Large Sensor Research Articles

Low-Temperature Coefficient and Large Range Pressure Sensor Based on Single End Fixed-Support U-Beam Resonator

Low-Temperature Coefficient and Large Range Pressure Sensor Based on Single End Fixed-Support U-Beam Resonator

A column bus self-acceleration circuit for large array CMOS image sensor

This paper presents a column bus self-acceleration circuit based on dynamically regulating bias current, which aims to reduce the settling time of pixel output signal caused by the parasitic effect of the column bus. Through the mathematical model of the column bus, the relationship between the settling time and the bias current is analyzed. The proposed circuit is designed by a 110 nm CMOS process. Under the condition with a parasitic capacitance of 10.61 pF and a parasitic resistance of 6.3 kΩ, simulation results show that the proposed self-acceleration circuit can reduce the settling time of pixel output signal from 13.8μs to 4.1μs. Compared with the traditional column bus structure, the settling time of the pixel output signal is reduced by 71 %. The self-acceleration circuit can effectively improve the readout speed of large array CMOS image sensor.

A high consistency ramp circuit design method for negative feedback adaptive adjustment mechanism applied to large area array CMOS image sensors

A high consistency ramp circuit design method for negative feedback adaptive adjustment mechanism applied to large area array CMOS image sensors

Flexible Magnetoelectric Fiber for Self-Powered Human-Machine Interactive.

Flexible large strain sensors are an ideal choice for monitoring human motion, but the current use of flexible strain gauges is hindered by the need for external power sources and long-term operation requirements. Fiber-based sensors, due to their high flexibility, excellent breathability, and the ease with which they can be embedded into everyday clothing, have the potential to become a novel type of wearable electronic device. This paper proposes a flexible self-powered strain sensing material based on the electromagnetic induction effect, composed of a uniform mixture of Ecoflex and Nd2Fe14B, which has good skin-friendliness and high stretchability of over 100%. The voltage output of the magnetoelectric composite fiber remains stable over 5000 stretch-release cycles, reaching up to 969 μV. Based on this novel sensing material, a remote smart car control scheme for a human-machine interaction system was designed, enabling real-time gesture interaction.

Characterization of newly developed large area SiC sensors for the NUMEN experiment

First prototypes of large area, p–n junction, silicon carbide (SiC) detectors have been produced as part of an ongoing programme to develop a new particle identification wall for the focal plane detector of the MAGNEX magnetic spectrometer, in preparation for future NUMEN experimental campaigns. First characterizations of sensors from two wafers obtained with epitaxial silicon carbide growth and with different doping concentration are presented. Current (I–V) and capacitance (C–V) characteristics are investigated in order to determine the full depletion voltage and the doping profile. Radioactive α-sources are used to measure the energy resolution and estimate the depletion depth.

Cluster-guided denoising graph auto-encoder for enhanced traffic data imputation and fault detection

In an era of increasing digitalization, the integration of diverse sensors has become commonplace. Within the transportation sector, state Departments of Transportation have extensively deployed traffic sensors to support various engineering endeavors. However, these sensors are prone to faults such as data loss, random noise, bias, and drift due to sensor aging, defects, or environmental influences. Therefore, detecting these faults is imperative to maintain data integrity. This paper presents a novel approach for detecting faults in traffic data based on a cluster-guided data reconstruction process. First, a traffic sensor clustering module is designed using a dual-encoding attention graph auto-encoder (DA-GAE) to identify clusters of traffic sensors. This module leverages a joint embedding of node and edge features in a low-dimensional vector space. Subsequently, utilizing the identified clusters, a cluster-guided denoising graph auto-encoder (CG-DGAE) is devised and trained for data reconstruction. The CG-DGAE employs a diffusion graph convolutional network (DGCN) and is trained with a cluster-wise sampling strategy. Extensive experiments were conducted using traffic data obtained from a real-world large sensor network, demonstrating superior performance of the CG-DGAE model in data reconstruction compared to various baseline methods. For fault detection, a score function is devised to discern potential faults by contrasting the sensor data sequence and the reconstructed data sequence. The fault detection results show that our proposed CG-DGAE model achieved an impressive overall accuracy of 99.09%, a precision of 99.13%, a recall of 99.53%, and a F1 score of 99.53%. The superior capability of CG-DGAE in data reconstruction and fault detection is attributable to the cluster-wise spatial–temporal context leveraged by the model.

