Sensor Parameters Research Articles

Recent Advances in Self-Powered Tactile Sensing for Wearable Electronics.

With the arrival of the Internet of Things era, the demand for tactile sensors continues to grow. However, traditional sensors mostly require an external power supply to meet real-time monitoring, which brings many drawbacks such as short service life, environmental pollution, and difficulty in replacement, which greatly limits their practical applications. Therefore, the development of a passive self-power supply of tactile sensors has become a research hotspot in academia and the industry. In this review, the development of self-powered tactile sensors in the past several years is introduced and discussed. First, the sensing principle of self-powered tactile sensors is introduced. After that, the main performance parameters of the tactile sensors are briefly discussed. Finally, the potential application prospects of the tactile sensors are discussed in detail.

Remote monitoring of sleep disorder using FBG sensors and FSO transmission system enabled smart vest

Optical sensors, particularly fiber Bragg grating (FBG) sensors have achieved a fast ingress into the fields of medical diagnostic and vital signs monitoring. Wearable smart textiles equipped with FBG sensors are catching huge research attention in different applications for measurement and monitoring of physiological parameters. In this paper, we report a simple technique for remote monitoring of sleep disorder using a smart vest implemented with four FBG stress sensors located at different sides of the vest and free space optics (FSO) transmission system. The sleep disorder of the patient is monitored in real time through shifts in the original Bragg wavelengths of sensors by stress loading during random changes in patient’s sleeping postures. The reflected wavelength from a stress loaded sensor at a certain posture is transmitted over 0.5 km long FSO channel towards remote medical center, photodetected, and then can be processed in a PC to record the restlessness in a certain time interval in terms of total number of times sleeping postures are changed, total time spent at a certain posture etc. To correctly detect the stress loaded FBG sensor at the medical center, various parameters of FBG sensors and demultiplexer are carefully adjusted to minimize the power leakages from unloaded sensors that may result into errors in the detection. Maximum dynamic range around 45 dB has been achieved ensuring accurate detection. This study not only provides a cost-efficient and non-intrusive solution for monitoring the sleep disorder of patients but also can be used for real-time monitoring of various other ailments, such as lung, brain, and cardiac diseases in future.

Cost-Effective Optical Wireless Sensor Networks: Enhancing Detection of Sub-Pixel Transmitters in Camera-Based Communications.

In the domain of the Internet of Things (IoT), Optical Camera Communication (OCC) has garnered significant attention. This wireless technology employs solid-state lamps as transmitters and image sensors as receivers, offering a promising avenue for reducing energy costs and simplifying electronics. Moreover, image sensors are prevalent in various applications today, enabling dual functionality: recording and communication. However, a challenge arises when optical transmitters are not in close proximity to the camera, leading to sub-pixel projections on the image sensor and introducing strong channel dependence. Previous approaches, such as modifying camera optics or adjusting image sensor parameters, not only limited the camera's utility for purposes beyond communication but also made it challenging to accommodate multiple transmitters. In this paper, a novel sub-pixel optical transmitter discovery algorithm that overcomes these limitations is presented. This algorithm enables the use of OCC in scenarios with static transmitters and receivers without the need for camera modifications. This allows increasing the number of transmitters in a given scenario and alleviates the proximity and size limitations of the transmitters. Implemented in Python with multiprocessing programming schemes for efficiency, the algorithm achieved a 100% detection rate in nighttime scenarios, while there was a 89% detection rate indoors and a 72% rate outdoors during daylight. Detection rates were strongly influenced by varying transmitter types and lighting conditions. False positives remained minimal, and processing times were consistently under 1 s. With these results, the algorithm is considered suitable for export as a web service or as an intermediary component for data conversion into other network technologies.

