4,033 research outputs found
A Learning Approach for Robust Carrier Recovery in Heavily Noisy Visible Light Communication
International audienceVisible Light Communication (VLC) exploits optical frequencies, diffused by usual LED lamps, for adding data communication features to illuminating systems. This paradigm has attracted a growing interest in both scientific and industrial community in the latter decade. Nevertheless, classical wireless communication mechanisms for physical and Medium Access Control (MAC) layers are hardly available for VLC, due to the massive external interference caused by sunlight. A correct signal carrier recover in high noise conditions represent a significant challenge. In this work, it is shown that the synchronization frame length affects the performance of the system in terms of Bit Error Ratio (BER). Since different external conditions require different minimum preamble lengths, we considered an Artificial Intelligence (AI) approach, based on multi-arm bandit formulation, for obtaining a low impact in both BER and goodput of the communication. A low-cost hardware VLC system, implementing a learning algorithm on a Frequency Shift Keying Modulation (FSK), has been designed and tested in different environmental conditions. Experimental results show that a proper choice of preamble length overcomes, in terms of BER and goodput, the classical approach based on fixed preambles
Classification and modeling of power line noise using machine learning techniques
A thesis submitted in ful lment of the requirements
for the degree of Doctor of Philosophy
in the
School of Electrical and Information Engineering
Faculty of Engineering and Built Environment
June 2017The realization of robust, reliable and e cient data transmission have been the theme of
recent research, most importantly in real channel such as the noisy, fading prone power
line communication (PLC) channel. The focus is to exploit old techniques or create new
techniques capable of improving the transmission reliability and also increasing the transmission
capacity of the real communication channels. Multi-carrier modulation scheme such
as Orthogonal Frequency Division Multiplexing (OFDM) utilizing conventional single-carrier
modulation is developed to facilitate a robust data transmission, increasing transmission capacity
(e cient bandwidth usage) and further reducing design complexity in PLC systems.
On the contrary, the reliability of data transmission is subjected to several inhibiting factors
as a result of the varying nature of the PLC channel. These inhibiting factors include noise,
perturbation and disturbances. Contrary to the Additive White Gaussian noise (AWGN)
model often assumed in several communication systems, this noise model fails to capture
the attributes of noise encountered on the PLC channel. This is because periodic noise or
random noise pulses injected by power electronic appliances on the network is a deviation
from the AWGN. The nature of the noise is categorized as non-white non-Gaussian and
unstable due to its impulsive attributes, thus, it is labeled as Non-additive White Gaussian
Noise (NAWGN). These noise and disturbances results into long burst errors that corrupts
signals being transmitted, thus, the PLC is labeled as a horrible or burst error channel.
The e cient and optimal performance of a conventional linear receiver in the white Gaussian
noise environment can therefore be made to drastically degrade in this NAWGN environment.
Therefore, transmission reliability in such environment can be greatly enhanced if we
know and exploit the knowledge of the channel's statistical attributes, thus, the need for
developing statistical channel model based on empirical data. In this thesis, attention is
focused on developing a recon gurable software de ned un-coded single-carrier and multicarrier
PLC transceiver as a tool for realizing an optimized channel model for the narrowband
PLC (NB-PLC) channel.
First, a novel recon gurable software de ned un-coded single-carrier and multi-carrier PLC
transceiver is developed for real-time NB-PLC transmission. The transceivers can be adapted
to implement di erent waveforms for several real-time scenarios and performance evaluation.
Due to the varying noise parameters obtained from country to country as a result of
the dependence of noise impairment on mains voltages, topology of power line, place and
time, the developed transceivers is capable of facilitating constant measurement campaigns
to capture these varying noise parameters before statistical and mathematically inclined
channel models are derived.
Furthermore, the single-carrier (Binary Phase Shift Keying (BPSK), Di erential BPSK
(DBPSK), Quadrature Phase Shift Keying (QPSK) and Di erential QPSK (DQPSK)) PLC
transceiver system developed is used to facilitate a First-Order semi-hidden Fritchman
Markov modeling (SHFMM) of the NB-PLC channel utilizing the e cient iterative Baum-
Welch algorithm (BWA) for parameter estimation. The performance of each modulation
scheme is evaluated in a mildly and heavily disturbed scenarios for both residential and
laboratory site considered. The First-Order estimated error statistics of the realized First-
Order SHFMM have been analytically validated in terms of performance metrics such as:
log-likelihood ratio (LLR), error-free run distribution (EFRD), error probabilities, mean
square error (MSE) and Chi-square ( 2) test. The reliability of the model results is also
con rmed by an excellent match between the empirically obtained error sequence and the
SHFMM regenerated error sequence as shown by the error-free run distribution plot.
