Sensor Configuration Research Articles

Monitoring of static and vibration responses of laminated composite materials using integrated carbon nanotube fibers

Carbon nanotube fibers or yarns (CNTYs) are lightweight, stiff, strong, electrically, and thermally conductive fiber-like materials that exhibit a piezoresistive response and could be integrated in glass-fiber/epoxy laminated composite materials to measure strain and to detect damage. Aiming at extending the scope of previous studies, this work is about the piezoresistive response of CNTY sensors integrated in composite laminates of industrial interest, accounting for interactions between the CNTY and the typical heterogeneous, anisotropic surrounding media, including the effects induced by the curing process of the composite matrix. This study reports experimental results on the mechanical response of laminated composite materials under quasi-static and vibration loading monitored using integrated CNTY sensors. A combination of CNTY sensor configurations and experimental setups were used to monitor the deformation and strains among the various layers of the laminated composites. As the laminated composites were mechanically loaded under quasi-static four-point bending, the CNTY sensors captured instantaneously the deformation as demonstrated by the change in their electrical resistance. Also, as the laminated composites were subjected to sinusoidal loading at specific frequencies, the integrated CNTY sensors were able to capture the loading cycles exactly including durations and peaks. Integrated sensing using CNTYs may offer a highly adaptive, practical, and sensitive structural monitoring method for a variety of applications.

Control-oriented wind turbine load estimation based on local linear neuro-fuzzy models

To enhance energy production, the wind energy industry builds increasingly larger wind turbines (WT) in terms of hub height and rotor diameter. This enlargement results in higher structural loads, which emphasizes the importance of load-reducing WT control alongside the nominal control of power and rotational speed. Generally, it is not possible to directly measure these structural loads, so a controller needs to include their online estimation. Here we extend a baseline WT state estimator in the form of an Extended Kalman Filter (EKF) to include two unaccounted loads. These loads are a change of thrust in the main bearing and a yaw moment in the rotor hub. We incorporate these loads via data-driven local linear neuro-fuzzy models (LLNFM). These LLNFMs can represent nonlinear relationships while maintaining limited complexity. We use alaska/Wind to generate the underlying regression data and to perform the state estimation both in simulation. The regression data consists of nominal WT operation under turbulent conditions for different average wind speeds. We further superimpose the individual pitch angles to excite the WT. The evaluation is performed in a different scenario, using another turbulent wind field and a realistic noisy sensor configuration. While we can estimate the change of thrust in the one per revolution (1P) frequency range, we achieve more precise results for the yaw moment due to the incorporation of sensors with a strong correlation to it. The estimation of the change of thrust and the yaw moment appear to be sufficiently precise for load-reducing WT control in practically relevant frequency ranges.

Structural Health Monitoring of Fiber Reinforced Composites Using Integrated a Linear Capacitance Based Sensor.

The demand for fiber-reinforced polymers (FRPs) has significantly increased in various industries due to their attributes, including low weight, high strength, corrosion resistance, and cost-efficiency. Nevertheless, FRPs, such as glass and Kevlar fiber composites, exhibit anisotropic properties and relatively low interlaminar strength, rendering them susceptible to undetected damage. The integration of real-time damage detection processes can effectively mitigate this issue. This paper introduces a novel method for fabricating embedded capacitive sensors within FRPs using a coating technique. The study encompasses two types of fibers, namely glass and Kevlar fiber/epoxy composites. The physical vapor deposition (PVD) technique is employed to coat bundle fibers with conductive material, thus creating embedded electrodes. The results demonstrate the uniform distribution of nanoparticles of gold (Au) along the fibers using PVD, resulting in a favorable resistance of approximately ≈100 Ω. Two sensor configurations are explored: axial and lateral embedding of the coated yarn (electrodes) to investigate the influence of load direction on the coating yarn. Axial-sensor configuration specimens undergo tensile testing, showcasing a linear response to axial loads with average sensitivities of 1 for glass and 1.5 for Kevlar fiber/epoxy composites. Additionally, onset damage is detected in both types of fiber composites, occurring before final fracture, with average stress at the turning point measuring 208 MPa for glass and 144 MPa for Kevlar. The lateral-sensor configuration for glass fiber-reinforced polymer (GFRP) exhibits good linearity towards strain until failure, with average gauge factors of 0.25 and -2.44 in the x and y axes, respectively.

