Passive Sensors Research Articles

A Passive Wireless Smart Washer for Locking Force Monitoring on the Orthopedic Pedicle Screw

A pedicle screw is a component for fixation in spine fusion surgery, often for patients with osteoporosis. Spine fusion condition highly depends on whether the pedicle screw is tightly fixed on spine, if not, spine fusion will not work properly. After the surgery, the first 3 months is the most crucial period, and bone healing situation cannot be shown through X-ray before the first radiologic images taken around the sixth week. Therefore, it is helpful to have a nonradiative method to monitor the locking force of the pedicle screw after surgery, especially during the early stage. Here a passive wireless force sensor is developed for monitoring the locking force of the pedicle screw, as we call it a smart washer. By integrating a capacitive ring-shape force sensor with an inductor, a passive LC sensor can be built by measuring the resonant frequency wirelessly. The smart washer is designed and calibrated to establish the relation between the locking force and resonant frequency, and then it is fixed with a screw in a porcine femur, with and without medium between the reader and inductor. When the locking force decreases from 8.3 to 0.9 N, the error is less than 0.5 N, and the maximum wireless sensing distance is 72 mm. However, the medium between the reader and the sensor inductor will affect the resonant frequency, but not the sensitivity. Therefore, the locking force variation can still be calculated by the resonant frequency shift accurately. Furthermore, by designing another five LC sensors with different operating resonant frequency ranges, it is possible to identify locking forces at different locations for more pedicle screws. To our knowledge, no LC force sensor was proposed to monitor the locking force of pedicle screws after surgery in the past.

Passive Wireless Partial Discharge Sensors with Multiple Resonances.

Partial discharge (PD) is the dominant insulating defect in Gas-Insulated Switchgear (GIS). The existing detection methods are mainly divided into built-in wire-connected disk antennas with destructive drilling and external ultra-high frequency antennas with poor anti-interference ability. This research introduces a passive wireless PD sensor implanted inside GIS on the observation window. The sensor is implemented by a sheeting branch-inductor with multiple resonances which is able to enhance detection sensitivity. A coaxially aligned readout circuit, positioned outside the GIS, interrogates the PD sensor to wirelessly obtain the PD signal. The proposed sensing scheme improves signal-to-noise ratio and ensures minimal disruption to the electric field distribution inside GIS. An experimental setup was established in a controlled laboratory environment to benchmark the multi-resonant sensor against the commercial UHF sensor. A 2.5-times enhancement of signal strength was observed. Since our sensor was implanted inside the GIS, a high signal-to-noise ratio (68.82 dB) was obtained. Moreover, we constructed a wireless calibration test to investigate the accuracy of the proposed sensor. The precision of the signal test was as high as 0.72 pC. The pulse phase distribution information was collected to demonstrate a phase-resolved partial discharge (PRPD) pattern. The experiment results validate the effectiveness of the proposed method and demonstrate excellent performance in PD detection.

3D printed sub-terahertz photonic crystal for wireless passive biosensing

Monitoring pathogens has become a major challenge for society and research in recent years. Of great interest are refractive index sensors, which are based on the interaction between analytes and electromagnetic waves and allow label-free and fast detection. In addition, the electromagnetic waves can be exploited for wireless communication. However, current refractive index biosensors can only be read from a few centimeters. Here, we demonstrate an innovative concept of a passive wireless sensor based on a sub-terahertz photonic crystal resonator. The fabricated sensors have a reading range of up to 0.9 m and elevation and azimuth acceptance angles of around 90°. We demonstrate the stand-off detection of sub-µm thin-film proteins as test analytes. The proposed wireless sensor opens the door to a non-electronic, compact, and low-cost solution and can be extended to a wireless sensor network monitoring airborne pathogen, which may provide a pre-infection detection to prevent their spread efficiently.

Hybrid Water-Harvesting Channels Delivering Wide-Range and Supersensitive Passive Fluorescence Humidity Sensors.

