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2008 Journal article Open Access OPEN

Active video surveillance based on stereo and infrared imaging
Pieri G., Moroni D.
Video surveillance is a very actual and critical issue at the present time. Within this topics, we address the problem of firstly identifying moving people in a scene through motion detection techniques, and subsequently categorising them in order to identify humans for tracking their movements. The use of stereo cameras, coupled with infrared vision, allows to apply this technique to images acquired through different and variable conditions, and allows an a priori filtering based on the characteristics of such images to give evidence to objects emitting a higher radiance (i.e., higher temperature).Source: EURASIP Journal on Advances in Signal Processing (Online) Article ID 380210 (2008). doi:10.1155/2008/380210
DOI: 10.1155/2008/380210

See at: EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | asp.eurasipjournals.com Restricted | CNR ExploRA Restricted


2008 Conference article Restricted

An infrastructure for mining medical multimedia data
Colantonio S., Salvetti O., Tampucci M.
Biomedical research processes related to disease diagnosis, prognosis and monitoring would great benefit from advanced tools able not exclusively to store and manage multimodal data but also to process and extract significant relations and then novel knowledge from them. Indeed, making a prediction on a disease outcome usually requires considering heterogeneous pieces of information obtained from several sources which should be compared and related. Mining medical multimedia objects is aimed at discovering and making available the hidden useful knowledge embedded in collections of data and is, then, of key importance for supporting clinical decision-making. In this paper, we report current results of a medical warehouse we are developing in an integrated environment for mining clinical data acquired by different media. In particular, focus is herein given to the infrastructure of the warehouse and its current functionalities not limited to storage and management but including intelligent representation and annotation of multimedia objects.Source: Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. 8th Industrial Conference, pp. 102–113, Leipzig, Germany, 16-18 July 2008
DOI: 10.1007/978-3-540-70720-2_8

See at: academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | doi.org Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted | www.springerlink.com Restricted


2008 Journal article Restricted

Biomedical signal and image processing for decision support in heart failure
Chiarugi F., Colantonio S., Emmanouilidou D., Moroni D., Salvetti O.
Signal and imaging investigations are currently a basic step of the diagnostic, prognostic and follow-up processes of heart diseases. Besides, the need of a more efficient, cost-effective and personalized care has lead nowadays to a renaissance of clinical decision support systems (CDSS). The purpose of this paper is to present an effective way to achieve a high-level integration of signal and image processing methods in the general process of care, by means of a clinical decision support system, and to discuss the advantages of such an approach. Among several heart diseases, we treat heart failure, that for its complexity highlights best the benefits of this integration. Architectural details of the related components of the CDSS are provided with special attention to their seamless integration in the general IT infrastructure. In particular, significant and suitably designed image and signal processing algorithms are introduced to objectively and reliably evaluate important features that, in collaboration with the CDSS, can facilitate decisional problems in the heart failure domain. Furthermore, additional signal and image processing tools enrich the model base of the CDSS.Source: Lecture notes in computer science 5108 (2008): 38–51. doi:10.1007/978-3-540-70715-8_4
DOI: 10.1007/978-3-540-70715-8_4

See at: academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted | www.microsoft.com Restricted | www.researchgate.net Restricted | www.springerlink.com Restricted | www.springerlink.com Restricted


2008 Journal article Open Access OPEN

Cohomology of affine artin groups and applications
Callegaro F., Moroni D., Salvetti M.
The result of this paper is the determination of the cohomology of Artin groups of type A_n, B_n and A. _n with non-trivial local coefficients. The main result is an explicit computation of the cohomology of the Artin group of type B_n with coefficients over the module Q[q±1, t±1]. Here the first n - 1 standard generators of the group act by (-q)-multiplication, while the last one acts by (-t)-multiplication. The proof uses some technical results from previous papers plus computations over a suitable spectral sequence. The remaining cases follow from an application of Shapiro's lemma, by considering some well-known inclusions: we obtain the rational cohomology of the Artin group of affine type A. _n as well as the cohomology of the classical braid group Br_n with coefficients in the n-dimensional representation presented in Tong, Yang, and Ma (1996). The topological counterpart is the explicit construction of finite CW-complexes endowed with a free action of the Artin groups, which are known to be K(p, 1) spaces in some cases (including finite type groups). Particularly simple formulas for the Euler-characteristic of these orbit spaces are derived.Source: Transactions of the American Mathematical Society 360 (2008): 4169–4188. doi:10.1090/S0002-9947-08-04488-7
DOI: 10.1090/s0002-9947-08-04488-7

