Mohamed A. Zohdy

Design of robust discrete control with desirable quadratic stability

In this paper, a design of robust discrete control with desirable quadratic stability is proposed. The design procedure is the extended discrete version of the continuous robust quadratic stabilization technique proposed by Gu et al. [K. Gu, Y.H. Chen, M.A. Zohdy, N.K. Loh, Quadratic stabilizability of uncertain systems: a two level optimization setup, Automatica 27 (1) (1991) 161-165.]. The effect of the sampling time selection, and the effect of the domain on the robustness, is examined. The presented algorithm is applied to a discrete mass spring system, and a discrete simplified car steering system to demonstrate the feasibility of the procedure, and the effect of the time sampling on the robustness. The robustness increases in both examples considered, as the sampling time decreases to some degree.

Linear Phase FIR Low Pass Filter Design Based on Firefly Algorithm

Zigbee technology has been developed for short range wireless sensor networks and it follows IEEE 802.15.4 standard. For such sensors, several considerations should be taken including; low data rate and less design complexity in order to achieve efficient performance considering to the transceiver systems. This research focuses on implementing a digital transceiver system for Zigbee sensor based on IEEE 802.15.4 . The system is implemented using offset quadrature phase shift keying (OQPSK) modulation technique with half sine pulse-shaping method. Direct conversion scheme has been used in the design of Zigbee receiver in order to fulfill the requirements mentioned above. System performance is analyzed considering to BER when it encountered adaptive white Gaussian noise (AWGN), besides showing the effect of using direct sequence spread spectrum (DSSS) technique.The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.This paper presents a novel technique for numeral reading in Indian language speech synthesis systems using the rule-based Concatenative speech synthesis technique. The model uses a set of rules to determine the context of the numeral pronunciation and is being integrated with the waveform concatenation technique to produce speech out of the input text in Indian languages. To analyze the performance of the proposed technique, a set of numerals are considered in different context and a comparison of the proposed technique with an existing numeral reading method is also presented to show the effectiveness of the proposed technique in producing intelligible speech out of the entered text.This paper presents a data processing system based on an architecture comprised of multiple stacked layers of computational processes that transforms Raw Binary Pollution Data com- ing directly from Two EUMETSAT Metop satellites to our servers, into ready to interpret and visualise continuous data stream in near real time using techniques varying from task automation, data preprocessing and data analysis to machine learning using feed forward ar- tificial neural networks. The proposed system handles the acquisition, cleaning, processing, normalizing, and predicting of Pollution Data in our area of interest of Morocco.Advanced Communication Systems are wideband systems to support multiple applications such as audio, video and data so and so forth. These systems require high spectral efficiency and data rates. In addition, they should provide multipath fading and inter-symbol interference (ISI) free transmission. Multiple input multiple output orthogonal frequency division multiplexing (MIMO OFDM) meets these requirements Hence, MIMO-OFDM is the most preferable technique for long term evaluation advanced (LTE-A). The primary objective of this paper is to control bit error rate (BER) by proper channel coding, pilot carriers, adaptive filter channel estimation schemes and space time coding (STC). A combination of any of these schemes results in better BER performance over individual schemes. System performance is analyzed for various digital modulation schemes. In this paper,adaptive filter channel estimated MIMO OFDM system is proposed by integrating channel coding, adaptivefilter channel estimation, digital modulation and space time coding. From the simulation results, channel estimated 2×2 MIMO OFDM system shows superior performance over individual schemes.Electricity markets are different from other markets as electricity generation cannot be easily stored in large amounts and in order to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a large extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks in order to predict short-term (72 hours ahead) wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. Implementing an in-house self-adaptive model should yield good results in the dynamic uncertain Irish energy market. We have identified the features that such a model demands and outline it here.Received May 2, 2018 Revised Jul 9, 2018 Accepted Aug 2, 2018 Zigbee technology has been developed for short range wireless sensor networks and it follows IEEE 802.15.4 standard. For such sensors, several considerations should be taken including; low data rate and less design complexity in order to achieve efficient performance considering to the transceiver systems. This research focuses on implementing a digital transceiver system for Zigbee sensor based on IEEE 802.15.4. The system is implemented using offset quadrature phase shift keying (OQPSK) modulation technique with half sine pulse-shaping method. Direct conversion scheme has been used in the design of Zigbee receiver in order to fulfill the requirements mentioned above. System performance is analyzed considering to BER when it encountered adaptive white Gaussian noise (AWGN), besides showing the effect of using direct sequence spread spectrum (DSSS) technique. Keyword:This paper presents the use of Simelectronics Program for modeling and control of a two degrees-of freedom coupled mass-spring-damper mechanical system.The aims of this paper are to establish a mathematical model that represents the dynamic behaviour of a coupled mass-spring damper system and effectively control the mass position using both Simulink and Simelectronics.The mathematical model is derived based on the augmented Lagrange equation and to simulate the dynamic accurately a PD controller is implemented to compensate for the oscillation sustained by the system as a result of the complex conjugate pair poles near to the imaginary axis.The input force has been subjected to an obstacle to mimic actual challenges and to validate the mathematical model a Simulink and Simelectronics models were developed, consequently, the results of the models were compared. According to the result analysis, the controller tracked the position errors and stabilized the positions to zero within a settling time of 6.5sec and significantly reduced the overshoot by 99.5% and 99. 7% in Simulink and Simelectronics respectively. Furthermore, it is found that Simelectronics model proved to be capable having advantages of simplicity, less time-intense and requires no mathematical model over the Simulink approach.