Energy Harvesting System

Abstract: This paper presents a novel energy harvesting system designed to harness ambient mechanical energy using piezoelectric sensors, with the Arduino Uno microcontroller serving as the central processing unit. The system's primary objective is to convert mechanical vibrations into electrical energy, storing it for potential use in powering low-energy electronic devices. The proposed setup incorporates piezoelectric materials strategically positioned to capture mechanical vibrations from various sources. The core of the system is an Arduino Uno microcontroller, programmed to efficiently manage the energy harvesting process. The harvested energy is stored in a rechargeable battery, ensuring a consistent power supply for applications with intermittent energy demands. To validate the effectiveness of the system, a light bulb is employed as a demonstrative load, allowing for real-time observation of the energy harvesting capabilities. The paper details the experimental setup, including the placement and configuration of piezoelectric sensors, the Arduino Uno programming logic, and the integration of a light bulb for visual confirmation of the generated current. Performance metrics such as voltage output, current production, and power generation efficiency are thoroughly analyzed under varying environmental conditions and mechanical vibrations. Results demonstrate the feasibility of the proposed energy harvesting system, showcasing its potential as a sustainable power source for low-power electronic devices. The combination of piezoelectric materials, Arduino Uno control, and a practical load application illustrates a promising approach to energy harvesting, paving the way for advancements in self-sufficient and ecofriendly power solutions

Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review

Breathing pattern assessment holds critical importance in clinical practice for detecting respiratory dysfunctions and their impact on health and wellbeing. This systematic literature review investigates the efficacy of inertial sensors in assessing adult human breathing patterns, exploring various methodologies, challenges, and limitations. Utilizing the PSALSAR framework, incorporating the PICOC method and PRISMA statement for comprehensive research, 22 publications were scrutinized from the Scopus, Web of Science, and PubMed databases. A diverse range of sensor fusion methods, data signal analysis techniques, and classifier performances were investigated. Notably, Madgwick’s algorithm and the Principal Component Analysis showed superior performance in tracking respiratory movements. Classifiers like Long Short-Term Memory Recurrent Neural Networks exhibited high accuracy in detecting breathing events. Motion artifacts, limited sample sizes, and physiological variability posed challenges, highlighting the need for further research. Optimal sensor configurations were explored, suggesting improvements with multiple sensors, especially in different body postures. In conclusion, this systematic literature review elucidates methods, challenges, and potential future developments in using inertial sensors for assessing adult human breathing patterns. Overcoming the challenges related to sensor placement, motion artifacts, and algorithm development is essential for progress. Future research should focus on extending sensor applications to clinical settings and diverse populations, enhancing respiratory health management.

Characterization of IMIC, an implantable needle-shaped positron sensitive monolithic active pixel sensor for preclinical molecular neuroimaging

The correlation of molecular neuroimaging and behavior studies in preclinical PET imaging is of major interest to unlock progress in the understanding of brain processes and assess the validity of preclinical studies in drug development. However, fully achieving this ambition requires performing molecular images of awake and freely moving animals, whereas most of the preclinical imaging procedures are currently performed under anesthesia. To overcome this issue, the MAPSSIC project aims to develop a pixelated intracerebral probe to be implanted into awake and freely moving rats. The aforementioned probe relies on IMIC (Imageur Moléculaire Intra Cérébral), a Monolithic Active Pixel Sensor (MAPS) prototype set to directly detect positrons. The IMIC sensors were produced in 5 different configurations. Measurements using a 204Tl source showed that the sensor parameters can be optimized to boost its performance allowing to increase the sensitivity and reduce the average cluster size. In addition, comparisons between sensor configurations show a clear gain provided by the introduction of CMOS process modifications. Finally, the choice of the optimal sensor configuration will depend on the expected in vivo conditions.

