Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-19T00:52:56.306Z Has data issue: false hasContentIssue false

Part I - Fundamentals

Published online by Cambridge University Press:  23 December 2021

Marco Tartagni
Affiliation:
University of Bologna
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Further Reading

Brillouin, L., Science and Information Theory, 2nd ed. Mineola, NY: Dover Publications, 1962.CrossRefGoogle Scholar
Pierce, J. R., An Introduction to Information Theory: Symbols, Signals & Noise. Mineola, NY: Dover Publications, 1961.Google Scholar
von Helmholtz, H., Popular Lectures on Scientific Subjects, Harvard Un. D. Appleton, 1873. L.CrossRefGoogle Scholar

Further Reading

Carlson, A. B., Communication Systems: An Introduction to Signal and Noise in Electrical Communication. New York: McGraw-Hill, 1986.Google Scholar
Duda, R., Hart, P., and David, S., Pattern Classification. New York: John Wiley & Sons, 2001.Google Scholar
Gregorian, R. and Temes, G. C., Analog MOS Integrated Circuits. New York: John Wiley & Sons, 1986.Google Scholar
Johns, D., and Martin, K., Analog Integrated Circuit Design. New York: John Wiley & Sons, 1997.Google Scholar
Joint Committee for Guides in Metrology, Evaluation of measurement data – Guide to the expression of uncertainty in measurement (GUM). Working Paper, Geneva, 2008.Google Scholar
Kester, W., Ed., The Data Conversion Handbook. Philadelphia: Elsevier, 2004.Google Scholar
Maloberti, F., Data Converters. New York: Springer Science+Business Media, 2007.Google Scholar
Taylor, J. R., An Introduction to Error Analysis. Sausalito, CA: University Science Books, 1997.Google Scholar
Widrow, B., and Kollar, I., Quantization Noise. Cambridge: Cambridge University Press, 2008.CrossRefGoogle Scholar

Further Reading

Cover, J. A., and Thomas, T. M., Elements of Information Theory. New York: John Wiley & Sons, 1991.Google Scholar
Gregorian, R., and Temes, G. C., Analog MOS Integrated Circuits. New York: John Wiley & Sons, 1986.Google Scholar
Kester, W., Ed., The Data Conversion Handbook. Philadelphia: Elsevier, 2004.Google Scholar
Schreier, R., and Temes, G. C., Understanding Delta-Sigma Data Converters. New York: IEEE Press, 2005.Google Scholar
Stone, J. V., Information Theory: A Tutorial Introduction. Sebtel Press, 2015.Google Scholar
Walden, R. H., Analog-to-digital converter survey and analysis, IEEE J. Sel. Areas Commun., vol. 17, no. 4, pp. 539550, 1999.CrossRefGoogle Scholar
Widrow, B., and Kollar, I., Quantization Noise. Cambridge: Cambridge University Press, 2008.CrossRefGoogle Scholar
Zhirnov, V., and Cavin, R. K. III, Microsystems for Bioelectronics. Philadelphia: Elsevier, 2015.Google Scholar

Further Reading

Carlson, A. B., Communication Systems: An Introduction to Signal and Noise in Electrical Communication. New York: McGraw-Hill, 1986.Google Scholar
Gardner, W. A., An Introduction to Random Processes with Application to Signal and Systems. New York: McGraw-Hill, 1990.Google Scholar
Oppenheim, A. V., and Schafer, R. W., Discrete-Time Signal Processing. Upper Saddle River, NJ: Pearson Education, 2011.Google Scholar
Oppenheim, A. V., and Willsky, A. S., Signals and Systems. Upper Saddle River, NJ: Pearson Education, 2013.Google Scholar
Papoulis, A., and Pillai, S. U., Probability, Random Variables, and Stochastic Processes. New York: McGraw-Hill, 2002.Google Scholar
Proakis, J. G., and Manolakis, D. G., Digital Signal Processing. Upper Saddle River, NJ: Pearson Prentice Hall, 2007.Google Scholar

