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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.
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.
Adaptive Short Time Fourier Transform (STFT) Analysis of seismic electric signal (SES): A comparison of Hamming and rectangular window
(IEEE, 2012-09-23) Astuti, W.; Sediono, W.; Aibinu, A. M.; Akmeliawati, R.; Salami, Momoh-Jimoh E.
Seismic electric signal (SES) is one of features for predicting earthquakes (EQs) because of its significant changes in the amplitude of the signal prior to the earthquake. This paper presents detailed analysis of SES recorded prior to earthquake that occurred in Greece in the period from January 1, 2008 to June 30, 2008. During this period of time 5 earthquakes were recorded with magnitudes greater than 6R. In this analysis STFT involving adaptively sliding window technique is used, in which Hamming and rectangular window functions are applied and compared. The comparison shows that Hamming window gives better results in analyzing the first significantly changes of SES prior to the EQ. The application of Hamming window resulted in less rippled spectrum shape which is more suitable to be used in characterizing the SES.
Advanced Control And Development of Hydro and Diesel Generator Hybrid Power System Models for Renewable Energy Microgrids
(JOURNAL LA MULTIAPP, 2021-08) Austin, Oshin Ola
The Nigerian power problem resulted to incessant and erratic supply of electricity which has destroyed many industrial processes in the country. It has reduced productivity and has increased unemployment rates in the country to over 50million (over 70% of Nigerian youths). This has led many of the youths in the country to crime. As of 2016, the electricity energy consumption in the world from the world fact book revealed that the average power per capita (watts per person) in the United States is 1,377 Watts. In South Africa, it is 445 and in Australia, average power per capita is as high as 1,112 Watts. Whereas, the average electricity consumed in watts per person in Nigeria is just 14 Watts putting the country in a rank of 189 out of 219 countries estimated. In this research work, a Hybrid Electric Power System (HEPS) which comprises Hydro Electric Power Plant (HEPP) and Diesel Generator (DG) was modelled and a control algorithm was established to improve the performance of the system. Hybrid power system mathematical and Simulink models were developed. The output power of the developed Simulink model was be optimized using optimum power point optimization techniques and control algorithms. Simulink models of the two components of the Hybrid Electric Power System were produced using MATLAB/Simulink software. The results obtained revealed that the problems associated with conventional methods of power generation was overcomed by the development of this Hybrid Electric Power System (HEPS) models.
Ag films grown by remote plasma enhanced atomic layer deposition on different substrates
(American Vacuum Society, 2015-11) Amusan, Akinwumi A.; Kalkofen, Bodo; Gargouri, Hassan; Wandel, Klauss; Pinnow, Cay; Lisker, Marco; Burte, Edmund P.
Plasma-assisted atomic layer deposition (PALD) was carried for growing thin boron oxide films onto silicon aiming at the formation of dopant sources for shallow boron doping of silicon by rapid thermal annealing (RTA). A remote capacitively coupled plasma source powered by GaN microwave oscillators was used for generating oxygen plasma in the PALD process with tris(dimethylamido)borane as boron containing precursor. ALD type growth was obtained; growth per cycle was highest with 0.13 nm at room temperature and decreased with higher temperature. The as-deposited films were highly unstable in ambient air and could be protected by capping with in-situ PALD grown antimony oxide films. After 16 weeks of storage in air, degradation of the film stack was observed in an electron microscope. The instability of the boron oxide, caused by moisture uptake, suggests the application of this film for testing moisture barrier properties of capping materials particularly for those grown by ALD. Boron doping of silicon was demonstrated using the uncapped PALD B2O3 films for RTA processes without exposing them to air. The boron concentration in the silicon could be varied depending on the source layer thickness for very thin films, which favors the application of ALD for semiconductor doping processes
Analysis of Formant Frequencies of the Correct Pronunciation of Quranic Alphabets Between Kids and Adults
(IDOSI, 2017) Badaruddin, Syarifah N. S.; Ahmad, Salmiah; Altalmas, Tareq M. K.; Salami, Momoh-Jimoh E.; Hashim, Nik Nur W. N.; Embong, Abdul H.; Khairuddin, Safiah
