Convolution Power Spectrum Analysis for fMRI Data Based on Prior Image Signal

Functional MRI (fMRI) data-processing methods based on changes in the time domain involve, among other things, correlation analysis and use of the general linear model with statistical parametric mapping (SPM). Unlike conventional fMRI data analysis methods, which aim to model the blood-oxygen-level-dependent (BOLD) response of voxels as a function of time, the theory of power spectrum (PS) analysis focuses completely on understanding the dynamic energy change of interacting systems. We propose a new convolution PS (CPS) analysis of fMRI data, based on the theory of matched filtering, to detect brain functional activation for fMRI data. First, convolution signals are computed between the measured fMRI signals and the image signal of prior experimental pattern to suppress noise in the fMRI data. Then, the PS density analysis of the convolution signal is specified as the quantitative analysis energy index of BOLD signal change. The data from simulation studies and in vivo fMRI studies, including block-design experiments, reveal that the CPS method enables a more effective detection of some aspects of brain functional activation, as compared with the canonical PS SPM and the support vector machine methods. Our results demonstrate that the CPS method is useful as a complementary analysis in revealing brain functional information regarding the complex nature of fMRI time series.      

Analysis of interchannel crosstalk in a dispersion-managed analog transmission link

A technique for computing the effect of cross-phase modulation (XPM) on two copropagating analog channels in an optical fiber link is presented. In this approach, the interaction between the two channels is linearized by keeping the self-phase modulation (SPM) and XPM interactions in the strong optical carrier components only at lowest order and then at the next order, deriving the effect on the modulation components of both channels when the optical carrier is strong relative to the other components of the channel. In contrast to some previously suggested approaches, it is not assumed that the pump is undistorted, and therefore, this method accurately describes distortions due to SPM, XPM, and dispersion management in both channels. This method is easily applied to systems with multiple spans employing dispersion management with loss and gain. The expressions for the received radio frequency power and crosstalk between the two channels when direct detection is used are then provided. Using this approach, new expressions for the amplitude modulation and phase modulation modes of the two channels are derived, and the way they exchange energy when SPM, XPM, and dispersion are all considered is explained. This method yields excellent agreement between theory and experimental data.     

Classification of fMRI time series in a low-dimensional subspace with a spatial prior

We propose a new method for detecting activation in functional magnetic resonance imaging (fMRI) data. We project the fMRI time series on a low-dimensional subspace spanned by wavelet packets in order to create projections that are as non-Gaussian as possible. Our approach achieves two goals: it reduces the dimensionality of the problem by explicitly constructing a sparse approximation to the dataset and it also creates meaningful clusters allowing the separation of the activated regions from the clutter formed by the background time series. We use a mixture of Gaussian densities to model the distribution of the wavelet packet coefficients. We expect activated areas that are connected, and impose a spatial prior in the form of a Markov random field. Our approach was validated with in vivo data and realistic synthetic data, where it outperformed a linear model equipped with the knowledge of the true hemodynamic response.     

Color image canonical correlation analysis for face feature extraction and recognition

Canonical correlation analysis (CCA) is a powerful statistical analysis technique, which can extract canonical correlated features from two data sets. However, it cannot be directly used for color images that are usually represented by three data sets, i.e., red, green and blue components. Current multi-set CCA (mCCA) methods, on the other hand, can only provide the iterative solutions, not the analytical solutions, when processing multiple data sets. In this paper, we develop the CCA technique and propose a color image CCA (CICCA) approach, which can extract canonical correlated features from three color components and provide the analytical solution. We show the mathematical model of CICCA, prove that CICCA can be cast as solving three eigen-equations, and present the realization algorithm of CICCA. Experimental results on the AR and FRGC-2 public color face image databases demonstrate that CICCA outperforms several representative color face recognition methods.     

