Passive Sensors Research Articles

Passive UHF RFID-based real-time intravenous fluid level sensor.

Ultra high frequency (UHF) passive radio frequency identification (RFID) tag-based sensors are proposed for intravenous (IV) fluid level monitoring in medical Internet of Things (IoT) applications. Two versions of the sensor are proposed: a binary sensor (i.e., full vs. empty state sensing) and a real-time (i.e., continuous level) sensor. The operating principle is demonstrated using full-wave electromagnetic simulation at 910 MHz and validated with experimental results. Generalized Additive Model (GAM) and random forest algorithms are employed for each interrogation dataset. Real-time sensing is accomplished with small deviations across the models. A minimum of 72% and a maximum of 97% of cases are within a 20% error for the GAM model and 62% to 98% for the random forest model. The proposed sensor is battery-free, lightweight, low-cost, and highly reliable. The read range of the proposed sensor is 4.6 m.

Analysis of RFI From MEO Inter-Satellite Links to LEO Passive Sensors

Analysis of RFI From MEO Inter-Satellite Links to LEO Passive Sensors

Misalignment-Tolerant Planar Spiral Coil Pair Design for 13.56 MHz Inductive Coupling of Wireless Resistive Analog Passive Sensors

Long-term daily-life body signal monitoring offers numerous advantages, such as timely response to health alerts, diseases monitoring, and reducing time and expenses related to clinical trials. Access to physiological data can be achieved with low-cost and comfortable wireless wearable sensors. In our previous publication, we reported a low-cost, easy to implement, and unobtrusive wireless resistive analog passive (WRAP) sensor to provide a feasible bio-signal monitoring technique by using a pair of printed spiral coils (PSC) in a near field connection. Sensitivity, defined as the response to the transducer, is a critical feature in the establishment of a reliable system. In the previous publication, we presented the utilization of a Genetic Algorithm to design a pair of coils and related components to maximize sensitivity. Although the coils’ misalignment can significantly affect the optimized sensitivity, it was not incorporated into the optimization process. This paper focuses on optimizing the coils and components in order to maximize both their sensitivity and their resilience against movements of the PSC pair. In a square-shaped pair comprising a primary coil of 60 mm and a secondary coil of 20 mm dimensions, we found that the sensitivity is maximized at 1.3 mƱ for a 16 mm axial distance. Additionally, it remains above 0.65 mƱ within ±11.25 mm lateral and +14 mm axial displacements.

Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis.

Mental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation. This study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care. A systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework. Overall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues. Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered. PROSPERO International Prospective Register of Systematic Reviews CRD42022331698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331698.

Narrow gap welding seam deflection correction study based on passive vision

PurposeThe purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.Design/methodology/approachThis paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.FindingsThe results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.Originality/valueAn adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

Sequential centralized fusion of multiple passive acoustic sensors with unknown propagation delays

In this paper, we address the problem of target tracking using multiple acoustic sensors to observe the target state with unknown propagation delays. This problem occurs because the measurements received by multiple acoustic sensors located at different positions are from different unknown emission times of the acoustic signal even if the sensors receive measurements simultaneously; thus, they cannot be stacked up directly for centralized fusion as usual. However, the target states at different unknown emission times can be aligned to a common measurement received time by the retrodiction of state prediction. On this basis, herein we propose the centralized fusion of multiple acoustic sensors via sequential processing, namely, sequential centralized fusion (SCF). First, the measurement received time is chosen as the target state time, and the target state is predicted to this time for tracking. Second, state prediction is retrodicted to the signal emission times by solving augmented implicit nonlinear equations through Wegstein's method. Third, the state prediction is updated with acoustic measurements sequentially at measurement received time. Compared with the existing distributed fusion methods, our proposed SCF method has smaller computational complexity and better tracking performance. Illustrative examples demonstrate that SCF outperforms covariance intersection and the largest ellipsoid approximation.

Camera-Based Net Avoidance Controls of Underwater Robots.

