Proposed Sensor System Research Articles

Ultrasensitive Online NO Sensor Based on a Distributed Parallel Self-Regulating Neural Network and Ultraviolet Differential Optical Absorption Spectroscopy for Exhaled Breath Diagnosis.

The concentration of fractional exhaled nitric oxide (FeNO) is closely related to human respiratory inflammation, and the detection of its concentration plays a key role in aiding diagnosing inflammatory airway diseases. In this paper, we report a gas sensor system based on a distributed parallel self-regulating neural network (DPSRNN) model combined with ultraviolet differential optical absorption spectroscopy for detecting ppb-level FeNO concentrations. The noise signals in the spectrum are eliminated by discrete wavelet transform. The DPSRNN model is then built based on the separated multipeak characteristic absorption structure of the UV absorption spectrum of NO. Furthermore, a distributed parallel network structure is built based on each absorption feature region, which is given self-regulating weights and finally trained by a unified model structure. The final self-regulating weights obtained by the model indicate that each absorption feature region contributes a different weight to the concentration prediction. Compared with the regular convolutional neural network model structure, the proposed model has better performance by considering the effect of separated characteristic absorptions in the spectrum on the concentration and breaking the habit of bringing the spectrum as a whole into the model training in previous related studies. Lab-based results show that the sensor system can stably achieve high-precision detection of NO (2.59-750.66 ppb) with a mean absolute error of 0.17 ppb and a measurement accuracy of 0.84%, which is the best result to date. More interestingly, the proposed sensor system is capable of achieving high-precision online detection of FeNO, as confirmed by the exhaled breath analysis.

Pitot Tube Sensor Probe System for Simultaneous Airflow and Pressure Measurement of Expiration Inside Pulmonary Airway

Respiratory diseases, including the cronic obstructive pulmonary disease (COPD) are among the leading causes of death worldwide. To date, there is no physical examination procedure that can quantify important parameters within a specific lesion area inside the lung. In this study, we propose a novel sensor probe system consisting of a basket forceps and 2 pressure sensors to simultaneously measure the expiratory airflow and the static pressure inside the pulmonary airway. A Pitot tube mechanism using 2 pressure sensors is introduced in the system for measuring both the physical quantities. Small fiber‐optic pressure sensors with 0.3 mm diameter were utilized, and both the airflow rate and the static pressure characteristics were evaluated for obtaining the calibration curves. Finally, the proposed sensor system was implemented to measure both the airflow and pressure in an artificial ventilator and an experimental animal. The measurement of expired airflow and static pressure inside the airway of a rat with the diameter of 1.8 mm were demonstrated. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Low-Cost Optical Sensors for Soil Composition Monitoring.

Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time and resource consumption. Contrastingly, sensor techniques effectively gather environmental data, though they mostly lack in situ studies. Despite this, sensors offer substantial on-site data generation potential in a non-invasive manner and can be included in wireless sensor networks. Therefore, the aim of the paper is to develop a low-cost red, green, and blue (RGB)-based sensor system capable of detecting changes in the composition of the soil. The proposed sensor system was found to be effective when the sample materials, including salt, sand, and nitro phosphate, were determined under eight different RGB lights. Statistical analyses showed that each material could be classified with significant differences based on specific light variations. The results from a discriminant analysis documented the 100% prediction accuracy of the system. In order to use the minimum number of colors, all the possible color combinations were evaluated. Consequently, a combination of six colors for salt and nitro phosphate successfully classified the materials, whereas all the eight colors were found to be effective for classifying sand samples. The proposed low-cost RGB sensor system provides an economically viable and easily accessible solution for soil classification.

Advanced self-convergent calibration for selenized two-dimensional film gas sensors

This research presents a gas sensor system featuring a selenized two-dimensional (2D) film as its primary sensing material, integrated with metal-gate-coupled floating gate devices to enable self-convergent calibration. The inherent variability in resistance levels of 2D gas-sensing materials across different devices has been a significant challenge, resulting in substantial deviations of the output signal within the sensing circuit. To address this issue, we introduce a novel self-convergent operational technique, which effectively mitigates the impact of resistance variations thereby enhancing the precision and reliability of gas-sensing outcomes. The proposed gas sensor system promises to deliver consistent and accurate results, even with device-to-device resistance variations, making it a valuable contribution to gas-sensing technology. This work holds substantial potential for various applications requiring highly precise gas detection and quantification.

