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Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on

Issue 9 • Date Sep 1993

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Displaying Results 1 - 8 of 8
  • A fast mean-distance-ordered partial codebook search algorithm for image vector quantization

    Publication Year: 1993 , Page(s): 576 - 579
    Cited by:  Papers (48)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (360 KB)  

    A new fast search algorithm for vector quantization using the mean of image vectors is proposed. The codevectors are sorted according to their component means, and the search for the codevector having the minimum Euclidean-distance to a given input vector starts with the one having the minimum mean-distance to it, making use of our observation that the two codevectors are close to each other in most real images. The search is then made to terminate as soon as a simple yet novel test reports that any remaining vector in the codebook should have a larger Euclidean distance. Simulations show that the number of calculations can be reduced to as low as a fourth the number achievable by an algorithm known as the partial distance method View full abstract»

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  • Area efficient VLSI architectures for Huffman coding

    Publication Year: 1993 , Page(s): 568 - 575
    Cited by:  Papers (27)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (724 KB)  

    In this paper, we present simple and area efficient VLSI architectures for Huffman coding, an industrial standard proposed by MPEG, JPEG, and others. We use a memory of size O(n log n) bits to store a Huffman code tree, where a is the number of symbols. This storage scheme supports real-time encoding and decoding. In addition, few simple arithmetic operations are performed on the chip for encoding and decoding. Based on our scheme, we show a design for I-bit symbols. The proposed design requires 256×9 and 64×18-bit memory modules to process 8-bit symbols. The chip occupies a silicon area of 3.5×3.5 mm2 using 1.2 micron CMOSN standard library cells. Compared with a known parallel implementation which requires up to 65536 PE's, the proposed architecture leads to a single PE design. It requires significantly less area than the known single PE design. Different Huffman codes can be stored by changing the contents of the memory, without changing the design View full abstract»

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  • Error analysis and simulation for APMI

    Publication Year: 1993 , Page(s): 579 - 581
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (188 KB)  

    Presents an error analysis for a system consisting of an all-pole model with multiple impulse excitation (APMI). This model has been successfully used in low bit-rate speech processing, seismic processing and echo cancellation. Bounds are derived for the performance of the optimal algorithm in the presence of noise in terms of the eigenvalues of the underlying covariance matrices. The bounds are verified with simulations using actual multipulse data View full abstract»

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  • Parallel, self-organizing, hierarchical neural networks with competitive learning and safe rejection schemes

    Publication Year: 1993 , Page(s): 556 - 567
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1028 KB)  

    A new neural network learning algorithm with competitive learning and multiple safe rejection schemes is proposed in the context of parallel, self-organizing, hierarchical neural networks (PSHNN). After reference vectors are computed using competitive learning in a stage of PSHNN, safe rejection schemes are constructed for reference vectors. The purpose of safe rejection schemes is to reject the input vectors which are hard to classify. The next stage neural network is trained with the nonlinearly transformed values of only those training vectors that were rejected in the previous stage neural network Two different kinds of safe rejection schemes, RADPN and RAD, are developed and used together. Experimental results comparing the performance of the proposed algorithms with those of backpropagation and the PSHNN with the delta rule learning algorithm are discussed. The proposed learning network produced higher classification accuracy and much faster learning. The classification accuracies of two methods for learning the reference vectors were compared. When the reference vectors are computed separately for each class (Method II), higher classification accuracy was obtained as compared to the method in which the reference vectors are computed together for all the classes (Method I). This conclusion has to do with rejection of hard vectors, and is the opposite of what is normally expected. In addition, Method II has the advantage of parallelism by which the reference vectors for all the classes can be computed simultaneously View full abstract»

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  • Design of 2D FIR digital filters by McClellan transformation and least squares eigencontour mapping

    Publication Year: 1993 , Page(s): 546 - 555
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (620 KB)  

    A powerful eigenfilter approach is proposed to determine the optimal coefficients of McClellan transformation. It can design arbitrary shape transformation contours to map from 1D prototypes to 2D FIR filters very effectively. This paper presents the design methods for 2D fan filters with general slope and inclination angle, elliptically symmetric filters of arbitrary orientation, circularly symmetric filters, and diamond-shaped filters. Several numerical examples are given to demonstrate the usefulness and the efficiency of the present method View full abstract»

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  • The uniqueness in designing multidimensional causal recursive digital filters possessing magnitude hyperspherical symmetry

    Publication Year: 1993 , Page(s): 533 - 545
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1148 KB)  

    It is shown that magnitude hyperspherically symmetric transfer functions of multidimensional (MD) causal recursive digital filters must have numerator and denominator polynomials that are separately magnitude hyperspherically symmetric. Further, the exact reference-domain magnitude hyperspherically symmetric denominator polynomial is of infinite order, possessing only one free parameter, and the magnitude hyperspherically symmetric numerator polynomial itself has to be a radial even function. The corresponding MD design problem is shown to be essentially a one-dimensional design problem. Filter transfer functions having good symmetry and moderate degree can be designed by using the presented procedure View full abstract»

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  • Circuit implementation of a peak detector neural network

    Publication Year: 1993 , Page(s): 585 - 591
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (564 KB)  

    Peak detection is a basic data analysis problem which is essential in a large number of applications. In applications such as image processing, the large computational effort to locate peaks may prohibit operation in real-time. A Hopfield neural network is proposed for the peak detector to solve the real-time problem. Analytical expressions are derived for input separation, neuron gain, and restrictions on initial conditions. Hardware limitations are discussed and a modified circuit model is suggested for the Hopfield neuron. Solution time under thirty microseconds is obtainable with general purpose operational amplifiers independent of the number of inputs. Results obtained from a twenty-five neuron hardware implementation of the network lend credence to the theoretical results View full abstract»

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  • Unconstrained Hartley domain least mean square adaptive filter

    Publication Year: 1993 , Page(s): 582 - 585
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (312 KB)  

    An efficient adaptive filtering algorithm named as the unconstrained Hartley domain least mean square (UHLMS) algorithm has been proposed. It is found from computer simulation that the proposed algorithm has similar performance to the time domain least mean square (LMS) algorithm for uncorrelated signals; but yields faster and better convergence for highly correlated signals. The UHLMS algorithm has identical performance to that of the unconstrained frequency domain least mean square (UFLMS) algorithm, but requires significantly less computation View full abstract»

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Aims & Scope

This title ceased production in 2003. The current updated title is IEEE Transactions on Circuits and Systems II: Express Briefs.

Full Aims & Scope