Fabien Ferrero

The efficiency of wireless systems critically depends on the ability of antennas to transfer power from the transmitter circuitry into free space. Although maximum power transfer is theoretically achieved under perfect impedance matching, IoT devices rarely meet this condition due to the ever-changing conditions of the surrounding environment. As a result, a portion of the transmitted power is reflected, reducing the effectively radiated power and degrading system performance. In addition to these radiated losses, load mismatch at the power amplifier output can lead to gain degradation, increased power dissipation, and impaired performance of linearization schemes such as digital predistortion. Such an effect is well-known but has never been quantified. The purpose of this paper is to quantify not only the losses arising from reflection due to impedance mismatch but also those associated with the reduction in amplifier gain by considering both antenna- and amplifier-level perspectives. Theoretical calculations of mismatch losses are first developed and analysed. These results are subsequently validated in an idealised environment, followed by experimental demonstrations in realistic device scenarios, where substantial discrepancies with theoretical predictions and controlled measurements are observed. The findings quantitatively separate and superimpose, for the first time in a unified experimental framework, the radiative mismatch losses (antenna and matching network) from the additional power amplifier gain degradation under realistic load conditions. This demonstrates that passive antenna measurements alone significantly underestimate the total radiated power loss in practical IoT devices. The results emphasise the need to account for real-world operating conditions when evaluating mismatch-induced losses and highlight the importance of co-design and adaptive strategies for both antennas and power amplifiers in future wireless and IoT systems.

A review of the recent research work on high-power and high-speed (HPHS) Ge-on-Si photodiode design is presented, using Silicon Photonics (SiPh) technology, suitable for Radio-over-Fiber base station schemes. The Photodiode (PD) principle of operation, its structure for high RF photogenerated power, and the achieved PD wide bandwidth are presented. Then, the PD equivalent circuit models are introduced to obtain the PD S-parameters and operating bandwidth, such that efficient power coupling to mmWave loads is realized. Then, the PD theoretical transit-time and RC-time bandwidths are presented, and the PD photocurrent behavior against input optical power, and the optical signal manipulation techniques to improve the PD performance are also presented. After that, the impedance matching techniques between the PD output impedance and antenna input impedance are presented. Finally, recent photonic mmWave antenna designs are introduced.

A review of the recent research work on High-Power and High-Speed (HPHS) Ge-on-Si photodiodes design is presented, using Silicon Photonics (SiPh) technology, suitable for Radio-over-Fiber scheme. The Photodiode (PD) principle of operation, its structure for high RF photogenerated power, and the achieved wide bandwidth are presented. Followed by the PD equivalent circuit models which are introduced such that it is allowed to obtain the PD S-parameters and bandwidth. Then, the PD theoretical transit-time and RC-time bandwidths are presented, and the PD photocurrent behavior against input optical power and the optical signal manipulation techniques to improve the PD performance are also presented. After that, the impedance matching techniques between the PD output impedance and antenna input impedance are presented. Finally, the recent photonic mmWave antenna designs are introduced.

To build an Internet of Things (IoT) infrastructure that provides flood susceptibility forecasts for granular geographic levels, an extensive network of IoT weather sensors in local regions is crucial. However, these IoT devices may exhibit anomalistic behavior due to factors such as diminished signal strength, physical disturbance, low battery life, and more. To ensure that incorrect readings are identified and addressed appropriately, we devise a novel method for multi-stream sensor data verification and anomaly detection. Our method uses time-series anomaly detection to identify incorrect readings. We expand on the state-of-the-art by incorporating sensor fusion mechanisms between nearby devices to improve anomaly detection ability. Our system pairs nearby devices and fuses them by creating a new time series with the difference between the corresponding readings. This new time series is then input into a time-series anomaly detection model which identifies if any readings are anomalistic. By testing our system with nine different machine learning anomaly detection methods on synthetic data based on one year of real weather data, we find that our system outperforms the previous anomaly detection methods by improving F1-Score by 10.8%.

This study presents a novel approach to enhance GNSS (Global Navigation Satellite System) signal reception using an eight-port beamformer tailored for satellite navigation applications. The beamformer architecture integrates key stages including filtering, amplification, phase shifting, and signal combination to optimize signal reception. For demonstration, a GNSS signal generator simulates a four-element phased array antenna, emitting GPS L1 constellation signals across four outputs connected to the beamformer ports. By manipulating phase settings on each port, the beamformer enables beam steering, showcasing improved signal detection. The results highlight the efficacy of the custom beamformer in enhancing GNSS signal reception and suggest promising implications for satellite signal tracking and navigation. This research contributes valuable insights into advanced antenna design and signal processing strategies for optimizing GNSS performance.

