Pair Of Sensors Research Articles

Identifying gas mixtures based on acoustic relaxation spectroscopy and attention recurrent neural network

Qualitative and quantitative detection of the composition of gaseous mixtures remains challenging in acoustic gas sensing. Traditional feature-based methods attempt to extract local features from complex numerical models, but tend to ignore crucial information owing to the lack of a global view. Deep learning can achieve competitive performance in acquiring information. This study proposes an attention recurrent gas detection (ARGD) neural network for gas sensing based on acoustic relaxation spectroscopy. This model combines a bidirectional gated recurrent unit to extract gas information and an attention module to determine the crucial features. The processed features were input into a double-task network for simultaneous prediction of the types of gas and their respective concentrations. Samples of natural gas involving CO2, CH4, and N2 were identified with six acoustic sensor pairs. The simulated and experimental results demonstrate the effectiveness and robustness of the proposed model. A classification accuracy of up to 99% was achieved, with the root mean squared error declining to 0.67%. The combination of deep learning and acoustic relaxation spectroscopy provides new ideas for further research of acoustic gas detection.

An Affordable phototherapy intensity meter using machine learning to improve the quality of care system for Hyperbilirubinemia in Indonesia.

Hyperbilirubinemia is more frequently seen in low and middle-income countries like Indonesia. One of the contributing factors is a substandard dose of Phototherapy irradiance. This research aims to design a phototherapy intensity meter called PhotoInMeter using readily available low-cost components. PhotoInMeter is designed by using a microcontroller, light sensor, color sensor, and an ND (neutral-density) filter. We use machine learning to create a mathematical model that converts the emission from the color sensor and light sensor into light intensity measurements that are close to Ohmeda Biliblanket's measurements. Our prototype collects sensor reading data and pairs them with Ohmeda Biliblanket Light Meter to create a training set for our machine learning algorithm. We create a multivariate linear regression, random forest, and XGBoost model based on our training set to convert sensor readings to Ohmeda Biliblanket Light Meter measurement. We successfully devised a prototype that costs 20 times less to produce compared to our reference intensity meter while still having high accuracy. Compared to Ohmeda Biliblanket Light Meter, our PhotoInMeter has a Mean Absolute Error (MAE) of 0.83 and achieves more than a 0.99 correlation score in all six different devices for intensity in the range of 0-90 μW/cm2/nm. Our prototypes show consistent reading between PhotoInMeter devices, having an average difference of 0.435 among all six devices.

Trilinear decomposition based near-field source localization with MIMO velocity vector sensor arrays

This paper studies the near-field (NF) parameter estimation problem for a multiple-input multiple-output (MIMO) array system, which employs multiple pairs of orthogonal velocity sensors at both the transmitter and the receiver. A trilinear decomposition method is proposed to estimate the four-dimensional (4-D) parameters, including the direction of departure (DOD), the range from transmitter to target (RFTT), the direction of arrival (DOA), and the range from target to receiver (RFTR). Firstly, the output of the matched filter at the receiver is formulated in a third-order parallel factor (PARAFAC) model; secondly, the initial coarse estimates of DOD, RFTT, DOA and RFTR embedded in the velocity vector sensors are obtained through trilinear decomposition, and then more accurate estimates of DOD, RFTT, DOA and RFTR are achieved from the steering vector. The proposed method is search-free and has a close form, with automatically paired results. Its performance is demonstrated via numerical examples.

Laboratory investigation of hydraulic fracturing in granitic rocks using active and passive seismic monitoring

