3,126 research outputs found
Study of L0-norm constraint normalized subband adaptive filtering algorithm
Limited by fixed step-size and sparsity penalty factor, the conventional
sparsity-aware normalized subband adaptive filtering (NSAF) type algorithms
suffer from trade-off requirements of high filtering accurateness and quicker
convergence behavior. To deal with this problem, this paper proposes variable
step-size L0-norm constraint NSAF algorithms (VSS-L0-NSAFs) for sparse system
identification. We first analyze mean-square-deviation (MSD) statistics
behavior of the L0-NSAF algorithm innovatively in according to a novel
recursion form and arrive at corresponding expressions for the cases that
background noise variance is available and unavailable, where correlation
degree of system input is indicated by scaling parameter r. Based on
derivations, we develop an effective variable step-size scheme through
minimizing the upper bounds of the MSD under some reasonable assumptions and
lemma. To realize performance improvement, an effective reset strategy is
incorporated into presented algorithms to tackle with non-stationary
situations. Finally, numerical simulations corroborate that the proposed
algorithms achieve better performance in terms of estimation accurateness and
tracking capability in comparison with existing related algorithms in sparse
system identification and adaptive echo cancellation circumstances.Comment: 15 pages,15 figure
Data-driven multivariate and multiscale methods for brain computer interface
This thesis focuses on the development of data-driven multivariate and multiscale methods
for brain computer interface (BCI) systems. The electroencephalogram (EEG), the
most convenient means to measure neurophysiological activity due to its noninvasive nature,
is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its
multichannel recording nature require a new set of data-driven multivariate techniques to
estimate more accurately features for enhanced BCI operation. Also, a long term goal
is to enable an alternative EEG recording strategy for achieving long-term and portable
monitoring.
Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully
data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary
EEG signal into a set of components which are highly localised in time and frequency. It
is shown that the complex and multivariate extensions of EMD, which can exploit common
oscillatory modes within multivariate (multichannel) data, can be used to accurately
estimate and compare the amplitude and phase information among multiple sources, a
key for the feature extraction of BCI system. A complex extension of local mean decomposition
is also introduced and its operation is illustrated on two channel neuronal
spike streams. Common spatial pattern (CSP), a standard feature extraction technique
for BCI application, is also extended to complex domain using the augmented complex
statistics. Depending on the circularity/noncircularity of a complex signal, one of the
complex CSP algorithms can be chosen to produce the best classification performance
between two different EEG classes.
Using these complex and multivariate algorithms, two cognitive brain studies are
investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user
attention to a sound source among a mixture of sound stimuli, which is aimed at improving
the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments
elicited by taste and taste recall are examined to determine the pleasure and displeasure
of a food for the implementation of affective computing. The separation between two
emotional responses is examined using real and complex-valued common spatial pattern
methods.
Finally, we introduce a novel approach to brain monitoring based on EEG recordings
from within the ear canal, embedded on a custom made hearing aid earplug. The new
platform promises the possibility of both short- and long-term continuous use for standard
brain monitoring and interfacing applications
Robust adaptive filtering algorithms for system identification and array signal processing in non-Gaussian environment
This dissertation proposes four new algorithms based on fractionally lower order statistics for adaptive filtering in a non-Gaussian interference environment. One is the affine projection sign algorithm (APSA) based on L₁ norm minimization, which combines the ability of decorrelating colored input and suppressing divergence when an outlier occurs. The second one is the variable-step-size normalized sign algorithm (VSS-NSA), which adjusts its step size automatically by matching the L₁ norm of the a posteriori error to that of noise. The third one adopts the same variable-step-size scheme but extends L₁ minimization to Lp minimization and the variable step-size normalized fractionally lower-order moment (VSS-NFLOM) algorithms are generalized. Instead of variable step size, the variable order is another trial to facilitate adaptive algorithms where no a priori statistics are available, which leads to the variable-order least mean pth norm (VO-LMP) algorithm, as the fourth one. These algorithms are applied to system identification for impulsive interference suppression, echo cancelation, and noise reduction. They are also applied to a phased array radar system with space-time adaptive processing (beamforming) to combat heavy-tailed non-Gaussian clutters. The proposed algorithms are tested by extensive computer simulations. The results demonstrate significant performance improvements in terms of convergence rate, steady-state error, computational simplicity, and robustness against impulsive noise and interference --Abstract, page iv
On Kalman smoothing with random packet loss
The abstract is included in the text
Heterogeneous data fusion for brain psychology applications
This thesis aims to apply Empirical Mode Decomposition (EMD), Multiscale Entropy
(MSE), and collaborative adaptive filters for the monitoring of different brain
consciousness states. Both block based and online approaches are investigated, and
a possible extension to the monitoring and identification of Electromyograph (EMG)
states is provided.