Formulation analysis and follow-up study of multi-turn configuration for performance enhancement of MEMS piezoresistive pressure sensor utilized for low-pressure measurement applications

PurposeThe purpose of this research is to study the performance of piezoresistive pressure sensors using polysilicon as the piezoresistive material, which is typically used to measure pressure in high-temperature environments.Design/methodology/approachThe performance of this sensor is enhanced by studying the influence of multi-turn configuration at which the piezoresistors are arranged. Different configurations are studied and compared by laying down their analytical solution.FindingsThe validation of analytical results is accomplished through finite element analysis using the software COMSOL Multiphysics. The best configuration, which uses a partial triple-turn configuration, was able to achieve a sensitivity of 116.00 mV/V/MPa over a simulated pressure range of 0 to 500 KPa.Originality/valueThe literature shows the study of single-turn and double-turn meander-shaped configuration of micro-electromechanical systems piezoresistive pressure sensor but multi-turn meander-shaped configuration using a square silicon diaphragm has not been reported. Its study has reflected promising results than its counterparts based on key performance parameters such as sensitivity and linearity and are more effective to be used for automotive, aviation, biomedical and consumer electronics applications.

A Proposal for a Solar Position Sensor System with Multifiber Optical Cable.

A solar position sensor is an essential optoelectronic device used to monitor the sun's position in solar tracking systems. In closed-loop systems, this sensor is responsible for providing feedback signals to the control system, allowing motor adjustments to optimize the angle of incidence and minimize positioning errors. The accuracy required for solar tracking systems varies depending on the specific photovoltaic concentration. In the case of the concentrator photovoltaic (CPV), it is normally essential to track the sun with a position error of less than ±0.6°. To achieve such precision, a proposed sensor configuration composed of low-cost embedded electronics and multifiber optical cable is subjected to characterization through a series of measurements covering range, sensitivity, and resolution. These measurements are performed in controlled indoor environments as well as outdoor conditions. The results obtained exhibit a resolution of 2.6×10-3 degrees when the sensor is illuminated within its designated field of view of ±0.1°, particularly in external conditions. Considering the performance demonstrated by the proposed solar position sensor, coupled with its straightforward modeling and assembly compared to position sensors documented in the literature, it emerges as a promising candidate for integration into solar tracking systems.

Planning and resource allocation of a hybrid IoT network using artificial intelligence

This paper introduces a pioneering hybrid topology tailored for Internet of Things (IoT) applications, integrating mesh and star wireless sensor configurations. This hybridized approach aims to optimize energy consumption efficiency while ensuring comprehensive network coverage for sensor deployment. The formulation of the network strategy is rooted in empirical data collected from real sensors deployed across two neighboring municipalities within the State of Espírito Santo, Brazil. Specifically, our analysis encompasses 380 strategically positioned sensors throughout Vitória city, all intended for connectivity to a central gateway located in Vila Velha. To establish mesh network clusters, we employed the k-Medoids algorithm for clustering fusion, while the GA with a binary solution was utilized to determine the star network points. In this approach, Dijkstra and genetic algorithms with real solutions are incorporated to facilitate efficient resource allocation within the mesh (utilizing ZigBee) and star (utilizing LoRa) networks. These resource allocation strategies are devised with the overarching objective of minimizing energy consumption. The findings of this investigation demonstrate that through the implementation of planning and resource allocation algorithms, we were able to effectively reduce the number of mesh networks and allocate resources to each designated end-point sensor.

Developing an IoT-enabled probabilistic model for quick identification of hidden radioactive materials in maritime port operations to strengthen global supply chain security

Uncovering hidden radioactive materials continues to be a major hurdle in worldwide supply chains. Recent research has not adequately investigated practical Internet of Things (IoT)-based approaches for improving and implementing efficient data fusion techniques. Current systems often misuse resources, leading to security vulnerabilities in typical settings. Our research delves into the fundamental principles of detection using both single and multiple sensor configurations, adopting a probabilistic method for merging data. We introduce a model aimed at accelerating the detection of radiation emissions in actual port operations. The results highlight the model’s effectiveness in rapid identification and determine the best conditions for its application in scenarios involving stacked containers, whether they are on ships or positioned in storage areas.

