Sensor Array Optimization Research Articles

Robot-assisted optimized array design for accurate multi-component gas quantification

Designing sensor arrays is a common strategy for detecting mixtures. However, a sensor array designed based on human experience leads to inaccuracies due to cross-sensitivity and information overlap. This difficulty led to the realization that arrays should be optimized as a whole. The conventional array optimizing method involves selecting the best subarray from a limited sensor pool. However, this method did not consider the vast continuous multi-dimensional variable space of each sensing element’s recipe, thus only generating a simpler array with limited detection capacity. To address this problem, we developed a Robot-assisted Optimized Array Design (ROAD) method. This innovative method holistically optimizes the sensor array across a continuous and vast variable space, overcoming the bottlenecks of high dimensionality and high-quality data collection. We applied ROAD to an optoelectronic nose, tasked with detecting a mixture of CO2, NH3, and water vapor. The method explored an immense space, estimated at the order of 2^(10^7). The implementation of ROAD led to a revolutionary quantitative accuracy, achieving an average relative standard deviation of 1.90%. This advancement has propelled the application of optoelectronic nose from qualitative to quantitative. The successful ROAD of system-wide array optimization has been demonstrated in the optoelectronic nose, validating the potential of the holistically optimized sensor array for accurately quantifying each component in a gas mixture.

Sensor Selection for an Electronic Tongue for the Rapid Detection of Paralytic Shellfish Toxins: A Case Study

The performance of an electronic tongue can be optimized by varying the number and types of sensors in the array and by employing data-processing methods. Sensor selection is typically performed empirically, with sensors picked up either by analyzing their characteristics or through trial and error, which does not guarantee an optimized sensor array composition. This study focuses on developing a method for sensor selection for an electronic tongue using simulated sensor data and Lasso regularization. Simulated sensor responses were calculated using sensor parameters such as sensitivity and selectivity, which were determined in the individual analyte solutions. Sensor selection was carried out using Lasso regularization, which removes redundant or highly correlated variables without much loss of information. The objective of the optimization of the sensor array was twofold, aiming to minimize both quantification errors and the number of sensors in the array. The quantification of toxins belonging to one of the groups of marine toxins—paralytic shellfish toxins (PSTs)—using arrays of potentiometric chemical sensors was used as a case study. Eight PSTs corresponding to the toxin profiles in bivalves due to the two common toxin-producing phytoplankton species, G. catenatum (dcSTX, GTX5, GTX6, and C1+2) and A. minitum (STX, GTX2+3), as well as total sample toxicity, were included in the study. Experimental validation with mixed solutions of two groups of toxins confirmed the suitability of the proposed method of sensor array optimization with better performance obtained for the a priori optimized sensor arrays. The results indicate that the use of simulated sensor responses and Lasso regularization is a rapid and efficient method for the selection of an optimized sensor array.

A Novel Rapid Bacterial Infection Screening Multisensor System With Feature Selection and Sensor Array Optimization

A Novel Rapid Bacterial Infection Screening Multisensor System With Feature Selection and Sensor Array Optimization

Electronic sensing combined with machine learning models for predicting soil nutrient content

Traditional methods for detecting soil nutrient content usually involve laborious and time-consuming experimental procedures, hindering the efficiency of soil analysis and making them less suitable for large-scale soil testing. Therefore, there exists a pressing need to develop innovative solutions. This study aimed to investigate the potential relationship between soil pyrolysis gas and soil nutrient content by establishing a model that combined electronic sensing and machine learning to predict soil nutrient content. The initial data sets were derived from utilizing pyrolysis gas response curves of soil samples acquired from 10 distinct gas sensors. Afterward, we innovatively proposed a sensor array optimization (SAO) method based on the dynamic feature importance (DFI) of the Random Forest-Pearson Correlation Coefficient (RF-PCC) to identify the optimal sensor combinations for four soil nutrients. Furthermore, Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Multilayer Perceptron-Random Forest (MLP-RF) models were employed to predict the soil nutrient content based on the electronic sense to address the challenges associated with developing a unified model. The MLP-RF models showed superior performance compared with the other three models, with the coefficient of determination (R2) for the four soil nutrients ranging from 0.54 to 0.94.Overall, our research not only provides approximately quantitative predictive outcomes but also contributes to a better understanding ofa new approach for detecting soil nutrient content.

