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Currently, there is a lot of interest in smart sensors and integrated composite materials in various industries such as construction, aviation, automobile, medical, information technology, communication, and manufacturing. Here, a new conceptual design for a force and temperature sensor system is developed using fiber-optic Bragg grating sensors embedded within composite materials, and a mathematical model is proposed that allows one to estimate strain and temperature based on signals obtained from the optical Bragg gratings. This is important for understanding the behaviors of sensors under different conditions and for creating effective monitoring systems. Describing the strain gradient distribution, especially considering different materials with different Young's modulus values, provides insight into how different materials respond to applied forces and temperature changes. The shape of the strain gradient distribution was obtained, which is a quadratic function with a maximum value of 1500 µ, with a maximum value at the center of the lattice and a symmetrically decreasing strain value with distance from the central part of the fiber Bragg grating. With the axial strain at the installation site of the Bragg grating sensor under applied force values ranging from 10 to 11 N, the change in strain was linear. As a result of theoretical research, it was found that the developed system with fiber-optic sensors based on Bragg gratings embedded in composite materials is resistant to external influences and temperature changes. [ABSTRACT FROM AUTHOR]

Food processing is an indispensable sector crucial for controlling the food losses. Discrete measures were introduced and implemented to enhance the food shelf life and preservation techniques. However, poor handling and preservation methods usher to the food deprivation. In such milieu, amalgamation of futuristic technologies like sophisticated sensors tether with Internet of Things (IoT) could shoot up the food safety and minimize the deprivation. Research across the globe have proved that integration of IoT‐smart sensors in scrutinizing ecological factors such as temperature, radiation, gaseous composition, relative humidity, and moisture content that are critical for food processing and preservation. IoT has the prospects to ameliorate nationwide explicable execution, slash energy depletion, slash manufacturing expenses, inflate worker health and safety during food processing unit. Smart agricultural techniques also enable measurement of temperature, relative humidity, soil moisture, and nitrogen contents in smart farming and helps the user to determine the status of crops and commodity. This article aims to focus a few aspects and budding areas of IoT in the food and agricultural sectors. With this outlook, advancement and smartness in agriculture and food processing can be created by collaborating with IoT technology. [ABSTRACT FROM AUTHOR]

Summary: Human activity recognition is a well‐established research problem in ubiquitous computing. The increased dependency on various smart devices in our daily lives allows us to investigate the sensor data world produced by multimodal sensors embedded in smart devices. However, the raw sensor data are often unlabeled and annotating this vast amount of data are a costly exercise that can often lead to privacy breaches. Self‐supervised learning‐based approaches are at the forefront of learning semantic representation from unlabeled sensor data, including when applied to human activity recognition tasks. As inferring human activity depends on multimodal sensors, addressing the modality difference and inter‐modality dependencies in a model is an important process. This paper proposes a novel self‐supervised learning approach, modality aware contrastive learning (MACL), for representation learning using multimodal sensor data. The approach uses different sensing modalities to create different views of an input signal. Thus, the model is able to learn the representations by maximizing the similarity among different sensing modalities of the same input signal. Extensive experiments were performed on four publicly available human activity recognition data sets to verify the effectiveness of our proposed MACL method. The experimental evaluation results show that the MACL method attains a comparable performance for human activity recognition to the compared baseline models, directly exceeding the performance of models using standard augmentation transformation strategies. [ABSTRACT FROM AUTHOR]

Human vision system remains alert for dangers and adopts high‐speed and low‐power coding methods to convert the image information to spike signals. To meet the demand for danger alert in machine vision, it is important to design intelligent sensors to integrate the functions of image perception and high‐efficiency coding for high‐priority analysis. Inspired by the human visual system, a MoS2 phototransistor is introduced on SiNx substrate enabling simultaneous image perception and time‐to‐first‐spike (TTFS) coding. The device demonstrates exceptional performance in encoding 3‐bit grayscale images, achieving a low mean squared error of 0.008 and a high structural similarity index of 0.9784. Spiking neural networks (SNN) with TTFS coding achieve high recognition accuracy (98.86%) while reducing spike count by 75%. The device array also perceives motion direction and object states by converting data temporally. This work establishes a hardware foundation to promote the performance of SNNs in efficiently identifying crucial information. [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]

The advancements in tactile sensor technology have found wide-ranging applications in robotic fields, resulting in remarkable achievements in object manipulation and overall human-machine interactions. However, the widespread availability of high-resolution tactile skins remains limited, due to the challenges of incorporating large-sized, robust sensing units and increased wiring complexity. One approach to achieve high-resolution and robust tactile skins is to integrate a limited number of sensor units (taxels) into a flexible surface material and leverage signal processing techniques to achieve super-resolution sensing. Here, we present a magnetic skin consisting of multi-direction magnetized flexible films and a contactless Hall sensor array. The key features of the proposed sensor include the specific magnetization arrangement, K-Nearest Neighbors (KNN) clustering algorithm and convolutional neural network (CNN) model for signal processing. Using only an array of 4*4 taxels, our magnetic skin is capable of achieving super-resolution perception over an area of 48400 mm2, with an average localization error of 1.2 mm. By employing neural network algorithms to decouple the multi-dimensional signals, the skin can achieve multi-point and multi-scale perception. We also demonstrate the promising potentials of the proposed sensor in intelligent control, by simultaneously controlling two vehicles with trajectory mapping on the magnetic skin. [ABSTRACT FROM AUTHOR]

Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head posture (FHP). This paper proposes a wearable device that combines the acquisition of electromyographic signals from the cervical region with inertial data from inertial measurement units (IMUs) to assess the occurrence of FHP. To improve electronics integration and wearability, e-textiles are explored for the development of surface electrodes and conductive tracks that connect the different electronic modules. Tensile strength and abrasion tests of 22 samples consisting of textile electrodes and conductive tracks produced with three fiber types (two from Shieldex and one from Imbut) were conducted. Imbut's Elitex fiber outperformed Shieldex's fibers in both tests. The developed surface electromyography (sEMG) acquisition hardware and textile electrodes were also tested and benchmarked against an electromyography (EMG) gold standard in dynamic and isometric conditions, with results showing slightly better root mean square error (RMSE) values (for 4 × 2 textile electrodes (10.02%) in comparison to commercial Ag/AgCl electrodes (11.11%). The posture monitoring module was also validated in terms of joint angle estimation and presented an overall error of 4.77° for a controlled angular velocity of 40°/s as benchmarked against a UR10 robotic arm. [ABSTRACT FROM AUTHOR]

Grasping and object manipulation have been considered key domains of Cyber-Physical Systems (CPS) since the beginning of automation, as they are the most common interactions between systems, or a system and its environment. As the demand for automation is spreading to increasingly complex fields of industry, smart tools with sensors and internal decision-making become necessities. CPS, such as robots and smart autonomous machinery, have been introduced in the meat industry in recent decades; however, the natural diversity of animals, potential anatomical disorders and soft, slippery animal tissues require the use of a wide range of sensors, software and intelligent tools. This paper presents the development of a smart robotic gripper for deployment in the meat industry. A comprehensive review of the available robotic grippers employed in the sector is presented along with the relevant recent research projects. Based on the identified needs, a new mechatronic design and early development process of the smart gripper is described. The integrated force sensing method based on strain measurement and magnetic encoders is described, including the adjacent laboratory and on-site tests. Furthermore, a combined slip detection system is presented, which relies on an optical flow-based image processing algorithm using the video feed of a built-in endoscopic camera. Basic user tests and application assessments are presented. [ABSTRACT FROM AUTHOR]

Electronic textiles (e-textiles) are a branch of wearable technology based on integrating smart systems into textile materials creating different possibilities, transforming industries, and improving individuals' quality of life. E-textiles hold vast potential, particularly for use in personal protective equipment (PPE) by embedding sensors and smart technologies into garments, thus significantly enhancing safety and performance. Although this branch of research has been active for several decades now, only a few products have made it to the market. Achieving durability, reliability, user acceptance, sustainability, and integration into current manufacturing processes remains challenging. High levels of reliability and user acceptance are critical for technical textiles, such as those used in PPE. While studies address washing reliability and field tests, they often overlook end user preferences regarding smart textiles. This paper presents a narrow fabric-based e-textile system co-developed by engineers, garment and textiles' manufacturers, and firefighters. It highlights material choices and integration methods, and evaluates the system's reliability, sustainability, and user experience, providing comprehensive insights into developing and analyzing e-textile products, particularly in the PPE field. [ABSTRACT FROM AUTHOR]

The automatic detection of smoke by analyzing the video stream acquired by traditional surveillance cameras is becoming a more and more interesting problem for the scientific community thanks to the necessity to prevent fires at the very early stages. The adoption of a smart visual sensor, namely a computer vision algorithm running in real time, allows one to overcome the limitations of standard physical sensors. Nevertheless, this is a very challenging problem, due to the strong similarity of the smoke with other environmental elements like clouds, fog and dust. In addition to this challenge, data available for training deep neural networks is limited and not fully representative of real environments. Within this context, in this paper we propose a new method for smoke detection based on the combination of motion and appearance analysis with a modern convolutional neural network (CNN). Moreover, we propose a new dataset, called the MIVIA Smoke Detection Dataset (MIVIA-SDD), publicly available for research purposes; it consists of 129 videos covering about 28 h of recordings. The proposed hybrid method, trained and evaluated on the proposed dataset, demonstrated to be very effective by achieving a 94% smoke recognition rate and, at the same time, a substantially lower false positive rate if compared with fully deep learning-based approaches (14% vs. 100%). Therefore, the proposed combination of motion and appearance analysis with deep learning CNNs can be further investigated to improve the precision of fire detection approaches. [ABSTRACT FROM AUTHOR]

