Evaluation of sample processing methods for the polar contaminant analysis of sewage sludge using liquid chromatography - mass spectrometry (LC/MS)

Monitoring of sewage sludge has proved the presence of many polar anthropogenic pollutants since LC/MS techniques came into routine use. While advanced techniques may improve characterizations, flawed sample processing procedures, however, may disturb or disguise the presence and fate of many target compounds present in this type of complex matrix before analytical process starts. Freeze-drying or oven-drying, in combination with centrifugation or filtration as sample processing techniques were performed followed by visual pattern recognition of target compounds for assessment of pretreatment processes. The results shown that oven-drying affected the sludge characterization, while freeze-drying led to less analytical misinterpretations.

Demonstration Platform for Visual Quality Metrics

This thesis presents two graphical user interfaces for the project DigiQ - Fusion of Digital and Visual Print Quality, a project for computationally modeling the subjective human experience of print quality by measuring the image with certain metrics. After presenting the user interfaces, methods for reducing the computation time of several of the metrics and the image registration process required to compute the metrics, and details of their performance are given. The weighted sample method for the image registration process was able to signifigantly decrease the calculation times while resulting in some error. The random sampling method for the metrics greatly reduced calculation time while maintaining excellent accuracy, but worked with only two of the metrics.

Computational visual quality of digitally printed images

The problem of understanding how humans perceive the quality of a reproduced image is of interest to researchers of many fields related to vision science and engineering: optics and material physics, image processing (compression and transfer), printing and media technology, and psychology. A measure for visual quality cannot be defined without ambiguity because it is ultimately the subjective opinion of an “end-user” observing the product. The purpose of this thesis is to devise computational methods to estimate the overall visual quality of prints, i.e. a numerical value that combines all the relevant attributes of the perceived image quality. The problem is limited to consider the perceived quality of printed photographs from the viewpoint of a consumer, and moreover, the study focuses only on digital printing methods, such as inkjet and electrophotography. The main contributions of this thesis are two novel methods to estimate the overall visual quality of prints. In the first method, the quality is computed as a visible difference between the reproduced image and the original digital (reference) image, which is assumed to have an ideal quality. The second method utilises instrumental print quality measures, such as colour densities, measured from printed technical test fields, and connects the instrumental measures to the overall quality via subjective attributes, i.e. attributes that directly contribute to the perceived quality, using a Bayesian network. Both approaches were evaluated and verified with real data, and shown to predict well the subjective evaluation results.

Comparison of Local Feature Detectors and Descriptors for Visual Object Categorization

Local features are used in many computer vision tasks including visual object categorization, content-based image retrieval and object recognition to mention a few. Local features are points, blobs or regions in images that are extracted using a local feature detector. To make use of extracted local features the localized interest points are described using a local feature descriptor. A descriptor histogram vector is a compact representation of an image and can be used for searching and matching images in databases. In this thesis the performance of local feature detectors and descriptors is evaluated for object class detection task. Features are extracted from image samples belonging to several object classes. Matching features are then searched using random image pairs of a same class. The goal of this thesis is to find out what are the best detector and descriptor methods for such task in terms of detector repeatability and descriptor matching rate.

Bag-of-Features Approach to Unsupervised Visual Object Categorisation

The large and growing number of digital images is making manual image search laborious. Only a fraction of the images contain metadata that can be used to search for a particular type of image. Thus, the main research question of this thesis is whether it is possible to learn visual object categories directly from images. Computers process images as long lists of pixels that do not have a clear connection to high-level semantics which could be used in the image search. There are various methods introduced in the literature to extract low-level image features and also approaches to connect these low-level features with high-level semantics. One of these approaches is called Bag-of-Features which is studied in the thesis. In the Bag-of-Features approach, the images are described using a visual codebook. The codebook is built from the descriptions of the image patches using clustering. The images are described by matching descriptions of image patches with the visual codebook and computing the number of matches for each code. In this thesis, unsupervised visual object categorisation using the Bag-of-Features approach is studied. The goal is to find groups of similar images, e.g., images that contain an object from the same category. The standard Bag-of-Features approach is improved by using spatial information and visual saliency. It was found that the performance of the visual object categorisation can be improved by using spatial information of local features to verify the matches. However, this process is computationally heavy, and thus, the number of images must be limited in the spatial matching, for example, by using the Bag-of-Features method as in this study. Different approaches for saliency detection are studied and a new method based on the Hessian-Affine local feature detector is proposed. The new method achieves comparable results with current state-of-the-art. The visual object categorisation performance was improved by using foreground segmentation based on saliency information, especially when the background could be considered as clutter.

