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Sensors have become more versatile and sophisticated in recent years to fulfill the increasing demands for human health applications. Physiological information such as electrocardiogram, pulse rate, and respiration are essential indications of personal health, often collected as vitals, which are typically collected from medical‐grade electrocardiogram (ECG) machines. In‐textile sensors are a fast‐growing sub‐category of wearable sensors embedded in smart textiles to acquire physiological information and movement index and provide harmful chemical warnings without compromising the comfortable nature of clothing. Recent literature has shown that integrating new materials has greatly improved the stability, specificity, and selectivity of in‐textile sensors. For example, polyvinylidene fluoride nanofiber produced a highly stretchable sensor to measure ECG readings during movement without losing data quality. This review discusses a group of nanomaterial‐based in‐textile sensors for consumer use in the home, workplace, and healthcare environments. This review will focus on exploring and analyzing the latest developments in these nanomaterial‐based e‐textiles due to their ability to be more easily integrated for daily use and their great potential for medical applications. Future work will be necessary to incorporate recycled materials, improve the method of powering these sensors, and ultimately refine the designs to be appropriate for more sustainable use. [ABSTRACT FROM AUTHOR]

This paper presents here a novel electrical sensor for detecting alcohol concentration in the exhale of an individual. We highlight fabrication of the sensor followed by subsequent assemblage into a handheld unit. The prototype yields a remarkable sensitivity along with a linear range of ~ 250–800 ppm. Additionally, we demonstrate the implementation of it in the helmet of a biker—which is further validated by in situ results related to monitoring exhale activity of few subjects. Through proper tuning with the ignition control system, the prototype can act as a protective gear for drunken drivers. Once the concentration of alcohol is sensed above a permissible level, the ignition system can be momentarily stopped; thereby averting plausible accidents. We believe that this sensor can be upgraded to more advanced protocol via a little tweaking with Internet of Things. [ABSTRACT FROM AUTHOR]

Achieving convenient, sensitive, low‐cost, and non‐contact detection of trace addictive drugs is a challenging problem. Herein, a novel ionically conductive metal‐organic framework (IC‐MOFs) is designed through a controlled interface assembly strategy. The active metal anions are incorporated into the layered MOFs with a porous structure, forming charge carriers that served as effective adsorption/binding sites for N‐methylphenethylamine (MPEA), a crucial simulator of addictive drugs. The in situ integrated Zn3(HHTP)2‐MOF sensor device designed in this study demonstrated real‐time detection of sub‐ppb level MPEA at room temperature, with an exceptionally low theoretical detection limit of 20 ppt. The overall sensing performance of this sensor surpassed all previously reported chemical sensors for detecting methamphetamine. The Zn3(HHTP)2‐MOF sensor exhibited outstanding selectivity, rapid response time (ca. 5 s), excellent long‐term stability, amenable miniaturization, and device consistency. The device successfully passed dual 85 test (500 h at 85 °C and 85% of humidity), which is rarely report previously. Density functional theoretical calculations (DFT) and spectral characterization confirmed that the prominent selectivity of Zn3(HHTP)2‐MOF toward MPEA is attributed to the strong binding ability. The general and straightforward strategy provides brand‐new route to exploiting smart sensors for drug prevention and surveillance. [ABSTRACT FROM AUTHOR]

Machining is extensively used for producing functional parts in various industries such as aerospace, automotive, energy, etc. There is a growing demand for improved part quality and performance at lower costs from increasingly difficult-to-machine materials. Whilst modern machine tools are equipped with sensors for closed loop control of their axes' movements and position, they provide minimal information regarding the cutting performance and tool condition. The integration of additional sensors into cutting tools, machine tools and/or their components can provide an insight into the machining performance. It also provides an opportunity to improve the machining process and reduce the need for post-process inspection and rework. This paper presents a comprehensive analysis of various sensors utilised for in-process and on-machine measurement and monitoring of machining performance parameters such as cutting forces, vibrations, tool wear, surface integrity, etc. Data transfer and communication methods, as well as power supply options for sensor-integrated systems are also investigated. Sensor integrated machining systems can potentially improve machining performance and part quality by early detection of errors and damages, maximising tool usage and preventing machining and tool wear induced damages. A combination of sensor data collection and intelligent sensor signal processing can further increase the capabilities of sensor integrated systems from process monitoring to active process control. [ABSTRACT FROM AUTHOR]

Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent decision-making, and any malicious activity of these sensors could potentially lead to a system-wide collapse. To ensure safety and security, it is essential to have a reliable system that can automatically detect and prevent any malicious activity, and modern detection systems are created based on machine learning (ML) models. Most often, the dataset generated from the sensor node for detecting malicious activity is highly imbalanced because the Malicious class is significantly fewer than the Non-Malicious class. To address these issues, we proposed a hybrid data balancing technique in combination with a Cluster-based Under Sampling and Synthetic Minority Oversampling Technique (SMOTE). We have also proposed an ensemble machine learning model that outperforms other standard ML models, achieving 99.7% accuracy. Additionally, we have identified the critical features that pose security risks to the sensor nodes with extensive explainability analysis of our proposed machine learning model. In brief, we have explored a hybrid data balancing method, developed a robust ensemble machine learning model for detecting malicious sensor nodes, and conducted a thorough analysis of the model's explainability. [ABSTRACT FROM AUTHOR]

Ferrograph-based wear debris analysis (WDA) provides significant information for wear fault analysis of mechanical equipment. After decades of offline application, this conventional technology is being driven by the online ferrograph sensor for real-time wear state monitoring. However, online ferrography has been greatly limited by the low imaging quality and segmentation accuracy of particle chains when analyzing degraded lubricant oils in practical applications. To address this issue, an integrated optimization method is developed that focuses on two aspects: the structural re-design of the online ferrograph sensor and the intelligent segmentation of particle chains. For enhancing the imaging quality of wear particles, the magnetic pole of the online ferrograph sensor is optimized to enable the imaging system directly observe wear particles without penetrating oils. Furthermore, a light source simulation model is established based on the light intensity distribution theory, and the LED installation parameters are determined for particle illumination uniformity in the online ferrograph sensor. On this basis, a Mask-RCNN-based segmentation model of particle chains is constructed by specifically establishing the region of interest (ROI) generation layer and the ROI align layer for the irregular particle morphology. With these measures, a new online ferrograph sensor is designed to enhance the image acquisition and information extraction of wear particles. For verification, the developed sensor is tested to collect particle images from different degraded oils, and the images are further handled with the Mask-RCNN-based model for particle feature extraction. Experimental results reveal that the optimized online ferrography can capture clear particle images even in highly-degraded lubricant oils, and the illumination uniformity reaches 90% in its imaging field. Most importantly, the statistical accuracy of wear particles has been improved from 67.2% to 94.1%. [ABSTRACT FROM AUTHOR]