The development of optical humidity detection has been of considerable interest in highly integrated wearable electronics and packaged equipment. However, improving their capacities for color recognition at ultralow humidity and response-recovery rate remains a significant challenge. Herein, we propose a type of hybrid water-harvesting channel to construct brand-new passive fluorescence humidity sensors (PFHSs). Specifically, the hybrid water-harvesting channels involve porous metal-organic frameworks and a hydrophilic poly(acrylic acid) network that can capture water vapors from the ambient environment even at ultralow humidity, into which polar-responsive aggregation-induced emission molecules are doped to impart humidity-sensitive luminescence colors. As a result, the PFHSs exhibit clearly defined fluorescence signals within 0-98% RH coupling with desirable performances such as a fast response rate, precise quantitative feedback, and durable reversibility. Given the flexible processability of this system, we further upgrade the porous structure via electrostatic spinning to furnish a kind of Nano-PFHSs, demonstrating an impressive response time (<100 ms). Finally, we validate the promising applications of these sensors in electronic humidity monitoring and successfully fabricate a portable and rapid humidity indicator card.

A Disturbance Compensation Control Strategy for Rotational Speed Standard Device Based on AMB System.

The rotational speed standard device that can carry loads is the key device for calibrating passive rotational speed sensors. The rotor of the passive rotational speed sensor is connected to the rotor of the standard speed device through a coupling, and the standard reference speed is provided by the standard device. Due to the rotor eccentricity, the unbalanced force of the rotor occurs, and it can not only affect the rotational speed accuracy but can also damage the mechanical bearings of the standard speed device. To solve this issue, a method for suppressing the unbalanced force of the speed standard device based on an active magnetic bearing (AMB) force compensation system is proposed. First, the overall structure of the system is briefly introduced. Then, the force feedback control system model with the AMB as the force actuator is established, and a PI controller is designed to achieve the disturbed force control. Finally, a semi-physical simulation experimental platform is built to verify the effectiveness of the proposed method. The experimental results show that the AMB force compensation system can reduce 84.4%, 81.6%, and 79.8% of the unbalanced vibration force at the frequency of 30 Hz, 90 Hz, and 150 Hz, respectively.

Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches

The fusion of active and passive microwave measurements is expected to provide more robust surface soil moisture (SSM) mapping across various environmental conditions compared to the use of a single sensor. Thus, the integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) and the active (i.e., the Advanced Scatterometer, ASCAT) observations provides an opportunity for SSM mapping with improved accuracy. However, this integration remains largely underexplored. In this context, the integration of SMAP brightness temperature (TB) and ASCAT backscattering coefficients for global-scale SSM estimation was investigated, by fully considering the potential error sources in conventional radiative transfer models (RTMs) as well as other SSM linked factors. Based on ground measurements from globally distributed dense networks with mitigated mismatch issues and spatial/temporal independent evaluation strategies, this study: (i) comprehensively evaluated four classical machine learning approaches, including Random Forest (RF), Long-Short Term Memory (LSTM), Support Vector Machine (SVM), and Cascaded Neural Network (CNN), and chose the best performing RF method to implement the final integration of SSM; (ii) compared the integration retrievals to those made using data from a single sensor (SMAP or ASCAT) with the same machine learning framework, as well as to the SMAP passive, ASCAT active, and ESA CCI active-passive combined SSM products. The results show the integration retrievals achieve satisfactory performance by obtaining an averaged unbiased root mean squared error (ubRMSE) of 0.042 m3/m3 and a temporal correlation of 0.756, which are superior to machine learning based SSM estimated from a single active or passive sensor, and also outperform the SMAP, ASCAT, and ESA CCI products. Moreover, the temporal resolution is evidently improved compared to the SMAP and ASCAT SSM products, with a temporal ratio exceeding 60% for most areas across the globe. Therefore, blending active and passive measurements affords a more reliable SSM mapping with increased sampling at the global scale, and could contribute to improved hydro-ecological applications.

Effective ecological monitoring using passive acoustic sensors: Recommendations for conservation practitioners

AbstractPassive acoustic recorders have emerged as powerful tools for ecological monitoring. However, effective monitoring is not simply an act of recording sounds. To have meaning for conservation and management, acoustic monitoring needs to be properly planned and analyzed to yield high quality information. Here, we provide a set of considerations for the design of an effective acoustic monitoring program. We argue that such a program, has the following attributes: (1) has established appropriate partnerships with landowners, Traditional Owners, researchers, or other relevant stakeholders, (2) is based on clear objectives and questions, (3) is explicit in its target sound signals, (4) has considered in‐field sensor placement for a range of factors, including experimental design, statistical power, background noise, and potential impacts on human privacy and animal disturbance, (5) has a justified recording schedule and periodicity, (6) has methods to process sound data in line with objectives, and (7) has protocols for permanent data storage and access. Acoustic monitoring is increasingly used in large‐scale programs and will be important in addressing global biodiversity targets and new biodiversity markets. It is critical that new monitoring programs are designed to effectively and efficiently capture data that address pertinent and emerging issues in conservation.