Evaluation of Bubble Formation and Break Up in Suppression Pools by Using Pattern Recognition Methods

During a possible loss of coolant accident in BWRs, a large amount of steam will be released from the reactor pressure vessel to the suppression pool. Steam will be condensed into the suppression pool causing dynamic and structural loads to the pool. The formation and break up of bubbles can be measured by visual observation using a suitable pattern recognition algorithm. The aim of this study was to improve the preliminary pattern recognition algorithm, developed by Vesa Tanskanen in his doctoral dissertation, by using MATLAB. Video material from the PPOOLEX test facility, recorded during thermal stratification and mixing experiments, was used as a reference in the development of the algorithm. The developed algorithm consists of two parts: the pattern recognition of the bubbles and the analysis of recognized bubble images. The bubble recognition works well, but some errors will appear due to the complex structure of the pool. The results of the image analysis were reasonable. The volume and the surface area of the bubbles were not evaluated. Chugging frequencies calculated by using FFT fitted well into the results of oscillation frequencies measured in the experiments. The pattern recognition algorithm works in the conditions it is designed for. If the measurement configuration will be changed, some modifications have to be done. Numerous improvements are proposed for the future 3D equipment.

Lean daily management, visual management and continuous improvement

This thesis was part of lean adaptation project started at Outotec Lappeenranta factory in early 2013. The purpose of this thesis was to develop and propose lean tools that could be used in daily management, visual management and continuous improvement. This thesis was “outsiders” view, and as such, did not study the current processes deeply. As result of this thesis, two different Daily Management -boards were designed, one for parallel processes and one for sequential processes. In addition, methods of doing continuous improvement and daily task accountability were framed and standard work for the leaders outlined. The tools presented in this thesis are general tools which support work in lean environment. They are visual and, if used correctly, they provide a basis from which continuous improvement can be done. Lean philosophy emphasizes the deep understanding of the current situation and it would be against the lean principles to blindly implement anything developed “on the outside”. The tools presented should be reviewed and modified further by the people working on the factory floor.

Weed suppression in the formation of brachiarias under three sowing methods

The success of conservation systems such as no-till depends on adequate soil cover throughout the year, which is possible through the use of cover crops. For this purpose the species belonging to the genus Urochloa has stood out by virtue of its hardiness and tolerance to drought. Aiming ground cover for the no-till system, the objective was to evaluate the establishment of two species of the genus Urochloa, in three sowing methods, in the weed suppression and the sensitivity of these forages to glyphosate. The study design was a randomized block with a 2 x 3 x 3 factorial arrangement, in which factor A was composed of Urochloa ruziziensis and Urochloa hybrid CIAT 36087 cv. Mulato II, factor B was formed by sowing methods: sown without embedding, sown with light embedding and sown in rows, and factor C was composed of three doses of glyphosate (0.975, 1.625 and 2.275 kg ha-1 of acid equivalent). For determination of weed suppression, assessment of biomass yield and soil cover was performed, by brachiaria and weeds, at 30, 60, 90, 120 and 258 days after sowing. Visual assessment of the desiccation efficiency at 7 and 14 days after herbicide application was performed. It is concluded that embedding Urochloa seeds stands out in relation to sowing in the soil surface. Urochloa ruziziensis is more efficient in the dry weight yield, weed suppression, in addition to being more sensitive to glyphosate herbicide.

Frequent occurrence of nicotinic acetylcholine receptors in GABAergic neurons of the chick visual system

Double-labeling immunohistochemical methods were used to investigate the occurrence of the alpha8 and alpha5 nicotinic receptor subunits in presumptive GABAergic neurons of the chick nervous system. Nicotinic receptor immunoreactivity was often found in cells exhibiting GABA-like immunoreactivity, especially in the visual system. The alpha8 subunit appeared to be present in presumptive GABAergic cells of the ventral lateral geniculate nucleus, nucleus of the basal optic root of the accessory optic system, and the optic tectum, among several other structures. The alpha5 subunit was also found in GABA-positive neurons, as observed in the lentiform nucleus of the mesencephalon and other pretectal nuclei. The numbers of alpha8- and alpha5-positive neurons that were also GABA-positive represented high percentages of the total number of neurons containing nicotinic receptor labeling in several brain areas, which indicates that most of the alpha8 and alpha5 nicotinic receptor subunits are present in GABAergic cells. Taken together with data from other studies, our results indicate an important role of the nicotinic acetylcholine receptors in the functional organization of GABAergic circuits in the visual system.

