Research Articles in Electrical and Electronics Engineering

Permanent URI for this collectionhttps://repository.elizadeuniversity.edu.ng/handle/20.500.12398/19

Browse

Now showing 1 - 20 of 171

35 Transient Multiexponential Data Selection Using Cramer Rao Lower Bound
(ASME Press, 2012) Jibia, Abdussamad U.; Salami, Momoh-Jimoh E.
Previously, analysis of transient multiexponential data using a combination of Gardner transform and parametric methods was shown to yield good results. However, one problem that remains unsolved is that of the nonstationarity of the data resulting from the associated deconvolution. Hitherto, trial and error methods have been used to select the qualitative length of the deconvolved data. In this paper, Cramer Rao Lower Bound (CRLB) is used to select the data truncation points for use with the MUSIC (Multiple Signal Classification), minimum norm and ARMA (autoregressive moving average) methods. Several simulations are made based on which truncation points are recommended for each of the three parametric methods.
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.
A LabVIEW based data acquisition system for vibration monitoring and analysis
(IEEE, 2002-07-17) Gani, Asan; Salami, Momoh-Jimoh E.
LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is gaining popularity as a graphical programming language, especially for data acquisition and measurement. This is due to the vast array of data acquisition cards and measurement systems which can be supported by LabVIEW as well as the relative ease with which advanced software can be programmed. One area of application of LabVIEW is the monitoring and analysis of vibration signals. The analysis and monitoring of the signal are of concern for fault detection and predictive maintenance. This paper describes LabVIEW based data acquisition and analysis developed specifically for vibration monitoring and used with vibration fault simulation systems (VFSS). On-line displays of time and frequency domains of the vibration signal provide a user-friendly data acquisition interface.
A LabVIEW based data acquisition system for vibration monitoring and analysis
(2002/7/17, 2002-07-17) Gani, Asan; Salami, Momoh-Jimoh E.
LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is gaining popularity as a graphical programming language, especially for data acquisition and measurement. This is due to the vast array of data acquisition cards and measurement systems which can be supported by LabVIEW as well as the relative ease with which advanced software can be programmed. One area of application of LabVIEW is the monitoring and analysis of vibration signals. The analysis and monitoring of the signal are of concern for fault detection and predictive maintenance. This paper describes LabVIEW based data acquisition and analysis developed specifically for vibration monitoring and used with vibration fault simulation systems (VFSS). On-line displays of time and frequency domains of the vibration signal provide a user-friendly data acquisition interface.
A modified Otsu’s algorithm for improving the performance of the energy detector in cognitive radio
(Urban & Fischer, 2017-09-01) Onumanyi, A. J.; Onwuka, E. N.; Aibinu, A. M.; Ugweje, O. C.; Salami, Momoh-Jimoh E.
In this paper, we present a modified Otsu’s algorithm for solving the automatic threshold estimation problem in energy detection based Cognitive Radio (CR) application. The modified algorithm was tested extensively and compared with other known algorithms using both simulated and real datasets. In particular, our findings reveal that the modified algorithm provides an averagely lower false alarm rate than the other techniques compared with in this paper. Furthermore, the results obtained show that the algorithm is independent of the bandwidth’s size, while having a total complexity of O (V), where V is the total sample size. Thus, from the results of this paper, full and effective automatic blind spectrum sensing using an Energy Detector is possible in CR. This can be achieved at a Signal-to-Noise Ratio of 5 dB to meet the IEEE 802.22 draft standard of P D> 90% and P FA< 10%.
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.
A new method of vascular point detection using artificial neural network
(IEEE, 2012-12-17) Kader, S.; Aibinu, A. M.; Salami, Momoh-Jimoh E.
Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5×5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database.
A novel hybrid artificial intelligence technique for colpitts oscillator design
(Springer US, 2014-02-01) Ghazali, Mohamad; Amsa, Ameer; Aibinu, A. M.; Salami, Momoh-Jimoh E.
