Journal articles: 'Sensor Array Optimization'

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Men, Hong, Hai Yan Liu, Lei Wang, and Xuan Zhou. "Optimization of Electronic Nose Sensor Array and its Application in the Classification of Vinegar." Advanced Materials Research 121-122 (June 2010): 27–32. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.27.

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Five kinds of vinegars were measured by a gas sensor array composed of six TGS gas sensors. The sensor array should be optimized by the minimal Wilks statistic value, then, the four best sensor array used to detect the type of vinegars were formed, Principal Component Analysis (PCA) and Linear discriminant analysis (LDA) were applied to analyze the data of primary and optimized sensor array. The results indicated that optimization sensor array could be more adaptable to recognize the five kinds of vinegars. Thereby the given optimization method is effective.

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Liu, Jiancheng, Feng Shi, Yecheng Sun, and Peng Li. "An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays." Applied Sciences 10, no. 9 (May 2, 2020): 3176. http://dx.doi.org/10.3390/app10093176.

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The Mills Cross sonar sensor array, achieved by the virtual element technology, is one way to build a low-complexity and low-cost imaging system while not decreasing the imaging quality. This type of sensor array is widely investigated and applied in sensor imaging. However, the Mills Cross array still holds some redundancy in sensor spatial sampling, and it means that this sensor array may be further thinned. For this reason, the Almost Different Sets (ADS) method is proposed to further thin the Mills Cross array. First, the original Mills Cross array is divided into several transversal linear arrays and one longitudinal linear array. Secondly, the Peak Side Lobe Level (PSLL) of each virtual linear array is estimated in advance. After the ADS parameters are matched according to the thinned ratio of the expectant array, all linear arrays are thinned in order. In the end, the element locations in the thinned linear array are used to determine which elements are kept or discarded from the original Mills array. Simulations demonstrate that the ADS method can be used to thin the Mills array and to further decrease the complexity of the imaging system while retaining beam performance.

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Chen, Shengzhi, Minghua Zhu, Qing Zhang, Xuesong Cai, and Bo Xiao. "Design and optimization of sensor array for magnetic gradient tensor system." Sensor Review 40, no. 1 (December 4, 2019): 121–29. http://dx.doi.org/10.1108/sr-03-2019-0074.

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Purpose The differential magnetic gradient tensor system is usually constructed from the three-axis magnetic sensor array. While the effects of measurement error, sensor performance and baseline distance on localization performance of such systems have been widely reported, the research about the effect of spatial design of sensor array is less presented. This paper aims to provide a spatial design method of sensor array and corresponding optimization strategy to localization based on magnetic tensor gradient to get the optimum design of the sensor array. Based on the results of simulation, magnetic localization systems constructed from the proposed array and the traditional array have been built to carry out a localization experiment. The results of experiment have verified the effectiveness of magnetic localization based on the proposed array. Design/methodology/approach The authors focus on the localization of the magnetic target based on magnetic gradient by using three-axis magnetic sensor array and combine a design method with corresponding optimization strategy to get the optimum design of the sensor array. Findings This paper provides an array design and optimization method for magnetic target localization based on magnetic gradient to improve the localization performance. Originality/value In this paper, the authors focus on the magnetic localization based on magnetic gradient by using three-axis magnetic sensors and study the effect of the spatial design of sensor array on localization performance.

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Tong, Jin, Chengxin Song, Tianjian Tong, Xuanjie Zong, Zhaoyang Liu, Songyang Wang, Lidong Tan, Yinwu Li, and Zhiyong Chang. "Design and Optimization of Electronic Nose Sensor Array for Real-Time and Rapid Detection of Vehicle Exhaust Pollutants." Chemosensors 10, no. 12 (November 22, 2022): 496. http://dx.doi.org/10.3390/chemosensors10120496.

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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.

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Shulkind, Gal, Stefanie Jegelka, and Gregory W. Wornell. "Sensor Array Design Through Submodular Optimization." IEEE Transactions on Information Theory 65, no. 1 (January 2019): 664–75. http://dx.doi.org/10.1109/tit.2018.2873795.

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Wang, Jin, Cheng Zhang, Meizhuo Chang, Wei He, Xiaohui Lu, Shaomei Fei, and Guodong Lu. "Optimization of Electronic Nose Sensor Array for Tea Aroma Detecting Based on Correlation Coefficient and Cluster Analysis." Chemosensors 9, no. 9 (September 17, 2021): 266. http://dx.doi.org/10.3390/chemosensors9090266.

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The electronic nose system is widely used in tea aroma detecting, and the sensor array plays a fundamental role for obtaining good results. Here, a sensor array optimization (SAO) method based on correlation coefficient and cluster analysis (CA) is proposed. First, correlation coefficient and distinguishing performance value (DPV) are calculated to eliminate redundant sensors. Then, the sensor independence is obtained through cluster analysis and the number of sensors is confirmed. Finally, the optimized sensor array is constructed. According to the results of the proposed method, sensor array for green tea (LG), fried green tea (LF) and baked green tea (LB) are constructed, and validation experiments are carried out. The classification accuracy using methods of linear discriminant analysis (LDA) based on the average value (LDA-ave) combined with nearest-neighbor classifier (NNC) can almost reach 94.44~100%. When the proposed method is used to discriminate between various grades of West Lake Longjing tea, LF can show comparable performance to that of the German PEN2 electronic nose. The electronic nose SAO method proposed in this paper can effectively eliminate redundant sensors and improve the quality of original tea aroma data. With fewer sensors, the optimized sensor array contributes to the miniaturization and cost reduction of the electronic nose system.

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Boopalan, Navaamsini, Agileswari K. Ramasamy, and Farrukh Hafiz Nagi. "A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array." Global Journal of Engineering and Technology Review Vol.4 (4) October-December. 2019 4, no. 4 (December 30, 2019): 101–9. http://dx.doi.org/10.35609/gjetr.2019.4.4(4).