2D shape reconstruction with Fiber Bragg Gratings embedded in rubbers using machine learning methods

This paper presents the development, signal analysis and application of an FBG array embedded smart rubber carpet for 2D shape reconstruction. The proposed sensor system is based on the optical fiber integration between two rubber layers, where the optical fibers have 7 FBGs for the quasi-distributed force assessment. The sensor system analysis is performed at room temperature in difference set of tests, namely the stability tests (without any applied force), the force characterization tests (forces applied directly on the FBG region) and the shape reconstruction tests (different forces applied at different positions in the carpet). The stability tests indicated a maximum variation below 0.02 nm considering all 7 FBGs, whereas the force characterization tests indicated a mean sensitivity of around 80 N/nm. However, it is worth to mention that there is a variation on the sensitivities considering all FBGs, which is related to minor thickness differences in the rubber layer that directly effects the strain transmission from the rubber layer to the optical fiber beneath it. Thereafter, the shape reconstruction occurs in two steps, the first one is the cubic interpolation between the FBGs, considering the boundary conditions on the carpet following the beam theory. Then, the k-nearest neighbors regression algorithm is used on the force distribution assessment with a dataset divided into training and testing with a 70/30 proportion. The shape reconstruction results indicate the feasibility of the proposed approach, where root mean squared errors below 0.65 N are obtained in the range of applied force between 3 N and 35 N. Therefore, the proposed smart carpet as a feasible option for different 2D shape reconstruction applications ranging from biomechanics to structural health monitoring.

Noncollocated Proprioceptive Sensing for Lightweight Flexible Robotic Manipulators

This article presents the design of a noncollocated feedback system for flexible serial manipulators. The device is a passive serial chain of encoders and lightweight links, mounted in parallel with the manipulator. This measuring arm effectively decouples the manipulator's proprioception from its actuators by providing information on the actual end effector pose, accounting for both joint and link flexibility. The kinematic redundancy of the measuring chain allows for safe operation in the context of human–robot interaction. A simple yet effective error model is introduced to assess the suitability of the proposed sensor system in the context of robotic control. The practicality of the device is first demonstrated by building a physical joint-encoder assembly and a simplified planar measuring arm prototype. With this additional feedback, a task-space position controller is devised and tested in simulation. Finally, the simulation results are validated with an experimental 3-DoF lightweight manipulator prototype equipped with a five-joint measuring arm.

A Broad Range Triboelectric Stiffness Sensor for Variable Inclusions Recognition

HighlightsWe propose a broad range triboelectric sensor system employing elastic sponge and shielding layers, which can realize fast stiffness recognition within 1.0 s at a low cost.A novel algorithm is proposed for rapid stiffness identification by extracting signal characteristics, effectively reducing demand of computing resources.The proposed sensor system can identify the multi-layer stiffness structure of objects, enabling effective recognition of variable inclusions in soft objects with an accuracy of 99.7%.

Cylindrical-cam modulation instantaneous angular speed sensor for reciprocating-rotary machine

This paper presents the theory and implementation of a novel sensor system for measuring the instantaneous angular speed (IAS) of a shaft with reciprocating rotary motion. The system comprises a laser displacement sensor and an S-shaped cylindrical cam sleeve fixed to the shaft. As the shaft rotates with reciprocating rotary motion, the cylindrical cam modulates the IAS signal of the machine to achieve a change in profile radius. The signal is then demodulated to obtain the IAS of the reciprocating rotary motion. The sensor system is experimentally validated using a vibration-assisted tapping machine. Results show that the IAS readings are consistent with the machine’s set parameters. Furthermore, the novel sensor produces considerably smooth and stable traces of the IAS, demonstrating its high accuracy and reliability in obtaining the IAS of reciprocating rotary motion transients. The frequency error rate of the system is less than 3%, and the highest resolution of the angular amplitude is 4.788″. Finally, a cutting experiment confirmed the feasibility and validity of the proposed sensor system in optimizing the parameters of the vibration-assisted tapping machine.

