4 resultados para Vector notation

em Bucknell University Digital Commons - Pensilvania - USA


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Two experiments plus a pilot investigated the role of melodic structure on short-term memory for musical notation by musicians and nonmusicians. In the pilot experiment, visually similar melodies that had been rated as either "good" or "bad" were presented briefly, followed by a 15-sec retention interval and then recall. Musicians remembered good melodies better than they remembered bad ones: nonmusicians did not distinguish between them. In the second experiment, good, bad, and random melodies were briefly presented, followed by immediate recall. The advantage of musicians over nonmusicians decreased as the melody type progressed from good to bad to random. In the third experiment, musicians and nonmusicians divided the stimulus melodies into groups. For each melody, the consistency of grouping was correlated with memory performance in the first two experiments. Evidence was found for use of musical groupings by musicians and for use of a simple visual strategy by nonmusicians. The nature of these musical groupings and how they may be learned are considered. The relation of this work to other studies of comprehension of symbolic diagrams is also discussed.

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The performance of the parallel vector implementation of the one- and two-dimensional orthogonal transforms is evaluated. The orthogonal transforms are computed using actual or modified fast Fourier transform (FFT) kernels. The factors considered in comparing the speed-up of these vectorized digital signal processing algorithms are discussed and it is shown that the traditional way of comparing th execution speed of digital signal processing algorithms by the ratios of the number of multiplications and additions is no longer effective for vector implementation; the structure of the algorithm must also be considered as a factor when comparing the execution speed of vectorized digital signal processing algorithms. Simulation results on the Cray X/MP with the following orthogonal transforms are presented: discrete Fourier transform (DFT), discrete cosine transform (DCT), discrete sine transform (DST), discrete Hartley transform (DHT), discrete Walsh transform (DWHT), and discrete Hadamard transform (DHDT). A comparison between the DHT and the fast Hartley transform is also included.(34 refs)

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A new idea for waveform coding using vector quantisation (VQ) is introduced. This idea makes it possible to deal with codevectors much larger than before for a fixed bit per sample rate. Also a solution to the matching problem (inherent in the present context) in the &-norm describing a measure of neamess is presented. The overall computational complexity of this solution is O(n3 log, n). Sample results are presented to demonstrate the advantage of using this technique in the context of coding of speech waveforms.

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We carry out some computations of vector-valued Siegel modular forms of degree two, weight (k, 2) and level one, and highlight three experimental results: (1) we identify a rational eigenform in a three-dimensional space of cusp forms; (2) we observe that non-cuspidal eigenforms of level one are not always rational; (3) we verify a number of cases of conjectures about congruences between classical modular forms and Siegel modular forms. Our approach is based on Satoh's description of the module of vector-valued Siegel modular forms of weight (k, 2) and an explicit description of the Hecke action on Fourier expansions. (C) 2013 Elsevier Inc. All rights reserved.