- Read more about QUALITY ASSESSMENT OF MPEG-4 AVC/H.264 AND HEVC COMPRESSED VIDEO IN A TELEMEDICINE CONTEXT
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- Read more about AUTOMATIC DELINEATION OF MACULAR REGIONS BASED ON A LOCALLY DEFINED CONTRAST FUNCTION
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We consider the problem of fovea segmentation and develop
a technique for delineation of macular regions based on the
active-disc formalism that we recently introduced. The outlining
problem is posed as one of the optimization of a locally
defined contrast function using gradient-ascent maximization
with respect to the affine transformation parameters
that characterize the active disc. For automatic localization
of the fovea and initialization of the active disc, we
use the directional-derivative-based matched filter. We report
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- Read more about CSMSDL: A Common Sequential Dictionary Learning Algorithm for Multi-Subject fMRI Data Sets Analysis
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Sequential dictionary learning algorithms has gained widespread acceptance in functional magnetic resonance imaging (fMRI) data analysis. However, many problems in fMRI data analysis involve the analysis of multiple-subject fMRI data sets and the existing algorithms do not extend naturally to this case. In this paper we propose an algorithm dedicated to multiple-subject fMRI data analysis. The algorithm is named SMSDL for sequential multi-subject dictionary learning and differs from existing dictionary learning algorithms in its dictionary update stage.
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- Read more about RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION
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poster.pdf
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While convex optimization for low-light imaging has received some attention by the imaging community, non-convex optimization techniques for photon-limited imaging are still in their nascent stages. In this thesis, we developed a stage-based non-convex approach to recover high-resolution sparse signals from low-dimensional measurements corrupted by Poisson noise. We incorporate gradient-based information to construct a sequence of quadratic subproblems with an $\ell_p$-norm ($0 \leq p < 1$) penalty term to promote sparsity.
PhDForum.pdf
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- Read more about Reduction of Necessary Data Rate for Neural Data Through Exponential and Sinusoidal Spline Decomposition using the Finite Rate of Innovation Framework
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The sampling of neural signals plays an important role in modern neuroscience, especially for prosthetics. However, due to hardware and data rate constraints, only spike trains can get recovered reliably. State of the art prosthetics can still achieve impressive results, but to get higher resolutions the used data rate needs to be reduced. In this paper, this is done by expressing the data with exponential and sinusoidal splines.
FRIVortrag.pdf
FRIVortrag.pdf
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- Read more about EVENT-RELATED SYNCHRONISATION RESPONSES TO N-BACK MEMORY TASKS DISCRIMINATE BETWEEN HEALTHY AGEING, MILD COGNITIVE IMPAIRMENT, AND MILD ALZHEIMER’S DISEASE
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In this study we investigate whether or not event-related (de)synchronisation (ERD/ERS) can be used to differenti- ate between 27 healthy elderly, 21 subjects diagnosed with amnestic mild cognitive impairment (aMCI) and 16 mild Alzheimer’s disease (AD) patients. Using 32-channel EEG recordings, we measured ERD responses to a three-level vi- sual N-back task (N = 0, 1, 2) on the well-known delta, theta, alpha, beta and gamma bands.
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- Read more about COMPRESSIVE SENSING BASED ECG MONITORING WITH EFFECTIVE AF DETECTION
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Atrial fibrillation (AF) patients need long-term electrocardiography (ECG) monitoring to detect occurrence of AF. We can acquire ECG signals under low power by compressive sensing based sensor and detect AF by existing algorithms. However, the compression ratio of AF signal is low when DWT basis is applied for CS reconstruction. On the other hand the complexity of AF detection algorithms is high. In this paper, we propose a CS-based ECG monitoring system with effective AF detection. We exploit dictionary learning to improve 2.5x better compression ratio than existing works.
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In this paper, we propose a novel sparse common spatial pattern (CSP) algorithm to optimally select channels of EEG signals. Compared to the traditional CSP, which maximizes the variance of signals in one class and minimizes the variance of signals in the other class,the classification accuracy is guaranteed by a constraint that the ratio
of variances of signals in two different classes is lower bounded.Then, a sparse spatial filter is achieved by minimizing the l1-norm of filter coefficients and channels of EEG signals can be further optimized.
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- Read more about Fast and Stable Signal Deconvolution via Compressible State-Space Models
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Objective: Common biological measurements are in
the form of noisy convolutions of signals of interest with possibly
unknown and transient blurring kernels. Examples include EEG
and calcium imaging data. Thus, signal deconvolution of these
measurements is crucial in understanding the underlying biological
processes. The objective of this paper is to develop fast and
stable solutions for signal deconvolution from noisy, blurred and
undersampled data, where the signals are in the form of discrete
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