in the auditory portion of this region, dorsal TP (dTP; Poremba et al. 2003). The present study examines single-unit activity of dTP in rhesus monkeys performing a delayed matching-to-sample task utilizing auditory stimuli, wherein two sounds are determined to be the same or different. Neurons of dTP encode several task-relevant events during the delayed matching-to-sample task, and encoding of auditory cues in this region is associated with accurate recognition performance. Population activity in dTP shows a match suppression mechanism to identical, repeated sound stimuli similar to that observed in the visual object identification pathway located ventral to dTP (Desimone 1996; Nakamura and Kubota 1996). However, in contrast to sustained visual delay-related activity in nearby analogous regions, auditory delay-related activity in dTP is transient and limited. Neurons in dTP respond selectively to different sound stimuli and often change their sound response preferences between experimental contexts. Current findings suggest a significant role for dTP in auditory recognition memory similar in many respects to the visual nervous system, while delay memory firing patterns are not prominent, which may relate to monkeys' shorter forgetting thresholds for auditory vs. visual objects.

  14. Neural correlates of auditory recognition memory in the primate dorsal temporal pole

    Science.gov (United States)

    Ng, Chi-Wing; Plakke, Bethany

    2013-01-01

    Temporal pole (TP) cortex is associated with higher-order sensory perception and/or recognition memory, as human patients with damage in this region show impaired performance during some tasks requiring recognition memory (Olson et al. 2007). The underlying mechanisms of TP processing are largely based on examination of the visual nervous system in humans and monkeys, while little is known about neuronal activity patterns in the auditory portion of this region, dorsal TP (dTP; Poremba et al. 2003). The present study examines single-unit activity of dTP in rhesus monkeys performing a delayed matching-to-sample task utilizing auditory stimuli, wherein two sounds are determined to be the same or different. Neurons of dTP encode several task-relevant events during the delayed matching-to-sample task, and encoding of auditory cues in this region is associated with accurate recognition performance. Population activity in dTP shows a match suppression mechanism to identical, repeated sound stimuli similar to that observed in the visual object identification pathway located ventral to dTP (Desimone 1996; Nakamura and Kubota 1996). However, in contrast to sustained visual delay-related activity in nearby analogous regions, auditory delay-related activity in dTP is transient and limited. Neurons in dTP respond selectively to different sound stimuli and often change their sound response preferences between experimental contexts. Current findings suggest a significant role for dTP in auditory recognition memory similar in many respects to the visual nervous system, while delay memory firing patterns are not prominent, which may relate to monkeys' shorter forgetting thresholds for auditory vs. visual objects. PMID:24198324

  15. Expression, crystallization and preliminary X-ray analysis of extracellular modules of the neural cell-adhesion molecules NCAM and L1

    DEFF Research Database (Denmark)

    Kulahin, Nikolaj; Kasper, Christina; Gajhede, Michael

    2004-01-01

    Recombinant proteins consisting of either the four or five amino-terminal immunoglobulin (Ig) modules of the rat neural cell-adhesion molecule NCAM or the whole extracellular part [six Ig and five fibronectin type III (F3) modules] of mouse L1 have been expressed in Drosophila S2 cells. The prote......Recombinant proteins consisting of either the four or five amino-terminal immunoglobulin (Ig) modules of the rat neural cell-adhesion molecule NCAM or the whole extracellular part [six Ig and five fibronectin type III (F3) modules] of mouse L1 have been expressed in Drosophila S2 cells...

  16. Modulation of the homophilic interaction between the first and second Ig modules of neural cell adhesion molecule by heparin

    DEFF Research Database (Denmark)

    Kulahin, Nikolaj; Rudenko, Olga; Kiselyov, V.

    2005-01-01

    The second Ig module (IgII) of the neural cell adhesion molecule (NCAM) is known to bind to the first Ig module (IgI) of NCAM (so-called homophilic binding) and to interact with heparan sulfate and chondroitin sulfate glycoconjugates. We here show by NMR that the heparin and chondroitin sulfate......-binding sites (HBS and CBS, respectively) in IgII coincide, and that this site overlaps with the homophilic binding site. Using NMR and surface plasmon resonance (SPR) analyses we demonstrate that interaction between IgII and heparin indeed interferes with the homophilic interaction between IgI and Ig......II. Accordingly, we show that treatment of cerebellar granule neurons (CGNs) with heparin inhibits NCAM-mediated outgrowth. In contrast, treatment with heparinase III or chondroitinase ABC abrogates NCAM-mediated neurite outgrowth in CGNs emphasizing the importance of the presence of heparan/chondroitin sulfates...

  17. Neural correlates of the in-group memory advantage on the encoding and recognition of faces.

    Science.gov (United States)

    Herzmann, Grit; Curran, Tim

    2013-01-01

    People have a memory advantage for faces that belong to the same group, for example, that attend the same university or have the same personality type. Faces from such in-group members are assumed to receive more attention during memory encoding and are therefore recognized more accurately. Here we use event-related potentials related to memory encoding and retrieval to investigate the neural correlates of the in-group memory advantage. Using the minimal group procedure, subjects were classified based on a bogus personality test as belonging to one of two personality types. While the electroencephalogram was recorded, subjects studied and recognized faces supposedly belonging to the subject's own and the other personality type. Subjects recognized in-group faces more accurately than out-group faces but the effect size was small. Using the individual behavioral in-group memory advantage in multivariate analyses of covariance, we determined neural correlates of the in-group advantage. During memory encoding (300 to 1000 ms after stimulus onset), subjects with a high in-group memory advantage elicited more positive amplitudes for subsequently remembered in-group than out-group faces, showing that in-group faces received more attention and elicited more neural activity during initial encoding. Early during memory retrieval (300 to 500 ms), frontal brain areas were more activated for remembered in-group faces indicating an early detection of group membership. Surprisingly, the parietal old/new effect (600 to 900 ms) thought to indicate recollection processes differed between in-group and out-group faces independent from the behavioral in-group memory advantage. This finding suggests that group membership affects memory retrieval independent of memory performance. Comparisons with a previous study on the other-race effect, another memory phenomenon influenced by social classification of faces, suggested that the in-group memory advantage is dominated by top

  18. Lack of the pattern recognition molecule mannose-binding lectin increases susceptibility to influenza A virus infection

    Directory of Open Access Journals (Sweden)

    Hartshorn Kevan L

    2010-12-01

    Full Text Available Abstract Background Mannose-binding lectin (MBL, a pattern recognition innate immune molecule, inhibits influenza A virus infection in vitro. MBL deficiency due to gene polymorphism in humans has been associated with infection susceptibility. These clinical observations were confirmed by animal model studies, in which mice genetically lacking MBL were susceptible to certain pathogens, including herpes simplex virus 2. Results We demonstrate that MBL is present in the lung of naïve healthy wild type (WT mice and that MBL null mice are more susceptible to IAV infection. Administration of recombinant human MBL (rhMBL reverses the infection phenotype, confirming that the infection susceptibility is MBL-mediated. The anti-viral mechanisms of MBL include activation of the lectin complement pathway and coagulation, requiring serum factors. White blood cells (WBCs in the lung increase in WT mice compared with MBL null mice on day 1 post-infection. In contrast, apoptotic macrophages (MΦs are two-fold higher in the lung of MBL null mice compared with WT mice. Furthermore, MBL deficient macrophages appear to be susceptible to apoptosis in vitro. Lastly, soluble factors, which are associated with lung injury, are increased in the lungs of MBL null mice during IAV infection. These results suggest that MBL plays a key role against IAV infection. Conclusion MBL plays a key role in clearing IAV and maintaining lung homeostasis. In addition, our findings also suggest that MBL deficiency maybe a risk factor in IAV infection and MBL may be a useful adjunctive therapy for IAV infection.

  19. Neural nets with varying topology for high energy particle recognition. Theory and applications

    International Nuclear Information System (INIS)

    Perrone, A.L.; Basti, G.; Messi, R.; Paoluzi, L.; Picozza, P.

    1995-01-01

    In this paper we propose a strategy to solve the problem of parallel compuation based on a dynamic definition of the net topology showing its effectiveness for problems of particle track recognition in high-energy physics. In this way, we can maintain the linear architecture like in the geometric perceptron, but with a partial and dynamic connectivity so to overcome the intrinsic limiations of the geometric perceptron. Namely, the computation is truly parallel because of the partial connectivity but the net topology is always the optimal one because of its dynamic redefinition on the single input pattern. For these properties, we call this new architecture dynamic perceptron

  20. Functional cross-talk between the cellular prion protein and the neural cell adhesion molecule is critical for neuronal differentiation of neural stem/precursor cells.

    Science.gov (United States)

    Prodromidou, Kanella; Papastefanaki, Florentia; Sklaviadis, Theodoros; Matsas, Rebecca

    2014-06-01

    Cellular prion protein (PrP) is prominently expressed in brain, in differentiated neurons but also in neural stem/precursor cells (NPCs). The misfolding of PrP is a central event in prion diseases, yet the physiological function of PrP is insufficiently understood. Although PrP has been reported to associate with the neural cell adhesion molecule (NCAM), the consequences of concerted PrP-NCAM action in NPC physiology are unknown. Here, we generated NPCs from the subventricular zone (SVZ) of postnatal day 5 wild-type and PrP null (-/-) mice and observed that PrP is essential for proper NPC proliferation and neuronal differentiation. Moreover, we found that PrP is required for the NPC response to NCAM-induced neuronal differentiation. In the absence of PrP, NCAM not only fails to promote neuronal differentiation but also induces an accumulation of doublecortin-positive neuronal progenitors at the proliferation stage. In agreement, we noted an increase in cycling neuronal progenitors in the SVZ of PrP-/- mice compared with PrP+/+ mice, as evidenced by double labeling for the proliferation marker Ki67 and doublecortin as well as by 5-bromo-2'-deoxyuridine incorporation experiments. Additionally, fewer newly born neurons were detected in the rostral migratory stream of PrP-/- mice. Analysis of the migration of SVZ cells in microexplant cultures from wild-type and PrP-/- mice revealed no differences between genotypes or a role for NCAM in this process. Our data demonstrate that PrP plays a critical role in neuronal differentiation of NPCs and suggest that this function is, at least in part, NCAM-dependent. © 2014 AlphaMed Press.

  1. Neural correlates of the in-group memory advantage on the encoding and recognition of faces.

    Directory of Open Access Journals (Sweden)

    Grit Herzmann

    Full Text Available People have a memory advantage for faces that belong to the same group, for example, that attend the same university or have the same personality type. Faces from such in-group members are assumed to receive more attention during memory encoding and are therefore recognized more accurately. Here we use event-related potentials related to memory encoding and retrieval to investigate the neural correlates of the in-group memory advantage. Using the minimal group procedure, subjects were classified based on a bogus personality test as belonging to one of two personality types. While the electroencephalogram was recorded, subjects studied and recognized faces supposedly belonging to the subject's own and the other personality type. Subjects recognized in-group faces more accurately than out-group faces but the effect size was small. Using the individual behavioral in-group memory advantage in multivariate analyses of covariance, we determined neural correlates of the in-group advantage. During memory encoding (300 to 1000 ms after stimulus onset, subjects with a high in-group memory advantage elicited more positive amplitudes for subsequently remembered in-group than out-group faces, showing that in-group faces received more attention and elicited more neural activity during initial encoding. Early during memory retrieval (300 to 500 ms, frontal brain areas were more activated for remembered in-group faces indicating an early detection of group membership. Surprisingly, the parietal old/new effect (600 to 900 ms thought to indicate recollection processes differed between in-group and out-group faces independent from the behavioral in-group memory advantage. This finding suggests that group membership affects memory retrieval independent of memory performance. Comparisons with a previous study on the other-race effect, another memory phenomenon influenced by social classification of faces, suggested that the in-group memory advantage is dominated by

  2. A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network

    Directory of Open Access Journals (Sweden)

    Jianzheng Liu

    2016-01-01

    Full Text Available This paper presents a method for recognizing human faces with facial expression. In the proposed approach, a motion history image (MHI is employed to get the features in an expressive face. The face can be seen as a kind of physiological characteristic of a human and the expressions are behavioral characteristics. We fused the 2D images of a face and MHIs which were generated from the same face’s image sequences with expression. Then the fusion features were used to feed a 7-layer deep learning neural network. The previous 6 layers of the whole network can be seen as an autoencoder network which can reduce the dimension of the fusion features. The last layer of the network can be seen as a softmax regression; we used it to get the identification decision. Experimental results demonstrated that our proposed method performs favorably against several state-of-the-art methods.

  3. Wireless Indoor Location Estimation Based on Neural Network RSS Signature Recognition (LENSR)

    Energy Technology Data Exchange (ETDEWEB)

    Kurt Derr; Milos Manic

    2008-06-01

    Location Based Services (LBS), context aware applications, and people and object tracking depend on the ability to locate mobile devices, also known as localization, in the wireless landscape. Localization enables a diverse set of applications that include, but are not limited to, vehicle guidance in an industrial environment, security monitoring, self-guided tours, personalized communications services, resource tracking, mobile commerce services, guiding emergency workers during fire emergencies, habitat monitoring, environmental surveillance, and receiving alerts. This paper presents a new neural network approach (LENSR) based on a competitive topological Counter Propagation Network (CPN) with k-nearest neighborhood vector mapping, for indoor location estimation based on received signal strength. The advantage of this approach is both speed and accuracy. The tested accuracy of the algorithm was 90.6% within 1 meter and 96.4% within 1.5 meters. Several approaches for location estimation using WLAN technology were reviewed for comparison of results.

  4. Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

    Science.gov (United States)

    Szu, Harold H.

    1990-01-01

    In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.

  5. Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors.

    Science.gov (United States)

    Dominguez-Morales, Juan P; Jimenez-Fernandez, Angel F; Dominguez-Morales, Manuel J; Jimenez-Moreno, Gabriel

    2018-02-01

    Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of the main causes of death in the world. Heart murmurs are the most common abnormal finding when a patient visits the physician for auscultation. These heart sounds can either be innocent, which are harmless, or abnormal, which may be a sign of a more serious heart condition. However, the accuracy rate of primary care physicians and expert cardiologists when auscultating is not good enough to avoid most of both type-I (healthy patients are sent for echocardiogram) and type-II (pathological patients are sent home without medication or treatment) errors made. In this paper, the authors present a novel convolutional neural network based tool for classifying between healthy people and pathological patients using a neuromorphic auditory sensor for FPGA that is able to decompose the audio into frequency bands in real time. For this purpose, different networks have been trained with the heart murmur information contained in heart sound recordings obtained from nine different heart sound databases sourced from multiple research groups. These samples are segmented and preprocessed using the neuromorphic auditory sensor to decompose their audio information into frequency bands and, after that, sonogram images with the same size are generated. These images have been used to train and test different convolutional neural network architectures. The best results have been obtained with a modified version of the AlexNet model, achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%, PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid cardiologists and primary care physicians in the auscultation process, improving the decision making task and reducing type-I and type-II errors.

  6. Chronic Restraint Stress Induces an Isoform-Specific Regulation on the Neural Cell Adhesion Molecule in the Hippocampus

    Science.gov (United States)

    Touyarot, K.; Sandi, C.

    2002-01-01

    Existing evidence indicates that 21-days exposure of rats to restraint stress induces dendritic atrophy in pyramidal cells of the hippocampus. This phenomenon has been related to altered performance in hippocampal-dependent learning tasks. Prior studies have shown that hippocampal expression of cell adhesion molecules is modified by such stress treatment, with the neural cell adhesion molecule (NCAM) decreasing and L1 increasing, their expression, at both the mRNA and protein levels. Given that NCAM comprises several isoforms, we investigated here whether chronic stress might differentially affect the expression of the three major isoforms (NCAM-120, NCAM-140, NCAM-180) in the hippocampus. In addition, as glucocorticoids have been implicated in the deleterious effects induced by chronic stress, we also evaluated plasma corticosterone levels and the hippocampal expression of the corticosteroid mineralocorticoid receptor (MR) and glucocorticoid receptor (GR). The results showed that the protein concentration of the NCAM-140 isoform decreased in the hippoampus of stressed rats. This effect was isoform-specific, because NCAM-120 and NCAM-180 levels were not significantly modified. In addition, whereas basal levels of plasma corticosterone tended to be increased, MR and GR concentrations were not significantly altered. Although possible changes in NCAM-120, NCAM-180 and corticosteroid receptors at earlier time points of the stress period cannot be ignored; this study suggests that a down-regulation of NCAM-140 might be implicated in the structural alterations consistently shown to be induced in the hippocampus by chronic stress exposure. As NCAM-140 is involved in cell-cell adhesion and neurite outgrowth, these findings suggest that this molecule might be one of the molecular mechanisms involved in the complex interactions among neurodegeneration-related events. PMID:12757368

  7. Probabilistic neural network with homogeneity testing in recognition of discrete patterns set.

    Science.gov (United States)

    Savchenko, A V

    2013-10-01

    The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n-grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%-7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. Copyright © 2013 Elsevier Ltd. All rights reserved.

  8. The neural cell adhesion molecule-derived peptide, FGL, attenuates lipopolysaccharide-induced changes in glia in a CD200-dependent manner

    DEFF Research Database (Denmark)

    Cox, F F; Berezin, V; Bock, E

    2013-01-01

    Fibroblast growth loop (FGL) is a neural cell adhesion molecule (NCAM)-mimetic peptide that mimics the interaction of NCAM with fibroblast growth factor receptor (FGFR). FGL increases neurite outgrowth and promotes neuronal survival in vitro, and it has also been shown to have neuroprotective eff...

  9. Transmembrane neural cell-adhesion molecule (NCAM), but not glycosyl-phosphatidylinositol-anchored NCAM, down-regulates secretion of matrix metalloproteinases

    DEFF Research Database (Denmark)

    Edvardsen, K; Chen, W; Rucklidge, G

    1993-01-01

    proteinases, and proteinase inhibitors all participate in the construction, maintenance, and remodeling of extracellular matrix by cells. The neural cell-adhesion molecule (NCAM)-negative rat glioma cell line BT4Cn secretes substantial amounts of metalloproteinases, as compared with its NCAM-positive mother...

