Passive Sensors Research Articles

Fish Injury from Movements across Hydraulic Structures: A Review

Fish migration is essential to maintain healthy aquatic ecosystems, but hydraulic structures across rivers have impeded natural fish migration worldwide. While efforts have been made to allow fish to pass some hydraulic structures, there is limited understanding of hydrodynamic effects that cause fish injury in different hydraulic systems, such as spillways and stilling basins as well as hydropower systems. This study reviewed available literature on this topic to identify the current knowledge of fish injury thresholds in laboratory- and field-based studies of hydraulic systems. Often, the hydraulic effects that lead to fish injury have been described with time-averaged simplified parameters including shear stress, pressure changes, acceleration, vortical motions, aeration, collision, and strike, while these hydrodynamic effects often occur simultaneously in the turbulent flows across hydraulic structures, making it difficult to link specific fish injuries to a particular hydrodynamic effect. Strong variations of injury may occur, depending on the type and the intensity of hydrodynamic effects, as well as the fish species and fish sizes. Modelling can provide information of stressors, but real-world tests are needed to accurately assess fish injury and mortality. Fish injury mechanisms at hydropower turbines are well understood, however, clear understanding at other sites is lacking. Future studies should aim to report holistic hydrodynamic thresholds with associated fish injury rates. Multidisciplinary systematic research is required, including laboratory and field studies, using passive tracer sensor packages and state-of-the art instrumentation in conjunction with live fish. This can quantify stressors with meaningful parameters, aiming to improve fish safety with more sustainable design of water infrastructure that reduces fish injury when passing across hydraulic structures.

ABS-PLA-Al composite for digital twinning of heritage buildings

In the past two decades, significant studies have been reported on the use of 3D-printed thermal sensors for online health monitoring of heritage buildings. But hitherto little has been reported on the 4D capability of such thermal sensors for the digital twinning of heritage buildings. In this study, 80% acrylonitrile butadiene styrene (ABS) and 20% polylactic acid (PLA) matrix (by wt %), was reinforced with 5% Al powder (≈45-50 µm) based on melt flow index (MFI). The substrate (as a passive sensor) of ABS-PLA-5 %Al was 3D printed with fused filament fabrication (FFF) for the construction of a micro-strip patch antenna (MPA) as an internet of things (IoT) based solution for online health monitoring and digital twinning of heritage buildings. The dielectric properties (dielectric constant (ɛr) and loss tangent (tanδ)) were explored by using a ring resonator test to ascertain the dimensions and design of the passive sensor. For 4D capabilities, the prepared sensor was exposed to two stimuli (temperature and moisture) for exploring its use as a digital twinning component.

Delineating Polynya Area Using Active and Passive Microwave Sensors for the Western Ross Sea Sector of Antarctica

A polynya is an area of open water or reduced concentration of sea ice surrounded by either concentrated sea ice or land ice. They are often seen as sites of intense ocean–atmosphere heat exchange and as ice production factories. Given their importance, it is crucial to quantify the accuracy of satellite-derived polynya information. Polynyas in their early evolution phase are generally narrow and occur at scales likely too fine to be detected by widely used passive microwave (PMW) radiometric sensors. We derived 40 m scale polynya information over the western Ross Sea from high-resolution Synthetic Aperture Radar (SAR) Sentinel-1 C-band data and examined discrepancies with larger-scale estimates. We utilized two automated algorithms, supervised (a rule-based approach) and unsupervised (a combination of texture analysis with k-means clustering), to accurately identify the polynya areas. We generated data for validation using Sentinel-1 data at instances where polynyas can be visually delineated. Results from PMW sensors (NSIDC and AMSR2) and SAR-based algorithms (rule-based and texture-based) are compared with manually delineated polynya areas obtained through Sentinel-1. Analysis using PMW sensors revealed that NSIDC overestimates larger polynyas and underestimates smaller polynyas compared to AMSR2. We were more accurately able to identify polynya presence and area using Sentinel-1 SAR observations, especially in clear cases and cases when PMW data miscalculates the polynya’s presence. Of our SAR-based algorithms, the rule-based approach was more accurate than the texture-based approach at identifying clear polynyas when validated against manually delineated regions. Altogether, we emphasize the need for finer spatio-temporal resolution data for polynya studies.

