Sensor System Research Articles

Optical Surface Roughness Estimation Using Artificial Intelligence

ABSTRACTA system for analysing steel plates is presented for an application in the wind power industry. The objective of the system is to assess the surface roughness as a preliminary step for the application of an anti‐corrosion coating. A number of noncontact sensor systems were considered, with the laser triangulation scanner proving to be the optimal solution. The samples are analysed in a test stand and the roughness is measured with this optical system. At the same time, the surface is recorded with a CMOS camera. An AI model is created from the sensor fusion of both systems, which can classify individual segments of the surface as well or poorly blasted. In the following step, the surface roughness is estimated as the Rz parameter by another AI model. An error of less than 6 μm is achieved.

On the Dynamics of a Novel Liquid-Coupled Piezoelectric Micromachined Ultrasonic Transducer Designed to Have a Reduced Resonant Frequency and Enhanced Ultrasonic Reception Capabilities.

This paper introduces a novel design for a liquid-deployed Piezoelectric Micromachined Ultrasonic Transducer (PMUT). This design was specifically developed to resonate at a lower ultrasonic frequency than a PMUT with a circular, fully clamped diaphragm with the same diameter. Furthermore, the novel design was also optimised to enhance its ultrasonic radiation reception capabilities. These parametric enhancements were necessary to develop a PMUT device that could form part of an eventual microscale sensory device used for the Structural Health Monitoring (SHM) of reinforced concrete (RC) structures. Through these two enhancements, an eventual microscale sensor can be made smaller, thus taking up a smaller die footprint and also be able to be deployed further apart from each other. Eventually, this would reduce the developed distributed sensor system's cost. The innovative design employed a configuration where the diaphragm was only pinned at particular points along its circumference. This paper presents results from Finite Element Modelling (FEM), as well as experimental work that was conducted to develop and test this novel PMUT. The experimental work presented involved both laser vibrometry and ultrasonic radiation lab work. The results show that when compared to a clamped diaphragm design, the novel device managed to achieve the required reduction in resonant frequency and presented an enhanced sensitivity to incoming ultrasonic radiation.

Fully Implantable Wireless Cardiac Pacing and Sensing System Integrated with Hydrogel Electrodes.

Cardiac pacemakers play a crucial role in arrhythmia treatment. Existing devices typically rely on rigid electrode components, leading to potential issues such as heart damage and detachment during prolonged cardiac motion due to the mechanical mismatch with cardiac tissue. Additionally, traditional pacemakers, with their batteries and percutaneous leads, introduce infection risks and limit freedom of movement. A wireless, battery-free multifunctional bioelectronic device for cardiac pacing is developed. This device integrates highly conductive (160Sm-1), flexible (Young's modulus of 80kPa is similar to that of mammalian heart tissue), and stretchable (270%) soft hydrogel electrodes, providing high signal-to-noise ratio (≈28dB) electrocardiogram (ECG)recordings and effective pacing of the beating heart. The versatile device detects physiological and biochemical signals in the cardiac environment and allows for adjustable pacing in vivo studies. Remarkably, it maintained recording and pacing capabilities 31 days post-implantation in rats. Additionally, the wireless bioelectronic device can be fully implanted in rabbits for pacing. By addressing a major shortcoming of conventional pacemakers, this device paves the way for implantable flexible bioelectronics, which offers promising opportunities for advanced cardiac therapies.

Development of a Compact NDIR CO2 Gas Sensor for a Portable Gas Analyzer

A carbon dioxide (CO2) gas sensor based on non-dispersive infrared (NDIR) technology has been developed and is suitable for use in portable devices for high-precision CO2 detection. The NDIR gas sensor comprises a MEMS infrared emitter, a MEMS thermopile detector with an integrated optical filter, and a compact gas cell with high optical coupling efficiency. A dual-ellipsoid mirror optical system was designed, and based on optical simulation analysis, the structure of the dual-ellipsoid reflective gas chamber was designed and optimized, achieving a coupling efficiency of up to 54%. Optical and thermal simulations were conducted to design the sensor structure, considering thermal management and light analysis. By optimizing the gas cell structure and conditioning circuit, we effectively reduced the sensor’s baseline noise, enhancing the overall reliability and stability of the system. The sensor’s dimensions were 20 mm × 10 mm × 4 mm (L × W × H), only 15% of the size of traditional NDIR gas sensors with equivalent detection resolution. The developed sensor offers high sensitivity and low noise, with a sensitivity of 15 μV/ppm, a detection limit of 90 ppm, and a resolution of 30 ppm. The total power consumption of the whole sensor system is 6.5 mW, with a maximum power consumption of only 90 mW.