A passive ladder-shaped sensor architecture with failure detection based on fiber Bragg grating

This study proposes a fiber Bragg grating (FBG)-based passive ladder-shaped sensor architecture with failure detection capabilities. The sensor architecture primarily includes optocoupler (OCP) lines and FBG lines and can detect multiple failure points. In order to verify the failure detection capability of the system, we set six failure modes and conducted simulation experiments. In the first four cases, the OCP lines are failure-free, while in the last two cases, the OCP line is faulty. The experimental results show that in the first four cases, the sensor network can detect multiple failure points; in the last two cases, although the break-point position cannot be determined, it can help to locate the failure area. In addition, strain sensing is also analyzed. The results show that the proposed sensor system can not only perform strain sensing, but also detect the failure point.

Multi‐Modal Modular Textile Sensor for Physical Human–Robot Interaction Using Band‐Stop Filters

AbstractFor safe coexistence between robots and humans, it is important for robots to detect the presence of nearby humans as well as any physical contacts made to its body. The design of a modular textile sensor array and an algorithm for multi‐modal sensing of human touches and other contacts with their contact forces proposed. Each sensor module in the array is capable of multi‐modal sensing, and the entire array with multiple modules requires only two wires to read the outputs from all the modules using band‐stop filter circuits. The proposed sensor system shows the structural modularity, achieved by simple fabrication of sequential lamination of conductive and non‐conductive textile materials, realizing electrical connections through conductive snap buttons that connect the modules to the circuit. The functional modularity is also achieved through the compensation algorithm, derived from the analysis of the transfer function in the frequency domain. The algorithm significantly reduces signal interferences between modules. The multi‐modality, the textile‐based design, and the structural and functional modularity of the proposed system enable practical applications to various robotic systems, including robotic skin for a collaborative robot, a wearable sensor, a robot hand sensor, and a human–computer interface, as demonstrated in this study.

Numerical calculation and analysis of a novel complex impedance sensing approach for in-flow droplet detection utilizing the C4D technique

This study proposed a C4D sensor structure with a novel approach utilizing the complex impedance analysis method, which includes both resistance and reactance analysis. The complex impedance analysis method provides additional information to characterize the electrical properties of solutions and strange objects moving in the fluidic channel, thus enhancing the sensitivity of conventional C4D structure. In addition, the effects of the fluidic properties, object size, excitation frequency, and sensor geometry on the performance of the proposed C4D sensor were investigated. The proposed sensor system used the finite element method analysis to investigate the performance of the proposed method. The simulation results show that the largest resistance change is approximately 27 kΩ while the largest reactance change is about 10 kΩ according to the passage of a 400 μm radius oil droplet over the sensing area in the NaCl solution channel. Furthermore, this work indirectly verifies the possibility and feasibility of the proposed impedance analysis method by utilizing the conventional C4D structure thanks to its advantages and lays a good foundation for future research work.

A wearable medical sensors system for in-situ monitoring vital signs of patients with hemorrhagic shock in big disaster scenes

In big disaster sites, such as earthquakes, wars, and traffic accidents, monitoring the vital signs of people with hemorrhagic shock is crucial for accurate injury classification and timely rescue. Clinical medical equipment is unsuitable for in-situ detection since large size and complex operations. In this paper, we propose a set of wearable medical sensors system that integrates bio-radar, wristwatch, nose clip, and biochemical sensor to monitor the vital signs of patients in big disaster scenes. This system offers portable advantages over bulky medical equipment and flexible measurement of blood pressure, heart rate, blood oxygen saturation, respiratory rate, body temperature, and levels of glucose, lactate, and hemoglobin (Hb). By addressing individual differences and improving the signal-to-noise ratio, the sensors system can achieve highly sensitive measurements online. Experimental studies on 15 patients in ICU wards reveal high uniformity of measurement results between our wearable sensors system and clinical equipment. Based on the data collected by the wearable sensor system and processed through a pre-established assessment model, color labels indicating severity levels will be presented to classify the injury degree for efficient rescue. Our proposed wearable medical sensor system contributes to saving more lives in big disaster scenes through accurate and timely measurement of vital signs.

Plasmonic Cavity for Self-Powered Chemical Detection and Performance Boosted Surface-Enhanced Raman Scattering Detection.

