Real-time Monitoring Applications Research Articles

Crowd Density Estimation via Global Crowd Collectiveness Metric

Drone-captured crowd videos have become increasingly prevalent in various applications in recent years, including crowd density estimation via measuring crowd collectiveness. Traditional methods often measure local differences in motion directions among individuals and scarcely handle the challenge brought by the changing illumination of scenarios. They are limited in their generalization. The crowd density estimation needs both macroscopic and microscopic descriptions of collective motion. In this study, we introduce a Global Measuring Crowd Collectiveness (GMCC) metric that incorporates intra-crowd and inter-crowd collectiveness to assess the collective crowd motion. An energy spread process is introduced to explore the related crucial factors. This process measures the intra-crowd collectiveness of individuals within a crowded cluster by incorporating the collectiveness of motion direction and the velocity magnitude derived from the optical flow field. The global metric is adopted to keep the illumination-invariance of optical flow for intra-crowd motion. Then, we measure the motion consistency among various clusters to generate inter-crowd collectiveness, which constitutes the GMCC metric together with intra-collectiveness. Finally, the proposed energy spread process of GMCC is used to merge the inter-crowd collectiveness to estimate the global distribution of dense crowds. Experimental results validate that GMCC significantly improves the performance and efficiency of measuring crowd collectiveness and crowd density estimation on various crowd datasets, demonstrating a wide range of applications for real-time monitoring in public crowd management.

Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future Directions

Low-Code Development Platforms (LCDPs) empower users to create and deploy custom software with little to no programming. These platforms streamline development, offering benefits like faster time-to-market, reduced technical barriers, and broader participation in software creation, even for those without traditional coding skills. This study explores the application of LCDPs in Precision Agriculture (PA) through a systematic literature review (SLR). By analyzing the general characteristics and challenges of LCDPs, alongside insights from existing PA research, we assess their feasibility and potential impact in agricultural contexts. Our findings suggest that LCDPs can enable farmers and agricultural professionals to create tailored applications for real-time monitoring, data analysis, and automation, enhancing farming efficiency. However, challenges such as scalability, extensibility, data security, and integration with complex IoT systems must be addressed to fully realize the benefits of LCDPs in PA. This study contributes to the growing knowledge base in agricultural technology, offering valuable insights for researchers, practitioners, and policymakers looking to leverage LCDPs for sustainable and efficient farming practices.

The Development of a Flexible Humidity Sensor Using MWCNT/PVA Thin Films.

The exponential demand for real-time monitoring applications has altered the course of sensor development, from sensor electronics miniaturization, e.g., resorting to printing techniques, to low-cost, flexible and functional wearable materials. Humidity sensing has been used in the prevention and diagnosis of medical conditions, as well as in the assessment of physical comfort. This paper presents a resistive flexible humidity sensor composed of silver interdigitated electrodes (IDTs) screen printed onto polyimide film and an active layer of multiwall carbon nanotubes (MWCNT) dispersed in a water-soluble polymer, polyvinyl alcohol (PVA). Different MWCNT/PVA sensor sizes and MWCNT percentages are tested to study their effect on the initial electrical resistance (Ri) values and sensor response at different humidity percentages. The results show that the Ri values decrease with the increase in % MWCNT. The sensor size did not influence the sensor response, while the % MWCNT affected the sensor behavior upon relative humidity (RH) increments. The 1% MWCNT/PVA sensor showed the best response, reaching a relative electrical resistance, ΔR/R0, of 509% at 99% RH. Comparable with other reported sensors, the produced MWCNT/PVA flexible sensor is simpler, greener and shows a good sensitivity to humidity, being easily incorporated in wearable monitoring applications, from sports to medical fields.

