Image Segmentation Using Information Bottleneck Method

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

Determination of etoricoxib in human plasma using automated on-line solid-phase extraction coupled with LC-APCI/MS/MS

A liquid chromatography-tandem mass spectrometry method with atmospheric pressure chemical ionization (LC-APCI/MS/MS) was validated for the determination of etoricoxib in human plasma using antipyrin as internal standard, followed by on-line solid-phase extraction. The method was performed on a Luna C18 column and the mobile phase consisted of acetonitrile:water (95:5, v/v)/ammonium acetate (pH 4.0; 10 mM), run at a flow rate of 0.6 mL/min. The method was linear in the range of 1-5000 ng/mL (r²>0.99). The lower limit of quantitation was 1 ng/mL. The recoveries were within 93.72-96.18%. Moreover, method validation demonstrated acceptable results for the precision, accuracy and stability studies.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

THE APPLICATION OF AUTOMATED CORRELATION OPTIMIZED WARPING TO THE QUALITY EVALUATION OF Radix Puerariae thomsonii: CORRECTING RETENTION TIME SHIFT IN THE CHROMATOGRAPHIC FINGERPRINTS

The application of automated correlation optimized warping (ACOW) to the correction of retention time shift in the chromatographic fingerprints of Radix Puerariae thomsonii (RPT) was investigated. Twenty-seven samples were extracted from 9 batches of RPT products. The fingerprints of the 27 samples were established by the HPLC method. Because there is a retention time shift in the established fingerprints, the quality of these samples cannot be correctly evaluated by using similarity estimation and principal component analysis (PCA). Thus, the ACOW method was used to align these fingerprints. In the ACOW procedure, the warping parameters, which have a significant influence on the alignment result, were optimized by an automated algorithm. After correcting the retention time shift, the quality of these RPT samples was correctly evaluated by similarity estimation and PCA. It is demonstrated that ACOW is a practical method for aligning the chromatographic fingerprints of RPT. The combination of ACOW, similarity estimation, and PCA is shown to be a promising method for evaluating the quality of Traditional Chinese Medicine.

Comparison Structure Analysis

This study presents an automatic, computer-aided analytical method called Comparison Structure Analysis (CSA), which can be applied to different dimensions of music. The aim of CSA is first and foremost practical: to produce dynamic and understandable representations of musical properties by evaluating the prevalence of a chosen musical data structure through a musical piece. Such a comparison structure may refer to a mathematical vector, a set, a matrix or another type of data structure and even a combination of data structures. CSA depends on an abstract systematic segmentation that allows for a statistical or mathematical survey of the data. To choose a comparison structure is to tune the apparatus to be sensitive to an exclusive set of musical properties. CSA settles somewhere between traditional music analysis and computer aided music information retrieval (MIR). Theoretically defined musical entities, such as pitch-class sets, set-classes and particular rhythm patterns are detected in compositions using pattern extraction and pattern comparison algorithms that are typical within the field of MIR. In principle, the idea of comparison structure analysis can be applied to any time-series type data and, in the music analytical context, to polyphonic as well as homophonic music. Tonal trends, set-class similarities, invertible counterpoints, voice-leading similarities, short-term modulations, rhythmic similarities and multiparametric changes in musical texture were studied. Since CSA allows for a highly accurate classification of compositions, its methods may be applicable to symbolic music information retrieval as well. The strength of CSA relies especially on the possibility to make comparisons between the observations concerning different musical parameters and to combine it with statistical and perhaps other music analytical methods. The results of CSA are dependent on the competence of the similarity measure. New similarity measures for tonal stability, rhythmic and set-class similarity measurements were proposed. The most advanced results were attained by employing the automated function generation – comparable with the so-called genetic programming – to search for an optimal model for set-class similarity measurements. However, the results of CSA seem to agree strongly, independent of the type of similarity function employed in the analysis.

