2 edition of **Minimum-cross-entropy spectral analysis of multiple signals** found in the catalog.

Minimum-cross-entropy spectral analysis of multiple signals

Rodney W Johnson

- 52 Want to read
- 27 Currently reading

Published
**1981**
by Naval Research Laboratory in Washington, D.C
.

Written in English

- Spectrum analysis,
- Power spectra,
- Entropy (Information theory)

**Edition Notes**

Statement | R. Johnson and J. Shore |

Series | NRL memorandum report -- 4492 |

Contributions | Shore, John E, Naval Research Laboratory (U.S.). Computer Science and Systems Branch |

The Physical Object | |
---|---|

Pagination | iii, 31 p. : |

Number of Pages | 31 |

ID Numbers | |

Open Library | OL14859290M |

The beta distribution has been applied in acoustic analysis to assess damage to gears, as the kurtosis of the beta distribution has been reported to be a good indicator of the condition of a gear[1]. Kurtosis has also been used to distinguish the seismic signal generated by a person's footsteps from other signals. International Journal of Engineering and Advanced Technology (IJEAT) covers topics in the field of Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences.

R. B. Randall, J. Antoni, S. Chobsaard, The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals, Mechanical Systems and Signal Processing, 15 () Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good .

C. Li, E. Shlomot and V. Cuperman, "Quantization of variable dimension spectral vectors," Proceedings of the 32nd Asilomar Conference on Signals, Systems & Computers, pp. , Oct S. Gadkari and K. Rose, "Unequally Protected Multi-stage Vector Quantization for Time-varying Channels," IEEE International Conference on Communication. This book is a collection of manuscripts selected from those ones accepted for publication J. P. Burg, Maximum Entropy Spectral Analysis, Ph.D. thesis, Stanford University, J.E Shore and R.W Johnson, Axiomatic Derivation of the Principle of Maximum Entropy and The Principle of Minimum Cross-Entropy, IEEE Trans. on Information.

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Get this from a library. Minimum-cross-entropy spectral analysis of multiple signals. [Rodney W Johnson; John E Shore; Naval Research Laboratory.

Johnson and J. Shore () Minimum cross-entropy spectral analysis of multiple signals, IEEE Trans. Acoust. Speech Signal Process. ASSP, –Author: Rodney W. Johnson, John E. Shore. Cross-entropy Minimum cross-entropy spectral analysis (MCESA) Minimum cross-entropy power spectrum given auto-correlation Cross-entropy between input and output of linear filter Comparison Towards.

ISBN: OCLC Number: Description: 1 online resource (ix, pages) Contents: Incomplete Information and Generalized Inverse Problems --Where Do We Go from Here. --to Minimum-Cross-Entropy Spectral Analysis of Multiple Signals --On an Alleged Breakdown of the Maximum-Entropy Principle --Algorithms and Applications.

Abstract. This chapter provides a historical perspective of the concept of entropy, Shannon’s reasoning, and the axioms that justify the principles of entropy optimization, namely, the maximum entropy and minimum cross-entropy mathematical forms of various entropy optimization problems are also discussed along with references to the existing by: 1.

Minimum cross-entropy analysis with entropy-type constraints Article (PDF Available) in Journal of Computational and Applied Mathematics 39(2). How is Minimum Cross-Entropy Spectral Analysis (speech signal separation) abbreviated.

MCESA stands for Minimum Cross-Entropy Spectral Analysis (speech signal separation). MCESA is defined as Minimum Cross-Entropy Spectral Minimum-cross-entropy spectral analysis of multiple signals book (speech signal separation) very. This volume contains the text of the twenty-five papers presented at two workshops entitled Maximum-Entropy and Bayesian Methods in Applied Statistics, which were held at the University of Wyoming from June 8 to 10,and from August 9 to 11, The workshops were organized to bring together.

