Relevant bibliographies by topics / Sensor Array Optimization

Academic literature on the topic 'Sensor Array Optimization'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Sensor Array Optimization"

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Sundar, Meghana. "Sensor array optimization application of cluster analysis and genetic algorithms for sensor selection /." Diss., Online access via UMI:, 2007.

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Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2007.
Includes bibliographical references.
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Lee, Shira M. (Shira Miriam). "Finite element simulation and parameter optimization of a flexible tactile pressure sensor array." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32948.

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Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.
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A finite element model was developed to optimize design of a flexible tactile sensor. The sensor consists of layers of thin-film copper and PDMS, and the model can be used to determine the effects on sensor sensitivity and durability of variations in material properties and geometry. The model was used to study the effect of variations in copper thickness. Four copper thicknesses, 0.3[mu]m, 0.5[mu]m, 3[mu]m, and 9[mu]m, were analyzed under a range of pressure loads. The thickness of the copper affected both the stress in the material and the displacement of the copper when a pressure load was applied to the sensor model. The stress in the sensor was highest for 3[mu]m copper, potentially causing decreased durability in this sensor. The separation between the copper strips beneath the pressure load was highest for 9[mu]m copper, so this sensor may have lower accuracy for small loads. Thin copper strips are challenging to manufacture, so the largest but most accurate and durable copper strip thickness, 0.5[mu]m, is recommended from this analysis.
by Shira M. Lee.
S.B.
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Ekdahl, Filipsson Fabian. "Trajectory and Pulse Optimization for Active Towed Array Sonar using MPC and Information Measures." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420532.

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In underwater tracking and surveillance, the active towed array sonar presents a way of discovering and tracking adversarial submerged targets that try to stay hidden. The configuration consist of listening and emitting hydrophones towed behind a ship. Moreover, it has inherent limitations, and the characteristics of sound in the ocean are complex. By varying the pulse form emitted and the trajectory of the ship the measurement accuracy may be improved. This type of optimization constitutes a sensor management problem. In this thesis, a model of the tracking scenario has been constructed derived from Cramér-Rao bound analyses. A model predictive control approach together with information measures have been used to optimize a filter's estimated state of the target. For the simulations, the MATLAB environment has been used. Different combinations of decision horizons, information measures and variations of the Kalman filter have been studied. It has been found that the accuracy of the Extended Kalman filter is too low to give consistent results given the studied information measures. However, the Unscented Kalman filter is sufficient for this purpose.
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Ullah, Anayat [Verfasser]. "Optimization of the Mechanical and Optical Properties of Tunable Optical Sensor Arrays (TOSA) for a Nanospectrometer in the Visible and Near Infrared Spectral Range / Anayat Ullah." Kassel : Universitätsbibliothek Kassel, 2014. http://d-nb.info/1065080360/34.

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Le, Ny Mathieu. "Diagnostic non invasif de piles à combustible par mesure du champ magnétique proche." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00844407.

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Cette thèse propose une technique innovante de diagnostic non invasive pour les systèmes piles à combustible. Cette technique s'appuie sur la mesure de la signature magnétique générée par ces systèmes. A l'aide de ces champs magnétiques externes, il est possible d'obtenir une cartographie de la densité de courant interne par résolution d'un problème inverse. Ce problème est néanmoins mal posé : la solution est non unique et est extrêmement sensible au bruit. Des techniques de régularisation ont ainsi été mises en place pour filtrer les erreurs de mesures et obtenir une solution physiquement acceptable. Afin d'augmenter la qualité de reconstruction des courants, nous avons conçu notre outil de diagnostic de manière à ce qu'il soit uniquement sensible aux défaillances de la pile (capteur de défauts). De plus, cette reconstruction se base sur un nombre extrêmement faible de mesures. Une telle approche facilite l'instrumentation du système et augmente la précision et la rapidité de celui-ci. La sensibilité de notre outil à certaines défaillances (assèchements, appauvrissement en réactifs, dégradations) est démontrée.
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Ortman, Robert L. "Sensory input encoding and readout methods for in vitro living neuronal networks." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44856.

