658 research outputs found
Spectrum Sensing in Cognitive Radio: Bootstrap and Sequential Detection Approaches
In this thesis, advanced techniques for spectrum sensing in cognitive radio are addressed. The problem of small sample size in spectrum sensing is considered, and resampling-based methods are developed for local and collaborative spectrum sensing. A method to deal with unknown parameters in sequential testing for spectrum sensing is proposed. Moreover, techniques are developed for multiband sensing, spectrum sensing in low signal to noise ratio, and two-bits hard decision combining for collaborative spectrum sensing.
The assumption of using large sample size in spectrum sensing often raises a problem when the devised test statistic is implemented with a small sample size. This is because, for small sample sizes, the asymptotic approximation for the distribution of the test statistic under the null hypothesis fails to model the true distribution.
Therefore, the probability of false alarm or miss detection of the test statistic is poor. In this respect, we propose to use bootstrap methods, where the distribution of the test statistic is estimated by resampling the observed data. For local spectrum sensing, we propose the null-resampling bootstrap test which exhibits better performances than the pivot bootstrap test and the asymptotic test, as common approaches. For collaborative spectrum sensing, a resampling-based Chair-Varshney fusion rule is developed. At the cognitive radio user,
a combination of independent resampling and moving-block resampling is proposed to estimate the local probability of detection. At the fusion center, the parametric bootstrap is applied
when the number of cognitive radio users is large.
The sequential probability ratio test (SPRT) is designed to test a simple hypothesis against a simple alternative hypothesis.
However, the more realistic scenario in spectrum sensing is to deal with composite hypotheses, where the parameters are not uniquely defined. In this thesis, we generalize the sequential probability ratio test to cope with composite hypotheses, wherein the thresholds are updated in an adaptive manner, using the parametric bootstrap.
The resulting test avoids the asymptotic assumption made in earlier works. The proposed bootstrap based sequential probability ratio test minimizes decision errors due to errors induced by employing maximum likelihood estimators in the generalized sequential probability ratio test. Hence, the proposed method achieves the sensing objective. The average sample number (ASN) of the proposed method is better than that of the conventional method which uses the asymptotic assumption. Furthermore, we propose a mechanism to reduce the computational cost incurred by the bootstrap, using a convex combination of the latest K bootstrap distributions. The reduction in the computational cost does not impose a significant increase on the ASN, while the protection against decision errors is even better. This work is motivated by the fact that the sequential probability ratio test produces a smaller sensing time than its counterpart of fixed sample size test. A smaller sensing time is preferable to improve the throughput of the cognitive radio network.
Moreover, multiband spectrum sensing is addressed, more precisely by using multiple testing procedures. In a context of a fixed sample size, an adaptive Benjamini-Hochberg procedure is suggested to be used, since it produces a better balance between the familywise error rate and the familywise miss detection, than the conventional Benjamini-Hochberg. For the sequential probability ratio test, we devise a method based on ordered stopping times. The results show that our method has smaller ASNs than the Bonferroni procedure.
Another issue in spectrum sensing is to detect a signal when the signal to noise ratio is very low. In this case, we derive a locally optimum detector that is based on the assumption that the underlying noise is Student's t-distributed. The resulting scheme outperforms the energy detector in all scenarios. Last but not least, we extend the hard decision combining in collaborative spectrum sensing to include a quality information bit. In this case, the multiple thresholds are determined by a distance measure criterion. The hard decision combining with quality information performs better than the conventional hard decision combining
Spectral density correction of a signal at frequency variable transformation
The goal of this paper is to determine analytical expression for the spectral density function of a signal, affected by a known frequency transformation, which do not modify the process energy. Such transformations of frequency variable can frequently appear on spectral density function of a signal, due to physical events (e.g. Doppler effect) or mathematical considerations (e.g. changing the coordinate system). In this case, all components of the spectral density function are modified. The formulas are valid for every spectral component and can be used in signal processing, for model simulation or implementation of advanced algorithm. A case study is illustrated on wave spectrum correction
Untangling hotel industryâs inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio
New approaches for EEG signal processing: artifact EOG removal by ICA-RLS scheme and tracks extraction method
Localizing the bioelectric phenomena originating from the cerebral cortex
and evoked by auditory and somatosensory stimuli are clear objectives to
both understand how the brain works and to recognize different pathologies.
