Measurement of EEG event-related potential (ERP) data has been most commonly undertaken in the time-domain, which can be complicated to interpret when separable activity overlaps in time. When the overlapping activity...
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ISBN:
(纸本)9780819472946
Measurement of EEG event-related potential (ERP) data has been most commonly undertaken in the time-domain, which can be complicated to interpret when separable activity overlaps in time. When the overlapping activity has distinct frequency characteristics, however, time-frequency (TF) signalprocessing techniques can be useful. The current report utilized ERP data from a cognitive task producing typical feedback-related negativity (FRN) and P300 ERP components which overlap in time. TF transforms were computed using the binomial reduced interference distribution (RID), and the resulting TF activity was then characterized using principal components analysis (PCA). Consistent with previous work, results indicate that the FRN was more related to theta activity (3-7 Hz) and P300 more to delta activity (below 3 Hz). At the same time, both time-domain measures were shown to be mixtures of TF theta and delta activity, highlighting the difficulties with overlapping activity. The TF theta and delta measures, on the other hand, were largely independent from each other, but also independently indexed the feedback stimulus parameters investigated. Results support the view that TF decomposition can greatly improve separation of overlapping EEG/ERP activity relevant to cognitive models of performance monitoring.
Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of se...
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ISBN:
(纸本)9780819472946
Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may, exhibit insufficient spatial and temporal resolution. In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). The stability of these methods depends on having more than one image of the same frame. Differences between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of current SR, techniques we describe two recently developed SR methods by the authors. First, we introduce a variational method that minimizes a regularized energy function with respect to the high resolution image and blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods.
When combined with advanced FEC techniques such as the turbo code and LDPC code, soft-output MIMO sphere decoders significantly outperform hard-output sphere decoders. Hence, algorithms and implementations of soft-out...
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When combined with advanced FEC techniques such as the turbo code and LDPC code, soft-output MIMO sphere decoders significantly outperform hard-output sphere decoders. Hence, algorithms and implementations of soft-output sphere decoders have attracted intensive interest in recent years. Practical soft-output sphere decoder implementations often consist of a list generator and a LLR generator. Most existing implementations focus on the list generator, and the LLR generator is implemented in a relatively straightforward way. However, the LLR generator accounts for a great part of the complexity. Our contribution is an implementation friendly low complexity multiplierless LLR generator. We apply selective and incremental updating, algebraic simplifications and strength reductions to reduce the algorithmic complexity and to eliminate all multiplications. When integrated with the SSFE list generator, our scheme not only remove 100% multiplications, but also remove 26% to 83% additions, 76% to 94% bit-shifts and 63% to 91% memory operations. Besides the algorithmic aspects, we extract the key data-flow block with well-defined control signals. This can be easily mapped onto micro-architectures and implemented as the data-path in ASICs, or a function unit in ASIPs.
SPA (simple power analysis) attacks against RSA cryptosystems are enhanced by using chosen-message scenarios. One of the most powerful chosen-message SPA attacks was proposed by Yen et. al. in 2005, which can be appli...
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SPA (simple power analysis) attacks against RSA cryptosystems are enhanced by using chosen-message scenarios. One of the most powerful chosen-message SPA attacks was proposed by Yen et. al. in 2005, which can be applied to various algorithms and architectures, and can defeat the most popular SPA countermeasure using dummy multiplication. Special input values of -1 and a pair of -X and X can be used to identify squaring operations performed depending on key bit stream. However, no experimental result on actual implementation was reported. In this paper, we implemented some RSA processors on an FPGA platform and demonstrated that Yen's attack with a signal filtering technique clearly reveal the secret key information in the actual power waveforms.
We formulate in a simple fashion the concept of invariance for a linear system. We show that one must define what we call an "associated Hermitian operator"' which commutes with the system function. We s...
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ISBN:
(纸本)9780819468451
We formulate in a simple fashion the concept of invariance for a linear system. We show that one must define what we call an "associated Hermitian operator"' which commutes with the system function. We show that it is this Hermitian operator that defines the invariance and also determines the appropriate transform and other connections between input and output relations.
