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International Conference on Computer, Electrical and Electronics Engineering (ICCEEE)

作者： Zayid, Elrasheed Ismail Mohommoud Akay, Mehmet Fatih Univ Elimam Elmahdi Dept Comp Engn Kosti 11588 Sudan Cukurova Univ Dept Comp Engn Adana 01330 Turkey

ISBN: (纸本)9781467362313;9781467362320

In this study we develop a model for predicting the performance measures of a distributed shared memory (DSM) multiprocessor architecture by using a reliable attributes selection method. The structure of a DSM platform is interconnected by the Simultaneous optical Multiprocessor Exchange Bus (SOME-Bus), which is a low latency high bandwidth fiber-optic interconnection network. OPNET Modeler is used to simulate the SOME-Bus multiprocessor architecture and to create the datasets. The input variables chosen for the prediction model include the ratio service time over packet transfer time (varies from 0.01 to 1), traffic patterns (uniform, hot region, bit reverse and perfect shuffle), DSM protocol type, node number (varies to 16, 32 and 64), thread number (varies from 1 to 6). The attributes selection method examined the models using different machine learning tools. These tools include: multilayer feed forward artificial neural network (MFANNs), support vector regression with radial basis function (SVR-RBF) and multiple linear regression (MLR). Cross validation (CV) technique is applied using 10 folds. The results show that MFANN-based model gives the best results (i.e. SEE=11.1 and R = 0.998587 for CWT;SEE=18.96 and R = 0.997 for NRT;SEE=60.46 and R=0.8638 for IWT;SEE=0.04795 and R = 0.9838 for PU;SEE=0.0348 and R=0.9990 for CU). Results of the constructed new selected subset are compared with the original feature space and the findings prove the accuracy and reliability of the model.

关键词： distributed shared memory parallel multiprocessor architectures and artificial neural networks

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Mixed Norm-Based Image Restoration Using neural network

Mixed Norm-Based Image Restoration Using Neural Network

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Yuan-nan Xu Jing Wang Yan-bing Dong Science and Technology on Optical Radiation Laboratory Beijing P.R. China

In this paper, a novel image restoration model is presented based on the adaptive mixed norm regularization and Hop field neural network. The new error function of this image restoration model combines the L2-norm and L1-norm. To fit the neural network processing, the nonlinear gradient operator of L1-norm is decomposed to the sum of linear operators. Two methods of calculating the adaptive scale control parameter and the modified implementation technique using neural network are presented. Experimental results demonstrate the proposed algorithms are more effective than the traditional and total variation image restoration algorithms.

关键词： Image restoration Signal processing algorithms Adaptation models Image edge detection Hopfield neural networks Noise

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Contactless Fingerprint Recognition: a neural Approach for Perspective and Rotation Effects Reduction

Contactless Fingerprint Recognition: a Neural Approach for P...

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IEEE Symposium on Computational Intelligence in Biometrics and Identity Management

作者： Ruggero Donida Labati Angelo Genovese Vincenzo Piuri Fabio Scotti Department of Computer Science Universita degli Studi di Milano

ISBN: (纸本)9781467358781

Contactless fingerprint recognition systems are being researched in order to reduce intrinsic limitations of traditional biometric acquisition technologies, encompassing the release of latent fingerprints on the sensor platen, non-linear spatial distortions in the captured samples, and relevant image differences with respect to the moisture level and pressure of the fingertip on the sensor surface. Fingerprint images captured by single cameras, however, can be affected by perspective distortions and deformations due to incorrect alignments of the finger with respect to the camera optical axis. These non-idealities can modify the ridge pattern and reduce the visibility of the fingerprint details, thus decreasing the recognition accuracy. Some systems in the literature overcome this problem by computing three-dimensional. models of the finger. Unfortunately, such approaches are usually based on complex and expensive acquisition setups, which limit their portability in consumer devices like mobile phones and tablets. In this paper, we present a novel approach able to recover perspective deformations and improper fingertip alignments in single camera systems. The approach estimates the orientation difference between two contactless fingerprint acquisitions by using neural networks, and permits to register the considered samples by applying the estimated rotation angle to a synthetic three-dimensional model of the finger surface. The generalization capability of neural networks offers a significant advantage by allowing processing a robust estimation of the orientation difference with a very limited need of computational resources with respect to traditional techniques. Experimental results show that the approach is feasible and can effectively enhance the recognition accuracy of single-camera biometric systems. On the evaluated dataset of 800 contactless images, the proposed method permitted to decrease the equal error rate of the used biometric system from 3.04% to 2.2

