Espinosa Loera, Ricardo Abel
Main Affiliation
Preferred name
Espinosa Loera, Ricardo Abel
Official Name
Espinosa Loera, Ricardo Abel
ORCID
0000-0003-2573-7853
Scopus Author ID
57211517018
20 results
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Item type:Publication, Prompt Assisted Enhancement for Correcting Illumination Artifacts in Endoscopic Images(Springer Nature Switzerland, 2025-10-24); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deploying Real-Time Speech Recognition on ESP32 Using TinyML and Edge Impulse(Springer Nature Switzerland, 2025); ; The emergence of Tiny Machine Learning (TinyML) has enabled real-time on-device inference on ultra-low-power microcontrollers, eliminating reliance on cloud computing while significantly reducing latency, power consumption, and bandwidth requirements. This study explores the deployment of a TinyML-based speech recognition system on an ESP32 microcontroller, leveraging Edge Impulse for model development, Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, and TensorFlow Lite for Microcontrollers (TFLM) for efficient inference. The model was trained on a curated subset of the Google Speech Commands Dataset, incorporating background noise augmentation to enhance robustness in real-world environments. Using Edge Impulse’s EON Compiler, the model was fully quantized and optimized, achieving a 37% reduction in RAM usage and 27% in ROM. The final model attained 87.14% accuracy on testing data and 97.1% average classification confidence during real-time inference, with excellent noise rejection (99.6%) and latency of 266 ms. Compared to state-of-the-art systems deployed on more powerful platforms, the proposed approach achieves competitive accuracy while maintaining real-time inference and minimal resource consumption on ultra-low-power hardware. This makes it particularly suitable for battery-powered IoT, robotics, and embedded automation applications where connectivity and energy efficiency are critical. By balancing performance and efficiency, this research highlights the viability of deploying speech recognition systems on constrained microcontrollers. Future work will explore advanced architectures and enhanced feature extraction strategies to further improve recognition accuracy, especially for short or phonetically similar commands. ©The authors ©Springer. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A deep learning-based image pre-processing pipeline for enhanced 3D colon surface reconstruction robust to endoscopic illumination artifacts(2024); 28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Color-aware Exposure Correction for Endoscopic Imaging using a Lightweight Vision Transformer(2024); 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Click-event sound detection in automotive industry using machine/deep learning(2021); ; Gutiérrez, SebastiánIn the automotive industry, despite the robotic systems on the production lines, factories continue employing workers in several custom tasks getting for semi-automatic assembly operations. Specifically, the assembly of electrical harnesses of engines comprises a set of connections between electrical components. Despite the task is easy to perform, employees tend not to notice that a few components are not being connected properly due to physical fatigue provoked by repetitive tasks. This yields a low quality of the assembly production line and possible hazards. In this work, we propose a sound detection system based on machine/deep learning (ML/DL) approaches to identify click sounds produced when electrical harnesses are connected. The purpose of this system is to count the number of connections properly made and to feedback to the employees. We collect and release a public dataset of 25,000 click sounds of 25 ms length at 22 kHz during three months of assembly operations in an automotive production line located in Mexico. Then, we design an ML/DL-based methodology for click sound detection of assembled harnesses under real conditions of a noisy environment (noise level ranging from −16.67 dB to −12.87 dB) including other machinery sounds. Our best ML/DL model (i.e., a combination between five acoustic features and an optimized convolutional neural network) is able to detect click sounds in a real assembly production line with an accuracy of 94.55±0.83 %. To the best of our knowledge, this is the first time a click sounds detection system in assembling electrical harnesses of engines for giving feedback to the workers is proposed and implemented in a real-world automotive production line. We consider this work valuable for the automotive industry on how to apply ML/DL approaches for improving the quality of semi-automatic assembly operations. © 2021 Elsevier B.V.Scopus© Citations 22 24 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras(2020); ; Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning technique offers more reliable and robust solutions on detection and classification problems. Focusing only on a vision-based approach, for this work we used images from a new public multimodal data set for fall detection (UP-Fall Detection dataset) published by our research team. In this chapter we present fall detection system using a 2D CNN analyzing multiple camera information. This method analyzes images in fixed time window frames extracting features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accuracy with a simple CNN network architecture compared with other state-of-the-art methods.Scopus© Citations 21 51 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Renewable Energy Prediction through Machine Learning Algorithms(2020)Scopus© Citations 10 9 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design and Implementation of a Node Geolocation System for Fire Monitoring through LoRaWAN(2020)Scopus© Citations 4 10 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A deep learning-based image pre-processing pipeline for enhanced 3D colon surface reconstruction robust to endoscopic illumination artifacts(2024); 29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning(2021); Scopus© Citations 15 6 2