Sensor Measurements Research Articles

A unified sensor and actuator fault diagnosis in digital twins for remote operations

This paper explores the development of a unified hybrid approach for sensor and actuator fault diagnosis in digital twins for remote operations. Central to this approach is the implementation of a robust adaptive Kalman filter algorithm, which forms the backbone of the proposed unified algorithm. The essence of this unified algorithm lies in its capability to effectively filter the sensor measurements. The algorithm is enriched with tuning parameters, offering flexibility in adjusting the convergence rate to suit operational requirements. Noteworthy for its robustness, our approach excels in handling uncertainties and diverse types of faults, including drift, bias, noise, and freeze fault scenarios. Through comprehensive simulation and experimental evaluations conducted on a small surface vessel, the method demonstrates remarkable proficiency in accurately identifying sensor and actuator faults. This precision enables early detection and prompt mitigation of anomalies, contributing to heightened operational resilience.

Flexible Arc-Shaped Micro-Fiber Bragg Grating Array Three-Dimensional Tactile Sensor for Fingertip Signals Detection and Human Pulse Monitoring.

A flexible arc-shaped micro-Fiber Bragg Grating (mFBG) array three-dimensional tactile sensor for fingertip signal detection and human pulse monitoring is presented. It is based on a three mFBGs array which is embedded in an arc-shaped poly (dimethylsiloxane) (PDMS) elastomer, which can effectively discriminate the normal force, left force, and right force by monitoring the reflected intensity variation of the three mFBGs. Different from the traditional FBG sensors, this sensor measures force by detecting changes in light intensity, effectively avoiding the wavelength cross-sensitivity impact of temperature variations on the sensor performance. This design strategy simplifies the sensor structure, reduces the system complexity and signal interrogation cost, and enhances reliability and practicality. Through systematic experiments, we successfully validated the sensor's superior performance, achieving a minimum detection force of 0.01 N and providing robust data support for practical applications. In addition, the sensor has been used to monitor human pulse accurately. The successful fabrication and experimental validation of this sensor lay a foundation for its widespread application in fields such as robot perception and human vital signal detection.

Frequency shift of a PVDF surface acoustic wave sensor on a curved surface

Wearable sensors have generated a significant attention across various research domains, including the monitoring of human health, pressure sensing, and body health monitoring. Notably, substantial research has been focused on the utilization of piezoelectric sensors for precise pressure measurements in diverse applications, such as medical devices and structural health monitoring. This paper explains the external pressure measurement employing sensors crafted from Polyvinylidene Fluoride (PVDF), known for its remarkable ability to conform consistently to various surface shapes and curvatures. The primary objective of this study is to present an integrated experimental and numerical approach to quantifying the frequency shift of piezoelectric PVDF surface acoustic wave (SAW) sensors when deployed on curved surfaces, a crucial step in optimizing their performance for real-world applications. We aim to explain how changes in surface geometry impact frequency shifts concerning external pressure and movement. Our findings reveal a linear relationship between frequency shifts and geometric variations in a certain range, as supported by experimental data. Furthermore, it is observed that PVDF samples can be used to successfully measure the internal pressure of a canister. The consistency between experimental and numerical results underscores the validity and reliability of our approach. In summary, this paper contributes to our understanding of piezoelectric PVDF SAW sensor behavior when placed on curved surfaces. Our novel methodology combines experimental measurements and numerical simulations to quantify the impact of geometric changes on frequency shifts, providing valuable insights for future sensor applications.