  10. Differential expression of neural cell adhesion molecule and cadherins in pancreatic islets, glucagonomas, and insulinomas

    DEFF Research Database (Denmark)

    Møller, C J; Christgau, S; Williamson, M R

    1992-01-01

    (delta-cells), and pancreatic polypeptide (PP-cells) in a sequential order. The endocrine cells are believed to arise from a stem cell with neuronal traits. The developmental lineage from a common neuron-like progenitor is evidenced by: transient coexpression of more than one cell type-specific hormone......-cadherin in brain. Insulinoma cells express E-cadherin but differ from primary islet cells by expressing a second cadherin molecule, which is similar to N-cadherin. The expression of NCAM and cadherin isoforms in the glucagonoma suggest that this transformed alpha-cell type has converted to an immature phenotype......The endocrine cells of the pancreas develop from the endoderm and yet display several characteristics of a neuronal phenotype. During embryonic life, ductal epithelial cells give rise to first the glugagon-producing cells (alpha-cells) and then cells that express insulin (beta-cells), somatostatin...

  11. Artificial Neural Network for the Prediction of Tyrosine-Based Sorting Signal Recognition by Adaptor Complexes

    Directory of Open Access Journals (Sweden)

    Debarati Mukherjee

    2012-01-01

    Full Text Available Sorting of transmembrane proteins to various intracellular compartments depends on specific signals present within their cytosolic domains. Among these sorting signals, the tyrosine-based motif (YXXØ is one of the best characterized and is recognized by μ-subunits of the four clathrin-associated adaptor complexes (AP-1 to AP-4. Despite their overlap in specificity, each μ-subunit has a distinct sequence preference dependent on the nature of the X-residues. Moreover, combinations of these residues exert cooperative or inhibitory effects towards interaction with the various APs. This complexity makes it impossible to predict a priori, the specificity of a given tyrosine-signal for a particular μ-subunit. Here, we describe the results obtained with a computational approach based on the Artificial Neural Network (ANN paradigm that addresses the issue of tyrosine-signal specificity, enabling the prediction of YXXØ-μ interactions with accuracies over 90%. Therefore, this approach constitutes a powerful tool to help predict mechanisms of intracellular protein sorting.

  12. Pattern recognition in lithology classification: modeling using neural networks, self-organizing maps and genetic algorithms

    Science.gov (United States)

    Sahoo, Sasmita; Jha, Madan K.

    2017-03-01

    Effective characterization of lithology is vital for the conceptualization of complex aquifer systems, which is a prerequisite for the development of reliable groundwater-flow and contaminant-transport models. However, such information is often limited for most groundwater basins. This study explores the usefulness and potential of a hybrid soft-computing framework; a traditional artificial neural network with gradient descent-momentum training (ANN-GDM) and a traditional genetic algorithm (GA) based ANN (ANN-GA) approach were developed and compared with a novel hybrid self-organizing map (SOM) based ANN (SOM-ANN-GA) method for the prediction of lithology at a basin scale. This framework is demonstrated through a case study involving a complex multi-layered aquifer system in India, where well-log sites were clustered on the basis of sand-layer frequencies; within each cluster, subsurface layers were reclassified into four depth classes based on the maximum drilling depth. ANN models for each depth class were developed using each of the three approaches. Of the three, the hybrid SOM-ANN-GA models were able to recognize incomplete geologic pattern more reasonably, followed by ANN-GA and ANN-GDM models. It is concluded that the hybrid soft-computing framework can serve as a promising tool for characterizing lithology in groundwater basins with missing lithologic patterns.

  13. Neural network recognition of nuclear power plant transients. Final report, April 15, 1992--April 15, 1995

    Energy Technology Data Exchange (ETDEWEB)

    Bartlett, E.B.

    1995-05-15

    The objective of this report is to describe results obtained during the second year of funding that will lead to the development of an artificial neural network (A.N.N) fault diagnostic system for the real-time classification of operational transients at nuclear power plants. The ultimate goal of this three-year project is to design, build, and test a prototype diagnostic adviser for use in the control room or technical support center at Duane Arnold Energy Center (DAEC); such a prototype could be integrated into the plant process computer or safety-parameter display system. The adviser could then warn and inform plant operators and engineers of plant component failures in a timely manner. This report describes the work accomplished in the second of three scheduled years for the project. Included herein is a summary of the second year`s results as well as descriptions of each of the major topics undertaken by the researchers. Also included are reprints of the articles written under this funding as well as those that were published during the funded period.

  14. Neural network recognition of nuclear power plant transients. Final report, April 15, 1992--April 15, 1995

    International Nuclear Information System (INIS)

    Bartlett, E.B.

    1995-01-01

    The objective of this report is to describe results obtained during the second year of funding that will lead to the development of an artificial neural network (A.N.N) fault diagnostic system for the real-time classification of operational transients at nuclear power plants. The ultimate goal of this three-year project is to design, build, and test a prototype diagnostic adviser for use in the control room or technical support center at Duane Arnold Energy Center (DAEC); such a prototype could be integrated into the plant process computer or safety-parameter display system. The adviser could then warn and inform plant operators and engineers of plant component failures in a timely manner. This report describes the work accomplished in the second of three scheduled years for the project. Included herein is a summary of the second year's results as well as descriptions of each of the major topics undertaken by the researchers. Also included are reprints of the articles written under this funding as well as those that were published during the funded period

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

    Science.gov (United States)

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

    2018-01-01

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

  16. Pattern recognition based on time-frequency analysis and convolutional neural networks for vibrational events in φ-OTDR

    Science.gov (United States)

    Xu, Chengjin; Guan, Junjun; Bao, Ming; Lu, Jiangang; Ye, Wei

    2018-01-01

    Based on vibration signals detected by a phase-sensitive optical time-domain reflectometer distributed optical fiber sensing system, this paper presents an implement of time-frequency analysis and convolutional neural network (CNN), used to classify different types of vibrational events. First, spectral subtraction and the short-time Fourier transform are used to enhance time-frequency features of vibration signals and transform different types of vibration signals into spectrograms, which are input to the CNN for automatic feature extraction and classification. Finally, by replacing the soft-max layer in the CNN with a multiclass support vector machine, the performance of the classifier is enhanced. Experiments show that after using this method to process 4000 vibration signal samples generated by four different vibration events, namely, digging, walking, vehicles passing, and damaging, the recognition rates of vibration events are over 90%. The experimental results prove that this method can automatically make an effective feature selection and greatly improve the classification accuracy of vibrational events in distributed optical fiber sensing systems.

  17. Quickprop method to speed up learning process of Artificial Neural Network in money's nominal value recognition case

    Science.gov (United States)

    Swastika, Windra

    2017-03-01

    A money's nominal value recognition system has been developed using Artificial Neural Network (ANN). ANN with Back Propagation has one disadvantage. The learning process is very slow (or never reach the target) in the case of large number of iteration, weight and samples. One way to speed up the learning process is using Quickprop method. Quickprop method is based on Newton's method and able to speed up the learning process by assuming that the weight adjustment (E) is a parabolic function. The goal is to minimize the error gradient (E'). In our system, we use 5 types of money's nominal value, i.e. 1,000 IDR, 2,000 IDR, 5,000 IDR, 10,000 IDR and 50,000 IDR. One of the surface of each nominal were scanned and digitally processed. There are 40 patterns to be used as training set in ANN system. The effectiveness of Quickprop method in the ANN system was validated by 2 factors, (1) number of iterations required to reach error below 0.1; and (2) the accuracy to predict nominal values based on the input. Our results shows that the use of Quickprop method is successfully reduce the learning process compared to Back Propagation method. For 40 input patterns, Quickprop method successfully reached error below 0.1 for only 20 iterations, while Back Propagation method required 2000 iterations. The prediction accuracy for both method is higher than 90%.

  18. Recognition of unnatural variation patterns in metal-stamping process using artificial neural network and statistical features

    Science.gov (United States)

    Rahman, Norasulaini Abdul; Masood, Ibrahim; Nasrull Abdol Rahman, Mohd

    2016-11-01

    Unnatural process variation (UPV) is vital in quality problem of a metalstamping process. It is a major contributor to a poor quality product. The sources of UPV usually found from special causes. Recently, there is still debated among researchers in finding an effective technique for on-line monitoring-diagnosis the sources of UPV. Control charts pattern recognition (CCPR) is the most investigated technique. The existing CCPR schemes were mainly developed using raw data-based artificial neural network (ANN) recognizer, whereby the process samples were mainly generated artificially using mathematical equations. This is because the real process samples were commonly confidential or not economically available. In this research, the statistical features - ANN recognizer was utilized as the control chart pattern recognizer, whereby process sample was taken directly from an actual manufacturing process. Based on dynamic data training, the proposed recognizer has resulted in better monitoring-diagnosis performance (Normal = 100%, Unnatural = 100%) compared to the raw data- ANN (Normal = 66.67%, Unnatural = 26.97%).

  19. Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks

    Science.gov (United States)

    Umarov, Ramzan Kh.

    2017-01-01

    Accurate computational identification of promoters remains a challenge as these key DNA regulatory regions have variable structures composed of functional motifs that provide gene-specific initiation of transcription. In this paper we utilize Convolutional Neural Networks (CNN) to analyze sequence characteristics of prokaryotic and eukaryotic promoters and build their predictive models. We trained a similar CNN architecture on promoters of five distant organisms: human, mouse, plant (Arabidopsis), and two bacteria (Escherichia coli and Bacillus subtilis). We found that CNN trained on sigma70 subclass of Escherichia coli promoter gives an excellent classification of promoters and non-promoter sequences (Sn = 0.90, Sp = 0.96, CC = 0.84). The Bacillus subtilis promoters identification CNN model achieves Sn = 0.91, Sp = 0.95, and CC = 0.86. For human, mouse and Arabidopsis promoters we employed CNNs for identification of two well-known promoter classes (TATA and non-TATA promoters). CNN models nicely recognize these complex functional regions. For human promoters Sn/Sp/CC accuracy of prediction reached 0.95/0.98/0,90 on TATA and 0.90/0.98/0.89 for non-TATA promoter sequences, respectively. For Arabidopsis we observed Sn/Sp/CC 0.95/0.97/0.91 (TATA) and 0.94/0.94/0.86 (non-TATA) promoters. Thus, the developed CNN models, implemented in CNNProm program, demonstrated the ability of deep learning approach to grasp complex promoter sequence characteristics and achieve significantly higher accuracy compared to the previously developed promoter prediction programs. We also propose random substitution procedure to discover positionally conserved promoter functional elements. As the suggested approach does not require knowledge of any specific promoter features, it can be easily extended to identify promoters and other complex functional regions in sequences of many other and especially newly sequenced genomes. The CNNProm program is available to run at web server http

  20. Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks

    KAUST Repository

    Umarov, Ramzan

    2017-02-03

    Accurate computational identification of promoters remains a challenge as these key DNA regulatory regions have variable structures composed of functional motifs that provide gene-specific initiation of transcription. In this paper we utilize Convolutional Neural Networks (CNN) to analyze sequence characteristics of prokaryotic and eukaryotic promoters and build their predictive models. We trained a similar CNN architecture on promoters of five distant organisms: human, mouse, plant (Arabidopsis), and two bacteria (Escherichia coli and Bacillus subtilis). We found that CNN trained on sigma70 subclass of Escherichia coli promoter gives an excellent classification of promoters and non-promoter sequences (Sn = 0.90, Sp = 0.96, CC = 0.84). The Bacillus subtilis promoters identification CNN model achieves Sn = 0.91, Sp = 0.95, and CC = 0.86. For human, mouse and Arabidopsis promoters we employed CNNs for identification of two well-known promoter classes (TATA and non-TATA promoters). CNN models nicely recognize these complex functional regions. For human promoters Sn/Sp/CC accuracy of prediction reached 0.95/0.98/0,90 on TATA and 0.90/0.98/0.89 for non-TATA promoter sequences, respectively. For Arabidopsis we observed Sn/Sp/CC 0.95/0.97/0.91 (TATA) and 0.94/0.94/0.86 (non-TATA) promoters. Thus, the developed CNN models, implemented in CNNProm program, demonstrated the ability of deep learning approach to grasp complex promoter sequence characteristics and achieve significantly higher accuracy compared to the previously developed promoter prediction programs. We also propose random substitution procedure to discover positionally conserved promoter functional elements. As the suggested approach does not require knowledge of any specific promoter features, it can be easily extended to identify promoters and other complex functional regions in sequences of many other and especially newly sequenced genomes. The CNNProm program is available to run at web server http://www.softberry.com.

  1. A machine for neural computation of acoustical patterns with application to real time speech recognition

    Science.gov (United States)

    Mueller, P.; Lazzaro, J.

    1986-08-01

    400 analog electronic neurons have been assembled and connected for the analysis and recognition of acoustical patterns, including speech. Input to the net comes from a set of 18 band pass filters (Qmax 300 dB/octave; 180 to 6000 Hz, log scale). The net is organized into two parts, the first performs in real time the decomposition of the input patterns into their primitives of energy, space (frequency) and time relations. The other part decodes the set of primitives. 216 neurons are dedicated to pattern decomposition. The output of the individual filters is rectified and fed to two sets of 18 neurons in an opponent center-surround organization of synaptic connections (``on center'' and (``off center''). These units compute maxima and minima of energy at different frequencies. The next two sets of neutrons compute the temporal boundaries (``on'') and ``off'') and the following two the movement of the energy maxima (formants) up or down the frequency axis. There are in addition ``hyperacuity'' units which expand the frequency resolution to 36, other units tuned to a particular range of duration of the ``on center'' units and others tuned exclusively to very low energy sounds. In order to recognize speech sounds at the phoneme or diphone level, the set of primitives belonging to the phoneme is decoded such that only one neuron or a non-overlapping group of neurons fire when the sound pattern is present at the input. For display and translation into phonetic symbols the output from these neurons is fed into an EPROM decoder and computer which displays in real time a phonetic representation of the speech input.

  2. Neural correlates of own and close-other’s name recognition: ERP evidence

    Directory of Open Access Journals (Sweden)

    Pawel eTacikowski

    2014-04-01

    Full Text Available One’s own name seems to have a special status in the processing of incoming information. In event-related potential (ERP studies this preferential status has mainly been associated with higher P300 to one’s own name than to other names. Some studies showed preferential responses to own name even for earlier ERP components. However, instead of just being self-specific, these effects could be related to the processing of any highly relevant and/or frequently encountered stimuli. If this is the case: (1 processing of other highly relevant and highly familiar names (e.g., names of friends, partners, siblings, etc. should be associated with similar ERP responses as processing of one's own name; and (2 processing of own and close others' names should result in larger amplitudes of early and late ERP components than processing of less relevant and less familiar names (e.g., names of famous people, names of strangers, etc.. To test this hypothesis we measured and analyzed ERPs from 62 scalp electrodes in 22 subjects. Subjects performed a speeded two-choice recognition task - familiar vs. unfamiliar - with one’s own name being treated as one of the familiar names. All stimuli were presented visually. We found that amplitudes of P200, N250 and P300 did not differ between one’s own and close-other’s names. Crucially, they were significantly larger to own and close-other’s names than to other names (unknown and famous for P300 and unknown for P200 and N250. Our findings suggest that preferential processing of one’s own name is due to its personal-relevance and/or familiarity factors. This pattern of results speaks for a common preference in processing of different kinds of socially relevant stimuli.

  3. IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion

    Directory of Open Access Journals (Sweden)

    Omid Dehzangi

    2017-11-01

    Full Text Available The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF expansion of human gait cycles in order to capture joint 2 dimensional (2D spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level and late (decision score level multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class

  4. IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion.

    Science.gov (United States)

    Dehzangi, Omid; Taherisadr, Mojtaba; ChangalVala, Raghvendar

    2017-11-27

    The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF) expansion of human gait cycles in order to capture joint 2 dimensional (2D) spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN) learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level) and late (decision score level) multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF) method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class identification task

  5. Low-level lead exposure attenuates the expression of three major isoforms of neural cell adhesion molecule.

    Science.gov (United States)

    Hu, Qiansheng; Fu, Hongjun; Song, Hong; Ren, Tieling; Li, Liquan; Ye, Liuqing; Liu, Tao; Dong, Shengzhang

    2011-03-01

    Toxic lead (Pb) exposure poses serious risks to human health, especially to children at developmental stages, even at low exposure levels. Neural cell adhesion molecule (NCAM) is considered to be a potential early target in the neurotoxicity of Pb due to its role in cell adhesion, neuronal migration, synaptic plasticity, and learning and memory. However, the effect of low-level Pb exposure on the specific expression of NCAM isoforms has not been reported. In the present study, we found that Pb could concentration-dependently (1-100 nM) inhibit the expression of three major NCAM isoforms (NCAM-180, -140, and -120) in primary cultured hippocampal neurons. Furthermore, it was verified that levels of all three major isoforms of NCAM were reduced by Pb exposure in human embryonic kidney (HEK)-293 cells transiently transfected with NCAM-120, -140, or -180 isoform cDNA constructs. In addition, low-level Pb exposure delayed the neurite outgrowth and reduced the survival rate of cultured hippocampal neurons at different time-points. Together, our results demonstrate that developmental low-level Pb exposure can attenuate the expression of all three major NCAM isoforms, which may contribute to the observed Pb-mediated neurotoxicity. Copyright © 2010 Elsevier Inc. All rights reserved.