A gold nano-urchin-decorated quasi-freestanding graphene-based humidity sensor with enhanced responsivity and a wide relative humidity detection range for real-time applications

Excellent humidity response and detection threshold are essential attributes of a humidity sensor. In this study, quasi-freestanding graphene (QFSG) was grown epitaxially on vicinal silicon carbide via thermal annealing, followed by decoration with hydrophilic gold nano-urchins (AuNUs). The resulting humidity sensor consistently achieved highly effective non-contact humidity-sensing performance, which can be used to monitor breathing and skin moisture. The proposed humidity sensor demonstrated a non-linear response of ∼119.98 % over a wide relative humidity detection range of 4–96 % at a frequency of 1 kHz, outperforming a QFSG-only humidity-sensing device (∼11.92 % and 22–84 % relative humidity, respectively). This 10-fold increase in humidity response and wider detection range is attributed to the hydrophilicity and plasmonic behavior of the AuNUs. It also demonstrated transient impedance response/recovery time of 4.5 and 3.0 s, respectively; low hysteresis of 3.6 %; and stability over 25 days, which are key factors for effective humidity sensing. The fast response and sensitivity of the AuNUs/QFSG humidity sensor made it possible to test for various real-time monitoring applications such as human breathing and non-contact proximity testing.

A review of the application of continuous real-time monitoring in noise risk management

Background: In the year 2020, the Mineral's Council Chief Executive Officer (CEO) Zero Harm Forum (CEOZHF) reviewed the slow progress towards achieving the 2024 Occupational Health Milestones and agreed that there was a need to improve the current measures in place to reduce occupational diseases. Following discussions with Group Environmental Engineers, it was proposed to adopt key occupational health indicators for tracking progress through Continuous Real-Time Monitoring (CRTM) to reduce workplace exposures. Methods: A systematic review methodology was employed to gather data from reputable academic databases. Results: CRTM in Noise Risk Management enables an encompassing practical and intuitive analysis of building noise pollution with prompt alerts for poor acoustic comfort. Real-time graphical display of noise measurements, utilizing a warning signal framework, facilitated area-specific alerts and responses. Additionally, CRTM enabled the visualization of noise levels and sources through frequency analysis, contributing to noise pollution analysis for zoning and strategic planning in smart cities. Conclusions: While CRTM has been extensively applied in noise monitoring globally, its utilization in the mining industry, especially in South Africa, remains limited. This study underscores the feasibility of implementing CRTM in Noise Risk Management, emphasizing the need for further research within the mining industry.

Smart paper-based materials incorporating nitrogen and boron co-doped MXene quantum dots for rapid adsorption and sensitive detection of Cr2O72−

Dichromate ion (Cr2O72−) is a highly toxic chromium-containing compound that poses significant hazards to the digestive, respiratory systems, skin, and mucous membranes. Currently, the detection and adsorption of Cr2O72− face significant challenges, including the time-consuming and low sensitivity nature of traditional analytical methods. The limited efficiency and capacity of existing adsorbents hinder their practical application in real-time water quality monitoring and environmental remediation. Herein, using polyethyleneimine-functionalized (PEI) pulp fiber paper as the substrate, we developed smart paper-based materials (designated as NB-MQDs@PP) incorporated with nitrogen and boron co-doped MXene quantum dots (NB-MQDs) for rapid adsorption and sensitive detection of Cr2O72−. Compared to undoped MQDs, NB-MQDs exhibited longer excitation wavelength and enhanced oxidation stability. As anticipated, NB-MQDs achieved rapid (response time of 10 s) and sensitive (detection limit of 1.2 μM) recognition of Cr2O72− within a wide pH range with a quenching efficiency of 99.9%. Simultaneously, two on-site detection methods, immersion and cyclic filtration, were constructed based on NB-MQDs@PP. The detection limit of the immersion method was 17.0 nM, while the cyclic filtration method had a detection limit as low as 3.8 nM, surpassing the majority of those reported literatures. Remarkably, NB-MQDs@PP exhibited outstanding enrichment capacity towards Cr2O72−, with an adsorption capacity of up to 162.4 mg/g. This work provides a novel strategy for creating unique paper-based materials with excellent capture and monitoring dual-function, which can be customized according to the requirements of various application scenarios.