In situ assembly enabling adhesive-free bonding of large area electronic sensors to concrete for structural health monitoring

Abstract Cracks developed in concrete infrastructure are one of the primary mechanisms that degrade their structural integrity, which may result in structural failures. Previous research on soft elastomeric capacitors (SEC) has shown their viability for structural health monitoring of structural materials, including concrete, steel, and fiberglass composites. The SEC, or its derivative version with a corrugated geometry termed corrugated SEC or cSEC, is a parallel plate capacitor. Prior work demonstrated that it was possible to directly paint the electrode interfacing with the structural material onto the structure and adhere the rest of the pre-fabricated sensor onto the wet interface, thereby eliminating the need for a joining epoxy. This demonstration was conducted on steel and fiberglass. The study on concrete was left to future work as concrete exhibits a much rougher surface and structure/sensor capacitive coupling, causing a significant amplification in signal noise. This study advances structural health monitoring in concrete applications by investigating the in situ assembly of the SEC on concrete where the carbon black (CB)-based electrode plate of the cSEC is directly painted onto the concrete surface and serves as both the adhesive and electrode for the sensor. A series of free-vibration and compression tests were designed and conducted to evaluate the sensing performance of in situ assembled cSEC compared to that of an epoxy-bonded cSEC. Additionally, the bonding strength of the in situ assembled and epoxy-bonded cSEC is evaluated through a peel test. Results show good strain sensing capabilities of the in situ assembled SEC with an R 2 value of 0.986 and a resolution of 45 ± μ ε . Even though the epoxied cSEC demonstrated a higher bonding strength than the in situ assembled cSEC, the in situ assembled cSEC demonstrated adequate bonding strength for the application. This research contributes to the scientific understanding of sensor adhesion and opens avenues for practical applications in infrastructure monitoring, potentially leading to more resilient and sustainable urban environments.

Performance Analysis and Optimal Resource Allocation for Large Scale Joint Sensing and Communication

Performance Analysis and Optimal Resource Allocation for Large Scale Joint Sensing and Communication

Soft Thermoplastic Polyurethane/Silver Nanowire Membranes with Low Hysteresis for Large Strain Sensing and Joule Heating

Soft Thermoplastic Polyurethane/Silver Nanowire Membranes with Low Hysteresis for Large Strain Sensing and Joule Heating

A wireless multiparameter cryogenic monitoring method using passive backscatter sensors

In this paper, we proposed the first wireless multiparameter monitoring method for cryogenic environments (−196 ℃). Present-day cryogenic industries implement a large array of wired sensors to monitor key parameters in fabrication processes. The wirings for a large sensor array greatly complicate the monitoring system. We presented the first wireless multiparameter monitoring method for cryogenic environments by making two efforts. First, a novel wireless passive vibration and pressure sensor is developed. We transfer vibrations and pressures to a relative displacement between a magnet and a tunnel magnetoresistor (TMR). This affects the magnetic field at the TMR and changes the TMR’s resistance. By integrating the TMR on a backscattering antenna, the antenna’s wireless return loss is modulated by both vibrations and pressures. We proposed a decoupling method for simultaneously monitoring vibration and pressure by a single sensor. Second, we demonstrated and evaluated the first wireless multiparameter cryogenic monitoring system. A wireless passive temperature sensor is developed by integrating a resistance temperature detector (RTD) on a backscattering antenna. The developed vibration and pressure sensor and temperature sensor are tuned to separate frequency bands, embedded in cryogenic environments, and calibrated in situ. Vibrations, pressures, and temperatures in cryogenic environments are synchronously obtained by our wireless cryogenic monitoring system, with an accuracy of 80∼90 %. This research provides a wireless option in large-scale automated cryogenic monitoring, thus it is desirable in implementing the Internet of Things in cryogenic industries.