A model for an infrared (M) flame detection system using artificial neural networks (ANN) is presented. The joint time-frequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extract...
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ISBN:
(纸本)9780819468451
A model for an infrared (M) flame detection system using artificial neural networks (ANN) is presented. The joint time-frequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extracting relevant input features for a set of ANNs. Each ANN is trained using the backpropagation conjugate-gradient (CG) method to distinguish all hydrocarbon flames from a particular type of environmental nuisance and background noise. signal saturation caused by the increased intensity of IR sources at closer distances is resolved by an adjustable gain control. A classification scheme with trained ANN connection weights was implemented on a digital signal processor for use in an industrial hydrocarbon flame detector.
Given the moments of a time-frequency distribution, one can, in principle, construct the characteristic function from which one then obtains the distribution by Fourier transformation. However, often one can not find ...
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ISBN:
(纸本)9780819468451
Given the moments of a time-frequency distribution, one can, in principle, construct the characteristic function from which one then obtains the distribution by Fourier transformation. However, often one can not find a closed form for the characteristic function and hence one can not obtain the distribution in a direct manner. We formulate the problem of constructing time-frequency representations from moments without first constructing the characteristic function. Our method is based on expanding the distribution in terms of a complete set of functions where the expansion coefficients are dependent directly on the moments. We apply the method to a case where the even moments are manifestly positive which is a necessary condition for obtaining a proper time-frequency representation.
The proceedings contain 29 papers. The topics discussed include: optimization of spanning tree adders;estimating adders for a low density parity check decoder;sublinear constant multiplication algorithms;new identitie...
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ISBN:
(纸本)0819463922
The proceedings contain 29 papers. The topics discussed include: optimization of spanning tree adders;estimating adders for a low density parity check decoder;sublinear constant multiplication algorithms;new identities and transformations for hardware power operators;interconnection scheme for networks of online modules;reconfigurable architecture for the efficient solution of large-scale non-Hermitian eigenvalue problems;high-resolution iris image reconstruction from low-resolution imagery;using mean-squared error to assess visual image quality;time-frequency analysis of classical and quantum noise;application of time-frequency analysis methods to speaker verification;time-frequency decomposition based on information;time-frequency approximations with applications to filtering, modulation, and propagation;and on the development of a high-order texture analysis using the PWD and Rènyl entropy.
In this paper, we consider the use of a seismic sensor array for the localization and tracking of a wideband moving source. The proposed solution consists of two steps: source Direction-Of-Arrival (DOA) estimation and...
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ISBN:
(纸本)9780819468451
In this paper, we consider the use of a seismic sensor array for the localization and tracking of a wideband moving source. The proposed solution consists of two steps: source Direction-Of-Arrival (DOA) estimation and localization via DOA estimates. Three DOA estimation methods are considered. The Covariance Matrix Analysis and the Surface Wave Analysis are previously published DOA estimation algorithms shown to be effective in the localization of a stationary wideband source. This paper investigates their performance on moving wideband sources. A novel DOA estimation algorithm, the Modified Kirlin's Method was also developed for the localization of a moving Source. The DOAs estimated by these algorithms are combined rising a least-squares optimization for source localization. The application of these algorithms to real-life data show the effectiveness of both the Surface Wave Analysis and the Modified Kirlin's Method in locating and tracking a wideband moving source.
We are presenting a new method for super resolution tracking of frequency modulated sinusoids in white noise. The method is specifically designed to handle the rapid transient problem, i.e. the problem of tracking a c...
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ISBN:
(纸本)9780819468451
We are presenting a new method for super resolution tracking of frequency modulated sinusoids in white noise. The method is specifically designed to handle the rapid transient problem, i.e. the problem of tracking a continuous, rapidly changing instantaneous frequency contour. The proposed method employs to components: 1) an adaptive generalized scale transform 1, 2 which applies a localized change of time-frequency coordinates within the given signal, and 2) an estimation of signal parameters by rotational invariance techniques 3 (ESPRIT). With experiments we have shown that the proposed method provides a significantly higher estimation accuracy than conventional methods. 3 With an optimal choice of transform parameters the estimation error can be reduced dramatically. Error reductions of over 40% have been observed.
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