Automatic Sensor Research Articles

A Study the Water Quality Measuring Equipment with Automatic Sensor System

This paper studied the water quality in Dong Khoai Village, Xaythani District, Vientiane Capital, which is the primary water pumping location for agriculture. The area has a population of over 1735 citizens and cover more than 450 hectares. Common issue found in Markhiaw river. Stream is its toxicity to certain rice field, causing it inability to grow rice despite using fertilizer. Furthermore, this stream gives dissatisfaction odor, negatively affects people who get into the water for fishing and occasionally found death fish in the areas. In order to assess the water quality and disseminate the results to the public. Therefore, this study aims to investigate the creating of an automated sensor system for high precision water quality monitoring in the water. Four sensors; pH (hydrogen potential), EC (electro conductivity), TDS (total dissolved solid), and temperature are used in this procedure. The sensor system was tested, and build a small station uses solar energy and saves information into the SD card and available online by Blynk, and compare the test results with national environmental standards. The research revealed that, in comparison to the standard instrument S5 pro parameter, the measuring stations for the water quality values of all four sensors are accuracy value of 92.91% to 99.34%. Based on the information gathered over the course of two months, from February 13 and March 18, the pH value will be low, at 5. The pH value is high, at 11, between 13, 17 and 18 February 2024. These numbers are considered to be in the standard ranks. Regarding the dissolved solids in the water vary from 3 to 100 mg/L, and the electrical conductivity is between 100 and 180 µS/cm. They are also fall upon the National Environmental Standard. The Temperature value is high, at 40.81 ºC on 4 February 2024 and from 02 March 2024, the Temperature value will be low, at 24.4ºC.

Rancang Bangun Mesin Pencacah Plastik Pet dengan Kapasitas 20 Kg/Jam

Shredding is one of the common recycling methods used for plastic waste processing. Along with the development of the era, conventional tools have begun to change to semi-automatic. Therefore, a shredding machine is needed for plastic waste processing with an automatic sensor system to facilitate operation. This design aims to determine the performance of a PET plastic shredding machine with the shredder shredding method. Pahl and Beitz is the method used in making this tool. In accordance with the method applied, the manufacture of the tool starts from planning, concept design, shape design, detailed design, documentation creation and realization of the tool. The testing process is carried out by testing the function of the machine and the function of the automatic sensor. The designed shredder machine has the following specifications: Dimensions of the tool are 950 x 365 x 1060 mm, capacity is 20 kg/hour, 3 phase ½ HP dynamo motor, rotation speed is 70 rpm, number of blades is 14 and material used on the blade is AISI 1080. The test results of this tool show that the tool can work to shred PET plastic, can shred 1 kg of plastic bottles in 2 minutes 45 seconds, the first shredded result is 150 mm maximum smallest size is 16 mm with 3 shredding processes, and can be operated with a sensor control system for the tool's work process. Keywords: shredding machine, garbage, plastic

Enhancing Automatic Inertial Sensor Calibration Algorithm for Accurate Joint Angle Estimation in High Flexion Postures

Enhancing Automatic Inertial Sensor Calibration Algorithm for Accurate Joint Angle Estimation in High Flexion Postures

A Study on the Factors Controlling the Kinematics of a Reactivated and Slow-Moving Landslide in the Eastern Liguria Region (NW Italy) through the Integration of Automatic Geotechnical Sensors

This paper deals with the investigation of factors influencing the movement patterns of a reactivated slow-moving landslide situated in the eastern Liguria region (NW Italy) through the analysis of extensive ground-based hydrological and geotechnical monitoring data. Subsurface horizontal displacement and pore water pressure data were acquired simultaneously by means of automatic sensors positioned at pre-existing and localized failure zones. The joint examination of field measurements enabled us to explore the connections between rain, pore water pressure, and displacements. The results of continuous displacement monitoring showed that the landslide kinematics involved phases of extremely slow movements alternated with periods of relative inactivity. Both stages occurred prevalently at seasonal scale displaying similar durations. The slow-motion phases took place at relatively constant pore water pressure and were ascribed to mechanisms of viscous shear displacements along failure surfaces. Inactive phases entailed no significant deformations, mostly corresponding to prolonged dry periods. The two motion patterns were interrupted by episodic sharp deformations triggered by delayed (preparation periods from 4 to 11 days) rainfall-induced pore water pressure peaks, which were ascribed to sliding mechanisms taking place through rigid-plastic frictional behaviour. During these deformation events, hysteresis relationships between pore water pressure and displacement were found, revealing far more complex hydro-mechanical behaviour.

Fire detection and suppression in gas-plants using automatic sensors among electrical/electronic technology education graduates in Nigeria

Software systems such as Proteus, Matlab, and an Arduino Uno programming software system, comprising an Infra-red flame sensor, a 5v Motor and other components like Buzzer, relay, light emitting diode, transistor, resistor and capacitors, were used in order to detect smoke, flame or fire and suppress it in a laboratory. The methodology of the system's design, as well as the operation of the system, were schematically highlighted. The control measures and maintenance strategies to be deployed for the smooth functioning of the system design were also discussed, wherever they are used. Solutions to checkmate fire disasters were proffered. The workings of the system are that when the sensor registers or turns zero (0), it means that there is no fire, flame or smoke to suppress, while when it turns to one (1), it indicates that there is smoke or fire to suppress. The process recorded 95% success.

Multi-scale assessment of a cosmic-ray neutron probe observation of soil moisture for surface layer applications in a mountainous forest environment

Soil moisture is critical to the Earth's water cycle as it influences water exchange between the land, atmosphere, and oceans. This study assessed the potential of a cosmic-ray neutron probe (CRNP) to monitor soil moisture dynamics in a forested mountain region, which is critical for water resource management and ecosystem health. Accurate soil moisture monitoring is essential for effectively managing forested mountainous areas, but various limitations exist in such environments. To overcome the complexities of soil moisture monitoring, an observatory consisting of a CRNP, automatic weather sensors, and dataloggers was established. Fifteen capacitance sensor (CS) stations were placed strategically within the CRNP's footprint to ensure strict evaluation. Although each CS measurement revealed the heterogeneity of the experimental site, temporal stability analysis determined the representative soil moisture. The validation of the CRNP and the average results of the 15 surface CS sensors revealed that they were strongly correlated (R = 0.94). It means that the CRNP can describe the dynamics of soil moisture that fluctuate according to physical properties, underscoring its reliability in estimating intermediate scale soil moisture. This correlation remained robust despite differences in rainfall patterns over three years. These findings indicate that the CRNP may be a valuable tool for monitoring soil moisture in a challenging environment. Additionally, the evaluation of the Advanced Microwave Scanning Radiometer-2 (AMSR2), Advanced Scatterometer (ASCAT), and Global Land Data Assimilation System (GLDAS) demonstrated the usefulness of CRNP in supplying reference data. The AMSR2 showed an inadequate temporal pattern (R = 0.35), and the ASCAT achieved the highest correlation agreement (R = 0.67) with the CRNP. The GLDAS also showed close agreement, although with some discrepancies (R = 0.52). The results of this study support the utilization of CRNP and provide insight into establishing and expanding a soil moisture network.

Data-driven approaches linking wastewater and source estimation hazardous waste for environmental management

Industrial enterprises are major sources of contaminants, making their regulation vital for sustainable development. Tracking contaminant generation at the firm-level is challenging due to enterprise heterogeneity and the lack of a universal estimation method. This study addresses the issue by focusing on hazardous waste (HW), which is difficult to monitor automatically. We developed a data-driven methodology to predict HW generation using wastewater big data which is grounded in the availability of this data with widespread application of automatic sensors and the logical assumption that a correlation exists between wastewater and HW generation. We created a generic framework that used representative variables from diverse sectors, exploited a data-balance algorithm to address long-tail data distribution, and incorporated causal discovery to screen features and improve computation efficiency. Our method was tested on 1024 enterprises across 10 sectors in Jiangsu, China, demonstrating high fidelity (R² = 0.87) in predicting HW generation with 4,260,593 daily wastewater data.

Spatial Variation of Airborne Pollen Concentrations Locally around Brussels City, Belgium, during a Field Campaign in 2022–2023, Using the Automatic Sensor Beenose

As a growing part of the world population is suffering from pollen-induced allergies, increasing the number of pollen monitoring stations and developing new dedicated measurement networks has become a necessity. To this purpose, Beenose, a new automatic and relatively low-cost sensor, was developed to characterize and quantify the pollinic content of the air using multiangle light scattering. A field campaign was conducted at four locations around Brussels, Belgium, during summer 2022 and winter–spring 2023. First, the consistency was assessed between the automatic sensor and a collocated reference Hirst-type trap deployed at Ixelles, south-east of Brussels. Daily average total pollen concentrations provided by the two instruments showed a mean error of about 15%. Daily average pollen concentrations were also checked for a selection of pollen species and revealed Pearson and Spearman correlation coefficients ranging from 0.71 to 0.93. Subsequently, a study on the spatial variability of the pollen content around Brussels was conducted with Beenose sensors. The temporal evolution of daily average total pollen concentrations recorded at four sites were compared and showed strong variations from one location to another, up to a factor 10 over no more than a few kilometers apart. This variation is a consequence of multiple factors such as the local vegetation, the wind directions, the altitude of the measurement station, and the topology of the city. It is therefore highly necessary to multiply the number of measurement stations per city for a better evaluation of human exposure to pollen allergens and for more enhanced pollen allergy management.