See at: arXiv.org e-Print Archive Open Access | Transactions of the American Mathematical Society Open Access | Transactions of the American Mathematical Society Restricted | Transactions of the American Mathematical Society Restricted | Transactions of the American Mathematical Society Restricted | CNR ExploRA Restricted | www.ams.org Restricted | Transactions of the American Mathematical Society Restricted | Transactions of the American Mathematical Society Restricted


2008 Journal article Open Access OPEN

Component separation methods for the PLANCK mission
Leach S. M., Cardoso J., Baccigalupi C., Barreiro R. B., Betoule M., Bobin J., Bonaldi A., Delabrouille J., De Zotti G., Dickinson C., Eriksen H. K., Gonzalez-nuevo J., Hansen F. K., Herranz D., Le Jeune M., Lopez-caniego M., Martinez-gonzalez E., Massardi M., Melin J., Miville-dechêne M., Patanchon G., Prunet S., Ricciardi S., Salerno E., Sanz J. L., Stark J., Stivoli F., Stolyarov V., Stompor R., Vielva P.
Context. The planck satellite will map the full sky at nine frequencies from 30 to 857 GHz. The CMB intensity and polarization that are its prime targets are contaminated by foreground emission. Aims. The goal of this paper is to compare proposed methods for separating CMB from foregrounds based on their di?erent spectral and spatial characteristics, and to separate the foregrounds into "components" with di?erent physical origins (Galactic synchrotron, free-free and dust emissions; extra-galactic and far-IR point sources; Sunyaev-Zeldovich e?ect, etc.). Methods. A component separation challenge has been organised, based on a set of realistically complex simulations of sky emission. Several methods including those based on internal template subtraction, maximum entropy method, parametric method, spatial and harmonic cross correlation methods, and independent component analysis have been tested. Results. Di?erent methods proved to be e?ective in cleaning the CMB maps of foreground contamination, in reconstructing maps of di?use Galactic emissions, and in detecting point sources and thermal Sunyaev-Zeldovich signals. The power spectrum of the residuals is, on the largest scales, four orders of magnitude lower than the input Galaxy power spectrum at the foreground minimum. The CMB power spectrum was accurately recovered up to the sixth acoustic peak. The point source detection limit reaches 100 mJy, and about 2300 clusters are detected via the thermal SZ e?ect on two thirds of the sky.We have found that no single method performs best for all scientific objectives. Conclusions. We foresee that the final component separation pipeline for planck will involve a combination of methods and iterations between processing steps targeted at di?erent objectives such as di?use component separation, spectral estimation, and compact source extraction.Source: Astronomy & astrophysics (Print) 491 (2008): 597–615. doi:10.1051/0004-6361:200810116
DOI: 10.1051/0004-6361:200810116

See at: arXiv.org e-Print Archive Open Access | SISSA Digital Library Open Access | Caltech Authors Open Access | Astronomy and Astrophysics Open Access | Astronomy and Astrophysics Restricted | Astronomy and Astrophysics Restricted | Astronomy and Astrophysics Restricted | HAL-UPMC Restricted | CNR ExploRA Restricted | Astronomy and Astrophysics Restricted | www.aanda.org Restricted


