Accuracy Of Sensor Research Articles

Enzymeless Gabapentin Sensor Based on Prussian Blue-modified Indium Tin Oxide Electrodes for Sensing Gabapentin in Capsules

Background: In this study, we reported on developing a susceptible, accurate, simple, and economical electrochemical sensor for gabapentin determination in capsules. Methods: The ITO electrode was modified with a layer of Prussian blue nanoparticles and then used as the working electrode. Gabapentin was extracted from commercial capsules, and a series of concentrations of gabapentin were prepared for studying the efficacy of the proposed sensor. The electrochemical measurements were performed using cyclic voltammetry and square wave voltammetry techniques. Results: The sensitivity and selectivity of the developed electrode toward gabapentin in different interferences, including citric acid, glucose, and urea, were investigated. The modified electrode showed detection and quantification limits of 31.58 and 94.74 nM, respectively, over a dynamic range of concentrations from 100 nM to 3 μM. Conclusion: The proposed sensor displayed a high sensitivity and selectivity for monitoring gabapentin in pharmaceutical drugs without a noticeable interference. Hence, the modified electrodes are great candidates for gabapentin routine analysis.

Reliability Analysis of Steam Turbine Instrumentation Using the Failure Mode and Effect Analysis (FMEA) Method at PT. PLN Nusantara Power UP Tenayan

PT. PLN Nusantara Power Unit Generation Tenayan is a company operating in the Steam Power Plant sector in Pekanbaru, Indonesia. One of the main components of a steam power plant (PLTU) is a steam turbine. A steam turbine is a machine that functions to convert kinetic energy into electrical current. Operational failures are often caused by less than optimal instrumentation in the steam turbine. This research uses the failure mode and effect analysis (FMEA) method with the aim of identifying the type of failure, the cause of the failure, the effect of the failure, and determining the RPN value. The analysis results from this research show that the turbine instrumentation components still meet performance standards because the risk priority number (RPN) value is less than 200. The conclusion from this research is that errors in steam turbine instrumentation are inaccurate sensor readings, switches cannot deactivate the equipment, and readings control room does not match local readings, the instrumentation component with the highest risk priority number (RPN) is the solenoid valve with a value of 140, and the instrumentation component with the lowest risk priority number (RPN) is the pressure indicator with a value of 30. The component given top priority in action The recommendation is for the solenoid valve component, namely to carry out maintenance every 6 months and add an overspeed protection system

Electrochemical immunosensor based on gold nanoparticles for the detection of creatine kinase as a cardiac marker

This study presents the development of an electrochemical immunosensor utilizing gold nanoparticles for the detection of creatine kinase (CK), a crucial cardiac marker. Gold nanoparticles with an average diameter of 18.3 ± 2.1 nm were synthesized and characterized, exhibiting a characteristic surface plasmon resonance peak at 520 nm. The construction of the immunosensor was observed through cyclic voltammetry and electrochemical impedance spectroscopy, showing notable alterations in electron transfer rates during each step of the assembly process. Optimal performance was achieved at pH 7.4, with an antibody concentration of 100 μg/mL and an incubation time of 45 minutes. The sensor demonstrated a sensitivity of 152.6 Ω/(ng/mL) and maintained its response in the presence of common interfering substances, with maximum signal deviation of 7.2 %. Testing with spiked human serum samples yielded recovery rates between 96.5 % and 103.8 %. Comparison with ELISA using clinical samples further validated the sensor's accuracy. This AuNP-based immunosensor offers a promising platform for rapid and sensitive CK detection in cardiac health assessment.

An Invisible Dermal Nanotattoo-Based Smart Wearable Sensor for eDiagnostics of Jaundice.