  6. Expression of neural cell adhesion molecule in human liver development and in congenital and acquired liver diseases.

    Science.gov (United States)

    Libbrecht, L; Cassiman, D; Desmet, V; Roskams, T

    2001-09-01

    In the liver, neural cell adhesion molecule (NCAM) is a marker of immature cells committed to the biliary lineage and is expressed by reactive bile ductules in human liver diseases. We investigated the possible role of NCAM in the development of intrahepatic bile ducts and aimed at determining whether immature biliary cells can contribute to the repair of damaged bile ducts in chronic liver diseases. Therefore, we performed immunohistochemistry for NCAM and bile duct cell markers cytokeratin 7 and cytokeratin 19 on frozen sections of 85 liver specimens taken from 14 fetuses, 10 donor livers, 18 patients with congenital liver diseases characterized by ductal plate malformations (DPMs), and 43 cirrhotic explant livers. Duplicated ductal plates and incorporating bile ducts during development showed a patchy immunoreactivity for NCAM, while DPMs were continuously positive for NCAM. Bile ducts showing complete or patchy immunoreactivity for NCAM were found in cirrhotic livers, with higher frequency in biliary than in posthepatitic cirrhosis. Our results suggest that NCAM may have a function in the development of the intrahepatic bile ducts and that NCAM-positive immature biliary cells can contribute to the repair of damaged bile ducts in chronic liver diseases.

  7. Regulation of neural cell adhesion molecule polysialylation: evidence for nontranscriptional control and sensitivity to an intracellular pool of calcium.

    Science.gov (United States)

    Brusés, J L; Rutishauser, U

    1998-03-09

    The up- and downregulation of polysialic acid-neural cell adhesion molecule (PSA-NCAM) expression on motorneurons during development is associated respectively with target innervation and synaptogenesis, and is regulated at the level of PSA enzymatic biosynthesis involving specific polysialyltransferase activity. The purpose of this study has been to describe the cellular mechanisms by which that regulation might occur. It has been found that developmental regulation of PSA synthesis by ciliary ganglion motorneurons is not reflected in the levels of polysialyltransferase-1 (PST) or sialyltransferase-X (STX) mRNA. On the other hand, PSA synthesis in both the ciliary ganglion and the developing tectum appears to be coupled to the concentration of calcium in intracellular compartments. This study documents a calcium dependence of polysialyltransferase activity in a cell-free assay over the range of 0.1-1 mM, and a rapid sensitivity of new PSA synthesis, as measured in a pulse-chase analysis of tissue explants, to calcium ionophore perturbation of intracellular calcium levels. Moreover, the relevant calcium pool appears to be within a specific intracellular compartment that is sensitive to thapsigargin and does not directly reflect the level of cytosolic calcium. Perturbation of other major second messenger systems, such as cAMP and protein kinase-dependent pathways, did not affect polysialylation in the pulse chase analysis. These results suggest that the shuttling of calcium to different pools within the cell can result in the rapid regulation of PSA synthesis in developing tissues.

  8. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

    Directory of Open Access Journals (Sweden)

    Dat Tien Nguyen

    2017-03-01

    Full Text Available Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT, speed-up robust feature (SURF, local binary patterns (LBP, histogram of oriented gradients (HOG, and weighted HOG. Recently, the convolutional neural network (CNN method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

  9. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

    Science.gov (United States)

    Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

    2017-01-01

    Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

  10. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

    Science.gov (United States)

    Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

    2017-03-20

    Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

  11. Association of the pattern recognition molecule H-ficolin with incident microalbuminuria in an inception cohort of newly diagnosed type 1 diabetic patients

    DEFF Research Database (Denmark)

    Østergaard, Jakob A; Thiel, Steffen; Hovind, Peter

    2014-01-01

    AIMS/HYPOTHESIS: Increasing evidence links complement activation through the lectin pathway to diabetic nephropathy. Adverse complement recognition of proteins modified by glycation has been suggested to trigger complement auto-attack in diabetes. H-ficolin (also known as ficolin-3) is a pattern...... recognition molecule that activates the complement cascade on binding to glycated surfaces, but the role of H-ficolin in diabetic nephropathy is unknown. We aimed to investigate the association between circulating H-ficolin levels and the incidence of microalbuminuria in type 1 diabetes. METHODS: We measured...... baseline H-ficolin levels and tracked the development of persistent micro- and macroalbuminuria in a prospective 18 year observational follow-up study of an inception cohort of 270 patients with newly diagnosed type 1 diabetes. RESULTS: Patients were followed for a median of 18 years (range 1-22 years...

  12. A Neural Model Combining Attentional Orienting to Object Recognition: Preliminary Explorations on the Interplay Between Where and What

    National Research Council Canada - National Science Library

    Miau, Florence

    2001-01-01

    ... ("where") pathway and an object recognition ("what") pathway. The fast visual attention front-end rapidly selects the few most conspicuous image locations, and the slower object recognition back-end identifies objects at the selected locations...

  13. Point-of-Care Determination of Acetaminophen Levels with Multi-Hydrogen Bond Manipulated Single-Molecule Recognition (eMuHSiR).

    Science.gov (United States)

    Zhang, Yan; Huang, Zhongyuan; Wang, Letao; Wang, Chunming; Zhang, Changde; Wiese, Tomas; Wang, Guangdi; Riley, Kevin; Wang, Zhe

    2018-04-03

    This work aims to face the challenge of monitoring small molecule drugs accurately and rapidly for point-of-care (POC) diagnosis in current clinical settings. Overdose of acetaminophen (AP), a commonly used over the counter (OTC) analgesic drug, has been determined to be a major cause of acute liver failure in the US and the UK. However, there is no rapid and accurate detection method available for this drug in the emergency room. The present study examined an AP sensing strategy that relies on a previously unexplored strong interaction between AP and the arginine (Arg) molecule. It was found that as many as 4 hydrogen bonds can be formed between one Arg molecule and one AP molecule. By taking advantages of this structural selectivity and high tenability of hydrogen bonds, Arg, immobilized on a graphene surface via electrostatic interactions, was utilized to structurally capture AP. Interestingly, bonded AP still remained the perfect electrochemical activities. The extent of Arg-AP bonds was quantified using a newly designed electrochemical (EC) sensor. To verify the feasibility of this novel assay, based on multihydrogen bond manipulated single-molecule recognition (eMuHSiR), both pharmaceutical and serum sample were examined. In commercial tablet measurement, no significant difference was seen between the results of eMuHSiR and other standard methods. For measuring AP concentration in the mice blood, the substances in serum, such as sugars and fats, would not bring any interference to the eMuHSiR in a wide concentration range. This eMuHSiR method opens the way for future development of small molecule detection for the POC testing.

  14. Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells.

    Directory of Open Access Journals (Sweden)

    Muthu Subash Kavitha

    Full Text Available Pluripotent stem cells can potentially be used in clinical applications as a model for studying disease progress. This tracking of disease-causing events in cells requires constant assessment of the quality of stem cells. Existing approaches are inadequate for robust and automated differentiation of stem cell colonies. In this study, we developed a new model of vector-based convolutional neural network (V-CNN with respect to extracted features of the induced pluripotent stem cell (iPSC colony for distinguishing colony characteristics. A transfer function from the feature vectors to the virtual image was generated at the front of the CNN in order for classification of feature vectors of healthy and unhealthy colonies. The robustness of the proposed V-CNN model in distinguishing colonies was compared with that of the competitive support vector machine (SVM classifier based on morphological, textural, and combined features. Additionally, five-fold cross-validation was used to investigate the performance of the V-CNN model. The precision, recall, and F-measure values of the V-CNN model were comparatively higher than those of the SVM classifier, with a range of 87-93%, indicating fewer false positives and false negative rates. Furthermore, for determining the quality of colonies, the V-CNN model showed higher accuracy values based on morphological (95.5%, textural (91.0%, and combined (93.2% features than those estimated with the SVM classifier (86.7, 83.3, and 83.4%, respectively. Similarly, the accuracy of the feature sets using five-fold cross-validation was above 90% for the V-CNN model, whereas that yielded by the SVM model was in the range of 75-77%. We thus concluded that the proposed V-CNN model outperforms the conventional SVM classifier, which strongly suggests that it as a reliable framework for robust colony classification of iPSCs. It can also serve as a cost-effective quality recognition tool during culture and other experimental

  15. Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

    International Nuclear Information System (INIS)

    Cen Haiyan; Bao Yidan; He Yong

    2006-01-01

    Visible and near-infrared reflectance (visible-NIR) spectroscopy is applied to discriminate different varieties of bayberry juices. The discrimination of visible-NIR spectra from samples is a matter of pattern recognition. By partial least squares (PLS), the spectrum is reduced to certain factors, which are then taken as the input of the backpropagation neural network (BPNN). Through training and prediction, three different varieties of bayberry juice are classified based on the output of the BPNN. In addition, a mathematical model is built and the algorithm is optimized. With proper parameters in the training set,100% accuracy is obtained by the BPNN. Thus it is concluded that the PLS analysis combined with the BPNN is an alternative for pattern recognition based on visible and NIR spectroscopy

  16. Can color changes alter the neural correlates of recognition memory? Manipulation of processing affects an electrophysiological indicator of conceptual implicit memory.

    Science.gov (United States)

    Cui, Xiaoyu; Gao, Chuanji; Zhou, Jianshe; Guo, Chunyan

    2016-09-28

    It has been widely shown that recognition memory includes two distinct retrieval processes: familiarity and recollection. Many studies have shown that recognition memory can be facilitated when there is a perceptual match between the studied and the tested items. Most event-related potential studies have explored the perceptual match effect on familiarity on the basis of the hypothesis that the specific event-related potential component associated with familiarity is the FN400 (300-500 ms mid-frontal effect). However, it is currently unclear whether the FN400 indexes familiarity or conceptual implicit memory. In addition, on the basis of the findings of a previous study, the so-called perceptual manipulations in previous studies may also involve some conceptual alterations. Therefore, we sought to determine the influence of perceptual manipulation by color changes on recognition memory when the perceptual or the conceptual processes were emphasized. Specifically, different instructions (perceptually or conceptually oriented) were provided to the participants. The results showed that color changes may significantly affect overall recognition memory behaviorally and that congruent items were recognized with a higher accuracy rate than incongruent items in both tasks, but no corresponding neural changes were found. Despite the evident familiarity shown in the two tasks (the behavioral performance of recognition memory was much higher than at the chance level), the FN400 effect was found in conceptually oriented tasks, but not perceptually oriented tasks. It is thus highly interesting that the FN400 effect was not induced, although color manipulation of recognition memory was behaviorally shown, as seen in previous studies. Our findings of the FN400 effect for the conceptual but not perceptual condition support the explanation that the FN400 effect indexes conceptual implicit memory.

  17. 24-GOPS 4.5- mm² digital cellular neural network for rapid visual attention in an object-recognition SoC.

    Science.gov (United States)

    Lee, Seungjin; Kim, Minsu; Kim, Kwanho; Kim, Joo-Young; Yoo, Hoi-Jun

    2011-01-01

    This paper presents the Visual Attention Engine (VAE), which is a digital cellular neural network (CNN) that executes the VA algorithm to speed up object-recognition. The proposed time-multiplexed processing element (TMPE) CNN topology achieves high performance and small area by integrating 4800 (80 × 60) cells and 120 PEs. Pipelined operation of the PEs and single-cycle global shift capability of the cells result in a high PE utilization ratio of 93%. The cells are implemented by 6T static random access memory-based register files and dynamic shift registers to enable a small area of 4.5 mm(2). The bus connections between PEs and cells are optimized to minimize power consumption. The VAE is integrated within an object-recognition system-on-chip (SoC) fabricated in the 0.13- μm complementary metal-oxide-semiconductor process. It achieves 24 GOPS peak performance and 22 GOPS sustained performance at 200 MHz enabling one CNN iteration on an 80 × 60 pixel image to be completed in just 4.3 μs. With VA enabled using the VAE, the workload of the object-recognition SoC is significantly reduced, resulting in 83% higher frame rate while consuming 45% less energy per frame without degradation of recognition accuracy.

  18. Recognition of the Major Histocompatibility Complex (MHC) Class Ib Molecule H2-Q10 by the Natural Killer Cell Receptor Ly49C.

    Science.gov (United States)

    Sullivan, Lucy C; Berry, Richard; Sosnin, Natasha; Widjaja, Jacqueline M L; Deuss, Felix A; Balaji, Gautham R; LaGruta, Nicole L; Mirams, Michiko; Trapani, Joseph A; Rossjohn, Jamie; Brooks, Andrew G; Andrews, Daniel M

    2016-09-02

    Murine natural killer (NK) cells are regulated by the interaction of Ly49 receptors with major histocompatibility complex class I molecules (MHC-I). Although the ligands for inhibitory Ly49 were considered to be restricted to classical MHC (MHC-Ia), we have shown that the non-classical MHC molecule (MHC-Ib) H2-M3 was a ligand for the inhibitory Ly49A. Here we establish that another MHC-Ib, H2-Q10, is a bona fide ligand for the inhibitory Ly49C receptor. H2-Q10 bound to Ly49C with a marginally lower affinity (∼5 μm) than that observed between Ly49C and MHC-Ia (H-2K(b)/H-2D(d), both ∼1 μm), and this recognition could be prevented by cis interactions with H-2K in situ To understand the molecular details underpinning Ly49·MHC-Ib recognition, we determined the crystal structures of H2-Q10 and Ly49C bound H2-Q10. Unliganded H2-Q10 adopted a classical MHC-I fold and possessed a peptide-binding groove that exhibited features similar to those found in MHC-Ia, explaining the diverse peptide binding repertoire of H2-Q10. Ly49C bound to H2-Q10 underneath the peptide binding platform to a region that encompassed residues from the α1, α2, and α3 domains, as well as the associated β2-microglobulin subunit. This docking mode was conserved with that previously observed for Ly49C·H-2K(b) Indeed, structure-guided mutation of Ly49C indicated that Ly49C·H2-Q10 and Ly49C·H-2K(b) possess similar energetic footprints focused around residues located within the Ly49C β4-stand and L5 loop, which contact the underside of the peptide-binding platform floor. Our data provide a structural basis for Ly49·MHC-Ib recognition and demonstrate that MHC-Ib represent an extended family of ligands for Ly49 molecules. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  19. Insulin and IGF1 modulate turnover of polysialylated neural cell adhesion molecule (PSA-NCAM) in a process involving specific extracellular matrix components.

    Science.gov (United States)

    Monzo, Hector J; Park, Thomas I H; Dieriks, Birger V; Jansson, Deidre; Faull, Richard L M; Dragunow, Mike; Curtis, Maurice A

    2013-09-01

    Cellular interactions mediated by the neural cell adhesion molecule (NCAM) are critical in cell migration, differentiation and plasticity. Switching of the NCAM-interaction mode, from adhesion to signalling, is determined by NCAM carrying a particular post-translational modification, polysialic acid (PSA). Regulation of cell-surface PSA-NCAM is traditionally viewed as a direct consequence of polysialyltransferase activity. Taking advantage of the polysialyltransferase Ca²⁺-dependent activity, we demonstrate in TE671 cells that downregulation of PSA-NCAM synthesis constitutes a necessary but not sufficient condition to reduce cell-surface PSA-NCAM; instead, PSA-NCAM turnover required internalization of the molecule into the cytosol. PSA-NCAM internalization was specifically triggered by collagen in the extracellular matrix (ECM) and prevented by insulin-like growth factor (IGF1) and insulin. Our results pose a novel role for IGF1 and insulin in controlling cell migration through modulation of PSA-NCAM turnover at the cell surface. Neural cell adhesion molecules (NCAMs) are critically involved in cell differentiation and migration. Polysialylation (PSA)/desialylation of NCAMs switches their functional interaction mode and, in turn, migration and differentiation. We have found that the desialylation process of PSA-NCAM occurs via endocytosis, induced by collagen-IV and blocked by insulin-like growth factor (IGF1) and insulin, suggesting a novel association between PSA-NCAM, IGF1/insulin and brain/tumour plasticity. © 2013 International Society for Neurochemistry.

  20. Recognition of HLA class II molecules by antipeptide antibodies elicited by synthetic peptides selected from regions of HLA-DP antigens.

    Science.gov (United States)

    Chersi, A; Houghten, R A; Morganti, M C; Muratti, E

    1987-01-01

    Repeated immunizations of rabbits with chemically synthesized peptides from selected regions of HLA-DP histocompatibility antigens resulted in the production of specific antibodies that were then isolated from the immune sera by chromatography on Sepharose-peptide immunoadsorbents. The purified antibodies, when tested with an enzyme-linked immunosorbent assay, specifically bound to the inciting fragments; moreover, two of them recognized glycoproteins extracted by nonionic detergents from human chronic lymphocytic leukemia cells, as revealed by binding assays. The results suggest that amino acid stretches 51-61 of the alpha chain and 80-90 of the beta chain of HLA-DP histocompatibility antigens are likely exposed on the surface of the protein molecule. The specific recognition of DP regions is strongly suggested by the difference in the binding of those antibodies to soluble membrane proteins, as compared to the binding of monomorphic anti-Class II monoclonal antibodies to the same antigens.

  1. Neural cell adhesion molecule-180-mediated homophilic binding induces epidermal growth factor receptor (EGFR) down-regulation and uncouples the inhibitory function of EGFR in neurite outgrowth

    DEFF Research Database (Denmark)

    Povlsen, Gro Klitgaard; Berezin, Vladimir; Bock, Elisabeth

    2008-01-01

    The neural cell adhesion molecule (NCAM) plays important roles in neuronal development, regeneration, and synaptic plasticity. NCAM homophilic binding mediates cell adhesion and induces intracellular signals, in which the fibroblast growth factor receptor plays a prominent role. Recent studies...... on axon guidance in Drosophila suggest that NCAM also regulates the epidermal growth factor receptor (EGFR) (Molecular and Cellular Neuroscience, 28, 2005, 141). A possible interaction between NCAM and EGFR in mammalian cells has not been investigated. The present study demonstrates for the first time...

