Search results for "communications"
showing 10 items of 3827 documents
Preamble Transmission Prediction for mMTC Bursty Traffic : A Machine Learning based Approach
2020
The evolution of Internet of things (IoT) towards massive IoT in recent years has stimulated a surge of traffic volume among which a huge amount of traffic is generated in the form of massive machine type communications. Consequently, existing network infrastructure is facing challenges when handling rapidly growing traffic load, especially under bursty traffic conditions which may more often lead to congestion. By proactively predicting the occurrence of congestion, we can implement necessary means and conceivably avoid congestion. In this paper, we propose a machine learning (ML) based model for predicting successful preamble transmissions at a base station and subsequently forecasting th…
Fall Detection Based on the Instantaneous Doppler Frequency : A Machine Learning Approach
2019
Modern societies are facing an ageing problem which comes with increased cost of healthcare. A major share of this ever-increasing cost is due to fall related injuries, which urges the development of fall detection systems. In this context, this paper paves the way for building of a radio-frequency-based fall detection system. This paper presents an activity simulator that generates the complex channel gain of indoor channels in the presence of one person performing three different activities, namely, slow fall, fast fall, and walking. We built a machine learning framework for activity recognition based on the complex channel gain. We assess the recognition accuracy of three different class…
Web Usage Mining by Neural Hybrid Prediction with Markov Chain Components
2021
This paper presents and evaluates a two-level web usage prediction technique, consisting of a neural network in the first level and contextual component predictors in the second level. We used Markov chains of different orders as contextual predictors to anticipate the next web access based on specific web access history. The role of the neural network is to decide, based on previous behaviour, whose predictor’s output to use. The predicted web resources are then prefetched into the cache of the browser. In this way, we considerably increase the hit rate of the web browser, which shortens the load times. We have determined the optimal configuration of the proposed hybrid predictor on a real…
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.