DSpace Repository :: Browsing by Author "Shafie, Amir A."

Browsing by Author "Shafie, Amir A."

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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.
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.
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.
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.
Development of a New Concept for Fire Fighting Robot Propulsion System
(International Conference on Material, Industrial and Mechanical Engineering (ICMIME2016), 2006) Ajala, Mosud T.; Khan, M. R.; Shafie, Amir A.; Salami, Momoh-Jimoh E.
An additional cost to human loss and property destruction during fire disaster is fire fighters injuries and death. The recent statistics of 63,350 fire fighters injuries that occurred during the year 2014 confirms that firefighting still presents great risks of personal injury to the fire fighters [1]. The lack of details on information about the victims trapped in fire and situation in the fire zone increase the risk to fire fighters [2, 3]. To reduce these fatalities fire fighting robots (FFRs) emerged as possible solutions therefore they are developed and researched on. The FFRs are designed for either prevention or emergency (same as intervention) tasks of fire and are applied indoor or outdoor. However, the prime movers of the majority of the FFRs are electrically powered [4] which made them to be suitable for preventive task alone and inappropriate for the emergency task. Their inappropriateness is due to the vulnerability in high temperature environment that characterised fire emergency. Thus, alternative propulsion systems for the mobility of fire fighting robots in emergency setting are evolving. Furthermore, literature survey reveals that water powered hydraulic propulsion system has been the only alternative to the drawbacks of dc motors in the hot environment. The mechanism was implemented on snake fire fighting robot for tunnel fire application [5]. In the mechanism, hydraulic motor was used to actuate the snake joints for mobility while water provides power for the hydraulic motors. However, the snake robot was designed for outdoor application. Consequently, the need for an autonomous fire fighting robot with a novel propulsion system becomes imminent.
Dynamic approach for real-time skin detection
(Springer Berlin Heidelberg, 2015-06-01) Bilal, Sara; Akmeliawati, Rini; Salami, Momoh-Jimoh E.; Shafie, Amir A.
Human face and hand detection, recognition and tracking are important research areas for many computer interaction applications. Face and hand are considered as human skin blobs, which fall in a compact region of colour spaces. Limitations arise from the fact that human skin has common properties and can be defined in various colour spaces after applying colour normalization. The model therefore, has to accept a wide range of colours, making it more susceptible to noise. We have addressed this problem and propose that the skin colour could be defined separately for every person. This is expected to reduce the errors. To detect human skin colour pixels and to decrease the number of false alarms, a prior face or hand detection model has been developed using Haar-like and AdaBoost technique. To decrease the cost of computational time, a fast search algorithm for skin detection is proposed. The level of performance reached in terms of detection accuracy and processing time allows this approach to be an adequate choice for real-time skin blob tracking.
Hidden Markov model for human to computer interaction: a study on human hand gesture recognition
(Springer Netherlands, 2013-12-01) Bilal, Sara; Akmeliawati, Rini; Shafie, Amir A.; Salami, Momoh-Jimoh E.
Human hand recognition plays an important role in a wide range of applications ranging from sign language translators, gesture recognition, augmented reality, surveillance and medical image processing to various Human Computer Interaction (HCI) domains. Human hand is a complex articulated object consisting of many connected parts and joints. Therefore, for applications that involve HCI one can find many challenges to establish a system with high detection and recognition accuracy for hand posture and/or gesture. Hand posture is defined as a static hand configuration without any movement involved. Meanwhile, hand gesture is a sequence of hand postures connected by continuous motions. During the past decades, many approaches have been presented for hand posture and/or gesture recognition. In this paper, we provide a survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications.
Hidden Markov model for human to computer interaction: a study on human hand gesture recognition
(Springer Netherlands, 2013-12-01) Bilal, Sara; Akmeliawati, Rini; Shafie, Amir A.; Salami, Momoh-Jimoh E.
Human hand recognition plays an important role in a wide range of applications ranging from sign language translators, gesture recognition, augmented reality, surveillance and medical image processing to various Human Computer Interaction (HCI) domains. Human hand is a complex articulated object consisting of many connected parts and joints. Therefore, for applications that involve HCI one can find many challenges to establish a system with high detection and recognition accuracy for hand posture and/or gesture. Hand posture is defined as a static hand configuration without anymovement involved. Meanwhile, hand gesture is a sequence of hand postures connected by continuous motions. During the past decades, many approaches have been presented for hand posture and/or gesture recognition. In this paper, we provide a survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications.
Human Upper Body Pose Region Estimation
(Springer, Berlin, Heidelberg, 2013) Bilal, Sara; Akmeliawati, Rini; Shafie, Amir A.; Salami, Momoh-Jimoh E.
The objective of this chapter is to estimate 2D human pose for action recognition and especially for sign language recognition systems which require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. We propose an approach that progressively reduces the search space for body parts and can greatly improve chance to estimate the HUB pose. This involves two contributions: (a) a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. (b) Scaling the extracted parts during body orientation was attained using partial estimation of face size. The outcome of the system makes it applicable for real-time applications such as sign languages recognition systems. The method is fully automatic and self-initializing using a Haar-like face region. The tracking the HUB pose is based on the face detection algorithm. Our evaluation was done mainly using 50 images from INRIA Person Dataset.
A hybrid method using haar-like and skin-color algorithm for hand posture detection, recognition and tracking
(IEEE, 2010-08-04) Bilal, S.; Akmeliawati, Rini; Salami, Momoh-Jimoh E.; Shafie, Amir A.; Bouhabba, El Mehdi
Human hand posture detection and recognition is a challenging problem in computer vision. We introduce an algorithm that is capable to recognize hand posture in a sophisticated background. The system combines two algorithms to achieve better detection rate for hand. Recently Viola et al. in have introduced a rapid object detection scheme; we use this approach to detect the hand posture in the first set of consecutive frames. The chromatic color distribution of skin can be found within this cluster. As the shape of hand posture keep changing in the subsequent frames, the skin regions updated dynamically. The classification of hand posture makes use of static feature for locating and counting hand fingers. Kalman Filter is used to track the face and hand blobs based on their position. In the experiments, we have tested our system in various environments, and results showed effectiveness of the approach.
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.
Modeling of Human Upper Body for Sign Language Recognition
(IEEE, 2011-12-06) Bilal, Sara; Akmeliawati, Rini; Shafie, Amir A.; Salami, Momoh-Jimoh E
Sign Language Recognition systems require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. Head, face, forehead, shoulders and chest are very crucial parts that can carry a lot of positioning information of hand gestures in gesture classification. In this paper as the main contribution, a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. Scaling the extracted parts during body orientation was attained using partial estimation of face size. Tracking the extracted parts for front and side view was achieved using CAMSHIFT [24]. The outcome of the system makes it applicable for real-time applications such as Sign Languages Recognition (SLR) systems.
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.
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.
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.
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.
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
Vision-based hand posture detection and recognition for Sign Language—A study
(IEEE, 2011-05-17) Bilal, Sara; Akmeliawati, Rini; Salami, Momoh-Jimoh E.; Shafie, Amir A.
Unlike general gestures, Sign Languages (SLs) are highly structured so that it provides an appealing test bed for understanding more general principles for hand shape, location and motion trajectory. Hand posture shape in other words static gestures detection and recognition is crucial in SLs and plays an important role within the duration of the motion trajectory. Vision-based hand shape recognition can be accomplished using three approaches 3D hand modelling, appearance-based methods and hand shape analysis. In this survey paper, we show that extracting features from hand shape is so essential during recognition stage for applications such as SL translators.

Browsing by Author "Shafie, Amir A."

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