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The advancement of multi-channel synthetic aperture radar (SAR) system is considered as an upgraded technology for surveillance activities. SAR sensors onboard provide data for coastal ocean surveillance and a view of the oceanic surface features. Vessel monitoring has earlier been performed using Constant False Alarm Rate (CFAR) algorithm which is not a smart technique as it lacks decision-making capabilities, therefore we introduce wavelet transformation-based Convolution Neural Network approach to recognize objects from SAR images during the heavy naval traffic, which corresponds to the numerous object detection. The utilized information comprises Sentinel-1 SAR-C dual-polarization data acquisitions over the western coastal zones of India and with help of the proposed technique we have obtained 95.46% detection accuracy. Utilizing this model can automatize the monitoring of naval objects and recognition of foreign maritime intruders., Comment: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

Melt quenching technique is used to prepare the glasses composition (KF-CaO-B2O3) with different concentration of manganese oxide in the range of 0.1-0.4 wt %. Nature of the fabricated glasses is confirmed by X-ray diffraction analysis. All calculated stability factors from differential thermal analysis increases with increasing the content of MnO. Infrared spectral analysis provided the information that those glass samples exhibited some symmetrical as well as asymmetrical bands belong to the borate group. The absorption results show a shifting in the absorption band, which is corresponding to the exchange of Mn2+ into the Mn3+ ions. Electron paramagnetic resonance results are shown two signals, which is characterized by Mn2+ and Mn3+. The variation in the intensity of these signals is discovered an improvement into glass stability with increased MnO content. The dielectric analyses is presented with different parameters with the variation of frequency from 103 Hz to 105 Hz and temperature 20 ?-400 ?., QC 20220912