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advanced signal-processing algorithms, architectures, and implementations

作者： Baraniuk, Richard G. Jones, Douglas L. Univ of Illinois Urbana United States

Current bilinear time-frequency representations apply a fixed kernel to smooth the Wigner distribution. However, the choice of a fixed kernel limits the class of signals that can be analyzed effectively. This paper presents optimality criteria for the design of signal-dependent kernels that suppress cross-components while passing as much auto-component energy as possible, irrespective of the form of the signal. A fast algorithm for the optimal kernel solution makes the procedure competitive computationally with fixed kernel methods. Examples demonstrate the superior performance of the optimal kernel for a frequency modulated signal.

关键词： signal processing

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Non-parametric estimation of autocorrelation and spectra using cumulants and polyspectra

Non-parametric estimation of autocorrelation and spectra usi...

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advanced signal-processing algorithms, architectures, and implementations

作者： Giannakis, Georgios B. Delopoulos, Anastasios Univ of Virginia Charlottesville United States

Autocorrelation and spectra of linear random processes can be can be expressed in terms of cumulants and polyspectra, respectively. The insensitivity of the latter to additive Gaussian noise of unknown covariance, is exploited in this paper to develop spectral estimators of deterministic and linear non-Gaussian signals using polyspectra. In the time-domain, windowed projections of third-order cumulants are shown to yield consistent estimators of the autocorrelation sequence. Both batch and recursive algorithms are derived. In the frequency-domain, a Fourier-slice solution and a least-squares approach are described for performing spectral analysis through windowed bi-periodograms. Asymptotic variance expressions of the time- and frequency-domain estimators are also presented. Two-dimensional extensions are indicated, and potential applications are discussed. Simulations are provided to illustrate the performance of the proposed algorithms and compare them with conventional approaches.

关键词： signal processing

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Implementation of the phase gradient algorithm

Implementation of the phase gradient algorithm

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advanced signal-processing algorithms, architectures, and implementations

作者： Wahl, Daniel E. Eichel, Paul H. Jakowatz Jr., Charles v. Sandia Natl Lab Albuquerque United States

The recently introduced Phase Gradient Autofocus (PGA) algorithm is a non-parametric autofocus technique which has been shown to be quite effective for phase correction of Synthetic Aperture Radar (SAR) imagery. This paper will show that this powerful algorithm can be executed at near real-time speeds and also be implemented in a relatively small piece of hardware. A brief review of the PGA will be presented along with an overview of some critical implementation considerations. In addition, a demonstration of the PGA algorithm running on a 7 inches × 10 inches printed circuit board containing a TMS320C30 digital signal processing (DSP) chip will be given. With this system, using only the 20 range bins which contain the brightest points in the image, the algorithm can correct a badly degraded 256×256 image in as little as 3 seconds. Using all range bins, the algorithm can correct the image in 9 seconds.

关键词： Radar

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Object-based media and stream-based computing

Object-based media and stream-based computing

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Multimedia Hardware architectures 1998

作者： Bove Jr., v. Michael Massachusetts Institute Technology Media Laboratory 20 Ames Street Cambridge MA 02139 United States

Object-Based Media refers to the representation of audiovisual information as a collection of objects - the result of scene-analysis algorithms - and a script describing how they are to be rendered for display. Such multimedia presentations can adapt to viewing circumstances (e.g. size and aspect ratio of display) as well as to viewer preferences and behavior, and can provide a richer link between content creator and consumer. With faster networks and processors, such ideas become applicable to live interpersonal communications as well, creating a more natural and productive alternative to traditional videoconferencing. I outline an example of object-based media algorithms and applications developed by my group, and present new hardware architectures and software methods that we have developed to enable meeting the computational requirements of object-based and other advanced media representations. In particular I will describe stream-based processing, which enables automatic run-time parallelization of multidimensional signal processing tasks even given heterogeneous computational resources.