Fishing nets are dangerous obstacles for an underwater robot whose aim is to reach a goal in unknown underwater environments. This paper proposes how to make the robot reach its goal, while avoiding fishing nets that are detected using the robot's camera sensors. For the detection of underwater nets based on camera measurements of the robot, we can use deep neural networks. Passive camera sensors do not provide the distance information between the robot and a net. Camera sensors only provide the bearing angle of a net, with respect to the robot's camera pose. There may be trailing wires that extend from a net, and the wires can entangle the robot before the robot detects the net. Moreover, light, viewpoint, and sea floor condition can decrease the net detection probability in practice. Therefore, whenever a net is detected by the robot's camera, we make the robot avoid the detected net by moving away from the net abruptly. For moving away from the net, the robot uses the bounding box for the detected net in the camera image. After the robot moves backward for a certain distance, the robot makes a large circular turn to approach the goal, while avoiding the net. A large circular turn is used, since moving close to a net is too dangerous for the robot. As far as we know, our paper is unique in addressing reactive control laws for approaching the goal, while avoiding fishing nets detected using camera sensors. The effectiveness of the proposed net avoidance controls is verified using simulations.

X as a Passive Sensor to Identify Opinion Leaders: A Novel Method for Balancing Visibility and Community Engagement.

The identification of opinion leaders is a matter of great significance for companies and authorities, as these individuals are able to shape the opinions and attitudes of entire societies. In this paper, we consider X (formerly Twitter) as a passive sensor to identify opinion leaders. Given the unreliability of the traditional follower count metric due to the presence of fake accounts and farm bots, our approach combines the measures of visibility and community engagement to identify these influential individuals. Through an experimental evaluation involving approximately 4 million tweets, we showed two important findings: (i) relying solely on follower count or post frequency is inadequate for accurately identifying opinion leaders, (ii) opinion leaders are able to build community and gain visibility around specific themes. The results showed the benefits of using X as a passive sensor to identify opinion leaders, as the proposed method offers substantial advantages for those who are involved in social media communication strategies, including political campaigns, brand monitoring, and policymaking.

Joint energy beamforming and bandwidth allocation for cooperative localization in passive sensor networks

Passive sensor networks (PSNs) harvest continuous radio-frequency (RF) energy to work. However, the location information is difficult to maintain with unstable power supply for each passive sensor node. Cooperative localization is a promising solution utilizes the cooperation between the nodes with unknown positions to maintain the location information. And the localization performance relies on the attained energy from the energy access point (E-AP) and also the allocated bandwidth for each node. In this paper, we analyze the impacts of energy beamforming and bandwidth allocation on the cooperative localization performance of PSNs. We formulate the Fisher information matrix (FIM) and deduce the squared position error bound (SPEB) for the proposed network. Then, we propose an alternative optimization method for jointly energy beamforming and bandwidth allocation. Firstly, we employ the first-order Taylor expansions based on the trust region method to allocate the bandwidth of each node. Secondly, the Lagrangian multiplier based energy beamforming strategy is proposed. Then, the bandwidth allocation and energy beamforming calculations are executed alternatively and iteratively until the global optimal solution is achieved. The simulation results indicate that our proposed two-step method can achieve high precision positioning in different scenarios, and the SPEBs of proposed method reach 10−3 m2.

Detector development for particle physics

In high-energy physics experiments, tracking and vertexing is nowadays mostly done using semiconductor detectors. Among the employed detectors are hybrid pixel sensors, passive sensors and recently also depleted monolithic active pixel sensors (DMAPS), which integrate the particle sensor with front-end electronics. Currently, the dominant material for the production of such sensors is silicon. However, the use of silicon carbide is currently being investigated. In this work we report on our progress on the development of silicon-based DMAPS. Further, we present two approaches for reading out passive silicon carbide detectors at particle rates from the kHz to the GHz range.