The influence of the rectangular dielectric grating filling factor on the grating-based sensor’s sensitive element characteristics

The influence of grating filling factor on the spectral sensitivity of sensors, characterized by full width at half maximum (FWHM) and a figure of merit (FOM), operating on the principle of waveguide mode resonance in dielectric gratings formed on a substrate, has been investigated. Resonant wavelengths were determined for varying grating thicknesses and filling factors. Sensitivity values were obtained for normally and obliquely incident plane waves, as well as for Gaussian beams. Additionally, a scaling rule is presented, allowing determination of sensor parameters using a calculator based on identical grating parameters and corresponding resonant wavelengths. This method holds when linear dimensions of the grating and tested wavelengths are scaled proportionally.

A novel method for measuring roll angle

The precise measurement of the spin speed of a high-speed autonomous unmanned aerial vehicle (HSA-UAV) is a key element in mastering flight stability, and the measurement of roll angle is the key to determining the accuracy of navigation control systems. We put forward a novel method for measuring roll angle. This method starts from the time–frequency domain analysis of the output signal of the geomagnetic sensor, extracts the time–frequency ridge of the time–frequency matrix (TFM) to obtain the spin speed of the HSA-UAV, and reconstructs the output signal of the geomagnetic sensor. It can calculate the roll angle when the calibration parameters of the geomagnetic sensor are unknown and have good engineering practical value. In addition, we also propose an improved nonlinear short-time Fourier transform with high-frequency resolution and a forward penalty dynamic path ridge-extraction method with frequency jump suppression to extract instantaneous frequency in the TFM.

Nanowire sensor calibration and performance evaluation in microfluidic flow velocity monitoring

A microfluidic chip, integrated with a contact flow velocity sensor, was fabricated. A single gold nanowire prepared by nanoskiving was positioned across the bottom of a microfluidic channel. Real-time monitoring of the microfluidic flow velocity is achieved by leveraging the principle of forced heat exchange between the fluid and the solid nanowire. To harmonize theoretical calculations and experiments, leading to the derivation of a conversion formula between the flow equivalence factor and the flow velocity. Analysis of experimental data, specifically curves of resistance versus time, yields two characteristic parameters for the nanowire sensor: the step size of the flow equivalence factor and the resistivity variation. The performance of nanowire sensor was further analyzed by examining the patterns exhibited in the relationship between characteristic parameters (resolution and response characteristics) and both voltage and inlet flow rate. This work has potential applications in biomedical sensing, microfluidic mixing, and other areas where flow velocity control is required.

An IoT-Based Smart Electronic Nose System for Non-Destructive Meat Freshness Monitoring

In recent years, food hygiene and safety violations have become a painful problem, especially in developing countries. It is essential to have tools or systems that can both monitor food quality and promptly warn users. Sensor technology as well as wireless communication technology pave the way to build a system that meets the above needs. In this paper, an electronic nose system based on the Internet of Things platform is proposed to monitor the freshness of food without changing the test sample. To evaluate the effectiveness of the proposed system, samples of three types of meat including pork, chicken and fish were collected and processed. In addition, the system also develops an application platform on smartphone devices to notify users about assessment results in real time. The system has been practically built and tested on product samples under many different testing conditions. The results show that the electronic nose system can effectively assess the freshness of meat through appropriate sensor parameters and provide timely warnings to the desired users.

The Impacts of Heat Stress on Rumination, Drinking, and Locomotory Behavior, as Registered by Innovative Technologies, and Acid-Base Balance in Fresh Multiparous Dairy Cows.

