Research Title: Embedding Mixed-Reality Laboratories into E-Learning Systems for Engineering Education

Author: Kasim Mousa Al-Aubidy, Published Year: 2013

International Conference on E-Learning and Blended Education (ICELBE2013), Jordan

Faculty: Engineering and Technology

Abstract: E-learning, virtual learning and mixed reality techniques are now a global integral part of the academic and educational systems. They provide easier access to educational opportunities to a very wide spectrum of individuals to pursue their educational and qualification objectives. These modern techniques have the potentials to improve the quality of the teaching and learning process and elevate its performance to higher standards. Furthermore, e-learning in conjunction with mixed reality techniques can reduce the cost of higher education at both institutional and individual learner levels. In this paper, the focus will be on teaching-learning of applied science such as engineering. These studies demand special requirements, such as acquiring specific technical skills and practices through training. Our objective in this paper is the explanation and design of remote laboratories in mixed-reality mode. Decision making and evaluation of performance using fuzzy logic will be embedded in the proposed design.

Keywords: e-learning, engineering education, virtual labs, remote labs, mixed- reality, fuzzy decision making.

Research Title: Fundamental Issues in the Stability of Adaptive IIR Filters

Author: Mohammad Abdul Qader Abu Naser, Published Year: 2009

Digital Signal Processing Workshop, USA

Faculty: Engineering and Technology

Abstract: Adaptive IIR filter analysis is more complicated than for the FIR case because (a) some algorithm signals are generated by the adaptive filter itself, and (b) the prediction error relates to the adapted parameters via a filtering operation. Averaging analyses of stability address the first issue by linearization about the convergence point, and the second by using passivity of the error operator. However, published results do not fully account for signal dynamics in the linearization, nor have initial conditions in the passivity analysis been considered. This paper addresses these gaps. Our motivation to revisit these broadly applicable issues is for analyzing recently developed adaptive algorithms that have application to biological systems.

Keywords: IIR filters, Algorithm design and analysis, Stability analysis

Research Title: Convergence of Adaptive Estimators of Time-Varying Linear Systems using Basis Functions: Continuous Time Results

Author: Mohammad Abdul Qader Abu Naser, Published Year: 2007

International Conference on Acoustics, Speech, and Signal Processing, USA

Faculty: Engineering and Technology

Abstract: The convergence properties of adaptive filtering algorithms are investigated in situations where the optimal filter is modeled as a time-varying linear system whose parameters are expanded over basis functions. This type of model is one approach when parameters cannot be considered as slowly varying, and is appropriate for modeling certain mobile radio channels and in the identification of the dynamics of vascular autoregulation in kidneys. Appropriate adaptive algorithms are developed in a continuous-time setting, and the local convergence of these algorithms is studied. Conditions for convergence are shown to include an excitation condition on the algorithm regressor and a passivity condition on an algorithm operator. The excitation conditions are interpreted in terms of system signals and the parameter basis functions using previously established results in the discrete-time case. A test for the passivity condition is developed whose application is presented via an illustrative example.

Keywords: Adaptive algorithm, Parameter estimation, Convergence

Research Title: Convergence properties of adaptive estimators of time-varying linear systems using basis functions

Author: Mohammad Abdul Qader Abu Naser, Published Year: 2006

Digital Signal Processing Workshop, USA

Faculty: Engineering and Technology

Abstract: The estimation of time-varying linear systems using a basis function approach has been applied in various fields such as equalization of mobile radio channels and in estimation of dynamics in biological systems. Typically, time-varying finite impulse response system models have been employed with recursive least squares or least mean squares adaptation. In this paper the convergence properties of these and other adaptive algorithms employed in this setting are formulated. The use of time-varying ARMA models is also included in the framework that is examined. The relation of the prediction error with the parameter error and the system regressor is exposed, indicating that a previously analyzed class of adaptive algorithms is appropriate for these problems. The convergence of this class of algorithms is dependent on a persistent excitation condition on system signals and a passivity condition on a system operator. Requirements for system regressors to be persistently exciting are derived for the time-varying linear system identification using basis functions, and the relevant operator conditions are described.

Keywords: Adaptive algorithm, Parameter estimation, Convergence

Research Title: Vascular resistance estimation in renal hemodynamics using a time-varying windkessel model

Author: Mohammad Abdul Qader Abu Naser, Published Year: 2005

International Conference on Acoustics, Speech, and Signal Processing, USA

Faculty: Engineering and Technology

Abstract: In studies of the dynamics of renal vascular response to blood pressure variations, measurements of pressure and flow rate are typically utilized to characterize a dynamic response with pressure as input and flow as output. However, the primary regulatory effect is the adjustment of vascular resistance, so that a record of a resistance time series would better serve as the regulated output. Toward this goal, a technique is developed for estimating the parameters of a three-element, time-varying Windkessel model of the renal vasculature that enables resistance estimation. The method is described, analyzed, and applied to renal pressure/flow data from rats.

Keywords: Vascular resistance measurement, Hemodynamics, renal vasculature

Research Title: Analyzing the Dynamic Relationship between Budget Deficit, Inflation, and Interest Rate (A Case from Jordan)

Author: Izzeddien Naef Ananzeh, Published Year: 2016

European Journal of Business and Management, Vol.8, No.29, 2016

Faculty: Business

Abstract: This study, gathered the most important economic variables that influence different countries interest rate such as,inflation and public deficit. Interest rate and inflation play an important role in monetary policy, and influence different countries decisions making regarding economic practices. Also, budget deficit can be used as a tool tomeasure governments’ financial performances. This study comes to investigate the dynamic relationship between budget deficit, inflation, and interest rate in Jordan for the period span from 1992 to 2015. Through employingmore advanced methodologies such as, Johansen Co Integration Test, and Granger Causality Test.Taking into consideration the econometrics analysis and johansen co integration test our study reported for a long-termrelationship between budget deficit, inflation, and interest rate. Also according to the VECM model which refers to refers to there is a long run causality running from interest rate and inflation rate toward budget deficit. Also the results report for a short run causality running from inflation rate, and interest rate toward budget deficit. Finally according to the Granger Causality Test confirm only for a single directional causality running inflation rate to budget deficit. This result imply for short-run impact between budget deficit, and inflation. Finally, Granger Causality Test confirms a single directional causality when comparing running inflation rate to budget deficit, and this result implies for short-term impact between budget deficit, and inflation.