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Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

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Wang, Jiang, Xingang Fan, Yongchao Zhang, Jianyu Yang, Yuming Du, and Jianxin He. "Methods for Assessing and Optimizing Solar Orientation by Non-Planar Sensor Arrays." Sensors 19, no. 11 (June 5, 2019): 2561. http://dx.doi.org/10.3390/s19112561.

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Non-planar sensor arrays are used to determine solar orientation based on the orientation matrix formed by orientation vectors of the sensor planes. Solar panels or existing photodiodes can be directly used without increasing the size or mass of the spacecraft. However, a limiting factor for the improvement of the accuracy of orientation lies with the lack of an assessment-based approach. A formulation was developed for the supremum (i.e., the least upper bound) of orientation error of an arbitrary orientation matrix in terms of its influencing factors. The new formulation offers a way to evaluate the supremum of orientation error considering interference with finite energy and interference with infinite energy but finite average energy. For a given non-planar sensor array, a sub-matrix of the full orientation matrix would reach the optimal accuracy of orientation if its supremum of orientation error is the least. Principles for designing an optimal sensor array relate to the configuration of the orientation matrix, which can be pre-determined for a given number of sensors. Simulations and field experiment tested and validated the methods, showing that our sensor array optimization method outperforms the existing methods, while providing a way of assessment and optimization.

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Rahman, Md Mizanur, Chalie Charoenlarpnopparut, Prapun Suksompong, and Pisanu Toochinda. "Sensor Array Optimization for Complexity Reduction in Electronic Nose System." ECTI Transactions on Electrical Engineering, Electronics, and Communications 15, no. 1 (September 28, 2016): 49–59. http://dx.doi.org/10.37936/ecti-eec.2017151.171295.

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An Electronic nose (E-Nose) can be used to assess food quality and fruit ripeness without personal bias. A set of relevant sensors must be identified to design an effective E-Nose and reduce implementation cost and complexity. The analysis of tropical fruit odour in terms of pattern recognition errors is carried out to determine the minimum number of sensors and their combinations. Two new methods namely 1) principal component loading and mutual information between sensor data, and 2) threshold based approach are proposed in this work to evaluate and optimize the sensor set. Four pattern recognition methods, namely multilayer perceptron neural network (MLPNN), radial basis function neural network (RBFNN), support vector machine (SVM), and k-nearest neighbour (k-NN) are also compared in terms of classification performance. The pattern recognition error of SVM with the optimal set of sensors is as low as 2.78% and that of k-NN is 9.72%. The results conclude that the pattern classification error with MLPNN, and RBFNN is higher than the error from k-NN and SVM.

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Wang, Lening, Hangfang Zhao, and Qide Wang. "Underwater Sparse Acoustic Sensor Array Design under Spacing Constraints Based on a Global Enhancement Whale Optimization Algorithm." Applied Sciences 12, no. 22 (November 21, 2022): 11825. http://dx.doi.org/10.3390/app122211825.

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Sparse arrays with low cost and engineering complexity are widely applied in many fields. However, the high peak sidelobe level (PSLL) of a sparse array causes the degradation of weak target detection performance. Particularly for the large size of underwater low-frequency sensors, the design problem requires a minimum spacing constraint, which further increases the difficulty of PSLL suppression. In this paper, a novel swarm-intelligence-based approach for sparse sensor array design is proposed to reduce PSLL under spacing constrains. First, a global enhancement whale optimization algorithm (GEWOA) is introduced to improve the global search capability for optimal arrays. A three-step enhanced strategy is used to enhance the ergodicity of element positions over the aperture. In order to solve the adaptation problem for discrete array design, a position decomposition method and a V-shaped transfer function are introduced into off-grid and on-grid arrays, respectively. The effectiveness and superiority of the proposed approach is validated using experiments for designing large-scale low-frequency arrays in the marine environment. The PSLL of the off-grid array obtained by GEWOA was nearly 3.8 dB lower than that of WOA. In addition, compared with other intelligent algorithms, the on-grid array designed using GEWOA had the lowest PSLL.

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Xu, Zhe, Xiajing Shi, and Susan Lu. "Integrated sensor array optimization with statistical evaluation." Sensors and Actuators B: Chemical 149, no. 1 (August 2010): 239–44. http://dx.doi.org/10.1016/j.snb.2010.05.038.

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Tholin-Chittenden, Carl, and Manuchehr Soleimani. "Planar Array Capacitive Imaging Sensor Design Optimization." IEEE Sensors Journal 17, no. 24 (December 15, 2017): 8059–71. http://dx.doi.org/10.1109/jsen.2017.2719579.

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Mao, Zhenghao, Jianchao Wang, Youjin Gong, Heng Yang, and Shunping Zhang. "A Set of Platforms with Combinatorial and High-Throughput Technique for Gas Sensing, from Material to Device and to System." Micromachines 9, no. 11 (November 19, 2018): 606. http://dx.doi.org/10.3390/mi9110606.

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In a new E-nose development, the sensor array needs to be optimized to have enough sensitivity and selectivity for gas/odor classification in the application. The development process includes the preparation of gas sensitive materials, gas sensor fabrication, array optimization, sensor array package and E-nose system integration, which would take a long time to complete. A set of platforms including a gas sensing film parallel synthesis platform, high-throughput gas sensing unmanned testing platform and a handheld wireless E-nose system were presented in this paper to improve the efficiency of a new E-nose development. Inkjet printing was used to parallel synthesize sensor libraries (400 sensors can be prepared each time). For gas sensor selection and array optimization, a high-throughput unmanned testing platform was designed and fabricated for gas sensing measurements of more than 1000 materials synchronously. The structures of a handheld wireless E-nose system with low power were presented in detail. Using the proposed hardware platforms, a new E-nose development might only take one week.