Diseases such as Parkinsonâs, Alzheimerâs, schizophrenia and epilepsy are intensively
studied to find a cure or accurate diagnosis.
Epilepsy is considered the disease with major prevalence within disorders
with neurological origin. The recurrent and sudden incidence of seizures can
lead to dangerous and possibly life-threatening situations. Since disturbance
of consciousness and sudden loss of motor control often occur without any
warning, the ability to predict epileptic seizures would reduce patientsâ anxiety,
thus considerably improving quality of life and safety.
The common procedure for epilepsy seizure detection is based on brain
activity monitorization via electroencephalogram (EEG) data. This process
consumes a lot of time, especially in the case of long recordings, but the major
problem is the subjective nature of the analysis among specialists when
analyzing the same record. From this perspective, the identification of hidden
dynamical patterns is necessary because they could provide insight into
the underlying physiological mechanisms that occur in the brain.
Time-frequency distributions (TFDs) and adaptive methods have demonstrated
to be good alternatives in designing systems for detecting neurodegenerative
diseases. TFDs are appropriate transformations because they offer
the possibility of analyzing relatively long continuous segments of EEG data
even when the dynamics of the signal are rapidly changing. On the other
hand, most of the detection methods proposed in the literature assume a
clean EEG signal free of artifacts or noise, leaving the preprocessing problem
opened to any denoising algorithm.
In this thesis we have developed two proposals for EEG signal processing:
the first approach consists in electrooculogram (EOG) removal method based
on a combination of ICA and RLS algorithms which automatically cancels
the artifacts produced by eyes movement without the use of external âad
hocâ electrode. This method, called ICA-RLS has been compared with other
techniques that are in the state of the art and has shown to be a good
alternative for artifacts rejection. The second approach is a novel method
in EEG features extraction called tracks extraction (LFE features). This
method is based on the TFDs and partial tracking. Our results in pattern
extractions related to epileptic seizures have shown that tracks extraction is
appropriate in EEG detection and classification tasks, being practical, easily applicable in medical environment and has acceptable computational cost
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Magnetoencephalographic studies of neural systems associated with higher order processes in humans
This thesis has been concerned with the neuromagnetic fields associated with the processing of faces and sentences in humans. In four, largely independent sub-projects, results were obtained using novel methods of analysis to extract neurophysiologically relevant information from magnetoencephalographic MEG readings. Using the MEG facility of the Helsinki University of Technology, Finland, the research has led to four main suggestions: a) there are early latency face-specific neural systems in humans that are predominantly in right inferior occipito-temporal cortex, b) MEG recordings are useful in the study of autism, in that autistic subjects exhibit different responses to normal subjects following face presentation, c) phase-locked y-band activity has a specific role in semantic processing of sentences in normal subjects, and d) the late components of responses to face images are modified by endogenous priming, which is detectable before stimulus arrival in normal subjects.
In order to pursue these neuroscience objectives, new methods for treating MEG data were developed, implemented and used. These comprise: a) an improved parameterisation of signal power over regions of interest, b) the use of re-sampling strategies to achieve statistical assessment of spectral coefficients within subjects, and c) a prestimulus method for the study of face processing using a tailored state-space representation approach
Electroencephalography brain computer interface using an asynchronous protocol
A dissertation submitted to the Faculty of Science,
University of the Witwatersrand, in ful llment of the
requirements for the degree of Master of Science. October 31, 2016.Brain Computer Interface (BCI) technology is a promising new channel for communication
between humans and computers, and consequently other humans. This technology has the
potential to form the basis for a paradigm shift in communication for people with disabilities or
neuro-degenerative ailments. The objective of this work is to create an asynchronous BCI that
is based on a commercial-grade electroencephalography (EEG) sensor. The BCI is intended
to allow a user of possibly low income means to issue control signals to a computer by using
modulated cortical activation patterns as a control signal. The user achieves this modulation
by performing a mental task such as imagining waving the left arm until the computer performs
the action intended by the user. In our work, we make use of the Emotiv EPOC headset to
perform the EEG measurements. We validate our models by assessing their performance when
the experimental data is collected using clinical-grade EEG technology. We make use of a
publicly available data-set in the validation phase.
We apply signal processing concepts to extract the power spectrum of each electrode from
the EEG time-series data. In particular, we make use of the fast Fourier transform (FFT).
