Data-adaptive harmonic spectra and multilayer Stuart-Landau models

Harmonic decompositions of multivariate time series are considered for which we adopt an integral operator approach with periodic semigroup kernels. Spectral decomposition theorems are derived that cover the important cases of two-time statistics drawn from a mixing invariant measure. The corresponding eigenvalues can be grouped per Fourier frequency, and are actually given, at each frequency, as the singular values of a cross-spectral matrix depending on the data. These eigenvalues obey furthermore a variational principle that allows us to define naturally a multidimensional power spectrum. The eigenmodes, as far as they are concerned, exhibit a data-adaptive character manifested in their phase which allows us in turn to define a multidimensional phase spectrum. The resulting data-adaptive harmonic (DAH) modes allow for reducing the data-driven modeling effort to elemental models stacked per frequency, only coupled at different frequencies by the same noise realization. In particular, the DAH decomposition extracts time-dependent coefficients stacked by Fourier frequency which can be efficiently modeled---provided the decay of temporal correlations is sufficiently well-resolved---within a class of multilayer stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators. Applications to the Lorenz 96 model and to a stochastic heat equation driven by a space-time white noise, are considered. In both cases, the DAH decomposition allows for an extraction of spatio-temporal modes revealing key features of the dynamics in the embedded phase space. The multilayer Stuart-Landau models (MSLMs) are shown to successfully model the typical patterns of the corresponding time-evolving fields, as well as their statistics of occurrence.Comment: 26 pages, double columns; 15 figure

Emotion Generation using LPC Synthesis

S speech synthesis means artificial production of human speech . A system used for this purpose is called a speech synthesizer . The most important qualities of a speech synthesis system are naturalness and intelligibility . Naturalness describes how closely the output sounds like human speech, while intelligibility is the ease with which the ou tput is understood. Emotion is an important element in expressive speech synthesis. T his paper describes LPC analysis and synthesis technique . The LPC s are analyse d for each speech segmen t and pitch p eriod is detected . At synthesis the speech samples equal to the samples in one pitch period are reconstructed using LPC inverse synthesis. Thus by using LPC Synthesis we can implement pitch modification or duration modification or spectrum modification to introduce emotion in the neutral speech, such as happiness or anger

Discrimination of signal and noise events on seismic recordings by linear threshold estimation theory

The object of this study is the investigation of a linear threshold element technique for identifying surface multiples on a single seismic trace. Traces of seismic events were generated which contained primaries, surface multiples, and various levels of Gaussian random noise. Since it was necessary to separate the events as much as possible, the traces were subjected to pulse-compression deconvolution processing prior to LTE analysis. Mean frequency, peak frequency, amplitude spectrum variance, periodicity, and polarity were employed as pattern parameters. A set of weights was found that would maximize the moment of inertia of the S line distribution of the patterns subject to the constraint that the sum of the squared values of the weights was minimized. It is shown that the problem of the maximization of the moment of inertia reduces to the solution of a simple eigenvalue problem. Furthermore, the optimum set of weights is the eigenvector corresponding to the largest eigenvalue of a matrix proportional to the autocovariance matrix of the pattern vectors. The classes of patterns representing primaries and multiples on traces with high signal-to-noise ratios were clustered and separated, making identification by inspection a simple procedure. Clustering and separation of classes on traces with low signal-to-noise ratios was less than optimum --Abstract, page ii

