Single Sensor Research Articles

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.

Robust location parameter estimation in the presence of adversary

In classical parameter estimation settings, sensor observation models are often assumed to be known. However, when the sensors themselves become unreliable, the traditional observation models may no longer hold. It is then expected that estimation performance would degrade due to the abnormal behavior of sensor observations. We formulate the estimation problem as a two-person zero-sum game and propose a mini-max estimator with the optimization goal to minimize the worst possible estimation error. We show that there exists a saddle-point solution for a single sensor observation. We then apply our result and characterize the estimation performance for networks with multiple sensors.

Research on transformer fire alarm threshold based on multi-sensor data analysis

Based on literature, on-site investigation and accident case analysis, this paper selects multi-sensor for transformer fire alarm, builds the transformer fire test platform, and carries out the transformer fire test and interference test to obtain the training and test data of neural network data fusion. This paper determines the fire alarm threshold of multi-sensor signal. It provides a benchmark for comparing the alarm results of multi-sensor data simulation fusion with those of single sensor. This paper studies the preprocessing multi-sensor data, compiles the corresponding algorithm, and obtains the multi-sensor preprocessed data after dimension reduction and noise reduction. It provides data support for multi-sensor data fusion.

Dual-band complementary metamaterial perfect absorber for multispectral molecular sensing.

Metamaterial perfect absorbers (MPAs) show great potential in achieving exceptional sensing performance, particularly in the realm of surface-enhanced infrared absorption (SEIRA) spectroscopy. To this aim, it is highly desirable for the localized hotspots to be readily exposed and accessible to analyte with strong mode confinement to enhance absorption. Here, we propose a quasi-three-dimensional MPA based on cross-shaped coupled complementary plasmonic arrays for highly sensitive refractive index sensing and molecular vibrational sensing. Dual-band perfect absorption can be approached with the two plasmonic resonances corresponding to the electric dipole-like mode of cross antenna array and the magnetic dipole-like mode of cross hole array, respectively. Large portions of the electric field of the hotspots are exposed and concentrated in the gap between the elevated cross antenna and its complementary structure on the substrate, leading to improved sensing sensitivities. An ultrathin polymethyl methacrylate (PMMA) film induces a significant redshift of the magnetic dipole-like mode with an 11.8 nm resonance shift per each nanometer polymer thickness. The value is comparable to the reported sensitivity of single molecule layer sensors. Additionally, the simultaneous detection of the C = O and C-H vibrations of PMMA molecules is enabled with the two plasmonic resonances adjusted by changing the lengths of the two cross branches. Remarkably, the observed mode splitting and anti-crossing behavior imply the strong interaction between plasmonic resonance and molecular vibration. Our dual-band MPA based on coupled complementary plasmonic arrays opens a new avenue for developing highly sensitive sensors for the detection of refractive index and multispectral molecular vibrations.

Monitoring Rheological Changes Using Acoustic Emissions for Complex Formulated Fluids Manufacturing

AbstractThe measurement capabilities of a newly developed in‐situ rheometric device based on a single passive acoustic emission sensor and machine learning algorithms were investigated. Two surfactant structured fluids demonstrating complex non‐Newtonian rheology (Power‐law and Herschel‐Bulkley models) were examined. Furthermore, a static evaluation on the laboratory scale in comparison to dynamic processing on the pilot scale was conducted. The results indicate that the machine learning algorithms of this technology can identify, in > 90 % of scenarios, the correct type of rheology or the manufacturing process step across both scales. This identification is based on solving a classification problem using quadratic support vector machine learning algorithms, which have proven to deliver the most robust predictions across a choice of 24 different algorithms tested. Additionally, a new format of in situ rheology display was introduced, referred to as RRF™ factor.