  2. Rhenium-188-labeled anti-neural cell adhesion molecule antibodies with 2-iminothiolane modification for targeting small-cell lung cancer.

    Science.gov (United States)

    Hosono, M N; Hosono, M; Mishra, A K; Faivre-Chauvet, A; Gautherot, E; Barbet, J; Knapp, F F; Chatal, J F

    2000-06-01

    We have evaluated the potential of 188Re-labeled monoclonal antibodies (MAbs) modified with 2-iminothiolane (2IT) for targeting small-cell lung cancer (SCLC). Radiolabeled MAbs NK1NBL1 and C218 recognizing neural cell adhesion molecule were injected i.v. into athymic mice inoculated with human SCLC tumors, and the biodistribution was examined. NK1NBL1 localized in the tumors better than C218. 188Re-labeled MAbs cleared from the blood faster than 125I-labeled counterparts, resulting in higher tumor-to-blood ratios. In conclusion, the 188Re-labeled MAbs are attractive candidates for imaging and therapy of SCLC.

  3. Neural Networks: Implementations and Applications

    NARCIS (Netherlands)

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  4. A Single Amino Acid Difference within the α-2 Domain of Two Naturally Occurring Equine MHC Class I Molecules Alters the Recognition of Gag and Rev Epitopes by Equine Infectious Anemia Virus-Specific CTL1

    Science.gov (United States)

    Mealey, Robert H.; Lee, Jae-Hyung; Leib, Steven R.; Littke, Matt H.; McGuire, Travis C.

    2012-01-01

    Although CTL are critical for control of lentiviruses, including equine infectious anemia virus, relatively little is known regarding the MHC class I molecules that present important epitopes to equine infectious anemia virus-specific CTL. The equine class I molecule 7-6 is associated with the equine leukocyte Ag (ELA)-A1 haplotype and presents the Env-RW12 and Gag-GW12 CTL epitopes. Some ELA-A1 target cells present both epitopes, whereas others are not recognized by Gag-GW12-specific CTL, suggesting that the ELA-A1 haplotype comprises functionally distinct alleles. The Rev-QW11 CTL epitope is also ELA-A1-restricted, but the molecule that presents Rev-QW11 is unknown. To determine whether functionally distinct class I molecules present ELA-A1-restricted CTL epitopes, we sequenced and expressed MHC class I genes from three ELA-A1 horses. Two horses had the 7-6 allele, which when expressed, presented Env-RW12, Gag-GW12, and Rev-QW11 to CTL. The other horse had a distinct allele, designated 141, encoding a molecule that differed from 7-6 by a single amino acid within the α-2 domain. This substitution did not affect recognition of Env-RW12, but resulted in more efficient recognition of Rev-QW11. Significantly, CTL recognition of Gag-GW12 was abrogated, despite Gag-GW12 binding to 141. Molecular modeling suggested that conformational changes in the 141/Gag-GW12 complex led to a loss of TCR recognition. These results confirmed that the ELA-A1 haplotype is comprised of functionally distinct alleles, and demonstrated for the first time that naturally occurring MHC class I molecules that vary by only a single amino acid can result in significantly different patterns of epitope recognition by lentivirus-specific CTL. PMID:17082657

  5. In the face of threat: neural and endocrine correlates of impaired facial emotion recognition in cocaine dependence.

    Science.gov (United States)

    Ersche, K D; Hagan, C C; Smith, D G; Jones, P S; Calder, A J; Williams, G B

    2015-05-26

    The ability to recognize facial expressions of emotion in others is a cornerstone of human interaction. Selective impairments in the recognition of facial expressions of fear have frequently been reported in chronic cocaine users, but the nature of these impairments remains poorly understood. We used the multivariate method of partial least squares and structural magnetic resonance imaging to identify gray matter brain networks that underlie facial affect processing in both cocaine-dependent (n = 29) and healthy male volunteers (n = 29). We hypothesized that disruptions in neuroendocrine function in cocaine-dependent individuals would explain their impairments in fear recognition by modulating the relationship with the underlying gray matter networks. We found that cocaine-dependent individuals not only exhibited significant impairments in the recognition of fear, but also for facial expressions of anger. Although recognition accuracy of threatening expressions co-varied in all participants with distinctive gray matter networks implicated in fear and anger processing, in cocaine users it was less well predicted by these networks than in controls. The weaker brain-behavior relationships for threat processing were also mediated by distinctly different factors. Fear recognition impairments were influenced by variations in intelligence levels, whereas anger recognition impairments were associated with comorbid opiate dependence and related reduction in testosterone levels. We also observed an inverse relationship between testosterone levels and the duration of crack and opiate use. Our data provide novel insight into the neurobiological basis of abnormal threat processing in cocaine dependence, which may shed light on new opportunities facilitating the psychosocial integration of these patients.

  6. The Neural Substrates of Recognition Memory for Verbal Information: Spanning the Divide between Short- and Long-Term Memory

    Science.gov (United States)

    Buchsbaum, Bradley R.; Padmanabhan, Aarthi; Berman, Karen Faith

    2011-01-01

    One of the classic categorical divisions in the history of memory research is that between short-term and long-term memory. Indeed, because memory for the immediate past (a few seconds) and memory for the relatively more remote past (several seconds and beyond) are assumed to rely on distinct neural systems, more often than not, memory research…

  7. A Study in Speech Recognition Using a Kohonen Neural Network Dynamic Programming and Multi-Feature Fusion

    Science.gov (United States)

    1989-12-01

    parallel fashion. Biological neural networks appear to process information in a manner that is different from digital computers. At the cell level, each...of data values. It is possible that altering the position of the individual vectors in the hyperspace could disrupt the delicate interrelationship

  8. Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks

    Science.gov (United States)

    Nikelshpur, Dmitry O.

    2014-01-01

    Similar to mammalian brains, Artificial Neural Networks (ANN) are universal approximators, capable of yielding near-optimal solutions to a wide assortment of problems. ANNs are used in many fields including medicine, internet security, engineering, retail, robotics, warfare, intelligence control, and finance. "ANNs have a tendency to get…

  9. Complete abolition of reading and writing ability with a third ventricle colloid cyst: implications for surgical intervention and proposed neural substrates of visual recognition and visual imaging ability.

    Science.gov (United States)

    Barker, Lynne Ann; Morton, Nicholas; Romanowski, Charles A J; Gosden, Kevin

    2013-10-24

    We report a rare case of a patient unable to read (alexic) and write (agraphic) after a mild head injury. He had preserved speech and comprehension, could spell aloud, identify words spelt aloud and copy letter features. He was unable to visualise letters but showed no problems with digits. Neuropsychological testing revealed general visual memory, processing speed and imaging deficits. Imaging data revealed an 8 mm colloid cyst of the third ventricle that splayed the fornix. Little is known about functions mediated by fornical connectivity, but this region is thought to contribute to memory recall. Other regions thought to mediate letter recognition and letter imagery, visual word form area and visual pathways were intact. We remediated reading and writing by multimodal letter retraining. The study raises issues about the neural substrates of reading, role of fornical tracts to selective memory in the absence of other pathology, and effective remediation strategies for selective functional deficits.

  10. Dual small-molecule targeting of SMAD signaling stimulates human induced pluripotent stem cells toward neural lineages.

    Directory of Open Access Journals (Sweden)

    Methichit Wattanapanitch

    Full Text Available Incurable neurological disorders such as Parkinson's disease (PD, Huntington's disease (HD, and Alzheimer's disease (AD are very common and can be life-threatening because of their progressive disease symptoms with limited treatment options. To provide an alternative renewable cell source for cell-based transplantation and as study models for neurological diseases, we generated induced pluripotent stem cells (iPSCs from human dermal fibroblasts (HDFs and then differentiated them into neural progenitor cells (NPCs and mature neurons by dual SMAD signaling inhibitors. Reprogramming efficiency was improved by supplementing the histone deacethylase inhibitor, valproic acid (VPA, and inhibitor of p160-Rho associated coiled-coil kinase (ROCK, Y-27632, after retroviral transduction. We obtained a number of iPS colonies that shared similar characteristics with human embryonic stem cells in terms of their morphology, cell surface antigens, pluripotency-associated gene and protein expressions as well as their in vitro and in vivo differentiation potentials. After treatment with Noggin and SB431542, inhibitors of the SMAD signaling pathway, HDF-iPSCs demonstrated rapid and efficient differentiation into neural lineages. Six days after neural induction, neuroepithelial cells (NEPCs were observed in the adherent monolayer culture, which had the ability to differentiate further into NPCs and neurons, as characterized by their morphology and the expression of neuron-specific transcripts and proteins. We propose that our study may be applied to generate neurological disease patient-specific iPSCs allowing better understanding of disease pathogenesis and drug sensitivity assays.

  11. Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

    Science.gov (United States)

    Zhang, Jianhua; Li, Sunan; Wang, Rubin

    2017-01-01

    In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

  12. Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Jianhua Zhang

    2017-05-01

    Full Text Available In this paper, we deal with the Mental Workload (MWL classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers and parameter optimization algorithms for the Convolutional Neural Networks (CNN. The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

  13. Altered Expression Profile of IgLON Family of Neural Cell Adhesion Molecules in the Dorsolateral Prefrontal Cortex of Schizophrenic Patients

    Directory of Open Access Journals (Sweden)

    Karina Karis

    2018-01-01

    Full Text Available Neural adhesion proteins are crucial in the development and maintenance of functional neural connectivity. Growing evidence suggests that the IgLON family of neural adhesion molecules LSAMP, NTM, NEGR1, and OPCML are important candidates in forming the susceptibility to schizophrenia (SCZ. IgLON proteins have been shown to be involved in neurite outgrowth, synaptic plasticity and neuronal connectivity, all of which have been shown to be altered in the brains of patients with the diagnosis of schizophrenia. Here we optimized custom 5′-isoform-specific TaqMan gene-expression analysis for the transcripts of human IgLON genes to study the expression of IgLONs in the dorsolateral prefrontal cortex (DLPFC of schizophrenic patients (n = 36 and control subjects (n = 36. Uniform 5′-region and a single promoter was confirmed for the human NEGR1 gene by in silico analysis. IgLON5, a recently described family member, was also included in the study. We detected significantly elevated levels of the NEGR1 transcript (1.33-fold increase and the NTM 1b isoform transcript (1.47-fold increase in the DLPFC of schizophrenia patients compared to healthy controls. Consequent protein analysis performed in male subjects confirmed the increase in NEGR1 protein content both in patients with the paranoid subtype and in patients with other subtypes. In-group analysis of patients revealed that lower expression of certain IgLON transcripts, mostly LSAMP 1a and 1b, could be related with concurrent depressive endophenotype in schizophrenic patients. Additionally, our study cohort provides further evidence that cannabis use may be a relevant risk factor associated with suicidal behaviors in psychotic patients. In conclusion, we provide clinical evidence of increased expression levels of particular IgLON family members in the DLPFC of schizophrenic patients. We propose that alterations in the expression profile of IgLON neural adhesion molecules are associated with brain

  14. Minimum Constructive Back Propagation Neural Network Based on Fuzzy Logic for Pattern Recognition of Electronic Nose System

    Directory of Open Access Journals (Sweden)

    Radi Radi

    2011-08-01

    Full Text Available Constructive Back Propagation Neural Network (CBPNN is a kind of back propagation neural network trained with constructive algorithm. Training of CBPNN is mainly conducted by developing the network’s architecture which commonly done by adding a number of new neuron units on learning process. Training of the network usually implements fixed method to develop its structure gradually by adding new units constantly. Although this method is simple and able to create an adaptive network for data pattern complexity, but it is wasteful and inefficient for computing. New unit addition affects directly to the computational load of training, speed of convergence, and structure of the final neural network. While increases training load significantly, excessive addition of units also tends to generate a large size of final network. Moreover, addition pattern with small unit number tends to drop off the adaptability of the network and extends time of training. Therefore, there is important to design an adaptive structure development pattern for CBPNN in order to minimize computing load of training. This study proposes Fuzzy Logic (FL algorithm to manage and develop structure of CBPNN. FL method was implemented on two models of CBPNN, i.e. designed with one and two hidden layers, used to recognize aroma patterns on an electronic nose system. The results showed that this method is effective to be applied due to its capability to minimize time of training, to reduce load of computational learning, and generate small size of network.

  15. Haptic recognition memory following short-term visual deprivation: Behavioral and neural correlates from ERPs and alpha band oscillations.

    Science.gov (United States)

    Santaniello, Gerardo; Sebastián, Manuel; Carretié, Luis; Fernández-Folgueiras, Uxia; Hinojosa, José Antonio

    2018-03-01

    In the current study, we investigated the effects of short-term visual deprivation (2 h) on a haptic recognition memory task with familiar objects. Behavioral data, as well as event-related potentials (ERPs) and induced event-related oscillations (EROs) were analyzed. At the behavioral level, deprived participants showed speeded reaction times to new stimuli. Analyses of ERPs indicated that starting from 1000 ms the recognition of old objects elicited enhanced positive amplitudes only for the visually deprived group. Visual deprivation also influenced EROs. In this sense, we observed reduced power in the lower-1 alpha band for the processing of new compared to old stimuli between 500 and 750 ms. Overall, our data showed improved haptic recognition memory after a short period of visual deprivation. These effects were thought to reflect a compensatory mechanism that might have developed as an adaptive strategy for dealing with the environment when visual information is not available. Copyright © 2018 Elsevier B.V. All rights reserved.

  16. Recognition tunneling

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  17. Recognition tunneling

    Energy Technology Data Exchange (ETDEWEB)

    Lindsay, Stuart; He Jin; Zhang Peiming; Chang Shuai; Huang Shuo [Biodesign Institute, Arizona State University, Tempe, AZ 85287 (United States); Sankey, Otto [Department of Physics, Arizona State University, Tempe, AZ 85287 (United States); Hapala, Prokop; Jelinek, Pavel [Institute of Physics, Academy of Sciences of the Czech Republic, Cukrovarnicka 10, 1862 53, Prague (Czech Republic)

    2010-07-02

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

  18. Dennexin peptides modeled after the homophilic binding sites of the neural cell adhesion molecule (NCAM) promote neuronal survival, modify cell adhesion and impair spatial learning

    DEFF Research Database (Denmark)

    Køhler, Lene B; Christensen, Claus; Rossetti, Clara

    2010-01-01

    Neural cell adhesion molecule (NCAM)-mediated cell adhesion results in activation of intracellular signaling cascades that lead to cellular responses such as neurite outgrowth, neuronal survival, and modulation of synaptic activity associated with cognitive processes. The crystal structure...... of the immunoglobulin (Ig) 1-2-3 fragment of the NCAM ectodomain has revealed novel mechanisms for NCAM homophilic adhesion. The present study addressed the biological significance of the so called dense zipper formation of NCAM. Two peptides, termed dennexinA and dennexinB, were modeled after the contact interfaces...... between Ig1 and Ig3 and between Ig2 and Ig2, respectively, observed in the crystal structure. Although the two dennexin peptides differed in amino acid sequence, they both modulated cell adhesion, reflected by inhibition of NCAM-mediated neurite outgrowth. Both dennexins also promoted neuronal survival...

  19. Quantitative Structure-Activity Relationships of Noncompetitive Antagonists of the NMDA Receptor: A Study of a Series of MK801 Derivative Molecules Using Statistical Methods and Neural Network

    Directory of Open Access Journals (Sweden)

    T. Lakhlifi

    2003-04-01

    Full Text Available Abstract: From a series of 50 MK801 derivative molecules, a selected set of 44 compounds was submitted to a principal components analysis (PCA, a multiple regression analysis (MRA, and a neural network (NN. This study shows that the compounds' activity correlates reasonably well with the selected descriptors encoding the chemical structures. The correlation coefficients calculated by MRA and there after by NN, r = 0.986 and r = 0.974 respectively, are fairly good to evaluate a quantitative model, and to predict activity for MK801 derivatives. To test the performance of this model, the activities of the remained set of 6 compounds are deduced from the proposed quantitative model, by NN. This study proved that the predictive power of this model is relevant.

  20. Rhenium-188-labeled anti-neural cell adhesion molecule antibodies with 2-iminothiolane modification for targeting small-cell lung cancer

    Energy Technology Data Exchange (ETDEWEB)

    Hosono, Masako N. [Osaka City Univ. (Japan); Hosono, Makoto; Mishra, A.K.; Faivre-Chauvet, A.; Gautherot, E.; Barbet, J.; Knapp, F.F.R. Jr; Chatal, J.F.

    2000-06-01

    We have evaluated the potential of {sup 188}Re-labeled monoclonal antibodies (MAbs) modified with 2-iminothiolane (2IT) for targeting small-cell lung cancer (SCLC). Radiolabeled MAbs NK1NBL1 and C218 recognizing neural cell adhesion molecule were injected i.v. into athymic mice inoculated with human SCLC tumors, and the biodistribution was examined. NK1NBL1 localized in the tumors better than C218. {sup 188}Re-labeled MAbs cleared from the blood faster than {sup 125}I-labeled counterparts, resulting in higher tumor-to-blood ratios. In conclusion, the {sup 188}Re-labeled MAbs are attractive candidates for imaging and therapy of SCLC. (author)

  1. Door and cabinet recognition using convolutional neural nets and real-time method fusion for handle detection and grasping

    DEFF Research Database (Denmark)

    Maurin, Adrian Llopart; Ravn, Ole; Andersen, Nils Axel

    2017-01-01

    In this paper we present a new method that robustly identifies doors, cabinets and their respective handles, with special emphasis on extracting useful features from handles to be then manipulated. The novelty of this system relies on the combination of a Convolutional Neural Net (CNN), as a form...... of reducing the search space, several methods to extract point cloud data and a mobile robot to interact with the objects. The framework consists of the following components: The implementation of a CNN to extract a Region of Interest (ROI) from an image corresponding to a door or cabinet. Several vision...