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Establishing and maintaining successful communication stands as a critical prerequisite for achieving the goals of inducing and studying advanced computation in small-scale living neuronal networks. The following work establishes a novel and effective method for communicating arbitrary "sensory" input information to cultures of living neurons, living neuronal networks (LNNs), consisting of approximately 20 000 rat cortical neurons plated on microelectrode arrays (MEAs) containing 60 electrodes. The sensory coding algorithm determines a set of effective codes (symbols), comprised of different spatio-temporal patterns of electrical stimulation, to which the LNN consistently produces unique responses to each individual symbol. The algorithm evaluates random sequences of candidate electrical stimulation patterns for evoked-response separability and reliability via a support vector machine (SVM)-based method, and employing the separability results as a fitness metric, a genetic algorithm subsequently constructs subsets of highly separable symbols (input patterns). Sustainable input/output (I/O) bit rates of 16-20 bits per second with a 10% symbol error rate resulted for time periods of approximately ten minutes to over ten hours. To further evaluate the resulting code sets' performance, I used the system to encode approximately ten hours of sinusoidal input into stimulation patterns that the algorithm selected and was able to recover the original signal with a normalized root-mean-square error of 20-30% using only the recorded LNN responses and trained SVM classifiers. Response variations over the course of several hours observed in the results of the sine wave I/O experiment suggest that the LNNs may retain some short-term memory of the previous input sample and undergo neuroplastic changes in the context of repeated stimulation with sensory coding patterns identified by the algorithm.
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Hossain, Md Eftekhar, and odour volatile. "Fabrication and optimization of a sensor array for incipient grain spoilage monitoring." 2010. http://hdl.handle.net/1993/4142.

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During storage of grain, there may have significant damage to its quality due to unfavorable physical and biological interactions and thus requires continuous monitoring. Therefore, an easy, cost-effective and environmentally friendly method is necessary for efficient monitoring of stored-grain. Arrays of sensors are being used for classifying liquors, perfumes, quality of food products mimicking mammalian olfactory systems. Monitoring of stored grain is a new application of sensor arrays. The main objective was to fabricate a carbon black polymer sensor array which can easily monitor incipient grain spoilage by detecting spoiling stored grain volatiles (benzene derivatives and aliphatic hydrocarbon derivatives) with minimum interference from relative humidity. Various aspects of a good sensor were analyzed using statistical analysis (RSD, LDA, PCA, t-test). The developed sensor array can identify red flour beetle-infected and uninfected wheat and fungal volatiles at ambient conditions as well as some stored grain conditions (MC 16%, RH 52%).
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Hossain, Md Eftekhar. "Fabrication and optimization of a sensor array for incipient grain spoilage monitoring." 2010. http://hdl.handle.net/1993/4142.

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During storage of grain, there may have significant damage to its quality due to unfavorable physical and biological interactions and thus requires continuous monitoring. Therefore, an easy, cost-effective and environmentally friendly method is necessary for efficient monitoring of stored-grain. Arrays of sensors are being used for classifying liquors, perfumes, quality of food products mimicking mammalian olfactory systems. Monitoring of stored grain is a new application of sensor arrays. The main objective was to fabricate a carbon black polymer sensor array which can easily monitor incipient grain spoilage by detecting spoiling stored grain volatiles (benzene derivatives and aliphatic hydrocarbon derivatives) with minimum interference from relative humidity. Various aspects of a good sensor were analyzed using statistical analysis (RSD, LDA, PCA, t-test). The developed sensor array can identify red flour beetle-infected and uninfected wheat and fungal volatiles at ambient conditions as well as some stored grain conditions (MC 16%, RH 52%).
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Chou, Yu-Wei, and 周育瑋. "Optimization for Localized Surface Plasmon Resonance Vapor Sensor Array Using Polymer Coated Gold Nanoshells." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/42786874240043254986.

