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.
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.
Fiber Sensing in the 6G Era: Vision Transformers for ϕ-OTDR-Based Road-Traffic Monitoring
This article adds to the emergent body of research that examines the potential of 6G as a platform that can combine wired and wireless sensing modalities. We apply vision transformers (ViTs) in a distributed fiber-optic sensing system to evaluate road traffic parameters in smart cities. Convolutional neural networks (CNNs) are also assessed for benchmarking. The experimental setup is based on a direct-detection phase-sensitive optical time-domain reflectometer ($\phi$-OTDR) implemented using a narrow linewidth source. The monitored fibers are buried on the university campus, creating a smart city environment. Backscattered traces are consolidated into space–time matrices, illustrating traffic patterns and enabling analysis through image processing algorithms. The ground truth is established by traffic parameters obtained by processing video camera images monitoring the same street using the YOLOv8 model. The results indicate that ViTs outperform CNNs for estimating the number of vehicles and the mean vehicle speed. While a ViT necessitates a significantly larger number of parameters, its complexity is similar to that of a CNN when considering multiply–accumulate operations and random access memory usage. The processed dataset has been made publicly available for benchmarking.
The Role of Photonics in Neuromorphic Computing for Augmenting the von Neumann Paradigm
The rapid expansion of Big Data and Artificial Intelligence has exposed fundamental inefficiencies in conventional von Neumann computing architectures, thus necessitating a search for alternative solutions. Neuromorphic computing, inspired by biological neural systems, has emerged as a promising route toward faster and more energy-efficient information processing approaches. Optical neuromorphic computing exploits light as a signal modality for stimulation, data delivery, information processing, and signal readout, offering intrinsic advantages in speed, parallelism, and power consumption. This review outlines the core principles of neuromorphic computing and examines recent advances in its optical implementations, with a particular focus on optical artificial synapses, photonic layers integrated into hybrid photonic-digital architectures, and optical neural networks. These emerging approaches define a coherent framework for advancing computing systems that offer clear advantages over purely von Neumann systems.
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.
Mobile-compatible neuromorphic optical computing enabled by dual-emission photonic materials
Artificial intelligence (AI) and neuromorphic computing demand hardware platforms that combine energy efficiency with physically informed data processing. Photonics offers unique advantages in this context, but optical neuromorphic systems in which memory and information processing arise from intrinsic material dynamics remain scarce. We demonstrate a photonic layer in which history-dependent material photophysics implements physically embedded information processing before digital learning, using luminescent phosphors. The photonic layer exhibits dual fluorescence and phosphorescence together with excitation-history-dependent photoactivation dynamics that emulate synaptic functionalities, including short-term memory, long-term memory, and synaptic potentiation. Quantitative analysis and modelling reveal efficient nanoscale interlamellar energy transfer consistent with the lamellar material morphology, establishing a link between structure and function. When integrated as an active optical front-end within a hybrid photonic–digital AI architecture, the photonic layer performs a material-based transformation of input data before digital learning. Using a laboratory-based optical readout, classification accuracy comparable to state-of-the-art hybrid neuromorphic computing (approximately 94%) is achieved while requiring fewer training epochs. A mobile-compatible implementation based on smartphone optical readout yields similar accuracy, demonstrating robustness to non-specialized optical hardware. These results outline a general strategy for neuromorphic photonic systems in which functional materials act as adaptive computational primitives, enabling energy-efficient computing architectures.