RVFL-Based Nonlinearity Compensation for MISO VLC Systems: Experimental Validation and Performance Analysis
Visible Light Communication (VLC) systems employing multiple Light-Emitting Diode (LED) transmitters face significant challenges from aggregate nonlinear distortions that degrade signal quality and limit practical deployment. This work presents experimental validation of Random Vector Functional Link (RVFL) networks for post-distortion compensation in Multiple-Input Single-Output (MISO) VLC systems. RVFL’s closed-form training and memoryless architecture offer distinct computational advantages over iterative methods. Comprehensive experimental validation across variable drive amplitude (300-900 mV) and bias current (50-350 mA) in a 2×1 dual-luminaire testbed demonstrates that RVFL achieves EVM of 7.81% for 16-QAM and 3.97% for 64-QAM at 200 cm distance, enabling reliable transmission where uncompensated signals exceed thresholds. Comparative analysis reveals superior computational efficiency: RVFL inference requires 45.5 ms versus 502.5 ms for Artificial Neural Networks (ANN) and 338.5 ms for Volterra, achieving 11× faster inference than ANN. These results position RVFL as an attractive candidate for practical VLC receivers where computational efficiency is critical.
Experimental Performance Comparison of a Modular Water-Based Photovoltaic–Thermal System Under Multiple Hydraulic Operating Modes in a Tropical Climate
In Brazil, more than 80% of households rely on electricity for water heating, representing approximately 13% of residential electricity consumption and significantly contributing to peak grid demand. As a prominent alternative for supplying household thermal energy and reducing grid stress, this study experimentally evaluates, under tropical climate conditions, the performance of a modular water-based photovoltaic–thermal (PVT) system and compares it with a conventional photovoltaic (PV) system operating simultaneously under identical environmental conditions. The PVT system, based on commercial PV modules coupled to roll-bond heat exchangers, a storage tank, and a shower outlet, was tested under three hydraulic regimes: natural thermosiphon, closed-loop, and Forced circulation. A dedicated ESP32-based data acquisition system, integrated with a cloud platform, continuously monitors electrical, thermal, and meteorological variables. Results show that PVT modules exhibit a small electrical efficiency reduction due to increased cell temperatures, which is largely compensated by the simultaneous thermal generation, yielding overall efficiency gains of 74.04%, 76.53%, and 7.62% over the reference PV system for Normal, Forced, and Closed circulation, respectively. The comparative analysis identifies Forced-circulation scheduling and the matching between thermal generation and consumption as key factors for performance optimization. The findings provide practical guidelines for deploying PVT systems to replace electric showers in tropical regions, reducing residential electricity consumption and mitigating peak-demand stress on the grid.
Bridging Legacy Smart Meters to Modern IoT Networks Based on LoRaWAN
The integration of legacy energy meters into modern smart grids remains a challenge due to the unviable cost and logistical complexity of full-scale replacement. In Brazil, this problem is acute, with millions of meters operating on a sort of Protocol for Advanced Measurement Infrastructure (AMI) legacy called Protocolo para Infraestrutura de Medição Avançada (PIMA), provoking incompatibility with modern internet of things (IoT) networks and impeding the rollout of AMIs. This letter presents a novel low-power sensor interface platform designed to resolve physical-layer data acquisition barriers in legacy infrastructure. The system architecture addresses the specific sensing constraints of open-collector serial outputs, decoding proprietary PIMA frames with high precision and transmitting key measurements over long range wide area networks (LoRaWAN). An experimental validation confirmed the sensing accuracy, with a perfect match between transmitted data and the meter’s physical display. This retrofit solution provides a verified and viable pathway for the rapid, scalable, and economical integration of legacy assets into modern AMI, bypassing the need for costly meter replacement.
Dispersion turning point enhanced on-chip bimodal interferometry
A transverse-magnetic (TM) polarized strip bimodal waveguide (BiMW) sensor operating near a dispersion turning point (DTP) is proposed and experimentally demonstrated on a silicon-on-insulator platform. To enhance the sensitivity of common-path BiMW sensors, near-DTP operation is realized by driving the modal group-index difference toward zero. By exploiting the intrinsic width-dependent dispersion of the TM modes, the DTP is achieved in a standard strip waveguide configuration through waveguide-width optimization, without complex dispersion-engineering structures. With a sensing length of only 0.8 mm, the sensor achieves a refractive-index sensitivity of 7456 nm/RIU and a limit of detection (LoD) of 1.57 × 10−6 RIU. This work provides a compact, fabrication-accessible, and experimentally validated approach for high-performance on-chip refractive-index sensing, with potential applications in biochemical sensing.
Versatile neuromorphic photonic processor for next-generation optical communication systems
Building upon an 8-tap delayed silicon-on-insulator (SOI) photonic neural network (PNN) pre-equalizer validated at 10-GHz bandwidth orthogonal frequency-division multiplexing (OFDM) intensity-modulation direct detection (IMDD), we experimentally probe the operational boundaries of this architecture by scaling to 20-GHz bandwidth operation and 16-QAM modulation. We quantify performance across 5/10/20-GHz OFDM bandwidths and QAM4/QAM16, approaching the sampling limit imposed by the fixed 25-ps tap spacing. In this way, we experimentally demonstrate its applicability to preserve the signal integrity near the Nyquist limit with improvements in all tested configurations.
