We present a generic approach that identifies and differentiates among signals for wide range of problems. Originally our algorithm was developed to detect the presence of a specific vehicle belonging to a certain class via the analysis of the acoustic signals emitted while it is moving. A crucial factor in having a successful detection (no false alarm) is to construct signatures built from characteristic features that enable to discriminate between the class of interest and the residual information such as background. We construct the signatures of certain classes by the distribution of the energies among blocks which consist of wavelet packet coefficients. We developed an efficient procedure for adaptive selection of the characteristic blocks. We modified the CART algorithm in order to utilize it to be a decision unit in our scheme. However, this technology, which has many algorithmic variations, can be used to solve a wide range of classification and detection problems which are based on acoustic processing and, more generally, for classification and detection of signals which have near-periodic structure. We present results of successful application of the properly modified algorithm to detection of early symptoms of arterial hypertension in children via real-time analysis of pulse signals.
KEYWORDS: Wavelets, Quantization, Data compression, Signal to noise ratio, Data acquisition, Multimedia, Image compression, Fourier transforms, Image processing, Data processing
The main drive behind the use of data compression in seismic data is the very large size of seismic data acquired. Some of the most recent acquired marine seismic data sets exceed 10 Tbytes, and in fact there are currently seismic surveys planned with a volume of around 120 Tbytes. Nevertheless, seismic data are quite different from the typical images used in image processing and multimedia applications. Some of their major differences are the data dynamic range exceeding 100 dB in theory, very often it is data with extensive oscillatory nature, the x and y directions represent different physical meaning, and there is significant amount of coherent noise which is often present in seismic data. The objective of this paper is to achieve higher compression ratio, than achieved with the wavelet/uniform quantization/Huffman coding family of compression schemes, with a comparable level of residual noise. The goal is to achieve above 40dB in the decompressed seismic data sets. One of the conclusions is that adaptive multiscale local cosine transform with different windows sizes performs well on all the seismic data sets and outperforms the other methods from the SNR point of view. Comparison with other methods (old and new) are given in the full paper. The main conclusion is that multidimensional adaptive multiscale local cosine transform with different windows sizes perform well on all the seismic data sets and outperforms other methods from the SNR point of view. Special emphasis was given to achieve faster processing speed which is another critical issue that is examined in the paper. Some of these algorithms are also suitable for multimedia type compression.
We present a library of biorthogonal wavelet transforms and the related library of biorthogonal symmetric waveforms. For the construction we use interpolatory, quasiinterpolatory and smoothing splines with finite masks (local splines). With this base we designed a set of perfect reconstruction infinite and finite impulse response filter banks with linear phase property. The construction is performed in a lifting manner. The developed technique allows to construct wavelet transforms with arbitrary prescribed properties such as the number of vanishing moments, shape of wavelets, and frequency resolution. Moreover, the transforms contain some scalar control parameters which enable their flexible tuning in either time or frequency domains. The transforms are implemented in a fast way. The transforms, which are based on interpolatory splines, are implemented through recursive filtering. We present encouraging results towards image compression.
In this paper, we present a new family of biorthogonal wavelet transforms and a related library of biorthogonal periodic symmetric waveforms. For the construction we used the interpolatory discrete splices which enabled us to design a library of perfect reconstruction filter banks. These filter banks are related to Buttersworth filters. The construction is performed in a 'lifting' manner. The difference from the conventional lifting scheme is that all the transforms are implemented in the frequency domain with the use of the fast Fourier transform. Two ways to choose the control filters are suggested. The proposed scheme is based on interpolation and, as such, it involves only samples of signals and it does not require any use of quadrature formulas. These filters have linear phase property and the basic waveforms are symmetric. In addition, these filters yield perfect frequency resolution.
We detect the presence of a vehicle or an air borne target form a certain class via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. To achieve this detection with no false alarms we construct the acoustic signatures of certain targets to be found by the distribution of the energies among blocks which consists of wavelet packet coefficients. We developed an efficient procedure for adaptive selection of the characteristic blocks. We modified the CART algorithm in order to utilize it as a decision unit in our scheme. A wide series of field experiments manifested a remarkable efficiency of the algorithm. The detecting had been achieved practically with no false alarms even under severe conditions such a the acoustic recording of sought- after object was a superposition of the acoustics emitted from other targets that belong to other classes. The detection was even immune to severe noisy surroundings.
The main contribution of this work is a new paradigm for image compression. We describe a new multi-layered representation technique for images. An image is encoded as the superposition of one main approximation, and a sequence of residuals. The strength of the multi-layered method comes from the fact that we use different bases to encode the main approximation and the residuals. For instance, we can use: a wavelet basis to encode a coarse main approximation of the image, wavelet packet bases to encode textured patterns, brushlet bases to encode localized oriented textured features, etc.
KEYWORDS: Wavelets, Signal to noise ratio, Quantization, Distortion, Digital signal processing, Linear filtering, Chromium, Ear, Wavelet transforms, Computer programming
We present an algorithm for speech compression which uses the wavelet packet transform, vector quantization, entropy coding and postfiltering of the decoded speech. We address the following issue: obtaining the best speech quality for a given bit rate with minimal algorithmic delay (applying it on the possible shortest segment). The wavelet packet transform provides good compression since it is based on a very close relation between the transform and the actual physical processes in the human ear. The experimental results demonstrate that we can compress speech by factor of 6 - 10 and still have reasonable intelligibility and perceivability of the output speech using an algorithmic delay of 8 msec (64 speech samples). In addition, the proposed algorithm fits well DSP architecture and can be easily ported into any current 40MIPS DSP. By comparing the proposed algorithm in this paper with new CELP-oriented algorithm one can conclude that the former has less delay with higher compression ratio. The postfiltering was found to improve the quality of the decoded speech. We see that by using fixed size segments with 64 samples with wrap-around in the segments border does not degrade the performance in comparison to FIR-implementation without wrap-around. In addition, it is useful to implement different filter in each level of the decomposition.
The patch clamp technique opened a new field in biological research and shed light on membrane permittivity for ionic currents. The key element in patch clamp measurements is the detection of the ionic currents in a single biological channel. It is known that the channels open and close at random times, thus modulating the ionic currents. The measured current switches between two levels corresponding to the open and close states of the channel. Determining the statistics of the open and closed periods is of crucial importance to the experimenter, because it reflects the response of channel protein to drugs and other factors. The detected signal is strongly corrupted by instrumentation and other noises, rendering the detection of the opening and closing moments extremely difficult. We describe the use of the wavelet transform and its associated multiresolution (multiscale) analysis to detect the currents through single ionic channels corrupted with noise.
This paper presents the Local Cosine Transform as a new method for the reduction and smoothing of the blocking effect that appears at low bit rates in image coding algorithms based on the Discrete Cosine Transform. In particular, the blocking effect appears in the JPEG baseline sequential algorithm.
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