Sensor Output Research Articles

Artificial Neural Networks as Digital Twins for Whispering Gallery Mode Optical Sensors in Robotics Applications

This paper investigates the use of artificial neural networks (ANNs) as a viable digital twin or alternative to the typical whispering gallery mode (WGM) optical sensors in engineering systems, especially in dynamic environments like robotics. Because of its fragility and limited endurance, the WGM sensor which is based on micro-optical resonators is inappropriate in these kinds of situations. In order to address these issues, the paper suggests an ANN that is specifically designed for the system and makes use of the WGM sensor’s high-quality factor (Q-factor). By extending the applicability and endurance to dynamic contexts and reducing fragility problems, the ANN seeks to give high-resolution measurement. In order to minimize post-processing requirements and maintain system robustness, the study goal is for the ANN to function as a representative predictor of the WGM sensor output. The GUCnoid 1.0 humanoid robot is used in the paper as an example to show how the WGM optical sensors may improve humanoid robot performance for a variety of applications. The results of the experiments demonstrate that the sensitivity, precision, and resolution of ANN outputs and actual WGM shifts are equivalent. As a consequence, current obstacles to the widespread use of high-precision sensing in the robotics industry are removed, and the potential of ANNs as virtual substitutes or the digital twin for genuine WGM sensors in robotics systems is validated. So, this paper can be very beneficial not only to the sensing technologies that are used in robotics, which are subjected to the dynamic environments, but also to the industrial automation and human-machine interface.

POF helical structure: a non-contact approach for wide-range force measurements based on the coupling method

Abstract This study proposes a non-contacting force measuring sensor design by utilizing the polymer optical fiber. The sensor design uses two separate fibers (i.e. sensor input and sensor output fibers). Sensor input fiber is used to helically wound on the supportive circular pipe, aiming to create the bend losses at every bend loop. The sensor output fiber is getting advantages by coupling the variable optical power from different bend loops of the sensor input fiber. The variable amount of the optical power coupling is directly associated with the applying force value on the spring. Where, the spring is intermediate in transferring force value to the sensor. Further, for variable ranges of the forces, the three different springs (i.e. Spring-A, B, and C) are used, which sustain variable force ranges (0 to 20 N, 0 to 40 N, and 0 to 60 N), respectively. The proposed sensor design addresses the critical need for force sensors capable of effectively measuring force across a wide dynamic range. The results of the present study support the sensor design, which has a wide range of force measuring capabilities from low, medium, and high range with a close repetitive response. The low range force measuring (0 to 20 N) indicates the sensor’s high sensitivity of 0.567 µW/N. Further, the sensor is also capable for a large force measuring range (0 to 60 N) with a sensitivity of 0.189 µW/N.

Vertical line arrays of vector sensors.

A vertical line array (VLA) of vector sensors has a multi-component vector sensor at each sensor position. Vector sensors consist of an omni-directional receiver (Omni), two orthogonal horizontal dipoles, and possibly a vertical dipole. VLA beamforming may be factored into azimuthal processing at each sensor by using a weighted sum of the sensor components and vertical beamforming that combines the vector sensor outputs. Alternative sensor weights, which may depend on vertical steering angle, including cardioids, limacons, and the optimal weights for azimuthally symmetric noise are compared. The vertical dipole's usefulness is limited to sidelobe and grating lobe reduction. For steep vertical steering angles, the optimal weight on the horizontal dipoles becomes large, resulting in an undesirable sensitivity to white noise and tolerance errors. The optimum weights that minimize beam noise subject to an inequality constraint on sensitivity to white noise are derived. For non-symmetric noise, optimal beamforming may cause the mainlobe to squint in azimuth away from the steering direction in pursuit of reduction in directional noise. A simplified optimization approach is introduced that requires a global peak in the steering direction and completely prevents undesirable squinting. The performance of tilted dipoles in azimuthally symmetric noise is compared with other configurations.

