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Huey-Long Chen

Bio: Huey-Long Chen is an academic researcher from Lan Yang Institute of Technology. The author has contributed to research in topics: Time domain & Frequency domain. The author has an hindex of 5, co-authored 6 publications receiving 2385 citations.

Papers
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Book ChapterDOI
01 Jan 2003
TL;DR: In this article, it is shown that the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time.
Abstract: Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e. the value of the signal at each instant in time is well defined. However, the time representation of a signal is poorly localized in frequency, i.e. little information about the frequency content of the signal at a certain frequency can be known by looking at the signal in the time domain. On the other hand, the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time.

2,317 citations

Book
31 Jul 2003
TL;DR: This paper presents a meta-analysis of the evolution of Continuous Spectral Analysis in Hydrologic and Climatic Data and its applications to Batchelor Spectrum Sampling and Segmentation, which revealed promising results for detection of change points in synthetic series.
Abstract: 1. Introduction.- 2. Data Used in the Book.- 2.1. Hydrologic and Climatic Data.- 2.2. Synthetic and Observed Environmental Data.- 2.2.1. Synthetic Data Sampling from Batchelor Spectrum.- 2.2.2. Details of Data Generated by Sampling from the Batchelor Spectrum.- 2.2.3. Synthetic Data from AR Model.- 2.3. Observed Data.- 2.3.1. Measured Temperature Gradient Profiles.- 3. Time Domain Analysis.- 3.1. Introduction.- 3.2. Visual Inspection of Time Series.- 3.3. Statistical Tests of Significance.- 3.3.1. Parametric Tests.- 3.3.2. Non-parametric Tests.- 3.4. Testing Autocorrelated Data.- 3.5. Application of Trend Tests to Hydrologic Data.- 3.5.1. Visual Inspection of Data.- 3.5.2. Statistical Trend Tests.- 3.5.3. Sub-period Trend Analysis.- 3.6. Conclusions.- 4. Frequency Domain Analysis.- 4.1. Introduction.- 4.2. Conventional Spectral Analysis.- 4.3. Multi-Taper Method (MTM) of Spectral Analysis.- 4.4. Maximum Entropy Spectral Analysis.- 4.5. Spectral Analysis of Hydrologic and Climatic Data.- 4.5.1. Results from MEM Analysis.- 4.5.2. Results from MTM Analysis.- 4.6. Discussion of Results.- 4.7. Conclusions.- 5. Time-Frequency Analysis.- 5.1. Introduction.- 5.2. Evolutionary Spectral Analysis.- 5.3. Evolution of Line Components in Hydrologic and Climatic Data.- 5.4. Evolution of Continuous Spectra in Hydrologic and Climatic Data.- 5.5. Conclusions.- 6. Time-Scale Analysis.- 6.1. Introduction.- 6.2. Wavelet Analysis.- 6.3. Wavelet Trend Analysis.- 6.4. Identification of Dominant Scales.- 6.5. Time-Scale Distribution.- 6.6. Behavior of Hydrologic and Climatic Time Series at Different Scales.- 6.7. Conclusions.- 7. Segmentation of Non-Stationary Time Series.- 7.1. Introduction.- 7.2. Tests based on AR Models.- 7.2.1. Test 1 (de Souza and Thomson, 1982).- 7.2.2. Test 2 (Imberger and Ivey, 1991).- 7.2.3. Test 3 (Davis, Huang and Yao, 1995).- 7.2.4. Test 4 (Tsay, 1988).- 7.3. A test based on wavelet analysis.- 7.4. Segmentation algorithm.- 7.5. Variations of test statistics with the AR order p.- 7.6. Sensitivity of test statistics for detecting change points.- 7.6.1. Detection results for synthetic series from model 2.1.2.- 7.6.2. Detection results for synthetic series from model 2.1.3.- 7.6.3. Detection results for synthetic series from model 2.1.4.- 7.6.4. Detection results for synthetic series from model 2.1.5.- 7.6.5. Conclusions on performances of tests 1-5.- 7.7. Performances of algorithms with and without boundary optimization.- 7.7.1. Detection of non-stationary segment.- 7.7.2. Detection of multi-segment series.- 7.8. Conclusions about the segmentation algorithm.- 8. Estimation of Turbulent Kinetic Energy Dissipation.- 8.1. Introduction.- 8.2. Multi-taper Spectral Estimation.- 8.3. Batchelor Curve Fitting.- 8.4. Comparison of Spectral Estimation Methods.- 8.5. Batchelor Curve Fitting to Synthetic Series.- 8.5.1. Batchelor curve fitting using the first error function.- 8.5.2. Batchelor curve fitting using the second error function.- 8.5.3. Batchelor curve fitting using the third error function.- 8.6. Conclusions on Batchelor curve fitting.- 9. Segmentation of Observed Data.- 9.1. Introduction.- 9.2. Temperature Gradient Profiles.- 9.2.1. Ratios of Unresolved, Bad-Fit and Good-Fit Segments.- 9.2.2. Estimated Values of ? and XT from Resolved Spectra.- 9.2.3. Estimated Values of ? and XT from Profiles in the Same Lake.- 9.2.4. Estimated Values of ? and XT from Different Lakes.- 9.3. Conclusions on Segmentation of Temperature Gradient Profiles.- 9.4. Hydrologic Series.- 9.4.1. Stationary Segments from Hydrologic Series.- 9.4.2. Change Points in Hydrologic Series.- 9.5. Conclusions on Segmentation of Hydrologic Series.- 10. Linearity and Gaussianity Analysis.- 10.1. Introduction.- 10.2. Tests for Gaussianity and Linearity (Hinich, 1982).- 10.3. Testing for Stationary Segments.- 10.3.1. Testing Temperature Gradient Profiles.- 10.3.2. Testing Hydrologic Series.- 10.4. Conclusions about Testing the Hydrologic Series.- 11. Bayesian Detection of Shifts in Hydrologic Time Series.- 11.1. Introduction.- 11.2. Data Used in this Chapter.- 11.3. A Bayesian Method to Detect Shifts in Data.- 11.3.1. Theory.- 11.3.1.1. Parameters of the distribution and the change point n1.- 11.3.1.2. The Unconditional Posterior Distributions of ?, ? and ?.- 11.3.1.3. The Conditional Posterior Distributions of ?i, ?21 and ?i.- 11.3.2. Computation Sequences.- 11.4. Discussion of Results.- 11.4.1. The Posterior Distribution of the Change point n1.- 11.4.2. The Unconditional Posterior Distributions of ?, ? and ?.- 11.4.3. The Conditional Posterior Distributions of ?i,?2i and ?i.- 11.5. Conclusions.- 12. References.- 13. Index.