2008 Journal article Restricted

Detection of signs of brain dysfunction in epileptic children by recognition of transient changes in the correlation of seizure-free EEG
Righi M., Barcaro U., Starita A., Karakonstantaki E., Micheloyannis S.
Seizure-free EEG signals recorded from epileptic children were compared with EEG signals recorded from normal children. The comparison was based on the detection of transient events characterized by decrease in the correlation between different traces. For this purpose, a conceptually and mathematically simple method was applied. Two clear and remarkable phenomena, able to quantitatively discriminate between the two groups of subjects, were evidenced, with high statistical significance. In fact, it was observed that: (a) The number of events for the epileptic group was larger; (b) Applying restrictive criteria for event definition, the number of subjects in the epileptic group presenting events was larger. The results support the hypothesis of a decrease in brain correlation in children with epilepsy under treatment. This confirms the efficacy of the EEG signal in evaluating cortical functional differences not visible by visual inspection, independently of the cause (epilepsy or drugs), and demonstrate the specific effectiveness of the analysis method applied.Source: Brain topography 21 (2008): 43–51. doi:10.1007/s10548-008-0057-2
DOI: 10.1007/s10548-008-0057-2

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2008 Journal article Restricted

Estimation of time-varying AR SaS processes using Gibbs sampling
Gencaga D., Kuruoglu E. E., Ertuzun A., Yildirim S.
In this work, we present a novel method for modeling time-varying autoregressive impulsive signals driven by symmetric alpha stable distributions. The proposed method can be interpreted as a two-stage Gibbs sampler composed of a particle filter, which is capable of estimating the unknown time-varying autoregressive coefficients, and a hybrid Monte Carlo method for estimating the unknown but constant distribution parameters of a symmetric alpha stable process. This method is an alternative to a recently published technique in which both the autoregressive coefficients and the distribution parameters are estimated jointly within a single sequential Monte Carlo framework-the single particle filter technique. The proposed method achieves lower error variances in estimating the distribution parameters compared with the single sequential Monte Carlo technique, and thus, successfully models symmetric impulsive signals.Source: Signal processing (Print) 88 (2008): 2564–2572. doi:10.1016/j.sigpro.2008.03.021
DOI: 10.1016/j.sigpro.2008.03.021

See at: Signal Processing Restricted | Signal Processing Restricted | Signal Processing Restricted | Signal Processing Restricted | CNR ExploRA Restricted | Signal Processing Restricted


2008 Conference article Restricted

Evaluation of feature subset selection, feature weighting, and prototype selection for biomedical applications
Little S., Salvetti O., Perner P.
Many medical diagnosis applications are characterized by datasets that contain under-represented classes due to the fact that the disease appears more rarely than the normal case. In such a situation classifiers that generalize over the data such as decision trees and Naïve Bayesian are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far are more appropriate for such a task. We propose to calculate the contingency table and class specific evaluation measures despite the overall accuracy for evaluation purposes of classifiers for these specific data characteristics. We evaluate the different options of our case-based classifier and compare the performance to decision trees and Naïve Bayesian. Finally, we give an outlook for further work.Source: ECCBR 2008 - Advances in Case-Based Reasoning, 9th European Conference, pp. 312–324, Trier, Germany, 1-4 September 2008
DOI: 10.1007/978-3-540-85502-6

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2008 Journal article Restricted

Modeling of non-stationary autoregressive alpha-stable processes by particle filters
Gencaga D., Ertuzun A., Kuruoglu E. E.
In the literature, impulsive signals are mostly modeled by symmetric alpha-stable processes. To represent their temporal dependencies, usually autoregressive models with time-invariant coefficients are utilized. We propose a general sequential Bayesian odeling methodology where both unknown autoregressive coefficients and distribution parameters can be estimated successfully, even when they are time-varying. In contrast to most work in the literature on signal processing with alpha-stable distributions, our work is general and models also skewed alpha-stable processes. Successful performance of our method is demonstrated by computer simulations. We support our empirical results by providing posterior Cramer-Rao lower bounds. The proposed method is also tested on a practical application where seismic data events are modeled.Source: Digital signal processing (Print) 18 (2008): 465–478. doi:10.1016/j.dsp.2007.04.01
DOI: 10.1016/j.dsp.2007.04.01