Novel calibration method for improved UWB sensor distance measurement in the context of application for 3D analysis of human movement

Integrated UWB and MIMU sensor systems have become popular for pedestrian tracking and indoor localization, since this facilitates data fusion that improves position estimation accuracy by exploiting the complementary nature of their error sources. Integrated UWB/MIMU sensors also have great potential in only on-body use for 3D analysis of human movement, as with MIMU sensors alone accurate direct estimation of (relative) body segment position is not possible. For this, a position estimation accuracy with errors smaller than 1 cm is deemed required. The lowest position estimating error with integrated UWB/MIMU systems, reported so far, is around 5 cm. The main accuracy limiting factors were found to be the systematic errors in the distance estimates from the UWB sensor. Multiple reported attempts to calibrate for these systematic errors failed to achieve the desired accuracy. This article presents a novel distance-bias calibration method that minimizes the residual systematic distance estimate errors using multiple sensors in a swarm configuration. Validation was performed against synthetic reference data and against reference data measured with an optical motion tracking system. Significantly reduced systematic distance estimate errors (≤0.5 cm) were found. These results promise to facilitate significantly better position estimates in future UWB/MIMU data fusion.

Use of a structure-borne sound-based in-process sensor system to identify Weld seam irregularities during electron beam welding

In this publication, an in-process quality assurance method for electron beam welding based on a structure-borne sound emission test for the detection of weld irregularities arising in the process is presented. For this purpose, different sheet materials, i.e., AISI 304, AZ31 and AlMg3, were welded in a butt-joint and the resulting process noises were recorded by means of two acoustic emission sensors specifically designed for structure-borne sound. During the welding experiments, typical irregularities, e.g. incidence points, pore lines and cracks, were deliberately induced. Subsequently, the recorded acoustic signals were examined with regard to defect-specific abnormalities. Various methods in the time and frequency domain as well as pre-trained machine learning models were used to analyze the acoustic emission data. The results show that the investigated irregularities can be identified and distinguished from other process emissions, eventually enabling a robust means of identification for weld seam irregularities and, thus, opening pathways towards cost-effective in-process quality control.

Operation Temperature Effects on a Microwave Gas Sensor with and without Sensitive Material.

Microwave gas sensors have garnered attention for their high sensitivity and selectivity in the detection of volatile organic compounds (VOCs). However, traditional gas sensors generally rely on sensitive materials that degrade over time and are easily affected by the environment, compromising their stability and accuracy. This study proposes a microwave VOC gas sensor based on the condensation effect. The sensor adopts a novel design without sensitive materials, utilizing the condensation effect to detect acetone gas. The sensor system consists of a microwave sensor and a temperature control device. As the sensor temperature is lowered below the boiling point of acetone, the condensation of acetone gas on the sensor surface is achieved, enabling accurate detection of acetone gas. Experimental results indicate that the accumulated amount of acetone on the sensor surface is positively correlated with its response, with the maximum response of 3000 ppm acetone gas reaching 0.34 dB. Additionally, this study investigated the detection mechanism of the sensor after adding the sensitive material MXene and compared the performance of the sensor at different temperatures (-10 °C, 0 °C, and 60 °C). The results show that at -10 °C the sensor mainly captures acetone through physical adsorption, while at 25 and 60 °C, it primarily responds through chemical adsorption, with a maximum response of 0.29 dB. The VOC sensor based on the condensation effect without sensitive materials not only achieves the same sensitivity as traditional microwave sensors but also demonstrates stronger stability and anti-interference capabilities.

A high stretchability micro-crack tactile sensor system based on strain-isolation substrate

The integration of inflexible constituents onto pliable substrates is widely acknowledged as the most pragmatic approach for the realization of stretchable electronics. Nevertheless, the assurance of enduring connectivity between rigid electrode components and these compliant substrates poses a formidable quandary. In the scope of our investigation, we proffer a resolution by conceptualizing a PDMS substrate replete with strain isolation partitions, which can generate Young's modulus difference of approximately 30 times. These partitions efficaciously safeguard the steadfast linkage between rigid components and electrodes, even under diverse strain provocations, a stable connection can be maintained even when able to withstand strain exceeding 120 %. Using this substrate, we constructed a visual deformation sensing system based on microcrack type sensors. Compared with traditional flexible substrates (2 % strain), systems based on strain isolation substrates have better tensile stability (10 % strain). This groundbreaking innovation bestows stretchable micro-crack strain-sensing systems the resilience to contend with the potentially formidable rigors of everyday application.

