Summary form only given. Deoxyribonucleic acid (DNA) has become one of the most examined molecules on the planet. Scientist around the world have been trying to unravel its secrets for many purposes. For example, gene...
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Summary form only given. Deoxyribonucleic acid (DNA) has become one of the most examined molecules on the planet. Scientist around the world have been trying to unravel its secrets for many purposes. For example, genetic information is currently used to raise better plants and animals, create enhanced pharmaceuticals for humans, and for gene therapy in medicine. Science as a whole has benefited from the study of genetics because of the increased understanding of biological process that all organisms share. In recent decades, a significant amount of research has been directed towards sequencing and understanding the entire human genome through the Human Genome Project (HGP) launched in 1986. The goal of the HGP was to find the location of the approximately 1×105 human genes, and read all the sequence of human genome (about 3×109 base pairs, bp). An exponential grow rate of that research has resulted in reaching the goal by 2003. Similarly, the speed of finding genes and their locations is also increasing rapidly. On the other hand, the traditional methods of finding genes and their location at chromatosomes through testing their biological function have been inherently slow. Although numerous faster techniques have been developed, there is still a need to augment them with new approaches. Therefore, robust computational solutions to the gene-finding problem could provide a valuable resource for the HGP and for the molecular-biology community. Most of the current research in the deciphering the meaning of DNA sequences is approached from the lowest base-pair level. Its main objective is to search for patterns or correlations existing in the DNA sequence related to codons, amino acids, and proteins. A number of gene-finding systems have been developed in recent decades. These systems use a variety of sophisticated computational data-miming techniques, including neural networks, dynamic programming, rule-based methods, decision trees, probability reasoning, hidden Marko
This paper describes how the selection of parameters for the variance fractal dimension (VFD) multiscale time-domain algorithm can create an amplification of the fractal dimension trajectory that is obtained for a nat...
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This paper describes how the selection of parameters for the variance fractal dimension (VFD) multiscale time-domain algorithm can create an amplification of the fractal dimension trajectory that is obtained for a natural-speech waveform in the presence of ambient noise. The technique is based on the variance fractal dimension trajectory (VFDT) algorithm that is used not only to detect the external boundaries of an utterance, but also its internal pauses representing the unvoiced speech. The VFDT algorithm can also amplify internal features of phonemes. This fractal feature amplification is accomplished when the time increments are selected in a dyadic manner rather than selecting the increments in a unit distance sequence. These amplified trajectories for different phonemes are more distinct, thus providing a better characterization of the individual segments in the speech signal. This approach is superior to other energy-based boundary-detection techniques. These observations are based on extensive experimental results on speech utterances digitized at 44.1 kilosamples per second, with 16 bits in each sample.
This paper presents feature extraction and estimations of multifractal measures for deoxyribonucleic acid (DNA) sequences, and demonstrates the intriguing possibility of identifying biological functionality using info...
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This paper presents feature extraction and estimations of multifractal measures for deoxyribonucleic acid (DNA) sequences, and demonstrates the intriguing possibility of identifying biological functionality using information contained within the DNA sequence. We have developed a technique that seeks patterns or correlations in the DNA sequence at a higher level. The technique has three main steps: (i) transforms the DNA sequence symbols into a modified Levy walk, (ii) transforms the Levy walk into a signal spectrum, and (iii) breaks the spectrum into subspectra and treats each of these as an attractor from which the multifractal dimension spectrum is estimated. An optimal minimum window size and volume element size are found for estimation of the multifractal measures. Experimental results show that DNA is a multifractal, and that the multifractality changes depending upon the location (coding or noncoding region) in the sequence.
This paper describes a novel approach of fractal modelling and coding of residuals for excitation in the linear predictive coding of speech. This work was motivated by reducing the bit rate to 1200 bps, while maintain...
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This paper describes a novel approach of fractal modelling and coding of residuals for excitation in the linear predictive coding of speech. This work was motivated by reducing the bit rate to 1200 bps, while maintaining a good quality of speech. Linear prediction based speech coders differ primarily in the modelling of the residual. The design trade-off in the modelling of the residual is between quality and bit-rate. In this paper fractal modelling is used to model the residual. We show that fractal modelling reduces the bit-rate while maintaining quality. A 6 kbps speech coder was implemented using the piecewise self-affine fractal model. The new coder has a signal-to-noise ratio of 10.9 dB. An informal subjective measure found the perceptual quality to be comparable to that of the 13 kbps GSM coder.
The last few decades of physics, chemistry, biology, computer science, engineering, and social sciences have been marked by major developments of views on cognitive systems, dynamical systems, complex systems, complex...
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ISBN:
(纸本)9781424425389
The last few decades of physics, chemistry, biology, computer science, engineering, and social sciences have been marked by major developments of views on cognitive systems, dynamical systems, complex systems, complexity, self-organization, and emergent phenomena that originate from the interactions among the constituent components (agents) and with the environment, without any central authority. How can measures of complexity capture the intuitive sense of pattern, order, structure, regularity, evolution of features, memory, and correlation? This paper describes several key ideas, including dynamical systems, complex systems, complexity, and quantification of complexity. As there is no single definition of a complex system, its complexity and complexity measures too have many definitions. This papers also addresses some practical aspects of acquiring the observables.
