Sensor Array Research Articles

FABRICATION AND PERFORMANCE OF AUTONOMOUS VEHICLE

Line following and obstacle avoidance are essential functionalities in robotics. A Line Following Robot utilizes arrays of optical sensors to track and follow lines autonomously. Equipped with four sensors, it ensures precise movement along the line. DC gear motors drive its wheels, controlled by an Arduino Uno interfacing algorithms for speed regulation and steering. Additionally, an LCD interface displays the traveled distance. This versatile robot finds applications in industrial automation, household tasks, and guiding systems in museums. Conversely, an obstacle avoidance robotic vehicle, built with an ATmega328 microcontroller and ultrasonic sensors, demonstrates intelligent navigation. When obstacles are detected, the microcontroller redirects the vehicle by actuating motors through a motor driver. Unlike infrared sensors, the ultrasonic sensors offer compatibility and reliability. This project amalgamates both functionalities to create a comprehensive robotic system capable of navigating along lines while autonomously avoiding obstacles. Key components include the Arduino UNO, motor shield L293d, ultrasonic sensor HC-SR04, DC motors, and servo motor.represents a significant leap towards a future where vehicles navigate with unprecedented efficiency and safety, heralding a new era of transportation. Keywords: Line Following Robot, Optical Sensors, DC Gear Motors, Arduino Uno.

High-density, highly sensitive sensor array of spiky carbon nanospheres for strain field mapping

While accurate mapping of strain distribution is crucial for assessing stress concentration and estimating fatigue life in engineering applications, conventional strain sensor arrays face a great challenge in balancing sensitivity and sensing density for effective strain mapping. In this study, we present a Fowler-Nordheim tunneling effect of monodispersed spiky carbon nanosphere array on polydimethylsiloxane as strain sensor arrays to achieve a sensitivity up to 70,000, a sensing density of 100 pixel cm−2, and logarithmic linearity over 99% within a wide strain range of 0% to 60%. The highly ordered assembly of spiky carbon nanospheres in each unit also ensures high inter-unit consistency (standard deviation ≤3.82%). Furthermore, this sensor array can conformally cover diverse surfaces, enabling accurate acquisition of strain distributions. The sensing array offers a convenient approach for mapping strain fields in various applications such as flexible electronics, soft robotics, biomechanics, and structure health monitoring.

Identification of Microorganism in Infected Wounds by Positively Charged Selective Sensor Array and Deep Learning Algorithm.

Microorganism are ubiquitous and intimately connected with human health and disease management. The accurate and fast identification of pathogenic microorganisms is especially important for diagnosing infections. Herein, three tetraphenylethylene derivatives (S-TDs: TBN, TPN, and TPI) featuring different cationic groups, charge numbers, emission wavelengths, and hydrophobicities were successfully synthesized. Benefiting from distinct cell wall binding properties, S-TDs were collectively utilized to create a sensor array capable of imaging various microorganisms through their characteristic fluorescent signatures. Furthermore, the interaction mechanism between S-TDs and different microorganisms was explored by calculating the binding energy between S-TDs and cell membrane/wall constituents, including phospholipid bilayer and peptidoglycan. Using a combination of the fluorescence sensor array and a deep learning model of residual network (ResNet), readily differentiation of Gram-negative bacteria (G-), Gram-positive bacteria (G+), fungi, and their mixtures was achieved. Specifically, by extensive training of two ResNet models with large quantities of images data from 14 kinds of microorganism stained with S-TDs, identification of microorganism was achieved at high-level accuracy: over 92.8% for both Gram species and antibiotic-resistant species, with 90.35% accuracy for the detection of mixed microorganism in infected wound. This novel method provides a rapid and accurate method for microbial classification, potentially aiding in the diagnosis and treatment of infectious diseases.

Soft Monolithic Shielded Sensors to Measure Shear and Normal Forces for Local Slip Detection

AbstractSoft force sensors enable closed loop control or autonomous operation of compliant robot systems. A stretchable force sensing surface containing an array of independent sensor units is reported. This design allows for the measurement of local shear and normal forces at multiple locations, independently, providing real‐time, multi‐axis force information at each unit. The sensor array is based on capacitive strain sensing, using liquid metal electrodes and air microchannel for high sensitivity. Electromechanical interference between adjacent sensor units is addressed and eliminated through design optimization and the use of compliant electrical shield layers. A 1D sensor array is applied to gripping tasks, where the independent sensor readings enable detecting local and partial slipping. This study illustrates how the device can be used to explore the dynamics of local forces during the stages of pick up, slip, and release.