Multimodal reaching-position prediction for ADL support using neural networks

This study aimed to develop daily living support robots for patients with hemiplegia and the elderly. To support the daily living activities using robots in ordinary households without imposing physical and mental burdens on users, the system must detect the actions of the user and move appropriately according to their motions. We propose a reaching-position prediction scheme that targets the motion of lifting the upper arm, which is burdensome for patients with hemiplegia and the elderly in daily living activities. For this motion, it is difficult to obtain effective features to create a prediction model in environments where large-scale sensor system installation is not feasible and the motion time is short. We performed motion-collection experiments, revealed the features of the target motion and built a prediction model using the multimodal motion features and deep learning. The proposed model achieved an accuracy of 93% macro average and F1-score of 0.69 for a 9-class classification prediction at 35% of the motion completion.

Research on Strategies to Improve Teachers' Sense of Gain in Private Universities

In the higher education system, private colleges and universities play an increasingly important role, and the stability and development of the teaching team are one of the key factors to ensure the quality of education. This study aims to conduct an in-depth analysis of the structure, acquisition paths, evaluation and improvement methods of teachers' sense of gain in private colleges and universities. Through a combination of literature review and field research, a theoretical framework of teachers' sense of gain in private colleges and universities is constructed and its multi-dimensional structure is clarified. By identifying the main factors that affect teachers' sense of gain, this study proposes corresponding improvement strategies, evaluates the current evaluation system of sense of gain, and puts forward specific suggestions for improvement. The study points out that by improving the working environment, optimizing the salary and welfare system, and broadening career development channels, the sense of gain of teachers in private universities can be significantly improved, thus promoting the overall development of private higher education.

Graphene-Doped Thermoplastic Polyurethane Nanocomposite Film-Based Triboelectric Nanogenerator for Self-Powered Sport Sensor.

Triboelectric nanogenerators (TENGs), as novel electronic devices for converting mechanical energy into electrical energy, are better suited as signal-testing sensors or as components within larger wearable Internet of Things (IoT) or Artificial Intelligence (AI) systems, where they handle small-device power supply and signal acquisition. Consequently, TENGs hold promising applications in self-powered sensor technology. As global energy supplies become increasingly tight, research into self-powered sensors has become critical. This study presents a self-powered sport sensor system utilizing a triboelectric nanogenerator (TENG), which incorporates a thermoplastic polyurethane (TPU) film doped with graphene and polytetrafluoroethylene (PTFE) as friction materials. The graphene-doped TPU nanocomposite film-based TENG (GT-TENG) demonstrates excellent working durability. Furthermore, the GT-TENG not only consistently powers an LED but also supplies energy to a sports timer and an electronic watch. It serves additionally as a self-powered sensor for monitoring human movement. The design of this self-powered motion sensor system effectively harnesses human kinetic energy, integrating it seamlessly with sport sensing capabilities.

Room-temperature rapid oxygen monitoring system in high humidity hydrogen gas environment towards water electrolysis application

Water electrolysis is one of the key processes for carbon-free hydrogen (H2) generation, yet it inherently presents the risk of oxygen (O2) permeation, a significant concern that must be addressed. Contrary to external atmospheric conditions, the environment within a water electrolysis system is characterized exclusively by high-purity H2, little O2, and moisture (H2O). However, there has been limited research on sensors capable of detecting O2 crossover in such a pure H2 atmosphere under high humidity conditions. In this study, we developed an electrolysis O2 monitoring (E-OM) sensor based on a semiconducting metal oxide (SMO) that can operate at a room-temperature with such high humidity through photoactivation with 365 nm LED light. A porous In2O3 nanofilm, prepared by glancing angle deposition (GLAD), was used as the gas sensing material, and copper nanoparticles (Cu NPs) were decorated on the In2O3 surface through electron beam evaporation to accelerate the O2 adsorption. Remarkably, the E-OM sensor showed a humidity-independent response to O2 gas in the H2 atmosphere. Meanwhile, since the response of the E-OM sensor itself was not fast enough for water electrolysis system application, convolutional neural network (CNN)-based signal processing in the time domain was applied. As a result, our E-OM sensor system could predict O2 concentrations from 0 to 2 vol% in an H2 environment with a relative humidity (RH) of 30–90 %, and the detection time was under 5 s. This development could enable safe, rapid, and cost-effective monitoring of O2 crossover in H2 production infrastructure.