A data‐driven intelligent stress monitoring methodology is proposed for a robust manufacturing of a phone display with the extremely narrow bottom bezel. For the narrow bottom bezel, a bottom of a display panel should be bent 180 degrees with asymmetric bended protrusion by tight attachment. The mechanical stress of the bended panel increases rapidly depending on the amount attached after bending 180 degrees. For stable operation of assembly process with high risk process method, the virtual stress monitoring are applied for total product samples during the process. The intelligent stress sensor produces the virtual stress/strain data using a surrogated model, which is learned with the dataset of measured data and simulated data of the tightly bending attachment using it as input. The virtual stress/strain data which matches 99.4% of the simulation value with regression error of less than 1% can be obtained in real time from all products during assembly process with bending of the display panel. The crack risks of the bending 180 degrees are monitored in real time for anomaly detection using the virtual stress data produced by the proposed virtual sensor. With these approaches, we believe that the manufacturing processes having high risks could be stably controlled for a real infinite display creating an incredibly immersive viewing experience. [ABSTRACT FROM AUTHOR]

Smart and connected physical products enable numerous novel value creations based on embedded sensor data. However, during the product design process, it appears that these value-creation opportunities are not sufficiently explored. To address this gap, a general and accessible ideation tool is proposed to structure a systematic exploration. The tool has been developed from the data sheets of a large number of various embedded sensors. A non-exhaustive list of 14 main sensor capabilities was thus structured to form the core of the tool. During the creative phases of product design, these capabilities are combined with a convenient set of inputs to support the generation of value-creation concepts. A focus group to evaluate the tool was carried out with a medium-sized manufacturer of connected shared city bikes. Expert product designers generated concepts with and without the tool. The results of the experiment suggest that the tool enables the generation of concepts that are more novel and of better quality in comparison to traditional ideation approaches. In addition, participants reported a high level of appreciation for the tool. They also noted a high level of creative exploration and collaboration facilitated by the tool. [ABSTRACT FROM AUTHOR]

This paper presents a suitably general model for resistive displacement sensors where the model parameters depend on the current sensor conditions, thereby capturing wearout and failure, and proposes a novel fault detection method that can be seamlessly applied during sensor operation, providing self-diagnostic capabilities. On the basis of the estimation of model parameters, an innovative self-compensation method is derived to increase the accuracy of sensors subject to progressive wearout. The proposed model and methods have been validated by both numerical simulations and experimental tests on two real resistive displacement sensors, placed in undamaged and faulty conditions, respectively. The fault detection method has shown an accuracy of 97.2%. The position estimation error is < ±0.2% of the full-scale span for the undamaged sensor, while the self-compensation method successfully reduces the position estimation error from ±15% to approximately ±2% of the full-scale span for the faulty sensor. [ABSTRACT FROM AUTHOR]

The detection of Activities of Daily Living (ADL) holds significant importance in a range of applications, including elderly care and health monitoring. Our research focuses on the relevance of ADL detection in elderly care, highlighting the importance of accurate and unobtrusive monitoring. In this paper, we present a novel approach that that leverages smartphone data as the primary source for detecting ADLs. Additionally, we investigate the possibilities offered by ambient sensors installed in smart home environments to complement the smartphone data and optimize the ADL detection. Our approach uses a Long Short-Term Memory (LSTM) model. One of the key contributions of our work is defining ADL detection as a multilabeling problem, allowing us to detect different activities that occur simultaneously. This is particularly valuable since in real-world scenarios, individuals can perform multiple activities concurrently, such as cooking while watching TV. We also made use of unlabeled data to further enhance the accuracy of our model. Performance is evaluated on a real-world collected dataset, strengthening reliability of our findings. We also made the dataset openly available for further research and analysis. Results show that utilizing smartphone data alone already yields satisfactory results, above 50% true positive rate and balanced accuracy for all activities, providing a convenient and non-intrusive method for ADL detection. However, by incorporating ambient sensors, as an additional data source, one can improve the balanced accuracy of the ADL detection by 7% and 8% of balanced accuracy and true positive rate respectively, on average. [ABSTRACT FROM AUTHOR]

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making them more intelligent and connected. However, this advancement comes with challenges related to the effectiveness of IoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensure their proper functionality. The success of smart systems relies on their seamless operation and ability to handle faults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore, sensor faults can compromise the system's reliability and undermine the trustworthiness of smart environments. To address these concerns, various techniques and algorithms can be employed to enhance the performance of IoT devices through effective fault detection. This paper conducted a thorough review of the existing literature and conducted a detailed analysis. This analysis effectively links sensor errors with a prominent fault detection technique capable of addressing them. This study is innovative because it paves the way for future researchers to explore errors that have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essential factors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and their corresponding recommended techniques. Additionally, the paper presents a methodical overview of fault detection techniques employed in smart devices, including the metrics used for evaluation. Furthermore, the paper examines the body of academic work related to sensor faults and fault detection techniques within the domain. This reflects the growing inclination and scholarly attention of researchers and academicians toward strategies for fault detection within the realm of the Internet of Things. [ABSTRACT FROM AUTHOR]

