Overcoming the Limits of Cross-Sensitivity:Pattern Recognition Methods for Chemiresistive Gas Sensor Array

As information acquisition terminals for artificial olfaction,chemiresistive gas sensors are often troubled by their cross-sensitivity,and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area.Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors.It is crucial to choose an appropriate pattern recognition method for enhancing data analysis,reducing errors and improving system reliability,obtaining better classification or gas concentration prediction results.In this review,we analyze the sensing mechanism of crosssensitivity for chemiresistive gas sensors.We further examine the types,working principles,characteristics,and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays.Additionally,we report,summarize,and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification.At the same time,this work showcases the recent advancements in utilizing these methods for gas identification,particularly within three crucial domains:ensuring food safety,monitoring the environment,and aiding in medical diagnosis.In conclusion,this study anticipates future research prospects by considering the existing landscape and challenges.It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

Flexible capacitive pressure sensor based on interdigital electrodes with porous microneedle arrays for physiological signal monitoring

Flexible pressure sensors have many potential applications in the monitoring of physiological signals because of their good biocompatibil-ity and wearability.However,their relatively low sensitivity,linearity,and stability have hindered their large-scale commercial application.Herein,aflexible capacitive pressure sensor based on an interdigital electrode structure with two porous microneedle arrays(MNAs)is pro-posed.The porous substrate that constitutes the MNA is a mixed product of polydimethylsiloxane and NaHCO3.Due to its porous and interdigital structure,the maximum sensitivity(0.07 kPa-1)of a porous MNA-based pressure sensor was found to be seven times higher than that of an imporous MNA pressure sensor,and it was much greater than that of aflat pressure sensor without a porous MNA structure.Finite-element analysis showed that the interdigital MNA structure can greatly increase the strain and improve the sensitivity of the sen-sor.In addition,the porous MNA-based pressure sensor was found to have good stability over 1500 loading cycles as a result of its bilayer parylene-enhanced conductive electrode structure.Most importantly,it was found that the sensor could accurately monitor the motion of afinger,wrist joint,arm,face,abdomen,eye,and Adam’s apple.Furthermore,preliminary semantic recognition was achieved by monitoring the movement of the Adam’s apple.Finally,multiple pressure sensors were integrated into a 33 array to detect a spatial pressure distribu-×tion.Compared to the sensors reported in previous works,the interdigital electrode structure presented in this work improves sensitivity and stability by modifying the electrode layer rather than the dielectric layer.

Microarrow sensor array with enhanced skin adhesion for transdermal continuous monitoring of glucose and reactive oxygen species

Conventional blood sampling for glucose detection is prone to cause pain and fails to continuously record glucose fluctuations in vivo.Continuous glucose monitoring based on implantable electrodes could induce pain and potential tissue inflammation,and the presence of reactive oxygen species(ROS)due to inflammationmay affect glucose detection.Microneedle technology is less invasive,yet microneedle adhesion with skin tissue is limited.In this work,we developed a microarrow sensor array(MASA),which provided enhanced skin surface adhesion and enabled simultaneous detection of glucose and H_(2)O_(2)(representative of ROS)in interstitial fluid in vivo.The microarrows fabricated via laser micromachining were modified with functional coating and integrated into a patch of a three-dimensional(3D)microneedle array.Due to the arrow tip mechanically interlocking with the tissue,the microarrow array could better adhere to the skin surface after penetration into skin.The MASA was demonstrated to provide continuous in vivo monitoring of glucose and H_(2)O_(2) concentrations,with the detection of H_(2)O_(2) providing a valuable reference for assessing the inflammation state.Finally,the MASA was integrated into a monitoring system using custom circuitry.This work provides a promising tool for the stable and reliable monitoring of blood glucose in diabetic patients.

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

Early non-invasive diagnosis of coronary heart disease(CHD)is critical.However,it is challenging to achieve accurate CHD diagnosis via detecting breath.In this work,heterostructured complexes of black phosphorus(BP)and two-dimensional carbide and nitride(MXene)with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy.A light-activated virtual sensor array(LAVSA)based on BP/Ti_(3)C_(2)Tx was prepared under photomodulation and further assembled into an instant gas sensing platform(IGSP).In addition,a machine learning(ML)algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD.Due to the synergistic effect of BP and Ti_(3)C_(2)Tx as well as photo excitation,the synthesized heterostructured complexes exhibited higher performance than pristine Ti_(3)C_(2)Tx,with a response value 26%higher than that of pristine Ti_(3)C_(2)Tx.In addition,with the help of a pattern recognition algorithm,LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols,ketones,aldehydes,esters,and acids.Meanwhile,with the assistance of ML,the IGSP achieved 69.2%accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients.In conclusion,an immediate,low-cost,and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD,which provided a generalized solution for diagnosing other diseases and other more complex application scenarios.

