Sampling (signal processing)
About: Sampling (signal processing) is a(n) research topic. Over the lifetime, 26855 publication(s) have been published within this topic receiving 218111 citation(s).
Papers published on a yearly basis
TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.
Abstract: 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, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.
TL;DR: The state-of-the-art of ADCs is surveyed, including experimental converters and commercially available parts, and the distribution of resolution versus sampling rate provides insight into ADC performance limitations.
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.
01 Mar 2008
TL;DR: This paper overviews the recent work on compressive sensing, a new approach to data acquisition in which analog signals are digitized for processing not via uniform sampling but via measurements using more general, even random, test functions.
Abstract: This paper overviews the recent work on compressive sensing, a new approach to data acquisition in which analog signals are digitized for processing not via uniform sampling but via measurements using more general, even random, test functions. In stark contrast with conventional wisdom, the new theory asserts that one can combine "low-rate sampling" with digital computational power for efficient and accurate signal acquisition. Compressive sensing systems directly translate analog data into a compressed digital form; all we need to do is "decompress" the measured data through an optimization on a digital computer. The implications of compressive sensing are promising for many applications and enable the design of new kinds of analog-to-digital converters, cameras, and imaging systems.
01 Jan 1993
Abstract: We present a straightforward and robust algorithm for periodicity detection, working in the lag (autocorrelation) domain. When it is tested for periodic signals and for signals with additive noise or jitter, it proves to be several orders of magnitude more accurate than the methods commonly used for speech analysis. This makes our method capable of measuring harmonics-to-noise ratios in the lag domain with an accuracy and reliability much greater than that of any of the usual frequency-domain methods. By definition, the best candidate for the acoustic pitch period of a sound can be found from the position of the maximum of the autocorrelation function of the sound, while the degree of periodicity (the harmonics-to-noise ratio) of the sound can be found from the relative height of this maximum. However, sampling and windowing cause problems in accurately determining the position and height of the maximum. These problems have led to inaccurate timedomain and cepstral methods for pitch detection, and to the exclusive use of frequency-domain methods for the determination of the harmonics-to-noise ratio. In this paper, I will tackle these problems. Table 1 shows the specifications of the resulting algorithm for two spectrally maximally different kinds of periodic sounds: a sine wave and a periodic pulse train; other periodic sounds give results between these. Table 1. The accuracy of the algorithm for a sampled sine wave and for a correctly sampled periodic pulse train, as a function of the number of periods that fit in the duration of a Hanning window. These results are valid for pitch frequencies up to 80% of the Nyquist frequency. These results were measured for a sampling frequency of 10 kHz and window lengths of 40 ms (for pitch) and 80 ms (for HNR), but generalize to other sampling frequencies and window lengths (see section 5).
01 Jan 1977
TL;DR: This chapter introduces the z-Transform, a new type of transform that combines Laplace Transforms, z-Transforms and Modified Z-TRANSFORMS with Convolution Integral to achieve state-of-the-art control of Discrete-Data Systems.
Abstract: Each chapter ends with Problems and References Chapter One includes only References) Introduction 1. Signal Conversion and Processing 2. The z-Transform 3. Transfer Functions, Block Diagrams and Signal Flow Graphs 4. The State Cariable Technique 5. Controllability, Observability and Stability 6. Frequency-Domain Analysis 7. Digital Simulation and Digital Redesign 8. Design of Discrete-Data Systems 9. Optimal Control 10. Micro-processor and DSP Controls Appendices: A. Fixed-Point and Floating-Point Numbers B. Mathematical Modeling of Sampling by Convolution Integral C. Table of Laplace Transforms, z-Transforms and Modified Z-TRANSFORMS D. General Gain Formula for Signal Flow Graphs E. Routh's Tabulation for Stability Analysis F. Galil DMC-100 Motion Controller Board