Aluminium foil micro-defect detection method based on motion-induced eddy current using differential TMR sensor spatial position compensation

ABSTRACT Lithium-ion batteries are a key technology in the energy storage field, and aluminum foil, a common cathode current collector, can develop micro-defects that significantly affect battery performance, especially during high-speed manufacturing. Motion-induced eddy current (MIEC) sensing is effective for detecting defects in conductive materials. However, lift-off fluctuations degrade the signal-to-noise ratio (SNR), making it challenging to detect small or shallow defects. Traditional methods, such as differential sensor outputs, help reduce these fluctuations, but baseline signal deviations caused by varying relative positions between the sensor and the permanent magnet limit their effectiveness. To address this, this study proposes a spatial compensation method for differential tunnel magnetoresistance (TMR) sensors, which reduces baseline deviations caused by position differences, mitigating lift-off fluctuations and improving the SNR for micro-defect detection.

Comparison and performance optimization of two absolute circuit designs based on Multisim

Digital circuits are crucial in today's technology, underpinning infrastructure such as computing, communicating, and intelligent devices. Its high efficiency, accuracy and programmable capability drive the development of information processing, data transmission and automation systems. The application of digital circuits has promoted innovation in artificial intelligence, the Internet of Things and other fields, providing strong support for the intelligence and interconnection of modern society. The absolute value circuit is discussed in this paper. The absolute value circuit is implemented for converting the negative value of the input signal to a positive value, ensuring that the output signal is always non-negative. Its significance is to handle application scenarios that require non-negative values, such as signal processing, audio amplification, and sensor output. Through the absolute value circuit, negative value can avoid the negative effect on the subsequent circuit, and improve the stability and accuracy of the system. This paper first introduces all the gates used in the circuit, points out the functions of each gate circuit, and then puts forward two absolute value circuit designs, explaining their working principles, differences and advantages and disadvantages. Then the minimum delay is analyzed by the critical path logic and the sizes of each logic gate is given. Then the supply voltage is optimized after selecting the design with the smallest delay to reduce energy consumption.

Research on the Sensitivity Enhancement Method of Inductive Conductivity Sensors Based on Impedance Matching.

This paper presents the design and performance evaluation of an inductive conductivity sensor with a double tuning impedance matching network to enhance sensitivity and improve linearity. The sensor's equivalent circuit model is analyzed and verified through simulation, and impedance matching is shown to significantly increase the sensor's output signal, particularly at low conductivity measurements. Double tuning impedance matching expands the frequency response range and optimizes power transfer efficiency, achieving a higher power factor across a broader frequency range. Experimental results confirm that the sensor's sensitivity increases by approximately 30% after impedance matching with optimal performance at a frequency of 9865 Hz. Furthermore, while the impedance matching improves sensitivity, it also introduces some nonlinear errors, which are evaluated using a performance function that balances sensitivity and linearity. The results demonstrate that impedance matching enhances the sensor's measurement capability, making it more effective in practical applications where conductivity changes need to be accurately monitored across varying frequencies.

Experimental Validation of Virtual Torque Sensing for Wind Turbine Gearboxes Based on Strain Measurements

ABSTRACTIn efforts to reduce the operation and maintenance cost of wind turbines, there is an increasing interest to monitor key turbine quantities such as the torque load on the gearbox. Monitoring the torque paves the way for the calculation of remaining useful lifetime, leading to cost reductions through improved reliability and maintenance planning. In order to avoid expensive direct torque sensors, this paper investigates the potential of virtual torque sensing, a technique based on 3 basic components: first, a set of non‐intrusive sensors installed on the gearbox. Three groups of strain gauges on the gearbox as well an angular encoder are considered in this paper. Second is a physics‐based model, capable of predicting the response of aforementioned sensors. These models are constructed with a purposeful balance between accuracy and computational cost. Model validation and updating are performed to ensure efficient and accurate prediction of the sensor output. Finally, an augmented extended Kalman filter (AEKF) is used to combine the measured response with predictions from the model to infer the gearbox input torque. Since a key factor determining the performance of the AEKF is the tuning of the AEKF covariance matrices, multiple methods are introduced to systematically tune the covariance matrices. Experimental validation results show that the virtual torque sensor can detect the load torque with a normalized mean absolute error (NMAE) between 3.41% and 7.47%, depending on the sensor set. The influence of the amount of sensors used and the tuning method are also investigated.