关键词： CONTACT Perspective Fingerprint recognition Fingerprints Fingers moisture level image difference neural network Error analysis Cameras three dimensional models

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Intelligent sensor system for discrimination of material type using neural networks

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APPLIED SOFT computing 2012年第1期12卷 543-552页

作者： Charniya, Nadir N. Dudul, Sanjay V. Lokmanya Tilak Coll Engn Navi Mumbai Maharashtra India SGB Amravati Univ Dept Appl Elect Amravati Maharashtra India

neural network based classification of material type even with the variation in the sensor parameter is investigated in this paper. The sensor is developed by means of a lightweight plunger probe and an optical mouse sensor. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The experiment is conducted to obtain the bouncing signals for plain surface of an objects kept at different distances from the probe. During the bouncing of the probe, time varying signals are generated from optical mouse that are recorded in data files on PC. Some dominant unique features are then extracted using signal processing tools to optimize neural network based classifier. The time and features of bouncing signal are related to the material type, and each material has a unique set of such properties. It is found that the sensor system is intelligent due to its ability to classify the material type even with the variation in the sensor parameter (distance between the sensor probe and plain objects). The classifiers are developed using two neural networks configurations, namely a well-known Multi-layer Perceptron neural networks (MLP NN), and Radial Basis Function neural networks (RBF NN). MLP NN and RBF NN models are designed to maximize accuracy under the constraints of minimum network dimension. The optimal parameters of MLP NN and RBF NN models based on various performance measures that include percentage classification accuracy (PCLA) on the testing data, and area under Receiver Operating Characteristics (ROC), and are determined. For the sensor data set, the PCLA of both the classifiers are found reasonable consistently in respect of rigorous testing using different data partitions. The areas under the ROC curves are close to unity. Performances of the two classifiers have been compared. It has been found that the RBF NN is more robust to noise, and epochs required for tra

关键词： Classification neural networks Signal processing Sensor optical mouse

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Adaptive and efficient colour quantisation based on a growing self-organising map

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IET IMAGE PROCESSING 2012年第5期6卷 463-472页

作者： Teng, W. -G. Chang, P. -L. Yang, C. -T. Natl Cheng Kung Univ Dept Engn Sci Tainan 70101 Taiwan Univ London Imperial Coll Sci Technol & Med Dept Comp London England Natl Cheng Kung Univ Dept Psychol Tainan 70101 Taiwan Natl Cheng Kung Univ Inst Cognit Sci Tainan 70101 Taiwan

Studies on colour quantisation have indicated that its applications range from the relaxation of displaying hardware constraints in early years to a modern usage of facilitating content-based image retrieval tasks. Among many alternatives, approaches based on neural network models are generally accepted to be able to produce quality results in colour quantisation. However, these approaches using n quantised neurons require O(n) for a full search strategy, which is inefficient when n becomes large. In view of this, we propose to incorporate a growing quadtree structure into a self-organising map (GQSOM) which reaches a search time O(logn). Specifically, the strategy of inheriting from parent neurons hierarchically facilitates a much more efficient and flexible learning process. Both theoretical and empirical studies have shown that our approach is adaptive in determining an appropriate number of quantised colours, and the performance is significantly improved without compromise of the quantisation quality.

关键词： neural computing techniques displaying hardware constraints self-organising feature maps neural network models self-organising map growing quadtree structure efficient colour quantisation quantisation quality Computer vision and image processing techniques flexible learning process image retrieval content-based image retrieval tasks optical, image and video signal processing image colour analysis GQSOM

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Design and simulation of a multiport neural network heteroassociative memory for optical pattern recognitions

Design and simulation of a multiport neural network heteroas...