This study hypothesizes that heat stress adversely affects dairy cows, resulting in reduced rumination, altering eating and drinking behaviors, changes in their locomotory patterns, and significant variations in their acid-base balance. The aim of this study was to investigate the impacts of heat stress on rumination, drinking, and locomotory behavior, as registered by innovative technologies, and acid-base balance in fresh multiparous dairy cows. This study was conducted during the summer, from 15 June to 8 July 2023, on a Lithuanian commercial dairy farm. We assessed 350 German Holstein cows that produced an average of 11,400 kg of milk annually throughout their second and subsequent lactation periods. We used the temperature-humidity index (THI) to divide the cows under investigation into three periods: I. high HS-THI >78 (period: 15-23 June 2023); II. medium HS-THI 72-78 (period: 24-30 June 2023); and III. low HS-THI <72 (period: 1-8 July 2023). The appropriate RumiWatch sensor (RWS) parameters were assessed between 15 June 2023 and 8 July 2023. Cows were acclimatized to the rumination, drinking, and locomotory behavior parameters during the adaptation period (1-30 June 2023). The registration process started on 15 June 2023 and terminated on 8 July 2023 and was performed every hour during the 24 h day. The acid-base balance was recorded from 15 June 2023 until 8 July 2023, once per week. The cows' activity increased by 11.75% in the high HS period compared to the low HS period (p < 0.01); high mean differences were detected for rumination, which was 17.67% higher in the high HS period and 13.80% higher in the medium HS period compared to the low HS period (p < 0.01); and the change in activity was 12.82% higher in the low HS compared to the medium HS period (p < 0.01). Cows under high HS had higher blood urea nitrogen (BUN) levels compared with cows under medium HS (p < 0.01). The observed alterations in the rumination, drinking, and locomotory behaviors, in addition to the acid-base balance, highlight the multifaceted impacts of varying heat stress on the physiological and behavioral responses of dairy cows. This suggests that the utilization of advanced technologies may assist dairy farmers in effectively monitoring and controlling heat stress in cows. Additionally, regularly assessing blood urea nitrogen levels can enable farmers to modify their feeding practices, thus promoting optimal cow well-being and productivity.

The semi-analytical model of capacitive imaging (CI) sensors based on the renormalization group method

Purpose This study aims to design and validate a theoretical model for capacitive imaging (CI) sensors that incorporates the interelectrode shielding and surrounding shielding electrodes. Through experimental verification, the effectiveness of the theoretical model in evaluating CI sensors equipped with shielding electrodes has been demonstrated. Design/methodology/approach The study begins by incorporating the interelectrode shielding and surrounding shielding electrodes of CI sensors into the theoretical model. A method for deriving the semianalytical model is proposed, using the renormalization group method and physical model. Based on random geometric parameters of CI sensors, capacitance values are calculated using both simulation models and theoretical models. Three different types of CI sensors with varying geometric parameters are designed and manufactured for experimental testing. Findings The study’s results indicate that the errors of the semianalytical model for the CI sensor are predominantly below 5%, with all errors falling below 10%. This suggests that the semianalytical model, derived using the renormalization group method, effectively evaluates CI sensors equipped with shielding electrodes. The experimental results demonstrate the efficacy of the theoretical model in accurately predicting the capacitance values of the CI sensors. Originality/value The theoretical model of CI sensors is described by incorporating the interelectrode shielding and surrounding shielding electrodes into the model. This comprehensive approach allows for a more accurate evaluation of the detecting capability of CI sensors, as well as optimization of their performance.

Research on proton displacement damage effects in charge-domain accumulation type TDI CMOS image sensors

TDI CMOS image sensors are the most widely used photodetectors in space earth observation and multispectral imaging. The displacement damage caused by high-energy proton irradiation in the space environment leads to the continuous degradation of TDI CMOS image sensor parameters, seriously affecting its performance, reliability, and service life. This paper focuses on a space-based TDI CMOS image sensor chip and carries out 3 MeV proton irradiation tests and post-irradiation annealing tests to obtain the change rules of optical and electrical parameters. The degradation mechanism of sensitive parameters of TDI CMOS image sensors caused by proton irradiation is analyzed, and the sensitive region of proton displacement damage are discussed. The study demonstrates that after proton irradiation, the parameters especially full well capacity and temporal dark noise of the device degrade less, with small sensitivity to proton irradiation. However, the dark current, CTE, and pixel column FPN significantly degrade, and the degree of degradation is linearly related to the proton irradiation dose. In addition, the test also obtained the short-term and long-term annealing changes of sensitive parameters after proton irradiation of TDI CMOS image sensors, providing scientific advice for TDI CMOS image sensors space proton radiation effect tests and evaluations. The research results of this paper provide important references for the evaluation of radiation effects, on-orbit performance estimation, and radiation resistance reinforcement of space-based TDI CMOS image sensors.