Error analysis of wave intercomparison based on sampling variability

Quantitative analysis of error sources in wave measurement intercomparison under real-world conditions remains challenging due to various factors such as spatial and temporal offsets in the field, diverse configurations of wave sensors and platforms, and distinct measurement algorithms. This study introduces a modified error analysis method based on sampling variability. Measurements from two co-deployed Datawell Waverider buoys at the National Marine Test Site (Weihai) in China offer an opportunity to assess the spatial variability error. The results reveal that for non-directional wave parameters, like significant wave height and mean zero-crossing period, the wave conditions in the field can be considered stationary, with random errors primarily arising from inherent sampling variability. Spatial offsets significantly impact directional wave parameters at peak frequencies, with large deviations (greater than 20°) in peak wave direction primarily attributed to bimodal distribution. For data with significant wave heights exceeding 0.5 m, random errors in the mean wave direction at the same frequency can be mainly attributed to sampling variability. The bulk directional wave parameters weighted by the energy spectrum demonstrate lower sensitivity to spatial offsets, and there is excellent agreement between the bulk mean directions when considering only the unimodal distribution data.

Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets.

This paper presents a transfer learning method to enhance locomotion intent prediction in novel transfemoral amputee subjects, particularly in data-sparse scenarios. Transfer learning is done with three pre-trained models trained on separate datasets: transfemoral amputees, able-bodied individuals, and a mixed dataset of both groups. Each model is subsequently fine-tuned using data from a new transfemoral amputee subject. While subject-dependent models, trained and tested using individual user data, can achieve the least error rate, they require extensive training datasets. In contrast, our transfer learning approach yields comparable error rates while requiring significantly less data. This highlights the benefit of using preexisting, pre-trained features when data is scarce. As anticipated, the performance of transfer learning improves as more data from the subject is made available. We also explore the performance of the intent prediction system under various sensor configurations. We identify that a combination of a thigh inertial measurement unit and load cell offers a practical and efficient choice for sensor setup. These findings underscore the potential of transfer learning as a powerful tool for enhancing intent prediction accuracy for new transfemoral amputee subjects, even under data-limited conditions.

Optimized strategies of cloud droplet distribution retrieval using satellite multi-directional polarimetric optical measurements: information content approach

Multi-directional polarized optical sensors are increasingly vital in passive remote sensing, deepening our understanding of global cloud properties. Nevertheless, uncertainty lingers on how these observations can contribute to our knowledge of cloud diversity. The variability in cloud PSD (Particle Size Distribution) significantly influences a wide array of cloud characteristics, while unidentified factors in RT (Radiative Transfer) may introduce errors into the cloud PSD retrieval algorithm. Therefore, establishing unified evaluation criteria for both optical device configuration and inversion methods is crucial. Our study, based on Bayesian theory and RT, assesses the information content of both cloud effective radius and effective variance retrieval, along with the key factors affecting their retrieval in multi-directional polarized observations, using the calculation of DFS (Degree of Freedom for Signals).We consider the process of solar incidence, cloud scattering, and sensor reception, and discuss the impact of various sensor configurations, cloud characteristics, and other components on the retrieval of cloud PSD. Correspondingly, we observed a 48% improvement in the information content of cloud PSD with the incorporation of multi-directional polarized measurements in the rainbow region. Cloud droplet concentration significantly influences inversion, but its PSD does not cause monotonic linear interference on information content. The blending of particle mixtures with different PSD has a significant negative impact on DFS. In cases where the AOD (Aerosol Optical Depth) is less than 0.5 and the COT (Cloud Optical Thickness) exceeds 5, the influence of aerosol and surface contributions on inversion can be neglected. Our findings would serve as a foundation for future instrument design improvements and enhancements to retrieval algorithms.

Applications of Chipless RFID Humidity Sensors to Smart Packaging Solutions.