Development of a Fleet IoT-Based System Management in Institutions

IoT-based systems are increasingly important for managing assets and activities, and implementing such a system can greatly benefit institutions like WASAC. The purpose of this system is to effectively manage WASAC's fleet, improving vehicle tracking, dispatching, fuel management, maintenance planning, driver behavior and safety, accident reduction, vehicle lifespan, customer service, unauthorized vehicle usage control, and data analysis and reporting. Currently, WASAC relies on paper-based methods for data recording and fuel management, resulting in financial losses and inadequate driver monitoring. By developing an IoT-based system, several sensors and devices such as a CAN adapter FMB140, automatic alcohol test sensor, fuel sensor, relay, I-button, and buzzer will be employed. These devices and sensors will be connected to the FMB140 IoT device, which will capture data and store them in a server database. This data will then be used to create an interactive web-based application using socket programming for fleet managers and the monitoring team. The application will be developed using PHP and React technologies. By implementing this IoT in fleet management will enhance safety and security measures for institutions specifically within the WASAC fleet management system, Real-time tracking, geofencing, prevention of fuel theft, driver behavior monitoring, and remote engine cut-off will significantly improve driver and vehicle safety while boosting fleet productivity. Overall, the IoTbased system will streamline operations, reduce costs, and enhance efficiency for WASAC's fleet management.

A Healthcare System Employing Lightweight CNN for Disease Prediction with Artificial Intelligence

Introduction/Background This research introduces the EO-optimized Lightweight Automatic Modulation Classification Network (EO-LWAMCNet) model, employing AI and sensor data for forecasting chronic illnesses within the Internet of Things framework. A transformative tool in remote healthcare monitoring, it exemplifies AI's potential to revolutionize patient experiences and outcomes. This study unveils a novel Healthcare System integrating a Lightweight Convolutional Neural Network (CNN) for swift disease prediction through Artificial Intelligence. Leveraging the efficiency of lightweight CNN, the model holds promise for revolutionizing early diagnosis and enhancing overall patient care. By merging advanced AI techniques, this healthcare model holds the potential for revolutionizing early diagnosis and improving overall patient care. Materials and Methods The Lightweight Convolutional Neural Network (CNN) is implemented to analyze sensor data in real-time within an Internet of Things (IoT) framework. The methodology also involves the integration of the EO-LWAMCNet model into a cloud-based IoT ecosystem, demonstrating its potential for reshaping remote healthcare monitoring and expanding access to high-quality care beyond conventional medical settings. Results Utilizing the Chronic Liver Disease (CLD) and Brain Disease (BD) datasets, the algorithm achieved remarkable accuracy rates of 94.8% and 95%, respectively, showcasing the robustness of the model as a reliable clinical tool. Discussion These outcomes affirm the model's reliability as a robust clinical tool, particularly crucial for diseases benefiting from early detection. The potential transformative impact on healthcare is emphasized through the model's integration into a cloud-based IoT ecosystem, suggesting a paradigm shift in remote healthcare monitoring beyond traditional medical confines. Conclusion Our proposed model presents a cutting-edge solution with remarkable accuracy in forecasting chronic illnesses. The potential revolutionization of remote healthcare through the model's integration into a cloud-based IoT ecosystem underscores its innovative impact on enhancing patient experiences and healthcare outcomes.

Efficacy of chlorine-based disinfectants to control Legionella within premise plumbing systems

Legionella is an opportunistic waterborne pathogen that causes Legionnaires’ disease. It poses a significant public health risk, especially to vulnerable populations in health care facilities. It is ubiquitous in manufactured water systems and is transmitted via inhalation or aspiration of aerosols/water droplets generated from water fixtures (e.g., showers and hand basins). As such, the effective management of premise plumbing systems (building water systems) in health care facilities is essential for reducing the risk of Legionnaires’ disease. Chemical disinfection is a commonly used control method and chlorine-based disinfectants, including chlorine, chloramine, and chlorine dioxide, have been used for over a century. However, the effectiveness of these disinfectants in premise plumbing systems is affected by various interconnected factors that can make it challenging to maintain effective disinfection. This systematic literature review identifies all studies that have examined the factors impacting the efficacy and decay of chlorine-based disinfectant within premise plumbing systems. A total of 117 field and laboratory-based studies were identified and included in this review. A total of 20 studies directly compared the effectiveness of the different chlorine-based disinfectants. The findings from these studies ranked the typical effectiveness as follows: chloramine > chlorine dioxide > chlorine. A total of 26 factors were identified across 117 studies as influencing the efficacy and decay of disinfectants in premise plumbing systems. These factors were sorted into categories of operational factors that are changed by the operation of water devices and fixtures (such as stagnation, temperature, water velocity), evolving factors which are changed in-directly (such as disinfectant concentration, Legionella disinfectant resistance, Legionella growth, season, biofilm and microbe, protozoa, nitrification, total organic carbon(TOC), pH, dissolved oxygen(DO), hardness, ammonia, and sediment and pipe deposit) and stable factors that are not often changed(such as disinfectant type, pipe material, pipe size, pipe age, water recirculating, softener, corrosion inhibitor, automatic sensor tap, building floor, and construction activity). A factor-effect map of each of these factors and whether they have a positive or negative association with disinfection efficacy against Legionella in premise plumbing systems is presented. It was also found that evaluating the effectiveness of chlorine disinfection as a water risk management strategy is further complicated by varying disinfection resistance of Legionella species and the form of Legionella (culturable/viable but non culturable, free living/biofilm associated, intracellular replication within amoeba hosts). Future research is needed that utilises sensors and other approaches to measure these key factors (such as pH, temperature, stagnation, water age and disinfection residual) in real time throughout premise plumbing systems. This information will support the development of improved models to predict disinfection within premise plumbing systems. The findings from this study will inform the use of chlorine-based disinfection within premise plumbing systems to reduce the risk of Legionnaires disease.

Drone-based photogrammetry combined with deep learning to estimate hail size distributions and melting of hail on the ground

Abstract. Hail is a major threat associated with severe thunderstorms, and estimating the hail size is important for issuing warnings to the public. For the validation of existing operational, radar-derived hail estimates, ground-based observations are necessary. Automatic hail sensors, for example within the Swiss Hail Network, record the kinetic energy of hailstones to estimate the hail sizes. Due to the small size of the observational area of these sensors (0.2 m2), the full hail size distribution (HSD) cannot be retrieved. To address this issue, we apply a state-of-the-art custom trained deep learning object detection model to drone-based aerial photogrammetric data to identify hailstones and estimate the HSD. Photogrammetric data of hail on the ground were collected for one supercell thunderstorm crossing central Switzerland from southwest to northeast in the afternoon of 20 June 2021. The hail swath of this intense right-moving supercell was intercepted a few minutes after the passage at a soccer field near Entlebuch (canton of Lucerne, Switzerland) and aerial images were taken by a commercial DJI drone, equipped with a 45-megapixel full-frame camera system. The resulting images have a ground sampling distance (GSD) of 1.5 mm per pixel, defined by the focal length of 35 mm of the camera and a flight altitude of 12 m above the ground. A 2-dimensional orthomosaic model of the survey area (750.4 m2) is created based on 116 captured images during the first drone mapping flight. Hail is then detected using a region-based convolutional neural network (Mask R-CNN). We first characterize the hail sizes based on the individual hail segmentation masks resulting from the model detections and investigate the performance using manual hail annotations by experts to generate validation and test data sets. The final HSD, composed of 18 207 hailstones, is compared with nearby automatic hail sensor observations, the operational weather-radar-based hail product MESHS (Maximum Expected Severe Hail Size) and crowdsourced hail reports. Based on the retrieved data set, a statistical assessment of sampling errors of hail sensors is carried out. Furthermore, five repetitions of the drone-based photogrammetry mission within 18.65 min facilitate investigations into the hail-melting process on the ground.

Comparison of Frequency and Stay Time between Normal and Abnormal Elimination Behavior of Cats Using a Litter Box with Automatic Sensor

Comparison of Frequency and Stay Time between Normal and Abnormal Elimination Behavior of Cats Using a Litter Box with Automatic Sensor

Uncertainty evaluation for wind speed measurement part (2): “Input quantity correlation”

When the “Guide to the expression of uncertainty in measurement” (GUM) is used to evaluate the uncertainty of wind speed sensors, there is a lack of input quantities correlation research, which leads to the deviation of the uncertainty evaluation result. This paper takes the calibration result of an automatic weather station's three-cup wind speed sensor as an example to evaluate the uncertainty of wind speed sensor measurement based on the correlation of input quantities. First, the calibration point of 10.00 m/s wind speed is taken as an example, and the original wind speed record is obtained by calibrating the three-cup wind speed sensor. Then, according to the GUM evaluation method, the uncertainty of the wind speed measurement result is analyzed and evaluated, And analyze and explore the correlation between the input quantities (considering the influence of the stability and uniformity of the wind tunnel on the measurement result, and the existence of a certain interdependence between them, an experimental plan was developed to test the stability and uniformity of the wind tunnel, and the correlation coefficient between them was calculated to be 0.1561 at the wind speed point of 10.00 m/s). Finally, the input quantities correlation (the correlation coefficient of wind tunnel stability and uniformity) is integrated into the uncertainty evaluation, and the uncertainty evaluation result is obtained at 10.00 m/s wind speed point (where U = 0.22 m/s, U95 = 0.22 m/s). Through comparison, the uncertainty results evaluated without considering the correlation of input quantities have generated negative deviation, with deviation values being −0.00022 m/s. Due to the relatively small deviation, in practical applications, it can be determined whether to correct it based on the accuracy requirements of the measurement result and actual needs.

Conjunctive use of floodwater harvesting for managed aquifer recharge and irrigation on a date farm in Morocco

AbstractIn arid regions, harvesting floodwater can mitigate irrigation‐induced groundwater depletion by providing additional surface water and recharging aquifers. We designed an experimental protocol to quantify these fluxes on a date farm located along the Wadi Satt, whose flow originates from the Anti‐Atlas Mountains in south‐eastern Morocco. Automatic barometric sensors were used to monitor the water level in a 6500 m3 floodwater harvesting pond and in surrounding boreholes. Six flood events occurred from 2021 to 2023. The pond water balance indicated that most stored water is pumped for irrigation (56% of harvested floodwater). More than 40% infiltrates at a rate of approximately 90 mm day−1, and the remainder evaporated. Analytical modelling of the pond water table system showed that the radius of the piezometric mound resulting from pond infiltration is less than 360 m. Groundwater recharge from the irrigated plot could be observed after two close floods that enabled continuous pumping for several weeks, suggesting that in this specific context, over‐irrigation using surface water allows the aquifer to be recharged. The hydrological effects of possible future expansion of these ponds at the watershed scale should be analysed to assess possible negative impacts on downstream water resources.

Automated Generation of Clinical Reports Using Sensing Technologies with Deep Learning Techniques.

This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient-doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes.

Performance Evaluation of Micro Automatic Pressure Measurement Sensor for Enhanced Accuracy

The major objective of this research is to design sensitive components, conversion components, and various sensor circuits to achieve the miniaturization design for more accurate measurements. This article conducts performance testing on the designed miniaturized pressure sensor to determine whether it meets the qualified standards. The response time of designed sensor results increases with the increase of pressure under experimental conditions of different pressure application values (5MPa to 50MPa). The detection accuracy of the micro automatic pressure measurement sensor designed in this paper can reach 0.0452\%; the average pressure is 0.00364%, and the insulation resistance is 68.44 megohms, which meets reliability requirements. The sensitivity is 0.0582%; the nonlinearity is 0.0741%; the hysteresis is 0.0266%; the repeatability of 0.0625% meets the qualification standard for this instrument. Still, compared with traditional sensors, the sensor reduces the response time of results by about 60%. However, the author conducted the detection in an ideal environment. The actual working environment of sensors is relatively good. Therefore, the detection results obtained in this article may have some errors compared to the actual situation, and further analysis and testing are needed to optimize the performance of the designed sensor.

ASCD: Automatic sensing and control device for crop irrigation scheduling

Traditional agricultural irrigation systems suffer from wasting a lot of water allocated for irrigation through evaporation and drainage. To address water waste and increase crop productivity, irrigation scheduling is used, as irrigation scheduling depends on four elements: soil moisture, soil characteristics, weather conditions, and plant type. This paper proposes a new design for monitoring agricultural conditions and controlling the amount of soil moisture by determining the required irrigation water ratios and the ideal time period for irrigating crops. An intelligent control and monitoring algorithm was created using experimental data for a wide range of plants (for soil moisture from 21 to 80 kPa), where plants were classified into three groups according to their common irrigation starting point. Based on the proposed plant classification, three models were provided, where irrigation ratios as the required parameter, soil moisture, plant type, and time interval as input parameters. This algorithm was used to program a custom-made automatic sensor and control device (ASCD). The ASCD is equipped with two input ports to communicate with two types of soil moisture sensors (a resistive sensor and a capacitive sensor) and with three output ports to drive three types of electronic irrigation valves. The ASCD can read from one sensor and drive one electronic valve at a time, the choice of which is made by the farmer. ASCD based the new algorithm showed high accuracy compared to a number of experimental results. The average absolute relative deviation (AAPD) of the new models and experimental data is 5.46 %. The application of the new algorithm shows a reduction in the amount of water used for crop irrigation during the day versus irrigation at night. On the other hand, ASCD has proven its success in sensing and controlling, and it works automatically and independently.

Multivariate Functional Clustering with Variable Selection and Application to Sensor Data from Engineering Systems

Multisensor data that track system operating behaviors are widely available nowadays from various engineering systems. Measurements from each sensor over time form a curve and can be viewed as functional data. Clustering of these multivariate functional curves is important for studying the operating patterns of systems. One complication in such applications is the possible presence of sensors whose data do not contain relevant information. Hence, it is desirable for the clustering method to equip with an automatic sensor selection procedure. Motivated by a real engineering application, we propose a functional data clustering method that simultaneously removes noninformative sensors and groups functional curves into clusters using informative sensors. Functional principal component analysis is used to transform multivariate functional data into a coefficient matrix for data reduction. We then model the transformed data by a Gaussian mixture distribution to perform model-based clustering with variable selection. Three types of penalties, the individual, variable, and group penalties, are considered to achieve automatic variable selection. Extensive simulations are conducted to assess the clustering and variable selection performance of the proposed methods. The application of the proposed methods to an engineering system with multiple sensors shows the promise of the methods and reveals interesting patterns in the sensor data. History: Kwok Tsui served as the senior editor for this article. Funding: The research by J. Min and Y. Hong was partially supported by the National Science Foundation [Grant CMMI-1904165] to Virginia Tech. The work by Y. Hong was partially supported by the Virginia Tech College of Science Research Equipment Fund. Data Ethics & Reproducibility Note: The original dataset is proprietary and cannot be shared. The full code to replicate the results in this paper, based on summary statistics of the original data, is available at https://github.com/jiem3/MultiFuncClustering . The code applied to a simplified version is available at https://codeocean.com/capsule/4041000/tree/v1 , which covers the data analysis and part of the simulation scenarios with a single dataset under each scenario using a fixed set of hyperparameters, for reducing computation time, and at https://doi.org/10.1287/ijds.2022.0034 .

Design of magnetic field sensor with software for a wide range of applications

This paper outlines a highly efficient wirelessly magnetic field-based information transmission method, enabling real-time data acquisition. Emphasizing stringent demands on magnetic devices for high sensitivity across a wide range of field strengths, it highlights the need for rapid response times and minimal power consumption. The work introduces a prefabricated magnetic sensor programmed on an Arduino board using Hall’s Effect. This principle exploits electron transfer within a conductor under a magnetic field, inducing a transverse potential difference. Meticulously chosen solid-state materials and geometries generate detectable pulses, subsequently amplified for measuring various magnetic field components. Operating within a voltage range of 2.7-6.5V, aligned with the Arduino's 5V standard, the sensor demonstrates zero signal levels at 2.25-2.75V. Sensitivity ranges from 1.0-1.75 mV/gauss, mandating pre-calibration for accuracy, facilitated by a pre-calibration function or reset button. Output voltage ranges from 1.0-4.0V when powered by 5V, suitable for analog-to-digital conversion. With a minimum measurement range of ±650 gauss, typically extending to ±1000 gauss, and a swift response time of 3 ms, the sensor allows measurements up to tens of kHz. Operating currents between 6-10 mA are suitable for battery-powered applications, while a temperature-induced error of 0.1%/°C equates to 3 mT. Notably, the sensor measures magnetic fields along and perpendicular to the axis. To enhance accessibility and accuracy, a specialized Python-based software tool has been developed, featuring automatic sensor identification. This work encapsulates the paper's focus on advancing magnetic field measurement technology with practical implications for diverse applications.