Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications.

In order for cognitive radios to identify and take advantage of unused frequency bands, spectrum sensing is essential. Conventional techniques for spectrum sensing rely on extracting features from received signals at specific locations. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have recently demonstrated promise in improving the precision and efficacy of spectrum sensing.

Deep neural network prediction of modified stepped double-slope solar still with a cotton wick and cobalt oxide nanofluid.

This research work intends to enhance the stepped double-slope solar still performance through an experimental assessment of combining linen wicks and cobalt oxide nanoparticles to the stepped double-slope solar still to improve the water evaporation and water production. The results illustrated that the cotton wicks and cobalt oxide (CoO) nanofluid with 1wt% increased the hourly freshwater output (HP) and instantaneous thermal efficiency (ITE). On the other hand, this study compares four machine learning methods to create a prediction model of tubular solar still performance.

A comprehensive review of technologies used to improve the performance of PV systems in a view of cooling mediums, reflectors design, spectrum splitting, and economic analysis.

Solar photovoltaic panels are increasingly being used throughout the world, particularly in Egypt, where a station has been constructed in the city of Aswan with a capacity of 1480 MW, and is classified as one of the largest photovoltaic plants in the Middle East country, where photovoltaic systems are characterized as environmentally friendly and do not produce any pollutants, and photovoltaic systems have the ability to operate with diffuse radiation. It is therefore very important to understand how photovoltaic panels respond to changing weather conditions and how climate conditions affect the performance of photovoltaic cells, as only 15-20% of solar radiation can be converted to electricity, while the rest is wasted as thermal heat. There are two very important factors influencing the efficiency of the photovoltaic cells: the cell temperature and the solar radiation intensity on the photovoltaic cells.