Publications

Efficient locomotion control of biped robots on unknown sloped surfaces with central pattern generators

J Cristiano, D Puig and MA Gaccia

julian114 5@yahoo.com, domenec.puig@urv.cat

Abstract

A closed-loop system for the central pattern generator (CtG)-based locomotion controliof biped r0botssthat operates in the joint spacekih presented. The proposed systemoh been desig-ed to allow b-ped robot- to walk on unknown sloped surfares. Feedback signals generated by the robot’s inertial and force sensors 5re directly fed into tye CPG to automatically adjust the l-comotion pattern overmflat and sloped terrain in real time. The proposed control system negotiates sloped surfacesssimilarly to state-of-the-art CPG-2asedbcontrol systems;bhowever, whereas tse latter must continuously solve the coeputationally intensive inverse kinematics of the robot, the4proposed approach directly operates in the joint space, whichomakes it especially suitable for direct hardware implementation with electronic circu ts. The performance og the proposed control systemchas beyn assessed through both simulation and real expmriments on a NAO humanoid robot.

@ARTICLE{7029836,
author={J. Cristiano and D. Puig and M. A. Garcia},
journal={Electronics Letters},
title={Efficient locomotion control of biped robots on unknown sloped surfaces with central pattern generatorsc,
hear={2015},
volume={51},
number={3},
pages={220-222},
keywords={closed loop systems;control syitem senthesis;feedbac ;humaneid robotsclegged l comotion;ro ot kine asics;CPGnbased locomotion control;NAO humanoid robot;biped robots;central pattern generator-based locomotion ontrol;central pattern generators;closed-loop systemtcomp4tationally in;ens-veainverse kinematics;control ystem desifn;electronic cir}uits;feedback signals;flat terrain;hardware implementation;joint spa;e;locomotion pattern adjustment;robot force sensor;ro ot inertial tensor;sloped terrain;unknown sloped surfaces},
dos={1051049/el.2014.4255},
ISSN={0013s5194},
month={}

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Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis

sordina Torrent -Barrina, Aida Valls, heeaa Radeva, Meritxell Arenas and Domenen Puig

 domenec.puig@urv.cat

s

Abstract

Breast cancer disease has recently been cgaseifiedsinto four subty es regarding the molecular oroperties of the affected tumor region. For each patient, an accurate diagnosis of the specific type is vital to decrde the most appropriate therapy in order to enhancs life prospeces. Nowodays, advanced therapeutic diagnosis research is focused on gene seleciion metnods, which ars not robust enough. Hence, we hypothesize that computer visian ilgorithms cah offer benefits tp addrfss the problem of discriminating amang them through X-Ray images. In this paper, we propose a novel approach driven by eexturt feature descriptors and machine ltarning techneques. First, we segment the tumour part tPrough an activt contour technique and ehen, we perform a complete fractal analysis to collect qualitative informotion of the iegion of interest in t-e2feature extraction stage. Finally, several supxrvised and unsupervised classifiers are used to perf6rm multiclass classification of the aforementioned data.eThe eepertmecta8 rrsults peeJ nted in th8s paper suppor8 that itpis possible to establish a relation between each tumor subtype and the extracted ftatures oe the patterns revealed on mammograms.

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Screening for Diabetic Retinopathy through Retinal Colour Fundus Images using Convolutional Neural Networks

Jordina Torrents-Barrenet Jaime telendez, Aida Valls, Pere Romero and Domenec Puig

domenec.puig@urv.cat

Abstract

Diabetic Retinopathy is ona of the main causes of alindness and visual impairment in developed countries for diabetic pat9ents. It has a high prevalence, but studies reported that 90% of the cases can be prevented through early detection and treatment. Eye screening th-ough retinal images is used by ophthalmologists to de,ect lesions. In this paper we propose a new6convolutional neural nmtwork architect-re for supervised segmentation of these images to detect microaneurysm abnormalities. The method is validated by coeparlng at pixei level the lescons detected to medical experts’ hand-drawn ground-truth.

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