Objective: This study introduces a novel methodology combining rapid stretch compound training with blood flow restriction (BFR) to investigate post activation performance enhancement (PAPE) in basketball players, a field that has been predominantly explored for lower limbs. We aimed to assess the efficacy of this combined approach on upper limb muscle performance in athletes. Methods: We employed a randomized, self-controlled crossover trial with ten male basketball players. The bench press throw (BPT) served as the primary metric, with players undergoing four interventions post-baseline: (1) STR—plyometric training; (2) BFR—blood flow restriction; (3) COMB—STR integrated with BFR; and (4) CON—control. Innovatively, we utilized an intelligent tracking sensor to precisely measure peak power (PP), peak velocity (PV), mean power (MP), and mean velocity (MV) at 4, 8, and 12 min post-intervention, providing a detailed temporal analysis of PAPE. Results: The COMB intervention demonstrated superior PAPE effects at 4 min, significantly outperforming STR and BFR alone and the control group across all measured indices (p < 0.05). Notably, the COMB group maintained these improvements for PV, PP, and H up to 12 min post-intervention, suggesting a prolonged effect. Conclusion: (1) The COMB stimulation has been shown to successfully induce PAPE more effectively than STR and BFR modality alone. (2) It appears that the optimal effects of PAPE are achieved within 4 min of exercising under this COMB. By the 12 min mark, only the COMB group continued to show significant improvements in PV, PP, and H compared to both the baseline and the CON group, while the effects in the STR and BFR groups further diminished. This suggests that although the PAPE effect is maintained over time, its optimal performance may peak at the 4 min mark and then gradually weaken as time progresses. [ABSTRACT FROM AUTHOR]

This work demonstrates that the packaging material commonly called "bubble wraps" (Aeroplast) can be leveraged to serve as a probe to visible sunlight exposure. This probe relies on a newly developed layered metal selenide photocatalyst with the general formula (DMAH)2xMnxSn3–xSe6 (DMSe‐1) (x = 1.3–1.7; DMAH+ = dimethylammonium), featuring a narrow bandgap of 0.76 eV, in addition to an indicator dye and a reducing agent. The photochemically sensitive probe is introduced into the air‐filled compartments of bubble wraps and undergoes photocatalytic degradation, resulting in a chromatic response to sunlight exposure. The probe's sensitivity to variable irradiation doses can be adjusted by varying the amount of the photocatalyst. The color intensity correlates with the absorbed irradiation dose, allowing for qualitative assessment by the naked eye or quantitative measurement using the RGB color system. The results obtained from the new probe agree with those obtained from standard sunlight pyranometers (r = 0.98), with an average error of <15%. This suggests that beyond their use as protective coatings, bubble wraps can be successfully repurposed as visible light sensors. Furthermore, this study describes the initial use of metal chalcogenides as visible light probes, potentially paving the way for the development of innovative light‐sensitive materials. [ABSTRACT FROM AUTHOR]

Here, the implementation of a smartphone‐based portable molecularly imprinted photonic crystal hydrogel (MIPCH) colorimetric sensor for the visual and label‐free detection of bisphenol A (BPA) in water samples is reported. The sensor is prepared by photopolymerizing BPA‐added hydrogel precursor solution within the voids of a photonic crystal opal film, followed by the extraction of BPA molecules. Rebinding of the BPA analyte to the MIPCH causes the hydrogel to swell, leading to a significant redshift in the diffracting spectrum. The MIPCH sensor exhibits a low limit of detection of 0.96 × 10−15m. The sensor also shows linear response in the femtomolar range, rapid responsiveness (≤6 min), selective detection of BPA in complex matrices, long‐term stability, and reproducibility. Images of the sensor responses are used to train a deep‐learning‐based regression model on a smartphone to predict the BPA concentration quantitatively. Integration of the regression model with the developed sensor provides an accurate and portable smart sensor platform well‐suited for real‐time and on‐field monitoring of BPA. [ABSTRACT FROM AUTHOR]

Tactile sensing provides robots the ability of object recognition, fine operation, natural interaction, etc. However, in the actual scenario, robotic tactile recognition of similar objects still faces difficulties such as low efficiency and accuracy, resulting from a lack of high-performance sensors and intelligent recognition algorithms. In this paper, a flexible sensor combining a pyramidal microstructure with a gradient conformal ionic gel coating was demonstrated, exhibiting excellent signal-to-noise ratio (48 dB), low detection limit (1 Pa), high sensitivity (92.96 kPa−1), fast response time (55 ms), and outstanding stability over 15,000 compression-release cycles. Furthermore, a Pressure-Slip Dual-Branch Convolutional Neural Network (PSNet) architecture was proposed to separately extract hardness and texture features and perform feature fusion. In tactile experiments on different kinds of leaves, a recognition rate of 97.16% was achieved, and surpassed that of human hands recognition (72.5%). These researches showed the great potential in a broad application in bionic robots, intelligent prostheses, and precise human–computer interaction. [ABSTRACT FROM AUTHOR]

Wearable smart sensors have broad prospects in medical health and physical exercise. In this study, the core–shell conductive fiber membrane was prepared by adding multiwalled carbon nanotubes during polymerization of aniline on spun fibers. In comparison to the conventionally utilized polymerized aniline, the integration of carbon nanotube particulates enhances the conductivity and surface area, provides more aniline polymerization sites, and simultaneously increases the stability of the composite film. The pressure sensor has a sensitivity of 61.12, 40.32, and 23.17 kPa−1 in the range of 0–7.1 kPa, 7.1–11.1 kPa, and 11.1–19.54 kPa. In addition, it has a fast response time (loading time of 120 ms and unloading time of only 22 ms) and exhibits excellent cyclic stability through numerous loading–unloading cycles. These properties enable the sensor to be applied to real‐time monitoring of human sports health, such as knee and elbow bending, mouse clicking, beaker holding, and so on. This research method provides a new strategy and technology for conducting polymers as materials for flexible electronic equipment. [ABSTRACT FROM AUTHOR]