Star point positioning for large dynamic star sensors in near space based on capsule network

In order to solve the problem that the star point positioning accuracy of the star sensor in near space is decreased due to atmospheric background stray light and rapid maneuvering of platform, this paper proposes a star point positioning algorithm based on the capsule network whose input and output are both vectors. First, a PCTL (Probability-Coordinate Transformation Layer) is designed to represent the mapping relationship between the probability output of the capsule network and the star point sub-pixel coordinates. Then, Coordconv Layer is introduced to implement explicit encoding of space information and the probability is used as the centroid weight to achieve the conversion between probability and star point sub-pixel coordinates, which improves the network’s ability to perceive star point positions. Finally, based on the dynamic imaging principle of star sensors and the characteristics of near-space environment, a star map dataset for algorithm training and testing is constructed. The simulation results show that the proposed algorithm reduces the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) of the star point positioning by 36.1% and 41.7% respectively compared with the traditional algorithm. The research results can provide important theory and technical support for the scheme design, index demonstration, test and evaluation of large dynamic star sensors in near space.

Developing a robust sensor for infrared imaging bolometers.

A new type of large area sensor for infrared imaging bolometers has been developed. It replaces the thin and fragile free-standing metal foils, which typically have been used, with a multi-layer coated sapphire (or diamond) substrate. Sapphire is transparent to mid-infrared wavelengths, is robust against transients, and can be thick enough to even be the vacuum window. The primary radiation absorber is still a thin deposited metal layer, but now it is partially insulated from the supporting sapphire substrate by a black (carbon-based) layer, which also acts as a blackbody remitter. Test results indicate 6× more noise equivalent power density (estimated NEPD = 23 W/m2 at 5ms camera exposure time, foil temperature decay time 60ms) for a 2μm gold-coated sapphire disk compared to estimated NEP = 4 W/m2 at 1.8ms exposure time, with foil decay time 420ms, for a nominal 2.5μm thick platinum-free-standing foil.

Generating interpretable rainfall-runoff models automatically from data

A sudden surge of data has created new challenges in water management, spanning quality control, assimilation, and analysis. Few approaches are available to integrate growing volumes of data into interpretable results. Process-based hydrologic models have not been designed to consume large amounts of data. Alternatively, new machine learning tools can automate data analysis and forecasting, but their lack of interpretability and reliance on very large data sets limits the discovery of insights and may impact trust. To address this gap, we present a new approach, which seeks to strike a middle ground between process-, and data-based modeling. The contribution of this work is an automated and scalable methodology that discovers differential equations and latent state estimations within hydrologic systems using only rainfall and runoff measurements. We show how this enables automated tools to learn interpretable models of 6 to 18 parameters solely from measurements. We apply this approach to nearly 400 stream gaging sites across the US, showing how complex catchment dynamics can be reconstructed solely from rainfall and runoff measurements. We also show how the approach discovers surrogate models that can replicate the dynamics of a much more complex process-based model, but at a fraction of the computational complexity. We discuss how the resulting representation of watershed dynamics provides insight and computational efficiency to enable automated predictions across large sensor networks.

Experimental qualification and modelling of the non-linear response of the DEPFET active pixel sensor for the DSSC camera

A modified Depleted P-Channel Field Effect Transistor (DEPFET) is the key feature of the DEPFET Sensor with Signal Compression (DSSC), a 1 Mpixel X-ray camera aiming at ultra-fast imaging of soft X-rays at the European XFEL. Operation of large-area devices with a large number of DSSC-type DEPFET pixels requires the accurate knowledge of the sensitivity of the response to the relevant DEPFET parameters. The paper presents the experimental qualification of the response of DSSC-type DEPFET pixels in the space of 4 relevant parameters: drain current, source voltage, drain voltage and back side voltage. The obtained results allow estimation of the impact of the inevitable parameter fluctuations in large monolithic sensors and of the actual trimming range of the shape of signal compression in view of the pixelwise calibration of the 1 Mpixel DSSC camera.

