Precise Sensor Research Articles

Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL

Abstract. To design a monitoring network for estimating CO2 fluxes in an urban area, a high-resolution observing system simulation experiment (OSSE) is performed using the transport model Graz Mesoscale Model (GRAMMv19.1) coupled to the Graz Lagrangian Model (GRALv19.1). First, a high-resolution anthropogenic emission inventory which is considered as the truth serves as input to the model to simulate CO2 concentration in the urban atmosphere on 10 m horizontal resolution in a 12.3 km × 12.3 km domain centred in Heidelberg, Germany. By sampling the CO2 concentration at selected stations and feeding the measurements into a Bayesian inverse framework, CO2 fluxes on a neighbourhood scale are estimated. Different configurations of possible measurement networks are tested to assess the precision of posterior CO2 fluxes. We determine the trade-off between the quality and quantity of sensors by comparing the information content for different set-ups. Decisions on investing in a larger number or in more precise sensors can be based on this result. We further analyse optimal sensor locations for flux estimation using a Monte Carlo approach. We examine the benefit of additionally measuring carbon monoxide (CO). We find that including CO as tracer in the inversion enables the disaggregation of different emission sectors. Finally, we quantify the benefit of introducing a temporal correlation into the prior emissions. The results of this study have implications for an optimal measurement network design for a city like Heidelberg. The study showcases the general usefulness of the inverse framework developed using GRAMM/GRAL for planning and evaluating measurement networks in an urban area.

Commutation torque ripple suppression method for brushless DC motors with minimum phase shift angle

SummaryCommutation torque ripple occurs when brushless DC (BLDC) motor runs in the high‐speed range, which degrades operation performance. To overcome the problem, this paper presents a commutation torque ripple suppression method based on the minimum phase shift angle (MPSA) of hall signals. In the paper, the effect of the phase shift angle (PSA) of hall signals on phase current is analyzed, and the MPSA is deduced on the basis of the unit duty cycle of the incoming and non‐commutation phase. With the proposed method, two‐phase conduction mode is used in the full speed range, commutation period and non‐commutation period, which avoids switching modulation mode and enhances system reliability. The proposed method is implemented through shifting hall signals by the MPSA, and it is simple and feasible without the requirement to redesign a controller. Moreover, no additional power devices and precise sensors are required, which reduces design cost and volume size. The proposed method is experimentally compared with different methods under steady operation condition, and the results show superior performance on suppressing commutation torque ripple. The loading experiment demonstrates that the proposed method exhibits good dynamic response. The frequency spectrum of the electromagnetic torque is analyzed, and it is verified that the 6nf harmonics are diminished with the proposed method.

The intersection of mechatronics and precision engineering: Synergies and future directions

The convergence of mechatronics and precision engineering has sparked significant interest due to its potential to revolutionize various industries, ranging from manufacturing to healthcare. This review explores the synergies between these two domains and outlines potential future directions for research and application. Mechatronics, an interdisciplinary field combining mechanical engineering, electrical engineering, computer science, and control engineering, focuses on the design and development of intelligent systems with integrated sensing, actuation, and control capabilities. Precision engineering, on the other hand, emphasizes the fabrication of high-accuracy components and systems, often at micro or nanoscales, to meet stringent performance requirements. The intersection of mechatronics and precision engineering offers numerous synergistic opportunities. One such opportunity lies in the development of precision actuators and sensors with embedded intelligent control systems, enabling precise motion control and feedback mechanisms in complex systems. Furthermore, advancements in microfabrication techniques allow for the integration of sensors and actuators directly onto precision components, leading to miniaturized and efficient mechatronic systems. In manufacturing, this synergy facilitates the creation of highly automated and flexible production processes capable of producing intricate and high-quality products with minimal human intervention. In healthcare, it enables the development of precision medical devices for diagnostics, surgery, and drug delivery, enhancing patient outcomes and reducing the invasiveness of procedures. Future directions in this interdisciplinary field include exploring novel materials and manufacturing techniques to further improve the precision and performance of mechatronic systems. Additionally, advancements in artificial intelligence and machine learning are poised to enhance the autonomy and adaptability of mechatronic systems, enabling them to learn from data and adapt to changing environments in real-time. the intersection of mechatronics and precision engineering holds great promise for addressing complex engineering challenges and driving innovation across various industries. By leveraging their complementary strengths, researchers and engineers can unlock new opportunities to create smarter, more efficient, and more precise systems with transformative impacts on society.

Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology’s precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies.

Development of end-to-end low-cost IoT system for densely deployed PM monitoring network: an Indian case study

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive, and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors, specifically focusing on PM10 and PM2.5, the most health-impacting particulates. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad, of which 43 devices were developed as part of this work, and six devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor and were particularly adjusted to accurately measure PM10 and PM2.5 levels. A thorough analysis of data collected for 7 months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation, and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM10 and PM2.5. In addition, event-driven spatio-temporal analysis is done for PM2.5 and PM10 values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.

A 3D-printed hollow microneedle-based electrochemical sensing device for in situ plant health monitoring

Plant health monitoring is devised as a new concept to elucidate in situ physiological processes. The need for increased food production to nourish the growing global population is inconsistent with the dramatic impact of climate change, which hinders crop health and exacerbates plant stress. In this context, wearable sensors play a crucial role in assessing plant stress. Herein, we present a low-cost 3D-printed hollow microneedle array (HMA) patch as a sampling device coupled with biosensors based on screen-printing technology, leading to affordable analysis of biomarkers in the plant fluid of a leaf. First, a refinement of the 3D-printing method showed a tip diameter of 25.9 ± 3.7 μm with a side hole diameter on the microneedle of 228.2 ± 18.6 μm using an affordable 3D printer (<500 EUR). Notably, the HMA patch withstanded the forces exerted by thumb pressing (i.e. 20-40 N). Subsequently, the holes of the HMA enabled the fluid extraction tested in vitro and in vivo in plant leaves (i.e. 13.5 ± 1.1 μL). A paper-based sampling strategy adapted to the HMA allowed the collection of plant fluid. Finally, integrating the sampling device onto biosensors facilitated the in situ electrochemical analysis of plant health biomarkers (i.e. H2O2, glucose, and pH) and the electrochemical profiling of plants in five plant species. Overall, this electrochemical platform advances precise and versatile sensors for plant health monitoring. The wearable device can potentially improve precision farming practices, addressing the critical need for sustainable and resilient agriculture in changing environmental conditions.

Guaranteed Robust Performance of $\mathcal {H}_{\infty }$ Filters With Sparse and Low Precision Sensing

The performance of estimation algorithms depends on the available sensors and their precisions. While higher precisions of sensors provide better estimation accuracy, it may lead to unnecessarily expensive designs and higher operational costs due to higher costs of high-precision sensing modalities. Also, higher precision can cause interference for other sensors in the environment, such as RADARS, resulting in degradation in the overall system's performance. This paper presents a framework for co-designing a sparse sensing network with the least precise sensors and the filter that guarantees the prescribed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> estimation accuracy. Convex optimization problems for minimizing sensor precisions are formulated for continuous and discrete-time linear time-invariant systems, with and without model uncertainties. Different heuristics for determining a sparse sensor set with the least precise sensors are discussed, and their performances are compared using numerical simulations. The application of the proposed framework is demonstrated using numerical examples.

Optical control of coherent magneto-optical resonances in potassium

The influence of Light Induced Atomic Desorption (LIAD) on the potassium D2 line magneto-optical resonances in uncoated buffer-gas optical cell is investigated. LIAD effect reduces the drawbacks of conventional heating for achieving high atomic density that is essential for many spectroscopy-based applications. Another feature of LIAD is the impact on the dwelling time of the atoms when colliding with the surface of the cell. In this work we investigate LIAD from point of view to distinguish the influence of LIAD on the atomic density from the dwelling time as well as to control and improve the parameters of magneto-optical resonances in potassium vapor. The results are interesting for development of new precise optical sensors and devices for various applications.

Selective Detection of Creatinine Using Enzymatic Route for Diagnosis of Chronic Kidney Diseases

Chronic kidney diseases are becoming most common health issues due to growing age, hypertension and high cholesterol that causes poor kidney functioning and eventually death also. Creatinine is a serum present in blood and urine that measures the kidney functioning. We have designed a highly sensitive and precise sensor for the creatinine sensing. Eu—doping in tin oxide (SnO2) is used to enhance the sensitivity and limit of detection. A precise working range of this sensor is also found by the calibration curve between FWHM and figure of Merit with respect to the concentration of creatinine. A selectivity experiment with enzymatic route is followed to make this sensor selective for creatinine only. This sensor may find plentiful deliberation in the field of sensing and mainly in medical applications.

