Abstract
The increasing use of wearable electronics calls for sustainable energy solutions. Biomechanical energy harvesting appears as an attractive solution to replace the use of batteries in wearables, as the body generates sufficient power to drive small electronics. In particular, triboelectric nanogenerators (TENGs) have emerged as a promising approach due to its lightweight and high power density. In this work, a TENG is hybridized with an electromagnetic generator (EMG) to harvest energy from the foot strike. An enclosed radial-flow turbine is optimized and used to convert the foot-strike low-frequency linear movement into a higher-frequency rotational motion (by a factor of & AP;12). Besides increasing the motion frequency, the employed mechanism is physically robust and enables a continuous operation from irregular mechanical excitations. A single TENG unit operating in the freestanding mode generated an optimal power of 4.72 & mu;W and transferred a short-circuit charge of 2.3 nC. The TENG+EMG hybridization allows to power a digital pedometer even after the mechanical input stopped. Finally, the energy harvester is incorporated into a commercial shoe to power the same pedometer from foot walking. The obtained results validate the developed prototype ability to serve as a portable power source that can drive sensors and wearable electronics.

Abstract
This paper introduces two machine learning optimization algorithms to significantly enhance position estimation in Reconfigurable Intelligent Surface (RIS) aided localization for mobile user equipment in Non-Line-of-Sight conditions. Leveraging the strengths of these algorithms, we present two methods capable of achieving extremely high accuracy, reaching sub-centimeter or even sub-millimeter levels at 3.5 GHz. The simulation results highlight the potential of these approaches, showing significant improvements in indoor mobile localization. The demonstrated precision and reliability of the proposed methods offer new opportunities for practical applications in real-world scenarios, particularly in Non-Line-of-Sight indoor localization. By evaluating four optimization techniques, we determine that a combination of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) results in localization errors under 30 cm in 90 % of the cases, and under 5 mm for close to 85 % of cases when considering a simulated room of 10 m by 10m where two of the walls are equipped with RIS tiles. © 2023 IEEE.

Abstract
For reconfigurable radios where the signals can be easily routed from one band to another band, new radio frequency switches (RF) are a fundament. The main factor driving the power consumption of the reconfigurable intelligent system (RIS) is the need for an intermediate device with static power consumption to maintain a certain surface configuration state. Since power usage scales quadratically with the RIS area, there is a relevant interest in mitigating this drawback so that this technology can be applied to everyday objects without needing such a high intrinsic power consumption. Current switch technologies such as PIN diodes, and field effect transistors (FETs) are volatile electronic devices, resulting in high static power. In addition, dynamic power dissipation related to switching event is also considerable. Regarding energy efficiency, non-volatile radio frequency resistive switch (RFRS) concept may be better alternative solution due to several advantages: smaller area, zero-hold voltage, lower actuation bias for operation, short switching time, scalability and capable to be fabricated in the backend-of-line of standard CMOS process.

Abstract
Future telecommunications aim to be ubiquitous and efficient, as widely deployed connectivity will allow for a variety of edge/fog based services. Challenges are numerous, e.g., spectrum overuse, energy efficiency, latency and bandwidth, battery life and computing power of edge devices. Addressing these challenges is key to compose the backbone for the future Internet-of-Things (IoT). Among IoT applications are Indoor Positioning System and indoor Real-Time-Location-Systems systems, which are needed where GPS is unviable. The Bluetooth Low Energy (BLE) 5.1 specification introduced Direction Finding to the protocol, allowing for BLE devices with antenna arrays to derive the Angle-of-Arrival (AoA) of transmissions. Well known algorithms for AoA calculation are computationally demanding, so recent works have addressed this, since the low-cost of BLE devices may provide efficient solutions for indoor localization. In this paper, we present a system topology and algorithms for self-localization where a receiver with an antenna array utilizes the AoAs from fixed battery powered beacons to self-localize, without a centralized system or wall-power infrastructure. We conduct two main experiments using a BLE receiver of our own design. Firstly, we validate the expected behaviour in an anechoic chamber, computing the AoA with an RMSE of 10.7 degrees conduct a test in an outdoor area of 12 by 12 meters using four beacons, and present pre-processing steps prior to computing the AoAs, followed by position estimations achieving a mean absolute error of 3.6 m for 21 map positions, with a minimum as low as 1.1 m.

Abstract
PPG signal is a valuable resource for continuous heart rate monitoring; however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events.

Abstract
Amongst the different types of cancer, lung cancer is the one with the highest mortality rate and consequently, there is an urgent need to develop early detection methods to improve the survival probabilities of the patients. Due to the millions of deaths that are caused annually by cancer, there is large interest int the scientific community to developed deep learning models that can be employed in computer aided diagnostic tools.Currently, in the literature, there are several works in the Radiomics field that try to develop new solutions by employing learning models for lung nodule classification. However, in these types of application, it is usually required to extract the lung nodule from the input images, while using a segmentation mask made by a radiologist. This means that in a clinical scenario, to be able to employ the developed learning models, it is required first to manually segment the lung nodule. Considering the fact that several patients are attended daily in the hospital with suspicion of lung cancer, the segmentation of each lung nodule would become a tiresome task. Furthermore, the available algorithms for automatic lung nodule segmentation are not efficient enough to be used in a real application.In response to the current limitations of the state of the art, the proposed work attempts to evaluate a multitasking approach where both the segmentation and the classification task are executed in parallel. As a baseline, we also study a sequential approach where first we employ DL models to segment the lung nodule, corp the lung nodule from the input image and then finally, we classify the cropped nodule. Our results show that the multitasking approach is better than to sequentially execute the segmentation and classification task for lung nodule classification. For instances, while the multitasking approach was able to achieve an AUC of 84.49% in the classification task, the sequential approach was only able to achieve an AUC of 72.43%. These results show that the proposed multitasking approach can become a viable alternative to the classification and segmentation of lung nodules.