Ponencias (Electrónica y Electromagnetismo)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/10841

Examinar

Mostrando 1 - 20 de 184

Positive Neuroblastoma differentiation with AC electrical-stimulation
(IEEE, 2024) Martín Fernández, Daniel; Fernandez Scagliusi, Santiago Joaquin; Pérez García, Pablo; Olmo Fernández, Alberto; Algarín Pérez, Antonio; Yúfera García, Alberto; Huertas Sánchez, Gloria; Daza Navarro, María Paula; Biología Celular; Tecnología Electrónica; Electrónica y Electromagnetismo; Agencia Estatal de Investigación. España
Electrical Stimulation (ES) is an excellent technique to promote the differentiation and proliferation of precursor cells towards new linages. We studied the effects of alternated current (AC) electrical stimulation on mouse neuroblastoma cell line N2A differentiation towards neuronal tissue. We developed an ES system with electronics, culture-ware with electrodes and encapsulation, designing a setup and protocols for stimulation. The applied ES were biphasic voltage pulses, with programmable amplitudes and frequencies. After ES, N2A cells were analyzed using microscope images. Differentiated and non-differentiated cells were counted. Results show that ES facilitates the differentiation of N2A cells in N2A. The best values for the applied electric field (pulse biphasic signals) in terms of amplitude and frequency, are around 250-500 mV/mm and 100 Hz, these conditions are proposed as the most suitable for future ES of N2A.
Integrated Electrode-Based Systems for Stem-Cell Stimulation
(IEEE, 2024-12) Algarín Pérez, Antonio; Martín, D.; Daza Navarro, María Paula; Huertas Sánchez, Gloria; Yúfera García, Alberto; Tecnología Electrónica; Biología Celular; Electrónica y Electromagnetismo; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España
This paper is conceived as an update of integrated electrode-based systems involved on Stem Cells (SC) differentiation processes based on electric stimulation, actual and future progress. These techniques are applied in different biological and medical protocols with varied objectives: cell linage derivation, tissue engineering, cellular therapy, cancer research, cell motility, etc. The general procedure of SC electric stimulation tries to emulate the biological processes by applying an electrical signal to the cell culture, and to evaluate the cell response to it. Basically, cell metabolism is electrically sensitive, answering in some way to the applied stimuli. What is really happening at the cell is not well known actually, but it is clear that the ion density changes, positive or negative, at the cell membrane neighbourhood must excite in some way the cell metabolism (receptors) activating its “differentiation” as response to the electrical stimulus. In this review, we will try to progress towards a compilation of the proposed system setups, and specifications required, to find and know the local conditions or variables at cell environment that activate differentiation processes. Two features of stimulation (STI) will be reviewed: the setup employed for STI; and the circuits for STI. The nexus between both are the electrodes, as required interfaces to apply electric signals to cell cultures; so we will focus our interest on integrated Micro-Electrode Arrays (MEAs) realizations mainly, and the problems and specifications imposed by its use. This approach will allow to centre this review on fully integrated realization of electrodes, at the same scale of cell sizes.
Spectrogram-Based Spectrum Prediction for AI-managed Cognitive-Radio Edge Devices
(IEEE, 2025-06-27) Rojas Bustos, Andrés Bolivar; Liñán-Cembrano, G.; Dolecek, G. Jovanovic; Rosa Utrera, José Manuel de la; Electrónica y Electromagnetismo; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España
This paper presents a Radio-Frequency (RF) spectrum prediction system based on a Convolutional Neural Network (CNN) architecture for image-based forecasting intended for Cognitive Radio (CR) terminals. Compared to previous approaches based on the use of time series, we propose a more efficient way for occupancy spectrum prediction based on channel availability tables (derived from spectrograms) to train a deep learning model. As a result, the neural engine is able to predict the best band available for Secondary User (SU) transmission in edge devices. As a proof of concept, a simple CR demonstrator combining a computational model for the neural engine – previously modeled and trained in Python using the Keras API – in MATLAB/SIMULINK with two Software-Defined Radio (SDR) boards has been developed. Three different table sizes were used as input for the predictor and a comparison is presented. The system performance is assessed using real over-the-air signals captured in the 2.412 GHz central frequency with 60 MHz bandwidth to validate the presented approach1.
Multi-Domain Feature Extraction for ML-Based Over-The-Air RF Signal Classification
(IEEE, 2025-11) Suarez Bonilla, F. D.; Rosa Utrera, José Manuel de la; Linan-Cembrano, G.; Electrónica y Electromagnetismo; Agencia Estatal de Investigación. España; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
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 (167 k 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.11The funding for these actions/grants and contracts comes from the European Union's Recovery and Resilience Facility-Next Generation, in the framework of the General Invitation of the Spanish Government's public business entity Red.es to participate in talent attraction and retention programmes within Investment 4 of Component 19 of the Recovery, Transformation and Resilience Plan.
