8,511 research outputs found
Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions
The Internet of Things (IoT) is a collection of Internet connected devices
capable of interacting with the physical world and computer systems. It is
estimated that the IoT will consist of approximately fifty billion devices by
the year 2020. In addition to the sheer numbers, the need for IoT security is
exacerbated by the fact that many of the edge devices employ weak to no
encryption of the communication link. It has been estimated that almost 70% of
IoT devices use no form of encryption. Previous research has suggested the use
of Specific Emitter Identification (SEI), a physical layer technique, as a
means of augmenting bit-level security mechanism such as encryption. The work
presented here integrates a Nelder-Mead based approach for estimating the
Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA
fingerprinting. The performance of this estimator is assessed for degrading
signal-to-noise ratio and compared with least square and minimum mean squared
error channel estimators. Additionally, this work presents classification
results using RF-DNA fingerprints that were extracted from received signals
that have undergone Rayleigh fading channel correction using Minimum Mean
Squared Error (MMSE) equalization. This work also performs radio discrimination
using RF-DNA fingerprints generated from the normalized magnitude-squared and
phase response of Gabor coefficients as well as two classifiers. Discrimination
of four 802.11a Wi-Fi radios achieves an average percent correct classification
of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a
Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE
Transactions on Information Forensics and Securit
Directed Flow of Information in Chimera States
We investigated interactions within chimera states in a phase oscillator
network with two coupled subpopulations. To quantify interactions within and
between these subpopulations, we estimated the corresponding (delayed) mutual
information that -- in general -- quantifies the capacity or the maximum rate
at which information can be transferred to recover a sender's information at
the receiver with a vanishingly low error probability. After verifying their
equivalence with estimates based on the continuous phase data, we determined
the mutual information using the time points at which the individual phases
passed through their respective Poincar\'{e} sections. This stroboscopic view
on the dynamics may resemble, e.g., neural spike times, that are common
observables in the study of neuronal information transfer. This discretization
also increased processing speed significantly, rendering it particularly
suitable for a fine-grained analysis of the effects of experimental and model
parameters. In our model, the delayed mutual information within each
subpopulation peaked at zero delay, whereas between the subpopulations it was
always maximal at non-zero delay, irrespective of parameter choices. We
observed that the delayed mutual information of the desynchronized
subpopulation preceded the synchronized subpopulation. Put differently, the
oscillators of the desynchronized subpopulation were 'driving' the ones in the
synchronized subpopulation. These findings were also observed when estimating
mutual information of the full phase trajectories. We can thus conclude that
the delayed mutual information of discrete time points allows for inferring a
functional directed flow of information between subpopulations of coupled phase
oscillators
Studies in Signal Processing Techniques for Speech Enhancement: A comparative study
Speech enhancement is very essential to suppress the background noise and to increase speech intelligibility and reduce fatigue in hearing. There exist many simple speech enhancement algorithms like spectral subtraction to complex algorithms like Bayesian Magnitude estimators based on Minimum Mean Square Error (MMSE) and its variants. A continuous research is going and new algorithms are emerging to enhance speech signal recorded in the background of environment such as industries, vehicles and aircraft cockpit. In aviation industries speech enhancement plays a vital role to bring crucial information from pilot’s conversation in case of an incident or accident by suppressing engine and other cockpit instrument noises. In this work proposed is a new approach to speech enhancement making use harmonic wavelet transform and Bayesian estimators. The performance indicators, SNR and listening confirms to the fact that newly modified algorithms using harmonic wavelet transform indeed show better results than currently existing methods. Further, the Harmonic Wavelet Transform is computationally efficient and simple to implement due to its inbuilt decimation-interpolation operations compared to those of filter-bank approach to realize sub-bands
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
JIDT: An information-theoretic toolkit for studying the dynamics of complex systems
Complex systems are increasingly being viewed as distributed information
processing systems, particularly in the domains of computational neuroscience,
bioinformatics and Artificial Life. This trend has resulted in a strong uptake
in the use of (Shannon) information-theoretic measures to analyse the dynamics
of complex systems in these fields. We introduce the Java Information Dynamics
Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3
licensed) open-source code implementation for empirical estimation of
information-theoretic measures from time-series data. While the toolkit
provides classic information-theoretic measures (e.g. entropy, mutual
information, conditional mutual information), it ultimately focusses on
implementing higher-level measures for information dynamics. That is, JIDT
focusses on quantifying information storage, transfer and modification, and the
dynamics of these operations in space and time. For this purpose, it includes
implementations of the transfer entropy and active information storage, their
multivariate extensions and local or pointwise variants. JIDT provides
implementations for both discrete and continuous-valued data for each measure,
including various types of estimator for continuous data (e.g. Gaussian,
box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time
due to Java's object-oriented polymorphism. Furthermore, while written in Java,
the toolkit can be used directly in MATLAB, GNU Octave, Python and other
environments. We present the principles behind the code design, and provide
several examples to guide users.Comment: 37 pages, 4 figure
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