Sensor Operation Research Articles

Directional Resonant MEMS Acoustic Sensor and Associated Acoustic Vector Sensor.

This paper reports on the design, modeling, analysis, and evaluation of a micro-electromechanical systems acoustic sensor and the novel design of an acoustic vector sensor array (AVS) which utilized this acoustic sensor. This research builds upon previous work conducted to develop a small, lightweight, portable system for the detection and location of quiet or distant acoustic sources of interest. This study also reports on the underwater operation of this sensor and AVS. Studies were conducted in the lab and in the field utilizing multiple acoustic sources (e.g., generated tones, gun shots, drones). The sensor operates at resonance, providing for high acoustic sensitivity and a high signal-to-noise ratio (SNR). The sensor demonstrated a maximum SNR of 88 dB with an associated sensitivity of -84.6 dB re 1 V/μPa (59 V/Pa). The sensor design can be adjusted to set a specified resonant frequency to align with a known acoustic signature of interest. The AVS demonstrated an unambiguous, 360-degree, in-plane, azimuthal coverage and was able to provide an acoustic direction of arrival to an average error of within 3.5° during field experiments. The results of this research demonstrate the potential usefulness of this sensor and AVS design for specific applications.

Исследование амплитудной и фазовой ошибок датчика-опоры при ультразвуковой сварке пластмасс

The paper presents a technique making it possible to evaluate amplitude and phase errors of the magnetoelastic support sensor used in the plastics ultrasonic welding. Measurement of the support sensor vibration amplitude allows monitoring alterations in the basic physical properties of the polymer being welded, i.e. its temperature and thickness. The support sensor operation dynamic mode is described. It is established that evaluating measurement errors with the proposed method in manufacture of any support sensor of different design makes it possible to accurately determine its performance with parameters of the polymer material ultrasonic welding process. The problem of evaluating accuracy of the magnetoelastic support sensor operation used in the welding process was considered. The support sensor amplitude and phase errors during ultrasonic welding were studied. A magnetoelastic sensor was designed and developed ensuring improved accuracy in measuring the support vibrations amplitude during the polymer material ultrasonic welding. The experiments carried out confirmed clear relationship between the support vibrations amplitude and heating of the welded samples, their thickness and properties during the ultrasonic welding.

Investigation of the influence of external factors on the operation of a fiber-optic dielectric temperature sensor

The article presents the design and test results of a fiber-optic dielectric temperature sensor intended for use under the influence of external electromagnetic field and aggressive environment.

Analysis of the Response Signals of an Electronic Nose Sensor for Differentiation between Fusarium Species.

Fusarium is a genus of fungi found throughout the world. It includes many pathogenic species that produce toxins of agricultural importance. These fungi are also found in buildings and the toxins they spread can be harmful to humans. Distinguishing Fusarium species can be important for selecting effective preventive measures against their spread. A low-cost electronic nose applying six commercially available TGS-series gas sensors from Figaro Inc. was used in our research. Different modes of operation of the electronic nose were applied and compared, namely, gas adsorption and desorption, as well as modulation of the sensor's heating voltage. Classification models using the random forest technique were applied to differentiate between measured sample categories of four species: F. avenaceum, F. culmorum, F. greaminarum, and F. oxysporum. In our research, it was found that the mode of operation with modulation of the heating voltage had the advantage of collecting data from which features can be extracted, leading to the training of machine learning classification models with better performance compared to cases where the sensor's response to the change in composition of the measured gas was exploited. The optimization of the data collection time was investigated and led to the conclusion that the response of the sensor at the beginning of the heating voltage modulation provides the most useful information. For sensor operation in the mode of gas desorption/absorption (i.e., modulation of the gas composition), the optimal time of data collection was found to be longer.

Understanding the Role of Sensor Optimisation in Complex Systems.