Sensor array optimization for the electronic nose via different deep learning methods

A sensor array is a key component of an electronic nose (E-nose). However, the practical applications of the E-nose are often inhibited by its size and energy consumption arising from the number of gas sensors. Achieving a high-performance E-nose with a minimum number of sensors is key and challenging for its practical applications. In this study, different machine learning models have been studied and compared to optimize the performance of the E-nose. The results show that the convolutional neural network (CNN) is the best-performing model, which has an accuracy of 0.986 for classification, and an R-square score of 0.979 for concentration prediction, outperforming the gated recurrent unit, long short-term memory, multi-layer perceptron neural network, and support vector machine. The performance of the E-nose experiences only a minor decrease when the number of sensors that participated in pattern recognition is reduced from eight to four, where the CNN model can yield an accuracy of 0.905 for classification and an R-square of 0.972 for concentration prediction. To further quantify the pros and cons of array optimization, a cost-effective metric is designed to reveal the suitable array size under different scenarios. This work can provide valuable guidance in the design of portable E-nose devices with a smaller size and optimal performance.

A new sensor configuration design method for source term estimation in urban neighborhood with complex conditions under different wind directions

When hazardous substances leak into the atmosphere, Source Term Estimation (STE) can quickly determine the information of the leaked substance to control the threat of the leakage event to the environment and public safety. Reasonable sensor configuration is the foundation of STE. Existing sensor configuration schemes have limited applicability in STE processes. When urban neighborhood has complex building structures and airflow conditions, there is a lack of a method that can provide reasonable sensor configurations to ensure good STE performance. In this study, a new sensor configuration design method is proposed, which weights the joint entropy of adjoint concentration information of multi wind directions to obtain the cost function, and then uses Simulated Annealing (SA) algorithm to minimize the cost function. This article selects a real complex urban neighborhood for simplified modeling, and designs the optimal sensor array for it. STE results of several leakage scenarios are compared with the optimal and uniform sensor configurations at different leakage locations and wind directions. Compared to traditional uniform sensor configurations, the optimal sensor configuration has significantly improved estimation performance in both source location and strength, with a reduction of 27 %–51 % in the bias of source location identification and 17 %–39 % in the bias of source strength estimation.

Agent-based sensor location strategy for smart irrigation of large crop fields

Efficient monitoring of large crop fields is important to ensure the optimal use of resources such as water in irrigation policies, but at the same time represents a challenge to determine the structure of the sensor network. A balance must be accomplished between the acquisition, operation, and maintenance costs of this sensor network with the amount of information that can be collected in real-time to support the optimal use of the resource, e.g., an optimal irrigation policy. In this study, a sensor location strategy is proposed based on an agent-based model (ABM) of the crop–soil system, a state estimation algorithm reconstructing non-measured variables, and an objective function balancing the convergence of the estimation technique and the costs of the sensor network. The ABM model describes the crop–soil dynamics and allows conveniently representing uneven landscapes where water exchanges take place between different portions of the land. Various state estimation techniques can be considered and an extended Kalman filter is implemented in the present study, whose error covariance matrix can be exploited to assess practical observability and observer convergence. Finally, an economic cost function combines the observability measure with the sensor costs in order to select an optimal or suboptimal sensor array. For validation purposes, a numerical simulation case study, corresponding to a rugged land located in Colombia, is used to test various scenarios including the variability of climatic inputs.

Research on the location of space debris impact spacecraft based on genetic neural network

The existence of space debris will pose a serious threat to the safety of spacecraft in orbit and cause serious impact damage to spacecraft. Therefore, the timely and accurate determination of the impact location of space debris is very important to improve the safety and reliability of the orbiting spacecraft in the long run. This paper proposes an intelligent back-propagation artificial neural network-based algorithm for locating the impact source of space debris. The method of building and training the algorithm is described, based on the moment of arrival of the stress waves detected by the PVDF (Polyvinylidene Fluoride) sensor array and the sensor coordinates. Based on the ABAQUS finite element simulation data, the neural network is trained. The result shows that compared with the traditional TOA (Time of Arrival) localization algorithm, the localization accuracy of the BP (Back Propagation) neural network localization algorithm is improved by 34.7%. The optimal objective is to minimize the error between the space debris impact point and the localization point. The parameters of the PVDF sensor array (diameter d of the circular PVDF thin film sensor, radius R of the PVDF sensor array) were optimized by genetic algorithm, and the optimal sensor array parameters were obtained based on the optimization algorithm.