Texture Structure Analysis

abstract: Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture Structure Analysis is the process of studying the structure present in the textures. This structure can be expressed in terms of perceived regularity. Our human visual system (HVS) uses the perceived regularity as one of the important pre-attentive cues in low-level image understanding. Similar to the HVS, image processing and computer vision systems can make fast and efficient decisions if they can quantify this regularity automatically. In this work, the problem of quantifying the degree of perceived regularity when looking at an arbitrary texture is introduced and addressed. One key contribution of this work is in proposing an objective no-reference perceptual texture regularity metric based on visual saliency. Other key contributions include an adaptive texture synthesis method based on texture regularity, and a low-complexity reduced-reference visual quality metric for assessing the quality of synthesized textures. In order to use the best performing visual attention model on textures, the performance of the most popular visual attention models to predict the visual saliency on textures is evaluated. Since there is no publicly available database with ground-truth saliency maps on images with exclusive texture content, a new eye-tracking database is systematically built. Using the Visual Saliency Map (VSM) generated by the best visual attention model, the proposed texture regularity metric is computed. The proposed metric is based on the observation that VSM characteristics differ between textures of differing regularity. The proposed texture regularity metric is based on two texture regularity scores, namely a textural similarity score and a spatial distribution score. In order to evaluate the performance of the proposed regularity metric, a texture regularity database called RegTEX, is built as a part of this work. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score (MOS) for the perceived regularity of textures. The proposed method is also shown to be robust to geometric and photometric transformations and outperforms some of the popular texture regularity metrics in predicting the perceived regularity. The impact of the proposed metric to improve the performance of many image-processing applications is also presented. The influence of the perceived texture regularity on the perceptual quality of synthesized textures is demonstrated through building a synthesized textures database named SynTEX. It is shown through subjective testing that textures with different degrees of perceived regularities exhibit different degrees of vulnerability to artifacts resulting from different texture synthesis approaches. This work also proposes an algorithm for adaptively selecting the appropriate texture synthesis method based on the perceived regularity of the original texture. A reduced-reference texture quality metric for texture synthesis is also proposed as part of this work. The metric is based on the change in perceived regularity and the change in perceived granularity between the original and the synthesized textures. The perceived granularity is quantified through a new granularity metric that is proposed in this work. It is shown through subjective testing that the proposed quality metric, using just 2 parameters, has a strong correlation with the MOS for the fidelity of synthesized textures and outperforms the state-of-the-art full-reference quality metrics on 3 different texture databases. Finally, the ability of the proposed regularity metric in predicting the perceived degradation of textures due to compression and blur artifacts is also established.Dissertation/ThesisPh.D. Electrical Engineering 201

Remote Heart Rate Estimation Using Consumer-Grade Cameras

There are many ways in which the remote non-contact detection of the human heart rate might be useful. This is especially true if it can be done using inexpensive equipment such as consumer-grade cameras. Many studies and experiments have been performed in recent years to help reliably determine the heart rate from video footage of a person. The methods have taken an analysis approach which involves temporal Itering and frequency spectrum examination. This study attempts to answer questions about the noise sources which inhibit these methods from estimating the heart rate. Other statistical processes are examined for their use in reducing the noise in the system. Methods for locating the skin of a moving individual are explored and used with the purpose for acquiring the heart rate. Alternative methods borrowed from other fields are also introduced to find if they have merit in remote heart rate detection

Harnessing Spatial Intensity Fluctuations for Optical Imaging and Sensing

Properties of light such as amplitude and phase, temporal and spatial coherence, polarization, etc. are abundantly used for sensing and imaging. Regardless of the passive or active nature of the sensing method, optical intensity fluctuations are always present! While these fluctuations are usually regarded as noise, there are situations where one can harness the intensity fluctuations to enhance certain attributes of the sensing procedure. In this thesis, we developed different sensing methodologies that use statistical properties of optical fluctuations for gauging specific information. We examine this concept in the context of three different aspects of computational optical imaging and sensing. First, we study imposing specific statistical properties to the probing field to image or characterize certain properties of an object through a statistical analysis of the spatially integrated scattered intensity. This offers unique capabilities for imaging and sensing techniques operating in highly perturbed environments and low-light conditions. Next, we examine optical sensing in the presence of strong perturbations that preclude any controllable field modification. We demonstrate that inherent properties of diffused coherent fields and fluctuations of integrated intensity can be used to track objects hidden behind obscurants. Finally, we address situations where, due to coherent noise, image accuracy is severely degraded by intensity fluctuations. By taking advantage of the spatial coherence properties of optical fields, we show that this limitation can be effectively mitigated and that a significant improvement in the signal-to-noise ratio can be achieved even in one single-shot measurement. The findings included in this dissertation illustrate different circumstances where optical fluctuations can affect the efficacy of computational optical imaging and sensing. A broad range of applications, including biomedical imaging and remote sensing, could benefit from the new approaches to suppress, enhance, and exploit optical fluctuations, which are described in this dissertation