On the Mode Synthesis in the Synchrosqueezing Method

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201

Separation of Agile Waveform Time-Frequency Signatures from Coexisting Multimodal Systems

abstract: As the demand for wireless systems increases exponentially, it has become necessary for different wireless modalities, like radar and communication systems, to share the available bandwidth. One approach to realize coexistence successfully is for each system to adopt a transmit waveform with a unique nonlinear time-varying phase function. At the receiver of the system of interest, the waveform received for process- ing may still suffer from low signal-to-interference-plus-noise ratio (SINR) due to the presence of the waveforms that are matched to the other coexisting systems. This thesis uses a time-frequency based approach to increase the SINR of a system by estimating the unique nonlinear instantaneous frequency (IF) of the waveform matched to the system. Specifically, the IF is estimated using the synchrosqueezing transform, a highly localized time-frequency representation that also enables reconstruction of individual waveform components. As the IF estimate is biased, modified versions of the transform are investigated to obtain estimators that are both unbiased and also matched to the unique nonlinear phase function of a given waveform. Simulations using transmit waveforms of coexisting wireless systems are provided to demonstrate the performance of the proposed approach using both biased and unbiased IF estimators.Dissertation/ThesisMasters Thesis Electrical Engineering 201

An adaptive denoising approach to powerline interference reduction in ECG recording

939-951The objective of this paper is to develop an adaptive denoising algorithm for the reduction of powerline interference (PLI) in electrocardiogram (ECG) recording. The problem of mode mixing and inability to deal with non-stationary characteristics have the major drawbacks posed by existing PLI removal techniques. The technique used in the proposed work has been combined with the concept of synchrosqueezing transform with an adaptive filter to overcome such limitations. The synchrosqueezing transform has facilitated as a mode decomposition tool to disintegrate the signal into a set of intrinsic mode functions and then applying adaptive filter on decomposition results to estimate PLI so as to recover noise free ECG. The performance assessment of the proposed techniques have been carried out with several established ECG database available in physio net and its adaptability to powerline frequency drift has also been studied. A comparative analysis has been conducted to validate the proposed techniques. Experimental results have suggested that the proposed SST based PLI reduction approaches provide better denoising effect than state of the art methods

An adaptive denoising approach to powerline interference reduction in ECG recording

The objective of this paper is to develop an adaptive denoising algorithm for the reduction of powerline interference (PLI) in electrocardiogram (ECG) recording. The problem of mode mixing and inability to deal with non-stationary characteristics have the major drawbacks posed by existing PLI removal techniques. The technique used in the proposed work has been combined with the concept of synchrosqueezing transform with an adaptive filter to overcome such limitations. The synchrosqueezing transform has facilitated as a mode decomposition tool to disintegrate the signal into a set of intrinsic mode functions and then applying adaptive filter on decomposition results to estimate PLI so as to recover noise free ECG. The performance assessment of the proposed techniques have been carried out with several established ECG database available in physio net and its adaptability to powerline frequency drift has also been studied. A comparative analysis has been conducted to validate the proposed techniques. Experimental results have suggested that the proposed SST based PLI reduction approaches provide better denoising effect than state of the art methods

Data-driven Signal Decomposition Approaches: A Comparative Analysis

Signal decomposition (SD) approaches aim to decompose non-stationary signals into their constituent amplitude- and frequency-modulated components. This represents an important preprocessing step in many practical signal processing pipelines, providing useful knowledge and insight into the data and relevant underlying system(s) while also facilitating tasks such as noise or artefact removal and feature extraction. The popular SD methods are mostly data-driven, striving to obtain inherent well-behaved signal components without making many prior assumptions on input data. Among those methods include empirical mode decomposition (EMD) and variants, variational mode decomposition (VMD) and variants, synchrosqueezed transform (SST) and variants and sliding singular spectrum analysis (SSA). With the increasing popularity and utility of these methods in wide-ranging application, it is imperative to gain a better understanding and insight into the operation of these algorithms, evaluate their accuracy with and without noise in input data and gauge their sensitivity against algorithmic parameter changes. In this work, we achieve those tasks through extensive experiments involving carefully designed synthetic and real-life signals. Based on our experimental observations, we comment on the pros and cons of the considered SD algorithms as well as highlighting the best practices, in terms of parameter selection, for the their successful operation. The SD algorithms for both single- and multi-channel (multivariate) data fall within the scope of our work. For multivariate signals, we evaluate the performance of the popular algorithms in terms of fulfilling the mode-alignment property, especially in the presence of noise.Comment: Resubmission with changes in the reference lis