Despite substantial progress in the diagnosis of jaundice/hyperbilirubinemia as the most common disease and cause of hospitalization of newborns, on the eve of Industry/Healthcare 5.0, the development of accurate and reliable wearable diagnostic sensors for noninvasive smart monitoring of bilirubin (BIL) is still in high demand. Aiming to fabricate a smart wearable sensor for early diagnosis of neonatal jaundice and its therapeutic monitoring, we here report a fluorescent dermal nanotattoo that further coupled with an IoT-integrated wearable optoelectronic reader for minimally invasive, continuous, and real-time monitoring of BIL in interstitial fluid. Selective recovery of quenched fluorescence of the dermal tattoo sensor, composed of biocompatible dissolving/hydrogel microneedles loaded with fluorescent carbon quantum dots, upon blue light exposure used for jaundice phototherapy was utilized for highly selective BIL sensing. The fascinating features of our developed smart wearable tattoo sensor and its successful results with high correlation with blood BIL results make it a highly promising sensor for easy, minimally invasive, reliable, and smart eDiagnostics and continuous therapeutic eMonitoring of jaundice and other BIL-induced diseases at the point of care. We envision that the developed nanotattoo sensing bioplatform will inspire the development of future smart tattoo sensors in various diagnostic and monitoring scenarios.

Sensitivity and accuracy of single-mode fiber optic sensor for non-invasive glucose measurement

Abstract Routine glucose level monitoring is essential for diabetic patients. Standard measurement methods based on finger pricking are inconvenient and susceptible to infection risk. Therefore, simple and non-invasive measurements are urgently needed. Compared to other approaches, glucose level measurement using optical methods offers many advantages, such as set-up simplicity, high sensitivity, and high accuracy. We optimize the sensing performance of glucose level sensors based on a single-mode fiber optic. The fiber optic serves as a medium for light propagation from a diode laser to a photodiode and, at the same time, serves as an active sensing medium. In the sensing area, the fiber optic cladding is partly (i.e., 1 – 5 cm) stripped to allow direct contact, which leads to evanescent wave absorption by glucose molecules. The length of fiber optic stripping and bending radius, which determine the sensitivity and accuracy of the sensor, varied. Bent fiber optic sensors consistently show a higher sensitivity and accuracy than straight ones. Maximum sensitivity of 2.35 mV/(mg/dL) was attained for a 3 cm stripping length and 4.5 cm bending radius. The lowest sensitivity of the sensor, 0.453 mV/(mg/dL), was acquired at the stripping length of 5 cm for the straight fiber optic sensor. The highest accuracy of the developed sensor is 99% for bent fiber optic sensors.

Fast and Robust 6-DoF LiDAR-Based Localization of an Autonomous Vehicle Against Sensor Inaccuracy

Fast and Robust 6-DoF LiDAR-Based Localization of an Autonomous Vehicle Against Sensor Inaccuracy

Wireless sensor fusion localization method for target vehicles in a collaborative environment of vehicle road trajectory information

Abstract Vehicle positioning technology is the foundation for achieving traffic management. Vehicle positioning errors are significant in complex environments, making it challenging to ensure positioning accuracy. To reduce positioning errors, this paper explores a wireless sensor fusion localization method for target vehicles in a collaborative environment of vehicle road trajectory information. Based on wireless sensor data, we achieve collaborative environmental perception of vehicle and road trajectory information and construct a target vehicle trajectory optimization model. Through a fuzzy neural network, data from different sensors are integrated to overcome the problem of limited positioning accuracy of a single sensor in a complex environment. The experimental results show that both the heading angle and rolling angle errors float between −0.1° and 0.1°, and the positioning error is less than 0.1 m, which proves that the method has high positioning accuracy and good application value.

A multi-source approach to mapping habitat diversity: Comparison and combination of single-date hyperspectral and multi-date multispectral satellite imagery in a Mediterranean Natural Reserve