See at: CNR ExploRA Restricted | scienceserver.cilea.it Restricted


2008 Journal article Restricted

Multi-scale representation and persistency for shape description
Moroni D., Salvetti M., Salvetti O.
Extraction, organization and exploitation of topological features are emerging topics in computer vision and graphics. However, such kind of features often exhibits weak robustness with respect to small perturbations and it is often unclear how to distinguish truly topological features from topological noise. In this paper, we present an introduction to persistence theory, which aims at analyzing multi-scale representations from a topological point of view. Besides, we extend the ideas of persistency to a more general setting by defining a set of discrete invariants.Source: Lecture notes in computer science 5108 (2008): 123–138. doi:10.1007/978-3-540-70715-8_11
DOI: 10.1007/978-3-540-70715-8_11

See at: academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted


2008 Journal article Restricted

Usefulness of the analysis of links among dream sources in therapy
Barcaro U., Rizzi P.
A study of the links among the memory sources of dreams can be carried out by means of an automatic analysis of text files including dream reports and associations. Heuristic criteria can provide plausible explanations for the existence of these links, which generally present a logical and at the same time emotional significance. The aim of this paper is to support the idea that the study of the link patterns among dream sources, in addition to being interesting from the cognitive viewpoint can be also useful for the therapeutic process. An interaction schema is described including four operators: the dreamer (patient), the therapist, the detector of possible links, and the proposer of plausible explanations. Two examples are given of application of this schema. (PsycINFO Database Record (c) 2012 APA, all rights reserved)Source: Dreaming (N.Y.N.Y.) 18 (2008): 139–157. doi:10.1037/a0012899
DOI: 10.1037/a0012899

See at: Dreaming Restricted | Dreaming Restricted | Dreaming Restricted | Dreaming Restricted | psycnet.apa.org Restricted | Dreaming Restricted | CNR ExploRA Restricted


2008 Contribution to conference Open Access OPEN

Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Perner P., Salvetti O.
The automatic analysis of signals and images together with the characterization and elaboration of their representation features is still a challenging activity in many relevant scientific and hi-tech fields such as medicine, biotechnology, and chemistry. Multidimensional and multisource signal processing can generate a number of information patterns which can be useful to increase the knowledge of several domains for solving complex problems. Furthermore, advanced signal and image manipulation allows relating specific application problems into pattern recognition problems, often implying also the development of KDD and other computational intelligence procedures. Nevertheless, the amount of data produced by sensors and equipments used in biomedicine, biotechnology and chemistry is usually quite huge and structured, thus strongly pushing the need of investigating advanced models and efficient computational algorithms for automating mass analysis procedures. Accordingly, signal and image understanding approaches able to generate automatically expected outputs become more and more essential, including novel conceptual approaches and system architectures. The purpose of this third edition of the International Conference on Mass Data Analysis of Signals and Images in Medicine, Biotechnology, Chemistry and Food Industry (MDA 2008; www.mda-signals.de) was to present the broad and growing scientific evidence linking mass data analysis with challenging problems in medicine, biotechnology and chemistry. Scientific and engineering experts convened at the workshop to present the current understanding of image and signal processing and interpretation methods useful for facing various medical and biological problems and exploring the applicability and effectiveness of advanced techniques as solutions. The primary goal of the conference was to disseminate this knowledge to a multidisciplinary community and encourage cooperative proactive collaboration in all the interested fields. We were pleased to see that the idea of the conference was taken up by a growing number of researchers and that we could start to bundle the activities in this area. We appreciate the help and understanding of the editorial staff at Springer, and in particular Alfred Hofmann, who supported the publication of these proceedings in the LNAI series. Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference. The next International Conferences on Mass Data Analysis of Signals and Images (www.mda-signals.de) will be held in July 2009. We are looking forward to your submissions.

See at: link.springer.com Open Access | CNR ExploRA Open Access


2008 Contribution to journal Open Access OPEN

Human-Activity Analysis in Multimedia Data
Salvetti O., Cetin E. A., Pauwels E.
Many important applications in multimedia revolve around the detection of humans and the interpretation of their behavior. These include surveillance and intrusion detection, video conferencing applications, assisted living applications, and automatic analysis of sports videos, broadcasts, and movies, to name just a few. Success in these tasks often requires the integration of various sensor or data modalities such as video, audio, motion, and accompanying text, and typically hinges on a host of machine-learning methodologies to handle the inherent variability and complexity of the ensuing features. The computational efficiency of the resulting algorithms is critical since the amount of data to be processed in multimedia applications is typically large, and in real-time systems, speed is of the essence. There have been several recent special issues dealing with the dection of humans and the analysis of their activity relying solely on video footage. In this special issue, we have tried to provide a platform to contributions that make use of a broader spectrum of multimedia information, complementing video with audio or text information as well as other types of sensor signals, whenever available. The first group of papers in the special issue addresses the joint use of audio and video data. The paperDOI: 10.1155/2008/293453

