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The Sentinel-2 satellites deliver 13 band multi-spectral imagery with bands having 10 m, 20 m or 60 m spatial resolution. The low-resolution bands can be upsampled to match the high resolution bands to extract valuable information at higher spatial resolution. This paper presents a Wasserstein Generative Adversarial Network (WGAN) based approach named as DSen2-WGAN to super-resolve the low-resolution (i.e., 20 m and 60 m) bands of Sentinel-2 images to a spatial resolution of 10 m. A proposed generator is trained in an adversarial manner using the min-max game to super-resolve the low-resolution bands with the guidance of available high-resolution bands in an image. The performance evaluated using metrics such as Signal Reconstruction Error (SRE) and Root Mean Squared Error (RMSE) shows the effectiveness of the proposed approach as compared to the state-of-the-art method, DSen2 as the DSen2-WGAN reduced RMSE by 14.68% and 7%, while SRE improved by almost 4% and 1.6% for 6 × and 2 × super-resolution. Lastly, for further evaluation, we have used trained DSen2-WGAN model to super-resolve the bands of EuroSAT dataset, a satellite image classification dataset based on Sentinel-2 images. The per band classification accuracy of low-resolution bands shows significant improvement after super-resolution using our proposed approach. [ABSTRACT FROM AUTHOR]

Electromyography (EMG) is the study of electrical activity in the muscles. We classify EMG signals from surface electrodes (channels) using Artificial Neural Network (ANN). We evaluate classification performance of 10 different hand motions using several feature-channel combinations with wrapper method. Highest classification accuracy of 98.7% is achieved with each feature-channel combination. Compared to previous studies, we achieve the highest accuracy for 10 classes with lower number of feature-channel combination. We reduce ANN complexity without compromising the classification accuracy for deployment in low-end hardware with limited computational power along with improving the design of a low-cost hardware for EMG signal acquisition. [ABSTRACT FROM AUTHOR]

Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. With lack of ground observations in Pakistan, microwave sensors are viable technologies for retrieving soil moisture from space. Thus, this study compares soil moisture data from Soil Moisture and Ocean Salinity (SMOS) with Advanced Microwave Scanning Radiometer (AMSR-E) for 2010 and 2011, which were years where massive flooding occurred. We find suitable correlations (r = 0.47) for Level-2 soil moisture values for both satellites and report temporal variability during the pre-monsoon, monsoon, post-monsoon and winter seasons. This study motivates using satellite retrievals as a start towards more comprehensive studies in Pakistan. [ABSTRACT FROM PUBLISHER]