Design, fabrication, and hypervelocity impact testing of screen-printed flexible micrometeoroid and orbital debris impact sensors for long-duration spacecraft health monitoring

Micrometeoroid and orbital debris (MMOD) are a key risk for spacecraft damage that could compromise missions. Detecting and evaluating MMOD damage is therefore a crucial component in the health monitoring of spacecraft, especially for long duration, deep space expeditions. In this work, we developed a passive sensor system fabricated from conductive metal ink screen-printed on flexible Kapton, using roll-to-roll manufacturing suitable for low-cost fabrication of large areas of sensors, as a MMOD sensor for a spacecraft shield. The sensor is integrated into a low density, two-wall Whipple shield comprising of thin aluminum sheets sandwiching a polyimide foam. The shield with the sensors were tested with hypervelocity impacts at approximately 7 km/s using different particle diameters. Data collected from the sensors were successfully used to determine the impact size, impact location, and predict the impact energy of the damage.

Wireless Human Motion Detection with a Highly Sensitive Wearable Pressure Sensing Technology

AbstractWireless flexible sensing devices using inductive‐capacitive (LC) resonator‐enabled transmission technologies are attractive in the areas of human motion detection, health inspection, and implantable medical devices. However, challenges remain in LC‐based wireless sensing devices, including low device sensitivity/resolution and slow signal readout speed based on sweeping excitation frequencies. Herein, an LC resonator‐based wireless pressure sensing technology (LC‐WPS) is proposed for flexible contact pressure measurements in a real‐time manner. It includes a fully flexible passive LC pressure sensor and a signal‐reading circuit for wireless communication with the sensor and real‐time data processing. The passive sensor detects external pressure by the changes in resonant frequency. Importantly, by optimizing the design of the LC resonator in the sensor, the LC‐WPS converts the pressure‐dependent resonant frequency into circuit output voltage to a great extent, achieving a high sensitivity (1.23 × 10−2 kPa−1) and resolution (54 Pa) within a pressure range of 0–15 kPa. In addition, the LC‐WPS utilizes a single operating frequency (i.e., 13.56 MHz) instead of sweeping the operating frequencies to identify the sensor resonant frequency, featuring fast and real‐time data analysis and processing. Real‐time monitoring of human motions, such as laryngeal movement, neck bending and wrist rotation, is demonstrated, verifying the applicability of the LC‐WPS in the fields of wearable medical care and sports biomechanics.

Integrated sensor for humidity, temperature, light, and carbon dioxide based on passive RFID

In recent years, radio frequency identification (RFID) technology has been widely used in smart agriculture. In order to measure the multi-dimensional environmental information during the planting of agricultural products, this paper proposes an integrated humid temperature phosgene (HTLC) sensor based on passive RFID. The structure is optimized by the HFSS software and the multi-dimensional integrated sensor is prepared by engraving. The passive RFID HTLC multidimensional sensor operates stably at 10–50 %RH, 5–25 ℃, 700–1900 lux and 800–1600 ppm. The frequency shift is 80 MHz at 10–50 %RH and the sensitivity is 2 MHz/%RH. In the temperature range of 5–25 ℃, the amplitude change is 12.58 dB and the sensitivity is 0.63 dB/℃. For 700–1900 lux light, the amplitude variation is 10.03 dB and the sensitivity is 0.02 dB/Lux at room temperature. For CO2 concentrations of 800–1600 ppm, the sensor has an amplitude change of 9.43 dB and a sensitivity of 0.012 dB/ppm. The sensor has the advantages of low cost, fast response, good repeatability and good stability, and can collect and display the parameter information of the crop growth environment in multiple dimensions, making the monitoring and management more accurate and intelligent, and helping to adjust the growth environment of the crop in time, thereby improving the yield of the crop.