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碩士
國立臺灣師範大學
化學系
100
This research uses gold nanoshells as the localized surface plasmon resonance (LSPR) sensors that were surface-modified with various polymer thin films to provide chemical selectivity for detecting volatile organic compounds (VOCs). The changes of LSPR spectrum were measured by real-time UV-visible spectrum. Distinguishable patterns of the tested organic vapors with different functional groups were generated by one unmodified and seven surface-modified gold nanoshell LSPR sensors. The polymer modification altered the surface chemical affinity without increasing the response time. In order to find out the sensor set which would obtain the best recognition rate, five analytical methods were introduced: maximum distance, sum of variance, maximum area, F-test, and Wilcoxon-Wilcox test. The sum of variance method indicated that the {DB-1, PIB, PECH} sensor set had a better result, whereas the maximum distance and maximum area method showed the higher efficiency of the {PEG, PIB, PECH} sensor set. The result of F-test represented that {PEG, PIB}, {PEG, PECH}, and {PMMA, PECH} would be the possible sets, while Wilcoxon-Wilcox test indicated that {PEG, PIB} and {PEG, PECH} were the sets with significant differences. According to these analytical methods, the sensor set with obvious patterns would be easy to figure out from large amounts of sensing data without time-consuming.
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Cervantes, Téllez Gabriela Andrea. "Engineering in the optimization of resolution of nanohole arrays in metal films for refractive index sensing." Thesis, 2012. http://hdl.handle.net/1828/4077.

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Label free detection techniques such as surface plasmon resonance, carbon nanotubes, nanowires, and interferometry have been progressing rapidly for biosensing applications. Surface plasmon resonance is considered one of the most promising label free optical techniques. The use of nanohole arrays in a metal film allows for extraordinary transmission and has been motivated by their application as biosensors. Nanohole arrays present several advantages like smaller foot print, dense integration, lower limits of detection, and collinear optical detection. This thesis presents the design parameters for the optimization of sensitivity and resolution of nanohole arrays for refractive index sensing. A systematic study is provided of the influence of the nanohole array periodicity, diameter, and gold thickness. Focused ion beam was used to fabricate the nanohole arrays. A microfluidic device with a set of embedded nanohole arrays was developed and used to measure the sensing characteristics. The results are encouraging for potential future biosensing tests.
Graduate
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Books on the topic "Sensor Array Optimization"

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L, Palumbo Daniel, and Langley Research Center, eds. Performance of optimized actuator and sensor arrays in an active noise control system. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1996.

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Book chapters on the topic "Sensor Array Optimization"

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Saha, P., S. Ghorai, B. Tudu, R. Bandyopadhyay, and N. Bhattacharyya. "Optimization of Sensor Array in Electronic Nose by Combinational Feature Selection Method." In Sensing Technology: Current Status and Future Trends II, 189–205. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02315-1_9.

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Zhao, Leilei, Fengchun Tian, Junhui Qian, Ran Liu, and Anyan Jiang. "Robust Sparse Learning Based Sensor Array Optimization for Multi-feature Fusion Classification." In Lecture Notes in Computer Science, 176–86. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15937-4_15.

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Chung, Wan-Young, Jun-Woo Lim, and Duk-Dong Lee. "Thermal Optimization of a Novel Micro Gas Sensor Array With Different Operating Temperatures." In Transducers ’01 Eurosensors XV, 1672–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59497-7_395.

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Mazzia, Vittorio, Angelo Tartaglia, Marcello Chiaberge, and Dario Gandini. "Deep Learning Algorithms for Complex Pattern Recognition in Ultrasonic Sensors Arrays." In Machine Learning, Optimization, and Data Science, 24–35. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37599-7_3.

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Leite Cavalcanti, Welchy, Elli Moutsompegka, Konstantinos Tserpes, Paweł H. Malinowski, Wiesław M. Ostachowicz, Romain Ecault, Neele Grundmann, et al. "Integrating Extended Non-destructive Testing in the Life Cycle Management of Bonded Products—Some Perspectives." In Adhesive Bonding of Aircraft Composite Structures, 331–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-92810-4_6.

