The Arpoador beach is located between the Copacabana Fort and the corner of Francisco Otaviano St. and Vieira Souto St. in Ipanema.
The place is famous for its rock formations, called Pedra do Arpoador. Many people go to the place to enjoy the sea views, to hang out
with friends, or to see the sunset. Near the Arpoador rock, there is the Devil's Beach or Praia do Diabo, which is a bit hidden.
Many surfers choose Arpoador Beach and Diabo Beach because their waves are higher than other beaches and therefore better for surfing.
I. Connecting the Dots: Identifying Network Structure of Complex Data via Graph Signal Processing
Gonzalo Mateos, University of Rochester, USA
Santiago Segarra, Rice University, USA
Under the assumption that the signals are related to the topology of the graph where they are supported, the goal of graph signal processing (GSP) is to develop algorithms that fruitfully leverage this relational structure, and can make inferences about these relationships even when they are only partially observed. Many GSP efforts to date assume that the underlying network is known, and then analyze how the graph’s algebraic and spectral characteristics impact the properties of the graph signals of interest. However, such an assumption is often untenable in practice and arguably most graph construction schemes are largely informal, distinctly lacking an element of validation. In this tutorial we offer an overview of network topology inference methods developed to bridge the aforementioned gap, by using information available from graph signals along with innovative GSP tools and models to infer the underlying graph structure. It will also introduce the attendees to challenges and opportunities for SP research in emerging topic areas at the crossroads of modeling, prediction, and control of complex behavior arising with large-scale networked systems that evolve over time. Accordingly, this tutorial stretches all the way from (nowadays rather mature) statistical approaches including correlation analyses to recent GSP advances in a comprehensive and unifying manner. Through rigorous problem formulations and intuitive reasoning, concepts are made accessible to SP researchers not well versed in network-analytic issues. A diverse gamut of network inference challenges and application domains will be selectively covered, based on importance and relevance to SP expertise, as well as the authors’ own experience and contributions.
II. Statistical methods for physical layer security
Arsenia Chorti, CY Université, ENSEA, CNRS, Cergy, France
It is foreseen that in the next few years we will witness major leaps in the area of quantum computing, with serious implications for the domain of security. For example, NIST key management guidelines suggest that RSA key lengths of more than 15,360-bits are necessary to attain a security strength similar to AES-256, and a selection process has been initiated by NIST to choose new post-quantum cryptographic primitives. On the other hand, with 5G just being launched, massive connectivity and ultra-low delays are envisioned. In this context, the increasing complexity and related overhead of quantum resistant cryptographic schemes may clash with one of the central requirements of the massive Internet of things (IoT) and low latency of beyond (B5G) systems to connect people, machines and ultimately everything with diminishing delays and increasing autonomy. Currently, there is an intense discussion in progress in the statistical signal processing and wireless communities on possible alternative approaches to secure the B5G wireless edge, building on the premise that the physical layer can be exploited in building novel security approaches. These arguments are particularly pertinent in the case of massive IoT. The area of physical layer security (PLS) is gaining momentum in this framework, with the hope that it can play a vital role in reducing both the latency as well as the complexity of novel security standards. In this aspect, it is vitally important to review the application of statistical signal processing techniques in the realm of PLS.
III. Efficient Processing of Multidimensional Data via Tensor-Based Techniques
Martin Haardt, Ilmenau University of Technology, Germany
Lucas Nogueira Ribeiro, Ilmenau University of Technology, Germany
Tensor-based signal processing enables the exploitation of the rich multi-dimensional structure of multi-dimensional data, to achieve improved estimation accuracy, improved identifiability of the multi-dimensional data model, or a significant reduction of the computational complexity. There are many inter-disciplinary applications where such a benefit can be exploited including image processing, video processing, hyperspectral image denoising, data mining, social network analysis, machine learning, pattern recognition, array signal processing, wireless communications, and biomedical signal processing.
In this tutorial, we review the fundamental concepts and tools of tensor algebra. Then, we turn to the most important tensor decompositions that can be interpreted as generalizations of the matrix SVD, including the Higher-Order Singular Value Decomposition (HOSVD), its denoising property via truncation, and the decomposition of a given multi-dimensional signal into a sum of rank-one components, which is often called CANDECOMP / PARAFAC or Canonical Polyadic Decomposition (CPD). We also review several algorithms for the efficient computation of an approximate low-rank CPD from noise-corrupted multi-dimensional measurements.