The SQUID Controller Unit for the LiteBIRD Space Mission: Description, Functional Tests and Early Performance Assessment

AbstractLiteBIRD is a satellite mission to be launched by JAXA in the early 2030s. It will measure the Cosmic Microwave Background (CMB) primordial B-modes with an unprecedented sensitivity. Microwave radiation will be detected by Transition Edge Sensors (TESs) arrays multiplexed in frequency domain and read by Superconducting QUantum Interference Devices (SQUIDs). The LiteBIRD SQUID Controller Unit (SCU), based on the heritage of the successful design used for the ground-based SPT3G experiment, presents some novel elements that make it suitable for a space-borne application. We compare our first breadboard model with the ground-based, Off-The-Shelf Components (COTS) version, by driving the same SQUID Array Amplifier (SAA) at 4 K, measuring relevant quantities such as noise, gain and bandwidth. We demonstrate that the noise added by our first prototype (including a switching part for redundancy purposes) never exceeds the noise added by the COTS-based electronics board, representing our benchmark. We also present the first noise estimates with the SAA cooled below 1 K, going closer to the conditions expected for LiteBIRD operation.

Dual Sensing Signal Decoupling Based on Thermoelectric Polymer Aerogels for Precise Temperature and Pressure Recognition

AbstractThe capability to emulate skin‐like temperature and pressure sensing is fundamental for next‐generation artificial intelligence products. However, detecting temperature and pressure simultaneously with a single sensor without signal interference is challenging. Herein, a novel PCC aerogel sensor composed of PEDOT:PSS, CNTs, and CNF via directional freezing technology is developed. The PCC sensor can decouple temperature and pressure stimuli into individual voltage and resistance signals. It exhibits high‐precision temperature sensing capabilities, boasting an exceptionally high Seebeck coefficient of 30.4 µV K‐1 and the ability to detect temperature variations as low as 0.1 K. PCC sensors show excellent sensitivity and fast response times for detecting static and dynamic pressures, as well as high stability after 1000 cycles. Its maximum pressure sensitivity can reach 159.1% kPa−1, and the lowest detection limit is 10 Pa. Additionally, its excellent thermoelectric properties also enable to generating thermopower from human skin for self‐powered pressure sensing. A 3×3 PCC sensor array has been proposed to simulate the unique features of human skin in temperature and pressure recognition. This work provides a scalable manufacturing strategy for multi‐functional tactile sensors.

All-Printed Finger-Inspired Tactile Sensor Array for Microscale Texture Detection and 3D Reconstruction.

Electronic skins are expected to replicate a human-like tactile sense, which significantly detects surface information, including geometry, material, and temperature. Although most texture features can be sensed in the horizontal direction, the lack of effective approaches for detecting vertical properties limits the development of artificial skin based on tactile sensors. In this study, an all-printed finger-inspired tactile sensor array is developed to realize the 3D detection and reconstruction of microscale structures. A beam structure with a suspended multilayer membrane is proposed, and a tactile sensor array of 12 units arranged in a dual-column layout is developed. This architecture enables the tactile sensor array to obtain comprehensive geometric information of micro-textures, including 3D morphology and clearance characteristics, and optimizes the 3D reconstruction patterns by self-calibration. Moreover, an innovative screen-printing technology incorporating multilayer printing and sacrificial-layer techniques is adopted to print the entire device. In additon, a Braille recognition system utilizing this tactile sensor array is developed to interpret Shakespeare's quotes printed in Grade 2 Braille. The abovementioned demonstrations reveal an attractive future vision for endowing bioinspired robots with the unique capability of touching and feeling the microscale real world and reconstructing it in the cyber world.

Esters-targeted colorimetric sensor array for the authenticity discrimination of strong-aroma baijiu with different origins

Esters-targeted colorimetric sensor array for the authenticity discrimination of strong-aroma baijiu with different origins

A fluorescent sensor array based on antibiotic-stabilized metal nanoclusters for the multiplex detection of bacteria.