Direct-Ink-Writing Printed Strain Rosette Sensor Array with Optimized Circuit Layout

The full-field multiaxial strain measurement is highly desired for application of structural monitoring but still challenging,especially when the manufacturing and assembling for largearea sensing devices is quite difficult.Compared with the traditional procedure of gluing commercial strain gauges on the structure surfaces for strain monitoring,the recently developed Direct-Ink-Writing(DIW)technology provides a feasible way to directly print sensors on the structure.However,there are still crucial issues in the design and printing strategies to be probed and improved.Therefore,in this work,we propose an integrated strategy from layered circuit scheme to rapid manufacturing of strain rosette sensor array based on the DIW technology.Benefit from the innovative design with simplified circuit layout and the advantages of DIW for printing multilayer structures,here we achieve optimization design principle for strain rosette sensor array with scalable circuit layout,which enable a hierarchical printing strategy for multiaxial strain monitoring in large scale or multiple domains.The strategy is highly expected to adapt for the emerging requirement in various applications such as integrated soft electronics,nondestructive testing and small-batch medical devices.

Driving and Braking Control of PM Synchronous Motor Based on Low-resolution Hall Sensor for Battery Electric Vehicle

Resolvers are normally employed for rotor positioning in motors for electric vehicles, but resolvers are expensive and vulnerable to vibrations. Hall sensors have the advantages of low cost and high reliability, but the positioning accuracy is low. Motors with Hall sensors are typically controlled by six-step commutation algorithm, which brings high torque ripple. This paper studies the high-performance driving and braking control of the in-wheel permanent magnetic synchronous motor （PMSM） based on low-resolution Hall sensors. Field oriented control （FOC） based on Hall-effect sensors is developed to reduce the torque ripple. The positioning accuracy of the Hall sensors is improved by interpolation between two consecutive Hall signals using the estimated motor speed. The position error from the misalignment of the Hall sensors is compensated by the precise calibration of Hall transition timing. The braking control algorithms based on six-step commutation and FOC are studied. Two variants of the six-step commutation braking control, namely, half-bridge commutation and full-bridge commutation, are discussed and compared, which shows that the full-bridge commutation could better explore the potential of the back electro-motive forces （EMF）, thus can deliver higher efficiency and smaller current ripple. The FOC braking is analyzed with the phasor diagrams. At a given motor speed, the motor turns from the regenerative braking mode into the plug braking mode if the braking torque exceeds a certain limit, which is proportional to the motor speed. Tests in the dynamometer show that a smooth control could be realized by FOC driving control and the highest efficiency and the smallest current ripple could be achieved by FOC braking control, compared to six-step commutation braking control. Therefore, FOC braking is selected as the braking control algorithm for electric vehicles. The proposed research ensures a good motor control performance while maintaining low cost and high reliability.

Magnetic Array Assisted Triboelectric Nanogenerator Sensor for Real‑Time Gesture Interaction

In human-machine interaction,robotic hands are useful in many scenarios.To operate robotic hands via gestures instead of handles will greatly improve the convenience and intuition of human-machine interaction.Here,we present a magnetic array assisted sliding triboelectric sensor for achieving a real-time gesture interaction between a human hand and robotic hand.With a finger’s traction movement of flexion or extension,the sensor can induce positive/negative pulse signals.Through counting the pulses in unit time,the degree,speed,and direction of finger motion can be judged in realtime.The magnetic array plays an important role in generating the quantifiable pulses.The designed two parts of magnetic array can transform sliding motion into contact-separation and constrain the sliding pathway,respectively,thus improve the durability,low speed signal amplitude,and stability of the system.This direct quantization approach and optimization of wearable gesture sensor provide a new strategy for achieving a natural,intuitive,and real-time human-robotic interaction.