  2. Morphofunctional plasticity in the adult hypothalamus induces regulation of polysialic acid-neural cell adhesion molecule through changing activity and expression levels of polysialyltransferases.

    Science.gov (United States)

    Soares, S; von Boxberg, Y; Ravaille-Veron, M; Vincent, J D; Nothias, F

    2000-04-01

    Polysialic acid-neural cell adhesion molecule (PSA-NCAM) expression in the adult nervous system is restricted to regions retaining a capacity for morphological plasticity. For the female rat hypothalamoneurohypophysial system (HNS), we have previously shown that lactation induces a dramatic decrease in PSA-NCAM, while leaving the level of total NCAM protein unchanged. Here, we wanted to elucidate the molecular mechanisms leading to a downregulation of PSA, thereby stabilizing newly established synapses and neurohemal contacts that accompany the increased activity of oxytocinergic neurons. First, we show that the overall specific activity of polysialyltransferases present in tissue extracts from supraoptic nuclei decreases by approximately 50% during lactation. So far, two polysialyltransferase enzymes, STX and PST, have been characterized for their capacity to transfer PSA onto NCAM in vitro. Using a competitive RT-PCR on RNA extracts from the HNS, we demonstrate furthermore a significant decrease in the expression levels of both STX and PST mRNAs in lactating versus virgin animals. Interestingly, this downregulation of NCAM polysialylation is not correlated with the post-transcriptional regulation of variable alternative spliced exon splicing, in contrast to neural development. The control of polysialylation via a regulation of both enzyme activity and expression underlines the important role of this post-translational modification of NCAM in morphofunctional plasticity in adult brain.

  3. Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

    Science.gov (United States)

    Mimura, Masato; Sakai, Shinsuke; Kawahara, Tatsuya

    2015-12-01

    We propose an approach to reverberant speech recognition adopting deep learning in the front-end as well as b a c k-e n d o f a r e v e r b e r a n t s p e e c h r e c o g n i t i o n s y s t e m, a n d a n o v e l m e t h o d t o i m p r o v e t h e d e r e v e r b e r a t i o n p e r f o r m a n c e of the front-end network using phone-class information. At the front-end, we adopt a deep autoencoder (DAE) for enhancing the speech feature parameters, and speech recognition is performed in the back-end using DNN-HMM acoustic models trained on multi-condition data. The system was evaluated through the ASR task in the Reverb Challenge 2014. The DNN-HMM system trained on the multi-condition training set achieved a conspicuously higher word accuracy compared to the MLLR-adapted GMM-HMM system trained on the same data. Furthermore, feature enhancement with the deep autoencoder contributed to the improvement of recognition accuracy especially in the more adverse conditions. While the mapping between reverberant and clean speech in DAE-based dereverberation is conventionally conducted only with the acoustic information, we presume the mapping is also dependent on the phone information. Therefore, we propose a new scheme (pDAE), which augments a phone-class feature to the standard acoustic features as input. Two types of the phone-class feature are investigated. One is the hard recognition result of monophones, and the other is a soft representation derived from the posterior outputs of monophone DNN. The augmented feature in either type results in a significant improvement (7-8 % relative) from the standard DAE.

  4. Recognition of Paddy, Brown Rice and White Rice Cultivars Based on Textural Features of Images and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    I Golpour

    2015-03-01

    Full Text Available Identification of rice cultivars is very important in modern agriculture. Texture properties could be used to identify of rice cultivars among of the various factors. The digital images processing can be used as a new approach to extract texture features. The objective of this research was to identify rice cultivars using of texture features with using image processing and back propagation artificial neural networks. To identify rice cultivars, five rice cultivars Fajr, Shiroodi, Neda, Tarom mahalli and Khazar were selected. Finally, 108 textural features were extracted from rice images using gray level co-occurrence matrix. Then cultivar identification was carried out using Back Propagation Artificial Neural Network. After evaluation of the network with one hidden layer using texture features, the highest classification accuracy for paddy cultivars, brown rice and white rice were obtained 92.2%, 97.8% and 98.9%, respectively. After evaluation of the network with two hidden layers, the average accuracy for classification of paddy cultivars was obtained to be 96.67%, for brown rice it was 97.78% and for white rice the classification accuracy was 98.88%. The highest mean classification accuracy acquired for paddy cultivars with 45 features was achieved to be 98.9%, for brown rice cultivars with 11 selected features it was 93.3% and it was 96.7% with 18 selected features for rice cultivars.

  5. Mitigation of Effects of Occlusion on Object Recognition with Deep Neural Networks through Low-Level Image Completion

    Directory of Open Access Journals (Sweden)

    Benjamin Chandler

    2016-01-01

    Full Text Available Heavily occluded objects are more difficult for classification algorithms to identify correctly than unoccluded objects. This effect is rare and thus hard to measure with datasets like ImageNet and PASCAL VOC, however, owing to biases in human-generated image pose selection. We introduce a dataset that emphasizes occlusion and additions to a standard convolutional neural network aimed at increasing invariance to occlusion. An unmodified convolutional neural network trained and tested on the new dataset rapidly degrades to chance-level accuracy as occlusion increases. Training with occluded data slows this decline but still yields poor performance with high occlusion. Integrating novel preprocessing stages to segment the input and inpaint occlusions is an effective mitigation. A convolutional network so modified is nearly as effective with more than 81% of pixels occluded as it is with no occlusion. Such a network is also more accurate on unoccluded images than an otherwise identical network that has been trained with only unoccluded images. These results depend on successful segmentation. The occlusions in our dataset are deliberately easy to segment from the figure and background. Achieving similar results on a more challenging dataset would require finding a method to split figure, background, and occluding pixels in the input.

  6. An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks

    Directory of Open Access Journals (Sweden)

    Carlos E. Galván-Tejada

    2016-01-01

    Full Text Available This work presents a human activity recognition (HAR model based on audio features. The use of sound as an information source for HAR models represents a challenge because sound wave analyses generate very large amounts of data. However, feature selection techniques may reduce the amount of data required to represent an audio signal sample. Some of the audio features that were analyzed include Mel-frequency cepstral coefficients (MFCC. Although MFCC are commonly used in voice and instrument recognition, their utility within HAR models is yet to be confirmed, and this work validates their usefulness. Additionally, statistical features were extracted from the audio samples to generate the proposed HAR model. The size of the information is necessary to conform a HAR model impact directly on the accuracy of the model. This problem also was tackled in the present work; our results indicate that we are capable of recognizing a human activity with an accuracy of 85% using the HAR model proposed. This means that minimum computational costs are needed, thus allowing portable devices to identify human activities using audio as an information source.

  7. The genetically modified polysialylated form of neural cell adhesion molecule-positive cells for potential treatment of X-linked adrenoleukodystrophy.

    Science.gov (United States)

    Jang, Jiho; Kim, Han-Soo; Kang, Joon Won; Kang, Hoon-Chul

    2013-01-01

    Cell transplantation of myelin-producing exogenous cells is being extensively explored as a means of remyelinating axons in X-linked adrenoleukodystrophy. We determined whether 3,3',5-Triiodo-L-thyronine (T3) overexpresses the ABCD2 gene in the polysialylated (PSA) form of neural cell adhesion molecule (NCAM)-positive cells and promotes cell proliferation and favors oligodendrocyte lineage differentiation. PSA-NCAM+ cells from newborn Sprague-Dawley rats were grown for five days on uncoated dishes in defined medium with or without supplementation of basic fibroblast growth factor (bFGF) and/or T3. Then, PSA-NCAM+ spheres were prepared in single cells and transferred to polyornithine/fibronectin-coated glass coverslips for five days to determine the fate of the cells according to the supplementation of these molecules. T3 responsiveness of ABCD2 was analyzed using real-time quantitative polymerase chain reaction, the growth and fate of cells were determined using 5-bromo-2-deoxyuridine incorporation and immunocytochemistry, respectively. Results demonstrated that T3 induces overexpression of the ABCD2 gene in PSA-NCAM+ cells, and can enhance PSA-NCAM+ cell growth in the presence of bFGF, favoring an oligodendrocyte fate. These results may provide new insights into investigation of PSA-NCAM+ cells for therapeutic application to X-linked adrenoleukodystrophy.

  8. The Dynamic Multisensory Engram: Neural Circuitry Underlying Crossmodal Object Recognition in Rats Changes with the Nature of Object Experience.

    Science.gov (United States)

    Jacklin, Derek L; Cloke, Jacob M; Potvin, Alphonse; Garrett, Inara; Winters, Boyer D

    2016-01-27

    Rats, humans, and monkeys demonstrate robust crossmodal object recognition (CMOR), identifying objects across sensory modalities. We have shown that rats' performance of a spontaneous tactile-to-visual CMOR task requires functional integration of perirhinal (PRh) and posterior parietal (PPC) cortices, which seemingly provide visual and tactile object feature processing, respectively. However, research with primates has suggested that PRh is sufficient for multisensory object representation. We tested this hypothesis in rats using a modification of the CMOR task in which multimodal preexposure to the to-be-remembered objects significantly facilitates performance. In the original CMOR task, with no preexposure, reversible lesions of PRh or PPC produced patterns of impairment consistent with modality-specific contributions. Conversely, in the CMOR task with preexposure, PPC lesions had no effect, whereas PRh involvement was robust, proving necessary for phases of the task that did not require PRh activity when rats did not have preexposure; this pattern was supported by results from c-fos imaging. We suggest that multimodal preexposure alters the circuitry responsible for object recognition, in this case obviating the need for PPC contributions and expanding PRh involvement, consistent with the polymodal nature of PRh connections and results from primates indicating a key role for PRh in multisensory object representation. These findings have significant implications for our understanding of multisensory information processing, suggesting that the nature of an individual's past experience with an object strongly determines the brain circuitry involved in representing that object's multisensory features in memory. The ability to integrate information from multiple sensory modalities is crucial to the survival of organisms living in complex environments. Appropriate responses to behaviorally relevant objects are informed by integration of multisensory object features

  9. Pattern recognition, neural networks, genetic algorithms and high performance computing in nuclear reactor diagnostics. Results and perspectives

    International Nuclear Information System (INIS)

    Dzwinel, W.; Pepyolyshev, N.

    1996-01-01

    The main goal of this paper is the presentation of our experience in development of the diagnostic system for the IBR-2 (Russia - Dubna) nuclear reactor. The authors show the principal results of the system modifications to make it work more reliable and much faster. The former needs the adaptation of new techniques of data processing, the latter, implementation of the newest computational facilities. The results of application of the clustering techniques and a method of visualization of the multi-dimensional information directly on the operator display are presented. The experiences with neural nets, used for prediction of the reactor operation, are discussed. The genetic algorithms were also tested, to reduce the quantity of data nd extracting the most informative components of the analyzed spectra. (authors)

  10. Adaptation of the Neural Network Recognition System of the Helicopter on Its Acoustic Radiation to the Flight Speed

    Directory of Open Access Journals (Sweden)

    V. K. Hohlov

    2015-01-01

    Full Text Available The article concerns the adaptation of a neural tract that recognizes a helicopter from the aerodynamic and ground objects by its acoustic radiation to the helicopter flight speed. It uses non-centered informative signs-indications of estimating signal spectra, which correspond to the local extremes (maximums and minimums of the power spectrum of input signal and have the greatest information when differentiating the helicopter signals from those of tracked vehicles. The article gives justification to the principle of the neural network (NN adaptation and adaptation block structure, which solves problems of blade passage frequency estimation when capturing the object and track it when tracking a target, as well as forming a signal to control the resonant filter parameters of the selection block of informative signs. To create the discriminatory characteristics of the discriminator are used autoregressive statistical characteristics of the quadrature components of signal, obtained through the discrete Hilbert Converter (DGC that perforMathematical modeling of the tracking meter using the helicopter signals obtained in real conditions is performed. The article gives estimates of the tracking parameter when using a tracking meter with DGC by sequential records of realized acoustic noise of the helicopter. It also shows a block-diagram of the adaptive NN. The scientific novelty of the work is that providing the invariance of used informative sign, the counts of local extremes of power spectral density (PSD to changes in the helicopter flight speed is reached due to adding the NN structure and adaptation block, which is implemented as a meter to track the apparent passage frequency of the helicopter rotor blades using its relationship with a function of the autoregressive acoustic signal of the helicopter.Specialized literature proposes solutions based on the use of training classifiers with different parametric methods of spectral representations

  11. Paradigms in object recognition

    International Nuclear Information System (INIS)

    Mutihac, R.; Mutihac, R.C.

    1999-09-01

    A broad range of approaches has been proposed and applied for the complex and rather difficult task of object recognition that involves the determination of object characteristics and object classification into one of many a priori object types. Our paper revises briefly the three main different paradigms in pattern recognition, namely Bayesian statistics, neural networks, and expert systems. (author)

  12. Post-Training Intrahippocampal Injection of Synthetic Poly-Alpha-2,8-Sialic Acid-Neural Cell Adhesion Molecule Mimetic Peptide Improves Spatial Long-Term Performance in Mice

    Science.gov (United States)

    Florian, Cedrick; Foltz, Jane; Norreel, Jean-Chretien; Rougon, Genevieve; Roullet, Pascal

    2006-01-01

    Several data have shown that the neural cell adhesion molecule (NCAM) is necessary for long-term memory formation and might play a role in the structural reorganization of synapses. The NCAM, encoded by a single gene, is represented by several isoforms that differ with regard to their content of alpha-2,8-linked sialic acid residues (PSA) on their…

  13. Neurite outgrowth induced by a synthetic peptide ligand of neural cell adhesion molecule requires fibroblast growth factor receptor activation

    DEFF Research Database (Denmark)

    Rønn, L C; Doherty, P; Holm, A

    2000-01-01

    identified a neuritogenic ligand, termed the C3 peptide, of the first immunoglobulin (lg) module of NCAM using a combinatorial library of synthetic peptides. Here we investigate whether stimulation of neurite outgrowth by this synthetic ligand of NCAM involves FGFRs. In primary cultures of cerebellar neurons...... from wild-type mice, the C3 peptide stimulated neurite outgrowth. This response was virtually absent in cultures of cerebellar neurons from transgenic mice expressing a dominant-negative form of the FGFR1. Likewise, in PC12E2 cells transiently expressing a dominant-negative form of the mouse FGFR1......, induction of neurites by the C3 peptide was abrogated. These findings suggest that the neuritogenic effect of the C3 peptide requires the presence of functional FGFRs and support the hypothesis that FGFRs are essential in cell adhesion molecule-stimulated neurite outgrowth. The C3 peptide appears...

  14. Subtle Changes in Peptide Conformation Profoundly Affect Recognition of the Non-Classical MHC Class I Molecule HLA-E by the CD94-NKG2 Natural Killer Cell Receptors

    Energy Technology Data Exchange (ETDEWEB)

    Hoare, Hilary L; Sullivan, Lucy C; Clements, Craig S; Ely, Lauren K; Beddoe, Travis; Henderson, Kate N; Lin, Jie; Reid, Hugh H; Brooks, Andrew G; Rossjohn, Jamie [Monash; (Melbourne)

    2008-03-31

    Human leukocyte antigen (HLA)-E is a non-classical major histocompatibility complex class I molecule that binds peptides derived from the leader sequences of other HLA class I molecules. Natural killer cell recognition of these HLA-E molecules, via the CD94-NKG2 natural killer family, represents a central innate mechanism for monitoring major histocompatibility complex expression levels within a cell. The leader sequence-derived peptides bound to HLA-E exhibit very limited polymorphism, yet subtle differences affect the recognition of HLA-E by the CD94-NKG2 receptors. To better understand the basis for this peptide-specific recognition, we determined the structure of HLA-E in complex with two leader peptides, namely, HLA-Cw*07 (VMAPRALLL), which is poorly recognised by CD94-NKG2 receptors, and HLA-G*01 (VMAPRTLFL), a high-affinity ligand of CD94-NKG2 receptors. A comparison of these structures, both of which were determined to 2.5-Å resolution, revealed that allotypic variations in the bound leader sequences do not result in conformational changes in the HLA-E heavy chain, although subtle changes in the conformation of the peptide within the binding groove of HLA-E were evident. Accordingly, our data indicate that the CD94-NKG2 receptors interact with HLA-E in a manner that maximises the ability of the receptors to discriminate between subtle changes in both the sequence and conformation of peptides bound to HLA-E.

  15. Recognition of Damaged Arrow-Road Markings by Visible Light Camera Sensor Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Husan Vokhidov

    2016-12-01

    Full Text Available Automobile driver information as displayed on marked road signs indicates the state of the road, traffic conditions, proximity to schools, etc. These signs are important to insure the safety of the driver and pedestrians. They are also important input to the automated advanced driver assistance system (ADAS, installed in many automobiles. Over time, the arrow-road markings may be eroded or otherwise damaged by automobile contact, making it difficult for the driver to correctly identify the marking. Failure to properly identify an arrow-road marker creates a dangerous situation that may result in traffic accidents or pedestrian injury. Very little research exists that studies the problem of automated identification of damaged arrow-road marking painted on the road. In this study, we propose a method that uses a convolutional neural network (CNN to recognize six types of arrow-road markings, possibly damaged, by visible light camera sensor. Experimental results with six databases of Road marking dataset, KITTI dataset, Málaga dataset 2009, Málaga urban dataset, Naver street view dataset, and Road/Lane detection evaluation 2013 dataset, show that our method outperforms conventional methods.

  16. High-resolution subsurface imaging and neural network recognition: Non-intrusive buried substance location. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Sternberg, B.K.; Poulton, M.M.

