<p>An effective way to extract combustion parameters from a spark ignited engine is to measure the level of ionization. One way to do this is to use the spark plug as a sensor.</p><p>Until now this has been achieved by applying a DC voltage over the spark gap which causes an electrical field. The electrical field together with the ionization process gives cause to an ion current which can be measured and analyzed. Previous research suggests that it would be beneficial to replace the DC voltage with an AC voltage.</p><p>The focus in this thesis is on the hardware and how to best implement an AC voltage to the existing ion sensing system. Both simulation- and hardware models will be constructed. These models will be tested and analyzed to evaluate both benefits and drawbacks of an AC ion current sensing system.</p>

An effective way to extract combustion parameters from a spark ignited engine is to measure the level of ionization. One way to do this is to use the spark plug as a sensor. Until now this has been achieved by applying a DC voltage over the spark gap which causes an electrical field. The electrical field together with the ionization process gives cause to an ion current which can be measured and analyzed. Previous research suggests that it would be beneficial to replace the DC voltage with an AC voltage. The focus in this thesis is on the hardware and how to best implement an AC voltage to the existing ion sensing system. Both simulation- and hardware models will be constructed. These models will be tested and analyzed to evaluate both benefits and drawbacks of an AC ion current sensing system.

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. / 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. / Sensor,