Using home monitoring technology to study the effects of traumatic brain injury on older multimorbid adults: protocol for a feasibility study

IntroductionThe prevalence of traumatic brain injury (TBI) among older adults is increasing exponentially. The sequelae can be severe in older adults and interact with age-related conditions such as multimorbidity. Despite...

Jointly Prediction of Activities, Locations, and Starting Times for Isolated Elderly People.

Restrictive public health measures such as isolation and quarantine have been used to reduce the pandemic viruss transmission. With no proper treatment, older adults have been specifically advised to stay home, given their vulnerability to COVID-19. This pandemic has created an increasing need for new and innovative assistive technologies capable of easing the lives of people with special needs. Smart home systems have become widely popular in providing such assistive services to isolated older adults. These systems can provide better services to assist older people if it anticipates what activities inhabitants will perform ahead of time. For example, a smart home can prompt inhabitants to initiate essential activities like taking medicine using activity prediction. This paper proposes a multi- task activity prediction system that jointly predicts labels, lo- cations, and starting times of future activities. The observed sequence of previous activities characterizes future activities. We use body activity information from wearable sensors and motion information from passive environmental sensors to sense activities of daily living of older adults. The activity prediction system consists of recurrent neural networks to capture temporal dependencies. This work also carries out several experiments on collected and existing real datasets to evaluate the systems performance.

Promoting Occupancy Detection Accuracy Using On-Device Lifelong Learning

Our recently developed Synchronized Low Energy Electronically chopped Passive Infra-Red (SLEEPIR) sensor node enables the stationary occupancy detection capability of traditional Passive Infra-Red sensors. A Machine Learning (ML) algorithm reports occupancy based on a locally collected dataset from the sensor node. Though promising, the ML algorithm’s detection accuracy depends on the diversity of the collected dataset- provided the dataset contains a wide variety of infrared noise and occupancy patterns. Thus, it is challenging to train a universal ML model that contains all possible patterns. We propose an efficient K-Nearest Neighbor (KNN) occupancy classifier that incrementally adapts to the novel data from the sensor. The proposed algorithm ensures that only the relevant noise and occupancy patterns are learned. The fact that training observations are gathered on the same sensor node where the inference is made, keeps the proposed classifier accurate even with the bounded size of the dataset. A small dataset and an architecture like KNN, both enable the training and inference to be executed on a resource-constrained Internet of Things device. Thus, the proposed On-Device Lifelong Learning approach eliminates the need for over-the-cloud ML model updates. The dataset was collected for two distinct floorplans over two months. Results indicate an average occupancy accuracy improvement of 20.8% compared to a statically trained Long Short-Term Memory (LSTM) model. The proposed KNN model delivers comparable detection accuracy while remaining orders of magnitude faster in terms of computational performance when compared to the LSTM-based occupancy detection algorithm.

Hybrid Frame-Based Current Control Scheme for LC-Equipped PMSM With Non-Sinusoidal Back-EMF

Permanent-magnet synchronous machine (PMSM) drive with a sine wave <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> filter has the features of low insulation stress and current harmonics. However, few studies consider the impact of back-electromotive force (EMF) harmonics on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> -equipped PMSM drive system. The non-sinusoidal back-EMF can cause current harmonics, meanwhile, it also affects the stability analysis and high-frequency resonance suppression. In order to solve these two problems simultaneously, a hybrid frame-based current control scheme is proposed in this paper. The quantitative effect of back-EMF harmonics on control behaviors is analyzed first. A virtual triple three-phase system with auxiliary currents is developed to decompose the fundamental and harmonic contents, which not only contributes to independent current harmonic regulation in multiple synchronous reference frames, but also simplifies system stability analysis considering non-sinusoidal back-EMF. Then, an active damping method based on stator current feedback is implemented in the stationary frame to suppress the resonance, and no additional passive components or sensors are required. Finally, experimental results are provided to verify the effectiveness and superiority of the proposed method.