See at: EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | CNR ExploRA Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access


2008 Contribution to journal Restricted

Introduction to the issue on Signal Processing for Space Research and Astronomy
Leshem A., Christou J., Jeffs B. D., Kuruoglu E. E., Van Der Veen A.
SPACE research in general, and astronomy in particular, are some of the most challenging application areas for signal processing. Digital signal and image processing techniques have been widely used for optical astronomy and radio astronomy as well as in deep-space communication. Several new instruments are being designed for radio, optical, and other frequencies. These instruments will push our understanding of the universe even further and ambitious design goals for these instruments will rely on advanced signal processing techniques. Traditionally, radio telescope design was in the forefront of electrical engineering technology. Technological advances in the last decade have created possibilities for large distributed interferometric radio and optical telescopes with very large receiving areas, extremely large aperture, and a sensitivity which is one to two orders of magnitude better than the current generation. Increased sensitivity implies receiving more interfering signals; therefore, RFI detection and removal is now an important topic in radio astronomy. Fortunately, massive digital phased-array technology has also greatly advanced during this period and can provide increased flexibility to filter out interference as well as the possibility of directing multiple beams simultaneously. Several major, international research groups are working on next generations of phased-array instruments. The most ambitious one falls under the framework of the Square Kilometer Array programm (SKA), with a target commissioning date of 2020. A second instrument, which is a distributed phasedarray radio telescope is the Low Frequency Array (LOFAR) currently under construction in The Netherlands, and slated for 2009. The LOFAR design calls for an instrument consisting of about 13 000Source: New York: IEEE, 2008
DOI: 10.1109/jstsp.2008.2006397

See at: IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted


2008 Journal article Restricted

Automatic computation of left ventricle ejection fraction from dynamic ultrasound images
Barcaro U., Moroni D., Salvetti O.
Left Ventricle (LV) Ejection Fraction (EF) is a fundamental parameter for heart function assessment. Being based on border tracing, however, manual computation of EF is time-consuming and extremely prone to inter- and intra-observer variability. In this paper we present an automatic method for EF computation which provides results in agreement with those provided by expert observers. The segmentation strategy consists of two stages: first, the region of interest is identified by means of mimetic criteria; then, the identified region is used for initialization of an active contour based on a variational formulation of level set methods, which provides accurate segmentation of the LV cavity. Volume calculation is then performed according to the conventional Simpson's rule and, finally, the EF is computed.Source: Pattern recognition and image analysis 18 (2008): 351–358. doi:10.1134/S1054661808020247
DOI: 10.1134/s1054661808020247

See at: Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | CNR ExploRA Restricted | www.springerlink.com Restricted


2008 Journal article Restricted

Cell image analysis ontology
Colantonio S., Martinelli M., Salvetti O., Gurevich I. B., Trusova Y.
Cell image analysis in microscopy is the core activity of cytology and cytopathology for assessing cell physiological (cellular structure and function) and pathological properties. Biologists usually make evaluations by visually and qualitatively inspecting microscopic images: this way, they are particularly able to recognize deviations from normality. Nevertheless, automated analysis is strongly preferable for obtaining objective, quantitative, detailed, and reproducible measurements, i.e., features , of cells. Yet, the organization and standardization of the wide domain of features used in cytometry is still a matter of challenging research. In this paper, we present the Cell Image Analysis Ontology (CIAO), which we are developing for structuring the cell image features domain. CIAO is a structured ontology that relates different cell parts or whole cells, microscopic images, and cytometric features. Such an ontology has incalculable value since it could be used for standardizing cell image analysis terminology and features definition. It could also be suitably integrated into the development of tools for supporting biologists and clinicians in their analysis processes and for implementing automated diagnostic systems. Thus, we also present a tool developed for using CIAO in the diagnosis of hematopoietic diseases.Source: Pattern recognition and image analysis 18 (2008): 332–341. doi:10.1134/S1054661808020211
DOI: 10.1134/s1054661808020211