关键词： Image compression

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Comparison of time-frequency-based techniques for estimating instantaneous frequency parameters of nonstationary processes

Comparison of time-frequency-based techniques for estimating...

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Proceedings of the 1999 advanced signal processing algorithms, architectures, and implementations IX

作者： Bonato, Paolo Erim, Zeynep Roy, Serge H. De Luca, Carlo J. Boston Univ Boston United States

The aim of this work is to contrast techniques used to estimate two instantaneous frequency parameters of the surface electromyographic (EMG) signal, the instantaneous median frequency and the instantaneous mean frequency, based on their estimation error. Three methods are compared: Cohen class and Cohen-Posch class time-frequency representations are used to compute both the above-mentioned instantaneous frequency parameters, and a cross-time-frequency based technique is adopted to derive the instantaneous mean frequency. The results demonstrate that the algorithm based on Cohen-Posch class transformations leads to a standard deviation of the instantaneous frequency parameters that is smaller than that obtained using Cohen class representations. However, the cross-time-frequency estimation procedure for instantaneous mean frequency produced the smallest standard deviation compared to the other techniques. The algorithms based on Cohen class and Cohen-Posch class transformations often provided a lower bias than the cross-time-frequency based technique. This advantage was particularly evident when the instantaneous mean frequency varied non-linearly within the epochs used to derive the cross-time-frequency representation of the surface EMG signal.

关键词： signal processing

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Intensity and phase stacked analysis of a 40-OTDR system using deep transfer learning and recurrent neural networks

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APPLIED OPTICS 2023年第7期62卷 1753-1764页

作者： Kayan, Ceyhun Efe Aldogan, Kivilcim Yuksel Gumus, Abdurrahman Izmir Inst Technol Elect & Elect Engn Izmir Turkiye

Distributed acoustic sensors (DAS) are effective apparatuses that are widely used in many application areas for recording signals of various events with very high spatial resolution along optical fibers. To properly detect and recognize the recorded events, advanced signal processing algorithms with high computational demands are crucial. Convolutional neural networks (CNNs) are highly capable tools to extract spatial information and are suitable for event recognition applications in DAS. Long short-term memory (LSTM) is an effective instrument to process sequential data. In this study, a two-stage feature extraction methodology that combines the capabilities of these neural network architectures with transfer learning is proposed to classify vibrations applied to an optical fiber by a piezoelectric transducer. First, the differential amplitude and phase information is extracted from the phasesensitive optical time domain reflectometer (40-OTDR) recordings and stored in a spatiotemporal data matrix. Then, a state-of-the-art pre-trained CNN without dense layers is used as a feature extractor in the first stage. In the second stage, LSTMs are used to further analyze the features extracted by the CNN. Finally, a dense layer is used to classify the extracted features. To observe the effect of different CNN architectures, the proposed model is tested with five state-of-the-art pre-trained models (vGG-16, ResNet-50, DenseNet-121, MobileNet, and Inception-v3). The results show that using the vGG-16 architecture in the proposed framework manages to obtain a 100% classification accuracy in 50 trainings and got the best results on the 40-OTDR dataset. The results of this study indicate that pre-trained CNNs combined with LSTM are very suitable to analyze differential amplitude and phase information represented in a spatiotemporal data matrix, which is promising for event recognition operations in DAS applications. (c) 2023 Optica Publishing Group

关键词： Feature extraction Fiber optic cables Machine vision Neural networks signal processing Spatial resolution

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Classification of vehicles using nonlinear dynamics and array processing

Classification of vehicles using nonlinear dynamics and arra...