Intelligent sensor devices are widely used in the fields of aerospace, automobile, biomedicine, and electronics. However, using traditional bulk sensors, it is challenging to meet the needs of health monitoring, human‐computer interaction, environmental monitoring, and wearable portable electronic devices because of the limitations of flexibility and wearability. Novel flexible sensors should be characterized with stretchable, bendable, breathability, and distributed sensing properties. Herein, a flexible multi‐functional fiber sensor for stress and strain monitoring is prepared using thermal drawing technology. The as‐developed sensor is used for real‐time monitoring of small stress and strain with an accuracy of 1 g (1.7 kPa), positioning spatial resolution of 2 cm, and a strain range of 30 000 µɛ with an accuracy of 86 µɛ. It can sense single‐ and multi‐points weak pressure at the same time during keyboard testing. Furthermore, pulse and breath monitoring are also demonstrated. These results reveal the high sensitivity, lightweight, and good flexibility of this multi‐functional fiber sensor, promoting its applications in fields such as biosensing, aircrafts, and intelligent robot. [ABSTRACT FROM AUTHOR]

This document discusses the role of artificial intelligence (AI), sensors, and robotics in smart agriculture. Traditional agriculture practices are facing challenges such as low productivity, degraded land, and reliance on inorganic fertilizers. Smart agriculture aims to improve efficiency, implement green production methods, and promote long-term agricultural viability. AI can be used in precision farming, autonomous machinery, and predictive analytics to optimize resource allocation and mitigate risks. Sensors, including soil moisture sensors and weather sensors, can provide data for scientific analysis and improve productivity. Robotics can accelerate plant breeding and precision farming with reduced labor inputs. The combination of AI, sensors, and robotics enables farmers to collect and analyze data to improve production yield quality. [Extracted from the article]

Smart devices with sensors now enable continuous measurement of activities of daily living. Accordingly, various human activity recognition (HAR) experiments have been carried out, aiming to convert the measures taken from smart devices into physical activity types. HAR can be applied in many research areas, such as health assessment, environmentally supported living systems, sports, exercise, and security systems. The HAR process can also detect activity-based anomalies in daily life for elderly people. Thus, this study focused on sensor-based activity recognition, and we developed a new 1D-CNN-based deep learning approach to detect human activities. We evaluated our model using raw accelerometer and gyroscope sensor data on three public datasets: UCI-HAPT, WISDM, and PAMAP2. Parameter optimization was employed to define the model's architecture and fine-tune the final design's hyper-parameters. We applied 6, 7, and 12 classes of activity recognition to the UCI-HAPT dataset and obtained accuracy rates of 98%, 96.9%, and 94.8%, respectively. We also achieved an accuracy rate of 97.8% and 90.27% on the WISDM and PAMAP2 datasets, respectively. Moreover, we investigated the impact of using each sensor data individually, and the results show that our model achieved better results using both sensor data concurrently. [ABSTRACT FROM AUTHOR]

This article mainly analyzes the correlation between the carrier energy in robotic arms, according to enhance its performance. This task is achieved because of the sensors/actuators based on nanostructures properties: short response time and high robustness, which proportionated the possibility to execute intricate instructions by the control subsystem of the robotic arm. Therefore, the instructions executed by the controller are supported by a polynomial design, this algorithm helped to evaluate every response signal as a consequence of the main control system, which was consequently by the short response time from the main sensors "flow (carrier energy) and speed of the robotic arm", moreover, the advantage of the proposed system is given by the extra time obtained also to verify the stability of the robotic arm based in Lyapunov models correlated with Lagrange, as well as every equations were solved and organized by neural network. [ABSTRACT FROM AUTHOR]

In this post-Moore era, on-chip components have transitioned into the "more Moore" and "more than Moore" technology routes. Serving as the foundational point and data source for all intelligent devices, sensors are undergoing development for multifunctionality, systematization, and integration. However, the current micro/nano multifunctional sensor fabrication technology is not sufficiently advanced to meet the requirements of practical applications. In this study, we prepared high-precision microwire arrays as multifunctional microsensors. This microsensor exhibited a highly sensitive response to humidity and bending strain. It has a sensitivity of up to 8520% and response time as low as 0.9/3.5 s (response/recovery) over a wide range of humidity variations from 23% to 97%. In the bending strain range of −0.4–0.4%, the microsensor exhibits a gauge factor of up to 267. This multifunctional microsensor has the potential for use in integrated intelligent devices. [ABSTRACT FROM AUTHOR]

With the ongoing advancement in sensor technologies, the development of intelligent, secure, and low‐carbon vehicles will be possible in the near future. However, providing continuous power to numerous onboard sensors consumes substantial amounts of power from vehicle, resulting in the shortened running times. Herein, a self‐powered noncontact triboelectric nanogenerator (SNC‐TENG) with ultralong‐distance detection and a high sensitivity is proposed, which can be used as ambient perception sensors for smart vehicles. The SNC‐TENG is constructed from a MXene/silicone nanocomposite with a fully embedded conductive sponge, which significantly enriches the tribo‐charges and leads to an excellent capability and performance. This SNC‐TENG is capable of perceiving human activity from a distance of 2 m. To the best of authors knowledge, the perceiving distance per unit area of this SNC‐TENG is the highest among previously reported non‐contact TENGs. Furthermore, the applicability of the SNC‐TENG is explored for vehicle sentry mode and blind spot detection for the first time. The use of this technology in a real vehicle demonstrates its feasibility for use in practical applications. Overall, this study provides a new design scheme for contactless self‐powered sensors, and the presented SNC‐TENG is expected to be applicable in various fields where non‐contact sensing is required. [ABSTRACT FROM AUTHOR]

The integration of 3D printed sensors into hosting structures has become a growing area of research due to simplified assembly procedures, reduced system complexity, and lower fabrication cost. Embedding 3D printed sensors into structures or bonding the sensors on surfaces are the two techniques for the integration of sensors. This review extensively discusses the fabrication of sensors through different additive manufacturing techniques. Various additive manufacturing techniques dedicated to manufacture sensors as well as their integration techniques during the manufacturing process will be discussed. This review will also discuss the basic sensing mechanisms of integrated sensors and their applications. It has been proven that integrating 3D printed sensors into infrastructures can open new possibilities for research and development in additive manufacturing and sensor materials for smart goods and the Internet of Things. [ABSTRACT FROM AUTHOR]