Highly sensitive strain sensors with ultra-low detection limit based on pre-defined serpentine cracks.

Flexible and stretchable strain sensors have garnered significant interest due to their potential applications in various fields including human health monitoring and human-machine interfaces. Previous studies have shown that strain sensors based on microcracks can exhibit both high sensitivity and a wide sensing range by manipulating the opening and closing of randomly generated cracks within conductive thin films. However, the uncontrolled nature of microcrack formation can cause a drift in the sensor's performance over time, affecting its accuracy and reliability. In this study, by pre-defining the cracks, we introduce a novel resistive strain sensor with high sensitivity, excellent linearity, an ultra-low detection limit, and robustness against off-axis deformation. The sensor operates on a simple mechanism involving the modulation of ohmic contact within intricately designed conductive serpentine curves, which are encapsulated by pre-stretched thin films. This design facilitates a high gauge factor of 495, exceptional linearity (R2 > 0.98), and an ultra-low detection threshold of 0.01% strain. Moreover, it maintains performance integrity during off-axis deformations such as bending and twisting, features that are indispensable for accurately monitoring human motion. To explore practical applications, a driving scenario was simulated where a sensor array was positioned on the driver's neck. The sensor output was analyzed using machine learning algorithms to successfully determine the presence of driver fatigue. This demonstration underlines the potential of our sensor technology in applications ranging from healthcare monitoring to wearable biomechanical systems and human-machine interfaces.

A Remote Monitoring System for Wind Speed and Direction Based on Non-Contact Triboelectric Nanogenerator

A Remote Monitoring System for Wind Speed and Direction Based on Non-Contact Triboelectric Nanogenerator

Proton Cumulative Radiation Effects on Saturation Output in CMOS Image Sensors

Complementary metal oxide semiconductor (CMOS) image sensors have become increasingly widely used in the field of radiation environments due to their numerous advantages, and their radiation effects have also attracted much attention. Some experimental studies have shown a decrease in the saturation output of CMOS image sensors after irradiation, while others have reported an increase. This article conducts further in-depth research on the inconsistent result based on proton irradiation experiments and TCAD simulations, analyzing the degradation mechanism in full well capacity (FWC), conversion factor (CVF), and saturation output of the 4T pinned photodiode (PPD) CMOS image sensors due to proton cumulative radiation effects. In experiments, the sensors are irradiated by 12 MeV and 60 MeV protons with a fluence up to 2×10<sup>12</sup> p/cm<sup>2</sup>. The sensors are unbiased during irradiation. The experimental results show that proton irradiation at 12 MeV and 60 MeV results in an increase of 8.2% and 7.3% in conversion gain, respectively, and a decrease of 7.3% and 3.8% in full well capacity, respectively. The saturation output shows no significant change trend under 12 MeV proton irradiation, but increases by 3% under 60 MeV proton irradiation. In TCAD simulation, a three-dimensional 4T PPD pixel model is constructed. A simulation method that combines the Traps and Gamma Radiation model within TCAD and minority carrier lifetime mathematical model is employed to conduct global and local cumulative proton irradiation simulations for analyzing degradation mechanisms. It is proposed that the degradation of saturation output at the pixel level is determined by the FWC of PPD, the physical characteristics of the reset transistor and the capacitance of floating diffusion, but they have opposite effects. Proton irradiation leads to the accumulation of oxide-trapped positive charges in the shallow trench isolation on both sides of PPD, resulting in the formation of leakage current path in silicon, thereby reducing the full well capacity. A decrease in FWC leads to a decrease in saturation output. While, the radiation effect of the reset transistor causes the FD potential to increase during the FD reset phase, further leading to an increase in saturation output. Irradiation causes a decrease in the capacitance of the floating diffusion, resulting in an increase in conversion factor and consequently increasing the saturation output. The difference in radiation sensitivity among the three influence factors at the pixel level may result in a decrease or increase in saturation output with proton fluence. The above work comprehensively reveals and analyzes the mechanism of degradation in FWC, CVF and saturation output after irradiation, and the research results have certain guiding significance for the radiation damage analysis of CMOS image sensors.