Visual analytics for behavioral and niche market segmentation

Companies require information in order to gain an improved understanding of their customers. Data concerning customers, their interests and behavior are collected through different loyalty programs. The amount of data stored in company data bases has increased exponentially over the years and become difficult to handle. This research area is the subject of much current interest, not only in academia but also in practice, as is shown by several magazines and blogs that are covering topics on how to get to know your customers, Big Data, information visualization, and data warehousing. In this Ph.D. thesis, the Self-Organizing Map and two extensions of it – the Weighted Self-Organizing Map (WSOM) and the Self-Organizing Time Map (SOTM) – are used as data mining methods for extracting information from large amounts of customer data. The thesis focuses on how data mining methods can be used to model and analyze customer data in order to gain an overview of the customer base, as well as, for analyzing niche-markets. The thesis uses real world customer data to create models for customer profiling. Evaluation of the built models is performed by CRM experts from the retailing industry. The experts considered the information gained with help of the models to be valuable and useful for decision making and for making strategic planning for the future.

Comparison of the reliability of multifocal visual evoked cortical potentials generated by pattern reversal and pattern pulse stimulation

This study compared the effectiveness of the multifocal visual evoked cortical potentials (mfVEP) elicited by pattern pulse stimulation with that of pattern reversal in producing reliable responses (signal-to-noise ratio >1.359). Participants were 14 healthy subjects. Visual stimulation was obtained using a 60-sector dartboard display consisting of 6 concentric rings presented in either pulse or reversal mode. Each sector, consisting of 16 checks at 99% Michelson contrast and 80 cd/m² mean luminance, was controlled by a binary m-sequence in the time domain. The signal-to-noise ratio was generally larger in the pattern reversal than in the pattern pulse mode. The number of reliable responses was similar in the central sectors for the two stimulation modes. At the periphery, pattern reversal showed a larger number of reliable responses. Pattern pulse stimuli performed similarly to pattern reversal stimuli to generate reliable waveforms in R1 and R2. The advantage of using both protocols to study mfVEP responses is their complementarity: in some patients, reliable waveforms in specific sectors may be obtained with only one of the two methods. The joint analysis of pattern reversal and pattern pulse stimuli increased the rate of reliability for central sectors by 7.14% in R1, 5.35% in R2, 4.76% in R3, 3.57% in R4, 2.97% in R5, and 1.78% in R6. From R1 to R4 the reliability to generate mfVEPs was above 70% when using both protocols. Thus, for a very high reliability and thorough examination of visual performance, it is recommended to use both stimulation protocols.

Chromatic spatial contrast sensitivity estimated by visual evoked cortical potential and psychophysics

The purpose of the present study was to measure contrast sensitivity to equiluminant gratings using steady-state visual evoked cortical potential (ssVECP) and psychophysics. Six healthy volunteers were evaluated with ssVECPs and psychophysics. The visual stimuli were red-green or blue-yellow horizontal sinusoidal gratings, 5° × 5°, 34.3 cd/m2 mean luminance, presented at 6 Hz. Eight spatial frequencies from 0.2 to 8 cpd were used, each presented at 8 contrast levels. Contrast threshold was obtained by extrapolating second harmonic amplitude values to zero. Psychophysical contrast thresholds were measured using stimuli at 6 Hz and static presentation. Contrast sensitivity was calculated as the inverse function of the pooled cone contrast threshold. ssVECP and both psychophysical contrast sensitivity functions (CSFs) were low-pass functions for red-green gratings. For electrophysiology, the highest contrast sensitivity values were found at 0.4 cpd (1.95 ± 0.15). ssVECP CSF was similar to dynamic psychophysical CSF, while static CSF had higher values ranging from 0.4 to 6 cpd (P < 0.05, ANOVA). Blue-yellow chromatic functions showed no specific tuning shape; however, at high spatial frequencies the evoked potentials showed higher contrast sensitivity than the psychophysical methods (P < 0.05, ANOVA). Evoked potentials can be used reliably to evaluate chromatic red-green CSFs in agreement with psychophysical thresholds, mainly if the same temporal properties are applied to the stimulus. For blue-yellow CSF, correlation between electrophysiology and psychophysics was poor at high spatial frequency, possibly due to a greater effect of chromatic aberration on this kind of stimulus.