Multipath delay estimation using a superresolution PN-correlationmethod

This paper addresses the problem of high-resolution estimation of a multipath channel delay profile. We propose several improvements to the so-called superresolution pseudo-noise sequence correlation method (SPM) and analyze its performance on time-varying channels. SPM is based on the multiple signal classification (MUSIC) algorithm, which requires decorrelation of the multipath echoes. The proposed improvements enable SPM-based delay estimation in the presence of narrowband interference, and they reduce the necessary transmission window while preserving multipath echo decorrelation. These improvements are analyzed and are applied to underwater acoustic experimental data     

Bayesian approach to segmentation of statistical parametric maps

A contextual segmentation technique to detect brain activation from functional brain images is presented in the Bayesian framework. Unlike earlier similar approaches [Holmes and Ford (1993) and Descombes et al. (1998)], a Markov random field (MRF) is used to represent configurations of activated brain voxels, and likelihoods given by statistical parametric maps (SPM's) are directly used to find the maximum a posteriori (MAP) estimation of segmentation. The iterative segmentation algorithm, which is based on a simulated annealing scheme, is fully data-driven and capable of analyzing experiments involving multiple-input stimuli. Simulation results and comparisons with the simple thresholding and the statistical parametric mapping (SPM) approaches are presented with synthetic images, and functional MR images acquired in memory retrieval and event-related working memory tasks. The experiments show that an MRF is a valid representation of the activation patterns obtained in functional brain images, and the present technique renders a superior segmentation scheme to the context-free approach and the SPM approach.     

Variable Partitioning and Scheduling for MPSoC with Virtually Shared Scratch Pad Memory

One of the most critical components that determine the success of an MPSoC based architecture is its on-chip memory. Scratch Pad Memory (SPM) is increasingly being applied to substitute cache as the on-chip memory of embedded MPSoCs due to its superior chip area, power consumption and timing predictability. SPM can be organized as a Virtually Shared SPM (VS-SPM) architecture that takes advantage of both shared and private SPM. However, making effective use of the VS-SPM architecture strongly depends on two inter-dependent problems: variable partitioning and task scheduling. In this paper, we decouple these two problems and solve them in phase-ordered manner. We propose two variable partitioning heuristics based on an initial schedule: High Access Frequency First (HAFF) variable partitioning and Global View Prediction (GVP) variable partitioning. Then, we present a loop pipeline scheduling algorithm known as Rotation Scheduling with Variable Partitioning (RSVP) to improve overall throughput. Our experimental results obtained on MiBench show that the average performance improvements over IDAS (Integrated Data Assignment with Scheduling) are 23.74% for HAFF and 31.91% for GVP on four-core MPSoC. The average schedule length generated by RSVP is 25.96% shorter than that of list scheduling with optimal variable partition.     

Critic-driven ensemble classification

We develop new rules for combining the estimates obtained from each classifier in an ensemble, in order to address problems involving multiple (>2) classes. A variety of techniques have been previously suggested, including averaging probability estimates from each classifier, as well as hard (0-1) voting schemes. In this work, we introduce the notion of a critic associated with each classifier, whose objective is to predict the classifier's errors. Since the critic only tackles a two class problem, its predictions are generally more reliable than those of the classifier and, thus, can be used as the basis for improved combination rules. Several such rules are suggested here. While previous techniques are only effective when the individual classifier error rate is p<0.5, the new approach is successful, as proved under an independence assumption, even when this condition is violated-in particular, so long as p+q<1, with q the critic's error rate. More generally, critic-driven combining is found to achieve significant performance gains over alternative methods on a number of benchmark data sets. We also propose a new analytical tool for modeling ensemble performance, based on dependence between experts. This approach is substantially more accurate than the analysis based on independence that is often used to justify ensemble methods     

Removal of ballistocardiogram artifacts from simultaneously recorded EEG and fMRI data using independent component analysis

Simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been studied to identify areas related to EEG events. EEG data recorded in the magnetic resonance (MR) scanner with MR imaging is suffered from two specific artifacts, imaging artifact, and ballistocardiogram (BCG). In this paper, we focus on BCG. In preceding studies, average subtraction was often used for this purpose. However, average subtraction requires an assumption that BCG waveforms are precisely periodic, which seems unrealistic because BCG is a biomedical artifact. We propose the application of independent component analysis (ICA) with a postprocessing of high-pass filtering for the removal of BCG. With this approach, it is not necessary to assume that the BCG waveform is periodic. Empirically, we show that our proposed method removes BCG artifacts as well as does the average subtraction method. Power spectral density analysis of the two approaches shows that, with ICA, distortion of recovered EEG data is also as small as that associated with the average subtraction approach. We also propose a hypothesis for how head movement causes BCGs and show why ICA can remove BCG artifacts arising from this source.     