With the popularization of the Internet of Things, the application of chemical sensors has become more and more extensive. However, it is difficult for a single functional sensor to meet multiple needs at the same time. For the next generation of chemical sensors, in addition to rapid qualitative and quantitative detection, it is also necessary to solve the problem of a distributed sensor power supply. Triboelectric nanogenerator (TENG) and surface-enhanced Raman scattering (SERS) are two emerging technologies that can be used for chemical testing. The combination of TENG and SERS technology is proposed to be an attractive research strategy to implement qualitative and quantitative analysis, as well as self-powered detection in one device. Herein, the Ag nanoparticle (NP)@polydimethylsiloxane (PDMS) plasmonic cavity is demonstrated, which can be exploited not only as a SERS substrate for qualitative analysis of the target molecules but also as a TENG based self-powered chemical sensor for rapid quantitative analysis. More importantly, the as-designed plasmonic cavity enables prolonged triboelectric field generated by the phenomena of triboelectricity, which in turn enhances the "hot spot" intensities from Ag NPs in the cavity and boosts the SERS signals. In this way, the device can have good feasibility and versatility for chemical detection. Specifically, the measurement of the concentration of many analytes can be successfully realized, including ions and small molecules. The results verify that the proposed sensor system has the potential for self-powered chemical sensors for environmental monitoring and analytical chemistry.

Microwave imaging sensor system using metamaterial lens for subwavelength resolution

Lenses made of negative refractive index metamaterials can overcome the diffraction-limited resolution of imaging at working distances in the far-field. This paper describes a novel negative index metamaterial (NIM) lens imaging sensor system for subwavelength microwave nondestructive evaluation (NDE) using homodyne detection measurements. The split-ring resonator (SRR) - wire based NIM lens is optimized to work at 6.3 GHz (wavelength λ = 48 mm). Coherent homodyne detection scheme provides a simple, low-cost, and highly sensitive NIM lens imaging sensor system that can be used in the field under practical conditions. Imaging of an omnidirectional source using negative refraction is shown using the proposed system. A focal spot of size 0.62λ was obtained in the image plane of the metamaterial lens with a source to image distance of 1.67λ. The focal spot position was further shifted in the azimuthal plane by simple movement of the source. The study demonstrates unique focus-scanning capability of NIM lenses for target localization without the need for complex tuning mechanisms. The feasibility of the system to image subwavelength defects inside dielectric materials in reflection mode is also investigated for its potential application as an NDE sensor. Defects comprising subwavelength hole of diameter 0.25λ and a groove of dimensions 0.31λ x 0.1λ placed at the focal plane of the lens was imaged using the proposed sensor system.

Chemically engineered unzipped multiwalled carbon nanotube and rGO nanohybrid for ultrasensitive picloram detection in rice water and soil samples

Picloram (4-Amino-3,5,6-trichloro pyridine-2-carboxylic acid) is a chlorinated herbicide that has been discovered to be tenacious and relatively durable in both soil and water. It is known to have adverse and unpleasant effects on humans causing several health complications. Therefore, the determination of picloram is profoundly effective because of its bio-accumulative and persistent nature. Because of this, a sensitive, rapid, and robust detection system is essential to detect traces of this molecule. In this study, we have constructed a novel nanohybrid system comprising of an UZMWCNT and rGO decorated on AuNPs modified glassy carbon electrode (UZMWCNT + rGO/AuNPs/GCE). The synthesized nanomaterials and the developed system were characterized using techniques such as SEM, XRD, SWV, LSV, EIS, and chronoamperometry. The engineered sensor surface showed a broad linear range of 5 × 10–2 nM to 6 × 105 nM , a low limit of detection (LOD) of 2.31 ± 0.02 (RSD < 4.1%) pM and a limit of quantification (LOQ) of 7.63 ± 0.03 pM. The response time was recorded to be 0.2 s, and the efficacy of the proposed sensor system was studied using rice water and soil samples collected from the agricultural field post filtration. The calculated recovery % for picloram in rice water was found to be 88.58%—96.70% (RSD < 3.5%, n = 3) and for soil it was found to be 89.57%—93.24% (RSD < 3.5%, n = 3). In addition, the SWV responses of both the real samples have been performed and a linear plot have been obtained with a correlation coefficient of 0.97 and 0.96 for rice and soil samples, respectively. The interference studies due to the coexisting molecules that may be present in the samples have been found to be negligible. Also, the designed sensor has been evaluated for stability and found to be highly reproducible and stable towards picloram detection.

Rapid Manufacturing Approach of an Ultrathin Moisture Sensor for Health Monitoring.