Monitoring Soil Temperature and Humidity in an IoT-Based Green House to Improve Plant Management

This study aims to develop and validate a soil temperature and humidity monitoring system using DHT 11 and YL 69 sensors connected to an ESP 8266 microcontroller. Calibration was performed by comparing sensor data with a reference instrument, followed by regression analysis to determine sensor accuracy. Validation involved calculating the standard deviation and accuracy of the sensor measurements. Temperature and soil humidity monitoring was conducted over five days in September, with average temperature measurements ranging from 22.4°C to 32.6°C and soil humidity ranging from 66.47% to 88.78%. The results showed that the temperature sensor had an average accuracy of 99.19%, while the humidity sensor had an average accuracy of 97.95%. The low standard deviation values for both sensors indicate good measurement consistency. These findings align with the literature emphasizing the importance of high-performance sensors for environmental monitoring in smart agriculture systems. The implementation of this system is expected to enhance the efficiency and effectiveness of agricultural management and support the sustainability of agricultural practices.

Hybrid Twins Modeling of a High-Level Radioactive Waste Cell Demonstrator for Long-Term Temperature Monitoring and Forecasting.

Monitoring a deep geological repository for radioactive waste during the operational phases relies on a combination of fit-for-purpose numerical simulations and online sensor measurements, both producing complementary massive data, which can then be compared to predict reliable and integrated information (e.g., in a digital twin) reflecting the actual physical evolution of the installation over the long term (i.e., a century), the ultimate objective being to assess that the repository components/processes are effectively following the expected trajectory towards the closure phase. Data prediction involves using historical data and statistical methods to forecast future outcomes, but it faces challenges such as data quality issues, the complexity of real-world data, and the difficulty in balancing model complexity. Feature selection, overfitting, and the interpretability of complex models further contribute to the complexity. Data reconciliation involves aligning model with in situ data, but a major challenge is to create models capturing all the complexity of the real world, encompassing dynamic variables, as well as the residual and complex near-field effects on measurements (e.g., sensors coupling). This difficulty can result in residual discrepancies between simulated and real data, highlighting the challenge of accurately estimating real-world intricacies within predictive models during the reconciliation process. The paper delves into these challenges for complex and instrumented systems (multi-scale, multi-physics, and multi-media), discussing practical applications of machine and deep learning methods in the case study of thermal loading monitoring of a high-level waste (HLW) cell demonstrator (called ALC1605) implemented at Andra's underground research laboratory.

Serial kinematic analysis using inertial measurement units in growing dogs at risk of hip dysplasia

In this study, 54 dogs were examined at regular intervals from 12 weeks to 15 months of age using a gait analysis system based on inertial measurement sensors. At the end of the study, the dogs were examined for hip dysplasia (HD) and elbow dysplasia (ED) under sedation and officially classified at the Dysplasia Commission in Zurich. Gait parameters which are characteristic for the gait pattern of dogs, were calculated according to recent publications. These parameters were analysed for variance throughout the entire study period and assigned to healthy dogs and those suffering from HD. The findings of the study show that dogs suffering from HD exhibit a more unsteady gait pattern, e.g. higher variance, as they grow.

Predicting Wall Pressure in Shock Wave/Boundary Layer Interactions with Convolutional Neural Networks

Within the dynamic realm of variable-geometry shock wave/boundary layer interactions, the wall parameters of the flow field undergo real-time fluctuations. The conventional approach to sensing these changes in wall pressure through sensor measurements is encumbered by a cumbersome process, leading to diminished efficiency and an inability to provide swift predictions of wall parameters. This paper introduces a data-driven methodology that leverages non-contact schlieren imaging to predict wall pressure within the flow field, a technique that holds promise for informing the optimized design of variable-geometry systems. A sophisticated deep learning framework, predicated on Convolutional Neural Networks (CNN), has been engineered to anticipate alterations in wall pressure stemming from high-speed shock wave/boundary layer interactions. Utilizing an impulsive wind tunnel with a Mach number of 6, we have procured a sequence of schlieren images and corresponding wall pressure measurements, capturing the continuous variations induced by an attack angle from a shock wave generator. These data have been instrumental in compiling a comprehensive dataset for the training and evaluation of the CNN. The CNN model, once trained, has adeptly deduced the distribution of wall pressure from the schlieren imagery. Notwithstanding, it was observed that the CNN’s predictive prowess is marginally diminished in regions where pressure variations are most pronounced. To assess the model’s generalization capabilities, we have segmented the dataset according to different temporal intervals for network training. Our findings indicate that while the generalization of all models crafted was less than optimal, Model 4 demonstrated superior generalization. It is thus suggested that augmenting the training set with additional samples and refining the network architecture will be a worthwhile endeavor in subsequent research initiatives.

