Time-frequency signal analysis
01 Jan 2009-
About: The article was published on 2009-01-01 and is currently open access. It has received 139 citations till now. The article focuses on the topics: Signal transition & Analog signal.
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Book•
17 Dec 2015
TL;DR: Time Frequency Signal Analysis and Processing focuses on advanced techniques and methods that allow a refined extraction and processing of information, allowing efficient and effective decision making that would not be possible with classical techniques.
Abstract: Time Frequency Signal Analysis and Processing covers fundamental concepts, principles and techniques, treatment of specialised and advanced topics, methods and applications, including results of recent research. This book deals with the modern methodologies, key techniques and concepts that form the core of new technologies used in IT, multimedia, telecommunications as well as most fields of engineering, science and technology. It focuses on advanced techniques and methods that allow a refined extraction and processing of information, allowing efficient and effective decision making that would not be possible with classical techniques.
1,130 citations
03 Jun 2008
TL;DR: Comparison analysis of different approaches to spectral signal representation such as power spectral density (PSD) techniques, atomic decompositions, time-frequency (t-f) energy distributions, continuous and discrete wavelet approaches, from which band power features can be extracted and used in the framework of MI classification are performed.
Abstract: The quantification of the spectral content of electroencephalogram (EEG) recordings has a substantial role in clinical and scientific applications. It is of particular relevance in the analysis of event-related brain oscillatory responses. This work is focused on the identification and quantification of relevant frequency patterns in motor imagery (MI) related EEGs utilized for brain-computer interface (BCI) purposes. The main objective of the paper is to perform comparative analysis of different approaches to spectral signal representation such as power spectral density (PSD) techniques, atomic decompositions, time-frequency (t-f) energy distributions, continuous and discrete wavelet approaches, from which band power features can be extracted and used in the framework of MI classification. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during imagination of either left- or right-hand movement. Feature separability is quantified in the offline study using the classification accuracy (CA) rate obtained with linear and nonlinear classifiers. PSD approaches demonstrate the most consistent robustness and effectiveness in extracting the distinctive spectral patterns for accurately discriminating between left and right MI induced EEGs. This observation is based on an analysis of data recorded from eleven subjects over two sessions of BCI experiments. In addition, generalization capabilities of the classifiers reflected in their intersession performance are discussed in the paper.
283 citations
Cites background from "Time-frequency signal analysis"
...the WVD transform is theoretically defined as [23]...
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01 Aug 2002
TL;DR: The letter defines an IFR estimation algorithm and theoretically analyzes it and is seen to be asymptotically optimal at the center of the data record for high signal-to-noise ratios.
Abstract: This letter introduces a two-dimensional bilinear mapping operator referred to as the cubic phase (CP) function. For first-, second-, or third-order polynomial phase signals, the energy of the CP function is concentrated along the frequency rate law of the signal. The function, thus, has an interpretation as a time-frequency rate representation. The peaks of the CP function yield unbiased estimates of the instantaneous (angular) frequency rate (IFR) and, hence, can be used as the basis for an IFR estimation algorithm. The letter defines an IFR estimation algorithm and theoretically analyzes it. The estimation is seen to be asymptotically optimal at the center of the data record for high signal-to-noise ratios. Simulations are provided to verify the theoretical claims.
178 citations
TL;DR: Comparative results indicate that the new ( t, f ) features give better performance as compared to time-only or frequency-only features for the detection of abnormalities in newborn EEG signals.
169 citations
Cites background from "Time-frequency signal analysis"
...A typical model for such signals was originally proposed in ([15], p....
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TL;DR: In this article, a set of time-frequency based power quality indices are developed based on the timefrequency distribution of a transient disturbance, which allow one to quantify the effects of transient disturbances with high resolution and accuracy.
Abstract: For reasonable power quality assessment of transient disturbances in electric power systems, new transient power quality indices are developed based on a signal processing technique, time-frequency analysis. Based on the time-frequency distribution of a transient disturbance, a set of time-frequency based power quality indices are developed. In this paper, the instantaneous disturbance energy ratio, normalized instantaneous disturbance energy ratio, instantaneous frequency, and instantaneous K-factor are suggested for transient power quality assessment. Time-frequency based power quality indices allow one to quantify the effects of transient disturbances with high-resolution and accuracy.
116 citations
Cites background from "Time-frequency signal analysis"
...The fast capacitor switching is caused by a restrike on opening; if a contactor does not successfully open during the deenegizing process, an arc is generated by re-energizing the capacitor....
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...The conclusion is drawn in Section V. 0885-8977/$20.00 © 2006 IEEE...
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References
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TL;DR: It is argued that insertion of a watermark under this regime makes the watermark robust to signal processing operations and common geometric transformations provided that the original image is available and that it can be successfully registered against the transformed watermarked image.
Abstract: This paper presents a secure (tamper-resistant) algorithm for watermarking images, and a methodology for digital watermarking that may be generalized to audio, video, and multimedia data. We advocate that a watermark should be constructed as an independent and identically distributed (i.i.d.) Gaussian random vector that is imperceptibly inserted in a spread-spectrum-like fashion into the perceptually most significant spectral components of the data. We argue that insertion of a watermark under this regime makes the watermark robust to signal processing operations (such as lossy compression, filtering, digital-analog and analog-digital conversion, requantization, etc.), and common geometric transformations (such as cropping, scaling, translation, and rotation) provided that the original image is available and that it can be successfully registered against the transformed watermarked image. In these cases, the watermark detector unambiguously identifies the owner. Further, the use of Gaussian noise, ensures strong resilience to multiple-document, or collusional, attacks. Experimental results are provided to support these claims, along with an exposition of pending open problems.
6,194 citations
TL;DR: In this article, the Boltzmann formula for lower temperatures has been developed for a correction term, which can be developed into a power series of h. The formula is developed for this correction by means of a probability function and the result discussed.
Abstract: The probability of a configuration is given in classical theory by the Boltzmann formula exp [— V/hT] where V is the potential energy of this configuration. For high temperatures this of course also holds in quantum theory. For lower temperatures, however, a correction term has to be introduced, which can be developed into a power series of h. The formula is developed for this correction by means of a probability function and the result discussed.
5,865 citations
Book•
01 Jan 1968
TL;DR: Detection, estimation, and modulation theory, Detection, estimation and modulation theorists, اطلاعات رسانی کشاورزی .
Abstract: Detection, estimation, and modulation theory , Detection, estimation, and modulation theory , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
3,908 citations
01 Jul 1989
TL;DR: A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented with emphasis on the diversity of concepts and motivations that have gone into the formation of the field.
Abstract: A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented. The objective of the field is to describe how the spectral content of a signal changes in time and to develop the physical and mathematical ideas needed to understand what a time-varying spectrum is. The basic gal is to devise a distribution that represents the energy or intensity of a signal simultaneously in time and frequency. Although the basic notions have been developing steadily over the last 40 years, there have recently been significant advances. This review is intended to be understandable to the nonspecialist with emphasis on the diversity of concepts and motivations that have gone into the formation of the field. >
3,568 citations