954 resultados para Binary Coding
Resumo:
Combustion behaviour of ammonium perchlorate-potassium perchlorate pellets is studied using Crawford strand burners. At low concentrations of potassium perchlorate (up to 30 percent potassium perchlorate) the burning rate of ammonium perchlorate-potassium perchlorate condensed mixtures increases with potassium perchlorate content. Above 40 percent potassium perchlorate content, combustion sustenance becomes difficult. Decomposition products of ammonium perchlorate sensitize the melting and subsequent decomposition of potassium perchlorate. The results are explained in terms of the melt layer thickness, flame temperature and the resultant surface temperature, and heat wave penetration into the solid. The study suggests the importance of melt layer on the burning surface in the deflagration behaviour of ammonium perchlorate-potassium perchlorate condensed mixtures
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The low-frequency (5–100 kHz) dielectric constant epsilon (Porson) has been measured in the temperature range 7 × 10−5 < t = (T − Tc)/Tc < 8 × 10−2. Near Tc an exponent ≈0.11 characterizes the power law behaviour of Image consistent with the theoretically predicted t−α singularity. However, over the full range of t an exponent ≈0.35 is obtained.
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The purpose of this study is to describe the development of application of mass spectrometry for the structural analyses of non-coding ribonucleic acids during past decade. Mass spectrometric methods are compared of traditional gel electrophoretic methods, the characteristics of performance of mass spectrometric, analyses are studied and the future trends of mass spectrometry of ribonucleic acids are discussed. Non-coding ribonucleic acids are short polymeric biomolecules which are not translated to proteins, but which may affect the gene expression in all organisms. Regulatory ribonucleic acids act through transient interactions with key molecules in signal transduction pathways. Interactions are mediated through specific secondary and tertiary structures. Posttranscriptional modifications in the structures of molecules may introduce new properties to the organism, such as adaptation to environmental changes or development of resistance to antibiotics. In the scope of this study, the structural studies include i) determination of the sequence of nucleobases in the polymer chain, ii) characterisation and localisation of posttranscriptional modifications in nucleobases and in the backbone structure, iii) identification of ribonucleic acid-binding molecules and iv) probing of higher order structures in the ribonucleic acid molecule. Bacteria, archaea, viruses and HeLa cancer cells have been used as target organisms. Synthesised ribonucleic acids consisting of structural regions of interest have been frequently used. Electrospray ionisation (ESI) and matrix-assisted laser desorption ionisation (MALDI) have been used for ionisation of ribonucleic analytes. Ammonium acetate and 2-propanol are common solvents for ESI. Trihydroxyacetophenone is the optimal MALDI matrix for ionisation of ribonucleic acids and peptides. Ammonium salts are used in ESI buffers and MALDI matrices as additives to remove cation adducts. Reverse phase high performance liquid chromatography has been used for desalting and fractionation of analytes either off-line of on-line, coupled with ESI source. Triethylamine and triethylammonium bicarbonate are used as ion pair reagents almost exclusively. Fourier transform ion cyclotron resonance analyser using ESI coupled with liquid chromatography is the platform of choice for all forms of structural analyses. Time-of-flight (TOF) analyser using MALDI may offer sensitive, easy-to-use and economical solution for simple sequencing of longer oligonucleotides and analyses of analyte mixtures without prior fractionation. Special analysis software is used for computer-aided interpretation of mass spectra. With mass spectrometry, sequences of 20-30 nucleotides of length may be determined unambiguously. Sequencing may be applied to quality control of short synthetic oligomers for analytical purposes. Sequencing in conjunction with other structural studies enables accurate localisation and characterisation of posttranscriptional modifications and identification of nucleobases and amino acids at the sites of interaction. High throughput screening methods for RNA-binding ligands have been developed. Probing of the higher order structures has provided supportive data for computer-generated three dimensional models of viral pseudoknots. In conclusion. mass spectrometric methods are well suited for structural analyses of small species of ribonucleic acids, such as short non-coding ribonucleic acids in the molecular size region of 20-30 nucleotides. Structural information not attainable with other methods of analyses, such as nuclear magnetic resonance and X-ray crystallography, may be obtained with the use of mass spectrometry. Sequencing may be applied to quality control of short synthetic oligomers for analytical purposes. Ligand screening may be used in the search of possible new therapeutic agents. Demanding assay design and challenging interpretation of data requires multidisclipinary knowledge. The implement of mass spectrometry to structural studies of ribonucleic acids is probably most efficiently conducted in specialist groups consisting of researchers from various fields of science.
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This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.