Night-time blood pressure dipping, but not 24-h blood pressure level, is linked to increased 24-h ocular volume slope.

This study investigated the relationship between blood pressure and intraocular pressure in treatmentnaive, non-glaucoma patients with different blood pressure statuses, focusing on the 24-h ocular volume and nocturnal blood pressure decline. Treatment-naive, non-glaucoma patients undergoing hypertension evaluation were enrolled as study participants. Simultaneous 24-h ambulatory blood pressure measurement and 24-h ocular volume recording with a contact lens sensor. We also compared ocular volume curve parameters between normotensive and hypertensive patients, as well as between those with and without nocturnal blood pressure decline. A total of 21 patients, including 7 normotensive and 14 treatment-naive hypertensive individuals, were included in the study. of them, 11 were dippers and 10 were non-dippers. No significant difference in the 24-h ocular volume slope was observed between the hypertensive and normotensive patients (p=0.284). However, dippers had a significantly higher 24-h ocular volume slope (p=0.004) and nocturnal contact lens sensor output (p=0.041) than non-dippers. Nocturnal blood pressure decline, rather than the blood pressure level, is associated with the increased 24-h ocular volume slope and nocturnal ocular volume. Further studies are required to determine whether the acceleration of glaucoma progression in dippers is primarily due to low blood pressure, high intraocular pressure, or a combination of both.

Comparative Analysis of Fire-Detection Characteristics of Si-Based RGB Color Sensors and Existing Fire-Detection Sensors

The industrial development and urbanization in modern society have accelerated the trend toward larger and taller buildings, thus resulting in more frequent large-scale fire incidents. Consequently, fire-detection technology has become more important. However, conventional fire detectors continue to be affected by issues such as false alarms caused by non-fire situations. Hence, this study proposes a novel fire-detection technology that utilizes Si-based RGB color sensors. An experimental fire-detection apparatus is developed, and the sensor detection characteristics under smoke and flame conditions are analyzed. The results confirm that the detection speed and sensor output vary depending on the type of combustible material used. Specifically, the Si-based RGB color sensor detected fires 26.7 and 43.7 s faster than temperature sensors in smoke and flame experiments, respectively, and 26.6 and 15.4 s faster than flame-detection sensors, respectively. Therefore, the proposed Si-based RGB color sensor performs better than the conventional fire detectors by offering rapid and more accurate fire detection, thus contributing to improved fire safety.

FEATURES OF GENERATING THE OUTPUT SIGNAL OF THE INFRARED RADIATION SENSOR

The paper deals with infrared radiation detectors that can register the thermal radiation of a living organism. The human body has a temperature in the range from 31°C to 42°C, depending on the heat exchange between the skin and the environment, and infrared radiation in the wavelength range from 4 to 50 microns. The analysis of the phenomenon of the pyroelectric effect and the features of the use of pyroelectric sensitive elements, which are the basis of the operation of the sensors, has been carried out. The principles of the formation of thermal radiation in the detection zone of the sensor are given, and the formulas for determining the amount of radiation of the thermal flux are presented. The process of forming the output signal during the action of thermal energy on the sensor was studied. It has been established that for the rational use of methods and algorithms for improving the interference immunity of an infrared sensor, it is necessary to know the main parameters of the signal, namely the amplitude, shape, duration, dependence on the speed of human movement, and background temperature. Experimental graphs of the shape and spectrum of the signal at the input of the pyroelectric sensor under different input conditions are presented. The influence of temperature contrast on the formation of the sensor output signal is analyzed.

Dual-Gate Modulation in a Quantum Dots/MoS2 Thin-Film Transistor Gas Sensor.

