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Application of principal component analysis and artificial neural networks to recognize the individual VOCs of methanol/2-propanol in a binary mixture by SAW multi-sensor array

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sensorS AND ACTUATORS B-CHEMICAL 2003年第3期89卷 269-284页

作者： Penza, M Cassano, G ENEA CR Brindisi Mat & New Technol Unit I-72100 Brindisi Italy

This work reports on the performance of a volatile organic compounds (VOCs) identification system based on a surface acoustic wave (SAW) multi-sensor array with four acoustic sensing elements, developed in dual configuration as multiplexed two-port resonator 433.92 MHz oscillators and a reference SAW element, in order to recognize the different individual components in a binary mixture of VOCs such as methanol (CH3OH) and 2-propanol (C3H7OH), in the range 20-140 and 5-70 ppm, respectively. The SAW sensors, operating at room temperature, have been specifically coated by sensing thin films belonging to various chemical classes such as arachidic acid (fatty acids), carbowax (stationary phases), triethanolamine (amines), acrylated polysiloxane, (polysiloxanes) to ensure cross-sensitivity towards VOCs under test. By using the relative frequency change as the output signal of the SAW multi-sensor array with an artificial neural network (ANN), a recognition system has been realized for the identification of tested VOCs. The features extraction from output signals of the SAW multi-sensor array, exposed to the binary component mixture of methanol and 2-propanol, has been also performed by pattern recognition techniques such as principal component analysis (PCA). The feedforward multi-layer neural network with a hidden layer and trained by a back-propagation learning algorithm has been implemented in order to classify and identify the tested VOCs patterns. Both the normalized responses of four SAW sensors array and the selected principal components (PCs) scores have been used as inputs to the multi-layer perceptron ANN by resulting in a 100% success recognition rate with the four SAW sensors normalized responses and with the first two principal components scores of the original data of the primary matrix. The different strategies used to recognize the VOCs patterns by the ANNs such as the 'Leave-one-out' method and 'Training-and-Test' method are discussed. Our experimental re

关键词： SAW chemical sensors multi-sensor array vapor sensing thin films pattern recognition principal component analysis artificial neural networks

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Study Of Acquisition Streetlights Background Signal By multi-sensor array

Study Of Acquisition Streetlights Background Signal By Multi...

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International Conference on Control, Automation and Systems (ICCAS 2010)

作者： Wu Yue Shi Changhong Yang Wei North Univ China Natl Key Lab Elect Measurement Technol Taiyuan 030051 Peoples R China North Univ China Mech & Elect Engn Coll Taiyuan 030051 Peoples R China PLA Quartermaster Inst Gen Logist Dept Beijing 100010 Peoples R China

ISBN: (纸本)9788993215021

The multi-sensor array, such as the sound, light, infrared, vibration etc, is used to get the street lights environmental information. Combined with a variety of clock control strategy for control lamps, it can achieve the background information of perception, detection, identification, and collect the typical characteristics of an effective signal, to rationally determine the threshold value range in the circuit design and lay a solid foundation for the realization of intelligent control of lights. The joint acquisition streetlights background signal through a variety of sensors carried out in a typical experimental design and preparation, principles, introduction, experimental methods and experimental analysis of the results, not only can test the design of control circuit, but also optimize the choice of the sensor models to provide true comparative data. In order to facilitate in a real environment to detect and further identify goals, to achieve the typical information data fusion of specific objectives, the ultimate street intelligent control can be achieved. Experimental results show that the magnetic sensors and infrared sensors detect close proximity, so the model of sensor needs to be replaced or further improved design (such as the optimization of pre-level filtering and amplification circuit design). Acoustic sensors and vibration sensors to detect distance, can meet night lighting control at residents of the community as well as tourist attractions. But there are a lot of distances to meet the use demands of roads and high-speed traffic, the more optimal designs and experiments is need. In addition, a range problem of information synchronize data collection, background noise and a variety of information data crosstalk need to be solved in the joint acquisition experiments, which in the paper were discussed.