Segmentation of Industrial Customers – Case: Finnish Mechanical Engineering Companies

Segmentointi on perinteisesti ollut erityisesti kuluttajamarkkinoinnin työkalu, mutta siirtymä tuotteista palveluihin on lisännyt segmentointitarvetta myös teollisilla markkinoilla. Tämän tutkimuksen tavoite on löytää selkeästi toisistaan erottuvia asiakasryhmiä suomalaisen liikkeenjohdon konsultointiyritys Synocus Groupin tarjoaman case-materiaalin pohjalta. K-means-klusteroinnin avulla löydetään kolme potentiaalista markkinasegmenttiä perustuen siihen, mitkä tarjoamaelementit 105 valikoitua suomalaisen kone- ja metallituoteteollisuuden asiakasta ovat maininneet tärkeimmiksi. Ensimmäinen klusteri on hintatietoiset asiakkaat, jotka laskevat yksikkökohtaisia hintoja. Toinen klusteri koostuu huolto-orientoituneista asiakkaista, jotka laskevat tuntikustannuksia ja maksimoivat konekannan käyttötunteja. Tälle kohderyhmälle kannattaisi ehkä markkinoida teknisiä palveluja ja huoltosopimuksia. Kolmas klusteri on tuottavuussuuntautuneet asiakkaat, jotka ovat kiinnostuneita suorituskyvyn kehittämisestä ja laskevat tonnikohtaisia kustannuksia. He tavoittelevat alempia kokonaiskustannuksia lisääntyneen suorituskyvyn, pidemmän käyttöiän ja alempien huoltokustannusten kautta.

Speaker diarization: Segmentation and clustering of speeches

Speaker diarization is the process of sorting speeches according to the speaker. Diarization helps to search and retrieve what a certain speaker uttered in a meeting. Applications of diarization systemsextend to other domains than meetings, for example, lectures, telephone, television, and radio. Besides, diarization enhances the performance of several speech technologies such as speaker recognition, automatic transcription, and speaker tracking. Methodologies previously used in developing diarization systems are discussed. Prior results and techniques are studied and compared. Methods such as Hidden Markov Models and Gaussian Mixture Models that are used in speaker recognition and other speech technologies are also used in speaker diarization. The objective of this thesis is to develop a speaker diarization system in meeting domain. Experimental part of this work indicates that zero-crossing rate can be used effectively in breaking down the audio stream into segments, and adaptive Gaussian Models fit adequately short audio segments. Results show that 35 Gaussian Models and one second as average length of each segment are optimum values to build a diarization system for the tested data. Uniting the segments which are uttered by same speaker is done in a bottom-up clustering by a newapproach of categorizing the mixture weights.

Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.

Automated Software Testing in Machine Automation

The problem of software (SW) defaults is becoming more and more topical because of increasing amount of the SW and its complication. The majority of these defaults are founded during the test part that consumes about 40-50% of the development efforts. Test automation allows reducing the cost of this process and increasing testing effectiveness. In the middle of 1980 the first tools for automated testing appeared and the automated process was implemented in different kinds of SW testing. In short time, it became obviously, automated testing can cause many problems such as increasing product cost, decreasing reliability and even project fail. This thesis describes automated testing process, its concept, lists main problems, and gives an algorithm for automated test tools selection. Also this work presents an overview of the main automated test tools for embedded systems.

Benefits of automated acclimatization during the pre-milking phase of lactating girolando cows

The aim of this paper was to evaluate the automated acclimatization effects during pre-milking of cows on thermal conditioning, physiology, milk production and cost-benefit of the automated adiabatic evaporative cooling system (AECS). The treatments 20; 30; 40 min and control consisted of exposure time of pre-milking cows to the automated AECS. Sixteen cows were used with an average daily milk yield of 19 kg, distributed in a 4 x 4 Latin square design. The Tukey's test (P<0.05) was used to compare the means. The environmental variables, dry bulb temperature (DBT, ºC) and relative humidity (RH, %), were recorded every minute, which allowed the determination of the system efficiency through the Temperature and Humidity Index (THI). The respiratory rate (RR), rectal temperature (RT) and temperature of the coat (TC) were measured before and after the acclimatization. The 40 min treatment kept the environmental variables and the comfort indexes within recommended limits. The physiological variables (RR, RT and TC) were lower in the 40 min treatment and reflected positively on milk production, which increased 3.66% compared to the control treatment. The system was profitable, having a 43 days return on investment and a monthly revenue increase of R$ 1,992.67.