Time-Frequency Ridge Analysis Based on the Reassignment Vector

International audienceThis paper considers the problem of detecting and estimating AM/FM components in the time-frequency plane. It introduces a new algorithm to estimate the ridges corresponding to the instantaneous frequencies of the components, and to segment the time-frequency plane into different `basins of attraction', each basin corresponding to one mode. The technique is based on the structure of the reassignment vector, which is commonly used for sharpening time-frequency representations. Compared with previous approaches, this new method does not need extra parameters, exhibits less sensitivity to the choice of the window and shows better reconstruction performance. Its effectiveness is demonstrated on simulated and real datasets

Multivariate time-frequency analysis

Recent advances in time-frequency theory have led to the development of high resolution time-frequency algorithms, such as the empirical mode decomposition (EMD) and the synchrosqueezing transform (SST). These algorithms provide enhanced localization in representing time varying oscillatory components over conventional linear and quadratic time-frequency algorithms. However, with the emergence of low cost multichannel sensor technology, multivariate extensions of time-frequency algorithms are needed in order to exploit the inter-channel dependencies that may arise for multivariate data. Applications of this framework range from filtering to the analysis of oscillatory components. To this end, this thesis first seeks to introduce a multivariate extension of the synchrosqueezing transform, so as to identify a set of oscillations common to the multivariate data. Furthermore, a new framework for multivariate time-frequency representations is developed using the proposed multivariate extension of the SST. The performance of the proposed algorithms are demonstrated on a wide variety of both simulated and real world data sets, such as in phase synchrony spectrograms and multivariate signal denoising. Finally, multivariate extensions of the EMD have been developed that capture the inter-channel dependencies in multivariate data. This is achieved by processing such data directly in higher dimensional spaces where they reside, and by accounting for the power imbalance across multivariate data channels that are recorded from real world sensors, thereby preserving the multivariate structure of the data. These optimized performance of such data driven algorithms when processing multivariate data with power imbalances and inter-channel correlations, and is demonstrated on the real world examples of Doppler radar processing.Open Acces

Direct Signal Separation Via Extraction of Local Frequencies with Adaptive Time-Varying Parameters

In nature, real-world phenomena that can be formulated as signals (or in terms of time series) are often affected by a number of factors and appear as multi-component modes. The natural approach to understand and process such phenomena is to decompose, or even better, to separate the multi-component signals to their basic building blocks (called sub-signals or time-series components, or fundamental modes). Recently the synchro-squeezing transform (SST) and its variants have been developed for nonstationary signal separation. More recently, a direct method of the time-frequency approach, called signal separation operation (SSO), was introduced for multi-component signal separation. While both SST and SSO are mathematically rigorous on the instantaneous frequency (IF) estimation, SSO avoids the second step of the two-step SST method in signal separation, which depends heavily on the accuracy of the estimated IFs. In the present paper, we solve the signal separation problem by constructing an adaptive signal separation operator (ASSO) for more effective separation of the blind-source multi-component signal, via introducing a time-varying parameter that adapts to local IFs. A recovery scheme is also proposed to extract the signal components one by one, and the time-varying parameter is updated for each component. The proposed method is suitable for engineering implementation, being capable of separating complicated signals into their sub-signals and reconstructing the signal trend directly. Numerical experiments on synthetic and real-world signals are presented to demonstrate our improvement over the previous attempts