The performance of Wi-Fi fingerprinting indoor localization systems (ILS) in indoor environments depends on the channel state information (CSI) that is usually restricted because of the fading effect of the multipath. Commonly referred to as the next positioning generation (NPG), the Wi-Fi™, IEEE 802.11az standard offers physical layer characteristics that allow positioning and enhanced ranging using conventional methods. Therefore, it is essential to create an indoor environment dataset of fingerprints of CIR based on 802.11az signals, and label all these fingerprints by their location data estimate STA locations based on a portion of the dataset for fingerprints. This work develops a model for training a convolutional neural network (CNN) for positioning and localization through generating IEEE® 802.11data. The study includes the use of a trained CNN to predict the position or location of several stations according to fingerprint data. This includes evaluating the performance of the CNN for multiple channel impulses responses (CIRs). Deep learning and Fingerprinting algorithms are employed in Wi-Fi positioning models to create a dataset through sampling the fingerprints channel at recognized positions in an environment. The model predicts the locations of a user according to a signal acknowledged of an unidentified position via a reference database. The work also discusses the influence of antenna array size and channel bandwidth on performance. It is shown that the increased training epochs and number of STAs improve the network performance. The results have been proven by a confusion matrix that summarizes and visualizes the undertaking classification technique. We use a limited dataset for simplicity and last in a short simulation time but a higher performance is achieved by training a larger data.

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This work aims to prepare magnesium oxide MgO nanopowder using the coprecipitation method and prepare nanocomposites by mixing MgO prepared nanopowder with epoxy resin by weight percentages (0.5, 1, 1.5, 2, and 2.5) using hand lay up molding. These prepared chemical materials are added to many consumer products to meet fire safety codes and prevent these items from catching fire quickly. If the flame retarded material or an adjacent material has ignited, the flame retardant will slow down combustion and often prevent the fire from spreading to other items. Especially some of these chemicals can accumulate in parts of electrical equipment, cars, airplanes, and building components. Using non toxic nanofillers in polymers to achieve flame retardancy is a viable option. The prepared powder has a cubic structure,  space group, and 4.2165 Å unit cell parameters according to X-ray diffraction XRD data and using Dicvol 91 indexing program. The grain size of the prepared powder was measured using Sherrer's equation to be 12.45 nm. The scanning electron microscope SEM micrograph of MgO nanopowder showed a spherical shape. The effect of MgO on flame retardancy of epoxy resin was investigated using limiting oxygen index LOI, rate of burning RB, and maximum flame height HF tests. According to the results of the three standard tests, the best flame retardancy with a strong and well intumescent char is obtained from the sample with 2 wt. % of MgO nanopowder, which has the highest LOI value of 21.95, RB value of 1.65 cm/min, and HF value of 5.44 cm. This data of using MgO nanopowder as flame retardant was valuable and necessary because it showed MgO nanopowder help prevent and slow fires of epoxy resin, therefore, protecting property and saving lives.

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