Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The science and technology of ultracapacitors are reviewed for a number of electrode materials, including carbon, mixed metal oxides, and conducting polymers. More work has been done using microporous carbons than with the other materials and most of the commercially available devices use carbon electrodes and an organic electrolytes. The energy density of these devices is 3¯5 Wh/kg with a power density of 300¯500 W/kg for high efficiency (90¯95%) charge/discharges. Projections of future developments using carbon indicate that energy densities of 10 Wh/kg or higher are likely with power densities of 1¯2 kW/kg. A key problem in the fabrication of these advanced devices is the bonding of the thin electrodes to a current collector such the contact resistance is less than 0.1 cm2. Special attention is given in the paper to comparing the power density characteristics of ultracapacitors and batteries. The comparisons should be made at the same charge/discharge efficiency.

Ultracapacitors: Why, How, and Where is the Technology

https://math.duke.edu/~jianfeng/paper/synsquez.pdf

Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool

Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography

Time-Frequency Analysis.

In this paper, the use of the reassignment method, first applied by Kodera, Gendrin, and de Villedary (1976) to the spectrogram, is generalized to any bilinear time-frequency or time-scale distribution. This method creates a modified version of a representation by moving its values away from where they are computed, so as to produce a better localization of the signal components. We first propose a new formulation of this method, followed by a thorough theoretical study of its characteristics. Its practical use for a large variety of known time-frequency and time-scale distributions is then addressed. Finally, some experimental results are reported to demonstrate the performance of this method. >

/pdf/improving-the-readability-of-time-frequency-and-time-scale-nogp1msss6.pdf

Improving the readability of time-frequency and time-scale representations by the reassignment method

This article provides a general overview of time-frequency (T-F) reassignment and synchrosqueezing techniques applied to multicomponent signals, covering the theoretical background and applications. We explain how synchrosqueezing can be viewed as a special case of reassignment enabling mode reconstruction and place emphasis on the interest of using such T-F distributions throughout with illustrative examples.

/pdf/time-frequency-reassignment-and-synchrosqueezing-an-overview-4junkqjood.pdf

Time-Frequency Reassignment and Synchrosqueezing: An Overview

The Kalman filter (KF) has received a huge interest from the industrial electronics community and has played a key role in many engineering fields since the 1970s, ranging, without being exhaustive, trajectory estimation, state and parameter estimation for control or diagnosis, data merging, signal processing, and so on. This paper provides a brief overview of the industrial applications and implementation issues of the KF in six topics of the industrial electronics community, highlighting some relevant reference papers and giving future research trends.

/pdf/industrial-applications-of-the-kalman-filter-a-review-tqdmwc4feq.pdf

Industrial Applications of the Kalman Filter: A Review

Note: Publie sous les auspices du Centre National de la Recherche Scientifique (CNRS), France et de la Rice University, USA Reference Record created on 2004-09-07, modified on 2016-08-08

Time - frequency toolbox for use with MATHLAB

Time–frequency analysis (TF) is a field that has experienced a number of qualitative and quantitative changes during the last two decades. Whereas most of classical signal processing studies of the 1970s were aimed at stationary signals and processes, many efforts were devoted to less idealized situations during the 1980s, and the idea of TF progressively emerged as a new paradigm for nonstationarity. It is now well recognized that many signal processing problems can be advantageously phrased in a TF language, and the issue may no longer be designing brand new methods from scratch, but instead in adequately using some of the many tools that we have at our disposal, or in improving them for specific tasks. In some sense, the purpose of this chapter has to be understood from this second generation perspective, because what is discussed here essentially builds on the methods that have already been extensively studied and used. New advances nevertheless are to be provided, thanks to fresh interpretations that have been made possible by recent developments in TF analysis.

/pdf/time-frequency-reassignment-from-principles-to-algorithms-mcgma8srri.pdf

François Auger

Papers

Improving the readability of time-frequency and time-scale representations by the reassignment method

Time-Frequency Reassignment and Synchrosqueezing: An Overview

Industrial Applications of the Kalman Filter: A Review

Time - frequency toolbox for use with MATHLAB

Time-Frequency Reassignment: From Principles to Algorithms