scholarly journals Methods for Assessing and Optimizing Solar Orientation by Non-Planar Sensor Arrays

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2561
Author(s):  
Jiang Wang ◽  
Xingang Fan ◽  
Yongchao Zhang ◽  
Jianyu Yang ◽  
Yuming Du ◽  
...  
Keyword(s):  
Sensor Array ◽  
Sensor Arrays ◽  
Average Energy ◽  
Finite Energy ◽  
Optimization Method ◽  
Limiting Factor ◽  
Orientation Error ◽  
Orientation Matrix ◽  
New Formulation ◽  
Solar Orientation

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.

scholarly journals An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays

2020 ◽  
Vol 10 (9) ◽  
pp. 3176
Author(s):  
Jiancheng Liu ◽  
Feng Shi ◽  
Yecheng Sun ◽  
Peng Li
Keyword(s):  
Sensor Array ◽  
Linear Array ◽  
Imaging System ◽  
Low Cost ◽  
Sensor Arrays ◽  
Optimization Method ◽  
Low Complexity ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Linear Arrays

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.

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 304-311 ◽  
Author(s):  
Pengfei Jia ◽  
Fengchun Tian ◽  
Shu Fan ◽  
Qinghua He ◽  
Jingwei Feng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wound Infection ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Particle Swarm ◽  
Optimization Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type

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.

Fundamental Sensor Development in Electrical Resistance Tomography

2015 ◽  
Vol 73 (6) ◽  
Author(s):  
Ling En Hong ◽  
Ruzairi Hj. Abdul Rahim ◽  
Anita Ahmad ◽  
Mohd Amri Md. Yunus ◽  
Khairul Hamimah Aba ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Electrical Resistance ◽  
Sensor Array ◽  
Sensor Arrays ◽  
Region Of Interest ◽  
Display System ◽  
Electrical Resistance Tomography ◽  
Fundamental Understanding ◽  
Collection Strategies

This paper will provide a fundamental understanding of one of the most commonly used tomography, Electrical Resistance Tomography (ERT). Unlike the other tomography systems, ERT displayed conductivity distribution in the Region of Interest (ROI) and commonly associated to Sensitivity Theorem in their image reconstruction. The fundamental construction of ERT includes a sensor array spaced equally around the imaged object periphery, a Data Acquisition (DAQ), image reconstruction and display system. Four ERT data collection strategies that will be discussed are Adjacent Strategy, Opposite Strategy, Diagonal Strategy and Conducting Boundary Strategy. We will also explain briefly on some of the possible Data Acquisition System (DAQ), forward and inverse problems, different arrangements for conducting and non-conducting pipes and factors that influence sensor arrays selections. 

scholarly journals Curating Metal-Organic Frameworks to Compose Robust Gas Sensor Arrays in Dilute Conditions

2019 ◽  
Author(s):  
Arni Sturluson ◽  
Rachel Sousa ◽  
Yujing Zhang ◽  
Melanie T. Huynh ◽  
Caleb Laird ◽  
...  
Keyword(s):  
Sensor Array ◽  
Gas Sensing ◽  
Metal Organic Frameworks ◽  
Sensor Arrays ◽  
Gas Composition ◽  
Composition Space ◽  
The Matrix ◽  
Metal Organic ◽  
Analyte Detection ◽  
Value Decomposition

Metal-organic frameworks (MOFs)-- tunable, nano-porous materials-- are alluring recognition elements for gas sensing. Mimicking human olfaction, an array of cross-sensitive, MOF-based sensors could enable analyte detection in complex, variable gas mixtures containing confounding gas species. Herein, we address the question: given a set of MOF candidates and their adsorption properties, how do we select the optimal subset to compose a sensor array that accurately and robustly predicts the gas composition via monitoring the adsorbed mass in each MOF? We first mathematically formulate the MOF-based sensor array problem under dilute conditions. Instructively, the sensor array can be viewed as a linear map from <i>gas composition space</i> to <i>sensor array response space</i> defined by the matrix <b>H</b> of Henry coefficients of the gases in the MOFs. Characterizing this mapping, the singular value decomposition of <b>H </b>is a useful tool for evaluating MOF subsets for sensor arrays, as it determines the sensitivity of the predicted gas composition to measurement error, quantifies the magnitude of the response to changes in composition, and recovers which direction in gas composition space elicits the largest/smallest response. To illustrate, on the basis of experimental adsorption data, we curate MOFs for a sensor array with the objective of determining the concentration of CO<sub>2</sub> and SO<sub>2</sub> in the gas phase.

scholarly journals Development of a Pattern Recognition Tool for the Classification of Electronic Tongue Signals Using Machine Learning

2021 ◽  
Vol 5 (1) ◽  
pp. 21
Author(s):  
Edgar G. Mendez-Lopez ◽  
Jersson X. Leon-Medina ◽  
Diego A. Tibaduiza
Keyword(s):  
Machine Learning ◽  
Dimensionality Reduction ◽  
Performance Measures ◽  
Sensor Array ◽  
Three Dimensional ◽  
Electronic Tongue ◽  
Sensor Arrays ◽  
Classification Performance ◽  
Supervised Machine Learning ◽  
Electrochemical Tests

Electronic tongue type sensor arrays are made of different materials with the property of capturing signals independently by each sensor. The signals captured when conducting electrochemical tests often have high dimensionality, which increases when performing the data unfolding process. This unfolding process consists of arranging the data coming from different experiments, sensors, and sample times, thus the obtained information is arranged in a two-dimensional matrix. In this work, a description of a tool for the analysis of electronic tongue signals is developed. This tool is developed in Matlab® App Designer, to process and classify the data from different substances analyzed by an electronic tongue type sensor array. The data processing is carried out through the execution of the following stages: (1) data unfolding, (2) normalization, (3) dimensionality reduction, (4) classification through a supervised machine learning model, and finally (5) a cross-validation procedure to calculate a set of classification performance measures. Some important characteristics of this tool are the possibility to tune the parameters of the dimensionality reduction and classifier algorithms, and also plot the two and three-dimensional scatter plot of the features after reduced the dimensionality. This to see the data separability between classes and compatibility in each class. This interface is successfully tested with two electronic tongue sensor array datasets with multi-frequency large amplitude pulse voltammetry (MLAPV) signals. The developed graphical user interface allows comparing different methods in each of the mentioned stages to find the best combination of methods and thus obtain the highest values of classification performance measures.