This thesis also reports a novel development of a low cost, low complexity Frequency-shift
keying (FSK) - On-o keying (OOK) in-house hybrid PLC and VLC system. The functionality
of this hybrid PLC-VLC transceiver system was ascertained at both residential and
laboratory site at three di erent times of the day: morning, afternoon and evening. A First
and Second-Order SHFMM of the hybrid system is realized. The error statistics of the realized
First and Second-Order SHFMMs have been analytically validated in terms of LLR,
EFRD, error probabilities, MSE and Chi-square ( 2). The Second-Order SHFMMs have
also been analytically validated to be superior to the First-Order SHFMMs although at the
expense of added computational complexity. The reliability of both First and Second-Order
SHFMM results is con rmed by an excellent match between the empirical error sequences
and SHFMM re-generated error sequences as shown by the EFRD plot.
In addition, the multi-carrier (QPSK-OFDM, Di erential QPSK (DQPSK)-OFDM) and
Di erential 8-PSK (D8PSK)-OFDM) PLC transceiver system developed is used to facilitate
a First and Second-Order modeling of the NB-PLC system using the SHFMM and BWA
for parameter estimation. The performance of each OFDM modulation scheme in evaluated
and compared taking into consideration the mildly and heavily disturbed noise scenarios
for the two measurement sites considered. The estimated error statistics of the realized
SHFMMs have been analytically validated in terms of LLR, EFRD, error probabilities, MSE
and Chi-square ( 2) test. The estimated Second-Order SHFMMs have been analytically
validated to be outperform the First-Order SHFMMs although with added computational
complexity. The reliability of the models is con rmed by an excellent match between the
empirical data and SHFMM generated data as shown by the EFRD plot.
The statistical models obtained using Baum-Welch to adjust the parameters of the adopted
SHFMM are often locally maximized. To solve this problem, a novel Metropolis-Hastings
algorithm, a Bayesian inference approach based on Markov Chain Monte Carlo (MCMC)
is developed to optimize the parameters of the adopted SHFMM. The algorithm is used to
optimize the model results obtained from the single-carrier and multi-carrier PLC systems
as well as that of the hybrid PLC-VLC system. Consequently, as deduced from the results,
the models obtained utilizing the novel Metropolis-Hastings algorithm are more precise, near
optimal model with parameter sets that are closer to the global maxima.
Generally, the model results obtained in this thesis are relevant in enhancing transmission
reliability on the PLC channel through the use of the models to improve the adopted modulation
schemes, create adaptive modulation techniques, develop and evaluate forward error
correction (FEC) codes such as a concatenation of Reed-Solomon and Permutation codes and
other robust codes suitable for exploiting and mitigating noise impairments encountered on
the low voltage NB-PLC channel. Furthermore, the recon gurable software de ned NB-PLC
transceiver test-bed developed can be utilized for future measurement campaign as well as
adapted for multiple-input and multiple-output (MIMO) PLC applications.MT201
Classification and modeling of power line noise using machine learning techniques
A thesis submitted in ful lment of the requirements
for the degree of Doctor of Philosophy
in the
School of Electrical and Information Engineering
Faculty of Engineering and Built Environment
June 2017The realization of robust, reliable and e cient data transmission have been the theme of
recent research, most importantly in real channel such as the noisy, fading prone power
line communication (PLC) channel. The focus is to exploit old techniques or create new
techniques capable of improving the transmission reliability and also increasing the transmission
capacity of the real communication channels. Multi-carrier modulation scheme such
as Orthogonal Frequency Division Multiplexing (OFDM) utilizing conventional single-carrier
modulation is developed to facilitate a robust data transmission, increasing transmission capacity
(e cient bandwidth usage) and further reducing design complexity in PLC systems.
On the contrary, the reliability of data transmission is subjected to several inhibiting factors
as a result of the varying nature of the PLC channel. These inhibiting factors include noise,
perturbation and disturbances. Contrary to the Additive White Gaussian noise (AWGN)
model often assumed in several communication systems, this noise model fails to capture
the attributes of noise encountered on the PLC channel. This is because periodic noise or
random noise pulses injected by power electronic appliances on the network is a deviation
from the AWGN. The nature of the noise is categorized as non-white non-Gaussian and
unstable due to its impulsive attributes, thus, it is labeled as Non-additive White Gaussian
Noise (NAWGN). These noise and disturbances results into long burst errors that corrupts
signals being transmitted, thus, the PLC is labeled as a horrible or burst error channel.
The e cient and optimal performance of a conventional linear receiver in the white Gaussian
noise environment can therefore be made to drastically degrade in this NAWGN environment.