一种针对基于SVM入侵检测系统的毒性攻击方法

在机器学习被广泛应用的背景下,本文提出一种针对基于SVM（Support Vector Machine）入侵检测系统的新颖攻击方法——毒性攻击.该方法通过篡改训练数据,进而误导SVM的机器学习过程,降低入侵检测系统的分类模型对攻击流量的识别率.本文把这种攻击建模为最优化问题,利用数值方法得到攻击样本.通过包含多种攻击类型的NSL-KDD数据集进行实验,从攻击流量的召回率和精度这两个指标对攻击效果进行评估,与已有方法相比,实验结果表明本文方法可更有效地降低入侵检测系统的识别率.本文希望通过该研究进一步认识针对机器学习的新颖攻击,为下一步研究对应的防御机制提供研究基础.     

Lifetime distribution based degradation analysis

The methods commonly used for degradation analysis deduce the lifetime distribution of a product from the distribution of the random parameters in the degradation path model for the product. This approach requires a functional form of the joint distribution of the random parameters, which poses certain practical difficulties. In this paper, we propose an alternative approach which makes inference directly on the lifetime distribution itself. In the proposed approach, degradation data are first used to derive predictive intervals of individual lifetimes. Then an imputation algorithm is invoked to obtain the estimate of the lifetime distribution. The approach has the following advantages: 1) the adequacy of the assumption on the lifetime distribution can be easily examined, 2) the estimated lifetime distribution has a closed form, and 3) the procedure can be more easily implemented. A simulation study is reported to demonstrate the efficiency of the proposed approach. The approach is applied to two real degradation data sets, and compared with Lu-Meeker's two stage method in the applications.     

What is the best similarity measure for motion correction in fMRI time series?

It has been shown that the difference of squares cost function used by standard realignment packages (SPM and AIR) can lead to the detection of spurious activations, because the motion parameter estimations are biased by the activated areas. Therefore, this paper describes several experiments aiming at selecting a better similarity measure to drive functional magnetic resonance image registration. The behaviors of the Geman-McClure (GM) estimator, of the correlation ratio, and of the mutual information (MI) relative to activated areas are studied using simulated time series and actual data stemming from a 3T magnet. It is shown that these methods are more robust than the usual difference of squares measure. The results suggest also that the measures built from robust metrics like the GM estimator may be the best choice, while MI is also an interesting solution. Some more work, however, is required to compare the various robust metrics proposed in the literature.     

Joint Detection-Estimation of Brain Activity in Functional MRI: A Multichannel Deconvolution Solution

Analysis of functional magnetic resonance imaging (fMRI) data focuses essentially on two questions: first, a detection problem that studies which parts of the brain are activated by a given stimulus and, second, an estimation problem that investigates the temporal dynamic of the brain response during activations. Up to now, these questions have been addressed independently. However, the activated areas need to be known prior to the analysis of the temporal dynamic of the response. Similarly, a typical shape of the response has to be assumed a priori for detection purpose. This situation motivates the need for new methods in neuroimaging data analysis that are able to go beyond this unsatisfactory tradeoff. The present paper raises a novel detection-estimation approach to perform these two tasks simultaneously in region-based analysis. In the Bayesian framework, the detection of brain activity is achieved using a mixture of two Gaussian distributions as a prior model on the “neural” response levels, whereas the hemodynamic impulse response is constrained to be smooth enough in the time domain with a Gaussian prior. All parameters of interest, as well as hyperparameters, are estimated from the posterior distribution using Gibbs sampling and posterior mean estimates. Results obtained both on simulated and real fMRI data demonstrate first that our approach can segregate activated and nonactivated voxels in a given region of interest (ROI) and, second, that it can provide spatial activation maps without any assumption on the exact shape of the Hemodynamic Response Function (HRF), in contrast to standard model-based analysis.     

Handover mechanism and speed considerations in personal communication systems

In this paper, we discuss views concerning different approaches to the analysis of personal communication systems (PCSs): an outside observer's point of view, a user's point of view, and a base station's point of view. In these three approaches the paper focuses on the handover mechanism rather than on the data traffic modeling. In particular, we reexamine these different methods of analysis, combine these views with our approach, and propose a new queueing model. We take into account the speed factor and show that the ordinary measurements that are based on the state probabilities are not appropriate. In addition, we propose a layered queueing model in which low-speed and high-speed users are treated differently.     