Specific bands in the power spectra are used to construct a vector that represents an abstract
state the brain is in at that particular moment. The selected bands are motivated by insights
from neuroscience. The state vector is used in conjunction with a model that performs classification. The exact purpose of the model is to associate the input data with an abstract
classification result which can then used to select the appropriate set of instructions to be executed
by the computer. In our work, we make use of probabilistic graphical models to perform
this association.
The performance of two probabilistic graphical models is evaluated in this work. As a
preliminary step, we perform classification on pre-segmented data and we assess the performance
of the hidden conditional random fields (HCRF) model. The pre-segmented data has a trial
structure such that each data le contains the power spectra measurements associated with only
one mental task. The objective of the assessment is to determine how well the HCRF models the
spatio-spectral and temporal relationships in the EEG data when mental tasks are performed
in the aforementioned manner. In other words, the HCRF is to model the internal dynamics
of the data corresponding to the mental task. The performance of the HCRF is assessed over
three and four classes. We find that the HCRF can model the internal structure of the data
corresponding to different mental tasks.
As the final step, we perform classification on continuous data that is not segmented and
assess the performance of the latent dynamic conditional random fields (LDCRF). The LDCRF
is used to perform sequence segmentation and labeling at each time-step so as to allow the
program to determine which action should be taken at that moment. The sequence segmentation
and labeling is the primary capability that we require in order to facilitate an asynchronous
BCI protocol. The continuous data has a trial structure such that each data le contains the
power spectra measurements associated with three different mental tasks. The mental tasks
are randomly selected at 15 second intervals. The objective of the assessment is to determine
how well the LDCRF models the spatio-spectral and temporal relationships in the EEG data,
both within each mental task and in the transitions between mental tasks. The performance of
the LDCRF is assessed over three classes for both the publicly available data and the data we
obtained using the Emotiv EPOC headset. We find that the LDCRF produces a true positive
classification rate of 82.31% averaged over three subjects, on the validation data which is in the
publicly available data. On the data collected using the Emotiv EPOC, we find that the LDCRF
produces a true positive classification rate of 42.55% averaged over two subjects.
In the two assessments involving the LDCRF, the random classification strategy would
produce a true positive classification rate of 33.34%. It is thus clear that our classification
strategy provides above random performance on the two groups of data-sets. We conclude that
our results indicate that creating low-cost EEG based BCI technology holds potential for future
development. However, as discussed in the final chapter, further work on both the software and
low-cost hardware aspects is required in order to improve the performance of the technology as
it relates to the low-cost context.LG201
Metabolic functions and inheritance of the microsporidian mitosome
Phd ThesisMicrosporidia are a group of obligate intracellular parasites of economic and
medical importance. Many aspects of microsporidian genomes and cell biology are a
result of an extensive reductive evolution during adaptation to their intracellular
parasitic lifestyle. Microsporidian mitochondrial homologues called mitosomes have
only a single known conserved metabolic function in biosynthesis of the essential
iron-sulfur clusters. Based on genomic data an additional function of the mitosome in
the alternative respiratory pathway (ARP) was proposed for some microsporidians
including human pathogenic Trachipleistophora hominis. This thesis provides the first
direct experimental evidence for a mitosomal localization of the two components of
the ARP in T. hominis. Quantitative analyses of the immunofluorescence data
together with western blotting experiments provided results consistent with the life
cycle-stage specific function of the organelle. In the proliferative stages of the T.
hominis life cycle, capable of stealing ATP from the host, mitosomes seem to
function mostly in the biosynthesis of the essential iron sulfur clusters. The ARP
proteins are enriched in the T. hominis spores, which is consistent with the
hypothetical functions of the mitosome in energy metabolism of the spore that is
unable to rely on its host for ATP production. This thesis also provides the first
bioinformatics characterization of the molecular machineries involved in the
processes required for inheritance of the microsporidian mitosomes: organelle fission
and segregation during the cell division. Specific antibodies were generated and
used to detect the microsporidian spindle pole body (SPB), an organelle
hypothesized to play a role in inheritance of the microsporidian mitosomes. Double
labelling experiments using the specific antibodies against the SPB and mitosomal
markers provide evidence for a stable connection between the two organelles
throughout the life cycle of the parasite.European Union as a part of a
Marie Curie Initial Training Network Symbiomics
Automation and Control Architecture for Hybrid Pipeline Robots
The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management.
The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform.
The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions
Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robotâs parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPRâs mechanical and environmental constraints.
As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive
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