Improved parametrized multiple window spectrogram with application in ship navigation systems

In analyzing non-stationary noisy signals with time-varying frequency content, it's convenient to use distribution methods in joint, time and frequency, domains. Besides different adaptive data-driven time-frequency (TF) representations, the approach with multiple orthogonal and optimally concentrated Hermite window functions is an effective solution to achieve a good trade-off between low variance and minimized stable bias estimates. In this paper, we propose a novel spectrogram method with multiple optimally parameterized Hermite window functions, with parameterization which includes a pair of free parameters to regulate the shape of the window functions. The computation is performed in the optimization process to minimize the variable projection (VP) functional problem. The proposed parametrized distribution method improves TF concentration and instantaneous frequency (IF) estimation accuracy, as shown in experimental results for synthetic signals and real-life ship motion response signals. With the optimization of nonlinear least-squares approximation of the ship response signals, the Hermite spectra are centralized, and only up to 15 basis functions are sufficient for concentration improvement in the TF domain

Time-Frequency Analysis of the Auditory Brainstem Response

This thesis is about time-frequency analysis of the brainstem auditory evoked potential (BAEP). The work can be divided into two parts. One part where a model is built up from a very simple example to a more complex model resulting in a model consisting of a sum of sinusoids with stochastic starting points and amplitudes. Dierent time-frequency methods have been evaluated for these models and the multi window spectrogram with Hermitian base functions performs the best in a real life situation with more than one component and a high level of noise. The second part consists of investigating real BAEP data. BAEP data from ve patients were available. Each patient has two data sets which have been studied. One while the patient is awake and one while it is asleep. A hypothesis is that there exists some sort of dierence between these two datasets. It turns out that it does. The earlier peaks dier slightly in latency and the later peaks for the sleeping data seem to disappear. This result is concluded from dierent time frequency methods, where the spectrogram and the multi-window spectrogram are the most successful methods. An attempt to make a bootstrap simulation in order to estimate the mean and condence bounds of each peak is also made for one dataset

Spectral Analysis for Signal Detection and Classification : Reducing Variance and Extracting Features

Spectral analysis encompasses several powerful signal processing methods. The papers in this thesis present methods for finding good spectral representations, and methods both for stationary and non-stationary signals are considered. Stationary methods can be used for real-time evaluation, analysing shorter segments of an incoming signal, while non-stationary methods can be used to analyse the instantaneous frequencies of fully recorded signals. All the presented methods aim to produce spectral representations that have high resolution and are easy to interpret. Such representations allow for detection of individual signal components in multi-component signals, as well as separation of close signal components. This makes feature extraction in the spectral representation possible, relevant features include the frequency or instantaneous frequency of components, the number of components in the signal, and the time duration of the components. Two methods that extract some of these features automatically for two types of signals are presented in this thesis. One adapted to signals with two longer duration frequency modulated components that detects the instantaneous frequencies and cross-terms in the Wigner-Ville distribution, the other for signals with an unknown number of short duration oscillations that detects the instantaneous frequencies in a reassigned spectrogram. This thesis also presents two multitaper methods that reduce the influence of noise on the spectral representations. One is designed for stationary signals and the other for non-stationary signals with multiple short duration oscillations. Applications for the methods presented in this thesis include several within medicine, e.g. diagnosis from analysis of heart rate variability, improved ultrasound resolution, and interpretation of brain activity from the electroencephalogram

EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing

We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments

Sparse and structured decomposition of audio signals on hybrid dictionaries using musical priors

International audienceThis paper investigates the use of musical priors for sparse expansion of audio signals of music, on an overcomplete dual-resolution dictionary taken from the union of two orthonormal bases that can describe both transient and tonal components of a music audio signal. More specifically, chord and metrical structure information are used to build a structured model that takes into account dependencies between coefficients of the decomposition, both for the tonal and for the transient layer. The denoising task application is used to provide a proof of concept of the proposed musical priors. Several configurations of the model are analyzed. Evaluation on monophonic and complex polyphonic excerpts of real music signals shows that the proposed approach provides results whose quality measured by the signal-to-noise ratio is competitive with state-of-the-art approaches, and more coherent with the semantic content of the signal. A detailed analysis of the model in terms of sparsity and in terms of interpretability of the representation is also provided, and shows that the model is capable of giving a relevant and legible representation of Western tonal music audio signals

Seismic Data Conditioning for Quantitative Interpretation of Unconventional Reservoirs

Shale resource plays are fairly new to the petroleum industry, but they have reinvigorated oil and gas production in North America. Brittleness and TOC are the two most important parameters for shale resource characterization. Ideally, of the multilinear and non-linear regression can be used to correlate TOC and brittleness measured on core to well logs forming a proxy for TOC and brittleness with in the seismic survey. In turn seismic attributes correlated to TOC and brittleness predictions from well logs. The success of such integration depends on data quality. In Texas and the mid-continent much of our seismic data have been merged and reprocessed using modern technology. I will expose one pitfall on merged seismic surveys due to offset range variation. Other pitfalls are best addressed by seismic modeling. Legacy seismic data acquired in the mid-continent region have low fold, resulting in a rise to low signal to noise ratio. Such data often exhibit a strong acquisition footprint, which can be caused by the presence of aliased ground roll. Conventional processing techniques cannot suppress such groundroll without damaging the signal. I developed and applied a coherence-based technique to remove highly aliased ground roll present in a survey of North Central Texas Mississippi Lime play. The predicted TOC and brittleness volumes showed a fair correlation with production in the Barnett Shale of Fort Worth Basin. The areas of good production are associated with high brittleness in the vicinity of high TOC

ON SOME COMMON COMPRESSIVE SENSING RECOVERY ALGORITHMS AND APPLICATIONS

Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its’ common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in the theoretical part of the paper. The commonly used algorithms for missing data reconstruction are presented. The Compressive Sensing applications have gained significant attention leading to an intensive growth of signal processing possibilities. Hence, some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well