See at: Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | CNR ExploRA Restricted | www.springerlink.com Restricted


2008 Journal article Restricted

Descriptive approach to medical image mining. An algorithmic scheme for analysis of cytological specimens
Gurevich I. B., Yashina V., Koryabkina I., Niemann H., Salvetti O.
The present paper is devoted the development and formal representation of a descriptive model for an information technology to automate the morphological analysis of cytologic preparations (a tumor of the lymphatic system). The theoretical basis of the model is a descriptive approach to image analysis and understanding and its main mathematical tools. Practical application of the algebraic tools of the descriptive approach is demonstrated, and the algorithmic scheme of the technology is described in the language of descriptive image algebras.Source: Pattern recognition and image analysis 18 (2008): 542–562. doi:10.1134/S1054661808040032
DOI: 10.1134/s1054661808040032

See at: Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | CNR ExploRA Restricted | www.springerlink.com Restricted


2008 Journal article Restricted

Fully bayesian source separation of astrophysical images modelled by mixture of Gaussians
Wilson S., Kuruoglu E. E., Salerno E.
We address the problem of source separation in the presence of prior information. We develop a fully Bayesian source separation technique that assumes a very flexible model for the sources, namely the Gaussian mixture model with an unknown number of factors, and utilize Markov chain Monte Carlo techniques for model parameter estimation. The development of this methodology is motivated by the need to bring an efficient solution to the separation of components in the microwave radiation maps to be obtained by the satellite mission Planck which has the objective of uncovering cosmic microwave background radiation. The proposed algorithm successfully incorporates a rich variety of prior information available to us in this problem in contrast to most of the previous work which assumes completely blind separation of the sources. We report results on realistic simulations of expected Planck maps and on WMAP 5th year results. The technique suggested is easily applicable to other source separation applications by modifying some of the priors.Source: IEEE journal of selected topics in signal processing 2 (2008): 685–696. doi:10.1109/JSTSP.2008.2005320
DOI: 10.1109/jstsp.2008.2005320

See at: IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted | IEEE Journal of Selected Topics in Signal Processing Restricted


2008 Journal article Restricted

Real-time tracking of sound parameters in a multimedia system
Bertini G., Magrini M., Lunardi S., Lucia G.
Modern multimedia performances and presentations offer innovative methods for user interaction, in order to be more appealing to the audience. Following this trend and in collaboration with VIS S.r.l. (an SME in Rome), the Institute of Information Sciences and Technology (ISTI-CNR) has developed a system called Pandora, which controls real-time video effects applied to filmed or synthesized scenes by means of parameters extracted from sound signals. The system can be used both for artistic interactive multimedia performances and also for other non-artistic applications.Source: ERCIM news 73 (2008): 46–47.
Project(s): EPOCH via OpenAIRE

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2008 Journal article Restricted

Statistical analysis of electrophoresis time series for improving basecalling in DNA sequencing
Tonazzini A., Bedini L.
In automated DNA sequencing, the final algorithmic phase, referred to as basecalling, consists of the translation of four time signals in the form of peak sequences (electropherogram) to the corresponding sequence of bases. Commercial basecallers detect the peaks based on heuristics, and are very efficient when the peaks are distinct and regular in spread, amplitude and spacing. Unfortunately, in the practice the signals are subject to several degradations, among which peak superposition and peak merging are the most frequent. In these cases the experiment must be repeated and human intervention is required. Recently, there have been attempts to provide methodological foundations to the problem and to use statistical models for solving it. In this paper, we exploit a priori information and Bayesian estimation to remove degradations and recover the signals in an impulsive form which makes basecalling straightforward.Source: International journal of signal and imaging systems engineering (Print) 1 (2008): 36–40. doi:10.1504/IJSISE.2008.017772
DOI: 10.1504/ijsise.2008.017772
DOI: 10.1007/978-3-540-76300-0_15

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