Optical chemical gas sensor based on spectral autocorrelation: A method for online detection of nitric oxide and ammonia in exhaled breath

The detection of nitric oxide (NO) and ammonia (NH3) in exhaled breath (EB) plays a crucial role in non-invasive diagnosis of airway inflammatory and kidney diseases. Here we report an optical chemical gas sensor for NO and NH3 based on spectral autocorrelation. In our sensor system, the target gas is removed through a chemical reaction with a sensing material and the spectrum of the target gas is extracted from the spectra obtained before and after the reaction by autocorrelation. Our method focuses on the ability of the material to eliminate the target gas without the need for detecting signals of the reaction, greatly relaxing the requirements on material properties. Fundamentally different from optical sensors that obtain spectra by spectral decoupling, our sensor relies on autocorrelation operation to acquire the spectrum of the target gas, thereby overcoming spectra overlap. Lab-based results show that the sensor enables detection of NO (2.28–451.82 ppb) and NH3 (11.52–12,725.42 ppb) with mean absolute errors (MAEs) of 0.79 ppb and 6.13 ppb and measurement accuracies of 0.8 % and 1.4 %, respectively. The EB experiment proves that the sensor can detect exhaled NO and NH3 and distinguish EB between healthy subjects and simulated patients.

Sensing signal augmentation by flow rate modulation of carrier gas for accurate differentiation of complex odours

ABSTRACT For olfactory sensors, clear differentiation of complex odour samples requires diverse information. To obtain such information, hardware modifications, such as introducing additional channels with different physical/chemical properties, are usually needed. In this study, we present a new approach to augmenting the sensing signals of an olfactory sensor by modulating the flow rate of the carrier gas. The headspace vapour of complex odours is measured using a sensing system of nanomechanical sensor (Membrane-type Surface stress Sensor, MSS). The resulting data set is quantitatively evaluated using the Davies-Bouldin index (DBI) of principal component analysis (PCA). The increasing number of sensing signals obtained at different gas flow rates leads to a decrease in the DBI, achieving better cluster separation between different odours. Such gas flow effects can be attributed to several factors, including the sample evaporation and the equilibrium of the gas-liquid and gas-solid interfaces. Proton-transfer-reaction time-of-flight mass spectrometry (PTR-TOF-MS) experiments reveal that the compositions of odour samples vary with the different gas flow rates. It is demonstrated that a simple technique for modulating gas flow rates can significantly improve the differentiation performance of complex odours, providing an additional degree of freedom in olfactory sensing.

A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture

This paper comprehensively explores the development of a standalone and compact microwave sensing system tailored for automated radio frequency (RF) scattered parameter acquisitions. Coupled with an emitting RF device (antenna, resonator, open waveguide), the system could be used for non-invasive monitoring of external matter or latent environmental variables. Central to this design is the integration of a NanoVNA and a Raspberry Pi Zero W platform, allowing easy recording of S-parameters (scattering parameters) in the range of the 50 kHz–4.4 GHz frequency band. Noteworthy features include dual recording modes, manual for on-demand acquisitions and automatic for scheduled data collection, powered seamlessly by a single battery source. Thanks to the flexibility of the system’s architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. As a case study, the potential application of the integrated sensor system with an RF patch antenna is explored in the context of greenwood hydration detection within the field of smart agriculture. This innovative system enables non-invasive monitoring of wood hydration levels by analyzing scattering parameters (S-parameters). These S-parameters are then processed using ML techniques to automate the monitoring process, enabling real-time and predictive analysis of moisture levels.

The Oxylipin Dependent Quorum Sensing System enhances Pseudomonas aeruginosa dissemination during burn-associated infection.

