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Multivariate Exploration and Processing of Sensor Data-applications with multidimensional sensor systems
Linköping University, Department of Physics, Chemistry and Biology, Applied Physics . Linköping University, The Institute of Technology.
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A sensor is a device that transforms a physical, chemical, or biological stimulus into a readable signal. The integral part that sensors make in modern technology is considerable and many are those trying to take the development of sensor technology further. Sensor systems are becoming more and more complex and may contain a wide range of different sensors, where each may deliver a multitude of signals.Although the data generated by modern sensor systems contain lots of information, the information may not be clearly visible. Appropriate handling of data becomes crucial to reveal what is sought, but unfortunately, that process is not always straightforward and there are many aspects to consider. Therefore, analysis of multidimensional sensor data has become a science.The topic of this thesis is signal processing of multidimensional sensordata. Surveys are given on methods to explore data and to use the data to quantify or classify samples. It is also discussed how to avoid the rise of artifacts and how to compensate for sensor deficiencies. Special interest is put on methods being practically applicable to chemical gas sensors. The merits and limitations of chemical sensors are discussed and it is argued that multivariate data analysis plays an important role using such sensors.

The contribution made to the public by this thesis is primarily on techniques dealing with difficulties related to the operation of sensors in applications. In the second paper, a method is suggested that aims at suppressing the negative effects caused by unwanted sensor-to-sensor differences. If such differences are not suppressed sufficiently, systems where sensors occasionally must be replaced may degrade and lose performance. The strong-point of the suggested method is its relative ease of use considering large-scale production of sensor components and when integrating sensors into mass-market products. The third paper presents a method that facilitates and speeds up the process of assembling an array of sensors that is optimal for a particular application. The method combines multivariate data analysis with the `Scanning Light Pulse Technique'. In the first and fourth papers, the problem of source separation is studied. In two separate applications, one using gas sensors for combustion control and one using acoustic sensors for ground surveillance, it has been identified that the current sensors outputs mixtures of both interesting- and interfering signals. By different means, the two papers applies and evaluates methods to extract the relevant information under such circumstances.

Abstract [sv]

En sensor är en komponent som överför en fysikalisk, kemisk, eller biologisk storhet eller kvalitet till en utläsbar signal. Sensorer utgör idag en viktig del i flertalet högteknologiska produkter och sensorforskning är ett aktivt område.

Komplexiteten på sensorbaserade system ökar och det blir möjligt att registrera allt er olika typer av mätsignaler. Mätsignalerna är inte alltid direkt tydbara, varvid signalbehandling blir ett väsentligt verktyg för att vaska fram den viktiga information som sökes. Signalbehandling av sensorsignaler är dessvärre inte en okomplicerad procedur och det finns många aspekter att beakta. Av denna anledning har signalbehandling och analys av sensorsignaler utvecklats till ett eget forskningsområde.

Denna avhandling avhandlar metoder för att analysera komplexa multidimensionella sensorsignaler. En introduktion ges till metoder för att, utifrån mätningar, klassificera och kvantifiera egenskaper hos mätobjekt. En överblick ges av de effekter som kan uppstå på grund av imperfektioner hos sensorerna och en diskussion föres kring metoder för att undvika eller lindra de problem som dessa imperfektioner kan ge uppkomst till. Speciell vikt lägges vid sådana metoder som medför en direkt applicerbarhet och nytta för system av kemiska sensorer.

I avhandlingen ingår fyra artiklar, som vart och en belyser hur de metoder som beskrivits kan användas i praktiska situationer.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press , 2008. , 66 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1162
Keyword [en]
Chemical sensor, Pattern recognition, sensor selcection, Combustion monitoring, Vector Classification
National Category
Chemical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-14879ISBN: 978-91-7393-841-9 (print)OAI: oai:DiVA.org:liu-14879DiVA: diva2:25491
Public defence
2008-09-12, Planck, Fysikhuset, Campus Valla, Linköpings universitet, Linköping, 10:00 (Swedish)
Opponent
Supervisors
Projects
Sensor,
Available from: 2008-09-29 Created: 2008-09-29 Last updated: 2009-05-12Bibliographically approved
List of papers
1. Initial studies on the possibility to use chemical sensors to monitor and control boilers
Open this publication in new window or tab >>Initial studies on the possibility to use chemical sensors to monitor and control boilers
2005 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 111-112, 487-493 p.Article in journal (Refereed) Published
Abstract [en]

Small-scale boilers are quite often installed in facilities like schools, households and at local heat distributors. Because of economical considerations such boilers often lack appropriate control-systems, which results in inefficient and pollutant combustions with high levels of carbon monoxides, hydrocarbons, and in ashes with unburned charcoal. Monitoring of oxygen, carbon monoxide, and hydrocarbons, which is essential to be able to control a boiler, requires expensive instruments like flame-ionization detectors, IR- and mass-spectrometers.

We demonstrate the possibility to use a low-cost chemical sensor array to monitor a small-scaled boiler. By using metal oxide sensors, metal insulator silicon carbide field effect transistors, and by applying multivariate data modeling, promising results have been obtained. The data modeling was made using a joint approach based on blind source separations and multiple linear regressions. This approach showed similar result compared to results from the well-known PLSR algorithm.