Packaging solutions have recently evolved to become smart and intelligent thanks to technologies such as RFID tracking and communication systems, but the integration of sensing functionality in these systems is still under active development. In this paper, chipless RFID humidity sensors suitable for smart packaging are proposed together with a novel strategy to tune their performances and their operating range. The sensors are flexible, fast, low-cost and easy to fabricate and can be read wirelessly. The sensitivity and the humidity range where they can be used are adjustable by changing one of the sensor's structural parameters. Moreover, these sensors are proposed as double parameter sensors, using both the frequency shift and the intensity variation of the resonance peak for the measure of the relative humidity. The results show that the sensitivity can vary remarkably among the sensors proposed, together with the operative range. The sensor suitability in two specific smart packaging applications is discussed. In the first case, a threshold sensor in the low-humidity range for package integrity verification is analyzed, and in the second case, a more complex measurement of humidity in non-hermetic packages is investigated. The discussion shows that the sensor configuration can easily be adapted to the different application needs.

Adjustable optical fiber displacement-curvature sensor based on macro-bending losses with a coding of optical signal intensity

In this work, a simple design and cost-effective displacement-curvature sensor based on macro-bend single-mode fiber (SMF) is planned and demonstrated. The optical fiber sensor encompasses a bending configuration and the macro-bending losses were exploited as the sensing process for the capturing of angular displacement motion in an angle span between 0° and 90° (that corresponds to curvature displacement between 0 and 49.5 cm). A straight SMF shape and other four bent sensor structures (U-like, drop-like, Knot-like, and eight-like) have been fabricated from sections of 90 cm length SMF. These five sensor configurations will provide high maneuvering for versatile applications. In these adjustable sensing schemes, the fiber sensors were described by monitoring the power loss corresponding to the diverse angular displacement at various bending structures in terms of the shape of the bend, the bending diameter, and the number of wrapping turns. Then, these measurement schemes based on the macro-bending losses were employed to detect the human motion elbow joint, and a proper sensing scheme was adopted to examine the flexion/extension joint motion in detail. The proposed sensors showed enhanced sensitivity to flexion/extension joint motion range between −0.25 and −0.477 dB/° and good sensitivity to curvature ranging between −0.552 and −1.052 dB/cm. The demonstrated and planned sensing techniques are adjustable and accessible for different types of human and mechanical movements.

A highly-sensitive wearable capacitance pressure sensor based on calcium copper titanate/polydimethysiloxane/graphene oxide and polydimethysiloxane/silver nanowires sanwich strustures combination for human body monitoring

In recent years, flexible pressure sensors, with their high sensitivity, low detection limit, wide working pressure range and fast response speed, have been playing an important role in the development of wearable artificial devices, human-computer interaction and healthcare systems, and have attracted wide attention. However, the pressure sensors face challenges related to the weak tightness of bipolar materials, low charge density, and limitation in dielectric layers. In this paper, we present a novel configuration of flexible capacitive pressure sensors, utilizing a polydimethysiloxane (PDMS) thin film covered by silver nanowires (PDMS/SN, PSN) as the electrode materials and a porous material composed of calcium copper titanate (@CCTO), PDMS and graphene oxide (GO)(@CCTO/PDMS/GO, @CPG) as the positive electrode material. The sacrificial material, sugar, is employed to create dense internal pores within the substrate material, @CPG, leading to the formation of a profoundly porous substrate. The integration of the flexible capacitive pressure sensor using @CPG/PSN results in enhanced output performance. The modified sensors achieves a sensitivity of 0.22 kPa−1, which is 7 times higher than that of a pure PDMS dielectric layer sensor. Moreover, the sensors has a response time of 87 ms, a low detection pressure of 5 Pa with an extensive detection range from 0 to 150 kPa, and are capable of enduring 7200 cycles without sustaining damage. To validate the practical performance of the @CPG/PSN sensor, various human body activities are detected, including swallowing, coughing, neck bending, wrist bending, elbow bending, finger flexing, and holding objects like cups using a digitally designed glove capable of accommodating different materials. The pressure sensor based on @CPG/PSN demonstrates significant promise in detecting both low and high pressures, thus expanding the potential applications of capacitive pressure sensors.

FO-SPR Model for Full-Spectrum Signal Analysis of Back-reflecting FO-SPR Sensors to Monitor MOF Deposition.