The increasing availability of spaceborne hyperspectral satellite imagery opens new opportunities for forest habitat mapping and monitoring, but the limitation of its generally low temporal resolution must be considered. In this study, we compare the ability of single-date PRISMA (PRecursore IperSpettrale della Missione Applicativa), the hyperspectral satellite from the Italian Space Agency, with that of both single-date and multi-date Sentinel-2 (S2) and PlanetScope (PS) to detect and correctly classify various EUNIS habitat types distributed over a relatively small spatial extent (6000 ha) in a natural reserve in Central Italy. The case study deals with multiple levels of spectral similarity, as the dominant canopy species of the target forest habitat classes belong to the same genus (Quercus spp., both deciduous and evergreen species) as well as of different taxa (Pinus and Fraxinus spp.). We performed a pixel-based classification with the Random Forest algorithm using a set of 28 spectral indices computed on PRISMA bands, 22 on S2, and 12 on PS. A Canopy Height Model (CHM) was also used as an input variable for the classification. Our results showed that PRISMA considerably outperforms the two multispectral satellites in single-date classifications, with an overall accuracy of 84 % compared to PlanetScope's 69 % and Sentinel-2's 72 %. Regarding the comparison between multi-date multispectral and single-date hyperspectral, 10-fold cross-validation results revealed that S2 achieves an out-of-bag error rate of approximately 16 %, while PRISMA achieves 17 % and PS 19 %. This demonstrates that a combination of spectral indices calculated during the growing season can capture phenological or physiological differences among the target species, which consequently results in a significant improvement in the classification accuracy of the multispectral sensors. Ultimately, classification results from all three sensors were combined to create probability maps for each forest class, identifying areas classified with a higher degree of certainty by each satellite tested and potentially contributing to forest management by defining areas with varying conservation levels.

Embedded Star Catalog Calculation Tool for Autonomous Star Trackers

Autonomous star trackers are crucial attitude sensors for spacecraft three-axis control. They are the most accurate commercial-off-the-shelf sensors available. An autonomous star tracker is an optoelectronic device that autonomously determines the spacecraft’s attitude, using an embedded star catalog that is compared to in-orbit images to perform stellar field recognition. This work presents a computational tool to calculate the embedded star catalog for any specified star sensor. To optimize the star sensor design, the tool developed provides users with sensor accuracy analysis regarding the observed starfield.

Indoor mapping accuracy comparison between the apple devices’ LiDAR sensor and terrestrial laser Scanner

ABSTRACT Indoor 3D mapping is crucial for various architectural uses, including transforming physical floor plans and side walls into digital format, offering an interactive visualization of plans, sections, and elevations, and enabling data accessibility for different engineering needs, according to the available scanning tools, 3D interior modelling for surveying indoors usually uses terrestrial laser scanners, as they provide high accuracy but are expensive and take a long time, especially in data processing. Apple recently released smartphones with LiDAR sensors that can create 3D models. This study evaluates the accuracy of these LiDAR sensors, which are nearly cost 2.5% of the terrestrial laser scanners price and produce promising 3D model for indoor mapping. Evaluation is through a comparison between Apple’s iPad Pro 2021 LiDAR sensor and the terrestrial laser scanner Trimble Tx8, a 3D model created of a hall in the university campus using the Laser Scanner Trimble Tx8 and another two-3D models of the same hall using the iPad’s Pro 2021 LiDAR sensor, one of which is dynamic acquisition since the iPad was in motion and the other is static acquisition where the iPad stayed still and scanned the same hall through multiple overlapping scans, Comparisons were conducted depending on lengths, widths, hight and area measurements. In the static acquisition model, linear readings were 1–2 cm off, while area measurements were around 1-meter square accuracy.

Actual problems of remote patient monitoring

Remote patient monitoring (RPM) is an innovative technology that provides continuous monitoring of patients' health from a distance. The RPM system is versatile because it can be used in the treatment of acute and chronic conditions, helping doctors keep their patients under control between ward rounds or the absence of personal care. However, the implementation of this system is accompanied by numerous problems: from technical to hospital staff management problems. The object of this study is the technical challenges associated with the use of the RPM system. The problem lies in the imperfection of many elements of RPM, including: data management, sensor accuracy, compatibility and user acceptance of the technology. The research found that these technical challenges arise from insufficient standardization, poor sensor data quality, and interoperability issues between medical platforms. These results are explained by the complexity of the technological infrastructure and the need to improve the qualifications of medical workers. The main goal of the current article is to solve the scientific and technical problem of RPM through the proposal of an effective practical development - an automated system for remote monitoring of patients. Since most processes are moving towards their automation, by using the latest scientific developments, among which we can recall machine learning and neural networks and Internet of Things (IoT) technology, it was decided to consider the factors of their application in remote patient monitoring systems. The results of this study can be used to optimize RPM systems in hospitals, as well as to improve the reliability and efficiency of new automated monitoring systems, which is extremely important in the context of war, which causes a shortage of medical personnel and an increase in the number of potential patients per doctor.