A small-period long-period fiber grating biosensor based on immobilization of concanavalin A on polydopamine nanospheres for simultaneous detection of D-glucose and temperature

The accurate and sensitive monitoring of glucose levels plays a crucial role in diagnosing diabetes in clinical settings. In this work, a novel small-period long-period fiber grating (SP-LPG) biosensor was first developed for D-glucose detection based on the immobilization of concanavalin A (Con A) on polydopamine (PDA) nanospheres. SP-LPG was fabricated by using femtosecond laser direct writing technology and the obtained grating region was only 2.4 mm. The recognition element of D-glucose, Con A, was attached to the surface of PDA nanospheres. Due to the high specific surface area of PDA nanospheres, a large number of binding sites were available for Con A. The as-prepared sensor showed selective and sensitive to D-glucose measurement in the range of 10 nM to 10 mM and a low detection limit of 1.95 nM (S/N = 3) was achieved. Moreover, the small grating period of SP-LPG allows for the observation of Bragg reflection, which can be utilized for temperature sensing. This property eliminates the issue of temperature cross-sensitivity present in refractive index sensing. And the sensor exhibited a thermal sensitivity of 10 pm/°C within the range of 30 ∼ 100 °C. The developed sensor offers a convenient and efficient platform of simultaneously measuring multiple parameters, including target molecules and temperature, for real-time medical monitoring applications.

Speech recognition inspired features for acoustic emission

For the application of acoustic emission methods in the field of condition monitoring, extracting meaningful information out of signals is an essential step in signal processing and mandatory for machine learning. However, when dealing with acoustic emission signals, the computational intensity of extracting features is becoming more important due to its large amount of data. The methodology in this study, hence, investigates established methods from descriptive statistics as typically used in acoustic emission and techniques commonly employed in speech recognition. Thereby, the objective is to improve the feature extraction of acoustic emission signals, emphasizing the synergies between the methods of both approaches. Extracting traditional acoustic emission features based on descriptive statistics alone usually yields robust features capturing the significant patterns within the time and frequency domain. However, traditional acoustic emission features may neglect more detailed information present in the signals due to averaging or mere evaluation of extreme values. To address this limitation, the methodology is extended by incorporating techniques from the field of speech recognition, such as applying window functions and digital filter banks to calculate the so-called cepstral coefficients. When comparing machine learning models that incorporate these cepstral coefficients alongside those that do not, this approach demonstrates that speech recognition techniques can result in more effective models. Here, the basic concept for speech recognition-based feature extraction and its implementation are presented, and necessary considerations and techniques are discussed, which are crucial for performant signal processing, particularly for real-time monitoring applications. The methodology is demonstrated, using acoustic emission signals derived from industrial-scale processes , such as e.g. milling, drilling or friction stir welding.

Enhanced ammonia detection using rGO-wrapped Zn3V2O8 nanostructures

This study presents the synthesis and characterization of reduced graphene oxide (rGO)-wrapped Zn3V2O8 nanostructures, highlighting their enhanced ammonia sensing capabilities. Utilizing advanced techniques such as XRD, TEM, HRTEM, XPS, and Raman spectroscopy, the microstructural, compositional, and crystalline properties of the rGO/ Zn3V2O8 composites were thoroughly analyzed. The integration of rGO significantly improves the electrical conductivity, surface area, and chemical reactivity of the Zn3V2O8, resulting in superior sensing performance. Notably, the rGO/Zn3V2O8:2 composite exhibits rapid response and recovery times when detecting ammonia at room temperature, demonstrating its potential as an effective material for advanced gas sensing applications. These findings contribute to the ongoing development of high-performance ammonia sensors, offering a promising approach for real-time environmental monitoring and industrial applications.