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Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is available, but efficient computational tools for analyzing the data are missing. In this research, we develop efficient algorithms for a commonly occurring search problem - searching for the statistically most significant dependency rules in binary data. We consider dependency rules of the form X->A or X->not A, where X is a set of positive-valued attributes and A is a single attribute. Such rules describe which factors either increase or decrease the probability of the consequent A. A classical example are genetic and environmental factors, which can either cause or prevent a disease. The emphasis in this research is that the discovered dependencies should be genuine - i.e. they should also hold in future data. This is an important distinction from the traditional association rules, which - in spite of their name and a similar appearance to dependency rules - do not necessarily represent statistical dependencies at all or represent only spurious connections, which occur by chance. Therefore, the principal objective is to search for the rules with statistical significance measures. Another important objective is to search for only non-redundant rules, which express the real causes of dependence, without any occasional extra factors. The extra factors do not add any new information on the dependence, but can only blur it and make it less accurate in future data. The problem is computationally very demanding, because the number of all possible rules increases exponentially with the number of attributes. In addition, neither the statistical dependency nor the statistical significance are monotonic properties, which means that the traditional pruning techniques do not work. As a solution, we first derive the mathematical basis for pruning the search space with any well-behaving statistical significance measures. The mathematical theory is complemented by a new algorithmic invention, which enables an efficient search without any heuristic restrictions. The resulting algorithm can be used to search for both positive and negative dependencies with any commonly used statistical measures, like Fisher's exact test, the chi-squared measure, mutual information, and z scores. According to our experiments, the algorithm is well-scalable, especially with Fisher's exact test. It can easily handle even the densest data sets with 10000-20000 attributes. Still, the results are globally optimal, which is a remarkable improvement over the existing solutions. In practice, this means that the user does not have to worry whether the dependencies hold in future data or if the data still contains better, but undiscovered dependencies.
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The possibility of observing gravitational spin precession due to spin-orbit coupling in a binary pulsar system is considered. An analysis is presented which can aid in delineating the relevant physical effects from pulse-structure data. In this analysis, it is assumed that the pulsar radiation emanates from a cone whose axis is tilted with respect to the axis of rotation. It is found that the time-averaged pulse width and polarization sweep vary periodically with time and that this variation has a periodicity of the order of the spin-precession frequency averaged over a complete revolution. It is concluded that for an orbital period of about 180 years, it suffices to measure polarization data with an accuracy of a few parts in 100 over a period of six months to a year in order to uncover the effects of spin precession. The consistency of the analysis is checked, and the calculations are applied to a recently discovered binary pulsar.
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The signal-to-noise (S/N) ratio in the reconstructed image from a binary hologram has been quantitatively related to the amplitude and phase quantization levels. The S/N ratio increases monotonically with increasing number of quantization levels. This observation is further supported by experimental results.
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The system CS2 + CH3NO2 shows β=0.315±0.004 over 10-6<ε=|T-Tc| / Tc<2�10-1 with no indication of a classical value ½ even far away from Tc. The diameter shows a curvature and is of the form �c+b ε+fε7 / 8exp(-gεh).
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Electrical resistance measurements are reported on the binary liquid mixtures CS2 + CH3CN and CS2 + CH3NO2 with special reference to the critical region. Impurity conduction seems to be the dominant mechanism for charge transport. For the liquid mixture filled at the critical composition, the resistance of the system aboveT c follows the relationR=R c−A(T−T c) b withb=0·6±0·1. BelowT c the conductivities of the two phases obey a relation σ2−σ1=B(T c−T)β with β=0·34±0·02, the exponent of the transport coefficient being the same as the exponent of the order parameter, an equilibrium property.
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Two different designs for negative binary adder-subtracter are compared. Ono design uses the method of a hybrid-carry—borrow, while the other 11303 the method of polarization and addition.
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For five binary liquid systems CS2+CH3CN, CS2+CH3NO2, CS2+(CH3CO)2O, C6H12+(CH3CO)2O, n-C7H16+(CH3CO)2O, the electrical resistance has been measured near the critical solution temperatures. The behaviour is universal. Below Tc, the conductivities of the two phases follow σ1−σ2 β, where = T−Tc Tc with β≈0.35. In the one phase region with b≈0.35±0.1 and is positive in some cases and negative in others.
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We investigate the use of a two stage transform vector quantizer (TSTVQ) for coding of line spectral frequency (LSF) parameters in wideband speech coding. The first stage quantizer of TSTVQ, provides better matching of source distribution and the second stage quantizer provides additional coding gain through using an individual cluster specific decorrelating transform and variance normalization. Further coding gain is shown to be achieved by exploiting the slow time-varying nature of speech spectra and thus using inter-frame cluster continuity (ICC) property in the first stage of TSTVQ method. The proposed method saves 3-4 bits and reduces the computational complexity by 58-66%, compared to the traditional split vector quantizer (SVQ), but at the expense of 1.5-2.5 times of memory.
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Scaling relations between the critical indices are derived for two similar systems exhibiting λ lines: binary liquid systems and ferromagnets under pressure. In addition to the usual scaling relations, this procedure gives information about other weakly divergent quantities like isothermal compressibility and thermal expansion. Suggestions for more detailed investigations are made.
Resumo:
The system CS2 + CH3NO2 shows β=0.315±0.004 over 10-6<ε=|T-Tc| / Tc<2-10-1 with no indication of a classical value ½ even far away from Tc. The diameter shows a curvature and is of the form - c+b ε+fε7 / 8exp(-gεh).