RBFNN-based angles-only orbit determination method for non-cooperative space targets

Angles-only orbit determination during space situational awareness missions for non-cooperative targets such as space debris is subject to the well-known observability problem, that is, the orbital state is weakly observable or even unobservable because of missing range measurements. The key point to solve this problem is how to quickly capture the nonlinearity of orbit dynamics if only one space-based passive sensor is available to observe the target and translational maneuvers are not executed. To solve the short-arc angles-only problem, this study develops a simple and fast orbit determination method by exploiting the advantages of radial basis function neural network (RBFNN). Emphasis was placed on the design of the RBFNN, which was trained to construct a mapping network model from line-of-sight (LOS) measurements to the relative orbit. First, the perturbed relative orbit dynamics considering the Earth's non-spherical J2 term and air drag was established with the LOS measurement model. Second, a RBFNN-based angles-only orbit determination frame was developed, in which the generator of the training dataset, data preprocessing algorithm, and RBF network structure were designed. Finally, the proposed method was verified and tested using a set of numerical simulations of low-Earth orbit cases. The anti-noise and generalization performance of the designed network and the sensitivity of the solution accuracy to the measurement frequency and span were presented and discussed. Additionally, the advantages of the RBFNN-based method were confirmed by comparison with the results obtained using the BPNN (Back Propagation Neural Network)-based method. The simulation results show that the average orbit prediction error for LEO scenarios is approximately 0.08% using the index of initial relative distance, where only three sets of LOS angles are measured within a span of 24 s.

Ultrasparse Ultrasonic Synthetic Aperture Focus Imaging by Passive Sensing.

Ultrasonic synthetic aperture focus techniques (SAFTs) using less than the total number of available array elements to transmit ("sparse" transmissions) have been recently used in both medical imaging and industrial nondestructive testing (NDT) imaging to increase test speed and simplify multiplexer hardware. The challenge of sparse arrays is to obtain a reasonable image quality given the reduced transmitter-receiver combinations available to the beamforming process. This article proposes a "ultrasparse" SAFT method that employs a minimum number of transmitter elements (from one to four elements only) to obtain an entire full-matrix capture (FMC) set of waveforms. Specifically, a "virtual" FMC is obtained from normalized cross-power spectra between each array element pair in an implementation of "passive" ultrasonic sensing. In order to maintain high image quality without sacrificing imaging speed (e.g., applying a minimal initial time delay and keeping a short time recording window), several key steps have to be taken in this "passive" imaging mode, specifically: 1) the use of carefully designed segment-averaged normalized cross-power spectrum (NCPS) for robust passive reconstruction of the ultrasonic impulse response function (IRF) between two receivers; 2) the use of both the causal and acausal portions of the passively reconstructed IRFs; and 3) the compounding of multiple wave modes in the beamforming process. These steps also ensure the elimination of the near-field blind zone hence potentially enabling near-field imaging. The article first reviews the theory of passive IRF reconstruction between two receivers, comparing time-averaged cross correlation versus segment-averaged NCPS, and then demonstrates the application to ultrasparse SAFT FMC imaging of drilled holes in an aluminum block using a linear transducer array where only one to four elements are used in transmission.

Bioinspired Potentiometric and Passive Strain Sensing Based on Mechanical Regulation of Ionically Conductive Percolating Networks

AbstractFlexible strain sensors are essential components for wearable devices, human‐machine interaction systems, and intelligent soft robots. Current active strain sensors have high power consumption, while passive strain sensors exhibit limitations in detecting static or slowly‐varying stimulations. Here, inspired by the strain sensing behavior of Ruffini endings in natural skin, a new type of potentiometric, fully passive, and highly stretchable strain sensors are presented based on the mechanical regulation of ionically conductive percolating networks. Specifically, an artificial potential difference is first created with two electrochemical active electrodes, followed by encoding external strain stimulations into the potential difference variations using a new class of highly elastic and ionically conductive composites with configurable ionic percolating networks. Based on the proposed passive strain sensing mechanism, both static and slowly‐varying strain stimulations (up to 100%) can be monitored with ultralow power consumption (at nW level). Promising applications of the passive strain sensors in human motion monitoring, Morse code communication, and hand gesture recognition are demonstrated. This work opens up a new avenue to constructing novel strain sensing devices with high stretchability, ultralow power consumption, and desirable sensing performance.