Exploring the Significance of Carbon Quantum Dots and Graphene Quantum Dots in Sarcosine Sensing: A Key Biomarker for Prognosticating Prostate Cancer

AbstractLuminescent carbon nanoparticles, such as carbon quantum dots (CQDs) and graphene quantum dots (GQDs), are increasingly utilized for sarcosine detection due to their exceptional water permeability, sustained luminescence, and biocompatibility. Sarcosine as a biomarker holds significance for therapeutic purposes and disease identification. Carbon‐based sensors offer eco‐friendliness, recyclability, and affordability, making them sustainable for sensing applications across various domains. Despite the widespread use of standard sensors for sarcosine measurement, they face limitations. This paper presents a focused review of recent developments in biosensors utilizing luminescent carbon quantum dots and graphene quantum dots for sarcosine detection. Key processes are characterized by their selectivity and sensitivity to different forms of sarcosine and interfering substances like metallic ions and amino acids. The significant reaction mechanisms and detection methods for sarcosine are outlined and compared. Additionally, the challenges in developing functionalized CQDs and GQDs for further investigation are explored. In summary, luminescent carbon nanoparticles offer a promising avenue for sarcosine detection, addressing limitations of standard sensors through their cost‐effectiveness, sensitivity, and rapid response, thus holding potential for diverse sensing applications.

PMF based Sagnac interferometric sensor for simultaneous measurement of strain, temperature, and torsion

We propose a Sagnac interferometric sensor for simultaneous strain, temperature, and torsion measurement based on a polarization-maintaining fiber (PMF). The Sagnac sensor consists of a single-mode fiber (SMF), PMF and SMF spliced sequentially to form an SMF-PMF-SMF structure. The designed Sagnac interferometric sensor exhibits different wavelength shifts for strain, temperature and torsion measurements. Therefore, we observed three selected dips in the interferogram and measured strain, temperature and torsion simultaneously by their wavelength shifts. The maximum sensitivity of the sensor to strain, temperature, and torsion were experimentally measured to be 28.9 pm/µε. (10-12 m), −1.7504 nm/°C, and −1.0964 nm/(rad/m), and the measurement ranges were 0∼720 με, 25 °C–39 °C, and −7.679 rad/m∼4.887 rad/m (−110°–70°), respectively. In the subsequent experiments, we also verified the feasibility of the sensor for simultaneous measurements, and the errors of the results obtained were less than 10%. And we have successfully increased the temperature measurement range to 25°C∼85°C by cascading FBG in the original structure. The designed sensor has the advantages of easy fabrication, high sensitivity, and bi-directional torsional measurement, which have a wide range of applications in the fields of structural health detection, industrial control, and aircraft attitude recognition.

High-Sensitivity Curvature Fiber Sensor Based on Miniature Two-Path Mach-Zehnder Interferometer.

This paper introduces a new high-sensitivity curvature fiber sensor based on a miniature two-path Mach-Zehnder interferometer (MTP-MZI). The sensor is fabricated by coupling and fusing the multimode fiber (MMF) with the single-mode fiber (SMF) using arc fusion technology (AFT), resulting in a centimeter-level two-path MZI structure. The sensor represents an innovative approach to MZI coupling technology, which reduces device size, simplifies manufacturing, and lowers costs. In curvature-sensing experiments, the MTP-MZI sensor achieves a maximum curvature sensitivity of -96.70 dB/m-1 in the curvature range of 0.0418 m-1 to 0.0888 m-1, which is an extremely high sensitivity among intensity-modulated curvature sensors. Additionally, temperature-sensing measurements of the MTP-MZI sensor show a maximum temperature sensitivity of 212 pm/°C in the range of 30 °C to 70 °C, which is low temperature sensitivity and solves the cross-sensitivity problem. Thanks to the miniature two-path structure of the MTP-MZI, it provides a new perspective for developing multidimensional and multiparameter measurement methods in MZI fiber sensors.

