Szerző szerinti böngészés "Al-Rawi, Muaayed F."
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Tétel Szabadon hozzáférhető Novel approach in measurement instrument based on computer(Akadémiai Kiadó, 2021-08) Al-Rawi, Muaayed F.; F. Al-Rawi, Muhanned; nemMost applications in engineering use a data acquisition device hooked up to a personal computer for data processing. Finding less costly, easily accessible and reliable devices will make personal computer (PC) based data acquisition systems less difficult. A soundcard may be used as such a device for it is standard in almost every PC. It can also process any voltage signal within its limits. This paper proposes a way to enable the PC to be used as an oscilloscope. A voltage signal is acquired via the soundcard LINE IN port. The maximum and minimum input signal amplitude requirements for the soundcard are established to be +1 V and –1 V respectively. Based on these findings, hardware circuitry is designed to clip any high amplitude input signals to the range of ±1 V while allowing low amplitude signals to go through to the soundcard unclipped. MATLAB is then employed to acquire, process and display the signal. The final output from MATLAB is compared with the original signal to determine accuracy of the designed oscilloscope. Analysis of the results obtained shows that the final oscilloscope designed enables the soundcard to process input signals with a high level of accuracy. The final design yields a hardware cost at a fraction of an iPod while providing an elegant user interface. This makes it suitable for college students, basement hackers and even professional engineers.Tétel Szabadon hozzáférhető Novel technique based on cascade classifiers for smoke image detection(Akadémiai Kiadó, 2021-12) Al-Rawi, Muaayed F.; Alyouzbaki, Yasameen A. Ghani; nemThis article contributes a novel technique based on cascade classifiers for smoke detection by utilizing the image processing method. It has been a difficult issue for ten years or so due to its variety in shape, texture, and color. In this article, a machine learning methodology is represented to tackle this issue and simulated with MATLAB software. The smoke detection issue acted like a classification issue. The solution is demonstrated as a binary classification issue. Hence, the support vector machine (SVM) is represented for classification. In order to train and test the SVM classifier, both samples of positive and negative are gathered. Two SVM classifiers are utilized in the cascade. The first classifier distinguishes the presence of smoke if smoke presents in a provided input image; the second classifier is utilized to find the locale of smoke in a provided input image. The size of the window is set to 32 × 32 and slided across the whole image to identify the smoke in a zone of the window. The novel technique is a training dataset and utilizing linear kernel function. In this manner, the novel technique is tested with a test dataset. The first SVM classifier obtained 100% accuracy in training and 96% accuracy in testing. A training accuracy of 96% and a test accuracy of 93.6% were obtained by the second SVM classifier. This novel technique proved to be more proficient and cost-savvy than the traditional strategies.