A CuNi/C Nanosheet Array Based on a Metal–Organic Framework Derivate as a Supersensitive Non-Enzymatic Glucose Sensor

Bimetal catalysts are good alternatives for nonenzymatic glucose sensors owing to their low cost, high activity, good conductivity, and ease of fabrication. In the present study, a self-supported CuNi/C electrode prepared by electrodepositing Cu nanoparticles on a Ni-based metal–organic framework(MOF) derivate was used as a non-enzymatic glucose sensor. The porous construction and carbon scaffold inherited from the Ni-MOF guarantee good kinetics of the electrode process in electrochemical glucose detection. Furthermore, Cu nanoparticles disturb the array structure of MOF derived films and evidently enhance their electrochemical performances in glucose detection. Electrochemical measurements indicate that the CuNi/C electrode possesses a high sensitivity of17.12 mA mM^(-1) cm^(-2), a low detection limit of 66.67 nM,and a wider linearity range from 0.20 to 2.72 mM. Additionally, the electrode exhibits good reusability, reproducibility, and stability, thereby catering to the practical use of glucose sensors. Similar values of glucose concentrations in human blood serum samples are detected with our electrode and with the method involving glucose-6-phosphate dehydrogenase; the results further demonstrate the practical feasibility of our electrode.

Smart gas sensor arrays powered by artificial intelligence

Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision,hearing,touch,smell,and taste.A gas sensor array(GSA),also known as electronic nose,is a possible solution for a robotic olfactory system that can detect and discriminate a wide variety of gas molecules.Artificial intelligence(AI)applied to an electronic nose involves a diverse set of machine learning algorithms which can generate a smell print by analyzing the signal pattern from the GSA.A combination of GSA and AI algorithms can empower intelligent robots with great capabilities in many areas such as environmental monitoring,gas leakage detection,food and beverage production and storage,and especially disease diagnosis through detection of different types and concentrations of target gases with the advantages of portability,low-powerconsumption and ease-of-operation.It is exciting to envisage robots equipped with a"nose"acting as family doctor who will guard every family member's health and keep their home safe.In this review,we give a summary of the state-of the-art research progress in the fabrication techniques for GSAs and typical algorithms employed in artificial olfactory systems,exploring their potential applications in disease diagnosis,environmental monitoring,and explosive detection.We also discuss the key limitations of gas sensor units and their possible solutions.Finally,we present the outlook of GSAs over the horizon of smart homes and cities.

Array gain for a conformal acoustic vector sensor array:An experimental study

An acoustic vector sensor can measure the components of particle velocity and the acoustic pressure at the same point simultaneously, which provides a larger array gain against the ambient noise and a higher angular resolution than the omnidirectional pressure sensor. This paper presents an experimental study of array gain for a conformal acoustic vector sensor array in a practical environment. First, the manifold vector is calculated using the real measured data so that the effects of array mismatches can be minimized. Second, an optimal beamformer with a specific spatial response on the basis of the stable directivity of the ambient noise is designed, which can effectively suppress the ambient noise. Experimental results show that this beamformer for the conformal acoustic vector sensor array provides good signal-to- noise ratio enhancement and is more advantageous than the delay-and-sum and minimum variance distortionless response beamformers.

Analysis of SNR for Acoustic Vector Sensor Linear Array in Volume and Surface-generated Noise Fields

Acoustic vector sensor consists of pressure and particle velocity sensors,which measure the three-dimensional acoustic particle velocity,as well as the pressure at one location at the same time.By preserving the amplitude and phase information of the pressure and particle velocity,they possess a number of advantages over traditional scalar sensors.Signal-to-noise ratio (SNR) gain (which is often called array gain) is one of such advantages and is always interested by all of us.But it is not unchangeable if the spatial correlation of the noise field varies.Much more important,it is difficult to be given if the noise becomes complex.In this paper,spatial correlation of the vector field of isotropic volume-noise and surface-generated noise has been introduced briefly.Based on the results,the combined SNR output of a vector linear array is investigated and the maximum gain is given in the specified noise.Computer simulation shows that the output of one array in the same noise is not the same in different gestures.And then we find the best gesture through SNR calculation and obtain the biggest gain,which has important meaning to guide how to deploy an array in practice.We also should use the array with respect to the characteristics of the real ambient noise,especially in anisotropic noise field.

Simulation on the Models of Small Scale Passive Acoustic Sensor Arrays Based on Time-Delay Estimation Method

The passive acoustic localization with planar sensor array is introduced. Based on a method to eliminate the influence of effective sound velocity in passive detection, a new five-sensors solid array and its localization model are put forward. The factors that influence the precision of the localization are analyzed. Considering the errors from the factors synchronously, the simulation compares the solid array with the planar array. It can be proved that the five-sensor solid array is better than the four-sensor planar array in the estimation of bearing elements.

