Anthropometric measurements of the orbita and gender prediction with three-dimensional computed tomography images

Background: The aim of the study was to investigate the orbital anthropometric variations in the normal population using three-dimensional computed tomography (3D-CT) images and to define the effects of age and gender on orbital anthropometry.Materials and methods: Three-dimensional orbita CT of 280 patients, obtained for various reasons, were retrospectively evaluated in 772-bed referral and tertiary-care hospital between April 2011 and June 2012. Using 3D images, orbital width, height, biorbital-interorbital diameter and orbital index were measured. Measurements were obtained comparing right and left sides and male to female. The relation of the results with age and gender was analysed.Results: Right orbit was found to be wider than left (p < 0.0001). Male patients had wider (p < 0.0001) and higher (p = 0.0001) orbits. Right orbital index was found to be smaller than the left one (p = 0.005). No differences were found between the genders in terms of right and left orbital indexes (p > 0.05). Biorbital (p < 0.0001) and interorbital (p = 0.01) widths were found to be higher in males. There was no relation between the age change and the parameters defined (p > 0.05).Conclusions: No relation was found between age and orbital measurements. It was concluded that orbital images obtained with 3D-CT may be used as a method for gender evaluation

Physical Layer-Based IoT Security: An Investigation Into Improving Preamble-Based SEI Performance When Using Multiple Waveform Collections

The Internet of Things (IoT) is a collection of inexpensive, semi-autonomous, Internet-connected devices that sense and interact within the physical world. IoT security is of paramount concern because most IoT devices use weak or no encryption at all. This concern is exacerbated by the fact that the number of IoT deployments continues to grow, IoT devices are being integrated into key infrastructures, and their weak or lack of encryption is being exploited. Specific Emitter Identification (SEI) is being investigated as an effective, cost-saving IoT security approach because it is a passive technique that uses inherent, distinct features that are unintentionally imparted to the waveform during its formation and transmission by the IoT device’s Radio Frequency (RF) front-end. Despite the amount of research conducted, SEI still faces roadblocks that hinder its integration within operational networks. Our work focuses on the lack of feature permanence across time and environments, which is designated herein as the “multi-day” problem. We present results and analysis for six distinct experiments focused on improving multi-day SEI performance through multiple waveform representations, deeper Convolutional Neural Networks (CNNs), increasing numbers of waveforms, channel model impacts, and two-channel mitigation techniques. Our work shows improved multi-day SEI performance using the waveform’s frequency-domain representation and a CNN comprised of four convolutional layers. However, the traditional channel model and both channel mitigation techniques fail to sufficiently mitigate or remove real-world channel impacts, which suggests that the channel may not be the dominant effect hindering multi-day SEI performance