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In this paper, we present an approach to estimate fractal complexity of discrete time signal waveforms based on computation of area bounded by sample points of the signal at different time resolutions. The slope of best straight line fit to the graph of log(Ark/ rk2) versus log(1/rk) is estimated, where Ark is the area computed at different time resolutions and rk time resolutions at which the area...
Support Vector Machine (SVM) is one of the state-of-the-art tools for linear and nonlinear pattern classification. One of the design issues in SVM classifier is reducing the number of support vectors without compromising the classification accuracy. In this paper, a novel technique known as Diminishing Learning (DL) is proposed for an SVM based multi-class pattern recognition system. In this technique,...
Electroencephalograms (EEG) are the brain signals that provide us the valuable information about the normal or epileptic state of the brain. In this paper the EEG signals were characterized by wavelet, sample and spectral entropy approach and the recurrent neural network classifier is used for the automated detection of epileptic seizures.
Preprocessing is an important step for automatic check processing in Indian scenario where there is huge variation in writing style, especially the way in which Courtesy Amount is terminated and the fractional (Paisa) amount is written. Courtesy Amount Recognition (CAR) and Legal Amount Recognition (LAR) form the core of automated check processing system. For CAR identification, a number of approaches...
Support Vector Machines (SVMs) have become an increasingly popular tool for machine learning tasks involving classification and regression, and have shown superior performance compared to other machine learning techniques. In this paper we propose a hybrid classification technique to extract fuzzy rules from the support vector machine and evaluate the rules against decision tree classifier constructed...
This paper presents a hybrid technique to enhance the quality of the rule-based approach to generate prosody for Malay speech synthesis by integrating prosody parametric manipulation with template parametric manipulation so as to increase the intonation variability of the synthesized output. Basically the prosodic features of the neutral synthesized speech are manipulated in an attempt to express...
This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation...
Wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle monitoring systems that ensure safe and secure operations of the rail vehicle. To make an energy-efficient WSN application, power consumption due to raw data collection and pre-processing needs to be kept to a minimum level. In this paper, an energy-efficient data acquisition...
We propose a classical approach in the segmentation of canonical syllables of Telugu document images. Connected component approach is a simple strategy adopted until recent period. In this paper we propose a canonical syllable model which is equivalent to canonical structure C(C(CV))). The model consists of zone separation and component extraction phases as independent parts. The relation between...
In distributed multisensor fusion, local estimates may have to be communicated to a distant central processor. Hence, the communication channel noise is an important factor to the fusion algorithm. Optimal linear methods can be applied when channel noise is supposed to be Gaussian. In practice, the channel noise is not Gaussian and usually modeled by a contaminated Gaussian distribution. A two-stage...
This contribution deals with identification of fractional-order dynamical systems. System identification, which refers to estimation of process parameters, is a necessity in control theory. Accurate estimation is particularly important for systems having varying parameters, which is the usual case with physical processes. Real processes are usually of fractional order as opposed to the ideal integral...
This paper describes electrocardiogram (ECG) pattern classification using QRS morphological features and the artificial neural network. Four types of ECG patterns were chosen from the MIT-BIH database to be classified, including normal sinus rhythm, premature ventricular contraction, atrial premature beat and left bundle branch block beat. Authors propose a set of six ECG morphological features to...
As the expansion of power systems continue, the challenge of real monitoring and control gets bigger. Important network parameters are measured at various points on the system and transferred to the control center, where the data is used for various energy management system (EMS) functions. State estimation forms one of the primary EMS functions at the control center. Accuracy of the state estimation...
We are addressing the problem of jointly using multiple noisy speech patterns for automatic speech recognition (ASR), given that they come from the same class. If the user utters a word K times, the ASR system should try to use the information content in all the K patterns of the word simultaneously and improve its speech recognition accuracy compared to that of the single pattern based speech recognition...
State-of-the-art Speaker Identification (SI) systems use Gaussian Mixture Models (GMM) for modeling speakerspsila data. Using GMM, a speaker can be identified accurately even from a large number of speakers, when model complexity is large. However, lower ordered speaker model using GMM show poor accuracy as lesser number of Gaussian are involved. In SI context, not much attention have been paid towards...
In this paper, a new feature set is presented and evaluated based on sinusoidal modeling of audio signals. Duration of the longest sinusoidal model frequency track, as a measure of the harmony, is used and compared to typical features as input into an audio classifier. The performance of this sinusoidal model feature is evaluated through classification of audio to speech and music using both the GMM...
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