A feasibility study was conducted to segment 1.5T fMRIs into gray matter and large veins using individual pixel intensity and temporal phase delay as two correlated parameters in gradient echo images. The time-course ...
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ISBN:
(纸本)0819417815
A feasibility study was conducted to segment 1.5T fMRIs into gray matter and large veins using individual pixel intensity and temporal phase delay as two correlated parameters in gradient echo images. The time-course of each pixel in gradient echo images acquired during visual stimulation with a checkerboard flashing at 8Hz was correlated to the stimulation 'on'-'off' sequence to identify activated pixels. The temporal delay of each activated pixels was estimated by fitting its time-course to a reference sinusoidal function. The mean signal intensity difference of the activated pixels was computed by subtracting the average of the 'on' images from the average of the 'off' images. After replacing each activated pixel with 2D features (i.e., intensity and time-delay), a clustering method based on a k-meansalgorithm was employed to classify vein and tissue pixels. Good demarcation between large veins and activated gray matter was achieved with this method.
In agriculture, paddy crop monitoring placed a crucial role because it supports food security control. Water shortage, high cost of fertilizers, and soil deterioration were identified as some of the difficulties encou...
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In agriculture, paddy crop monitoring placed a crucial role because it supports food security control. Water shortage, high cost of fertilizers, and soil deterioration were identified as some of the difficulties encountered when monitoring rice crops through satellite images acquired by remote sensing. This study developed a deep learning method-assisted clusteringalgorithm (DLCA) which helps to improve the paddy growth identification process and enables the transparency of agricultural activity. Convolution neural network (CNN) has been utilized to identify crop growth which helps to understand drip irrigation and water scarcity for a particular crop. The experimental research shows that the proposed model is improved in identifying the paddy growth, soil availability, high cost of fertilizers, and soil degradation in monitoring paddy crop production through the satellite image process. Overall, the findings of the experiments have been carried out, and the proposed DLCA to achieve a lower error rate of 0.03 and high accuracy of 98.52%, MCC attains 98.43%, and F1-score 99.02% compared to other popular methods.
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