Engineering

Permanent URI for this communityhttps://repository.elizadeuniversity.edu.ng/handle/20.500.12398/6

These are the scholarly outputs of the Faculty of Engineering

Browse

Now showing 1 - 20 of 362

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 Comparative Performance and Emission Analysis of Blended Groundnut Oil and Mineral Oil Based Lubricants Using a Spark Ignition Engine
(The CIGR E journal Manuscript, 2008-09) Ejilah, I. R; Asere, A. A.
This paper presents the performance and emission behaviour of a spark ignition engine run on groundnut oil blended lubricants. However, for the purpose of comparism, benchmark tests were carried out on multigrade and monograde motor oils. A 5.0 h.p engine test-bed incorporated with an A.C dynamometer and flue gas analyzer was used to conduct the tests. At maximum engine speed, the groundnut oil blended lubricants generated an exhaust temperature of 6.02% higher than multigrade oil, and 6.26% higher than monograde oil. The brake power was 12.70% and 10.94% higher than the monograde and multigrade oils. In terms of fuel economy, the monograde and groundnut oil blended lubricants are 7.50% and 2.18% higher than multigrade oil respectively. It is shown that as the proportion of viscosity modifier in the oil sample increases, exhaust temperature, brake power, brake mean effective pressure (b.m.ep) also increases, while the specific fuel consumption diminishes. At various speeds, CO2 level emitted by the use of groundnut oil blended lubricants was observed to be higher than it was the case for multigrade and monograde oils. In the light of the fast depleting fossil fuel reserve in Nigeria, this work has demonstrated the feasibility of using groundnut oil blends as lubricant substitutes in spark ignition engines.
A Comparative Performance and Emission Analysis of Blended Groundnut Oil and Mineral Oil Based Lubricants Using a Spark Ignition Engine.
(Agricultural Engineering International: The CIGR E journal Manuscript EE 07017., 2008-09) Ejilah, I. R; Asere, A. A.
This paper presents the performance and emission behaviour of a spark ignition engine run on groundnut oil blended lubricants. However, for the purpose of comparism, benchmark tests were carried out on multigrade and monograde motor oils. A 5.0 h.p engine test-bed incorporated with an A.C dynamometer and flue gas analyzer was used to conduct the tests. At maximum engine speed, the groundnut oil blended lubricants generated an exhaust temperature of 6.02% higher than multigrade oil, and 6.26% higher than monograde oil. The brake power was 12.70% and 10.94% higher than the monograde and multigrade oils. In terms of fuel economy, the monograde and groundnut oil blended lubricants are 7.50% and 2.18% higher than multigrade oil respectively. It is shown that as the proportion of viscosity modifier in the oil sample increases, exhaust temperature, brake power, brake mean effective pressure (b.m.e.p) also increases, while the specific fuel consumption diminishes. At various speeds, CO2 level emitted by the use of groundnut oil blended lubricants was observed to be higher than it was the case for multigrade and monograde oils. In the light of the fast depleting fossil fuel reserve in Nigeria, this work has demonstrated the feasibility of using groundnut oil blends as lubricant substitutes in spark ignition engines.
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 numerical modelling study of the hydroclimatology of the Niger River Basin, West Africa
(Taylor & Francis, 2016-01-02) Oguntunde, Phillip G.; Abiodun, Babatunde J; Lischeid, Gunnar
Adequate water resources management at the basin level needs quality downscaling of climate change scenarios for application to impact assessment and adaptation work. This study evaluates the ability of a regional climate model (RegCM3) to simulate the present-day climate and regional water balance over the Niger River Basin (NRB). RegCM3 gives a good simulation of the NRB hydroclimatic features. The mean bias error for monthly temperature is 1.5°C, 0.3 mm d-1 for rainfall, and 0.4 mm d-1 for runoff. Moderate to high correlations (0.66– 0.95) were found between the modelled and the observed variables. RegCM3-based water cycling indices were not statistically different from the observation. Seasonal moistening efficiency (m) ranges between 19% and 37%; 66% of the available atmospheric moisture over NRB precipitates between June and September, of which 21% originates from local evaporation. The result suggests that the moisture sink period is July to October with very high precipitation efficiency over the basin. The model reproduces the hydroclimatology of the NRB and hence is a suitable tool for further studies relating to the assessment of climate change impacts on river basin water systems
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 OF THE ENERGY SITUATION IN UGANDA
(International Journal of Scientific and Research Publications,, 2014-01) Adeyemi, K.O.; Asere, A.A.
This paper reviews the energy sector of Uganda. As is typical of East Africa, it is characterized by excessive use of Biomass to provide over 90% of the energy needs. Uganda is one of the least developed countries in the region. Hydropower provides over 90% of the total electricity generated in the country while wind, solar and geothermal are under developed. Energy policies are geared towards the use of modern, clean and energy efficient technologies. This is stimulating public private partnerships, attracting multilateral and bilateral agencies to provide funding, grants and technical assistance in renewable energy projects. The Clean Development Mechanism (CDM) of the United Nations avail project developers the opportunity to obtain carbon finance. Several challenges hindering the development in the energy sector and utilization of renewable energy resources have been identified. Strategies for achieving the United Nations Sustainable Energy for All Initiative have been presented.
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 semi-empirical model for estimating surface albedo of wetland rice field
(International Commission of Agricultural Engineering, 2007) Oguntunde, Phillip G.; Olukunle, O.J.; Ijatuyi, O.A.; Olufayo, A.A.
Surface albedo plays a vital role in the evapotranspiration component of the wetland rice water balance. This paper examines the influence of the phenological stages of rice (Oriza sativa) field on observed albedo at a tropical site (Ghana) during the year 2002, with a view to parametrizing a simple albedo model suitable for inclusion in models to estimate evapotranspiration in wetland rice cropping systems. Crop management was similar for the two planting dates used in this study. Measurements were taken from 10 m x 10 m plots within rice fields. Four phenological stages were distinguished: emergence, vegetative, flowering and physiological maturity. Surface albedo (α) was measured and simulated, using a calibrated semi-empirical model, at solar zenith angles of 15°, 30°, 45°, 60°, and 75°. Leaf area index (LAI) and crop height (h) were also monitored. Generally, albedo increases from emergence to flowering for both planting dates but slightly decreases after flowering. The correlation coefficient (r) between α and LAI equals 0.985 and the correlation coefficient between α and h equals 0.908. The composed albedo model adequately predicted the observed albedos with an overall r = 0.946 and mean bias error (MBE) of 0.002. The extinction coefficient of the rice crop albedo was estimated as 0.75. Data presented are valuable inputs in agricultural water management, rice production models, and especially as vital sub-routine inputs in calculating evaporation and transpiration from wetland rice.

Browse

Browsing Engineering by Title