Study of Large Strain OFDR Sensor Based on Inclined Transfer Structure

In the fields of structural health monitoring, civil engineering safety monitoring, aerospace and other fields, the key challenge is to increase the range of strain measurements to solve with the information monitoring of large strains. Reducing the strain sensitivity of the sensor is an important way to solve the problem of small sensor ranges. In this paper, the inclined transfer structure is proposed to achieve large strain distributed optical fiber sensing. When the fiber is axially stretched directly to fracture, the inclined transfer structure is used to increase the strain measurement range of the system. The theoretical relationship between the strain monitored by single mode fiber sensors and the matrix strain is established. It is experimentally verified that the inclined transfer structure using polypropylene material as the matrix achieves the strain measurement from 36667[Formula: see text][Formula: see text] at sensing spatial resolution of 8[Formula: see text]mm, 10[Formula: see text]nm wavelength tuning range of the laser source and [Formula: see text] inclined angle, obtaining a good linear fit of more than 0.99. One hour stability of less than 11[Formula: see text]pm is obtained, corresponding to the strain of 9[Formula: see text][Formula: see text].

Secure Routing Protocol for Low-Energy Medium and Large Wireless Sensor Networks

Secure Routing Protocol for Low-Energy Medium and Large Wireless Sensor Networks

A pragmatic data processing system for large resistive sensor arrays.

Large resistive sensor arrays (RSAs) show great potential in tactile perception. However, the large number of sensors can result in great hardware overhead and bring difficulties for acquiring and processing mass data timely in transient measurement applications. This paper implements a field programmable gate array (FPGA)-based data processing system for a large RSA of 96 × 96, which shows good power consumption and high-speed wireless data update. For crosstalk-free measure, the zero potential method is improved with bus switches, leading to fewer operational amplifiers required and less negative power consumption. A real-time embedded data processing system is realized by FPGA for excellent parallel processing ability. A high-speed wireless transfer scheme with automatic regulated transfer size is proposed and realized by a wireless fidelity module, which allows timely data analysis at the remote end. Moreover, fault identification of RSAs fabricated by micro-electromechanical system technology is achieved. Tests carried out on a 32 × 32 RSA show that the total power consumption is 2209 mW, including 1261 mW of processors and 948 mW of readout circuits, corresponding to 2.15 mW/pixel. The total negative power consumption of 549 mW has been reduced by 50% compared with the zero potential method. The scanning speed is 400fps, and the wireless transfer speed is up to 120fps when the transceiver and receiver are 5m apart.

Large measurement range curvature sensor based on all-fiber Mach-Zehnder interferometer using triple-ring-core few-mode fiber.

This study experimentally demonstrates a large measurement range curvature sensor based on a Mach-Zehnder interferometer (MZI) in a triple-ring-core fiber (TRCF). The sensor is fabricated by fusion splicing a segment of TRCF between two pieces of single-mode fiber (SMF), forming the SMF-TRCF-SMF sandwich structure. Since the TRCF can support the propagation of a few guided modes, the fundamental mode interferes with high-order modes in the sensing part to produce a periodic interference spectrum. Bending causes the effective refractive index of the mode to change, which shifts the interference spectrum. The bending measurement is achieved by monitoring the wavelength variation of interference dips. The proposed sensors can realize curvature measurements up to 100 m-1, with a bending sensitivity of -225.9 pm/m-1. It is believed that the proposed MZI sensor may have potential applications in robot hand gesture recognition and control.

An observational analysis to evaluate the influence of occlusion on body posture and plantar pressure - An in vivo study.

