Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Modeling of Gold Cyanidation

The chemistry of gold dissolution in alkaline cyanide solution has continually received attention and new rate equations expressing the gold leaching are still developed. The effect of leaching parameters on gold gold cyanidation is studied in this work in order to optimize the leaching process. A gold leaching model, based on the well-known shrinking-core model, is presented in this work. It is proposed that the reaction takes place at the reacting particle surface which is continuously reduced as the reaction proceeds. The model parameters are estimated by comparing experimental data and simulations. The experimental data used in this work was obtained from Ling et al. (1996) and de Andrade Lima and Hodouin (2005). Two different rate equations, where the unreacted amount of gold is considered in one equation, are investigated. In this work, it is presented that the reaction at the surface is the rate controlling step since there is no internal diffusion limitation. The model considering the effect of non-reacting gold shows that the reaction orders are consistent with the experimental observations reported by Ling et al. (1996) and de Andrade Lima and Hodouin (2005). However, it should be noted that the model obtained in this work is based on assumptions of no side reactions, no solid-liquid mass transfer resistances and no effect from temperature.

Photoelectric Measurements and Modeling of Bacteriorhodopsin

Recent technology has provided us with new information about the internal structures and properties of biomolecules. This has lead to the design of applications based on underlying biological processes. Applications proposed for biomolecules are, for example, the future computers and different types of sensors. One potential biomolecule to be incorporated in the applications is bacteriorhodopsin. Bacteriorhodopsin is a light-sensitive biomolecule, which works in a similar way as the light sensitive cells of the human eye. Bacteriorhodopsin reacts to light by undergoing a complicated series of chemical and thermal transitions. During these transitions, a proton translocation occurs inside the molecule. It is possible to measure the photovoltage caused by the proton translocations when a vast number of molecules is immobilized in a thin film. Also the changes in the light absorption of the film can be measured. This work aimed to develop the electronics needed for the voltage measurements of the bacteriorhodopsin-based optoelectronic sensors. The development of the electronics aimed to get more accurate information about the structure and functionality of these sensors. The sensors used in this work contain a thick film of bacteriorhodopsin immobilized in polyvinylalcohol. This film is placed between two transparent electrodes. The result of this work is an instrumentation amplifier which can be placed in a small space very close to the sensor. By using this amplifier, the original photovoltage can be measured in more detail. The response measured using this amplifier revealed two different components, which could not be distinguished earlier. Another result of this work is the model for the photoelectric response in dry polymer films.

Precursor nanoscale modulations in ferromagnets: Modeling and thermodynamic characterization

Using a Ginzburg-Landau model for the magnetic degrees of freedom with coupling to disorder, we demonstrate through simulations the existence of stripelike magnetic precursors recently observed in Co-Ni-Al alloys above the Curie temperature. We characterize these magnetic modulations by means of the temperature dependence of local magnetization distribution, magnetized volume fraction, and magnetic susceptibility. We also obtain a temperature-disorder strength phase diagram in which a magnetic tweed phase exists in a small region between the paramagnetic and dipolar phases.

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

Unsupervised visual object categorization

The number of digital images has been increasing exponentially in the last few years. People have problems managing their image collections and finding a specific image. An automatic image categorization system could help them to manage images and find specific images. In this thesis, an unsupervised visual object categorization system was implemented to categorize a set of unknown images. The system is unsupervised, and hence, it does not need known images to train the system which needs to be manually obtained. Therefore, the number of possible categories and images can be huge. The system implemented in the thesis extracts local features from the images. These local features are used to build a codebook. The local features and the codebook are then used to generate a feature vector for an image. Images are categorized based on the feature vectors. The system is able to categorize any given set of images based on the visual appearance of the images. Images that have similar image regions are grouped together in the same category. Thus, for example, images which contain cars are assigned to the same cluster. The unsupervised visual object categorization system can be used in many situations, e.g., in an Internet search engine. The system can categorize images for a user, and the user can then easily find a specific type of image.

