Publications

2025

1. DCIS

   

   Multi-Domain Feature Extraction for ML-Based Over-the-Air RF Signal Classification

   Félix David Suárez Bonilla, Gustavo Liñán-Cembrano, and José Manuel Rosa Utrera

   40th Conference on Design of Circuits and Integrated Systems (DCIS 2025), Nov 2025

   Abs

   This paper presents a system for automatic classification of telecommunication signals using signal processing, multi-domain features fusion, and machine learning techniques. Our system achieves a 97.72% classification accuracy across a wide range of SNR values (-20 dB to 18 dB) using an over-the-air radio-frequency (RF) signals dataset, while maintaining a relatively low complexity (167k learnable parameters). We employ a comprehensive feature extraction methodology that combines time-frequency representations, wavelet transform coefficients, and frequency domain statistics which are processed through a multi-layer architecture. This work demonstrates a systematic approach to signal classification that balances accuracy, computational efficiency, and generalization capability, with potential applications in spectrum monitoring, electronic defense, and cognitive radio systems.

2021

1. TransAI

   

   BETO Emotion Analysis of Facebook Users Reacting to Major Media Outlets in Costa Rica

   Félix Suárez Bonilla, Natyari Vargas Oconitrillo, José David Garro Castro, and 2 more authors

   2021 Third International Conference on Transdisciplinary AI, Sep 2021

   Abs DOI

   An emotion analysis service, based on Transfer Learning, was built using BETO, and applied to Facebook posts written in Spanish. The system was tested using comments related to the anniversary of major media outlets in Costa Rica, the comments on these posts provide insight into the public opinion towards the mentioned media outlet. After collecting the comments and processing them into the emotion analyzer, the most common feelings for each media where: Amelia Rueda (joy), La Nación (anger and sadness) and Teletica.com (joy and anger).

2020

1. ITNEC

   

   Robotic architecture to map an action language to a low level motion commands for skillful manipulation

   Félix Suárez Bonilla, and Federico Ruiz Ugalde

   2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, Jun 2020

   Abs DOI

   This paper gives an overview of a robotic architecture meant for skillful manipulation. This design is meant to close the gap between the high-level layer (reasoning and planing layer) and the object model system (physical control layer). This architecture proposes an interface layer that allows, in a meaningful way, to connect atomic tasks with controller inputs. In this paper, we discuss how specific complex tasks can be resolved by this system; we analyze the affordance unit design and, we overview the future challenges in the implemenation of the whole system.

2019

1. IRC

   

   Automatic Translation of Spanish Natural Language Commands to Control Robot Commands Based on LSTM Neural Network

   Félix Suárez Bonilla, and Federico Ruiz Ugalde

   2019 Third IEEE International Conference on Robotic Computing, Feb 2019

   Abs DOI

   In this paper, we propose a high level layer able to translate motion commands in natural spanish language to a formal intermediate representation called Robot Control Language (RCL). The layer was built by using the seq2seq TensorFlow library, with a single forward LSTM for encoder and decoder respectively. We were able to achieve a 4.3e-08 loss employing a manually generated corpus in Spanish.

2018

1. IWOBI

   

   Analysis of Atomic Manipulation Tasks in Human-Shared Scenarios for a Humanoid Robot

   Félix Suárez Bonilla, and Federico Ruiz Ugalde

   2018 IEEE International Work Conference on Bioinspired Intelligence, Jul 2018

   Abs DOI

   In this paper we analyze the vocabulary of an action description language to be used, together with a low level object controller, by a humanoid robot in two specific scenarios: a kitchen and a collaborative workshop table. A methodology to acquire this knowledge is presented and then it is applied to these two scenarios. The collected data allows to establish a priority of which actions needs to be implemented with robot controllers, which objects need to be manipulated and how much variation they exhibit between different scenarios or different complex tasks.