Following severe burn injury, Pseudomonas aeruginosa is the leading cause of life-threatening infection. Herein, we unveil how P. aeruginosa strategically employs host-derived oleic acid, released as consequence of burn-injury, to induce a hypervirulent phenotype via its Oxylipin Dependent Quorum Sensing system (ODS). ODS activation enhanced P. aeruginosa invasion of burned skin and promoted its dissemination to distant organs in vivo . ODS regulation of P. aeruginosa virulence involved the control of nitic oxide levels, a key signaling molecule in bacteria, through upregulation of the nitric oxide reductases NorCB. Immunization with OdsA, one of the enzymes involved in oxylipin generation, or treatment with a pharmacological inhibitor of OdsA, protected mice against lethal P. aeruginosa infection following burn-injury. Our findings reveal a new mechanism underlying P. aeruginosa hypervirulence in burn wounds and identifies OdsA as a promising target for preventing disseminated infections following burns.

Page Turning Using Assistive Robot with Low-Degree-of-Freedom Hand

This paper proposes a page-turning strategy using an assistive robot that has a low-degree-of-freedom robotic hand. The robotic hand is based on human object handling characteristics, which significantly reduces the number of fingers and joints required to handle various objects. The robotic hand has right and left planar fingers that can transform their shape to handle various objects. To turn a page, the robot uses the planar fingers to push the surface of the page and then rotates the fingers. The design concept, mechanism, sensor system, strategy for page turning, and control system of the robotic hand are presented. The experimental results show that the robot can turn pages using the proposed method; however, it sometimes failed to turn the page when the robotic hand height was too low and too close to the book because the rotation of the fingers was stopped by the book. When the hand detects excessive force during page turning, the control system changes the shape of the fingers and releases the force from the book. The experimental results show the effectiveness of the control system.

FacePsy: An Open-Source Affective Mobile Sensing System - Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings

Depression, a prevalent and complex mental health issue affecting millions worldwide, presents significant challenges for detection and monitoring. While facial expressions have shown promise in laboratory settings for identifying depression, their potential in real-world applications remains largely unexplored due to the difficulties in developing efficient mobile systems. In this study, we aim to introduce FacePsy, an open-source mobile sensing system designed to capture affective inferences by analyzing sophisticated features and generating real-time data on facial behavior landmarks, eye movements, and head gestures - all within the naturalistic context of smartphone usage with 25 participants. Through rigorous development, testing, and optimization, we identified eye-open states, head gestures, smile expressions, and specific Action Units (2, 6, 7, 12, 15, and 17) as significant indicators of depressive episodes (AUROC=81%). Our regression model predicting PHQ-9 scores achieved moderate accuracy, with a Mean Absolute Error of 3.08. Our findings offer valuable insights and implications for enhancing deployable and usable mobile affective sensing systems, ultimately improving mental health monitoring, prediction, and just-in-time adaptive interventions for researchers and developers in healthcare.

A Study on Sensitivity Improvement of the Fiber Optic Acoustic Sensing System Based on Sagnac Interference.

A new pickup structure was introduced and modified to improve the resolution of the linear Sagnac optical fiber acoustic sensing system. The maximum strains corresponding to the material, diameter, wall thickness, and height of the pickups were analyzed by simulation. An aluminum cylinder with a diameter of 110 mm, a wall thickness of 3 mm, and a height of 120 mm was chosen as the basic pickup. A four-groove pickup with a vertical width of 80 mm and a horizontal width of 20 mm was introduced to improve the sensitivity of the system. The experiments showed that the average peak-to-peak sensitivity of the four-groove pickup increased by 215.54% to 106.806 mV/Pa. The improved pickup can be applied in areas to monitor the situation of invasion of the Sagnac optical fiber acoustic sensing system.

An image-based bridge displacement field measurement method with self-vibration elimination

Displacement is a critical indicator that reflects the degeneration of overall stiffness for bridges. However, for most bridges, contact displacement measurement is difficult to achieve due to the lack of reference points and technical limitations. In this paper, a unique dual-camera visual sensor system for the whole field displacement measurement of bridge undersurfaces was developed. The visual sensor system relies on feature point algorithm and several algorithms are involved to improve the accuracy and efficiency. The dual-camera equipment can independently eliminate the impact of the camera vibration, thereby solving the problem of camera vibration caused by ground vibrations and wind loads at outdoor inspection sites when measuring bridge displacement. In a laboratory testing environment, a steel plate simulated a bridge and different experiments were conducted to test the visual system in situations with and without vibration disturbances to the camera. The results verify the accuracy of the developed visual sensor, especially the effectiveness of the vibration reduction method.