Therefore, transmission reliability in such environment can be greatly enhanced if we
know and exploit the knowledge of the channel's statistical attributes, thus, the need for
developing statistical channel model based on empirical data. In this thesis, attention is
focused on developing a recon gurable software de ned un-coded single-carrier and multicarrier
PLC transceiver as a tool for realizing an optimized channel model for the narrowband
PLC (NB-PLC) channel.
First, a novel recon gurable software de ned un-coded single-carrier and multi-carrier PLC
transceiver is developed for real-time NB-PLC transmission. The transceivers can be adapted
to implement di erent waveforms for several real-time scenarios and performance evaluation.
Due to the varying noise parameters obtained from country to country as a result of
the dependence of noise impairment on mains voltages, topology of power line, place and
time, the developed transceivers is capable of facilitating constant measurement campaigns
to capture these varying noise parameters before statistical and mathematically inclined
channel models are derived.
Furthermore, the single-carrier (Binary Phase Shift Keying (BPSK), Di erential BPSK
(DBPSK), Quadrature Phase Shift Keying (QPSK) and Di erential QPSK (DQPSK)) PLC
transceiver system developed is used to facilitate a First-Order semi-hidden Fritchman
Markov modeling (SHFMM) of the NB-PLC channel utilizing the e cient iterative Baum-
Welch algorithm (BWA) for parameter estimation. The performance of each modulation
scheme is evaluated in a mildly and heavily disturbed scenarios for both residential and
laboratory site considered. The First-Order estimated error statistics of the realized First-
Order SHFMM have been analytically validated in terms of performance metrics such as:
log-likelihood ratio (LLR), error-free run distribution (EFRD), error probabilities, mean
square error (MSE) and Chi-square ( 2) test. The reliability of the model results is also
con rmed by an excellent match between the empirically obtained error sequence and the
SHFMM regenerated error sequence as shown by the error-free run distribution plot.
This thesis also reports a novel development of a low cost, low complexity Frequency-shift
keying (FSK) - On-o keying (OOK) in-house hybrid PLC and VLC system. The functionality
of this hybrid PLC-VLC transceiver system was ascertained at both residential and
laboratory site at three di erent times of the day: morning, afternoon and evening. A First
and Second-Order SHFMM of the hybrid system is realized. The error statistics of the realized
First and Second-Order SHFMMs have been analytically validated in terms of LLR,
EFRD, error probabilities, MSE and Chi-square ( 2). The Second-Order SHFMMs have
also been analytically validated to be superior to the First-Order SHFMMs although at the
expense of added computational complexity. The reliability of both First and Second-Order
SHFMM results is con rmed by an excellent match between the empirical error sequences
and SHFMM re-generated error sequences as shown by the EFRD plot.
In addition, the multi-carrier (QPSK-OFDM, Di erential QPSK (DQPSK)-OFDM) and
Di erential 8-PSK (D8PSK)-OFDM) PLC transceiver system developed is used to facilitate
a First and Second-Order modeling of the NB-PLC system using the SHFMM and BWA
for parameter estimation. The performance of each OFDM modulation scheme in evaluated
and compared taking into consideration the mildly and heavily disturbed noise scenarios
for the two measurement sites considered. The estimated error statistics of the realized
SHFMMs have been analytically validated in terms of LLR, EFRD, error probabilities, MSE
and Chi-square ( 2) test. The estimated Second-Order SHFMMs have been analytically
validated to be outperform the First-Order SHFMMs although with added computational
complexity. The reliability of the models is con rmed by an excellent match between the
empirical data and SHFMM generated data as shown by the EFRD plot.
The statistical models obtained using Baum-Welch to adjust the parameters of the adopted
SHFMM are often locally maximized. To solve this problem, a novel Metropolis-Hastings
algorithm, a Bayesian inference approach based on Markov Chain Monte Carlo (MCMC)
is developed to optimize the parameters of the adopted SHFMM. The algorithm is used to
optimize the model results obtained from the single-carrier and multi-carrier PLC systems
as well as that of the hybrid PLC-VLC system. Consequently, as deduced from the results,
the models obtained utilizing the novel Metropolis-Hastings algorithm are more precise, near
optimal model with parameter sets that are closer to the global maxima.
