Towards Cognitive Service Delivery on B5G through AIaaS Architecture

Artificial Intelligence (AI) is pivotal in advancing mobile network systems by facilitating smart capabilities and automation. The transition from 4G to 5G has substantial implications for AI in consolidating a network predominantly geared towards business verticals. In this context, 3GPP has specified and introduced the Network Data Analytics Function (NWDAF) entity at the network's core to provide insights based on AI algorithms to benefit network orchestration. This paper proposes a framework for evolving NWDAF that presents the interfaces necessary to further empower the core network with AI capabilities B5G and 6G. In addition, we identify a set of research directions for realizing a distributed e-NWDAF.

Texture analysis using volume-radius fractal dimension

Texture plays an important role in computer vision. It is one of the most important visual attributes used in image analysis, once it provides information about pixel organization at different regions of the image. This paper presents a novel approach for texture characterization, based on complexity analysis. The proposed approach expands the idea of the Mass-radius fractal dimension, a method originally developed for shape analysis, to a set of coordinates in 3D-space that represents the texture under analysis in a signature able to characterize efficiently different texture classes in terms of complexity. An experiment using images from the Brodatz album illustrates the method performance.

Shape analysis using fractal dimension: a curvature based approach

The present work shows a novel fractal dimension method for shape analysis. The proposed technique extracts descriptors from the shape by applying a multiscale approach to the calculus of the fractal dimension of that shape. The fractal dimension is obtained by the application of the curvature scale-space technique to the original shape. Through the application of a multiscale transform to the dimension calculus, it is obtained a set of numbers (descriptors) capable of describing with a high precision the shape in analysis. The obtained descriptors are validated in a classification process. The results demonstrate that the novel technique provides descriptors highly reliable, confirming the precision of the proposed method.