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.
Delayed Luminescence of Algae Under Silver Stress
The stress of algae samples after exposure to Silver Nitrate (AgNO3) is expressed in alterations of the decay dynamics of their delayed luminescence patterns. Indicative results are obtained just one hour after inoculation, with sensitivity comparable to those of standard tests (obtained after 72h) but using less consumables/work-hours. Further tests using different metals might validate the new data analysis suggested in the article.
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.
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.
Quantum Sensors: A Comprehensive Review and Future Perspectives
Quantum sensing is an emerging field that exploits the principles of quantum mechanics to achieve unprecedented levels of precision, sensitivity, and accuracy in measuring physical quantities. From navigation and artificial intelligence to environmental monitoring and medical diagnostics, these technologies have the potential to transform a wide range of applications completely. This review provides an exploration of the fundamental principles, state-of-the-art developments, and future perspectives of quantum sensors and perception. We discuss the underlying quantum phenomena, such as superposition, entanglement, and quantum coherence, and their role in enabling advanced sensing capabilities. We also look at how quantum sensors can be incorporated into several applications. Lastly, we address the field’s challenges and outline future research directions.
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.
High-Resolution Distributed Optical Fiber Sensor for Multiparameter Sensing, Local Inhomogeneity Detection, and Poisson’s Ratio Estimation in Concrete Structures
The structural health monitoring (SHM) concept emerges as an important approach for the continuous assessment of civil infrastructure, directly aligning with the demands for sustainability, safety, and smart devices in modern urbanization and smart cities. Addressing these demands, this article presents the development and validation of a multiparameter distributed optical fiber sensor (DOFS) for the simultaneous measurement of temperature, longitudinal strain, and transverse strain with subcentimeter-scale spatial resolution. Based on optical frequency domain reflectometry (OFDR), the system utilizes a geometrically configured commercial fiber to act as a continuous spatial rosette. The sensor was characterized, achieving linear sensitivities of 0.1253 GHz/ μϵ and 1.251 GHz/°C, with a limit of detection of 0.1 μϵ and 0.1 ∘ C and a spatial resolution of 2.5 mm. Application tests on concrete cylinders under compressive loading demonstrated the system’s capability to decouple multiaxial strain components while inherently compensating for thermal cross-sensitivity, maintaining a stable temperature variation of 0.3 ∘ C during testing. The decomposed experimental strain profiles were validated against a numerical finite element analysis (FEA). In contrast with the idealized numerical model, the distributed sensor successfully mapped real-world localized strain concentrations and inherent material inhomogeneities, registering spatial strain variations of up to 619.22 μϵ (longitudinal) and 113.75 μϵ (transverse) near the elastic limit (160 kN). By aggregating the synchronized multitest data, the Poisson’s ratio of the concrete was estimated at 0.16. Furthermore, the system captured the complete spatiotemporal evolution of damage, precisely identifying the macroscopic rupture region. Finally, the results demonstrate that the proposed device provides a robust, single-fiber solution for 2-D strain field mapping and multiparameter SHM, filling key technological gaps for future smart infrastructure applications.
POF-Based Sensor Device for Assessment of Human-Robot Interaction Forces
This article presents the development and validation of a polymer optical fiber (POF) sensor system for quantifying human–robot interaction (HRI) torque in knee exoskeletons. The device consists of a four-channel POF array embedded in a shank cuff that provides online torque signals during gait tasks. The channels were calibrated against a reference load cell, yielding dynamic force RMSE in the range 3.93–5.14 N and correlation between 0.88 and 0.94. In quasi-static interface tests with six participants, the reconstructed POF torque tracked full torque transfer through the cuff, with RMSE of 1.4–1.8 N ⋅ m for sinusoidal and triangular actuator profiles. In treadmill trials, the POF-based torque was used as a reference to assess disturbance observer (DOB) techniques. All observers reproduced the main interaction-torque patterns but showed attenuation and temporal shifts with respect to the POF reference, with RMSE between 0.8 and 1.3 N ⋅ m. These results indicate that the POF cuff delivers a consistent interaction-torque signal at the interface and can serve as a practical reference when analyzing bandwidth and robustness limitations of observer-based estimators.