Search results for "Multidimensional signal processing"
showing 9 items of 9 documents
Adaptive Importance Sampling: The past, the present, and the future
2017
A fundamental problem in signal processing is the estimation of unknown parameters or functions from noisy observations. Important examples include localization of objects in wireless sensor networks [1] and the Internet of Things [2]; multiple source reconstruction from electroencephalograms [3]; estimation of power spectral density for speech enhancement [4]; or inference in genomic signal processing [5]. Within the Bayesian signal processing framework, these problems are addressed by constructing posterior probability distributions of the unknowns. The posteriors combine optimally all of the information about the unknowns in the observations with the information that is present in their …
Fractional wavelet transform
1997
The wavelet transform, which has had a growing importance in signal and image processing, has been generalized by association with both the wavelet transform and the fractional Fourier transform. Possible implementations of the new transformation are in image compression, image transmission, transient signal processing, etc. Computer simulations demonstrate the abilities of the novel transform. Optical implementation of this transform is briefly discussed.
On the metric properties of dynamic time warping
1987
Recently, some new and promising methods have been proposed to reduce the number of Dynamic Time Warping (DTW) computations in Isolated Word Recognition. For these methods to be properly applicable, the verification of the Triangle Inequality (TI) by the DTW-based Dissimilarity Measure utilized seems to be an important prerequisite.
Real-time acquisition of wideband signals data using non-uniform sampling
2009
Wideband signal acquisition often faces a problem of bulk data recording. It is a critical factor in real-time systems. The paper discusses an approach that deals with this problem incorporating Digital Alias-free Signal Processing and Virtual Instrumentation technologies. Such combination is advantageous for real-time applications and useful in the development of measurement and software defined radio systems. Non-uniform sampling technique allows considerably decrease the data of wideband signals so, that they can be transferred in real-time to a typical PC through high-speed PCI Express communication interface. The experimental prototype system has been developed and obtained results are…
Elementary transformation analysis for Array-OL
2009
Array-OL is a high-level specification language dedicated to the definition of multidimentional intensive signal processing applications. It allows to specify both the task parallelism and the data parallelism of these applications on focusing on their complex multidimensional data access patterns. Several tools exist for implementing an Array-OL specification as a data parallel program. While Array-OL can be used directly, it is often convenient to be able to deduce part of the specification from a sequential version of the application. This paper proposes such an analysis and examines its feasibility and its limits.
Discrete wavelet transform implementation in Fourier domain for multidimensional signal
2002
Wavelet transforms are often calculated by using the Mallat algorithm. In this algorithm, a signal is decomposed by a cascade of filtering and downsampling operations. Computing time can be important but the filtering operations can be speeded up by using fast Fourier transform (FFT)-based convolutions. Since it is necessary to work in the Fourier domain when large filters are used, we present some results of Fourier-based optimization of the sampling operations. Acceleration can be obtained by expressing the samplings in the Fourier domain. The general equations of the down- and upsampling of digital multidimensional signals are given. It is shown that for special cases such as the separab…
<title>Reaction-diffusion electrical network for image processing</title>
2006
We consider an experimental setup, modelling the FitzHugh-Nagumo equation without recovery term and composed of a 1D nonlinear electrical network made up of discrete bistable cells, resistively coupled. In the first place, we study the propagation of topological fronts in the continuum limit, then in more discrete case. We propose to apply these results to the domain of signal processing. We show that erosion and dilation of a binary signal, can be obtained. Finally, we extend the study to 2D lattices and show that it can be of great interest in image processing techniques.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted fo…
Discrete Time Signal Processing Framework with Support Vector Machines
2007
Digital signal processing (DSP) of time series using SVM has been addressed in the literature with a straightforward application of the SVM kernel regression, but the assumption of independently distributed samples in regression models is not fulfilled by a time-series problem. Therefore, a new branch of SVM algorithms has to be developed for the advantageous application of SVM concepts when we process data with underlying time-series structure. In this chapter, we summarize our past, present, and future proposal for the SVM-DSP frame-work, which consists of several principles for creating linear and nonlinear SVM algorithms devoted to DSP problems. First, the statement of linear signal mod…
Introduction to Digital Signal Processing
2018
Signal processing deals with the representation, transformation, and manipulation of signals and the information they contain. Typical examples include extracting the pure signals from a mixture observation (a field commonly known as deconvolution) or particular signal (frequency) components from noisy observations (generally known as filtering). This chapter outlines the basics of signal processing and then introduces the more advanced concepts of time‐frequency and time‐scale representations, as well as emerging fields of compressed sensing and multidimensional signal processing. When moving to multidimensional signal processing, a modern approach is taken from the point of view of statis…