Development of an automated bench top electronic penetrometer

The aim of this study was to develop a an automated bench top electronic penetrometer (ABEP) that allows performing tests with high rate of data acquisition (up to 19,600 Hz) and with variation of the displacement velocity and of the base area of cone penetration. The mechanical components of the ABEP are: a supporting structure, stepper motor, velocity reducer, double nut ball screw and six penetration probes. The electronic components of ABEP are: a "driver" to control rotation and displacement, power supply, three load cells, two software programs for running and storing data, and a data acquisition module. This penetrometer presented in compact size, portable and in 32 validation tests it proved easy to operate, and showed high resolution, high velocity in reliability in data collection. During the validation tests the equipment met the objectives, because the test results showed that the ABEP could use different sizes of cones, allowed work at different velocities, showed for velocity and displacement, were only 1.3% and 0.7%, respectively, at the highest velocity (30 mm s-1) and 1% and 0.9%, respectively for the lowest velocity (0.1 mm s-1).

Customer Segmentation in Industrial Markets - CASE: Labelstock Company

The aim of this thesis is to study segmentation in industrial markets and develop a segmenting method proposal and criteria case study for a labelstock manufacturing company. An industrial company is facing many different customers with varying needs. Market segmentation is a process for dividing a market into smaller groups in which customers have the same or similar needs. Segmentation gives tools to the marketer to better match the product or service more closely to the needs of the target market. In this thesis a segmentation tool proposal and segmenting criteria is case studied for labelstock company’s Europe, Middle East and Africa business area customers and market. In the developed matrix tool different customers are planned to be evaluated based on customer characteristic variables. The criteria for the evaluating matrix are based on the customer’s buying organizations characteristics and buying behaviour. There are altogether 13 variables in the evaluating matrix. As an example of variables there are loyalty, size of the customer, estimated growth of the customer purchases and customer’s decision-making and buying behaviour. These characteristic variables will help to identify market segments to target and the customers belonging to those segments.

Automated Purchase to Pay Process Value Modeling and Comparative Process Speeds

The importance of efficient supply chain management has increased due to globalization and the blurring of organizational boundaries. Various supply chain management technologies have been identified to drive organizational profitability and financial performance. Organizations have historically been concentrating heavily on the flow of goods and services, while less attention has been dedicated to the flow of money. While supply chains are becoming more transparent and automated, new opportunities for financial supply chain management have emerged through information technology solutions and comprehensive financial supply chain management strategies. This research concentrates on the end part of the purchasing process which is the handling of invoices. Efficient invoice processing can have an impact on organizations working capital management and thus provide companies with better readiness to face the challenges related to cash management. Leveraging a process mining solution the aim of this research was to examine the automated invoice handling process of four different organizations. The invoice data was collected from each organizations invoice processing system. The sample included all the invoices organizations had processed during the year 2012. The main objective was to find out whether e-invoices are faster to process in an automated invoice processing solution than scanned invoices (post entry into invoice processing solution). Other objectives included looking into the longest lead times between process steps and the impact of manual process steps on cycle time. Processing of invoices from maverick purchases was also examined. Based on the results of the research and previous literature on the subject, suggestions for improving the process were proposed. The results of the research indicate that scanned invoices were processed faster than e-invoices. This is mostly due to the more complex processing of e-invoices. It should be noted however that the manual tasks related to turning a paper invoice into electronic format through scanning are ignored in this research. The transitions with the longest lead times in the invoice handling process included both pre-automated steps as well as manual steps performed by humans. When the most common manual steps were examined in more detail, it was clear that these steps had a prolonging impact on the process. Regarding invoices from maverick purchases the evidence shows that these invoices were slower to process than invoices from purchases conducted through e-procurement systems and from preferred suppliers. Suggestions on how to improve the process included: increasing invoice matching, reducing of manual steps and leveraging of different value added services such as invoice validation service, mobile solutions and supply chain financing services. For companies that have already reaped all the process efficiencies the next step is to engage in collaborative financial supply chain management strategies that can benefit the whole supply chain.