Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks

This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing spectral utilization and interference management and supporting efficient enforcement of access regulations. Existing emitter classification approaches successfully leverage convolutional neural networks (CNNs) to recognize RAT visual features in spectrograms and other time-frequency representations; however, the corresponding classification accuracy degrades severely under harsh propagation conditions, and the computational cost of CNNs may limit their adoption in resource-constrained network edge scenarios. In this work, we propose a novel emitter classification solution consisting of a Denoising Autoencoder (DAE), which feeds a CNN classifier with lower dimensionality, denoised representations of channel-corrupted spectrograms. We demonstrate—using a standard-compliant simulation of various RATs including LTE and four latest Wi-Fi standards—that in harsh channel conditions including non-line-of-sight, large scale fading, and mobility-induced Doppler shifts, our proposed solution outperforms a wide range of standalone CNNs and other machine learning models while requiring significantly less computational resources. The maximum achieved accuracy of the emitter classifier is 100%, and the average accuracy is 91% across all the propagation conditions

Tooth Shade Relationship with Age, Gender, and Skin Color in a Saudi Population: A Cross-Sectional Study

Objectives: The aim of this study was to assess the relationship between tooth shade among different groups of patients according to their age, gender, and skin color in a Saudi population. Materials and methods: Participants were divided based on age into Group 1 (10–20 years), Group 2 (21–30 years), Group 3 (31–40 years), and Group 4 (41+ years), and according to gender. Tooth shade was measured by Vita easyshade, Shade scanner, 3D Master shade system. The skin color was determined according to the Firzpatrick Scale. It consists of six shades, namely: I, II, III, IV, V, and VI. The skin complexion of the participants was divided into six categories: white/very fair, fair, light brown, moderate brown, dark brown, and black. Results: One hundred and ninety-eight individuals were recruited. Around 70% were males. Females had 25.4% A2 followed by 22% A1, and 22% A3 shade types, while males had B3 shade (18%) followed by A2 and A3 (15.8%). A statistically significant difference was observed between shade and gender (p < 0.05). A statistically significant difference was observed between shade and age group (p < 0.05), where increased age was correlated with darker teeth shades. Shade A1 was correlated with type I skin color in 57.1% of individuals. Skin color type II had A2 as a dominant shade by 34.1%. A2 and A3 shades were equally observed in skin color III by 20.3%. Overall, statistically significant differences were observed between shade and skin color groups (p < 0.05). Conclusions: In conclusion, the most frequent classical shade noted among male and female participants was shade type A, which represents reddish brownish. Group 2 (21–30 years) had the B3 shade as the most prominent shade type among age groups. Gender, age, and skin types all showed a significant relation with the tooth shade

Tooth Shade Relationship with Age, Gender, and Skin Color in a Saudi Population: A Cross-Sectional Study

Objectives: The aim of this study was to assess the relationship between tooth shade among different groups of patients according to their age, gender, and skin color in a Saudi population. Materials and methods: Participants were divided based on age into Group 1 (10–20 years), Group 2 (21–30 years), Group 3 (31–40 years), and Group 4 (41+ years), and according to gender. Tooth shade was measured by Vita easyshade, Shade scanner, 3D Master shade system. The skin color was determined according to the Firzpatrick Scale. It consists of six shades, namely: I, II, III, IV, V, and VI. The skin complexion of the participants was divided into six categories: white/very fair, fair, light brown, moderate brown, dark brown, and black. Results: One hundred and ninety-eight individuals were recruited. Around 70% were males. Females had 25.4% A2 followed by 22% A1, and 22% A3 shade types, while males had B3 shade (18%) followed by A2 and A3 (15.8%). A statistically significant difference was observed between shade and gender (p p p Conclusions: In conclusion, the most frequent classical shade noted among male and female participants was shade type A, which represents reddish brownish. Group 2 (21–30 years) had the B3 shade as the most prominent shade type among age groups. Gender, age, and skin types all showed a significant relation with the tooth shade