EMI-based classification of multiple closely spaced subsurface objects via independent component analysis

Previous work in subsurface object discrimination using electromagnetic induction data has shown that discrimination algorithms based on statistical signal processing techniques are effective for classifying data from objects that occur in isolation. However, for multiple closely spaced subsurface objects, the raw (unprocessed) measurement is a mixture of the responses from several objects and as such cannot be used directly to determine the identity of each of the individual objects. Thus, we propose to separate individual signatures from the mixture by posing the problem as a blind source separation (BSS) problem and effecting signature separation using independent component analysis. We propose to apply BSS to separate the mixed signatures and then follow the separation process with a Bayesian classifier. This approach is evaluated using both simulated data and data from unexploded ordnance items. The results show that this approach can be used to effectively classify multiple closely spaced objects.     

A new computational approach for cortical imaging

Estimation of current or potential distribution on the cortex is used to obtain information about neural sources from the scalp recorded electroencephalogram. If the active sources in the brain are superficial, the estimated field distribution on the cortex also yields information about the active source configuration. In these cases, these methods can be used as source localization methods. In this study, we concentrate on finite-element-based cortex potential estimation. Usually these methods require surface interpolation of the recorded voltages at the electrodes onto the entire scalp surface. We propose a new computational approach which does not require the use of surface interpolation but does it implicitly and uses only the recorded data at the electrodes. We refer to this method as the systematic approach (SA). We compare the SA with the surface interpolation approach (IA) and show that the SA is able to produce somewhat better accuracy than the IA. However, the main asset is that the sensitivity of the cortical potential maps to the regularization parameter is significantly lower than with the IA.     

Image content analysis for sector-wise JPEG fragment classification

In this paper, we propose a sector-wise JPEG fragment classification approach to classify normal and erroneous JPEG data fragments with the minimum size of 512 bytes per fragment. Our method is based on processing each read-in sector of 512 bytes with using the DCT coefficient analysis methods for extracting the features of visual inconsistencies. The classification is conducted before the inverse DCT and can be performed simultaneously with JPEG decoding. The contributions of this work are two-folds: (1) a sector-wise JPEG erroneous fragment classification approach is proposed (2) new DCT coefficient analysis methods are introduced for image content analysis. Testing results on a variety of erroneous fragmented and normal JPEG files prove the strength of this operator for the purpose of forensics analysis, data recovery and abnormal fragment inconsistencies classification and detection. Furthermore, the results also show that the proposed DCT coefficient analysis methods are efficient and practical in terms of classification accuracy. In our experiment, the proposed approach yields a false positive rate of 0.32% and a true positive rate of 96.1% in terms of erroneous JPEG fragment classification.     

Estimating the soil dielectric constant via scattering measurements along the specular direction

We propose a soil dielectric constant retrieval scheme based on the use of the ratio of power densities scattered at HH and VV polarizations along the specular direction for different incidence angles and/or frequencies. The method relies on the minimum squares technique and is based on the observation that at variance with the backscattering case, in the specular case the small perturbation method (SPM) and the Kirchhoff approach both lead to the same expression of the copolarized ratio. Accordingly, it is expected that its evaluation is rather robust, to hold under a wide range of surface roughnesses. We present method-of-moments (MoM) simulations that confirm this expectation, and we test the validity of the overall retrieval scheme, as well as its sensitivity to measurement errors (calibration errors and fading or speckle noise), by applying the algorithm to data simulated by using the MoM. Theoretical and practical problems related to the implementation of measurements along the specular direction are also discussed. The proposed method is amenable to the new generation of bistatic remote sensing instruments.     

Reliable fusion of ToF and stereo data based on joint depth filter

To obtain reliable depth images with high resolution, a novel method is proposed in this study that fuses data acquired from time-of-flight (ToF) and stereo cameras, through which the advantages of both active and passive sensing are utilised. Based on the classic error model of the ToF, gradient information is introduced to establish the likelihood distribution for all disparity candidates. The stereo likelihood is estimated in parallel based on a 3D adaptive support-weight approach. The two independent likelihoods are unified using a maximum likelihood estimation, a process also referred to as a joint depth filter herein. Conventional post-processing methods such as a mutual consistency check are also used after applying a joint depth filter. We also propose a novel hole-filling method based on the seed-growing algorithm to retrieve missing disparities. Experiment results show that the proposed fusion method can produce reliable high-resolution depth maps and outperforms other compared methods.