Analytical FIR Optical Filter Based on Time-Delayed Photonic Neural Networks for 40 Gbps NRZ Dispersion Compensation
This paper presents an analytical method for designing FIR filters based on the hardware of time-delayed photonic neural networks for chromatic dispersion compensation in long-reach passive optical networks. The model employs a Fourier-based synthesis of the compensation function, allowing for the closed-form determination of phase and amplitude weights for each optical path. Different implementation strategies are considered, including solutions with phase-only control and others with additional amplitude control using tunable attenuators. Simulations over a 100 km standard single-mode fiber link demonstrate effective dispersion compensation for NRZ-OOK signals. In particular, the symmetric FIR-based design with 23 optical paths and 25 ps delay spacing achieved BER < 10-3 at data rates up to 40 Gbps under amplified spontaneous emission noise. These results confirm the analytical approach's potential to provide scalable, power-efficient, and high-speed solutions for future long-reach optical access networks.
Integrated Photonic Enabling Massive Data Transfer: A Short Review
We have been living in a digital revolution, with the creation of new online services, the popularization of electronics, the advent of generative AI, and a growing trend of virtualization of things, allowing the creation of a digital twin version of a real object. As this revolution pushes society forward, there is a high performance demand for its infrastructure, such as telecommunication networks. There is a constant need to transmit a massive amount of data, which is increasing with the market demand. The scientific community has been working to bring new solutions to this demand. One possibility is simplifying the edge devices, adding open issues, and reducing system latency. In this context, photonic integrated circuits can play a significant role. This work concisely reviews the current development of photonics integrated circuits (PIC) for optical systems with strict requirements and the demand for massive data transfer. As the current telecommunication scenario rapidly evolves due to market demands and the creation of new requirement-sensitive services, it is crucial to understand the role of integrated photonics in telecommunication networks as discussed in this work. Also, it is presented how open issues, such as chromatic dispersion, can be handled by PIC, leveraging the overall performance of optical telecommunication systems.
Characterization and functional evaluation of a fiber-optic instrumented impedance-controlled upper-limb exoskeleton
The use of active exoskeletons for musculoskeletal disorder (MSD) prevention is increasing in industrial environments. Wearable sensing technologies enable closed-loop control of these devices. However, magnetic disturbances and sensor fixation issues can compromise measurement reliability and affect exoskeleton behavior. Polymer optical fiber (POF) sensors offer intrinsic immunity to electromagnetic interference and mechanical compliance, making them a promising alternative for wearable kinematic sensing. Nevertheless, their integration within the control loop of active exoskeletons remains largely unexplored. This study investigates the impact of POF sensing modality integrated within the impedance control loop of an upper-limb exoskeleton, focusing on control performance and system dynamics. A two-stage experimental protocol was conducted under an approved ethical protocol. First, a test-bench evaluation showed that POF sensing modality achieves comparable rise time and mean absolute error to IMU sensing modality, though it resulted in a 0.565 s increase in settling time. Second, participants performed standardized lifting tasks under assisted (Exo-POF and Exo-IMU) and unassisted (No-Assist) conditions. Kinematic and electromyographic (EMG) data showed a 16–23% reduction in muscle activation while preserving range of motion. These findings demonstrate that POF sensing modality significantly influences system dynamics and human–robot interaction. Subsequently, the proof of concept suggests the feasibility of the proposed approach during lifting, handling, and lowering tasks, opening the way for future validation under representative industrial environments.
Modeling Real-Time Bicep Muscle Fatigue With IMU Sensors Using LSTM Regression
Muscle fatigue is a leading contributor to musculoskeletal disorders (MSDs), which cause significant workplace disability and absenteeism. The biceps brachii are especially susceptible in manual labor due to prolonged use during tasks like lifting and carrying. Early detection of fatigue can help prevent MSDs by enabling timely interventions, such as workload adjustments or the use of assistive devices. This study presents a real-time, noninvasive, and cost-effective system for modeling biceps muscle fatigue during dumbbell curl exercises, which simulate industrial lifting movements. Two inertial measurement units (IMUs) captured arm kinematics, and a long short-term memory (LSTM) neural network was trained to predict fatigue levels on a 0–7 scale using participants’ perceived exertion ratings. After offline training and optimization, the model was implemented in real-time and evaluated across 18 experiments, achieving a root-mean-squared error (RMSE) of 1.2. Accuracy was assessed using RMSE thresholds (±5, ±3.3, and ±2), reaching 100% at ±5 and exceeding 87% for offline and online implementations. Compared to previous studies, this approach achieved higher accuracy with fewer sensors and demonstrated reliable real-time performance, highlighting its potential for workplace fatigue monitoring and injury prevention.
Microstrip T-Resonator: Review, Design, and Error Assessment for Dielectric Characterization
This paper presents a comprehensive study of the Microstrip T-Resonator (MTR) for dielectric characterization of PCB substrates. Due to conflicting assumptions in existing models, we evaluate two MTR configurations: MTR-S, corresponding to the short-substrate configuration, and MTR-E, corresponding to the extended-substrate configuration. Microstrip discontinuities, particularly the open end, significantly affect dielectric characterization. Specifically, the effective length used for relative permittivity extraction relies on an open-end correction derived for extended substrates, making MTR-E the appropriate choice for this parameter. Loss tangent estimation, in turn, depends on radiation loss models that assume the substrate terminates at the open end of the resonator, as in the MTR-S configuration. Thus, this work investigates the MTR methodology and the effects of substrate extension on dielectric characterization accuracy. Relative permittivity and loss tangent are estimated using measured and simulated data, and the results are employed to assess the error and deviation of each configuration. The analysis highlights that aligning device geometry with model assumptions significantly improves estimation accuracy, particularly for loss tangent, which exhibits greater variation with substrate termination.