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Liu, Yixin, Kai Zhou, and Yu Lei. "Using Bayesian Inference Framework towards Identifying Gas Species and Concentration from High Temperature Resistive Sensor Array Data." Journal of Sensors 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/351940.

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High temperature gas sensors have been highly demanded for combustion process optimization and toxic emissions control, which usually suffer from poor selectivity. In order to solve this selectivity issue and identify unknown reducing gas species (CO, CH4, and CH8) and concentrations, a high temperature resistive sensor array data set was built in this study based on 5 reported sensors. As each sensor showed specific responses towards different types of reducing gas with certain concentrations, based on which calibration curves were fitted, providing benchmark sensor array response database, then Bayesian inference framework was utilized to process the sensor array data and build a sample selection program to simultaneously identify gas species and concentration, by formulating proper likelihood between input measured sensor array response pattern of an unknown gas and each sampled sensor array response pattern in benchmark database. This algorithm shows good robustness which can accurately identify gas species and predict gas concentration with a small error of less than 10% based on limited amount of experiment data. These features indicate that Bayesian probabilistic approach is a simple and efficient way to process sensor array data, which can significantly reduce the required computational overhead and training data.

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Zhang, Shunping, Changsheng Xie, Dawen Zeng, Huayao Li, Yuan Liu, and Shuizhou Cai. "A sensor array optimization method for electronic noses with sub-arrays." Sensors and Actuators B: Chemical 142, no. 1 (October 2009): 243–52. http://dx.doi.org/10.1016/j.snb.2009.08.015.

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Häcker, P., S. Uhlich, and B. Yang. "Fast beampattern evaluation by polynomial rooting." Advances in Radio Science 9 (July 29, 2011): 145–52. http://dx.doi.org/10.5194/ars-9-145-2011.

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Abstract. Current automotive radar systems measure the distance, the relative velocity and the direction of objects in their environment. This information enables the car to support the driver. The direction estimation capabilities of a sensor array depend on its beampattern. To find the array configuration leading to the best angle estimation by a global optimization algorithm, a huge amount of beampatterns have to be calculated to detect their maxima. In this paper, a novel algorithm is proposed to find all maxima of an array's beampattern fast and reliably, leading to accelerated array optimizations. The algorithm works for arrays having the sensors on a uniformly spaced grid. We use a general version of the gcd (greatest common divisor) function in order to write the problem as a polynomial. We differentiate and root the polynomial to get the extrema of the beampattern. In addition, we show a method to reduce the computational burden even more by decreasing the order of the polynomial.

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N. Jabbar, Ahmed, and Ibrahim A. Murdas. "Developing a graphical package for sensor arrays design, optimization and maintenance." International Journal of Engineering & Technology 7, no. 3 (July 8, 2018): 1388. http://dx.doi.org/10.14419/ijet.v7i3.14113.

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The sensor arrays are now an essential part of any communication, medical and remote sensing system. These arrays should be designed with utmost performance to ensure the maximum link efficiency. The available commercial sensor arrays design packages are expensive, complicated and cannot be easily modified to accommodate the users’ needs. This work suggests a solution that is to design an open source specialized application to serve the ever-changing needs of the users. This package is called Sensor Array Design and Optimization (SADO) and it is developed to allow the unexperienced users and the researchers to design, test and optimize their sensor arrays using effi-cient optimization algorithms. The optimization is trying to reduce the sidelobe levels to reduce the interference. The application is simple and friendly to use, with professional graphical results. The predesigned arrays configurations supplied with this package are uniform and random arrays. The built in optimization algorithms are: Artificial Bee Colony (ABC), Biogeography-Based Optimization (BBO) and Teaching Learning Based Optimization (TLBO). The results for various designs and optimization results are also given and compared to indicate the best settings for the user. Some optimization ratios might reach about 50% that represent -3dB reduction in sidelobe level.

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Rabinkin, Daniel V., Richard J. Renomeron, and James L. Flanagan. "Microphone array sensor placement optimization in reverberant environments." Journal of the Acoustical Society of America 102, no. 5 (November 1997): 3207–8. http://dx.doi.org/10.1121/1.420954.

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Jia, Pengfei, Fengchun Tian, Shu Fan, Qinghua He, Jingwei Feng, and Simon X. Yang. "A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization." Sensor Review 34, no. 3 (June 10, 2014): 304–11. http://dx.doi.org/10.1108/sr-02-2013-630.

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Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

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Chaudhri, Shiv Nath, Navin Singh Rajput, Saeed Hamood Alsamhi, Alexey V. Shvetsov, and Faris A. Almalki. "Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm." Sensors 22, no. 8 (April 15, 2022): 3039. http://dx.doi.org/10.3390/s22083039.

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Ultra-low-power is a key performance indicator in 6G-IoT ecosystems. Sensor nodes in this eco-system are also capable of running light-weight artificial intelligence (AI) models. In this work, we have achieved high performance in a gas sensor system using Convolutional Neural Network (CNN) with a smaller number of gas sensor elements. We have identified redundant gas sensor elements in a gas sensor array and removed them to reduce the power consumption without significant deviation in the node’s performance. The inevitable variation in the performance due to removing redundant sensor elements has been compensated using specialized data pre-processing (zero-padded virtual sensors and spatial augmentation) and CNN. The experiment is demonstrated to classify and quantify the four hazardous gases, viz., acetone, carbon tetrachloride, ethyl methyl ketone, and xylene. The performance of the unoptimized gas sensor array has been taken as a “baseline” to compare the performance of the optimized gas sensor array. Our proposed approach reduces the power consumption from 10 Watts to 5 Watts; classification performance sustained to 100 percent while quantification performance compensated up to a mean squared error (MSE) of 1.12 × 10−2. Thus, our power-efficient optimization paves the way to “computation on edge”, even in the resource-constrained 6G-IoT paradigm.