In this study, we explore the full-spectrum capabilities of fiber-optic surface plasmon resonance (FO-SPR) for analyzing heterogeneous samples with increased comprehensiveness. Our approach involves refining a literature-derived FO-SPR model to more precisely reflect experimental data obtained using a back-reflecting sensor configuration. Key enhancements in our model include adjustments to the thickness and permittivity of the gold SPR-active layer on the FO-SPR sensor as well as improvements to the angular distribution of light within the system. We apply this optimized model to the investigation of the deposition process of a metal-organic framework (MOF), specifically ZIF-8, using FO-SPR. By closely examining the temporal variations in the FO-SPR signal during MOF layer formation, we simultaneously determine the evolving thickness and refractive index (RI) of the MOF layer, offering a dual-parameter analysis. Our results demonstrate that a full-spectrum analysis of the FO-SPR signal can extract critical information from samples exhibiting radial heterogeneity. This advancement significantly enhances the quantitative assessment of various phenomena that alter the refractive index in the sensor's domain, such as adsorption and binding processes. This work thus represents a significant step forward in the field of FO-SPR sensor technology, promising broad applications in areas requiring the precise detection and analysis of complex samples.

A Comprehensive Analysis of Trapezius Muscle EMG Activity in Relation to Stress and Meditation

Introduction: This study analyzes the efficacy of trapezius muscle electromyography (EMG) in discerning mental states, namely stress and meditation. Methods: Fifteen healthy participants were monitored to assess their physiological responses to mental stressors and meditation. Sensors were affixed to both the right and left trapezius muscles to capture EMG signals, while simultaneous electroencephalography (EEG) was conducted to validate cognitive states. Results: Our analysis of various EMG features, considering frequency ranges and sensor positioning, revealed significant changes in trapezius muscle activity during stress and meditation. Notably, low-frequency EMG features facilitated enhanced stress detection. For accurate stress identification, sensor configurations can be limited to the right trapezius muscle. Furthermore, the introduction of a novel method for determining asymmetry in EMG features suggests that applying sensors on bilateral trapezius muscles can improve the detection of mental states. Conclusion: This research presents a promising avenue for efficient cognitive state monitoring through compact and convenient sensing.

Investigation into the seamless construction for hundred-meter scale super-length raft structure based on magnesia expansive agent concrete

This research introduces an innovative construction method based on magnesia expansive agent concrete for the seamless construction of hundred-meter scale super-length raft structures, corroborated by the on-site test. The basic principle of this construction method is to use the pre compression stress generated by magnesia expansive agent to offset temperature and shrinkage stress. A temperature-strain monitoring system was employed to gather data, affirming the technique’s applicability and safety. Through the examination of temperature and strain dispersion trends in super-length raft structure, recommendations for the configuration of temperature-strain sensors have been put forth. Through the scrutiny of the temporal evolution pattern of temperature, the specific temporal and spatial coordinates that warrant particular vigilance during the surveillance of the raft’s inner-surface temperature difference were identified. Upon evaluating the correlation between strain dispersion and strain-temperature differential in the raft’s thickness dimension, a novel temperature control index (the bottom-surface temperature difference) was introduced. The threshold for this metric was established at 30°C, derived from empirical test outcomes conducted on-site. Furthermore, the critical regions for monitoring the bottom-surface temperature difference were specified.

Delving Into the Devils of Bird's-Eye-View Perception: A Review, Evaluation and Recipe.

Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. Conventional approaches for most autonomous driving algorithms perform detection, segmentation, tracking, etc., in a front or perspective view. As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance. BEV perception inherits several advantages, as representing surrounding scenes in BEV is intuitive and fusion-friendly; and representing objects in BEV is most desirable for subsequent modules as in planning and/or control. The core problems for BEV perception lie in (a) how to reconstruct the lost 3D information via view transformation from perspective view to BEV; (b) how to acquire ground truth annotations in BEV grid; (c) how to formulate the pipeline to incorporate features from different sources and views; and (d) how to adapt and generalize algorithms as sensor configurations vary across different scenarios. In this survey, we review the most recent works on BEV perception and provide an in-depth analysis of different solutions. Moreover, several systematic designs of BEV approach from the industry are depicted as well. Furthermore, we introduce a full suite of practical guidebook to improve the performance of BEV perception tasks, including camera, LiDAR and fusion inputs. At last, we point out the future research directions in this area. We hope this report will shed some light on the community and encourage more research effort on BEV perception.