In this paper, a single-phase boost type ac-dc converter with power factor correction (PFC) technique is designed and implemented. A current mode control at a constant switching frequency is used as a control strategy for PFC converter. The PFC converter is a single-stage singleswitch boost converter that uses a current shaping technique to reshape the non-sinusoidal input current drawn by the bulky capacitor in the conventional rectifier. This technique should provide an input current with almost free-harmonics, comply with the IEC61000-3-2 limits, and a system operates with near unity power factor. The other function of the boost converter that should be
accomplished is to provide a regulated DC output voltage. The complete designed

The traditional centralized network management approach presents severe efficiency and scalability limitations in large scale networks. The process of data collection and analysis typically involves huge transfers of management data to the manager which cause considerable network throughput and bottlenecks at the manager side. All these problems processed using the Agent technology as a solution to distribute the management functionality over the network elements. The proposed system consists of the server agent that is working together with clients agents to monitor the logging (off, on) of the clients computers and which user is working on it. file system watcher mechanism is used to indicate any change in files. The results were presente

Agent technology has a widespread usage in most of computerized systems. In this paper agent technology has been applied to monitor wear test for an aluminium silicon alloy which is used in automotive parts and gears of light loads. In addition to wear test monitoring، porosity effect on
wear resistance has been investigated. To get a controlled amount of porosity, the specimens have
been made by powder metallurgy process with various pressures (100, 200 and 600) MPa. The aim of
this investigation is a proactive step to avoid the failure occurrence by the porosity.
A dry wear tests have been achieved by subjecting three reciprocated loads (1000, 1500 and 2000)g
for three periods (10, 45 and 90)min. The weight difference a

The objective of the research is to identify the effect of an instructional design according to the active learning modelsالباحثين in the achievement of the students of the fifth grade, the instructional design was constructed according to the active learning models for the design of education. The research experience was applied for a full academic year (the first & the second term of 2017-2018). The sample consisted of 58 students, 28 students for the experimental group and 30 students for the control group. The experimental design was adopted with partial and post-test, the final achievement test consisted of (50) objectives and essays items on two terms, the validity of the test was verified by the adoption of the Kudoric

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

It is often needed to have circuits that can display the decimal representation of a binary number and specifically in this paper on a 7-segment display. In this paper a circuit that can display the decimal equivalent of an n-bit binary number is designed and it’s behavior is described using Verilog Hardware Descriptive Language (HDL).
This HDL program is then used to configure an FPGA to implement the designed circuit.

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k