Design of 3D Unit Cells for Reconfigurable Intelligent Surfaces Based on Slot Rings
(IEEE, 2025) Dijkstra, Mats Kohler; Palomares-Caballero, Ángel; Gillard, Raphaël; Molero Jiménez, Carlos; García Vigueras, María; Padilla, Pablo; Electrónica y Electromagnetismo; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; European Union (UE)
Neural Architecture Search for Over-the-Air Telecommunication Signal Classification
(IEEE, 2025-11) Suarez Bonilla, F. D.; Rosa Utrera, José Manuel de la; Linan-Cembrano, G.; Electrónica y Electromagnetismo; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España
This paper presents a Neural Architecture Search (NAS) approach for automatic classification of telecommunication signals using Convolutional Neural Networks (CNNs). Our optimized CNN architecture achieved 96.29% accuracy using Neural Architecture Search with only 91,367 trainable parameters, demonstrating improved computational efficiency compared to manually designed architectures. We explored 63 million possible architectural configurations using Bayesian optimization to systematically identify optimal network designs for spectrogram-based signal classification. The search process evaluated 20 different trials over approximately 1 hour and 21 minutes, ultimately discovering an architecture that balances accuracy and computational efficiency. This work demonstrates how automated architecture search can effectively optimize telecommunication signal processing applications for spectrum monitoring, electronic defense, and cognitive radio systems.
Design Considerations for Tunable Bandpass Delta-Sigma ADCs
(IEEE, 2025-11-25) Gorji, Javad; Camuñas Mesa, Luis Alejandro; Rosa Utrera, José Manuel de la; Electrónica y Electromagnetismo; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España
This paper discusses alternative implementations of radio-frequency (RF) analog-to-digital converters (ADCs) by using bandpass delta-sigma modulators (BP−ΔΣMs). Main architectural strategies, including loop-filter order, quantizer resolution, notch-frequency position, undersampling, and feedback digital-to-analog converter (DAC), are overviewed while considering their practical limitations in terms of system complexity, stability, dynamics, and power consumption. Additionally, the combination of tunable notch-frequency and bandpass finite impulse response (FIR) filtered DAC is discussed as an efficient approach, offering reduced sensitivity to nonlinearities and clock jitter compared to the prior art1.
Combining Machine Learning and Optimization Techniques for the High-Level Design of ΣMs
(IEEE, 2025-07-29) Linan-Cembrano, Gustavo; Rosa Utrera, José Manuel de la; Electrónica y Electromagnetismo
This paper presents a toolbox designed for optimizing and automating the design process of Analog-to-Digital Converters (ADCs). The tool combines Artificial Neural Networks (ANNs) and optimization algorithms together with behavioral simulations to perform high-level sizing, turning system-level specifications into component-level requirements. The ANNs are trained to choose the most suitable ADC architecture for a given set of specifications and to find the optimal design parameters that meet those specifications. The predicted designs are further refined through time-domain behavioral simulations, using different optimization engines such as Simulated Annealing (SA), to maximize the figure of merit. The toolbox, demonstrated for Sigma-Delta Modulators (Σ∆Ms), includes a MATLAB-based Graphical User Interface (GUI) that assists users through the entire process, from specifications to optimization and validation1.
When Barkhausen's Criterion Does Not Suffice and you Must Rely on the Forgotten Art of Oscillator Design
(Institute of Electrical and Electronics Engineers (IEEE), 2024-08-01) Rodríguez Vázquez, Ángel Benito; Leñero Bardallo, Juan Antonio; Electrónica y Electromagnetismo
This paper recasts methods known for a long time to complement Barkhausen's criterion when designing oscillators. These methods rely on describing functions to linearize nonlinear systems and Routh-Hurwitz's criterion to study natural frequency movements around the imaginary axis. The paper set links to Lab exercises that employ Excel datasheet and SIMULINK models and are designed for students to gain insight into fundamental concepts about oscillator design.
Enhancing Dynamic Vision Sensors Performance with a Photovoltaic Receptor
(Institute of Electrical and Electronics Engineers (IEEE), 2025-01-28) Fernández Peramo, Pablo; Leñero Bardallo, Juan Antonio; Rodríguez Vázquez, Ángel Benito; Electrónica y Electromagnetismo; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Ministerio de Asuntos Económicos y Transformación Digital. España
We introduce a Dynamic Vision Sensor (DVS) with a unique pixel architecture. Unlike conventional designs, the proposed architecture substitutes the photoreceptor front-end with a single diode operating in photovoltaic regime. Acting as a solar cell, this diode provides competitive sensitivity to transient illumination changes, especially in low-light settings, thanks to its self-biased condition and signal-noise ratio. This design offers compactness compared to logarithmic photorecep-tors. Additionally, it showcases highly competitive latency and noise performance.