Precise sensors in detecting stress in workers in the industry: A review

Stress in the work environment has an impact on worker welfare and industrial productivity. This paper reviews the use of precision sensors in detecting workers’ physiological responses to stress in industry. The focus is on sensors that measure heart rate, sweat rate, body temperature, muscle activity and sleep patterns. The wearable sensor is the main highlight. Despite implementation challenges, precision sensors offer the potential for real-time monitoring to create healthier and more productive work environments.

Advanced temperature sensing with Er3+/Yb3+ co-doped Ba2GdV3O11 phosphors through upconversion luminescence.

Optical thermometry is a non-contact temperature sensing technique with widespread applications. It offers precise measurements without physical contact, making it ideal for situations where contact-based methods are impractical. However, improving the accuracy of optical thermometry remains an ongoing challenge. Herein, enhancing the thermometric properties of luminescent thermometers through novel materials or strategies is crucial for developing more precise sensors. Hence, the present study focuses on the application of four-mode luminescence thermometric techniques in sol-gel synthesized Er3+/Yb3+ co-doped Ba2GdV3O11 phosphors for optical temperature sensing in the temperature range of 298-573 K. The upconversion (UC) luminescence is achieved under excitations of 980 nm or 1550 nm, resulting in bright yellow-green emission in the visible spectral range. Temperature sensing is realized by exploiting the UC emissions of 4S3/2, 2H11/2 and 4F7/2 bands, which represent intensity ratios of thermally coupled levels (TCELs) and non-thermally coupled levels (NTCELs) of Er3+/Yb3+, along with the emission lifetimes at 4S3/2. The relative sensitivity (Sr) values for TCELs exhibit a gradual decrease with rising temperature, reaching a maximum of 1.1% K-1 for 980 nm excitation and 0.86% K-1 for 1550 nm excitation at 298 K. Conversely, for NTCELs, the highest Sr value observed is 0.9% K-1 at 298 K for 1550 nm excitation. Moreover, the emission lifetimes at 4S3/2 yield notably high Sr values of up to 5.0% μs K-1 (at 425 K). Furthermore, the studied phosphors have a sub-degree thermal resolution, making them excellent materials for accurate temperature sensing. Overall, this study provides a promising new direction for the development of more precise and reliable optical thermometry techniques, which could have important implications for a range of scientific and industrial optical temperature sensing applications.

Efficient detection of p-nitrophenol via a polypyrrole flower-decorated nickel foam-based electrochemical sensor.

p-Nitrophenol (p-NP) is known as a common contaminant found in wastewater, agricultural runoff, and industrial effluents which can degrade water quality and cause potential carcinogenic and toxic effects on the human body. Its detection is essential for public health, industrial safety, environmental protection, and regulatory compliance, underscoring its broad applicability. In this study, a novel electrochemical sensor based on polypyrrole (PPy) flowers assembled via nanotubes was developed for the sensitive determination of p-NP. The nickel (Ni) foam modified with PPy flowers functioned as the working electrode and showed selectivity toward p-NP in a phosphate buffer medium at pH 7.0. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques were utilized for the sensitive determination of p-NP. Under the optimum conditions, the peak currents of DPV versus the concentrations of p-NP in the range of 0.01-20 nM showed a good linear relationship (R2 = 0.9943), and the limit of detection (LOD) was calculated to be 7.18 pM (signal-to-noise ratio of 3, S/N = 3). The fabricated electrochemical p-NP sensor exhibited high sensitivity, a low detection limit, and a low response time. The recoveries of p-NP in real samples of groundwater and tap water using the PPy Fls/Ni foam electrode were in the range of 91.0-108.4% with a relative standard deviation (RSD) in the range of 6.65%. Consequently, the PPy Fls/Ni foam electrode could be applied as a rapid, precise, and sensitive electrochemical sensor platform for aqueous p-NP quantification and determination.

