In recent years, the demand for compact and inexpensive sensor systems for the digitalization of production processes has risen. One solution to meet this demand are sensor-integrating machine elements. These are mach...
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In recent years, the demand for compact and inexpensive sensor systems for the digitalization of production processes has risen. One solution to meet this demand are sensor-integrating machine elements. These are machine elements with integrated sensors, whose geometry is not altered and thus allows an uncomplicated exchange of the conventional machine elements with the sensor-integrating machine elements. In the current work, a numerical model for a sensor-integrating jaw coupling is presented. The aim of the sensor-integration is to determine the deformation of the teeth of the gear rim with dielectric elastomer sensors (DES) and thus draw conclusions about the applied torque. In the following, a model for the DES is presented which is validated with experimental results. It was shown that the experimental and simulation results for the capacitance agree well, if only 50% of the change in area of the electrodes is taken into account. After that, a finite element model for the sensor-integrating jaw coupling itself is presented, which is created with the commercial software ABAQUS. Finally, the advantages and disadvantages for different positions of the sensor inside the gear rim of the jaw coupling are evaluated.
Two-dimensional magnetic sensors of soft magnetic film/planar coil laminated have outstanding advantages in miniaturization and integration. However, due to the cumbersome process and complicated structure of the sens...
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Two-dimensional magnetic sensors of soft magnetic film/planar coil laminated have outstanding advantages in miniaturization and integration. However, due to the cumbersome process and complicated structure of the sensor, its yield rate is extremely low, and the gap between magnetic films is large, resulting in increased leakage flux and low sensitivity. Therefore, a high-performance soft magnetic film with through-holes/planar coil laminated micromagnetic sensor with mutual inductance (MI) is designed and the sensor is fabricated by micro-electromechanical system technology. The micromagnetic sensor can be processed in 15 steps, with a yield rate of more than 95%, and the gap between magnetic films can be effectively reduced, resulting in reduced leakage flux and improved sensitivity. Moreover, the effect of coil resistance is eliminated, the noise level of the proposed MI micromagnetic sensor is reduced and the detectivity can be significantly enhanced. The experiment result shows that the sensitivity of the proposed MI micromagnetic sensor is 46.5 mV/Oe in the range of 0 Oe-0.3 Oe, the voltage noise is as low as 0.8 mu V/root Hz, and the noise level reaches 17.2 mu Oe/v Hz. Therefore, the detectivity of the proposed MI micromagnetic sensor can reach 17.2 mu Oe (1.72 nT), which has significant potential in several areas, such as biosensing and geomagnetic navigation.
Extracting sufficient volumes of sweat consistently in various scenarios is necessary for next-generation wearable sweat sensors. Here, we developed a sensor patch for Joule-heating sweating and comfortable biofluid m...
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Extracting sufficient volumes of sweat consistently in various scenarios is necessary for next-generation wearable sweat sensors. Here, we developed a sensor patch for Joule-heating sweating and comfortable biofluid monitoring. The Ti3C2Tx nanosheet provides silk fabrics with good electrical conductivity which can serve as an efficient low-voltage electrothermal platform. This biosensor can induce significant sweating in volunteers within just 5 minutes through mild heat stimulation, allowing real-time assessment of metabolic syndrome risks, including Na+, K+, pH, and uric acid levels. Unlike traditional sweat sensors that rely on iontophoresis or physical movement, our approach offered enhanced comfort and efficiency, particularly in sedentary scenarios. Such integration of Joule-heating sweating and sweat analysis endows its potential as a versatile and userfriendly tool in precision medicine and healthcare monitoring.
This review critically examines the progress in unmanned aerial vehicle (UAV) detection and classification technologies from 2020 to the present. It highlights a range of detection methods, including radar, radio freq...
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This review critically examines the progress in unmanned aerial vehicle (UAV) detection and classification technologies from 2020 to the present. It highlights a range of detection methods, including radar, radio frequency (RF), optical, and acoustic sensors, with particular emphasis on the integration of these technologies through advanced sensor fusion techniques. The paper explores the core technologies driving improvements in detection accuracy, range, and reliability, with a special focus on the transformative role of artificial intelligence and machine learning. These innovations have significantly enhanced system performance, enabling more precise and efficient UAV detection. The review concludes with insights into emerging trends and future developments that promise to further refine UAV detection technologies, ensuring greater security and operational reliability.