Dynamic Slope Detection: A High-Compression Fidelity-Preserving Approach for ECG Signal Acquisition
(Institute of Electrical and Electronics Engineers (IEEE), 2024-09-16) Sáenz-Noval, Jorge J.; Leñero Bardallo, Juan Antonio; Gontard, Lionel C.; Tang, Wei; Electrónica y Electromagnetismo; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; European Union (UE)
This paper introduces a novel Dynamic Slope De-tection (DSD) system for acquiring electrocardiogram (ECG) signals. DSD addresses the critical challenge of balancing data storage requirements with signal fidelity, particularly in resource-constrained environments like wearable devices. The system leverages the slope information of the ECG signal to guide efficient and adaptive data sampling. Validation using ten samples from the publicly available MIT-BIH Arrhythmia Database confirmed significant data reduction compared to traditional sampling. The proposed method achieves a compression ratio of up to 12.5× while maintaining RR interval estimation error below ±0.1 msec.
Design Lab-Based Learning of Analog Front-End Circuits: A Sigma-Delta Modulator Lab
(Institute of Electrical and Electronics Engineers (IEEE), 2024-08-01) Rodríguez Vázquez, Ángel Benito; Leñero Bardallo, Juan Antonio; Gómez Merchán, Rubén; Méndez Romero, Roberto José; Electrónica y Electromagnetismo
This paper details a Lab exercise that combines electrical and behavioral models to design ∑ΔMs. This Lab is suitable for students following an undergraduate course dealing with the design of analog-to-digital converters. Throughout the lab activities, students use different tools and modeling levels for the objects, similar to what they do in the profession. Data converter architectures employ high-level Simulink models to capture the nominal function and the thermal noise error. Active building blocks such as opamps and comparators employ electrical level and Spice simulations. Students are confronted with realistic active circuits and follow a guided methodology with a top-down complexity increase across the lab activities. Lab activities end by simulating mixed-signal circuits at the electrical level, which are complex and involve a large variety of building blocks (opamps, comparators, resistor string DACs, logic blocks, etc.). Tools employed are available at most universities, and the models are conceptually similar to those used in the profession.
Live Demonstration: RF Frame Detection Using YOLOv8 for Spectrum Sensing
(IEEE, 2025-06) Rojas Bustos, Andrés Bolivar; Liñán-Cembrano, G.; Dolecek, G. Jovanovic; Rosa Utrera, José Manuel de la; Electrónica y Electromagnetismo; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España
This demo shows how to use the You Only Look Once (YOLO) version 8 framework to identify radio-frequency (RF) signals for spectrum sensing in Software-Defined-Radio (SDR) and Cognitive-Radio (CR) systems. To this purpose, a trained YOLOv8 nano framework was embedded in an IoT device based on a Raspberry Pi 5 and connected to an ADALM-PLUTO SDR board to detect and classify the activity of RF frames around the industrial, scientific, and medical (ISM) frequency band. This framework detects wireless standards such as Bluetooth and WiFi. This hardware demonstrator operates in real-time and can detect RF frames showing its potential application in SDR/CR terminals1.
Analysis and Simulation of Reset Leakage Currents and Quantization Error in a PFM Digital Pixel
(Institute of Electrical and Electronics Engineers (IEEE), 2024-11-11) Palomeque Mangut, Sergio; Fernández Peramo, Pablo; Leñero Bardallo, Juan Antonio; Rodríguez Vázquez, Ángel Benito; Electrónica y Electromagnetismo; European Defence Fund
This paper analyzes the design and performance optimization of the intensity-to-frequency converter in a Pulse-Frequency Modulated (PFM) Digital Pixel Sensor (DPS). We modeled the leakage currents induced by the self-reset feedback switch, which have a significant impact on its behavior. The work presents simulation results conducted in an advanced CMOS technology, illustrating the benefits of utilizing an NMOS reset to mitigate leakage, as opposed to a PMOS device. Additionally, the paper explores the consequences of quantization errors on sensor performance.
Live Demonstration: AI-Assisted High-Level Design of Sigma-Delta Modulators
(IEEE, 2025) Manrique-Merchán, P.; Liñán-Cembrano, G.; Rosa Utrera, José Manuel de la; Electrónica y Electromagnetismo; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España
This demo shows a toolbox for the optimization and automated high-level design of Analog-to-Digital Converters (ADCs). The tool integrates Artificial Neural Networks (ANNs) with behavioral simulation to optimize the high-level sizing process, mapping system-level specifications to building-block requirements. ANNs are trained to determine the optimal ADC architecture based on given specifications, as well as the ideal set of design parameters that meet these requirements. The ANN-generated designs are refined through iterative simulations to achieve the best figure of merit. The toolbox, applied to Sigma-Delta Modulators (Σ∆Ms), features a Graphical User Interface (GUI) implemented in MATLAB, which guides users through the entire process—from specifications to verification 1.