To address the need for facile, rapid detection of pathogens in water supplies, a fluorescent sensing array platform based on antibiotic-stabilized metal nanoclusters was developed for the multiplex detection of pathogens. Using five common antibiotics, eight different nanoclusters (NCs) were synthesized including ampicillin stabilized copper NCs, cefepime stabilized gold and copper NCs, kanamycin stabilized gold and copper NCs, lysozyme stabilized gold NCs, and vancomycin stabilized gold/silver and copper NCs. Based on the different interaction of each NC with the bacteria strains, unique patterns were generated. Various machine learning algorithms were employed for pattern discernment, among which the artificial neural networks proved to have the highest performance, with an accuracy of 100%. The developed prediction model performed well on an independent test dataset and on real samples gathered from drinking water, tap water and the Anzali Lagoon water, with prediction accuracy of 96.88% and 95.14%, respectively. This work demonstrates how generic antibiotics can be implemented for NC synthesis and used as recognition elements for pathogen detection. Furthermore, it displays how merging machine learning techniques can elevate sensitivity of analytical devices.

3D-Printed Soft Temperature Sensors Based on Thermoelectric Effects for Fast Mapping of Localized Temperature Distributions.

We propose a novel design of thermoelectric (TE) effect-based soft temperature sensors for directly monitoring localized subtle temperature stimuli. This design integrates rheology-engineered three-dimensional (3D) printing of high-performance carbon-based TE materials and polymer-based viscoelastic materials with low thermal conductivity. Rheological engineering of carbon nanotube (CNT) TE inks ensures the 3D printing of highly sensitive TE sensing units on directly written 3D soft platforms. Additionally, we pre-dope CNT inks with p- and n-type organic dopants to achieve high sensitivity and a fast response to temperature changes. The introduced 3D soft platforms with low thermal conductivity lead to an efficient thermal gradient on TE sensing units in the out-of-plane direction. Furthermore, encapsulating the temperature sensor array with the same polymer-based materials as the 3D soft platforms facilitates independent detection of localized temperature stimuli by minimizing thermal interaction between sensing units, resulting in precise temperature mapping by localized detection. Our 3D-printed soft temperature sensors exhibit high sensitivity to relatively small temperature changes, with a minimum sensing resolution of 0.1 K within tens of milliseconds. Moreover, the temperature sensor array not only detects localized temperature stimuli by imaging the temperature distribution but also demonstrates remarkable mechanical reliability against repetitive deformation with high accuracy.

Unraveling the Structure-Chirality Sensing Relationship between Achiral Anthracene-Based Tetracationic Nanotubes and Nucleosides in Aqueous Host-Guest Complexation

In biological systems, nucleosides play crucial roles in various physiological processes. In this study, we designed and synthesized four achiral anthracene-based tetracationic nanotubes (1 – 4) as artificial hosts and chiroptical sensors for nucleosides in aqueous media. Notably, different nanotubes exhibit varied chirality sensing on circular dichroism (CD)/circularly polarized luminescence (CPL) spectra through the host-guest complexation, which prompted us to explore the factors influencing their chiroptical responses. Through systematic host-guest experiments, the structure-chirality sensing relationship between achiral anthracene-based tetracationic nanotubes and nucleosides in the host-guest complexation was unraveled. Firstly, the CD response originates from the anthracene rings situated at the side-wall position, resulting from the right-handed (P)- or left-handed (M)-twisted conformation of the macrocyclic structure. Secondly, the CPL signal is influenced by the presence of anthracene rings at the linking-wall position, which results from intermolecular chiral twisted stacking between these anthracene rings. Therefore, these nanotubes can serve as chiroptical sensor arrays to enhance the accuracy of nucleotide recognition through principal component analysis (PCA) analysis based on the diversified CD spectra. This study provides insights for the construction of adaptive chirality from achiral nanotubes with dynamic conformational nature and might facilitate further design of chiral functional materials for several applications.

A bimetallic peroxidase-mimicking nanozyme with antifouling property for construction of sensor array to identify phosphoproteins and diagnose cancers

A bimetallic peroxidase-mimicking nanozyme with antifouling property for construction of sensor array to identify phosphoproteins and diagnose cancers

Surface heat fluxes at coarse blocky Murtèl rock glacier (Engadine, eastern Swiss Alps)