Indoor Human Detection Based on Thermal Array Sensor Data and Adaptive Background Estimation

Low Resolution Thermal Array Sensors are widely used in several applications in indoor environments. In particular, one of these cheap, small and unobtrusive sensors provides a low-resolution thermal image of the environment and, unlike cameras;it is capable to detect human heat emission even in dark rooms. The obtained thermal data can be used to monitor older seniors while they are performing daily activities at home, to detect critical situations such as falls. Most of the studies in activity recognition using Thermal Array Sensors require human detection techniques to recognize humans passing in the sensor field of view. This paper aims to improve the accuracy of the algorithms used so far by considering the temperature environment variation. This method leverages an adaptive background estimation and a noise removal technique based on Kalman Filter. In order to properly validate the system, a novel installation of a single sensor has been implemented in a smart environment: the obtained results show an improvement in human detection accuracy with respect to the state of the art, especially in case of disturbed environments.

Research on data diagnosis method of acoustic array sensor device based on spectrogram

Acoustic array sensor device for partial discharge detection is widely used in power equipment inspection with the advantages of non-contact and precise positioning compared with partial discharge detection methods such as ultrasonic method and pulse current method.However,due to the sensitivity of the acoustic array sensor and the influence of the equipment operation site interference,the acoustic array sensor device for partial discharge type diagnosis by phase resolved partial discharge(PRPD)map might occasionally presents incorrect results,thus affecting the power equipment operation and maintenance strategy.The acoustic array sensor detection device for power equipment developed in this paper applies the array design model of equal-area multi-arm spiral with machine learning fast fourier transform clean(FFT-CLEAN)sound source localization identification algorithm to avoid the interference factors in the noise acquisition system using a single microphone and conventional beam forming algorithm,improves the spatial resolution of the acoustic array sensor device,and proposes an acoustic array sensor device based on the acoustic spectrogram.The analysis and diagnosis method of discharge type of acoustic array sensor device can effectively reduce the system misjudgment caused by factors such as the resolution of the acoustic imaging device and the time domain pulse of the digital signal,and reduce the false alarm rate of the acoustic array sensor device.The proposed method is tested by selecting power cables as the object,and its effectiveness is proved by laboratory verification and field verification.

Least-Square Collaborative Beamforming Linear Array for Steering Capability in Green Wireless Sensor Networks

This paper presents a collaborative beamforming （CB） technique to organize the sensor node＇s location in a linear array for green wireless sensor network （WSN） applications. In this method, only selected clusters and active CB nodes are needed each time to perform CB in WSNs. The proposed least-square linear array （LSLA） manages to select nodes to perform as a linear antenna array （LAA）, which is similar to and as outstanding as the conventional uniform linear array （ULA）. The LSLA technique is also able to solve positioning error problems that exist in the random nodes deployment. The beampattern fluctuations have been analyzed due to the random positions of sensor nodes. Performances in terms of normalized power gains are given. It is demonstrated by a simulation that the proposed technique gives similar performances to the conventional ULA and at the same time exhibits lower complexity.

Low-Complexity DOA Estimation of Noncircular Signals for Coprime Sensor Arrays

This paper presents a low?complexity method for the direction?of?arrival(DOA)estimation of noncircular signals for coprime sensor arrays.The noncircular property is exploited to improve the performance of DOA estimation.To reduce the computational complexity,the rotational invariance propagator method(RIPM)is included in the algorithm.First,the extended array output is reconstructed by combining the array output and its conjugated counterpart.Then,the RIPM is utilized to obtain two sets of DOA estimates for two subarrays.Finally,the true DOAs are estimated by combining the consistent results of the two subarrays.This illustrates the potential gain that both noncircularity and coprime arrays provide when considered together.The proposed algorithm has a lower computational complexity and a better DOA estimation performance than the standard estimation of signal parameters by the rotational invariance technique and Capon algorithm.Numerical simulation results illustrate the effectiveness and superiority of the proposed algorithm.