    1997-01-26

    A high-frequency, high-resolution electromagnetic (EM) imaging system has been developed for environmental geophysics surveys. Some key features of this system include: (1) rapid surveying to allow dense spatial sampling over a large area, (2) high-accuracy measurements which are used to produce a high-resolution image of the subsurface, (3) measurements which have excellent signal-to-noise ratio over a wide bandwidth (31 kHz to 32 MHz), (4) elimination of electric-field interference at high frequencies, (5) large-scale physical modeling to produce accurate theoretical responses over targets of interest in environmental geophysics surveys, (6) rapid neural network interpretation at the field site, and (7) visualization of complex structures during the survey. Four major experiments were conducted with the system: (1) Data were collected for several targets in our physical modeling facility. (2) The authors tested the system over targets buried in soil. (3) The authors conducted an extensive survey at the Idaho National Engineering Laboratory (INEL) Cold Test Pit (CTP). The location of the buried waste, category of waste, and thickness of the clay cap were successfully mapped. (4) The authors ran surveys over the acid pit at INEL. This was an operational survey over a hot site. The interpreted low-resistivity region correlated closely with the known extent of the acid pit.

  17. High-resolution subsurface imaging and neural network recognition: Non-intrusive buried substance location. Final report, January 26, 1997

    Energy Technology Data Exchange (ETDEWEB)

    Sternberg, B.K.; Poulton, M.M.

    1998-12-31

    A high-frequency, high-resolution electromagnetic (EIVI) imaging system has been developed for environmental geophysics surveys. Some key features of this system include: (1) rapid surveying to allow dense spatial sampling over a large area, (2) high-accuracy measurements which are used to produce a high-resolution image of the subsurface, (3) measurements which have excellent signal-to-noise ratio over a wide bandwidth (31 kHz to 32 MHZ), (4) elimination of electric-field interference at high frequencies, (5) large-scale physical modeling to produce accurate theoretical responses over targets of interest in environmental geophysics surveys, (6) rapid neural network interpretation at the field site, and (7) visualization of complex structures during the survey. Four major experiments were conducted with the system: (1) Data were collected for several targets in our physical modeling facility. (2) We tested the system over targets buried in soil. (3) We conducted an extensive survey at the Idaho National Engineering Laboratory (INEL) Cold Test Pit (CTP). The location of the buried waste, category of waste, and thickness of the clay cap were successfully mapped. (4) We ran surveys over the acid pit at INEL. This was an operational survey over a hot site. The interpreted low-resistivity region correlated closely with the known extent of the acid pit.

  18. High-resolution subsurface imaging and neural network recognition: Non-intrusive buried substance location. Final report, January 26, 1997

    International Nuclear Information System (INIS)

    Sternberg, B.K.; Poulton, M.M.

    1998-01-01

    A high-frequency, high-resolution electromagnetic (EIVI) imaging system has been developed for environmental geophysics surveys. Some key features of this system include: (1) rapid surveying to allow dense spatial sampling over a large area, (2) high-accuracy measurements which are used to produce a high-resolution image of the subsurface, (3) measurements which have excellent signal-to-noise ratio over a wide bandwidth (31 kHz to 32 MHZ), (4) elimination of electric-field interference at high frequencies, (5) large-scale physical modeling to produce accurate theoretical responses over targets of interest in environmental geophysics surveys, (6) rapid neural network interpretation at the field site, and (7) visualization of complex structures during the survey. Four major experiments were conducted with the system: (1) Data were collected for several targets in our physical modeling facility. (2) We tested the system over targets buried in soil. (3) We conducted an extensive survey at the Idaho National Engineering Laboratory (INEL) Cold Test Pit (CTP). The location of the buried waste, category of waste, and thickness of the clay cap were successfully mapped. (4) We ran surveys over the acid pit at INEL. This was an operational survey over a hot site. The interpreted low-resistivity region correlated closely with the known extent of the acid pit

  19. Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution

    Science.gov (United States)

    Aichinger, Wolfgang; Krappe, Sebastian; Cetin, A. Enis; Cetin-Atalay, Rengul; Üner, Aysegül; Benz, Michaela; Wittenberg, Thomas; Stamminger, Marc; Münzenmayer, Christian

    2017-03-01

    The analysis and interpretation of histopathological samples and images is an important discipline in the diagnosis of various diseases, especially cancer. An important factor in prognosis and treatment with the aim of a precision medicine is the determination of so-called cancer stem cells (CSC) which are known for their resistance to chemotherapeutic treatment and involvement in tumor recurrence. Using immunohistochemistry with CSC markers like CD13, CD133 and others is one way to identify CSC. In our work we aim at identifying CSC presence on ubiquitous Hematoxilyn and Eosin (HE) staining as an inexpensive tool for routine histopathology based on their distinct morphological features. We present initial results of a new method based on color deconvolution (CD) and convolutional neural networks (CNN). This method performs favorably (accuracy 0.936) in comparison with a state-of-the-art method based on 1DSIFT and eigen-analysis feature sets evaluated on the same image database. We also show that accuracy of the CNN is improved by the CD pre-processing.

  20. A Real-Time Angle- and Illumination-Aware Face Recognition System Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Hisateru Kato

    2012-01-01

    Full Text Available Automatic authentication systems, using biometric technology, are becoming increasingly important with the increased need for person verification in our daily life. A few years back, fingerprint verification was done only in criminal investigations. Now fingerprints and face images are widely used in bank tellers, airports, and building entrances. Face images are easy to obtain, but successful recognition depends on proper orientation and illumination of the image, compared to the one taken at registration time. Facial features heavily change with illumination and orientation angle, leading to increased false rejection as well as false acceptance. Registering face images for all possible angles and illumination is impossible. In this work, we proposed a memory efficient way to register (store multiple angle and changing illumination face image data, and a computationally efficient authentication technique, using multilayer perceptron (MLP. Though MLP is trained using a few registered images with different orientation, due to generalization property of MLP, interpolation of features for intermediate orientation angles was possible. The algorithm is further extended to include illumination robust authentication system. Results of extensive experiments verify the effectiveness of the proposed algorithm.

  1. Polysialylated-neural cell adhesion molecule (PSA-NCAM in the human trigeminal ganglion and brainstem at prenatal and adult ages

    Directory of Open Access Journals (Sweden)

    Melis Tiziana

    2008-11-01

    , the distribution pattern remains fairly steady, whereas the density of immunoreactive structures and the staining intensity may change and are usually higher in newborn than in adult specimens. Conclusion The results obtained show that, in man, the expression of PSA-NCAM in selective populations of central and peripheral neurons occurs not only during prenatal life, but also in adulthood. They support the concept of an involvement of this molecule in the structural and functional neural plasticity throughout life. In particular, the localization of PSA-NCAM in TG primary sensory neurons likely to be involved in the transmission of protopathic stimuli suggests the possible participation of this molecule in the processing of the relevant sensory neurotransmission.

  2. Overexpression of Polysialylated Neural Cell Adhesion Molecule Improves the Migration Capacity of Induced Pluripotent Stem Cell-Derived Oligodendrocyte Precursors

    Science.gov (United States)

    Czepiel, Marcin; Leicher, Lasse; Becker, Katja; Boddeke, Erik

    2014-01-01

    Cell replacement therapy aiming at the compensation of lost oligodendrocytes and restoration of myelination in acquired or congenital demyelination disorders has gained considerable interest since the discovery of induced pluripotent stem cells (iPSCs). Patient-derived iPSCs provide an inexhaustible source for transplantable autologous oligodendrocyte precursors (OPCs). The first transplantation studies in animal models for demyelination with iPSC-derived OPCs demonstrated their survival and remyelinating capacity, but also revealed their limited migration capacity. In the present study, we induced overexpression of the polysialylating enzyme sialyltransferase X (STX) in iPSC-derived OPCs to stimulate the production of polysialic acid-neuronal cell adhesion molecules (PSA-NCAMs), known to promote and facilitate the migration of OPCs. The STX-overexpressing iPSC-derived OPCs showed a normal differentiation and maturation pattern and were able to downregulate PSA-NCAMs when they became myelin-forming oligodendrocytes. After implantation in the demyelinated corpus callosum of cuprizone-fed mice, STX-expressing iPSC-derived OPCs demonstrated a significant increase in migration along the axons. Our findings suggest that the reach and efficacy of iPSC-derived OPC transplantation can be improved by stimulating the OPC migration potential via specific gene modulation. PMID:25069776

  3. Glial cell line-derived neurotrophic factor (GDNF) induces neuritogenesis in the cochlear spiral ganglion via neural cell adhesion molecule (NCAM)

    Science.gov (United States)

    Euteneuer, Sara; Yang, Kuo H.; Chavez, Eduardo; Leichtle, Anke; Loers, Gabriele; Olshansky, Adel; Pak, Kwang; Schachner, Melitta; Ryan, Allen F.

    2013-01-01

    Glial cell line-derived neurotrophic factor (GDNF) increases survival and neurite extension of spiral ganglion neurons (SGNs), the primary neurons of the auditory system, via yet unknown signaling mechanisms. In other cell types, signaling is achieved by the GPI-linked GDNF family receptor α1 (GFRα1) via recruitment of transmembrane receptors: Ret (re-arranged during transformation) and/or NCAM (neural cell adhesion molecule). Here we show that GDNF enhances neuritogenesis in organotypic cultures of spiral ganglia from 5-day-old rats and mice. Addition of GFRα1-Fc increases this effect. GDNF/GFRα1-Fc stimulation activates intracellular PI3K/Akt and MEK/Erk signaling cascades as detected by Western blot analysis of cultures prepared from rats at postnatal days 5 (P5, before the onset of hearing) and 20 (P20, after the onset of hearing). Both cascades mediate GDNF stimulation of neuritogenesis, since application of the Akt inhibitor Wortmannin or the Erk inhibitor U0126 abolished GDNF/GFRα1-Fc stimulated neuritogenesis in P5 rats. Since cultures of P5 NCAM-deficient mice failed to respond by neuritogenesis to GDNF/GFRα1-Fc, we conclude that NCAM serves as a receptor for GDNF signaling responsible for neuritogenesis in early postnatal spiral ganglion. PMID:23262364

  4. Learning-associated regulation of polysialylated neural cell adhesion molecule expression in the rat prefrontal cortex is region-, cell type- and paradigm-specific.

    Science.gov (United States)

    Ter Horst, Judith P F; Loscher, Jennifer S; Pickering, Mark; Regan, Ciaran M; Murphy, Keith J

    2008-08-01

    The prefrontal cortex (PFC) is an interconnected set of cortical areas that function in the synthesis of a diverse range of information and production of complex behaviour. It is now clear that these frontal structures, through bidirectional excitatory communication with the hippocampal formation, also play a substantial role in long-term memory consolidation. In the hippocampus, morphological synaptic plasticity, supported by regulation of neural cell adhesion molecule (NCAM) polysialylation status, is crucial to information storage. The recent description of polysialylated neurons in the various fields of the medial PFC suggests these structures to possess a similar capacity for synaptic plasticity. Here, using double-labelling immunohistochemistry with glutamic acid decarboxylase 67, we report that the nature of NCAM polysialic acid-positive neurons in the PFC is region-specific, with a high proportion (30-50%) of a gamma-aminobutyric acid (GABA)ergic phenotype in the more ventral infralimbic, orbitofrontal and insular cortices compared with just 10% in the dorsal structures of the cingulate, prelimbic and frontal cortices. Moreover, spatial learning was accompanied by activations in polysialylation expression in ventral PFC structures, while avoidance conditioning involved downregulation of this plasticity marker that was restricted to the dorsomedial PFC--the cingulate and prelimbic cortices. Thus, in contrast to other structures integrated functionally with the hippocampus, memory-associated plasticity mobilized in the PFC is region-, cell type- and task-specific.

  5. Reconhecimento de variedades de soja por meio do processamento de imagens digitais usando redes neurais artificiais Soybean varieties recognition through the digital image processing using artificial neural network

    Directory of Open Access Journals (Sweden)

    Oleg Khatchatourian

    2008-12-01

    Full Text Available Neste trabalho, foi aplicado o processamento de imagens digitais auxiliado pelas Redes Neurais Artificiais (RNA com a finalidade de identificar algumas variedades de soja por meio da forma e do tamanho das sementes. Foram analisadas as seguintes variedades: EMBRAPA 133, EMBRAPA 184, COODETEC 205, COODETEC 206, EMBRAPA 48, SYNGENTA 8350, FEPAGRO 10 e MONSOY 8000 RR, safra 2005/2006. O processamento das imagens foi constituído pelas seguintes etapas: 1 Aquisição da imagem: as amostras de cada variedade foram fotografadas por máquina fotográfica Coolpix995, Nikon, com resolução de 3.34 megapixels; 2 Pré-processamento: um filtro de anti-aliasing foi aplicado para obter tons acinzentados da imagem; 3 Segmentação: foi realizada a detecção das bordas das sementes (Método de Prewitt, dilatação dessas bordas e remoção de segmentos não-necessários para a análise. 4 Representação: cada semente foi representada na forma de matriz binária 130x130, e 5 Reconhecimento e interpretação: foi utilizada uma rede neural feedforward multicamadas, com três camadas ocultas. O treinamento da rede foi realizado pelo método backpropagation. A validação da RNA treinada mostrou que o processamento aplicado pode ser usado para a identificação das variedades consideradas.Digital image processing with Artificial Neural Network (ANN was used to identify some soybean varieties through the form and size of the seeds. The following varieties were analyzed: EMBRAPA 133, EMBRAPA 184, COODETEC 205, COODETEC 206, EMBRAPA 48, SYNGENTA 8350, FEPAGRO 10 and MONSOY 8000 RR, 2005/2006 harvest. The image processing was constituted by the following stages: 1 Image acquisition: the samples of each variety were photographed by photographic camera Coolpix995, Nikon, with resolution of 3.34 megapixels; 2 Pre-processing: an anti-aliasing filter was applied to convert the true-color image to the grayscale intensity image; 3 Segmentation: it was made the seeds edges

  6. Crossing borders to bind proteins--a new concept in protein recognition based on the conjugation of small organic molecules or short peptides to polypeptides from a designed set.

    Science.gov (United States)

    Baltzer, Lars

    2011-06-01

    A new concept for protein recognition and binding is highlighted. The conjugation of small organic molecules or short peptides to polypeptides from a designed set provides binder molecules that bind proteins with high affinities, and with selectivities that are equal to those of antibodies. The small organic molecules or peptides need to bind the protein targets but only with modest affinities and selectivities, because conjugation to the polypeptides results in molecules with dramatically improved binder performance. The polypeptides are selected from a set of only sixteen sequences designed to bind, in principle, any protein. The small number of polypeptides used to prepare high-affinity binders contrasts sharply with the huge libraries used in binder technologies based on selection or immunization. Also, unlike antibodies and engineered proteins, the polypeptides have unordered three-dimensional structures and adapt to the proteins to which they bind. Binder molecules for the C-reactive protein, human carbonic anhydrase II, acetylcholine esterase, thymidine kinase 1, phosphorylated proteins, the D-dimer, and a number of antibodies are used as examples to demonstrate that affinities are achieved that are higher than those of the small molecules or peptides by as much as four orders of magnitude. Evaluation by pull-down experiments and ELISA-based tests in human serum show selectivities to be equal to those of antibodies. Small organic molecules and peptides are readily available from pools of endogenous ligands, enzyme substrates, inhibitors or products, from screened small molecule libraries, from phage display, and from mRNA display. The technology is an alternative to established binder concepts for applications in drug development, diagnostics, medical imaging, and protein separation.

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

    Science.gov (United States)

    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.

  8. Role of glial cell line-derived neurotrophic factor (GDNF)-neural cell adhesion molecule (NCAM) interactions in induction of neurite outgrowth and identification of a binding site for NCAM in the heel region of GDNF

    DEFF Research Database (Denmark)

    Nielsen, Janne; Gotfryd, Kamil; Li, Shizhong

    2009-01-01

    The formation of appropriate neuronal circuits is an essential part of nervous system development and relies heavily on the outgrowth of axons and dendrites and their guidance to their respective targets. This process is governed by a large array of molecules, including glial cell line......-derived neurotrophic factor (GDNF) and the neural cell adhesion molecule (NCAM), the interaction of which induce neurite outgrowth. In the present study the requirements for NCAM-mediated GDNF-induced neurite outgrowth were investigated in cultures of hippocampal neurons, which do not express Ret. We demonstrate...

  9. Derivation of mesenchymal stromal cells from pluripotent stem cells through a neural crest lineage using small molecule compounds with defined media.

    Directory of Open Access Journals (Sweden)

    Makoto Fukuta

    Full Text Available Neural crest cells (NCCs are an embryonic migratory cell population with the ability to differentiate into a wide variety of cell types that contribute to the craniofacial skeleton, cornea, peripheral nervous system, and skin pigmentation. This ability suggests the promising role of NCCs as a source for cell-based therapy. Although several methods have been used to induce human NCCs (hNCCs from human pluripotent stem cells (hPSCs, such as embryonic stem cells (ESCs and induced pluripotent stem cells (iPSCs, further modifications are required to improve the robustness, efficacy, and simplicity of these methods. Chemically defined medium (CDM was used as the basal medium in the induction and maintenance steps. By optimizing the culture conditions, the combination of the GSK3β inhibitor and TGFβ inhibitor with a minimum growth factor (insulin very efficiently induced hNCCs (70-80% from hPSCs. The induced hNCCs expressed cranial NCC-related genes and stably proliferated in CDM supplemented with EGF and FGF2 up to at least 10 passages without changes being observed in the major gene expression profiles. Differentiation properties were confirmed for peripheral neurons, glia, melanocytes, and corneal endothelial cells. In addition, cells with differentiation characteristics similar to multipotent mesenchymal stromal cells (MSCs were induced from hNCCs using CDM specific for human MSCs. Our simple and robust induction protocol using small molecule compounds with defined media enabled the generation of hNCCs as an intermediate material producing terminally differentiated cells for cell-based innovative medicine.

  10. Self-assembly of 5,11,17,23-Tetranitro-25,26,27,28-tetramethoxythiacalix[4]arene with Neutral Molecules and its Use for Anion Recognition

    Czech Academy of Sciences Publication Activity Database

    Macková, M.; Himl, M.; Budka, J.; Pojarová, M.; Císařová, I.; Eigner, V.; Cuřínová, Petra; Dvořáková, H.; Lhoták, P.