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Ojha, Varun Kumar, and Paramartha Dutta. "Performance analysis of neuro swarm optimization algorithm applied on detecting proportion of components in manhole gas mixture." Artificial Intelligence Research 1, no. 1 (July 5, 2012): 31. http://dx.doi.org/10.5430/air.v1n1p31.

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The article presents performance analysis of the neuro swarm optimization algorithm applied for the detection of proportion of the component gases found in manhole gas mixture. The hybrid neuro swarm optimization technique is used for implementing an intelligent sensory system for the detection of component gases present in manhole gas mixture. The manhole gas mixture typically contains toxic gases such as Hydrogen Sulfide, Ammonia, Methane, Carbon Dioxide, Nitrogen Oxide, and Carbon Monoxide. A semiconductor based gas sensor array used for sensing the gas components consists of many sensor elements, where each sensor element is responsible for sensing particular gas component. Presence of multiple gas sensors for detecting multiple gases results in cross-sensitivity. The central theme of this article is the performance analysis of the algorithm which offers solution to multiple gas detection issue. The article also presents study on the computational cost incurred by the algorithm.

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Men, Hong, Zong Nian Ge, Hai Yan Liu, Rui Xia Wen, and Zhi Ming Xu. "Optimization of Sensor Array Data with Various Pattern Recognition Techniques." Applied Mechanics and Materials 20-23 (January 2010): 694–99. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.694.

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An electronic tongue which composed of selective electrodes is applied to mineral water recognition (optimization of sensor array). The task of the system is to distinguish among five brands of mineral water. For this purpose, various pattern recognition (PARC) procedures are employed: principle components analysis (PCA), independent component analysis, linked-like adaptive genetic algorithm (LAGA), et al. LAGA networks are proved to exhibit the best performance both in array optimization and mineral water recognition. Their further advantages, such as fast training and robustness, make them the suggested pattern classifiers for sensor array data.

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Chen, Rong Rong, De Han Luo, Yu Sun, Yun Long Sun, and H. Gholam Hossini. "A Sensor Array Optimization Method Based on Variance Difference for Machine Olfaction." Applied Mechanics and Materials 618 (August 2014): 523–27. http://dx.doi.org/10.4028/www.scientific.net/amm.618.523.

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In machine olfaction or electronic nose, sensor optimization is important to enhance pattern recognition efficiency and reduce redundant information. Highly correlated response of one sensor to two different odors implies less contribution of this sensor to the classification of these two odors. Variance difference is a significant index to measure the similarity of sensor responses. A sensor optimization method based on variance difference is proposed in this paper; both the average value of variance difference and cluster analysis of variance difference matrix were considered to identify several possible sensor subsets. Six Chinese herbal medicines and linear discrimination analysis (LDA) were applied to test the classification results in order to determine the best subset. LDA results indicated that the optimized sensor subset performed well in classification of the six Chinese medicines. The proposed sensor array optimization method could be applied to other kinds of odors classification as a novel method.

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Rahmantyo, Wikan Haryo, and Danang Lelono. "Analisis Respons Sensor Electroni Tongue terhadap Sampel Ganja menggunakan Support Vector Machine." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 9, no. 2 (October 31, 2019): 141. http://dx.doi.org/10.22146/ijeis.49173.

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Electronic tongue sensors consisting of 16 sensor array made of TOMA and OA lipids that have been used to classify samples of pure cannabis, cannabis mixed with tea and cannabis mixed with tobacco does not involve the feature selection technique so that a lot of duplicated data is generated from data sampling. Feature selection is performed using PCA. Data analysis resulted in loading values shows the contribution of each sensor, and the similarity in sensor performance in characterizing samples, then analyzed using the correlation test so that the sensors that produce redundant information are known. Validation is performed using the SVM method and the classification performance is compared to the original sensor.The sensor optimization produces a subset of features with 6 sensors (Sensor 7, Sensor 10, Sensor 12, Sensors 13, Sensor 14 and Sensor 15) in the cannabis-tea sample test and a feature subset with 3 sensors (Sensor 3, Sensor 7 and Sensor 14) in the cannabis-tobacco sample test. Sensor optimization that has been done produced classification accuracy by 100% and shorten the running time by a difference of 0.578 microseconds in the test of cannabis-tea samples and a difference of 1.696 microseconds in the test of cannabis-tobacco samples.

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Choi, Jang-Sik, Jin-Young Jeon, and Hyung-Gi Byun. "Investigation of Chemical Sensor Array Optimization Methods for DADSS." Journal of Sensor Science and Technology 25, no. 1 (January 31, 2016): 13–19. http://dx.doi.org/10.5369/jsst.2016.25.1.13.

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Xu, Zhe, and Susan Lu. "Multi-objective optimization of sensor array using genetic algorithm." Sensors and Actuators B: Chemical 160, no. 1 (December 2011): 278–86. http://dx.doi.org/10.1016/j.snb.2011.07.048.

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Wang, Wen Hu, Chao Hu, Wei Xing Lin, and Jian Meng Bao. "Sensor Arrangement Optimization of Wearable Model in Magnetic Localization System." Advanced Materials Research 902 (February 2014): 306–11. http://dx.doi.org/10.4028/www.scientific.net/amr.902.306.

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A magnetic localization and orientation system is used to track the movement of the capsule endoscope during the gastrointestinal examination process. The system is made of a magnetic sensors array collecting the intensity of the magnetic field from a magnet in the capsule. In this paper, we try to optimize the sensor arrangement of wearable model that likes elliptical cylinder around the human body, to improve the tracking precision. Different sensor arrangement schemes are evaluated, and the tracking accuracy can be significantly increased with the appropriate arrangement. In addition, we presented a real time localization algorithm to compute uncertain 3D locus for estimating the tracing performance of the capsule.

