3,344 research outputs found
Very Low Cost Entropy Source Based on Chaotic Dynamics Retrofittable on Networked Devices to Prevent RNG Attacks
Good quality entropy sources are indispensable in most modern cryptographic
protocols. Unfortunately, many currently deployed networked devices do not
include them and may be vulnerable to Random Number Generator (RNG) attacks.
Since most of these systems allow firmware upgrades and have serial
communication facilities, the potential for retrofitting them with secure
hardware-based entropy sources exists. To this aim, very low-cost, robust, easy
to deploy solutions are required. Here, a retrofittable, sub 10$ entropy source
based on chaotic dynamics is illustrated, capable of a 32 kbit/s rate or more
and offering multiple serial communication options including USB, I2C, SPI or
USART. Operation is based on a loop built around the Analog to Digital
Converter (ADC) hosted on a standard microcontroller.Comment: 4 pages, 6 figures. Pre-print from conference proceedings; IEEE 21th
International Conference on Electronics, Circuits, and Systems (ICECS 2014),
pp. 175-178, Dec. 201
Optimal association of mobile users to multi-access edge computing resources
Multi-access edge computing (MEC) plays a key role in fifth-generation (5G) networks in bringing cloud functionalities at the edge of the radio access network, in close proximity to mobile users. In this paper we focus on mobile-edge computation offloading, a way to transfer heavy demanding, and latency-critical applications from mobile handsets to close-located MEC servers, in order to reduce latency and/or energy consumption. Our goal is to provide an optimal strategy to associate mobile users to access points (AP) and MEC hosts, while contextually optimizing the allocation of radio and computational resources to each user, with the objective of minimizing the overall user transmit power under latency constraints incorporating both communication and computation times. The overall problem is a mixed-binary problem. To overcome its inherent computational complexity, we propose two alternative strategies: i) a method based on successive convex approximation (SCA) techniques, proven to converge to local optimal solutions; ii) an approach hinging on matching theory, based on formulating the assignment problem as a matching game
The edge cloud: A holistic view of communication, computation and caching
The evolution of communication networks shows a clear shift of focus from
just improving the communications aspects to enabling new important services,
from Industry 4.0 to automated driving, virtual/augmented reality, Internet of
Things (IoT), and so on. This trend is evident in the roadmap planned for the
deployment of the fifth generation (5G) communication networks. This ambitious
goal requires a paradigm shift towards a vision that looks at communication,
computation and caching (3C) resources as three components of a single holistic
system. The further step is to bring these 3C resources closer to the mobile
user, at the edge of the network, to enable very low latency and high
reliability services. The scope of this chapter is to show that signal
processing techniques can play a key role in this new vision. In particular, we
motivate the joint optimization of 3C resources. Then we show how graph-based
representations can play a key role in building effective learning methods and
devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing:
Principles and Applications", P. Djuric and C. Richard Eds., Academic Press,
Elsevier, 201
The END: Estimation Network Design for games under partial-decision information
Multi-agent decision problems are typically solved via distributed iterative
algorithms, where the agents only communicate between themselves on a
peer-to-peer network. Each agent usually maintains a copy of each decision
variable, while agreement among the local copies is enforced via consensus
protocols. Yet, each agent is often directly influenced by a small portion of
the decision variables only: neglecting this sparsity results in redundancy,
poor scalability with the network size, communication and memory overhead. To
address these challenges, we develop Estimation Network Design (END), a
framework for the design and analysis of distributed algorithms, generalizing
several recent approaches. END algorithms can be tuned to exploit
problem-specific sparsity structures, by optimally allocating copies of each
variable only to a subset of agents, to improve efficiency and minimize
redundancy. We illustrate the END's potential by designing new algorithms for
generalised Nash equilibrium (GNE) seeking under partial-decision information,
that can leverage the sparsity in cost functions, constraints and aggregation
values. Finally, we test numerically our methods on a unicast rate allocation
problem, revealing greatly reduced communication and memory costs.Comment: 12 pages, 3 figure
Who is the director of this movie? Automatic style recognition based on shot features
We show how low-level formal features, such as shot duration, meant as length
of camera takes, and shot scale, i.e. the distance between the camera and the
subject, are distinctive of a director's style in art movies. So far such
features were thought of not having enough varieties to become distinctive of
an author. However our investigation on the full filmographies of six different
authors (Scorsese, Godard, Tarr, Fellini, Antonioni, and Bergman) for a total
number of 120 movies analysed second by second, confirms that these
shot-related features do not appear as random patterns in movies from the same
director. For feature extraction we adopt methods based on both conventional
and deep learning techniques. Our findings suggest that feature sequential
patterns, i.e. how features evolve in time, are at least as important as the
related feature distributions. To the best of our knowledge this is the first
study dealing with automatic attribution of movie authorship, which opens up
interesting lines of cross-disciplinary research on the impact of style on the
aesthetic and emotional effects on the viewers
Linear convergence in time-varying generalized Nash equilibrium problems
We study generalized games with full row rank equality constraints and we
provide a strikingly simple proof of strong monotonicity of the associated KKT
operator. This allows us to show linear convergence to a variational
equilibrium of the resulting primal-dual pseudo-gradient dynamics. Then, we
propose a fully-distributed algorithm with linear convergence guarantee for
aggregative games under partial-decision information. Based on these results,
we establish stability properties for online GNE seeking in games with
time-varying cost functions and constraints. Finally, we illustrate our
findings numerically on an economic dispatch problem for peer-to-peer energy
markets
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