The improved adaptive threshold decision method based on the fusion of otsu and equilibrium measurement to overcome the influence of flickering steel strip background is presented for high-speed moving target detectio...
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The southwest coast of India has changed dramatically as a result of natural and anthropogenic influences. The Mandaikadu coast in Tamilnadu has witnessed severe erosion activities. To understand the spatial and tempo...
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The southwest coast of India has changed dramatically as a result of natural and anthropogenic influences. The Mandaikadu coast in Tamilnadu has witnessed severe erosion activities. To understand the spatial and temporal beach morphological changes of this coast is being studied for the years 2018 and 2019. An attempt is made to map the coastal reforms using active Sentinel-1 synthetic aperture radar (SAR) remote sensing data by delineating shoreline based on backscattering coefficient (sigma degrees dB). The shoreline tracking and beach profiling were collected using a real-time kinematic global positioning system (RTK-GPS) for the two seasons. The study to examine;(i) SAR being able to distinguish between beach and sea in real-time conditions based on their backscattering coefficient values (ii) otsu threshold algorithm is being used to study the backscattering signal response at grey-scale for conformity (iii) DSAS being used to compute the statistical shoreline change rate by calculating End Point Rate (EPR) for short-term changes. The results showed that the cumulative distribution difference between beach and sea backscattering values (sigma degrees(B) -sigma degrees(S)) tends to yield a higher value of maximum probability in co-polarised sigma degrees(VV) (53%) accuracy compared to cross-polarised sigma degrees(VH) (16%). The study concluded that the Mandaikadu coast experienced an average erosion rate of (-55.34 m/yr) from 2018 to 2019 mainly due to the reduction of sediment discharge and other man-made activities. This study aims to help compensate for existing shoreline mapping especially with adverse weather conditions during the monsoon season. In the future, based on the findings of the study will be carried out for the entire Indian coast.
The diagnosis of brain tumours (BT) is time-consuming and heavily dependent on the radiologists' abilities. Multiple algorithms have been developed for detecting and classifying BT that are both accurate and fast....
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The diagnosis of brain tumours (BT) is time-consuming and heavily dependent on the radiologists' abilities. Multiple algorithms have been developed for detecting and classifying BT that are both accurate and fast. Recent years have seen an increase in the popularity of deep learning, especially when it comes to developing automated systems that can diagnose and segment BT more accurately and with less time. In this paper, a novel Brain Hexagonal Pattern Network (BHPN) has been proposed to classify the MEG and PET images into normal, benign and malignant tumours. For pre-processing, a bilateral filter is employed to remove noise artifacts from the collected MEG and PET images. To remove the outer cortical and skull region, skull stripping is used, to be implemented to raise the volume of the training datasets. The pre-processed images are segmented using the otsu threshold algorithm to segment the BT. These segmented tumours are taken as input to the Reversing Hexagonal algorithm to generate the hexagonal feature sets with and without a segmentation mask. In order to categorize tumours into normal, benign and malignant cases, a Spiking Dilated Convolutional Neural Network (SDCNN) classifier system is implemented. The classification accuracy of the Proposed BHPN approach is 99.54%. The Proposed BHPN approach improves the overall accuracy by 1.49%, 2.52%, and 3.93% better than hybrid deep autoencoder (DAE) and Bayesian Fuzzy Clustering (BFC), Deep CNN, and Neutrosophy and Convolutional Neural Network (NS-CNN) respectively.
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