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Jeong, Taekyeong, Janggon Yoo, and Daegyoum Kim. "Deep learning model inspired by lateral line system for underwater object detection." Bioinspiration & Biomimetics 17, no. 2 (January 24, 2022): 026002. http://dx.doi.org/10.1088/1748-3190/ac3ec6.

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Abstract Inspired by the lateral line systems of various aquatic organisms that are capable of hydrodynamic imaging using ambient flow information, this study develops a deep learning-based object localization model that can detect the location of objects using flow information measured from a moving sensor array. In numerical simulations with the assumption of a potential flow, a two-dimensional hydrofoil navigates around four stationary cylinders in a uniform flow and obtains two types of sensory data during a simulation, namely flow velocity and pressure, from an array of sensors located on the surface of the hydrofoil. Several neural network models are constructed using the flow velocity and pressure data, and these are used to detect the positions of the hydrofoil and surrounding objects. The model based on a long short-term memory network, which is capable of learning order dependence in sequence prediction problems, outperforms the other models. The number of sensors is then optimized using feature selection techniques. This sensor optimization leads to a new object localization model that achieves impressive accuracy in predicting the locations of the hydrofoil and objects with only 40% of the sensors used in the original model.

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Wang, Peng, Yujun Kong, and Mingxing Zhang. "Error Self-Calibration Algorithm for Acoustic Vector Sensor Array." Journal of Sensors 2019 (September 4, 2019): 1–10. http://dx.doi.org/10.1155/2019/9052547.

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In this paper, the errors of acoustic vector sensor array are classified, the impact factor of each error for the array signal model is derived, and the influence of each type of error on the direction-of-arrival (DOA) estimation performance of the array is compared by Monte Carlo experiments. Converting the directional error and location error to amplitude and phase errors, the optimization model and error self-calibration algorithm for acoustic vector sensor array are proposed. The simulation experiments and field experiment data processing of MEMS vector sensor array show that the proposed self-calibration algorithm has good parameter estimation performance and certain engineering practicability.

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Nie, Lei, Yifan Huang, Yehan Yin, Mengran Liu, Lili Wu, and Haoming Yang. "Internal defect identification method of TSV 3D packaging based on built-in integrated sensor." Advances in Mechanical Engineering 14, no. 9 (September 2022): 168781322211214. http://dx.doi.org/10.1177/16878132221121480.

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TSV (Through Silicon Via) is a key technology for three-dimensional (3D) packaging due to its unique vertical interconnection method. However, its particular manufacturing process of-ten leads to internal defects, such as gaps, bottom voids, filling missing, which are usually difficult to be detected by common means. In order to discover the internal defect of TSV packaging effectively, a novel non-destructive inspection method based on built-in integrated temperature sensor array is proposed. The relationship between temperature distribution and internal defect is dis-covered and then corresponding sensor array layout is designed. The simulation analysis shows that this kind of sensor array can recognize the internal TSV defect. And supervised machine learning is used to construct the classification model by which different defects can be found and classified with relatively high accuracy, and the classification accuracy rate can reach 95.625%. Experiments were conducted and the rationality of this built-in sensing array was verified. The research provides a non-destructive testing method for TSV internal defects based on bulit-in-integrated sensors, and verifies the feasibility of sensor arrangement through simulation, laying a foundation for the realization of later TSV design optimization.

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Ajo-Franklin, Jonathan B. "Optimal experiment design for time-lapse traveltime tomography." GEOPHYSICS 74, no. 4 (July 2009): Q27—Q40. http://dx.doi.org/10.1190/1.3141738.

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Geophysical monitoring techniques offer the only noninvasive approach capable of assessing the spatial and temporal dynamics of subsurface fluid processes. Increasingly, permanent sensor arrays in boreholes and on the ocean floor are being deployed to improve the repeatability and increase the temporal sampling of monitoring surveys. Because permanent arrays require a large up-front capital investment and are difficult (or impossible) to reconfigure once installed, a premium is placed on selecting a geometry capable of imaging the desired target at minimum cost. We have taken a simple approach to optimizing downhole sensor configurations for monitoring experiments making use of differential seismic traveltimes. We used a design quality metric based on the accuracy of tomographic reconstructions for a suite of imaging targets. By not requiring an explicit singular value decomposition of the forward operator, evaluation of this objective function scaled to problems with a large numberof unknowns. We restricted the design problem by recasting the array geometry into a low-dimensional form more suitable for optimization at a reasonable computational cost. We tested two search algorithms on the design problem: the Nelder-Mead downhill simplex method and the multilevel coordinate search algorithm. The algorithm was tested for four crosswell acquisition scenarios relevant to continuous seismic monitoring, a two-parameter array optimization, several scenarios involving four-parameter length/offset optimizations, and a comparison of optimal multisource designs. In the last case, we also examined trade-offs between source sparsity and the quality of tomographic reconstructions. Asymmetrical array lengths improved localized image quality in crosswell experiments with a small number of sources and a large number of receivers. Preliminary results also suggested that high-quality differential images could be generated using only a small number of optimally positioned sources in tandem with a more extensive receiver array.

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Zhong, Qian Wen, Yang Xu, Jie Yang, and Zhuo Meng. "PV Array Mathematical Model Optimization Based on Monitoring Data." Applied Mechanics and Materials 376 (August 2013): 336–40. http://dx.doi.org/10.4028/www.scientific.net/amm.376.336.

