DLS and zeta potential - What they are and what they are not?

Journal of Colloid and Interface Science. Editorial.

The culmination of more than twenty years of research, this authoritative resource provides you with a practical understanding of time-frequency signal analysis. The book offers in-depth coverage of critical concepts and principles, along with discussions on key applications in a wide range of signal processing areas, from communications and optics, to radar and biomedicine. Supported with over 140 illustrations and more than 1,700 equations, this detailed reference explores the topics you need to understand for your work in the field, such as Fourier analysis, linear time frequency representations, quadratic time-frequency distributions, higher order time-frequency representations, and analysis of non-stationary noisy signals. This unique book also serves as an excellent text for courses in this area, featuring numerous examples and problems at the end of each chapter.

Time-Frequency Signal Analysis With Applications

Empirical mode decomposition (EMD) is an adaptive and data-driven approach for analyzing multicomponent nonlinear and nonstationary signals. The stop criterion, envelope technique, and mode-mixing problem are the most important topics that need to be addressed in order to improve the EMD algorithm. In this paper, we study the envelope technique and the mode-mixing problem caused by separating multicomponent AM-FM signals with the EMD algorithm. We present a new necessary condition on the envelope that questions the current assumption that the envelope passes through the extreme points of an intrinsic mode function (IMF). Then, we present a solution to the mode-mixing problem that occurs when multicomponent AM-FM signals are separated. We experiment on several signals, including simulated signals and real-life signals, to demonstrate the efficacy of the proposed method in resolving the mode-mixing problem.

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EMD Revisited: A New Understanding of the Envelope and Resolving the Mode-Mixing Problem in AM-FM Signals

A tacho-less order tracking technique for large speed variations

Dynamic Light Scattering (DLS) is a widely used technique for measuring the size distributions of nano- and micro-particles dispersed in a liquid. The principle is based on the analysis of the temporal fluctuations of light scattered by the particles at a given scattering angle. However, single-angle DLS measurements may lack resolution and robustness, particularly for multimodal and/or polydisperse samples. Multi-angle Dynamic Light Scattering (MDLS) provides more robust, reproducible and accurate Particle Size Distributions (PSDs) than single-angle DLS. In this paper, a novel inversion method based on Bayesian inference is proposed for the estimation of the number PSD from MDLS measurements. The efficiency and robustness of this method is demonstrated through simulated and real data.

Multiangle dynamic light scattering for the improvement of multimodal particle size distribution measurements

https://hal.archives-ouvertes.fr/hal-00200115/document

Estimation of the instantaneous amplitude and frequency of non-stationary short-time signals

In this paper, we propose an original strategy for estimating and reconstructing monocomponent signals having a high nonstationarity and long-time duration. We locally apply to short-time duration intervals the strategy developed in our previous work about nonstationary short-time signals. This paper describes a nonsequential time segmentation that provides segments whose lengths are suitable for modeling both the instantaneous amplitude and frequency locally with low-order polynomials. Parameter estimation is done independently for each segment by maximizing the likelihood function by means of the simulated annealing technique. The signal is then reconstructed by merging the estimated segments. The strategy proposed is sufficiently flexible for estimating a large variety of nonstationarity and specifically applicable to high-order polynomial phase signals. The estimation of a high-order model is not necessary. The error propagation phenomenon occurring with the known approach, the higher ambiguity function (HAF)-based method, is avoided. The proposed strategy is evaluated using Monte Carlo noise simulations and compared with the Cramer-Rao bounds (CRBs). The signal of a songbird is used as a real example of its applicability.

https://hal.archives-ouvertes.fr/hal-00112552/document

A New Flexible Approach to Estimate the IA and IF of Nonstationary Signals of Long-Time Duration

A generating model of realistic synthetic heart sounds for performance assessment of phonocardiogram processing algorithms

This paper aims at comparing different forecasting strategies combined with the STL decomposition method. STL is a versatile and robust time series decomposition method. The forecasting strategies we consider are as follows: three statistical methods (ARIMA, ETS, and Theta), five machine learning methods (KNN, SVR, CART, RF, and GP), and two versions of RNNs (CNN-LSTM and ConvLSTM). We conduct the forecasting test on six horizons (1, 6, 12, 18, and 24 months). Our results show that, when applied to monthly industrial M3 Competition data as a preprocessing step, STL decomposition can benefit forecasting using statistical methods but harms the machine learning ones. Moreover, the STL-Theta combination method displays the best forecasting results on four over the five forecasting horizons.

https://www.mdpi.com/2673-4591/5/1/42/pdf

Meryem Jabloun

Papers

Multiangle dynamic light scattering for the improvement of multimodal particle size distribution measurements

Estimation of the instantaneous amplitude and frequency of non-stationary short-time signals

A New Flexible Approach to Estimate the IA and IF of Nonstationary Signals of Long-Time Duration

A generating model of realistic synthetic heart sounds for performance assessment of phonocardiogram processing algorithms

STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones