Proximity Sensors Research Articles

Design Smart Trash Based On the Inductive Proximity Sensor

Smart waste management, including a waste sorting system based on type, strongly supports the "Intelligent waste management and categorization system" program to support the development of modern cities. Therefore, this research is related to the design of smart waste based on an inductive distance sensor that uses Arduino Uno as a microcontroller. The method used in this research is research and development which consists of mechanical design and control system programming. The results obtained from this research are an intelligent waste design based on an inductive distance sensor that uses Arduino Uno as a microcontroller and uses a conveyor system as a waste transporter. The test results on the ultrasonic sensor were that several objects could be detected at a distance of 15 cm and the conveyor stopped for 0.5 seconds. The test results on the proximity sensor are that the object is read by the proximity sensor when the object is 2 cm from the sensor, and if the object is metal waste then the servo motor moves 30 degrees to the right, if it is non-metallic waste then the servo motor moves 30 degrees to the left

Studies on effect of temperature on carbonization of silk (Bombyx mori) and its application as proximity sensor

Silk, a pyroprotein with its unique micro-structural features comprises of soft amorphous ▪ regions containing compact ▪-sheet crystals resulting in its potent characteristics. Annealing in inert atmosphere causes carbonization followed by graphitization of these stable ▪-sheet-rich silk fibroins (SFs). Here we carry out systematic studies of annealed silk fibroin (Bombyx mori) in the temperature range of 200-1200°C by various analytical techniques. Herein the structural changes in SFs at different annealing temperature have been discussed using Raman, FTIR, XPS, XAES, SEM, XRD characterization techniques. Notably XPS and XAES studies were executed to examine the influence of sp2/sp3 ratio and the presence of hetero-atom on D-parameter value in silk ▪-sheet material on gradual increase in annealing temperature. The mechanical properties of SFs were measured using UTM. The application of carbonized silk fibroins (CSF) as a proximity sensor is also investigated and proposed as a probable active material.

RANCANG BANGUN TEMPAT SAMPAH OTOMATIS DENGAN PEMILAH SAMPAH ORGANIK DAN ANORGANIK DISERTAI NOTIFIKASI SMS BERBASIS ARDUINO

Garbage is a material left over from human activities which is an environmental issue and causes environmental pollution. Humans themselves are lazy to throw garbage in its place or are accustomed to littering, causing garbage to be scattered everywhere which in turn can cause garbage pollution, unpleasant odors and in the end can cause flooding. In this study, we will make a tool to sort organic and inorganic waste along with an Arduino-based SMS notification, namely by making a tool to sort out the types of organic waste and inorganic waste types which will later be classified separately and collected in their own place so that the waste is not mixed. To distinguish organic and inorganic waste using a capacitive proximity sensor as a separator of organic waste, an inductive proximity sensor as a separator of inorganic waste, arduino nano as a microcontroller to run all the work of the tool system. Servo motor as a sorting drive. GSM SIM 800l module as a sender of SMS notification of garbage that has been successfully sorted.

Precision Nitrogen Fertilization for Opium Poppy Using Combined Proximal and Remote Sensor Data Fusion

Proper management of within-field variability is crucial for maximizing crop yield, production outcomes and resource use efficiency and reducing environmental impacts. This study evaluated the agroeconomic and environmental feasibilities of site-specific nitrogen fertilization (SNF) in opium poppy (Papaver somniferum L.). On-line visible and near-infrared reflectance spectroscopy was used to estimate soil pH, organic carbon (OC), soil organic matter (SOM), P, K, Mg, Ca, Na, moisture content (MC), Ca:Mg and K:Mg for one field in Spain. Normalized difference vegetation indexes of the previous crop were retrieved from Sentine-2 images. Rasterization of soil and crop data layers created a spatially homogenous dataset followed by delineation of a management zone (MZ) map using a k-means cluster analysis. MZ clusters were ranked relying on the within-cluster soil fertility attributes. A strip experiment was conducted by creating parallel stripes distributed over the MZ map, over which two SNF treatments (i.e., SNF-Kings approach [KA] and SNF-Robin Hood approach [RHA]) were compared against the uniform rate N (URN) control treatment. In SNF-KA, the highest and lowest N dose was applied in the most and least fertile MZ, respectively, whereas the opposite approach was adopted in the SNF-RHA treatment. Yield and cost–benefit analyses provided both SNF treatments to produce more yield (KA = 2.72 and RHA = 2.74 t ha−1) than the URN (2.64 t ha−1) treatment, leading to increasing gross margins by EUR 91 ha−1 (SNF–KA) and EUR 88.5 ha−1 (SNF–RHA). While SNF-KA reduced N input by 66.54 kg N ha−1, SNF–RHA applied more N by 17.90 kg N ha−1 than URN. Additionally, SNF–RHA attempted to equalize yield responses to N across MZ classes, with a small increase in N input. This study, therefore, suggests adopting SNF–RHA for increasing yield and gross margin and accurate distribution of N according to per MZ N response. Future studies, however, should address the limitations of the current study by delineating MZ maps with the incorporation of additional soil information (e.g., mineral N and clay) for optimizing N doses as well as evaluating agroeconomic performance across multiple sites and years using a full-budget analysis.

Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality

The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R2) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.

Capacitive proximity skin for non-contact liquid state measurement of robot grippers with different structures

Handling liquid-filled containers is a frequently encountered yet challenging task for robotics. In this paper, a novel capacitive proximity skin was developed to measure liquid levels before grasping, improving the performance of robotics in application areas like domestic service, manufacturing, healthcare, and food processing. This capacitive proximity skin features a flexible structure that is readily adaptable to a variety of grippers, and it seamlessly integrates with Robot Operating System (ROS)-based control systems. Experiments were performed to compare this proximity skin against non-flexible capacitive proximity sensors, focusing on detection frequency and working distance. The results showed that the capacitive proximity skin achieved data-lossless detection in 0.08 s (12.5 Hz) and contactless measurement of liquid level within 0.7 cm, making it highly suitable for grasping operations. This capacitive proximity skin offers as a versatile solution for robotic applications that involve unsealed liquid-filled containers, and it has the potential for application across a wide range of industries.

Visible and near-infrared spectroscopy predicted leaf nitrogen contents of potato varieties under different growth and management conditions

Visible-Near Infrared (vis-NIR) spectroscopy can provide a faster, cost-effective, and user-friendly solution to monitor leaf N status, potentially overcoming the limitations of current techniques. The objectives of the study were to develop and validate partial least square regression (PLSR) to estimate the total N contents of fresh and removed leaves of potatoes using the vis-NIR spectral range (350–2500 nm) generated from a handheld proximal sensor. The model was built using data collected from Hancock Agricultural Research Station, WI, USA in 2020 and was validated using samples collected in 2021 for four different conditions. The conditions included two sites (Coloma and Hancock), four potato varieties (Burbank, Norkotah, Goldrush, and Silverton), two N rates (unfertilized and 308 kg N ha−1), and four growth stages (vegetative, tuber initiation, tuber bulking, and tuber maturation). The calibration and validation models had high predictive performance for leaf total N with R2 > 0.8 and RPD > 2. The model accuracy was affected by the total N contents in the leaf samples where the model underpredicted the samples with total leaf N contents greater than 6%.

A Flexible Magnetic Field Sensor Based on PZT/CFO Bilayer via van der Waals Oxide Heteroepitaxy.

Magnetoelectric (ME) magnetic field sensors utilize ME effects in ferroelectric ferromagnetic layered heterostructures to convert magnetic signals into electrical signals. However, the substrate clamping effect greatly limits the design and fabrication of ME composites with high ME coefficients. To reduce the clamping effect and improve the ME response, a flexible ME sensor based on PbZr0.2Ti0.8O3 (PZT)/CoFe2O4 (CFO) ME bilayered heterostructure was deposited on mica substrates via van der Waals oxide heteroepitaxy. A saturated magnetization of 114.5 emu/cm3 was observed in the bilayers. The flexible sensor exhibited a strong ME coefficient of 6.12 V/cm·Oe. The local ME coupling has been confirmed by the evolution of the ferroelectric domain under applied magnetic fields. The flexible ME sensor possessed a stable response with high sensitivity to both AC and DC weak magnetic fields. A high linearity of 0.9988 and sensitivity of 72.65 mV/Oe of the ME sensor were obtained under flat states. The ME output and limit-of-detection under different bending states showed an inferior trend as the bending radius increased. A flexible proximity sensor has been demonstrated, indicating a promising avenue for wearable device applications and significantly broadening the potential application of the flexible ME magnetic field sensors.

Would it be better for partition prediction of heavy metal concentration in soils based on the fusion of XRF and Vis-NIR data?

Heavy metal (HM) contamination in soil necessitates effective methods to diagnose suspected contaminated areas and control rehabilitation processes. The synergistic use of proximal sensors demonstrates significant potential for rapid detection via accurate surveys of soil HM pollution at large scales and high sampling densities, and necessitates the selection of appropriate data mining and modeling methods for early diagnosis of soil pollution. The aim of this study is to evaluate the performance of a subarea model based on geographically partitioned and global models based on high-precision energy dispersive X-ray fluorescence (HD-XRF) and visible near-infrared (vis-NIR) spectra using a random forest model for predicting soil Cu and Pb concentrations. A total of 166 soil samples are acquired from a contaminated plot in Baiyin, Gansu Province, China. The soil samples are subjected to HM analysis and proximal sensor scanning in a laboratory. Vis-NIR spectral data are preprocessed using the Savitzky Golay (SG) and first-order derivative with Savitzky Golay (SGFD) methods. The results show that for predicting Cu and Pb concentrations in soil, the subarea models performs better than the global models in terms of quantitative prediction, based solely on individual HD-XRF data. For the subarea and global models, the R2 values are 0.961 and 0.981, respectively; the RMSE values are 27.8 and 79.6, respectively; and the RPD values are 4.96 and 7.38, respectively. However, making use of the random forest algorithm trained with data fusion obtained from the HD-XRF and vis-NIR sensors, the global model achieves the best predictions for Cu and Pb concentrations via HD-XRF + vis-NIR (SGFD) and HD-XRF + vis-NIR (SG), respectively. The results will provide a new perspective for modeling approaches to rapidly invert HM concentrations based on proximal sensor data fusion within a large scope of the study area.

Multinational prediction of soil organic carbon and texture via proximal sensors

AbstractNovel technologies help to monitor the environmental impact of human activities, but tests involving datasets from several countries, encompassing a large variability of soil properties, are still scarce. This study utilized proximal sensors to predict soil organic carbon (OC) and soil texture of samples from Brazil, France, India, Mozambique, and United States. A total of 1749 samples were analyzed by portable X‐ray fluorescence (pXRF) spectrometry and visible near‐infrared diffuse reflectance spectroscopy. Sand (R2 = 0.89), silt (0.87), and clay (0.84) predictions were very accurate, despite contrasting climates, soil parent materials, and weathering degrees. Soil OC predictions were similarly successful (0.74) using samples from five countries. pXRF was the optimal sensor for soil texture predictions. The addition of international data may improve local models. Proximal soil sensing can be successfully used with a multinational soil database offering a clean, rapid, and accurate alternative to estimate soil texture and OC with international datasets.

Comparative analysis and visualization of soil profiles at the meter spatial scale utilizing novel matrix and volume rendering techniques

This research introduces a soil characterization technique involving four data visualization tools to help researchers and stakeholders interpret high dimensional soil data at the field scale. This technique involves visualizing a reduced dimensionality representation of elemental concentration and color data gathered via portable X-ray fluorescence (pXRF) spectrometer and NixPro color proximal sensors, respectively. Soil cores were collected from sites located in Lubbock and Lamb Counties, West Texas, USA. Thirteen core samples were collected from these sites in a star pattern with readings from proximal sensors at depths ranging between 0 and 100 cm at 10 cm intervals. The dimensionality reduction techniques utilize four visualization tools to represent soil composition data through multiple user-adjustable variables (i.e., mg kg−1 elemental concentrations and soil profiles), offering more insight and control compared to a single-variable approach. Through these tools and techniques, qualitative and quantitative conclusions regarding soil characteristics (e.g., elemental concentration variation, delineation of soil horizons, changes in soil color) can be formulated from the data and used in various applications. Areas where these novel software tools can be utilized potentially include rapid contaminant mapping in soils, characterization of diagnostic soil horizons (e.g., calcic, spodic, gypsic, etc.), micronutrient distribution at a field scale for precision agricultural purposes, and pedometrics.

Tungsten-Doped Indium Tin Oxide Thin-Film Transistors for Dual-mode Proximity Sensing Application.