The ignition interlock device, an onboard alcohol detection system for supervising cases of driving under the influence (DUI), is crucial for ensuring traffic safety. As a key component of this system, ethanol sensor ...
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The ignition interlock device, an onboard alcohol detection system for supervising cases of driving under the influence (DUI), is crucial for ensuring traffic safety. As a key component of this system, ethanol sensor faces challenges like integration difficulties and poor anti-humidity property, limiting its potential applications. Herein, from the perspective of miniaturization and integration, a carbon-based field effect transistor (FET) gas sensor with a floating-gate (FG) structure is proposed for fast ethanol detection in exhaled breath. The proposed sensing FG structure enables the traditional FET to acquire the perceptibility toward target gas, and the FET with excellent amplification ability further enhances the gas-sensing response. The detection range of the sensor is 0-600 ppm, with a response deviation of merely 11 % toward 50 ppm ethanol within a humidity range of 10 %90 % relative humidity (RH). Additionally, benefited from the good consistency of the semiconductor technology, relative standard deviation (RSD) of the responses from different batches is 2.1 %, confirming its potential for mass production. The tiny size (1.5 mmx1.5 mm) of the sensor and the compatible fabrication process with MEMS process are conducive to the on-chip integration. This work provides a boost to the process of advancing the research and development of automotive alcohol locks, and facilitate their popularization.
The efficacy of treatment with removable orthodontic appliances depends to a large extent on the length of time that patients use them. Orthodontists often observe a difference between the time of appliance usage clai...
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The efficacy of treatment with removable orthodontic appliances depends to a large extent on the length of time that patients use them. Orthodontists often observe a difference between the time of appliance usage claimed by patients and the actual clinical results. To address the inconsistency in patients' self-reported usage data, microsensors are used. In this study, we developed a low-power integrated system that functions as an intelligent cervical headgear (HG), capable of recording temperature and movement position at customizable intervals and storing these collected data in a memory device. In this study, we used the output data from sensors and the proposed algorithm to determine the duration of orthodontic appliance usage. The results were then stored as encoded records of zeros and ones in a low-power, nonvolatile memory that allows for read and write operations. Using clinical results obtained from ten smart cervical HGs used by orthodontic patients, over a period of ten months, an average accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 89.5%, 92.5%, 89.1%, 90.3%, and 88.7% were achieved, respectively ( P<0.05 ). Furthermore, the integration of temperature and motion data enhances the reliability in determining the duration of orthodontic appliance worn by the patient. These findings underscore the effective functionality of the smart cervical HG in accurately recording and identifying the correct duration of orthodontic appliance usage by patients. This device enables more precise prediction treatment duration and facilitates the selection of alternative treatment plans based on patient cooperation and documented evidence.
In prognostics and health management, the system's degradation condition assessment and corresponding remaining useful life prediction are the most important tasks. Both of these processes are heavily dependent on...
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In prognostics and health management, the system's degradation condition assessment and corresponding remaining useful life prediction are the most important tasks. Both of these processes are heavily dependent on information gathered by multiple sensors, which eventually causes data fusion-related complex problems. Typically, sensor information contains the speed, pressure, temperature, and similar other types of various system data. These systems' data obtained through sensors can be utilized as a part of the evidence in the evidence-based estimation method. In this work, an artificial intelligence-based novel framework for estimating the remaining useful life using data fusion has been presented. The Dempster-Shafer extended theory is adopted for sensor information modeling and data fusion. Besides, two different scenarios are introduced to determine the similarity between the studied system and the available evidence. As a case study, the turbofan dataset is demonstrated to assess the proposed method. Based on the results, our integrated proposed method performs very competitively compared with the existing methods based on standard scores and performance criteria.
This paper investigates methods that leverage physical contact between a robot's structure and its environment to enhance task performance, with a primary emphasis on improving precision. Two main approaches are e...
