Automobiles of today must constantly change their speeds in reaction to changing road and traffic circumstances as the pace and density of road traffic increases. In sophisticated automobiles, the Anti-lock Braking System (ABS) is a vehicle safety system that enhances the vehicle's stability and steering capabilities by varying the torque to maintain the slip ratio at a safe level. This paper analyzes the performance of classical control, model reference adaptive control (MRAC), and intelligent control for controlling the (ABS). The ABS controller's goal is to keep the wheel slip ratio, which includes nonlinearities, parametric uncertainties, and disturbances as close to an optimal slip value as possible. This will decrease the stopping distance and guarantee safe vehicle operation during braking. A Bang-bang controller, PID, PID based Model Reference Adaptive Control (PID-MRAD), Fuzzy Logic Control (FLC), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controller are used to control the vehicle model. The car was tested on a dry asphalt and ice road with only straight-line braking. Based on slip ratio, vehicle speed, angular velocity, and stopping time, comparisons are performed between all control strategies. To analyze braking characteristics, the simulation changes the road surface condition, vehicle weight, and control methods. The simulation results revealed that our objectives were met. The simulation results clearly show that the ANFIS provides more flexibility and improves system-tracking precision in control action compared to the Bang-bang, PID, PID-MRAC, and FLC.

Antennas play very important role in the performance and consistency of any wireless communication system. In this work we have design, investigate, and optimize the various parameters as well as the bandwidth of a multi-band microstrip antenna using different positions of slot patch and defected ground plane structures (DGS) techniques. A Roger RT/ duroid 5880 substrate with dielectric constant 2.2, and ground plane with an area of 80mm×90mm have been chosen for operation at three different frequency bands centered at (2.30-2.7) GHz, (4.0-4.7) GHz and (5.2-5.8) GHz. The operation scope of the proposed structures is intended to work for WLAN, IMT (International mobile telecommunications) and WiMAX applications. Ansoft-High Frequency Structured Simulation package (Ansoft-HFSS) has been used as realization software of the proposed antennas.

In first stage of each microwave receiver there is Low Noise Amplifier (LNA) circuit, and this stage has important rule in minimizing the system noise figure, provide enough gain with sufficient linearity, and assure a stable 50 Ω input impedance at a low power consumption. The design of a LNA in Radio Frequency/Microwave (RF/MW) circuit requires the trade-off many importance characteristics such as gain, Noise Figure (NF), stability, power consumption and complexity. This situation Forces the designers to make choices in the design of RF circuits. In this paper the aim is to design and simulate a LNA circuit with high gain and low noise using E-pHEMT MGA665P8 transistor for frequency of 4 GHz. The amplifier is manually designed using conventional technique, Smith chart was used to do a matching of the input and output of the amplifier. The matching network was designed with microstrip lines and it’s single-stub with minimum lengths. A completed design of amplifier was optimized using Hewlett- Packard Advanced Design System (HP-ADS) software. A single stage LNA has successfully designed with 21.032 dB forward gain and 0.697 dB noise figure and working stably in the desired frequency.

The spread of ICT in education and learning has changed the way of teaching and learning, and

this raised the issue of getting e-learning systems to adapt/personalize with respect to student's

knowledge achievement. However, the relation between the concepts in the programming domain

are not definitive and they are fuzzily related. In this Paper, we describe a practical experience for

developing adaptable e-learning system using fuzzy logic to model and evaluate student's

knowledge for the purpose of adaptation in the field of Computer Programming. Fuzzy logic

technique modeled as a plugin on MOODLE Learning Management System at the Libyan

Academy for Postgraduate Studies is developed. Conclusions highlighted some important lessons

in increasing learning effectiveness and learner's satisfaction.

In this paper, the data filtering based Recursive Least Squares algorithm (RLS) of linear Box–Jenkins systems is proposed for fault detection. The system is decomposed into two subsystems, one containing the parameters of the system model and the other containing the parameters of the noise model to improve the computational load. In addition, the parameters of the system model and the noise model are estimated and their accuracy are developed. The model validation is tested using two statistical methods, histogram and mean square errors. The residual is generated based on the proposed algorithm to design the threshold and therefore, this designed thresholdis used for fault detection. Simulation results are performed to illustrate the algorithm performance.

For Arabic letters recognition, we achieve three of pattern recognition approaches namely gray level co-occurrence matrix (GLCM), local binary pattern recognition (LBP) and artificial neural network (ANN) and compare between them to result best performance. Two of these methods level co-occurrence matrix and local binary pattern recognition are used for feature extraction whereas in artificial neural network (ANN) we use the intensity values of pixels for input of the neural network. Two classifiers are used, the K-Nearest Neighbor classifier (KNN) for the LBP, GLCM and neural network classifier for (ANN) artificial neural network. Also, we evaluate the results by using leave one person out approach, fold classification and leave one out.

this paper derives extended three-stage recursive identification algorithm of MISO for (CARARMA) systems. Based on The decomposition technique, four subsystems are obtained and the parameters of each subsystem are identified. Some model validation methods are computed to measure the model value and Akaike’s Final Prediction Error Criterion (FPE) is used to verify the selection of system order. The algorithm has a high computational efficiency because the covariance matrices dimensions become small in each subsystem. Finally, this algorithm effectiveness is demonstrated in simulation example.

This paper aims to demonstrate the application of two different control techniques, namely the Linear Quadratic Regulator (LQR) and a neural network-based controller to evaluate and control the vibrations that occurred in the car's suspension system. When the car suspension is designed, a quarter car model with 1-DOF is used. A complete control system is needed to provide the desired suspension performance and characteristics such as passenger comfort, road handling, and suspension deflection, this control system performed by using the Matlab software and includes three parts: input signals (actuator force and road profile), Controller, and the suspension system model. The simulation results show a comparison between the uncontrolled suspension system and the suspension system with a neural network-based controller and the active suspension system of the car based on the linear-quadratic regulator, and it is explained thoroughly.