An ingenious chemiluminescence sensing strategy for recalcitrant triphenyl phosphate based on oxidant-free UV-activated MIL-100(Fe) gel system

BackgroundOrganophosphate flame retardants (OPFRs) are notorious emerging contaminants threatening the environment and human health. Triphenyl phosphate (TPHP), which has an extremely serious biotoxicity, is a typical harmful OPFR. Due to its wide use, TPHP has been discovered in various environmental mediums. Moreover, it is pretty recalcitrant to the removal process, resulting in the need for a technique to understand it better. Hence, accurate and quick discrimination of TPHP in the environment is critical to further evaluate its potential effect on ecosystems and human health. ResultsAn ingenious oxidant-free chemiluminescence (CL) sensor based on the oxidant-free UV/MIL-100(Fe) gel system was established for TPHP detection. The oxidation of luminol in the UV-activated MIL-100(Fe) gel has resulted in remarkable CL emission, which is contributed by reactive oxygen species (ROS) generated by it. Notably, the CL intensity was inhibited significantly after introducing TPHP. An investigation into the mechanism underlying the effect of CL suppression demonstrated that TPHP competed with luminol to consume ROS from UV-activated MIL-100(Fe) gel, contributing to CL inhibition. The subsequent sensing performance experiments demonstrated the advantages of environmentally friendly, economic efficiency, user-friendly operation, rapid determination, potential for compact size, high selectivity, and sensitivity. Additionally, these investigations confirmed the low limit of detection (210 ng L−1) and wide linear range (10–1000 μg L−1). SignificanceIn this paper, a green, economical, and oxidant-free CL sensing strategy for TPHP has been established. It has the advantage of being rapid, having the potential for compact size, high selectivity, and sensitivity. This ingenious method has promising applications in real-time and online environmental monitoring, and it paves the way for the rapid and environmentally friendly identification of emerging contaminants that are structurally stable and recalcitrant to remove.

Retraction Note: Application of IoT real-time monitoring based on optical sensors in residents’ sports and health service system

Retraction Note: Application of IoT real-time monitoring based on optical sensors in residents’ sports and health service system

Research on vector bending sensors based on taper-drawn seven-core fiber Bragg grating

We present a novel vector bending sensor that enables simultaneous measurement of the bending response in six outer cores of a seven-core fiber (SCF) without the need for fan-in/fan-out configurations. Utilizing femtosecond laser technology, we inscribe seven Fiber Bragg Gratings (FBGs) with distinct wavelengths across the SCF cores. The sensor’s tapering process, tailored for the bending sensing scenario, effectively combines the reflection peaks of the FBGs into one detection channel, thereby substantially improving the measurement efficiency. Our experimental results demonstrate a strong directional dependence of the bending response, with a maximum sensitivity of 127 pm/m−1. The bending angle and curvature magnitude are reconstructed by analyzing the wavelength shifts of any two non-diagonal outer cores, offering a versatile solution for real-time monitoring applications. This sensor design, by eliminating the need for additional fan-in/fan-out devices, simplifies the system architecture and reduces both measurement time and sensor size, making it highly suitable for applications in precision manufacturing, environmental monitoring, and robotics.

Rapid photo-crosslinking, highly conductive, and anti-freezing acrylamide-based hydrogels applied for ECG sensors

In recent years, ionic conductive hydrogels have attracted significant attention in the wearable sensors field due to their simplified preparation process, cost- effectiveness, and high conductivity. However, a large amount of water in traditional hydrogels inevitably condense into ice at low temperatures and make them lose various properties in their practical applications. Hence, this study proposes a method to rapidly synthesized a water/glycerol binary anti-freezing hydrogel only in 10 s through photo-initiated free-radical polymerization. The introduction of LiCl into the crosslinked network composed of acrylamide (AAm) and poly (ethylene glycol) diacrylate (PEGDA) regulated the ionic conductivity of the hydrogel. The hydrogel exhibits a fracture strain of 1062.1 %, a tensile strength of 31 kPa, and an adhesive strength of 6.49 kPa. The synergistic effects of glycerol and LiCl imparted excellent anti-freezing properties (−60 °C). The three-dimensional network structure of the hydrogel facilitates ion transport, exhibiting superior conductivity (30.25 S/m), maintaining high conductivity capability of 1.46 S/m even at a low temperature of −55 °C. Notably, the hydrogel demonstrates prolonged durability and stability in real-time human motion monitoring applications while concurrently exhibiting remarkable sensitivity as an ECG sensor. Hence, this as-prepared hydrogel can adapt to extreme environments and deliver comprehensive performance as an ECG sensor.