SUMMARY Knowledge of the fracturing processes can be important for the optimization of pressurized fluid injection operations in the deep underground rock mass. Active and passive seismic monitoring techniques have been used in the field for tracking or mapping the propagating hydraulic fracture. Although both these monitoring techniques provide valuable information about the generated fracture network, it is difficult for either technique to comprehensibly identify the different processes associated with hydraulic fracturing. The combined active and passive monitoring has the potential for better characterization of the complex hydraulic fracturing phenomena. In this study, laboratory hydraulic fracturing experiments with combined active and passive seismic monitoring were conducted on true triaxially loaded Barre granite cubes with different fluid injection rates. The seismic inelastic fracturing was detected by 16 passive acoustic emission sensors, where 3678 and 2370 seismic source events were detected for the high and low injection rate experiments, respectively. For active monitoring, strong variations in the attributes of signals were observed which were transmitted through four source–receiver pairs, placed both perpendicular and parallel to the generated hydraulic fracture. Positive velocity changes were observed for active sensor pairs with ray paths passing through the generated hydraulic fracture indicating fluid permeation, whereas isolated dry deformation was characterized by a slight but permanent velocity decrease. Compared to velocity, the energy of the active signals was 1–2 orders of magnitude more sensitive to different hydraulic fracturing processes. However, the sensitivity and signatures of the active signal attributes were found to be dependent on the frequency range and direction of ray path with respect to the location of the generated fracture network. Using the coupled evaluation of the active and passive signals we were able to systematically identify various hydraulic fracturing processes including: (1) aseismic deformation, (2) fracture initiation and fluid permeation, (3) pressure build-up, (4) fracture propagation and (5) pressure release and leak-off. The results of this study showed that combining the respective advantages of active and passive seismic techniques and using both of them to monitor the failure processes can facilitate a more comprehensive understanding and better control of the hydraulic stimulations in subsurface operations.

Empirical Parameterization of Broadband VLF Attenuation in the Earth‐Ionosphere Waveguide

AbstractThe Earth‐ionosphere waveguide (EIW) determines the propagation of Very Low Frequency (VLF; ∼3−30 kHz) waves. Characterizing the waveguide is a longstanding challenge due to its large spatial scale and the complex variability of the lower ionosphere. Here we apply a novel linear basis function regression technique to characterize attenuation in the EIW using broadband measurements of lightning‐generated radio waves. The process begins with defining a basis function set, which ideally encompasses a feature set that can predict the variability seen in VLF attenuation properties. With this basis set defined, a system of linear equations is then constructed using sensor pair observations to eliminate the dependency on source amplitude in each observation. Using this formalism, an empirical attenuation model for broadband signals from lightning is constructed and the dependence on attenuation properties with the boundary conditions is explored. The empirically derived results show attenuation rates over ice that are 12 dB/Mm higher compared to paths over saltwater. Well‐known east/west attenuation rate asymmetry stemming from anisotropic reflection coefficients of the ionosphere is also demonstrated and investigated. For example, under a daytime ionosphere, westward‐propagating waves suffer up to 2.8 dB/Mm greater attenuation compared to eastward‐propagating waves. The trained model is used for propagation corrections in a global lightning locating system (LLS), but this technique can be expanded to further study VLF attenuation rates by employing different sets of basis functions.

Degraded Ballast Stiffness Characterization Using Bender Element Field Sensor and PANDA® Penetrometer

The progressive degradation of railway ballast over time increases the degree of ballast fouling and poses significant drainability concerns leading to frequent maintenance activities. To avoid such issues, an effective and continuous characterization of the physical properties of the ballast layer is deemed necessary. This paper introduces the combined use of a bender element (BE) piezoelectric field sensor and a PANDA® dynamic cone penetrometer for periodic evaluations of ballast condition over the traffic use. To simulate various levels of ballast degradation, box tests were conducted using a dolomitic ballast material compacted in a laboratory-sized testbed with fouling indices ranging from zero to 39%. BE sensor pairs were embedded in the ballast layer to calculate the small strain modulus behavior of the ballast assessed through shear wave velocity measurements under different confinement conditions. Strength profiles of the ballast at different fouling levels were also measured using the PANDA® penetrometer. Experimental findings from both test methods indicate the existence of a dense state of ballast linked to a certain fouling index that maximized stiffness and strength characteristics of the tested ballast in dry condition. Accordingly, the potential use of BE field sensors embedded within in-service ballasted track coupled with periodic penetration testing can be a viable approach to evaluate ballast layer degradation. Various void packing conditions associated with ballast fouling levels can be linked to important ballast layer modulus and strength behavior. Further, such smart sensing of ballasted track behavior can provide long-term performance monitoring solutions for ballast cleaning and maintenance scheduling.