Dynamic transient response measurement method for parity-time-symmetric LC telemetry sensors

Parity-time symmetry concept has been utilized to develop high precision LC passive wireless sensors. However, they often use the traditional frequency sweeping method for measurements, thus the measurement precision and speed are strongly influenced by the performance of the frequency domain analysis instrument. To solve this issue, herein we proposed a time domain measurement method and extracted sensing information from the transient response signals of the reader. Its measurement speed was much faster than that using the frequency domain analysis instrument. A distance sensing system was developed to demonstrate the feasibility of the new method. It showed a resolution of less than 300 nm for detections of centimeter range, and the measurement time was as short as 100 μs, which was at least 1000 times faster than that using the traditional method. This technology can be explored as an innovative strategy for LC passive telemetry sensing.

WSN-Driven Advances in Soil Moisture Estimation: A Machine Learning Approach

Soil moisture estimation is crucial for agricultural productivity and environmental management. This study explores the integration of Wireless Sensor Networks (WSNs) with machine learning (ML) and deep learning (DL) techniques to optimize soil moisture estimation. By combining data from WSN nodes with satellite and climate data, this research aims to enhance the accuracy and resolution of soil moisture estimation, enabling more effective agricultural planning, irrigation management, and environmental monitoring. Five ML models, including linear regression, support vector machines, decision trees, random forests, and long short-term memory networks (LSTM), are evaluated and compared using real-world data from multiple geographical regions, which includes a dataset from NASA’s SMAP project, supplemented by climate data, which employs both active and passive sensors for data collection. The outcomes demonstrate that the LSTM model consistently outperforms other ML algorithms across various evaluation metrics, highlighting the effectiveness of WSN-driven approaches to soil moisture estimation. The study contributes to the advancement of soil moisture monitoring technologies, offering insights into the potential of WSNs combined with ML and DL for sustainable agriculture and environmental management practices.

Bioresorbable, wireless, passive sensors for continuous pH measurements and early detection of gastric leakage.

Continuous monitoring of biomarkers at locations adjacent to targeted internal organs can provide actionable information about postoperative status beyond conventional diagnostic methods. As an example, changes in pH in the intra-abdominal space after gastric surgeries can serve as direct indicators of potentially life-threatening leakage events, in contrast to symptomatic reactions that may delay treatment. Here, we report a bioresorbable, wireless, passive sensor that addresses this clinical need, designed to locally monitor pH for early detection of gastric leakage. A pH-responsive hydrogel serves as a transducer that couples to a mechanically optimized inductor-capacitor circuit for wireless readout. This platform enables real-time monitoring of pH with fast response time (within 1 hour) over a clinically relevant period (up to 7 days) and timely detection of simulated gastric leaks in animal models. These concepts have broad potential applications for temporary sensing of relevant biomarkers during critical risk periods following diverse types of surgeries.

FamilyScope: Visualizing Affective Aspects of Family Social Interactions using Passive Sensor Data

This work presents FamilyScope, a sensor-based family informatics system that enables reflection upon family data collected from family activity scenarios (e.g., game playing and movie watching) that include affective aspects of a family's social interactions. We conducted a user study with ten families (n=30) in a smart home testbed to observe how our system supports data reflection of the affective and behavioral states among family members. Our findings showed that FamilyScope facilitated family data reflection on affective and behavioral aspects of family interactions. Overall, families reported that the system well reflected family members' general tendencies in terms of affective and behavioral responses and even helped them gain new insights about each other. Based on the findings, we provide practical design approaches for collective reflection in family informatics systems.