Keywords: Budget Deficit, Interest Rate, Inflation Rate, VAR Model, VECM Model.

Research Title: Examining the Dynamics Relationship between Gold, Oil prices and Stock Markets: Experience from Jordan Economy

Author: Izzeddien Naef Ananzeh, Published Year: 2016

European Journal of Business and Management, 8, No.27, 2016

Faculty: Business

Abstract: The volatilities of gold and oil prices have extensive impacts on the financial activities of any country in the world. Consequently, financial markets and these two commodities have seen a period of extreme volatility raise the issue of the transmission the shocks and contagion between these markets through turmoil periods . for that reason this paper came in order to examine the dynamics relationship between the return of Amman Stock Exchange (ASE) and the price of the most important commodities in the world (crude oil , and gold ) for the period span from Jan 1993 to Apr 2016 . The main conclusion refer for a Long-run causality running from gold prices and oil prices to Amman Stock Market Returns. Also for existing co integration among fluctuations in gold price, and oil price on the stock prices of ASE which has remarkable implications for all investors in the region.

Keywords: Gold, Crude Oil, Stock Markets, VAR, Granger Causality Test.

Research Title: Weak Form Efficiency of the Amman Stock Exchange: An Empirical Analysis (2000-2013)

Author: Izzeddien Naef Ananzeh, Published Year: 2016

International Journal of Business and Management;, Vol. 11, No. 1; 2016

Faculty: Business

Abstract: The Efficient Market Hypothesis (EMH) has been a lot of debates in the literature of finance because of its important implication, and there is no clear-cut case regarding the efficiency of the financial markets for both developed and emerging markets. This empirical study conducted to examine EMH at the weak form level of Amman stock Exchange (ASE) by using daily observations for the period span from 2000 to 2013. Recent econometric procedures utilized for testing the randomness of stock prices for ASE. The results of serial correlation reject the existence of random walks in daily returns of the ASE, and the unit root tests also conclude the return series of ASE are stationary and inefficient at the weak-level. Also the runs tests verify that the stock returns series on ASE are not random, and our final conclusion reports that the ASE is inefficient at the weak form level.

Keywords: efficient market hypotheses, randomness, run test, serial correlation

Research Title: Factors Effecting Trading Volume: A Test of Mixed Distribution Hypothesis

Author: Izzeddien Naef Ananzeh, Published Year: 2015

International Journal of Financial Research, Vol. 6, No. 4; 2015

Faculty: Business

Abstract: This paper investigates the empirical relationship between trading volume and conditional volatility using data from Amman Stock Exchange (ASE) within the framework of Mixed Distribution Hypothesis (MDH). Our sample covered 27 securities, which is most active stocks traded for the period span from 2002 to 2012. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) k Exchange model employed in order to test the persistence in the volatility of stock returns. Our results confirm positive and strong relationship between trading volume for individual stocks and conditional volatility of returns. Moreover, the degree of volatility persistence reduced through the process of adding the contemporaneous volume into the conditional variance equation of GARCH model, and this is according to the predictions of the Mixture of Distributions Hypothesis (MDH).

Keywords: conditional volatility, trading volume, volatility persistence, mixture of distribution hypothesis, Generalized Autoregressive Conditional Heteroskedasticity (GARCH)

Research Title: Power Peaks Allocation Based on Averaging-Adaptive Wavelet Transform

Author: Qadri Jamal Al-Hamarsheh, Published Year: 2016

INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING , Volume 10

Faculty: Engineering and Technology

Abstract: a One of Orthogonal Frequency Division Multiplexing deficiency has been taken into consideration in this work. A proposition has been made to tackle the Peak to Average Power Ratio (PAPR) problem. The proposed work will be based on a special averaging adaptive wavelet transformation (SAAWT) process. It will be compared with two main works that has been published previously; a neural network (NN)-based and a special averaging technique (SAT)-based. In the NN work, the learning process makes use of a previously published work that is based on three linear coding techniques. The proposed work (SAAWT) consists of three main stages; extracting the needed features, de-noising and the optimization criterion. SAAWT has an enhancement over the SAT that will take the noise clearance enhancement into its consideration. It uses 136880 different combinations of de-noising parameters that are experimentally computed to get the most efficient result with respect to the MSE, SNR and PSNR values. A MATLAB simulation-based of such works has been made in order to check the proposition performance. In this simulation, both of the BER and CCDF curves have been taken into consideration. Furthermore, the bandwidth and channel behaviors have been remain constant. Moreover, two kinds of data have been imposing to this simulation; a random data that is generated randomly by making use of the MATLAB features and a practical data that have been extracted from a funded project entitled by ECEM. From the previously published work the SAT shows promising results in reducing the PAPR effect reached up to 75% over the work in the literature and over the NN-based work. Under the cost of increasing complexity, SAAWT gives further reduction over the SAT reaches up to 6%. This drawback will be examined in the future work.

Keywords: Orthogonal Frequency Division Multiplexing, Neural Network, Linear Codes, De-noising Parameters, Wavelet, Moving Average Filter.