Abstract. We estimate the surface energy balance (SEB) of the Murtèl rock glacier, a seasonally snow-covered permafrost landform with a ventilated coarse blocky active layer (AL) located in the eastern Swiss Alps. We focus on the parameterisation of the turbulent heat fluxes. Seasonally contrasting atmospheric conditions occur in the Murtèl cirque, with downslope katabatic jets in winter and a strongly unstable atmosphere over the heated blocky surface in summer. We use a novel comprehensive sensor array both above the ground surface and in the coarse blocky AL to track the rapid coupling by convective heat and moisture fluxes between the atmosphere, the snow cover, and the AL for the time period September 2020–September 2022. The in situ sensor array includes a sonic anemometer for eddy-covariance flux above-ground and sub-surface long-wave radiation measurements in a natural cavity between the AL blocks. During the thaw seasons, the measurements suggest an efficient (∼ 90 %) export of the available net radiation by sensible and latent turbulent fluxes, thereby strongly limiting the heat available for melting ground ice. Turbulent export of heat and moisture drawn from the permeable AL contributes to the well-known insulating effect of the coarse blocky AL and partly explains the climate resiliency of rock glaciers. This self-cooling capacity is counteracted by an early snow melt-out date, exposing the low-albedo blocky surface to the intense June–July insolation and causing reduced evaporative cooling due to exacerbated moisture scarcity in the near-surface AL during dry spells. With climate change, earlier snowmelt and increased frequency, duration, and intensity of heat waves and droughts are projected. Regarding the parameterisation of the turbulent fluxes, we estimated the year-round turbulent fluxes using a modified Louis (1979) scheme. The monthly SEB is closed within 20 W m−2 except during the snowmelt months and under katabatic drainage winds in winter. Detected sensible turbulent fluxes from nocturnal ventilation processes, although a potentially important ground cooling mechanism, are within our 20 W m−2 uncertainty because nighttime wind speeds are low. Wintertime katabatic wind speeds needed to be scaled to close the SEB, which hints at the limits of parameterisations based on the Monin–Obukhov similarity theory in complex mountain terrain and katabatic drainage winds. The present work contributes to the process understanding of the SEB and climate sensitivity of coarse blocky landforms.

ANKLE JOINT MOTION RECOGNITION SYSTEM AND ALGORITHM OPTIMIZATION BASED ON PLANTAR PRESSURE

Due to the current focus of research on ankle rehabilitation robots on structural design, there is still limited research on ankle human–machine interaction technology. In order to enable rehabilitation robots to conduct personalized rehabilitation training based on patients’ ankle movement intentions, we propose a new ankle motion recognition method based on plantar pressure. First, we designed a plantar pressure collection system based on array sensors. Then, we collected nine types of ankle joint motion pressure data from five volunteers and conducted algorithm selection, data processing, and algorithm optimization. Finally, we proposed a small sample optimization algorithm based on support vector machine, with an average recognition rate of 93.16%. The recognition method proposed in this paper can be combined with ankle rehabilitation robots to achieve active rehabilitation functions, laying the foundation for the clinical application of active rehabilitation technology.

Equilibrium and Stability of the Solar System Rely on a Human-Like Program of Substance and Information Transfer

This study bridges the realms of astronomy and life science, illuminating the intricate interplay between celestial phenomena and terrestrial biology. While humans may perceive the palpable effects of seasons, climate, geographical location, and the movements of the Sun and moon, other phenomena such as changes in Earth's magnetic field and seismic activity are often sensed by animals but elude human perception. However, with disciplined and sustained training, humans possess the potential to harness the rich array of sensors and sensory systems within the body, thereby unlocking the ability to discern and validate the cosmic influences documented by ancient civilizations. In instances where traditional astronomical inquiries, such as the maintenance of galactic equilibrium, present enigmatic puzzles, insights from cutting-edge life science research may offer promising leads. Newly unveiled mechanisms such as the "Function Enhancement Program of Five-Organs through Umbilical Access" and the rhythmic orchestration of the "Rotating Presidency of Twelve-Organs" elucidate the intricate operations of visceral organs within the human body. Indirect evidence suggests a correlation between these biological processes and the transfer of gaseous substances among celestial bodies, including Mercury, Venus, the Sun, and Jupiter, hinting at a temporal synchronicity that may contribute to the equilibrium and stability of the solar system. Confirmation of substance and information transfer processes could unlock new avenues of inquiry in astronomy, shedding light on vexing conundrums such as dark energy and matter, binary pulsars, gravitational waves, inflation fields, and alternative theories to Einstein's General Relativity. Thus, by bridging the disciplines of astronomy and life science, this study offers fresh perspectives and potential breakthroughs in our understanding of the cosmos.

