• Publications
  • Influence
TOPS: new DOA estimator for wideband signals
TLDR
This paper introduces a new direction-of-arrival (DOA) estimation algorithm for wideband sources called test of orthogonality of projected subspaces (TOPS). Expand
  • 203
  • 31
Evidential Deep Learning to Quantify Classification Uncertainty
TLDR
We treat predictions of a neural net as subjective opinions and learn the function that collects the evidence leading to these opinions by a deterministic neural net from data. Expand
  • 104
  • 19
  • PDF
GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams
TLDR
We propose GeoBurst, a method that enables effective and real-time local event detection from geo-tagged tweet streams in real time. Expand
  • 113
  • 16
  • PDF
On the quality and value of information in sensor networks
TLDR
The increasing use of sensor-derived information from planned, ad-hoc, and/or opportunistically deployed sensor networks provides enhanced visibility to everyday activities and processes, enabling fast-paced data-to-decision in personal, social, civilian, military, and business contexts. Expand
  • 97
  • 12
Fractal estimation from noisy data via discrete fractional Gaussian noise (DFGN) and the Haar basis
TLDR
The authors show that the application of the discrete wavelet transform (DWT) using the Haar basis to the increments of fractional Brownian motion (fBm), also known as discrete fractional Gaussian noise (DFGN), yields coefficients which are weakly correlated and have a variance that is exponentially related to scale. Expand
  • 115
  • 12
  • PDF
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks
TLDR
We propose a novel embedding learning framework-ASPEM-to preserve the semantic information in HINs based on multiple aspects, where the aspects can be selected from the given HIN. Expand
  • 48
  • 11
  • PDF
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks
TLDR
In this paper, we re-examine similarity search in HINs and propose a novel embedding-based framework to explore network structure-embedded similarity. Expand
  • 67
  • 8
  • PDF
On truth discovery in social sensing: A maximum likelihood estimation approach
TLDR
This paper addresses the challenge of truth discovery from noisy social sensing data. Expand
  • 191
  • 7
  • PDF
Maximum likelihood methods for bearings-only target localization
TLDR
We develop four maximum likelihood (ML) methods to localize a moving target using a network of acoustical sensor arrays, which employs one of the localization techniques. Expand
  • 152
  • 7