关键词： multi-sensor array joint acquisition streetlights background signal

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Passive Detection of a Random Signal Common to multi-sensor Reference and Surveillance arrays

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第7期73卷 10106-10117页

作者： Ramirez, David Santamaria, Ignacio Scharf, Louis L. Univ Carlos III Madrid Dept Signal Theory & Commun Madrid 28915 Spain Gregorio Maranon Hlth Res Inst Madrid 28007 Spain Univ Cantabria Dept Commun Engn Santander 39005 Spain Colorado State Univ Dept Math Ft Collins CO 80523 USA

This paper addresses the passive detection of a common rank-one subspace signal received in two multi-sensor arrays. We consider the case of a one-antenna transmitter sending a common Gaussian signal, independent Gaussian noises with arbitrary spatial covariance, and known channel subspaces. The detector derived in this paper is a generalized likelihood ratio (GLR) test. For all but one of the unknown parameters, it is possible to find closed-form maximum likelihood (ML) estimator functions. We can further compress the likelihood to only an unknown vector whose ML estimate requires maximizing a product of ratios in quadratic forms, which is carried out using a trust-region algorithm. We propose two approximations of the GLR that do not require any numerical optimization: one based on a sample-based estimator of the unknown parameter whose ML estimate cannot be obtained in closed-form, and one derived under low-SNR conditions. Notably, all the detectors are scale-invariant, and the approximations are functions of beamformed data. However, they are not GLRTs for data that has been pre-processed with a beamformer, a point that is elaborated in the paper. These detectors outperform previously published correlation detectors on simulated data, in many cases quite significantly. Moreover, performance results quantify the performance gains over detectors that assume only the dimension of the subspace to be.

关键词： Detectors Surveillance Passive radar Covariance matrices Maximum likelihood estimation Transmitters Doppler effect Coherence generalized likelihood ratio (GLR) hypothesis test multi-sensor array passive radar

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Scale-Invariant Subspace Detectors Based on First- and Second-Order Statistical Models

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 6432-6443页

作者： Santamaria, Ignacio Scharf, Louis L. Ramirez, David Univ Cantabria Dept Commun Engn Santander 39005 Spain Colorado State Univ Dept Math & Stat Ft Collins CO 80523 USA Univ Carlos III Madrid Dept Signal Theory & Commun Leganes 28911 Spain Gregorio Maranon Hlth Res Inst Madrid 28911 Spain

The problem is to detect a multi-dimensional source transmitting an unknown sequence of complex-valued symbols to a multi-sensor array. In some cases the channel subspace is known, and in others only its dimension is known. Should the unknown transmissions be treated as unknowns in a first-order statistical model, or should they be assigned a prior distribution that is then used to marginalize a first-order model for a second-order statistical model? This question motivates the derivation of subspace detectors for cases where the subspace is known, and for cases where only the dimension of the subspace is known. For three of these four models the GLR detectors are known, and they have been reported in the literature. But the GLR detector for the case of a known subspace and a second-order model for the measurements is derived for the first time in this paper. When the subspace is known, second-order generalized likelihood ratio (GLR) tests outperform first-order GLR tests when the spread of subspace eigenvalues is large, while first-order GLR tests outperform second-order GLR tests when the spread is small. When only the dimension of the subspace is known, second-order GLR tests outperform first-order GLR tests, regardless of the spread of signal subspace eigenvalues. For a dimension-1 source, first-order and second-order statistical models lead to equivalent GLR tests. This is a new finding.

关键词： Detection generalized likelihood ratio (GLR) likelihood multi-sensor array multivariate normal model (MVN) scale-invariant detector subspace signals

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Towards the Green Analytics: Design and Development of Sustainable Drinking Water Quality Monitoring System for Shekhawati Region in Rajasthan

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MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA 2021年第4期36卷 843-857页

作者： Khatri, Punit Gupta, Karunesh Kumar Gupta, Raj Kumar Panchariya, P. C. Birla Inst Technol & Sci BITS Dept Elect & Elect Engn Pilani Rajasthan India Birla Inst Technol & Sci BITS Dept Phys Pilani Rajasthan India Cent Elect Engn Res Inst CSIR CEERI Pilani Rajasthan India

In rural areas, there is limited monitoring of drinking water quality. Reliable water quality monitoring stations are expensive and require high costs for maintenance and calibration process. In this paper, the development of a sustainable water quality monitoring system is proposed. The green analytics principles were considered for developing the proposed system to reduce the measurement's time consumption and labor cost. Five water quality parameters [pH, oxidation reduction potential (ORP), dissolved oxygen (DO), electrical conductivity (EC), and temperature] have been measured using the developed system. The overall drinking water quality is measured by the proposed partial least squares regression (PLSR) model. The developed system's performance is determined by mean average percentage error (MAPE), root-mean-square error (RMSE), and R-2. The traceability of water quality sensors is defined with required uncertainty in water quality parameters. The measured uncertainty is 0.002, 0.892, 0.015, 0.029, and 0.017 for pH, EC, DO, ORP, and temperature, respectively. The relation between estimated and predicted water quality parameters (R-2 > 0.93) shows that the developed system can be a suitable replacement for traditional water quality monitoring techniques.