Contact localization from soft tactile array sensor using tactile image

Purpose This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method. Design/methodology/approach This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. Findings This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations. Originality/value The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

A sparse data gas sensor array feature mining method for rubber Mooney viscosity measurement

Mooney viscosity is an important index to reflect the performance and quality of rubber. At present, the rubber Mooney test has the problems of large time delay, destructive and unable to detect online, which restricts the development of rubber industry. In this paper, a gas sensor array-based online inspection method for rubber Mooney viscosity is proposed to improve the problems of measurement delay and raw material waste in the traditional method. A multiple generator time series generative adversarial network (MGTSGAN) structure is proposed to address the problem that the lack of sample data volume and uneven data distribution make it difficult to model. Transformer is introduced to solve the problem of traditional generative adversarial networks in dealing with long sequential dependencies. In the experimental part, a rubber Mooney viscosity detection device is built to verify the effectiveness of the proposed method. The performance of different generative models on two gas sensor datasets is compared to verify the advancement and generalization of the proposed method. The experimental results show that the correct rate of this paper’s method for rubber Mooney viscosity classification is higher than 96%. The correct rates are all improved after data enhancement, in which the MGTSGAN proposed in this paper obtains the highest correct rate of 98.35%. For the classification experiments on Gas sensor array under flow modulation dataset also achieved relatively good results. Among them, the highest correct classification rate of 95.63% is achieved after data enhancement using MGTSGAN.

Strain and Temperature Sensing Based on Different Temperature Coefficients fs-FBG Arrays for Intelligent Buoyancy Materials

The temperature and strain fields monitoring during the preparation process of buoyancy materials, as well as the health status after molding, are important for mastering the mechanical properties of buoyancy materials and ensuring the safety of operators and equipment. This paper proposes a short and high-density femtosecond fiber Bragg grating (fs-FBG) array based on different temperature coefficients fibers. By optimizing the parameters of femtosecond laser point-by-point writing technology, high-performance fs-FBG arrays with millimeter level gating length and millimeter level spatial resolution were prepared on two types of fibers. These were successfully embedded in buoyancy materials to achieve in-situ online monitoring of the curing process and after molding. The experimental results show that the fs-FBG array sensor has good anti-chirp performance and achieves online monitoring of millimeter-level spatial resolution. Intelligent buoyancy materials can provide real-time feedback on the health status of equipment in harsh underwater environments. The system can achieve temperature monitoring with an accuracy of 0.56 °C and deformation monitoring with sub-millimeter accuracy; the error is in the order of micrometers, which is of great significance in the field of deep-sea exploration.

Method for measuring non-stationary motion attitude based on MEMS-IMU array data fusion and adaptive filtering

Abstract The error characteristics of a typical commercial MEMS-IMU were analyzed. Using 15 ICM-42688 sensors, a 3*5 MEMS-IMU array was designed, and a weighted data fusion method based on Allan variance for the MEMS-IMU array was proposed. This effectively reduces the random measurement errors of the MEMS-IMU, providing a data foundation for the precise measurement of motion and attitude of robots, vehicles, aircraft, and other systems under low-cost conditions. The text describes a measurement method for non-stationary motion attitudes of sports vehicles based on adaptive Kalman-Mahony. Specifically, it first uses adaptive Kalman filtering on array sensor data to calculate the measurement noise in real-time and adaptively adjust the filtering gain. Then, it determines the compensation coefficient of the accelerometer to the gyroscope angular velocity based on the motion state of the vehicle, and solves the attitude through complementary filtering to obtain the motion attitude quaternion. Finally, it converts it into the roll angle, pitch angle, and yaw angle of the sports vehicle. Experimental results show that the proposed MEMS-IMU array weighted data fusion method based on Allan variance has significant advantages over traditional single MEMS-IUM methods and traditional average weighting methods in reducing sensor angle random walk and zero drift instability. The proposed adaptive Kalman-Mahony attitude measurement method also shows a significant improvement in the accuracy of non-stationary motion attitude measurement compared to traditional methods.

The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient’s exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection is attracting more researchers’ attention. However, the low accuracy, high-complexity computation and large number of parameters make the CNN algorithms difficult to transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models, such as EfficientNet, LTNet has fewer parameters (32 K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than the scores of the six existing models, and it also offers the shortest training (844.38 s and 584.67 s) and inference times (23 s and 14 s) in the same validation sets. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.