Unmanned vehicles in health monitoring and medicine delivery with swarm algorithm innovations

The article delves into modern approaches and innovative technologies designed to monitor the health of military personnel using unmanned vehicles, while also assessing their potential for delivering medicines in combat environments. It thoroughly examines the wide range of sensors and technologies that enable remote measurement of vital signs, including but not limited to thermal imaging cameras, photoplethysmography sensors, and radio frequency sensors, highlighting their effectiveness in various operational contexts. The article also reviews numerous real-world applications of drones in both medical and humanitarian projects, showcasing the transformative impact these technologies can have in providing timely healthcare and support in areas where traditional medical infrastructure may be lacking. In addition to discussing the practical benefits of using unmanned vehicles for healthcare delivery, the article also addresses the challenges posed by their integration into military operations, such as logistical, technical, and regulatory hurdles. Furthermore, it presents a detailed example of how these innovative systems can be effectively combined to enhance the medical care provided to military personnel, especially during combat, where traditional access to medical services is often limited or compromised. The article proposes several key areas for future research and development, emphasizing the importance of continued technological advancements to fully harness the potential of unmanned vehicles for medical applications in the military sector. These areas include improving sensor accuracy, developing better communication systems, and creating more autonomous and efficient delivery mechanisms. Here are examples that prove, the article suggests that unmanned vehicles could play a critical role in revolutionizing both battlefield medicine and military logistics, offering new solutions for healthcare delivery in challenging environments.

Analysis and comparison of sensor accuracy of autonomous vehicles

Autonomous driving is gradually becoming one of the major directions in the development of automotive technology nowadays. Environmental detection technology is indispensable for existing intelligent vehicles, especially when such cars are used in daily life, where many complex road environments cannot be helped by environmental detection technology. Environmental detection technology cannot be divorced from hardware and software support. This study will discuss the different sensors used in autonomous driving environment detection technology, to gain a deeper understanding of the characteristics, functions and applications of these sensors, and to discuss the advantages and limitations of each sensor. Then a comparison of the three widely used sensors in the world is conducted, in terms of the detection range and angle, the accuracy of road detection, and the stability of the detection. The three sensors are a camera, millimeter wave radar and laser radar. A more suitable and stable one from these three sensors will be chosen for in-depth consideration. On this basis, new ideas for the future development of existing sensors are provided, while the direction of improvement of existing sensors is summarized based on the results of existing analyses.

A Novel Bioluminescent Biosensor Quantifying Intramolecular Interaction and Levels of Pyroptosis Effector GSDMD.

Gasdermin D (GSDMD) is a key executor of pyroptosis, a form of inflammation-induced programmed cell death. Recently, GSDMD has been shown to play important roles in the development of various inflammatory-related human diseases including heart failure and cancer, suggesting that it is a promising therapeutic target for these diseases. While extensive studies on GSDMD's role in pyroptosis have been reported, it is challenging to study its function due to the lack of enzymatic activity of GSDMD. In this study, we used the NanoBiT technology to develop a novel GSDMD bioluminescent biosensor (GSDMD-BS) that detects the amount of non-cleaved GSDMD. This sensor allows us to quantify GSDMD's intramolecular interactions, the amounts of uncleaved GSDMD after caspase-1 cleavage, and expression levels in living cells. In vitro experiments using purified GSDMD-BS also confirmed the sensor's accuracy in reporting GSDMD levels and cleavage. Moreover, the potential for in vivo application was demonstrated in a xenograft mouse model. In conclusion, we have developed a GSDMD biosensor that is a valuable tool for real-time monitoring of GSDMD dynamics and pyroptosis. This biosensor will significantly expedite pyroptosis research and can be used for high-throughput screening for drugs targeting GSDMD for the therapy of many inflammation-related diseases.