46 citations

Book ChapterDOI
01 Jan 2003
TL;DR: In many practical situations, a model of piecewise-stationary time series with successive stationary segments is assumed for nonstationary series, and segmentation algorithms have been developed to detect segment boundaries and estimate the parameters characterizing each segment.
Abstract: In many practical situations, a model of piecewise-stationary time series with successive stationary segments is assumed for nonstationary series (213-1). In order to establish this piecewise-stationary time series model, segmentation algorithms have been developed to detect segment boundaries and estimate the parameters characterizing each segment. Techniques of segmentation without requiring a priori spectral information have been developed based on autoregressive (AR) models. It is assumed in these techniques that statistical properties described by a set of AR parameters remain the same in each segment. If these algorithms yield a single series — the original series — then the series is stationary.

6 citations

Book ChapterDOI
01 Jan 2003
TL;DR: In this article, the power spectrum of a time series is used to identify cyclic components which contribute most to the overall variability of the time series and the shape of the spectrum also reveals features of the process that are useful in selecting the types of models which are suitable for analyzing the observed data.
Abstract: The results discussed in chapter 3 reveal the existence of trends in the data. The results from trend tests, however, suggest that this variability is more likely to be non-monotonic in nature; i.e. periodic or cyclic. Spectral analysis in the frequency domain is suitable for analyzing cyclic behavior in time series. The basic idea of spectral analysis is to represent the time series as a sum of sinusoidal components of different frequencies. The power spectrum of a time series, which is the squared amplitude of these sinusoids reflects the distribution of the variance of the stochastic process over these frequencies. By studying the power spectrum, those cyclic components which contribute most to the overall variability of the time series can be identified. The shape of the spectrum also reveals features of the process that are useful in selecting the types of models which are suitable for analyzing the observed data (Jenkins and Watts, 1968).

6 citations


Cited by
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Journal ArticleDOI
Simon Haykin1
TL;DR: Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks: radio-scene analysis, channel-state estimation and predictive modeling, and the emergent behavior of cognitive radio.
Abstract: Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.

12,172 citations

Book
01 Jan 2005
TL;DR: 1. Basic Concepts. 2. Nonparametric Methods. 3. Parametric Methods for Rational Spectra.
Abstract: 1. Basic Concepts. 2. Nonparametric Methods. 3. Parametric Methods for Rational Spectra. 4. Parametric Methods for Line Spectra. 5. Filter Bank Methods. 6. Spatial Methods. Appendix A: Linear Algebra and Matrix Analysis Tools. Appendix B: Cramer-Rao Bound Tools. Appendix C: Model Order Selection Tools. Appendix D: Answers to Selected Exercises. Bibliography. References Grouped by Subject. Subject Index.

2,620 citations

Journal ArticleDOI
TL;DR: Hilbert-Huang transform, consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has been used extensively in geophysical research.
Abstract: [1] Data analysis has been one of the core activities in scientific research, but limited by the availability of analysis methods in the past, data analysis was often relegated to data processing. To accommodate the variety of data generated by nonlinear and nonstationary processes in nature, the analysis method would have to be adaptive. Hilbert-Huang transform, consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has been used extensively in geophysical research. In this review, we will briefly introduce the method, list some recent developments, demonstrate the usefulness of the method, summarize some applications in various geophysical research areas, and finally, discuss the outstanding open problems. We hope this review will serve as an introduction of the method for those new to the concepts, as well as a summary of the present frontiers of its applications for experienced research scientists.

1,533 citations

Journal ArticleDOI
TL;DR: In this paper, the micro-Doppler effect was introduced in radar data, and a model of Doppler modulations was developed to derive formulas of micro-doppler induced by targets with vibration, rotation, tumbling and coning motions.
Abstract: When, in addition to the constant Doppler frequency shift induced by the bulk motion of a radar target, the target or any structure on the target undergoes micro-motion dynamics, such as mechanical vibrations or rotations, the micro-motion dynamics induce Doppler modulations on the returned signal, referred to as the micro-Doppler effect. We introduce the micro-Doppler phenomenon in radar, develop a model of Doppler modulations, derive formulas of micro-Doppler induced by targets with vibration, rotation, tumbling and coning motions, and verify them by simulation studies, analyze time-varying micro-Doppler features using high-resolution time-frequency transforms, and demonstrate the micro-Doppler effect observed in real radar data.

1,373 citations

Journal ArticleDOI
TL;DR: The various methodologies and algorithms for EMG signal analysis are illustrated to provide efficient and effective ways of understanding the signal and its nature to help researchers develop more powerful, flexible, and efficient applications.
Abstract: Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

1,195 citations