Flexible epidermal sensors based on conductive hydrogels hold great promise for various applications, such as wearable electronics and personal healthcare monitoring. However, the integration of conductive hydrogel epidermal sensors into multiple applications remains challenging. In this study, a multifunctional PAAm/PEG/hydrolyzed keratin (Hereinafter referred to as HK)/MXene conductive hydrogel (PPHM hydrogel) was designed as a high-performance therapeutic all-in-one epidermal sensor. This sensor not only accelerates wound healing but also provides wearable human-computer interaction. The developed sensor possesses highly sensitive sensing properties (Gauge Factor = 4.82 at high strain), strong mechanical tensile properties (capable of achieving a maximum elongation at break of 600 %), rapid self-healing capability, stable self-adhesive capability, biocompatibility, freeze resistance at −20 °C, and adjustable photo-thermal conversion capability. This therapeutic all-in-one sensor can sensitively monitor human movements, enabling the detection of small electrophysiological signals for diagnosing relevant activities and diseases. Furthermore, using a rat frostbite model, we demonstrated that the composite hydrogel sensor can serve as an effective wound dressing to accelerate the healing process. This study serves as a valuable reference for the development of multifunctional flexible epidermal sensors for personal smart health monitoring. Accelerated wound healing reduces the risk of wound infection, and conductive hydrogel-based sensors can monitor physiological signals. The multifunctional application of conductive hydrogel sensors combined with wound diagnostic and therapeutic capabilities can meet personalized medical requirements for wound healing and sensor monitoring. The aim of this study is to develop a multifunctional hydrogel patch. The multifunctional hydrogel can be assembled into a flexible wearable high-performance diagnostic and therapeutic integrated sensor that can effectively accelerate the healing of frostbite wounds and satisfy the real-time monitoring of multi-application scenarios. We expect that this study will inform efforts to integrate wound therapy and sensor monitoring. [Display omitted] [ABSTRACT FROM AUTHOR]

Explainable Machine Learning (XML) has revolutionized the field of machine learning by offering interpretability, detailed explanations, and enhanced decision-making abilities. It allows for a comprehensive understanding of complex machine learning models, opening pathways for the explicit clarification of their functionalities. In relation to smart industries, where the accuracy and dependability of sensor data are crucial, XML provides a promising solution for evaluating sensor quality and establishing their trustworthiness. This paper introduces an innovative framework that employs XML techniques to interpret the quality of sensors in intelligent industrial settings, thus, produce comprehensible explanations. The framework integrates feature extraction, model training, and interpretability methods to achieve its goals. Initially, sensor data is gathered from a variety of sources, and pertinent features are isolated to identify critical attributes. Subsequently, machine learning models are trained using the compiled data to comprehend the patterns and correlations between sensor readings and their dependability. The proposed framework aspires to predict the reliability of sensors, facilitating informed decision-making and improving overall operational productivity. [ABSTRACT FROM AUTHOR]

Boulders and cobbles embedded on the body of landslides are carried downstream under the action of gravity, and the study of their transport can give important insight on their dynamics and hence the related hazard. The study examines the reliability of smart sensors to track movements of a cobble and discern between intensity and mode of movement in laboratory experiments. A tag equipped with accelerometer, gyroscope, and magnetometer sensors was installed inside a cobble. The experiments consisted of letting the cobble fall on an inclined plane. By tilting the inclined plane at different angles, different modes of movement such as rolling, bouncing, or sliding were generated. Sliding was generated by embedding the cobble within a thin layer of sand. The position of the cobble travelling down the slope was derived from camera videos. Raw sensor data allowed detection of movement and separation of two modes of movement, namely rolling, and sliding. Additionally, reliable values for the position, velocity, and acceleration were determined by feeding a Kalman filter with smart sensor measurements and camera-based positions. Furthermore, by testing LoRaWAN wireless transmission through sand, the study showed that the signal strength tended to decrease for thicker sand layers. These findings confirm the potential to use these sensors to improve early warning systems and further studies are in progress to assess practicalities of their use in field settings. [ABSTRACT FROM AUTHOR]

High‐performance and versatile multifunctional photodetection devices are essential for various applications, but they often face severe challenges, such as limited operating modes and low detection sensitivity. In this study, a multifunctional detector based on InSe/VO2 heterojunction that integrates light and electric field perceiving capabilities and can dynamically switch operating modes is presented. The device can serve as a self‐powered photodetector, logic gates, and an artificial nociceptor, showcasing its versatility and potential for various applications. The unique metal‐insulator transition (MIT) of VO2 enables the device to dynamically switch between self‐powered photovoltaic and photoconductive modes. A significant advance over traditional photodetectors that can only operate in a single mode has been realized. The device exhibits high detectivity up to 2.09 × 1013 Jones and excellent responsivity of 6.15 A W−1, making it a reliable and accurate tool for photodetection. In addition, it functions as "AND" and "OR" logic gates, providing opportunities for signal processing and communication. Moreover, by adding electric pulses to the InSe/VO2 heterojunction, the device can also function as an artificial nociceptor, with potential implications for medical applications and prosthetics. This work presents remarkable progress toward intelligent sensors and systems, with transformative potential for electronic and photonic devices. [ABSTRACT FROM AUTHOR]

This paper proposes a time- and event-triggered hybrid scheduling for remote state estimation with limited communication resources. A smart sensor observes a physical process and decides whether to send the local state estimate to a remote estimator via a wireless communication channel; the estimator computes the state estimate of the process according to the received data packets and the known scheduling mechanism. Based on the existing optimal time-triggered scheduling, we employ a stochastic event trigger to save precious communication chances and further improve the estimation performance. The minimum mean-squared error (MMSE) state estimate is derived since the Gaussian property is preserved. The estimation performance upper bound and communication rate are analyzed. The main results are illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

