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March 9, 2018

see it

icra2018_guiding_robot_video from Assistive Robotics Group, NCTU on Vimeo.



Visiting professor

March 2, 2018

From March 5th to March 23rd I will be at Jku (John Kepler university) to teach a course on identification and data analysis.

Guest Lecture in Taiwan

February 28, 2018

March 1st, 2018, I will teach a GUEST LECTURE (remotely) in the ASSISTIVE TECHNOLOGY course @ the Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Chiao Tung University, Taiwan.


Joint Thesis Proposal

February 21, 2018

Joint Thesis UNIPA-UNIMORE Laura


Papers on assisitive robotics

February 15, 2018

Two conference papers on assistive robotics will be presented soon:

  • Tzu-Kuan Chuang*, Ni-Ching Lin, Jih Shi Chen, Chen-Hao Hung, Yi-Wei Huang, Chunchih Teng, Haikun Huang, Lap-Fai Yu, Laura Giarré, Hsueh-Cheng Wang
    Deep Trail Following Robotic Guide Dog in Pedestrian Environments for People Who Are Blind and Visually Impaired – Learning from Virtual and Real Worlds
    2018 IEEE International Conference on Robotics and Automation
  • Giovanni Galioto, Ilenia Tinnirello, Daniele Croce, Federica Pascucci, Federica Inderst, Laura Giarre’* Sensor Fusion Localization and Navigation for Visually Impaired People 2018 European Control Conference


Free link for one month

February 14, 2018

IF you desire to freely access to the published paper Mixed ℓ2 and ℓ1-norm regularization for adaptive detrending with ARMA modeling and read it, you can use the following link that is active for 30 days



December 13, 2017

Mixed l2 and l1-norm regularization for adaptive detrending with ARMA modeling

L. Giarré and F. Argenti

To be published in Journal of the Franklin Institute

In this paper, the problem of detrending a time series and/or estimating a wandering baseline is addressed. We propose a new methodology that adaptively minimizes different regularized cost functions by introducing an ARMA model of the underlying trend. Mixed l_1 / l2-norm penalty functions are taken into consideration and novel RLS and LMS solutions are derived for the model parameters estimation. The proposed methods are applied to typical trend estimation/removal problems that can be found in the analysis of economic time series or biomedical signal acquisition. Comparisons with standard noncausal filtering techniques are also presented.