Technologies for human-machine interactions are booming now. In order to achieve multifunctional sensing abilities of electronic skins, further developments of various sensors are in urgent demand. Herein, a dual-mode proximity sensor based on an oxide thin-film transistor (TFT) is reported. Although InSnO (ITO) is featured with high mobility, the inherent high carrier concentration limits its use as a channel material for thin-film transistors. Herein, the tungsten element was introduced as a carrier suppressor to develop ITO-based semiconducting materials and devices. TFTs with amorphous tungsten-doped ITO (ITWO) channel layers were fabricated. As for a flat panel display application, the TFT device from 250 °C-annealed ITWO layer with an atomic ratio of In/Sn/W = 86:9:5 presented the optimal device performance with carrier mobility of 11.53 cm2 V-1 s-1, swing subthreshold of 0.66 V dec-1, threshold voltage of -2.18 V, and Ion/Ioff ratio of 3.33 × 107 and much small hysteresis of transfer characteristic. ITWO TFT devices were further developed as dual-mode proximity sensors that could work with both extended-gate and compact configurations, where the drain current was directly related to the surface potential of a charged object and the distance between the sensing end and the object, enabling the proximity sensing of charged stimuli. For extended-gate-configured proximity sensing, a charged object modulated the formation of a conductive channel at the semiconductor/SiO2 interface, while this conductive channel occurred at the semiconductor/air interface for compact-configured sensing. Formation of the conductive channel of the compact transistor was modulated by the electric field component in the direction perpendicular to the interface, and the drain current was sensitive to the orientation of the approaching object, which implied the capacity of angle sensing to the approach of a charged object. This work further emphasizes that the basic device performance should be optimized according to its specific application scenarios rather than only considering the requirements of the panel display.

Communicating a sense of safety: the public experience of Swedish Police Instagram communication

PurposeThe research aim of this article is to generate novel insights into how public sector organizations (PSOs) strategically communicate with the public about critical issues on social media. To this end, the study explores the public's experiences of the Swedish Police's sense of safety communication on Instagram in the third largest city in Sweden, where the lack of a sense of public safety is a main societal challenge.Design/methodology/approachThe research was designed as a case study employing photo-elicitation interviews as a method to collect the empirical material. A phenomenography approach was used to analyze public experiences of the Swedish Police's Instagram communication in Malmö, Sweden.FindingsFindings show that the police's strategic communication of safety on Instagram is experienced along the dimensions of a sense of protection, a sense of proximity and a sense of ambiguity. Taken together, these dimensions broaden and develop the knowledge of what communicating a sense of safety in the public sphere entails.Originality/valueThis study adds to previous research on strategic communication in public sector organizations by demonstrating what strategic communication accomplishes at the receiving end outside of the organization.

Measurement of the voltage evolution on a load of X-pinch plasma system using the Pockels effect

A diagnostic system using the Pockels effect (linear electro-optic effect) has been developed to measure a voltage on a load of the SNU X-pinch device [Ryu et al., Rev. Sci. Instrum. 92, 053533 (2021)]. The sensor component of the diagnostic system comprises of a lithium niobate (LN) crystal and its mount. When the LN crystal is subjected to an external electric field, the refractive indices of the LN crystal change due to the Pockels effect, leading to a change in the polarization state of the propagating laser beam through the crystal. This concept allows the separation and shielding of electronic devices from the intense electric pulses generated by the X-pinch system. Furthermore, the compact size and high electric field tolerance of the LN crystal enable us to position the sensor in a close proximity to the load of the X-pinch, facilitating the measurement of voltage evolution on the load. In contrast to circuit-based voltage probes, our sensor possesses low inductance, allowing us to measure resistive-dominant voltage even in the presence of rapid current changes within the X-pinch system. In this paper, we outline the configuration of our voltage measurement system using the LN crystal within the X-pinch device and provide measurement results of the load voltage. Additionally, we explore the temporal evolution of the voltage in relation to the phase transition of the load.

DESIGN OF AUTOMATIC BREAD CUTTING MACHINE

Before being marketed, white bread will usually be cut first. Small to medium scale bread entrepreneurs still use conventional tools in the form of standard bread knives, whose use is still less effective. This bread cutting machine is designed for small to medium sized bakers to make their work easier and more efficient. This machine is designed using a vertical blade movement method with an AC motor as the driving force and is controlled using a sequential circuit. The initial process of this machine is to press the pushbutton, then place the bread on the bread tray which has a proximity sensor as a bread detector and the cutting process is carried out, when the sensor does not detect the presence of bread the machine will stop working automatically. Electronic system control is carried out sequentially which is more efficient in its application. From the test results obtained that the thickness of the bread is 12 mm. The main driver of the cutting is 1 phase and uses a dimer to adjust the rotational speed of the 1 phase motor. The cutting process with a maximum motor speed can cut 1 loaf for 38 seconds.

