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Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

We propose two-channel critically-sampled filter banks for signals on undirected graphs that utilize spectral domain sampling. Unlike conventional approaches based on vertex domain sampling, our transforms have the following desirable properties.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Beamforming is an attractive technique to improve the system performance for multi-input multi-output (MIMO) communications. Previous works mainly focus on improving the data transmission quality. However, the potential of beamforming for improving the localization quality is not yet fully studied.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Two-dimensional (2-D) spectrum sensing is addressed in the context of a cognitive radar to gather real-time space-frequency electromagnetic awareness. Assuming a sensor equipped with multiple receive antennas, a discrete-time sensing signal model formally accounting for multiple snapshots of observations is introduced.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

The optimal mean-reverting portfolio (MRP) design problem is an important task for statistical arbitrage, also known as pairs trading, in the financial markets. The target of the problem is to construct a portfolio of the underlying assets (possibly with an asset selection target) that can exhibit a satisfactory mean reversion property and a desirable variance property.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

The state-of-the-art graph wavelet decomposition was constructed by maximum spanning tree (MST)-based downsampling and two-channel graph wavelet filter banks. In this work, we first show that: 1) the existing MST-based downsampling could become unbalanced, i.e., the sampling rate is far from 1/2, which eventually leads to low representation efficiency of the wavelet decomposition; and 2) not only low-pass components, but also some high-pass ones can be decomposed to potentially achieve better decomposition performance.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Graph-based representations play a key role in machine learning. The fundamental step in these representations is the association of a graph structure to a dataset. In this paper, we propose a method that finds a block sparse representation of the data by associating a graph, whose Laplacian matrix admits the sparsifying dictionary as its eigenvectors.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Recently, nested and coprime arrays have attracted considerable interest due to their capability of providing increased array aperture, enhanced degrees of freedom (DOFs), and reduced mutual coupling effect compared to uniform linear arrays (ULAs). These features are critical to improving the performance of direction-of-arrival estimation and adaptive beamforming. 

Title:  Machine Learning/Speech research internship  @ Seattle, WA Time: Summer/Fall 2019 About You A wiliness to be a better you on a day by day basis. About Us: We enable hundreds of millions of commercial and social interactions among our users, between consumers and merchants, and among businesses every day though speech. What You’ll Do

Section: Events

November 3-6, 2019 Location: Pacific Grove, CA, USA

Section: Events

November 18-21, 2019 Location: Lanzhou, China