Janus Conductive Mechanism: An Innovative Strategy Enabling Ultra‐Wide Linearity Range Pressure Sensing for Multi‐Scenario Applications

AbstractThe wide range of pressure detection and the exceptional linearity are essential performance parameters for flexible pressure sensors, enabling them to adapt to diverse scenarios and acquire information accurately. However, currently available “ultra‐wide range” piezoresistive sensors lack an optimal solution that effectively balances sensing properties, device thickness, and process cost. This study proposes a distinctive approach by introducing a Janus conductive structure assembled with dual resistive sensitive layers. The design allows for a pressure‐induced staged transformation of the current transport path, effectively mitigating variations and saturation in sensor resistance over a wide pressure range. The resulting piezoresistive sensor demonstrates an unprecedented detection range of 0–3800 kPa, showcasing remarkable sensitivity of 4.11 kPa−1 and outstanding linearity of 99.9% within the range of 0–1000 kPa. Additionally, the sensor boasts a thickness of only ≈200 µm, made possible through the utilization of a cellulose nanofibers material matrix. These performance achievements stand at the forefront when compared to existing reports. This research explores the potential applications of these sensors and extended arrays in the domains of human health and motion monitoring. It investigates their utility in gait analysis for assisted posture correction, as well as in the assessment and rehabilitation of gait instability.

A Wearable Sandwich Heterostructure Multimode Fiber Optic Microbend Sensor for Vital Signal Monitoring.

This work proposes a highly sensitive sandwich heterostructure multimode optical fiber microbend sensor for heart rate (HR), respiratory rate (RR), and ballistocardiography (BCG) monitoring, which is fabricated by combining a sandwich heterostructure multimode fiber Mach-Zehnder interferometer (SHMF-MZI) with a microbend deformer. The parameters of the SHMF-MZI sensor and the microbend deformer were analyzed and optimized in detail, and then the new encapsulated method of the wearable device was put forward. The proposed wearable sensor could greatly enhance the response to the HR signal. The performances for HR, RR, and BCG monitoring were as good as those of the medically approved commercial monitors. The sensor has the advantages of high sensitivity, easy fabrication, and good stability, providing the potential for application in the field of daily supervision and health monitoring.

Dynamic bubble tracking in fluidized beds via electrical capacitance volume tomography

This work sets forth the development of a novel, dynamic bubble detection algorithm for use with electrical capacitance volume tomography (ECVT) sensors for bubbling fluidized beds. Starting with an in-depth review of existing bubble detection methods, the novel phase detection method was developed to address the shortcomings of the other published methods by more fully utilizing the three-dimensional capability of the sensors. After the sensor parameters were optimized to verify capability of bubble detection, measurements were made with glass beads and quartz sand. A range of air velocities in a 10 cm diameter bubbling fluidized bed were used. The results largely agree with the fundamentals of bubbling fluidization and results from publications with similar experimental setups. Within each measurement the bubble dimensions, velocities and frequencies over the axial and radial position could be evaluated. Due to the three-dimensional nature of the novel bubble detection technique, insight into the directional tendencies of detected bubbles was gained. For example, bubble migration toward the radial center of the bed, radial and axial bubble coalescence, and splitting are more evident from the trends that are producible with this method.

Analytical Analysis of Factors Affecting the Accuracy of a Dual-Heat Flux Core Body Temperature Sensor.