Advancing Sensor Data Integrity with Deep Learning-Based Fault Detection

In the realm of modern data-driven systems, accurate and reliable sensor measurements are imperative for informed decision-making and system integrity. The objective of this project is to develop a robust sensor fault detection methodology leveraging the power of deep learning techniques. The ubiquity of sensors such as temperature and humidity sensors has led to a critical need for discerning between accurate readings and erroneous data, thereby enhancing the reliability of these measurements. This study addresses the prevailing challenge of sensor reliability by introducing a data-driven approach that harnesses deep learning algorithms to detect sensor faults promptly and accurately. A comprehensive dataset comprising sensor readings under diverse conditions forms the foundation of this investigation. Multiple cutting-edge deep learning architectures and techniques are systematically explored and evaluated against this dataset to identify the most efficient and precise sensor fault detection method. The outcomes of this research contribute significantly to advancing the state-of-the-art in sensor fault detection, with implications for a wide array of applications reliant on sensor measurements. By harnessing the capabilities of deep learning, this project presents a tangible solution to the challenge of accurately identifying faulty sensor data in real-time, thereby bolstering the dependability and efficacy of sensor-driven systems. Ultimately, this work underscores the potential of integrating advanced machine learning techniques in ensuring the reliability and precision of sensor data, heralding a new era of robust and trustworthy sensor-enabled environments.

Calcium Signalling in Microglia.

Intracellular Ca2+ signalling represents the substrate of microglial excitability. Spatially and temporally organised changes in the free cytoplasmic Ca2+ concentration ([Ca2+]i) are generated in response to physiological and pathological stimuli. Parameters of these intracellular Ca2+ signals are defined by Ca2+ signalling toolkits that may change with age or context therefore increasing adaptive capabilities of microglia. Main Ca2+ signalling pathways in microglial cells are associated with dynamic endoplasmic reticulum Ca2+ stores and with plasmalemmal Ca2+ entry mediated by several sets of Ca2+-permeable channels including transient receptor potential (TRP) channels, ORAI channels and P2X4/7 purinoceptors. Microglial Ca2+ dynamics is also linked to TREM2 signalling cascade, contributing to neuroprotection in neurodegenerative diseases. Microglial Ca2+ signals act as reliable and precise sensors of brain dyshomeostasis and pathological insults.

Research on hydrostatic leveling system to provide elevation constraints for control network adjustment

As the precise sensor system for monitoring the relative altitude changes among multiple points, the capacity hydrostatic leveling system (HLS) is widely used in particle accelerators. To expand its application in providing the elevation constraint for the control network adjustment, the research on the issue of the HLS for altitude difference measurement between multiple points is carried out. Based on the working principle of the HLS sensor, a comparison system composed of dual-frequency laser interferometer, high-precision Z stage, HLS sensors and others is designed and manufactured. The system is used to control multiple sensors to observe the same liquid level in the same coordinate system. The zero-position difference among sensors can be obtained by comparison. Then the altitude difference measurement can be realized, and it is verified that the measurement accuracy is better than 5 μm. In addition, a simulation experiment for 3D control network measurement is run, in which the HLS system provides the elevation constraint for the adjustment processing. The results show that for the 100m linear tunnel, the errors accumulation in the elevation direction is significantly improved compared to the classics adjustment.

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING POTENTIAL AS TOOLS FOR GEOREFERENCING AND FEATURES DETECTION USING UAV IMAGERY

Abstract. While Satellite imagery holds the advantage of encompassing expansive geographical regions. spanning square kilometers, its Spatial Resolution (SR) might prove inadequate for specific tasks. Conversely, Unmanned Aerial Vehicles (UAVs) excel in capturing high-resolution images with spatial resolutions in the range of a few centimeters or even millimeters. However, the accuracy of sensor locations during UAV flights is non-accurate enough by the Global Navigation Satellite Systems (GNSS) technology onboard. One of the key objectives of this research is to evaluate a technique aimed at generating precise sensor locations. This technique employs raw data from the drone's GNSS receiver and minimum Ground Control Points (GCPs) placed within a 2-meter diameter circle in the study area. The goal is to achieve accurate Digital Elevation Models (DEM) and orthomosaic images. Another focus of this research is on addressing challenges related to road lane detection. This is achieved through the enhancement of the You Only Look Once (YOLO) v3 algorithm. The proposed approach optimizes grid division, detection scales, and network architecture to enhance accuracy and real-time performance. The experimental results showcase an impressive 92.03% accuracy with a processing speed of 48 frames per second (fps), surpassing the performance of the original YOLOv3. In the rapidly evolving landscape of Artificial Intelligence (AI) and drone technology, this investigation underscores both the potential and complexities inherent in utilizing advanced AI models, such as YOLOv8, for building detection using UAV and satellite imagery. Furthermore, the research delves into robustness and real-time capabilities within building detection algorithms. The outlined strategy encompasses precise pre-processing, Field-Programmable Gate Array (FPGA) validation, and algorithm refinement. This comprehensive framework aims to elevate feature detection in intricate scenarios, ensuring accuracy, real-time efficiency, and adaptability.