In the design of the common base Colpitts oscillator, the resistance values of Thevenin’s resistors significantly influenced the transient time and steady state response of the resulting circuit. Various traditional approaches such as intuitive reasoning, mathematical calculation, and simulation-based techniques have been proposed in the literature for this purpose. Some of the aforementioned techniques involve rigorous mathematics, intuition, and experimentation in determining appropriate component values for optimal performance, stable steady state performance, and short transient response time from the resulting oscillator. In this paper, a new method of designing Colpitts oscillator using hybrid artificial intelligence comprising evolutionary-based Genetic Algorithm (GA) and artificial neural network (ANN) has been proposed. GA has been used in selecting various optimum resistance values of Thevenin’s resistors for maximizing long-term stability of the output waveform thus ensuring stable steady response of the designed circuit. ANN has been utilized in learning the nonlinear relationship between Thevenin’s resistors and transient time response of the Colpitts oscillator. Upon ANN convergence, optimum resistance values of obtained from GA process are fed into the trained ANN in predicting transient response time of each circuit. Optimized values with the shortest transient response time are finally selected for the Colpitts oscillator. The designed circuit successfully achieved optimization between its transient time response and steady state response. Hence, successfully reducing computation associated with existing traditional techniques in designing similar optimum Colpitts oscillator and achieving stable steady state output. Furthermore, this work has also demonstrated that ANN is capable of predicting the transient time of circuit with reasonable accuracy.
A novel palmprint segmentation technique
(IEEE, 2011-12-12) Rotinwa-Akinbile, M. O.; Aibinu, A. M.; Salami, Momoh-Jimoh E.
Recent paradigm shift from the conventional contact based palmprint recognition to contactless based systems (CBS) has necessitated the development of a variety of these systems. A major challenge of these systems is it robustness to illumination variation in unconstrained environment, thus making segmentation difficult. In this paper, the acquired image undergoes color space conversion and the output is filtered using coefficients obtained from the training of an artificial neural network (ANN) based model coefficient determination technique. Performance analysis of the proposed technique shows better performance in term of mean square error, true positive rate and accuracy when compared with two other techniques. Furthermore, it has also been observed that the proposed method is illumination invariant hence its suitability for deployment in contactless palmprint recognition systems.
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.
A real valued neural network based autoregressive energy detector for cognitive radio application
(Hindawi, 2014) Onumanyi, A. J.; Onwuka, E. N.; Aibinu, A. M.; Ugweje, O. C.; Salami, Momoh-Jimoh E.
A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application.
A Reference-Optimizing Antiwindup Control for Input-Constrained Systems
(IEEE Conference on Control, Decision and Information Technologies, 2019) Alli-Oke, Razak Olusegun
Control limits due to saturation constraints may result in the directionality problem in addition to the controller windup effect. In solving the directionality problem, this paper explores the concept of modifying the reference signal to be an element of the maximal output admissible set of the closedloop dynamics. This is achieved by an online constrained optimization of a tracking-related cost function associated with the closed-loop system. The design procedure for this reference-optimizing directional compensator is applicable to most existing antiwindup schemes. The effectiveness of the proposed control structure is demonstrated via simulation of benchmark case study examples.
A Review of Forecasting Techniques
(The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2012) Ghazali, Mohamad; Amsa, Ameer; Aibinu, A. M.; Salami, Momoh-Jimoh E.; Balogun, Wasiu
This work examines recent publications in forecasting in various fields, these include: wind power forecasting; electricity load forecasting; crude oil price forecasting; gold price forecasting energy price forecasting etc. In this review, categorization of the processes involve in forecasting are divided into four major steps namely: input features selection; data pre-processing; forecast model development and performance evaluation. The various methods involve are discussed in order to provide the overall view about possible options for development of forecasting system. It is intended that the classification of the steps into small categories with definitions of terms and discussion of evolving techniques will provide guidance for future forecasting sytem designers.
A review: Intelligent controllers for tropical food storage system
(IEEE, 2011-05-17) Adeyinka, Oluwo A.; Salami, Momoh-Jimoh E.; Raisuddin, K.; Musa, Aibinu A.
Food security can be assured by improving on post harvest storage methods. Food stored under improper storage conditions are prone to increased respiration and transpiration processes which often result in depletion and weight loss of edible material. Storage temperature and relative humidity are major factors that ultimately determine product quality and quantity. This paper presents a survey on methods of post harvest storage systems. The indigenous methods of tropical food storage common to the West African region are discussed. The attendant problems associated with these methods are highlighted. Intelligent control methods are also discussed. A novel intelligent controller is proposed to sustain product quality and quantity by optimizing the storage process.
A Robust Kalman Conjecture For First-Order Plants
(7th IFAC Symposium on Robust Control Design The International Federation of Automatic Control, 2012) Alli-Oke, Razak; Carrasco, Joaquin; Heath, William P.; Lanzon, Alexander