Error Modeling and Accuracy Analysis of a Novel Mobile Hybrid Parallel Robot

Over the last decades, calibration techniques have been widely used to improve the accuracy of robots and machine tools since they only involve software modification instead of changing the design and manufacture of the hardware. Traditionally, there are four steps are required for a calibration, i.e. error modeling, measurement, parameter identification and compensation. The objective of this thesis is to propose a method for the kinematics analysis and error modeling of a newly developed hybrid redundant robot IWR (Intersector Welding Robot), which possesses ten degrees of freedom (DOF) where 6-DOF in parallel and additional 4-DOF in serial. In this article, the problem of kinematics modeling and error modeling of the proposed IWR robot are discussed. Based on the vector arithmetic method, the kinematics model and the sensitivity model of the end-effector subject to the structure parameters is derived and analyzed. The relations between the pose (position and orientation) accuracy and manufacturing tolerances, actuation errors, and connection errors are formulated. Computer simulation is performed to examine the validity and effectiveness of the proposed method.

3D Surface Modeling From Point Clouds

The goal of this thesis is to implement software for creating 3D models from point clouds. Point clouds are acquired with stereo cameras, monocular systems or laser scanners. The created 3D models are triangular models or NURBS (Non-Uniform Rational B-Splines) models. Triangular models are constructed from selected areas from the point clouds and resulted triangular models are translated into a set of quads. The quads are further translated into an estimated grid structure and used for NURBS surface approximation. Finally, we have a set of NURBS surfaces which represent the whole model. The problem wasn’t so easy to solve. The selected triangular surface reconstruction algorithm did not deal well with noise in point clouds. To handle this problem, a clustering method is introduced for simplificating the model and removing noise. As we had better results with the smaller point clouds produced by clustering, we used points in clusters to better estimate the grids for NURBS models. The overall results were good when the point cloud did not have much noise. The point clouds with small amount of error had good results as the triangular model was solid. NURBS surface reconstruction performed well on solid models.

Study and modeling of the desintegration kinetics of coated paper

The disintegration of recovered paper is the first operation in the preparation of recycled pulp. It is known that the defibering process follows a first order kinetics from which it is possible to obtain the disintegration kinetic constant (KD) by means of different ways. The disintegration constant can be obtained from the Somerville index results (%lsv and from the dissipated energy per volume unit (Ss). The %slv is related to the quantity of non-defibrated paper, as a measure of the non-disintegrated fiber residual (percentage of flakes), which is expressed in disintegration time units. In this work, disintegration kinetics from recycled coated paper has been evaluated, working at 20 revise rotor speed and for different fiber consistency (6, 8, 10, 12 and 14%). The results showed that the values of experimental disintegration kinetic constant, Ko, through the analysis of Somerville index, as function of time. Increased, the disintegration time was drastically reduced. The calculation of the disintegration kinetic constant (modelled Ko), extracted from the Rayleigh’s dissipation function, showed a good correlation with the experimental values using the evolution of the Somerville index or with the dissipated energy

Determination of corn stalk fibers' strength through modeling of the mechanical properties of its composites

Worldwide cultivation of corn is expanding, due in part to the increasing production of bioethanol. In consequence, huge amounts of corn stalks residues are been produced. Instead of incineration, we transformed the corn stalks into a semichemical pulp and successfully applied it as reinforcement in polypropylene composites. PP composites reinforced with 40% wt corn stalk single fibers were prepared, and their mechanical properties were evaluated. Through mechanical properties modeling of the composites, the intrinsic tensile strength of the cellulosic fibers that constitute the corn stalk have been determined