It is an obligation for a Muslim to become skilled and proficient in reciting Al-Quran considering that Al-Quran is the fundamental source of revelation from Allah SWT. In Al-Quran, there are 28 alphabets where each of them has their own unique sound. The Quranic alphabets produce sound that are characterized from their point of articulation (Makhraj) and their characteristics (Sifaat). Knowing the correct way of pronunciation through engineering perspective may help Muslim in learning Al-Quran, in the sense that the signal of the experts can be used in Quranic teaching and learning as a reference model. Since both adults and children possess different vocal tract, therefore there will be different outcomes of the pronunciation between both experts. The features identification of the pronunciation of both experts is needed to represent the actual and correct pronunciation that will be used as a reference for Quranic teaching and learning at later. In this paper, the focus was on the identification and analysis of the correct pronunciation of the Quranic alphabets on the data obtained from adults and children experts. The first and second formant frequencies (F1 and F2) were used as the features where they were used to represent the pronunciation of each alphabet for both adults and children category. The speech analysis software PRAAT was used to accomplish the pre-processing of the data using Spectral Subtraction technique and also used to measure the F1 and F2 values. Linear Discriminant Analysis (LDA) was used for classification of the signals and results shows that some of the alphabets can be identified uniquely using F1 and F2 features of the two categories.
Analysis of multicomponent transient signals using MUSIC superresolution technique
(IEEE, 2008-05-13) Jibia, Abdussamad U.; Salami, Momoh-Jimoh E.; Khalifa, Othman O.
The problem of estimating the parameters of transient signals consisting of real decay constants has for long been a subject of study by many researchers. Such signals arise in many problems in Science and Engineering like nuclear magnetic resonance for medical diagnosis, deep-level transient spectroscopy, fluorescence decay analysis, etc. Many techniques have been suggested by researchers to analyse these signals but they often produce mixed results. A new method of analysis using modified MUSIC (multiple signal classification) subspace algorithm is successfully applied to the analysis of this signal. A noisy multiexponential signal is subjected to a preprocessing procedure consisting of Gardenerspsila transformation and inverse filtering. Modified MUSIC algorithm is then applied to the deconvolved data. The parameters of focus in this paper are the number of components and decay constants. It is shown that with this technique parameter estimates do not significantly change with signal to noise ratio. The superiority of this algorithm over conventional MUSIC algorithm is also shown.
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.
Analysis of transient multiexponential signals using cepstral deconvolution
(Academic Press, 2010-07-01) Jibia, Abdussamad U.; Salami, Momoh-Jimoh E.; Khalifa, Othman O.; Aibinu, A. M.
We propose and test a new method of multiexponential transient signal analysis. The method based on cepstral deconvolution is fast and computationally inexpensive. The multiexponential signal is initially converted to a deconvolution model using Gardners' transformation after which the proposed method is used to deconvolve the data. Simulation and experimental results indicate that this method is good for determining the number of components but performs poorly in accurately estimating the decay rates. Influence of noise is not considered in this paper.
Analysis of transient multiexponential signals using exponential compensation deconvolution
(Elsevier, 2012-01-01) Jibia, Abdussamad U.; Salami, Momoh-Jimoh E.
A three-step procedure for the parameter estimation of transient multiexponential signals is proposed. The first step involves the use of the classical Gardner transform to convert the data signal into a convolution model which is deconvolved using exponential compensation deconvolution technique in the second step. In the third step, eigenvector algorithms are used to process the resulting complex exponentials to obtain better estimates of decay rates and number of components. Simulation and experimental results show that this method outperforms previous approaches if a number of preprocessing parameters are correctly selected.
Animal sound activity detection using multi-class support vector machines
(IEEE, 2005-05-17) Astuti, W.; Aibinu, A. M.; Salami, Momoh-Jimoh E.; Akmelawati, R.; Muthalif, Asan G. A.