Subjective and Objective Day-to-Day Performance Measures of People with Essential Tremor.

This paper aims to map the daily functional characteristics of people diagnosed with essential tremor (ET) based on their subjective self-reports. In addition, we provide objective measurements of a cup-drinking task. This study involved 20 participants diagnosed with ET who completed the Columbia University Assessment of Disability in Essential Tremor (CADET) questionnaire that included five additional tasks related to digital equipment operation we wrote. Participants also described task-performance modifications they implemented. To create objective personal performance profiles, they performed a cup-drinking task while being monitored using a sensor measurement system. The CADET's subjective self-report results indicate that the most prevalent tasks participants reported as having difficulty with or requiring modifications were writing, threading a needle, carrying a cup, using a spoon, pouring, and taking a photo or video on a mobile phone. Analysis of participants' modifications revealed that holding the object with two hands or with one hand supporting the other were the most prevalent types. No significant correlation was found between the CADET total scores and the cup drinking objective measures. Capturing patients' perspectives on their functional disability, alongside objective performance measures, is envisioned to contribute to the development of custom-tailored interventions aligned with individual profiles, i.e., patient-based/smart healthcare.

Single-point calibration process based integrated electrical impedance analyzer for multi-selective gas detection

Impedance analysis is a powerful technique that has become increasingly important in various applications, it represents a leap forward in the field of electronic measurments and diagnostics. In this work, we present the development of miniaturized, multiplexed, and connected platform for impedance spectroscopy. Designed for online measurements and adapted to wireless network architectures, our platform has been tested and optimized to be used for multi-selective chemical organic sensor nodes. This compact and versatile circuit is built from low cost and low power consumption (250 mW) microelectronics components that achieve long duration operability (5 days and 16 h) without compromising on sensor measurement accuracy and precision. We used the well-known impedance network analyzer AD5933 (Analog Devices, Norwood, MA, USA) chip which can measure a spectrum of impedances in the range 5 kHz to 100 kHz. The proposed system is based on ESP32-C3 Microcontroller enabling the management of the AD5933 through its I2C interface. Our system benefits from two multiplexer components CD74HC4067 allowing calibration process and the interface of 15 conductimetric sensors with real time acquisition (less than 90 ms per acquisition). The system is capable of relaying information through the network for data analysis and storage. The paper describes the microelectronics design, the impedance response over time, the measurement’s sensitivity and accuracy and the testing of the platform with embedded chemical sensors for gas classification and recognition.

Linearizing AFM raster scans acquired using dual-stage lateral scanners

Dual-stage scanners can successfully increase the range of atomic force microscope (AFM) scanners in space and/or frequency by designing and constructing two nanopositioners with significant differences in static and dynamic characteristics. In such cases, the positioning signal has to be split to meet the displacement and frequency limitations of each stage. Without closed-loop displacement measurement sensors, linearizing images acquired using signals split in time and frequency can be challenging. A common method, often cumbersome and time consuming, uses inverse model-based feedforward compensation. In this work, we show that image-based linearization can be used to achieve acceptable results for raster scans acquired using dual-stage scanners. In particular, we apply the feedforward and image-based methods to our homemade dual-stage lateral scanner and high-speed AFM (HS-AFM) system. The acquired scans compare well for the two methods, indicating that image-based raster scan linearization can be used in place of inverse model-based feedforward approaches.