The validity domain of sensor fusion in sensing soil quality indicators

Soil health has gained increasing attention under the rapid development of industrialization and the requirement for green agriculture. Therefore, up-to-date soil information related to soil health is urgently needed to ensure food security and biodiversity protection. Previous studies have shown the potential of proximal soil sensing in measuring soil information, while it remains challenging to get cost-efficient and robust estimates of multiple soil health indicators simultaneously via sensor fusion. In this study, we investigated the potential of visible near-infrared (vis-NIR), and mid-infrared (MIR) spectroscopy as well as three model averaging methods in predicting three soil health properties, including soil organic matter (SOM), pH, cation exchange capacity (CEC). The model averaging methods are not only used for model fusion but also for high-level sensor fusion, which include Granger-Ramanathan (GR), Bayesian Model Averaging and Spectral-Guided Ensemble Modelling (S-GEM). Here, S-GEM is a recently proposed algorithm that can improve soil spectroscopic prediction by including spectral information in ensemble modelling. Four widely used prediction models were evaluated, including partial least square regression, Cubist, memory based learning and convolutional neural network. For SOM, sensor fusion based on model averaging algorithms was comparable to that of Sensorsingle + Modelmultiple (MIR singly based on S-GEM) with R2 of 0.86. However, MIR only with S-GEM performed the best among all methods (LCCC of 0.92, RMSE of 3.66 g kg−1 and RPIQ of 3.68). The 10-fold cross-validation results indicated that Sensorsingle + Modelmultiple (MIR singly based on S-GEM) performed best among all methods for pH, with R2 of 0.84, LCCC of 0.90, RMSE of 0.45 and RPIQ of 3.65. For CEC, Sensormultiple + Modelmultiple based on GR performed best with R2 of 0.66, LCCC of 0.80, RMSE of 3.48 cmol + kg−1 and RPIQ was 2.22. Our results also showed that sensor fusion failed to improve spectral prediction of soil information when the performance among sensors differed a lot (△R2 > 0.2), and the use of a single best sensor is therefore suggested in this case. When the sensors have a close model performance (△R2 < 0.2), Sensormultiple + Modelmultiple based on GR was recommended. The outcome of this study can provide a reference for determining the validity domain of sensor fusion methods in improving the accuracy of soil health prediction.

Assessing the Performance of Satellite-Based Models for Crop Yield Estimation in the Canadian Prairies

Timely monitoring of crop production using a remote sensing-based approach offers promise toward enhancing food security. Statistical models developed using satellite data typically employ a single vegetation index from a single sensor for yield estimation. With the increasing availability of satellite datasets, there is now an opportunity to investigate the potential of available vegetation indices from different sensors in estimating yields. The key objective of this study was to develop a best-performing satellite-based yield model for the Canadian Prairies for wheat, barley, and canola, trained using municipality-level data from 2009 to 2019. We tested the statistical performance of models built using (a) indices from different sensors (Landsat and Sentinel-2), (b) indices sensitive to different yield properties, and (c) single versus multiple vegetation indices. Results showed Landsat-NDWI as the best performing single-index across all indices and sensors for each crop. Sentinel-2-EVI performed best for wheat and canola and Sentinel-2-SR for barley; but these models were built using only 4 years of data. We found that best-performing single-index models recorded similar predictive accuracy as multi-index models during model validation. The results from this work suggest that satellite-based yield estimation can be improved by selecting the right index related to different crop properties.

Productivity Measurement through IMU-Based Detailed Activity Recognition Using Machine Learning: A Case Study of Masonry Work.

Although measuring worker productivity is crucial, the measurement of the productivity of each worker is challenging due to their dispersion across various construction jobsites. This paper presents a framework for measuring productivity based on an inertial measurement unit (IMU) and activity classification. Two deep learning algorithms and three sensor combinations were utilized to identify and analyze the feasibility of the framework in masonry work. Using the proposed method, worker activity classification could be performed with a maximum accuracy of 96.70% using the convolutional neural network model with multiple sensors, and a minimum accuracy of 72.11% using the long short-term memory (LSTM) model with a single sensor. Productivity could be measured with an accuracy of up to 96.47%. The main contributions of this study are the proposal of a method for classifying detailed activities and an exploration of the effect of the number of IMU sensors used in measuring worker productivity.