Experimental analysis of low-duty cycle campus deployed IoT network using LoRa technology

This study emphases on the implementation and evaluation of a LoRa network in indoor environments through an experimental setup at Amity University, Greater Noida (AUGN) campus. Aimed at scrutinizing the link-level performance and reliability of LoRa technology, extensive measurements have been conducted with prototype device integrating the SX1278 LoRa radio module at 433 MHz. The performance has been analysed in terms of Signal-to-Noise Ratio (SNR), Received Signal Strength Indicator (RSSI), and Packet Reception Rate (PRR) across various line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. The results reveal that network performance is highly sensitive to parameter configurations, such as spreading factor (SF) and coding rate (CR). In LoS environments, SF = 9 and CR = 4/5 provided an RSSI of −89.12 dBm and an SNR of 9.91 dB, while highly obstructive areas saw optimal performance with SF = 7 and CR = 4/7. The study also identified configurations ensuring 100 % packet delivery accuracy and maximum throughput. This work stands out due to its thorough examination of LoRa for indoor IoT applications, notably addressing duty cycle restrictions, a topic often overlooked in existing literature. The valuable insights presented are crucial for maximizing the potential of LoRa in indoor environments with high population density. The findings strongly advocate for the use of LoRa for real-time environmental monitoring and innovative IoT applications within university campuses.

The Implementation of a Cooling and Storage System for Bakpia Pathok Based on IoT Technology (Case Study : Bakpia Pathok 79)

Bakpia Pathok 79, a food product made from flour, has a short shelf life and faces challenges in maintaining quality due to inadequate storage and conventional cooling methods. This study aims to design a temperature control and spoilage detection system for Bakpia Pathok 79 using gas sensors and the DHT-22 temperature sensor. Over a 6-day test period, results indicated that bakpia is best consumed immediately after cooling and remains optimal until the fourth day. In showcase 1, the MQ4 sensor detected methane levels below 195 ppm, and the MQ3 sensor detected ethanol levels below 300 ppm. In showcase 2, the MQ4 readings were below 200 ppm, and the MQ3 readings were below 260 ppm. The highest temperature of bakpia before cooling was 32°C, stabilizing at 29°C within 7 minutes. The system connects to an Android application for real-time temperature monitoring and spoilage detection, providing notifications when the bakpia is ready for packaging. Test results show the system is effective in detecting spoilage and accurately monitoring temperature.

Time synchronization method of wireless distributed sensor node and its application for real-time dust monitoring

Time synchronization method of wireless distributed sensor node and its application for real-time dust monitoring

A probabilistic machine learning framework for stiffness tensor estimation of carbon composite laminate

The potential uses of carbon composite material are vast, particularly in the civil, mechanical and aero-structures. Nonetheless, the practical utilization of carbon composite faces challenges due to complex experimental methods and imprecise algorithms for inverting material properties. Characterization of full set of stiffness tensor for an anisotropic material in particular orthotropic material can be considered as a complex non-linear inversion problem. The inversion process requires a robust optimization algorithm and forward models. The complexity of this inversion process impedes the practical utility in large-scale automation and in real-time structural health monitoring (SHM) application. To cater this, in this work an advanced probabilistic machine learning framework based on multi-output Gaussian process regression (moGPR) is proposed as an inversion algorithm. The inversion algorithm proposed is based on probabilistic framework that utilises the dispersion of Lamb waves as an input for optimal estimation of stiffness tensor. Experimental measurements of full wavefield data of propagating waves are conducted by scanning laser Doppler vibrometer. Further, an optimal training dataset is generated by employing a forward model based on stiffness matrix method (SMM). In the presence of measurement uncertainties, the effectiveness of the optimal prediction algorithm is showcased through a comparison between the estimated parameter and its ground truth. This comparison reveals that the proposed inversion algorithm is both efficient and robust, delivering satisfactory performance even in the presence of substantial measurement uncertainties.

