Milan Jovovic

Publications

• Brain wave synergies, analysis and cosing

  Milan Jovovic

  http://milanjovovic.wordpress.com/author/milanjov/. 01/2011;

  Computation of renormalized synergies is proposed in signal analysis and coding. It quantizes information in a compact code to be used efficiently in storage, transmission, and in data encryption. We propose it within a quantum information theory that lays down a new perspective to networked systems... [more] Computation of renormalized synergies is proposed in signal analysis and coding. It quantizes information in a compact code to be used efficiently in storage, transmission, and in data encryption. We propose it within a quantum information theory that lays down a new perspective to networked systems dynamics and computation.

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• Foundation of re-normalized synergetics - issues of computability and complexity

  Milan Jovovic

  http://milanjovovic.wordpress.com/. 01/2010;

  We consider issues of computability and complexity in statistical physics from the perspective of information theory. It assumes information coupling by a mass conservation. Finally, we explain here our view on the 'mass phenomenon' in the clusters of information.... [more] We consider issues of computability and complexity in statistical physics from the perspective of information theory. It assumes information coupling by a mass conservation. Finally, we explain here our view on the 'mass phenomenon' in the clusters of information.

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• Foundation of re-normalized synergetics applied to robotics

  Milan Jovovic

  01/2009;

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• Where dimensions come from? -dynamical approach to scaling

  Milan Jovovic

  Belgrade University - presentation; 01/2008

  Dimensional and systems complexity analysis of 3D clusters resonance.

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• Multi-dimensional data scaling – dynamical cascade approach

  M. Jovovic, G. Fox

  IU, report. 01/2007;

  In this report a multi-dimensional data scaling approach is proposed in data mining and knowledge discovery applications. We derive the method based on an analogy to the physical computation of signal distortion. A dynamical cascade computation diagrams result from the statistical physics model comp... [more] In this report a multi-dimensional data scaling approach is proposed in data mining and knowledge discovery applications. We derive the method based on an analogy to the physical computation of signal distortion. A dynamical cascade computation diagrams result from the statistical physics model computation in the free energy decomposition. We assess the scale invariance of various data sets, such as with the image motion sequences, and with the high dimensional chemical data sets. Theoretical model of error propagation is given by the numerical computational schemes. Statistical mapping of the data is analyzed through dynamical cascades, as a way of approaching its coding and control data structure. We show how it correlates by segmenting set of chemical compounds observations in a high dimensional property space. The proposed algorithm, also, is suitable for the implementation in parallel computer architectures. An example implementation on the multicore processors is given in the end of this report.

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• Hierarchical Scale Decomposition of Images: Singular Feature Analysis

  M. Jovovic

  INRIA, report. 01/2003;

  In this paper we propose a method for image analysis, processing and coding, based on physical computation of signal distortion. A binary tree data structure of coupled system of data sets was initially proposed in [9, 10], derived from the statistical physics model of free energy. We assess the sca... [more] In this paper we propose a method for image analysis, processing and coding, based on physical computation of signal distortion. A binary tree data structure of coupled system of data sets was initially proposed in [9, 10], derived from the statistical physics model of free energy. We assess the scale invariance, in the method, by hierarchically clustering data. Theoretical model of error propagation is given in such a computational scheme. This decomposition of image information is analyzed by multifractal model formalism. We study how it correlates with the convective structure in clouds, that is associated with rain. The results are shown for MeteoSat IR images, provided by Thalweg ARC. project. The regularity constraints of data are used in the hierarchical scale decomposition of images. Accordingly, the reconstruction formula is derived based on the Laplacian system of diffusion of the residual information from the most singular sets. This gives us an effective way of compressing and progressive coding of information in image sequences. The proposed algorithm, also, is suitable for the implementation in parallel computer architectures.

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• Automatic Synthesis of Synergies for Control of Reaching --- Hierarchical Clustering

  Milan Jovovic, Slavica Jonic, Dejan Popovic

  01/2001;

  In this paper we describe a novel method for determining synergies between joint motions in reaching movements by hierarchical clustering. A set of recorded elbow and shoulder trajectories is used in a learning algorithm to determine the relationships between angular velocities at elbow and shoulder... [more] In this paper we describe a novel method for determining synergies between joint motions in reaching movements by hierarchical clustering. A set of recorded elbow and shoulder trajectories is used in a learning algorithm to determine the relationships between angular velocities at elbow and shoulder joints. The learning algorithm is based on optimal criteria for obtaining the hierarchy of descriptions of movement trajectories. We show that this method finds complex synergism between optimal joint trajectories for a given set of data and angular velocities at the shoulder and elbow joints. Three other machine learning techniques (ML) are used for comparison with our method of hierarchical clustering of trajectories. These MLs are: (1) radial basis functions (RBF), (2) inductive learning (IL), and (3) adaptive-network-based fuzzy inference system (ANFIS). Better error characteristics were obtained using the method of hierarchical clustering in comparison with the other techniques. The advantage of the method of hierarchical clustering with respect to the other MLs is in integrating the spatial and temporal elements of reaching movements. Determination and analysis of spatio-temporal events of movement trajectories is a useful tool in designing control systems for functional electrical stimulation (FES) assisted manipulation. 1999 IPEM. Published by Elsevier Science Ltd. All rights reserved.

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• Space-Color Quantization of Multispectral Images in Hierarchy of Scales

  M. Jovovic

  ICIP01. 01/2001; I:914-917.

  In this paper a novel model for multiscale space-color quantization of multispectral images, is described. The approach is based on the hierarchical clustering technique, derived from the statistical physics model of free energy �3, 4�. The group vectors for image color are computed on the adaptivel... [more] In this paper a novel model for multiscale space-color quantization of multispectral images, is described. The approach is based on the hierarchical clustering technique, derived from the statistical physics model of free energy �3, 4�. The group vectors for image color are computed on the adaptively selected windows of computation, as contrasted to the block-size windows, optimizing the accuracy of the computation of the group vectors with the density of sampling an image by the group windows. The algorithm is suitable for the implementation in parallel computer architectures. The results of quantization of color images by our algorithm are compared with 3 image compression techniques: 1) wavelets, 2) discrete cosine transform (DCT), and, 3) quad tree (QT). Contextual information of spatial coherency of the data is used in the segmentation process, in our algorithm. As a result, much better spatial resolution and small size of compressed images are obtained by our algorithm, as compared to the other techniques, for any error level of compression selected. Major spatial features are optimally color-coded along the hierarchy of scales of computation. The images quantized with our algorithm are suitable for the run-length encoding scheme of the hierarchy of binary images.

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• Image segmentation for feature selection from motion and photometric information by clustering

  M. Jovovic

  SPIE Symp. on Visual Inform. Processing V, Orlando, USA; 04/1996