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Conference on optical Pattern Recognition XXIII

作者： Krasilenko, Vladimir Lazarev, Alexander Grabovlyak, Sveta Vinnitsa Social Econ Inst Keletskaya Str 86-131 UA-21021 Vinnitsa Ukraine Vinnitsa Natl Tech Univ UA-21021 Vinnitsa Ukraine

ISBN: (纸本)9780819490766

The modified matrix equivalently models (MMEMs) of multiport neural network heteroassociative memory (MP_NN_HAM) with double adaptive - equivalently weighing (DAEW) for recognition of 1D and 2D-patterns (images) are offered. It is shown, that computing process in MP_NN_HAM under using the proposed MMEMs, is reduced to two-step and multi-step algorithms and step-by-step matrix-matrix (tensor-tensor) procedures. The base operations and structural components for construction of MP_NN_HAM are matrix-matrix multipliers and matrixes of nonlinear converters, including threshold transformations. Advantages of such MMEMs for MP_NN_HAM were shown and confirmed by computer simulation results. The aim of paper is research of improved models and MP_NN_HAM for input 1D and 2D signals with unipolar coding and their capacity determination. The given results of computer simulations confirmed the perspective of such models. Results were also received for case of a MP_NN_HAM on base of MMEMs capacity exceeded a neurons amount. This memory is intended to recognize parallel and refresh P input distorted images (N-element vector). Such MP_NN_HAM is a kind of combination consisting of P independently functioning NN_HAM with common memory. Variants of optical realization of MP_NN_HAM architectures are considered in paper. A whole system is consists of two matrix-matrix (for 1D patterns) or two tensor-tensor (for 2D patterns) equivalentors (E) (or nonequivalentors (NE)) (MME and MMNE or TTE and TTNE). The proposed E (or NE) architecture with temporary integration has more large dimension of HAM and more simple design.

关键词： optoelectronic neural networks time-pulse coded signals matrix optoelectronic computing equivalence and nonequivalence operations

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Crack Detection in Concrete Surfaces using Image Processing, Fuzzy Logic, and neural networks

Crack Detection in Concrete Surfaces using Image Processing,...

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IEEE 5th International Conference on Advanced Computational Intelligence (ICACI)

作者： Choudhary, Gajanan K. Dey, Sayan Indian Inst Technol Dept Civil Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9781467317443

Automation in structural health monitoring has generated a lot of interest in recent years, especially with the introduction of cheap digital cameras. This paper presents fuzzy logic and artificial neural network based models for accurate crack detection on concrete. Features are extracted from digital images of concrete surfaces using image processing which incorporates the edge detection technique. The properties of extracted features are fed into the models for detecting cracks. Two kinds of approaches have been implemented in this study: the image approach which classifies an image as a whole, and the object approach which classifies each component or object in an image into cracks and noise. The models have been tested on 205 images and evaluated on the basis of five measures of performance.

关键词： automatic optical inspection concrete condition monitoring crack detection edge detection feature extraction fuzzy logic image classification neural nets structural engineering computing

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neural network RULE EXTRACTION BY A NEW ENSEMBLE CONCEPT AND ITS THEORETICAL AND HISTORICAL BACKGROUND: A REVIEW

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2013年第4期12卷 1340006-1340006页

作者： Hayashi, Yoichi Meiji Univ Dept Comp Sci Tama Ku Kawasaki Kanagawa 2148571 Japan

This paper presents theoretical and historical backgrounds related to neural network rule extraction. It also investigates approaches for neural network rule extraction by ensemble concepts. Bologna pointed out that although many authors had generated comprehensive models from individual networks, much less work had been done to explain ensembles of neural networks. This paper carefully surveyed the previous work on rule extraction from neural network ensembles since 1988. We are aware of three major research groups i.e., Bologna' group, Zhou' group and Hayashi' group. The reason of these situations is obvious. Since the structures of previous neural network ensembles were quite complicated, the research on the efficient rule extraction algorithm from neural network ensembles was few although their learning capability was extremely high. Thus, these issues make rule extraction algorithm for neural network ensemble dificult task. However, there is a practical need for new ideas for neural network ensembles in order to realize the extremely high-performance needs of various rule extraction problems in real life. This paper successively explain nature of artificial neural networks, origin of neural network rule extraction, incorporating fuzziness in neural network rule extraction, theoretical foundation of neural network rule extraction, computational complexity of neural network rule extraction, neuro-fuzzy hybridization, previous rule extraction from neural network ensembles and dificulties of previous neural network ensembles. Next, this paper address three principles of proposed neural network rule extraction: to increase recognition rates, to extract rules from neural network ensembles, and to minimize the use of computing resources. We also propose an ensemble-recursive-rule extraction (E-Re-RX) by two or three standard backpropagation to train multi-layer perceptrons (MLPs), which enabled extremely high recognition accuracy and the extraction of comprehensible r

