Chaotic synchronization in 2D lattice for scene segmentation

Synchronization and chaos play important roles in neural activities and have been applied in oscillatory correlation modeling for scene and data analysis. Although it is an extensively studied topic, there are still few results regarding synchrony in locally coupled systems. In this paper we give a rigorous proof to show that large numbers of coupled chaotic oscillators with parameter mismatch in a 2D lattice can be synchronized by providing a sufficiently large coupling strength. We demonstrate how the obtained result can be applied to construct an oscillatory network for scene segmentation. (C) 2007 Elsevier B.V. All rights reserved.

An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation. (C) 2010 Elsevier B.V. All rights reserved.

Thermodynamic characterization of the palm tree Roystonea regia peroxidase stability

The structural stability of a peroxidase, a dimeric protein from royal palm tree (Roystonea regia) leaves, has been characterized by high-sensitivity differential scanning calorimetry, circular dichroism, steady-state tryptophan fluorescence and analytical ultracentifugation under different solvent conditions. It is shown that the thermal and chemical (using guanidine hydrochloride (Gdn-HCl)) folding/unfolding of royal palm tree peroxidase (RPTP) at pH 7 is a reversible process involving a highly cooperative transition between the folded dimer and unfolded monomers, with a free stabilization energy of about 23 kcal per mol of monomer at 25 degrees C. The structural stability of RPTP is pH-dependent. At pH 3, where ion pairs have disappeared due to protonation, the thermally induced denaturation of RPTP is irreversible and strongly dependent upon the scan rate, suggesting that this process is under kinetic control. Moreover, thermally induced transitions at this pH value are dependent on the protein concentration, allowing it to be concluded that in solution RPTP behaves as dimer, which undergoes thermal denaturation coupled with dissociation. Analysis of the kinetic parameters of RPTP denaturation at pH 3 was accomplished on the basis of the simple kinetic scheme N ->(k) D, where k is a first-order kinetic constant that changes with temperature, as given by the Arrhenius equation; N is the native state, and D is the denatured state, and thermodynamic information was obtained by extrapolation of the kinetic transition parameters to an infinite heating rate. Obtained in this way, the value of RPTP stability at 25 degrees C is ca. 8 kcal per mole of monomer lower than at pH 7. In all probability, this quantity reflects the contribution of ion pair interactions to the structural stability of RPTP. From a comparison of the stability of RPTP with other plant peroxidases it is proposed that one of the main factors responsible for the unusually high stability of RPTP which enhances its potential use for biotechnological purposes, is its dimerization. (c) 2008 Elsevier Masson SAS. All rights reserved.

Crystal structure and statistical coupling analysis of highly glycosylated peroxidase from royal palm tree (Roystonea regia)

Royal palm tree peroxidase (RPTP) is a very stable enzyme in regards to acidity, temperature, H(2)O(2), and organic solvents. Thus, RPTP is a promising candidate for developing H(2)O(2)-sensitive biosensors for diverse applications in industry and analytical chemistry. RPTP belongs to the family of class III secretory plant peroxidases, which include horseradish peroxidase isozyme C, soybean and peanut peroxidases. Here we report the X-ray structure of native RPTP isolated from royal palm tree (Roystonea regia) refined to a resolution of 1.85 angstrom. RPTP has the same overall folding pattern of the plant peroxidase superfamily, and it contains one heme group and two calcium-binding sites in similar locations. The three-dimensional structure of RPTP was solved for a hydroperoxide complex state, and it revealed a bound 2-(N-morpholino) ethanesulfonic acid molecule (MES) positioned at a putative substrate-binding secondary site. Nine N-glycosylation sites are clearly defined in the RPTP electron-density maps, revealing for the first time conformations of the glycan chains of this highly glycosylated enzyme. Furthermore, statistical coupling analysis (SCA) of the plant peroxidase superfamily was performed. This sequence-based method identified a set of evolutionarily conserved sites that mapped to regions surrounding the heme prosthetic group. The SCA matrix also predicted a set of energetically coupled residues that are involved in the maintenance of the structural folding of plant peroxidases. The combination of crystallographic data and SCA analysis provides information about the key structural elements that could contribute to explaining the unique stability of RPTP. (C) 2009 Elsevier Inc. All rights reserved.

