Browsing by Author "Aibinu, Abiodun M."

Now showing 1 - 14 of 14
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    Analysis of the ECG signal using SVD-based parametric modelling technique
    (IEEE, 2011-01-17) Baali, Hamza; Salami, Momoh-Jimoh E.; Akmeliawati, Rini; Aibinu, Abiodun M.
    A new parametric modeling technique for the analysis of the ECG signal is presented in this paper. This approach involves the projection of the excitation signal on the right eigenvectors of the impulse response matrix of the LPC filter. Each projected value is then weighted by the corresponding singular value, leading to an approximated sum of exponentially damped sinusoids (EDS). A two-stage procedure is then used to estimate the EDS model parameters. Prony's algorithm is first used to obtain initial estimates of the model, while the Gauss-Newton method is applied to solve the non-linear least-square optimisation problem. The performance of the proposed model is evaluated on abnormal clinical ECG data selected from the MIT-BIH database using objective measures of distortion. A good compression ratio per beat has been obtained using the proposed algorithm which is quite satisfactory when compared to other techniques.
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    Artificial neural network based autoregressive modeling technique with application in voice activity detection
    (Pergamon, 2019-09-01) Aibinu, Abiodun M.; Salami, Momoh-Jimoh E.; Shafie, Amir A.
    A new method of estimating the coefficients of an autoregressive (AR) model using real-valued neural network (RVNN) technique is presented in this paper. The coefficients of the AR model are obtained from the synaptic weights and adaptive coefficients of the activation function of a two layer RVNN while the number of neurons in the hidden layer is estimated from over-constrained system of equations. The performance of the proposed technique has been evaluated using sinusoidal data and recorded speech so as to examine the spectral resolution and line splitting as well as its ability to detect voiced and unvoiced data section from a recorded speech. Results obtained show that the method can accurately resolve closely related frequencies without experiencing spectral line splitting as well as identify the voice and unvoiced segments in a recorded speech.
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    Automatic fruits identification system using hybrid technique
    (IEEE, 2011-01-17) Aibinu, Abiodun M.; Salami, Momoh-Jimoh E.; Shafie, Amir A.; Hazali, Norazlanshah; Termidzi, N.
    In this work, a combination of artificial neural network (ANN), Fourier descriptors (FD) and spatial domain analysis (SDA) has been proposed for the development of an automatic fruits identification and sorting system. Fruits images are captured using digital camera inclined at different angles to the horizontal. Segmentation is used for the classification of the preprocessed images into two non-overlapping clusters from which shape boundary and signatures are estimated using FD and SDA technique. Furthermore, color information obtained from the extracted red-green-blue color components of the fruits images during ANN training process is used in accurately detecting the color of such a fruit. The two independent paths are then combined for fruits sorting and identification purposes. The performance of the developed hybrid system has been evaluated at three different angles of camera inclination from which an accuracy of 99:1% was obtained.
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    Determination of complex-valued parametric model coefficients using artificial neural network technique
    (Hindawi, 2010) Aibinu, Abiodun M.; Salami, Momoh-Jimoh E.; Shafie, Amir A.
    A new approach for determining the coefficients of a complex-valued autoregressive (CAR) and complex-valued autoregressive moving average (CARMA) model coefficients using complex-valued neural network (CVNN) technique is discussed in this paper. The CAR and complex-valued moving average (CMA) coefficients which constitute a CARMA model are computed simultaneously from the adaptive weights and coefficients of the linear activation functions in a two-layered CVNN. The performance of the proposed technique has been evaluated using simulated complex-valued data (CVD) with three different types of activation functions. The results show that the proposed method can accurately determine the model coefficients provided that the network is properly trained. Furthermore, application of the developed CVNN-based technique for MRI K-space reconstruction results in images with improve resolution.
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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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    Intelligent Technique for Grading Tropical Fruit using Magnetic Resonance Imaging
    (International Journal of Scientific & Engineering Research, 2013) Balogun, Wasiu A.; Salami, Momoh-Jimoh E.; McCarthy, Michael J.; Mustafah, Yasir M.; Aibinu, Abiodun M.