Increasing the Accuracy of Spectrogram-based Spectrum Sensing Trained by a Deep Learning Network Using a Resnet-18 Model
(IEEE, 2024) Rojas Bustos, Andrés Bolivar; Dolecek, G. Jovanovic; Rosa Utrera, José Manuel de la; Linan-Cembrano, G.; Electrónica y Electromagnetismo; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España
This paper presents the evaluation of an image restoration approach based on a bilateral Gaussian filter (BGF) to address signal identification for spectrum sensing. The proposed methodology uses synthetic Long-Term Evolution (LTE) and 5G New Radio (NR) signals generated in MATLAB to build two spectrogram datasets. A Resnet-18 model has been trained with and without enhanced spectrograms to show the benefits of spectrogram preprocessing. Several case studies are considered using noisy and restored spectrograms. A noise recognition of 84.81% is obtained, showing the benefits of the proposed approach for spectrum sensing1.
A Verilog-A/MS Compact Model for the Temperature Dependency of the Open-Circuit Voltage for Integrated Diodes
(Institute of Electrical and Electronics Engineers (IEEE), 2024-11-11) Fernández Peramo, Pablo; Leñero Bardallo, Juan Antonio; Palomeque Mangut, Sergio; Rodríguez Vázquez, Ángel Benito; Electrónica y Electromagnetismo; European Defence Fund
The open-circuit voltage is a crucial parameter that determines the harvesting capabilities of diodes operating in the photovoltaic regime. It is highly dependent on temperature and illumination which may limit the diode operation in application scenarios with large temperature and illumination variations as space instrumentation. The former dependence has not been modeled in a Hardware Description Language. This work presents a Verilog-A/MS based compact model to study and simulate the phenomenon. Validation of the model was conducted through TCAD simulations across a broad operational spectrum. The results affirm the model’s capability to analyze the impact of temperature fluctuations on solar cell performance accurately.
A Discrete Approach to Dynamic Vision with Single-Photon Detectors
(Institute of Electrical and Electronics Engineers (IEEE), 2024-07-02) Gómez Merchán, Rubén; Leñero Bardallo, Juan Antonio; Fernández Peramo, Pablo; Rodríguez Vázquez, Ángel Benito; Electrónica y Electromagnetismo
This paper presents a novel approach to implement discrete dynamic vision utilizing Single-Photon Avalanche Diode (SPAD) detectors. Departing from conventional continuous-time methodologies, our approach encodes photon arrival rates into frequency-based spikes, enabling asynchronous event-driven processing. The theoretical framework is validated through experimental results. Probability models for event detection and background noise are developed, demonstrating excellent agreement with theoretical predictions. The proposed probability models serve as a key tool for analyzing trade-offs between contrast sensitivity, speed, and background noise. This work not only advances the field of dynamic vision but also lays the groundwork for further innovations in SPAD-based imaging technologies.
Live Demonstration: A Photovoltaic Dynamic Vision Sensor
(Institute of Electrical and Electronics Engineers (IEEE), 2025-06-27) Fernández Peramo, Pablo; Leñero Bardallo, Juan Antonio; Rodríguez Vázquez, Ángel Benito; Electrónica y Electromagnetismo
Event-Based Pulse Frequency Modulated (PFM) Digital ROIC for Infrared Sensors
(Spie the International Society for Optical Engineering, 2025-10-28) Palomeque Mangut, Sergio; Fernández Peramo, Pablo; Martín-Arenas, Rafael; Rodríguez Vázquez, Ángel Benito; Leñero Bardallo, Juan Antonio; Electrónica y Electromagnetismo
An infrared detector is limited by the charge-handling capacity of the integration capacitor in the readout integrated circuit (ROIC). This is especially true for longer light wavelengths, due to higher background photon flux and dark current. For that reason, pulse frequency modulated (PFM) digital ROICs (DROICs) have drawn attention from the infrared community. These encode light intensity into a pulse train by recycling the capacitor, with pixels continuously integrating their photogenerated charge and generating a pulse when the accumulated charge reaches a threshold. Conventional PFM DROICs embed a digital counter inside each pixel to accumulate the number of pulses. However, these in-pixel circuits impose area penalties that limit scalability. This paper reviews the properties and design trade-offs of state-of-the-art PFM imagers and introduces a novel DROIC architecture with off-pixel counting. By relocating the counter logic and using an event-based interface, the proposed approach is suited for next-generation PFM DROICs with less than 10 μm pixel pitch.

Examinar

Envíos recientes