Journal ArticleDOI
Time--frequency feature representation using energy concentration: An overview of recent advances
TLDR
Time-frequency domain signal processing using energy concentration as a feature is a very powerful tool and has been utilized in numerous applications and the expectation is that further research and applications of these algorithms will flourish in the near future.About:
This article is published in Digital Signal Processing.The article was published on 2009-01-01. It has received 646 citations till now. The article focuses on the topics: Feature (computer vision) & Multidimensional signal processing.read more
Citations
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Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Steven L. Brunton,J. Nathan Kutz +1 more
TL;DR: In this paper, the authors bring together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science, and highlight many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy.
Journal ArticleDOI
Fractional Fourier transform as a signal processing tool: An overview of recent developments
TL;DR: This paper is geared toward signal processing practitioners by emphasizing the practical digital realizations and applications of the FRFT, which is closely related to other mathematical transforms, such as time-frequency and linear canonical transforms.
Journal ArticleDOI
Applications of fault detection and diagnosis methods in nuclear power plants: A review
Jianping Ma,Jin Jiang,Jin Jiang +2 more
TL;DR: Popularity of FDD applications in NPPs will continuously increase as FDD theories advance and the safety and reliability requirement for NPP tightens.
Journal ArticleDOI
Matching Demodulation Transform and SynchroSqueezing in Time-Frequency Analysis
TL;DR: The authors introduce an iterative algorithm, called matching demodulation transform (MDT), to generate a time-frequency (TF) representation with satisfactory energy concentration, and the MDT-based synchrosqueezing algorithm is described to further enhance the concentration and reduce the diffusion of the curved IF profile in the TF representation of original syn chrosquEEzing transform.
Proceedings ArticleDOI
Deep learning for human activity recognition: A resource efficient implementation on low-power devices
TL;DR: A human activity recognition technique based on a deep learning methodology is designed to enable accurate and real-time classification for low-power wearable devices to obtain invariance against changes in sensor orientation, sensor placement, and in sensor acquisition rates.
References
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A wavelet tour of signal processing
TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Book
Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Journal ArticleDOI
Ten Lectures on Wavelets
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
Journal ArticleDOI
Matching pursuits with time-frequency dictionaries
Stéphane Mallat,Zhifeng Zhang +1 more
TL;DR: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions, chosen in order to best match the signal structures.
Journal ArticleDOI
Statistical Pattern Recognition
TL;DR: In this paper, the primary goal of pattern recognition is supervised or unsupervised classification, and the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been used.