Coronavirus (COVID-19) disease is an infectious disease caused by the newly and deadly pneumonia type identified Coronavirus2 (SARS-CoV-2). A real-time Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the main method and has been regarded as the gold standard for diagnosing the COVID-19. Strict requirements and the limited supply of RT-PCR kits for the laboratory environment leads to delay in the accurate diagnosis of patients in addition to the test takes 4-6 hours to obtain the results. To tackle this problem, radiological images such as chest X-rays and CT scan could be the answer to test the COVID-19 infection rapidly and more efficiently. In this paper, an efficient proposed Convolution Neural Network (CNN) architecture model for COVID-19 detection based on chest X-ray images is presented. The proposed model is developed to provide accurate detection for binary classification (Normal vs. COVID-19), three class classification (Normal vs. COVID-19 vs. Pneumonia), and four class classification (Normal vs. COVID-19 vs. Pneumonia vs. Tuberculosis (TB)). Our proposed model produced an overall testing accuracy of 99.7%, 95.02%, and 94.53% for binary, three, and four class classifications, respectively. A comparison is made between this work and others shows the superior of this work over the others.
Physical network topology is the configuration of computers, cables and other peripherals as a geometrical shape. There are many types of topologies each of them has many advantages and disadvantages. For high performance Local Area Networks (LANs), the builder of a network should select the best physical network topology. This paper analyzes three basic network topologies including bus, star and ring for different number of connected nodes (various network topology size) using OPNET simulator. Four scenarios are configured for each topology with number of connected nodes equal to 5, 10, 15 and 20 for senario1, 2, 3 and 4 respectively. In this analysis, four parameters investigated in Delay (Sec.) for the complete network, Load (Bits/Sec.), Traffic Received (Bits/Sec.) and the number of Collision for single node (server). Performance comparison is made in each topology separately based on various network size and another comparison is also made between these topologies for the same number of connected devices. The comparison results indicate that performance decreased when the network size increased and also show that bus topology is more effected than two other topologies.
This paper presents four different realizations of single-multiplier sine-cosine generators based on second-order digital filter structure. FPGA implementations of these four realizations are carried out on FPGA Spartan-3E Kit. Implementation results are compared from the view points of utilization resources and maximum frequency of operation. Another comparison is made between one of implementations of the derived structures and other two recent CORDIC-based implementations. The comparison results indicate that smaller chip area can be achieved in the case of the proposed structure of the sine-cosine generator. In addition, such structure can operate with higher circuit frequency as compared with the two others.
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