This study developed a rapid manufacturing approach for a moisture sensor based on contactless jet printing technology. A compact measurement system with ultrathin and flexure sensor electrodes was fabricated. The proposed sensor system focuses on continuous urine measurement, which can provide timely information on subjects to ensure efficient diagnosis and treatment. The obtained results verify that the proposed sensor system can exhibit a typical responsivity of up to -7.76 mV/%RH in the high-sensitivity range of 50-80 %RH. A preliminary field experiment was conducted on a hairless rat, and the effectiveness of the proposed ultrathin moisture sensor was verified. This ultrathin sensor electrode can be fabricated in the micrometer range, and its application does not affect the comfort of the user. The ultrathin electrode sensors can be printed directly on the diaper or undergarment of the user for in situ urine health monitoring, particularly of infants and the elderly.

Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor

Electronic nose (e-nose) technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications, such as smart factory and personal health monitoring. To overcome the cross-reactivity problem of chemoresistive sensors to various gas species, herein, we propose a novel sensing strategy based on a single micro-LED (μLED)-embedded photoactivated (μLP) gas sensor, utilizing the time-variant illumination for identifying the species and concentrations of various target gases. A fast-changing pseudorandom voltage input is applied to the μLED to generate forced transient sensor responses. A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation. The proposed sensor system achieves high classification (~96.99%) and quantification (mean absolute percentage error ~ 31.99%) accuracies for various toxic gases (methanol, ethanol, acetone, and nitrogen dioxide) with a single gas sensor consuming 0.53 mW. The proposed method may significantly improve the efficiency of e-nose technology in terms of cost, space, and power consumption.

UWB 통신을 이용한 센서 응용: 자동차 내 좌석 탑승 감지

UWB(ultra-wideband) technology has recently attracted attention owing to its ability to accurately measure distance and enable communication. Currently, applications such as sensors capable of recognizing situations are being considered, moving beyond communication and distance measurement. In this study, we developed a UWB-based sensor application system that detects whether a person is seated in a car. With this system, it is possible to send an alert to the driver’s smartphone if the driver leaves the car with a child in the back seat without a separate seat detection sensor. The proposed sensor system uses changes in the channel status of the vehicle, which are recognized when the UWB communication device performs the communication function, to detect whether all seats in a car are occupied. The proposed system, using the DW1000 module, demonstrated detection performance of 93 % or more in the case of one receiver, 97 % in the case of two receivers, and 100 % in the case of four receivers for all nine scenarios where four people were seated in a car in different combinations of seats.

A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection

Vision, Radar, and LiDAR sensors are widely used for autonomous vehicle perception technology. Especially object detection and classification are primarily dependent on vision sensors. However, under poor lighting conditions, dazzling sunlight, or bad weather an object might be difficult to be identified with general vision sensors. In this paper, we propose a sensor fusion system that combines a thermal infrared camera and a LiDAR sensor that can reliably detect and identify objects even in environments with poor visibility, such as day or night. The proposed method obtains the external parameters of the two sensors by designing and manufacturing a 3D calibration target to externally calibrate the thermal infrared camera and the LiDAR sensor. To verify the performance, experiments were conducted in day and night environments. The proposed sensor system and fusion algorithm show that it can reliably detect and identify objects even in environments with poor visibility, such as day or night.

A flow-based enzyme-free biosensor fabricated using track-etched membrane electrodes: Selective and sensitive detection of uric acid

A flow-based, enzyme-free biosensor for the selective and sensitive determination of uric acid was developed using track-etched membrane electrodes (TEMEs). The proposed sensor system comprises a pre-reactor electrode to decompose interfering species, such as L-ascorbic acid, and a detector electrode to detect uric acid. TEMEs were prepared by sputter deposition of gold on a track-etched membrane filter. Acetylene black (AB) was introduced as an electrode catalyst by simple filtration with the TEME, and the sensitivity for uric acid using the AB-decorated Au-TEME was much greater than that with bare Au-TEME. The insertion of the 2-aminoethanethiol-modified Au-TEMEs as pre-reactor electrodes upstream of the AB-decorated detector electrode eliminated the interference caused by the co-existence of L-ascorbic acid because the interfering species were electrolyzed at the pre-reactor electrode prior to detection of uric acid. The proposed sensor system was incorporated into a flow-injection analysis system to evaluate the sensor performance. The detection limit of uric acid was 0.6 μM. A recovery test by standard addition to human urine samples also showed satisfactory results.