This paper describes a fast multiscale time-domain technique for the analysis of natural speech waveforms in the presence of noise. The technique is based on the variance fractal dimension trajectory algorithm that is...
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ISBN:
(纸本)9781424425389
This paper describes a fast multiscale time-domain technique for the analysis of natural speech waveforms in the presence of noise. The technique is based on the variance fractal dimension trajectory algorithm that is used not only to detect the external boundaries of an utterance, but also its internal pauses representing the unvoiced speech. The algorithm can also identify internal features of phonemes. The features can be amplified so that the speech utterances can be segmented into sentences, words and phonemes. This approach is superior to other energy-based boundary-detection techniques. These observations are based on extensive experimental results on speech utterances digitized at 44.1 kilosamples per second, with 16 bits in each sample.
Numerous attempts are being made to develop machines that could act not only autonomously, but also in an increasingly intelligent and cognitive manner. Such cognitive machines ought to be aware of their environments ...
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Numerous attempts are being made to develop machines that could act not only autonomously, but also in an increasingly intelligent and cognitive manner. Such cognitive machines ought to be aware of their environments which include not only other machines, but also human beings. Such machines ought to understand the meaning of information in more human-like ways by grounding knowledge in the physical world and in the machines' own goals. The motivation for developing such machines range from self-evidenced practical reasons such as the expense of computer maintenance, to wearable computing in health care, and gaining a better understanding of the cognitive capabilities of the human brain. To achieve such an ambitious goal requires solutions to many problems, ranging from human perception, attention, concept creation, cognition, consciousness, executive processes guided by emotions and value, and symbiotic conversational human-machine interactions. This paper discusses some of the challenges emerging from this new design paradigm, including systemic problems, design issues, teaching the subjects to undergraduate students in electrical and computerengineering programs, research related to design.
This paper describes the use of single-scale measures to determine the level of randomness and complexity of a sequence. Such sequences originate from either various pseudorandom number generators or natural sources o...
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This paper describes the use of single-scale measures to determine the level of randomness and complexity of a sequence. Such sequences originate from either various pseudorandom number generators or natural sources of white and coloured broadband noise. The paper provides a study of seven classes of sequences using the algorithmic complexity measures (the Kolmogorov-Chaitin complexity) and the probabilistic entropy-based measures (Shannon entropy). The study shows the fundamental differences between the two measures. The single-scale measures are adequate to determine the relative randomness and complexity of a sequence. However, they are not capable of revealing the hidden patterns in scale-invariant (self-affine) sequences. This paper identifies the need for new measures for such self-affine stochastic and chaotic sequences, and investigates if the existing techniques could be modified for the multiscale measures.
Life sustaining biomedical signal processing demands a guarantee that the results produced are accurate and precise. The separation of sources (e.g., demixing two heart signals, one from a mother, and one from a fetus...
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Life sustaining biomedical signal processing demands a guarantee that the results produced are accurate and precise. The separation of sources (e.g., demixing two heart signals, one from a mother, and one from a fetus) based only on observations of those mixtures, known as the blind source separation problem, is seen by researchers and scientists as a necessary preprocessing step in order to obtain uncontaminated data for analysis. A method from the field of intelligent signal processing called independent component analysis (ICA) is a promising solution to this problem. However, ICA algorithms and their implementation must be robust to interference, including outliers. Unfortunately, contamination of biomedical recordings by outliers is an unavoidable aspect in signal processing. Mihoko and Eguchi developed an outlier robust ICA algorithm, but code for this algorithm is unavailable. This paper presents a Matlab implementation of their beta-divergence for blind source separation algorithm. The implementation uses a quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization, combined with an Armijo conditioned line-search, to minimize the beta-divergence between the density of the source estimates and the product of its hypothesized marginal densities to separate a mixture of statistically independent sources. The implementation is verified by repeating the source separation simulations published by Mihoko and Eguchi. In each simulation the separation results match visually to those published by Mihoko and Eguchi
This paper presents a derivation of a new relative fractal dimension spectrum, DRq. to measure the dissimilarity between two finite probability distributions originating from various signals. This measure is an extens...
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This paper presents a derivation of a new relative fractal dimension spectrum, DRq. to measure the dissimilarity between two finite probability distributions originating from various signals. This measure is an extension of the Kullback-Leibler (KL) distance and the Renyi fractal dimension spectrum, Dq. Like the KL distance, DRq determines the dissimilarity between two probability distibutions X and Y of the same size, but does it at different scales, while the scalar KL distance is a single-scale measure. Like the Renyi fractal dimension spectrum, the DRq is also a bounded vectorial measure obtained at different scales and for different moment orders, q. However, unlike the Dq, all the elements of the new DRq become zero when X and Y are the same. Experimental results show that this objective measure is consistent with the subjective mean-opinion-score (MOS) when evaluating the perceptual quality of images reconstructed after their compression. Thus, it could also be used in other areas of cognitive informatics.
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