References

Logan, B. F., Properties of High-Pass Signals. PhD thesis, Columbia University, New York, 1965.Google Scholar
Donoho, D. L. and Logan, B. F., Signal recovery and the large sieve, SIAM J. Appl. Math., vol. 52, no. 2, pp. 577591, 1992.CrossRefGoogle Scholar
Donoho, D. L. and Huo, X., Uncertainty principles and ideal atomic decomposition, IEEE Trans. Inf. Theory, vol. 47, no. 7, pp. 28452862, Nov. 2001.CrossRefGoogle Scholar
Candes, E. and Tao, T., Decoding by linear programming, IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 42034215, Dec. 2005.CrossRefGoogle Scholar
Cai, T. and Zhang, A., Sharp RIP bound for sparse signal and low-rank matrix recovery, Appl. Comput. Harmon. Anal., vol. 35, no. 1, pp. 7493, Aug. 2013.Google Scholar
Candès, E. J. and Wakin, M. B., An introduction to compressive sampling, IEEE Signal Proc. Magazine, vol. 25, no. 2, pp. 2130, 2008.CrossRefGoogle Scholar
Donoho, D. L. and Tanner, J., Precise undersampling theorems, Proc. IEEE, vol. 98, no. 6, pp. 913924, May 2010.Google Scholar
Baraniuk, R., Davenport, M. A., and Duarte, M. F., An introduction to compressive sensing. Connexions e-textbook, 2011.Google Scholar
Foucart, S. and Rauhut, H., A Mathematical Introduction to Compressive Sensing. New York: Springer Science+Business Media, 2013.CrossRefGoogle Scholar
Donoho, D., Compressed sensing, IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 12891306, April 2006.CrossRefGoogle Scholar
Candes, E. J., Romberg, J., and Tao, T., Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489509, Feb 2006.CrossRefGoogle Scholar
Candes, E. J. and Tao, T., Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 54065425, Dec. 2006.CrossRefGoogle Scholar
Elzanaty, A., Giorgetti, A., and Chiani, M., Limits on sparse data acquisition: RIC analysis of finite Gaussian matrices, IEEE Trans. Inf. Theory, vol. 65, no. 3, pp. 15781588, Mar. 2019.Google Scholar
Elzanaty, A., Giorgetti, A., and Chiani, M., Weak RIC analysis of finite Gaussian matrices for joint sparse recovery, IEEE Signal Proc. Lett., vol. 24, no. 10, pp. 14731477, Oct. 2017.Google Scholar
Candès, E. J., The restricted isometry property and its implications for compressed sensing, Comptes Rendus Math., vol. 346, no. 9, pp. 589592, May 2008.Google Scholar
Maleki, A. and Donoho, D. L., Optimally tuned iterative reconstruction algorithms for compressed sensing, IEEE J. Sel. Topics Signal Proc., vol. 4, no. 2, pp. 330341, Apr. 2010.Google Scholar
Yang, A. Y., Zhou, Z., Balasubramanian, A. G., Sastry, S. S., and Ma, Y., Fast ℓ1 minimization algorithms for robust face recognition, IEEE Trans. Image Proc., vol. 22, no. 8, pp. 32343246, Aug. 2013.Google Scholar
Wakin, M., Becker, S., Nakamura, E., Grant, M., Sovero, E., Ching, D., et al. A nonuniform sampler for wideband spectrally-sparse environments, IEEE J. Emerg. Sel. Topics Circuits Syst., vol. 2, no. 3, pp. 516529, 2012.Google Scholar
Chen, F., Chandrakasan, A. P., and Stojanovic, V. M., Design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors, IEEE J. Solid-State Circuits, vol. 47, no. 3, pp. 744756, Mar. 2012.Google Scholar
Haboba, J., Mangia, M., Pareschi, F., Rovatti, R., and Setti, G., A pragmatic look at some compressive sensing architectures with saturation and quantization, IEEE J. Emerg. Sel. Topics Circuits Syst., vol. 2, no. 3, pp. 443459, Sept. 2012.CrossRefGoogle Scholar
Gangopadhyay, D., Allstot, E. G., Dixon, A. M. R., Natarajan, K., Gupta, S., and Allstot, D. J., Compressed sensing analog front-end for bio-sensor applications, IEEE J. Solid-State Circuits, vol. 49, no. 2, pp. 426438, Feb 2014.CrossRefGoogle Scholar
Chen, F., Lim, F., Abari, O., Chandrakasan, A., and Stojanovic, V., Energy-aware design of compressed sensing systems for wireless sensors under performance and reliability constraints, IEEE Trans. Circuits Syst., vol. 60, no. 3, pp. 650661, March 2013.CrossRefGoogle Scholar
Elzanaty, A., Giorgetti, A., and Chiani, M., Lossy compression of noisy sparse sources based on syndrome encoding, IEEE Trans. Commun., vol. 67, no. 10, pp. 70737087, Oct. 2019.Google Scholar
Yoo, J., Becker, S., Loh, M., Monge, M., Candes, E., and Emami-Neyestanak, A., A 100 MHz–2GHz 12.5 x sub-Nyquist rate receiver in 90 nm CMOS,” in Proc. of 2012 IEEE Radio Frequency Integrated Circuits Symposium, June 2012, pp. 3134.CrossRefGoogle Scholar
Duarte, M. F., Davenport, M. A., Takhar, D., Laska, J. N., Sun, T., Kelly, K. E., et al., Single-pixel imaging via compressive sampling, IEEE Signal Process. Mag., vol. 25, no. 2, p. 83, March 2008.Google Scholar
Lustig, M., Donoho, D. L., Santos, J. M., and Pauly, J. M., Compressed sensing MRI, IEEE Signal Process. Mag., vol. 25, no. 2, pp. 7282, March 2008.CrossRefGoogle Scholar
Craven, D., McGinley, B., Kilmartin, L., Glavin, M., and Jones, E., Compressed sensing for bioelectric signals: a review, IEEE J. Biomed. Health Inform., vol. 19, no. 2, pp. 529540, 2015.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Fundamentals
  • Marco Tartagni, University of Bologna
  • Book: Electronic Sensor Design Principles
  • Online publication: 23 December 2021
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Fundamentals
  • Marco Tartagni, University of Bologna
  • Book: Electronic Sensor Design Principles
  • Online publication: 23 December 2021
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Fundamentals
  • Marco Tartagni, University of Bologna
  • Book: Electronic Sensor Design Principles
  • Online publication: 23 December 2021
Available formats
×