An in vitro demonstration of a passive, acoustic metamaterial as a temperature sensor with mK resolution for implantable applications

Wireless medical sensors typically utilize electromagnetic coupling or ultrasound for energy transfer and sensor interrogation. Energy transfer and management is a complex aspect that often limits the applicability of implantable sensor systems. In this work, we report a new passive temperature sensing scheme based on an acoustic metamaterial made of silicon embedded in a polydimethylsiloxane matrix. Compared to other approaches, this concept is implemented without additional electrical components in situ or the need for a customized receiving unit. A standard ultrasonic transducer is used for this demonstration to directly excite and collect the reflected signal. The metamaterial resonates at a frequency close to a typical medical value (5 MHz) and exhibits a high-quality factor. Combining the design features of the metamaterial with the high-temperature sensitivity of the polydimethylsiloxane matrix, we achieve a temperature resolution of 30 mK. This value is below the current standard resolution required in infrared thermometry for monitoring postoperative complications (0.1 K). We fabricated, simulated, in vitro tested, and compared three acoustic sensor designs in the 29–43 °C (~302–316 K) temperature range. With this concept, we demonstrate how our passive metamaterial sensor can open the way toward new zero-power smart medical implant concepts based on acoustic interrogation.

Active and passive sensors for diagnostics quasi-zenith ionospheric HF communication channels

Background. There is a growing need for active sensory diagnostics of partial HF channels to provide frequency support to quasi-zenith HF radio links in varying signal propagation conditions. Enhancing the efficiency of active sensor algorithms, particularly by reducing emission time, is topical. To address this, a transition from sequential to parallel (simultaneous) diagnostics is proposed. Another significant challenge in HF communication is narrowband interference, and overcoming this issue involves the method of passive sensory diagnostics. This method assesses the availability of partial channels by analyzing the spectral density of interference power within them.
 Aim. The goal of this study is to develop algorithms and software tools that implement spectral monitoring and parallel sensing of partial channels for sensory diagnostics of ionospheric channels in quasi-zenith HF communication.
 Methods. The proposed approach involves integrating dynamic diagnostic methods into the development of intelligent sensors for ionospheric HF radio links, along with the creation of data analysis methods. Specialized computer software is employed to address the defined tasks. Experimental studies are conducted using the developed devices, which include intelligent active and passive radio sensors for HF radio links, to assess the load on HF communication channels.
 Results. A sensor for orthogonal quasi-zenith ionospheric radio channels has been created, incorporating algorithms for synthesizing a group pulse with orthogonal subcarriers while minimizing the peak factor. Additionally, algorithms for separating subcarriers and calculating the correlation function at the reception have been developed. The sensor employs the OFDM-BPSK signal modulation method, enabling operation in simultaneous-sequential sounding mode across the potential frequency range for communication. This led to an 8-fold reduction in the total signal emission time.
 Conclusion. The scientific results obtained have broad practical applications, particularly in enhancing the efficiency of wideband HF communication systems using spread spectrum signals.

Into the ice: Exploration and data capturing in glacial moulins by a tethered robot

AbstractGlacial moulins (cylindrical meltwater drainage shafts) provide valuable insights into glacier dynamics, but are inaccessible and hazardous environments for humans to study. Exploring them using passive sensor probes has revealed their complex geometry, which has limited further exploration with passive sensor probes. To overcome these challenges, we propose a tethered robot capable of autonomously exploring and capturing data in glacial moulins. Our novel robot is equipped with a tether to support its motion. Combined with novel estimation and control algorithms, the tethered robot is able to safely and efficiently maneuver in confined, chimney‐like structures, such as moulins. Laboratory and field experiments confirm the feasibility of the proposed design, showing successful localization in environments with no access to positional measurements. Field trials on the Mer de Glace glacier demonstrate the robot's capabilities, descending into the largest moulin to depths of 25 m, capturing valuable scientific data. Onboard sensors are used to capture data to reconstruct the moulin's three‐dimensional geometry and two sampling mechanisms are presented and evaluated to extract samples from the icy surface of the moulin. Our results show promising potential for future exploration of moulins, demonstrating the effectiveness of our tethered robot for safely gathering data from these hazardous environments.

