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.

In the hundreds of bands of the photographed substance, hyperspectral imaging delivers a great density of spectral data. This allows the images to be used for a variety of purposes, including agriculture, geosciences, and biomedical imaging. Previous work didn’t discuss the best classifier with sufficient ground truth classes. This work presents the application of maximum abundance classification (MAC) for classifying a variety of areas over hyperspectral images. The allocation of an end-member throughout hyperspectral images can be described with abundance maps. Since each pixel's abundance values represent the proportion of each end-member that is present in that pixel, the pixels in a hyperspectral image will be classified in this study by determining the highest abundance rate of every pixel and allocating it to the corresponding end-member category. The ground truth classes are represented by nine end-members in the test data: Bitumen, Shadows, Self-Blocking Bricks, Bare Soil, highlighted Metal area, Gravel, Meadows, Trees, and Asphalt. By uniformly distributing the range of wavelength over the amount of spectral domains, we initially determine the central wavelength for each band to visualize loaded data and the end-member signatures of nine ground truth classes. Next, we estimate the end-members abundance maps. Finally, we classify the Max Abundance of every pixel to present a color-coded image, the overlaid, and the classified hyperspectral image areas over their category labels. The result demonstrates that brick, bare soil, trees, and asphalt zones have all been correctly identified in the photographs, which is beneficial for the identification or detection of materials