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"Fundamentals of Statistical Signal Processing: Estimation Theory." Technometrics, 37(4), pp. 465–466

Materias / palabras clave: Signal theory (Telecommunication); Signal processing; Telecomunicación

Materias / palabras clave: Signal processing; Signal; Procesamiento de señales; Señales

Materias / palabras clave: signal processing, emerging technologies; Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication; Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics

Materias / palabras clave: image processing, biomedical signal processing, radar, signal processing techniques, statistical methods; Technology: Electrical engineering. Electronics. Nuclear engineering

Materias / palabras clave: signal processing, information technology, electrical engineering; Technology: Electrical engineering. Electronics. Nuclear engineering

Materias / palabras clave: signal processing, signal detection, digital signal processing, signal approximation, signal decomposition; Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication

This paper demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Matching Pursuit (OMP) can reliably recover a signal with $m$ nonzero entries in dimension $d$ given $ {\rm O}(m \ln d)$ ra...

The proceedings of the 2001 Neural Information Processing Systems (NIPS) Conference. The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference ...

This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f/spl isin/C/sup N/ and a randomly chosen set of frequencies /spl Omega/. Is it possib...

Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standa...

The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order...

During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is ...

Papers from the 2006 flagship meeting on neural computation, with contributions from physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The annual Neural Information Processing Systems (...

Processing the signals received on an array of sensors for the location of the emitter is of great enough interest to have been treated under many special case assumptions. The general problem considers sensors with arbi...

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesima...

It has long been realized that in pulse-code modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely...

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinatio...

Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typica...

We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units. The algorithm does not assume any knowledge of the input distributions, and is defined here f...

We consider the class of iterative shrinkage-thresholding algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods, which can be viewed as an extension of the classi...

Preface to the Fourth Edition. 1 Biomechanics as an Interdiscipline. 1.0 Introduction. 1.1 Measurement, Description, Analysis, and Assessment. 1.2 Biomechanics and its Relationship with Physiology and Anatomy. 1.3 Scope ...

Suppose x is an unknown vector in Ropf m (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible by transform coding with a known transform...

Materias / palabras clave: signal processing, information processing, image processing, speech processing; Science: Mathematics: Instruments and machines: Electronic computers. Computer science