Fully Passive Sensor Coated With Phenylboronic Acid Hydrogel for Continuous Wireless Glucose Monitoring

Fully Passive Sensor Coated With Phenylboronic Acid Hydrogel for Continuous Wireless Glucose Monitoring

AF-FTTSnet: An end-to-end two-stream convolutional neural network for online quality monitoring of robotic welding

Online welding quality monitoring (WQM) is crucial for intelligent welding, and deep learning approaches considering spatiotemporal features for WQM tasks show great potential. However, one of the important challenges for existing approaches is to balance the spatiotemporal representation learning capability and computational efficiency, which makes it challenging to adapt welding processes with complex and drastic molten pool dynamic behavior. This paper proposes a novel approach for WQM using molten pool visual sensing and deep learning considering spatiotemporal features, the proposed deep learning network called attention fusion based frame-temporality two-stream network (AF-FTTSnet). Firstly, a passive vision sensor is used to acquire continuous dynamic molten pool images. Meanwhile, temporal difference images are computed to provide novel features and temporal representations. Then, a two-stream feature extraction module is designed to concurrently extract rich spatiotemporal features from molten pool images and temporal difference images. Finally, an attention fusion module with the ability to automatically identify and weight the most relevant features is designed to achieve optimal fusion of the two-stream features. The shop welding experimental results indicate that the proposed AF-FTTSnet model can effectively and robustly recognize five typical welding states during helium arc welding, with an accuracy of 99.26%. This model has been demonstrated to exhibit significant performance improvements compared to mainstream temporal sequence models. Available: https://github.com/Just199806/TSCNN/tree/master.

Wake-Responsive AUV Guidance Assisted by Passive Sonar Measurements

To make an Autonomous Underwater Vehicle (AUV) chase a moving target ship that generates wake, wake-responsive guidance can be used. This scenario is applicable to making an underwater torpedo pursue a moving target ship until hitting the target. The objective of our research is to make an AUV pursue a target ship assisted by passive sonar sensors as well as wake sensors. To track a maneuvering target without losing the target, the AUV applies both passive sonar sensors and two wake sensors. Two wake sensors are utilized to decide the turn direction of the AUV in zig-zag maneuvers. In practice, sharp maneuvers of the target can cause the AUV to exit the target’s wake abruptly. As the target ship’s wake is not detected by wake sensors and the AUV needs to search for the target ship, the AUV’s passive sonar measures the direction of sound generated from the target ship. Thereafter, the AUV chases the target ship until the target’s wake is detected again. As far as we know, our paper is novel in addressing wake-responsive AUV guidance assisted by passive sonar sensors. The effectiveness of the proposed guidance is verified using computer simulations.

Three-Dimensional Signal Source Localization with Angle-Only Measurements in Passive Sensor Networks

Some passive sensors can provide only relative angles of a signal source. To obtain the signal source location, multiple passive sensors can be constructed into a passive sensor network through communication links. This paper investigates the source localization problem with angle-only measurements in three-dimensional space. First, we present an intersection localization method, which estimates the target position by minimizing the sum of distances between lines formed by angle-only measurements. It has the same target position estimate as the widely used least-squares (LS) method, but with a lower computational cost. Furthermore, considering the differences in measurement accuracy of sensors, the weighted least-squares (WLS) algorithm can achieve better localization performance than the LS method. Unfortunately, since the coefficient matrix and the noise vector are correlated, the WLS method is biased. The bias-compensation WLS (BCWLS) method is also presented in this paper to reduce the bias by estimating the correlation between the coefficient matrix and the pseudolinear noise vector. To evaluate the performance of the presented algorithms, numerical simulations are conducted, indicating that the superiority of the intersection localization method in computational cost and the superiority of the BCWLS method in localization accuracy.

New use of an Ag electrode and a potentiodynamic method to control the presence of chlorides in porous media like concrete

Chloride detection in porous materials is important for the soil characterisation and the durability control of reinforced concrete structures. Chloride content in soil can affect soil fertility, and the presence of chlorides in reinforced concrete structures can trigger the corrosion of reinforcement, which leads to structure failure.In both cases, the material in which chlorides are detected is a porous medium. Traditionally in this type of materials, chlorides have been controlled by means of tedious laboratory tests, in which specimens of the material are used, as are passive sensor systems like potentiometric sensors. In the former, tests are usually costly and do not allow effective control in real time. In the latter, as the sensor is not excited, overlapping reactions occur on its surface that can lead to errors when estimating chloride content.In aqueous media, chloride detection is efficiently carried out by electrochemical sensors using potentiodynamic techniques. However, they have hardly been employed in porous media, in which the porous network contains the so-called "pore solution". The present work aims to evaluate the feasibility of using an Ag electrode together with a triangular sweep potentiodynamic signal using concretes of different porosities as a porous medium. This opens up a new study field in electrochemical sensor technology.

Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits.

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012-2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha-1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (-25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.