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Text-to-phoneme mapping is a very important preliminary step in any text-to-speech synthesis system. In this paper, we study the performances of the multilayer perceptron (MLP) neural network for the problem of text-to-phoneme mapping. Specifically, we study the influence of the input letter encoding in the conversion accuracy of such system. We show, that for large network complexities the orthogonal...
Single instruction, multiple data (SIMD) signal processors for wireless communications require efficient vectorized algorithms for radix-2 and mixed-radix Fast Fourier Transforms (FFTs). Especially, mixed-radix FFTs are challenging for a processor that operates on power-of-two length vectors. We analyze the vectorization of pure radix-2 and mixed-radix FFTs and demonstrate that both FFTs have different...
This paper presents a new approach for 3D face modeling and recognition. Motivated by finding a representation that embodies a high power of discrimination between face classes, a new type of 3D shape descriptors is suggested. We have developed a fully automatic system which uses an alignment algorithm to register 3D facial scans. In addition, scalability in both time and space is achieved by converting...
The paper proposed to apply a fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear plants. The parameters of the local recurrent neural network models are used for a local indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule based control system. The applicability of the proposed...
This paper proposes a new design method of an adaptive regulator based on output feedback control for nonlin- ear systems with higher order relative degree and disturbances. The proposed method is an adaptive output feedback control based on the OFEP (Output Feedback Exponentially Passive) properties of the controlled system. To realize a stable adaptive output feedback control system, we introduce...
Grid systems have emerged as a means of sharing computational resources and information. Providing services for accessing, sharing and modifying large databases is a crucial task for grid management systems. This paper proposes an artificial neural network (ANN) prediction mechanism that provides an enhancement to data replication solutions within grid systems. Current replication services often exhibit...
Recently, various techniques using cyclic redundancy check (CRC) codes for error correction have been proposed. In previous techniques, a small number of unreliable bits in a packet were toggled in order to change negative acknowledgement (NAK) into acknowledgement (ACK). The difficulty of using these techniques is that the worst case complexity is still high because the number of possible error patterns...
In this paper, space vector pulse width modulation (SVPWM) scheme for voltage fed inverter (VFI) using conventional method and artificial neural network (ANN) based approach are presented separately. In the conventional method, the difficulty of explicitly expressing cross-over and holding-angle as a function of modulation factor in overmodulation mode-I and mode-II respectively are overcome by introducing...
Human geography is a concept used to indicate the augmentation of standard geographic layers of information about an area with behavioral variations of the people in the area. In particular, the actions of people can be attributed to both local and regional variations in physical (i.e., terrain) and human (e.g., income, political, cultural) variables. In this paper, we study the utility of a human...
The snag in a DTC scheme based induction motors is the presence of high content of torque ripples in the output torque. This has been reduced by modifying the three-level torque controller to five-level torque controller. Moreover, the controllability of the torque in motor with no overshoot and minimal ripples, good transient and steady state responses forms the basis of performance analysis. This...
This article presents a methodology for detection of high impedance faults (HIF). HIF occurs when e.g. a cable makes contact with objects of high electric resistance, resulting in a nonsignificant increase in current. Such faults cannot be detected by traditional protection devices that operate due to overcurrent. The developed methodology is based on making use of variables from a power quality meter...
In this paper, space vector pulse width modulation (SVPWM) scheme for voltage fed inverter (VFI) using conventional method and artificial neural network (ANN) based approach are presented separately. In the conventional method, the difficulty of explicitly expressing cross-over and holding-angle as a function of modulation factor in overmodulation mode-I and mode-II respectively are overcome by introducing...
The popular i-vector approach to speaker recognition represents a speech segment as an i-vector in a low-dimensional space. It is well known that i-vectors involve both speaker and session variances, and therefore additional discriminative approaches are required to extract speaker information from the ‘total variance’ space. Among various methods, the probabilistic linear discriminant analysis (PLDA)...
Character Recognition has become an interesting and a challenge topic research in the field of pattern recognition in recent decade. It has numerous applications including bank cheques, address sorting and conversion of handwritten or printed character into machine-readable form. Artificial neural network including self-organization map and multilayer perceptron network with the learning ability could...
Adaptive networks (ANs) rely on local adaptive filters (AFs) and a cooperation protocol to achieve a common goal, e.g., estimating a set of parameters. This protocol fuses the information from the rest of the network based on local combiners whose design impacts directly the network performance. Indeed, although diffusion schemes improve network performance on average, heterogeneity in signal statistics...
The brain-inspired neural networks have demonstrated great potential in big data analysis. The spiking neural network (SNN), which encodes the real world data into spike trains, promises great performance in computational ability and energy efficiency. Moreover, it is much more biologically plausible than the traditional artificial neural network (ANN), which keeps the input data in its original form...
Classification and decision systems in data analysis are mostly based on accuracy optimization. This criterion is only a conditional informative value if the data are imbalanced or false positive/negative decisions cause different costs. Therefore more sophisticated statistical quality measures are favored in medicine, like precision, recall etc‥ Otherwise, most classification approaches in machine...
Applying weight regularisation to gradient-descent based neural network training methods such as backpropagation was shown to improve the generalisation performance of a neural network. However, the existing applications of weight regularisation to particle swarm optimisation are very limited, despite being promising. This paper proposes adding a regularisation penalty term to the objective function...
The realization of robotic systems that understands human intentions and produces accordingly complex behaviors is needed particularly for disabled persons, and would consequently benefit the aged. For this purpose, a control technique that recognizes human intentions from neural responses called brain machine interface (BMI) have been suggested. The unique ability to communicate with machines by...
Deep architectures have been used in transfer learning applications, with the aim of improving the performance of networks designed for a given problem by reusing knowledge from another problem. In this work we addressed the transfer of knowledge between deep networks used as classifiers of digit and shape images, considering cases where only the set of class labels, or only the data distribution,...
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