关键词： Ensemble concepts rule extraction Re-RX algorithm ensemble methods neural network rule extraction neural network ensembles backpropagation MLPs

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Unified Perception-Prediction Model for Context Aware Text Recognition on a Heterogeneous Many-Core Platform

Unified Perception-Prediction Model for Context Aware Text R...

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International Joint Conference on neural networks (IJCNN)

作者： Qiu, Qinru Wu, Qing Linderman, Richard SUNY Binghamton Binghamton NY 13902 USA USAF AFMC AFRL RITC Griffiss AFB NY 13441 USA

ISBN: (纸本)9781424496365

Existing optical character recognition (OCR) software tools can perform text image detection and pattern recognition with fairly high accuracy, however their performance will be significantly impaired when the image of the character is partially blocked or smudged. Such missing information does not hinder the human perception because we predict the missing part based on the word level and sentence level context of the character. In order to mimic the human cognitive behavior, we developed a hybrid cognitive architecture combining two neuromorphic computing models, i.e. brain-state-in-a-box (BSB) and cogent confabulation, to achieve context-aware text recognition. The BSB model performs the character recognition from input image while the confabulation models perform the context-aware prediction based on the word and sentence knowledge bases. The software tool is implemented on an 1824-core computing cluster. Its accuracy and performance are analyzed in the paper.

关键词： Brain modeling Character recognition Computational modeling Knowledge based systems Neurons OCR software tools Training context aware text recognition heterogeneous many-core platform hybrid cognitive architecture knowledge based systems knowledge bases neuromorphic computing object detection optical character recognition optical character recognition pattern recognition software tools text analysis text image detection ubiquitous computing unified perception-prediction model

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Application of artificial neural network in estimating monthly time series reference evapotranspiration with minimum and maximum temperatures

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PADDY AND WATER ENVIRONMENT 2011年第2期9卷 207-220页

作者： Jahanbani, Heerbod El-Shafie, Ahmed Hussein Univ Putra Malaysia Fac Engn Dept Civil Engn Serdang Malaysia Univ Kebangsaan Malaysia Fac Engn Dept Civil Engn Bangi 43600 Malaysia

There are various methods for computing reference evapotranspiration (ETo) using meteorological data. However, such models tend to perform well for predicting ETo close to the mean, but do not keep accurate performance with extreme observations. It is recognized that the Penman-Monteith (PM) model has the best performance when rich data is available to calculate the ETo, which is not frequently available to a certain extent. In case of poor data, such as prediction of futuristic ETo while investigating climate change effect, although there are models other than PM like Hargreaves-Samani (HGS), the universal sustainability of these models are not quit proved. Accordingly, the calculation of ETo still required numerous research to reach accurate estimation of ETo specially when there is lacking for data to utilize PM method. Recently, methods based on artificial intelligence (AI) have been suggested to provide reliable prediction model for several application in engineering. This manuscript employed artificial neural network (ANN) for predicting daily ETo at Rasht city located northern part of Iran using minimum and maximum daily temperatures collected from 1975 to 1988 of the region. A comprehensive data analysis utilizing the daily time series, minimum and maximum temperatures and solar radiation (T (min), T (max) and R (s)), as input pattern to predict daily ETo at the current month and for the following month is proposed. The employed ANN model was feed forward backpropagation (FFBP) type with Bayesian regulation backpropagation. The mean square error, mean absolute error, mean absolute relative error and regression coefficient are the statistical performance indices used to evaluate the model accuracy. The results showed that the proposed ANN model could successfully be used to predict daily ETo using only maximum and minimum temperatures with significant level of accuracy. In addition, results show that the proposed ANN model outperforms HGS method.

关键词： Evapotranspiration Rich and poor data Feed forward backpropagation

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