In this paper, a new method for determining the number of coherent/correlated signals in the presence of colored noise is proposed which is based on the Eigen Increment Threshold (EIT) method. First, we present a new approach which combines EIT criterion and eigenvalue More
In this paper, a new method for determining the number of coherent/correlated signals in the presence of colored noise is proposed which is based on the Eigen Increment Threshold (EIT) method. First, we present a new approach which combines EIT criterion and eigenvalue correction. The simulation results show that the new method estimates the number of noncoherent signals in the presence of colored noise with higher detection probability respect to MDL, AIC, EGM and conventional EIT. In addition, to apply the proposed EIT algorithm to detect the number of sources in the case of coherent and/or correlated sources, a spatial smoothing preprocessing is added. In this case, simulation results show 100% detection probability for signal to noise ratios greater than -5dB. Final version of the proposed EIT-based method is a simple and efficient way to increase the detection probability of EIT method in the presence of colored noise considering either coherent/correlated or noncoherent sources.
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In the diagnosis of retinal disease, Retinal vessels become an important role in determining certain diseases. Retina vessels are an important element with a variety of shapes and sizes, each human blood vessel also can determine the disease with various types, but the More
In the diagnosis of retinal disease, Retinal vessels become an important role in determining certain diseases. Retina vessels are an important element with a variety of shapes and sizes, each human blood vessel also can determine the disease with various types, but the feasibility of the pattern of retinal blood vessels is very important for the advanced diagnosis process in medical retina such as detection, identification and classification. Improvement and improvement of image quality in this case is very important by focusing on extracting or segmenting the retinal veins so that parameters such as accuracy, specifications, and sensitivity can be obtained that are better and meet the advanced system. Therefore we conducted experiments in order to develop extraction of retinal images to obtain binary images of retinal vessels in the medical world using Dynamic Threshold and Butterworth Bandpass Filter. Using a database DRIVE Accuracy of 94.77%, sensitivity of 54.48% and specificity of 98.71%.
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The Pareto optimal solution is unique in single objective Particle Swarm Optimization (SO-PSO) problems as the emphasis is on the variable space of the decision. A multi-objective-based optimization technique called Multi-Objective Particle Swarm Optimization (MO-PSO) i More
The Pareto optimal solution is unique in single objective Particle Swarm Optimization (SO-PSO) problems as the emphasis is on the variable space of the decision. A multi-objective-based optimization technique called Multi-Objective Particle Swarm Optimization (MO-PSO) is introduced in this paper for image segmentation. The multi-objective Particle Swarm Optimization (MO-PSO) technique extends the principle of optimization by facilitating simultaneous optimization of single objectives. It is used in solving various image processing problems like image segmentation, image enhancement, etc. This technique is used to detect the tumour of the human brain on MR images. To get the threshold, the suggested algorithm uses two fitness(objective) functions- Image entropy and Image variance. These two objective functions are distinct from each other and are simultaneously optimized to create a sequence of pareto-optimal solutions. The global best (Gbest) obtained from MO-PSO is treated as threshold. The MO-PSO technique tested on various MRI images provides its efficiency with experimental findings. In terms of “best, worst, mean, median, standard deviation” parameters, the MO-PSO technique is also contrasted with the existing Single-objective PSO (SO-PSO) technique. Experimental results show that Multi Objective-PSO is 28% advanced than SO-PSO for ‘best’ parameter with reference to image entropy function and 92% accuracy than Single Objective-PSO with reference to image variance function.
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