Machine Learning-Assisted Near-Infrared Spectral Fingerprinting for Macrophage Phenotyping.

Spectral fingerprinting has emerged as a powerful tool that is adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coupled with machine learning techniques, we uncover complex interactions between DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) and live macrophage cells, enabling in situ phenotype discrimination. Utilizing Raman microscopy, we showcase statistically higher DNA-SWCNT uptake and a significantly lower defect ratio in M1 macrophages compared to M2 and naive phenotypes. NIR fluorescence data also indicate that distinctive intraendosomal environments of these cell types give rise to significant differences in many optical features, such as emission peak intensities, center wavelengths, and peak intensity ratios. Such features serve as distinctive markers for identifying different macrophage phenotypes. We further use a support vector machine (SVM) model trained on SWCNT fluorescence data to identify M1 and M2 macrophages, achieving an impressive accuracy of >95%. Finally, we observe that the stability of DNA-SWCNT complexes, influenced by DNA sequence length, is a crucial consideration for applications, such as cell phenotyping or mapping intraendosomal microenvironments using AI techniques. Our findings suggest that shorter DNA-sequences like GT6 give rise to more improved model accuracy (>87%) due to increased active interactions of SWCNTs with biomolecules in the endosomal microenvironment. Implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time monitoring of in vivo cellular differentiation.

A Versatile pH-Dependent Fluorometric, Colorimetric, and Electrochemical Sensor for H2S Monitoring in Water.

Hydrogen sulfide (H2S) is a colorless, foul smelling, toxic substance that can be found in water bodies and waste waters, especially in occupational susceptible environments, and can lead to harmful effects in humans at higher concentrations. An H2S monitoring probe NNAP is synthesized, which displays pH-dependent electrochemical, colorimetric, and fluorescence responses. NNAP functions as a fluorometric sensor at pH 7.4, with a limit of detection (LOD) of 0.70 mM, and as a colorimetric sensor at pH 12, where visible color changes are discernible to the naked eye, with an LOD of 4.28 mM. Additionally, it demonstrates utility in electrochemical sensing at pH 2, with a LOD of 2.5 mM. Furthermore, NNAP-coated paper strips have been successfully utilized for real-time H2S monitoring applications.

Preparation and performance study of sodium alginate/bamboo fiber/gelatin ionic conductive self-healing hydrogel

This study has been successfully developed the Sodium alginate/Bamboo fiber /Gelatin（SA/BF/Gel）composite conductive hydrogel with adhesive and self-healing properties. Through in-depth research, the influence of Gel content on the tensile, adhesive, self-healing properties, and conductivity of the SA/BF/Gel composite conductive hydrogel was discussed. The sensing performance and sensing mechanism of the material were also investigated, along with a preliminary exploration of its potential applications. An attempt was made to apply the SA/BF/Gel composite conductive hydrogel to 3D printing technology, establishing a connection between the rheological properties of the hydrogel and its printing structure. The addition of Gel significantly improved the flexibility of the hydrogel, with a conductivity of up to 3.12 S/m at a Gel content of 1.5 %. When employed as a sensor, the material exhibited high sensitivity (GF = 2.21) and excellent cyclic stability, rendering it suitable for a wide range of applications in real-time monitoring of bending movements of fingers and wrists, as well as dynamic contact and variations in contact forces on the hydrogel surface. The SA/BF/Gel composite conductive hydrogel has the potential to be utilized in a multitude of applications, including the development of smart wearable devices, the monitoring of individual human beings, and the integration of human beings and machines. Furthermore, the research findings associated with this hydrogel will provide a strong foundation for the advancement of materials science and the integration of smart technologies.