Y. Guo

Publications198
h-index23
Citations1,711
Highly Influential Citations59
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  • Publications
  • Influence
A unified framework for group independent component analysis for multi-subject fMRI data
  • 48
  • 7
Localization for anchor-free underwater sensor networks
  • Y. Guo, Y. Liu
  • Engineering, Computer Science
  • Comput. Electr. Eng.
  • 1 August 2013
TLDR
We propose an Anchor-Free Localization Algorithm, called AFLA, for UWSN. Expand
  • 40
  • 7
Identification and Control for Heart Rate Regulation During Treadmill Exercise
TLDR
This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate profile tracking performance for an automated treadmill system. Expand
  • 129
  • 4
  • PDF
Perpendicular Intersection: Locating Wireless Sensors with Mobile Beacon
TLDR
In this work, we propose a mobile-assisted localization scheme called perpendicular intersection (PI), setting a dedicate tradeoff between range-free and range-based approaches. Expand
  • 42
  • 4
  • PDF
An Efficient Autocalibration Method for Triaxial Accelerometer
  • Lin Ye, Y. Guo, S. Su
  • Computer Science, Engineering
  • IEEE Transactions on Instrumentation and…
  • 12 June 2017
TLDR
This paper investigates the autocalibration of microelectromechanical systems (MEMS) triaxial accelerometer (TA) based on experimental design (DoE). Expand
  • 23
  • 4
A unified framework for group independent component analysis for multi-subject fMRI data
TLDR
In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. Expand
  • 75
  • 3
Stochastic stability for pantograph multi-group models with dispersal and stochastic perturbation
TLDR
This paper focuses on a novel model of pantograph multi-group models with dispersal and stochastic perturbation. Expand
  • 25
  • 2
Covering numbers for support vector machines
TLDR
We present an explicit calculation of covering numbers for an SV machine using a Gaussian kernel, which is significantly better than that implied by previous results. Expand
  • 19
  • 2
Bayesian Joint Modeling of Multiple Brain Functional Networks
TLDR
The proposed Bayesian Joint Network Learning approach develops flexible priors on the edge probabilities involving a common intrinsic baseline structure and differential effects specific to individual networks. Expand
  • 8
  • 2
  • PDF
Support vector machine based fault detection and diagnosis for HVAC systems
  • 5
  • 2
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