Multi-Objective Genetic Algorithm for Optimizing an ELM-Based Driver Distraction Detection System

Multi-Objective Genetic Algorithm for Optimizing an ELM-Based Driver Distraction Detection System

Autoría:
J. Echanobe; K. Basterretxea; I. del Campo; V. Martínez; N. Vidal
Año:
2022
Revista:
IEEE Transactions on Intelligent Transportation Systems
Volumen:
23 (8)
Página de inicio - Página de fin:
11946 - 11959
ISBN/ISSN:
1524-9050
DOI:
10.1109/TITS.2021.3108851

"An eco-driving approach for ride comfort improvement"

An eco-driving approach for ride comfort improvement

Autoría:
O. Mata-Carballeira, I. del Campo, E. Asua
Año:
2022
Revista:
IET INTELLIGENT TRANSPORT SYSTEMS
Volumen:
16(2)
Página de inicio - Página de fin:
186 - 205

Publicaciones

An FPGA-based multiprocessor-architecture for intelligent environments

Autoría:
J. Echanobe; I. del Campo; K. Basterretxea; M.V. Martínez; F. Doctor
Año:
2014
Revista:
Microprocessors and Microsystems
Volumen:
38
Página de inicio - Página de fin:
730 - 740
Descripción:

In this paper we propose a SoPC-based multiprocessor embedded system for controlling ambiental parameters in an Intelligent Inhabited Environment. The intelligent features are achieved by means of a Neuro-Fuzzy system which has the ability to learn from samples, reason and adapt itself to changes in the environment or in user preferences. In particular, a modified version of the well known ANFIS (Adaptive Neuro-Fuzzy Inference System) scheme is used, which allows the development of very efficient implementations. The architecture proposed here is based on two soft-core microprocessors: one microprocessor is dedicated to the learning and adaptive procedures, whereas the other is dedicated to the on-line response. This second microprocessor is endowed with 4 efficient ad hoc hardware modules intended to accelerate the neuro-fuzzy algorithms. The implementation has been carried out on a Xilinx Virtex-5 FPGA and obtained results show that a very high performance system is achieved.

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