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The type of Solar panels in photovoltaic power generating system is generally photovoltaic array. On the basis of the photovoltaic array mathematical model for engineering , using the mathematical software MATLAB Simulink tool to build a photovoltaic array simulation model and utilizing monitoring data acquired by the detection system-Sunny Sensor Box-for Donghua University photovoltaic power generating system, the mathematical model of the PV array for engineering is optimized and the optimization includes linear and natural exponential optimization. Furthermore, higher accuracy of the optimized models is validated.

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Rai, Pratiksha, and Syed Hasan Saeed. "Detection of harmful gases present in the environment." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 1 (April 1, 2023): 70. http://dx.doi.org/10.11591/ijeecs.v30.i1.pp70-80.

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The electronic nose (e-nose) is demonstrated in this research for detecting and identifying several forms of hazardous gases. We describe an e-noses for detecting several gases, including butane, acetone, methane, and ethanol. For dimensionality reduction in 3D representation, data processing approaches are based on the partial least square (PLS) method. The suggested system can be utilised for sensor optimization since different sensors with varied operating temperatures can be tested in many devices to find the best array for a specific detection or application. The results reveal that, depending on the sensor array characteristics, varying success rates in classification can be attained when discriminating contaminants. The preceding criteria lead to a new search for a portable, dependable, low-cost, and most efficient gas sensor. The major purpose of this study is to create a gas sensor array that can detect and monitor toxic and poisonous gases in the environment, as well as warn against dangerous organic compounds. Our goal is to create a sensor system that can distinguish the most significant decontamination gases while also being highly responsive, precise, low-effort, and low-power demanding.

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Xue, Qian, and Xijuan Chen. "Optimization of Planar Array Electrostatic Sensor for Metal Surface Defect Detection." Journal of Physics: Conference Series 2370, no. 1 (November 1, 2022): 012019. http://dx.doi.org/10.1088/1742-6596/2370/1/012019.

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Static electricity is usually generated in the damage area of the metal surface due to contact friction, and the charge distribution density can reflect the size, shape, and relative position of the damage area. Based on this, a planar array electrostatic sensor is designed to detect metal surface defect in this paper, and the shielding method, number of electrodes, electrode shape, and arrangement of the sensor are optimized taking account of the induced charge value, the uniformity of sensitivity and the image correlation coefficient. Different image reconstruction algorithms (e.g. Landweber algorithm, conjugate Gradient algorithm, Tikhonov regularization and primary dual interior point method) are utilized to evaluate the performance of the designed electrostatic sensor. The results demonstrated that the sensor with hexagonal electrode shape, integrated shielding, a new arrangement, a duty cycle of 80%, and a peripheral shielding electrode, has better image quality for all the tested damage models. When using the PDIPA algorithm for image reconstruction, the image correlation coefficient can exceed 0.9.

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Borowik, Piotr, Leszek Adamowicz, Rafał Tarakowski, Krzysztof Siwek, and Tomasz Grzywacz. "Odor Detection Using an E-Nose With a Reduced Sensor Array." Sensors 20, no. 12 (June 23, 2020): 3542. http://dx.doi.org/10.3390/s20123542.

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Recent advances in the field of electronic noses (e-noses) have led to new developments in both sensors and feature extraction as well as data processing techniques, providing an increased amount of information. Therefore, feature selection has become essential in the development of e-nose applications. Sophisticated computation techniques can be applied for solving the old problem of sensor number optimization and feature selections. In this way, one can find an optimal application-specific sensor array and reduce the potential cost associated with designing new e-nose devices. In this paper, we examine a procedure to extract and select modeling features for optimal e-nose performance. The usefulness of this approach is demonstrated in detail. We calculated the model’s performance using cross-validation with the standard leave-one-group-out and group shuffle validation methods. Our analysis of wine spoilage data from the sensor array shows when a transient sensor response is considered, both from gas adsorption and desorption phases, it is possible to obtain a reasonable level of odor detection even with data coming from a single sensor. This requires adequate extraction of modeling features and then selection of features used in the final model.

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Zhang, Wenbin, Peng Li, Nianrong Zhou, Chunguang Suo, Weiren Chen, Yanyun Wang, Jiawen Zhao, and Yincheng Li. "Method for Localization Aerial Target in AC Electric Field Based on Sensor Circular Array." Sensors 20, no. 6 (March 12, 2020): 1585. http://dx.doi.org/10.3390/s20061585.

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The traditional method of using electric field sensors to realize early warning of electric power safety distance cannot measure the distance of dangerous sources. Therefore, aiming at the electric field with a frequency of 50 to 60 Hz (AC electric field), a new method for localization of aerial AC target by the capacitive one-dimensional spherical electric field sensor circular array is studied. This method can directly calculate the distance, elevation, and azimuth of the detector from the dangerous source. By combining the measurement principle of the spherical electric field sensor and the plane circular array theory, a mathematical model for the localization of aerial targets in an AC electric field is established. An error model was established using Gaussian noise and the effects of different layout parameters on the localization error were simulated. Based on mutual interference between sensors, minimum induced charge, and localization error, an optimal model for sensor layout was established, and it was solved by using genetic algorithms. The optimization results show that when the number of sensors is 4, the array radius is 20 cm, and the sensor radius is 1.5 cm, the ranging error is 8.4%. The detector was developed based on the layout parameters obtained from the optimization results, and the localization method was experimentally verified at 10 and 35 kV alarm distances. The experimental results show that when the detector is located at 10 kV alarm distance, the distance error is 0.18 m, the elevation error is 6.8°, and the azimuth error is 4.57°, and when it is located at 35 kV alarm distance, the distance error is 0.2 m, the elevation error is 4.8°, and the azimuth error is 5.14°, which meets the safety distance warning requirements of 10 and 35 kV voltage levels.