Research on Multi-Sensor Fusion Firefighting Manpower Status Monitoring Technology

Abstract Firefighting soldiers are in danger during firefighting operations, and it is necessary to monitor the data of their physical indicators to ensure the safety of firefighting soldiers, improvement of the safety of firefighting personnel. A single sensor will appear the information blind spot, and has the defects of measurement limitation, low robustness and low precision, which can’t satisfy the need of accurately judging the current state of the firefighting soldier. Therefore, this paper investigates the distribution characteristics of multi-dimensional data of firefighting soldiers in the process of working movement, The goal of multi-sensor information fusion is based on the separation of observations from each sensor, and its ultimate goal is to utilize the advantages of multiple sensors operating together or in conjunction to improve the effectiveness of the entire sensor system, the distribution characteristics of multi-dimensional data effectively improve the shortcomings of a single sensor. By introducing the idea of improving the local optimum of back propagation of BP neural network algorithm and the defects of network weights, this paper proposes a relevant fusion algorithm based on firefighting soldiers, establishes a kinematic physiological monitoring model based on firefighting soldiers, and realizes the physiological state monitoring of firefighting soldiers in the complex working environment. Experimental results show that this method can improve the accuracy with which data from several sensors determine the condition of firefighters, decision making for firefighting singletons’ safety and security can be effectively improved by multi-sensor data fusion, and the accuracy of the improved FWA-BP data fusion algorithm model is increased by 3.64%, which improves the stability of the system.

Adaptive Techniques for the Online Estimation of Spatial Fields With Mobile Sensors

The regression-based reconstruction of spatial fields by static sensors requires the number of measurements be at least equal to the number of unknown parameters in order to provide a unique solution. Improvements include the use of mobile sensor networks to arrive at higher accuracy estimates. An alternate approach utilizes adaptive techniques to update the parameter estimates online. Such adaptive techniques need persistence of excitation to ensure convergence of the parameter and function estimates. It is shown that the motion of just a single sensor is a necessary condition for persistence of excitation and, subsequently, of function convergence. Having a prescribed sensor guidance is also a sufficient condition for function convergence. First, a Lyapunov-based adaptation and guidance is presented and it is shown that persistence of excitation necessitates the time dependence of the outer product of the regressor vector with itself and which in turn requires the motion of the sensor within the spatial domain. Subsequently, a gradient-based adaptive scheme relaxes the Lyapunov-based sensor guidance to any user-defined guidance as long as the outer product of the associated rank-one regressor vector with itself has a uniformly positive definite integral. The latter adaptation is conducive to the inclusion of platform dynamics resulting in a vehicle control design needed to attain parameter convergence. Extension of the centralized solution to distributed estimation using a network of mobile sensors is also examined. These results are demonstrated with simulations in one and two dimensions.

Intelligent Kitchen Safety and Emergency Response System using IoT

The potential dangers stemming from inadequate safety measures in kitchens, including gas leaks and temperature fluctuations, can lead to severe consequences such as explosions, fires, and health hazards. To counteract these risks, an innovative research centered around a dual-purpose sensor system is proposed. This system effectively detects both gas leaks and temperature anomalies using a single sensor, optimizing efficiency and affordability. Notably, an added feature involves a low-cost camera module that captures images during critical situations like gas leaks or extreme temperature changes. These images are then transmitted via IoT technology to a designated Telegram channel, providing an additional layer of visual data to aid in emergency responses. The research stands out for its cost-effective design, focusing on low energy consumption and practical usage, making it an ideal solution for elevating kitchen safety standards.

Driver Safety System using Arduino

The most frequent cause of auto accidents is poor driving. This occurs frequently when a driver is intoxicated or sleepy. Driver fatigue is often acknowledged as a significant factor contributing to collisions as 20% of the accidents occur only due to drowsy driving or tiredness. It is hoped that the technological advancements can at least partially, avoid these thanks to technological improvements. The research put froth utilizes an IR sensor, to measures and regulates eye blinking. The infrared waves that reach the eyes are sent using an IR transmitter. The reflected infrared rays from an eye are picked up by the IR receiver. If the eye is closed, the result is to raise the alarm. Also, the pulse sensor is used to alert people by sending SMS in case of any health issues to the driver. In this study, eye blink of the driver is monitored to prevent accidents during drowsy driving. Here, a single eye blink sensor is installed in a vehicle to alert the driver during fatigue. A Global Positioning System (GPS) is also used in the proposed design to send location alerts.