    Recent application of modern marketing techniques coupled with intelligent agricultural systems of production has transformed small scale farming into large scale, in most part of the world. Characteristically, most of the tropical fruits, such as orange, appeared edible physically but internally such fruits might be defective based on their tissue and juice. Eventually, these fruits, via the market and undetected, usually get to the consumers who encounter the unfavourable status of the fruits. Our purpose, in this study, is to develop a non-destructive method to predict the status of orange fruits, based on internal quality. Graph of histogram showing the levels of different four colour intensities were acquired and analysed. The features extracted from Magnetic Resonance Imaging (MRI), using any of the two proposed methods, were applied as an input to train artificial neural network (ANN) in order to predict the orange fruit status. Different structures of multi-layer perceptron neural networks with feed-forward and back-propagation learning algorithms were developed using
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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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    A new method of correcting uneven illumination problem in fundus image
    (2007) Aibinu, Abiodun M.; Iqbal, Muhammad I.; Nilsson, M.; Salami, Momoh-Jimoh E.
    Recent advancements in signal and image processing have reduced the time of diagnoses, effort and pressure on the screeners by providing auto diagnostic tools for different diseases. The success rate of these tools greatly depend on the quality of acquired images. Bad image quality can significantly reduce the specificity and the sensitivity which in turn forces screeners back to their tedious job of manual diagnoses. In acquired fundus images, some areas appear to be brighter than the other, that is areas close to the center of the image are always well illuminated, hence appear very bright while areas far from the center are poorly illuminated hence appears to be very dark. Several techniques including the simple thresholding, Naka Rushton (NR) filtering technique and histogram equalization (HE) method have been suggested by various researchers to overcome this problem. However, each of these methods has limitations at their own and hence the need to develop a more robust technique that will provide better performance with greater flexibility. A new method of compensating uneven (irregular) illumination in fundus images termed global-local adaptive histogram equalization using partially-overlapped windows (GLAPOW) is proposed in this paper. The developed algorithm has been tested and the results obtained show superior performance when compared to other known techniques for uneven illumination correction.
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    A novel signal diagnosis technique using pseudo complex-valued autoregressive technique
    (Pergamon, 2011-08-01) Aibinu, Abiodun M.; Salami, Momoh-Jimoh E.; Shafie, Amir A.
    In this paper, a new method of biomedical signal classification using complex- valued pseudo autoregressive (CAR) modeling approach has been proposed. The CAR coefficients were computed from the synaptic weights and coefficients of a split weight and activation function of a feedforward multilayer complex valued neural network. The performance of the proposed technique has been evaluated using PIMA Indian diabetes dataset with different complex-valued data normalization techniques and four different values of learning rate. An accuracy value of 81.28% has been obtained using this proposed technique.
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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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    Palmprint recognition using principal lines characterization
    (IEEE, 2011-12-12) Rotinwa-Akinbile, M. O.; Aibinu, Abiodun M.; Salami, Momoh-Jimoh E.
    In this paper, a novel contactless Palmprint recognition system using palm print principal line-based feature extraction technique has been proposed. The discriminative Palmprint features were extracted from a pre-processed acquired images using easily available and low cost camera. Distances from endpoints to endpoints and point of interception to endpoints were calculated and transformed to frequency domain by the application of Discrete Fourier Transformed (DFT) technique. The extracted K-points DFT coefficients has been used as the discriminating features for recognition and identification purposes using correlation technique, power spectral matching and Euclidean distance measure. The proposed technique has been observed to be rotation, scale and translation invariant and accuracy of 100% was achieved in a 1-to-4 recognition and classification verification.
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    Performance analysis of ANN based YCbCr skin detection algorithm
    (Elsevier, 2012-01-01) Aibinu, Abiodun M.; Shafie, Amir A.; Salami, Momoh-Jimoh E.
    Skin detection from acquired images has various areas of applications especially in automatic facial and human recognition system. The performance analysis of artificial neural network based –YcbCr skin recognition and three other techniques is evaluated in this work. Results obtained show that the use of YCbCr color model performs better than RGB colour model and the use of artificial neural network further improves the accuracy of the system.
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    Vascular intersection detection in retina fundus images using a new hybrid approach
    (Pergamon, 2010-01-01) Aibinu, Abiodun M.; Iqbal, Muhammad I.; Shafie, Amir A.; Salami, Momoh-Jimoh E.; Nilsson, Mikael
    The use of vascular intersection aberration as one of the signs when monitoring and diagnosing diabetic retinopathy from retina fundus images (FIs) has been widely reported in the literature. In this paper, a new hybrid approach called the combined cross-point number (CCN) method able to detect the vascular bifurcation and intersection points in FIs is proposed. The CCN method makes use of two vascular intersection detection techniques, namely the modified cross-point number (MCN) method and the simple cross-point number (SCN) method. Our proposed approach was tested on images obtained from two different and publicly available fundus image databases. The results show a very high precision, accuracy, sensitivity and low false rate in detecting both bifurcation and crossover points compared with both the MCN and the SCN methods