Coded data generation or conversion – Sample and hold – Having variable sampling rate
Reexamination Certificate
2007-10-30
2007-10-30
Williams, Howard L. (Department: 2819)
Coded data generation or conversion
Sample and hold
Having variable sampling rate
C702S064000
Reexamination Certificate
active
11465826
ABSTRACT:
Embodiments of the present invention provide a method and apparatus for compressed sensing. The method generally comprises forming a first compressed sensing matrix utilizing a first set of time indices corresponding to a first sampling rate, forming a second compressed sensing matrix utilizing a plurality of frequencies and a second set of time indices corresponding to a second sampling rate, forming a combined compressed sensing matrix from the first compressed sensing matrix and the second compressed sensing matrix, and reconstructing at least a portion of the input signal utilizing the combined compressed sensing matrix. The first and second sampling rates are each less than the Nyquist sampling rate for the input signal.
REFERENCES:
patent: 6473013 (2002-10-01), Velazquez et al.
patent: 6567567 (2003-05-01), Levin et al.
patent: 6819279 (2004-11-01), Pupalaikis
patent: 2005/0156775 (2005-07-01), Petre et al.
patent: 2006/0029279 (2006-02-01), Donoho
patent: 2006/0038705 (2006-02-01), Brady et al.
patent: 2006/0200035 (2006-09-01), Ricci et al.
patent: 2007/0027656 (2007-02-01), Baraniuk et al.
Haupt, Jarvis et al.; Compressive Sampling for Signal Classification; Fortieth Asilomar Conference on□□Signals, Systems and Computers, 2006. ACSSC '06. Oct.-Nov. 2006 pp. 1430-1434.
Friedlander, Benjamin; Random Projections for Sparse Channel Estimation and Equalization Signals; Fortieth Asilomar Conference on Systems and Computers, 2006. ACSSC '06. Oct.-Nov. 2006 pp. 453-457.
Kirolos, S. et al.; Practical Issues in Implementing Analog-to-Information Converters; The 6th International Workshop on System-on-Chip for Real-Time Applications, IEEE, Dec. 2006 pp. 141-146.
Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information by: Emmanuel Candes, Justin Romberg & Terence Tao; Jun. 2004, Rev: Aug. 2005; Los Angeles, CA.
Joint Sparsity Models for Distributed Compressed Sensing, by: Marco F. Duarte, Shriram Sarvotham, Michael B. Wakin, Dror Baron, & Richard G. Baraniuk; Rice University 2005.
Practical Signal Recovery from Random Projections, by: Emmanuel Candes & Justin Romberg; draft: Jan. 25, 2005.
Random Filters for Compressive Sampling and Reconstruction, by: Joel a. Tropp, Michael B. Wakin, Marco F. Duarte, Dror Baron & Richard G. Baraniuk; ICASSP, May 2006.
Signal Reconstruction from Noisy Random Projections, by: Jarvis Haupt & Robert Nowak; Dept of Electrical & Computer Engineering, University of Wisconsin-Madison, Mar. 2005.
Signal Recovery from Partial Information via Orthogonal Matching Pursuit, by: Joel A. Tropp & Anna C. Gilbert; University of Michigan; Apr. 2005.
Stable Signal Recovery from Incomplete and Inaccurate Measurements; Emmanuel Candes, Justin Romberg, and Terence Tao; Applied & Computational Mathematics, Caltech, Pasadena, CA; Feb. 2005, Revised Jun. 2005.
Near Optimal Signal Recovery from Random Projections: Universal Encoding Strategies; Emmanuel Candes & Terence Tao; Applied & Computational Mathematics, Caltech, Pasadena, CA; Oct. 2004.
Compressed Sensing; David Donoho, IEEE Transactions on Information Theory, vol. 52, No. 4, Apr. 2006.
Sparse Signal Detection from Incoherent Projections; Marco Duarte, Mark Davenport, Michael Wakin & Richard Baraniuk; Rice University; ICASSP, May 2006.
Distributed Compressed Sensing of Jointly Sparse Signals; Marco Duarte, Shriram Sarvotham, Dror Baron, Michael Wakin & Richard Baraniuk; Rice University; 2005.
Fast Reconsruction in Compressed Sensing; Marco Duarte; Rice University; Spring 2005.
Near-Optimal Sparse Fourier Representations via Sampling; A. Gilbert, S. Guha, P. Indyk, S. Muthukrishnan & M. Strauss; Montreal, Quebec, Canada, May 2002.
Applications of Sparse Approximation in Communications; A. Gilbert; J. Tropp; University of Michigan, 2005.
Minimum Rate Sampling & Reconstruction of Signals with Arbitrary Frequency Support; Cormac Herley, Ping Wah Wong; IEEE Transactions on Information theory, vol. 45, No. 5, Jul. 1999.
Ambiguities in the Harmonic Retrieval Problem using Non-Uniform Sampling; V. Lefkaditis & A. Manikas; IEEE Proceedings online No. 20010619; 2001.
Use of the Symmetrical Number System in Resolving Single-Frequency Undersampling Aliases; Phillip Pace, Richard Leino & David Styer; IEEE 1997.
Reduction of Aliasing Ambiguities Through Phase Relations; R. Sanderson, J Tsui & N. Freese; IEEE Transactions on Aerospace & Electronic Systems, vol. 28, No. 4, Oct. 1992.
A Non-Uniform Sampling Technique for A/D Conversion; N. Sayiner, H, Sorensen & T. Viswanathan; IEEE 1993.
Two-Channel RSNS Dynamic Range; D. Styer & P. Pace; IEEE 2002.
Simultaneous Sparse Approximation via Greedy Pursuit; J. Tropp, A. Gilbert & M. Strauss; IEEE 2005.
Extensions of Compressed Sensing; Yaakov Tsaig; David Donoho; Oct. 22, 2004.
An Efficient Frequency-Determination Algorithm from Multiple Undersampled Waveforms; Xiang-Gen Xia; IEEE 2000.
Sampling Signals with Finite Rate of Innovation, by: Martin Vetterli, Pina Marziliano & Thierry Blu; IEEE Transactions on Signal Processing, vol. 50, No. 6, Jun. 2002.
Fudge Gerald Lothair
Wood Mark L.
Yeh Chen-Chu Alex
Hovey & Williams, LLP
L-3 Communications Integrated Systems L.P.
Williams Howard L.
LandOfFree
Method and apparatus for compressed sensing does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method and apparatus for compressed sensing, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method and apparatus for compressed sensing will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3866872