Background: Integrating diverse sensor technologies into smart insoles offers significant potential for monitoring biomechanical metrics; enhancing sports performance; and managing therapeutic interventions, diseases, disorders, and other health-related issues. The variation in sensor types and applications requires a systematic review to synthesize existing evidence and guide future innovations. Objectives: This review aims to identify, categorize, and critically evaluate the various sensors used in smart insoles, focusing on their technical specifications, application scopes, and validity. Methods: Following the PRISMA guidelines, a search was conducted in three major electronic databases, namely, PubMed, Scopus, and Web of Science, for relevant literature published from 2014 to 2024. Other works not located in the mentioned databases were added manually by parallel searches on related themes and suggestions from the website of the databases. To be eligible, studies were required to describe sensor implementation in insoles, specify the sensor types, and report on either validation experiments or practical outcomes. Results: The search identified 33 qualifying studies. Proper analysis revealed a dominance of pressure sensors, with accelerometers and gyroscopes also being widely used. Critical applications included gait analysis, posture correction, and real-time athletic and rehabilitation feedback. The review also examined the relative effectiveness of different sensor configurations. Conclusions: This systematic review comprehensively classifies sensor technologies within smart insoles and highlights their broad application potential across various fields. Future research should aim to standardize measurement protocols, enhance sensor integration, and advance data processing techniques to boost functionality and clinical applicability. [ABSTRACT FROM AUTHOR]

Researchers have been focusing on developing intelligent sensing devices capable of capturing human movements or signals, particularly in sports and healthcare. These signals, encompassing biophysical and biochemical data, are increasingly utilized across various fields for purposes such as sports training, medical diagnostics, preventive healthcare, and rehabilitation. The urgency for real-time health monitoring has been underscored by the coronavirus disease 2019 (COVID-19) pandemic, hastening the demand for portable, flexible sensors enabling remote health signal measurement. Our investigation delves into recent advancements in flexible smart sensor technologies tailored for monitoring crucial signals in healthcare and sports. Within this comprehensive study, we provide an overview of the latest progress in smart sensors and systems engineered to track fitness and health indicators. These encompass a range of signals including electrocardiograms (ECGs), inertial data, motion, heart rate, and biological fluids (such as blood and sweat). Furthermore, we introduce a novel approach involving multimodal and integrated multidimensional sensing systems distinguished by their high sensitivity. Additionally, we elucidate current challenges in the field and offer insights into the future trajectory of smart sensors in capturing vital signals effectively. It is evident that the integration of modern fabrication techniques, stretchable electronic devices, the Internet of Things (IoT), and artificial intelligence (AI) algorithms has significantly augmented the capacity to monitor and utilize these signals for human health monitoring, including disease prediction. Smart sensors and systems that are multidimensional and multimodal might provide a more adaptable and comprehensive solution to meet the escalating healthcare demands. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a highly accurate smart temperature sensor designed in 65 nm CMOS technology. The sensor exhibits a ±0.15 °C (3σ) error across a wide temperature range from −40 °C to 125 °C, catering to diverse application needs. Leveraging advanced CMOS technology, the sensor employs an adaptive decimation filter that allows us to control the conversion time, ensuring that the accuracy of the conversion is maintained even in challenging conditions. The proposed sensor architecture integrates advanced techniques for temperature sensing for improved accuracy and reliability. Through meticulous circuit design and the usage of dynamic element matching, chopping, and calibration/trimming, the sensor demonstrates exceptional performance characteristics, making it suitable for various industrial, automotive, and consumer electronics applications demanding high precision temperature monitoring. [ABSTRACT FROM AUTHOR]

This paper introduces an MOS-based intelligent temperature sensor with a linear readout. Compared with similar designs, the proposed sensor utilizes the DIBL effect to reduce the precision requirement for the voltage reference source and compensate for the temperature measurement range. A compact voltage reference circuit is introduced, which generates two reference voltage bases using only three transistors. In addition, the proposed digital readout circuit does not require a subtractor or a divider, further saving area. Fabricated in a 55 nm CMOS process, the proposed sensor occupies a compact area of 3216 μ m 2 . Post-simulation results show it has a maximum error of −0.52/+0.28 °C within the temperature range of −20 °C to 120 °C after two-point calibration. The power supply voltage range of the sensor is 0.8 to 1.8 V. It has a maximum voltage sensitivity of 5.7 °C/V and its power consumption is only 166 nW, with a power supply voltage of 0.8 V. [ABSTRACT FROM AUTHOR]

Multimodal haptic perception is essential for enhancing perceptual experiences in augmented reality applications. To date, several artificial tactile interfaces have been developed to perceive pressure and precontact signals, while simultaneously detecting object type and softness with quantified modulus still remains challenging. Here, inspired by the campaniform sensilla on insect antennae, we proposed a hemispherical bimodal intelligent tactile sensor (BITS) array using the triboelectric effect. The system is capable of softness identification, modulus quantification, and material type recognition. In principle, due to the varied deformability of materials, the BITS generates unique triboelectric output fingerprints when in contact with the tested object. Furthermore, owing to the different electron affinities, the BITS array can accurately recognize material type (99.4% accuracy), facilitating softness recognition (100% accuracy) and modulus quantification. It is promising that the BITS based on the triboelectric effect has the potential to be miniaturized to provide real-time accurate haptic information as an artificial antenna toward applications of human-machine integration. [ABSTRACT FROM AUTHOR]