Firstly, EMD is employed as a multiscale time-frequency data driven tool to
decompose a signal into a number of band-limited oscillatory components; its data
driven nature makes EMD an ideal candidate for the analysis of nonlinear and non-stationary
data. This methodology is further extended to process multichannel real
world data, by making use of recent theoretical advances in complex and multivariate
EMD. It is shown that this can be used to robustly measure higher order features
in multichannel recordings to robustly indicate ‘QBD’. In the next stage, analysis is
performed in an information theory setting on multiple scales in time, using MSE.
This enables an insight into the complexity of real world recordings. The results of
the MSE analysis and the corresponding statistical analysis show a clear difference
in MSE between the patients in different brain consciousness states. Finally, an
online method for the assessment of the underlying signal nature is studied. This
method is based on a collaborative adaptive filtering approach, and is shown to be
able to approximately quantify the degree of signal nonlinearity, sparsity, and non-circularity
relative to the constituent subfilters. To further illustrate the usefulness
of the proposed data driven multiscale signal processing methodology, the final case
study considers a human-robot interface based on a multichannel EMG analysis.
A preliminary analysis shows that the same methodology as that applied to the
analysis of brain cognitive states gives robust and accurate results.
The analysis, simulations, and the scope of applications presented suggest
great potential of the proposed multiscale data processing framework for feature extraction
in multichannel data analysis. Directions for future work include further development
of real-time feature map approaches and their use across brain-computer
and brain-machine interface applications
Adaptive filtering algorithms for quaternion-valued signals
Advances in sensor technology have made possible the recoding of three and four-dimensional signals which afford a better representation of our actual three-dimensional world than the ``flat view'' one and two-dimensional approaches. Although it is straightforward to model such signals as real-valued vectors, many applications require unambiguous modeling of orientation and rotation, where the division algebra of quaternions provides crucial advantages over real-valued vector approaches.
The focus of this thesis is on the use of recent advances in quaternion-valued signal processing, such as the quaternion augmented statistics, widely-linear modeling, and the HR-calculus, in order to develop practical adaptive signal processing algorithms in the quaternion domain which deal with the notion of phase and frequency in a compact and physically meaningful way. To this end, first a real-time tracker of quaternion impropriety is developed, which allows for choosing between strictly linear and widely-linear quaternion-valued signal processing algorithms in real-time, in order to reduce computational complexity where appropriate. This is followed by the strictly linear and widely-linear quaternion least mean phase algorithms that are developed for phase-only estimation in the quaternion domain, which is accompanied by both quantitative performance assessment and physical interpretation of operations. Next, the practical application of state space modeling of three-phase power signals in smart grid management and control systems is considered, and a robust complex-valued state space model for frequency estimation in three-phase systems is presented. Its advantages over other available estimators are demonstrated both in an analytical sense and through simulations. The concept is then expanded to the quaternion setting in order to make possible the simultaneous estimation of the system frequency and its voltage phasors. Furthermore, a distributed quaternion Kalman filtering algorithm is developed for frequency estimation over power distribution networks and collaborative target tracking. Finally, statistics of stable quaternion-valued random variables, that include quaternion-valued Gaussian random variables as a special case, is investigated in order to develop a framework for the modeling and processing of heavy-tailed quaternion-valued signals.Open Acces
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