Estimating Forest Variables for Major Commercial Timber Plantations in Northern Spain Using Sentinel-2 and Ancillary Data

In this study, we used Spanish National Forest Inventory (SNFI) data, Sentinel-2 imagery and ancillary data to develop models that estimate forest variables for major commercial timber plantations in northern Spain. We carried out the analysis in two stages. In the first stage, we considered plots with and without sub-meter geolocation, three pre-processing levels for the Sentinel-2 images and two machine learning algorithms. In most cases, geometrically, radiometrically, atmospherically and topographically (L2A-ATC) corrected images and the random forest algorithm provided the best results, with topographic correction producing a greater gain in model accuracy as the average slope of the plots increased. Our results did not show any clear impact of the geolocation accuracy of SNFI plots on results, suggesting that the usual geolocation accuracy of SNFI plots is adequate for developing forest models with data obtained from passive sensors. In the second stage, we used all plots together with L2A-ATC-corrected images to select five different groups of predictor variables in a cumulative process to determine the influence of each group of variables in the final RF model predictions. Yield variables produced the best fits, with R2 ranging from 0.39 to 0.46 (RMSE% ranged from 44.6% to 61.9%). Although the Sentinel-2-based estimates obtained in this research are less precise than those previously obtained with Airborne Laser Scanning (ALS) data for the same species and region, they are unbiased (Bias% was always below 1%). Therefore, accurate estimates for one hectare are expected, as they are obtained by averaging the values of 100 pixels (model resolution of 10 m pixel−1) with an expected error compensation. Moreover, the use of these models will overcome the temporal resolution problem associated with the previous ALS-based models and will enable annual updates of forest timber resource estimates to be obtained.

Indium tin oxide thin film preparation and property relationship for humidity sensing: A review

AbstractThis review aims to present a critical overview of indium tin oxide (ITO) thin film preparation methods, structure–property relationship, and its humidity sensing. A range of passive and active humidity sensors with thin films (based on metal oxides) detects humidity. ITO thin film has advantageous properties, such as low resistivity and high stability, making it highly suitable for humidity sensing applications. ITO thin film has shown the efficient level of humidity sensing, and a compatible size of humidity sensor can monitor the interface conditions humidity. So far, the application of ITO thin film for humidity measurement has yet to be explored at commercial scale, specifically in the detection of lower environmental humidity range (below 5% relative humidity (RH)). The research reveals a gap in improving the ITO thin film properties with an optimal range of preparation conditions. The research opportunities in the preparation, properties, characteristics, and efficient humidity sensitivity of ITO thin film are reviewed in this work.

Sensing behavior change in chronic pain: a scoping review of sensor technology for use in daily life.

Technology offers possibilities for quantification of behaviors and physiological changes of relevance to chronic pain, using wearable sensors and devices suitable for data collection in daily life contexts. We conducted a scoping review of wearable and passive sensor technologies that sample data of psychological interest in chronic pain, including in social situations. Sixty articles met our criteria from the 2783 citations retrieved from searching. Three-quarters of recruited people were with chronic pain, mostly musculoskeletal, and the remainder with acute or episodic pain; those with chronic pain had a mean age of 43 (few studies sampled adolescents or children) and 60% were women. Thirty-seven studies were performed in laboratory or clinical settings and the remainder in daily life settings. Most used only 1 type of technology, with 76 sensor types overall. The commonest was accelerometry (mainly used in daily life contexts), followed by motion capture (mainly in laboratory settings), with a smaller number collecting autonomic activity, vocal signals, or brain activity. Subjective self-report provided "ground truth" for pain, mood, and other variables, but often at a different timescale from the automatically collected data, and many studies reported weak relationships between technological data and relevant psychological constructs, for instance, between fear of movement and muscle activity. There was relatively little discussion of practical issues: frequency of sampling, missing data for human or technological reasons, and the users' experience, particularly when users did not receive data in any form. We conclude the review with some suggestions for content and process of future studies in this field.