Damage detection of Kevlar woven fabric using optical fiber multimode interferometer

Kevlar woven fabric is crucial for the lightweight design of aerospace structures due to its unique advantages. However, it is susceptible to damage during operation, making effective damage detection essential for ensuring structural reliability. Nevertheless, conventional sensors used for damage detection have limitations such as complex manufacturing processes and weight. To address this issue, this paper proposed a simple singlemode-multimode-singlemode (SMS) optical fiber sensor to collect vibration signals and construct a damage recognition model based on natural frequency in order to identify the size of damages in Kevlar woven fabrics. To achieve a highly linear and accurate sensor, a length determination criterion for multimode optical fiber segments based on quasi-image, notch, and transmission spectrum loss was proposed. An optical sensor was fabricated based on this criterion. The feasibility and sensitivity of the sensor for vibration signal measurement were verified by constructing a vibration signal measurement device. An experimental study was conducted to detect damage in Kevlar tapes, where the natural frequency changes measured by the SMS sensor were integrated with an optimized multi-variable gray model for damage size detection. The experimental results indicate that the estimated error in damage size is 0.71%. The damage identification system proposed in this paper offers a simple and cost-effective solution for measuring damage in flexible structures using fiber optic sensors.

Fill, Fold, Photo: Preconcentration and Multiplex Detection of Trace Level Heavy Metals in Water.

Heavy metal contamination is an increasing global threat to human and environmental health, particularly in resource-limited areas. Traditional platforms for heavy metal detection are labor intensive and expensive and require lab facilities. While paper-based colorimetric sensors offer a simpler approach, their sensitivity limitations prevent them from meeting legislative requirements for many metals. Existing preconcentration systems, on the other hand, can achieve lower detection limits but typically focus on analyzing only one metal, making comprehensive monitoring difficult. We address these limitations by introducing a low-cost preconcentration system coupled with colorimetric analysis for the simultaneous detection of seven metal ions at low ppb levels without the need for external equipment outside a smartphone. The system achieved detection limits of 15 ppb (Ni(II)), 7 ppb (Cu(II)), 2 ppb (Fe(III)), 20 ppb (Cr(VI)), 13 ppb (Pb(II)), 26 ppb (Hg(II)), and 15 ppb (Mn(II)) with six out of seven limits of detection values falling well below EPA regulatory guidelines for drinking water. The user-friendly Fill, Fold, Photo approach eliminates complex pretreatment steps. Smartphone-based detection offers portable quantification within seconds. Employing masking strategies ensured higher selectivity for each assay on the card, while our packaging protocols enable system stability for over 4 weeks of study, facilitating mass production and deployment within a realistic time frame. To validate the sensor's performance in real-world scenarios, the sensor was tested with environmental water samples. The sensor demonstrated good recovery, ranging from 77% to 94% compared to the standard ICP-MS method. Furthermore, spike recovery analysis confirmed the sensor's accuracy, with a relative standard deviation (RSD) of less than 15%. This technology holds significant promise for future development as a convenient, portable solution for field-based monitoring of a broad spectrum of water contaminants, including pesticides, PFAS, fertilizers, and beyond.

LAVOLUTION: Tunable structured light for bridge displacement measurement

Assessing bridge conditions is critical, but field measurements often face challenges like sensor installation and accuracy requirements. This paper introduces a novel approach using structured light (SL) for non-target-based measurements, eliminating the need for physical markers. A tunable SL system, featuring four laser pointers aligned with a commercial camera, was developed to estimate the relative angle between the camera and the target structure, and to accurately calculate the scale factor. The proposed method involves three main processes: (1) laser alignment, (2) angle estimation, and (3) real-time displacement measurement. By precisely aligning the SL system and minimizing discrepancies between projected SL points and an ideal numerical model, the method achieves accurate results. Validation through numerical simulations, lab-scale tests, and full-scale bridge tests demonstrated a maximum error of 2.69% in scale and 2.53% in displacement, confirming the method’s effectiveness and applicability.

