This paper presents a radio frequency (RF) measurement method that allows to determine electrical characteristics such as scattering parameters or impedance of contactless passive chips, using common measurement devices such as oscilloscope, personal computer, and directional coupler. The measurements obtained using this method are as accurate as those obtained with vector network analyzer (VNA) measurements, while providing several improvements. First, the method makes it possible to measure a chip's impedance in activation state, which requires large input power, not compatible with VNA measurements. Second, this method realizes a real-time measurement, which consists in impedance measurement as a function of time. Therefore, this real-time measurement makes it possible to detect and measure the impedance changes of an actual contactless smart card's integrated circuit during a load modulation communication at different powers. Finally, this real-time impedance measurement gives important information on chips' electrical characteristics that can be used to optimize near-field communication devices design in order to reduce the power losses within an RF identification tag.

This paper introduces a pioneering hybrid framework that integrates Q-learning/deep Q-network (DQN) with a locally deployed large language model (LLM) to enhance obstacle avoidance in embedded robotic systems. The STM32WB55RG microcontroller handles real-time decision-making using sensor data, while a Raspberry Pi 5 computer runs a quantized TinyLlama LLM to dynamically refine navigation strategies. The LLM addresses traditional Q-learning limitations, such as slow convergence and poor adaptability, by analyzing action histories and optimizing decision-making policies in complex, dynamic environments. A selective triggering mechanism ensures efficient LLM intervention, minimizing computational overhead. Experimental results demonstrate significant improvements, including up to 41% higher deadlock recovery (81% vs. 40% for Q-learning + LLM), up to 34% faster time to goal (38 s vs. 58 s for Q-learning + LLM), and up to 14% lower collision rates (11% vs. 25% for Q-learning + LLM) compared to standalone Q-learning/DQN. This novel approach presents a solution for scalable, adaptive navigation in resource-constrained embedded robotics, with potential applications in logistics and healthcare.

Within an increasingly connected world, the exponential growth in the deployment of Internet of Things (IoT) applications presents a significant challenge in power and data transfer optimisation. Currently, the maximization of Radio Frequency (RF) system power gain depends on the design of efficient, commercial chips, and on the integration of these chips by using complex RF simulations to verify bespoke configurations. However, even if a standard 50$\Omega$ transmitter’s chip has an efficiency of 90%, the overall power efficiency of the RF system can be reduced by 10% if coupled with a standard antenna of 72$\Omega$. Hence, it is necessary for scalable IoT networks to have optimal RF system design for every transceiver: for example, impedance mismatching between a transmitter’s antenna and chip leads to a significant reduction of the corresponding RF system’s overall power efficiency. This work presents a versatile design framework, based on well-known theoretical methods (i.e., transducer gain, power wave approach, transmission line theory), for the optimal design in terms of power delivered to a load of a typical RF system, which consists of an antenna, a matching network, a load (e.g., integrated circuit) and transmission lines which connect all these parts. The aim of this design framework is not only to reduce the computational effort needed for the design and prototyping of power efficient RF systems, but also to increase the accuracy of the analysis, based on the explanatory analysis within our design framework. Simulated and measured results verify the accuracy of this proposed design framework over a 0–4 GHz spectrum. Finally, a case study based on the design of an RF system for Bluetooth applications demonstrates the benefits of this RF design framework.

The work presented in this article aims to detect the cardiac movement of a person in a non-invasive way and to correlate it with a reference signal in the medical field: the electrocardiogram. To achieve this goal, a measurement campaign was carried out on twenty consenting individuals. On the one hand, the mechanical signal, the movement of the subject’s chest induced by the heartbeat, is recorded via a FMCW radar and on the other hand, the electrical signal of the heart is recorded via an ECG acquisition board. Signal processing functions and different filtering will allow the correlation of the radar and ECG signals. This study is conducted on apnea recordings in order to remove the impact of breathing on the movement of the chest. When the subject holds his or her breath, the two important phases of cardiac movement via radar capture can be detected: the systole and the diastole. The delay between the mechanical signal of the heart and the electrical signal of the heart, already explained by medicine, is well noted. The accuracy of motion detection provided by the radar allowed us to highlight the reproducibility of the chest movements detected during a capture. Their correlation with ECG data validates the proposed hypotheses.

This paper presents a new topology for RFID antenna matched on all UHF and one of the microwave RFID's band. The antenna can be implemented on different standards of tag's sizes as credit card, 85 times 54 mm, or other common sizes 50 times 50 mm. The most criticals parameters of the antenna are studied in order to predict design rules for implemented different kind of dies on the antenna. Radiation pattern on UHF band is complementary to the radiation pattern on microwave band. The substrate is FR4, 0.8 mm, with epsiv_r = 4.4. Some purposes are made on future studied of a tri-band antenna on 13.56 MHz, all UHF and 2.45 GHz RFID's band.