    2013-01-01

    Roč. 69, č. 4 (2013), s. 1397-1402 ISSN 0040-4020 R&D Projects: GA ČR GA203/09/0691; GA AV ČR IAAX08240901 Institutional support: RVO:67985858 Keywords : thiacalixarenes * self assembly * anion recognition Subject RIV: CC - Organic Chemistry Impact factor: 2.817, year: 2013

  11. Use of pattern recognition and neural networks for non-metric sex diagnosis from lateral shape of calvarium: an innovative model for computer-aided diagnosis in forensic and physical anthropology.

    Science.gov (United States)

    Cavalli, Fabio; Lusnig, Luca; Trentin, Edmondo

    2017-05-01

    Sex determination on skeletal remains is one of the most important diagnosis in forensic cases and in demographic studies on ancient populations. Our purpose is to realize an automatic operator-independent method to determine the sex from the bone shape and to test an intelligent, automatic pattern recognition system in an anthropological domain. Our multiple-classifier system is based exclusively on the morphological variants of a curve that represents the sagittal profile of the calvarium, modeled via artificial neural networks, and yields an accuracy higher than 80 %. The application of this system to other bone profiles is expected to further improve the sensibility of the methodology.

  12. Enzymatic removal of polysialic acid from neural cell adhesion molecule interrupts gonadotropin releasing hormone (GnRH) neuron-glial remodeling.

    Science.gov (United States)

    Kumar, Sushil; Parkash, Jyoti; Kataria, Hardeep; Kaur, Gurcharan

    2012-01-02

    There is abundant evidence to prove that the astrocytes are highly dynamic cell type in CNS and under physiological conditions such as reproduction, these cells display a remarkable structural plasticity especially at the level of their distal processes ensheathing the gonadotropin releasing hormone (GnRH) axon terminals. The morphology of GnRH axon terminals and astrocytes in the median eminence region of hypothalamus show activity dependent structural plasticity during different phases of estrous cycle. In the current study, we have assessed the functional contribution of ∞-2,8-linked polysialic acid (PSA) on neural cell adhesion molecule (PSA-NCAM) in this neuronal-glial plasticity using both in vitro and in vivo model systems. In vivo experiments were carried out after stereotaxic injection of endoneuraminidase enzyme (endo-N) near median eminence region of hypothalamus to specifically remove PSA residues on NCAM followed by localization of GnRH, PSA-NCAM and glial fibrillary acidic protein (GFAP) by immunostaining. Using in vitro model, structural remodeling of GnV-3 cells, (a conditionally immortalized GnRH cell line) co-cultured with primary astrocytes was studied after treating the cells with endo-N. Marked morphological changes were observed in GnRH axon terminals in proestrous phase rats and control GnV-3 cells as compared to endo-N treatment i.e. after removal of PSA. The specificity of endo-N treatment was also confirmed by studying the expression of PSA-NCAM by Western blotting in cultures treated with and without endo-N. Removal of PSA from surfaces with endo-N prevented stimulation associated remodeling of GnRH axon terminals as well as their associated glial cells under both in vivo and in vitro conditions. The current data confirms the permissive role of PSA to promote dynamic remodeling of GnRH axon terminals and their associated glia during reproductive cycle in rats. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  13. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  14. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  15. Automated road marking recognition system

    Science.gov (United States)

    Ziyatdinov, R. R.; Shigabiev, R. R.; Talipov, D. N.

    2017-09-01

    Development of the automated road marking recognition systems in existing and future vehicles control systems is an urgent task. One way to implement such systems is the use of neural networks. To test the possibility of using neural network software has been developed with the use of a single-layer perceptron. The resulting system based on neural network has successfully coped with the task both when driving in the daytime and at night.

  16. Continuous monitoring of the lunar or Martian subsurface using on-board pattern recognition and neural processing of Rover geophysical data

    Science.gov (United States)

    Glass, Charles E.; Boyd, Richard V.; Sternberg, Ben K.

    1991-01-01

    The overall aim is to provide base technology for an automated vision system for on-board interpretation of geophysical data. During the first year's work, it was demonstrated that geophysical data can be treated as patterns and interpreted using single neural networks. Current research is developing an integrated vision system comprising neural networks, algorithmic preprocessing, and expert knowledge. This system is to be tested incrementally using synthetic geophysical patterns, laboratory generated geophysical patterns, and field geophysical patterns.

  17. Distortion Analysis Of Tamil Language Characters Recognition

    OpenAIRE

    Gowri N.; R. Bhaskaran

    2011-01-01

    This research work demonstrates how character recognition can be done with a back propagation network and shows how to implement this using the MATLAB Neural Network toolbox. This is a slightly enhanced version of the character recognition application based on the MATLAB Neural Network toolbox. In this research article we are focusing on the distortion analysis of Tamil Language Characters in order to recognize them effectively using the neural network we have developed. We have used the comm...

  18. The Regions on the Light Chain of Botulinum Neurotoxin Type A Recognized by T Cells from Toxin-Treated Cervical Dystonia Patients. The Complete Human T-Cell Recognition Map of the Toxin Molecule.

    Science.gov (United States)

    Oshima, Minako; Deitiker, Philip; Jankovic, Joseph; Atassi, M Zouhair

    2018-01-01

    We have recently mapped the in vitro proliferative responses of T cells from botulinum neurotoxin type A (BoNT/A)-treated cervical dystonia (CD) patients with overlapping peptides encompassing BoNT/A heavy chain (residues 449-1296). In the present study, we determined the recognition profiles, by peripheral blood lymphocytes (PBL) from the same set of patients, of BoNT/A light (L) chain (residues 1-453) by using 32 synthetic overlapping peptides that encompassed the entire L chain. Profiles of the T-cell responses (expressed in stimulation index, SI; Z score based on transformed SI) to the peptides varied among the patients. Samples from 14 patients treated solely with BoNT/A recognized 3-13 (average 7.2) peptides/sample at Z > 3.0 level. Two peptide regions representing residues 113-131 and 225-243 were recognized by around 40% of these patients. Regarding treatment parameters, treatment history with current BOTOX ® only group produced significantly lower average T-cell responses to the 32 L-chain peptides compared to treatments with mix of type A including original and current BOTOX ® . Influence of other treatment parameters on T-cell recognition of the L-chain peptides was also observed. Results of the submolecular T-cell recognition of the L chain are compared to those of the H chain and the T-cell recognition profile of the entire BoNT/A molecule is discussed. Abbreviations used: BoNT/A, botulinum neurotoxin type A; BoNT/A i , inactivated BoNT/A; BoNT/B, botulinum neurotoxin type B; CD, cervical dystonia; L chain, the light chain (residues 1-448) of BoNT/A; LNC, lymph node cells; H chain, the heavy chain (residues 449-1296) of BoNT/A; H C , C-terminal domain (residues 855-1296) of H chain; H N , N-terminal domain (residues 449-859) of H chain; MPA, mouse protection assay; SI, stimulation index (SI = cpm of 3 H-thymidine incorporated by antigen-stimulated T cells/cpm incorporated by unstimulated cells); TeNT, tetanus neurotoxin; TeNT i , inactivated TeNT.

  19. Identifying Broadband Rotational Spectra with Neural Networks

    Science.gov (United States)

    Zaleski, Daniel P.; Prozument, Kirill

    2017-06-01

    A typical broadband rotational spectrum may contain several thousand observable transitions, spanning many species. Identifying the individual spectra, particularly when the dynamic range reaches 1,000:1 or even 10,000:1, can be challenging. One approach is to apply automated fitting routines. In this approach, combinations of 3 transitions can be created to form a "triple", which allows fitting of the A, B, and C rotational constants in a Watson-type Hamiltonian. On a standard desktop computer, with a target molecule of interest, a typical AUTOFIT routine takes 2-12 hours depending on the spectral density. A new approach is to utilize machine learning to train a computer to recognize the patterns (frequency spacing and relative intensities) inherit in rotational spectra and to identify the individual spectra in a raw broadband rotational spectrum. Here, recurrent neural networks have been trained to identify different types of rotational spectra and classify them accordingly. Furthermore, early results in applying convolutional neural networks for spectral object recognition in broadband rotational spectra appear promising. Perez et al. "Broadband Fourier transform rotational spectroscopy for structure determination: The water heptamer." Chem. Phys. Lett., 2013, 571, 1-15. Seifert et al. "AUTOFIT, an Automated Fitting Tool for Broadband Rotational Spectra, and Applications to 1-Hexanal." J. Mol. Spectrosc., 2015, 312, 13-21. Bishop. "Neural networks for pattern recognition." Oxford university press, 1995.

  20. [Artificial neural networks in Neurosciences].

    Science.gov (United States)

    Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María

    2011-11-01

    This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.

  1. Pattern recognition in speech and language processing

    CERN Document Server

    Chou, Wu

    2003-01-01

    Minimum Classification Error (MSE) Approach in Pattern Recognition, Wu ChouMinimum Bayes-Risk Methods in Automatic Speech Recognition, Vaibhava Goel and William ByrneA Decision Theoretic Formulation for Adaptive and Robust Automatic Speech Recognition, Qiang HuoSpeech Pattern Recognition Using Neural Networks, Shigeru KatagiriLarge Vocabulary Speech Recognition Based on Statistical Methods, Jean-Luc GauvainToward Spontaneous Speech Recognition and Understanding, Sadaoki FuruiSpeaker Authentication, Qi Li and Biing-Hwang JuangHMMs for Language Processing Problems, Ri

  2. What are artificial neural networks?

    DEFF Research Database (Denmark)

    Krogh, Anders

    2008-01-01

    Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...

  3. Chronic stress in adulthood followed by intermittent stress impairs spatial memory and the survival of newborn hippocampal cells in aging animals: prevention by FGL, a peptide mimetic of neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Borcel, Erika; Pérez-Alvarez, Laura; Herrero, Ana Isabel

    2008-01-01

    , a peptide mimetic of neural cell adhesion molecule, during the 4 weeks of continuous stress not only prevented the deleterious effects of chronic stress on spatial memory, but also reduced the survival of the newly generated hippocampal cells in aging animals. FGL treatment did not, however, prevent......In this study, we examined whether chronic stress in adulthood can exert long-term effects on spatial-cognitive abilities and on the survival of newborn hippocampal cells in aging animals. Male Wistar rats were subjected to chronic unpredictable stress at midlife (12 months old) and then reexposed...... each week to a stress stimulus. When evaluated in the water maze at the early stages of aging (18 months old), chronic unpredictable stress accelerated spatial-cognitive decline, an effect that was accompanied by a reduction in the survival of newborn cells and in the number of adult granular cells...

  4. Quantum-chemical insights from deep tensor neural networks

    Science.gov (United States)

    Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R.; Tkatchenko, Alexandre

    2017-01-01

    Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol-1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

  5. Deep learning for single-molecule science

    Science.gov (United States)

    Albrecht, Tim; Slabaugh, Gregory; Alonso, Eduardo; Al-Arif, SM Masudur R.

    2017-10-01

    Exploring and making predictions based on single-molecule data can be challenging, not only due to the sheer size of the datasets, but also because a priori knowledge about the signal characteristics is typically limited and poor signal-to-noise ratio. For example, hypothesis-driven data exploration, informed by an expectation of the signal characteristics, can lead to interpretation bias or loss of information. Equally, even when the different data categories are known, e.g., the four bases in DNA sequencing, it is often difficult to know how to make best use of the available information content. The latest developments in machine learning (ML), so-called deep learning (DL) offer interesting, new avenues to address such challenges. In some applications, such as speech and image recognition, DL has been able to outperform conventional ML strategies and even human performance. However, to date DL has not been applied much in single-molecule science, presumably in part because relatively little is known about the ‘internal workings’ of such DL tools within single-molecule science as a field. In this Tutorial, we make an attempt to illustrate in a step-by-step guide how one of those, a convolutional neural network (CNN), may be used for base calling in DNA sequencing applications. We compare it with a SVM as a more conventional ML method, and discuss some of the strengths and weaknesses of the approach. In particular, a ‘deep’ neural network has many features of a ‘black box’, which has important implications on how we look at and interpret data.

  6. Neural networks at the Tevatron

    International Nuclear Information System (INIS)

    Badgett, W.; Burkett, K.; Campbell, M.K.; Wu, D.Y.; Bianchin, S.; DeNardi, M.; Pauletta, G.; Santi, L.; Caner, A.; Denby, B.; Haggerty, H.; Lindsey, C.S.; Wainer, N.; Dall'Agata, M.; Johns, K.; Dickson, M.; Stanco, L.; Wyss, J.L.

    1992-10-01

    This paper summarizes neural network applications at the Fermilab Tevatron, including the first online hardware application in high energy physics (muon tracking): the CDF and DO neural network triggers; offline quark/gluon discrimination at CDF; ND a new tool for top to multijets recognition at CDF

  7. Optoelectronic Implementation of Neural Networks

    Indian Academy of Sciences (India)

    optical neural network using photo refractive crystals and realized interconnection density of 10 8 to. 1010 per cm3. • B Javidi and others designed a correlato.,. based two-layer neural network associated with a supervised perceptron learning algorithm for r~al-time face recognition. electronic wiring altogether and replace it ...

  8. Physical model for recognition tunneling

    International Nuclear Information System (INIS)

    Krstić, Predrag; Ashcroft, Brian; Lindsay, Stuart

    2015-01-01

    Recognition tunneling (RT) identifies target molecules trapped between tunneling electrodes functionalized with recognition molecules that serve as specific chemical linkages between the metal electrodes and the trapped target molecule. Possible applications include single molecule DNA and protein sequencing. This paper addresses several fundamental aspects of RT by multiscale theory, applying both all-atom and coarse-grained DNA models: (1) we show that the magnitude of the observed currents are consistent with the results of non-equilibrium Green’s function calculations carried out on a solvated all-atom model. (2) Brownian fluctuations in hydrogen bond-lengths lead to current spikes that are similar to what is observed experimentally. (3) The frequency characteristics of these fluctuations can be used to identify the trapped molecules with a machine-learning algorithm, giving a theoretical underpinning to this new method of identifying single molecule signals. (paper)

  9. Designer molecule for molecular recognition and photoinduced ...

    Indian Academy of Sciences (India)

    amitava

    1H NMR spectroscopy or Change in redox potential value is also has been used. H NMR spectroscopy or Change in redox potential value ... Change in electronic spectra. Change in electronic spectra in visible region may allow naked ..... causes the inhibition of the plasma membrane ATPase activity and thus. 0.6. 0.9 b s o.

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

    Directory of Open Access Journals (Sweden)

    Yu Liu

    2014-05-01

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

  11. Molecule Matters

    Indian Academy of Sciences (India)

    is a very stable and inert molecule due to the formation of a triple bond between the two atoms. Surpris- ingly isoelectronic molecules are quite reactive making dinitrogen very useful and unique. Dinitrogen (N. 2. ) is such an innocuous molecule that you might not think it worthy of special attention. We take this molecule for.

  12. Pattern recognition in the ALFALFA.70 and Sloan Digital Sky Surveys: a catalogue of ˜500 000 H I gas fraction estimates based on artificial neural networks

    Science.gov (United States)

    Teimoorinia, Hossen; Ellison, Sara L.; Patton, David R.

    2017-02-01

    The application of artificial neural networks (ANNs) for the estimation of H I gas mass fraction (M_{H I}/{{M}_{*}}) is investigated, based on a sample of 13 674 galaxies in the Sloan Digital Sky Survey (SDSS) with H I detections or upper limits from the Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA). We show that, for an example set of fixed input parameters (g - r colour and I-band surface brightness), a multidimensional quadratic model yields M_{H I}/{{M}_{*}} scaling relations with a smaller scatter (0.22 dex) than traditional linear fits (0.32 dex), demonstrating that non-linear methods can lead to an improved performance over traditional approaches. A more extensive ANN analysis is performed using 15 galaxy parameters that capture variation in stellar mass, internal structure, environment and star formation. Of the 15 parameters investigated, we find that g - r colour, followed by stellar mass surface density, bulge fraction and specific star formation rate have the best connection with M_{H I}/{{M}_{*}}. By combining two control parameters, that indicate how well a given galaxy in SDSS is represented by the ALFALFA training set (PR) and the scatter in the training procedure (σfit), we develop a strategy for quantifying which SDSS galaxies our ANN can be adequately applied to, and the associated errors in the M_{H I}/{{M}_{*}} estimation. In contrast to previous works, our M_{H I}/{{M}_{*}} estimation has no systematic trend with galactic parameters such as M⋆, g - r and star formation rate. We present a catalogue of M_{H I}/{{M}_{*}} estimates for more than half a million galaxies in the SDSS, of which ˜150 000 galaxies have a secure selection parameter with average scatter in the M_{H I}/{{M}_{*}} estimation of 0.22 dex.