Wireless and Passive SAW-Based Humidity Sensor Employing SiO2 Thin Film

A high-sensitive wireless and passive surface acoustic wave (SAW) humidity sensor operating at 433 MHz was designed and developed. SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> thin-film with large specific surface was employed as the humidity sensitive interface and deposited along the sensing area of the SAW device patterned by reflective delay line by using plasma enhanced chemical vapor deposition (PECVD) to build the sensing chip. Coupling of modes (COM) model was utilized to simulate the SAW device prior to device fabrication to achieve low time-domain insertion and high signal-to-noise ratio (SNR). The transceiver based on frequency-stepped continuous wave (FSCW) and planar dipole antennas were developed to construct successfully the wireless measurement system. In wireless humidity sensing experiments, high humidity sensitivity of 11.8 °/% RH in measurement range of 45%-90% RH was achieved at the wireless distance of 0.5 m. The corresponding response and recovery time were evaluated as 60 s and 90 s respectively. Also, excellent repeatability and stability in humidity sensing were obtained.

WiSensor: Passive Sensor Data Transmission by Way of Ambient Wi-Fi Channels

WiSensor: Passive Sensor Data Transmission by Way of Ambient Wi-Fi Channels

Advanced Alerting System by Motion Detection

Abstract: Human technological advances have marked the emergence of alarm systems. The man began shouting a message to inform others of terrible information. Then people adopted new techniques to develop alarm systems to replace them. Security has always been a great concern in this developing nation. Security always plays a vital role in safeguarding and protecting property. In this internet age there is a new paradigm of technology that is clearly assisting the major concerns of security, i.e., 'Internet of things' and we thereby come with an Espressif Systems based microcontroller board alert system. The paper offers a camera module and microcontroller-based development platform that acts as a sophisticated alert indicator system embedded with passive infra-red sensor to detect the intruder motion. The designed prototype is configured with required functionality and obtained accurate results.

A Review on Remote Sensing as a Tool for Irrigation Monitoring and Management

Remote sensing has played a vital role in advancement of agriculture and is effective technical method for agriculture crop management. It is a technology which acquisite information regarding objects on earth surface as well as atmosphere from a distance without being in contact with the object. Researchers have proved its high potential with accuracy in the field of agriculture. After various experiments, the qualitative and quantitative assessment of soil, crop and atmosphere demonstrated the better understanding between the crop and its management practices. The collected spatial and temporal data via various passive and active sensors has been utilized not only for morphological study but also for monitoring the vegetation moisture content. The paper reviews about the potential studies carried out to investigate the water content in plant to make use in irrigation management. Diverse spectral reflectance indices have been mentioned from which special emphasis on NDWI has been given. It is an index which is used in remote sensing to assess the crop water status and can be utilized in efficient operation of irrigation to improve water use efficiency (WUE) in agriculture. In order to make irrigation practices more efficient by making the lab restricted irrigation scheduling methods like IW:CPE method applicable in regular practice by using remote sensing. This paper firstly identifies areas where researches and techniques have real-world application. Next, it identifies actual issues that remote sensing could address and solve with further research and its related development. All opportunities for managing agricultural water resources effectively to be explored and made successful through precision agriculture. Using the fast, impartial and reliable information offered by remote sensing is a significant difficulty in the field of water resource management.

Flexible, ultra-wideband acoustic device for ultrasound energy harvesting and passive wireless sensing

As a promising means of energy transmission and communication, acoustic devices with flexibility and wideband are always urgently required. Here, we developed a flexible, ultra-wideband triboelectric ultrasonic device (FUTUD). Benefiting from the low mechanical quality factor of an ultra-thin PI substrate and interlocking nanostructures (of the order of micro-strain or nano-strain), the bandwidth of the FUTUD reached up to 10 MHz, which is currently unprecedented in the study of triboelectric acoustic devices. Based on the wideband properties of the FUTUD, we proposed two application strategies. For low-frequency ultrasound (20–200 kHz), the FUTUD acted as an ultrasound energy harvester that converted acoustic energy into electrical energy to power implantable electronic devices in vivo. It could generate a voltage of 1.2 V and a current of 150 μA through porcine tissue of 10 mm thickness and charge a capacitor at a rate of 8.4 μC/s up to 3.2 V. For high-frequency ultrasound (0.2–10 MHz), the FUTUD was integrated with a flexible electromagnetic coil to transmit electrical signals containing physiological information from in vivo to ex vivo wirelessly as a passive wireless sensor. This research shows great promise for the development of intelligent and miniaturized implantable medical devices.