Ultrasensitive optical fiber SPR sensor enhanced by Au-NPs-film modified with functionalized CQDs for label-free detecting cobalt (II) ion

BackgroundCobalt, an essential trace element, is vital for maintaining human nervous system function, aiding in DNA synthesis, and contributing to red blood cell production. It is helpful for disease diagnosis and treatment plan evaluation by precisely monitoring its concentration changes in the human body. Despite extensive efforts made, due to its ultra-low concentration, the current limit of detection (LOD) as reported is still inadequate and cannot be satisfied with the precise clinical applications. Therefore, it is crucial to develop novel label-free sensors with high sensitivity and excellent selectivity for detecting trace amounts of Co2+. ResultsHere, an ultrasensitive optical fiber SPR sensor was designed and fabricated for label-free detection of Co2+ with ultra-low concentration. It is achieved by modifying the carboxyl-functionalized CQDs on the AuNPs/Au film-coated hetero-core fiber, which can specifically capture the Co2+, leading to changes in the fiber's surface refractive index (RI) and subsequent SPR wavelength shifts in the transmission spectrum. Both the Au film and AuNPs on the fiber are modified with CQDs, leveraging their large surface area to enhance the number of active sites and probes. The sensor exhibits an ultra-high sensitivity of approximately 6.67 × 1019 nm/M, and the LOD is obtained as low as 5.36 × 10−20 M which is several orders of magnitude lower compared to other conventional methods. It is also experimentally demonstrated that the sensor possesses excellent specificity, stability, and repeatability, which may be adapted for detecting real clinical samples. SignificanceThe CQDs-functionalized optical fiber SPR sensor exhibits substantial potential for precisely detecting Co2+ of trace amounts, which is especially vital for scarce clinical samples. Additionally, the sensing platform with sample sensor fabrication and measurement configuration introduces a novel, highly sensitive approach to biochemical analysis, particularly adapting for applications involving the detection of trace targets, which could also be employed to detect various biochemical targets by facile modification of CQDs with specific groups or biomolecules.

Establishment and application of real-time monitoring system for mine ventilation resistance

The monitoring of ventilation resistance in domestic coal mines is generally faced with two problems: huge workload and lagging monitoring data behind production, which is due to the backward monitoring technology. In recent years, with the progress of technology and equipment in the coal industry, ventilation resistance monitoring products have appeared in the market one after another, but they still face the following main problems: (1) the sensor measurement error needs to be further improved; (2) The monitoring scheme is relatively simple, and the use of absolute pressure multi-parameter monitoring faces two problems: measurement error and construction cost; (3) There is a lack of successful application cases of ventilation resistance monitoring in the industry, and the analysis and application of ventilation resistance monitoring data by the upper computer is insufficient. In view of the above problems, this paper studies the principle, monitoring scheme, system framework and practical application of ventilation resistance monitoring, aiming to solve the problem of real-time online monitoring and analysis of ventilation resistance.

Self‐triggered adaptive neural control for USVs with sensor measurement sensitivity under deception attacks

Self‐triggered adaptive neural control for USVs with sensor measurement sensitivity under deception attacks

Distributed energy-saving speech enhancement in wireless acoustic sensor networks

Wireless acoustic sensor network (WASN) has shown great potential for speech enhancement compared with traditional microphone arrays. As dealing with all sensor measurements demands a powerful centralized processor, a distributed framework for speech enhancement is developed in this work. We propose to suppress noises by jointly exploiting the temporal and spatial information of audio signals, based on the information weighted consensus (IWC) filtering. In order to reduce the computational cost as well as the transmission power, we activate only an informative subnetwork by maximizing the energy efficiency in the WASN, where the subnetwork connectivity is guaranteed by incorporating more constraints. To select sensors rapidly, we propose a near-optimal greedy method, which removes one sensor at each iteration until the subnetwork size equals a preset value. The proposed method can obtain the high-quality target signal in a distributed and energy-efficient way. Simulation and real-world experiment results confirm its validity.