Mastering the surface chemistry of quantum dots (QDs) has enabled a remarkable gas-sensing response as well as impressive air stability. To overcome the intrinsic receptor-transducer mismatch of QDs, PbS QDs used as sensitive NO2 receptors are spin-coated on top of a few-layer MoS2 and incorporated into a thin-film transistor (TFT) gas sensor. This architecture enables the separation of the electron transduction function from the chemical reception function. A comparison study through size engineering of QDs combined with TFT device modeling suggests a unique dual-gate modulation related to the capacitance coupling effect of QDs. The favorable increase in sensor output current by 3 orders of magnitude is ascribed to the high mobility of the few-layer MoS2. The optimal sensor exhibits a sensitive (LOD ∼ 0.6 ppb), selective, and recoverable response at room temperature. Because of the dual-gate modulation, the sensor performance is further optimized by varying the gate voltage (a two-fold increase in response to 1 ppm of NO2).

Real-Time Information Acquisition and Processing Method for Penetration Information Based on Multi-Information Fusion

To improve the real-time performance and the target adaptability of penetration fuze detonation control systems, and to enhance the system fusion processing capability for multi-sensor information, this paper uses a modular design concept to construct a miniaturized (ø38[Formula: see text]mm[Formula: see text]×[Formula: see text]4[Formula: see text]mm) fuze detonation control system that is capable of real-time processing of data from multiple information sources. The core component of this system is the GD32E230 microcontroller, which features a high dominant frequency and low power consumption. This device is integrated with a ferroelectric memory and signal processing circuits that match the sensors. To address the issue of unclear traditional acceleration signal penetration and the difficulties associated with the identification of these signals, the approach in this paper improves feature recognition accuracy through rapid acquisition and fusion of multiple types of sensor output signal, and self-adaptive identification of multilayered targets and single-layer thick targets is achieved. During the programming of the embedded system, the hardware register is operated directly, the instruction execution sequence is optimized, and the program execution efficiency is improved by using the function characteristic that some microcontroller unit peripherals do not occupy the central processing unit when working, thus allowing the intended purpose of improving the system’s real-time performance to be achieved. A semi-physical simulation method is then used to verify the performance of the penetration fuze detonation control system. The results obtained show that the system has 100%-layer counting accuracy for multilayered targets and a relative error of less than 1% for the calculated residual velocities of single-layer thick targets, thus validating the effectiveness of the system.

Gas-compensated thermal flow sensor using an integrated velocity-independent gas properties meter

Abstract A gas-compensated thermal flow sensor is presented that measures the flow rate in real-time, independent of the type of gas, by simultaneously measuring and compensating for the thermal conductivity and volumetric heat capacity of the gas. The thermal flow sensor consists of two free-hanging, heated wires, forming a calorimetric flow meter. The temperature difference between the two wires is a function of the flow rate and the fluid thermal properties. An additional heated wire is integrated on the same chip and used to measure the gas properties. This wire is suspended over a shallow V-groove cavity, and oriented perpendicular to the flow direction, so that it is only affected by the gas properties and not by the flow. DC excitation is used to measure the thermal conductivity, and AC excitation with the 3$\omega$ method is used to determine the volumetric heat capacity. The output of the thermal flow sensor is automatically corrected for the medium using these measured parameters. Measurements were performed with 11 different gases and gas mixtures, and in all cases the deviation between the applied flow rate and measured flow rate is less than 10\%.

Modelling heart rate dynamics in relation to speed and power output in sprint kayaking as a basis for training evaluation and optimisation.

With the development of power output sensors in the field of paddle sports and the ongoing advancements in dynamical analysis of exercise data, this study aims to model the measurements of external training intensity in relation to heart rate (HR) time-series during flat-water kayak sprint. Nine elite athletes performed a total of 47 interval training sessions with incremental intensity (light to (sub-) maximal effort levels). The data of HR, speed and power output were measured continuously and rating of perceived exertion and blood lactate concentration ([BLa]) were sampled at the end of each interval stage. Different autoregressive-exogenous (ARX) modelling configurations are tested, and we report on which combination of input (speed or power), model order (1st or 2nd), parameter estimation method (time-(in)variant) and training conditions (ergometer or on-water) is best suited for linking external to internal measures. Average model R2 values varied between 0.60 and 0.97, with corresponding average root mean square error values of 15.6 and 3.2 bpm. 1st order models with time-varying (TV) parameter estimates yield the best model performance (average R2=0.94). At the level of the individual athlete, the TV modelling features (i.e., the model parameters and derivatives such as time constant values) show significant repeated measure correlations in relation to measures of exercise intensity. In conclusion, the study provides a comprehensive description of how the dynamic relationship between external load and HR for sprint kayaking training data can be modelled. Such models can be used as a basis for improving training evaluation and optimisation.

Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data.

Axle load data and traffic survey data are both important outputs of highway sensors. This study targets highways and ordinary national and provincial highways, seeking to calculate axle load spectrum and equivalent axle times across the network. There is often an association in the spatial extent of traffic survey data and axle load detection data in highway networks. Initially, using the Highway Asphalt Pavement Design Specification, it analyzes the demand for these calculations in road sections. Considering the current axle load detection coverage, a method supported by highway traffic data is proposed. For integrating multi-source data, a generalized regression neural network model is established, enabling deep learning calculations. The method is validated and applied to Xuzhou's highway network. Results show consistency between the calculated average axle load spectrum and actual data. Among validation samples, 3-axle vehicles exhibit the smallest deviation, while 6-axle vehicles show the largest. Calculating equivalent axle numbers reveals the distribution and grading of heavily loaded road sections, aiding maintenance decisions.

Driver Drowsiness Detection System for Accident Prevention

The abstract highlights the importance of driver drowsiness detection as a critical component of vehicle safety technology, with the goal of preventing accidents brought on by drowsy drivers. According to studies, driver fatigue possibly a factor in 20% of traffic accidents, underscoring the importance of developing efficient accident-avoidance strategies. The research focuses on a particular illustration of an automated tiredness detection system intended to improve driver safety by tracking unsafe driving practices. The primary objective of the research is to develop an automated system capable of accurate analysis the blink patterns of the driver's eyes. It detects modifications to the distance between the eyes and reflects eye blink events using an infrared-based eye blink sensor. The location of the visual is indicated by the sensor's output, which is high when the eye is closed and low when it is open. The project includes a circuit that, in the event that it detects indicators of tiredness, such closed eyes, while driving, sounds an alarm, namely a buzzer next to the driver. By offering a real-time alarm mechanism, this technology seeks to address the risks related to driver weariness and enhance overall road safety.

Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network.

Ammonia is widely acknowledged to be a stressor and one of the most detrimental gases in animal enclosures. In livestock- and poultry-breeding facilities, a precise, rapid, and affordable method for detecting ammonia concentrations is essential. We design and develop an electronic nose system containing a bionic chamber that imitates the nasal-cavity structure of humans and canines. The sensors are positioned based on fluid simulation results. Response data for ammonia and ethanol gases and the response/ recovery times of an ammonia sensor under three concentrations are collected using the electronic nose system. Response data are classified and regressed using a sparrow search algorithm (SSA)-optimized backpropagation neural network (BPNN). The results show that the sensor has a relative mean deviation of 1.45%. The ammonia sensor's output voltage is 1.3-2.05 V when the ammonia concentration ranges from 15 to 300 ppm. The ethanol gas sensor's output voltage is 1.89-3.15 V when the ethanol gas concentration ranges from 8 to 200 ppm. The average response time of the ammonia sensor in the chamber is 13 s slower than that of the sensor directly exposed to the gas being measured, while the average recovery time is 19 s faster. In tests comparing the performance of the SSA-BPNN, support vector machine (SVM), and random forest (RF) models, the SSA-BPNN achieves a 99.1% classification accuracy, better than the SVM and RF models. It also outperforms the other models at regression prediction, with smaller absolute, mean absolute, and root mean square errors. Its coefficient of determination (R2) is greater than 0.99, surpassing those of the SVM and RF models. The theoretical and experimental results both indicate that the proposed electronic nose system containing a bionic chamber, when used with the SSA-BPNN, offers a promising approach for detecting ammonia in livestock- and poultry-breeding facilities.