Bluetooth sensors in intelligent transportation systems possess extensive coverage and access to a large number of identity (ID) data, but they cannot distinguish between vehicles and persons. This study aims to classify and differentiate raw data collected from Bluetooth sensors positioned between various origin–destination (i–j) points into vehicles and persons and to determine their distribution ratios. To reduce data noise, two different filtering algorithms are proposed. The first algorithm employs time series simplification based on Simple Moving Average (SMA) and threshold models, which are tools of statistical analysis. The second algorithm is rule-based, using speed data of Bluetooth devices derived from sensor data to provide a simplification algorithm. The study area was the Historic Peninsula Traffic Cord Region of Istanbul, utilizing data from 39 sensors in the region. As a result of time-based filtering, the ratio of person ID addresses for Bluetooth devices participating in circulation in the region was found to be 65.57% (397,799 person IDs), while the ratio of vehicle ID addresses was 34.43% (208,941 vehicle IDs). In contrast, the rule-based algorithm based on speed data found that the ratio of vehicle ID addresses was 35.82% (389,392 vehicle IDs), while the ratio of person ID addresses was 64.17% (217,348 person IDs). The Jaccard similarity coefficient was utilized to identify similarities in the data obtained from the applied filtering approaches, yielding a coefficient (J) of 0.628. The identity addresses of the vehicles common throughout the two date sets which are obtained represent the sampling size for traffic measurements. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT), the sophistication and intelligence of sensors are continually evolving, playing increasingly important roles in smart homes, industrial automation, and remote healthcare. However, these intelligent sensors face many security threats, particularly from malware attacks. Identifying and classifying malware is crucial for preventing such attacks. As the number of sensors and their applications grow, malware targeting sensors proliferates. Processing massive malware samples is challenging due to limited bandwidth and resources in IoT environments. Therefore, compressing malware samples before transmission and classification can improve efficiency. Additionally, sharing malware samples between classification participants poses security risks, necessitating methods that prevent sample exploitation. Moreover, the complex network environments also necessitate robust classification methods. To address these challenges, this paper proposes CSMC (Compressed Sensing Malware Classification), an efficient malware classification method based on compressed sensing. This method compresses malware samples before sharing and classification, thus facilitating more effective sharing and processing. By introducing deep learning, the method can extract malware family features during compression, which classical methods cannot achieve. Furthermore, the irreversibility of the method enhances security by preventing classification participants from exploiting malware samples. Experimental results demonstrate that for malware targeting Windows and Android operating systems, CSMC outperforms many existing methods based on compressed sensing and machine or deep learning. Additionally, experiments on sample reconstruction and noise demonstrate CSMC's capabilities in terms of security and robustness. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) have been widely applied in various edge computing devices based on intelligent sensors. However, due to the high computational demands of CNN tasks, the limited computing resources of edge intelligent terminal devices, and significant architectural differences among these devices, it is challenging for edge devices to independently execute inference tasks locally. Collaborative inference among edge terminal devices can effectively utilize idle computing and storage resources and optimize latency characteristics, thus significantly addressing the challenges posed by the computational intensity of CNNs. This paper targets efficient collaborative execution of CNN inference tasks among heterogeneous and resource-constrained edge terminal devices. We propose a pre-partitioning deployment method for CNNs based on critical operator layers, and optimize the system bottleneck latency during pipeline parallelism using data compression, queuing, and "micro-shifting" techniques. Experimental results demonstrate that our method achieves significant acceleration in CNN inference within heterogeneous environments, improving performance by 71.6% compared to existing popular frameworks. [ABSTRACT FROM AUTHOR]

A low power clock generator with self-calibration for UHF RFID tags compatible with the EPCglobal Class-1 Gen-2 (EPC Gen2) standard is presented. By utilizing the timing information of the reader to tag (R ⇒ T) link symbols, the frequency accuracy of the calibrated clock can meet the stringent requirement of the standard. Designed in a 0.18 μ m standard CMOS technology, simulation results show that the frequency error of the clock source is only from − 2.07 to + 1.4% of the target frequency of 2.56 MHz. The power consumption is only 720 nW. [ABSTRACT FROM AUTHOR]

Intelligent wearable sensors play a crucial role in the detection of toxic gases and monitoring physiological signals, thereby effectively ensuring environmental and personal health safety. Nonetheless, achieving the requirements for antibacterial properties, comfortable wear, and multifunctional detection remains a major challenge. In this study, a novel Def‐ZIF‐8/PPY/BC‐based flexible sensor is developed by in situ growth of zeolitic imidazolate frameworks‐8 (ZIF‐8) and polypyrrole (PPY) on bacterial cellulose (BC), followed by water immersion. The Def‐ZIF‐8/PPY/BC‐based flexible sensor demonstrates effective dual‐sensitivity responses to nitrogen dioxide (NO2) toxic gas and stress‐strain behaviors at room temperature. The structural characterization and theoretical calculations affirm that the innovative water treatment method successfully introduces defects into ZIF‐8, resulting in a significant 2.57‐fold improvement in response intensity to 80 ppm NO2. Stress‐strain sensing performance analysis reveals that the Def‐ZIF‐8/PPY/BC‐based flexible sensor exhibited high sensitivity (6.44 kPa−1), rapid response and recovery times (0.7/0.4 s), and exceptional cyclic stability (8000 cycles). Further, the inhibitory effect of ZIF‐8 on common bacterial strains contributes to the exceptional antibacterial properties (antibacterial rate exceeding 99%) of the Def‐ZIF‐8/PPY/BC based on the flexible sensor. This study offers a significant advancement in metal‐organic framework defect engineering and provides an effective strategy for developing multifunctional wearable sensors. [ABSTRACT FROM AUTHOR]