Laboratory investigation of hydraulic fracturing in granitic rocks using active and passive seismic monitoring

SUMMARY Knowledge of the fracturing processes can be important for the optimization of pressurized fluid injection operations in the deep underground rock mass. Active and passive seismic monitoring techniques have been used in the field for tracking or mapping the propagating hydraulic fracture. Although both these monitoring techniques provide valuable information about the generated fracture network, it is difficult for either technique to comprehensibly identify the different processes associated with hydraulic fracturing. The combined active and passive monitoring has the potential for better characterization of the complex hydraulic fracturing phenomena. In this study, laboratory hydraulic fracturing experiments with combined active and passive seismic monitoring were conducted on true triaxially loaded Barre granite cubes with different fluid injection rates. The seismic inelastic fracturing was detected by 16 passive acoustic emission sensors, where 3678 and 2370 seismic source events were detected for the high and low injection rate experiments, respectively. For active monitoring, strong variations in the attributes of signals were observed which were transmitted through four source–receiver pairs, placed both perpendicular and parallel to the generated hydraulic fracture. Positive velocity changes were observed for active sensor pairs with ray paths passing through the generated hydraulic fracture indicating fluid permeation, whereas isolated dry deformation was characterized by a slight but permanent velocity decrease. Compared to velocity, the energy of the active signals was 1–2 orders of magnitude more sensitive to different hydraulic fracturing processes. However, the sensitivity and signatures of the active signal attributes were found to be dependent on the frequency range and direction of ray path with respect to the location of the generated fracture network. Using the coupled evaluation of the active and passive signals we were able to systematically identify various hydraulic fracturing processes including: (1) aseismic deformation, (2) fracture initiation and fluid permeation, (3) pressure build-up, (4) fracture propagation and (5) pressure release and leak-off. The results of this study showed that combining the respective advantages of active and passive seismic techniques and using both of them to monitor the failure processes can facilitate a more comprehensive understanding and better control of the hydraulic stimulations in subsurface operations.

Passive IoT Optical Fiber Sensor Network for Water Level Monitoring with Signal Processing of Feature Extraction

This paper presents a real-time remote water level monitoring system based on dense wavelength division multiplexing (DWDM)-passive optical fiber sensor (OFS) network for the application of the Internet of Things (IoT). This network employs a broadband light source based on amplified spontaneous emission (ASE) as a seed light. This ASE light is spectrum-sliced by an athermal type arrayed waveguide grating (200 GHz × 16 channel), then distributed towards multiple sensing units (SU). Here, 16 SUs are installed vertically at the specified height in the water pool according to the design specification (i.e., spatial resolution). Then, each SU reflects an optical spectrum having a different reflection coefficient depending on the surrounding medium (e.g., air or water). By measuring these reflected optical spectra with an optical spectrum analyzer, the water level can be easily recognized in real time. However, as the sensing distance increases, system performance is severely degraded due to the Rayleigh Back-Scattering of the ASE light. As a result, the remote sensing capability is limited at a short distance (i.e., &lt;10 km). To overcome this limitation, we propose a simple signal processing technique based on feature extraction of received optical spectra, which includes embedding a peak detection algorithm with a signal validation check. For the specific, the proposed signal processing performs the peak power detection, signal quality monitoring, and determination/display of the actual water level through three function modules, i.e., data save/load module, signal processing module, and Human–Machine Interface display module. In particular, the signal quality of the remote sensing network can be easily monitored through several factors, such as the number of spectral peaks, the wavelength spacing between neighboring peaks and the pattern of detected peak power. Moreover, by using this validation check algorithm, it is also possible to diagnose various error types (such as peak detection error, loss of data and so on) according to the pattern of measured optical spectra. As a result, the IoT sensor network can recognize 17 different level statuses for the water level measurement from a distance of about 25 km away without active devices such as optical amplifiers (i.e., passive remote sensing).