This paper presents a 3D-printed fully dielectric bi-material reflectarray with bandgap characteristics for multi-band applications. To achieve bandgap characteristics, a “1D Bragg reflector” unit cell is used. The latter is a layered structure characterized by a spatial distribution of refractive index that varies periodically along one dimension. By appropriately selecting the dimensions, the bandgap can be shifted to cover the desired frequency bands. To validate this bandgap characteristic, a (121.5 mm × 121.5 mm) with an f/D ratio of 0.5 reflectarray was fabricated. The measured gain at 27 GHz is 27.22 dBi, equivalent to an aperture efficiency of 35.05%, demonstrating good agreement between simulated and measured performances within the frequency range of 26–30 GHz. Additionally, the transparency of the reflectarray was verified by measuring the transmission coefficient, which exhibited a high level of transparency of 0.32 dB at 39 GHz. These features make the proposed reflectarray a good candidate for multi-band frequency applications.

Near-infrared (NIR) spectroscopy has become a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on urea content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes—Safe and Unsafe—based on their urea content. A comprehensive NIR dataset comprising 11,960 spectra collected from eight distinct positions within the fish body was obtained from 299 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their urea content exceeded the permissible limit of 1000 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques, as well as ML algorithms, were explored. The results demonstrated that utilizing NIR data specifically obtained from the outer skin of the stomach yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their first derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers, achieving an accuracy of up to 83.9%.

Wireless Sensor Network (WSN) position is required in many applications and the Global Positioning System (GPS) is not compatible with battery life management issues. In this article, we propose an autonomous, low-cost and energy-efficient localization solution based on LoRa wireless network. Our design uses a low-power relay system to extend the coverage of a typical LoR-WAN gateway to improve the accuracy of Received Signal Strength Indicator (RSSI) based approaches. We then apply Artificial Intelligence (AI) techniques, mainly regression algorithms, to obtain accurate location estimates of the target node. The proposed ML-MTL (Machine Learning-Multilateration) algorithm-based localization approach better meets the demands of IoT (Internet of Things) applications and could achieve accuracy comparable to more intricate and energy intensive methods like Angle of Arrival (AoA), Time of Arrivals (ToA), and Time Difference of Arrive (TDoA).

This work introduces a novel approach to improve the precision of distance estimation in localization systems by using existing LoRaWAN and RSSI-based techniques. Despite the benefits of range and power efficiency, these systems exhibit limited accuracy in practical situations. To address the limitation, this study provides an innovative technique that greatly improves the precision of distance estimations, particularly in urban environments. The fundamental basis of this approach lies in the use of a dynamic path loss model. An additional element is to accommodate the varied and dynamic conditions of signal transmission in metropolitan areas. A better Kalman filter is also used in the study. This is important because it reduces the effects of multipath fading and environmental noise that often make RSSI-based localization in LoRaWAN networks less accurate. The study further examines the influence of the environmental exponent, also known as the path loss exponent, on the RSSI results and the precision of the distance measurements. This methodology achieves the average error under 1 meters for indoor environments and under 7 meters for outdoor environments. Finally, the Cumulative Density Function (CDF) shows 90 % of the distance estimation algorithm error for indoor environment is lower than 1.08 meters while for outdoor environment is lower than 7.55 meters. Based on these improvements, the introduced methodology not only enhances and improves existing approaches but also optimizes the precision and dependability of urban localization technologies, with substantial implications for a variety of practical applications.

Over the past few years, the Internet of Things paradigm has brought renewed significant interest to indoor positioning, tracking, and localization topics, principally since real-time locating technology allows a reference node to infer the position of tagged target nodes, creating the opportunity for millions of object-to-object awareness applications. This study first presents an overview of positioning localization techniques and discusses the use of ultra-wide bandwidth technology for complex environment monitoring, followed by consideration of the error sources that are present in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios between a reader and a tag. A technical review of the available industrial and commercial UWB real-time locating transceivers (RTLSs) is presented, with a focus on the frontend antennas that are integrated in these systems to establish the needed wireless communication for positioning. Then, the different characteristics of these antennas are summarized and discussed, along with their impact on the localization performance in terms of the reading range, position information accuracy, object-orientation-independent localization, and multipath mitigation. Solutions are suggested to achieve antenna-based improvements to the performance of RTLSs.