Mathematics of Analog‐to‐Digital Conversion

As the performance of digital circuits and the capacity of digital communication channels have advanced steadily over the past decades, digital signals have replaced analog signals in nearly every technological application domain, offering robustness, ever increasing quality, and speed. At the same time, many signals that we perceive audiovisually (sound and video), or measure via instrumentation (e.g., geophysical signals and medical images), are analog in their nature. Consequently, not only are the analog‐to‐digital (A/D) and digital‐to‐analog (D/A) conversion processes as crucial as ever, but also the performance of these processes must improve to meet the increasing demands at the digital processing end. Both A/D and D/A conversion is necessarily carried out via analog devices bound by physical limitations such as imprecisions and noise, which present continual technological as well as theoretical challenges to achieving this objective. This article aims to introduce some of the modern mathematical ideas, developments, and challenges in this context from the author's perspective. © 2012 Wiley Periodicals, Inc.

[1]  W. R. Bennett,et al.  Spectra of quantized signals , 1948, Bell Syst. Tech. J..

[2]  Claude E. Shannon,et al.  The mathematical theory of communication , 1950 .

[3]  W. Parry On theβ-expansions of real numbers , 1960 .

[4]  H. Inose,et al.  A Telemetering System by Code Modulation - Δ- ΣModulation , 1962, IRE Transactions on Space Electronics and Telemetry.

[5]  P. Zador DEVELOPMENT AND EVALUATION OF PROCEDURES FOR QUANTIZING MULTIVARIATE DISTRIBUTIONS , 1963 .

[6]  Hiroshi Inose,et al.  A unity bit coding method by negative feedback , 1963 .

[7]  T. Bernard From Sigma - Delta modulation to digital halftoning of images , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[8]  A. Karanicolas,et al.  A 15-b 1-Msample/s digitally self-calibrated pipeline ADC , 1993 .

[9]  Martin Vetterli,et al.  Data Compression and Harmonic Analysis , 1998, IEEE Trans. Inf. Theory.

[10]  Jeffrey C. Lagarias,et al.  On the robustness of single-loop sigma-Delta modulation , 2001, IEEE Trans. Inf. Theory.

[11]  K. Dajani,et al.  From greedy to lazy expansions and their driving dynamics , 2002 .

[12]  A. Robert Calderbank,et al.  The pros and cons of democracy , 2002, IEEE Trans. Inf. Theory.

[13]  I. Daubechies,et al.  Approximating a bandlimited function using very coarsely quantized data: A family of stable sigma-delta modulators of arbitrary order , 2003 .

[14]  N. Sidorov Almost every number has a continuum of beta-expansions , 2003 .

[15]  C. S. Gunturk,et al.  Approximating a bandlimited function using very coarsely quantized data: Improved error estimates in sigma-delta modulation , 2003 .

[16]  Steffen Dereich,et al.  High resolution coding of stochastic processes and small ball probabilities , 2003 .

[17]  C. S. Güntürk One‐bit sigma‐delta quantization with exponential accuracy , 2003 .

[18]  C. Sinan Güntürk,et al.  Refined error analysis in second-order Sigma-Delta modulation with constant inputs , 2004, IEEE Trans. Inf. Theory.

[19]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[20]  C. Sinan Güntürk,et al.  Ergodic dynamics in sigma-delta quantization: tiling invariant sets and spectral analysis of error , 2005, Adv. Appl. Math..

[21]  D. Donoho For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .

[22]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[23]  Özgür Yilmaz,et al.  Robust and Practical Analog-to-Digital Conversion With Exponential Precision , 2006, IEEE Transactions on Information Theory.

[24]  Ronald A. DeVore,et al.  A/D conversion with imperfect quantizers , 2006, IEEE Transactions on Information Theory.

[25]  Ronald A. DeVore,et al.  Deterministic constructions of compressed sensing matrices , 2007, J. Complex..

[26]  M. Rudelson,et al.  On sparse reconstruction from Fourier and Gaussian measurements , 2008 .

[27]  R. DeVore,et al.  Compressed sensing and best k-term approximation , 2008 .

[28]  Ting Sun,et al.  Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..

[29]  R. Ward,et al.  On Robustness Properties of Beta Encoders and Golden Ratio Encoders , 2008, IEEE Transactions on Information Theory.

[30]  Felix Krahmer,et al.  An optimal family of exponentially accurate one‐bit Sigma‐Delta quantization schemes , 2010, ArXiv.

[31]  Yonina C. Eldar,et al.  From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals , 2009, IEEE Journal of Selected Topics in Signal Processing.

[32]  Stephen P. Boyd,et al.  Compressed Sensing With Quantized Measurements , 2010, IEEE Signal Processing Letters.

[33]  Rachel Ward,et al.  Lower bounds for the error decay incurred by coarse quantization schemes , 2010, ArXiv.

[34]  Özgür Yilmaz,et al.  The Golden Ratio Encoder , 2008, IEEE Transactions on Information Theory.

[35]  Özgür Yılmaz,et al.  Sobolev Duals in Frame Theory and Sigma-Delta Quantization , 2010 .

[36]  Rayan Saab,et al.  Sigma delta quantization for compressed sensing , 2010, 2010 44th Annual Conference on Information Sciences and Systems (CISS).

[37]  Justin K. Romberg,et al.  Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals , 2009, IEEE Transactions on Information Theory.

[38]  Stephen J. Dilworth,et al.  Explicit constructions of RIP matrices and related problems , 2010, ArXiv.

[39]  Rayan Saab,et al.  Sobolev Duals for Random Frames and Sigma-Delta Quantization of Compressed Sensing Measurements , 2010, ArXiv.

[40]  Laurent Jacques,et al.  Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine , 2009, IEEE Transactions on Information Theory.

[41]  Yonina C. Eldar,et al.  Xampling: Analog to digital at sub-Nyquist rates , 2009, IET Circuits Devices Syst..