Complex systems involve monitoring, assessing, and predicting the health of various systems within an integrated vehicle health management (IVHM) system or a larger system. Health management applications rely on sensors that generate useful information about the health condition of the assets; thus, optimising the sensor network quality while considering specific constraints is the first step in assessing the condition of assets. The optimisation problem in sensor networks involves considering trade-offs between different performance metrics. This review paper provides a comprehensive guideline for practitioners in the field of sensor optimisation for complex systems. It introduces versatile multi-perspective cost functions for different aspects of sensor optimisation, including selection, placement, data processing and operation. A taxonomy and concept map of the field are defined as valuable navigation tools in this vast field. Optimisation techniques and quantification approaches of the cost functions are discussed, emphasising their adaptability to tailor to specific application requirements. As a pioneering contribution, all the relevant literature is gathered and classified here to further improve the understanding of optimal sensor networks from an information-gain perspective.

Optimization of the Geometrical Design for an All-Dielectric Metasurface Sensor with a High Refractive-Index Response

This study aims to develop a refractive-index sensor operating in the visible region using an all-dielectric metasurface, which was chosen for its advantages of low optical loss and narrow spectral bandwidth, compared to those of conventional metallic metasurfaces. COMSOL software was utilized as a calculation tool to simulate the resonant properties of an all-dielectric metasurface composed of a circular nanohole-structured titanium oxide (TiO2) thin film, with the aim of enhancing the sensitivity of the refractive index for sensing targets. The simulation focused on finding the best geometrical conditions for the all-dielectric metasurface to achieve high sensitivity. Two resonance modes observed in this metasurface were considered: the quasi-bound-state-in-the-continuum (qBIC) mode and the perfect-reflection (PR) mode. The simulated results demonstrated that high sensitivities of 257 nm/RIU at the PR mode and 94 nm/RIU at the qBIC mode in the visible spectral range could be obtained by periodically constructing the metasurface with a unit cell having a lattice constant of 350 nm, a nanohole radius of 160 nm, and a nanohole depth of 250 nm. Furthermore, the study showed that the resonance mode that enabled high sensitivity was the PR mode, with a sensitivity nearly three times larger than that of the qBIC mode and the ability to reach the highest reflectance at the resonance wavelength. The optimized feature had the highest reflectance at a resonant wavelength of 570.19 nm, and although the quality factor was 25.50, these designed parameters were considered sufficient for developing a refractive index biosensor with high sensitivity and optical efficiency when operating in the visible spectral range.

Fast multi-feature tracking method based on tightly coupled sensors

In contrast to conventional methods of optimizing feature-matching results at the data-processing level, we utilize the tightly coupled mode to fuse the information collected by the camera and inertial sensor at the data-gathering level, which contributed to better accuracy and robustness in the feature-matching process. Specifically, the proposed visual-inertial feature tracking method that combines inertial measurement unit (IMU) calibration, feature matching, and prediction algorithms. The approach includes a vision-aided multi-level IMU systemic calibration method and an inertial-aided image feature prediction algorithm, which effectively processes and utilizes information from multiple sensors. Our method addresses not only the influence of image distortion and blur resulting from illumination changes and fast camera motion but also the problem of measurement errors that can arise from the long-term operation of the inertial sensors. Extensive experiments have been conducted to demonstrate that the efficiency of it is superior to the state-of-the-art methods: With the accuracy rate remains the same level, the speed of feature matching is improved by 41.8%. Additionally, when it is applied to simultaneous localization and mapping systems, its localization performances are better than that of the VINS-mono method by 8.1%.

High-precision 4D tracking with large pixels using thin resistive silicon detectors

The basic principle of operation of silicon sensors with resistive read-out is built-in charge sharing. Resistive Silicon Detectors (RSD, also known as AC-LGAD), exploiting the signals seen on the electrodes surrounding the impact point, achieve excellent space and time resolutions even with very large pixels. In this paper, a TCT system using a 1064 nm picosecond laser is used to characterize RSD sensors produced by Fondazione Bruno Kessler. The paper first introduces the parametrization of the errors in the determination of the position and time coordinates in RSD, then outlines the reconstruction method, and finally presents the results. Three different pixel pitches are used in the analysis: 200 × 340, 450 × 450, and 1300 × 1300 μm2. At gain = 30, the 450 × 450 μm2 pixel achieves a time jitter of 20 ps and a spatial resolution of 15 μm concurrently, while the 1300 × 1300 μm2 pixel achieves 30 ps and 30 μm, respectively. The implementation of cross-shaped electrodes improves considerably the response uniformity over the pixel surface.