A simple physical model for simulation and design magneto-plethysmograph in application non-invasive hemoglobin measurement

The magneto-plethysmograph method is a combination of magnetic field and sensors used to detect changes in blood flow pulsation. However, to detect the magnetic properties of blood related to hemoglobin concentration, physical modeling and simulation are required. This approach involves designing simulations using magnetic field equations and magnetic susceptibility, where a permanent magnet is placed on the surface of blood vessels, and sensors based on giant magnetoresistance are placed at a distance r. The design originates from a simple approach involving the magnetization and detection of Fe atoms in hemoglobin. Parameters involved include the magnetic susceptibility of oxyhemoglobin and deoxyhemoglobin, with an external magnetic field exceeding 1 Tesla. From the physical modeling and simulation, graphs are obtained depicting the influence of hemoglobin concentration on the number of Fe atoms and its magnetization. This enables the design of non-invasive hemoglobin measurement sensor devices. The uniqueness of this simple physical model and simulation lies in its ability to produce specially designed device models for measuring hemoglobin concentration. This differs from other research focusing on blood flow pulse measurements; the results of this study provide new insights into the benefits of simple physics equations that can be developed for medical diagnostic research and device development.

Deep Brain Stimulation Inhibits Epileptic Seizures via Increase of Adenosine Release and Inhibition of ENT1, CD39, and CD73 Expression.

Deep brain stimulation (DBS) of the anterior nucleus of the thalamus is an efficacious treatment option for patients with refractory epilepsy. Our previous study demonstrates that adenosine is a potential target of DBS for the treatment of epilepsy. Equilibrative nucleoside transporters-1 (ENT1) and ectonucleotidases (CD39, CD73) function as regulators of extracellular adenosine in the brain. It is unclear whether ENT1, CD39, and CD73 are involved in the mechanism of DBS for epilepsy. A total of 48 SD male rats were divided into four groups: control (naïve rats), Pilo (pilocarpine induced rats with epilepsy), DBS (rats with epilepsy treated with DBS for 8weeks), and sham. In the present study, video electroencephalogram monitoring, Morris water maze assays, in vivo measurements of adenosine using fiber photometry, histochemistry, and western blot were performed on the hippocampus. DBS markedly attenuated spontaneous recurrent seizures (SRSs) and enhanced spatial learning in rats with epilepsy, assessed through video-EEG and water maze assays. Fibred photometry measurements of an adenosine sensor revealed dynamic increase in extracellular adenosine during DBS. The expressions of ENT1, CD39, and CD73 in Pilo group and sham group increased compared with the control group, while the expressions of ENT1, CD39, and CD73 in DBS group decreased compared to that of Pilo group and sham group. The findings indicate that DBS reduces the number of SRSs and improves spatial memory in rats with epilepsy with concomitant decrease of ENT1, CD39, and CD73 expressions. Adenosine-modulating enzymes might be the potential targets of DBS for the treatment of epilepsy.

Total Protein Determination by Recovery Fluorescence of Trinitrotoluene-Quenched Protein-Functionalized Gold Nanoclusters.

Determination of total protein content is very important for clinical, pharmaceutical and food chemistry, and the simplicity and rapidity of the existing total protein content assays should be improved. Here, a novel fluorescent sensor for total protein content measurement was described using near-infrared emitting fluorescent gold nanoclusters, which were prepared with egg whites. Trinitrotoluene served as highly efficient quencher to quench the fluorescence of gold nanoclusters. Interestingly, the trinitrotoluene-protein complex that formed by premixing of trinitrotoluene and protein-containing real samples maintained the fluorescence of gold nanoclusters. These new findings provided a novel mechanism to design a fluorescence sensor for determination of total protein levels in various real samples. With available and low-cost bovine serum albumin as reference, the obtained standard curve for total protein determination indicated a linear range of 0.1-3.5g L- 1 with a detection limit of 0.06g L- 1 (3σ rule) and a correlation efficiency (R2) of 0.9850. The applicability of the proposed sensor was validated by determination of total protein levels in real samples. Comparison with the existing spectrophotometric methods, the proposed sensor is advantageous of simplicity, rapidity, and cost-effectiveness.