  13. Individual Differences in the Speed of Facial Emotion Recognition Show Little Specificity but Are Strongly Related with General Mental Speed: Psychometric, Neural and Genetic Evidence

    Science.gov (United States)

    Liu, Xinyang; Hildebrandt, Andrea; Recio, Guillermo; Sommer, Werner; Cai, Xinxia; Wilhelm, Oliver

    2017-01-01

    findings keenly suggest that general speed abilities should be taken into account when the study of emotion recognition abilities is targeted in its specificity. PMID:28848411

  14. What and Where in auditory sensory processing: A high-density electrical mapping study of distinct neural processes underlying sound object recognition and sound localization

    Directory of Open Access Journals (Sweden)

    Victoria M Leavitt

    2011-06-01

    Full Text Available Functionally distinct dorsal and ventral auditory pathways for sound localization (where and sound object recognition (what have been described in non-human primates. A handful of studies have explored differential processing within these streams in humans, with highly inconsistent findings. Stimuli employed have included simple tones, noise bursts and speech sounds, with simulated left-right spatial manipulations, and in some cases participants were not required to actively discriminate the stimuli. Our contention is that these paradigms were not well suited to dissociating processing within the two streams. Our aim here was to determine how early in processing we could find evidence for dissociable pathways using better titrated what and where task conditions. The use of more compelling tasks should allow us to amplify differential processing within the dorsal and ventral pathways. We employed high-density electrical mapping using a relatively large and environmentally realistic stimulus set (seven animal calls delivered from seven free-field spatial locations; with stimulus configuration identical across the where and what tasks. Topographic analysis revealed distinct dorsal and ventral auditory processing networks during the where and what tasks with the earliest point of divergence seen during the N1 component of the auditory evoked response, beginning at approximately 100 ms. While this difference occurred during the N1 timeframe, it was not a simple modulation of N1 amplitude as it displayed a wholly different topographic distribution to that of the N1. Global dissimilarity measures using topographic modulation analysis confirmed that this difference between tasks was driven by a shift in the underlying generator configuration. Minimum norm source reconstruction revealed distinct activations that corresponded well with activity within putative dorsal and ventral auditory structures.

  15. Individual Differences in the Speed of Facial Emotion Recognition Show Little Specificity but Are Strongly Related with General Mental Speed: Psychometric, Neural and Genetic Evidence

    Directory of Open Access Journals (Sweden)

    Xinyang Liu

    2017-08-01

    or not. These findings keenly suggest that general speed abilities should be taken into account when the study of emotion recognition abilities is targeted in its specificity.

  16. Molecule nanoweaver

    Science.gov (United States)

    Gerald, II; Rex, E [Brookfield, IL; Klingler, Robert J [Glenview, IL; Rathke, Jerome W [Homer Glen, IL; Diaz, Rocio [Chicago, IL; Vukovic, Lela [Westchester, IL

    2009-03-10

    A method, apparatus, and system for constructing uniform macroscopic films with tailored geometric assemblies of molecules on the nanometer scale. The method, apparatus, and system include providing starting molecules of selected character, applying one or more force fields to the molecules to cause them to order and condense with NMR spectra and images being used to monitor progress in creating the desired geometrical assembly and functionality of molecules that comprise the films.

  17. Speech Recognition

    Directory of Open Access Journals (Sweden)

    Adrian Morariu

    2009-01-01

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

  18. Recognition of the smart card iconic numbers

    Directory of Open Access Journals (Sweden)

    Xin Xue Shi

    2016-01-01

    Full Text Available Manual way of recognizing and inputting the smart card iconic numbers leads to much time consuming, high resources consuming and low accuracy. This paper presents a new automatic method based on the technology of computer vision, including Harr operator for face detection, Fast operator for number location, image binarization, character segmentation and BP neural network for number recognition. Experimental results on 100 ID cards show that by adjusting the parameters of the BP neural network properly, the recognition accuracy is more than 99%, and the recognition time for each ID card is within 0.05 seconds.

  19. Multilingual Data Selection for Low Resource Speech Recognition

    Science.gov (United States)

    2016-09-12

    Multilingual Data Selection For Low Resource Speech Recognition Samuel Thomas, Kartik Audhkhasi, Jia Cui, Brian Kingsbury and Bhuvana Ramabhadran IBM...deep neural network- based multilingual frontends provide significant improvements to speech recognition systems in low resource settings. To ef...present speech recognition results on 7 very limited language pack (VLLP) languages from the second option period of the IARPA Babel program using

  20. Man machine interface based on speech recognition

    International Nuclear Information System (INIS)

    Jorge, Carlos A.F.; Aghina, Mauricio A.C.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.

    2007-01-01

    This work reports the development of a Man Machine Interface based on speech recognition. The system must recognize spoken commands, and execute the desired tasks, without manual interventions of operators. The range of applications goes from the execution of commands in an industrial plant's control room, to navigation and interaction in virtual environments. Results are reported for isolated word recognition, the isolated words corresponding to the spoken commands. For the pre-processing stage, relevant parameters are extracted from the speech signals, using the cepstral analysis technique, that are used for isolated word recognition, and corresponds to the inputs of an artificial neural network, that performs recognition tasks. (author)

  1. Molecule Matters

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 14; Issue 4. Molecule Matters – van der Waals Molecules - History and Some Perspectives on Intermolecular Forces ... Author Affiliations. E Arunan1. Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore 560 012, India.

  2. Molecule Matters

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 14; Issue 4. Molecule Matters – van der Waals Molecules - History and Some Perspectives on Intermolecular Forces. E Arunan. Feature Article Volume 14 Issue 4 April 2009 pp 346-356 ...

  3. Post-training intrahippocampal injection of synthetic poly-alpha-2,8-sialic acid-neural cell adhesion molecule mimetic peptide improves spatial long-term performance in mice.

    Science.gov (United States)

    Florian, Cédrick; Foltz, Jane; Norreel, Jean-Chrétien; Rougon, Geneviève; Roullet, Pascal

    2006-01-01

    Several data have shown that the neural cell adhesion molecule (NCAM) is necessary for long-term memory formation and might play a role in the structural reorganization of synapses. The NCAM, encoded by a single gene, is represented by several isoforms that differ with regard to their content of alpha-2,8-linked sialic acid residues (PSA) on their extracellular domain. The carbohydrate PSA is known to promote plasticity, and PSA-NCAM isoforms remain expressed in the CA3 region of the adult hippocampus. In the present study, we investigated the effect on spatial memory consolidation of a PSA gain of function by injecting a PSA mimetic peptide (termed pr2) into the dorsal hippocampus. Mice were subjected to massed training in the spatial version of the water maze. Five hours after the last training session, experimental mice received an injection of pr2, whereas control mice received PBS or reverse peptide injections in the hippocampal CA3 region. Memory retention was tested at different time intervals: 24 h, 1 wk, and 4 wk. The results showed that the post-training infusion of pr2 peptide significantly increases spatial performance whenever it was assessed after the training phase. By contrast, administration of the control reverse peptide did not affect retention performance. These findings provide evidence that (1) PSA-NCAM is involved in memory consolidation processes in the CA3 hippocampal region, and (2) PSA mimetic peptides can facilitate the formation of long-term spatial memory when injected during the memory consolidation phase.

  4. PANP is a novel O-glycosylated PILR{alpha} ligand expressed in neural tissues

    Energy Technology Data Exchange (ETDEWEB)

    Kogure, Amane [Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871 (Japan); Laboratory of Immunochemistry, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871 (Japan); Shiratori, Ikuo [Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871 (Japan); Wang, Jing [Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871 (Japan); Laboratory of Immunochemistry, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871 (Japan); Lanier, Lewis L. [Department of Microbiology and Immunology and the Cancer Research Institute, University of California San Francisco, San Francisco, CA 94143 (United States); Arase, Hisashi, E-mail: arase@biken.osaka-u.ac.jp [Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871 (Japan); Laboratory of Immunochemistry, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871 (Japan); JST CREST, Saitama 332-0012 (Japan)

    2011-02-18

    Research highlights: {yields} A Novel molecule, PANP, was identified to be a PILR{alpha} ligand. {yields} Sialylated O-glycan structures on PANP were required for PILR{alpha} recognition. {yields} Transcription of PANP was mainly observed in neural tissues. {yields} PANP seems to be involved in immune regulation as a ligand for PILR{alpha}. -- Abstract: PILR{alpha} is an immune inhibitory receptor possessing an immunoreceptor tyrosine-based inhibitory motif (ITIM) in its cytoplasmic domain enabling it to deliver inhibitory signals. Binding of PILR{alpha} to its ligand CD99 is involved in immune regulation; however, whether there are other PILR{alpha} ligands in addition to CD99 is not known. Here, we report that a novel molecule, PILR-associating neural protein (PANP), acts as an additional ligand for PILR{alpha}. Transcription of PANP was mainly observed in neural tissues. PILR{alpha}-Ig fusion protein bound cells transfected with PANP and the transfectants stimulated PILR{alpha} reporter cells. Specific O-glycan structures on PANP were found to be required for PILR recognition of this ligand. These results suggest that PANP is involved in immune regulation as a ligand of the PILR{alpha}.

  5. Neural networks for triggering

    International Nuclear Information System (INIS)

    Denby, B.; Campbell, M.; Bedeschi, F.; Chriss, N.; Bowers, C.; Nesti, F.

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab

  6. Atkins' molecules

    CERN Document Server

    Atkins, Peters

    2003-01-01

    Originally published in 2003, this is the second edition of a title that was called 'the most beautiful chemistry book ever written'. In it, we see the molecules responsible for the experiences of our everyday life - including fabrics, drugs, plastics, explosives, detergents, fragrances, tastes, and sex. With engaging prose Peter Atkins gives a non-technical account of an incredible range of aspects of the world around us, showing unexpected connections, and giving an insight into how this amazing world can be understood in terms of the atoms and molecules from which it is built. The second edition includes dozens of extra molecules, graphical presentation, and an even more accessible and enthralling account of the molecules themselves.

  7. Interstellar Molecules

    Science.gov (United States)

    Solomon, Philip M.

    1973-01-01

    Radioastronomy reveals that clouds between the stars, once believed to consist of simple atoms, contain molecules as complex as seven atoms and may be the most massive objects in our Galaxy. (Author/DF)

  8. Cognitively Inspired Neural Network for Situation Recognition

    Science.gov (United States)

    2010-01-14

    jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (ClRA), Intelligent Systems and Semiotics (ISAS...1996), Godel Theorem and Semiotics . In Proc. Conference on Intelligent Systems and Semiotics 󈨤. Gaithersburg, MD, vol.2, pp. 14-18. Perlovsky, l.1...New Math. and Natural Computation, 2(1), pp.43-55. Perlovsky, L.I . (2006d) . Symbols: Integrated Cognition and Language. Chapter in Semiotics and

  9. The First Quantum Theory of Molecules

    Indian Academy of Sciences (India)

    IAS Admin

    with a kinetic energy proportional to the excess energy of that particle, subsequently called a photon. After one has recognized the quantum laws of nature, or the ... think of molecules as static entities, but Clausius had proposed a dynamic model in which molecules vibrated and rotated, in accordance with the recognition by ...

  10. Speaker Recognition

    DEFF Research Database (Denmark)

    Mølgaard, Lasse Lohilahti; Jørgensen, Kasper Winther

    2005-01-01

    Speaker recognition is basically divided into speaker identification and speaker verification. Verification is the task of automatically determining if a person really is the person he or she claims to be. This technology can be used as a biometric feature for verifying the identity of a person...

  11. Contemporary deep recurrent learning for recognition

    Science.gov (United States)

    Iftekharuddin, K. M.; Alam, M.; Vidyaratne, L.

    2017-05-01

    Large-scale feed-forward neural networks have seen intense application in many computer vision problems. However, these networks can get hefty and computationally intensive with increasing complexity of the task. Our work, for the first time in literature, introduces a Cellular Simultaneous Recurrent Network (CSRN) based hierarchical neural network for object detection. CSRN has shown to be more effective to solving complex tasks such as maze traversal and image processing when compared to generic feed forward networks. While deep neural networks (DNN) have exhibited excellent performance in object detection and recognition, such hierarchical structure has largely been absent in neural networks with recurrency. Further, our work introduces deep hierarchy in SRN for object recognition. The simultaneous recurrency results in an unfolding effect of the SRN through time, potentially enabling the design of an arbitrarily deep network. This paper shows experiments using face, facial expression and character recognition tasks using novel deep recurrent model and compares recognition performance with that of generic deep feed forward model. Finally, we demonstrate the flexibility of incorporating our proposed deep SRN based recognition framework in a humanoid robotic platform called NAO.

  12. Face Recognition using Approximate Arithmetic

    DEFF Research Database (Denmark)

    Marso, Karol

    Face recognition is image processing technique which aims to identify human faces and found its use in various different fields for example in security. Throughout the years this field evolved and there are many approaches and many different algorithms which aim to make the face recognition as effective...... as possible. The use of different approaches such as neural networks and machine learning can lead to fast and efficient solutions however, these solutions are expensive in terms of hardware resources and power consumption. A possible solution to this problem can be use of approximate arithmetic. In many image...... processing applications the results do not need to be completely precise and use of the approximate arithmetic can lead to reduction in terms of delay, space and power consumption. In this paper we examine possible use of approximate arithmetic in face recognition using Eigenfaces algorithm....

  13. Adhesion molecules

    CERN Document Server

    Preedy, Victor R

    2016-01-01

    This book covers the structure and classification of adhesion molecules in relation to signaling pathways and gene expression. It discusses immunohistochemical localization, neutrophil migration, and junctional, functional, and inflammatory adhesion molecules in pathologies such as leukocyte decompression sickness and ischemia reperfusion injury. Highlighting the medical applications of current research, chapters cover diabetes, obesity, and metabolic syndrome; hypoxia; kidney disease; smoking, atrial fibrillation, and heart disease, the brain and dementia; and tumor proliferation. Finally, it looks at molecular imaging and bioinformatics, high-throughput technologies, and chemotherapy.

  14. Object recognition memory: neurobiological mechanisms of encoding, consolidation and retrieval.

    Science.gov (United States)

    Winters, Boyer D; Saksida, Lisa M; Bussey, Timothy J

    2008-07-01

    Tests of object recognition memory, or the judgment of the prior occurrence of an object, have made substantial contributions to our understanding of the nature and neurobiological underpinnings of mammalian memory. Only in recent years, however, have researchers begun to elucidate the specific brain areas and neural processes involved in object recognition memory. The present review considers some of this recent research, with an emphasis on studies addressing the neural bases of perirhinal cortex-dependent object recognition memory processes. We first briefly discuss operational definitions of object recognition and the common behavioural tests used to measure it in non-human primates and rodents. We then consider research from the non-human primate and rat literature examining the anatomical basis of object recognition memory in the delayed nonmatching-to-sample (DNMS) and spontaneous object recognition (SOR) tasks, respectively. The results of these studies overwhelmingly favor the view that perirhinal cortex (PRh) is a critical region for object recognition memory. We then discuss the involvement of PRh in the different stages--encoding, consolidation, and retrieval--of object recognition memory. Specifically, recent work in rats has indicated that neural activity in PRh contributes to object memory encoding, consolidation, and retrieval processes. Finally, we consider the pharmacological, cellular, and molecular factors that might play a part in PRh-mediated object recognition memory. Recent studies in rodents have begun to indicate the remarkable complexity of the neural substrates underlying this seemingly simple aspect of declarative memory.

  15. Molecule Matters

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 16; Issue 12. Molecule Matters - Dinitrogen. A G Samuelson J Jabadurai. Volume 16 Issue 12 ... Author Affiliations. A G Samuelson1 J Jabadurai1. Department of Inroganic and Physical Chemistry, Indian Institute of Science, Bangalore 560 012, India.

  16. Molecule Matters

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 13; Issue 5. Molecule Matters - N-Heterocyclic Carbenes - The Stable Form of R2 C: Anil J Elias. Feature Article Volume 13 Issue 5 May 2008 pp 456-467. Fulltext. Click here to view fulltext PDF. Permanent link:

  17. Molecule Matters

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 11; Issue 9. Molecule Matters - A Chromium Compound with a Quintuple Bond. K C Kumara Swamy. Feature Article Volume 11 Issue 9 September 2006 pp 72-75. Fulltext. Click here to view fulltext PDF. Permanent link:

  18. Chemical recognition software

    Energy Technology Data Exchange (ETDEWEB)

    Wagner, J.S.; Trahan, M.W.; Nelson, W.E.; Hargis, P.J. Jr.; Tisone, G.C.

    1994-12-01

    We have developed a capability to make real time concentration measurements of individual chemicals in a complex mixture using a multispectral laser remote sensing system. Our chemical recognition and analysis software consists of three parts: (1) a rigorous multivariate analysis package for quantitative concentration and uncertainty estimates, (2) a genetic optimizer which customizes and tailors the multivariate algorithm for a particular application, and (3) an intelligent neural net chemical filter which pre-selects from the chemical database to find the appropriate candidate chemicals for quantitative analyses by the multivariate algorithms, as well as providing a quick-look concentration estimate and consistency check. Detailed simulations using both laboratory fluorescence data and computer synthesized spectra indicate that our software can make accurate concentration estimates from complex multicomponent mixtures. even when the mixture is noisy and contaminated with unknowns.

  19. Chemical recognition software

    Energy Technology Data Exchange (ETDEWEB)

    Wagner, J.S.; Trahan, M.W.; Nelson, W.E.; Hargis, P.H. Jr.; Tisone, G.C.

    1994-06-01

    We have developed a capability to make real time concentration measurements of individual chemicals in a complex mixture using a multispectral laser remote sensing system. Our chemical recognition and analysis software consists of three parts: (1) a rigorous multivariate analysis package for quantitative concentration and uncertainty estimates, (2) a genetic optimizer which customizes and tailors the multivariate algorithm for a particular application, and (3) an intelligent neural net chemical filter which pre-selects from the chemical database to find the appropriate candidate chemicals for quantitative analyses by the multivariate algorithms, as well as providing a quick-look concentration estimate and consistency check. Detailed simulations using both laboratory fluorescence data and computer synthesized spectra indicate that our software can make accurate concentration estimates from complex multicomponent mixtures, even when the mixture is noisy and contaminated with unknowns.

  20. Cortical networks for visual self-recognition

    International Nuclear Information System (INIS)

    Sugiura, Motoaki

    2007-01-01

    This paper briefly reviews recent developments regarding the brain mechanisms of visual self-recognition. A special cognitive mechanism for visual self-recognition has been postulated based on behavioral and neuropsychological evidence, but its neural substrate remains controversial. Recent functional imaging studies suggest that multiple cortical mechanisms play self-specific roles during visual self-recognition, reconciling the existing controversy. Respective roles for the left occipitotemporal, right parietal, and frontal cortices in symbolic, visuospatial, and conceptual aspects of self-representation have been proposed. (author)