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In this paper, we propose a new face image restoration method based on the features extracted from the noisy images given by the principal components of the noise covariance matrix. This technique deliberate the additive normal scattered degradation classic and use of a code shrinkage technique to remove noise from the images. The proposed work have been a large number of artificial neural networks...
This paper addresses the problem of recognizing handwritten numerals for Gujarati Language. Three methods are presented for feature extraction. One belongs to the spatial domain and other two belongs to the transform domain. In first technique, a new method has been proposed for spatial domain which is based on Freeman chain code. This method obtains the global direction by considering n × n neighbourhood...
In this paper an artificial intelligence based framework for fault detection and diagnosis to support supervision of the cardboard production is presented. Cutting accuracy significantly affects the quality of the product and because there are many different causes of errors, their identification requires a sound knowledge and experience of the service staff. The authors observed that the sources...
Convolutional Neural Network (CNN) is a kind of deep artificial neural network. CNN has kinds of merits, such as multidimensional data input, and fewer parameters. However, the network always has the problem of overfitting due to lots of connection in the full connection layer. In order to overcome the overfitting problem, the denoising method is used to corrupt input data and hidden unit output which...
In this study, a novel method for estimating wrist forces from surface electromyogram (EMG) measured from the upper limb is proposed, which can be applied for unilateral transradial amputees. Three degrees of freedom (DoFs) of wrist including flexion-extension, abduction-adduction, and pronation-supination were used. We first classify feature vectors extracted from the EMG signals into three classes...
In this paper, we propose a novel deep convex network method for domain adaptation in multitemporal remote sensing imagery. We fuse the capabilities of the extreme learning machine (ELM) classifier and local feature descriptor techniques to boost the classification accuracy. We use the Affine Scale Invariant Feature Transform (ASIFT) to extract the key points from the image pair, i.e. source and target...
Yarn quality prediction plays an important role in modern textile production management. Due to the nonlinearity and non-stationarity of yarn quality indicator series, the accuracy of the commonly used conventional methods, including regression analyses and artificial neural networks (ANN), has been limited. A prediction model based on support vector regression (SVR) is proposed in this paper to solve...
Neural Networks trained with the Belief Propagation Inspired (BPI) algorithm are able to learn a number of associations close to the theoretical limit in time that is sublinear in the number of input. Using binary synapses, implemented by a memristor, a single layer perceptron with BPI has been proposed. It well know that perceptrons with step function type nonlinearity can be implemented by a suitable...
Heterogeneous computing, which combines devices with different architectures, is rising in popularity, and promises increased performance combined with reduced energy consumption. OpenCL has been proposed as a standard for programing such systems, and offers functional portability. It does, however, suffer from poor performance portability, code tuned for one device must be re-tuned to achieve good...
Microarray gene expression profiles are generally consists of very large number of genes. The total numbers of samples in the gene expression profiles are usually very less. Therefore, ranking of informative genes plays an important role in sample classification. In this paper, we present a comparative study on different gene ranking methods by applying them to two datasets. Support vector machine...
Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models...
The continuously increasing traffic demand faces us with increased CAPital Expenditures (CAPEX) and Operational Expenditure (OPEX). Self Organizing Network (SON) functions aim to lower these costs by automating the network tuning. A SON instance is a realization of a SON function which can tune one or a set of cells. Having several uncoordinated SON functions in the network creates a risk for conflicts...
This paper introduces an electrocardiogram beat classification method based on deep belief networks. This method includes two parts: feature extraction and classification. In the feature extraction part, features are extracted from the original electrocardiogram signal: including features extracted by deep belief networks and timing interval features. Several classifiers are selected to classify the...
Parkinson's disease is a complex condition currently monitored at home with paper diaries which rely on subjective and unreliable assessment of motor function at nonstandard time intervals. We present an innovative wearable and unobtrusive monitoring system for patients which can help provide physicians with significantly improved assessment of patients' responses to drug therapies and lead to better-targeted...
Solving the non-linear distortion problems in wireless communications is often based on developing the behavioral models of non-linear components. In this paper, a non-linear Volterra model up to third order is developed by using an artificial neural network (ANN) approach. The Volterra kernels are derived from the parameters of a feed-forward time delay neural network with a suitable activation function...
In wireless communication systems the amplifier non-linear distortion problems are extremely challenging. The linearization techniques based on behavioral models of amplifiers, seem to be very promising, therefore developing a suitable non-linear model is of the crucial importance. A rigorous approach for non-linear modeling is using the of Volterra series, however the calculation of the Volterra...
This paper proposes a novel approach for automatic estimation of four important traits of speakers, namely age, height, weight and smoking habit, from speech signals. In this method, each utterance is modeled using the i-vector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor Analysis (NFA) framework which is based on a...
This paper highlights the importance of edge detection in action recognition and presents an edge detection method based on Artificial Neural Network. To implement this concept the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used. The ANFIS is first designed, trained and checked for average error tolerance. The system is then tested with a few sample images whose results are discussed at...
Modern aerospace vehicles are expected to have non-conventional flight envelopes and, in order to operate in uncertain environments, they must guarantee a high level of robustness and adaptability. A Neural Networks (NN) controller, with real-time learning capability, can be used in applications with manned or unmanned aerial vehicles. In this paper we propose a realtime system, based on a NN model,...
Ship motion prediction plays a prominent role in the whole ship motion process. This paper presents a new approach for ship motion prediction. In order to obtain more effective prediction result, the paper studied the BP neural network and Volterra series model, and the chaos characteristics of ship motion time series. A novel method of single-output three-layer BP neural network to identify Volterra...
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