We argue that the standard definition of signal to noise ratio may be misleading when the signal or noise are nonstationary. We introduce a new measure that we call local signal to noise ratio (LSNR) which is well sui...
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ISBN:
(纸本)0819463922
We argue that the standard definition of signal to noise ratio may be misleading when the signal or noise are nonstationary. We introduce a new measure that we call local signal to noise ratio (LSNR) which is well suited to take into account nonstationary situations. The advantage of our measure is that it is a local property unlike the standard SNR which is a single number representing the total duration of the signal. We simulated a number of cases to show that our measure is more indicative of the noise and signal level for nonstationary situations.
signals with time-varying spectral content arise in a number of situations, such as in shallow water sound propagation, biomedical signals, machine and structural vibrations, and seismic signals, among others. The Wig...
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ISBN:
(纸本)0819463922
signals with time-varying spectral content arise in a number of situations, such as in shallow water sound propagation, biomedical signals, machine and structural vibrations, and seismic signals, among others. The Wigner distribution and its generalization have become standard methods for analyzing such time-varying signals. We derive approximations of the Wigner distribution that can be applied to gain insights into the effects of filtering, amplitude modulation, frequency modulation, and dispersive propagation on the time-varying spectral content of signals.
In array processing applications, it is desirable to extract the sources that generate the observed signals. There are various source separation and component extraction algorithms in literature including principal co...
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ISBN:
(纸本)0819463922
In array processing applications, it is desirable to extract the sources that generate the observed signals. There are various source separation and component extraction algorithms in literature including principal component analysis (PCA) and independent component analysis (ICA). However, most of these methods are not designed to deal with time-varying signals and thus are formulated in the time domain. In this paper, we introduce a new time-frequency based decomposition method using an information measure as the decomposition criteria. It is shown that under the assumption of disjoint source signals on the time-frequency plane, this method can extract the sources up to a scalar factor. Based on the QR decomposition of the mixing matrix, the source extraction algorithm is reduced to finding the optimal N-dimensional rotation of the observed time-frequency distributions. The proposed algorithm is implemented using the steepest descent approach to find the optimal rotation angle. The performance of the method is illustrated for example signals and compared to some well-known decomposition techniques.
We are presenting an extension to the classic multiple signal classification method (MUSIC) developed by Schmidt and Bienvenu in 1979.(1) While the classic MUSIC algorithm is limited to the detection of constant frequ...
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ISBN:
(纸本)0819463922
We are presenting an extension to the classic multiple signal classification method (MUSIC) developed by Schmidt and Bienvenu in 1979.(1) While the classic MUSIC algorithm is limited to the detection of constant frequency sinusoids in white noise, the proposed new method is capable of detecting signals with a continuously varying instantaneous frequency. The method is based on the development of a discrete-time version of the generalized scale transform (GST) which was introduced by Nickel and Williams in 1999.(2) As a byproduct we obtain techniques for discrete-time warp-shift invariant filtering which can be used in addition to the signal detection to separate signals with different instantaneous frequency contours.
Time-Frequency Analysis has previously been successfully applied to characterize and quantify a variety of acoustic signals, including marine mammal sounds. In this research, Time-Frequency analysis is applied to huma...
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ISBN:
(纸本)0819463922
Time-Frequency Analysis has previously been successfully applied to characterize and quantify a variety of acoustic signals, including marine mammal sounds. In this research, Time-Frequency analysis is applied to human speech signals in an effort to reveal signal structure salient to the biometric speaker verification challenge. Prior approaches to speaker verification have relied upon signalprocessing analysis such as linear prediction or weighted Cepstrum spectral representations of segments of speech and classification techniques based on stochastic pattern matching. The authors believe that the classification of identity of a speaker based on time-frequency representation of short time events occurring in speech could have substantial advantages. Using these ideas, a speaker verification algorithm was developed(1) and has been refined over the past several years. In this presentation, the authors describe the testing of the algorithm using a large speech database, the results obtained, and recommendations for further improvements.
We address the problem of efficient resolution, detection and estimation of weak tones in a potentially massive amount of data. Our goal is to produce a relatively small reduced data set characterizing the signals in ...
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ISBN:
(纸本)0819463922
We address the problem of efficient resolution, detection and estimation of weak tones in a potentially massive amount of data. Our goal is to produce a relatively small reduced data set characterizing the signals in the environment in time and frequency. The requirements for this problem are that the process must be computationally efficient, high gain and able to resolve signals and efficiently compress the signal information into a form that may be easily displayed and further processed. We base our process on the cross spectral representation we have previously applied to other problems. In selecting this method, we have considered other representations and estimation methods such as the Wigner distribution and Welch's method. We compare our method to these methods. The spectral estimation method we propose is a variation of Welch's method and the cross-power spectral (CPS) estimator which was first applied to signal estimation and detection in the mid 1980's. The CPS algorithm and the method we present here are based on the principles first described by Kodera et at. now frequently called the reassignment principle.
In [14], we have proposed two total variation (TV) minimization wavelet models for the problem of filling in missing or damaged wavelet coefficients due to lossy image transmission or communication. The proposed model...
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ISBN:
(纸本)0819463922
In [14], we have proposed two total variation (TV) minimization wavelet models for the problem of filling in missing or damaged wavelet coefficients due to lossy image transmission or communication. The proposed models can have effective and automatic control over geometric features of the inpainted images including sharp edges, even in the presence of substantial loss of wavelet coefficients, including in the low frequencies. In this paper, we investigate a modification of the model for noisy images to further improve the recovery properties by using multi-level parameters in the fitting term. Some new numerical examples are also shown to illustrate the effectiveness of the recovery.
Artificial neural networks have been used in applications that require complex procedural algorithms and in systems which lack an analytical mathematic model. By designing a large network of computing nodes based on t...
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ISBN:
(纸本)0819463922
Artificial neural networks have been used in applications that require complex procedural algorithms and in systems which lack an analytical mathematic model. By designing a large network of computing nodes based on the artificial neuron model, new solutions can be developed for computational problems in fields such as image processing and speech recognition. Neural networks are inherently parallel since each neuron, or node, acts as an autonomous computational element. Artificial neural networks use a mathematical model for each node that processes information from other nodes in the same region. The information processing entails computing a weighted average computation followed by a nonlinear mathematical transformation. Some typical artificial neural network applications use the exponential function or trigonometric functions for the nonlinear transformation. Various simple artificial neural networks have been implemented using a processor to compute the output for each node sequentially. This approach uses sequential processing and does not take advantage of the parallelism of a complex artificial neural network. In this work a hardware-based approach is investigated for artificial neural network applications. A Field Programmable Gate Arrays (FPGAs) is used to implement an artificial neuron using hardware multipliers, adders and CORDIC functional units. In order to create a large scale artificial neural network, area efficient hardware units such as CORDIC units are needed. High performance and low cost bit serial CORDIC implementations are presented. Finally, the FPGA resources and the performance of a hardware-based artificial neuron are presented.
We describe the VLSI implementation of MIMO detectors that exhibit close-to optimum error-rate performance, but still achieve high throughput at low silicon area. In particular, algorithms and VLSI architectures for s...
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ISBN:
(纸本)9783981080100
We describe the VLSI implementation of MIMO detectors that exhibit close-to optimum error-rate performance, but still achieve high throughput at low silicon area. In particular, algorithms and VLSI architectures for sphere decoding (SD) and K-best detection are considered, and the corresponding trade-offs between uncoded error-rate performance, silicon area, and throughput are explored. We show that SD with a per-block run-time constraint is best suited for practical implementations
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