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Proceedings of the 1999 advanced signal processing algorithms, architectures, and implementations IX

作者： Tung, Tai-Lai Yao, Kung Univ of California Los Angeles Los Angeles United States

The analysis of vehicle signals with methods derived from the theory of nonlinear dynamics is a potential tool to classify different vehicles. The nonlinear dynamical methodologies provide alternate system information that the linear analysis tools have ignored. In order to observe the nonlinear dynamic phenomena more clearly, and estimate system invariants more robustly, we exploit the maximum power blind beamforming algorithm as a signal enhancement and noise reduction method when locations of a source and sensors are unknown. The dynamical behavior of an acoustic vehicle signal is studied with the use of correlation dimension D2 and Lyapunov exponents. To characterize the nonlinear dynamic behavior of the acoustic vehicle signal, Taken's embedded theory is used to form an attractor in phase space based on a single observed time series. The time series is obtained from the coherently enhanced output of a blind beamforming array. Then the Grassberger-Procaccia algorithm and Sano-Sawada method are exploited to compute the correlation dimension and Lyapunov exponents. In this paper, we also propose some efficient computational methods for evaluating these system invariants. Experimental classification results show that the maximum power blind beamforming processing improves the estimation of the invariants of the nonlinear dynamic system. Preliminary results show that the nonlinear dynamics is useful for classification applications.

关键词： Acoustic signal processing

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Maximum-likelihood methods for array processing based on time-frequency distributions

Maximum-likelihood methods for array processing based on tim...

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Proceedings of the 1999 advanced signal processing algorithms, architectures, and implementations IX

作者： Zhang, Yimin Mu, Weifeng Amin, Moeness G. Villanova Univ Villanova United States

This paper proposes a novel time-frequency maximum likelihood (t-f ML) method for direction-of-arrival (DOA) estimation for non-stationary signals, and compares this method with conventional maximum likelihood DOA estimation techniques. Time-frequency distributions localize the signal power in the time-frequency domain, and as such enhance the effective SNR, leading to improved DOA estimation. The localization of signals with different t-f signatures permits the division of the time-frequency domain into smaller regions, each contains fewer signals than those incident on the array. The reduction of the number of signals within different time-frequency regions not only reduces the required number of sensors, but also decreases the computational load in multi-dimensional optimizations. Compared to the recently proposed time-frequency MUSIC (t-f MUSIC), the proposed t-f ML method can be applied in coherent environments, without the need to perform any type of preprocessing that is subject to both array geometry and array aperture.

关键词： signal detection

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Wavelets, tomography, and line segment image representations

Wavelets, tomography, and line segment image representations

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advanced signal-processing algorithms, architectures, and implementations

作者： Altes, Richard A. Chirp Corp La Jolla United States

The scale dependent wavelet transform can be augmented by a rotation dependent version as well as other generalizations. Tomographic analysis and line segment transforms are special cases of rotation dependent wavelet analysis. Other cases suggested by biological analogy9 are a rotation dependent edge segment transform (using edge segments rather than line segments) and a binocular rotation dependent wavelet transform that introduces depth information into the reconstructed image. Applications to robot vision and synthetic aperture radar appear particularly promising.

关键词： Image processing

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Minimal-window time-frequency distributions

Minimal-window time-frequency distributions

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Proceedings of the 1999 advanced signal processing algorithms, architectures, and implementations IX

作者： Williams, William J. Aviyente, Selin Univ of Michigan Ann Arbor United States

This paper outlines means of using special sets of orthonormally related windows to realize Cohen's class of time-frequency distributions (TFDs). This is accomplished by decomposing the kernel of the distribution in terms of the set of analysis windows to obtain short time Fourier transforms (STFTs). The STFTs obtained using these analysis windows are used to form spectrograms which are then linearly combined with proper weights to form the desired TFD. A set of orthogonal analysis windows which also have the scaling property proves to be very effective, requiring only 1 + log2(N - 1) distinct windows for an overall analysis of N + 1 points, where N = 2n, with n a positive integer. Application of this theory offers very fast computation of TFDs, since very few analysis windows needed and fast, recursive STFT algorithms can be used. Additionally, it is shown that a minimal set of specially derived orthonormal windows can represent most TFDs, including Reduced Interference Distributions (RIDs) with only three distinct windows plus an impulse window. Finally, the Minimal Window RID (MW-RID) which achieves RID properties with only one distinct window and an impulse window is presented.

关键词： signal processing

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