In our daily life, flexible flow sensors endow us with a 'sixth sense' capability, i.e. 'touch' the fluids, improving living quality. Although there are kinds of flexible flow sensors developed to implement this capability, they still have insufficient sensitivity and limited intelligent applications in daily life. Biomimetic engineering provides us with a powerful and effective approach to develop highly sensitive and intelligent flow sensing systems served in our life, comparable to that in creatures. Here, in this review, we present a comprehensive review of recent studies on the flexible flow sensors for human intelligent life. Firstly, we briefly introduce the excellent flow sensing systems selected by nature, and typical design strategies of artificial flexible flow sensors. Furthermore, we collect and exhibit kinds of flexible flow sensors and their applications in intelligent and digital life. Finally, we discuss the challenges and future perspectives of the flexible flow sensor for the metaverse applications. [ABSTRACT FROM AUTHOR]

Nearly all sensors inevitably suffer from environmental influence such as temperature fluctuation. To precisely process the external signals, independent temperature compensation devices or electronic circuits are usually required, making the overall system and algorithms sophisticated. Especially, when the environment temperature is as high as over 200 °C, both the sensors and temperature‐compensation devices encounter the reliability problem. In this work, an intelligent multifunctional sensor utilizing single‐crystal diamond (SCD) microelectromechanical (MEMS) resonators is demonstrated that can sense both magnetic fields and temperatures up to 300 °C, independently. The strategy is to integrate a magnetostrictive thin film and a non‐magnetic thin film on different diamond MEMS resonators on the same diamond chip for simultaneous magnetic field sensing and temperature monitoring, respectively. The multi‐resonators based on diamond MEMS offer a promising platform for multi‐parameter sensing immune to environment interference. [ABSTRACT FROM AUTHOR]

Flexible force sensors show great potential applications in smart wearable devices based on their softness and ease of deformation. Textiles are extremely compatible with the human body, where mechanical forces generated by human movements and physiological activities create rich application scenarios for flexible force sensors. Currently, the bottleneck in the development of flexible sensors depends on the breakthrough of mass production efficiency, which has been a research focus in recent years to accelerate the application of smart textiles in various fields. This work reviews the research progress of flexible force sensors based on fiber assemblies with mass production efficiency, which is discussed in four aspects: sensing mechanisms, structure design, preparation methods, and application. Finally, the challenges faced by mass production of flexible force sensors are analyzed and summarized, and a few inspirations for their future development direction are provided. [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]

In the field of e-healthcare, smart medical sensors are responsible for consistently collecting, transmitting, and communicating real-time data to support immediate decision-making, both within and between healthcare organizations. Privacy-preserving authentication is required, even when the sensors roam between different networks. Existing authentication protocols rely on centralized authentication servers and pose a number of challenges, including a single point of failure, performance bottlenecks, scalability challenges, and privacy concerns. Hence, a secure mutual authentication mechanism using Blockchain is essential in preventing attacks. In this paper, an Improved Chain Code Blockchain-based Key Agreement Authentication Mechanism (CCBKAAM) using the merits of SM9-based Secure Threshold Ring Signature (SM9-STRS) is proposed for achieving privacy and security. This proposed mechanism uses multiple key generation-based cipher identification algorithms for parameter maintenance. The integrated threshold ring signature prevents the limitations of the single key generation scheme. It uses Chain Codes over the Ethereum network for constructing the blocks during the process of mutual user authentication with trust equity score-based Improved PBFT Voting protocol for reaching consensus during authentication. This authentication protocol adopted blockchain for the objective of storing the identities and associated parameters to support the entities under communication during the process of authentication. The formal and informal verification of the proposed CCBKAAM confirmed its potentiality in resisting most of the possible attacks the IoMT is vulnerable to. The communication and computation overhead during the implementation are determined to be significantly lowered by 32.19% and 28.94%, better than the compared baseline blockchain-based mutual authentication. The performance analysis proved that this proposed CCBKAAM scheme is also potent in minimizing the storage overhead up to maximized level of 35.42%. [ABSTRACT FROM AUTHOR]

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Environment perception technology is the most important part of driverless technology, and driverless vehicles need to realize decision planning and control by virtue of perception feedback. This paper summarizes the most promising technology methods in the field of perception, namely visual perception technology, radar perception technology, state perception technology, and information fusion technology. Regarding the current development status in the field, the development of the main perception technology is mainly the innovation of information fusion technology and the optimization of algorithms. Multimodal perception and deep learning are becoming popular. The future of the field can be transformed by intelligent sensors, promote edge computing and cloud collaboration, improve system data processing capacity, and reduce the burden of data transmission. Regarding driverless vehicles as a future development trend, the corresponding technology will become a research hotspot. [ABSTRACT FROM AUTHOR]