关键词： Traditional water quality monitoring Green analytics multi-sensor array Water quality index Partial least squares regression Traceability

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Subspace-based interference removal methods for a multichannel biomagnetic sensor array

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JOURNAL OF NEURAL ENGINEERING 2017年第5期14卷 051001-051001页

作者： Sekihara, Kensuke Nagarajan, Srikantan S. Signal Anal Inc Hachioji Tokyo Japan Tokyo Med & Dent Univ Dept Adv Technol Med Bunkyo Ku 1-5-45 Yushima Tokyo 1138519 Japan Univ Calif San Francisco Biomagnet Imaging Lab 513 Parnassus AveS362 San Francisco CA 94143 USA

Objective. In biomagnetic signal processing, the theory of the signal subspace has been applied to removing interfering magnetic fields, and a representative algorithm is the signal space projection algorithm, in which the signal/interference subspace is defined in the spatial domain as the span of signal/interference-source lead field vectors. This paper extends the notion of this conventional (spatial domain) signal subspace by introducing a new definition of signal subspace in the time domain. Approach. It defines the time-domain signal subspace as the span of row vectors that contain the source time course values. This definition leads to symmetric relationships between the time-domain and the conventional (spatial-domain) signal subspaces. As a review, this article shows that the notion of the time-domain signal subspace provides useful insights over existing interference removal methods from a unified perspective. Main results and significance. Using the time-domain signal subspace, it is possible to interpret a number of interference removal methods as the time domain signal space projection. Such methods include adaptive noise canceling, sensor noise suppression, the common temporal subspace projection, the spatio-temporal signal space separation, and the recently-proposed dual signal subspace projection. Our analysis using the notion of the time domain signal space projection reveals implicit assumptions these methods rely on, and shows that the difference between these methods results only from the manner of deriving the interference subspace. Numerical examples that illustrate the results of our arguments are provided.

关键词： interference removal magnetoencephalography sensor array processing signal subspace biomagnetic imaging biomagnetism multi-sensor array

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Amperometric Bioelectronic Tongue for glucose determination

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Sensing and Bio-Sensing Research 2015年 3卷 31-37页

作者： Al-Issa, Yazan Njagi, John Schuckers, Stephanie C. Suni, Ian I. Department of Electrical and Computer Engineering Clarkson University Potsdam 13699 NY United States Department of Chemistry and Biomolecular Scinece Clarkson University Potsdam 13699 NY United States Materials Technology Center Department of Chemistry and Biochemistry Department of Mechanical Engineering and Energy Processes Southern Illinois University Carbondale 62901 IL United States

An amperometric Bioelectronic Tongue is reported for glucose determination that contains eight sensor electrodes constructed using different metal electrodes (Pt, Au), oxidoreductase enzymes (glucose oxidase, ascorbate oxidase, uricase), and membrane coatings (Nafion, chitosan). The response to varying concentrations of glucose, ascorbic acid, uric acid, and acetaminophen was tested for two models, concentration determination by current density measurements at individual electrodes and concentration determination by a linear regression model for the entire electrode array. The reduced chi-squared for the full array model was found to be about one order of magnitude lower than that for the individual-electrode model. Discrimination of glucose from chemical interference by the other three species is accomplished through a combination of enzyme catalysis, metal electrocatalysis, and membrane surface charge. The benefit of incorporating enzyme electrodes into the sensor array is illustrated by the lower correlation coefficients between different enzyme electrodes relative to non-enzyme coated electrodes. This approach can be more generally applied to detection of other substrates of oxidoreductase enzymes. © 2014 The Authors.