Generally, the model results obtained in this thesis are relevant in enhancing transmission
reliability on the PLC channel through the use of the models to improve the adopted modulation
schemes, create adaptive modulation techniques, develop and evaluate forward error
correction (FEC) codes such as a concatenation of Reed-Solomon and Permutation codes and
other robust codes suitable for exploiting and mitigating noise impairments encountered on
the low voltage NB-PLC channel. Furthermore, the recon gurable software de ned NB-PLC
transceiver test-bed developed can be utilized for future measurement campaign as well as
adapted for multiple-input and multiple-output (MIMO) PLC applications.MT201
A Noise Mitigation Approach for VLC Systems
International audienceVisible Light Communication (VLC) is based on the dual use of the illumination infrastructure for wireless data communication. The major interest on this communication technology lies on its specific features to be a secure, cost-effective wireless technology. Recently, this technology has gained an important role as potential candidate for complementing traditional RF communication systems. Anyway a major issue for the VLC development is a deep comprehension of the noise and its impact on the received signal at the receiver. In this work, we present a simple but effective approach to analyze the noise and drastically reduce it through a signal processing method. In order to validate the effectiveness of this analytical approach, we have developed an USRP-based testbed. Experimental results have been carried out by evaluating the symbol error rate (SER) and show the effectiveness of the noise mitigation approach in different interference conditions and at different distance between the transmitter and the receiver
'THz Torch' wireless communications links
The low-cost 'THz Torch’ technology, which exploits the thermal infrared spectrum (ca. 10 to 100 THz), was recently introduced to provide secure low data rate communications links across short ranges. In this thesis, the channel model for 'THz Torch’ wireless communications links is redeveloped from a thermodynamics perspective. Novel optimization-based channel estimators are also proposed to calibrate parameters in the channel model. Based on these theoretical advances, a cognitive 'THz Torch’ receiver, which combines conventional digital communications with state-of-the-art deep learning techniques, is presented to achieve cognitive synchronization and demodulation. The newly reported 'THz Torch’ wireless link is capable of bypassing the thermal time constant constraints normally associated with both the thermal emitter and sensor, allowing truly asynchronous data transfer with direct electronic modulation. Experimental results obtained in both laboratory environments and field trials demonstrate step-change improvements in channel range, bit rate, bit error rate and demodulation speed.
This work represents a paradigm shift in modulation-demodulation with a thermal-based physical layer and offers a practical solution for implementing future ubiquitous secure 'THz Torch’ wireless communications links. The cognitive receiver concept also has wide-ranging implications for future communications and sensor technologies, making them more resilient when operating in harsh environments.Open Acces
EFFICIENT LEARNING FOR HARDWARE SECURITY VALIDATION USING ELECTROMAGNETIC SIDE CHANNELS
The objective of this thesis is to combine the non-destructive monitoring advantages of standard and backscattering electromagnetic side channels with modern machine learning techniques to efficiently validate the authenticity of individual integrated circuits installed on a motherboard.
The authenticity of integrated circuits is of increasing concern as more steps in the device manufacturing supply chain are outsourced, especially in light of severe global semiconductor shortages. Common methods for integrated circuit validation rely on either destructive techniques before high resolution imaging of the circuit interconnects or functional testing of a variety of test inputs with automated test equipment. These methods are time-consuming or even intractable to detect counterfeit components or stealthy modifications of their underlying circuitry.
Side channels are any means of remotely leaking information related to a circuit's activity or architecture. Our work takes advantage of the electromagnetic (EM) side channel to remotely capture identifying information emitted from or backscattered off integrated circuits in the form of EM signals that can be used to validate their authenticity.
This research attempts to alleviate the need for time-consuming and expensive destructive validation methods for hardware security by robustly detecting inauthentic or modified integrated circuits with remote EM side-channel measurements. The first aim of this research is to apply deep learning methods to classify and detect counterfeits of major ICs on a variety of motherboards. The second aim is to leverage hyperspectral scanning with the backscattered EM side-channel and a novel active learning method to detect dormant hardware trojans several times smaller than before. The last aim is to develop a compressed sensing approach to heavily reduce sampling for hardware trojan detection as well as to develop a hyperspectral characterization of expected and anomalous circuits.Ph.D
On the use of deep learning for phase recovery
Phase recovery (PR) refers to calculating the phase of the light field from
its intensity measurements. As exemplified from quantitative phase imaging and
coherent diffraction imaging to adaptive optics, PR is essential for
reconstructing the refractive index distribution or topography of an object and
correcting the aberration of an imaging system. In recent years, deep learning
(DL), often implemented through deep neural networks, has provided
unprecedented support for computational imaging, leading to more efficient
solutions for various PR problems. In this review, we first briefly introduce
conventional methods for PR. Then, we review how DL provides support for PR
from the following three stages, namely, pre-processing, in-processing, and
post-processing. We also review how DL is used in phase image processing.
Finally, we summarize the work in DL for PR and outlook on how to better use DL
to improve the reliability and efficiency in PR. Furthermore, we present a
live-updating resource (https://github.com/kqwang/phase-recovery) for readers
to learn more about PR.Comment: 82 pages, 32 figure
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