On March 11th 2011, the whole world was taken aback by another tragic experience of Tsunami triggered by a magnitude 9.8 earthquake in Japan. Just few days after that, on March 25th 2011, another earthquake of magnitude 6.8 hit Myanmar deaths and destructions. Despite the loss incurred on properties and human being, available data show that relatively few numbers of animals died during most natural disasters. Prior to the occurrence of these disasters, available reports shows that animals do migrate to higher level or leave the areas en masse ahead of the event. Other related account show that animal sometimes behaves in unusual ways prior to the occurrence of these natural disasters. These overwhelming evidences point to the fact that animals might have the ability to sense impending natural disaster precursor signals ahead of time. This paper discusses the preliminary results obtained from the use of support vector machine (SVM) and Mel-frequency cepstral coefficients (MFCC) in the development of animal sound activity detection (ASAD) which is an integral part in the development of earthquake and natural disaster prediction using unusual animal behavior. The use of MFCC has been proposed for the features extraction stage while SVM has been proposed for classification of the extracted features. Preliminary results obtained shows that the MFCC and SVM can be used for features extraction and features classification respectively.
Anthropometric characteristics of roadside auto-mechanics: a case study
(Leonardo Journal of Sciences, 2018-01-11) Abiola, Oluranti A.; Oke, Adekola O.; Koya, Olufemi A.
The study evaluated the relevant/corresponding anthropometric characteristics of the people involved in engine-repair activities. The study was carried out on the selected roadside auto-engine repairers along Lagos-Ibadan express way, in Nigeria. This was with a view to providing ergonomic design data for optimal working condition among this set of workforce and redesigning the mechanics inspection-pit. Material and methods: The static and the functional anthropometric characteristics of the mechanics were measured. The data obtained from 110 auto-mechanics, randomly selected, were employed to evaluate efficient design parameter for roadside workstations. Results: The results indicated that inspection-pit is about 1626 mm deep; seat height ranges between 375 mm and 405 mm; optimal work posture sitting is between 483 mm and 622 mm. Conclusions: Adopting the data presented in this paper in optimizing the auto-mechanics working conditions for effective workplace comfort and productivity among the roadside auto-mechanics in Nigeria will be of immense advantage.
Application of ARMA modeling to multicomponent signals
(Elsevier, 1985-07-01) Nichols, S. T.; Smith, M. R.; Salami, Momoh-Jimoh E.
This paper investigates the problem of estimating the parameters of a multicomponent signal observed in noise. The process is modeled las a special nonstationary autoregressive moving average (ARMA) process. The parameters of the multicomponent signal are determined from the spectral estimate of the ARMA model The spectral lines are closely spaced and the ARMA model must be determined from very short data records. Two high-resolution ARMA algorithms are developed for determining the spectral estimates. The first ARMA algorithm modifies the extended Prony method to account for the nonstationary aspects of noise in the model.For comPonents signals with good signal to noise ratio (SNR) this algorithm provides excellent results, but for a lower SNR the performance degrades resulting in a loss in resolution. The second algorithm is based on the work of Cadzow. The algorithm presented overcomes the difficulties of Cadzow's and Kaye's algorithms and provides the coefficients for the complete model not just the spen ral estimate. This algorithm performs well in resolving multicomponent signals when the SNR is low.
Application of atomic layer deposited dopant sources for ultra‐shallow doping of silicon
(WILEY‐VCH Verlag GmbH, 2014-01) Kalkofen, Bodo; Amusan, Akinwumi A.; Lisker, Marco; Burte, Edmund P.
The advanced silicon semiconductor technology requires doping methods for production of ultra‐shallow junctions with sufficiently low sheet resistance. Furthermore, advanced 3‐dimensional topologies may require controlled local doping that cannot be achieved by ionimplantation. Here, the application of the atomic layer deposition (ALD) method for pre‐deposition of dopant sources is presented. Antimony oxide and boron oxide were investigated for such application. Ozone‐based ALD was carried out on silicon wafers by using triethylantimony or tris‐(dimethylamido)borane. Very homogeneous Sb2O5 deposition could be achieved on flat silicon wafers and in trench structures. The thermal stability of antimony oxide layers was investigated by rapid thermal annealing experiments. The layers were not stable above 750 °C. Therefore, this material failed to act as dopant source so far. In contrast, ultra‐shallow boron doping of silicon from ALD grown boron oxide films was successful. However, pure B2O3 films were highly unstable after exposure to ambient air. The boron oxide films could be protected by thin Sb2O5 or Al2O3 films that were in‐situ grown by ALD. Low temperature ALD of Al2O3 at 50 °C from trimethylaluminium (TMA) and ozone was investigated in detail with respect of its protective effect on boron oxide. Interestingly, it was observed that already one ALD cycle of TMA and O3 resulted in significant increase in stability of the boron oxide in air.
