no code implementations • 5 Oct 2020 • Lin Li, Charles K. Chui, Qingtang Jiang
In this paper, we propose an adaptive signal separation operation (ASSO) for effective and accurate separation of a single-channel blind-source multi-component signal, via introducing a time-varying parameter that adapts locally to IFs and using linear chirp (linear frequency modulation) signals to approximate components at each time instant.
no code implementations • 4 Oct 2020 • Lin Li, Ningning Han, Qingtang Jiang, Charles K. Chui
We use the chirplet transform (CT) to represent a multicomponent signal in the three-dimensional space of time, frequency and chirp rate and introduce a CT-based signal separation scheme (CT3S) to retrieve modes.
no code implementations • 31 Jan 2020 • Charles K. Chui, Ningning Han, Hrushikesh N. Mhaskar
This paper is concerned with the inverse problem of recovering the unknown signal components, along with extraction of their instantaneous frequencies (IFs), governed by the adaptive harmonic model (AHM), from discrete (and possibly non-uniform) samples of the blind-source composite signal.
no code implementations • 16 Dec 2019 • Charles K. Chui, Shao-Bo Lin, Bo Zhang, Ding-Xuan Zhou
The great success of deep learning poses urgent challenges for understanding its working mechanism and rationality.
no code implementations • 23 May 2019 • Yong Zheng Ong, Charles K. Chui, Haizhao Yang
This paper introduces a cross adversarial source separation (CASS) framework via autoencoder, a new model that aims at separating an input signal consisting of a mixture of multiple components into individual components defined via adversarial learning and autoencoder fitting.
no code implementations • 3 Apr 2019 • Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
Based on the tree architecture, the objective of this paper is to design deep neural networks with two or more hidden layers (called deep nets) for realization of radial functions so as to enable rotational invariance for near-optimal function approximation in an arbitrarily high dimensional Euclidian space.
no code implementations • 9 Mar 2018 • Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis and online advertisement, speech and handwriting recognition, computer vision and natural language processing, time series forecasting, and search engines.
no code implementations • 26 Jul 2017 • Charles K. Chui, Hrushikesh N. Mhaskar
Motivated by the interest of observing the growth of cancer cells among normal living cells and exploring how galaxies and stars are truly formed, the objective of this paper is to introduce a rigorous and effective method for counting point-masses, determining their spatial locations, and computing their attributes.
no code implementations • 26 Jul 2017 • Charles K. Chui, Hrushikesh N. Mhaskar
In this paper, motivated by diffraction of traveling light waves, a simple mathematical model is proposed, both for the multivariate super-resolution problem and the problem of blind-source separation of real-valued exponential sums.
no code implementations • 24 Jul 2016 • Charles K. Chui, H. N. Mhaskar
The problem of extending a function $f$ defined on a training data $\mathcal{C}$ on an unknown manifold $\mathbb{X}$ to the entire manifold and a tubular neighborhood of this manifold is considered in this paper.