Joint 2D-DOA and polarization estimation for sparse nonuniform rectangular array composed of spatially spread electromagnetic vector sensor

In this paper,a sparse nonuniform rectangular array based on spatially spread electromagnetic vector sensor(SNRASSEMVS)is introduced,and a method for estimating 2D-direction of arrival(DOA)and polarization is devised.Firstly,according to the special structure of the sparse nonuniform rectangular array(SNRA),a set of accurate but ambiguous direction-cosine estimates can be obtained.Then the steering vector of spatially spread electromagnetic vector sensor(SSEMVS)can be extracted from the array manifold to obtain the coarse but unambiguous direction-cosine estimates.Finally,the disambiguation approach can be used to get the final accurate estimates of 2DDOA and polarization.Compared with some existing methods,the SNRA configuration extends the spatial aperture and refines the parameters estimation accuracy without adding any redundant antennas,as well as reduces the mutual coupling effect.Moreover,the proposed algorithm resolves multiple sources without the priori knowledge of signal information,suffers no ambiguity in the estimation of the Poynting vector,and pairs the x-axis direction cosine with the y-axis direction cosine automatically.Simulation results are given to verify the effectiveness and superiority of the proposed algorithm.

Fiber Grating-Based Strain Sensor Array for Health Monitoring of Pipelines

Pipelines are one of the most important modern energy transportation methods,used especially for the transportation of certain dangerous energy media materials such as crude oil,natural gas,and chemical raw materials.New requirements have been put forward for the health monitoring and early security warning of pipelines because of the large-scale and complicated development trend of the pipe network system.To achieve an accurate assessment of the health conditions of pipeline infrastructure,obtaining as many precise operating parameters as possible,particularly at some critical parts of the pipeline,is necessary.Therefore,a novel type of fiber grating strain sensor array is proposed herein to monitor the pipeline hoop strain.The sensor utilizes fiber grating characteristics such as light weight,corrosion resistance,remote transmission,and strong environmental adaptability.The fiber containing the grating measurement points is implanted into the composite material to complete the sensitization encapsulation and protection of the bare fiber grating.The design of the sensor array fulfills the requirements for monitoring pipeline mass data,making it easy to form a pipeline health monitoring sensor network.The sensor sensitivity is researched by using a combination of theoretical and experimental analysis.A sensitivity test,as well as linearity and stability tests,are performed on the sensor.The experimental results show that the average sensitivity of the sensor is 14.86 pm/με,and the error from the theoretical calculation analysis value is 8.75%.Due to its high reliability,good linear response and long-term stability,and the ability to reflect the exact strain change of the outer wall of the pipeline,the designed sensor can support longterm online pipeline monitoring.The fiber grating sensor array network has successfully realized the monitoring of the pipeline’s internal operation by using external strain changes.In addition to the performance benefits,there are other merits associated with the applicability of the sensor namely simple structure,compact size,manufacturing ease,and exterior installation ease.

Analysis of Yellow Water in Liquor Fermentation with Sensor Array

Yellow water is a by-product of liquor in the solid state fermentation process, and contains a large amount of nutrients, such as acids, esters, alcohols and aldehydes produced by fermentation. The components in the yellow water reflect the fermentation information to a certain extent, so the fermentation process can be monitored by detecting the yellow water component online. A sensor array detection device is designed for detecting yellow water. In addition, chemical titration is used to obtain data such as acidity, reducing sugar and starch of yellow water. Principal component analysis and discriminant function analysis were performed on the data;and a multivariate linear regression was used to establish a prediction model for the data. The results showed that the prediction bias for acidity and alcohol was small, 0.39 and 0.43, respectively.

Development of Measurement System for Flow and Shape Using Array Ultrasonic Sensors

In Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, the fuel debris formed in the Reactor Pressure Vessel (RPV) and Primary Containment Vessel (PCV) at Unit 1 - 3. To accelerate and decide further decommissioning steps of the FDNPP, it is crucial to obtain realistic information of the debris and localize contaminated water leakage from PCV. Due to high radiation and dark environment inside the PCV, investigating instruments and techniques should necessarily to meet specification of radiation resistance, waterproofness, dust resistance and so on. This study focuses on development of ultrasonic measurement system using a couple of sectorial array sensors to localize contaminated water leakage and visualize shape of object that repre- senting fuel debris, simultaneously. In this study, Total Focusing Method (TFM) and Ultrasonic Velocity Profiler (UVP) methods are considered to visualize object shape and flow pattern around it, respectively. To demonstrate applicability and reliability of developed measurement system with sectorial array sensors, a mock-up experiment result of simulated water leakage and fuel debris shape were discussed in this paper.