Browsing by Author "Najeeb, Athaur R."

Now showing 1 - 6 of 6
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    Comparing autoregressive moving average (ARMA) coefficients determination using artificial neural networks with other techniques
    (Proc. of World Academy of Science, Engineering and Technol, 2008-06-28) Aibinu, A. M.; Salami, Momoh-Jimoh E.; Shafie, Amir A.; Najeeb, Athaur R.
    Autoregressive Moving average (ARMA) is a parametric based method of signal representation. It is suitable for problems in which the signal can be modeled by explicit known source functions with a few adjustable parameters. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Autocorrelation, Covariance and most recently, the use of Artificial Neural Network technique. In this paper, the method of using Artificial Neural network (ANN) technique is compared with some known and widely acceptable techniques. The comparisons is entirely based on the value of the coefficients obtained. Result obtained shows that the use of ANN also gives accurate in computing the coefficients of an ARMA system.
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    Evaluating the effect of voice activity detection in isolated Yoruba word recognition system
    (IEEE, 2011-05-17) Aibinu, Abiodun M.; Salami, Momoh-Jimoh E.; Najeeb, Athaur R.; Azeez, J. F.; Rajin, Ataul K.
    This paper discusses and evaluates the effect of voice Activity Detection (VAD) in an isolated Yoruba word recognition system (IYWRS). The word database used in this paper are collected from 22 speakers by repeating the numbers 1 to 9 three times each. A hybrid configuration of Mel-Frequency Cepstral coefficient (MFCC) and Linear Predictive Coding (LPC) have been used to extract the features of the speech samples. Artificial Neural Network algorithms are then used to classify these features. An overall accuracy of about 60% has been achieved from the two proposed feature extraction methods.
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    Increasing The Speed of Convergence of an Artificial Neural Network based ARMA Coefficients Determination Technique
    (World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2008-06-23) Aibinu, Abiodun M.; Salami, Momoh-Jimoh E.; Shafie, Amir A.; Najeeb, Athaur R.
    In this paper, novel techniques in increasing the accuracy and speed of convergence of a Feed forward Back propagation Artificial Neural Network (FFBPNN) with polynomial activation function reported in literature is presented. These technique was subsequently used to determine the coefficients of Autoregressive Moving Average (ARMA) and Autoregressive (AR) system. The results obtained by introducing sequential and batch method of weight initialization, batch method of weight and coefficient update, adaptive momentum and learning rate technique gives more accurate result and significant reduction in convergence time when compared t the traditional method of back propagation algorithm, thereby making FFBPNN an appropriate technique for online ARMA coefficient determination.
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    MRI reconstruction using discrete Fourier transform: a tutorial
    (World Academy of Science, Engineering and Technology, 2008-06-28) Aibinu, Abiodun M.; Salami, Momoh-Jimoh E.; Shafie, Amir A.; Najeeb, Athaur R.
    The use of Inverse Discrete Fourier Transform (IDFT) implemented in the form of Inverse Fourier Transform (IFFT) is one of the standard method of reconstructing Magnetic Resonance Imaging (MRI) from uniformly sampled K-space data. In this tutorial, three of the major problems associated with the use of IFFT in MRI reconstruction are highlighted. The tutorial also gives brief introduction to MRI physics; MRI system from instrumentation point of view; K-space signal and the process of IDFT and IFFT for One and two dimensional (1D and 2D) data.
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    Optimal model order selection for transient error autoregressive moving average (TERA) MRI reconstruction method
    (International Conference on Medical system Engineering (ICMSE), 2008-08) Aibinu, Abiodun M.; Najeeb, Athaur R.; Salami, Momoh-Jimoh E.; Shafie, Amir A.
    An alternative approach to the use of Discrete Fourier Transform (DFT) for Magnetic Resonance Imaging (MRI) reconstruction is the use of parametric modeling technique. This method is suitable for problems in which the image can be modeled by explicit known source functions with a few adjustable parameters. Despite the success reported in the use of modeling technique as an alternative MRI reconstruction technique, two important problems constitutes challenges to the applicability of this method, these are estimation of Model order and model coefficient determination. In this paper, five of the suggested method of evaluating the model order have been evaluated, these are: The Final Prediction Error (FPE), Akaike Information Criterion (AIC), Residual Variance (RV), Minimum Description Length (MDL) and Hannan and Quinn (HNQ) criterion. These criteria were evaluated on MRI data sets based on the method of Transient Error Reconstruction Algorithm (TERA). The result for each criterion is compared to result obtained by the use of a fixed order technique and three measures of similarity were evaluated. Result obtained shows that the use of MDL gives the highest measure of similarity to that use by a fixed order technique.
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    Performance Analysis of Clustering Based Genetic Algorithm
    (IEEE, 2016-07-26) Najeeb, Athaur R.; Aibinu, A. M.; Nwohu, M. N.; Salami, Momoh-Jimoh E.; Salau, Bello H.
    In this work, performance analysis of Clustering based Genetic Algorithm (CGA) proposed in the literature has been undertaken. The proposed CGA on which the performance analysis of this paper is based involve the use of two centroids based clustering technique as a new method of chromosomes selection at the reproduction stage in a typical Genetic Algorithm. Population Control and Polygamy mating techniques were introduced to improve the performance of the algorithm. Results obtained from the determination of optimal solutions to the: Sphere, Schwefel 2.4, Beale and another known optimization functions carried out in this work shows that the proposed CGA converges to global solutions within few iterations and can also be adopted for function optimization aside from the route optimization problem previously reported in Literature.