Modeling blockage in high directional wireless. systems

While the wireless word moves towards higher frequency bands, new challenges arises, due to the inherent characteristics of the transmission links, such as high path and penetration losses. Penetration losses causes blockages that in turn can significantly reduce the signal strength at the receiver. Most published contributions consider a binary blockage stage, i.e. either fully blocked or blockage-free links. However, in realistic scenarios, a link can be partially blocked. Motivated by this, in this paper, we present two low-complexity models that are based on tight approximations and accommodates the impact of partial blockage in high-frequency links. To demonstrate the applicability of the derived framework, we present closed-form expressions for the outage probability for the case in which the distance between the center of the receiver plane and the blocker's shadow center follow uniform distribution. Numerical results verify the derived framework and reveal how the transmission parameters affect blockage.

Pediatric Bone Age Assessment using Deep Learning Models

Bone age assessment (BAA) is a standard method for determining the age difference between skeletal and chronological age. Manual processes are complicated and necessitate the expertise of experts. This is where deep learning comes into play. In this study, pre-trained models like VGG-16, InceptionV3, XceptionNet, and MobileNet are used to assess the bone age of the input data, and their mean average errors are compared and evaluated to see which model predicts the best.