Active Damping, Vibration Isolation, and Shape Control of Space Structures: A Tutorial

This tutorial reviews the author’s contributions to the active control of precision space structures over the past 35 years. It is based on the Santini lecture presented at the IAC-2022 Astronautical Congress in Paris in September 2022. The first part is devoted to the active damping of space trusses with an emphasis on robustness. Guaranteed stability is achieved by using decentralized collocated actuator–sensor pairs. The so-called integral force feedback (IFF) is simple, robust, and effective, and the performances can be predicted easily with simple formulae based on modal analyses. These predictions have been confirmed by numerous experiments. The damping strategy for trusses has been extended to cable structures, and also confirmed experimentally. The second part addresses the problem of vibration isolation: isolating a sensitive payload from the vibration induced by the spacecraft (i.e., the unbalanced mass of attitude control reaction wheels and gyros). A six-axis isolator based on a Gough–Stewart platform is discussed; once again, the approach emphasizes robustness. Two different solutions are presented: The first one (active isolation) uses a decentralized controller with collocated pairs of the actuator and force sensor, with IFF control. It is demonstrated that this special implementation of the skyhook, unlike the classical one, has guaranteed stability, even if the two substructures it connects are flexible (typical of large space structures). A second approach (passive) discusses an electromagnetic implementation of the relaxation isolator where the classical dash-pot of the linear damper is substituted by a Maxwell unit, leading to an asymptotic decay rate of −40 dB/decade, similar to the skyhook (although much simpler in terms of electronics). The third part of the lecture summarizes more recent work done on the control of flexible mirrors: (i) flat mirrors for adaptive optics (AO) controlled by an array of piezoelectric ceramic (PZT) actuators and (ii) spherical thin shell polymer reflectors controlled by an array of piezoelectric polymer actuators (PVDF-TrFE) aimed at being deployed in space.

Evaluation of Airport Pavement Base Layer Stiffness Characteristics via Embedded Field Sensors

Real-time stiffness monitoring of unbound aggregate layers using embedded field sensors is crucial for advanced airport pavement design and management. This paper describes research findings on monitoring base layer stiffness characteristics for airport pavements using an embedded bender element (BE) field sensor, inductive coil sensors, and pressure cells. A full-scale pavement test section was constructed at the Federal Aviation Administration (FAA)’s National Airport Pavement Test Facility (NAPTF). A BE field sensor, inductive coil sensor pairs, and pressure cells were installed in the unbound aggregate base to evaluate the layer stiffness, applied load stress, and deformation characteristics. A triple dual tandem gear loading module applied 58,000 lb per wheel to the tested section with a wander pattern consisting of 66 passes arranged in nine lateral wander positions. Dynamic responses of coil sensors and pressure cells were collected during the traffic test, whereas BE signals were collected after completing each wander pattern. Laboratory repeated load triaxial tests with BE instrumentation were also conducted to establish a correlation for converting BE field sensor readings to layer modulus properties. The in situ modulus characteristics of the aggregate base layer were evaluated during full-scale testing using two methods: shear wave velocities from a BE field sensor, and strain and stress measurements from coil sensors and pressure cells. The moduli estimated using both approaches fell into typical ranges of crushed aggregate bases and were highly comparable. The moduli from the dynamic sensor measurements clearly showed the effect of vehicle wander on aggregate base layer measured responses.

Selective fiber optic TFBG-assisted biosensors featuring functional coatings

The paper reports on the results of the development of tilted fiber Bragg grating-assisted selective biosensors. The main sensing principle is based on the dependence of an optical evanescent field parameters on the composition of the medium under study. Sensors feature functional coatings of antibodies against protein molecules common in blood composition – fibrinogen and D-dimer. The work explores and clearly demonstrates the selective sensitivity of sensors to specific proteins by the example of simultaneous measurements using a pair of sensors functionalized with different antibodies.

Localization of HV Insulation Defects Using a System of Associated Capacitive Sensors

The issue of detecting and locating defects generating partial discharges (PDs) is very important for the proper functioning of power grids. Despite the existence of many localization methods, both very large and relatively small objects are still a challenge due to the problem of obtaining the required measurement accuracy. This article presents the idea of the method of PD localization in small objects of simple structure with the use of a system of four capacitive probes. Based on the relative difference in the amplitudes of the signals recorded by the pair of capacitive sensors and considering their distance characteristics, it is possible to determine the place where the PD pulses are generated. In the example of measurements made on a support insulator, it was shown that the location of a defect using the proposed method allows for an indication accuracy of up to 0.5 cm.