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AbstractIn this chapter, we outline some perspectives on embracing the datasets gathered using Extended Non-destructive Testing (ENDT) during manufacturing or repair process steps within the life cycle of bonded products. Ensuring that the ENDT data and metadata are FAIR, i.e. findable, accessible, interoperable and re-usable, will support the relevant stakeholders in exploiting the contained material-related information far beyond a stop/go decision, while a shorter time-to-information will facilitate a prompter time-to-decision in process and product management. Exploiting the value of ENDT (meta)data will contribute to increased performance by integrating all defined, measured, analyzed and controlled aspects of material transformation across process and company boundaries. This will facilitate the optimization of manufacturing and repair operations, boosting their energy efficiency and productivity. In this regard, some aspects that are currently driving activities in the field of pre-process, in-process and post-process quality assessment will be addressed in the following. Furthermore, some requirements will be contemplated for harmonized and conjoint data transfer ranging from a bonded product’s beginning-of-life through its end-of-life, the customization of stand-alone or linked ENDT tools, and the implementation of sensor arrays and networks in joints, devices and structural parts to gather material-related data during a product’s middle-of-life application phase, thereby fostering structural health monitoring (SHM).
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Sheik Althaf, M., K. P. Ray, Nethravathi K A, Bhishm Tripathi, and Ashish K. Adiga. "Non-Uniform Linear Nested Array Implementation for Improved Beamforming and Optimization." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220763.

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Beamforming has been an area of interest for a lot of research work as the multiantenna system has become an important topic in wireless communication. While both theoretical, as well as real-time practical implementation techniques for beamforming have been extensively researched, the existing techniques mainly focus on obtaining peak performance in the fastest and most accurate way possible and usually ignore applications providing improved performance with limited resource utilization. This paper further explores the concept of nonuniform spacing in such multiantenna receivers and shows how beamforming equivalent to regular uniform sensor arrays can be achieved and analyses many trade-offs that designers and users must consider for a minimalistic approach. The objective of this paper is to analyze various beamforming techniques on Uniform Linear Arrays (ULA) and the performance comparison between various techniques along with the analysis of output signal reconstruction and the array response. It also contains a detailed analysis of the uniformly spaced arrays and nested arrays using optimization techniques for error minimization. The construction of Non-uniform Linear Nested Arrays (NULNA) with optimum element spacing will be analyzed.
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Biswas, Rathindra Nath, Swarup Kumar Mitra, and Mrinal Kanti Naskar. "Preserving Security of Mobile Anchors Against Physical Layer Attacks." In Research Anthology on Securing Mobile Technologies and Applications, 93–118. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8545-0.ch006.

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This chapter introduces a new security scheme for mobile anchors avoiding the physical layer attacks towards localization in wireless sensor networks (WSNs). In a network, anchors are made location-aware equipping them with GPS (global positioning system) receivers. Direction finding capabilities are also incorporated with smart antennas. The proposed algorithm is based on adaptive beamforming of smart array that always minimizes the probabilities of successful attacks, keeping the adversaries beyond its beam coverage. Particle swarm optimization (PSO) technique is used to compute array excitation coefficients, generating the desired pattern. Thus, anchors remain secured through pattern irregularities, deteriorating the information retrieval process even though chances of occurring adequate RSS (received signal strength)/AoA (angle of arrival) measurements may exist. Moreover, anchors are assumed to send pseudo references towards stationary nodes over private links, preserving data integrity for localization. Simulation results validate its effectiveness over the existing methods.
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Biswas, Rathindra Nath, Swarup Kumar Mitra, and Mrinal Kanti Naskar. "Preserving Security of Mobile Anchors Against Physical Layer Attacks." In Cryptographic Security Solutions for the Internet of Things, 211–43. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5742-5.ch008.

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This chapter introduces a new security scheme for mobile anchors avoiding the physical layer attacks towards localization in wireless sensor networks (WSNs). In a network, anchors are made location-aware equipping them with GPS (global positioning system) receivers. Direction finding capabilities are also incorporated with smart antennas. The proposed algorithm is based on adaptive beamforming of smart array that always minimizes the probabilities of successful attacks, keeping the adversaries beyond its beam coverage. Particle swarm optimization (PSO) technique is used to compute array excitation coefficients, generating the desired pattern. Thus, anchors remain secured through pattern irregularities, deteriorating the information retrieval process even though chances of occurring adequate RSS (received signal strength)/AoA (angle of arrival) measurements may exist. Moreover, anchors are assumed to send pseudo references towards stationary nodes over private links, preserving data integrity for localization. Simulation results validate its effectiveness over the existing methods.
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Hari, Riitta, and Aina Puce. "Practicalities of Data Collection." In MEG-EEG Primer, edited by Riitta Hari and Aina Puce, 77–88. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190497774.003.0006.