Entanglement-enhanced optomechanical sensor array with application to dark matter searches

Squeezed light has long been used to enhance the precision of a single optomechanical sensor. An emerging set of proposals seeks to use arrays of optomechanical sensors to detect weak distributed forces, for applications ranging from gravity-based subterranean imaging to dark matter searches; however, a detailed investigation into the quantum-enhancement of this approach remains outstanding. Here, we propose an array of entanglement-enhanced optomechanical sensors to improve the broadband sensitivity of distributed force sensing. By coherently operating the optomechanical sensor array and distributing squeezing to entangle the optical fields, the array of sensors has a scaling advantage over independent sensors (i.e., sqrt{M}to M, where M is the number of sensors) due to coherence as well as joint noise suppression due to multi-partite entanglement. As an illustration, we consider entanglement-enhancement of an optomechanical accelerometer array to search for dark matter, and elucidate the challenge of realizing a quantum advantage in this context.

Wireless positioning method for transmission pipe gallery inspection robot based on the TDOA algorithm

A wireless sensor network for positioning an inspection robot is developed for a transmission pipe gallery environment. The time difference of the arrival algorithm optimized by the least squares method is utilized to realize the wireless positioning function of the robot in the inspection process. The developed system adopts a spiral configuration to install the sensor nodes at equal intervals on the sidewall of the transmission pipe gallery, and only 10 sensor nodes closest to the wireless signal transmission source are activated for each measurement, which avoids the poor long-distance measurement effect of the sensor, ensures sufficient accuracy, and reduces calculation costs. A wireless positioning simulation test was conducted in an experimental transmission corridor environment. The error of the simulation results is equivalent to the measurement error of a single sensor, and the error is not accumulated in the calculation of the algorithm, indicating that the algorithm has achieved good results.

Robot navigation based on multi-sensor fusion

Over the past years, with the rapid development of artificial intelligence technology, mobile robots have appeared in more and more fields. Different types of robots play different roles. Due to the complex indoor environment, there are still many technical problems to be solved for robots. Aiming at the shortcomings of single sensor, low mileage accuracy, and poor security in traditional mobile robot navigation systems, we propose a mobile robot autonomous navigation system based on multi-sensor perception. This system integrates mainstream synchronous positioning and mapping algorithms, path planning algorithms, and positioning algorithms, and uses multiple sensors to compensate each other to achieve the automatic navigation function of the mobile robot. In order to verify the reliability of the method, We have carried out multi angle analysis in the simulation environment, and the experimental results show that the proposed navigation system is reliable in operation, high in mileage accuracy, and good in robustness, which broadens the application scenario of mobile robots.

Fabrication and verification of a single aligned nanofibre gas sensor based on non‐contact direct writing method

AbstractIn this letter, the non‐contact direct writing method for producing single aligned fibre utilizing polyvinylpyrrolidone (PVP) polymer has been explored. It has been shown that with the help of this method, nanofibres can be created in a controlled manner with designed structures. This form of nanofibres is being studied with the goal of using it in carbon monoxide (CO) gas sensors. The temperature required for the annealing operation was 500°C, and finally the authors obtained nanofibres with diameter of 200 nm. In addition, a simulation technique built on the Monte Carlo method has also been introduced. It is shown that this simulation method is compatible with the experimental results with reasonable accuracy. The results showed that the authors have a correlation of about 0.934 between experimental results obtained in 30 ppb of CO gas and its simulation data.

A cross-reactive fluorescent ensemble based on perylene derivative/surfactant assemblies for discrimination of multiple aminoglycoside antibiotics in aqueous solution

Sensitive detection and discrimination of multiple aminoglycosides (AGs) hold paramount importance in the efforts to curtail antibiotic overutilization and to monitor environmental impact. The present work provides a novel discriminative sensor ensemble based on a perylene derivative and SDS assemblies that can recognize nine different aminoglycosides. The fluorescent binary ensemble emits very weak fluorescence due to the strong aggregation of perylene-based probes in SDS pre-micelles. The addition of AGs can induce fast and remarkable fluorescence enhancement. The acquisition of fluorescence variation data at three characteristic emission wavelengths enables the generation of distinctive recognition pattern to each AG. Principal component analysis reveals that the sensor ensemble can correctly identify multiple AGs in both buffered aqueous solution and in 10% human urine. The fluorescence enhancement is attributed to the disaggregation of perylene units caused by the variation of SDS assembly from pre-micelle to micelles resulting from multi-point interaction with AGs. The strategy of modulation effect on fluorescence emission of encapsulated fluorophores by surfactant assemblies has been demonstrated to be highly effective for fabricating single system-based discriminative sensors for aminoglycosides.

Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics.

One of the main challenges faced by iris recognition systems is to be able to work with people in motion, where the sensor is at an increasing distance (more than 1 m) from the person. The ultimate goal is to make the system less and less intrusive and require less cooperation from the person. When this scenario is implemented using a single static sensor, it will be necessary for the sensor to have a wide field of view and for the system to process a large number of frames per second (fps). In such a scenario, many of the captured eye images will not have adequate quality (contrast or resolution). This paper describes the implementation in an MPSoC (multiprocessor system-on-chip) of an eye image detection system that integrates, in the programmable logic (PL) part, a functional block to evaluate the level of defocus blur of the captured images. In this way, the system will be able to discard images that do not have the required focus quality in the subsequent processing steps. The proposals were successfully designed using Vitis High Level Synthesis (VHLS) and integrated into an eye detection framework capable of processing over 57 fps working with a 16 Mpixel sensor. Using, for validation, an extended version of the CASIA-Iris-distance V4 database, the experimental evaluation shows that the proposed framework is able to successfully discard unfocused eye images. But what is more relevant is that, in a real implementation, this proposal allows discarding up to 97% of out-of-focus eye images, which will not have to be processed by the segmentation and normalised iris pattern extraction blocks.

Integrated Design of a Dual-Mode Colorimetric Sensor Driven by Enzyme-like Activity Regulation Strategy for Ultratrace and Portable Detection of Hg2.

Colorimetric analysis for mercury detection has great application potential in the prevention of health damage caused by mercury in the environment. Sensitivity, selectivity, and portability are core competencies of sensors, and concentrating these properties in a single sensor for efficient mercury detection remains a great challenge. Herein, a hollow structure CuS@CuSe@PVP (CCP) was prepared in which the enzyme-like activities could be activated by Hg2+ due to the antagonism between Hg and Se, inspiring the establishment of a colorimetric method for Hg2+ detection. As for Hg2+ detection performance, the linear range (LR) and limit of detection (LOD) were 1-900 and 0.81 nM in the POD-like activity system, respectively. Also, 5-550 nM of LR and 2.34 nM of LOD were achieved in the OD-like activity system. Further, a smartphone-mediated portable RGB nanosensor was fabricated, with a LOD down to 6.65 nM in the POD-like system and 7.97 nM in the OD-like system. Moreover, the excellent self-calibration and satisfactory recovery of 94.77%-106.16% were shown in the application of real water samples analysis. This study represented advanced progress toward emerging applications of nanozymes with multiple enzyme-like activities in heavy metal detection and will accelerate the development of efficient and portable heavy metal sensors.

Investigation of MEMS Single Turn Meander-Shaped Silicon Carbide Piezoresistive Pressure Sensor on a Clamped Circular Diaphragm for High Pressure Harsh Environment Applications

Silicon-based Microelectromechanical System (MEMS) pressure sensors are extensively used and have the advantages of high accuracy and miniaturization. Despite this, due to inherent material limitations, they are not easily able to withstand high temperatures greater than 150°C. To overcome this disadvantage, Silicon Carbide (SiC) is the preferred material because it has excellent thermomechanical properties and operates above 600°C. Piezoresistive pressure sensors made of Silicon Carbide are ideally able to detect pressures beyond 600°C. Our work presents MEMS Single turn meander-shaped piezoresistive pressure sensor on a circular SiC diaphragm for high pressure applications of pressure range 0–40[Formula: see text]MPa in harsh environment. This work models and analyses the piezoresistive pressure sensor characteristics using an analytical modeling and simulation method to choose the best design. To ascertain its sensitivity, expressions are computationally simulated with MATLAB software using the thin plate and small deflection theory. To evaluate the viability of the model, COMSOL Multiphysics simulation is used. When compared to recent studies, our proposed sensor provides a sensitivity of near about 5.4[Formula: see text]mV/V/MPa across a pressure range of 0–40[Formula: see text]MPa.