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Zhang, Wenwen, Lei Wang, Lihua Ye, Peilong Li, and Mingxue Hu. "Gas Sensor Array Dynamic Measurement Uncertainty Evaluation and Optimization Algorithm." IEEE Access 7 (2019): 35779–94. http://dx.doi.org/10.1109/access.2019.2898881.

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Cai, Chang-Xin, Guan-Jun Huang, Fang-Qing Wen, Xin-Hai Wang, and Lin Wang. "2D-DOA Estimation for EMVS Array with Nonuniform Noise." International Journal of Antennas and Propagation 2021 (August 18, 2021): 1–9. http://dx.doi.org/10.1155/2021/9053864.

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Electromagnetic vector sensor (EMVS) array is one of the most potential arrays for future wireless communications and radars because it is capable of providing two-dimensional (2D) direction-of-arrival (DOA) estimation as well as polarization angles of the source signal. It is well known that existing subspace algorithm cannot directly be applied to a nonuniform noise scenario. In this paper, we consider the 2D-DOA estimation issue for EMVS array in the presence of nonuniform noise and propose an improved subspace-based algorithm. Firstly, it recasts the nonuniform noise issue as a matrix completion problem. The noiseless array covariance matrix is then recovered via solving a convex optimization problem. Thereafter, the shift invariant principle of the EMVS array is adopted to construct a normalized polarization steering vector, after which 2D-DOA is easily estimated as well as polarization angles by incorporating the vector cross-product technique and the pseudoinverse method. The proposed algorithm is effective to EMVS array with arbitrary sensor geometry. Furthermore, the proposed algorithm is free from the nonuniform noise. Several simulations verify the improvement of the proposed method.

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Sarma, Munmi, Noelia Romero, Xavier Cetó, and Manel del Valle. "Optimization of Sensors to be Used in a Voltammetric Electronic Tongue Based on Clustering Metrics." Sensors 20, no. 17 (August 25, 2020): 4798. http://dx.doi.org/10.3390/s20174798.

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Herein we investigate the usage of principal component analysis (PCA) and canonical variate analysis (CVA), in combination with the F factor clustering metric, for the a priori tailored selection of the optimal sensor array for a given electronic tongue (ET) application. The former allows us to visually compare the performance of the different sensors, while the latter allows us to numerically assess the impact that the inclusion/removal of the different sensors has on the discrimination ability of the ET. The proposed methodology is based on the measurement of a pure stock solution of each of the compounds under study, and the posterior analysis by PCA/CVA with stepwise iterative removal of the sensors that demote the clustering when retained as part of the array. To illustrate and assess the potential of such an approach, the quantification of paracetamol, ascorbic acid, and uric acid mixtures were chosen as the study case. Initially, an array of eight different electrodes was considered, from which an optimal array of four sensors was derived to build the quantitative ANN model. Finally, the performance of the optimized ET was benchmarked against the results previously reported for the analysis of the same mixtures, showing improved performance.

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Dibiase, Marco, Masoud Mohammadgholiha, and Luca De Marchi. "Optimal Array Design and Directive Sensors for Guided Waves DoA Estimation." Sensors 22, no. 3 (January 20, 2022): 780. http://dx.doi.org/10.3390/s22030780.

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The estimation of Direction of Arrival (DoA) of guided ultrasonic waves is an important task in many Structural Health Monitoring (SHM) applications. The aim is to locate sources of elastic waves which can be generated by impacts or defects in the inspected structures. In this paper, the array geometry and the shape of the piezo-sensors are designed to optimize the DoA estimation on a pre-defined angular sector, from acquisitions affected by noise and interference. In the proposed approach, the DoA of a wave generated by a single source is considered as a random variable that is uniformly distributed in a given range. The wave velocity is assumed to be unknown and the DoA estimation is performed by measuring the Differences in Time of Arrival (DToAs) of wavefronts impinging on the sensors. The optimization procedure of sensors positioning is based on the computation of the DoA and wave velocity parameters Cramér-Rao Matrix Bound (CRMB) with a Bayesian approach. An efficient DoA estimator is found based on the DToAs Gauss-Markov estimator for a three sensors array. Moreover, a novel directive sensor for guided waves is introduced to cancel out undesired Acoustic Sources impinging from DoAs out of the given angles range. Numerical results show the capability to filter directional interference of the novel sensor and a considerably improved DoA estimation performance provided by the optimized sensor cluster in the pre-defined angular sector, as compared to conventional approaches.

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Wang, Changhui. "Sample Density Clustering Method Considering Unbalanced Data Distribution." Mobile Information Systems 2022 (September 15, 2022): 1–8. http://dx.doi.org/10.1155/2022/7580468.

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The data distribution of the multidimensional array sensor is unbalanced in data sample collection. To improve the clustering ability of data samples, a data density clustering method of sparse scattered points and multisensor array sensor samples based on the analysis of unbalanced data distribution characteristics is proposed. The sparse scattered multisensor array network’s sample data collection structure is created using the Voronoi polygon topology. By analyzing the unbalanced parameters between data classes and reconstructing the characteristic space of data sample sequence, the time series of sample data collected by sparse scattered multisensor array is reorganized, and the statistical characteristic quantity and high-order cumulant of sample data collected by sparsely scattered multisensor array are extracted. Combined with the learning algorithm of unbalanced data distribution sample feature fusion, the fuzzy clustering of sample data information flow collected by sparse scattered multisensor array elements is realized. According to the feature clustering and convergence analysis, the sparse scattered feature detection method is adopted to realize the data density clustering and data structure optimization configuration of sparse scattered multisensor array elements. The test results show that the method in this paper has good convergence, strong spectrum expansion ability, and low error rate of data clustering when collecting samples with sparse scattered points and multisensor arrays.

