Programa del congreso

This paper presents a planar microwave sensor implemented in microstrip technology and operating in transmission. The device is devoted to detecting tiny changes in the dielectric constant of the medium surrounding the sensing element (or material under test -MUT). Such sensing element is a dumbbell defect-ground-structure (DB-DGS) resonator, transversally etched in the ground plane. The operating principle of the sensor is the change in the phase of the transmission coefficient at a specific (operating) frequency caused by variations in the dielectric constant of the MUT. To achieve a high sensitivity, an inductive strip is placed between the access lines, on top of the DB-DGS resonator. By this means, closely spaced resonance and antiresonance frequencies are generated, with the result of a high phase slope in between, a necessary condition for sensitivity enhancement. The maximum sensitivity of the reported prototype is -66.8º per unit of dielectric constant variation, and the figure of merit, or ratio between the maximum sensitivity and the area of the sensing region expressed in squared wavelengths, is FoM = 6243º/λ².

Este trabajo presenta un sensor resonante planar de microondas para medida de concentración de alcohol (etanol y metanol) en agua. El diseño del sensor se basa en la concentración de la energía eléctrica en la región de la muestra mediante un condensador interdigital y una línea de alta impedancia, sin plano de masa, formando una estructura semi-lumped. La respuesta en frecuencia del sensor se puede describir mediante un modelo circuital de elementos concentrados . El sensor se ha estudiado experimentalmente con disoluciones de etanol y metanol en agua, con mejores resultados en términos de sensibilidad para el etanol, en coherencia con las frecuencias de trabajo del sensor, más próximas a la región de relajación del etanol. Las sensibilidades experimentales para tres medidas (frecuencia de resonancia, ancho de banda a 1 dB y nivel mínimo de transmisión), alcanzan valores máximos respectivos de 3,0 MHz/%, 3,67 MHz/% y 0,069 dB/% para el etanol, y de 1,1 MHz/%, 2,10 MHz/% y 0,039 dB/% para el metanol.

Hidden Markov Models (HMMs) have proven to be powerful tools for modeling sequential data across various applications. This paper explores their use in RFID-based tag detection, focusing on improving accuracy and robustness in environments affected by signal noise and interference. We propose a methodology that leverages the probabilistic nature of HMMs to classify and predict RFID tag readings, thereby enhancing detection reliability. The effectiveness of the approach is evaluated through simulations, demonstrating its potential as a cornerstone for improving modern RFID reader performance. Our results indicate that HMMs can significantly reduce inaccuracies, providing a more reliable solution for RFID applications. Additionally, we analyze the impact of model parameters and propose optimizations to enhance performance in dynamic scenarios. This research contributes to the advancement of intelligent RFID systems by integrating probabilistic models for improved tag detection and identification.

In this work, we present an RFID-based indirect soil moisture sensor based on the application of Machine Learning. More specifically, we suggest an unsupervised approach that does not require information about the real height and moisture levels. This approach can be of great interest in practical agricultural deployments, where the careful deployment of tags at specific depths within the soil is challenging. It allows an estimation of the posterior probability of moisture, based on the available Received Signal Strength Indicator (RSSI) and phase. The suggested method enables the RFID system to operate as a sensor by probabilistically quantifying measurement uncertainty, which is a key distinction from existing methodologies. In this paper, we focus on two differentiated moisture cases to show the validity of our approach. Future research will extend the proposed methodology to a wider set of moisture levels.

This paper presents a highly sensitive microwave sensor for bioparticle detection, designed around a ring-gap resonator integrated with a microfluidic channel. The sensor's structure is optimized to enhance electric field confinement, improving its sensitivity to dielectric variations. Experimental validation using commercial microbeads as bioparticle models demonstrates clear resonance shifts, confirming the sensor's capability for precise detection of particles up to 5 µm. These results highlight the potential of the proposed approach for real­time, label-free biosensing applications, offering a promising tool for biomedical diagnostics and analytical chemistry.

With the accelerated growth of online transactions, counterfeiting of various products, such as pharmaceuticals, textiles, and food items, has increased. This work proposes the use of non-cloneable authentication labels for product protection, combining the substrate used and its geometry to obtain a unique spectral response. The labels are manufactured in two steps. In the first, a laser-induced graphene (LIG) layer is formed, which can be implemented using a laser (e.g., CO2 laser) to directly convert various precursors (e.g., polyimide) into graphene. The second step involves an electroplating process that generates traces with variable sheet resistance according to manufacturing parameters. A prototype of a scanner designed to characterize the electromagnetic spectrum is also presented. Results obtained with simple resonators and complex patterns demonstrate the feasibility of this technology.