In recent years, flexible sensors based on laser-induced graphene (LIG) have played an important role in areas such as smart healthcare, smart skin, and wearable devices. This paper presents the fabrication of flexible sensors based on LIG technology and their applications in human–computer interaction (HCI) systems. Firstly, LIG with a sheet resistance as low as 4.5 Ω per square was generated through direct laser interaction with commercial polyimide (PI) film. The flexible sensors were then fabricated through a one-step method using the as-prepared LIG. The applications of the flexible sensors were demonstrated by an HCI system, which was fabricated through the integration of the flexible sensors and a flexible glove. The as-prepared HCI system could detect the bending motions of different fingers and translate them into the movements of the mouse on the computer screen. At the end of the paper, a demonstration of the HCI system is presented in which words were typed on a computer screen through the bending motion of the fingers. The newly designed LIG-based flexible HCI system can be used by persons with limited mobility to control a virtual keyboard or mouse pointer, thus enhancing their accessibility and independence in the digital realm. [ABSTRACT FROM AUTHOR]

This study addresses the development of smart neural sensors to predict the powder mass flow and track clogging in real time during laser cladding. The challenges posed by powder granulometry and challenging environmental conditions that can lead to delivery failures are considered. An extensive experimental setup was conducted that included manipulation of key factors, such as laser power, travel speed, Z-step, N-layers, nozzle-to-substrate distance, and two types of process patterns. The mass flow rate of the powder was used as an independent variable to evaluate the predictive ability of the neural sensor with respect to the mass flow rate. Several models were trained and evaluated with different datasets and images of the cladding equipment. The model that integrated all data and images showed the best accuracy and precision also showed a strong predictive power for real-time estimation of the powder mass flow rate. Considering two practical rules—an error detection time of no more than one second and a confidence interval of less than 1.8 g/min—two strategies were proposed to meet these criteria. The first recommends the use of the comprehensive "all-features" model, while the second proposes a simplified model (with Z-step, N-slices, and the external camera as inputs) for efficient real-time error detection. The study provides an understanding of powder clogging prediction in laser cladding and suggests strategies for leaders in the field. Future research should validate these results and test these models in different environments to predict complex cladding properties and support the development of stand-alone laser cladding systems. [ABSTRACT FROM AUTHOR]

Simple Summary: Various measurements can be obtained on an animal. These measurements can provide valuable information on animal welfare. Sensors and smart algorithms can automatically perform those measurements and aid in achieving more automated meat inspection and welfare assessment at the slaughterhouse. This study provides an overview of animal welfare measurements at the slaughterhouse and gives examples of the available technologies to record and use these measurements. There are several technologies available, either applied in research, on farm or at the slaughterhouse. However, according to current European Union (EU) law the meat inspection must be performed by a veterinarian. Therefore, sensor technology cannot yet replace a human during the meat inspection, but it has potential to play an important role in the future. Currently it can already add value to the inspections and provide better insight into animal welfare issues than by human inspections alone. Animal-based measures (ABMs) are the preferred way to assess animal welfare. However, manual scoring of ABMs is very time-consuming during the meat inspection. Automatic scoring by using sensor technology and artificial intelligence (AI) may bring a solution. Based on review papers an overview was made of ABMs recorded at the slaughterhouse for poultry, pigs and cattle and applications of sensor technology to measure the identified ABMs. Also, relevant legislation and work instructions of the Dutch Regulatory Authority (RA) were scanned on applied ABMs. Applications of sensor technology in a research setting, on farm or at the slaughterhouse were reported for 10 of the 37 ABMs identified for poultry, 4 of 32 for cattle and 13 of 41 for pigs. Several applications are related to aspects of meat inspection. However, by European law meat inspection must be performed by an official veterinarian, although there are exceptions for the post mortem inspection of poultry. The examples in this study show that there are opportunities for using sensor technology by the RA to support the inspection and to give more insight into animal welfare risks. The lack of external validation for multiple commercially available systems is a point of attention. [ABSTRACT FROM AUTHOR]

High-performance pressure sensors provide the necessary conditions for smart shoe applications. In this paper, the elastic Macroporous Graphene Aerogel (MGA) was synthesized via the modified Hummers' method, and it was further combined with Expanded-Thermoplastic polyurethane (ETPU) particles to assemble MGA-ETPU flexible sensors. The MGA-ETPU has a low apparent density (3.02 mg/cm3), high conductivity (0.024 S/cm) and fast response time (50 ms). The MGA-ETPU has a large linear sensing range (0–10 kPa) and consists of two linear regions: the low-pressure region (0 to 8 kPa) and the high-pressure region (8 to 10 kPa), with sensitivities of 0.08 kPa−1, and 0.246 kPa−1, respectively. Mechanical test results show that the MGA-ETPU sensor showed 19% reduction in maximum stress after 400 loading–unloading compression cycles at 40% strain. Electrical performance tests showed that the resistance of MGA-ETPU sensor decreased by 12.5% when subjected to sudden compression at 82% strain and returned to its original state within 0.05 s. Compared to existing flexible sensors, the MGA-ETPU sensors offer excellent performance and several distinct advantages, including ease of fabrication, high sensitivity, fast response time, and good flexibility. These remarkable features make them ideally suited as flexible pressure sensors for smart shoes. [ABSTRACT FROM AUTHOR]

Bank erosion impacts sediment transport and river morphology, both important processes for river managers. Some important factors, such as timing and causes of bank erosion, are difficult to assess with established techniques. We explore the capability of smart sensors, actually developed for the agricultural sector, to measure soil moisture and temperature as well as movement in real time on banks of northern rivers in different geographical, climatological and landscape settings. Soil movement and its timing were easy to identify from the measurements, and in many cases, the reason for the onset of movement could be inferred. The sensors automatically upload data with a high temporal resolution for a long period of time, while only minimally disturbing the bank itself. However, the sensor approach has a low spatial resolution (point measurements), and therefore, complementing the measurements with approaches with a high spatial resolution, such as spatially continuous digital elevation models of differencing techniques or seismic sensors, is advised. Overall, the measurement approach using new agricultural sensors has several unique advantages and disadvantages and can be a good tool for the monitoring of river bank erosion and could lead to new insights. [ABSTRACT FROM AUTHOR]

The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O (n2)). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings. [ABSTRACT FROM AUTHOR]

