Sampling (signal processing)

Abstract: Abstract Offers a comprehensive introduction to distance sampling, a statistical method used by many biologists and conservationists to estimate animal abundance. The text discusses point transect sampling and line transect sampling and also describes several other related techniques. There are updates on study design and field methods, laser range finders, theodolites and the GPS and advice is given on a wide range of survey methods. Analysis methods have also been generalized, through the use of various types of multiplier and exercises for students in wildlife and conservation management are included.

Abstract: Analog-to-digital converters (ADCs) are ubiquitous, critical components of software radio and other signal processing systems. This paper surveys the state-of-the-art of ADCs, including experimental converters and commercially available parts. The distribution of resolution versus sampling rate provides insight into ADC performance limitations. At sampling rates below 2 million samples per second (Gs/s), resolution appears to be limited by thermal noise. At sampling rates ranging from /spl sim/2 Ms/s to /spl sim/4 giga samples per second (Gs/s), resolution falls off by /spl sim/1 bit for every doubling of the sampling rate. This behavior may be attributed to uncertainty in the sampling instant due to aperture jitter. For ADCs operating at multi-Gs/s rates, the speed of the device technology is also a limiting factor due to comparator ambiguity. Many ADC architectures and integrated circuit technologies have been proposed and implemented to push back these limits. The trend toward single-chip ADCs brings lower power dissipation. However, technological progress as measured by the product of the ADC resolution (bits) times the sampling rate is slow. Average improvement is only /spl sim/1.5 bits for any given sampling frequency over the last six-eight years.

Abstract: Compressed sensing is a technique to sample compressible signals below the Nyquist rate, whilst still allowing near optimal reconstruction of the signal. In this paper we present a theoretical analysis of the iterative hard thresholding algorithm when applied to the compressed sensing recovery problem. We show that the algorithm has the following properties (made more precise in the main text of the paper) - It gives near-optimal error guarantees. - It is robust to observation noise. - It succeeds with a minimum number of observations. - It can be used with any sampling operator for which the operator and its adjoint can be computed. - The memory requirement is linear in the problem size. - Its computational complexity per iteration is of the same order as the application of the measurement operator or its adjoint. - It requires a fixed number of iterations depending only on the logarithm of a form of signal to noise ratio of the signal. - Its performance guarantees are uniform in that they only depend on properties of the sampling operator and signal sparsity.

Abstract: Abstract Stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm can be viewed as three alternative sampling- (or Monte Carlo-) based approaches to the calculation of numerical estimates of marginal probability distributions. The three approaches will be reviewed, compared, and contrasted in relation to various joint probability structures frequently encountered in applications. In particular, the relevance of the approaches to calculating Bayesian posterior densities for a variety of structured models will be discussed and illustrated.