A Self-Sustained CPS Design for Reliable Wildfire Monitoring

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. The proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.

Attosecond-Level Delay Sensing via Temporal Quantum Erasing.

Traditional Hong-Ou-Mandel (HOM) interferometry, insensitive to photons phase mismatch, proved to be a rugged single-photon interferometric technique. By introducing a post-beam splitter polarization-dependent delay, it is possible to recover phase-sensitive fringes, obtaining a temporal quantum eraser that maintains the ruggedness of the original HOM with enhanced sensitivity. This setup shows promising applications in biological sensing and optical metrology, where high sensitivity requirements are coupled with the necessity to keep light intensity as low as possible to avoid power-induced degradation. In this paper, we developed a highly sensitive single photon birefringence-induced delay sensor operating in the telecom range (1550 nm). By using a temporal quantum eraser based on common path Hongr-Ou-Mandel Interferometry, we were able to achieve a sensitivity of 4 as for an integration time of 2·104 s.

Grease-lubricated triboelectric instantaneous angular speed sensor integrated with signal processing circuit for bearing fault diagnosis

The triboelectric sensor can be integrated with the rolling bearing to diagnose bearing faults by monitoring the periodic fluctuation of instantaneous angular speed, yet continuous dry friction between materials will reduce its signal-to-noise ratio (SNR). Moreover, the signal processing modules used by previous triboelectric sensors need additional wires to transmit the signal, and thus affect the reliability of the system. In this manuscript, a wireless grease-lubricated triboelectric instantaneous angular speed sensor (GL-TEIASS) integrated with the signal processing circuit for bearing fault diagnosis is proposed based on rotary freestanding triboelectric nanogenerators (TENGs). First, electrical insulating grease is presented as an ideal candidate lubricant used in the GL-TEIASS. Next, by establishing the charge density-dependent electromechanical model of grease-lubricated TENG, optimal structural parameters for maximum signal-to-noise ratio and angular resolution as well as minimum friction torque of the GL-TEIASS can be obtained. Moreover, the kurtosis-guided local polynomial fitting method is employed to extract the signal fluctuation caused by bearing faults. The results show that, compared to dry friction, the mass loss of the polymer under grease lubrication decreases by 91.7 % and the electrical output increases by 4.0 times. The durability of the GL-TEIASS in fault diagnosis is improved by at least 2.8 times compared with that of the sensor operating under dry friction. The error between the angular speed measured by the GL-TEIASS and a commercial sensor is less than 0.731 %, and the SNR of the output signal from in-situ monitoring by the GL-TEIASS can reach 39.1 dB, which is 4.9 times higher than that of vibration signal commonly used in fault diagnosis. The designed GL-TEIASS has good application prospects in self-sensing and self-diagnosis of operation state for smart bearings.

Protected and Energy-Efficient E-Healthcare System Based on Internet of Healthcare Services

The Internet of Things (IoT) is gaining a tons of attention in numerous industries due to its low-cost autonomous sensor operations. IoT devices in healthcare and medical activities establish an environment that recognizes the patients’ health status such as stress levels, oxygen supply, pulse and warmth, and responds quickly in the event of an emergency. Moreover, various systems founded on low-powered biosensor nodules have been proposed to monitor patients’ medical conditions utilizing Wireless Body Area Network (WBAN); despite the fact that controlling increasing power usage and communication expenses is time-consuming and attention-demanding. Another difficult research problem is data privacy and integrity in the presence of malicious traffic. Therefore, to overcome the above-stated limitations, this research introduces a Safe and the Energy-Efficient Framework for e-Healthcare using Internet of Medical Things (IoMT), whose main goal is to reduce transmission cost but also power usage among biomaterials while sending health records conveniently and, on the other hand, to protect patients’ medical data from unverified and malevolent base stations to increase internet confidentiality and protection.

