Moving objects detection, type recognition, and traffic analysis in video-based surveillance systems is an active area of research which has many applications in road traffic monitoring. This paper is on using classical approaches of image processing to develop an efficient algorithm for computer vision based on traffic surveillance system that can detect and classify moving vehicles, besides serving some other traffic analysis issues like finding vehicles speed and heading, tracking specified vehicles, and finding traffic load. The algorithm is designed to be flexible for modification to fulfill the changes in design objectives, having limited computation time, giving good accuracy, and serves inexpensive implementation. A 92% of success is achieved for the considered test, with the missed cases being abnormal that are not defined to the algorithm. The computation time, with a platform (hardware and software) dependent, the algorithm took to produce results was parts of milliseconds. A CNN based deep learning classifier was built and evaluated to judge the feasibility of involving a modern approach in the design for the targeted aims in this work. The modern NN based deep learning approach is very powerful and represents the choice for many very sophisticated applications, but when the purpose is restricted to limited requirements, as it is believed the case is here, the reason will be to use the classical image processing procedures. In making choice, it is important to consider, among many things, accuracy, computation time, and simplicity of design, development, and implementation.
Because of the unique properties, Ni-Ti based shape memory alloys (SMAs) are increasingly attractive for a wide variety of engineering applications such as actuators, biomedical, or robot coupling. In this work, a third alloying element, namely nanoparticles of Ag (which is insoluble in Ni-Ti matrix), is added by powder technology to the Ni-Ti alloy to produce a Ni-Ti-Ag alloy. The Nanoparticles of the Ag element are added at 3, 5, 7, and 10 wt. % to produce four alloy specimens with different mixtures. The mixing process was done by a horizontal mixer for 120 min with a speed of 350 rpm, and then the mixture was compacted by using a compacting pressure of 600 MPa. Afterward, the compacted specimens were sintered at 600/min for 6 hrs. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) were used to evaluate the microstructure and phases of the products. DSC examination was used to characterize the phase transformation temperatures in heating and cooling. Wear behavior was defined by using the pin-on-disc technique, and the hardness of the samples was calculated using Vickers's hardness apparatus. The results of this work showed that the nano-Ag added at 7 and 10 wt. % were distributed homogeneously in the Ni-Ti matrix, and that Ag slightly decreased hardness and increased the wear rate. The value of shape memory effect (SME) for the produced alloy was about 89.9% and the phase transformation in heating was at a temperature of about 186.48 and in cooling of about 140.3 for the specimen that contains 10 wt.% Ag nanoparticles.
Face detection technology is an essential step in almost all face related analysis applications such as face feature extraction, face alignment, face verification, face identification, face parsing, face recognition, age recognition, and gender classification. Numerous algorithms were introduced for face detection, one of which is the Viola-Jones algorithm being introduced in 2001. This algorithm is still widely used due to its simplicity and ability of detection in real-time with relatively high accuracy and low computational power requirements compared to other recent algorithms such as deep learning based algorithms. In this paper, Viola-Jones algorithm is implemented and evaluated through different tests. And its strengths, limitations, and affecting factors are provided according to the obtained results. This paper concentrates on the algorithm limitations and the reasons of these limitations, and suggests some solutions if possible. This can help in enhancing the algorithm performance by increasing the detection accuracy or reducing the time taken for detection or training…etc.
This work is concerned with designing two types of controllers, a PID and a Fuzzy PID, to be usedfor flying and stabilizing a quadcopter. The designed controllers have been tuned, tested, and compared using two performance indices which are the Integral Square Error (ISE) and the IntegralAbsolute Error (IAE), and also some response characteristics like the rise time, overshoot, settlingtime, and the steady state error. To try and test the controllers, a quadcopter mathematical model hasbeen developed. The model concentrated on the rotational dynamics of the quadcopter, i.e. the roll,pitch, and yaw variables. The work has been simulated with “MATLAB”. To make testing thesimulated model and the controllers more realistic, the testing signals have been applied by a userthrough a joystick interfaced to the computer. The results obtained indicated a general superiority inperformance for the Fuzzy PID controller over the PID controller used in this work. This conclusion is based by the following figures: lesser ISA for the roll, pitch, and yawconsequently, lesser IAE for the roll, pitch, and yaw consequently, lesser rise time and settling time for the roll and pitch consequently, and lesser settling time for the yaw. Moreover, the FPID gave zero overshoot versus , ,and in the PID case for the roll, pitch, and yaw consequently. Both controllers gave zero steady state error with close rise times for the yaw. This superiority of the FPID controller is gained as thefuzzy part of it continuously and online adapts the parameters of the PID part.
In this paper, an Integral Backstepping Controller (IBC) is designed and optimized for full control, of rotational and translational dynamics, of an unmanned Quadcopter (QC). Before designing the controller, a mathematical model for the QC is developed in a form appropriate for the IBC design. Due to the underactuated property of the QC, it is possible to control the QC Cartesian positions (X, Y, and Z) and the yaw angle through ordering the desired values for them. As for the pitch and roll angles, they are generated by the position controllers. Backstepping Controller (BC) is a practical nonlinear control scheme based on Lyapunov design approach, which can, therefore, guarantee the convergence of the position tracking error to zero. To improve controller capability in the steady state against disturbances, an integral action is used with the BC. To determine the optimal values of the IBC parameters, the Particle Swarm Optimization (PSO) is used. In the algorithm, the controller parameters are computed by minimizing a cost function that depends on the Integral Time Absolute Error (ITAE) performance index. Finally, different numerical simulations are provided in order to illustrate the performances of the designed controller. And for comparison purposes, a PID controller is designed and optimized using the PSO to control the quadcopter. The obtainediresults indicated a superiority in performance for the IBC over the PID controller based on some points among which are: a 13.3% and 30.5% lesser settling times for X and Y consequently, the ability to perform critical maneuvers that the quadcopter failed to do using the PID controller, and the capability of fast following up and conforming the changes of pitch (
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