By the 2010s, BIM is highly popular research area that gathers researchers from various domains. We tackle the major challenge of BIM and the main barrier for the application of BIM in smart environments: integration of BIM and wireless sensor networks. Existing studies in the literature have various dependencies and propose limited solutions. These solutions are dedicated to specific applications and domains. However, it is highly required to have a generic solution for real time indoor and outdoor environmental monitoring with full integration with the digital model of the building. To fulfill this need, in this study, we propose a novel approach to monitor and fulfill user requirements by a numerical model of the building, a novel framework and a BIMWSN integrated architecture based on the BIM principles for smart building environments. All the experiments are performed on a real testbed, physical sensor devices deployed on the classrooms of the university building. Such numerical model presents the digital model of the environment by gathering acquired data. Besides, data is gathered on a big data platform where it may be served to various other applications such as machine learning studies. [ABSTRACT FROM AUTHOR]

Using the example of an acoustoelectronic sensor of linear displacements on surface acoustic waves (SAW), a method of intellectualization of microelectronic sensors has been developed, the feature of which, in particular, is the frequency type of the output signal.The structure diagram of the SAW sensor with the intelligent support unit (ISU) is described. The ISU receives input data from elements on the SAW of the sensor, processes them according to the developed algorithm of intellectual support in two modes of operation, which provide high metrological characteristics, as well as interface and service functions.In the programming of embedded processors, the used C/C++ language is implemented by a binary array of const float variable constants with a floating point defined as const float Dat[N][T + 1]. [ABSTRACT FROM AUTHOR]

In this paper, with the assistance of some tools and a machine learning model, a smart wind turbine was formed that eliminates some expensive sensors and reduces sensor complexity. Squirrel cage induction generator (SCIG) and six rotor blades make up the proposed design, and depending on the wind's direction, the turbine itself can rotate the rotor hub to produce energy more effectively. Additionally, two stepper motors are coupled to the yaw mechanism with the aid of the rotor hub, and the entire controlling procedure will depend on the direction of the wind. The rotor hub must continuously revolve in the same direction as the wind to maximize wind energy utilization. Additionally, to correctly predict wind degrees, a machine learning model was deployed. Random forest regression was used to train and predict the wind direction. The model is deployed in Raspberry Pi, where the incoming sensor values are being stored. Using the generated data, machine learning model was trained and it can be concluded that the model can potentially replace some of the expensive sensors to reduce cost. The model can be used for similar weather conditions only based on machine learning model and fewer sensors. [ABSTRACT FROM AUTHOR]

Supramolecular polymer gels show promise for use in batteries, supercapacitors, and multifunctional sensors. However, their poor stiffness and ionic conductivity hinder their large‐scale utilization. Herein, a cellulose‐based supramolecular bio‐polymer gel (SBPG) with high ionic conductivity and exceptional stiffness is reported. These properties are translated into proof‐of‐concept iontronic pressure sensors with an ultra‐high sensitivity of 1 475 000 kPa−1. The technological potential is demonstrated via fabrication of a prototype smart glove using only eight pressure sensors based on this novel gel. Smart glove sensors with 90% classification accuracy for object recognition are demonstrated using a tactile feedback algorithm. [ABSTRACT FROM AUTHOR]

The launching of 5G technology provides excellent opportunity for the prosperous development of Internet of Things (IoT) devices and intelligent wireless sensor nodes. However, deploying of tremendous wireless sensor nodes network presents a great challenge to sustainable power supply and self‐powered active sensing. Triboelectric nanogenerator (TENG) has shown great capability for powering wireless sensors and work as self‐powered sensors since its discovery in 2012. Nevertheless, its inherent property of large internal impedance and pulsed "high‐voltage and low‐current" output characteristic seriously limit its direct application as stable power supply. Herein, a generic triboelectric sensor module (TSM) is developed toward managing the high output of TENG into signals that can be directly utilized by commercial electronics. Finally, an IoT‐based smart switching system is realized by integrating the TSM with a typical vertical contact–separation mode TENG and microcontroller, which is able to monitor the real‐time appliance status and location information. Such design of a universal energy solution for triboelectric sensors is applicable for managing and normalizing the wide output range generated from various working modes of TENGs and suitable for facile integration with IoT platform, representing a significant step toward scaling up TENG applications in future smart sensing. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications. [ABSTRACT FROM AUTHOR]

Wearable electronic sensors have attracted considerable interest in hand motion monitoring because of their small size, flexibility, and biocompatibility. However, the range of motion and sensitivity of many sensors are inadequate for complex and precise finger motion capture. Here, organic and inorganic materials were incorporated to fabricate a hybrid electronic sensor and optimized and woven into fabric for hand motion detection. The sensor was made from flexible porous polydimethylsiloxane (PDMS) filled with multiwalled carbon nanotubes (MWCNTs). The weight ratios of MWCNTs and geometric characteristics were optimized to improve the hybrid electronic sensor, which showed a high elongation at the breaking point (i.e., more than 100%) and a good sensitivity of 1.44. The strain-related deformation of the PDMS/MWCNT composite network resulted in a variation in the sensor resistance; thus, the strain level that corresponds to different finger motions is be calculated. Finally, the fabricated and optimized electronic sensor in filiform structure with a 6% MWCNT ratio was integrated with smart fabric to create a finger sleeve for real-time motion capture. In conclusion, a novel hybrid E-fiber sensor based on PDMS and MWCNTs was successfully fabricated in the current study with an optimal M/P ratio and structure, and textile techniques were adopted as new packaging approaches for such soft electronic sensors to create smart fabric for wearable and precise detection with highly enhanced sensing performance. The successful results in the current study demonstrate the great potential of such hybrid soft sensors in smart wearable healthcare management, including motion detection. [ABSTRACT FROM AUTHOR]

Energy harvesting systems are attracting significant attention as a new power‐supply technology to overcome the limitations of internet of things (IoT) devices usually established in an indoor environment such as power consumption, size, and usage time. Organic photovoltaics (OPVs) with high absorption coefficients at indoor light wavelengths are a promising energy source for the IoT‐based sensors. However, OPVs in sensor platforms are not well addressed because of their insufficient and inconsistent power supply under indoor circumstance. Herein, a self‐powered sensor system that combines highlyefficient indoor OPV modules with a boost converter for real‐time temperature measurements is demonstrated. The OPV module with a ternary blend is devised to achieve suitable morphology for low‐intensity indoor operation resulting in high efficiency and stability under 1000 lux 3000 K light‐emitting diode illumination. Moreover, the voltage of the OPV modules is effectively boosted through an optimized converter to adequately operate the sensor platform. The sensor system integrated with the OPV modules and boost converter successfully measures the real‐time changes in temperature in indoor environments. This study proposes an ideal self‐powered sensor platform that can be used in smart‐home systems using indoor lighting. [ABSTRACT FROM AUTHOR]