A fast and precise convolutional neural network (CNN) framework was built for classifying tea species using low-cost electrochemical profiles of samples. The model achieved an outstanding 96.3% accuracy in categorizing 9 major tea taxa from sensor input data alone. The approach combines simplicity with reliability gains over conventional analytical chemistry, thereby enabling field verification of labels along supply chains. Precision of 96% and recall exceeding 94% per class substantiate robust deployability. Charting of loss values and accuracy over training epochs validates learning efficiency. Comparative evaluation proves deep learning superiority over other models with up to 11% higher prediction rates. Normalization of input signatures was also found vital for inter-sample variability reduction and amplification of subtle inter-species differences that aid discrimination. Practical validation can entrench viability for scalable commercial adoption targeting small producers and vendors globally. In conclusion, an AI-powered rapid, low-cost sensor prototype allows tea sector modernization with data-driven quality assessments. [ABSTRACT FROM AUTHOR]

Irrigation refers to supplying water to soil through pipes, pumps, and spraying systems to ensure even distribution across the field. In traditional farming or gardening, the setup and usage of an agricultural irrigation system solely rely on the personal experience of farmers. The Food and Agriculture Organization of the United Nations (UN) has projected that by 2030, developing countries will expand their irrigated areas by 34%, while water consumption will only be up 14%. This discrepancy highlights the importance of accurately monitoring water flow and volume rather than people's rough estimations. The smart irrigation systems, a key subsystem of smart agriculture known as the cyber–physical system (CPS) in the agriculture domain, automate the administration of water flow, volume, and timing via using cutting-edge technologies, especially the Internet of Things (IoT) technology, to solve the challenges. This study explores a comprehensive three-dimensional problem space to thoroughly analyze the IoT's applications in irrigation systems. Our framework encompasses several critical domains in smart irrigation systems. These domains include soil science, sensor technology, communication protocols, data analysis techniques, and the practical implementations of automated irrigation systems, such as remote monitoring, autonomous operation, and intelligent decision-making processes. Finally, we discuss a few challenges and outline future research directions in this promising field. [ABSTRACT FROM AUTHOR]

Purpose: With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines. Design/methodology/approach: We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected. Findings: This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers' skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies. Originality/value: This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation. [ABSTRACT FROM AUTHOR]

In order to improve the multi-sensor intelligent control performance of environmental pollution detection, this paper proposes a design model of environmental pollution detection intelligent system based on Internet of Things technology. Firstly, this paper uses ZigBee's Internet of Things networking technology to design the network of the pollution detection system. Secondly, in order to verify the effectiveness of the environmental pollution detection method, this paper carries out system debugging analysis and simulation test, and uses visual dsp++simulation system to debug the system. The network of ZigBee Internet of Things networking technology is adopted to realize the network design of pollution detection system, and VIX bus technology is adopted to carry out environmental pollution detection data communication and information processing, so as to realize the optimal design of intelligent system of environmental pollution detection system. Finally, the experimental test and analysis were performed. The collection period of environmental pollution data is 10 s and the monitoring time is 22 h. Finally, according to the environmental model and parameters, we determine that the system can accurately detect environmental pollution information, and the results are accurate and reliable. [ABSTRACT FROM AUTHOR]

Knowledge of modern state-of-the-practice traffic flow sensors provides traffic managers, researchers, and students an understanding of the operation, strengths, and limitations of current sensor technologies and enables them to make an informed decision as to which is appropriate for a particular application. Accordingly, this article describes the intrusive and nonintrusive traffic flow sensor technologies in use today, their applications and selection criteria, and typical output data. Furthermore, it provides examples of representative sensor models. The technologies discussed are mature with respect to current traffic management applications, although some may not provide the data or accuracy required for a specific application or may not perform as needed under the operational conditions anticipated at the installation site. Sensors selected for a first-time application should be field tested under conditions that will be encountered in day-to-day operation before large-scale purchases of the devices are made. As alternative traffic data and information sources, such as commercial data vendors, Wi-Fi and Bluetooth sensing of smartphone locations, and connected and automated vehicle data, become increasingly available, they are progressively finding their way into modern traffic management systems as a complement to conventional roadside sensors. [ABSTRACT FROM AUTHOR]

The present research offers an innovative approach to enhance the safety and mobility of people with visual impairments by using a smart white cane sensor. Herein, an aluminum electrode, Kapton film, and an inexpensive PET substrate are employed to develop an origami‐shaped multilayered triboelectric nanogenerator (TENG) device. The multilayered integration of three‐unit TENGs produces output with a high power of 51 μW at 50 MΩ, along with 155 V and 4.5 μA. Capacitor charging, low‐power electronics powering, and the integration of mechanoluminescent materials are demonstrated. The integration of mechanoluminescent materials enables efficient identification of visually impaired people in regions with poor lighting. A single‐electrode TENG is also implemented for successful surface material detection. To further enhance the sensor's capabilities, digital signal processing techniques are employed for obstacle recognition on their route. The intelligent white cane sensor effectively detects obstructions, offering the user instantaneous feedback and enabling more secure navigation in sophisticated environments. This study highlights the possibility of integrating TENG technologies to provide a complete solution for people with visual impairments. The smart white cane sensor has the potential to greatly increase the safety and independence of visually impaired people in a variety of outdoor and public spaces. [ABSTRACT FROM AUTHOR]