Olfactory system-inspired electronic nose system using numerous low-cost homogenous and hetrogenous sensors

This paper presents an electronic nose system inspired by the biological olfactory system. When comparing the human olfactory system to that of a dog, it’s worth noting that dogs have 30 times more olfactory receptors and three times as many types of olfactory receptors. This implies that the number of olfactory receptors could be a more important parameter for classifying chemical compounds than the number of receptor types. Instead of using expensive precision sensors, the proposed electronic nose system relies on numerous low-cost homogeneous and heterogeneous sensors with poor cross-interference characteristics due to their low gas selectivity. Even if the same type of sensor shows a slightly different output for the same chemical compound, this variation becomes a unique signal for the target gas being measured. The electronic nose system comprises 30 sensors, the e-nose had 6 differing sensors with 5 replicates of each type. The characteristics of the electronic nose system are evaluated using three different volatile alcoholic compounds, more than 99% of which are the same. Liquid samples are supplied to the sensor chamber for 60 seconds using an air bubbler, followed by a 60-second cleaning of the chamber. Sensor signals are acquired at a sampling rate of 100 Hz. In this experimental study, the effects of data preprocessing methods and the number of sensors of the same type are investigated. By increasing the number of sensors of the same type, classification accuracy exceeds 99%, regardless of the deep learning model. The proposed electronic nose system, based on low-cost sensors, demonstrates similar results to commercial expensive electronic nose systems.

Comparing continuous and pulsed nitrogen-vacancy DC magnetometry in the optical-power-limited regime

Ensembles of nitrogen-vacancy (NV) center spins in diamond offer a robust, precise, and accurate magnetic sensor. As their applications move beyond the laboratory, practical considerations including size, complexity, and power consumption become important. Here, we compare two commonly employed NV magnetometry techniques—continuous-wave (CW) versus pulsed magnetic resonance—in a scenario limited by the total available optical power. We develop a consistent theoretical model for the magnetic sensitivity of each protocol that incorporates NV photophysics—in particular, including the incomplete spin polarization associated with limited optical power; after comparing the models’ behavior to experiments, we use them to predict the relative DC sensitivity of CW versus pulsed operation for an optical-power-limited, shot-noise-limited NV ensemble magnetometer. We find a ∼2−3× gain in sensitivity for pulsed operation, which is significantly smaller than seen in power-unlimited, single-NV experiments [Phys. Rev. B 84, 195204 (2011)PRBMDO0163-182910.1103/PhysRevB.84.195204]. Our results provide a resource for practical sensor development, informing protocol choice and identifying optimal operation regimes when optical power is constrained.

A planar cloverleaf antenna for circularly polarized microwave fields in atomic and molecular physics experiments

We report on the design and characterization of a compact microwave antenna for atomic and molecular physics experiments. The antenna is comprised of four loop antennas arranged in a cloverleaf shape, allowing for precise adjustment of polarization by tuning the relative phase of the loops. We optimize the antenna for left-circularly polarized microwaves at 3.5 GHz and characterize its near-field performance using ultracold NaCs molecules as a precise quantum sensor. Observing an unusually high Rabi frequency of 2π × 46.1(2) MHz, we extract an electric field amplitude of 33(2) V/cm at 22 mm distance from the antenna. The polarization ellipticity is 2.3(4)°, corresponding to a 24 dB suppression of right-circular polarization. The cloverleaf antenna is planar and provides large optical access, making it highly suitable for quantum control of atoms and molecules and potentially other quantum systems that operate in the microwave regime.

Urban open-air chemical sensing using a mobile quantum cascade laser dual-comb spectrometer

Detection of airborne chemical releases in densely populated urban environments requires precise sensors with high temporal and spatial resolution capable of covering large areas. For this purpose, we present a mobile mid-infrared quantum cascade laser dual-comb spectrometer for identification and quantification of chemical plumes. Field tests with the remote sensor were conducted during daytime in the downtown Boston area over a five day period during which chemical releases were simulated by intermittently emitting non-toxic substances. Open-air sensing was performed with retroreflectors positioned at up to 230 m distance and with sensitivities in the ppm m range for one second of averaging time. The field campaign demonstrates a step toward a semiconductor dual-comb spectroscopic sensor in the mid-infrared fingerprint region, suitable for long-term deployments. These types of sensors will be valuable complements to existing optical sensors for urban hazardous gas leak monitoring, air quality assessments, and localization of clandestine chemical production.