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.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Local politico-administrative perspectives on quality improvement based on national registry data in Sweden : a qualitative study using the Consolidated Framework for Implementation Research.

Background. Through a national policy agreement, over 167 million Euros will be invested in the Swedish National Quality Registries (NQRs) between 2012 and 2016. One of the policy agreement¿s intentions is to increase the use of NQR data for quality improvement (QI). However, the evidence is fragmented as to how the use of medical registries and the like lead to quality improvement, and little is known about non-clinical use. The aim was therefore to investigate the perspectives of Swedish politicians and administrators on quality improvement based on national registry data. Methods. Politicians and administrators from four county councils were interviewed. A qualitative content analysis guided by the Consolidated Framework for Implementation Research (CFIR) was performed. Results. The politicians and administrators perspectives on the use of NQR data for quality improvement were mainly assigned to three of the five CFIR domains. In the domain of intervention characteristics, data reliability and access in reasonable time were not considered entirely satisfactory, making it difficult for the politico-administrative leaderships to initiate, monitor, and support timely QI efforts. Still, politicians and administrators trusted the idea of using the NQRs as a base for quality improvement. In the domain of inner setting, the organizational structures were not sufficiently developed to utilize the advantages of the NQRs, and readiness for implementation appeared to be inadequate for two reasons. Firstly, the resources for data analysis and quality improvement were not considered sufficient at politico-administrative or clinical level. Secondly, deficiencies in leadership engagement at multiple levels were described and there was a lack of consensus on the politicians¿ role and level of involvement. Regarding the domain of outer setting, there was a lack of communication and cooperation between the county councils and the national NQR organizations. Conclusions. The Swedish experiences show that a government-supported national system of well-funded, well-managed, and reputable national quality registries needs favorable local politico-administrative conditions to be used for quality improvement; such conditions are not yet in place according to local politicians and administrators.

Entity Framework 4.0, enutvärdering av ett ORMramverk

När man kombinerar ett objektorienterat programmeringsspråk och en relationsdatabas uppstår en del problem för utvecklare eftersom objektorienterade programmeringsspråk och relationsdatabaser har olika fokus, objektorienterade programmeringsspråk fokuserar på att avbilda verkliga objekt och relationsdatabaser fokuserar på data. De problem som uppstår kallas med ett samlingsnamn för object-relational mismatch. Det finns flertalet ramverk för att hantera dessa problem. Ett av dem är Entity Framework.Syftet med detta projekt var att utvärdera hur utvecklare tycker att Entity Framework fungerar för att lösa problematiken runt object-relational mismatch, hur det är för utvecklare att lära sig använda Entity Framework samt hur tillgången på inlärningsmaterial är.Under vår studie har vi lärt oss använda Entity Framework samtidigt som vi gjort en studie av tillgången på inlärningsmaterial. Vi har också byggt om en applikation så att den använder Entity Framework. Vi har jämfört den ombyggda applikationen med den gamla applikationen för att kunna se vilken skillnad som Entity Framework bidrog till.Vi kom fram till att Entity Framework hanterar object-relational mismatch på ett bra sätt som bland annat gör att utvecklingsprocessen kortas ner då inte lika mycket kod behöver skrivas. Utvecklare med tidigare kunskaper i .NET-programmering upplever att det är lätt att lära sig Entity Framework. Att det upplevs lätt att lära sig Entity Framework hänger förmodligen ihop med att tillgången på inlärningsmaterial är god.

Testing for Seasonal Unit Roots when Residuals Contain Serial Correlations under HEGY Test Framework

This paper introduces a corrected test statistic for testing seasonal unit roots when residuals contain serial correlations, based on the HEGY test proposed by Hylleberg,Engle, Granger and Yoo (1990). The serial correlations in the residuals of test regressionare accommodated by making corrections to the commonly used HEGY t statistics. Theasymptotic distributions of the corrected t statistics are free from nuisance parameters.The size and power properties of the corrected statistics for quarterly and montly data are investigated. Based on our simulations, the corrected statistics for monthly data havemore power compared with the commonly used HEGY test statistics, but they also have size distortions when there are strong negative seasonal correlations in the residuals.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

How does employment density influence individuals’ wages? : A micro data approach.

We estimate the effect of employment density on wages in Sweden in a large geocoded data set on individuals and workplaces. Employment density is measured in four circular zones around each individual’s place of living. The data contains a rich set of control variables that we use in an instrumental variables framework. Results show a relatively strong but rather local positive effect of employment density on wages. Beyond 5 kilometers the effect becomes negative. This might indicate that the effect of agglomeration economies falls faster with distance than the effects of congestion.

Knowledge translation in Uganda : a qualitative study of Ugandan midwives' and managers' perceived relevance of the sub-elements of the context cornerstone in the PARIHS framework

BACKGROUND: A large proportion of the annual 3.3 million neonatal deaths could be averted if there was a high uptake of basic evidence-based practices. In order to overcome this 'know-do' gap, there is an urgent need for in-depth understanding of knowledge translation (KT). A major factor to consider in the successful translation of knowledge into practice is the influence of organizational context. A theoretical framework highlighting this process is Promoting Action on Research Implementation in Health Services (PARIHS). However, research linked to this framework has almost exclusively been conducted in high-income countries. Therefore, the objective of this study was to examine the perceived relevance of the subelements of the organizational context cornerstone of the PARIHS framework, and also whether other factors in the organizational context were perceived to influence KT in a specific low-income setting. METHODS: This qualitative study was conducted in a district of Uganda, where focus group discussions and semi-structured interviews were conducted with midwives (n = 18) and managers (n = 5) within the catchment area of the general hospital. The interview guide was developed based on the context sub-elements in the PARIHS framework (receptive context, culture, leadership, and evaluation). Interviews were transcribed verbatim, followed by directed content analysis of the data. RESULTS: The sub-elements of organizational context in the PARIHS framework--i.e., receptive context, culture, leadership, and evaluation--also appear to be relevant in a low-income setting like Uganda, but there are additional factors to consider. Access to resources, commitment and informal payment, and community involvement were all perceived to play important roles for successful KT. CONCLUSIONS: In further development of the context assessment tool, assessing factors for successful implementation of evidence in low-income settings--resources, community involvement, and commitment and informal payment--should be considered for inclusion. For low-income settings, resources are of significant importance, and might be considered as a separate subelement of the PARIHS framework as a whole.