Integrating low-cost GNSS and MEMS accelerometer for precise dynamic displacement monitoring

We assess the performance of the self-developed receiver coupling dual-frequency low-cost GNSS chipset and IMU MEMS sensor. The system also comprises software dedicated to vibration monitoring based on relative kinematic positioning in a post-processing mode. The observation assessment reveals high accuracy of low-cost GNSS and MEMS accelerometer data. The Septentrio Mosaic X5 chipset exhibits competitive to high-grade receivers’ phase observation noise, outperformance in code noise level and observation auto-correlation at a level that may be neglected even in high-rate applications. The evaluation of the MEMS accelerometer data proves that the sensor accuracy is adequate for vibration monitoring. With the shake table experiment, we examine the performance of vibration recovery. An advantage of the coupled solution over a GNSS-only one is documented with a 15 % reduction of the amplitude error. The backward smoothing of coupled solutions drives the next 20 % error drop. Finally, the high performance of the coupled solution based on low-cost GNSS & accelerometer is confirmed by a mean amplitude error of 0.4 mm. The frequency analyses indicate that both coupled and single-sensor solutions precisely detect the vibration frequencies; however, they also confirm the outperformance of the former over the latter. The power spectra of the recovered displacement time series accentuate the benefits of GNSS + accelerometer fusion and backward filtering for reducing displacement noise.

Spacecraft formation flying orbital control for earth observation mission

Synthetic Aperture Radar (SAR) sensors have gained much attention for multiple civil use cases, where NewSpace startups have launched and planning to launch sensors to orbit to harvest the unique capabilities of SAR for the Earth Observation (EO) market. Unlike optical, SAR can capture images in all lighting conditions and penetrate cloud cover, generating exceptional use cases, which makes SAR the most reliable sensor. For example, monitoring soil deformation with millimeter accuracy, oil spills, crop lodging, and typical EO use cases of optical sensors that are unable to penetrate the clouds or see at night. However, SAR is still limited in civil use cases because it is tough to interpret. In addition, unlike government users who can consume the SAR data directly, most civil end-users need analytics on top of the data. To unlock the potential civil use cases, many experts believe that optical EO data should be fused with SAR at the EO upstream, thus suggesting optical and SAR sensors fly in formation. Spacecraft Formation Flying (SFF) is one of the key technology enablers for space exploration and EO space missions. This paper presents an SFF EO mission (SAR and Optical) design considering orbit requirements to maximize user objectives inspired by OptiSAR™ Constellation.

Machine learning and deep learning models based grid search cross validation for short-term solar irradiance forecasting

In late 2023, the United Nations conference on climate change (COP28), which was held in Dubai, encouraged a quick move from fossil fuels to renewable energy. Solar energy is one of the most promising forms of energy that is both sustainable and renewable. Generally, photovoltaic systems transform solar irradiance into electricity. Unfortunately, instability and intermittency in solar radiation can lead to interruptions in electricity production. The accurate forecasting of solar irradiance guarantees sustainable power production even when solar irradiance is not present. Batteries can store solar energy to be used during periods of solar absence. Additionally, deterministic models take into account the specification of technical PV systems and may be not accurate for low solar irradiance. This paper presents a comparative study for the most common Deep Learning (DL) and Machine Learning (ML) algorithms employed for short-term solar irradiance forecasting. The dataset was gathered in Islamabad during a five-year period, from 2015 to 2019, at hourly intervals with accurate meteorological sensors. Furthermore, the Grid Search Cross Validation (GSCV) with five folds is introduced to ML and DL models for optimizing the hyperparameters of these models. Several performance metrics are used to assess the algorithms, such as the Adjusted R2 score, Normalized Root Mean Square Error (NRMSE), Mean Absolute Deviation (MAD), Mean Absolute Error (MAE) and Mean Square Error (MSE). The statistical analysis shows that CNN-LSTM outperforms its counterparts of nine well-known DL models with Adjusted R2 score value of 0.984. For ML algorithms, gradient boosting regression is an effective forecasting method with Adjusted R2 score value of 0.962, beating its rivals of six ML models. Furthermore, SHAP and LIME are examples of explainable Artificial Intelligence (XAI) utilized for understanding the reasons behind the obtained results.