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Practical Approaches for Software Components Integration in Telecommunications

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EMPoWER Hybrid Networks: Exploiting Multiple Paths over Wireless and ElectRical Mediums

Patrick Thiran, Julien Pierre Sacha Herzen, Christina Vlachou, Sébastien Christophe Henri

Several technologies, such as WiFi, Ethernet and power-line communications (PLC), can be used to build residential and enterprise networks. These technologies often co-exist; most networks use WiFi, and buildings are readily equipped with electrical wires ...

2016

Learning Network Structures from Firing Patterns

Martin Vetterli, Amin Karbasi, Amir Hesam Salavati

How can we decipher the hidden structure of a network based on limited observations? This question arises in many scenarios ranging from social to wireless and to neural networks. In such settings, we typically observe the nodes’ behaviors (e.g., the time ...

Ieee2016

On the Learning Behavior of Adaptive Networks - Part II: Performance Analysis

Ali H. Sayed

Part I of this paper examined the mean-square stability and convergence of the learning process of distributed strategies over graphs. The results identified conditions on the network topology, utilities, and data in order to ensure stability; the results ...

IEEE2015

Non-volatile memory as hardware synapse in neuromorphic computing: A first look at reliability issues

Irem Boybat Kara, Carmelo Di Nolfo

A large-scale artificial neural network, a three-layer perceptron, is implemented using two phase-change memory (PCM) devices to encode the weight of each of 164,885 synapses. The PCM conductances are programmed using a crossbar-compatible pulse scheme, an ...

IEEE2015

Functional Forwarding of Channel State Information

Michael Christoph Gastpar

Based on the recent compute-and-forward technique [1], a novel communication strategy is proposed under which functions of the channel state information are forwarded along the network. Those functions are chosen such that on the one hand, they can be effi ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Distributed machine learning in networks by consensus

Martin Hasler, Leonidas Georgopoulos

We propose an algorithm to learn from distributed data on a network of arbitrarily connected machines without exchange of the data-points. Parts of the dataset are processed locally at each machine, and then the consensus communication algorithm is employe ...

Elsevier2014

Is the Network Turing-Complete?

Dejan Kostic, Peter Peresini

Ensuring correct network behavior is hard. This is the case even for simple networks, and adding middleboxes only complicates this task. In this paper, we demonstrate a fundamental property of networks. Namely, we show a way of using a network to emulate t ...

2013

Definitive Consensus for Distributed Data Inference

Leonidas Georgopoulos

Inference from data is of key importance in many applications of informatics. The current trend in performing such a task of inference from data is to utilise machine learning algorithms. Moreover, in many applications that it is either required or is pref ...

EPFL2011

Power Efficient Hardware Implementation of a Fuzzy Neural Network

Rafal Tomasz Dlugosz

This paper presents a digital, transistor level implemented neo-fuzzy neural network. This type of neural network is particularly well suited for real-time applications like those encountered in signal processing and nonlinear system identification. We con ...

Technical University of Lodz2010

Local restoration for trees and arborescences

Laura Sanità

Protocols belonging to the Spanning Tree Protocol (STP) route traffic demands on tree topologies that are evaluated through shortest path procedures. In this paper we deal with the problem of assigning costs to the arcs of a network in order to guarantee t ...

Springer Berlin / Heidelberg2009