关键词： Amperometry Bioelectronic Tongue Chemical interference Glucose estimation multi-sensor array multivariate analysis

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Passive Detection of Rank-One Gaussian Signals for Known Channel Subspaces and Arbitrary Noise 48

Passive Detection of Rank-One Gaussian Signals for Known Cha...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Ramírez, D. Santamaria, I. Scharf, L.L. Universidad Carlos Iii de Madrid Dept. of Signal Theory and Communications Spain Gregorio Marañón Health Research Institute Madrid Spain Universidad de Cantabria Dept. of Communications Engineering Spain Colorado State University Dept. of Mathematics Fort CollinsCO United States

ISBN: (纸本)9781728163277

This paper addresses the passive detection of a common signal in two multi-sensor arrays. For this problem, we derive a detector based on likelihood theory for the case of one-antenna transmitters, independent Gaussian noises with arbitrary spatial structure, Gaussian signals, and known channel subspaces. The detector uses a likelihood ratio where all but one of the unknown parameters are replaced by their maximum likelihood (ML) estimates. The ML estimation of the remaining parameter requires a numerical search, and it is therefore estimated using a sample-based estimator. The performance of the proposed detector is illustrated by means of Monte Carlo simulations and compared with that of the detector for unknown channels, showing the advantage of this knowledge. © 2023 IEEE.

关键词： Generalized likelihood ratio (GLR) hypothesis test multi-sensor array passive radar

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A Turbo Approach to Distributed Acoustic Detection and Estimation

A Turbo Approach to Distributed Acoustic Detection and Estim...

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作者： Egger, Sean Robert Virginia Tech | University

Networked, multi-sensor array systems have proven to be advantageous in the sensor world. A large amount of research has been conducted with these systems, with a main interest in data fusion. Intelligently processing the large amounts of data collected by these systems is required in order to fully utilize the benefits of a multi-sensor array system. A robust but flexible simulation environment would provide a platform for accurately comparing current and future data fusion theories. This thesis proposes a simulator model for testing fusion theories for these acoustic multi-sensor networks. An iterative, lossless data fusion algorithm was presented as the model for simulation development. The arrangement and orientation of objects in the simulation environment, as well as most other system parameters are defined by the user before the simulation runs. The sensor data, including noise, is generated at the appropriate time delay and propagation loss before being processed by a delay and sum beamformer and a matched filter. The resulting range-Doppler maps are modified to probability density functions, and translated to a single point of reference. The data is then combined into a single world model. An iterative process is used to filter out false targets and amplify true target detections. Data is fused from each multi-sensor array and from each simulation run. Target amplitudes are gained if they are present in all combined world models, and are otherwise reduced. This thesis presents the results of the fusion algorithm used, including multiple iterations, to prove the algorithms effectiveness.

关键词： lossless information fusion multi-sensor array acoustic simulation Thesis

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multi-sensor Geomagnetic Prospection: A Case Study from Neolithic Thessaly, Greece

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REMOTE SENSING 2016年第11期8卷 966-966页

作者： Kalayci, Tuna Sarris, Apostolos Fdn Res & Technol Hellas FORTH Lab Geophys Remote Sensing & Archaeoenvironm GeoS Melissinou & Nik Fora 130POB 119 Iraklion 74100 Greece

multi-sensor prospecting is a fast-emerging paradigm in archaeological geophysics. Given suitable ground conditions for navigation, sensor arrays drastically increase efficiency in data collection. In particular, geomagnetic prospecting benefits from this development. Despite these advancements, data processing still lacks a best-practice approach. Conventional processing methods developed for gridded data has been challenged by sensor arrays "roaming" in the landscape. In realization of the issue, the Innovative Geophysical Approaches for the Study of Early Agricultural Villages of Neolithic Thessaly (IGEAN) Project explored various innovative techniques for the betterment of the multi-sensor geomagnetic data processing. As a result, a modular pipeline is produced with minimal user intervention. In addition to standard steps, such as data clipping, various other algorithms have been introduced. This pipeline is tested over 20 Neolithic settlements in Thessaly, Greece, three of which are presented here in detail. The proposed workflow provides drastic improvements over raw data. As a result of these improvements, the IGEAN project revealed astonishing details on architectural elements, settlement enclosures, and paleolandscapes, changing completely the existing perspective of the Neolithic habitation in Thessaly.

关键词： Neolithic Thessaly magoula geomagnetic prospection multi-sensor array

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