A Bulk Acoustic Wave Strain Sensor for Near-Field Passive Wireless Sensing.

Near-field passive wireless sensors can realize non-contact strain measurement, so these sensors have extensive applications in structural health monitoring. However, these sensors suffer from low stability and short wireless sensing distance. This paper presents a bulk acoustic wave (BAW) passive wireless strain sensor, which consists of two coils and a BAW sensor. The force-sensitive element is a quartz wafer with a high quality factor, which is embedded into the sensor housing, so the sensor can convert the strain of the measured surface into the shift of resonant frequency. A double-mass-spring-damper model is developed to analyze the interaction between the quartz and the sensor housing. A lumped parameter model is established to investigate the influence of the contact force on the sensor signal. Experiments show that a prototype BAW passive wireless sensor has a sensitivity of 4 Hz/με when the wireless sensing distance is 10 cm. The resonant frequency of the sensor is almost independent of the coupling coefficient, which indicates that the sensor can reduce the measurement error caused by misalignment or relative movement between coils. Thanks to the high stability and modest sensing distance, this sensor may be compatible with a UAV-based monitoring platform for the strain monitoring of large buildings.

Real-Time Monitoring of Tire Condition with Fast Detection Passive and Wireless TPMS

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Accurate tire pressure monitoring system (TPMS) is of great practical importance and the reliability and safety of its power supply module has great concern. The piezoelectric-based surface acoustic wave (SAW) sensor is considered to have great potential in this field because of its passive, wireless and small size advantages. This paper presents the application of passive and wireless SAW sensors for real-time tire condition monitoring. The pressure sensitive structure is optimized and a three-resonator structure is also designed sensing temperature and pressure. Furthermore, a fast detection system is developed to realize high-speed signal acquisition. At last, experiments are executed and the SAW temperature and pressure sensor property is measured. The results show that the designed SAW sensor can realize real-time monitoring of tire condition; the temperature measurement range can reach -40~120°C with an accuracy of ±1°C; the pressure measurement range can reach 0~2MPa with an accuracy of 0.2MPa. This paper indicates the wireless and passive SAW temperature and pressure sensor system has the potential for application in real-time monitoring of tire condition.&lt;/div&gt;&lt;/div&gt;

Multi-UAV Collaborative Absolute Vision Positioning and Navigation: A Survey and Discussion

The employment of unmanned aerial vehicles (UAVs) has greatly facilitated the lives of humans. Due to the mass manufacturing of consumer unmanned aerial vehicles and the support of related scientific research, it can now be used in lighting shows, jungle search-and-rescues, topographical mapping, disaster monitoring, and sports event broadcasting, among many other disciplines. Some applications have stricter requirements for the autonomous positioning capability of UAV clusters, requiring its positioning precision to be within the cognitive range of a human or machine. Global Navigation Satellite System (GNSS) is currently the only method that can be applied directly and consistently to UAV positioning. Even with dependable GNSS, large-scale clustering of drones might fail, resulting in drone cluster bombardment. As a type of passive sensor, the visual sensor has a compact size, a low cost, a wealth of information, strong positional autonomy and reliability, and high positioning accuracy. This automated navigation technology is ideal for drone swarms. The application of vision sensors in the collaborative task of multiple UAVs can effectively avoid navigation interruption or precision deficiency caused by factors such as field-of-view obstruction or flight height limitation of a single UAV sensor and achieve large-area group positioning and navigation in complex environments. This paper examines collaborative visual positioning among multiple UAVs (UAV autonomous positioning and navigation, distributed collaborative measurement fusion under cluster dynamic topology, and group navigation based on active behavior control and distributed fusion of multi-source dynamic sensing information). Current research constraints are compared and appraised, and the most pressing issues to be addressed in the future are anticipated and researched. Through analysis and discussion, it has been concluded that the integrated employment of the aforementioned methodologies aids in enhancing the cooperative positioning and navigation capabilities of multiple UAVs during GNSS denial.