A robust Kalman conjecture is defined for the robust Lur’e problem. Specifically, it is conjectured that the nonlinearity’s slope interval for which robust absolute stability is guaranteed corresponds to the robust interval of the uncertain plant. We verify this robust Kalman conjecture for first-order plants perturbed by various norm-bounded unstructured uncertainties. The analysis classifies the appropriate stability multipliers required for verification in these cases. Robust control of Lur’e-type nonlinear systems satisfying this novel conjecture can therefore be designed using linear robust control methods.
A SVD-based transient error method for analyzing noisy multicomponent exponential signals
(IEEE, 1987-04-06) Salami, Momoh-Jimoh E.; Nichols, S.; Smith, M.
The problem of estimating the parameters of noisy multicomponent signals using parametric modeling technique is considered in this paper. The multicomponent signal of interest is formed by a superposition of basic functions having the same location in time but different widths and amplitudes. Based on the modified Gardner transformation, some samples of deconvolved data are derived from the multicomponent signals. The deconvolved data are then modeled using a special nonstationary autoregressive moving average (ARMA) process in which the parameters of the ARMA model are obtained by linear least-squares procedure. The least-squares procedure is based on the singular value decomposition (SVD) to overcome the limitations of the transient error method (TEM) of analysis that uses cholesky decomposition to determine its AR coefficients. The moving average (MA) coefficients corresponds to the initial residual error sequences so as to account for the nonstationary noise in the deconvolved data. This new method of analysis, termed the SVD-based transient error method, produces high resolution estimates of the exponents of multicomponent signals at both low and high signal to noise (SNR) ratios.
A transform-based feature extraction approach for motor imagery tasks classification
(IEEE, 2015-10-16) Baali, Hamza; Khorshidtalab, Aida; Mesbah, Mostefa; Salami, Momoh-Jimoh E.
In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s T 2 statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.
Active suppression of vibration modes with piezoelectic patches: Modeling, Simulation and Experimentation”
(Proceedings of the IASTED International Conference, APPLIED SIMULATION AND MODELLING, 2003-09-03) Gani, Asan; Salami, Momoh-Jimoh E.; Khan, R.
Active vibration control of the first three modes of a vibrating cantilever beam using piezoelectric patches is examined in this paper. A model based on Euler-Bemoulli beam equation is adopted and extended to the case of three bonded piezoelectric patches which act as sensor. actuator and exciter respectively. The sensor and the actualor are collocated to achieve a minimum phase. A compensated inverse PID controller has been designed and developed to damp these modes. Simulation studies are caried using MATLAB. Individual controller has been designed for each mode and then combined in parallel to damp any of the three modes. Finally, the simulation results are compared and verified experimentally and the real-tinie implementation is carried out with xPC talget toolbox in MATLAB.
Active vibration control of a beam with piezoelectric patches: real-time implementation with xPC target
(IEEE, 2003-06-25) Gani, Asan; Salami, Momoh-Jimoh E.; Khan, R.
Active control of a vibrating beam using smart materials such as piezoelectric materials is examined in this paper. A model based on Euler-Bernoulli beam equation has been developed and then extended with bonded three piezoelectric patches which act as sensor, actuator and exciter. The sensor and actuator are collocated to achieve a minimum phase. The aim of this research work is to control the first three resonant modes. To achieve this, a compensated inverse PID controller is developed and tuned to damp these modes using MATLAB. The designed controller for damping each mode is then combined in parallel to damp any of the three modes. Finally, the simulation results are verified experimentally and the real-time implementation is carried out with xPC target toolbox in MATLAB.
Adaptive neuro-fuzzy control of wet scrubbing process
(IEEE, 2015-05-31) Salami, Momoh-Jimoh E.; Danzomo, Bashir A.; Khan, Raisuddin
Thenon-linear characteristics of wet scrubbing process have led to the application of intelligent control technique to adequately deal with these complexities by manipulating the liquid droplet size for the effective control of particulate matter (PM) contaminants. This includes the use of adaptive neuro-fuzzy inference system (ANFIS) to design an intelligent controller based on direct inverse model control strategy using default input and output membership functions (gaussmf and linear) and different number of input membership functions. This is followed by training of the fuzzy inference system to obtain inverse model which was tested as the intelligent controller. The controller developed using two-input membership functions have successfully achieved the main target of setting the PM concentration (process output) below the set point which is the allowable World health organization (WHO) emission level for 20g/μm within a short settling time of 2s. © 2015 IEEE.

Browse

Browsing Research Articles in Electrical and Electronics Engineering by Title