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This paper investigates methods that leverage physical contact between a robot's structure and its environment to enhance task performance, with a primary emphasis on improving precision. Two main approaches are examined: solving the inverse kinematics problem and employing quadratic programming, which offers computational efficiency by utilizing forward kinematics. Additionally, geometrical methods are explored to simplify robot assembly and reduce the complexity of control calculations. These approaches are implemented on a physical robotic platform and evaluated in real-time applications to assess their effectiveness. Through experimental evaluation, this study aims to understand how environmental contact can be utilized to enhance performance across various conditions, offering valuable insights for practical applications in robotics.
Wearable sensors, specifically microneedle sensors based on electrochemical methods, have expanded extensively with recent technological advances. Today's wearable electrochemical sensors present specific challeng...
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Wearable sensors, specifically microneedle sensors based on electrochemical methods, have expanded extensively with recent technological advances. Today's wearable electrochemical sensors present specific challenges: they show significant modulus disparities with skin tissue, implying possible discomfort in vivo, especially over extended wear periods or on sensitive skin areas. The sensors, primarily based on polyethylene terephthalate (PET) or polyimide (PI) substrates, might also cause pressure or unease during insertion due to the skin's irregular deformation. To address these constraints, we developed an innovative, wearable, all-fiber-structured electrochemical sensor. Our composite sensor incorporates polyurethane (PU) fibers prepared via electrospinning as electrode substrates to achieve excellent adaptability. Electrospun PU nanofiber films with gold layers shaped via thermal evaporation are used as base electrodes with exemplary conductivity and electrochemical catalytic attributes. To achieve glucose monitoring, gold nanofibers functionalized by gold nanoflakes (AuNFs) and glucose oxidase (GOx) serve as the working electrode, while Pt nanofibers and Ag/AgCl nanofibers serve as the counter and reference electrode. The acrylamide-sodium alginate double-network hydrogel synthesized on electrospun PU fibers serves as the adhesive and substance-transferring layer between the electrodes. The all-fiber electrochemical sensor is assembled layer-by-layer to form a robust structure. Given the stretchability of PU nanofibers coupled with a high specific surface area, the manufactured porous microneedle glucose sensor exhibits enhanced stretchability, superior sensitivity at 31.94 mu A (lg(mM))-1 cm-2, a broad detection range (1-30 mM), and a significantly low detection limit (1 mM, S/N = 3), as well as satisfactory biocompatibility. Therefore, the novel electrochemical microneedle design is well-suited for wearable or even implantable continuous monitoring applicatio
Flexible and wearable devices play a pivotal role in the realm of smart portable electronics due to their diverse applications in healthcare monitoring, soft robotics, human-machine interfaces, and artificial intellig...
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Flexible and wearable devices play a pivotal role in the realm of smart portable electronics due to their diverse applications in healthcare monitoring, soft robotics, human-machine interfaces, and artificial intelligence. Nonetheless, the extensive integration of intelligent wearable sensors into mass production faces challenges within a resource-limited environment, necessitating low-cost manufacturing, high reliability, stability, and multi-functionality. In this study, a cost-effective fiber laser direct writing method (fLDW) was illustrated to create highly responsive and robust flexible sensors. These sensors integrate laser-induced graphene (LiG) with mixed metal oxides on a flexible polyimide film. fLDW simplifies the synthesis of graphene, functionalization of carbon structures into graphene oxides and reduced graphene oxides, and deposition of metal-oxide nanoparticles within a single experimental laser writing setup. The preparation and surface modification of dense oxygenated graphene networks and semiconducting metal oxide nanoparticles (CuOx, ZnOx, FeOx) enables rapid fabrication of LiG/MOx composite sensors with the ability to detect and differentiate various stimuli, including visible light, UV light, temperature, humidity, and magnetic fluxes. Further, this in situ customizability of fLDW-produced sensors allows for tunable sensitivity, response time, recovery time, and selectivity. The normalized current gain of resistive LiG/MOx sensors can be controlled between -2.7 to 3.5, with response times ranging from 0.02 to 15 s, and recovery times from 0.04 to 6 s. Furthermore, the programmable properties showed great endurance after 200 days in air and extended bend cycles. Collectively, these LiG/MOx sensors stand as a testament to the effectiveness of fLDW in economically mass-producing flexible and wearable electronic devices to meet the explicit demands of the Internet of Things.
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