Integration of Vehicle Dynamic Model and System Identification Model for Extending the Navigation Service Under Sensor Failures

Accurate and reliable localization is of great importance for autonomous vehicles (AV). Mainstream localization approaches in autonomous vehicles (AV) are limited by the reliability of onboard sensors, which could be vulnerable to sensor failure, such as signal outages of the camera and signal spoofing of the global navigation satellite systems (GNSS). Different from these active or passive sensors, the vehicle dynamic model (VDM), which is the application of physical laws to a vehicle in motion, is environmentally independent and is capable of providing vehicle motion estimation continuously. However, the performance of the VDM-based motion estimation is dominated by the accuracy of the system dynamics model. To tackle this issue, this study proposes a sensor-free localization method VDM-SI by integrating system identification into the design of vehicle dynamic models (VDM). A system identification process based on low-order process models is proposed to identify the system dynamics of the AV, where the identified system responses are taken as the control input of VDM to estimate the vehicular positioning. The localization experiments in two scenarios show that the mean absolute translation error of VDM-SI can be reduced by 70% compared to conventional VDM methods. In addition, VDM-SI is experimentally proven to improve the localization performance of sensor fusion-based localization systems with high noise levels. Furthermore, in the application of re-localization after sensors fail and recover, VDM-SI shows strength in enhancing the security of AVs in extreme conditions.

Joint Target and Transmitter Localization With AOA and DTD Using Passive Sensors

Joint Target and Transmitter Localization With AOA and DTD Using Passive Sensors

From Anatolian Plateau to American Plains: A transcontinental assessment of the EUMETSAT H SAF’s new generation snow water equivalent product over Türkiye and the conterminous U.S.

The main frame of this paper is to present the first validation results of the new generation hemi-spherical daily snow water equivalent (SWE) product of the EUMETSAT H SAF, namely, H65. It utilizes data from passive microwave radiometry sensors to estimate SWE. Operating at a suitable spatial scale, it offers insights into snow accumulation and melting dynamics, advancing satellite-based snow monitoring across diverse regions. The validation period covers the 2021 snow year, from January to March 2021, during which the dry snow conditions prevail. The validation is conducted in two distinct geographic regions, Türkiye, and the conterminous U.S. For Türkiye, the in-situ snow depth measurements provided by the Turkish State Meteorological Service are employed. On the other hand, the 1-km gridded SWE dataset of NOAA National Ice and Snow Data Center is used in the validation over the U.S. The validation results over Türkiye yields an overall RMSE of 39.27 mm, whereas it reads 15.19 mm for the U.S. These results indicate that the H65 SWE product complies with the product requirement thresholds for both flat (40 mm) and mountainous (45 mm) areas.

Laser-Guided, Self-Confined Graphitization for High-Conductivity Embedded Electronics.

Facile fabrication of highly conductive and self-encapsulated graphene electronics is in urgent demand for carbon-based integrated circuits, field effect transistors, optoelectronic devices, and flexible sensors. The current fabrication of these electronic devices is mainly based on layer-by-layer techniques (separate circuit preparation and encapsulation procedures), which show multistep fabrication procedures, complicated renovation/repair procedures, and poor electrical property due to graphene oxidation and exfoliation. Here, we propose a laser-guided interfacial writing (LaserIW) technique based on self-confined, nickel-catalyzed graphitization to directly fabricate highly conductive, embedded graphene electronics inside multilayer structures. The doped nickel is used to induce chain carbonization, which firstly enhances the photothermal effect to increase the confined temperature for initial carbonization, and the generated carbon further increases the light-absorption capacity to fabricate high-quality graphene. Meanwhile, the nickel atoms contribute to the accelerated connection of carbon atoms. This interfacial carbonization inherently avoids the exfoliation and oxidation of the as-formed graphene, resulting in an 8-fold improvement in electrical conductivity (~20,000 S/m at 7,958 W/cm2 and 2 mm/s for 20% nickel content). The LaserIW technique shows excellent stability and reproducibility, with ±2.5% variations in the same batch and ±2% variations in different batches. Component-level wireless light sensors and flexible strain sensors exhibit excellent sensitivity (665 kHz/(W/cm2) for passive wireless light sensors) and self-encapsulation (<1% variations in terms of waterproof, antifriction, and antithermal shock). Additionally, the LaserIW technique allows for one-step renovation of in-service electronics and nondestructive repair of damaged circuits without the need to disassemble encapsulation layers. This technique reverses the layer-by-layer processing mode and provides a powerful manufacturing tool for the fabrication, modification, and repair of multilayer, multifunctional embedded electronics, especially demonstrating the immense potential for in-space manufacturing.