Keyword
Chemical sensor array, Pattern recognition, Combustion monitoring, Blind source separation, Canonical correlation analysis
National Category
Chemical Engineering
Identifiers
urn:nbn:se:liu:diva-14866 (URN)10.1016/j.snb.2005.03.045 (DOI)
Available from: 2008-09-29 Created: 2008-09-29 Last updated: 2017-12-13
2. Calibration Transfer Procedures Based on Sensor Models
Open this publication in new window or tab >>Calibration Transfer Procedures Based on Sensor Models
2008 (English)Article in journal (Refereed) Submitted
Abstract [en]

This article treats the matter of calibration transfer, i.e. how to mathematicallysuppress sensor-to-sensor differences. We propose an approach in which the strategyis to find a model that compactly describes the behavior of a sensor type. Theparameters of the model serve as ‘data sheets’ and can be used to differentiate characteristicsbetween individual sensor devices. We illustrate that by keeping recordof these parameters it does not only becomes possible to characterize individualsensors, but also to calibrate their responses.

As will be discussed, the main virtue of the suggested approach comes by itscompact and device specific description of the sensor response pattern. This opensup for the construction of flexible calibration transfer schemes. The idea is to let asensor manufacturer construct and distribute ‘data sheets’ describing the characteristicsof each sensor component. Customers can then to use these to mathematicallycounteract for sensor-to-sensor differences. The counteraction can be made at sensorlevel, prior to the signals enter any host system. This is important if a customerwishes to avoid extensive adaptations being made to a product as soon as a slightlydifferent sensor component must be used.

The approach is tested on real experimental data made with Metal-Insulator-Silicon-Carbide-Transistors. Data driven modeling techniques were used to approximatethe sensor responses. The results of this paper positively demonstrate thatthe suggested approach can be used to counteract for sensor-to-sensor differences.

Keyword
Calibration transfer, pattern recognition, sensor model, chemical sensors, MISiCFET, commercialization
National Category
Chemical Engineering
Identifiers
urn:nbn:se:liu:diva-14867 (URN)
Available from: 2008-09-29 Created: 2008-09-29 Last updated: 2009-02-25
3. Sensor Array Optimization using Variable Selection and a Scanning Light Pulse Technique
Open this publication in new window or tab >>Sensor Array Optimization using Variable Selection and a Scanning Light Pulse Technique
2009 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 142, no 2, 435-445 p.Article in journal (Refereed) Published
Abstract [en]

In the design of a chemical sensor. the constructor has several degrees of freedom setting parameters that influence the final characteristics of the component. For applications where several sensors are required, the number of possible parameter configurations increases dramatically. The work of configuring a sensor array is therefore tedious and many test sensors may need to be processed before a final configuration is found. The Scanning Light Pulse Technique (SLPT) is a technique for investigating insulator-semiconductor interfaces and can be used to scan surfaces with non-uniform properties, Thereby a virtual pool of test components can be evaluated simultaneously eliminating the need for processing individual test sensors. We report here on a method combining SLPT with algorithmic sensor selection techniques. This is a powerful combination providing the user with a candidate array configuration containing combinations of sensors optimal for the current application and data analysis algorithms. The need to process many individual test sensors is eliminated and the only sensor components that must be produced are those included in the final array. The selection techniques evaluated here are based on forward selection and Asymmetric Class Projection (ACP), Canonical Correlation Analysis (CCA), Linear Discriminant Analysis (LDA), and Mutual Information (MI). The Suggested method is successfully evaluated using an experiment in which the purpose was to find means to detect small amounts of hydrogen in a background dominated by an interfering gas, in this case ammonia. In this particular study, the selection techniques based on ACP and CCA showed the most promising result.

Keyword
SLPT, chemical sensor, MIS, sensor selection, Assymetric Class Projection, CCA, LDA, mutual information, forward selection
National Category
Chemical Engineering
Identifiers
urn:nbn:se:liu:diva-14870 (URN)10.1016/j.snb.2009.04.029 (DOI)
Available from: 2008-09-29 Created: 2008-09-29 Last updated: 2017-12-13
4. Classification of Vehicles in a Multi-Object Scenario using Acoustic Sensor Arrays
Open this publication in new window or tab >>Classification of Vehicles in a Multi-Object Scenario using Acoustic Sensor Arrays
2008 (English)Article in journal (Refereed) Submitted
Abstract [en]

The organization of distributed sensor networks is a topic gaining much interest. Detection, tracking and classification of objects moving within a geographic area is one possible application for such network. This article consider the classification of vehicles moving in an area monitored by a network of acoustic sensors. It is studied how to classify a target vehicle from acoustic recordings in which also interfering vehicles are present. Classification is made using a support vector classifer. The focus of this paper is on a combined beam-former and independent component analysis procedure used to separate the acoustic signature of a target vehicle from signatures of present interferences. An evaluation of the method is made on real data. The results show that the source separation method can help to improve classification performance. Properties of the joint separation-classification technique is analyzed and it is discussed under which circumstances the separation is most effcient.

Keyword
Support Vector Classifcation, Beamforming, Independent Component Analysis, multi object classifcation, distributed sensor network
National Category
Atom and Molecular Physics and Optics
Identifiers
urn:nbn:se:liu:diva-14878 (URN)
Available from: 2008-09-29 Created: 2008-09-29 Last updated: 2009-02-25

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