This paper intends to design anartificial model of WDM Optical Network in respect of length, pump power and simple free space optical link which is designed and analysis of several FSO (Free-space optical communication) factors such as Bit Error Rate (BER), Q factor and received power have been measured with regard to the variants in the beam divergence and attenuation. The Simulation process of the System has been done using Optic simulation software to achieve gain enhancement and decrease noise factor of Erbium Doped Fiber Amplifier (EDFA) through optimized fiber length and pump power. This workfocused to construct artificial design and analyses of 16 channels WDM system using EDFA and the influence of irregular atmospheric Motions on the link performance has been inspected by fluctuating beam divergence, atmospheric disturbances and modulation format at the bit rate of 10 Gb/s. As slicing in WDM schemes, in the transmitter channel with the frequency of 1558nm and chip spacing 0.4nm, power value is equal to + 23.5dBm, whereas the optical power meter plus can be used to predict the output power in the form of NRZ modulation, while the input power is produced with a help of CW laser, the gain level has been enriched from 1564nm to 1558nm wavelength band with bit rate of 10GbpS.This system is prepared by using Optic simulation software version 7.
Ultrasound imaging is commonly used to diagnose internal anomalies. Imaging for abnormality detection is a challenging process in today’s world. Even though there is an advancement in technology, tele-radiographers face difficulty in the accurate diagnosis of abnormalities. In order to resolve this issue, tele-radiology has paved a new way for doctors around the world to access the Internet to share the radiological images from one location to another. But frequent online access is one of the bottleneck issues. In order to overcome this drawback, Computer Assisted Diagnosis (CAD) is preferred in this proposed study and it uses VIRTEX-6 FPGA to clearly identify abnormality in the platform and also manual control is minimized in this condition. The proposed algorithm includes five steps: pre-processing, segmentation, feature extraction, selection and classification. The classification is performed using the Iterative K-Nearest Neighbor (IKNN) classifier based on the selected features. Unlike popular KNN, the proposed IKNN algorithm performs the similarity measurement on selective neighbors for a number of times where the number of neighbors has been dynamically selected at each iteration. Also, at each iteration, the method would select a subset of features in a random way. For the features selected and with the neighbors selected, the method computes the similarity value of Hist-sim which is being measured according to the features selected from the histogram features where the method computes the Haralick similarity with the features selected from the Haralick features. Using the features selected, the method computes the value of cumulative class drive similarity (CCDS). At each iteration the class with maximum similarity is selected and finally, the class being selected for the most number of times is selected as a result of classification. This improves the performance of classification. While comparing with the existing algorithms such as Support Vector Machine (SVM) with the linear, Radial Basis Function (RBF) and polynomial kernels, greater accuracy is achieved via IKNN classification. The specificity is found to be 95, 80 and 75 for normal, cystic and stone kidneys.
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