Sensor array design of optically pumped magnetometers for accurately estimating source currents

An optically pumped magnetometer (OPM) is a new generation of magnetoencephalography (MEG) devices that is small, light, and works at room temperature. Due to these characteristics, OPMs enable flexible and wearable MEG systems. On the other hand, if we have a limited number of OPM sensors, we need to carefully design their sensor arrays depending on our purposes and regions of interests (ROIs). In this study, we propose a method that designs OPM sensor arrays for accurately estimating the cortical currents at the ROIs. Based on the resolution matrix of minimum norm estimate (MNE), our method sequentially determines the position of each sensor to optimize its inverse filter pointing to the ROIs and suppressing the signal leakage from the other areas. We call this method the Sensor array Optimization based on Resolution Matrix (SORM). We conducted simple and realistic simulation tests to evaluate its characteristics and efficacy for real OPM-MEG data. SORM designed the sensor arrays so that their leadfield matrices had high effective ranks as well as high sensitivities to ROIs. Although SORM is based on MNE, the sensor arrays designed by SORM were effective not only when we estimated the cortical currents by MNE but also when we did so by other methods. With real OPM-MEG data we confirmed its validity for real data. These analyses suggest that SORM is especially useful when we want to accurately estimate ROIs’ activities with a limited number of OPM sensors, such as brain-machine interfaces and diagnosing brain diseases.

Multiple-Sensor Detection System Design for Tea Identification Based on Mutual Information Array Optimization Scheme

In this paper, a multi-metal oxide sensor (MOS) detection system is developed for the identification of different grades of tea leaves. To cope with the interference brought by redundant information and further improve the detection capability of the system and reduce the number of sensors, sensor array optimization algorithms are further implemented. The main idea of sensor array optimization is to select sensors according to evaluation index which ranks the importance of sensors. This paper optimizes the sensor array based on information theory. Specifically, a weighted modified mutual information optimization algorithm that simultaneously considers relevancy, redundancy, and complementarity is developed. The experimental results show that the weighted modified mutual information optimization method can obtain the best system performance with the minimum number of sensors.

Optimization of Sensor Array for Detection of Abalone Freshness Based on Electronic Tongue

Optimization of Sensor Array for Detection of Abalone Freshness Based on Electronic Tongue

Array Optimization Based on Weighted and Hilbert–Schmidt Schemes of Multisensor Detection System

This paper presents a novel sensor array optimization scheme for multisensor electronic nose detection system. A system architecture with multisensor is first proposed to implement the medical detection, including the bacterial culture medium detection and animal wound infection detection. The system efficiency is evaluated by comparing with the field asymmetric ion mobility spectrometry (FAIMS) system. To further improve the detection effect and reduce the number of sensors of the electronic nose system, we then derive two sensor array optimization procedures based on factor analysis and Hilbert-Schmidt independence criterion, respectively. Specifically, the weighted factor analysis method and non-weighted factor analysis method are proposed via factor analysis. Besides, the Hilbert-Schmidt independence criterion optimization design of linear kernel function and Gaussian kernel function are also exploited. The experiment results highlight that compared with the existing approaches, the proposed weighted factor analysis optimization method and Hilbert-Schmidt independence criterion optimization method (Gaussian kernel function) can achieve a significant system performance.

Automatic Detection and Imaging of Rivet Hole Defects for Aircraft Structures With Optimized Sensor Array Using Eddy Current Method and Image Analysis

Fatigue cracking in riveted lap joints on aircraft could cause severe safety consequences in flight. The prior inspection for the integrity of the second layer of aircraft structure with rivet removed is necessary due to the priority of safety. Methods such as radiographic and ultrasonic testing are widely applied but require relatively expensive equipment, mechanical scanning or professional operators. Eddy current method can be adopted for non-destructive testing (NDT) inspection, which is safe and low-cost. In this paper, a hand-held eddy current (EC) sensor array was optimised based on sensitivity map analysis. The EC sensor array was used with a multi-channel EM instrument, which was capable of inspecting defects around the fastener locations covered by the head of rivets. A simulated sample of riveted joint with defects was examined with the eddy current sensing system and the collected data was processed by Hough Circle Transformation (HCT) and texture analysis. The analysis results could detect and locate fatigue cracks on the sample. The presented eddy current sensing system in this paper achieved an automatic, real-time, portable, convenient and accurate inspection method for defects in riveted joints.