An Effective Gas Sensor Array Optimization Method Based on Random Forest*

2018 ◽  
Author(s):  
Guangfen Wei ◽  
Jie Zhao ◽  
Zechuan Yu ◽  
Yanli Feng ◽  
Gang Li ◽  
...  
Keyword(s):  
Random Forest ◽  
Gas Sensor ◽  
Sensor Array ◽  
Optimization Method ◽  
Gas Sensor Array ◽  
Array Optimization

scholarly journals Sensor arrays: an inspired idea or an objective measurement of environmental odours?

2001 ◽  
Vol 44 (9) ◽  
pp. 53-58 ◽  
Author(s):  
R.M. Stuetz ◽  
J. Nicolas
Keyword(s):  
Sensor Array ◽  
Sensory System ◽  
Sensor Arrays ◽  
Objective Measurement ◽  
Chemical Compositions ◽  
Response Patterns ◽  
Human Response ◽  
Control And Prevention ◽  
On Line ◽  
The Right

The measure of annoyance odours from sewage tratment, landfill and agricultural practise has become highly significant in the control and prevention of dorous emissions from existing facilities and its crucial for new planning applications. Current methods (such as GC-MS analysis, H2S and NH3 measurements) provide an accurate description of chemical compositions or act as surrogates for odour strength, but tell us very little about the perceived effect, whereas olfactometry gives the right human response but is very subjective and expensive. The use of non-specific sensor arrays may offer an objective and on-line instrument for assessing olfactive annoyance. Results have shown that sensor array systems can discriminate between different odour sources (wastewater, livestock and landfill). The response patterns from these sources can be significantly different and that the intensity of sensor responses is proportional to the concentration of the volatiles. The correlation of the sensors responses against odour strengths have also shown that reasonable fits can be obtained for a range of odour concentrations (100-800,000 ou/m3). However, the influence of environmental fluctuations (humidity and temperature) on sensor baselines still remains an obstacle, as well as the need for periodic calibration of the sensory system and the choice of a suitable gas for different environmental odours.

scholarly journals Spline approximation-based data compression for sensor arrays in the wireless hydrologic monitoring system

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771769258
Author(s):  
Danyang Li ◽  
Wei Huangfu ◽  
Keping Long
Keyword(s):  
Data Compression ◽  
Monitoring System ◽  
Sensor Array ◽  
Spline Approximation ◽  
Sensor Arrays ◽  
Huffman Coding ◽  
Long Distance ◽  
Data Set ◽  
Array Data ◽  
Hydrologic Monitoring

A sensor array produces lots of bits of data every sample period, which may cause a heavy burden on the long-distance wireless data transmission, especially in the scenarios of wireless sensor networks. A lossy but error-bounded sensor array data compression algorithm is proposed, in which the major part of the sensor array data are approximated by the Catmull-Rom spline curve and the residual errors are quantized and encoded with Huffman coding. The performance of this algorithm has been evaluated with a real data set from the wireless hydrologic monitoring system deployed in Qinhuangdao Port of China. The results show that the algorithm performs well for the hydrologic sensor array data.

Disease-specific protein corona sensor arrays may have disease detection capacity

2019 ◽  
Vol 4 (5) ◽  
pp. 1063-1076 ◽  
Author(s):  
Giulio Caracciolo ◽  
Reihaneh Safavi-Sohi ◽  
Reza Malekzadeh ◽  
Hossein Poustchi ◽  
Mahdi Vasighi ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Sensor Array ◽  
Sensor Arrays ◽  
Protein Corona ◽  
Specific Protein ◽  
Disease Detection ◽  
P Protein ◽  
Array Technology ◽  
Disease Specific

Protein corona sensor array technology identifies diseases through specific proteomics pattern recognition.

scholarly journals Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 3842-3849
Author(s):  
Jicong Fan ◽  
Yuqian Zhang ◽  
Madeleine Udell
Keyword(s):  
Missing Data ◽  
Matrix Completion ◽  
Synthetic Data ◽  
Full Rank ◽  
Feature Space ◽  
Optimization Method ◽  
Transductive Learning ◽  
Intrinsic Dimension ◽  
Missing Data Imputation ◽  
New Formulation

This paper develops new methods to recover the missing entries of a high-rank or even full-rank matrix when the intrinsic dimension of the data is low compared to the ambient dimension. Specifically, we assume that the columns of a matrix are generated by polynomials acting on a low-dimensional intrinsic variable, and wish to recover the missing entries under this assumption. We show that we can identify the complete matrix of minimum intrinsic dimension by minimizing the rank of the matrix in a high dimensional feature space. We develop a new formulation of the resulting problem using the kernel trick together with a new relaxation of the rank objective, and propose an efficient optimization method. We also show how to use our methods to complete data drawn from multiple nonlinear manifolds. Comparative studies on synthetic data, subspace clustering with missing data, motion capture data recovery, and transductive learning verify the superiority of our methods over the state-of-the-art.

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