Analysis of thermal-offset drift of a high-resolution current probe using a planar Hall resistance sensor

Abstract. We developed a pin-type current probe with high sensitivity, targeting electrical-probing printed circuit boards (PCBs). The developed sensor showed good enough characteristics, with 1 mA resolution on current measurements and up to 1 MHz operating frequency for analyzing highly integrated PCBs. During its characterization, however, we experienced a monotonously varying output signal in the time range of a few tens of minutes. We modeled it as the thermal-offset drift, being caused by Joule heating during sensor operation, and showed several solutions for reducing the offset by modifying the planar Hall resistance (PHR) layout and electric operation conditions and applying sensor circuitry with pulse width modulation.

Detection of SARS‐CoV‐2 Virus Concentration Using a Simple Structure of Binary Photonic Crystal Sensor

The scientific community works hard on developing reliable, rapid, highly sensitive, and low‐cost techniques to detect severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2). The current techniques can occasionally produce false‐positive results. The development of novel approaches for the identification of SARS‐CoV‐2 is a significant area of research because of the limitations of current methods. A biosensor based on a defective binary photonic crystal (PCL) is proposed to detect SARS‐CoV‐2 concentration herein. The model of pathogen concentration‐dependent refractive index of the analyte is taken into consideration. The concept of operation of the PCL sensor is based on the defect mode shift when any change in the refractive index of the defect layer occurs. The transmission spectra of the PCL are plotted and studied. An optimum incident angle of 80° and a defect layer thickness of 5 μm are found. The relationship between the defect mode's wavelength and the concentration of SARS‐CoV‐2 is investigated and a sensitivity of 2648.3 nm RIU−1 is attained.

Satellite radio detection via dual-microwave Rydberg spectroscopy

Rydberg electric field sensors exploit the large number of Rydberg resonances to provide sensitivity over a broad range of the electromagnetic spectrum. However, due to the difficulty of accessing resonant Rydberg states at ultra-high frequency (UHF) and below, ubiquitous bands in the world's current wireless communications infrastructure, they currently fall, short in sensitivity in this range. We present a resonant Rydberg electric field sensor operating in the UHF band using a dual-optical dual-microwave spectroscopy scheme. Adding an additional microwave photon allows us to access transitions between Rydberg states with higher angular momentum (L=3→4), which have lower resonant frequencies than transitions typically used in Rydberg sensors. We discuss the applicability of this type of sensor across the UHF band and below and measure the resonant sensitivity of our system at 2.3 GHz to be 70(5) μV m−1 Hz−1∕2, 57 times better than the measured sensitivity with a far off-resonant probing scheme at this frequency. We also show the effectiveness of this sensing scheme by measuring Sirius XM satellite radio (2.320–2.345 GHz) received outside the laboratory and rebroadcast onto the atoms.

ZnO single nanowire gas sensor: A platform to investigate the sensitization of Pt

Platinum (Pt) has been extensively used as a catalyst in metal oxide semiconductor (MOS) gas sensors. However, a clear understanding on the size effect of Pt sensitization is still missing. Herein, we establish a ZnO single nanowire sensor as a platform to investigate the sensitization of Pt catalyst. Pt has been loaded on ZnO nanowires by both atomic layer deposition (ALD) and solution method to realize delicate control over the size and loading mount. With increasing ALD cycles, the Pt size increases from single atom to an average of 11.86 nm and the sensor resistance shows a decrease. The nanowire sensor with a medium Pt loading (0.61 at%) with a size of 3.95 nm shows the best response of 7.01 to 20 ppm NO2, revealing a 5-fold enhancement compared to pristine ZnO. The sensor operating temperature shows a remarkable decrease from 310 to 220 °C due to Pt sensitization. A control sensor with almost identical Pt loading of 0.59 at% and size of 4.98 nm prepared by solution impregnation displays a response of 5.04 to 20 ppm NO2, but showing much slower response and recovery. Our findings determinate the crucial effect of Pt size in chemical sensors.

A Comprehensive Methodology for Optimizing Read-Out Timing and Reference DAC Offset in High Frame Rate Image Sensing Systems.