Smart Turns: Revolutionizing Road Safety

Abstract: A technological replacement for traditional convex mirrors at blind curves and T-junctions is presented in the abstract of this research article. The suggested system makes use of ultrasonic proximity sensors to see oncoming cars and sends timely notifications to drivers on both sides of the turn. The technology also computes and shows the oncoming vehicle's speed. A typical signal system or direct roadside projection of the alerts are both options provided by the system. This approach facilitates safer navigation at blind bends and T-junctions by using proximity sensors to measure vehicle speed and improve driver awareness. Our method improves accuracy, range, and reliability in vehicle detection and alerting by overcoming the limitations of convex mirrors. This technology improves safer driving by enhancing driver awareness and reducing accidents.

Development of an experimental prototype of a manufacturing cell for process automation teaching

For the industrial sector, the filling, sealing and labeling system represents one of the most used processes with several applications, that involves the food and beverage industry until the chemical plants, among many others applications. The propouse of this work is to develop an academic prototype of a manufacturing cell for teaching the automation of industrial processes, in order to emulate the filling, sealing and labeling system. The goal is to design a learning methodology such that the acquired academic knowledge can be transported to a real industrial environment. To achieve this, the PLC S7-300, industrial proximity sensors, pneumatic cylinders of the Festo brand, as well as their respective solenoid valves, are used. The programming methodology is based on a functional graph controlled by stages and transitions (GRAFCET). In contrart with other project based on Arduino, this project employs standard signals and variable scaling.

ANALISIS SISTEM OTOMASI KONVEYOR PADA MESIN COOLANT FILTRATION DI PT. MATAHARI MEGAH

Coolant Filtration Machine is one of the products of PT. Matahari Megah. The purpose of this machine is to recycle residual coolant fluid that has been used in the machining process. Coolant Filtration Machine is working through automation system which involves conveyor system controlled by sensors. The components involved need to be analyzed and calculated properly to work as designed. Hence, calculation for required electricity power to move the wire mesh conveyor used in this machine was done. According to the process, it was concluded that the required power is 60 Watt. A pair of sensors are mounted to control the movement of this conveyor are proximity sensors which monitor the water container and lever limit switch monitoring the paper holder system.

Local Curvature Estimation and Grasp Stability Prediction Based on Proximity Sensors on a Multi-Fingered Robot Hand

This study aims to realize a precision grasp of unknown-shaped objects. Precision grasping requires a detailed understanding of the surface shapes such as concavity and convexity. If an accurate shape model is not given in advance, it must be addressed by sensing. We have proposed a method for recognizing detailed object shapes using proximity sensors equipped on each fingertip of a multi-fingered robot hand. Direct sensing of the object’s surface from the fingertips enables both avoidance of unintended collision during the approach process and recognition of surface profiles for use in planning and executing stable grasping. This paper introduces local surface curvature estimation to improve the accuracy of local surface recognition. We propose practical and accurate models to estimate local curvature based on various characteristic tests on the proximity sensor and to estimate the distance to the nearest point. In actual experiments, it was shown that it was possible to estimate the position of the nearest point with a mean error of less than 2 mm and to predict grasping stability in reasonable real-time for the object shape.

Upscaling plot-based measurements of RUSLE C-factor of different leaf-angled crops in semi-arid agroecosystems.

Several models have been used to assess temporal cover change trends by using remote and proximal sensing tools. Particularly, from the point of hydrologic and erosional processes and sustainable land and soil management, it is crucial to determine and understand the variation of protective canopy cover change within a development period. Concordantly, leaf angle distribution (LAD) is a crucial parameter when using the vegetation indices (VIs) to define the radiation reflected by the canopy when estimating the cover-management factor (C-factor). This research aims to assess the C-factor of cultivated lands with sunflower and wheat that have different leaf orientations (planophile and erectophile, respectively) with the help of reduced models of NDVI and LAI for estimating crop-stage SLR values with the help of a stepwise linear regression. Those equations with R-squared values of 0.85 and 0.93 were obtained for sunflower and wheat-planted areas, respectively. The Normalized Difference Vegetation Index (NDVI), one of the two plant indices used in this study, was measured by remote and proximal sensing tools. At the same time, the Leaf Area Index (LAI) was obtained by a proximal hand-held crop sensor alone. Soil loss ratio (SLR) was upscaled for the establishment period (1P) of sunflower and the maturing period (3P) of wheat to present different growth stages simultaneously with plant-specific equations that can be easily adapted to those aforementioned crops instead of doing field measurements with conventional techniques in semi-arid cropping systems.