The aim of this study is to evaluate the influence of occlusion on body posture and plantar arch pressure. An Observational analysis to Analyze the Influence of Occlusion on Plantar Pressure and Body Posture. A total of 30 asymptomatic subjects were selected for the study including 18 females and 12 males from age group 22 years to 28 years with a mean age of 24.83 years. Each subject underwent evaluation of occlusion at MIP using a Digital Occlusal analyzer (T-Scan III). At this point, the subjects were made to stand on a mat scan which consisted of a large postural platform sensor and a computer that displayed the plantar pressure data. The computer connected to the T Scan displayed the occlusal pressure analysis. This was followed by an evaluation of body posture using a posture grid where the photographs were taken and an evaluation of the frontal and lateral photos was done using the APECS - posture analysis app. Statistical package for social sciences (SPSS) for windows version 22.0 Released 2013, Armonk, N Y: IBM Corp., was used to perform Statistical Analysis. A chi-square test was applied for qualitative variables to find the association. Paired t-test was applied to compare the changes in the quantitative parameters in eye-open and eye-closed conditions. The level of significance was set at 5%. As observed from the results, occlusion for 40% of the subjects, where n = 12, was dominant on the right side. Occlusion for 23.3% of subjects, where n = 7, was dominant on the left side. Whereas, for 36.7% of subjects, where n = 11, the pressure distribution was almost equal on both sides. The inclination of body posture for 23.3% of subjects, where n = 7, was towards the right side. The inclination of body posture for 50% of subjects, where n = 11, was towards the left side. And, the inclination of body posture for 26.7% of subjects, where n = 8, was neutral i.e., balanced on the right and left side. Plantar pressure for 6.7% of subjects, where n = 2, was dominant on the right side. Plantar pressure for 36.7% of subjects, where n = 11, was dominant on the left side. Whereas, for 56.7% of subjects, where n = 17, the plantar pressure distribution was almost equal on both sides. On correlating the three parameters, it was found that occlusion for most of the subjects dominated on the right side, while body posture and plantar pressure dominated on the contralateral i.e., left side.

Spectroflat: A generic spectrum and flat-field calibration library for spectro-polarimetric data

Context. Flat-fielding spectro-polarimetric data with one spatial and one spectral dimension is inherently difficult as the imprint of the spectral lines needs to be separated from other wavelength-dependent instrumental effect (e.g., fringes or prefilter profiles) and wavelength-independent effects (e.g., dust and sensor response). Current approaches for spectrometers are often based on moving the grating or they depend on optical models and/or on lab calibration data. They are also limited to small spectral regions and are instrument-specific. Approaches that would be suitable for polarimeters have not been reported yet.Aims. We present an approach that allows for flat-field calibration data to be to obtained for diffraction-grating-based, long-slit spec-trographs combined with temporally modulated polarimetry from high-resolution solar telescopes. This approach is based on nominal flat-fielding procedures performed during the instrument’s science operations.Methods. We performed a precise and field-dependent correction of the spectrographic distortion effect (resulting in curved spectral lines, typically denoted as a “smile” effect) to ensure the orthogonality of the spectral and spatial dimensions. We identified distortions by tracking the position of multiple spectral lines within the full spectral field of view. From the raw modulated flats, we then removed the solar line imprints and derived separate flat-fields for sensor and slit dust features. Optionally, wavelength calibration and continuum correction can be included in this process.Results. We have created generic Python libraries that can be plugged into existing Python-based data reduction pipelines or used as a standalone calibration tool. We show that for spectrographs covering many spectral lines, a correction of the smile distortion based on optical models alone is not sufficient. Our results demonstrate a suppression of fringes, sensor artifacts, and fixed-pattern imprints in demodulated data by one order of magnitude. For intensity images, the photon noise level can be closely attained after calibration. Our correction works across the full spectral range. The algorithm was tested for different wavelength regimes with emission (EUV range) or absorption (near-UV, VIS, IR range) spectra, on data acquired with ground-based (SST/TRIPPLE-SP, GREGOR/GRIS), balloon-borne (SUNRISE-III/SUSI), and space-based (SolO/SPICE) instruments. The data calibrated with our method offer robust and precise inversion results.Conclusions. We have extended existing spectroscopic flat-field techniques to modern instruments with large imaging sensors covering many spectral lines simultaneously, and with polarimetric capabilities, where methods described so far are not adequate. We believe that our method is applicable as a standard calibration approach for most modern high resolution large-FOV, long-slit spectrographs – both with and without polarimetric capabilities.