Non-invasive core body temperature (CBT) measurements using temperature and heat-flux have become popular in health, sports, work safety, and general well-being applications. This research aimed to evaluate two commonly used sensor designs: those that combine heat flux and temperature sensors, and those with four temperature sensors. We used analytical methods, particularly uncertainty analysis calculus and Monte Carlo simulations, to analyse measurement accuracy, which depends on the accuracy of the temperature and flux sensors, mechanical construction parameters (such as heat transfer coefficient), ambient air temperature, and CBT values. The results show the relationship between the accuracy of each measurement method variant and various sensor parameters, indicating their suitability for different scenarios. All measurement variants showed unstable behaviour around the point where ambient temperature equals CBT. The ratio of the heat transfer coefficients of the dual-heat flux (DHF) sensor's channels impacts the CBT estimation uncertainty. An analysis of the individual components of uncertainty in CBT estimates reveals that the accuracy of temperature sensors significantly impacts the overall uncertainty of the CBT measurement. We also calculated the theoretical limits of measurement uncertainty, which varied depending on the method variant and could be as low as 0.05 °C.

LiMOX-A Point Cloud Lidar Model Toolbox Based on NVIDIA OptiX Ray Tracing Engine.

Virtual testing and validation are building blocks in the development of autonomous systems, in particular autonomous driving. Perception sensor models gained more attention to cover the entire tool chain of the sense-plan-act cycle, in a realistic test setup. In the literature or state-of-the-art software tools various kinds of lidar sensor models are available. We present a point cloud lidar sensor model, based on ray tracing, developed for a modular software architecture, which can be used stand-alone. The model is highly parametrizable and designed as a toolbox to simulate different kinds of lidar sensors. It is linked to an infrared material database to incorporate physical sensor effects introduced by the ray-surface interaction. The maximum detectable range depends on the material reflectivity, which can be covered with this approach. The angular dependence and maximum range for different Lambertian target materials are studied. Point clouds from a scene in an urban street environment are compared for different sensor parameters.

Fault Signal Perception of Nanofiber Sensor for 3D Human Motion Detection Using Multi-Task Deep Learning

Once a fault occurs in the nanofiber sensor, the scientific and reliable three-dimensional (3D) human motion detection results will be compromised. It is necessary to accurately and rapidly perceive the fault signals of the nanofiber sensor and determine the type of fault, to enable it to continue operating in a sustained and stable manner. Therefore, we propose a fault signal perception method for 3D human motion detection nanofiber sensor based on multi-task deep learning. First, through obtaining the fault characteristic parameters of the nanofiber sensor, the fault of the nanofiber sensor is reconstructed to complete the fault location of the nanofiber sensor. Second, the fault signal of the nanofiber sensor is mapped by the penalty function, and the feature extraction model of the fault signal of the nanofiber sensor is constructed by combining the multi-task deep learning. Finally, the multi-task deep learning algorithm is used to calculate the sampling frequency of the fault signal, and the key variable information of the fault of the nanofiber sensor is extracted according to the amplitude of the state change of the nanofiber sensor, to realize the perception of the fault signal of the nanofiber sensor. The results show that the proposed method can accurately perceive the fault signal of a nanofiber sensor in 3D human motion detection, the maximum sensor fault location accuracy is 97%, and the maximum noise content of the fault signal is only 5 dB, which shows that the method can be widely used in fault signal perception.

An Innovative Clinical Evaluation Protocol after Total Ankle Arthroplasty: A Pilot Study Using Inertial Sensors and Baropodometric Platforms

Background: Total ankle arthroplasty (TAA) has grown in popularity and indications, with encouraging results over time. Today, preoperative and postoperative evaluations are mainly performed using clinical test and diagnostic imaging, but there is a deficiency in objectively evaluating the biomechanics of the foot and ankle, which serve as the functional markers for monitoring the effectiveness and outcomes of surgery. Inertial measurement units associated with plantar pressure measurements may provide an accurate and reliable method of evaluating function through the analysis of gait and ankle joint mobility. The aim of this study was to introduce an innovative technology, to assess its accuracy and feasibility compared to standard clinical assessment methods and to objectify kinematic outcomes in patients with end-stage ankle OA before and after TAA surgery. Methods: A consecutive series of eight patients with symptomatic end-stage osteoarthritis and treated with TAA was prospectively evaluated using clinical scores (AOFAS, MOxFQ, VAS, SF-36, 17-IFFI), physical tests (FPI, ALT), plantar pressure measurements with FLEX EPS/R2 Letsense® baropodometric platform, gait analysis and wearable sensors-based ankle motion and kinematic outcomes using Wiva Science inertial sensors by Letsense®. Data were collected preoperatively and 4 months after surgery. Results: All PROMs exhibited statistical significance in improvement from pre- to postoperative periods, except for one. Physical examinations showed no significant changes of the foot shape and alignment. Plantar pressure analyses revealed no significant changes in static and dynamic evaluations, but a more uniform distribution of plantar pressure was observed between the two periods. Inertial sensor parameters demonstrated no significant differences, except for a significant reduction in stride length and step length for the operated foot after surgery. Conclusions: Gait analysis using inertial sensors and plantar pressure measurements offer ease of handling, cost effectiveness, portability and swift data reading, making them highly appealing for widespread clinical use. Integrating these tools into the routine assessments of patients with TAA holds promise for advancing precision of treating this condition and our depth of its understanding, contributing to more comprehensive and insightful patient care.