Sonar glass—Artificial vision: Comprehensive design aspects of a synchronization protocol for vision based sensors

Supporting visually impaired people during their navigation is a challenging task that involves localization, tracking, navigation, obstacle avoidance, and path guidance. Many researchers have experimented with Sonar, RFID, GPS, NFC, walking sticks, waist-based devices, and even computer vision modules for blind navigation. Using five sonar sensors for obstacle detection with direction and timestamp, we developed Sonar Glass. As humans see in left, right, front, top, and bottom directions based on eye angle and the head pose, the sonar glass is designed to provide obstacle information at the same angle. The head movement of the visually impaired person activates a pair of sensors for each module. Understanding human eye movement mechanisms and developing a synchronization protocol for each pair of visual sensors on sonar glass sitting on both eyes is the main goal of this paper. The major challenge lies in understanding and simulating the human vision mechanism and to realize how the field of Artificial Intelligence can be a contributor in producing technologies for the visually impaired. We are using log-polar transform to simulate human retinal image mapping. The Scale Invariant Feature Transform (SIFT) algorithm has also been implemented. It is the first time that both human eyes can be replaced by vision sensors. After comparing the estimated obstacle information from one sensor pair with that of the other sensors, the voice track is activated. The blind person uses the nearest obstacle information to avoid the obstacle and to extract spatial information about obstacles ahead of the user and provide an early warning. Unique in its design, the sonar Glass’s synchronization protocol for a pair of related sensors provides possible object information in that direction. The executions were simulated in MATLAB and the results obtained in real time are found to be promising as the tests were carried out both indoor and outdoor. The sonar Glass design is unique of its kind and the synchronization protocol for the pair of related sensors provides the possible vision about the object information at that particular direction.

End-Point Position Estimation of a Soft Continuum Manipulator Using Embedded Linear Magnetic Encoders.

Soft continuum robots are compliant mechanisms that rely on a deformable structure in order to achieve a desired posture. One of the challenges in designing and controlling this type of robot is to obtain the necessary proprioceptive information without resorting to external sensors, like cameras or 3D positioning devices. This requires a reliable and repeatable sensor that can be embedded in the highly deformable structure, distributed along its length, without imposing a significant change to the overall stiffness. This paper presents design considerations and practical results of estimating the tip position of a soft continuum manipulator module using embedded linear magnetic encoders. Three flexible scales with incremental tracks and a magnetic pole pitch of 2 mm are embedded in the robot structure as passive tendons, and six pairs of Hall effect linear sensors are used to measure the relative displacement between points along the outer surface of the structure. The curvature and tip position are then estimated from these measurements. Results are compared with the ground truth measurement of the tip position provided by a commercial optical tracker system. Average error estimates lower than 2.0 mm, with 8.7 mm peak value, were obtained for a robot module with a motion span of approximately 100 mm.

Design and Experiment of Real-Time Grain Yield Monitoring System for Corn Kernel Harvester

Real-time crop harvest data acquisition from harvesters during harvesting operations is an important way to understand the distribution of crop harvest in the field. Most real-time monitoring systems for grain yield using sensors are vulnerable to factors such as low accuracy and low real-time performance. To address this phenomenon, a real-time grain yield monitoring system was designed in this study. The real-time monitoring of yield was accomplished by adding three pairs of photoelectric sensors to the elevator of the corn kernel harvester. The system mainly consists of a signal acquisition and processing module, a positioning module and a visualization terminal; the signal acquisition frequency was set to 1 kHz and the response time was 2 ms. When the system operated, the signal acquisition and processing module detected the sensor signal duration of grain blocking the scrapers of the grain elevator in real-time and used the low-potential signal-based corn grain yield calculation model constructed in this study to complete the real-time yield measurement. The results of the bench tests, conducted under several different operating conditions with the simulated elevator test bench built, showed that the error of the system measurement was less than 5%. Field tests were conducted on a Zoomlion 4YZL-5BZH combined corn kernel harvester and the results showed that the average error of measured yield was 3.72%. Compared to the yield measurement method using the weighing method, the average error of the bench test yield measurement was 7.6% and the average error of yield measurement in field trials with a mass flow sensor yield measurement system was 16.38%. It was verified that the system designed in this study has high yield measurement accuracy and real-time yield measurement, and can provide reference for precision agriculture and high yield management.

In-Situ Measuring sEMG and Muscle Shape Change with a Flexible and Stretchable Hybrid Sensor for Hand Gesture Recognition.

