Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Characters Extraction for Traffic Sign Destination boards in video and still images

Traffic Control Signs or destination boards on roadways offer significant information for drivers. Regulation signs tell something like your speed, turns, etc; Warning signs warn drivers of conditions ahead to help them avoid accidents; Destination signs show distances and directions to various locations; Service signs display location of hospitals, gas and rest areas etc. Because the signs are so important and there is always a certain distance from them to drivers, to let the drivers get information clearly and easily even in bad weather or other situations. The idea is to develop software which can collect useful information from a special camera which is mounted in the front of a moving car to extract the important information and finally show it to the drivers. For example, when a frame contains on a destination drive sign board it will be text something like "Linkoping 50",so the software should extract every character of "Linkoping 50", compare them with the already known character data in the database. if there is extracted character match "k" in the database then output the destination name and show to the driver. In this project C++ will be used to write the code for this software.

Identification of cause of impairment in spiral drawings, using non-stationary feature extraction approach

Parkinson’s disease is a clinical syndrome manifesting with slowness and instability. As it is a progressive disease with varying symptoms, repeated assessments are necessary to determine the outcome of treatment changes in the patient. In the recent past, a computer-based method was developed to rate impairment in spiral drawings. The downside of this method is that it cannot separate the bradykinetic and dyskinetic spiral drawings. This work intends to construct the computer method which can overcome this weakness by using the Hilbert-Huang Transform (HHT) of tangential velocity. The work is done under supervised learning, so a target class is used which is acquired from a neurologist using a web interface. After reducing the dimension of HHT features by using PCA, classification is performed. C4.5 classifier is used to perform the classification. Results of the classification are close to random guessing which shows that the computer method is unsuccessful in assessing the cause of drawing impairment in spirals when evaluated against human ratings. One promising reason is that there is no difference between the two classes of spiral drawings. Displaying patients self ratings along with the spirals in the web application is another possible reason for this, as the neurologist may have relied too much on this in his own ratings.

Does speed matter? : the impact of the EU membership incentive on rule adoption in minority language rights protection

Denna avhandling tar sin utgångspunkt i ett ifrågasättande av effektiviteten i EU:s konditionalitetspolitik avseende minoritetsrättigheter. Baserat på den rationalistiska teoretiska modellen, External Incentives Model of Governance, syftar denna hypotesprövande avhandling till att förklara om tidsavståndet på det potentiella EU medlemskapet påverkar lagstiftningsnivån avseende minoritetsspråksrättigheter. Mätningen av nivån på lagstiftningen avseende minoritetsspråksrättigheter begränsas till att omfatta icke-diskriminering, användning av minoritetsspråk i officiella sammanhang samt minoriteters språkliga rättigheter i utbildningen. Metodologiskt används ett jämförande angreppssätt både avseende tidsramen för studien, som sträcker sig mellan 2003 och 2010, men även avseende urvalet av stater. På basis av det \"mest lika systemet\" kategoriseras staterna i tre grupper efter deras olika tidsavstånd från det potentiella EU medlemskapet. Hypotesen som prövas är följande: ju kortare tidsavstånd till det potentiella EU medlemskapet desto större sannolikhet att staternas lagstiftningsnivå inom de tre områden som studeras har utvecklats till en hög nivå. Studien visar att hypotesen endast bekräftas delvis. Resultaten avseende icke-diskriminering visar att sambandet mellan tidsavståndet och nivån på lagstiftningen har ökat markant under den undersökta tidsperioden. Detta samband har endast stärkts mellan kategorin av stater som ligger tidsmässigt längst bort ett potentiellt EU medlemskap och de två kategorier som ligger närmare respektive närmast ett potentiellt EU medlemskap. Resultaten avseende användning av minoritetsspråk i officiella sammanhang och minoriteters språkliga rättigheter i utbildningen visar inget respektive nästan inget samband mellan tidsavståndet och utvecklingen på lagstiftningen mellan 2003 och 2010.