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Chang, Li, Qing Hua Xu, and Ben Wei Liu. "Optimization Design of Electromagnetic Conductance Sensor for the Water Content of Crude Oil inside Pipeline." Applied Mechanics and Materials 55-57 (May 2011): 628–32. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.628.

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In the measurement of water content of crude oil inside pipeline, the traditional electromagnetic conductance sensors cannot achieve the measurement of water content due to the influence of pipeline closure and electromagnetic shielding. In theory, the double-coil array electromagnetic conductance sensor can obtain the measurement of water content with low frequency sine signal as the motivation. However, its longitudinal resolution is too low and the characteristic is poor. The four-coil model has been established so as to improve the sensor vertical resolution. The simulation results indicate that the useful signal is concentrated in the internal of main coils, which plays a good role of longitudinal focus.

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COURTE, DALE E., MATEEN M. RIZKI, LOUIS A. TAMBURINO, and RICARDO GUTIERREZ-OSUNA. "EVOLUTIONARY OPTIMIZATION OF GAUSSIAN WINDOWING FUNCTIONS FOR DATA PREPROCESSING." International Journal on Artificial Intelligence Tools 12, no. 01 (March 2003): 17–35. http://dx.doi.org/10.1142/s0218213003001095.

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The average classification accuracy of an odor classification system is improved using a genetic algorithm to determine optimal parameters for feature extraction. Gaussian windowing functions, called "kernels" are evolved to extract information from the transient response of an array of gas sensors, resulting in a reduced set of extracted features for a linear discriminant pattern classification system. Results show significant improvements are achieved when compared to results obtained using a predetermined and fixed set of four bell-shaped kernels for every sensor. Examination of the evolved kernels reveals the areas of the sensor responses where discriminating information was identified. A novel data migration approach during training helps prevent overtraining, and the fitness measure chosen incorporates adjustments for both population diversity and solution complexity. A variety of adjustable parameters, including the definition of a time-varying dynamic weighting factor, encourage experimentation in order to appropriately tune the sampling methods and fitness measure.

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Zhang, Xiaojun, Liming Fan, Peng Cheng, Chunlei Chen, Xuejun Liu, and Chong Kang. "A Method to Remotely Track a Magnetic Target Using a Scalar Magnetometer Array." Journal of Sensors 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6510980.

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The orientation of a vector magnetic sensor can affect the measurement accuracy of magnetic anomaly, thereby increasing the localization error of a magnetic target. Compared with vector magnetic sensor, the measurement of the scalar magnetic sensor is almost not influenced by its orientation. Therefore, we present a method for tracking the magnetic target with a static scalar magnetometer array. In this study, the magnitude of the target’s magnetic moment is a key parameter. We isolate it and formulate an optimization problem based on it to estimate the position and magnetic parameters of the target. To calculate the solution of this optimization problem, a dedicated particle swarm optimization (PSO) algorithm is developed. Then, we define a quality index to evaluate the solution calculated by the optimization problem. The proposed method was validated by the simulation and the real data collected when an SUV car was passing by the array on a straight path. The results show that the tracked trajectory is very close to the true trajectory and the quality index can be used as a criterion to allow accepting or rejecting the localization of the target.

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Tian, Bian, Yifan Xing, Xuefeng Zhang, Zhaojun Liu, Zhongkai Zhang, Jiangjiang Liu, Bingfei Zhang, Qijing Lin, and Zhuangde Jiang. "A High-Precision Three-Dimensional Probe Array Temperature Sensor." Chemosensors 10, no. 8 (August 5, 2022): 309. http://dx.doi.org/10.3390/chemosensors10080309.

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To meet the need for micro-volume devices for high-precision measurement of temperature, Cu-Constantan (CuNi45) thin films with a novel array structure of thermo-electrodes were designed and fabricated. The thermo-electrodes on the probe-type substrate were deposited by magnetron sputtering technology and the profiling mask was prepared by 3D printing technology. The comprehensive performance of the temperature sensor was improved by systematic optimization of the heat treatment process and accuracy correction algorithm. Results showed that the sensor can measure with an accuracy of up to ±0.19%FS from −60 °C to 200 °C. The three-dimensional probe array temperature sensor shows great advantages in sensitivity, reliability resolution, stability, and measurement accuracy.

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Durán, Cristhian, Juan Benjumea, and Jeniffer Carrillo. "Response Optimization of a Chemical Gas Sensor Array using Temperature Modulation." Electronics 7, no. 4 (April 21, 2018): 54. http://dx.doi.org/10.3390/electronics7040054.

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Kim, Kyoung-Youm, and Jaehoon Jung. "Multiobjective optimization for a plasmonic nanoslit array sensor using Kriging models." Applied Optics 56, no. 21 (July 13, 2017): 5838. http://dx.doi.org/10.1364/ao.56.005838.

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Subandri, Muhammad Asep, and Riyanarto Sarno. "E-Nose Sensor Array Optimization Based on Volatile Compound Concentration Data." Journal of Physics: Conference Series 1201 (May 2019): 012003. http://dx.doi.org/10.1088/1742-6596/1201/1/012003.

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Jung, Jaehoon. "Robust Optimization of Nanoslit Array Sensor Based on Extraordinary Optical Transmission." IEEE Sensors Journal 18, no. 21 (November 1, 2018): 8720–25. http://dx.doi.org/10.1109/jsen.2018.2870300.

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Wang, Hui, Xiaolin Wang, Matthew Wadsworth, Mohammad Faisal Ahmed, Zhe Liu, and Changchun Zeng. "Design, Fabrication, Structure Optimization and Pressure Sensing Demonstration of COC Piezoelectret Sensor and Sensor Array." Micromachines 13, no. 8 (July 26, 2022): 1177. http://dx.doi.org/10.3390/mi13081177.

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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).

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Journal articles on the topic 'Sensor Array Optimization'

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