Robust nonlinear position control of BLDC motor with friction

In this paper, the Control Lyapunov Function (CLF) approach is used for position control of permananent magnet BLDC motors. A nonlinear dynamic DC motor model including the nonlinear friction torque is used. The closed loop control used guarantees the systems stability and achieves the desired behaviour. The robustness of the control method is also proven in regards to the system parameters and to the control parameters which makes it a versatile method with only few control parameters.

Parameter estimation from experimental laboratory data of HSV-1 by using alternative regression method

In this paper, an estimation of model parameters is performed by using the Alternative Regression (AR) approach on an experimental data set of Herpes Simplex Virus type-1 (HSV-1) infection with innate immune response. Throughout the specified course of time, the measurements of monocytes, neutrophils, and viral load were obtained from the corneas of infected mice. C57BL/6 (B6) mice were used at Oakland University, Department of Biological Sciences, and the outcome measurements were divided into training and testing data sets. The HSV-1 nonlinear dynamic model is proposed based on the observed data patterns and biological system information. The simulation results of the proposed model showed that they consistently fit the experimental data set. In addition, the sensitivity test and model validation diagnostics are considered to determine the most significant key parameters that affect the dynamics of the HSV-1 system.

Developing the Theory of a Model-Based Dynamic Recurrent Neural Network

The theory lying behind a model-based dynamic recurrent neural network (MBDRNN) previously used to improve the linearized models of nonlinear systems is developed in this paper. The MBDRNN is initially based on the linearized system model, and then is trained to represent the systems nonlinearities by adapting the weights of its nodes activation functions using back-propagation . The details of the various computations necessary for a successful operation of the MBDRNN are presented.

Hybrid Neural Networks for Immunoinformatics

Hybrid set of optimally trained feed-forward, Hop-field and Elman neural networks were used as computational tools and were applied to immunoinformatics. These neural networks enabled a better understanding of the functions and key components of the adaptive immune system. A functional block representation was also created in order to summarize the basic adaptive immune system and the appropriate neural networks were employed to solve them. Training and learning accuracy of all neural networks were very good. Polymorphism, inheritance and encapsulation (PIE) learning concepts were adopted in order to predict the static and temporal behavior of adaptive immune system interactions in response to typical virus attacks

Robust quadratic stabilization applied to design of continuous-time and discrete-time observers

In this paper, an application of a robust quadratic stabilization algorithm (RQSA) to continuous-time and discrete-time observers is presented. The RQSA is based on checking end points of a bounding hyperpolyhedron. The RQSA applied to observers is introduced, and implemented for a simplified car model, in both continuous-time and discrete-time case. The paper concludes that the RQSA is feasible to estimate unavailable states, even though there are structured uncertainties in the system matrix and in the measurement matrix.

Analysis of chaotic physical systems and an algorithm for control

Numerous engineering, physics, meteorology and biomedical systems can be classified as projecting chaotic behavior. This can occur naturally or as a result of the underlying mathematical dynamical equations. In this paper, we consider comprehensive analysis of typical physical systems from several performance viewpoints: the power spectrum, the embedding dimension, Lyapunov exponents, return maps and Poincare animation. A discussion of a modern control and prediction algorithm follows. Finally, a proposal is made for the laboratory construction of a device to coalesce the analysis, prediction and control schemes on realistic data.