Generating segmented meshes from textured color images

This paper presents a new framework for generating triangular meshes from textured color images. The proposed framework combines a texture classification technique, called W-operator, with Imesh, a method originally conceived to generate simplicial meshes from gray scale images. An extension of W-operators to handle textured color images is proposed, which employs a combination of RGB and HSV channels and Sequential Floating Forward Search guided by mean conditional entropy criterion to extract features from the training data. The W-operator is built into the local error estimation used by Imesh to choose the mesh vertices. Furthermore, the W-operator also enables to assign a label to the triangles during the mesh construction, thus allowing to obtain a segmented mesh at the end of the process. The presented results show that the combination of W-operators with Imesh gives rise to a texture classification-based triangle mesh generation framework that outperforms pixel based methods. Crown Copyright (C) 2009 Published by Elsevier Inc. All rights reserved.

Minimum cycle cover and Chinese postman problems on mixed graphs with bounded tree-width

Let M = (V, E, A) be a mixed graph with vertex set V, edge set E and arc set A. A cycle cover of M is a family C = {C(1), ... , C(k)} of cycles of M such that each edge/arc of M belongs to at least one cycle in C. The weight of C is Sigma(k)(i=1) vertical bar C(i)vertical bar. The minimum cycle cover problem is the following: given a strongly connected mixed graph M without bridges, find a cycle cover of M with weight as small as possible. The Chinese postman problem is: given a strongly connected mixed graph M, find a minimum length closed walk using all edges and arcs of M. These problems are NP-hard. We show that they can be solved in polynomial time if M has bounded tree-width. (C) 2008 Elsevier B.V. All rights reserved.

The value of a saved tree : The lifecycle of CO2-emissions associated with the combination of printing paper and electricity production of wood in the U.S. and Sweden

The pulp- and paper production is a very energy intensive industry sector. Both Sweden and the U.S. are major pulpandpaper producers. This report examines the energy and the CO2-emission connected with the pulp- and paperindustry for the two countries from a lifecycle perspective.New technologies make it possible to increase the electricity production in the integrated pulp- andpaper mill through black liquor gasification and a combined cycle (BLGCC). That way, the mill canproduce excess electricity, which can be sold and replace electricity produced in power plants. In thisprocess the by-products that are formed at the pulp-making process is used as fuel to produce electricity.In pulp- and paper mills today the technology for generating energy from the by-product in aTomlinson boiler is not as efficient as it could be compared to the BLGCC technology. Scenarios havebeen designed to investigate the results from using the BLGCC technique using a life cycle analysis.Two scenarios are being represented by a 1994 mill in the U.S. and a 1994 mill in Sweden.The scenariosare based on the average energy intensity of pulp- and paper mills as operating in 1994 in the U.S.and Sweden respectively. The two other scenarios are constituted by a »reference mill« in the U.S. andSweden using state-of-the-art technology. We investigate the impact of varying recycling rates and totalenergy use and CO2-emissions from the production of printing and writing paper. To economize withthe wood and that way save trees, we can use the trees that are replaced by recycling in a biomassgasification combined cycle (BIGCC) to produce electricity in a power station. This produces extra electricitywith a lower CO2 intensity than electricity generated by, for example, coal-fired power plants.The lifecycle analysis in this thesis also includes the use of waste treatment in the paper lifecycle. Both Sweden and theU.S. are countries that recycle paper. Still there is a lot of paper waste, this paper is a part of the countries municipalsolid waste (MSW). A lot of the MSW is landfilled, but parts of it are incinerated to extract electricity. The thesis hasdesigned special scenarios for the use of MSW in the lifecycle analysis.This report is studying and comparing two different countries and two different efficiencies on theBLGCC in four different scenarios. This gives a wide survey and points to essential parameters to specificallyreflect on, when making assumptions in a lifecycle analysis. The report shows that there arethree key parameters that have to be carefully considered when making a lifecycle analysis of wood inan energy and CO2-emission perspective in the pulp- and paper mill in the U.S. and in Sweden. First,there is the energy efficiency in the pulp- and paper mill, then the efficiency of the BLGCC and last theCO2 intensity of the electricity displaced by BIGCC or BLGCC generatedelectricity. It also show that with the current technology that we havetoday, it is possible to produce CO2 free paper with a waste paper amountup to 30%. The thesis discusses the system boundaries and the assumptions.Further and more detailed research, including amongst others thesystem boundaries and forestry, is recommended for more specificanswers.

