530 research outputs found
Lucio Fontana. Il disegno negli anni Trenta
No full textIl catalogo della mostra tenuta a Varese, Sala Veratti, 24 marzo- 25 aprile 1996, a cura di Paolo Campiglio, raduna e studia per la prima volta un nucleo inedito di 197 disegni di Lucio Fontana degli anni trenta. Il libro è composto da un saggio introduttivo su Fontana e il disegno negli anni trenta, da un approfondimento su Riccardo Crippa, e apparati biografici, corredato dalle schede tecniche e bibliografiche dei disegni
"Editorial" of the International Journal of Computational Intelligence Studies (IJCIStudies)
No full text
“O maravilhoso número das imagens”: os primeiros “catálogos” de coleções de arte no renascimento
No full textObjective: To present and discuss the writings of Marcantonio Michiel, Anton Francesco Doni, Paolo Giovio and Frei Sabba de Castiglione as examples of the pioneering configuration of Art catalogs.
Methods: Itusesbibliographicresearchmethods,withaqualitativeapproach,analyzingthetextsfromasetoftheoretical premises about what lists and catalogs are BALSAMO, 2017; ECO, 2009; OTLET, 1934; SERRAI, 2001). The premise discusses how such materials should be handled.
Results: It is observed that the selected authors produced texts that worked epistemologically, contributing to the formation of knowledge, putting information into circulation, establishing taxonomies and other similar processes.
Conclusions: The analyzed texts are presented not only as lists, but also as narratives. This placed the need to observe the use of language in its rhetorical formulations. We observed that the authors use styles that are quite different from each other and, certainly, still far from contemporary formulas in art catalogs. Such differences can be observed when, for example, they describe the same work in different situations and moments (as we highlight in the case of the description of a source present in the museum of Giovio)
Multicomponent AM-FM demodulation: The state of the art after the development of the iterated Hilbert transform
No full textThis paper presents the state of the art of the multicomponent AM-FM demodulation techniques. On the basis of exhaustive comparisons between the current best practices: the impact of the iterated Hilbert transform on the milestones and the pioneering results of several decades of the advanced research done between MIT, Harvard, Bell-Labs, and NASA, has been analyzed. Finally, past performance, open problems, and future trends of AM-FM models have been considered and discussed
A non-probabilistic recognizer of stochastic signals based on KLT
No full textThis paper presents an efficient algorithm which is able to accurately recognize non-deterministic signals generated by synthetic non-chaotic and chaotic stochastic processes (SPs), as well as by natural phenomena (that are inherently stochastic) such as speech, image, and electroencephalographic signals. This recognition algorithm exploits a Karhunen–Loève transform (KLT)-based model able to characterize signals in terms of non-deterministic trajectories and consists of the concatenation of (i) a training stage, which iteratively extracts suitable parameter collections by means of the KLT and (ii) a recognition procedure based on ad hoc metric that measures the trajectory-proximities, in order to associate the unknown signal to the SP which this signal can be considered a realization of. The proposed methodology is able to recognize SPs without estimating their probability density function (pdf), thus requiring a low computational complexity to be implemented. Exhaustive experimentation on specific case-studies was performed and some experimental results were compared to other existing techniques such as hidden Markov model (HMM), vector quantization (VQ), and dynamic time warping (DTW). Recognition performance is similar to current best practices for non-chaotic signals and higher for chaotic ones. A better noise rejection was also achieved, and a reduction of two orders of magnitude in training-times compared with HMM was obtained, thus making the proposed methodology one of the current best practices in this field. Finally, the experimental results obtained by three different applications of the recognizer (an automatic speech recognition system, an automatic facial recognition system, and an automatic diagnosis system of the ictal and interictal epilepsy) clearly show excellent classification performance, and it is worth noting as complex filters are not needed unlike other current best practices
Efficient classification of chaotic signals with application to secure communications
No full textThis paper presents an exhaustive study on the classification capabilities of an efficient algorithm, which is able to accurately classify non-deterministic signals generated by chaotic dynamical systems, without estimating their probability density function (pdf). Experimental results were compared to other existing techniques such as hidden Markov model (HMM), vector quantization (VQ), and dynamic time warping (DTW). Classification performance is higher than current best practices for chaotic signals. A better noise rejection was also achieved, and a reduction of two orders of magnitude in training-times compared with HMM was obtained, thus making the proposed methodology one of the current best practices in this field. As an application example, the recognition of encrypted chaotic-signals in a secure-communication context, is reported and discussed
Information theoretical algorithm based on statistical models for blind identification of nonstationary dynamical systems
No full textThis paper presents an effective blind statistical identification technique for nonstationary nonlinear systems based on an information theoretical algorithm. This technique firstly extracts, from the output signals, the multivariate relationships in the Hilbert spaces by exploiting the separability properties of the signal outputs transformed by the Karhunen-Loeve transform (KLT). Then, the algorithm methodologically clusters the stochastic surfaces in the Hilbert spaces using the self-organizing maps (SOMs) and further develops their best statistical model under the fixed-rank condition. The resulting blind identification of the statistical system model is based on marginal probability density functions (PDFs), whose convergence to the statistical system model based on Monte Carlo simulations has also been demonstrated by asymptotically vanishing the Kullback-Leibler divergences. A large number of simulations on both synthetic and real systems demonstrated the validity and the excellent performances of this technique that is irrespective of the system order, the stochastic surface topology, the true marginal PDFs, and the knowledge of the statistics of the noise superimposed to the output signals. Finally, this approach could also represent a suitable and promising technique for the noninvasive diagnosis of a large class of medical pathologies originated by unknown physiological factors (nonlinear compositions of unknown input signals) and/or when they are difficult or unpractical to measure
A non probabilistic algorithm based on Karhunen-Loève transform for the recognition of stochastic signals
No full textThis paper proposes an efficient methodology that is able to accurately recognize nondeterministic signals generated by stochastic processes (SPs). This technique is based on (i) a training algorithm, which iteratively extracts suitable parameter collections; (ii) a recognition procedure that measures the trajectory-proximities by means of an ad-hoc metric, in order to associate the unknown signal to an SP by using a representation based on Karhunen-Loeve transform (KLT). The recognition algorithm exploits a modelling of several signal classes based on KLT, inasmuch this representation effectively characterizes projections of every SP signal in terms of nondeterministic trajectories defined on associated spaces. The methodology is able to recognize SPs without probability density function (pdf) estimation, and with low-computational complexity: exhaustive experimentations on specific case-studies have shown high recognition performance. As application examples, SPs generated by stochastic nonlinear-differential-equations (SNDEs), with different initial conditions and coefficients being random variables (RVs), have been considered
A statistical methodology for the design of high-performance current steering DAC's
No full textRandom device variations are a key factor limiting the performances of high-resolution CMOS current steering D/A converters. In this paper a novel design methodology based on statistical modeling of MOS drain current has been developed. This technique requires firstly an estimation of mean value and autocorrelation function of a single stochastic process, which all the process/device variations are lumped in. Then a behavioral model of D/A converters has been developed. Finally, the statistical simulation of static performances (DNL and INL) has been carried out for different DAC architectures
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