A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose

As the acquisition unit of gas information, the sensor array directly determines the overall performance of the electronic nose system (E-nose). This paper proposed a new method for optimizing the sensor array. Firstly, four evaluation indicators (sensitivity, selectivity, correlation, and repeatability) were selected to evaluate the sensor array. Subsequently, different evaluation indicators were assigned different weight values according to their contributions to the overall performance of the E-nose. Finally, a comprehensive evaluation model was established based on the EWM-TOPSIS algorithm to optimize the sensor array. In order to verify the effectiveness of the as-proposed model, it was applied to the optimization of the E-nose sensor array composed of 10 gas sensors, and the influence of the sensor array optimization on the gas recognition ability of the E-nose was investigated. The experimental results showed that the optimized sensor array can identify the CO-CH4 gas mixtures with an accuracy of 96.5%, which a significant improvement compared with the accuracy of 78.3% before the sensor array optimization.

Optimization of the Electronic Nose Sensor Array for Asthma Detection Based on Genetic Algorithm

Optimization of the Electronic Nose Sensor Array for Asthma Detection Based on Genetic Algorithm

A priori tailored selection of sensor arrays for electronic tongues

Electronic tongues (ETs) are multisensor systems based on the coupling of different sensors with high stability and cross-sensitivity towards different species, along with an appropriate chemometric tool that allow the classification of samples and/or the quantification of selected analytes. Despite the great advantages that the data processing stage may offer (e.g. allowing to offset matrix effects from the sample itself, or to resolve the presence of interferences, drifts or non-linearities obtained with the sensors), the stability of the sensors response together with its varied cross-sensitivity are of utmost importance. In this regard, despite the progress that has been made over the last years and the increasing number of publications dealing with ETs, researchers have focused mainly on the developed application itself or on the data treatment stage, but very few report on the choice of the sensors. In this direction, herein we explore on the development of a simple methodology that allows the a priori selection of the optimal sensor array to carry out a specific application. Concretely, the usage of principal component analysis (PCA) in combination with different clustering indices is proposed as a simple approach to simultaneously assess the cross-response and the reproducibility of the different sensors in a single step, what allows to define which sensors may constitute an ET array for a given qualitative or quantitative application. The above ideas have been applied to the study case of aromatic essential oils.

Design and Optimization of Electronic Nose Sensor Array for Real-Time and Rapid Detection of Vehicle Exhaust Pollutants

Traditional vehicle exhaust pollutant detection methods, such as bench test and remote sensing detection, have problems such as large volume, high cost, complex process, long waiting time, etc. In this paper, according to the main components of vehicle exhaust pollutants, an electronic nose with 12 gas sensors was designed independently for real-time and rapid detection of vehicle exhaust pollutants. In order to verify that the designed electronic nose based on machine learning classification method can accurately identify the exhaust pollutants from different engines or different concentration levels from the same engine. After feature extraction of the collected data, Random Forest (RF) was used as the classifier, and the average classification accuracy reached 99.92%. This result proved that the designed electronic nose combined with RF method can accurately and sensitively judge the concentration level of vehicle exhaust pollutants.. Then, in order to enable the electronic nose to be vehicle-mounted and to achieve real-time and rapid detection of vehicle exhaust pollutants. We used Recursive Feature Elimination with Cross Validation (RFECV), Random Forest Feature Selector (RFFS) and Principal Component Analysis (PCA) to optimize the sensor array. The results showed that these methods can effectively simplify the sensor array while ensuring the RF classifier’s classification recognition rate. After using RFECV and RFFS to optimize the sensor array, the RF classifier’s classification recognition rate of the optimized sensor arrays for vehicle exhaust pollutants reached 99.77% and 99.44%, respectively. The numbers of sensors in the optimized sensor arrays were six and eight respectively, which achieved the miniaturization and low-cost of the electronic nose. With the limitation of six sensors, RFECV is the best sensor array optimization method among the three methods.

