Abstract not found

Many applications require the management of spatial data. Clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. A good clustering approach should be efficient and detect clusters of arbitrary shape. It must be insensitive to the outliers (noise) and the order of input data. We pro-pose WaveCluster, a novel clustering approach based on wavelet transforms, which satisfies all the above requirements. Using multi-resolution property of wavelet transforms, we can effectively identify arbitrary shape clus-ters at different degrees of accuracy. We also demonstrate that WaveCluster is highly effi-cient in terms of time complexity. Experi-mental results on very large data sets are pre-sented which show the efficiency and effective-ness of the proposed approach compared to the other recent clustering methods.

AbstractÐMany machine vision applications, such as compression, pictorial database querying, and image understanding, often need to analyze in detail only a representative subset of the image, which may be arranged into sequences of loci called regions-of-interest, ROIs. We have investigated and developed a methodology that serves to automatically identify such a subset of aROIs (algorithmically detected ROIs) using different Image Processing Algorithms, IPAs, and appropriate clustering procedures. In human perception, an internal representation directs top-down, context-dependent sequences of eye movements to fixate on similar sequences of hROIs (human identified ROIs). In this paper, we introduce our methodology and we compare aROIs with hROIs as a criterion for evaluating and selecting bottom-up, context-free algorithms. An application is finally discussed. Index TermsÐEye movements, scanpath theory, regions of interest identification and comparison. 1

Abstract — Fast changing, increasingly complex, and diverse computing platforms pose central problems in scientific computing: How to achieve, with reasonable effort, portable optimal performance? We present SPIRAL that considers this problem for the performance-critical domain of linear digital signal processing (DSP) transforms. For a specified transform, SPIRAL automatically generates high performance code that is tuned to the given platform. SPIRAL formulates the tuning as an optimization problem, and exploits the domain-specific mathematical structure of transform algorithms to implement a feedback-driven optimizer. Similar to a human expert, for a specified transform, SPIRAL “intelligently ” generates and explores algorithmic and implementation choices to find the best match to the computer’s microarchitecture. The “intelligence” is provided by search and learning techniques that exploit the structure of the algorithm and implementation space to guide the exploration and optimization. SPIRAL generates high performance code for a broad set of DSP transforms including the discrete Fourier transform, other trigonometric transforms, filter transforms, and discrete wavelet transforms. Experimental results show that the code generated by SPIRAL competes with, and sometimes outperforms, the best available human tuned transform library code. Index Terms — library generation, code optimization, adaptation, automatic performance tuning, high performance computing, linear signal transform, discrete Fourier transform, FFT, discrete cosine transform, wavelet, filter, search, learning, genetic and evolutionary algorithm, Markov decision process I.

Transmitter redundancy introduced using filterbank precoders generalizes existing modulations including OFDM, DMT, TDMA, and CDMA schemes encountered with single- and multiuser communications. Sufficient conditions are derived to guarantee that with FIR filterbank precoders FIR channels are equalized perfectly in the absence of noise by FIR zero-forcing equalizer filterbanks, irrespective of the channel zero locations. Multicarrier transmissions through frequency-selective channels can thus be recovered even when deep fades are present. Jointly optimal transmitter-receiver filterbank designs are also developed, based on maximum output SNR and minimum mean-square error criteria under zero-forcing and fixed transmitted power constraints. Analytical performance results are presented for the zero-forcing filterbanks and are compared with mean-square error and ideal designs using simulations.

Perfect reconstruction oversampled filter banks are equivalent to a particular class of frames in ` (Z). These frames are the subject of this paper. First, necessary and sufficient conditions on a filter bank for implementing a frame or a tight frame expansion are established, as well as a necessary and sufficient condition for perfect reconstruction using FIR filters after an FIR analysis. Complete parameterizations of oversampled filter banks satisfying these conditions are given. Further, we study the condition under which the frame dual to the frame associated with an FIR filter bank is also FIR and give a parameterization of a class of filter banks satisfying this property. Then, we focus on nonsubsampled filter banks. Nonsubsampled filter banks implement transforms similar to continuous-time transforms and allow for very flexible design. We investigate relations of these filter banks to continuous-time filtering and illustrate the design flexibility by giving a procedure for designing maximally flat two-channel filter banks that yield highly regular wavelets with a given number of vanishing moments.