Toimitusketjun mallinnuksen tarpeet ja rakenne LOCOMOTIVE projekstissa

Työn tavoitteena oli kuvata ja priorisoida toimitusketjun dynaamisen mallinnustyökalun vaatimukset, sekä muodostaa tämän pohjalta ohjelmistokehitystä tukeva oliomalli. Vaatimuksia selvitettiin teoreettisen tarkastelun, aiemmin toteutettujen kyselytutkimusten sekä viiden pilottitapauksen avulla. Toimitusketjun hallinta ei ole pelkästään materiaalivirtojen vaan myös näihin liittyvän informaation hallintaa. Holististen toimitusketjuongelmien mallintaminen edellyttää siis informaatiovirtojen ja niitä saatelevien ohjausmekanisemien mallintamista. Markkinoilla on selkeästi tilaa tukijärjestelmille, jotka mahdollistaisivat multidimensionaalisten - tuotto, aika, palvelu - toimitusketjuongelmien tarkastelun. Systeemidynamiikan teorian mukaisesti oliomallin lähtökohdaksi valittiin tärkeimpien takaisinkytkentäsilmukkojen mallinnus. Takaisinkytkentäsilmukoiden avulla kyetään mallintamaan kompleksisia systeemejä ajan suhteen. Mallinnetut toimitusketjujen takaisinkytkentäsilmukkat ovat operaatio-, ohjaus-, kysyntä- ja strategiasilmukka. Toimitusketjun ohjausmekanismien, sekä systeemidynamiikan perusteiden pohjalta mallinnustyökalun vaatimuksista muodostettiin oliomalli. Muodostettu oliomalli on Locomotiven - toimitusketjun mallinnustyökalun - perusta.

Phase diagram and complex patterns in the modeling of the bromate-oxalic acid-Ce-acetone oscillating reaction

Simulations have been carried out on the bromate - oxalic acid - Ce(IV) - acetone oscillating reaction, under flow conditions, using Field and Boyd's model (J. Phys. Chem. 1985, 89, 3707). Many different complex dynamic behaviors were found, including simple periodic oscillations, complex periodic oscillations, quasiperiodicity and chaos. Some of these complex oscillations can be understood as belonging to a Farey sequence. The many different behaviors were systematized in a phase diagram which shows that some regions of complex patterns were nested with one inside the other. The existence of almost all known dynamic behavior for this system allows the suggestion that it can be used as a model for some very complex phenomena that occur in biological systems.

The Role of the Delay Time in the Modeling of Biological Range Expansions

The time interval between successive migrations of biological species causes a delay time in the reaction-diffusion equations describing their space-time dynamics. This lowers the predicted speed of the waves of advance, as compared to classical models. It has been shown that this delay-time effect improves the modeling of human range expansions. Here, we demonstrate that it can also be important for other species. We present two new examples where the predictions of the time-delayed and the classical (Fisher) approaches are compared to experimental data. No free or adjustable parameters are used. We show that the importance of the delay effect depends on the dimensionless product of the initial growth rate and the delay time. We argue that the delay effect should be taken into account in the modeling of range expansions for biological species

Sensor Fusion of Proprioception, Force and Vision in Estimation and Robot

Sensor-based robot control allows manipulation in dynamic environments with uncertainties. Vision is a versatile low-cost sensory modality, but low sample rate, high sensor delay and uncertain measurements limit its usability, especially in strongly dynamic environments. Force is a complementary sensory modality allowing accurate measurements of local object shape when a tooltip is in contact with the object. In multimodal sensor fusion, several sensors measuring different modalities are combined to give a more accurate estimate of the environment. As force and vision are fundamentally different sensory modalities not sharing a common representation, combining the information from these sensors is not straightforward. In this thesis, methods for fusing proprioception, force and vision together are proposed. Making assumptions of object shape and modeling the uncertainties of the sensors, the measurements can be fused together in an extended Kalman filter. The fusion of force and visual measurements makes it possible to estimate the pose of a moving target with an end-effector mounted moving camera at high rate and accuracy. The proposed approach takes the latency of the vision system into account explicitly, to provide high sample rate estimates. The estimates also allow a smooth transition from vision-based motion control to force control. The velocity of the end-effector can be controlled by estimating the distance to the target by vision and determining the velocity profile giving rapid approach and minimal force overshoot. Experiments with a 5-degree-of-freedom parallel hydraulic manipulator and a 6-degree-of-freedom serial manipulator show that integration of several sensor modalities can increase the accuracy of the measurements significantly.