Search results for "TELECOMMUNICATIONS"
showing 10 items of 1639 documents
Combining a context aware neural network with a denoising autoencoder for measuring string similarities
2020
Abstract Measuring similarities between strings is central for many established and fast-growing research areas, including information retrieval, biology, and natural-language processing. The traditional approach to string similarity measurements is to define a metric with respect to a word space that quantifies and sums up the differences between characters in two strings; surprisingly, these metrics have not evolved a great deal over the past few decades. Indeed, the majority of them are still based on making a simple comparison between character and character distributions without considering the words context. This paper proposes a string metric that encompasses similarities between str…
A Flexible 4G/5G Control Platform for Fingerprint-based Indoor Localization
2019
In this paper we propose a centralized SDN platform devised to control indoor femto-cells for supporting multiple network-wide optimizations and applications. In particular, we focus on an example localization application in order to enlighten the main functionalities and potentialities of the approach. First, we demonstrate that the platform can be exploited for reconfiguring some operational procedures, based on standard signalling mechanisms, at the programmable femto-cells; these procedures enable customized logics for collecting measurements reports from mobile terminals. Second, assuming that high-density devices such as smart objects are disseminated in the controlled indoor space, w…
The Use of Artificial Intelligence in Disaster Management - A Systematic Literature Review
2019
Whenever a disaster occurs, users in social media, sensors, cameras, satellites, and the like generate vast amounts of data. Emergency responders and victims use this data for situational awareness, decision-making, and safe evacuations. However, making sense of the generated information under time-bound situations is a challenging task as the amount of data can be significant, and there is a need for intelligent systems to analyze, process, and visualize it. With recent advancements in Artificial Intelligence (AI), numerous researchers have begun exploring AI, machine learning (ML), and deep learning (DL) techniques for big data analytics in managing disasters efficiently. This paper adopt…
An Encrypted Traffic Classification Framework Based on Convolutional Neural Networks and Stacked Autoencoders
2020
In recent years, deep learning-based encrypted traffic classification has proven to be effective; especially, using neural networks to extract features from raw traffic to classify encrypted traffic. However, most of the neural networks need a fixed-sized input, so that the raw traffic need to be trimmed. This will cause the loss of some information; for example, we do not know the number of packets in a session. To solve these problems, a framework, which implements both a convolutional neural network (CNN) and a stacked autoencoder (SAE), is proposed in this paper. This framework uses a CNN to extract high-level features from raw network traffic and uses an SAE to encode the 26 statistica…
Non-cooperative available bandwidth estimation towards ADSL links
2008
Existing tools for the estimation of the end- to-end available bandwidth require control of both end hosts of the path and this significantly limits their usability. In this paper we present ABwProbe, a single-ended tool for available bandwidth estimation against non-cooperative hosts. Although ABwProbe is general enough to be used on any Internet path, we focus our attention on ADSL links exploring the possibility of measuring the downlink available bandwidth of a non-cooperative ADSL host. We study the effect of cross-traffic on the uplink, finding that only large packets may deteriorate ABwProbe's measurements and we present two techniques to detect and filter the effect of uplink cross-…
Capacity Estimation of ADSL links
2008
Most tools designed to estimate the capacity of an Internet path require access on both end hosts of the path, which makes them difficult to deploy and use. In this paper we present a single-sided technique for measuring the capacity without the active cooperation of the destination host, focusing particularly on ADSL links. Compared to current methods used on broadband hosts, our approach generates two orders of magnitude less traffic and is much less intrusive. Our tool, DSLprobe, exploits the typical characteristics of ADSL, namely its bandwidth asymmetry and the relatively low absolute bandwidth, in order to measure both downlink and uplink capacities and to mitigate the impact of cross…
Significant correlations between certain spectra of atmospherics and different biological and pathological parameters.
1991
Atmospherics are very short naturally occurring electromagnetic impulses of between 4 and 50 kHz. In this review we summarize our results concerning the correlations between certain spectra of atmospherics and several biological and pathological parameters.
Efficient distributed average consensus in wireless sensor networks
2020
International audience; Computing the distributed average consensus in Wireless Sensor Networks (WSNs) is investigated in this article. This problem, which is both natural and important, plays a significant role in various application fields such as mobile agents and fleet vehicle coordination, network synchronization, distributed voting and decision, load balancing of divisible loads in distributed computing network systems, and so on. By and large, the average consensus' objective is to have all nodes in the network converged to the average value of the initial nodes' measurements based only on local nodes' information states. In this paper, we introduce a fully distributed algorithm to a…
Robust H-Infinity Filter Design for Uncertain Linear Systems Over Network with Network-Induced Delays and Output Quantization
2009
This paper investigates a convex optimization approach to the problem of robust H-Infinity filtering for uncertain linear systems connected over a common digital communication network. We consider the case where quantizers are static and the parameter uncertainties are norm bounded. Firstly, we propose a new model to investigate the effect of both the output quantization levels and the network conditions. Secondly, by introducing a descriptor technique, using Lyapunov-Krasovskii functional and a suitable change of variables, new required sufficient conditions are established in terms of delay-dependent linear matrix inequalities (LMIs) for the existence of the desired network-based quantize…
Steered Response Power Localization of Acoustic Passband Signals
2017
The vast majority of localization approaches using phase transform (PHAT) consider that the sources of interest are wideband low-pass sources. While this may be the usual case for common audio signals such as speech, PHAT methods are affected negatively by modulation artifacts when the sources to be localized are passband signals. In these cases, steered response power PHAT localization becomes less robust. This letter analyzes the form of generalized cross-correlation functions with PHAT when passband acoustic signals are considered, proposing approaches for increasing the localization performance through the mitigation of these negative effects.