Currently, an urgent need in the field of wearable electronics is the development of flexible sensors that can be attached to the human body to monitor various physiological indicators and movements. In this work, we propose a method for forming an electrically conductive network of multi-walled carbon nanotubes (MWCNT) in a matrix of silicone elastomer to make stretchable sensors sensitive to mechanical strain. The electrical conductivity and sensitivity characteristics of the sensor were improved by using laser exposure, through the effect of forming strong carbon nanotube (CNT) networks. The initial electrical resistance of the sensors obtained using laser technology was ~3 kOhm (in the absence of deformation) at a low concentration of nanotubes of 3 wt% in composition. For comparison, in a similar manufacturing process, but without laser exposure, the active material had significantly higher values of electrical resistance, which was ~19 kOhm in this case. The laser-fabricated sensors have a high tensile sensitivity (gauge factor ~10), linearity of >0.97, a low hysteresis of 2.4%, tensile strength of 963 kPa, and a fast strain response of 1 ms. The low Young's modulus values of ~47 kPa and the high electrical and sensitivity characteristics of the sensors made it possible to fabricate a smart gesture recognition sensor system based on them, with a recognition accuracy of ~94%. Data reading and visualization were performed using the developed electronic unit based on the ATXMEGA8E5-AU microcontroller and software. The obtained results open great prospects for the application of flexible CNT sensors in intelligent wearable devices (IWDs) for medical and industrial applications. [ABSTRACT FROM AUTHOR]

This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed. [ABSTRACT FROM AUTHOR]

Cementitious composites reinforced with conductive nanoparticles, also known as smart composite-based sensors, are emerging as novel sensors for real-time monitoring of structures. The present study deals with the development of embeddable smart composite-based sensor (called as e SCS), for monitoring the damage evolution in large-scale beam-column (B-C) sub-assemblages. Firstly, a systematic approach has been developed to fabricate the sensors through incorporation of functionalized (-COOH) multi-walled carbon nanotubes (MWCNTs). The optimum dosage of CNTs required for developing sensors with maximum conductivity and piezo-resistivity was ascertained by determining the percolation threshold limit. The best performing e SCS was then successfully embedded at the critical locations of full-scale B-C joint specimens. The embedded sensors are used, for the first time, for damage monitoring of the B-C sub-assemblages subjected to reverse cyclic loadings. From the study, it is found that e SCS with 0.50 wt.% COOH-MWCNT shows excellent piezoresistive behaviour and gauge factors are found to be approximately 730, 457 and 396 for the applied compressive stress ranges of 2–4, 2–6 and 2–8 MPa, respectively. The embedded sensors showed the excellent strain sensing capability under reverse cyclic loading. The present study demonstrates the suitability of embedded e SCS sensors for robust structural health monitoring applications. [ABSTRACT FROM AUTHOR]

A new class of eutectogel with hydrophilic/hydrophobic heterogeneous network is reported. The flexible and conductive eutectogel shows conspicuous transparency, solvent resistance, solvent retention and antibacterial performance. The assembled eutectogel sensor can be applied to detect human motion and underwater communication with satisfactory sensitivity, accuracy, and repeatability. [Display omitted] In recent years, gel-based ion conductor has been widely considered in wearable electronics because of the favorable flexibility and conductivity. However, it is of vital importance, yet rather challenging to adapt the gel for underwater and dry conditions. Herein, an anti-swelling and anti-drying, intrinsic conductor eutectogel is designed via a one-step radical polymerization of acrylic acid and 2, 2, 2‑trifluoroethyl methacrylate in binary deep eutectic solvents (DESs) medium. On the one hand, the synergistic effects of hydrophilic/hydrophobic heteronetworks can elicit the integrity stability of eutectogel in liquid environment. It is proved that both the mechanical property and conductivity are maintained after immersing in different salt, alkaline and acid solution and organic solvent for one month. On the other hand, the eutectogel inherits well conductivity (93 mS/m), anti-drying and antibacterial properties from DESs. Based on the above features, the resulting eutectogel can be assembled as smart sensor for stable information transmission in air and underwater with fast response time (1 s), high sensitivity (Gauge factor = 1.991) and long-time reproducibility (500 cycles, 70 % strain). Considering the simple preparation and integration of multiple functions, the binary cooperative complementary principle can provide insights into the development of next-generation conductive soft materials. [ABSTRACT FROM AUTHOR]

Microgrid is a power system that includes various energy sources (e.g., solar panels and wind turbines), where a number of device status and sensing data are collected and transmitted by smart sensors. Based on sensing-as-a-service in microgrid, sensor owners and sensor data consumers can effectively perform data sharing operations. However, the state-of-the-art sensor data sharing works in microgrid have the following two limitations: (i) cannot support fine-grained authorization for sensor owners and sensor data consumers and (ii) fail to simultaneously consider confidentiality and authenticity for sensor data sharing. To address the problems, in this article, we propose a lightweight privacy-preserving sensing data sharing system with fine-grained authorization in microgrid. Technically, we employed attribute-based signature methodology to design a fined-grained authorization mechanism for sensor data users. Moreover, a lightweight hyper elliptic curve-based signcryption scheme is employed to provide confidentiality and authenticity for sensor data sharing. To clarify the feasibility of our proposed system, we implement the system and evaluate the performance. The experimental results show that the system achieves small communication and time overhead, as well as highly acceptable gas consumption of smart contract. [ABSTRACT FROM AUTHOR]