The accurate recognition of hand motion intentions is an essential prerequisite for efficient human-machine interaction (HMI) systems such as multifunctional prostheses and rehabilitation robots. Surface electromyography (sEMG) signals and muscle shape change (MSC) signals which are usually detected with different types of sensors have been used for human hand motion intention recognition. However, using different sensors to measure sEMG and MSC respectively, it would be inconvenient and add deploying difficulty for human-machine interaction systems. In this study, a novel flexible and stretchable sensor was fabricated with a nano gold conductive material, which could simultaneously sense both sEMG and MSC signals. Accordingly, a wireless signal acquisition device was developed to record both sEMG and MSC signals with the fabricated hybrid sensors. The performance of the proposed in-situ dual-mode signal measurement (IDSM) system was evaluated by the recording signal quality and the accuracy of hand gesture recognition. The results demonstrated that by using two pairs of the hybrid sensors, the proposed IDSM system could obtain two-channel sEMG at a noise level of about 0.89 μVrms and four-channel MSC with a resolution of about 0.1 Ω. For a recognition task of 11 classes of hand gestures, the results showed that only with two pairs of the hybrid sensors, the average accuracy over all the subjects was 95.6 ± 2.9%, which was about 7% higher than that with two-channel sEMG and six-channel accelerometer signals. These results suggest that the proposed IDSM method would be an efficient way to simplify the human-machine interaction system with fewer sensors for high recognition accuracy of hand motions.

Sensor Selection with Composite Features in Identifying User-Intended Poses for Human-Prosthetic Interfaces.

A Human-Prosthetic Interface (HPI) serves to estimate and realise the limb pose intended by the human user, using the information obtained from sensors worn by the user. In recent studies, the HPI maps multi-joint limb poses (i.e. coordinated movement of the body and limbs) to the inputs of multiple sensors. This is in contrast to the conventional methods where each degree of freedom of the powered prosthesis is mapped to the input of one/a pair of sensors. In this approach, it is necessary to systematically select sensors that carry the most information for the intended set of poses, to improve system accuracy and/or minimise the number of sensors, thus the complexity, in the prosthetic system. In this paper, sensor selection process is systematically formulated to maximise the information contained in the input features for a given number of sensors. Most importantly, it accounts for composite features, which are features requiring information from multiple sensors. Such composite features exist and are important in HPIs as we seek to capture coordinated motion involving movements of multiple limb and body segments. A non-convex optimisation problem is formulated which accounts for the constraint introduced by the composite features. A projection matrix is utilised as the optimisation variable to select intended features for evaluation. The problem is solved by the proposed Sensor Selection with Composite Features (SS-CF) algorithm which adapts convex-relaxation techniques. The SS-CF is benchmarked against HPI with expert-selected sensors in the literature and against a greedy heuristic method. The outcome demonstrated the efficacy of the SS-CF algorithm.

Self-Attention Transformer-Based Architecture for Remaining Useful Life Estimation of Complex Machines

Meaningful feature extraction from multivariate time-series data is still challenging since it takes into account the correlation between pairs of sensors as well as the temporal information of each time-series. Meanwhile, the huge industrial system has evolved into a data-rich environment, resulting in the rapid development and deployment of deep learning for machine RUL prediction. RUL (Remaining Useful Life) examines a system's behavior over the course of its lifetime, that is, from the last inspection to when the system's performance deteriorates beyond a certain point. RUL has been addressed using Long-Short-Term Memory (LSTM) and Convolution Neural Network (CNN), particularly in complex tasks involving high-dimensional nonlinear data. The main focus, however, has been on degradation data. In 2021, a new realistic run-to-failure turbofan engine degradation dataset was released, which differs significantly from the simulation dataset. The key difference is that each cycle's flight duration varies, so the existing deep technique will be ineffective at predicting the RUL for real-world degradation data. We present a Self-Attention Transformer-Based Encoder model to address this problem. The encoder with the time-stamp encoder layer works in parallel to extract features from various sensors at various time stamps. Self-attention enables efficient processing of extended sequences and focuses on key elements of the input time series. Self-attention is used in the proposed Transformer model to access global characteristics from diverse time-series representations. Under real-world flight conditions, we conduct tests on turbofan engine degradation data using variable-length input. The proposed approach for estimating RUL of turbofan engines appears to be efficient based on empirical results.

Determining the spatio-temporal relationship between water quality monitors in drinking water distribution systems

A novel method to both assess the strength of connectivity and determine hydraulic transit times between water quality monitors from time series data is reported. It was developed using a network of over 50 mobile multi-parameter sensors deployed for 18 months across a UK drinking water distribution system, and then validated using a network of 18 sensors from a different UK utility. Correlation coefficients are calculated at different time shifts for each possible sensor pair, with strength of connectivity represented by the highest correlation coefficient, and with the temporal lag of this highest correlation also designates the transit time. The results demonstrate the potential to derive valuable spatio-temporal information, with potential to increase understanding of system performance and connectivity. This information can be used to assist with further analytics such as tracking water quality events and improving hydraulic and disinfection residual decay modelling.