This paper presents a comprehensive timing optimization methodology for power-efficient high-resolution image sensors with column-parallel single-slope analog-to-digital converters (ADCs). The aim of the method is to optimize the read-out timing for each period in the image sensor's operation, while considering various factors such as ADC decision time, slew rate, and settling time. By adjusting the ramp reference offset and optimizing the amplifier bandwidth of the comparator, the proposed methodology minimizes the power consumption of the amplifier array, which is one of the most power-hungry circuits in the system, while maintaining a small color linearity error and ensuring optimal performance. To demonstrate the effectiveness of the proposed method, a power-efficient 108 MP 3-D stacked CMOS image sensor with a 10-bit column-parallel single-slope ADC array was implemented and verified. The image sensor achieved a random noise of 1.4 e-rms, a column fixed-pattern noise of 66 ppm at an analog gain of 16, and a remarkable figure-of-merit (FoM) of 0.71 e-·nJ. The sensor utilized a one-row read-out time of 6.9 µs, an amplifier bandwidth of 1.1 MHz, and a reference digital-to-analog converter (DAC) offset of 512 LSB. This timing optimization methodology enhances energy efficiency in high-resolution image sensors, enabling higher frame rates and improved system performance. It could be adapted for various imaging applications requiring optimized performance and reduced power consumption, making it a valuable tool for designers aiming to achieve optimal performance in power-sensitive applications.

A hierarchical classifying and two-step training strategy for detection and diagnosis of anormal temperature in district heating system

Anormal temperature data caused by various reasons such as sensor faults and operation faults, which has a negative influence on heat metering and operation regulation in district heating system (DHS). However, it is difficult to detect and diagnose anormal temperature among massive unlabeled operation data. Therefore, this paper proposes a novel hierarchical classifying and two-step training strategy to facilitate the anormal temperature detection and diagnosis task. Firstly, self-defined feature change rate of operation data like water temperature, flow rate, and valve opening are constructed as additional training features to capture the characteristics of anormal temperature conditions. Then, a hierarchical classifying method is proposed to detect anormal temperature data. Finally, a two-step training strategy which combines expert knowledge with support vector machine (SVM) to fulfill anormal temperature type diagnosis. The proposed strategy is applied to a typical DHS in cold region of China. A total of 10,920 anormal data are detected. Four anormal temperature conditions are diagnosed including offline sensor, inversely connected sensor, anormal operation of heat source, and shutdown of heat station. The diagnosis accuracy for the 4 kinds of anormal temperature conditions all reached over 98%.

Magnetic Field Sensor Operating From Cryogenics to Elevated Temperatures

Magnetic Field Sensor Operating From Cryogenics to Elevated Temperatures

Optimization of electromagnetic-triboelectric wind energy harvester based on coaxial reversed mechanism with tip discharge

In recent years, the development of triboelectric-electromagnetic composite energy harvesters has revolutionized low-power urban devices by providing a cost-effective and high-output energy solution. However, most triboelectric nanogenerators (TENGs) have limited current output in the μA range, restricting their widespread application. Additionally, existing electromagnetic structures lack compactness and portability. To address these challenges, this paper proposes a compact triboelectric-electromagnetic composite wind energy harvester based on a coaxial reversing mechanism and tip discharge. It achieves gas ionization and increases the TENG's output current by utilizing TENG charge accumulation during rotation through a sidewall tip-copper structure. Compared to conventional TENGs, this harvester elevates the current from the μA level to the mA level. Moreover, it improves the output performance of the small volume electromagnetic generator (EMG) structure through the synchronous reversal of the magnet and coil at the same wind speed using two coaxial reversing propellers, intensifying the magnetic flux variation. Notably, the EMG structure outperforms a single rotating coil or magnet, achieving a remarkable 12% increase in output performance. At average urban wind speeds, this wind energy harvester can charge capacitors of 33 μF, 100 μF, and 220 μF to 2.5 V in 1.6 s, 2 s, and 3.6 s. Furthermore, it can power over 500 small LEDs and support the operation of temperature and humidity sensors. With its efficient wind energy harvesting technology, the proposed harvester holds great promise for self-powered Internet of Things (IoT) applications.