In the current era of data explosion, people's media have access to a large amount of information through the Internet. How to generate effective and realistic news and let interested people accept it is a hot topic in the current news media industry. The continuous development of sensor technology and the application of artificial intelligence technology enable the directional push of intelligent sensor news to be realized. Intelligent sensor media uses sensor technology to monitor and collect information and then combines artificial intelligence and big data to process the information. After obtaining effective media information, it will be pushed in a targeted way and tracked for analysis. By analyzing the current situation and difficulties of sensor news, this paper seeks for an effective combination of artificial intelligence technology and sensor news and establishes a whole process directional push model for sensor news production and thermal tracking analysis. This model is pioneering and practical in the media field. In order to further verify the effectiveness of the model, the important application field of sensor news is selected as the simulation research object, and the specific selection and layout of sensors, the data analysis obtained by sensors, the push of environmental news, and the thermal tracking analysis are simulated. The results show that through data analysis, news editing, and targeted push of water pollution environmental news, the news has gained a certain degree of attention among the main design media. The popularity index is estimated based on the number of likes and comments forwarded by the current mainstream Internet media platforms (Toutiao, Tiktok, and Weibo). The environmental pollution news thermal index of the sensor reached the highest thermal index score of 12 on the 26th day after the activity. On the Weibo client, the message reached the highest heat index of 14 points on the 28th. The simulation analysis of actual cases further illustrates the advantages of this design framework. [ABSTRACT FROM AUTHOR]

Strain and temperature are important physiological parameters for health monitoring, providing access to the respiration state, movement of joints, and inflammation processes. The challenge for smart wearables is to unambiguously discriminate strain and temperature using a single sensor element assuring a high degree of sensor integration. Here, a dual‐mode sensor with two electrodes and tubular mechanically heterogeneous structure enabling simultaneous sensing of strain and temperature without cross‐talk is reported. The sensor structure consists of a thermocouple coiled around an elastic strain‐to‐magnetic induction conversion unit, revealing a giant magnetoelastic effect, and accommodating a magnetic amorphous wire. The thermocouple provides access to temperature and its coil structure allows to measure impedance changes caused by the applied strain. The dual‐mode sensor also exhibits interference‐free temperature sensing performance with high coefficient of 54.49 µV °C−1, low strain and temperature detection limits of 0.05% and 0.1 °C, respectively. The use of these sensors in smart textiles to monitor continuously breathing, body movement, body temperature, and ambient temperature is demonstrated. The developed multifunctional wearable sensor is needed for applications in early disease prevention, health monitoring, and interactive electronics as well as for smart prosthetics and intelligent soft robotics. [ABSTRACT FROM AUTHOR]

In this article, a novel heterogeneous fusion of convolutional neural networks that combined an RGB camera and an active mmWave radar sensor for the smart parking meter is proposed. In general, the parking fee collector on the street outdoor surroundings by traffic flows, shadows, and reflections makes it an exceedingly tough task to identify street parking regions. The proposed heterogeneous fusion convolutional neural networks combine an active radar sensor and image input with specific geometric area, allowing them to detect the parking region against different tough conditions such as rain, fog, dust, snow, glare, and traffic flow. They use convolutional neural networks to acquire output results along with the individual training and fusion of RGB camera and mmWave radar data. To achieve real-time performance, the proposed algorithm has been implemented on a GPU-accelerated embedded platform Jetson Nano with a heterogeneous hardware acceleration methodology. The experimental results exhibit that the accuracy of the heterogeneous fusion method can reach up to 99.33% on average. [ABSTRACT FROM AUTHOR]

In this study, a compact smart-sensor tag is developed for estimating pork freshness. The smart sensor tag can be placed in areas where packaged meat is stored or displayed. Antennas and simulated models were developed to maximize the efficiency of radio frequency (RF) energy harvesting. The proposed smart sensor tag includes a red, green, and blue sensor that detects changes in the freshness of meat. To detect the color changes in pork stored at a perishable hot temperature in an outdoor environment, this study applies Hue, Saturation, and Value conversion using machine learning, through which the freshness can be determined with a high degree of accuracy. Validation experiments of the sensor tag performance demonstrate that meat freshness can be detected at distances up to 50 cm from the RF using only the RF energy harvesting without changing the battery source. The 1D convolutional neural network model outperforms the traditional MLP and ConvLSTM models in terms of accuracy and loss. [ABSTRACT FROM AUTHOR]

In this contribution, a 25 GHz planar antenna, designed and realized in microstrip technology, is exploited as a lightweight and compact liquid sensor. The high working frequency allows minimization of the sensor dimension. Moreover, particular attention was paid to keeping the design cost low. Indeed, the frequency of 25 GHz is widely exploited for many applications, e.g., up to the last decade concerning radars and, recently, 5G technology. Available commercial antennas allowed minimization of the effort that is usually required to design the microstrip sensor. The antenna was in-house realized, and the microstrip Cu conductor was modified through controlled anodic oxidation in order to enhance the sensing features. The sensor capability of detecting the presence and concentration of ethanol in water was experimentally demonstrated. In detail, a sensitivity of 0.21 kHz/(mg/L) and an average quality factor of 117 were achieved in a very compact size, i.e., 18 mm × 19 mm, and in a cost-effective way. As a matter of fact, the availability of devices able to collect data and then to send the related information wirelessly to a remote receiver represents a key feature for the next generation of connected smart sensors. [ABSTRACT FROM AUTHOR]

In this study, a simple optical luminescence smart sensor derived from organic ligand‐based metal–organic framework material with high oxidation state central metal (Zr4+) was developed. It also was screened to achieve Fe3+, Pb2+, and Co2+ sensitive detection through the strong coordination of amino groups or nitrogen center with 2‐acetylpyridine ligand. The intrinsic optical and fluorescence intensities of sensor were regulated by the ligand‐to‐metal charge transfer effect. The new sensor exhibits an excellent selectivity, ultrafast detection of Fe3+, Pb2+, and Co2+ ions in water with good detection limits (LODs and LOQs). Fluorometric and UV–Vis absorption spectroscopies were used to conduct the study. Brunauer, Emmett, and Teller, Fourier transform infrared (FT‐IR); X‐ray diffraction spectrometry; and X‐ray Photoelectron Spectroscopy were used to confirm the structure of the novel sensor. A real industrial wastewater sample was investigated against the synthesized sensor with a powerful detection efficiency. [ABSTRACT FROM AUTHOR]