Combining Local PWV and Quantified Arterial Changes for Calibration-Free Cuffless Blood Pressure Estimation: A Clinical Validation

This study proposes a novel calibration-free cuffless blood pressure monitor (BPM). Unlike the cuffless BPM that requires timing correction with the traditional BPM, the calibration-free cuffless BPM can directly estimate the arterial blood pressure (BP) based on the hemodynamics mathematical model. Our proposed device integrates a pair of piezoelectric ceramics as pressure sensors to sense the pressure wave in the radial artery. It calculates the local pulse wave velocity (PWV) with our waveform detection algorithm of our proposed one. A photoplethysmogram (PPG) probe is set between the pair of pressure wave sensors. This PPG probe is used to monitor the radial artery’s PPG intensity ratio (PIR). We design PPG signal processing algorithms to quantify the PIR. We recruited 129 participants for the BP monitoring experiment. Compared with the reference sphygmomanometer, the error mean ± standard deviations (STDs) systolic BP (SBP) was 2.1 ± 3.4, and the correlation r-value was 0.97. The diastolic BP (DBP) was 0.8 ± 4.2, the correlation r-value was 0.90, and p < 0.05 is taken as statistically significant. A new type of wearable continuous calibration-free BPM can replace the situation that requires the use of traditional ambulatory BPM and reduce patient discomfort. Our proposed BP measurement passed all ANSI/AAMI/ISO 81060-2:20181_5.2.4.1.2 data analysis criterion 1 and 2 standard requirements.

Seeking Repeating Anthropogenic Seismic Sources: Implications for Seismic Velocity Monitoring at Fault Zones.

Seismic velocities in rocks are highly sensitive to changes in permanent deformation and fluid content. The temporal variation of seismic velocity during the preparation phase of earthquakes has been well documented in laboratories but rarely observed in nature. It has been recently found that some anthropogenic, high-frequency (>1Hz) seismic sources are powerful enough to generate body waves that travel down to a few kilometers and can be used to monitor fault zones at seismogenic depth. Anthropogenic seismic sources typically have fixed spatial distribution and provide new perspectives for velocity monitoring. In this work, we propose a systematic workflow to seek such powerful seismic sources in a rapid and straightforward manner. We tackle the problem from a statistical point of view, considering that persistent, powerful seismic sources yield highly coherent correlation functions (CFs) between pairs of seismic sensors. The algorithm is tested in California and Japan. Multiple sites close to fault zones show high-frequency CFs stable for an extended period of time. These findings have great potential for monitoring fault zones, including the San Jacinto Fault and the Ridgecrest area in Southern California, Napa in Northern California, and faults in central Japan. However, extra steps, such as beamforming or polarization analysis, are required to determine the dominant seismic sources and study the source characteristics, which are crucial to interpreting the velocity monitoring results. Train tremors identified by the present approach have been successfully used for seismic velocity monitoring of the San Jacinto Fault in previous studies.

Flexible integrated sensor with asymmetric structure for simultaneously 3D tactile and thermal sensing

The human body detects tactile stimuli through a combination of pressure force and temperature signals via various cutaneous receptors. The development of a multifunctional artificial tactile perception system has potential benefits for future robotic technologies, human-machine interfaces, artificial intelligence, and health monitoring devices. However, constructing systems beyond simple pressure sensing capabilities remains challenging. Here, we propose an artificial flexible and ultra-thin (50 μ m) skin system to simultaneously capture 3D tactile and thermal signals, which mimics the human tactile recognition process using customized sensor pairs and compact peripheral signal-converting circuits. The 3D tactile sensors have a flower-like asymmetric structure with 5-ports and 4 capacitive elements in pairs. Differential and average signals would reveal the curl and amplitude values of the fore field with a resolution of 0.18/mm. The resistive thermal sensors are fabricated with serpentine lines and possess stable heat-sensing performance (165 mV/°C) under shape deformation conditions. Real-time monitoring of the skin stimuli is displayed on the user interface and stored on mobile clients. This work offers broad capabilities relevant to practical applications ranging from assistant prosthetics to artificial electronic skins.