The wireless body area network (WBAN) has emerged as a promising networking paradigm thanks to embedded systems, integrated circuit technologies, and wireless communications advancements. WBAN has the ability to send real-time biomedical data to remote medical personnel for clinical diagnostics through intelligent medical sensors in or around the patient's body. Moreover, WBANs have played an increasingly important role in modern medical systems over the past decade as part of the Internet of Things (IoT). In addition to their conveniences, WBANs present us with the data confidentiality challenge and protecting patient's privacy. The system requires a robust security mechanism to protect against threats because of the massive production of delay-sensitive data. This article presents a quantum key distribution (QKD) protocol-based authentication scheme for secure communication within WBAN infrastructure to prevent attacks and provide services free of security risks. To the best of our knowledge, we proposed a privacy preserving quantum authentication scheme for secure data sharing in WBANs to enhance security by encrypting medical data and safeguarding patients identities. Simulations conducted using Mathematica evaluate the proposed quantum authentication protocol both theoretically and practically, showing strong encryption performance against recent schemes. Results include computational cost of 6.832 ms, communication costs of 588 bits, and energy consumption of 432.98 μ J indicating the proposed quantum authentication protocol model exhibits superior reliability, feasibility, and efficiency in safeguarding WBAN data during transmission and storage. [ABSTRACT FROM AUTHOR]

Modern spacecraft and launch vehicle design is more oriented towards reducing system-level design and assembly complexities. In order to maintain high overall system performance while reducing these complexities, the use of smart materials and smart structural components is a well-known practice and is currently of rising interest to space systems' designers. The paper discusses a concept of smart space structures, in particular, a carbon fiber composites structure embedded with Optical Fiber Sensors (OFS) for spacecraft and launch vehicle applications. This study highlights the operational requirements for such tank and the smart features enabled by the optical fiber sensors. For the latter aspect, a quantitative comparison between Fiber Bragg Grating sensors (FBGs) and Distributed Optical Fiber Sensors (DOFS) based on Optical Frequency Domain Reflectometry (OFDR) is presented to state their core performance parameters, such as the sensitivity, sensing range, dynamic measurement capability, and spatial resolution. The increased performance and reliability in harsh environments associated with fiber optic sensors come with a reduction in size, mass, and power consumption compared to the conventional electronic sensors. Optical fiber sensors embedded in carbon fiber structures have proven their capability in providing accurate real-time measurements of temperature and monitoring structural integrity while detecting precisely possible points of rupture and failure as discussed and demonstrated in the literature review. The applications of fiber optic sensing in smart propellant tanks may extend to detecting fluid leakage, also providing increased precision in propellant gauging through temperature mapping, and can be used in on-ground qualification, pre-flight testing, as well as in-orbit operation, condition, and structural health monitoring. The article presents a statement for an optimal FOS embedding approach in composite pressure vessels and discusses the related placement and orientation method for the fiber optic sensors, coupled with a one component simplified analytical stress-strain transfer model deriving the stress component along the maximum principal direction (i.e., σ Max P r i n c i p a l ). The novel approach is believed to serve the optimal employment of embedded FOS in composite structures, e.g., pressure vessels and light-weight structures in spacecraft, among other applications. The simplified model is believed to pave the way for effective data interpretation and processing, utilizing the available limited computational resources on-board the spacecraft. • Smart propellant tank embedding fiber optic sensors for structural health monitoring. • The opto-mechanical model defines the local stress in the maximum principal strain direction. • Quasi-distributed optic fiber sensors employing arrays of FBGs for spacecraft monitoring. • Distributed fiber optic sensing based on OFDR for spacecraft applications. [ABSTRACT FROM AUTHOR]

In the context of the emerging Industry 4.0 paradigm, smart fabric sensors have been representing a novel addition to the textile industry. The proposed sensors utilize macro-bending techniques with varying fiber optic core sizes. The study involved the construction and testing of macro-bending sensors using single-mode (9 µm) and hetero-core (50-9-50 µm) fibers, configured into seven sinusoidal loops. The experiment was further extended to different types of elastic textiles. Spandex demonstrated superior linearity compared with jersey and rubber bands. The integration with the DOIT ESP32 DevKit facilitated real-time monitoring of respiratory rates. The results from the experiment indicated that the macro-bending sensor, fabricated using hetero-core optical fiber, exhibited superior sensitivity in comparison to the sensor assembled from single-mode optical fiber, with respective sensitivity values of 1.72 and 1.30 dB/cm. The designed sensors displayed closely aligned behavior during forward and reverse loading, indicating the reversibility of the fiber optic sensor. Given its simplistic design and low fabrication cost, the proposed sensor holds significant potential for practical applications. [ABSTRACT FROM AUTHOR]

Abstract With the rapid development of digital technologies such as intelligent control and information communication, digitization has gradually become an inevitable trend to promote technology innovation and industrial upgrading for the power grid. As the basis of digital technology, digital sensing technology will gradually penetrate all aspects of the power system for comprehensive perception and high operational reliability. First, this article introduces the application status and technical requirements of power intelligent sensor technology in various aspects of the power system, including the generation, transmission, transformation, distribution, and consumption of electricity. Next, domestic and foreign scholars' latest research achievements in power sensing technology are described from three major aspects, that is, optical fiber sensing technology, MEMS sensing technology, and sensor self‐powered technology. Finally, future challenges for the electric power intelligent sensors are discussed and an outlook of future widespread application in the clean, safe, and efficient digital power grid is provided.