EMG signals can be considered as the sum of scaled and delayed versions of a single prototype. We have applied the wavelet Transform choosing the mother wavelet so as to match the known shape of the basic component, a...
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ISBN:
(纸本)0819425915
EMG signals can be considered as the sum of scaled and delayed versions of a single prototype. We have applied the wavelet Transform choosing the mother wavelet so as to match the known shape of the basic component, and have compared the results obtained with different wavelets. The results in terms of MUAP detection and resolution are very encouraging even in the presence of asymmetric shape and high levels of additive noise.
In this paper we show that if wavelet domain processing is used with digital restoration, then pixel-scale features can be restored exactly in the absence of noise. In the presence of noise results are similar, except...
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ISBN:
(纸本)0819425915
In this paper we show that if wavelet domain processing is used with digital restoration, then pixel-scale features can be restored exactly in the absence of noise. In the presence of noise results are similar, except for some noise-amplification and ringing artifacts. wavelet domain modeling eliminates the need to discretize the image acquisition kernel and helps formulate image restoration as a discrete least squares problem. The performance of this technique is analyzed by model-based simulation using a comprehensive model to account for system blur at the image formation level, for the potentially important effects of aliasing, and for additive noise.
This paper investigates the relationship between the traditional wavelet (or matched filter) detector and the estimator correlator (EC) detector formulated in the wavelet domain. The EC detector is actually a weighted...
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ISBN:
(纸本)0819425915
This paper investigates the relationship between the traditional wavelet (or matched filter) detector and the estimator correlator (EC) detector formulated in the wavelet domain. The EC detector is actually a weighted wavelet detector, weighted by the scattering function that describes the medium and/or model. The wavelet detector is the optimum detector for point objects but it does not incorporate knowledge of the scattering environment. However, when imaging distributed objects, it is advantageous to take a priori information into account. The EC incorporates this information as a weight on the waveletimage and formulates an estimated spreading function which essentially achieves recombination of highlights and multipath energy. It can be shown that the EC reduces to the the wavelet detector when a point object is being imaged.
We investigate the application of adaptive wavelets for the representation and classification of signals in digitized speech and medical images. A class of wavelet basis functions are used to extract features from the...
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ISBN:
(纸本)0819425915
We investigate the application of adaptive wavelets for the representation and classification of signals in digitized speech and medical images. A class of wavelet basis functions are used to extract features from the regions of interest. These features are then used in an artificial neural network to classify the region as containing the desired object or belonging to the background clutter. The dilation and shift parameters of the wavelet functions are not fixed. These parameters are included in the training scheme. In this way the wavelets are adaptive to the expected shape and size of the signals. The results indicate that adaptive wavelet functions may outperform the classical fixed wavelet analysis in detection of subtle objects.
We present a viewpoint of studying biorthogonal wavelets by using wavelet operators. A characterization of MRA biorthogonal wavelets is given in the framework of wavelet operators. An efficient wavelet filtering algor...
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ISBN:
(纸本)0819425915
We present a viewpoint of studying biorthogonal wavelets by using wavelet operators. A characterization of MRA biorthogonal wavelets is given in the framework of wavelet operators. An efficient wavelet filtering algorithm based on this characterization is applied to X-ray computerized tomography (CT) for multiresolution reconstruction and reduced X-ray exposure. Simulation results indicate that wavelet based reconstruction allows satisfactory image quality in a region of interest from local wavelet and global scaling components of projection data. The results are directly applicable to medical X-ray CT.
If G is an orthonormal system in IL2 then for any function g is an element of G the function g(2) is a probability density. In this paper we discuss the properties of wavelet based densities and corresponding random v...
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ISBN:
(纸本)0819425915
If G is an orthonormal system in IL2 then for any function g is an element of G the function g(2) is a probability density. In this paper we discuss the properties of wavelet based densities and corresponding random variables.
We discuss a method for initializing the multi-wavelet decomposition algorithm by pre-filtering. The proposed pre-filtering operation projects the input signal into the space defined by the multi-scaling function asso...
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ISBN:
(纸本)0819425915
We discuss a method for initializing the multi-wavelet decomposition algorithm by pre-filtering. The proposed pre-filtering operation projects the input signal into the space defined by the multi-scaling function associated with the multi-wavelet. Since the approach is projection based, it is guaranteed to always have a solution. The space in which the original signal is contained is defined by multiple generating functions, making this work a generalization of our previous results.
We give many examples of bivariate nonseparable compactly supported orthonormal wavelets which are supported over [0,3]x[0,3]. The Holder continuity properties of these wavelets are studied.
ISBN:
(纸本)0819425915
We give many examples of bivariate nonseparable compactly supported orthonormal wavelets which are supported over [0,3]x[0,3]. The Holder continuity properties of these wavelets are studied.
In this paper, the relationship between wavelet transform and Differential Mapping Singularities Theory (DMST) is discussed in the context of image compression. DMST maps 3-D surfaces accurately, with exact results, a...
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ISBN:
(纸本)0819425915
In this paper, the relationship between wavelet transform and Differential Mapping Singularities Theory (DMST) is discussed in the context of image compression. DMST maps 3-D surfaces accurately, with exact results, and to construct an image compression algorithm based on an expanded set of operations. This set includes shift, scaling rotation, and homogenous nonlinear transformations. This approach permits the mathematical description of a full set of singularities that describe edges and other specific points of objects. The edges and specific points (degenerate critical points) are the product of mapping smooth 3-D surfaces, which can be described by a simple set of polynomials that are suitable for image compression and Automatic Target Recognition (ATR). In signal and imageprocessing, wavelets have been used for several years to provide multi-resolution data representation [1] Originally, wavelets were developed for one-dimensional signal decomposition. Subsequently, they were generalized to 2-D image coding. Now, wavelet transform is used to hierarchically decompose an input signal into a series of lower resolution reference signals and associated detail signals. At each level, a reference signal and its associated detail signal contain information required to reconstruct the reference signal at the next higher resolution level. Efficient image coding is enabled by allocating the bandwidth according to the relative importance of information in the reference and detail signals, and then applying the next level of the lossy and lossless compression algorithm.
A new approach to FPGA implementation of two-dimensional discrete wavelet transform is presented. This architecture allow high accurate and sampling rate DWT realization based on FIR filters of substantial length to b...
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ISBN:
(纸本)0819425915
A new approach to FPGA implementation of two-dimensional discrete wavelet transform is presented. This architecture allow high accurate and sampling rate DWT realization based on FIR filters of substantial length to be implemented on current generation FPGAs. The scheme is based on two parallel pipelined linear phase 17-tap FIR filters with common shift register, partial adders and look-up tables as coefficient multipliers with 4-stage pipelined architecture. The transform is realized in three stages controlled by the state machine, where temporary (L and H) and final subimages (LL, LH, HL, and HH) are created. High throughput (1050 MIPS) and external memory controller allow efficient concurrent cooperation with external processors.
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