S2S-Sim: A Benchmark Dataset for Ship Cooperative 3D Object Detection

The rapid development of vehicle cooperative 3D object-detection technology has significantly improved the perception capabilities of autonomous driving systems. However, ship cooperative perception technology has received limited research attention compared to autonomous driving, primarily due to the lack of appropriate ship cooperative perception datasets. To address this gap, this paper proposes S2S-sim, a novel ship cooperative perception dataset. Ship navigation scenarios were constructed using Unity3D, and accurate ship models were incorporated while simulating sensor parameters of real LiDAR sensors to collect data. The dataset comprises three typical ship navigation scenarios, including ports, islands, and open waters, featuring common ship classes such as container ships, bulk carriers, and cruise ships. It consists of 7000 frames with 96,881 annotated ship bounding boxes. Leveraging this dataset, we assess the performance of mainstream vehicle cooperative perception models when transferred to ship cooperative perception scenes. Furthermore, considering the characteristics of ship navigation data, we propose a regional clustering fusion-based ship cooperative 3D object-detection method. Experimental results demonstrate that our approach achieves state-of-the-art performance in 3D ship object detection, indicating its suitability for ship cooperative perception.

Measurement and separation of forces and lift-offs employing an inductance-to-digital converters (LDC)-based orthogonal LC resonance sensor

Inductor-capacitor (LC) resonance sensors based on the sensing materials’ electromagnetic properties have potential to replace strain gauges for wheel-rail contact force measurement. However, as traditional eddy current testing acts, the lift-off effects also affect the results of the LC resonance sensing which can cause the misinterpretation between forces or lift-offs. This paper proposed an LDC-based orthogonal LC resonance sensor, combined with an 8-node quadratic interpolation method to achieve the measurement and accurate separation of forces and lift-offs. The four characteristic parameters of the orthogonal LC resonance sensor, Rp1, Frequency1, Rp2, Frequency2, were measured at different applied forces and lift-offs. A new combination pattern referred to the Rp2-Rp1, frequency2-frequency1 plane was proposed, which contained force and lift-off information in both vertical and transverse directions. Through the 8-node isoparametric coordinate transformation, the applied force and lift-off were separated respectively, with the maximum relative error of 22.61% for forces and 1.66% for lift-offs. Results indicated that the proposed method is effective and can be used to measure force/stress under varying lift-offs.

A new procedure for onboard calibration of star sensors installed on low Earth orbit satellite

This paper suggests a new onboard calibration procedure for star sensors installed on low Earth orbit satellites. In this procedure, all the error factors are investigated, and an accurate model of the star sensor is presented. Then, a new scenario is proposed for producing real data from a star sensor installed on a satellite while the satellite is moving in orbit. These sensor data are entered into the unscented Kalman filter as measurement data, and the sensor parameters, including focal length and principal point, are estimated. In the following, the estimated parameters are entered into the linear Kalman filter, and the misalignment error is estimated. Finally, the proposed procedure is simulated, and its performance is checked. The simulation results show the high accuracy and convergence speed of the suggested algorithm.