Fingerprint Segmentation

In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable effect in the segmented image. Fingerprint recognition system is one of the oldest recognition systems in biometrics techniques. Everyone have a unique and unchangeable fingerprint. Based on this uniqueness and distinctness, fingerprint identification has been used in many applications for a long period. A fingerprint image is a pattern which consists of two regions, foreground and background. The foreground contains all important information needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false minutiae in the system. To avoid the extraction of false minutiae, there are many steps which should be followed such as preprocessing and enhancement. One of these steps is the transformation of the fingerprint image from gray-scale image to black and white image. This transformation is called segmentation or binarization. The aim for fingerprint segmentation is to separate the foreground from the background. Due to the nature of fingerprint image, the segmentation becomes an important and challenging task. The proposed algorithm is applied on FVC2000 database. Manual examinations from human experts show that the proposed algorithm provides an efficient segmentation results. These improved results are demonstrating in diverse experiments.

Spanning Tree Approach On The Snow Cleaning Problem

Snow cleaning is one of the important tasks in the winter time in Sweden. Every year government spends huge amount money for snow cleaning purpose. In this thesis we generate a shortest road network of the city and put the depots in different place of the city for snow cleaning. We generate shortest road network using minimum spanning tree algorithm and find the depots position using greedy heuristic. When snow is falling, vehicles start work from the depots and clean the snow all the road network of the city. We generate two types of model. Models are economic model and efficient model. Economic model provide good economical solution of the problem and it use less number of vehicles. Efficient model generate good efficient solution and it take less amount of time to clean the entire road network.

Color Segmentation using LVQ-Learning Vector Quantization

This thesis aims to present a color segmentation approach for traffic sign recognition based on LVQ neural networks. The RGB images were converted into HSV color space, and segmented using LVQ depending on the hue and saturation values of each pixel in the HSV color space. LVQ neural network was used to segment red, blue and yellow colors on the road and traffic signs to detect and recognize them. LVQ was effectively applied to 536 sampled images taken from different countries in different conditions with 89% accuracy and the execution time of each image among 31 images was calculated in between 0.726sec to 0.844sec. The method was tested in different environmental conditions and LVQ showed its capacity to reasonably segment color despite remarkable illumination differences. The results showed high robustness.

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

Evaluation of Red Colour Segmentation Algorithms in Traffic Signs Detection

Colour segmentation is the most commonly used method in road signs detection. Road sign contains several basic colours such as red, yellow, blue and white which depends on countries.The objective of this thesis is to do an evaluation of the four colour segmentation algorithms. Dynamic Threshold Algorithm, A Modification of de la Escalera’s Algorithm, the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm. The processing time and segmentation success rate as criteria are used to compare the performance of the four algorithms. And red colour is selected as the target colour to complete the comparison. All the testing images are selected from the Traffic Signs Database of Dalarna University [1] randomly according to the category. These road sign images are taken from a digital camera mounted in a moving car in Sweden.Experiments show that the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm are more accurate and stable to detect red colour of road signs. And the method could also be used in other colours analysis research. The yellow colour which is chosen to evaluate the performance of the four algorithms can reference Master Thesis of Yumei Liu.