Brazil nut sorting for aflatoxin prevention: a comparison between automatic and manual shelling methods

The impact of automatic and manual shelling methods during manual/visual sorting of different batches of Brazil nuts from the 2010 and 2011 harvests was evaluated in order to investigate aflatoxin prevention.The samples were tested as follows: in-shell, shell, shelled, and pieces in order to evaluate the moisture content (mc), water activity (Aw), and total aflatoxin (LOD = 0.3 µg/kg and LOQ 0.85 µg/kg) at the Brazil nut processing plant. The results of aflatoxins obtained for the manually shelled nut samples ranged from 3.0 to 60.3 µg/g and from 2.0 to 31.0 µg/g for the automatically shelled samples. All samples showed levels of mc below the limit of 15%; on the other hand, shelled samples from both harvests showed levels of Aw above the limit. There were no significant differences concerning the manual or automatic shelling results during the sorting stages. On the other hand, the visual sorting was effective in decreasing the aflatoxin contamination in both methods.

Wood species identification by visual characterization of fibers

Kandidaatintyö tehtiin osana PulpVision-tutkimusprojektia, jonka tarkoituksena on kehittää kuvapohjaisia laskenta- ja luokittelumetodeja sellun laaduntarkkailuun paperin valmistuksessa. Tämän tutkimusprojektin osana on aiemmin kehitetty metodi, jolla etsittiin kaarevia rakenteita kuvista, ja tätä metodia hyödynnettiin kuitujen etsintään kuvista. Tätä metodia käytettiin lähtökohtana kandidaatintyölle. Työn tarkoituksena oli tutkia, voidaanko erilaisista kuitukuvista laskettujen piirteiden avulla tunnistaa kuvassa olevien kuitujen laji. Näissä kuitukuvissa oli kuituja neljästä eri puulajista ja yhdestä kasvista. Nämä lajit olivat akasia, koivu, mänty, eukalyptus ja vehnä. Jokaisesta lajista valittiin 100 kuitukuvaa ja nämä kuvat jaettiin kahteen ryhmään, joista ensimmäistä käytettiin opetusryhmänä ja toista testausryhmänä. Opetusryhmän avulla jokaiselle kuitulajille laskettiin näitä kuvaavia piirteitä, joiden avulla pyrittiin tunnistamaan testausryhmän kuvissa olevat kuitulajit. Nämä kuvat oli tuottanut CEMIS-Oulu (Center for Measurement and Information Systems), joka on mittaustekniikkaan keskittynyt yksikkö Oulun yliopistossa. Yksittäiselle opetusryhmän kuitukuvalle laskettiin keskiarvot ja keskihajonnat kolmesta eri piirteestä, jotka olivat pituus, leveys ja kaarevuus. Lisäksi laskettiin, kuinka monta kuitua kuvasta löydettiin. Näiden piirteiden eri yhdistelmien avulla testattiin tunnistamisen tarkkuutta käyttämällä k:n lähimmän naapurin menetelmää ja Naiivi Bayes -luokitinta testausryhmän kuville. Testeistä saatiin lupaavia tuloksia muun muassa pituuden ja leveyden keskiarvoja käytettäessä saavutettiin jopa noin 98 %:n tarkkuus molemmilla algoritmeilla. Tunnistuksessa kuitujen keskimäärinen pituus vaikutti olevan kuitukuvia parhaiten kuvaava piirre. Käytettyjen algoritmien välillä ei ollut suurta vaihtelua tarkkuudessa. Testeissä saatujen tulosten perusteella voidaan todeta, että kuitukuvien tunnistaminen on mahdollista. Testien perusteella kuitukuvista tarvitsee laskea vain kaksi piirrettä, joilla kuidut voidaan tunnistaa tarkasti. Käytetyt lajittelualgoritmit olivat hyvin yksinkertaisia, mutta ne toimivat testeissä hyvin.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.