Application of intelligent technique for development of Colpitts oscillator
(IEEE, 2013-04-07) Salami, Momoh-Jimoh E.; Aibinu, A. M.
In this paper, new method of Colpitts oscillator designing through combination of Genetic Algorithm and Artificial Neural Network (ANN) has been suggested. The Thevenin's resistors for the common base Colpitts oscillator are optimized through application of GA and ANN. The developed common base Colpitts oscillator has shortest transient time response and stable Direct Current (DC) stability in the long term operation. Involvement of GA and ANN successfully optimize between transient time response and steady state response of common base oscillator. Application of these two artificial intelligent techniques assist faster selection of optimizes components values such as resistance values during circuit development rather than conventional method which used intuition techniques to develop the circuit.
Application of modeling techniques to diabetes diagnosis
(IEEE, 2010-11-30) Aibinu, A. M.; Salami, Momoh-Jimoh E.; Shafie, A. A.
In recent times, the introduction of complex-valued neural networks (CVNN) has widened the scope and applications of real-valued neural network (RVNN) and parametric modeling techniques. In this paper, new expert systems for automatic diagnosis and classification of diabetes using CVNN and RVNN based parametric modeling approaches have been suggested. Application of complex data normalization technique converts the real valued input data to complex valued data (CVD) by the process of phase encoding over unity magnitude. CVNN learn the relationship between the input and output phase encoded data during training and the coefficients of Complex-valued autoregressive (CAR) model can be extracted from the complex-valued weights and coefficients of the trained network. Classification of the obtained CAR or RVAR model coefficients results in required distinct classes for diagnosis purpose. Similar operations can be performed for real-valued autoregressive technique except for CVD normalization. The effect of data normalization techniques, activation functions, learning rate, number of neurons in the hidden layer and the number of epoch using the suggested techniques on PIMA INDIA diabetes dataset have been evaluated in this paper. Results obtained compares favorably with earlier reported results.
Artificial intelligent based friction modelling and compensation in motion control system
(INTECH Open Access Publisher, 2011) Ismaila, Tijani B.; Salami, Momoh-Jimoh E.; Akmeliawati, Rini; Alfaro, Horacio M.
The interest in the study of friction in control engineering has been driven by the need for 10 precise motion control in most of industrial applications such as machine tools, robot 11 systems, semiconductor manufacturing systems and Mechatronics systems. Friction has 12 been experimentally shown to be a major factor in performance degradation in various 13 control tasks. Among the prominent effects of friction in motion control are: steady state 14 error to a reference command, slow response, periodic process of sticking and sliding (stick-15 slip) motion, as well as periodic oscillations about a reference point known as hunting when 16 an integral control is employed in the control scheme. Table 1 shows the effects and type of 17 friction as highlighted by Armstrong et. al.(1994). It is observed that, each of task is 18 dominated by at least one friction effect ranging from stiction, or/and kinetic to negative 19 friction (Stribeck). Hence, the need for accurate compensation of friction has become 20 important in high precision motion control. Several techniques to alleviate the effects of 21 friction have been reported in the literature (Dupont and Armstrong, 1993; Wahyudi, 2003; 22 Tjahjowidodo, 2004; Canudas, et. al., 1986). 23 One of the successful methods is the well-known model-based friction compensation 24 (Armstrong et al., 1994; Canudas de Wit et al., 1995 and Wen-Fang, 2007). In this method, 25 the effect of the friction is cancelled by applying additional control signal which generates a 26 torque/force. The generated torque/force has the same value (or approximately the same) 27 with the friction torque/force but in opposite direction.
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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