10,376 research outputs found
Dynamic Decomposition of Spatiotemporal Neural Signals
Neural signals are characterized by rich temporal and spatiotemporal dynamics
that reflect the organization of cortical networks. Theoretical research has
shown how neural networks can operate at different dynamic ranges that
correspond to specific types of information processing. Here we present a data
analysis framework that uses a linearized model of these dynamic states in
order to decompose the measured neural signal into a series of components that
capture both rhythmic and non-rhythmic neural activity. The method is based on
stochastic differential equations and Gaussian process regression. Through
computer simulations and analysis of magnetoencephalographic data, we
demonstrate the efficacy of the method in identifying meaningful modulations of
oscillatory signals corrupted by structured temporal and spatiotemporal noise.
These results suggest that the method is particularly suitable for the analysis
and interpretation of complex temporal and spatiotemporal neural signals
Analysis of Signal Decomposition and Stain Separation methods for biomedical applications
Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis
Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data
Independent component analysis (ICA) has proven useful for modeling brain and
electroencephalographic (EEG) data. Here, we present a new, generalized method
to better capture the dynamics of brain signals than previous ICA algorithms.
We regard EEG sources as eliciting spatio-temporal activity patterns,
corresponding to, e.g., trajectories of activation propagating across cortex.
This leads to a model of convolutive signal superposition, in contrast with the
commonly used instantaneous mixing model. In the frequency-domain, convolutive
mixing is equivalent to multiplicative mixing of complex signal sources within
distinct spectral bands. We decompose the recorded spectral-domain signals into
independent components by a complex infomax ICA algorithm. First results from a
visual attention EEG experiment exhibit (1) sources of spatio-temporal dynamics
in the data, (2) links to subject behavior, (3) sources with a limited spectral
extent, and (4) a higher degree of independence compared to sources derived by
standard ICA.Comment: 21 pages, 11 figures. Added final journal reference, fixed minor
typo
Sketching for Large-Scale Learning of Mixture Models
Learning parameters from voluminous data can be prohibitive in terms of
memory and computational requirements. We propose a "compressive learning"
framework where we estimate model parameters from a sketch of the training
data. This sketch is a collection of generalized moments of the underlying
probability distribution of the data. It can be computed in a single pass on
the training set, and is easily computable on streams or distributed datasets.
The proposed framework shares similarities with compressive sensing, which aims
at drastically reducing the dimension of high-dimensional signals while
preserving the ability to reconstruct them. To perform the estimation task, we
derive an iterative algorithm analogous to sparse reconstruction algorithms in
the context of linear inverse problems. We exemplify our framework with the
compressive estimation of a Gaussian Mixture Model (GMM), providing heuristics
on the choice of the sketching procedure and theoretical guarantees of
reconstruction. We experimentally show on synthetic data that the proposed
algorithm yields results comparable to the classical Expectation-Maximization
(EM) technique while requiring significantly less memory and fewer computations
when the number of database elements is large. We further demonstrate the
potential of the approach on real large-scale data (over 10 8 training samples)
for the task of model-based speaker verification. Finally, we draw some
connections between the proposed framework and approximate Hilbert space
embedding of probability distributions using random features. We show that the
proposed sketching operator can be seen as an innovative method to design
translation-invariant kernels adapted to the analysis of GMMs. We also use this
theoretical framework to derive information preservation guarantees, in the
spirit of infinite-dimensional compressive sensing
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation
environment becomes more complicated. Some artificial intelligence
technologies, such as computer vision, can recognize, track and count the
sailing ships to ensure the maritime security and facilitates the management
for Smart Ocean System. Aiming at the scaling problem and boundary effect
problem of traditional correlation filtering methods, we propose a
self-selective correlation filtering method based on box regression (BRCF). The
proposed method mainly include: 1) A self-selective model with negative samples
mining method which effectively reduces the boundary effect in strengthening
the classification ability of classifier at the same time; 2) A bounding box
regression method combined with a key points matching method for the scale
prediction, leading to a fast and efficient calculation. The experimental
results show that the proposed method can effectively deal with the problem of
ship size changes and background interference. The success rates and precisions
were higher than Discriminative Scale Space Tracking (DSST) by over 8
percentage points on the marine traffic dataset of our laboratory. In terms of
processing speed, the proposed method is higher than DSST by nearly 22 Frames
Per Second (FPS)
- …