The concept of the maximum configuration entropy spectral method (CESA) was initially proposed by Frieden [] in the identification of fter, Gull and Daniell [] applied the concept in the field of astronomy for image the field of time series analysis, the CESA performs better than the BESA in the determination of spectral density function in the ARMA Cited by: 3.

The minimum cross-entropy MFCC features which were derived from the geometric mean of the power spectra computed over 20 ms, 30 ms, 40 ms and 50 ms long-analysis windows, have a WER of %. The proposed variable-scale system which adaptively chooses a window size in the range [20 ms, 60 ms], followed by the usual MFCC computation, has a % by: In this manuscript, a nonparametric spectral estimation method based on the singular spectral analysis (SSA) is proposed.

This is a postprocessing method to reconstruct the original signal from the quantized signal. Here, the input signal is assumed to be nearly : Weichao Kuang, Bingo Wing-Kuen Ling, Zhijing Yang.

The minimum cross entropy spectral analysis procedure (a generalization of maximum entropy spectral analysis) is formulated as a convex programming problem, and its unconstrained dual convex programming problem is shown.

In this dual setting the Lagrange Cited by: 4. Agricultural policies have impacts on land use, the economy, and the environment and their analysis requires disaggregated data at the local level with geographical references.

Thus, this study proposes a model for disaggregating agricultural data, which develops a supervised classification of satellite images by using a survey and empirical knowledge. To ensure the Author: António Xavier, Rui Fragoso, Maria de Belém Costa Freitas, Maria do Socorro Rosário, Florentino Vale.

This volume contains the text of the twenty-five papers presented at two workshops entitled Maximum-Entropy and Bayesian Methods in Applied Statistics, which were held at the University of Wyoming from June 8 to 10,and from August 9 to 11, Entropy optimization is a useful combination of classical engineering theory (entropy) with mathematical optimization.

The resulting entropyoptimization models have proved their usefulness with successful applications in areas such as image reconstruction, pattern recognition, statistical inference, queuing theory, spectral analysis, statistical mechanics, transportation planning.

A brief survey on the spectral radius and the spectral distribution of large dimensional random matrices with i.i.d. entries. Random Matrices and Their Applications,#xE;C.|Hwang.Author: Romain Couillet, Mérouane Debbah.

Robert Jackson Marks II is an American electrical contributions include the Zhao-Atlas-Marks (ZAM) time-frequency distribution in the field of signal processing, the Cheung–Marks theorem in Shannon sampling theory and the Papoulis-Marks-Cheung (PMC) approach in multidimensional sampling.

He was instrumental in the defining of the field of computational Main interests: Intelligent Design. The maximum entropy (ME) and minimum cross-entropy (MCE) formalisms provide a coherent tool for incorporating new information (in terms of constraints) into initial models and also an alternative tool for solving inverse by: 3.

Xianhua Jiang, Zhi-Quan Luo, T. Georgiou, Geometric Methods for Spectral Analysis, IEEE Transactions on Signal Robert M. Gray, Minimum Cross-Entropy Pattern Classification and Cluster This paper presents a parametric approach to classify the radiation pattern of an acoustic source given the signals captured by multiple microphones.

Cited by: Spectral analysis of the systolic blood pressure signal in secondary hypertension: a method for the identification of phaeochromocytoma.

J Hypertens 12(3) ; Baharav A, Mimouni M, Lehrman-Sagie T, Izraeli S, Akselrod S () Spectral analysis of heart rate in vasovagal syncope: the autonomic nervous system in vasovagal syncope. Purchase Quantification of Brain Function Using PET - 1st Edition. Print Book & E-Book. ISBNBook Edition: 1.A new version of the Perona and Malik theory for edge detection and image restoration is proposed.

This new version keeps all the improvements of the original model and avoids its drawbacks: it is proved to be stable in presence of noise, with existence and uniqueness by: This book and its sister volumes constitute the Proceedings of the Third International Symposium on Neural Networks (ISNN ) held in Chengdu in southwestern China during May 28–31, After a successful ISNN in Dalian and ISNN in Chongqing, ISNN became a well-established series of.