Design, Fabrication, Structure Optimization and Pressure Sensing Demonstration of COC Piezoelectret Sensor and Sensor Array.

This study reported on the design and fabrication of a pseudo-piezoelectric material (piezoelectret) from cyclic olefin copolymer (COC) based on a micropillar structure. The fabrication feasibility of such structure was explored and piezoelectret with the good piezoelectric activity (characterized by quasi-static piezoelectric coefficient d33) was demonstrated. Response surface method with a central composite design was employed to investigate the effects of the structure parameter on the piezoelectric coefficient d33. An optimal structure design was obtained and was validated by experiments. With the optimal design, d33 can reach an exceptional high value of ~9000 pC/N under low pressure. The charging process and the electrical and electromechanical characteristics were further investigated by experimentation and modeling. We further demonstrated the scalability of the fabrication process and demonstrated the application of these sensors in position specific pressure sensing (pressure mapping).

Ensemble machine learning approach for electronic nose signal processing

Electronic nose (e-nose) systems have been reported to be used in many areas as rapid, low-cost, and non-invasive instruments. Especially in meat production and processing, e-nose system is a powerful tool to process volatile compounds as a unique ‘fingerprint’. The ability of the pattern recognition algorithm to analyze e-nose signals is the key to the success of the e-nose system in many applications. On the other hand, ensemble methods have been reported for favorable performances in various data sets. This research proposes an ensemble learning approach for e-nose signal processing, especially in beef quality assessment. Ensemble methods are not only used for learning algorithms but also sensor array optimization. For sensor array optimization, three filter-based feature selection algorithms (FSAs) are used to build ensemble FSA such as reliefF, chi-square, and gini index. Ensemble FSA is developed to deal with different or unstable outputs of a single FSA on homogeneous e-nose data sets in beef quality monitoring. Moreover, ensemble learning algorithms are employed to deal with multi-class classification and regression tasks. Random forest and Adaboost are used that represent bagging and boosting algorithms, respectively. The results are also compared with support vector machine and decision tree as single learners. According to the experimental results, our ensemble approach has good performance and generalization in e-nose signal processing. Optimized sensor combination based on filter-based FSA shows stable results both in classification and regression tasks. Furthermore, Adaboost as a boosting algorithm produces the best prediction even though using a smaller number of sensors.

Sensor Array Optimization to Design and Develop an Electronic Nose System for the Detection of Water Stress in Khasi Mandarin Orange

Drought stress is one of the most significant abiotic stresses, adversely affecting the economy by tumbling or even eliminating agricultural productivity, development, and yield. Adverse effects of water scarcity can be reduced if certain precautionary actions could be taken in advance. Therefore, monitoring and early detection of the drought can be helpful for preparing a well-developed response plan. In response to the stresses, an intricate response system of the plants is involved which emits a range of Volatile Organic Compounds (VOCs) from different parts, such as flowers, leaves, roots and stems. These VOCs can be used as fingerprints for categorizing stressed and nonstressed plants. This paper addresses the optimization of an array of gas sensors used in an in-situ stress diagnosis system, and the data obtained after optimization have been used for the detection of induced stresses in plants by recording the VOCs emitted by the plants. The flow characteristics of the gas chamber were modeled using the Finite Element method before fabrication. The temperature modulation of the gas sensors used in the designed electronic nose system was accomplished. The optimization of the sensor array was performed using the radar plot and Wilks’ Lambda technique. The optimum operating temperatures for each gas sensor were selected using a radar plot. Furthermore, the number of the sensors in the sensor array was reduced by choosing the sensors having higher discriminant ability using the Wilks’ Lambda optimization technique. Twelve healthy Khasi Mandarin Orange saplings were considered for the investigation. Three different levels of water stresses are induced in the plants artificially for the experiments. The overall response and the optimized response of the developed electronic nose system are compared using Linear Discriminant Analysis (LDA) and bootstrap ensemble K-Nearest Neighbors (KNN) classifier. The Leaf Relative Water Content (LRWC) of the leaves is also measured concurrently to confirm the stress induction in the plants.