Earlier work by Driscoll and Healy [16] has produced an e#cient algorithm for computing the Fourier transform of band-limited functions on the 2-sphere. In this paper we present a reformulation and variation of the original algorithm which results in a greatly improved inverse transform, and consequent improved convolution algorithm for such functions. All require at most O(N log 2 N ) operations where N is the number of sample points. We also address implementation considerations and give heuristics for allowing reliable and computationally e#cient floating point implementations of slightly modified algorithms. These claims are supported by extensive numerical experiments from our implementation in C on DEC, HP and SGI platforms. These results indicate that variations of the algorithm are both reliable and e#cient for a large range of useful problem sizes. Performance appears to be architecture-dependent. The paper concludes with a brief discussion of a few potential applications. 1...

During the last decade, CD-quality digital audio has essentially replaced analog audio. Emerging digital audio applications for network, wireless, and multimedia computing systems face a series of constraints such as reduced channel bandwidth, limited storage capacity, and low cost. These new applications have created a demand for high-quality digital audio delivery at low bit rates. In response to this need, considerable research has been devoted to the development of algorithms for perceptually transparent coding of high-fidelity (CD-quality) digital audio. As a result, many algorithms have been proposed, and several have now become international and/or commercial product standards. This paper reviews algorithms for perceptually transparent coding of CD-quality digital audio, including both research and standardization activities. The paper is organized as follows. First, psychoacoustic principles are described with the MPEG psychoacoustic signal analysis model 1 discussed in some detail. Next, filter bank design issues and algorithms are addressed, with a particular emphasis placed on the Modified Discrete Cosine Transform (MDCT), a perfect reconstruction (PR) cosine-modulated filter bank that has become of central importance in perceptual audio coding. Then, we review methodologies that achieve perceptually transparent coding of FM- and CD-quality audio signals, including algorithms that manipulate transform components, subband signal decompositions, sinusoidal signal components, and linear prediction (LP) parameters, as well as hybrid algorithms that make use of more than one signal model. These discussions concentrate on architectures and applications of

This paper constructs K-regular M-band orthonormal wavelet bases. K-regularity of the wavelet basis is known to be useful in numerical analysis applications and in image coding using wavelet techniques. Several characterizations of K-regularity and their importance are described. An explicit formula is obtained for all minimal length M-band scaling filters. A new state-space approach to constructing the wavelet filters from the scaling filters is also described. When M-band wavelets are constructed from unitary filter banks they give rise to wavelet tight frames in general (not orthonormal bases). Conditions on the scaling filter so that the wavelet bases obtained from it is orthonormal is also described.

The implementation of software for embedded digital signal processing (DSP) applications is an extremely complex process. The complexity arises from escalating functionality in the applications; intense time-to-market pressures; and stringent cost, power and speed constraints. To help cope with such complexity, DSP system designers have increasingly been employing high-level, graphical design environments in which system specification is based on hierarchical dataflow graphs. Consequently, a significant industry has emerged for the development of dataflow -based DSP design environments. Leading products in this industry include SPW from Cadence, COSSAP from Synopsys, ADS from Hewlett Packard, and DSP Station from Mentor Graphics. This paper reviews a set of algorithms for compiling dataflow programs for embedded DSP applications into efficient implementations on programmable digital signal processors. The algorithms focus primarily on the minimization of code size, and the minimization of the memory required for the buffers that implement the communication channels in the input dataflow graph. These are critical problems because programmable digital signal processors have very limited amounts of on-chip memory, and the speed, power, and cost penalties for using off-chip memory are often prohibitively high for embedded applications. Furthermore, memory demands of applications are increasing at a significantly higher rate than the rate of increase in on-chip memory capacity offered by improved integrated circuit technology.