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This chapter provides a number of suggestions about optimization of MEG/EEG recording sessions to guarantee as good signal quality and as high signal-to-noise ratio as possible. It also advices for performing replicability checks on the data. The practical aspects of preparing and performing an EEG recording (skin preparation, electrode-impedance measurement) and postrecording infection control are presented. Similarly, practicalities of performing MEG recordings are discussed. Measurement of MEG sensor-array location with respect to landmarks on the scalp and different methods for the measurement of EEG electrode positions are presented. The chapter ends with a discussion on electrical safety in the MEG/EEG laboratory.
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Chen, Xiwu, and Zhe Huang. "Novel Tool of In-Bit Measurement for New Clean Geo-Energy Exploitation." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220404.

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With the global economy and industry development, environment-friendly and sustainable development have become the mainstream of energy exploration and utilization. However, due to the complex downhole environment, the rate of penetration increment is becoming an urgent need for the new clean geo-energy exploitation. As the direct actuator of rock breaking, the dynamic response of drilling bit is the embodiment of the relationship among drilling parameters, formation characteristics, and drilling string structures. The acquisition and mining of drilling bit’s dynamic response is the key to coordinate the game relation of drilling efficiency, economic benefit and risk control, and to lay the foundation of optimized drilling and ROP increment. In this paper, based on a series of researches on sensor array designing, hydraulic structure optimization, and data reckoning method, a new type of in-bit measurement tool was proposed for new clean geo-energy exploitation. The functionality and reliability of the novel tool were also proved by a field application in a shale gas development project.
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Conference papers on the topic "Sensor Array Optimization"

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Belotti, Pietro, Timo Berthold, and Kelligton Neves. "Algorithms for discrete nonlinear optimization in FICO Xpress." In 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2016. http://dx.doi.org/10.1109/sam.2016.7569658.

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Naghavi, Seyyed Hamed, and Yaser Norouzi. "TDOA sensor array optimization using Digital Elevation Model." In 2017 Iranian Conference on Electrical Engineering (ICEE). IEEE, 2017. http://dx.doi.org/10.1109/iraniancee.2017.7985335.

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Nik Abd Malik, Nik Noordini, Mazlina Esa, and Sharifah Kamilah Syed Yusof. "Optimization of adaptive linear sensor node array in Wireless Sensor Network." In 2009 Asia Pacific Microwave Conference - (APMC 2009). IEEE, 2009. http://dx.doi.org/10.1109/apmc.2009.5385453.

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Wang, Xingran, Tianyi Liu, Minh Trinh-Hoang, and Marius Pesavento. "GPU-accelerated parallel optimization for sparse regularization." In 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2020. http://dx.doi.org/10.1109/sam48682.2020.9104328.

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Rubsamen, Michael, and Alex B. Gershman. "Robust presteered broadband beamforming based on worst-case performance optimization." In 2008 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2008. http://dx.doi.org/10.1109/sam.2008.4606885.

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Ruan, Hang, and Rodrigo C. de Lamare. "Low-rank robust adaptive beamforming techniques using joint iterative optimization." In 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2016. http://dx.doi.org/10.1109/sam.2016.7569614.

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Pollak, Ilya. "Covariance estimation and related problems in portfolio optimization." In 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2012. http://dx.doi.org/10.1109/sam.2012.6250513.

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Zhu, Minghe, and Tsung-Hui Chang. "Optimization Inspired Learning Network for Multiuser Robust Beamforming." In 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2020. http://dx.doi.org/10.1109/sam48682.2020.9104277.

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Biao Jiang. "Low-complexity implementation for worst-case optimization-based robust adaptive beamforming." In 2008 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2008. http://dx.doi.org/10.1109/sam.2008.4606879.

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Rossetti, Gaia, and Sangarapillai Lambotharan. "Coordinated waveform design and receiver filter optimization for cognitive radar networks." In 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2016. http://dx.doi.org/10.1109/sam.2016.7569623.

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Reports on the topic "Sensor Array Optimization"

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Wegman, Edward J. Optimization Methods for Analyte Recognition from Optical Sensor Arrays. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada416421.

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